diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..30afab233d17ae0fee2527c6baf314466574fae8
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,62 @@
+# Mac OS files
+.DS_Store
+
+# IDEs
+.idea
+.vs
+.vscode
+.cache
+
+# GitHub files
+.github
+
+# Byte-compiled / optimized / DLL / cached files
+__pycache__/
+*.py[cod]
+*$py.class
+*.pyc
+.temp
+*.c
+*.so
+*.o
+
+# Developing mode
+_*.sh
+_*.json
+*.lst
+yard*
+*.out
+evaluation/evalset_selection
+mfa
+egs/svc/*wavmark
+egs/svc/custom
+egs/svc/*/dev*
+egs/svc/dev_exp_config.json
+bins/svc/demo*
+bins/svc/preprocess_custom.py
+data
+ckpts
+
+# Data and ckpt
+*.pkl
+*.pt
+*.npy
+*.npz
+!modules/whisper_extractor/assets/mel_filters.npz
+*.tar.gz
+*.ckpt
+*.wav
+*.flac
+pretrained/wenet/*conformer_exp
+
+# Runtime data dirs
+processed_data
+data
+model_ckpt
+logs
+*.ipynb
+*.lst
+source_audio
+result
+conversion_results
+get_available_gpu.py
\ No newline at end of file
diff --git a/config/audioldm.json b/config/audioldm.json
new file mode 100644
index 0000000000000000000000000000000000000000..c0cab48cda2f00ac6c1687da5ab3b6d6a263e317
--- /dev/null
+++ b/config/audioldm.json
@@ -0,0 +1,92 @@
+{
+  "base_config": "config/base.json",
+  "model_type": "AudioLDM",
+  "task_type": "tta",
+  "dataset": [
+    "AudioCaps"
+  ],
+  "preprocess": {
+    // feature used for model training
+    "use_spkid": false,
+    "use_uv": false,
+    "use_frame_pitch": false,
+    "use_phone_pitch": false,
+    "use_frame_energy": false,
+    "use_phone_energy": false,
+    "use_mel": false,
+    "use_audio": false,
+    "use_label": false,
+    "use_one_hot": false,
+    "cond_mask_prob": 0.1
+  },
+  // model
+  "model": {
+    "audioldm": {
+      "image_size": 32,
+      "in_channels": 4,
+      "out_channels": 4,
+      "model_channels": 256,
+      "attention_resolutions": [
+        4,
+        2,
+        1
+      ],
+      "num_res_blocks": 2,
+      "channel_mult": [
+        1,
+        2,
+        4
+      ],
+      "num_heads": 8,
+      "use_spatial_transformer": true,
+      "transformer_depth": 1,
+      "context_dim": 768,
+      "use_checkpoint": true,
+      "legacy": false
+    },
+    "autoencoderkl": {
+      "ch": 128,
+      "ch_mult": [
+        1,
+        1,
+        2,
+        2,
+        4
+      ],
+      "num_res_blocks": 2,
+      "in_channels": 1,
+      "z_channels": 4,
+      "out_ch": 1,
+      "double_z": true
+    },
+    "noise_scheduler": {
+      "num_train_timesteps": 1000,
+      "beta_start": 0.00085,
+      "beta_end": 0.012,
+      "beta_schedule": "scaled_linear",
+      "clip_sample": false,
+      "steps_offset": 1,
+      "set_alpha_to_one": false,
+      "skip_prk_steps": true,
+      "prediction_type": "epsilon"
+    }
+  },
+  // train
+  "train": {
+    "lronPlateau": {
+      "factor": 0.9,
+      "patience": 100,
+      "min_lr": 4.0e-5,
+      "verbose": true
+    },
+    "adam": {
+      "lr": 5.0e-5,
+      "betas": [
+        0.9,
+        0.999
+      ],
+      "weight_decay": 1.0e-2,
+      "eps": 1.0e-8
+    }
+  }
+}
\ No newline at end of file
diff --git a/config/autoencoderkl.json b/config/autoencoderkl.json
new file mode 100644
index 0000000000000000000000000000000000000000..72e677770da689af33d4c7bd6f3506927fb24380
--- /dev/null
+++ b/config/autoencoderkl.json
@@ -0,0 +1,69 @@
+{
+  "base_config": "config/base.json",
+  "model_type": "AutoencoderKL",
+  "task_type": "tta",
+  "dataset": [
+    "AudioCaps"
+  ],
+  "preprocess": {
+    // feature used for model training
+    "use_spkid": false,
+    "use_uv": false,
+    "use_frame_pitch": false,
+    "use_phone_pitch": false,
+    "use_frame_energy": false,
+    "use_phone_energy": false,
+    "use_mel": false,
+    "use_audio": false,
+    "use_label": false,
+    "use_one_hot": false
+  },
+  // model
+  "model": {
+    "autoencoderkl": {
+      "ch": 128,
+      "ch_mult": [
+        1,
+        1,
+        2,
+        2,
+        4
+      ],
+      "num_res_blocks": 2,
+      "in_channels": 1,
+      "z_channels": 4,
+      "out_ch": 1,
+      "double_z": true
+    },
+    "loss": {
+      "kl_weight": 1e-8,
+      "disc_weight": 0.5,
+      "disc_factor": 1.0,
+      "logvar_init": 0.0,
+      "min_adapt_d_weight": 0.0,
+      "max_adapt_d_weight": 10.0,
+      "disc_start": 50001,
+      "disc_in_channels": 1,
+      "disc_num_layers": 3,
+      "use_actnorm": false
+    }
+  },
+  // train
+  "train": {
+    "lronPlateau": {
+      "factor": 0.9,
+      "patience": 100,
+      "min_lr": 4.0e-5,
+      "verbose": true
+    },
+    "adam": {
+      "lr": 4.0e-4,
+      "betas": [
+        0.9,
+        0.999
+      ],
+      "weight_decay": 1.0e-2,
+      "eps": 1.0e-8
+    }
+  }
+}
\ No newline at end of file
diff --git a/config/base.json b/config/base.json
new file mode 100644
index 0000000000000000000000000000000000000000..5fa71dfcdde7058eae634007cdf4f034184e753b
--- /dev/null
+++ b/config/base.json
@@ -0,0 +1,220 @@
+{
+  "supported_model_type": [
+    "GANVocoder",
+    "Fastspeech2",
+    "DiffSVC",
+    "Transformer",
+    "EDM",
+    "CD"
+  ],
+  "task_type": "",
+  "dataset": [],
+  "use_custom_dataset": false,
+  "preprocess": {
+    "phone_extractor": "espeak", // "espeak, pypinyin, pypinyin_initials_finals, lexicon"
+    // trim audio silence
+    "data_augment": false,
+    "trim_silence": false,
+    "num_silent_frames": 8,
+    "trim_fft_size": 512, // fft size used in trimming
+    "trim_hop_size": 128, // hop size used in trimming
+    "trim_top_db": 30, // top db used in trimming sensitive to each dataset
+    // acoustic features
+    "extract_mel": false,
+    "mel_extract_mode": "",
+    "extract_linear_spec": false,
+    "extract_mcep": false,
+    "extract_pitch": false,
+    "extract_acoustic_token": false,
+    "pitch_remove_outlier": false,
+    "extract_uv": false,
+    "pitch_norm": false,
+    "extract_audio": false,
+    "extract_label": false,
+    "pitch_extractor": "parselmouth", // pyin, dio, pyworld, pyreaper, parselmouth, CWT (Continuous Wavelet Transform)
+    "extract_energy": false,
+    "energy_remove_outlier": false,
+    "energy_norm": false,
+    "energy_extract_mode": "from_mel",
+    "extract_duration": false,
+    "extract_amplitude_phase": false,
+    "mel_min_max_norm": false,
+    // lingusitic features
+    "extract_phone": false,
+    "lexicon_path": "./text/lexicon/librispeech-lexicon.txt", 
+    // content features
+    "extract_whisper_feature": false,
+    "extract_contentvec_feature": false,
+    "extract_mert_feature": false,
+    "extract_wenet_feature": false,
+    // Settings for data preprocessing
+    "n_mel": 80,
+    "win_size": 480,
+    "hop_size": 120,
+    "sample_rate": 24000,
+    "n_fft": 1024,
+    "fmin": 0,
+    "fmax": 12000,
+    "min_level_db": -115,
+    "ref_level_db": 20,
+    "bits": 8,
+    // Directory names of processed data or extracted features
+    "processed_dir": "processed_data",
+    "trimmed_wav_dir": "trimmed_wavs", // directory name of silence trimed wav
+    "raw_data": "raw_data",
+    "phone_dir": "phones",
+    "wav_dir": "wavs", // directory name of processed wav (such as downsampled waveform)
+    "audio_dir": "audios",
+    "log_amplitude_dir": "log_amplitudes",
+    "phase_dir": "phases",
+    "real_dir": "reals",
+    "imaginary_dir": "imaginarys",
+    "label_dir": "labels",
+    "linear_dir": "linears",
+    "mel_dir": "mels", // directory name of extraced mel features
+    "mcep_dir": "mcep", // directory name of extraced mcep features
+    "dur_dir": "durs",
+    "symbols_dict": "symbols.dict",
+    "lab_dir": "labs", // directory name of extraced label features
+    "wenet_dir": "wenet", // directory name of extraced wenet features
+    "contentvec_dir": "contentvec", // directory name of extraced wenet features
+    "pitch_dir": "pitches", // directory name of extraced pitch features
+    "energy_dir": "energys", // directory name of extracted energy features
+    "phone_pitch_dir": "phone_pitches", // directory name of extraced pitch features
+    "phone_energy_dir": "phone_energys", // directory name of extracted energy features
+    "uv_dir": "uvs", // directory name of extracted unvoiced features
+    "duration_dir": "duration", // ground-truth duration file
+    "phone_seq_file": "phone_seq_file", // phoneme sequence file
+    "file_lst": "file.lst",
+    "train_file": "train.json", // training set, the json file contains detailed information about the dataset, including dataset name, utterance id, duration of the utterance
+    "valid_file": "valid.json", // validattion set
+    "spk2id": "spk2id.json", // used for multi-speaker dataset
+    "utt2spk": "utt2spk", // used for multi-speaker dataset
+    "emo2id": "emo2id.json", // used for multi-emotion dataset
+    "utt2emo": "utt2emo", // used for multi-emotion dataset
+    // Features used for model training
+    "use_text": false,
+    "use_phone": false,   
+    "use_phn_seq": false,
+    "use_lab": false,
+    "use_linear": false,
+    "use_mel": false,
+    "use_min_max_norm_mel": false,
+    "use_wav": false,
+    "use_phone_pitch": false,
+    "use_log_scale_pitch": false,
+    "use_phone_energy": false,
+    "use_phone_duration": false,
+    "use_log_scale_energy": false,
+    "use_wenet": false,
+    "use_dur": false,
+    "use_spkid": false, // True: use speaker id for multi-speaker dataset
+    "use_emoid": false, // True: use emotion id for multi-emotion dataset
+    "use_frame_pitch": false,
+    "use_uv": false,
+    "use_frame_energy": false,
+    "use_frame_duration": false,
+    "use_audio": false,
+    "use_label": false,
+    "use_one_hot": false,
+    "use_amplitude_phase": false,
+    "data_augment": false,
+    "align_mel_duration": false
+  },
+  "train": {
+    "ddp": true,
+    "random_seed": 970227,
+    "batch_size": 16,
+    "max_steps": 1000000,
+    // Trackers
+    "tracker": [
+      "tensorboard"
+      // "wandb",
+      // "cometml",
+      // "mlflow",
+    ],
+    "max_epoch": -1,
+    // -1 means no limit
+    "save_checkpoint_stride": [
+      5,
+      20
+    ],
+    // unit is epoch
+    "keep_last": [
+      3,
+      -1
+    ],
+    // -1 means infinite, if one number will broadcast
+    "run_eval": [
+      false,
+      true
+    ],
+    // if one number will broadcast
+    // Fix the random seed
+    "random_seed": 10086,
+    // Optimizer
+    "optimizer": "AdamW",
+    "adamw": {
+      "lr": 4.0e-4
+      // nn model lr
+    },
+    // LR Scheduler
+    "scheduler": "ReduceLROnPlateau",
+    "reducelronplateau": {
+      "factor": 0.8,
+      "patience": 10,
+      // unit is epoch
+      "min_lr": 1.0e-4
+    },
+    // Batchsampler
+    "sampler": {
+      "holistic_shuffle": true,
+      "drop_last": true
+    },
+    // Dataloader
+    "dataloader": {
+      "num_worker": 32,
+      "pin_memory": true
+    },
+    "gradient_accumulation_step": 1,
+    "total_training_steps": 50000,
+    "save_summary_steps": 500,
+    "save_checkpoints_steps": 10000,
+    "valid_interval": 10000,
+    "keep_checkpoint_max": 5,
+    "multi_speaker_training": false, // True: train multi-speaker model; False: training single-speaker model;
+    "max_epoch": -1,
+    // -1 means no limit
+    "save_checkpoint_stride": [
+      5,
+      20
+    ],
+    // unit is epoch
+    "keep_last": [
+      3,
+      -1
+    ],
+    // -1 means infinite, if one number will broadcast
+    "run_eval": [
+      false,
+      true
+    ],
+    // Batchsampler
+    "sampler": {
+      "holistic_shuffle": true,
+      "drop_last": true
+    },
+    // Dataloader
+    "dataloader": {
+      "num_worker": 32,
+      "pin_memory": true
+    },
+    // Trackers
+    "tracker": [
+      "tensorboard"
+      // "wandb",
+      // "cometml",
+      // "mlflow",
+    ],
+  },
+}
diff --git a/config/comosvc.json b/config/comosvc.json
new file mode 100644
index 0000000000000000000000000000000000000000..19599e940be8f13e40fe6ac59ff82e720452d10e
--- /dev/null
+++ b/config/comosvc.json
@@ -0,0 +1,216 @@
+{
+    "base_config": "config/base.json",
+    "model_type": "DiffComoSVC",
+    "task_type": "svc",
+    "use_custom_dataset": false,
+    "preprocess": {
+        // data augmentations
+        "use_pitch_shift": false,
+        "use_formant_shift": false,
+        "use_time_stretch": false,
+        "use_equalizer": false,
+        // acoustic features
+        "extract_mel": true,
+        "mel_min_max_norm": true,
+        "extract_pitch": true,
+        "pitch_extractor": "parselmouth",
+        "extract_uv": true,
+        "extract_energy": true,
+        // content features
+        "extract_whisper_feature": false,
+        "whisper_sample_rate": 16000,
+        "extract_contentvec_feature": false,
+        "contentvec_sample_rate": 16000,
+        "extract_wenet_feature": false,
+        "wenet_sample_rate": 16000,
+        "extract_mert_feature": false,
+        "mert_sample_rate": 16000,
+        // Default config for whisper
+        "whisper_frameshift": 0.01,
+        "whisper_downsample_rate": 2,
+        // Default config for content vector
+        "contentvec_frameshift": 0.02,
+        // Default config for mert
+        "mert_model": "m-a-p/MERT-v1-330M",
+        "mert_feature_layer": -1,
+        "mert_hop_size": 320,
+        // 24k
+        "mert_frameshit": 0.01333,
+        // 10ms
+        "wenet_frameshift": 0.01,
+        // wenetspeech is 4, gigaspeech is 6
+        "wenet_downsample_rate": 4,
+        // Default config
+        "n_mel": 100,
+        "win_size": 1024,
+        // todo
+        "hop_size": 256,
+        "sample_rate": 24000,
+        "n_fft": 1024,
+        // todo
+        "fmin": 0,
+        "fmax": 12000,
+        // todo
+        "f0_min": 50,
+        // ~C2
+        "f0_max": 1100,
+        //1100,    // ~C6(1100), ~G5(800)
+        "pitch_bin": 256,
+        "pitch_max": 1100.0,
+        "pitch_min": 50.0,
+        "is_label": true,
+        "is_mu_law": true,
+        "bits": 8,
+        "mel_min_max_stats_dir": "mel_min_max_stats",
+        "whisper_dir": "whisper",
+        "contentvec_dir": "contentvec",
+        "wenet_dir": "wenet",
+        "mert_dir": "mert",
+        // Extract content features using dataloader
+        "pin_memory": true,
+        "num_workers": 8,
+        "content_feature_batch_size": 16,
+        // Features used for model training
+        "use_mel": true,
+        "use_min_max_norm_mel": true,
+        "use_frame_pitch": true,
+        "use_uv": true,
+        "use_frame_energy": true,
+        "use_log_scale_pitch": false,
+        "use_log_scale_energy": false,
+        "use_spkid": true,
+        // Meta file
+        "train_file": "train.json",
+        "valid_file": "test.json",
+        "spk2id": "singers.json",
+        "utt2spk": "utt2singer"
+    },
+    "model": {
+        "teacher_model_path": "[Your Teacher Model Path].bin",
+        "condition_encoder": {
+            "merge_mode": "add",
+            "input_melody_dim": 1,
+            "use_log_f0": true,
+            "n_bins_melody": 256,
+            //# Quantization (0 for not quantization)
+            "output_melody_dim": 384,
+            "input_loudness_dim": 1,
+            "use_log_loudness": true,
+            "n_bins_loudness": 256,
+            "output_loudness_dim": 384,
+            "use_whisper": false,
+            "use_contentvec": false,
+            "use_wenet": false,
+            "use_mert": false,
+            "whisper_dim": 1024,
+            "contentvec_dim": 256,
+            "mert_dim": 256,
+            "wenet_dim": 512,
+            "content_encoder_dim": 384,
+            "output_singer_dim": 384,
+            "singer_table_size": 512,
+            "output_content_dim": 384,
+            "use_spkid": true
+        },
+        "comosvc": {
+            "distill": false,
+            // conformer encoder
+            "input_dim": 384,
+            "output_dim": 100,
+            "n_heads": 2,
+            "n_layers": 6,
+            "filter_channels": 512,
+            "dropout": 0.1,
+            // karras diffusion
+            "P_mean": -1.2,
+            "P_std": 1.2,
+            "sigma_data": 0.5,
+            "sigma_min": 0.002,
+            "sigma_max": 80,
+            "rho": 7,
+            "n_timesteps": 40,
+        },
+        "diffusion": {
+            // Diffusion steps encoder
+            "step_encoder": {
+                "dim_raw_embedding": 128,
+                "dim_hidden_layer": 512,
+                "activation": "SiLU",
+                "num_layer": 2,
+                "max_period": 10000
+            },
+            // Diffusion decoder
+            "model_type": "bidilconv",
+            // bidilconv, unet2d, TODO: unet1d
+            "bidilconv": {
+                "base_channel": 384,
+                "n_res_block": 20,
+                "conv_kernel_size": 3,
+                "dilation_cycle_length": 4,
+                // specially, 1 means no dilation
+                "conditioner_size": 100
+            }
+        },
+    },
+    "train": {
+        // Basic settings
+        "fast_steps": 0,
+        "batch_size": 32,
+        "gradient_accumulation_step": 1,
+        "max_epoch": -1,
+        // -1 means no limit
+        "save_checkpoint_stride": [
+            10,
+            100
+        ],
+        // unit is epoch
+        "keep_last": [
+            3,
+            -1
+        ],
+        // -1 means infinite, if one number will broadcast
+        "run_eval": [
+            false,
+            true
+        ],
+        // if one number will broadcast
+        // Fix the random seed
+        "random_seed": 10086,
+        // Batchsampler
+        "sampler": {
+            "holistic_shuffle": true,
+            "drop_last": true
+        },
+        // Dataloader
+        "dataloader": {
+            "num_worker": 32,
+            "pin_memory": true
+        },
+        // Trackers
+        "tracker": [
+            "tensorboard"
+            // "wandb",
+            // "cometml",
+            // "mlflow",
+        ],
+        // Optimizer
+        "optimizer": "AdamW",
+        "adamw": {
+            "lr": 4.0e-4
+            // nn model lr
+        },
+        // LR Scheduler
+        "scheduler": "ReduceLROnPlateau",
+        "reducelronplateau": {
+            "factor": 0.8,
+            "patience": 10,
+            // unit is epoch
+            "min_lr": 1.0e-4
+        }
+    },
+    "inference": {
+        "comosvc": {
+            "inference_steps": 40
+        }
+    }
+}
\ No newline at end of file
diff --git a/config/diffusion.json b/config/diffusion.json
new file mode 100644
index 0000000000000000000000000000000000000000..7ef72f39d3dd578afc35d086a3d19fc892bbefda
--- /dev/null
+++ b/config/diffusion.json
@@ -0,0 +1,227 @@
+{
+    // FIXME: THESE ARE LEGACY
+    "base_config": "config/base.json",
+    "model_type": "diffusion",
+    "task_type": "svc",
+    "use_custom_dataset": false,
+    "preprocess": {
+        // data augmentations
+        "use_pitch_shift": false,
+        "use_formant_shift": false,
+        "use_time_stretch": false,
+        "use_equalizer": false,
+        // acoustic features
+        "extract_mel": true,
+        "mel_min_max_norm": true,
+        "extract_pitch": true,
+        "pitch_extractor": "parselmouth",
+        "extract_uv": true,
+        "extract_energy": true,
+        // content features
+        "extract_whisper_feature": false,
+        "whisper_sample_rate": 16000,
+        "extract_contentvec_feature": false,
+        "contentvec_sample_rate": 16000,
+        "extract_wenet_feature": false,
+        "wenet_sample_rate": 16000,
+        "extract_mert_feature": false,
+        "mert_sample_rate": 16000,
+        // Default config for whisper
+        "whisper_frameshift": 0.01,
+        "whisper_downsample_rate": 2,
+        // Default config for content vector
+        "contentvec_frameshift": 0.02,
+        // Default config for mert
+        "mert_model": "m-a-p/MERT-v1-330M",
+        "mert_feature_layer": -1,
+        "mert_hop_size": 320,
+        // 24k
+        "mert_frameshit": 0.01333,
+        // 10ms
+        "wenet_frameshift": 0.01,
+        // wenetspeech is 4, gigaspeech is 6
+        "wenet_downsample_rate": 4,
+        // Default config
+        "n_mel": 100,
+        "win_size": 1024,
+        // todo
+        "hop_size": 256,
+        "sample_rate": 24000,
+        "n_fft": 1024,
+        // todo
+        "fmin": 0,
+        "fmax": 12000,
+        // todo
+        "f0_min": 50,
+        // ~C2
+        "f0_max": 1100,
+        //1100,    // ~C6(1100), ~G5(800)
+        "pitch_bin": 256,
+        "pitch_max": 1100.0,
+        "pitch_min": 50.0,
+        "is_label": true,
+        "is_mu_law": true,
+        "bits": 8,
+        "mel_min_max_stats_dir": "mel_min_max_stats",
+        "whisper_dir": "whisper",
+        "contentvec_dir": "contentvec",
+        "wenet_dir": "wenet",
+        "mert_dir": "mert",
+        // Extract content features using dataloader
+        "pin_memory": true,
+        "num_workers": 8,
+        "content_feature_batch_size": 16,
+        // Features used for model training
+        "use_mel": true,
+        "use_min_max_norm_mel": true,
+        "use_frame_pitch": true,
+        "use_uv": true,
+        "use_frame_energy": true,
+        "use_log_scale_pitch": false,
+        "use_log_scale_energy": false,
+        "use_spkid": true,
+        // Meta file
+        "train_file": "train.json",
+        "valid_file": "test.json",
+        "spk2id": "singers.json",
+        "utt2spk": "utt2singer"
+    },
+    "model": {
+        "condition_encoder": {
+            "merge_mode": "add",
+            "input_melody_dim": 1,
+            "use_log_f0": true,
+            "n_bins_melody": 256,
+            //# Quantization (0 for not quantization)
+            "output_melody_dim": 384,
+            "input_loudness_dim": 1,
+            "use_log_loudness": true,
+            "n_bins_loudness": 256,
+            "output_loudness_dim": 384,
+            "use_whisper": false,
+            "use_contentvec": false,
+            "use_wenet": false,
+            "use_mert": false,
+            "whisper_dim": 1024,
+            "contentvec_dim": 256,
+            "mert_dim": 256,
+            "wenet_dim": 512,
+            "content_encoder_dim": 384,
+            "output_singer_dim": 384,
+            "singer_table_size": 512,
+            "output_content_dim": 384,
+            "use_spkid": true
+        },
+        // FIXME: FOLLOWING ARE NEW!!
+        "diffusion": {
+            "scheduler": "ddpm",
+            "scheduler_settings": {
+                "num_train_timesteps": 1000,
+                "beta_start": 1.0e-4,
+                "beta_end": 0.02,
+                "beta_schedule": "linear"
+            },
+            // Diffusion steps encoder
+            "step_encoder": {
+                "dim_raw_embedding": 128,
+                "dim_hidden_layer": 512,
+                "activation": "SiLU",
+                "num_layer": 2,
+                "max_period": 10000
+            },
+            // Diffusion decoder
+            "model_type": "bidilconv",
+            // bidilconv, unet2d, TODO: unet1d
+            "bidilconv": {
+                "base_channel": 384,
+                "n_res_block": 20,
+                "conv_kernel_size": 3,
+                "dilation_cycle_length": 4,
+                // specially, 1 means no dilation
+                "conditioner_size": 384
+            },
+            "unet2d": {
+                "in_channels": 1,
+                "out_channels": 1,
+                "down_block_types": [
+                    "CrossAttnDownBlock2D",
+                    "CrossAttnDownBlock2D",
+                    "CrossAttnDownBlock2D",
+                    "DownBlock2D"
+                ],
+                "mid_block_type": "UNetMidBlock2DCrossAttn",
+                "up_block_types": [
+                    "UpBlock2D",
+                    "CrossAttnUpBlock2D",
+                    "CrossAttnUpBlock2D",
+                    "CrossAttnUpBlock2D"
+                ],
+                "only_cross_attention": false
+            }
+        }
+    },
+    // FIXME: FOLLOWING ARE NEW!!
+    "train": {
+        // Basic settings
+        "batch_size": 64,
+        "gradient_accumulation_step": 1,
+        "max_epoch": -1,
+        // -1 means no limit
+        "save_checkpoint_stride": [
+            5,
+            20
+        ],
+        // unit is epoch
+        "keep_last": [
+            3,
+            -1
+        ],
+        // -1 means infinite, if one number will broadcast
+        "run_eval": [
+            false,
+            true
+        ],
+        // if one number will broadcast
+        // Fix the random seed
+        "random_seed": 10086,
+        // Batchsampler
+        "sampler": {
+            "holistic_shuffle": true,
+            "drop_last": true
+        },
+        // Dataloader
+        "dataloader": {
+            "num_worker": 32,
+            "pin_memory": true
+        },
+        // Trackers
+        "tracker": [
+            "tensorboard"
+            // "wandb",
+            // "cometml",
+            // "mlflow",
+        ],
+        // Optimizer
+        "optimizer": "AdamW",
+        "adamw": {
+            "lr": 4.0e-4
+            // nn model lr
+        },
+        // LR Scheduler
+        "scheduler": "ReduceLROnPlateau",
+        "reducelronplateau": {
+            "factor": 0.8,
+            "patience": 10,
+            // unit is epoch
+            "min_lr": 1.0e-4
+        }
+    },
+    "inference": {
+        "diffusion": {
+            "scheduler": "pndm",
+            "scheduler_settings": {
+                "num_inference_timesteps": 1000
+            }
+        }
+    }
+}
\ No newline at end of file
diff --git a/config/fs2.json b/config/fs2.json
new file mode 100644
index 0000000000000000000000000000000000000000..dba1799186592b63ab73ab08e1d921a4e8462a6a
--- /dev/null
+++ b/config/fs2.json
@@ -0,0 +1,118 @@
+{
+    "base_config": "config/tts.json",
+    "model_type": "FastSpeech2",
+    "task_type": "tts",
+    "dataset": ["LJSpeech"], 
+    "preprocess": {
+      // acoustic features
+      "extract_audio": true,
+      "extract_mel": true,
+      "mel_extract_mode": "taco",
+      "mel_min_max_norm": false,
+      "extract_pitch": true,
+      "extract_uv": false,
+      "pitch_extractor": "dio",
+      "extract_energy": true,
+      "energy_extract_mode": "from_tacotron_stft",
+      "extract_duration": true,
+      "use_phone": true,
+      "pitch_norm": true,
+      "energy_norm": true,
+      "pitch_remove_outlier": true,
+      "energy_remove_outlier": true,
+
+      // Default config 
+      "n_mel": 80,
+      "win_size": 1024,  // todo
+      "hop_size": 256,
+      "sample_rate": 22050,
+      "n_fft": 1024, // todo
+      "fmin": 0,
+      "fmax": 8000, // todo
+      "raw_data": "raw_data",
+      "text_cleaners": ["english_cleaners"],
+      "f0_min": 71,    // ~C2
+      "f0_max": 800, //1100,    // ~C6(1100), ~G5(800)
+      "pitch_bin": 256,
+      "pitch_max": 1100.0,
+      "pitch_min": 50.0,
+      "is_label": true,
+      "is_mu_law": true,
+      "bits": 8,
+
+      "mel_min_max_stats_dir": "mel_min_max_stats",
+      "whisper_dir": "whisper",
+      "content_vector_dir": "content_vector",
+      "wenet_dir": "wenet",
+      "mert_dir": "mert",
+      "spk2id":"spk2id.json",
+      "utt2spk":"utt2spk",
+
+      // Features used for model training
+      "use_mel": true,
+      "use_min_max_norm_mel": false,
+      "use_frame_pitch": false,
+      "use_frame_energy": false,
+      "use_phone_pitch": true,
+      "use_phone_energy": true,
+      "use_log_scale_pitch": false,
+      "use_log_scale_energy": false,
+      "use_spkid": false,
+      "align_mel_duration": true,
+      "text_cleaners": ["english_cleaners"],
+      "phone_extractor": "lexicon", // "espeak, pypinyin, pypinyin_initials_finals, lexicon (only for language=en-us right now)"
+      },
+    "model": {
+      // Settings for transformer
+      "transformer": {
+        "encoder_layer": 4,
+        "encoder_head": 2,
+        "encoder_hidden": 256,
+        "decoder_layer": 6,
+        "decoder_head": 2,
+        "decoder_hidden": 256,
+        "conv_filter_size": 1024,
+        "conv_kernel_size": [9, 1],
+        "encoder_dropout": 0.2,
+        "decoder_dropout": 0.2
+      },
+
+      // Settings for variance_predictor
+      "variance_predictor":{
+        "filter_size": 256,
+        "kernel_size": 3,
+        "dropout": 0.5
+      },
+    "variance_embedding":{
+        "pitch_quantization": "linear", // support 'linear' or 'log', 'log' is allowed only if the pitch values are not normalized during preprocessing
+        "energy_quantization": "linear", // support 'linear' or 'log', 'log' is allowed only if the energy values are not normalized during preprocessing
+        "n_bins": 256
+      },
+    "max_seq_len": 1000
+    },
+    "train":{
+      "batch_size": 16,
+      "sort_sample": true,
+      "drop_last": true,
+      "group_size": 4,
+      "grad_clip_thresh": 1.0,
+      "dataloader": {
+        "num_worker": 8,
+        "pin_memory": true
+      },
+      "lr_scheduler":{
+        "num_warmup": 4000
+      },
+      // LR Scheduler
+      "scheduler": "NoamLR",
+      // Optimizer
+      "optimizer": "Adam",
+      "adam": {
+        "lr": 0.0625,
+        "betas": [0.9, 0.98],
+        "eps": 0.000000001,
+        "weight_decay": 0.0
+      },
+    }
+
+}
diff --git a/config/ns2.json b/config/ns2.json
new file mode 100644
index 0000000000000000000000000000000000000000..6b8e5bdd43cc97c2a173465714d948d3528b38eb
--- /dev/null
+++ b/config/ns2.json
@@ -0,0 +1,88 @@
+{
+    "base_config": "config/base.json",
+    "model_type": "NaturalSpeech2",
+    "dataset": ["LibriTTS"],
+    "preprocess": {
+        "use_mel": false,
+        "use_code": true,
+        "use_spkid": true,
+        "use_pitch": true,
+        "use_duration": true,
+        "use_phone": true,
+        "use_len": true,
+        "use_cross_reference": true,
+        "train_file": "train.json",
+        "melspec_dir": "mel",
+        "code_dir": "code",
+        "pitch_dir": "pitch",
+        "duration_dir": "duration",
+        "clip_mode": "start"
+    },
+    "model": {
+        "latent_dim": 128,
+        "prior_encoder": {
+            "vocab_size": 100,
+            "pitch_min": 50,
+            "pitch_max": 1100,
+            "pitch_bins_num": 512,
+            "encoder": {
+                "encoder_layer": 6,
+                "encoder_hidden": 512,
+                "encoder_head": 8,
+                "conv_filter_size": 2048,
+                "conv_kernel_size": 9,
+                "encoder_dropout": 0.2,
+                "use_cln": true
+            },
+            "duration_predictor": {
+                "input_size": 512,
+                "filter_size": 512,
+                "kernel_size": 3,
+                "conv_layers": 30,
+                "cross_attn_per_layer": 3,
+                "attn_head": 8,
+                "drop_out": 0.5
+            },
+            "pitch_predictor": {
+                "input_size": 512,
+                "filter_size": 512,
+                "kernel_size": 5,
+                "conv_layers": 30,
+                "cross_attn_per_layer": 3,
+                "attn_head": 8,
+                "drop_out": 0.5
+            }
+        },
+        "diffusion": {
+            "wavenet": {
+                "input_size": 128,
+                "hidden_size": 512,
+                "out_size": 128,
+                "num_layers": 40,
+                "cross_attn_per_layer": 3,
+                "dilation_cycle": 2,
+                "attn_head": 8,
+                "drop_out": 0.2
+            },
+            "beta_min": 0.05,
+            "beta_max": 20,
+            "sigma": 1.0,
+            "noise_factor": 1.0,
+            "ode_solver": "euler"
+        },
+        "prompt_encoder": {
+            "encoder_layer": 6,
+            "encoder_hidden": 512,
+            "encoder_head": 8,
+            "conv_filter_size": 2048,
+            "conv_kernel_size": 9,
+            "encoder_dropout": 0.2,
+            "use_cln": false
+        },
+        "query_emb": {
+            "query_token_num": 32,
+            "hidden_size": 512,
+            "head_num": 8
+        }
+    }
+}
\ No newline at end of file
diff --git a/config/transformer.json b/config/transformer.json
new file mode 100644
index 0000000000000000000000000000000000000000..258bfddb36a21adb91b9e1921a57f738a789a835
--- /dev/null
+++ b/config/transformer.json
@@ -0,0 +1,180 @@
+{
+    "base_config": "config/base.json",
+    "model_type": "Transformer",
+    "task_type": "svc",
+    "use_custom_dataset": false,
+    "preprocess": {
+        // data augmentations
+        "use_pitch_shift": false,
+        "use_formant_shift": false,
+        "use_time_stretch": false,
+        "use_equalizer": false,
+        // acoustic features
+        "extract_mel": true,
+        "mel_min_max_norm": true,
+        "extract_pitch": true,
+        "pitch_extractor": "parselmouth",
+        "extract_uv": true,
+        "extract_energy": true,
+        // content features
+        "extract_whisper_feature": false,
+        "whisper_sample_rate": 16000,
+        "extract_contentvec_feature": false,
+        "contentvec_sample_rate": 16000,
+        "extract_wenet_feature": false,
+        "wenet_sample_rate": 16000,
+        "extract_mert_feature": false,
+        "mert_sample_rate": 16000,
+        // Default config for whisper
+        "whisper_frameshift": 0.01,
+        "whisper_downsample_rate": 2,
+        // Default config for content vector
+        "contentvec_frameshift": 0.02,
+        // Default config for mert
+        "mert_model": "m-a-p/MERT-v1-330M",
+        "mert_feature_layer": -1,
+        "mert_hop_size": 320,
+        // 24k
+        "mert_frameshit": 0.01333,
+        // 10ms
+        "wenet_frameshift": 0.01,
+        // wenetspeech is 4, gigaspeech is 6
+        "wenet_downsample_rate": 4,
+        // Default config
+        "n_mel": 100,
+        "win_size": 1024,
+        // todo
+        "hop_size": 256,
+        "sample_rate": 24000,
+        "n_fft": 1024,
+        // todo
+        "fmin": 0,
+        "fmax": 12000,
+        // todo
+        "f0_min": 50,
+        // ~C2
+        "f0_max": 1100,
+        //1100,    // ~C6(1100), ~G5(800)
+        "pitch_bin": 256,
+        "pitch_max": 1100.0,
+        "pitch_min": 50.0,
+        "is_label": true,
+        "is_mu_law": true,
+        "bits": 8,
+        "mel_min_max_stats_dir": "mel_min_max_stats",
+        "whisper_dir": "whisper",
+        "contentvec_dir": "contentvec",
+        "wenet_dir": "wenet",
+        "mert_dir": "mert",
+        // Extract content features using dataloader
+        "pin_memory": true,
+        "num_workers": 8,
+        "content_feature_batch_size": 16,
+        // Features used for model training
+        "use_mel": true,
+        "use_min_max_norm_mel": true,
+        "use_frame_pitch": true,
+        "use_uv": true,
+        "use_frame_energy": true,
+        "use_log_scale_pitch": false,
+        "use_log_scale_energy": false,
+        "use_spkid": true,
+        // Meta file
+        "train_file": "train.json",
+        "valid_file": "test.json",
+        "spk2id": "singers.json",
+        "utt2spk": "utt2singer"
+    },
+    "model": {
+        "condition_encoder": {
+            "merge_mode": "add",
+            "input_melody_dim": 1,
+            "use_log_f0": true,
+            "n_bins_melody": 256,
+            //# Quantization (0 for not quantization)
+            "output_melody_dim": 384,
+            "input_loudness_dim": 1,
+            "use_log_loudness": true,
+            "n_bins_loudness": 256,
+            "output_loudness_dim": 384,
+            "use_whisper": false,
+            "use_contentvec": true,
+            "use_wenet": false,
+            "use_mert": false,
+            "whisper_dim": 1024,
+            "contentvec_dim": 256,
+            "mert_dim": 256,
+            "wenet_dim": 512,
+            "content_encoder_dim": 384,
+            "output_singer_dim": 384,
+            "singer_table_size": 512,
+            "output_content_dim": 384,
+            "use_spkid": true
+        },
+        "transformer": {
+            "type": "conformer",
+            // 'conformer' or 'transformer'
+            "input_dim": 384,
+            "output_dim": 100,
+            "n_heads": 2,
+            "n_layers": 6,
+            "filter_channels": 512,
+            "dropout": 0.1,
+        }
+    },
+    "train": {
+        // Basic settings
+        "batch_size": 64,
+        "gradient_accumulation_step": 1,
+        "max_epoch": -1,
+        // -1 means no limit
+        "save_checkpoint_stride": [
+            10,
+            100
+        ],
+        // unit is epoch
+        "keep_last": [
+            3,
+            -1
+        ],
+        // -1 means infinite, if one number will broadcast
+        "run_eval": [
+            false,
+            true
+        ],
+        // if one number will broadcast
+        // Fix the random seed
+        "random_seed": 10086,
+        // Batchsampler
+        "sampler": {
+            "holistic_shuffle": true,
+            "drop_last": true
+        },
+        // Dataloader
+        "dataloader": {
+            "num_worker": 32,
+            "pin_memory": true
+        },
+        // Trackers
+        "tracker": [
+            "tensorboard"
+            // "wandb",
+            // "cometml",
+            // "mlflow",
+        ],
+        // Optimizer
+        "optimizer": "AdamW",
+        "adamw": {
+            "lr": 4.0e-4
+            // nn model lr
+        },
+        // LR Scheduler
+        "scheduler": "ReduceLROnPlateau",
+        "reducelronplateau": {
+            "factor": 0.8,
+            "patience": 10,
+            // unit is epoch
+            "min_lr": 1.0e-4
+        }
+    }
+}
\ No newline at end of file
diff --git a/config/tts.json b/config/tts.json
new file mode 100644
index 0000000000000000000000000000000000000000..d46670ebe858f478e3699e49b88e87ec7c0e34d4
--- /dev/null
+++ b/config/tts.json
@@ -0,0 +1,23 @@
+{
+  "base_config": "config/base.json",  
+  "supported_model_type": [
+    "Fastspeech2",
+    "VITS",
+    "VALLE",
+  ],
+  "task_type": "tts",
+  "preprocess": {
+    "language": "en-us",
+    // linguistic features
+    "extract_phone": true,
+    "phone_extractor": "espeak", // "espeak, pypinyin, pypinyin_initials_finals, lexicon (only for language=en-us right now)"
+    "lexicon_path": "./text/lexicon/librispeech-lexicon.txt",
+    // Directory names of processed data or extracted features
+    "phone_dir": "phones",
+    "use_phone": true,
+  },
+  "model": {
+      "text_token_num": 512,
+  }
+
+}
diff --git a/config/valle.json b/config/valle.json
new file mode 100644
index 0000000000000000000000000000000000000000..929f79c3b31abb56a826c85b97f67d42b9e2ead8
--- /dev/null
+++ b/config/valle.json
@@ -0,0 +1,53 @@
+{
+    "base_config": "config/tts.json",
+    "model_type": "VALLE",
+    "task_type": "tts",
+    "dataset": [
+        "libritts"
+    ],
+    "preprocess": {
+        "extract_phone": true,
+        "phone_extractor": "espeak", // phoneme extractor: espeak, pypinyin, pypinyin_initials_finals or lexicon 
+        "extract_acoustic_token": true,
+        "acoustic_token_extractor": "Encodec", // acoustic token extractor: encodec, dac(todo)
+        "acoustic_token_dir": "acoutic_tokens",
+        "use_text": false,
+        "use_phone": true,
+        "use_acoustic_token": true,
+        "symbols_dict": "symbols.dict",
+        "min_duration": 0.5, // the duration lowerbound to filter the audio with duration < min_duration
+        "max_duration": 14, //  the duration uperbound to filter the audio with duration > max_duration.              
+        "sample_rate": 24000, 
+        "codec_hop_size": 320
+    },
+    "model": {
+        "text_token_num": 512,
+        "audio_token_num": 1024,
+        "decoder_dim": 1024, // embedding dimension of the decoder model
+        "nhead": 16, // number of attention heads in the decoder layers
+        "num_decoder_layers": 12, // number of decoder layers
+        "norm_first": true, // pre or post Normalization.
+        "add_prenet": false, // whether add PreNet after Inputs
+        "prefix_mode": 0, //  mode for how to prefix VALL-E NAR Decoder, 0: no prefix, 1: 0 to random, 2: random to random, 4: chunk of pre or post utterance
+        "share_embedding": true, // share the parameters of the output projection layer with the parameters of the acoustic embedding
+        "nar_scale_factor": 1, // model scale factor which will be assigned different meanings in different models
+        "prepend_bos": false, // whether prepend <BOS> to the acoustic tokens -> AR Decoder inputs
+        "num_quantizers": 8, // numbert of the audio quantization layers
+        // "scaling_xformers": false, // Apply Reworked Conformer scaling on Transformers 
+    },
+    "train": {
+        "ddp": false,
+        "train_stage": 1, // 0: train all modules, For VALL_E, support 1: AR Decoder 2: NAR Decoder(s)
+        "max_epoch": 20, 
+        "optimizer": "AdamW", 
+        "scheduler": "cosine",
+        "warmup_steps": 16000, // number of steps that affects how rapidly the learning rate decreases
+        "base_lr": 1e-4, // base learning rate."
+        "valid_interval": 1000,
+        "log_epoch_step": 1000,
+        "save_checkpoint_stride": [
+            1,
+            1
+        ]
+    }
+}
diff --git a/config/vits.json b/config/vits.json
new file mode 100644
index 0000000000000000000000000000000000000000..d8de83ab4641f5c4efef7752e7017d0733a7ddac
--- /dev/null
+++ b/config/vits.json
@@ -0,0 +1,101 @@
+{
+    "base_config": "config/tts.json",
+    "model_type": "VITS",
+    "task_type": "tts",
+    "preprocess": {
+        "extract_phone": true,
+        "extract_mel": true,
+        "n_mel": 80,
+        "fmin": 0,
+        "fmax": null,
+        "extract_linear_spec": true,
+        "extract_audio": true,
+        "use_linear": true,
+        "use_mel": true,
+        "use_audio": true,
+        "use_text": false,
+        "use_phone": true,
+        "lexicon_path": "./text/lexicon/librispeech-lexicon.txt",
+        "n_fft": 1024,
+        "win_size": 1024,
+        "hop_size": 256,
+        "segment_size": 8192,
+        "text_cleaners": [
+            "english_cleaners"
+        ]
+    },
+    "model": {
+        "text_token_num": 512,
+        "inter_channels": 192,
+        "hidden_channels": 192,
+        "filter_channels": 768,
+        "n_heads": 2,
+        "n_layers": 6,
+        "kernel_size": 3,
+        "p_dropout": 0.1,
+        "resblock": "1",
+        "resblock_kernel_sizes": [
+            3,
+            7,
+            11
+        ],
+        "resblock_dilation_sizes": [
+            [
+                1,
+                3,
+                5
+            ],
+            [
+                1,
+                3,
+                5
+            ],
+            [
+                1,
+                3,
+                5
+            ]
+        ],
+        "upsample_rates": [
+            8,
+            8,
+            2,
+            2
+        ],
+        "upsample_initial_channel": 512,
+        "upsample_kernel_sizes": [
+            16,
+            16,
+            4,
+            4
+        ],
+        "n_layers_q": 3,
+        "use_spectral_norm": false,
+        "n_speakers": 0, // number of speakers, while be automatically set if n_speakers is 0 and multi_speaker_training is true
+        "gin_channels": 256,
+        "use_sdp": true
+    },
+    "train": {
+        "fp16_run": true,
+        "learning_rate": 2e-4,
+        "betas": [
+            0.8,
+            0.99
+        ],
+        "eps": 1e-9,
+        "batch_size": 16,
+        "lr_decay": 0.999875,
+        // "segment_size": 8192,
+        "init_lr_ratio": 1,
+        "warmup_epochs": 0,
+        "c_mel": 45,
+        "c_kl": 1.0,
+        "AdamW": {
+            "betas": [
+                0.8,
+                0.99
+            ],
+            "eps": 1e-9,
+        }
+    }
+}
\ No newline at end of file
diff --git a/config/vitssvc.json b/config/vitssvc.json
new file mode 100644
index 0000000000000000000000000000000000000000..70687cbb364dd5b7e3c2af122bfcd225f172bb84
--- /dev/null
+++ b/config/vitssvc.json
@@ -0,0 +1,192 @@
+{
+    "base_config": "config/base.json",
+    "model_type": "VITS",
+    "task_type": "svc",
+    "preprocess": {
+        "extract_phone": false,
+        "extract_mel": true,
+        "extract_linear_spec": true,
+        "extract_audio": true,
+        "use_linear": true,
+        "use_mel": true,
+        "use_audio": true,
+        "use_text": false,
+        "use_phone": true,
+        
+        "fmin": 0,
+        "fmax": null,
+        "f0_min": 50,
+        "f0_max": 1100,
+        // f0_bin in sovits
+        "pitch_bin": 256,
+        // filter_length in sovits
+        "n_fft": 2048,
+        // hop_length in sovits
+        "hop_size": 512,
+        // win_length in sovits
+        "win_size": 2048,
+        "segment_size": 8192,
+        "n_mel": 100,
+        "sample_rate": 44100,
+
+        "mel_min_max_stats_dir": "mel_min_max_stats",
+        "whisper_dir": "whisper",
+        "contentvec_dir": "contentvec",
+        "wenet_dir": "wenet",
+        "mert_dir": "mert",
+    },
+    "model": {
+        "condition_encoder": {
+            "merge_mode": "add",
+            "input_melody_dim": 1,
+            "use_log_f0": true,
+            "n_bins_melody": 256,
+            //# Quantization (0 for not quantization)
+            "output_melody_dim": 196,
+            "input_loudness_dim": 1,
+            "use_log_loudness": false,
+            "n_bins_loudness": 256,
+            "output_loudness_dim": 196,
+            "use_whisper": false,
+            "use_contentvec": false,
+            "use_wenet": false,
+            "use_mert": false,
+            "whisper_dim": 1024,
+            "contentvec_dim": 256,
+            "mert_dim": 256,
+            "wenet_dim": 512,
+            "content_encoder_dim": 196,
+            "output_singer_dim": 196,
+            "singer_table_size": 512,
+            "output_content_dim": 196,
+            "use_spkid": true
+        },
+        "vits": {
+            "filter_channels": 256,
+            "gin_channels": 256,
+            "hidden_channels": 192,
+            "inter_channels": 192,
+            "kernel_size": 3,
+            "n_flow_layer": 4,
+            "n_heads": 2,
+            "n_layers": 6,
+            "n_layers_q": 3,
+            "n_speakers": 512,
+            "p_dropout": 0.1,
+            "ssl_dim": 256,
+            "use_spectral_norm": false,
+        },
+        "generator": "hifigan",
+        "generator_config": {
+            "hifigan": {
+                "resblock": "1",
+                "resblock_kernel_sizes": [
+                    3,
+                    7,
+                    11
+                ],
+                "upsample_rates": [
+                    8,8,2,2,2
+                ],
+                "upsample_kernel_sizes": [
+                    16,16,4,4,4
+                ],
+                "upsample_initial_channel": 512,
+                "resblock_dilation_sizes": [
+                    [1,3,5],
+                    [1,3,5],
+                    [1,3,5]
+                ]
+            },
+            "melgan": {
+                "ratios": [8, 8, 2, 2, 2],
+                "ngf": 32,
+                "n_residual_layers": 3,
+                "num_D": 3,
+                "ndf": 16,
+                "n_layers": 4,
+                "downsampling_factor": 4
+            },
+            "bigvgan": {
+                "resblock": "1",
+                "activation": "snakebeta",
+                "snake_logscale": true,
+                "upsample_rates": [
+                    8,8,2,2,2,
+                ],
+                "upsample_kernel_sizes": [
+                    16,16,4,4,4,
+                ],
+                "upsample_initial_channel": 512,
+                "resblock_kernel_sizes": [
+                    3,
+                    7,
+                    11
+                ],
+                "resblock_dilation_sizes": [
+                    [1,3,5],
+                    [1,3,5],
+                    [1,3,5]
+                ]
+            },
+            "nsfhifigan": {
+                "resblock": "1",
+                "harmonic_num": 8,
+                "upsample_rates": [
+                    8,8,2,2,2,
+                ],
+                "upsample_kernel_sizes": [
+                    16,16,4,4,4,
+                ],
+                "upsample_initial_channel": 768,
+                "resblock_kernel_sizes": [
+                    3,
+                    7,
+                    11
+                ],
+                "resblock_dilation_sizes": [
+                    [1,3,5],
+                    [1,3,5],
+                    [1,3,5]
+                ]
+            },
+            "apnet": {
+              "ASP_channel": 512,
+              "ASP_resblock_kernel_sizes": [3,7,11],
+              "ASP_resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+              "ASP_input_conv_kernel_size": 7,
+              "ASP_output_conv_kernel_size": 7,
+        
+              "PSP_channel": 512,
+              "PSP_resblock_kernel_sizes": [3,7,11],
+              "PSP_resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]], 
+              "PSP_input_conv_kernel_size": 7,
+              "PSP_output_R_conv_kernel_size": 7,
+              "PSP_output_I_conv_kernel_size": 7,
+            }
+        },
+    },
+    "train": {
+        "fp16_run": true,
+        "learning_rate": 2e-4,
+        "betas": [
+            0.8,
+            0.99
+        ],
+        "eps": 1e-9,
+        "batch_size": 16,
+        "lr_decay": 0.999875,
+        // "segment_size": 8192,
+        "init_lr_ratio": 1,
+        "warmup_epochs": 0,
+        "c_mel": 45,
+        "c_kl": 1.0,
+        "AdamW": {
+            "betas": [
+                0.8,
+                0.99
+            ],
+            "eps": 1e-9,
+        }
+    }
+}
\ No newline at end of file
diff --git a/config/vocoder.json b/config/vocoder.json
new file mode 100644
index 0000000000000000000000000000000000000000..1ce29c67f4c1dc6406634b170ca73ef731d4b000
--- /dev/null
+++ b/config/vocoder.json
@@ -0,0 +1,84 @@
+{
+  "base_config": "config/base.json",
+  "dataset": [
+    "LJSpeech",
+    "LibriTTS",
+    "opencpop",
+    "m4singer",
+    "svcc",
+    "svcceval",
+    "pjs",
+    "opensinger",
+    "popbutfy",
+    "nus48e",
+    "popcs",
+    "kising",
+    "csd",
+    "opera",
+    "vctk",
+    "lijian",
+    "cdmusiceval"
+  ],
+  "task_type": "vocoder",
+  "preprocess": {
+    // acoustic features
+    "extract_mel": true,
+    "extract_pitch": false,
+    "extract_uv": false,
+    "extract_audio": true,
+    "extract_label": false,
+    "extract_one_hot": false,
+    "extract_amplitude_phase": false,
+    "pitch_extractor": "parselmouth",
+    // Settings for data preprocessing
+    "n_mel": 100,
+    "win_size": 1024,
+    "hop_size": 256,
+    "sample_rate": 24000,
+    "n_fft": 1024,
+    "fmin": 0,
+    "fmax": 12000,
+    "f0_min": 50,
+    "f0_max": 1100,
+    "pitch_bin": 256,
+    "pitch_max": 1100.0,
+    "pitch_min": 50.0,
+    "is_mu_law": false,
+    "bits": 8,
+    "cut_mel_frame": 32,
+    // Directory names of processed data or extracted features
+    "spk2id": "singers.json",
+    // Features used for model training
+    "use_mel": true,
+    "use_frame_pitch": false,
+    "use_uv": false,
+    "use_audio": true,
+    "use_label": false,
+    "use_one_hot": false,
+    "train_file": "train.json",
+    "valid_file": "test.json"
+  },
+  "train": {
+    "random_seed": 114514,
+    "batch_size": 64,
+    "gradient_accumulation_step": 1,
+    "max_epoch": 1000000,
+    "save_checkpoint_stride": [
+      20
+    ],
+    "run_eval": [
+      true
+    ],
+    "sampler": {
+      "holistic_shuffle": true,
+      "drop_last": true
+    },
+    "dataloader": {
+      "num_worker": 4,
+      "pin_memory": true
+    },
+    "tracker": [
+      "tensorboard"
+    ],
+  }
+}
\ No newline at end of file
diff --git a/egs/datasets/README.md b/egs/datasets/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..d6a4931373ad9986a52ed17d7574a6502c6b4d04
--- /dev/null
+++ b/egs/datasets/README.md
@@ -0,0 +1,381 @@
+# Datasets Format
+
+Amphion support the following academic datasets (sort alphabetically):
+
+- [Datasets Format](#datasets-format)
+  - [AudioCaps](#audiocaps)
+  - [CSD](#csd)
+  - [KiSing](#kising)
+  - [LibriTTS](#libritts)
+  - [LJSpeech](#ljspeech)
+  - [M4Singer](#m4singer)
+  - [NUS-48E](#nus-48e)
+  - [Opencpop](#opencpop)
+  - [OpenSinger](#opensinger)
+  - [Opera](#opera)
+  - [PopBuTFy](#popbutfy)
+  - [PopCS](#popcs)
+  - [PJS](#pjs)
+  - [SVCC](#svcc)
+  - [VCTK](#vctk)
+
+The downloading link and the file structure tree of each dataset is displayed as follows.
+
+## AudioCaps
+
+AudioCaps is a dataset of around 44K audio-caption pairs, where each audio clip corresponds to a caption with rich semantic information. You can download the dataset [here](https://github.com/cdjkim/audiocaps). The file structure tree is like:
+
+```plaintext
+[AudioCaps dataset path]
+┣ AudioCpas
+┃   ┣ wav
+┃   ┃   ┣ ---1_cCGK4M_0_10000.wav
+┃   ┃   ┣ ---lTs1dxhU_30000_40000.wav
+┃   ┃   ┣ ...
+```
+
+## CSD
+
+The official CSD dataset can be download [here](https://zenodo.org/records/4785016). The file structure tree is like:
+
+```plaintext
+[CSD dataset path]
+ ┣ english
+ ┣ korean
+ ┣ utterances
+ ┃ ┣ en001a
+ ┃ ┃ ┣ {UtterenceID}.wav
+ ┃ ┣ en001b
+ ┃ ┣ en002a
+ ┃ ┣ en002b
+ ┃ ┣ ...
+ ┣ README
+```
+
+## KiSing
+
+The official KiSing dataset can be download [here](http://shijt.site/index.php/2021/05/16/kising-the-first-open-source-mandarin-singing-voice-synthesis-corpus/). The file structure tree is like:
+
+```plaintext
+[KiSing dataset path]
+ ┣ clean
+ ┃ ┣ 421
+ ┃ ┣ 422
+ ┃ ┣ ...
+```
+
+## LibriTTS
+
+The official LibriTTS dataset can be download [here](https://www.openslr.org/60/). The file structure tree is like:
+
+```plaintext
+[LibriTTS dataset path]
+ ┣ BOOKS.txt
+ ┣ CHAPTERS.txt
+ ┣ eval_sentences10.tsv
+ ┣ LICENSE.txt
+ ┣ NOTE.txt
+ ┣ reader_book.tsv
+ ┣ README_librispeech.txt
+ ┣ README_libritts.txt 
+ ┣ speakers.tsv
+ ┣ SPEAKERS.txt
+ ┣ dev-clean (Subset)
+ ┃ ┣ 1272{Speaker_ID}
+ ┃ ┃ ┣ 128104 {Chapter_ID}
+ ┃ ┃ ┃ ┣ 1272_128104_000001_000000.normalized.txt
+ ┃ ┃ ┃ ┣ 1272_128104_000001_000000.original.txt
+ ┃ ┃ ┃ ┣ 1272_128104_000001_000000.wav
+ ┃ ┃ ┃ ┣ ...
+ ┃ ┃ ┃ ┣ 1272_128104.book.tsv
+ ┃ ┃ ┃ ┣ 1272_128104.trans.tsv
+ ┃ ┃ ┣ ...
+ ┃ ┣ ...
+ ┣ dev-other (Subset)
+ ┃ ┣ 116 (Speaker)
+ ┃ ┃ ┣ 288045 {Chapter_ID}
+ ┃ ┃ ┃ ┣ 116_288045_000003_000000.normalized.txt
+ ┃ ┃ ┃ ┣ 116_288045_000003_000000.original.txt
+ ┃ ┃ ┃ ┣ 116_288045_000003_000000.wav
+ ┃ ┃ ┃ ┣ ...
+ ┃ ┃ ┃ ┣ 116_288045.book.tsv
+ ┃ ┃ ┃ ┣ 116_288045.trans.tsv
+ ┃ ┃ ┣ ...
+ ┃ ┣ ...
+ ┃ ┣ ...
+ ┣ test-clean  (Subset)
+ ┃ ┣ {Speaker_ID}
+ ┃ ┃ ┣ {Chapter_ID}
+ ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.normalized.txt
+ ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.original.txt
+ ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.wav
+ ┃ ┃ ┃ ┣ ...
+ ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}.book.tsv
+ ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}.trans.tsv
+ ┃ ┃ ┣ ...
+ ┃ ┣ ...
+ ┣ test-other
+ ┃ ┣ {Speaker_ID}
+ ┃ ┃ ┣ {Chapter_ID}
+ ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.normalized.txt
+ ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.original.txt
+ ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.wav
+ ┃ ┃ ┃ ┣ ...
+ ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}.book.tsv
+ ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}.trans.tsv
+ ┃ ┃ ┣ ...
+ ┃ ┣ ...
+ ┣ train-clean-100
+ ┃ ┣ {Speaker_ID}
+ ┃ ┃ ┣ {Chapter_ID}
+ ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.normalized.txt
+ ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.original.txt
+ ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.wav
+ ┃ ┃ ┃ ┣ ...
+ ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}.book.tsv
+ ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}.trans.tsv
+ ┃ ┃ ┣ ...
+ ┃ ┣ ...
+ ┣ train-clean-360
+ ┃ ┣ {Speaker_ID}
+ ┃ ┃ ┣ {Chapter_ID}
+ ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.normalized.txt
+ ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.original.txt
+ ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.wav
+ ┃ ┃ ┃ ┣ ...
+ ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}.book.tsv
+ ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}.trans.tsv
+ ┃ ┃ ┣ ...
+ ┃ ┣ ...
+ ┣ train-other-500
+ ┃ ┣ {Speaker_ID}
+ ┃ ┃ ┣ {Chapter_ID}
+ ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.normalized.txt
+ ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.original.txt
+ ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}_{Utterance_ID}.wav
+ ┃ ┃ ┃ ┣ ...
+ ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}.book.tsv
+ ┃ ┃ ┃ ┣ {Speaker_ID}_{Chapter_ID}.trans.tsv
+ ┃ ┃ ┣ ...
+ ┃ ┣ ...
+```
+
+
+## LJSpeech
+
+The official LJSpeech dataset can be download [here](https://keithito.com/LJ-Speech-Dataset/). The file structure tree is like:
+
+```plaintext
+[LJSpeech dataset path]
+ ┣ metadata.csv
+ ┣ wavs
+ ┃ ┣ LJ001-0001.wav
+ ┃ ┣ LJ001-0002.wav 
+ ┃ ┣ ...
+ ┣ README
+```
+
+## M4Singer
+
+The official M4Singer dataset can be downloaded [here](https://drive.google.com/file/d/1xC37E59EWRRFFLdG3aJkVqwtLDgtFNqW/view). The file structure tree is like:
+
+```plaintext
+[M4Singer dataset path]
+ ┣ {Singer_1}#{Song_1}
+ ┃ ┣ 0000.mid
+ ┃ ┣ 0000.TextGrid
+ ┃ ┣ 0000.wav
+ ┃ ┣ ...
+ ┣ {Singer_1}#{Song_2}
+ ┣ ...
+ ┣ {Singer_2}#{Song_1}
+ ┣ {Singer_2}#{Song_2}
+ ┣ ...
+ ┗ meta.json
+```
+
+## NUS-48E
+
+The official NUS-48E dataset can be download [here](https://drive.google.com/drive/folders/12pP9uUl0HTVANU3IPLnumTJiRjPtVUMx). The file structure tree is like:
+
+```plaintext
+[NUS-48E dataset path]
+ ┣ {SpeakerID}
+ ┃ ┣ read
+ ┃ ┃ ┣ {SongID}.txt
+ ┃ ┃ ┣ {SongID}.wav
+ ┃ ┃ ┣ ...
+ ┃ ┣ sing
+ ┃ ┃ ┣ {SongID}.txt
+ ┃ ┃ ┣ {SongID}.wav
+ ┃ ┃ ┣ ...
+ ┣ ...
+ ┣ README.txt
+
+```
+
+## Opencpop
+
+The official Opera dataset can be downloaded [here](https://wenet.org.cn/opencpop/). The file structure tree is like:
+
+```plaintext
+[Opencpop dataset path]
+ ┣ midis
+ ┃ ┣ 2001.midi
+ ┃ ┣ 2002.midi
+ ┃ ┣ 2003.midi
+ ┃ ┣ ...
+ ┣ segments
+ ┃ ┣ wavs
+ ┃ ┃ ┣ 2001000001.wav
+ ┃ ┃ ┣ 2001000002.wav
+ ┃ ┃ ┣ 2001000003.wav
+ ┃ ┃ ┣ ...
+ ┃ ┣ test.txt
+ ┃ ┣ train.txt
+ ┃ ┗ transcriptions.txt
+ ┣ textgrids
+ ┃ ┣ 2001.TextGrid
+ ┃ ┣ 2002.TextGrid
+ ┃ ┣ 2003.TextGrid
+ ┃ ┣ ...
+ ┣ wavs
+ ┃ ┣ 2001.wav
+ ┃ ┣ 2002.wav
+ ┃ ┣ 2003.wav
+ ┃ ┣ ...
+ ┣ TERMS_OF_ACCESS
+ ┗ readme.md
+```
+
+## OpenSinger
+
+The official OpenSinger dataset can be downloaded [here](https://drive.google.com/file/d/1EofoZxvalgMjZqzUEuEdleHIZ6SHtNuK/view). The file structure tree is like:
+
+```plaintext
+[OpenSinger dataset path]
+ ┣ ManRaw
+ ┃ ┣ {Singer_1}_{Song_1}
+ ┃ ┃ ┣ {Singer_1}_{Song_1}_0.lab
+ ┃ ┃ ┣ {Singer_1}_{Song_1}_0.txt
+ ┃ ┃ ┣ {Singer_1}_{Song_1}_0.wav
+ ┃ ┃ ┣ ...
+ ┃ ┣ {Singer_1}_{Song_2}
+ ┃ ┣ ...
+ ┣ WomanRaw
+ ┣ LICENSE
+ ┗ README.md
+```
+
+## Opera
+
+The official Opera dataset can be downloaded [here](http://isophonics.net/SingingVoiceDataset). The file structure tree is like:
+
+```plaintext
+[Opera dataset path]
+ ┣ monophonic
+ ┃ ┣ chinese
+ ┃ ┃ ┣ {Gender}_{SingerID}
+ ┃ ┃ ┃ ┣ {Emotion}_{SongID}.wav
+ ┃ ┃ ┃ ┣ ...
+ ┃ ┃ ┣ ...
+ ┃ ┣ western
+ ┣ polyphonic
+ ┃ ┣ chinese
+ ┃ ┣ western
+ ┣ CrossculturalDataSet.xlsx
+```
+
+## PopBuTFy
+
+The official PopBuTFy dataset can be downloaded [here](https://github.com/MoonInTheRiver/NeuralSVB). The file structure tree is like:
+
+```plaintext
+[PopBuTFy dataset path]
+ ┣ data
+ ┃ ┣ {SingerID}#singing#{SongName}_Amateur
+ ┃ ┃ ┣ {SingerID}#singing#{SongName}_Amateur_{UtteranceID}.mp3
+ ┃ ┃ ┣ ...
+ ┃ ┣ {SingerID}#singing#{SongName}_Professional
+ ┃ ┃ ┣ {SingerID}#singing#{SongName}_Professional_{UtteranceID}.mp3
+ ┃ ┃ ┣ ...
+ ┣ text_labels
+ ┗ TERMS_OF_ACCESS
+```
+
+## PopCS
+
+The official PopCS dataset can be downloaded [here](https://github.com/MoonInTheRiver/DiffSinger/blob/master/resources/apply_form.md). The file structure tree is like:
+
+```plaintext
+[PopCS dataset path]
+ ┣ popcs
+ ┃ ┣ popcs-{SongName}
+ ┃ ┃ ┣ {UtteranceID}_ph.txt
+ ┃ ┃ ┣ {UtteranceID}_wf0.wav
+ ┃ ┃ ┣ {UtteranceID}.TextGrid
+ ┃ ┃ ┣ {UtteranceID}.txt
+ ┃ ┃ ┣ ...
+ ┃ ┣ ...
+ ┗ TERMS_OF_ACCESS
+```
+
+## PJS
+
+The official PJS dataset can be downloaded [here](https://sites.google.com/site/shinnosuketakamichi/research-topics/pjs_corpus). The file structure tree is like:
+
+```plaintext
+[PJS dataset path]
+ ┣ PJS_corpus_ver1.1
+ ┃ ┣ background_noise
+ ┃ ┣ pjs{SongID}
+ ┃ ┃ ┣ pjs{SongID}_song.wav
+ ┃ ┃ ┣ pjs{SongID}_speech.wav
+ ┃ ┃ ┣ pjs{SongID}.lab
+ ┃ ┃ ┣ pjs{SongID}.mid
+ ┃ ┃ ┣ pjs{SongID}.musicxml
+ ┃ ┃ ┣ pjs{SongID}.txt
+ ┃ ┣ ...
+```
+
+## SVCC
+
+The official SVCC dataset can be downloaded [here](https://github.com/lesterphillip/SVCC23_FastSVC/tree/main/egs/generate_dataset). The file structure tree is like:
+
+```plaintext
+[SVCC dataset path]
+ ┣ Data
+ ┃ ┣ CDF1
+ ┃ ┃ ┣ 10001.wav
+ ┃ ┃ ┣ 10002.wav
+ ┃ ┃ ┣ ...
+ ┃ ┣ CDM1
+ ┃ ┣ IDF1
+ ┃ ┣ IDM1
+ ┗ README.md
+```
+
+## VCTK
+
+The official VCTK dataset can be downloaded [here](https://datashare.ed.ac.uk/handle/10283/3443). The file structure tree is like:
+
+```plaintext
+[VCTK dataset path]
+ ┣ txt
+ ┃ ┣ {Speaker_1}
+ ┃ ┃ ┣ {Speaker_1}_001.txt
+ ┃ ┃ ┣ {Speaker_1}_002.txt
+ ┃ ┃ ┣ ...
+ ┃ ┣ {Speaker_2}
+ ┃ ┣ ...
+ ┣ wav48_silence_trimmed
+ ┃ ┣ {Speaker_1}
+ ┃ ┃ ┣ {Speaker_1}_001_mic1.flac
+ ┃ ┃ ┣ {Speaker_1}_001_mic2.flac
+ ┃ ┃ ┣ {Speaker_1}_002_mic1.flac
+ ┃ ┃ ┣ ...
+ ┃ ┣ {Speaker_2}
+ ┃ ┣ ...
+ ┣ speaker-info.txt
+ ┗ update.txt
+```
diff --git a/egs/metrics/README.md b/egs/metrics/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..882b31365cdd69b381cc8b9bef77509e94c9deb9
--- /dev/null
+++ b/egs/metrics/README.md
@@ -0,0 +1,94 @@
+# Amphion Evaluation Recipe
+
+## Supported Evaluation Metrics
+
+Until now, Amphion Evaluation has supported the following objective metrics:
+
+- **F0 Modeling**:
+  - F0 Pearson Coefficients (FPC)
+  - F0 Periodicity Root Mean Square Error (PeriodicityRMSE)
+  - F0 Root Mean Square Error (F0RMSE)
+  - Voiced/Unvoiced F1 Score (V/UV F1)
+- **Energy Modeling**:
+  - Energy Root Mean Square Error (EnergyRMSE)
+  - Energy Pearson Coefficients (EnergyPC)
+- **Intelligibility**:
+  - Character Error Rate (CER) based on [Whipser](https://github.com/openai/whisper)
+  - Word Error Rate (WER) based on [Whipser](https://github.com/openai/whisper)
+- **Spectrogram Distortion**:
+  - Frechet Audio Distance (FAD)
+  - Mel Cepstral Distortion (MCD)
+  - Multi-Resolution STFT Distance (MSTFT)
+  - Perceptual Evaluation of Speech Quality (PESQ)
+  - Short Time Objective Intelligibility (STOI)
+  - Scale Invariant Signal to Distortion Ratio (SISDR)
+  - Scale Invariant Signal to Noise Ratio (SISNR)
+- **Speaker Similarity**:
+  - Cosine similarity based on [Rawnet3](https://github.com/Jungjee/RawNet)
+  - Cosine similarity based on [WeSpeaker](https://github.com/wenet-e2e/wespeaker) (👨‍💻 developing)
+
+We provide a recipe to demonstrate how to objectively evaluate your generated audios. There are three steps in total:
+
+1. Pretrained Models Preparation
+2. Audio Data Preparation
+3. Evaluation
+
+## 1. Pretrained Models Preparation
+
+If you want to calculate `RawNet3` based speaker similarity, you need to download the pretrained model first, as illustrated [here](../../pretrained/README.md).
+
+## 2. Aduio Data Preparation
+
+Prepare reference audios and generated audios in two folders, the `ref_dir` contains the reference audio and the `gen_dir` contains the generated audio. Here is an example.
+
+```plaintext
+ ┣ {ref_dir}
+ ┃ ┣ sample1.wav
+ ┃ ┣ sample2.wav
+ ┣ {gen_dir}
+ ┃ ┣ sample1.wav
+ ┃ ┣ sample2.wav
+```
+
+You have to make sure that the pairwise **reference audio and generated audio are named the same**, as illustrated above (sample1 to sample1, sample2 to sample2).
+
+## 3. Evaluation
+
+Run the `run.sh` with specified refenrece folder, generated folder, dump folder and metrics.
+
+```bash
+cd Amphion
+sh egs/metrics/run.sh \
+	--reference_folder [Your path to the reference audios] \
+	--generated_folder [Your path to the generated audios] \
+	--dump_folder [Your path to dump the objective results] \
+	--metrics [The metrics you need] \
+	--fs [Optional. To calculate all metrics in the specified sampling rate]
+```
+
+As for the metrics, an example is provided below:
+
+```bash
+--metrics "mcd pesq fad"
+```
+
+All currently available metrics keywords are listed below:
+
+| Keys                  | Description                                |
+| --------------------- | ------------------------------------------ |
+| `fpc`                 | F0 Pearson Coefficients                    |
+| `f0_periodicity_rmse` | F0 Periodicity Root Mean Square Error      |
+| `f0rmse`              | F0 Root Mean Square Error                  |
+| `v_uv_f1`             | Voiced/Unvoiced F1 Score                   |
+| `energy_rmse`         | Energy Root Mean Square Error              |
+| `energy_pc`           | Energy Pearson Coefficients                |
+| `cer`                 | Character Error Rate                       |
+| `wer`                 | Word Error Rate                            |
+| `speaker_similarity`  | Cos Similarity based on RawNet3            |
+| `fad`                 | Frechet Audio Distance                     |
+| `mcd`                 | Mel Cepstral Distortion                    |
+| `mstft`               | Multi-Resolution STFT Distance             |
+| `pesq`                | Perceptual Evaluation of Speech Quality    |
+| `si_sdr`              | Scale Invariant Signal to Distortion Ratio |
+| `si_snr`              | Scale Invariant Signal to Noise Ratio      |
+| `stoi`                | Short Time Objective Intelligibility       |
diff --git a/egs/metrics/run.sh b/egs/metrics/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..6778f36454b788938befd19dca1bf2ef466f4cf9
--- /dev/null
+++ b/egs/metrics/run.sh
@@ -0,0 +1,42 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+######## Build Experiment Environment ###########
+exp_dir=$(cd `dirname $0`; pwd)
+work_dir=$(dirname $(dirname $exp_dir))
+
+export WORK_DIR=$work_dir
+export PYTHONPATH=$work_dir
+export PYTHONIOENCODING=UTF-8
+
+######## Parse the Given Parameters from the Commond ###########
+options=$(getopt -o c:n:s --long gpu:,reference_folder:,generated_folder:,dump_folder:,metrics:,fs: -- "$@")
+eval set -- "$options"
+
+while true; do
+  case $1 in
+    # Reference Audio Folder
+    --reference_folder) shift; ref_dir=$1 ; shift ;;
+    # Generated Audio Folder
+    --generated_folder) shift; deg_dir=$1 ; shift ;;
+    # Result Dumping Folder
+    --dump_folder) shift; dump_dir=$1 ; shift ;;
+    # Metrics to Compute
+    --metrics) shift; metrics=$1 ; shift ;;
+    # Sampling Rate
+    --fs) shift; fs=$1 ; shift ;;
+
+    --) shift ; break ;;
+    *) echo "Invalid option: $1" exit 1 ;;
+  esac
+done
+
+######## Calculate Objective Metrics ###########
+CUDA_VISIBLE_DEVICES=$gpu python "$work_dir"/bins/calc_metrics.py \
+    --ref_dir $ref_dir \
+    --deg_dir $deg_dir \
+    --dump_dir $dump_dir \
+    --metrics $metrics \
+    --fs $fs \
\ No newline at end of file
diff --git a/egs/svc/DiffComoSVC/README.md b/egs/svc/DiffComoSVC/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..aecf0d33fdb31f566339a1a29926457541efe8a3
--- /dev/null
+++ b/egs/svc/DiffComoSVC/README.md
@@ -0,0 +1,234 @@
+# Accelerating Diffusion-based Singing Voice Conversion through Consistency Distillation
+<br>
+<div align="center">
+<img src="../../../imgs/svc/DiffComoSVC.png" width="90%">
+</div>
+<br>
+
+This is an implement of [Consistency Models](https://arxiv.org/abs/2303.01469) for accelerating diffusion-based singing voice conversion. The overall architecture follows "[Leveraging Content-based Features from Multiple Acoustic Models for Singing Voice Conversion](https://arxiv.org/abs/2310.11160)" (NeurIPS 2023 Workshop on Machine Learning for Audio), only a slightly modification is applied on acoustic model. Specifically,
+
+* The acoustic model is a conformer which generates a coarse spectrogram and a diffusion decoder based on Bidirectional Non-Causal Dilated CNN which polish the former spectrogram for better. This is similar to [CoMoSpeech: One-Step Speech and Singing Voice Synthesis via Consistency Model](https://comospeech.github.io/)
+* To accelerate diffusion model, we apply consistency distillation from [Consistency Models](https://arxiv.org/abs/2303.01469). For teacher model, the diffusion schedule of the diffusion decoder follows [karras diffusion](https://arxiv.org/abs/2206.00364). For distilling teacher model, the condition encoder and the conformer part of acoustic model are frozen while the diffusion decoder model is updated via exponential moving average. See Figure above for details.
+
+There are five stages in total:
+
+1. Data preparation
+2. Features extraction
+3. Teacher Model Training
+4. Consistency Distillation
+5. Inference/conversion
+
+## 1. Data Preparation
+
+### Dataset Download
+
+By default, we utilize the five datasets for training: M4Singer, Opencpop, OpenSinger, SVCC, and VCTK. How to download them is detailed [here](../../datasets/README.md).
+
+### Configuration
+
+Specify the dataset paths in  `exp_config.json`. Note that you can change the `dataset` list to use your preferred datasets.
+
+```json
+    "dataset": [
+        "m4singer",
+        "opencpop",
+        "opensinger",
+        "svcc",
+        "vctk"
+    ],
+    "dataset_path": {
+        // TODO: Fill in your dataset path
+        "m4singer": "[M4Singer dataset path]",
+        "opencpop": "[Opencpop dataset path]",
+        "opensinger": "[OpenSinger dataset path]",
+        "svcc": "[SVCC dataset path]",
+        "vctk": "[VCTK dataset path]"
+    },
+```
+
+## 2. Features Extraction
+
+### Content-based Pretrained Models Download
+
+By default, we utilize the Whisper and ContentVec to extract content features. How to download them is detailed [here](../../../pretrained/README.md).
+
+### Configuration
+
+Specify the dataset path and the output path for saving the processed data and the training model in `exp_config.json`:
+
+```json
+    // TODO: Fill in the output log path
+    "log_dir": "[Your path to save logs and checkpoints]",
+    "preprocess": {
+        // TODO: Fill in the output data path
+        "processed_dir": "[Your path to save processed data]",
+        ...
+    },
+```
+
+### Run
+
+Run the `run.sh` as the preproces stage (set  `--stage 1`).
+
+```bash
+cd Amphion
+sh egs/svc/DiffComoSVC/run.sh --stage 1
+```
+
+Note: The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "1"`.
+
+## 3. Teacher Model Training
+
+### Configuration
+
+Set the `distill` in `config/comosvc.json` to `false` for teacher model training, you can also specify the detailed configuration for conformer encoder and diffusion process here: 
+
+```JSON
+"comosvc":{
+            "distill": false,
+            // conformer encoder
+            "input_dim": 384,
+            "output_dim": 100,
+            "n_heads": 2,
+            "n_layers": 6,
+            "filter_channels":512,
+            // karras diffusion
+            "P_mean": -1.2,
+            "P_std": 1.2,
+            "sigma_data": 0.5,
+            "sigma_min": 0.002,
+            "sigma_max": 80,
+            "rho": 7,
+            "n_timesteps": 40,
+        },
+```
+
+We provide the default hyparameters in the `exp_config.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on you GPU machines.
+
+```json
+"train": {
+        "batch_size": 32,
+        ...
+        "adamw": {
+            "lr": 2.0e-4
+        },
+        ...
+    }
+```
+
+### Run
+
+Run the `run.sh` as the training stage (set  `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `[Your path to save logs and checkpoints]/[YourExptName]`.
+
+```bash
+cd Amphion
+sh egs/svc/DiffComoSVC/run.sh --stage 2 --name [YourExptName]
+```
+
+Note: The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can specify it when running `run.sh` such as:
+
+```bash
+cd Amphion
+sh egs/svc/DiffComoSVC/run.sh --stage 2 --name [YourExptName] --gpu "0,1,2,3"
+```
+
+## 4. Consistency Distillation
+
+### Configuration
+
+Set the `distill` in `config/comosvc.json` to `true` for teacher model training, and specify the `teacher_model_path` for consistency distillation. You can also specify the detailed configuration for conformer encoder and diffusion process here: 
+
+```JSON
+"model": {
+    "teacher_model_path":"[Your_teacher_model_checkpoint].bin",
+    ...
+    "comosvc":{
+                "distill": true,
+                // conformer encoder
+                "input_dim": 384,
+                "output_dim": 100,
+                "n_heads": 2,
+                "n_layers": 6,
+                "filter_channels":512,
+                // karras diffusion
+                "P_mean": -1.2,
+                "P_std": 1.2,
+                "sigma_data": 0.5,
+                "sigma_min": 0.002,
+                "sigma_max": 80,
+                "rho": 7,
+                "n_timesteps": 40,
+            },
+```
+
+We provide the default hyparameters in the `exp_config.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on you GPU machines.
+
+```json
+"train": {
+        "batch_size": 32,
+        ...
+        "adamw": {
+            "lr": 2.0e-4
+        },
+        ...
+    }
+```
+
+### Run
+
+Run the `run.sh` as the training stage (set  `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `[Your path to save logs and checkpoints]/[YourExptName]`.
+
+```bash
+cd Amphion
+sh egs/svc/DiffComoSVC/run.sh --stage 2 --name [YourExptName]
+```
+
+Note: The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can specify it when running `run.sh` such as:
+
+```bash
+cd Amphion
+sh egs/svc/DiffComoSVC/run.sh --stage 2 --name [YourExptName] --gpu "0,1,2,3"
+```
+
+## 5. Inference/Conversion
+
+### Pretrained Vocoder Download
+
+We fine-tune the official BigVGAN pretrained model with over 120 hours singing voice data. The benifits of fine-tuning has been investigated in our paper (see this [demo page](https://www.zhangxueyao.com/data/MultipleContentsSVC/vocoder.html)). The final pretrained singing voice vocoder is released [here](../../../pretrained/README.md#amphion-singing-bigvgan) (called `Amphion Singing BigVGAN`).
+
+### Run
+
+For inference/conversion, you need to specify the following configurations when running `run.sh`:
+
+| Parameters                                          | Description                                                  | Example                                                      |
+| --------------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
+| `--infer_expt_dir`                                  | The experimental directory which contains `checkpoint`       | `[Your path to save logs and checkpoints]/[YourExptName]`    |
+| `--infer_output_dir`                                | The output directory to save inferred audios.                | `[Your path to save logs and checkpoints]/[YourExptName]/result` |
+| `--infer_source_file` or `--infer_source_audio_dir` | The inference source (can be a json file or a dir).          | The `infer_source_file` could be `[Your path to save processed data]/[YourDataset]/test.json`, and the `infer_source_audio_dir` is a folder which includes several audio files (*.wav, *.mp3 or *.flac). |
+| `--infer_target_speaker`                            | The target speaker you want to convert into. You can refer to `[Your path to save logs and checkpoints]/[YourExptName]/singers.json` to choose a trained speaker. | For opencpop dataset, the speaker name would be `opencpop_female1`. |
+| `--infer_key_shift`                                 | How many semitones you want to transpose.                    | `"autoshfit"` (by default), `3`, `-3`, etc.                  |
+
+For example, if you want to make `opencpop_female1` sing the songs in the `[Your Audios Folder]`, just run:
+
+```bash
+cd Amphion
+sh egs/svc/DiffComoSVC/run.sh --stage 3 --gpu "0" \
+    --infer_expt_dir [Your path to save logs and checkpoints]/[YourExptName] \
+    --infer_output_dir [Your path to save logs and checkpoints]/[YourExptName]/result \
+    --infer_source_audio_dir [Your Audios Folder] \
+    --infer_target_speaker "opencpop_female1" \
+    --infer_key_shift "autoshift"
+```
+Specially, you can configurate the inference steps for teacher model by setting `inference` at `exp_config`(student model is always one-step sampling):
+```json
+    "inference": {
+        "comosvc": {
+            "inference_steps": 40
+        }
+    }
+```
+
+# Reference
+https://github.com/zhenye234/CoMoSpeech
+
+https://github.com/openai/consistency_models
\ No newline at end of file
diff --git a/egs/svc/DiffComoSVC/exp_config.json b/egs/svc/DiffComoSVC/exp_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..37f977f21a09ea64eb218388b38fe1c1f7350a04
--- /dev/null
+++ b/egs/svc/DiffComoSVC/exp_config.json
@@ -0,0 +1,143 @@
+{
+    "base_config": "config/comosvc.json",
+    "model_type": "DiffComoSVC",
+    "dataset": [
+        "m4singer",
+        "opencpop",
+        "opensinger",
+        "svcc",
+        "vctk"
+    ],
+    "dataset_path": {
+        // TODO: Fill in your dataset path
+        "m4singer": "[M4Singer dataset path]",
+        "opencpop": "[Opencpop dataset path]",
+        "opensinger": "[OpenSinger dataset path]",
+        "svcc": "[SVCC dataset path]",
+        "vctk": "[VCTK dataset path]"
+    },
+    // TODO: Fill in the output log path
+    "log_dir": "[Your path to save logs and checkpoints]",
+    "preprocess": {
+        // TODO: Fill in the output data path
+        "processed_dir": "[Your path to save processed data]",
+        // Config for features extraction
+        "extract_mel": true,
+        "extract_pitch": true,
+        "extract_energy": true,
+        "extract_whisper_feature": true,
+        "extract_contentvec_feature": true,
+        "extract_wenet_feature": false,
+        "whisper_batch_size": 30, // decrease it if your GPU is out of memory
+        "contentvec_batch_size": 1,
+        // Fill in the content-based pretrained model's path
+        "contentvec_file": "pretrained/contentvec/checkpoint_best_legacy_500.pt",
+        "wenet_model_path": "pretrained/wenet/20220506_u2pp_conformer_exp/final.pt",
+        "wenet_config": "pretrained/wenet/20220506_u2pp_conformer_exp/train.yaml",
+        "whisper_model": "medium",
+        "whisper_model_path": "pretrained/whisper/medium.pt",
+        // Config for features usage
+        "use_mel": true,
+        "use_min_max_norm_mel": true,
+        "use_frame_pitch": true,
+        "use_frame_energy": true,
+        "use_spkid": true,
+        "use_whisper": true,
+        "use_contentvec": true,
+        "use_wenet": false,
+        "n_mel": 100,
+        "sample_rate": 24000
+    },
+    "model": {
+        "teacher_model_path":"[Your_teacher_model_checkpoint].bin",
+        "condition_encoder": {
+            // Config for features usage
+            "use_whisper": true,
+            "use_contentvec": true,
+            "use_wenet": false,
+            "whisper_dim": 1024,
+            "contentvec_dim": 256,
+            "wenet_dim": 512,
+            "use_singer_encoder": false,
+            "pitch_min": 50,
+            "pitch_max": 1100
+        },
+        "comosvc":{
+            "distill": false,
+            // conformer encoder
+            "input_dim": 384,
+            "output_dim": 100,
+            "n_heads": 2,
+            "n_layers": 6,
+            "filter_channels":512,
+            "dropout":0.1,
+            // karras diffusion
+            "P_mean": -1.2,
+            "P_std": 1.2,
+            "sigma_data": 0.5,
+            "sigma_min": 0.002,
+            "sigma_max": 80,
+            "rho": 7,
+            "n_timesteps": 40,
+        },
+        "diffusion": {
+            // Diffusion steps encoder
+            "step_encoder": {
+                "dim_raw_embedding": 128,
+                "dim_hidden_layer": 512,
+                "activation": "SiLU",
+                "num_layer": 2,
+                "max_period": 10000
+            },
+            // Diffusion decoder
+            "model_type": "bidilconv",
+            // bidilconv, unet2d, TODO: unet1d
+            "bidilconv": {
+                "base_channel": 384,
+                "n_res_block": 20,
+                "conv_kernel_size": 3,
+                "dilation_cycle_length": 4,
+                // specially, 1 means no dilation
+                "conditioner_size": 100
+            }
+        }
+    },
+    "train": {
+        "batch_size": 64,
+        "gradient_accumulation_step": 1,
+        "max_epoch": -1, // -1 means no limit
+        "save_checkpoint_stride": [
+            50,
+            50
+        ],
+        "keep_last": [
+            5,
+            -1
+        ],
+        "run_eval": [
+            false,
+            true
+        ],
+        "adamw": {
+            "lr": 4.0e-4
+        },
+        "reducelronplateau": {
+            "factor": 0.8,
+            "patience": 10,
+            "min_lr": 1.0e-4
+        },
+        "dataloader": {
+            "num_worker": 8,
+            "pin_memory": true
+        },
+        "sampler": {
+            "holistic_shuffle": false,
+            "drop_last": true
+        }
+    },
+    "inference": {
+        "comosvc": {
+            "inference_steps": 40
+        }
+    }
+}
\ No newline at end of file
diff --git a/egs/svc/DiffComoSVC/run.sh b/egs/svc/DiffComoSVC/run.sh
new file mode 120000
index 0000000000000000000000000000000000000000..f8daac3da463c177e36cdf041342566cc4243257
--- /dev/null
+++ b/egs/svc/DiffComoSVC/run.sh
@@ -0,0 +1 @@
+../_template/run.sh
\ No newline at end of file
diff --git a/egs/svc/MultipleContentsSVC/README.md b/egs/svc/MultipleContentsSVC/README.md
new file mode 100755
index 0000000000000000000000000000000000000000..ac999e6253076f79ca59ed05fac168e5679feaea
--- /dev/null
+++ b/egs/svc/MultipleContentsSVC/README.md
@@ -0,0 +1,153 @@
+# Leveraging Content-based Features from Multiple Acoustic Models for Singing Voice Conversion
+
+[![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2310.11160)
+[![demo](https://img.shields.io/badge/SVC-Demo-red)](https://www.zhangxueyao.com/data/MultipleContentsSVC/index.html)
+
+<br>
+<div align="center">
+<img src="../../../imgs/svc/MultipleContentsSVC.png" width="85%">
+</div>
+<br>
+
+This is the official implementation of the paper "[Leveraging Content-based Features from Multiple Acoustic Models for Singing Voice Conversion](https://arxiv.org/abs/2310.11160)" (NeurIPS 2023 Workshop on Machine Learning for Audio). Specially,
+
+- The muptile content features are from [Whipser](https://github.com/wenet-e2e/wenet) and [ContentVec](https://github.com/auspicious3000/contentvec).
+- The acoustic model is based on Bidirectional Non-Causal Dilated CNN (called `DiffWaveNetSVC` in Amphion), which is similar to [WaveNet](https://arxiv.org/pdf/1609.03499.pdf), [DiffWave](https://openreview.net/forum?id=a-xFK8Ymz5J), and [DiffSVC](https://ieeexplore.ieee.org/document/9688219).
+- The vocoder is [BigVGAN](https://github.com/NVIDIA/BigVGAN) architecture and we fine-tuned it in over 120 hours singing voice data.
+
+There are four stages in total:
+
+1. Data preparation
+2. Features extraction
+3. Training
+4. Inference/conversion
+
+> **NOTE:** You need to run every command of this recipe in the `Amphion` root path:
+> ```bash
+> cd Amphion
+> ```
+
+## 1. Data Preparation
+
+### Dataset Download
+
+By default, we utilize the five datasets for training: M4Singer, Opencpop, OpenSinger, SVCC, and VCTK. How to download them is detailed [here](../../datasets/README.md).
+
+### Configuration
+
+Specify the dataset paths in  `exp_config.json`. Note that you can change the `dataset` list to use your preferred datasets.
+
+```json
+    "dataset": [
+        "m4singer",
+        "opencpop",
+        "opensinger",
+        "svcc",
+        "vctk"
+    ],
+    "dataset_path": {
+        // TODO: Fill in your dataset path
+        "m4singer": "[M4Singer dataset path]",
+        "opencpop": "[Opencpop dataset path]",
+        "opensinger": "[OpenSinger dataset path]",
+        "svcc": "[SVCC dataset path]",
+        "vctk": "[VCTK dataset path]"
+    },
+```
+
+## 2. Features Extraction
+
+### Content-based Pretrained Models Download
+
+By default, we utilize the Whisper and ContentVec to extract content features. How to download them is detailed [here](../../../pretrained/README.md).
+
+### Configuration
+
+Specify the dataset path and the output path for saving the processed data and the training model in `exp_config.json`:
+
+```json
+    // TODO: Fill in the output log path. The default value is "Amphion/ckpts/svc"
+    "log_dir": "ckpts/svc",
+    "preprocess": {
+        // TODO: Fill in the output data path. The default value is "Amphion/data"
+        "processed_dir": "data",
+        ...
+    },
+```
+
+### Run
+
+Run the `run.sh` as the preproces stage (set  `--stage 1`).
+
+```bash
+sh egs/svc/MultipleContentsSVC/run.sh --stage 1
+```
+
+> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "1"`.
+
+## 3. Training
+
+### Configuration
+
+We provide the default hyparameters in the `exp_config.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on you GPU machines.
+
+```json
+"train": {
+        "batch_size": 32,
+        ...
+        "adamw": {
+            "lr": 2.0e-4
+        },
+        ...
+    }
+```
+
+### Run
+
+Run the `run.sh` as the training stage (set  `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `Amphion/ckpts/svc/[YourExptName]`.
+
+```bash
+sh egs/svc/MultipleContentsSVC/run.sh --stage 2 --name [YourExptName]
+```
+
+> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "0,1,2,3"`.
+
+## 4. Inference/Conversion
+
+### Pretrained Vocoder Download
+
+We fine-tune the official BigVGAN pretrained model with over 120 hours singing voice data. The benifits of fine-tuning has been investigated in our paper (see this [demo page](https://www.zhangxueyao.com/data/MultipleContentsSVC/vocoder.html)). The final pretrained singing voice vocoder is released [here](../../../pretrained/README.md#amphion-singing-bigvgan) (called `Amphion Singing BigVGAN`).
+
+### Run
+
+For inference/conversion, you need to specify the following configurations when running `run.sh`:
+
+| Parameters                                          | Description                                                                                                                                | Example                                                                                                                                                                            |
+| --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `--infer_expt_dir`                                  | The experimental directory which contains `checkpoint`                                                                                     | `Amphion/ckpts/svc/[YourExptName]`                                                                                                                                                 |
+| `--infer_output_dir`                                | The output directory to save inferred audios.                                                                                              | `Amphion/ckpts/svc/[YourExptName]/result`                                                                                                                                          |
+| `--infer_source_file` or `--infer_source_audio_dir` | The inference source (can be a json file or a dir).                                                                                        | The `infer_source_file` could be `Amphion/data/[YourDataset]/test.json`, and the `infer_source_audio_dir` is a folder which includes several audio files (*.wav, *.mp3 or *.flac). |
+| `--infer_target_speaker`                            | The target speaker you want to convert into. You can refer to `Amphion/ckpts/svc/[YourExptName]/singers.json` to choose a trained speaker. | For opencpop dataset, the speaker name would be `opencpop_female1`.                                                                                                                |
+| `--infer_key_shift`                                 | How many semitones you want to transpose.                                                                                                  | `"autoshfit"` (by default), `3`, `-3`, etc.                                                                                                                                        |
+
+For example, if you want to make `opencpop_female1` sing the songs in the `[Your Audios Folder]`, just run:
+
+```bash
+sh egs/svc/MultipleContentsSVC/run.sh --stage 3 --gpu "0" \
+	--infer_expt_dir Amphion/ckpts/svc/[YourExptName] \
+	--infer_output_dir Amphion/ckpts/svc/[YourExptName]/result \
+	--infer_source_audio_dir [Your Audios Folder] \
+	--infer_target_speaker "opencpop_female1" \
+	--infer_key_shift "autoshift"
+```
+
+## Citations
+
+```bibtex
+@article{zhang2023leveraging,
+  title={Leveraging Content-based Features from Multiple Acoustic Models for Singing Voice Conversion},
+  author={Zhang, Xueyao and Gu, Yicheng and Chen, Haopeng and Fang, Zihao and Zou, Lexiao and Xue, Liumeng and Wu, Zhizheng},
+  journal={Machine Learning for Audio Worshop, NeurIPS 2023},
+  year={2023}
+}
+```
diff --git a/egs/svc/MultipleContentsSVC/exp_config.json b/egs/svc/MultipleContentsSVC/exp_config.json
new file mode 100755
index 0000000000000000000000000000000000000000..7047855abd18c25760fcdd46ec63da5c4b7ad8ba
--- /dev/null
+++ b/egs/svc/MultipleContentsSVC/exp_config.json
@@ -0,0 +1,126 @@
+{
+    "base_config": "config/diffusion.json",
+    "model_type": "DiffWaveNetSVC",
+    "dataset": [
+        "m4singer",
+        "opencpop",
+        "opensinger",
+        "svcc",
+        "vctk"
+    ],
+    "dataset_path": {
+        // TODO: Fill in your dataset path
+        "m4singer": "[M4Singer dataset path]",
+        "opencpop": "[Opencpop dataset path]",
+        "opensinger": "[OpenSinger dataset path]",
+        "svcc": "[SVCC dataset path]",
+        "vctk": "[VCTK dataset path]"
+    },
+    // TODO: Fill in the output log path. The default value is "Amphion/ckpts/svc"
+    "log_dir": "ckpts/svc",
+    "preprocess": {
+        // TODO: Fill in the output data path. The default value is "Amphion/data"
+        "processed_dir": "data",
+        // Config for features extraction
+        "extract_mel": true,
+        "extract_pitch": true,
+        "extract_energy": true,
+        "extract_whisper_feature": true,
+        "extract_contentvec_feature": true,
+        "extract_wenet_feature": false,
+        "whisper_batch_size": 30, // decrease it if your GPU is out of memory
+        "contentvec_batch_size": 1,
+        // Fill in the content-based pretrained model's path
+        "contentvec_file": "pretrained/contentvec/checkpoint_best_legacy_500.pt",
+        "wenet_model_path": "pretrained/wenet/20220506_u2pp_conformer_exp/final.pt",
+        "wenet_config": "pretrained/wenet/20220506_u2pp_conformer_exp/train.yaml",
+        "whisper_model": "medium",
+        "whisper_model_path": "pretrained/whisper/medium.pt",
+        // Config for features usage
+        "use_mel": true,
+        "use_min_max_norm_mel": true,
+        "use_frame_pitch": true,
+        "use_frame_energy": true,
+        "use_spkid": true,
+        "use_whisper": true,
+        "use_contentvec": true,
+        "use_wenet": false,
+        "n_mel": 100,
+        "sample_rate": 24000
+    },
+    "model": {
+        "condition_encoder": {
+            // Config for features usage
+            "use_whisper": true,
+            "use_contentvec": true,
+            "use_wenet": false,
+            "whisper_dim": 1024,
+            "contentvec_dim": 256,
+            "wenet_dim": 512,
+            "use_singer_encoder": false,
+            "pitch_min": 50,
+            "pitch_max": 1100
+        },
+        "diffusion": {
+            "scheduler": "ddpm",
+            "scheduler_settings": {
+                "num_train_timesteps": 1000,
+                "beta_start": 1.0e-4,
+                "beta_end": 0.02,
+                "beta_schedule": "linear"
+            },
+            // Diffusion steps encoder
+            "step_encoder": {
+                "dim_raw_embedding": 128,
+                "dim_hidden_layer": 512,
+                "activation": "SiLU",
+                "num_layer": 2,
+                "max_period": 10000
+            },
+            // Diffusion decoder
+            "model_type": "bidilconv",
+            // bidilconv, unet2d, TODO: unet1d
+            "bidilconv": {
+                "base_channel": 512,
+                "n_res_block": 40,
+                "conv_kernel_size": 3,
+                "dilation_cycle_length": 4,
+                // specially, 1 means no dilation
+                "conditioner_size": 384
+            }
+        }
+    },
+    "train": {
+        "batch_size": 32,
+        "gradient_accumulation_step": 1,
+        "max_epoch": -1, // -1 means no limit
+        "save_checkpoint_stride": [
+            3,
+            50
+        ],
+        "keep_last": [
+            3,
+            2
+        ],
+        "run_eval": [
+            true,
+            true
+        ],
+        "adamw": {
+            "lr": 2.0e-4
+        },
+        "reducelronplateau": {
+            "factor": 0.8,
+            "patience": 30,
+            "min_lr": 1.0e-4
+        },
+        "dataloader": {
+            "num_worker": 8,
+            "pin_memory": true
+        },
+        "sampler": {
+            "holistic_shuffle": false,
+            "drop_last": true
+        }
+    }
+}
\ No newline at end of file
diff --git a/egs/svc/MultipleContentsSVC/run.sh b/egs/svc/MultipleContentsSVC/run.sh
new file mode 120000
index 0000000000000000000000000000000000000000..f8daac3da463c177e36cdf041342566cc4243257
--- /dev/null
+++ b/egs/svc/MultipleContentsSVC/run.sh
@@ -0,0 +1 @@
+../_template/run.sh
\ No newline at end of file
diff --git a/egs/svc/README.md b/egs/svc/README.md
new file mode 100755
index 0000000000000000000000000000000000000000..3207ecd790a614b850260086f6a99486fe33165e
--- /dev/null
+++ b/egs/svc/README.md
@@ -0,0 +1,34 @@
+# Amphion Singing Voice Conversion (SVC) Recipe
+
+## Quick Start
+
+We provide a **[beginner recipe](MultipleContentsSVC)** to demonstrate how to train a cutting edge SVC model. Specifically, it is also an official implementation of the paper "[Leveraging Content-based Features from Multiple Acoustic Models for Singing Voice Conversion](https://arxiv.org/abs/2310.11160)" (NeurIPS 2023 Workshop on Machine Learning for Audio). Some demos can be seen [here](https://www.zhangxueyao.com/data/MultipleContentsSVC/index.html).
+
+## Supported Model Architectures
+
+The main idea of SVC is to first disentangle the speaker-agnostic representations from the source audio, and then inject the desired speaker information to synthesize the target, which usually utilizes an acoustic decoder and a subsequent waveform synthesizer (vocoder):
+
+<br>
+<div align="center">
+  <img src="../../imgs/svc/pipeline.png" width="70%">
+</div>
+<br>
+
+Until now, Amphion SVC has supported the following features and models:
+
+- **Speaker-agnostic Representations**:
+  - Content Features: Sourcing from [WeNet](https://github.com/wenet-e2e/wenet), [Whisper](https://github.com/openai/whisper), and [ContentVec](https://github.com/auspicious3000/contentvec).
+  - Prosody Features: F0 and energy.
+- **Speaker Embeddings**:
+  - Speaker Look-Up Table.
+  - Reference Encoder (👨‍💻 developing): It can be used for zero-shot SVC.
+- **Acoustic Decoders**:
+  - Diffusion-based models:
+    - **[DiffWaveNetSVC](MultipleContentsSVC)**: The encoder is based on Bidirectional Non-Causal Dilated CNN, which is similar to [WaveNet](https://arxiv.org/pdf/1609.03499.pdf), [DiffWave](https://openreview.net/forum?id=a-xFK8Ymz5J), and [DiffSVC](https://ieeexplore.ieee.org/document/9688219).
+    - **[DiffComoSVC](DiffComoSVC)** (👨‍💻 developing): The diffusion framework is based on [Consistency Model](https://proceedings.mlr.press/v202/song23a.html). It can significantly accelerate the inference process of the diffusion model.
+  - Transformer-based models:
+    - **[TransformerSVC](TransformerSVC)**: Encoder-only and Non-autoregressive Transformer Architecture.
+  - VAE- and Flow-based models:
+    - **[VitsSVC](VitsSVC)**: It is designed as a [VITS](https://arxiv.org/abs/2106.06103)-like model whose textual input is replaced by the content features, which is similar to [so-vits-svc](https://github.com/svc-develop-team/so-vits-svc).
+- **Waveform Synthesizers (Vocoders)**:
+  - The supported vocoders can be seen in [Amphion Vocoder Recipe](../vocoder/README.md).
diff --git a/egs/svc/TransformerSVC/README.md b/egs/svc/TransformerSVC/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..1797e32f310994ce4c0a1ff3e7789b397b358907
--- /dev/null
+++ b/egs/svc/TransformerSVC/README.md
@@ -0,0 +1,164 @@
+# Transformer for Singing Voice Conversion
+
+This is an implementation of **vanilla transformer encoder**/**conformer** as acoustic model for singing voice conversion.
+
+There are four stages in total:
+
+1. Data preparation
+2. Features extraction
+3. Training
+4. Inference/conversion
+
+> **NOTE:** You need to run every command of this recipe in the `Amphion` root path:
+> ```bash
+> cd Amphion
+> ```
+
+## 1. Data Preparation
+
+### Dataset Download
+
+By default, we utilize the five datasets for training: M4Singer, Opencpop, OpenSinger, SVCC, and VCTK. How to download them is detailed [here](../../datasets/README.md).
+
+### Configuration
+
+Specify the dataset paths in  `exp_config.json`. Note that you can change the `dataset` list to use your preferred datasets.
+
+```json
+    "dataset": [
+        "m4singer",
+        "opencpop",
+        "opensinger",
+        "svcc",
+        "vctk"
+    ],
+    "dataset_path": {
+        // TODO: Fill in your dataset path
+        "m4singer": "[M4Singer dataset path]",
+        "opencpop": "[Opencpop dataset path]",
+        "opensinger": "[OpenSinger dataset path]",
+        "svcc": "[SVCC dataset path]",
+        "vctk": "[VCTK dataset path]"
+    },
+```
+
+## 2. Features Extraction
+
+### Content-based Pretrained Models Download
+
+By default, we utilize the Whisper and ContentVec to extract content features. How to download them is detailed [here](../../../pretrained/README.md).
+
+### Configuration
+
+Specify the dataset path and the output path for saving the processed data and the training model in `exp_config.json`:
+
+```json
+    // TODO: Fill in the output log path. The default value is "Amphion/ckpts/svc"
+    "log_dir": "ckpts/svc",
+    "preprocess": {
+        // TODO: Fill in the output data path. The default value is "Amphion/data"
+        "processed_dir": "data",
+        ...
+    },
+```
+
+### Run
+
+Run the `run.sh` as the preproces stage (set  `--stage 1`).
+
+```bash
+sh egs/svc/TransformerSVC/run.sh --stage 1
+```
+
+> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "1"`.
+
+## 3. Training
+
+### Configuration
+Specify the detailed configuration for transformer block in `exp_config.json`. For key `type`, `conformer` and `transformer` are supported:
+```json
+"model": {
+        ...
+        "transformer":{
+            // 'conformer' or 'transformer'
+            "type": "conformer",
+            "input_dim": 384,
+            "output_dim": 100,
+            "n_heads": 2,
+            "n_layers": 6,
+            "filter_channels":512,
+            "dropout":0.1,
+        }
+    }
+```
+We provide the default hyparameters in the `exp_config.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on you GPU machines.
+
+```json
+"train": {
+        "batch_size": 32,
+        ...
+        "adamw": {
+            "lr": 2.0e-4
+        },
+        ...
+    }
+```
+
+### Run
+
+Run the `run.sh` as the training stage (set  `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `Amphion/ckpts/svc/[YourExptName]`.
+
+```bash
+sh egs/svc/TransformerSVC/run.sh --stage 2 --name [YourExptName]
+```
+
+> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "0,1,2,3"`.
+
+## 4. Inference/Conversion
+
+### Pretrained Vocoder Download
+
+We fine-tune the official BigVGAN pretrained model with over 120 hours singing voice data. The benifits of fine-tuning has been investigated in our paper (see this [demo page](https://www.zhangxueyao.com/data/MultipleContentsSVC/vocoder.html)). The final pretrained singing voice vocoder is released [here](../../../pretrained/README.md#amphion-singing-bigvgan) (called `Amphion Singing BigVGAN`).
+
+### Run
+
+For inference/conversion, you need to specify the following configurations when running `run.sh`:
+
+| Parameters                                          | Description                                                                                                                                | Example                                                                                                                                                                            |
+| --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `--infer_expt_dir`                                  | The experimental directory which contains `checkpoint`                                                                                     | `Amphion/ckpts/svc/[YourExptName]`                                                                                                                                                 |
+| `--infer_output_dir`                                | The output directory to save inferred audios.                                                                                              | `Amphion/ckpts/svc/[YourExptName]/result`                                                                                                                                          |
+| `--infer_source_file` or `--infer_source_audio_dir` | The inference source (can be a json file or a dir).                                                                                        | The `infer_source_file` could be `Amphion/data/[YourDataset]/test.json`, and the `infer_source_audio_dir` is a folder which includes several audio files (*.wav, *.mp3 or *.flac). |
+| `--infer_target_speaker`                            | The target speaker you want to convert into. You can refer to `Amphion/ckpts/svc/[YourExptName]/singers.json` to choose a trained speaker. | For opencpop dataset, the speaker name would be `opencpop_female1`.                                                                                                                |
+| `--infer_key_shift`                                 | How many semitones you want to transpose.                                                                                                  | `"autoshfit"` (by default), `3`, `-3`, etc.                                                                                                                                        |
+
+For example, if you want to make `opencpop_female1` sing the songs in the `[Your Audios Folder]`, just run:
+
+```bash
+cd Amphion
+sh egs/svc/TransformerSVC/run.sh --stage 3 --gpu "0" \
+	--infer_expt_dir Amphion/ckpts/svc/[YourExptName] \
+	--infer_output_dir Amphion/ckpts/svc/[YourExptName]/result \
+	--infer_source_audio_dir [Your Audios Folder] \
+	--infer_target_speaker "opencpop_female1" \
+	--infer_key_shift "autoshift"
+```
+
+## Citations
+
+```bibtex
+@inproceedings{transformer,
+  author       = {Ashish Vaswani and
+                  Noam Shazeer and
+                  Niki Parmar and
+                  Jakob Uszkoreit and
+                  Llion Jones and
+                  Aidan N. Gomez and
+                  Lukasz Kaiser and
+                  Illia Polosukhin},
+  title        = {Attention is All you Need},
+  booktitle    = {{NIPS}},
+  pages        = {5998--6008},
+  year         = {2017}
+}
+```
\ No newline at end of file
diff --git a/egs/svc/TransformerSVC/exp_config.json b/egs/svc/TransformerSVC/exp_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..4ad85fbe47dd4945e579200a7e5d82a03a02457b
--- /dev/null
+++ b/egs/svc/TransformerSVC/exp_config.json
@@ -0,0 +1,108 @@
+{
+    "base_config": "config/transformer.json",
+    "model_type": "TransformerSVC",
+    "dataset": [
+        "m4singer",
+        "opencpop",
+        "opensinger",
+        "svcc",
+        "vctk"
+    ],
+    "dataset_path": {
+        // TODO: Fill in your dataset path
+        "m4singer": "[M4Singer dataset path]",
+        "opencpop": "[Opencpop dataset path]",
+        "opensinger": "[OpenSinger dataset path]",
+        "svcc": "[SVCC dataset path]",
+        "vctk": "[VCTK dataset path]"
+    },
+    // TODO: Fill in the output log path. The default value is "Amphion/ckpts/svc"
+    "log_dir": "ckpts/svc",
+    "preprocess": {
+        // TODO: Fill in the output data path. The default value is "Amphion/data"
+        "processed_dir": "data",
+        // Config for features extraction
+        "extract_mel": true,
+        "extract_pitch": true,
+        "extract_energy": true,
+        "extract_whisper_feature": true,
+        "extract_contentvec_feature": true,
+        "extract_wenet_feature": false,
+        "whisper_batch_size": 30, // decrease it if your GPU is out of memory
+        "contentvec_batch_size": 1,
+        // Fill in the content-based pretrained model's path
+        "contentvec_file": "pretrained/contentvec/checkpoint_best_legacy_500.pt",
+        "wenet_model_path": "pretrained/wenet/20220506_u2pp_conformer_exp/final.pt",
+        "wenet_config": "pretrained/wenet/20220506_u2pp_conformer_exp/train.yaml",
+        "whisper_model": "medium",
+        "whisper_model_path": "pretrained/whisper/medium.pt",
+        // Config for features usage
+        "use_mel": true,
+        "use_min_max_norm_mel": true,
+        "use_frame_pitch": true,
+        "use_frame_energy": true,
+        "use_spkid": true,
+        "use_whisper": true,
+        "use_contentvec": true,
+        "use_wenet": false,
+        "n_mel": 100,
+        "sample_rate": 24000
+    },
+    "model": {
+        "condition_encoder": {
+            // Config for features usage
+            "use_whisper": true,
+            "use_contentvec": true,
+            "use_wenet": false,
+            "whisper_dim": 1024,
+            "contentvec_dim": 256,
+            "wenet_dim": 512,
+            "use_singer_encoder": false,
+            "pitch_min": 50,
+            "pitch_max": 1100
+        },
+        "transformer": {
+            // 'conformer' or 'transformer'
+            "type": "conformer",
+            "input_dim": 384,
+            "output_dim": 100,
+            "n_heads": 2,
+            "n_layers": 6,
+            "filter_channels": 512,
+            "dropout": 0.1,
+        }
+    },
+    "train": {
+        "batch_size": 64,
+        "gradient_accumulation_step": 1,
+        "max_epoch": -1, // -1 means no limit
+        "save_checkpoint_stride": [
+            50,
+            50
+        ],
+        "keep_last": [
+            5,
+            -1
+        ],
+        "run_eval": [
+            false,
+            true
+        ],
+        "adamw": {
+            "lr": 4.0e-4
+        },
+        "reducelronplateau": {
+            "factor": 0.8,
+            "patience": 10,
+            "min_lr": 1.0e-4
+        },
+        "dataloader": {
+            "num_worker": 8,
+            "pin_memory": true
+        },
+        "sampler": {
+            "holistic_shuffle": false,
+            "drop_last": true
+        }
+    }
+}
\ No newline at end of file
diff --git a/egs/svc/TransformerSVC/run.sh b/egs/svc/TransformerSVC/run.sh
new file mode 120000
index 0000000000000000000000000000000000000000..f8daac3da463c177e36cdf041342566cc4243257
--- /dev/null
+++ b/egs/svc/TransformerSVC/run.sh
@@ -0,0 +1 @@
+../_template/run.sh
\ No newline at end of file
diff --git a/egs/svc/VitsSVC/README.md b/egs/svc/VitsSVC/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..6dc81ee9486a9a926bd132ff4afc68881ba39895
--- /dev/null
+++ b/egs/svc/VitsSVC/README.md
@@ -0,0 +1,125 @@
+# VITS for Singing Voice Conversion
+
+This is an implementation of VITS as acoustic model for end-to-end singing voice conversion. Adapted from [so-vits-svc](https://github.com/svc-develop-team/so-vits-svc), SoftVC content encoder is used to extract content features from the source audio. These feature vectors are directly fed into VITS without the need for conversion to a text-based intermediate representation.
+
+There are four stages in total:
+
+1. Data preparation
+2. Features extraction
+3. Training
+4. Inference/conversion
+
+> **NOTE:** You need to run every command of this recipe in the `Amphion` root path:
+> ```bash
+> cd Amphion
+> ```
+
+## 1. Data Preparation
+
+### Dataset Download
+
+By default, we utilize the five datasets for training: M4Singer, Opencpop, OpenSinger, SVCC, and VCTK. How to download them is detailed [here](../../datasets/README.md).
+
+### Configuration
+
+Specify the dataset paths in  `exp_config.json`. Note that you can change the `dataset` list to use your preferred datasets.
+
+```json
+    "dataset": [
+        "m4singer",
+        "opencpop",
+        "opensinger",
+        "svcc",
+        "vctk"
+    ],
+    "dataset_path": {
+        // TODO: Fill in your dataset path
+        "m4singer": "[M4Singer dataset path]",
+        "opencpop": "[Opencpop dataset path]",
+        "opensinger": "[OpenSinger dataset path]",
+        "svcc": "[SVCC dataset path]",
+        "vctk": "[VCTK dataset path]"
+    },
+```
+
+## 2. Features Extraction
+
+### Content-based Pretrained Models Download
+
+By default, we utilize ContentVec and Whisper to extract content features. How to download them is detailed [here](../../../pretrained/README.md).
+
+### Configuration
+
+Specify the dataset path and the output path for saving the processed data and the training model in `exp_config.json`:
+
+```json
+    // TODO: Fill in the output log path. The default value is "Amphion/ckpts/svc"
+    "log_dir": "ckpts/svc",
+    "preprocess": {
+        // TODO: Fill in the output data path. The default value is "Amphion/data"
+        "processed_dir": "data",
+        ...
+    },
+```
+
+### Run
+
+Run the `run.sh` as the preproces stage (set  `--stage 1`).
+
+```bash
+sh egs/svc/VitsSVC/run.sh --stage 1
+```
+
+> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "1"`.
+
+## 3. Training
+
+### Configuration
+
+We provide the default hyparameters in the `exp_config.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on you GPU machines.
+
+```json
+"train": {
+        "batch_size": 32,
+        ...
+        "adamw": {
+            "lr": 2.0e-4
+        },
+        ...
+    }
+```
+
+### Run
+
+Run the `run.sh` as the training stage (set  `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `Amphion/ckpts/svc/[YourExptName]`.
+
+```bash
+sh egs/svc/VitsSVC/run.sh --stage 2 --name [YourExptName]
+```
+
+> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "0,1,2,3"`.
+
+## 4. Inference/Conversion
+
+### Run
+
+For inference/conversion, you need to specify the following configurations when running `run.sh`:
+
+| Parameters                                          | Description                                                                                                                                                       | Example                                                                                                                                                                                                  |
+| --------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `--infer_expt_dir`                                  | The experimental directory which contains `checkpoint`                                                                                                            | `[Your path to save logs and checkpoints]/[YourExptName]`                                                                                                                                                |
+| `--infer_output_dir`                                | The output directory to save inferred audios.                                                                                                                     | `[Your path to save logs and checkpoints]/[YourExptName]/result`                                                                                                                                         |
+| `--infer_source_file` or `--infer_source_audio_dir` | The inference source (can be a json file or a dir).                                                                                                               | The `infer_source_file` could be `[Your path to save processed data]/[YourDataset]/test.json`, and the `infer_source_audio_dir` is a folder which includes several audio files (*.wav, *.mp3 or *.flac). |
+| `--infer_target_speaker`                            | The target speaker you want to convert into. You can refer to `[Your path to save logs and checkpoints]/[YourExptName]/singers.json` to choose a trained speaker. | For opencpop dataset, the speaker name would be `opencpop_female1`.                                                                                                                                      |
+| `--infer_key_shift`                                 | How many semitones you want to transpose.                                                                                                                         | `"autoshfit"` (by default), `3`, `-3`, etc.                                                                                                                                                              |
+
+For example, if you want to make `opencpop_female1` sing the songs in the `[Your Audios Folder]`, just run:
+
+```bash
+sh egs/svc/VitsSVC/run.sh --stage 3 --gpu "0" \
+	--infer_expt_dir Amphion/ckpts/svc/[YourExptName] \
+	--infer_output_dir Amphion/ckpts/svc/[YourExptName]/result \
+	--infer_source_audio_dir [Your Audios Folder] \
+	--infer_target_speaker "opencpop_female1" \
+	--infer_key_shift "autoshift"
+```
\ No newline at end of file
diff --git a/egs/svc/VitsSVC/exp_config.json b/egs/svc/VitsSVC/exp_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..bd3b4481829ce4c43bb2f352e08d3258fca775e9
--- /dev/null
+++ b/egs/svc/VitsSVC/exp_config.json
@@ -0,0 +1,162 @@
+{
+    "base_config": "config/vitssvc.json",
+    "model_type": "VitsSVC",
+    "dataset": [
+        "m4singer",
+        "opencpop",
+        "opensinger",
+        "svcc",
+        "vctk"
+    ],
+    "dataset_path": {
+        // TODO: Fill in your dataset path
+        "m4singer": "[M4Singer dataset path]",
+        "opencpop": "[Opencpop dataset path]",
+        "opensinger": "[OpenSinger dataset path]",
+        "svcc": "[SVCC dataset path]",
+        "vctk": "[VCTK dataset path]"
+    },
+    // TODO: Fill in the output log path. The default value is "Amphion/ckpts/svc"
+    "log_dir": "ckpts/svc",
+    "preprocess": {
+        // TODO: Fill in the output data path. The default value is "Amphion/data"
+        "processed_dir": "data",
+        
+        "f0_min": 50,
+        "f0_max": 1100,
+        // f0_bin in sovits
+        "pitch_bin": 256,
+        // filter_length in sovits
+        "n_fft": 2048,
+        // hop_length in sovits
+        "hop_size": 512,
+        // win_length in sovits
+        "win_size": 2048,
+        "segment_size": 8192,
+        "n_mel": 100,
+        "sample_rate": 44100,
+
+        // Config for features extraction
+        "extract_mel": true,
+        "extract_pitch": true,
+        "pitch_extractor": "parselmouth",
+        "extract_energy": false,
+        "extract_uv": true,
+        "extract_linear_spec": true,
+        "extract_audio": true,
+        // contentvec
+        "extract_contentvec_feature": true,
+        "contentvec_sample_rate": 16000,
+        "contentvec_batch_size": 1,
+        "contentvec_frameshift": 0.02,
+        // whisper
+        "extract_whisper_feature": true,
+        "whisper_sample_rate": 16000,
+        "whisper_frameshift": 0.01,
+        "whisper_downsample_rate": 2,
+        // Fill in the content-based pretrained model's path
+        "contentvec_file": "pretrained/contentvec/checkpoint_best_legacy_500.pt",
+        "wenet_model_path": "pretrained/wenet/20220506_u2pp_conformer_exp/final.pt",
+        "wenet_config": "pretrained/wenet/20220506_u2pp_conformer_exp/train.yaml",
+        "whisper_model": "medium",
+        "whisper_model_path": "pretrained/whisper/medium.pt",
+        // Config for features usage
+        "use_mel": true,
+        "use_frame_pitch": true,
+        "use_uv": true,
+        "use_spkid": true,
+        "use_contentvec": true,
+        "use_whisper": true,
+        "use_text": false,
+        "use_phone": false,
+
+        // Extract content features using dataloader
+        "pin_memory": true,
+        "num_workers": 8,
+        "content_feature_batch_size": 16,
+        // Meta file
+        "train_file": "train.json",
+        "valid_file": "test.json",
+        "spk2id": "singers.json",
+        "utt2spk": "utt2singer"
+    },
+    "model": {
+        "condition_encoder": {
+            // Config for features usage
+            "merge_mode": "add",
+            "input_melody_dim": 1,
+            "use_log_f0": true,
+            "n_bins_melody": 256,
+            //# Quantization (0 for not quantization)
+            "output_melody_dim": 192,
+
+            "use_contentvec": true,
+            "use_whisper": true,
+            "use_mert": false,
+            "use_wenet": false,
+            "whisper_dim": 1024,
+            "contentvec_dim": 256,
+            "content_encoder_dim": 192,
+            "output_singer_dim": 192,
+            "singer_table_size": 512,
+            "output_content_dim": 192,
+            "use_spkid": true,
+            
+            "pitch_max": 1100.0,
+            "pitch_min": 50.0,
+        },
+        "vits": {
+            "inter_channels": 192,
+            "hidden_channels": 192,
+            "filter_channels": 256,
+            "n_heads": 2,
+            "n_layers": 6,
+            "kernel_size": 3,
+            "p_dropout": 0.1,
+            "ssl_dim": 256,
+            "n_flow_layer": 4,
+            "n_layers_q": 3,
+            "gin_channels": 256,
+            "n_speakers": 512,
+            "use_spectral_norm": false,
+        },
+        "generator": "nsfhifigan",
+    },
+    "train": {
+        "batch_size": 32,
+        "learning_rate": 2e-4,
+        "gradient_accumulation_step": 1,
+        "max_epoch": -1, // -1 means no limit
+        "save_checkpoint_stride": [
+            3,
+            50
+        ],
+        "keep_last": [
+            3,
+            2
+        ],
+        "run_eval": [
+            true,
+            true
+        ],
+        "adamw": {
+            "lr": 2.0e-4
+        },
+        "reducelronplateau": {
+            "factor": 0.8,
+            "patience": 30,
+            "min_lr": 1.0e-4
+        },
+        "dataloader": {
+            "num_worker": 8,
+            "pin_memory": true
+        },
+        "sampler": {
+            "holistic_shuffle": false,
+            "drop_last": true
+        }
+    },
+    "inference": {
+        "batch_size": 1,
+    }
+}
\ No newline at end of file
diff --git a/egs/svc/VitsSVC/run.sh b/egs/svc/VitsSVC/run.sh
new file mode 120000
index 0000000000000000000000000000000000000000..f8daac3da463c177e36cdf041342566cc4243257
--- /dev/null
+++ b/egs/svc/VitsSVC/run.sh
@@ -0,0 +1 @@
+../_template/run.sh
\ No newline at end of file
diff --git a/egs/svc/_template/run.sh b/egs/svc/_template/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..8dc870fdef8b1464000021def5627f91d1676bbe
--- /dev/null
+++ b/egs/svc/_template/run.sh
@@ -0,0 +1,150 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+######## Build Experiment Environment ###########
+exp_dir=$(cd `dirname $0`; pwd)
+work_dir=$(dirname $(dirname $(dirname $exp_dir)))
+
+export WORK_DIR=$work_dir
+export PYTHONPATH=$work_dir
+export PYTHONIOENCODING=UTF-8
+
+######## Parse the Given Parameters from the Commond ###########
+options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,resume:,resume_from_ckpt_path:,resume_type:,infer_expt_dir:,infer_output_dir:,infer_source_file:,infer_source_audio_dir:,infer_target_speaker:,infer_key_shift:,infer_vocoder_dir: -- "$@")
+eval set -- "$options"
+
+while true; do
+  case $1 in
+    # Experimental Configuration File
+    -c | --config) shift; exp_config=$1 ; shift ;;
+    # Experimental Name
+    -n | --name) shift; exp_name=$1 ; shift ;;
+    # Running Stage
+    -s | --stage) shift; running_stage=$1 ; shift ;;
+    # Visible GPU machines. The default value is "0".
+    --gpu) shift; gpu=$1 ; shift ;;
+
+    # [Only for Training] Resume configuration
+    --resume) shift; resume=$1 ; shift ;;
+    # [Only for Training] The specific checkpoint path that you want to resume from.
+    --resume_from_ckpt_path) shift; resume_from_ckpt_path=$1 ; shift ;;
+    # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights.
+    --resume_type) shift; resume_type=$1 ; shift ;;
+
+    # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]"
+    --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;;
+    # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result"
+    --infer_output_dir) shift; infer_output_dir=$1 ; shift ;;
+    # [Only for Inference] The inference source (can be a json file or a dir). For example, the source_file can be "[Your path to save processed data]/[YourDataset]/test.json", and the source_audio_dir can be "$work_dir/source_audio" which includes several audio files (*.wav, *.mp3 or *.flac).
+    --infer_source_file) shift; infer_source_file=$1 ; shift ;;
+    --infer_source_audio_dir) shift; infer_source_audio_dir=$1 ; shift ;;
+    # [Only for Inference] Specify the target speaker you want to convert into. You can refer to "[Your path to save logs and checkpoints]/[Your Expt Name]/singers.json". In this singer look-up table, you can see the usable speaker names (all the keys of the dictionary). For example, for opencpop dataset, the speaker name would be "opencpop_female1".
+    --infer_target_speaker) shift; infer_target_speaker=$1 ; shift ;;
+    # [Only for Inference] For advanced users, you can modify the trans_key parameters into an integer (which means the semitones you want to transpose). Its default value is "autoshift".
+    --infer_key_shift) shift; infer_key_shift=$1 ; shift ;;
+    # [Only for Inference] The vocoder dir. Its default value is Amphion/pretrained/bigvgan. See Amphion/pretrained/README.md to download the pretrained BigVGAN vocoders.
+    --infer_vocoder_dir) shift; infer_vocoder_dir=$1 ; shift ;;
+
+    --) shift ; break ;;
+    *) echo "Invalid option: $1" exit 1 ;;
+  esac
+done
+
+
+### Value check ###
+if [ -z "$running_stage" ]; then
+    echo "[Error] Please specify the running stage"
+    exit 1
+fi
+
+if [ -z "$exp_config" ]; then
+    exp_config="${exp_dir}"/exp_config.json
+fi
+echo "Exprimental Configuration File: $exp_config"
+
+if [ -z "$gpu" ]; then
+    gpu="0"
+fi
+
+######## Features Extraction ###########
+if [ $running_stage -eq 1 ]; then
+    CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/svc/preprocess.py \
+        --config $exp_config \
+        --num_workers 4
+fi
+
+######## Training ###########
+if [ $running_stage -eq 2 ]; then
+    if [ -z "$exp_name" ]; then
+        echo "[Error] Please specify the experiments name"
+        exit 1
+    fi
+    echo "Exprimental Name: $exp_name"
+
+    if [ "$resume" = true ]; then
+        echo "Automatically resume from the experimental dir..."
+        CUDA_VISIBLE_DEVICES="$gpu" accelerate launch "${work_dir}"/bins/svc/train.py \
+            --config "$exp_config" \
+            --exp_name "$exp_name" \
+            --log_level info \
+            --resume
+    else
+        CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/svc/train.py \
+            --config "$exp_config" \
+            --exp_name "$exp_name" \
+            --log_level info \
+            --resume_from_ckpt_path "$resume_from_ckpt_path" \
+            --resume_type "$resume_type"
+    fi
+fi
+
+######## Inference/Conversion ###########
+if [ $running_stage -eq 3 ]; then
+    if [ -z "$infer_expt_dir" ]; then
+        echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]"
+        exit 1
+    fi
+
+    if [ -z "$infer_output_dir" ]; then
+        infer_output_dir="$expt_dir/result"
+    fi
+
+    if [ -z "$infer_source_file" ] && [ -z "$infer_source_audio_dir" ]; then
+        echo "[Error] Please specify the source file/dir. The inference source (can be a json file or a dir). For example, the source_file can be "[Your path to save processed data]/[YourDataset]/test.json", and the source_audio_dir should include several audio files (*.wav, *.mp3 or *.flac)."
+        exit 1
+    fi
+
+    if [ -z "$infer_source_file" ]; then
+        infer_source=$infer_source_audio_dir
+    fi
+
+    if [ -z "$infer_source_audio_dir" ]; then
+        infer_source=$infer_source_file
+    fi
+
+    if [ -z "$infer_target_speaker" ]; then
+        echo "[Error] Please specify the target speaker. You can refer to "[Your path to save logs and checkpoints]/[Your Expt Name]/singers.json". In this singer look-up table, you can see the usable speaker names (all the keys of the dictionary). For example, for opencpop dataset, the speaker name would be "opencpop_female1""
+        exit 1
+    fi
+
+    if [ -z "$infer_key_shift" ]; then
+        infer_key_shift="autoshift"
+    fi
+
+    if [ -z "$infer_vocoder_dir" ]; then
+        infer_vocoder_dir="$work_dir"/pretrained/bigvgan
+        echo "[Warning] You don't specify the infer_vocoder_dir. It is set $infer_vocoder_dir by default. Make sure that you have followed Amphoion/pretrained/README.md to download the pretrained BigVGAN vocoder checkpoint."
+    fi
+
+    CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/svc/inference.py \
+        --config $exp_config \
+        --acoustics_dir $infer_expt_dir \
+        --vocoder_dir $infer_vocoder_dir \
+        --target_singer $infer_target_speaker \
+        --trans_key $infer_key_shift \
+        --source $infer_source \
+        --output_dir $infer_output_dir  \
+        --log_level debug
+fi
\ No newline at end of file
diff --git a/egs/tta/README.md b/egs/tta/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..1d944688768697da9e0cee60445410d6ce115694
--- /dev/null
+++ b/egs/tta/README.md
@@ -0,0 +1,19 @@
+# Amphion Text-to-Audio (TTA) Recipe
+
+## Quick Start
+
+We provide a **[beginner recipe](RECIPE.md)** to demonstrate how to train a cutting edge TTA model. Specifically, it is designed as a latent diffusion model like [AudioLDM](https://arxiv.org/abs/2301.12503), [Make-an-Audio](https://arxiv.org/abs/2301.12661), and [AUDIT](https://arxiv.org/abs/2304.00830).
+
+## Supported Model Architectures
+
+Until now, Amphion has supported a latent diffusion based text-to-audio model:
+
+<br>
+<div align="center">
+<img src="../../imgs/tta/DiffusionTTA.png" width="65%">
+</div>
+<br>
+
+Similar to [AUDIT](https://arxiv.org/abs/2304.00830), we implement it in two-stage training:
+1. Training the VAE which is called `AutoencoderKL` in Amphion.
+2. Training the conditional latent diffusion model which is called `AudioLDM` in Amphion.
\ No newline at end of file
diff --git a/egs/tta/RECIPE.md b/egs/tta/RECIPE.md
new file mode 100644
index 0000000000000000000000000000000000000000..6682921fd88eb20f9c54f4defdfd856b6154c1c2
--- /dev/null
+++ b/egs/tta/RECIPE.md
@@ -0,0 +1,156 @@
+# Text-to-Audio with Latent Diffusion Model
+
+This is the quicktour for training a text-to-audio model with the popular and powerful generative model: [Latent Diffusion Model](https://arxiv.org/abs/2112.10752). Specially, this recipe is also the official implementation of the text-to-audio generation part of our NeurIPS 2023 paper "[AUDIT: Audio Editing by Following Instructions with Latent Diffusion Models](https://arxiv.org/abs/2304.00830)". You can check the last part of [AUDIT demos](https://audit-demo.github.io/) to see same text-to-audio examples.
+
+<br>
+<div align="center">
+<img src="../../imgs/tta/DiffusionTTA.png" width="65%">
+</div>
+<br>
+
+We train this latent diffusion model in two stages:
+1. In the first stage, we aims to obtain a high-quality VAE (called `AutoencoderKL` in Amphion), in order that we can project
+the input mel-spectrograms to an efficient, low-dimensional latent space. Specially, we train the VAE with GAN loss to improve the reconstruction quality.
+1. In the second stage, we aims to obtain a text-controllable diffusion model (called `AudioLDM` in Amphion). We use U-Net architecture diffusion model, and use T5 encoder as text encoder.
+
+There are four stages in total for training the text-to-audio model:
+
+1. Data preparation and processing
+2. Train the VAE model
+3. Train the latent diffusion model
+4. Inference
+
+> **NOTE:** You need to run every command of this recipe in the `Amphion` root path:
+> ```bash
+> cd Amphion
+> ```
+
+## Overview
+
+```sh
+# Train the VAE model
+sh egs/tta/autoencoderkl/run_train.sh
+
+# Train the latent diffusion model
+sh egs/tta/audioldm/run_train.sh
+
+# Inference
+sh egs/tta/audioldm/run_inference.sh
+```
+
+## 1. Data preparation and processing
+
+### Dataset Download
+
+We take [AudioCaps](https://audiocaps.github.io/) as an example, AudioCaps is a dataset of around 44K audio-caption pairs, where each audio clip corresponds to a caption with rich semantic information. You can download the dataset [here](https://github.com/cdjkim/audiocaps).
+
+<!-- How to download AudioCaps is detailed [here](../datasets/README.md) -->
+<!-- You can downlaod the dataset [here](https://github.com/cdjkim/audiocaps). -->
+
+### Data Processing
+
+- Download AudioCaps dataset to `[Your path to save tta dataset]` and modify `preprocess.processed_dir` in `egs/tta/.../exp_config.json`.
+
+```json
+{
+  "dataset": [
+    "AudioCaps"
+  ],
+  "preprocess": {
+    // Specify the output root path to save the processed data 
+    "processed_dir": "[Your path to save tta dataset]",
+    ...
+  }
+}
+```
+
+The folder structure of your downloaded data should be similar to:
+
+```plaintext
+.../[Your path to save tta dataset]
+┣ AudioCpas
+┃   ┣ wav
+┃   ┃   ┣ ---1_cCGK4M_0_10000.wav
+┃   ┃   ┣ ---lTs1dxhU_30000_40000.wav
+┃   ┃   ┣ ...
+```
+
+- Then you may process the data to mel-specgram and save it as `.npy` format. If you use the data we provide, we have processed all the wav data.
+
+- Generate a json file to save the metadata, the json file is like:
+
+```json
+[
+    {
+        "Dataset": "AudioCaps",
+        "Uid": "---1_cCGK4M_0_10000",
+        "Caption": "Idling car, train blows horn and passes"
+    },
+    {
+        "Dataset": "AudioCaps",
+        "Uid": "---lTs1dxhU_30000_40000",
+        "Caption": "A racing vehicle engine is heard passing by"
+    },
+    ...
+]
+```
+- Finally, the folder structure is like:
+
+```plaintext
+.../[Your path to save tta dataset]
+┣ AudioCpas
+┃   ┣ wav
+┃   ┃   ┣ ---1_cCGK4M_0_10000.wav
+┃   ┃   ┣ ---lTs1dxhU_30000_40000.wav
+┃   ┃   ┣ ...
+┃   ┣ mel
+┃   ┃   ┣ ---1_cCGK4M_0_10000.npy
+┃   ┃   ┣ ---lTs1dxhU_30000_40000.npy
+┃   ┃   ┣ ...
+┃   ┣ train.json
+┃   ┣ valid.json
+┃   ┣ ...
+```
+
+## 2. Training the VAE Model
+
+The first stage model is a VAE model trained with GAN loss (called `AutoencoderKL` in Amphion), run the follow commands:
+
+```sh
+sh egs/tta/autoencoderkl/run_train.sh
+```
+
+## 3. Training the Latent Diffusion Model
+
+The second stage model is a condition diffusion model with a T5 text encoder (called `AudioLDM` in Amphion), run the following commands:
+
+```sh
+sh egs/tta/audioldm/run_train.sh
+```
+
+## 4. Inference
+
+Now you can generate audio with your pre-trained latent diffusion model, run the following commands and modify the `text` argument.
+
+```sh
+sh egs/tta/audioldm/run_inference.sh \
+--text "A man is whistling"
+```
+
+## Citations
+
+```bibtex
+@article{wang2023audit,
+  title={AUDIT: Audio Editing by Following Instructions with Latent Diffusion Models},
+  author={Wang, Yuancheng and Ju, Zeqian and Tan, Xu and He, Lei and Wu, Zhizheng and Bian, Jiang and Zhao, Sheng},
+  journal={NeurIPS 2023},
+  year={2023}
+}
+
+@article{liu2023audioldm,
+  title={{AudioLDM}: Text-to-Audio Generation with Latent Diffusion Models},
+  author={Liu, Haohe and Chen, Zehua and Yuan, Yi and Mei, Xinhao and Liu, Xubo and Mandic, Danilo and Wang, Wenwu and Plumbley, Mark D},
+  journal={Proceedings of the International Conference on Machine Learning},
+  year={2023}
+}
+```
\ No newline at end of file
diff --git a/egs/tta/audioldm/exp_config.json b/egs/tta/audioldm/exp_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..5b57a771593094401fd38747c66951afe6d6d1f3
--- /dev/null
+++ b/egs/tta/audioldm/exp_config.json
@@ -0,0 +1,90 @@
+{
+    "base_config": "egs/tta/audioldm/exp_config_base.json",
+    "dataset": [
+      "AudioCaps"
+    ],
+    "preprocess": {
+      // Specify the output root path to save the processed data 
+      "processed_dir": "data",
+      // For example: "/home/TTADataset/processed_data"
+
+      // feature
+      "use_spkid": false,
+      "use_uv": false,
+      "use_frame_pitch": false,
+      "use_phone_pitch": false,
+      "use_frame_energy": false,
+      "use_phone_energy": false,
+      "use_mel": false,
+      "use_audio": false,
+      "use_label": false,
+      "use_one_hot": false,
+      // feature for text to audio
+      "use_caption": true,
+      "use_melspec": true,
+      "use_wav": false,
+      // feature dir
+      "melspec_dir": "mel",
+      "wav_dir": "wav"
+    },
+    // Specify the output root path to save model ckpts and logs
+    "log_dir": "ckpts/tta",
+    // For example: "/home/TTADataset/processed_data/logs"
+
+    // model
+    "model": {
+      "audioldm": {
+        "image_size": 32,
+        "in_channels": 4,
+        "out_channels": 4,
+        "model_channels": 256,
+        "attention_resolutions": [4, 2, 1],
+        "num_res_blocks": 2,
+        "channel_mult": [1, 2, 4],
+        "num_heads": 8,
+        "use_spatial_transformer": true,
+        "transformer_depth": 1,
+        "context_dim": 768,
+        "use_checkpoint": true,
+        "legacy": false
+      },
+      "autoencoderkl": {
+        "ch": 128,
+        "ch_mult": [1,1,2,2,4],
+        "num_res_blocks": 2,
+        "in_channels": 1,
+        "z_channels": 4,
+        "out_ch": 1,
+        "double_z": true
+      },
+      "noise_scheduler": {
+        "num_train_timesteps": 1000,
+        "beta_start": 0.00085,
+        "beta_end": 0.012,
+        "beta_schedule": "scaled_linear",
+        "clip_sample": false,
+        "steps_offset": 1,
+        "set_alpha_to_one": false,
+        "skip_prk_steps": true,
+        "prediction_type": "epsilon"
+      },
+      "autoencoder_path": "ckpts/tta/autoencoder_kl_debug/checkpoints/step-0445000_loss-0.3306.pt"
+    },
+
+    // train
+    "train": {
+      "adam": {
+        "lr": 5.0e-5
+      },
+      "ddp": false,
+      "random_seed": 12345,
+      "batch_size": 12,
+      "epochs": 50000,
+      "max_steps": 1000000,
+      "total_training_steps": 800000,
+      "save_summary_steps": 1000,
+      "save_checkpoints_steps": 5000,
+      "valid_interval": 5000,
+      "keep_checkpoint_max": 100
+    }
+  }
\ No newline at end of file
diff --git a/egs/tta/audioldm/exp_config_base.json b/egs/tta/audioldm/exp_config_base.json
new file mode 100644
index 0000000000000000000000000000000000000000..8201c070715112119d11750f68f76c83d80a9aa4
--- /dev/null
+++ b/egs/tta/audioldm/exp_config_base.json
@@ -0,0 +1,11 @@
+{
+    "base_config": "config/audioldm.json",
+    "model_type": "AudioLDM",
+    "dataset": [
+        "AudioCaps"
+    ],
+    "preprocess": {
+        "train_file": "train.json",
+        "valid_file": "vaild.json"
+    }
+}
\ No newline at end of file
diff --git a/egs/tta/audioldm/exp_config_latent_4_10_78.json b/egs/tta/audioldm/exp_config_latent_4_10_78.json
new file mode 100644
index 0000000000000000000000000000000000000000..09ae7c38cf332e74ecb4bc485d44a5f8985d43c5
--- /dev/null
+++ b/egs/tta/audioldm/exp_config_latent_4_10_78.json
@@ -0,0 +1,88 @@
+{
+    "base_config": "egs/tta/audioldm/exp_config_base.json",
+    "dataset": [
+      "AudioCaps"
+    ],
+    "preprocess": {
+      // Specify the output root path to save the processed data 
+      "processed_dir": "data",
+
+      // feature
+      "use_spkid": false,
+      "use_uv": false,
+      "use_frame_pitch": false,
+      "use_phone_pitch": false,
+      "use_frame_energy": false,
+      "use_phone_energy": false,
+      "use_mel": false,
+      "use_audio": false,
+      "use_label": false,
+      "use_one_hot": false,
+      // feature for text to audio
+      "use_caption": true,
+      "use_melspec": true,
+      "use_wav": false,
+      // feature dir
+      "melspec_dir": "mel",
+      "wav_dir": "wav"
+    },
+    // Specify the output root path to save model ckpts and logs
+    "log_dir": "ckpts/tta",
+
+    // model
+    "model": {
+      "audioldm": {
+        "image_size": 32,
+        "in_channels": 4,
+        "out_channels": 4,
+        "model_channels": 256,
+        "attention_resolutions": [4, 2, 1],
+        "num_res_blocks": 2,
+        "channel_mult": [1, 2, 4],
+        "num_heads": 8,
+        "use_spatial_transformer": true,
+        "transformer_depth": 1,
+        "context_dim": 768,
+        "use_checkpoint": true,
+        "legacy": false
+      },
+      "autoencoderkl": {
+        "ch": 128,
+        "ch_mult": [1,2,2,4],
+        "num_res_blocks": 2,
+        "in_channels": 1,
+        "z_channels": 4,
+        "out_ch": 1,
+        "double_z": true
+      },
+      "noise_scheduler": {
+        "num_train_timesteps": 1000,
+        "beta_start": 0.00085,
+        "beta_end": 0.012,
+        "beta_schedule": "scaled_linear",
+        "clip_sample": false,
+        "steps_offset": 1,
+        "set_alpha_to_one": false,
+        "skip_prk_steps": true,
+        "prediction_type": "epsilon"
+      },
+      "autoencoder_path": "ckpts/tta/autoencoder_kl_debug_latent_size_4_10_78/checkpoints/step-0390000_loss-0.2876.pt"
+    },
+
+    // train
+    "train": {
+      "adam": {
+        "lr": 2.0e-5
+      },
+      "ddp": false,
+      "random_seed": 12345,
+      "batch_size": 12,
+      "epochs": 50000,
+      "max_steps": 1000000,
+      "total_training_steps": 800000,
+      "save_summary_steps": 1000,
+      "save_checkpoints_steps": 5000,
+      "valid_interval": 5000,
+      "keep_checkpoint_max": 100
+    }
+  }
\ No newline at end of file
diff --git a/egs/tta/audioldm/run_inference.sh b/egs/tta/audioldm/run_inference.sh
new file mode 100644
index 0000000000000000000000000000000000000000..181244efece8e164eb44fb04f051dbb80721c248
--- /dev/null
+++ b/egs/tta/audioldm/run_inference.sh
@@ -0,0 +1,52 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+######## Build Experiment Environment ###########
+exp_dir=$(cd `dirname $0`; pwd)
+work_dir=$(dirname $(dirname $(dirname $exp_dir)))
+
+export WORK_DIR=$work_dir
+export PYTHONPATH=$work_dir
+export PYTHONIOENCODING=UTF-8
+
+######## Set Experiment Configuration ###########
+exp_config="$exp_dir/exp_config.json"
+exp_name="audioldm_debug_latent_size_4_5_39"
+checkpoint_path="$work_dir/ckpts/tta/audioldm_debug_latent_size_4_5_39/checkpoints/step-0570000_loss-0.2521.pt"
+output_dir="$work_dir/temp"
+vocoder_config_path="$work_dir/ckpts/tta/hifigan_checkpoints/config.json"
+vocoder_path="$work_dir/ckpts/tta/hifigan_checkpoints/g_01250000"
+num_steps=200
+guidance_scale=4.0
+
+export CUDA_VISIBLE_DEVICES="0" 
+
+######## Parse Command Line Arguments ###########
+while [[ $# -gt 0 ]]
+do
+key="$1"
+
+case $key in
+    --text)
+    text="$2"
+    shift # past argument
+    shift # past value
+    ;;
+    *)    # unknown option
+    shift # past argument
+    ;;
+esac
+done
+
+######## Run inference ###########
+python "${work_dir}"/bins/tta/inference.py \
+    --config=$exp_config \
+    --checkpoint_path=$checkpoint_path \
+    --text="$text" \
+    --vocoder_path=$vocoder_path \
+    --vocoder_config_path=$vocoder_config_path \
+    --num_steps=$num_steps \
+    --guidance_scale=$guidance_scale \
+    --output_dir=$output_dir
diff --git a/egs/tta/audioldm/run_inference_latent_4_10_78.sh b/egs/tta/audioldm/run_inference_latent_4_10_78.sh
new file mode 100644
index 0000000000000000000000000000000000000000..3c247e9e6d2919e14e395deb83397439bb22efc5
--- /dev/null
+++ b/egs/tta/audioldm/run_inference_latent_4_10_78.sh
@@ -0,0 +1,52 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+######## Build Experiment Environment ###########
+exp_dir=$(cd `dirname $0`; pwd)
+work_dir=$(dirname $(dirname $(dirname $exp_dir)))
+
+export WORK_DIR=$work_dir
+export PYTHONPATH=$work_dir
+export PYTHONIOENCODING=UTF-8
+
+######## Set Experiment Configuration ###########
+exp_config="$exp_dir/exp_config_v2.json"
+exp_name="audioldm_debug_latent_size_4_10_78"
+checkpoint_path="$work_dir/ckpts/tta/audioldm_debug_latent_size_4_10_78/checkpoints/step-0325000_loss-0.1936.pt"
+output_dir="$work_dir/temp"
+vocoder_config_path="$work_dir/ckpts/tta/hifigan_checkpoints/config.json"
+vocoder_path="$work_dir/ckpts/tta/hifigan_checkpoints/g_01250000"
+num_steps=200
+guidance_scale=4.0
+
+export CUDA_VISIBLE_DEVICES="0" 
+
+######## Parse Command Line Arguments ###########
+while [[ $# -gt 0 ]]
+do
+key="$1"
+
+case $key in
+    --text)
+    text="$2"
+    shift # past argument
+    shift # past value
+    ;;
+    *)    # unknown option
+    shift # past argument
+    ;;
+esac
+done
+
+######## Run inference ###########
+python "${work_dir}"/bins/tta/inference.py \
+    --config=$exp_config \
+    --checkpoint_path=$checkpoint_path \
+    --text="A man is whistling" \
+    --vocoder_path=$vocoder_path \
+    --vocoder_config_path=$vocoder_config_path \
+    --num_steps=$num_steps \
+    --guidance_scale=$guidance_scale \
+    --output_dir=$output_dir \
\ No newline at end of file
diff --git a/egs/tta/audioldm/run_train.sh b/egs/tta/audioldm/run_train.sh
new file mode 100644
index 0000000000000000000000000000000000000000..b1060db6cf02ccd46bdc8ecac304e26233cea354
--- /dev/null
+++ b/egs/tta/audioldm/run_train.sh
@@ -0,0 +1,26 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+######## Build Experiment Environment ###########
+exp_dir=$(cd `dirname $0`; pwd)
+work_dir=$(dirname $(dirname $(dirname $exp_dir)))
+
+export WORK_DIR=$work_dir
+export PYTHONPATH=$work_dir
+export PYTHONIOENCODING=UTF-8
+
+######## Set Experiment Configuration ###########
+exp_config="$exp_dir/exp_config.json"
+exp_name="audioldm_debug_latent_size_4_5_39"
+
+num_workers=8
+export CUDA_VISIBLE_DEVICES="0" 
+
+######## Train Model ###########
+python "${work_dir}"/bins/tta/train_tta.py \
+    --config=$exp_config \
+    --num_workers=$num_workers \
+    --exp_name=$exp_name \
+    --stdout_interval=25 \
\ No newline at end of file
diff --git a/egs/tta/audioldm/run_train_latent_4_10_78.sh b/egs/tta/audioldm/run_train_latent_4_10_78.sh
new file mode 100644
index 0000000000000000000000000000000000000000..f61c0de52983ba6d4976b115fdc22c65696cbc8d
--- /dev/null
+++ b/egs/tta/audioldm/run_train_latent_4_10_78.sh
@@ -0,0 +1,26 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+######## Build Experiment Environment ###########
+exp_dir=$(cd `dirname $0`; pwd)
+work_dir=$(dirname $(dirname $(dirname $exp_dir)))
+
+export WORK_DIR=$work_dir
+export PYTHONPATH=$work_dir
+export PYTHONIOENCODING=UTF-8
+
+######## Set Experiment Configuration ###########
+exp_config="$exp_dir/exp_config_latent_4_10_78.json"
+exp_name="audioldm_debug_latent_size_4_10_78"
+
+num_workers=8
+export CUDA_VISIBLE_DEVICES="0" 
+
+######## Train Model ###########
+python "${work_dir}"/bins/tta/train_tta.py \
+    --config=$exp_config \
+    --num_workers=$num_workers \
+    --exp_name=$exp_name \
+    --stdout_interval=25 \
\ No newline at end of file
diff --git a/egs/tta/autoencoderkl/exp_config.json b/egs/tta/autoencoderkl/exp_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..e0d401051fea5920ade4ad7d765e816fdbec5e9d
--- /dev/null
+++ b/egs/tta/autoencoderkl/exp_config.json
@@ -0,0 +1,49 @@
+{
+    "base_config": "egs/tta/autoencoderkl/exp_config_base.json",
+    "dataset": [
+      "AudioCaps"
+    ],
+    "preprocess": {
+      // Specify the output root path to save the processed data 
+      "processed_dir": "data",
+
+      // feature
+      "use_spk": false,
+      "use_spkid": false,
+      "use_uv": false,
+      "use_frame_pitch": false,
+      "use_phone_pitch": false,
+      "use_frame_energy": false,
+      "use_phone_energy": false,
+      "use_mel": false,
+      "use_audio": false,
+      "use_label": false,
+      "use_one_hot": false,
+      // feature for text to audio
+      "use_caption": true,
+      "use_melspec": true,
+      "use_wav": false,
+      // feature dir
+      "melspec_dir": "mel",
+      "wav_dir": "wav"
+    },
+    // Specify the output root path to save model ckpts and logs
+    "log_dir": "ckpts/tta",
+
+    // train
+    "train": {
+      "adam": {
+        "lr": 4.0e-5
+      },
+      "ddp": false,
+      "random_seed": 12345,
+      "batch_size": 12,
+      "epochs": 50000,
+      "max_steps": 1000000,
+      "total_training_steps": 800000,
+      "save_summary_steps": 1000,
+      "save_checkpoints_steps": 5000,
+      "valid_interval": 5000,
+      "keep_checkpoint_max": 100
+    }
+  }
\ No newline at end of file
diff --git a/egs/tta/autoencoderkl/exp_config_base.json b/egs/tta/autoencoderkl/exp_config_base.json
new file mode 100644
index 0000000000000000000000000000000000000000..d25badd4e99bb435e5eaf0cfc2ceeb96d75775e7
--- /dev/null
+++ b/egs/tta/autoencoderkl/exp_config_base.json
@@ -0,0 +1,11 @@
+{
+    "base_config": "config/autoencoderkl.json",
+    "model_type": "AutoencoderKL",
+    "dataset": [
+        "AudioCaps"
+    ],
+    "preprocess": {
+        "train_file": "train.json",
+        "valid_file": "vaild.json"
+    }
+}
\ No newline at end of file
diff --git a/egs/tta/autoencoderkl/exp_config_latent_4_10_78.json b/egs/tta/autoencoderkl/exp_config_latent_4_10_78.json
new file mode 100644
index 0000000000000000000000000000000000000000..911018a28c8e2d6a471a6505261e8050151e4d60
--- /dev/null
+++ b/egs/tta/autoencoderkl/exp_config_latent_4_10_78.json
@@ -0,0 +1,59 @@
+{
+    "base_config": "egs/tta/autoencoderkl/exp_config_base.json",
+    "dataset": [
+      "AudioCaps"
+    ],
+    "preprocess": {
+      // Specify the output root path to save the processed data 
+      "processed_dir": "data",
+
+      // feature
+      "use_spkid": false,
+      "use_uv": false,
+      "use_frame_pitch": false,
+      "use_phone_pitch": false,
+      "use_frame_energy": false,
+      "use_phone_energy": false,
+      "use_mel": false,
+      "use_audio": false,
+      "use_label": false,
+      "use_one_hot": false,
+      // feature for text to audio
+      "use_caption": true,
+      "use_melspec": true,
+      "use_wav": false,
+      // feature dir
+      "melspec_dir": "mel",
+      "wav_dir": "wav"
+    },
+    // Specify the output root path to save model ckpts and logs
+    "log_dir": "ckpts/tta",
+
+    "model": {
+      "autoencoderkl": {
+        "ch": 128,
+        "ch_mult": [1,2,2,4],
+        "num_res_blocks": 2,
+        "in_channels": 1,
+        "z_channels": 4,
+        "out_ch": 1,
+        "double_z": true
+      }
+    },
+    // train
+    "train": {
+      "adam": {
+        "lr": 4.0e-5
+      },
+      "ddp": false,
+      "random_seed": 12345,
+      "batch_size": 12,
+      "epochs": 50000,
+      "max_steps": 1000000,
+      "total_training_steps": 800000,
+      "save_summary_steps": 1000,
+      "save_checkpoints_steps": 5000,
+      "valid_interval": 5000,
+      "keep_checkpoint_max": 100
+    }
+  }
\ No newline at end of file
diff --git a/egs/tta/autoencoderkl/run_train.sh b/egs/tta/autoencoderkl/run_train.sh
new file mode 100644
index 0000000000000000000000000000000000000000..00a25693166dcee2d7b96dc6aa957ce96f8ef872
--- /dev/null
+++ b/egs/tta/autoencoderkl/run_train.sh
@@ -0,0 +1,26 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+######## Build Experiment Environment ###########
+exp_dir=$(cd `dirname $0`; pwd)
+work_dir=$(dirname $(dirname $(dirname $exp_dir)))
+
+export WORK_DIR=$work_dir
+export PYTHONPATH=$work_dir
+export PYTHONIOENCODING=UTF-8
+
+######## Set Experiment Configuration ###########
+exp_config="$exp_dir/exp_config.json"
+exp_name="autoencoder_kl_debug"
+
+num_workers=8
+export CUDA_VISIBLE_DEVICES="0" 
+
+######## Train Model ###########
+python "${work_dir}"/bins/tta/train_tta.py \
+    --config=$exp_config \
+    --num_workers=$num_workers \
+    --exp_name=$exp_name \
+    --stdout_interval=25 \
\ No newline at end of file
diff --git a/egs/tta/autoencoderkl/run_train_latent_4_10_78.sh b/egs/tta/autoencoderkl/run_train_latent_4_10_78.sh
new file mode 100644
index 0000000000000000000000000000000000000000..041627d9c43b56ee4f1657733062f18eb313e8b0
--- /dev/null
+++ b/egs/tta/autoencoderkl/run_train_latent_4_10_78.sh
@@ -0,0 +1,26 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+######## Build Experiment Environment ###########
+exp_dir=$(cd `dirname $0`; pwd)
+work_dir=$(dirname $(dirname $(dirname $exp_dir)))
+
+export WORK_DIR=$work_dir
+export PYTHONPATH=$work_dir
+export PYTHONIOENCODING=UTF-8
+
+######## Set Experiment Configuration ###########
+exp_config="$exp_dir/exp_config_latent_4_10_78.json"
+exp_name="autoencoder_kl_debug_latent_size_4_10_78"
+
+num_workers=8
+export CUDA_VISIBLE_DEVICES="0" 
+
+######## Train Model ###########
+python "${work_dir}"/bins/tta/train_tta.py \
+    --config=$exp_config \
+    --num_workers=$num_workers \
+    --exp_name=$exp_name \
+    --stdout_interval=25 \
\ No newline at end of file
diff --git a/egs/tts/FastSpeech2/README.md b/egs/tts/FastSpeech2/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..90108cf2a6c84d37dbb0537862e068376ea10c66
--- /dev/null
+++ b/egs/tts/FastSpeech2/README.md
@@ -0,0 +1,132 @@
+
+# FastSpeech2 Recipe
+
+In this recipe, we will show how to train [FastSpeech2](https://openreview.net/forum?id=piLPYqxtWuA) using Amphion's infrastructure. FastSpeech2 is a non-autoregressive TTS architecture that utilizes feed-forward Transformer blocks.
+
+There are four stages in total:
+
+1. Data preparation
+2. Features extraction
+3. Training
+4. Inference
+
+> **NOTE:** You need to run every command of this recipe in the `Amphion` root path:
+> ```bash
+> cd Amphion
+> ```
+
+## 1. Data Preparation
+
+### Dataset Download
+You can use the commonly used TTS dataset to train TTS model, e.g., LJSpeech, VCTK, LibriTTS, etc. We strongly recommend you use LJSpeech to train TTS model for the first time. How to download dataset is detailed [here](../../datasets/README.md).
+
+### Configuration
+
+After downloading the dataset, you can set the dataset paths in  `exp_config.json`. Note that you can change the `dataset` list to use your preferred datasets.
+
+```json
+    "dataset": [
+        "LJSpeech",
+    ],
+    "dataset_path": {
+        // TODO: Fill in your dataset path
+        "LJSpeech": "[LJSpeech dataset path]",
+    },
+```
+
+## 2. Features Extraction
+
+### Configuration
+
+Specify the `processed_dir` and the `log_dir` and for saving the processed data and the checkpoints in `exp_config.json`:
+
+```json
+    // TODO: Fill in the output log path
+    "log_dir": "ckpts/tts",
+    "preprocess": {
+        // TODO: Fill in the output data path
+        "processed_dir": "data",
+        ...
+    },
+```
+
+### Run
+
+Run the `run.sh` as the preproces stage (set  `--stage 1`):
+
+```bash
+sh egs/tts/FastSpeech2/run.sh --stage 1
+```
+
+## 3. Training
+
+### Configuration
+
+We provide the default hyparameters in the `exp_config.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on your GPU machines.
+
+```
+"train": {
+        "batch_size": 16,
+    }
+```
+
+### Run
+
+Run the `run.sh` as the training stage (set  `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `ckpts/tts/[YourExptName]`.
+
+```bash
+sh egs/tts/FastSpeech2/run.sh --stage 2 --name [YourExptName]
+```
+
+> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "0,1,2,3"`.
+
+
+## 4. Inference
+
+### Configuration
+
+For inference, you need to specify the following configurations when running `run.sh`:
+
+
+| Parameters                                          | Description                                                                                                                                                       | Example                                                                                                                                                                                                |
+| --------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `--infer_expt_dir`                              | The experimental directory which contains `checkpoint`                                                                                                            | `ckpts/tts/[YourExptName]`                                                                                                                                              |
+| `--infer_output_dir`                                | The output directory to save inferred audios.                                                                                                                     | `ckpts/tts/[YourExptName]/result`                                                                                                                                       |
+| `--infer_mode`                            | The inference mode, e.g., "`single`", "`batch`".  | "`single`" to generate a clip of speech, "`batch`" to generate a batch of speech at a time.                                     |
+| `--infer_dataset`                            | The dataset used for inference.  |  For LJSpeech dataset, the inference dataset would be `LJSpeech`.                                                                                                                                    |
+| `--infer_testing_set`                             | The subset of the inference dataset used for inference, e.g., train, test, golden_test | For LJSpeech dataset, the testing set would be  "`test`" split from LJSpeech at the feature extraction, or "`golden_test`" cherry-picked from test set as template testing set.                                                                                                                                    |
+| `--infer_text`                            | The text to be synthesized. | "`This is a clip of generated speech with the given text from a TTS model.`"                                                                                                                                    |
+
+### Run
+For example, if you want to generate speech of all testing set split from LJSpeech, just run:
+
+```bash
+sh egs/tts/FastSpeech2/run.sh --stage 3 \
+    --infer_expt_dir ckpts/tts/[YourExptName] \
+    --infer_output_dir ckpts/tts/[YourExptName]/result \
+    --infer_mode "batch" \
+    --infer_dataset "LJSpeech" \
+    --infer_testing_set "test"
+```
+
+Or, if you want to generate a single clip of speech from a given text, just run:
+
+```bash
+sh egs/tts/FastSpeech2/run.sh --stage 3 \
+    --infer_expt_dir ckpts/tts/[YourExptName] \
+    --infer_output_dir ckpts/tts/[YourExptName]/result \
+    --infer_mode "single" \
+    --infer_text "This is a clip of generated speech with the given text from a TTS model."
+```
+
+We will release a pre-trained FastSpeech2 model trained on LJSpeech. So you can download the pre-trained model and generate speech following the above inference instruction.
+
+
+```bibtex
+@inproceedings{ren2020fastspeech,
+  title={FastSpeech 2: Fast and High-Quality End-to-End Text to Speech},
+  author={Ren, Yi and Hu, Chenxu and Tan, Xu and Qin, Tao and Zhao, Sheng and Zhao, Zhou and Liu, Tie-Yan},
+  booktitle={International Conference on Learning Representations},
+  year={2020}
+}
+```
diff --git a/egs/tts/FastSpeech2/exp_config.json b/egs/tts/FastSpeech2/exp_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..6ba9022226ea987417913fb587e2a60ddb68bcb6
--- /dev/null
+++ b/egs/tts/FastSpeech2/exp_config.json
@@ -0,0 +1,21 @@
+{
+  "base_config": "config/fs2.json",
+  "model_type": "FastSpeech2",
+  "dataset": [
+    "LJSpeech"
+  ],
+  "dataset_path": {
+    // TODO: Fill in your dataset path
+    "LJSpeech": "[LJSpeech dataset path]"
+  },
+  // TODO: Fill in the output log path. The default value is "Amphion/ckpts/tts"
+  "log_dir": "ckpts/tts",
+  "preprocess": {
+    // TODO: Fill in the output data path. The default value is "Amphion/data"
+    "processed_dir": "data",
+    "sample_rate": 22050,
+  },
+  "train": {
+    "batch_size": 16,
+  }
+}
diff --git a/egs/tts/FastSpeech2/prepare_mfa.sh b/egs/tts/FastSpeech2/prepare_mfa.sh
new file mode 100644
index 0000000000000000000000000000000000000000..e8ce825b6a359f63e77e3ae001a48f14f3c69146
--- /dev/null
+++ b/egs/tts/FastSpeech2/prepare_mfa.sh
@@ -0,0 +1,14 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+#!/bin/bash
+mkdir mfa
+cd mfa
+wget https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner/releases/download/v1.1.0-beta.2/montreal-forced-aligner_linux.tar.gz
+tar -zxvf montreal-forced-aligner_linux.tar.gz
+cd mfa
+mkdir lexicon
+cd lexicon
+wget http://www.openslr.org/resources/11/librispeech-lexicon.txt
\ No newline at end of file
diff --git a/egs/tts/FastSpeech2/run.sh b/egs/tts/FastSpeech2/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..ad8da71e49783d5f47c3becb7d284506236678c2
--- /dev/null
+++ b/egs/tts/FastSpeech2/run.sh
@@ -0,0 +1,150 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+######## Build Experiment Environment ###########
+exp_dir=$(cd `dirname $0`; pwd)
+work_dir=$(dirname $(dirname $(dirname $exp_dir)))
+
+export WORK_DIR=$work_dir
+export PYTHONPATH=$work_dir
+export PYTHONIOENCODING=UTF-8
+
+cd $work_dir/modules/monotonic_align
+mkdir -p monotonic_align
+python setup.py build_ext --inplace
+cd $work_dir
+
+mfa_dir=$work_dir/mfa
+echo $mfa_dir
+
+######## Parse the Given Parameters from the Commond ###########
+# options=$(getopt -o c:n:s --long gpu:,config:,infer_expt_dir:,infer_output_dir:,infer_source_file:,infer_source_audio_dir:,infer_target_speaker:,infer_key_shift:,infer_vocoder_dir:,name:,stage: -- "$@")
+options=$(getopt -o c:n:s --long gpu:,config:,infer_expt_dir:,infer_output_dir:,infer_mode:,infer_dataset:,infer_testing_set:,infer_text:,name:,stage: -- "$@")
+eval set -- "$options"
+
+while true; do
+  case $1 in
+    # Experimental Configuration File
+    -c | --config) shift; exp_config=$1 ; shift ;;
+    # Experimental Name
+    -n | --name) shift; exp_name=$1 ; shift ;;
+    # Running Stage
+    -s | --stage) shift; running_stage=$1 ; shift ;;
+    # Visible GPU machines. The default value is "0".
+    --gpu) shift; gpu=$1 ; shift ;;
+
+    # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]"
+    --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;;
+    # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result"
+    --infer_output_dir) shift; infer_output_dir=$1 ; shift ;;
+    # [Only for Inference] The inference mode. It can be "batch" to generate speech by batch, or "single" to generage a single clip of speech.
+    --infer_mode) shift; infer_mode=$1 ; shift ;;
+    # [Only for Inference] The inference dataset. It is only used when the inference model is "batch".
+    --infer_dataset) shift; infer_dataset=$1 ; shift ;;
+    # [Only for Inference] The inference testing set. It is only used when the inference model is "batch". It can be "test" set split from the dataset, or "golden_test" carefully selected from the testing set.
+    --infer_testing_set) shift; infer_testing_set=$1 ; shift ;;
+    # [Only for Inference] The text to be synthesized from. It is only used when the inference model is "single". 
+    --infer_text) shift; infer_text=$1 ; shift ;;
+
+    --) shift ; break ;;
+    *) echo "Invalid option: $1" exit 1 ;;
+  esac
+done
+
+
+### Value check ###
+if [ -z "$running_stage" ]; then
+    echo "[Error] Please specify the running stage"
+    exit 1
+fi
+
+if [ -z "$exp_config" ]; then
+    exp_config="${exp_dir}"/exp_config.json
+fi
+echo "Exprimental Configuration File: $exp_config"
+
+if [ -z "$gpu" ]; then
+    gpu="0"
+fi
+
+######## Features Extraction ###########
+if [ $running_stage -eq 1 ]; then
+    if [ ! -d "$mfa_dir" ]; then
+        bash ${exp_dir}/prepare_mfa.sh
+    fi
+    CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/tts/preprocess.py \
+        --config=$exp_config \
+        --num_workers=4 \
+        --prepare_alignment=true
+fi
+
+######## Training ###########
+if [ $running_stage -eq 2 ]; then
+    if [ -z "$exp_name" ]; then
+        echo "[Error] Please specify the experiments name"
+        exit 1
+    fi
+    echo "Exprimental Name: $exp_name"
+
+    CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/tts/train.py \
+        --config $exp_config \
+        --exp_name $exp_name \
+        --log_level debug
+fi
+
+######## Inference ###########
+if [ $running_stage -eq 3 ]; then
+    if [ -z "$infer_expt_dir" ]; then
+        echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]"
+        exit 1
+    fi
+
+    if [ -z "$infer_output_dir" ]; then
+        infer_output_dir="$expt_dir/result"
+    fi
+
+    if [ -z "$infer_mode" ]; then
+        echo "[Error] Please specify the inference mode, e.g., "batch", "single""
+        exit 1
+    fi
+
+    if [ "$infer_mode" = "batch" ] && [ -z "$infer_dataset" ]; then
+        echo "[Error] Please specify the dataset used in inference when the inference mode is batch"
+        exit 1
+    fi
+
+    if [ "$infer_mode" = "batch" ] && [ -z "$infer_testing_set" ]; then
+        echo "[Error] Please specify the testing set used in inference when the inference mode is batch"
+        exit 1
+    fi
+ 
+    if [ "$infer_mode" = "single" ] && [ -z "$infer_text" ]; then
+        echo "[Error] Please specify the text to be synthesized when the inference mode is single"
+        exit 1
+    fi
+
+    if [ "$infer_mode" = "single" ]; then
+        echo 'Text: ' ${infer_text}
+        infer_dataset=None
+        infer_testing_set=None
+    elif [ "$infer_mode" = "batch" ]; then
+        infer_text=''
+    fi
+
+
+    CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/tts/inference.py \
+        --config $exp_config \
+        --acoustics_dir $infer_expt_dir \
+        --output_dir $infer_output_dir  \
+        --mode $infer_mode \
+        --dataset $infer_dataset \
+        --testing_set $infer_testing_set \
+        --text "$infer_text" \
+        --log_level debug \
+        --vocoder_dir /mntnfs/lee_data1/chenxi/processed_data/ljspeech/model_ckpt/hifigan/checkpoints
+
+
+
+fi
\ No newline at end of file
diff --git a/egs/tts/NaturalSpeech2/exp_config.json b/egs/tts/NaturalSpeech2/exp_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..170a15e7b1ceacda03bd6d7422f67a8adfa570b4
--- /dev/null
+++ b/egs/tts/NaturalSpeech2/exp_config.json
@@ -0,0 +1,39 @@
+{
+    "base_config": "egs/tts/NaturalSpeech2/exp_config_base.json",
+    "dataset": [
+      "LibriTTS"
+    ],
+    "preprocess": {
+      // Specify the output root path to save the processed data 
+      "processed_dir": "[LibriTTS dataset path]",
+      "train_file": "train.json",
+      "valid_file": "test.json",
+      "read_metadata": true,
+      "metadata_dir": "metadata"
+    },
+    // Specify the output root path to save model ckpts and logs
+    "log_dir": "ckpts/tts",
+    "train": {
+        // New trainer and Accelerator
+        "gradient_accumulation_step": 1,
+        "tracker": ["tensorboard"],
+        "max_epoch": 5000,
+        "save_checkpoint_stride": [1],
+        "keep_last": [1000],
+        "run_eval": [true],
+        "dataloader": {
+          "num_worker": 16,
+          "pin_memory": true
+        },
+        "adam": {
+            "lr": 1.0e-4
+        },
+        "use_dynamic_batchsize": true,
+        "batch_size": 8,
+        "max_tokens": 7500,
+        "max_sentences": 32,
+        "lr_warmup_steps": 5000,
+        "lr_scheduler": "cosine",
+        "num_train_steps": 800000
+    }
+  }
\ No newline at end of file
diff --git a/egs/tts/NaturalSpeech2/exp_config_base.json b/egs/tts/NaturalSpeech2/exp_config_base.json
new file mode 100644
index 0000000000000000000000000000000000000000..31edb71192f18d0079f66bf51217d7857f5f21ca
--- /dev/null
+++ b/egs/tts/NaturalSpeech2/exp_config_base.json
@@ -0,0 +1,118 @@
+{
+    "base_config": "config/ns2.json",
+    "model_type": "NaturalSpeech2",
+    "dataset": [
+        "LibriTTS"
+    ],
+    "preprocess": {
+        "use_mel": false,
+        "use_code": true,
+        "use_spkid": true,
+        "use_pitch": true,
+        "use_duration": true,
+        "use_phone": true,
+        "use_len": true,
+        "use_cross_reference": true,
+        "train_file": "train.json",
+        "valid_file": "test.json",
+        "melspec_dir": "mel",
+        "code_dir": "code",
+        "pitch_dir": "pitch",
+        "duration_dir": "duration",
+        "metadata_dir": "metadata",
+        "read_metadata": true,
+        "clip_mode": "start"
+    },
+    "model": {
+        "latent_dim": 128,
+        "prior_encoder": {
+            "vocab_size": 100,
+            "pitch_min": 50,
+            "pitch_max": 1100,
+            "pitch_bins_num": 512,
+            "encoder": {
+                "encoder_layer": 6,
+                "encoder_hidden": 512,
+                "encoder_head": 8,
+                "conv_filter_size": 2048,
+                "conv_kernel_size": 9,
+                "encoder_dropout": 0.2,
+                "use_cln": true
+            },
+            "duration_predictor": {
+                "input_size": 512,
+                "filter_size": 512,
+                "kernel_size": 3,
+                "conv_layers": 30,
+                "cross_attn_per_layer": 3,
+                "attn_head": 8,
+                "drop_out": 0.5
+            },
+            "pitch_predictor": {
+                "input_size": 512,
+                "filter_size": 512,
+                "kernel_size": 5,
+                "conv_layers": 30,
+                "cross_attn_per_layer": 3,
+                "attn_head": 8,
+                "drop_out": 0.5
+            }
+        },
+        "diffusion": {
+            "wavenet": {
+                "input_size": 128,
+                "hidden_size": 512,
+                "out_size": 128,
+                "num_layers": 40,
+                "cross_attn_per_layer": 3,
+                "dilation_cycle": 2,
+                "attn_head": 8,
+                "drop_out": 0.2
+            },
+            "beta_min": 0.05,
+            "beta_max": 20,
+            "sigma": 1.0,
+            "noise_factor": 1.0,
+            "ode_solver": "euler",
+            "diffusion_type": "diffusion"
+        },
+        "prompt_encoder": {
+            "encoder_layer": 6,
+            "encoder_hidden": 512,
+            "encoder_head": 8,
+            "conv_filter_size": 2048,
+            "conv_kernel_size": 9,
+            "encoder_dropout": 0.2,
+            "use_cln": false
+        },
+        "query_emb": {
+            "query_token_num": 32,
+            "hidden_size": 512,
+            "head_num": 8
+        },
+        "inference_step": 500
+    },
+    "train": {
+        "use_dynamic_batchsize": true,
+        "max_tokens": 7500,
+        "max_sentences": 32,
+        "lr_warmup_steps": 5000,
+        "lr_scheduler": "cosine",
+        "num_train_steps": 800000,
+        "adam": {
+            "lr": 7.5e-5
+        },
+        "diff_ce_loss_lambda": 0.5,
+        "diff_noise_loss_lambda": 1.0,
+        "ddp": false,
+        "random_seed": 114,
+        "batch_size": 32,
+        "epochs": 5000,
+        "max_steps": 1000000,
+        "total_training_steps": 800000,
+        "save_summary_steps": 500,
+        "save_checkpoints_steps": 2000,
+        "valid_interval": 2000,
+        "keep_checkpoint_max": 100
+    }
+}
\ No newline at end of file
diff --git a/egs/tts/NaturalSpeech2/run_inference.sh b/egs/tts/NaturalSpeech2/run_inference.sh
new file mode 100644
index 0000000000000000000000000000000000000000..b1a91b8c4efb20b0ec2ab1f913a9b84ab8fb2389
--- /dev/null
+++ b/egs/tts/NaturalSpeech2/run_inference.sh
@@ -0,0 +1,43 @@
+######## Build Experiment Environment ###########
+exp_dir=$(cd `dirname $0`; pwd)
+work_dir=$(dirname $(dirname $(dirname $exp_dir)))
+
+export WORK_DIR=$work_dir
+export PYTHONPATH=$work_dir
+export PYTHONIOENCODING=UTF-8
+
+######## Set Experiment Configuration ###########
+exp_config="$exp_dir/exp_config.json"
+exp_name="ns2_libritts"
+ref_audio="$work_dir/egs/tts/NaturalSpeech2/prompt_example/ref_audio.wav"
+checkpoint_path="$work_dir/ckpts/tts/ns2_libritts/checkpoint/epoch-0065_step-0376136_loss-7.126379"
+output_dir="$work_dir/output"
+mode="single"
+
+export CUDA_VISIBLE_DEVICES="0"
+
+######## Parse Command Line Arguments ###########
+while [[ $# -gt 0 ]]
+do
+key="$1"
+
+case $key in
+    --text)
+    text="$2"
+    shift # past argument
+    shift # past value
+    ;;
+    *)    # unknown option
+    shift # past argument
+    ;;
+esac
+done
+
+######## Train Model ###########
+python "${work_dir}"/bins/tts/inference.py \
+    --config=$exp_config \
+    --text="$text" \
+    --mode=$mode \
+    --checkpoint_path=$checkpoint_path \
+    --ref_audio=$ref_audio \
+    --output_dir=$output_dir \
\ No newline at end of file
diff --git a/egs/tts/NaturalSpeech2/run_train.sh b/egs/tts/NaturalSpeech2/run_train.sh
new file mode 100644
index 0000000000000000000000000000000000000000..561be9e8f3ce11b753f1148e2e797d7b5ef5b3f0
--- /dev/null
+++ b/egs/tts/NaturalSpeech2/run_train.sh
@@ -0,0 +1,18 @@
+######## Build Experiment Environment ###########
+exp_dir=$(cd `dirname $0`; pwd)
+work_dir=$(dirname $(dirname $(dirname $exp_dir)))
+
+export WORK_DIR=$work_dir
+export PYTHONPATH=$work_dir
+export PYTHONIOENCODING=UTF-8
+ 
+######## Set Experiment Configuration ###########
+exp_config="$exp_dir/exp_config.json"
+exp_name="ns2_libritts"
+
+######## Train Model ###########
+CUDA_VISIBLE_DEVICES="0" accelerate \
+    "${work_dir}"/bins/tts/train.py \
+    --config=$exp_config \
+    --exp_name=$exp_name \
+    --log_level debug \
\ No newline at end of file
diff --git a/egs/tts/README.md b/egs/tts/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..d1d40196af0e95f401958428511fd754d0d7e449
--- /dev/null
+++ b/egs/tts/README.md
@@ -0,0 +1,17 @@
+
+# Amphion Text-to-Speech (TTS) Recipe
+
+## Quick Start
+
+We provide a **[beginner recipe](VALLE/)** to demonstrate how to train a cutting edge TTS model. Specifically, it is Amphion's re-implementation for [Vall-E](https://arxiv.org/abs/2301.02111), which is a zero-shot TTS architecture that uses a neural codec language model with discrete codes.
+
+## Supported Model Architectures
+
+Until now, Amphion TTS supports the following models or architectures,
+- **[FastSpeech2](FastSpeech2)**: A non-autoregressive TTS architecture that utilizes feed-forward Transformer blocks.
+- **[VITS](VITS)**: An end-to-end TTS architecture that utilizes conditional variational autoencoder with adversarial learning
+- **[Vall-E](VALLE)**: A zero-shot TTS architecture that uses a neural codec language model with discrete codes.
+- **[NaturalSpeech2](NaturalSpeech2)** (👨‍💻 developing): An architecture for TTS that utilizes a latent diffusion model to generate natural-sounding voices.
+
+## Amphion TTS Demo
+Here are some [TTS samples](https://openhlt.github.io/Amphion_TTS_Demo/) from Amphion.
diff --git a/egs/tts/VALLE/README.md b/egs/tts/VALLE/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..63b837f832bb0aa5138ae31e5d232eeaca27574f
--- /dev/null
+++ b/egs/tts/VALLE/README.md
@@ -0,0 +1,139 @@
+# VALL-E Recipe
+
+In this recipe, we will show how to train [VALL-E](https://arxiv.org/abs/2301.02111) using Amphion's infrastructure. VALL-E is a zero-shot TTS architecture that uses a neural codec language model with discrete codes.
+
+There are four stages in total:
+
+1. Data preparation
+2. Features extraction
+3. Training
+4. Inference
+
+> **NOTE:** You need to run every command of this recipe in the `Amphion` root path:
+> ```bash
+> cd Amphion
+> ```
+
+## 1. Data Preparation
+
+### Dataset Download
+You can use the commonly used TTS dataset to train VALL-E model, e.g., LibriTTS, etc. We strongly recommend you use LibriTTS to train VALL-E model for the first time. How to download dataset is detailed [here](../../datasets/README.md).
+
+### Configuration
+
+After downloading the dataset, you can set the dataset paths in  `exp_config.json`. Note that you can change the `dataset` list to use your preferred datasets.
+
+```json
+    "dataset": [
+        "libritts",
+    ],
+    "dataset_path": {
+        // TODO: Fill in your dataset path
+        "libritts": "[LibriTTS dataset path]",
+    },
+```
+
+## 2. Features Extraction
+
+### Configuration
+
+Specify the `processed_dir` and the `log_dir` and for saving the processed data and the checkpoints in `exp_config.json`:
+
+```json
+    // TODO: Fill in the output log path. The default value is "Amphion/ckpts/tts"
+    "log_dir": "ckpts/tts",
+    "preprocess": {
+        // TODO: Fill in the output data path. The default value is "Amphion/data"
+        "processed_dir": "data",
+        ...
+    },
+```
+
+### Run
+
+Run the `run.sh` as the preproces stage (set  `--stage 1`):
+
+```bash
+sh egs/tts/VALLE/run.sh --stage 1
+```
+
+> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "1"`.
+
+
+## 3. Training
+
+### Configuration
+
+We provide the default hyparameters in the `exp_config.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on your GPU machines.
+
+```
+"train": {
+        "batch_size": 4,
+    }
+```
+
+### Run
+
+Run the `run.sh` as the training stage (set  `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `Amphion/ckpts/tts/[YourExptName]`.
+
+Specifically, VALL-E need to train a autoregressive (AR) model and then a non-autoregressive (NAR) model. So, you can set `--model_train_stage 1` to train AR model, and set `--model_train_stage 2` to train NAR model, where `--ar_model_ckpt_dir` should be set as the ckeckpoint path to the trained AR model.
+
+
+Train a AR moel, just run:
+
+```bash
+sh egs/tts/VALLE/run.sh --stage 2 --model_train_stage 1 --name [YourExptName]
+```
+
+Train a NAR model, just run:
+```bash
+sh egs/tts/VALLE/run.sh --stage 2 --model_train_stage 2 --ar_model_ckpt_dir [ARModelPath] --name [YourExptName]
+```
+<!-- > **NOTE:** To train a NAR model, `--checkpoint_path` should be set as the ckeckpoint path to the trained AR model. -->
+
+> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "0,1,2,3"`.
+
+
+## 4. Inference
+
+### Configuration
+
+For inference, you need to specify the following configurations when running `run.sh`:
+
+
+
+| Parameters            | Description                                                                            | Example                                                                                                                                                                         |
+| --------------------- | -------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `--infer_expt_dir`    | The experimental directory of NAR model which contains `checkpoint`                                 | `Amphion/ckpts/tts/[YourExptName]`                                                                                                                                              |
+| `--infer_output_dir`  | The output directory to save inferred audios.                                          | `Amphion/ckpts/tts/[YourExptName]/result`                                                                                                                                       |
+| `--infer_mode`        | The inference mode, e.g., "`single`", "`batch`".                                       | "`single`" to generate a clip of speech, "`batch`" to generate a batch of speech at a time.                                                                                     |
+| `--infer_text`        | The text to be synthesized.                                                            | "`This is a clip of generated speech with the given text from a TTS model.`"                                                                                                    |
+| `--infer_text_prompt`     | The text prompt for inference.                                                        | The text prompt should be aligned with the audio prompt.                                                                                                                |
+| `--infer_audio_prompt` | The audio prompt for inference. | The audio prompt should be aligned with text prompt.|
+| `--test_list_file` | The test list file used for batch inference. | The format of test list file is `text\|text_prompt\|audio_prompt`.|
+
+
+### Run
+For example, if you want to generate a single clip of speech, just run:
+
+```bash
+sh egs/tts/VALLE/run.sh --stage 3 --gpu "0" \
+    --infer_expt_dir Amphion/ckpts/tts/[YourExptName] \
+    --infer_output_dir Amphion/ckpts/tts/[YourExptName]/result \
+    --infer_mode "single" \
+    --infer_text "This is a clip of generated speech with the given text from a TTS model." \
+    --infer_text_prompt "But even the unsuccessful dramatist has his moments." \
+    --infer_audio_prompt egs/tts/VALLE/prompt_examples/7176_92135_000004_000000.wav
+```
+
+
+We released a pre-trained Amphion VALL-E model. So you can download the pre-trained model [here](https://huggingface.co/amphion/valle-libritts) and generate speech following the above inference instruction.
+
+```bibtex
+@article{wang2023neural,
+  title={Neural codec language models are zero-shot text to speech synthesizers},
+  author={Wang, Chengyi and Chen, Sanyuan and Wu, Yu and Zhang, Ziqiang and Zhou, Long and Liu, Shujie and Chen, Zhuo and Liu, Yanqing and Wang, Huaming and Li, Jinyu and others},
+  journal={arXiv preprint arXiv:2301.02111},
+  year={2023}
+}
+```
\ No newline at end of file
diff --git a/egs/tts/VALLE/exp_config.json b/egs/tts/VALLE/exp_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..f013dad0b7f85c57fcd99a578278fef51cf5d717
--- /dev/null
+++ b/egs/tts/VALLE/exp_config.json
@@ -0,0 +1,33 @@
+{
+    "base_config": "config/valle.json",
+    "model_type": "VALLE",
+    "dataset": [
+        "libritts"
+    ],
+    "dataset_path": {
+        "libritts": "[LibriTTS dataset path]"
+    },
+    "preprocess": {
+        "extract_phone": true,
+        "phone_extractor": "espeak", // "espeak, pypinyin, pypinyin_initials_finals, lexicon (only for language=en-us right now)"
+        "extract_acoustic_token": true,
+        "use_phone": true,
+        "use_acoustic_token": true,
+        "processed_dir": "Amphion/data/",
+        "sample_rate": 24000, // "Audio sampling rate." 
+        "codec_hop_size": "320", // "Audio codec hop size." 
+        "valid_file": "test.json", 
+    },
+    "model": {
+        "prefix_mode": 1, // "The mode for how to prefix VALL-E NAR Decoder, 0: no prefix, 1: 0 to random, 2: random to random, 4: chunk of pre or post utterance.",
+    },
+    "log_dir": "Amphion/ckpts/tts/valle",
+    "train": {
+        "batch_size": 4,
+        "train_stage": 1, // 0: train all modules, For VALL_E, support 1: AR Decoder 2: NAR Decoder(s)
+        "max_epoch": 20, // "Number of epochs to train."
+        "use_dynamic_batchsize": true, // If use dynamic batch size
+        "max_tokens": 4000, // If use dynamic batch size
+        "max_sentences": 10 // If use dynamic batch size
+    }
+}
diff --git a/egs/tts/VALLE/prompt_examples/260_123440_000010_000004.normalized.txt b/egs/tts/VALLE/prompt_examples/260_123440_000010_000004.normalized.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ed9d2b859d3c7bbb7bcc2476323d1a219ee8d53f
--- /dev/null
+++ b/egs/tts/VALLE/prompt_examples/260_123440_000010_000004.normalized.txt
@@ -0,0 +1 @@
+I almost think I can remember feeling a little different.
\ No newline at end of file
diff --git a/egs/tts/VALLE/prompt_examples/5142_33396_000002_000004.normalized.txt b/egs/tts/VALLE/prompt_examples/5142_33396_000002_000004.normalized.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4dab0ebd446cb814acccb2b97fee31a0d5b7444d
--- /dev/null
+++ b/egs/tts/VALLE/prompt_examples/5142_33396_000002_000004.normalized.txt
@@ -0,0 +1 @@
+Ten sons sat at meat with him, and I was the youngest.
\ No newline at end of file
diff --git a/egs/tts/VALLE/prompt_examples/6829_68771_000027_000000.normalized.txt b/egs/tts/VALLE/prompt_examples/6829_68771_000027_000000.normalized.txt
new file mode 100644
index 0000000000000000000000000000000000000000..04db64ff2711c35a78cbb210d11fce06865d831c
--- /dev/null
+++ b/egs/tts/VALLE/prompt_examples/6829_68771_000027_000000.normalized.txt
@@ -0,0 +1 @@
+The girl entered, and gave an involuntary cry of surprise.
\ No newline at end of file
diff --git a/egs/tts/VALLE/prompt_examples/7176_92135_000004_000000.normalized.txt b/egs/tts/VALLE/prompt_examples/7176_92135_000004_000000.normalized.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6400078bd5d5fd4043a0019fc1e8b276b408cd39
--- /dev/null
+++ b/egs/tts/VALLE/prompt_examples/7176_92135_000004_000000.normalized.txt
@@ -0,0 +1 @@
+But even the unsuccessful dramatist has his moments.
\ No newline at end of file
diff --git a/egs/tts/VALLE/run.sh b/egs/tts/VALLE/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..104f9f32619145952fac5218a3a24134837c4ad9
--- /dev/null
+++ b/egs/tts/VALLE/run.sh
@@ -0,0 +1,158 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+######## Build Experiment Environment ###########
+exp_dir=$(cd `dirname $0`; pwd)
+work_dir=$(dirname $(dirname $(dirname $exp_dir)))
+
+export WORK_DIR=$work_dir
+export PYTHONPATH=$work_dir
+export PYTHONIOENCODING=UTF-8
+
+cd $work_dir/modules/monotonic_align
+mkdir -p monotonic_align
+python setup.py build_ext --inplace
+cd $work_dir
+
+######## Parse the Given Parameters from the Commond ###########
+options=$(getopt -o c:n:s --long gpu:,config:,infer_expt_dir:,ar_model_ckpt_dir:,infer_output_dir:,infer_mode:,infer_test_list_file:,infer_text:,infer_text_prompt:,infer_audio_prompt:,model_train_stage:,name:,stage: -- "$@")
+eval set -- "$options"
+
+while true; do
+  case $1 in
+    # Experimental Configuration File
+    -c | --config) shift; exp_config=$1 ; shift ;;
+    # Experimental Name
+    -n | --name) shift; exp_name=$1 ; shift ;;
+    # Running Stage
+    -s | --stage) shift; running_stage=$1 ; shift ;;
+    # Visible GPU machines. The default value is "0".
+    --gpu) shift; gpu=$1 ; shift ;;
+
+    # [Only for Training] Model training stage. 
+    --model_train_stage) shift; model_train_stage=$1 ; shift ;;
+    # [Only for Training] The stage1 ckpt dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]"
+    --ar_model_ckpt_dir) shift; ar_model_ckpt_dir=$1 ; shift ;;
+
+    # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]"
+    --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;;
+    # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result"
+    --infer_output_dir) shift; infer_output_dir=$1 ; shift ;;
+    
+    # [Only for Inference] The inference mode. It can be "batch" to generate speech by batch, or "single" to generage a single clip of speech.
+    --infer_mode) shift; infer_mode=$1 ; shift ;;
+    # [Only for Inference] The inference test list file. It is only used when the inference model is "batch".
+    --infer_test_list_file) shift; infer_test_list_file=$1 ; shift ;;
+    # [Only for Inference] The text to be synthesized from. It is only used when the inference model is "single". 
+    --infer_text) shift; infer_text=$1 ; shift ;;
+    # [Only for Inference] The inference text prompt. It is only used when the inference model is "single".
+    --infer_text_prompt) shift; infer_text_prompt=$1 ; shift ;;
+    # [Only for Inference] The inference audio prompt. It is only used when the inference model is "single".
+    --infer_audio_prompt) shift; infer_audio_prompt=$1 ; shift ;;
+
+    --) shift ; break ;;
+    *) echo "Invalid option: $1" exit 1 ;;
+  esac
+done
+
+
+### Value check ###
+if [ -z "$running_stage" ]; then
+    echo "[Error] Please specify the running stage"
+    exit 1
+fi
+
+if [ -z "$exp_config" ]; then
+    exp_config="${exp_dir}"/exp_config.json
+fi
+echo "Exprimental Configuration File: $exp_config"
+
+if [ -z "$gpu" ]; then
+    gpu="0"
+fi
+
+######## Features Extraction ###########
+if [ $running_stage -eq 1 ]; then
+    CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/tts/preprocess.py \
+        --config=$exp_config \
+        --num_workers=4
+fi
+
+######## Training ###########
+if [ $running_stage -eq 2 ]; then
+    if [ -z "$exp_name" ]; then
+        echo "[Error] Please specify the experiments name"
+        exit 1
+    fi
+
+    if [ "$model_train_stage" = "2" ] && [ -z "$ar_model_ckpt_dir" ]; then
+        echo "[Error] Please specify the ckeckpoint path to the trained model in stage1."
+        exit 1
+    fi
+
+    if [  "$model_train_stage" = "1" ]; then
+        ar_model_ckpt_dir=None
+    fi
+
+    echo "Exprimental Name: $exp_name"
+
+    CUDA_VISIBLE_DEVICES=$gpu accelerate launch --main_process_port 29510 \
+    "${work_dir}"/bins/tts/train.py \
+        --config $exp_config \
+        --exp_name $exp_name \
+        --log_level debug \
+        --train_stage $model_train_stage \
+        --checkpoint_path $ar_model_ckpt_dir
+fi
+
+
+######## Inference ###########
+if [ $running_stage -eq 3 ]; then
+    if [ -z "$infer_expt_dir" ]; then
+        echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]"
+        exit 1
+    fi
+
+    if [ -z "$infer_output_dir" ]; then
+        infer_output_dir="$expt_dir/result"
+    fi
+
+    if [ -z "$infer_mode" ]; then
+        echo "[Error] Please specify the inference mode, e.g., "batch", "single""
+        exit 1
+    fi
+
+    if [ "$infer_mode" = "batch" ] && [ -z "$infer_test_list_file" ]; then
+        echo "[Error] Please specify the test list file used in inference when the inference mode is batch"
+        exit 1
+    fi
+
+    if [ "$infer_mode" = "single" ] && [ -z "$infer_text" ]; then
+        echo "[Error] Please specify the text to be synthesized when the inference mode is single"
+        exit 1
+    fi
+
+    if [ "$infer_mode" = "single" ]; then
+        echo 'Text: ' ${infer_text}
+        infer_test_list_file=None
+    elif [ "$infer_mode" = "batch" ]; then
+        infer_text=""
+        infer_text_prompt=""
+        infer_audio_prompt=""
+    fi
+
+
+    CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/tts/inference.py \
+        --config $exp_config \
+        --log_level debug \
+        --acoustics_dir $infer_expt_dir \
+        --output_dir $infer_output_dir  \
+        --mode $infer_mode \
+        --text "$infer_text" \
+        --text_prompt "$infer_text_prompt" \
+        --audio_prompt $infer_audio_prompt\
+        --test_list_file $infer_test_list_file \
+
+fi
diff --git a/egs/tts/VITS/README.md b/egs/tts/VITS/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..258f386d8abed927a7aecebff3c37fd55a07255e
--- /dev/null
+++ b/egs/tts/VITS/README.md
@@ -0,0 +1,135 @@
+
+# VITS Recipe
+
+In this recipe, we will show how to train [VITS](https://arxiv.org/abs/2106.06103) using Amphion's infrastructure. VITS is an end-to-end TTS architecture that utilizes conditional variational autoencoder with adversarial learning.
+
+There are four stages in total:
+
+1. Data preparation
+2. Features extraction
+3. Training
+4. Inference
+
+> **NOTE:** You need to run every command of this recipe in the `Amphion` root path:
+> ```bash
+> cd Amphion
+> ```
+
+## 1. Data Preparation
+
+### Dataset Download
+You can use the commonly used TTS dataset to train TTS model, e.g., LJSpeech, VCTK, LibriTTS, etc. We strongly recommend you use LJSpeech to train TTS model for the first time. How to download dataset is detailed [here](../../datasets/README.md).
+
+### Configuration
+
+After downloading the dataset, you can set the dataset paths in  `exp_config.json`. Note that you can change the `dataset` list to use your preferred datasets.
+
+```json
+    "dataset": [
+        "LJSpeech",
+    ],
+    "dataset_path": {
+        // TODO: Fill in your dataset path
+        "LJSpeech": "[LJSpeech dataset path]",
+    },
+```
+
+## 2. Features Extraction
+
+### Configuration
+
+Specify the `processed_dir` and the `log_dir` and for saving the processed data and the checkpoints in `exp_config.json`:
+
+```json
+    // TODO: Fill in the output log path. The default value is "Amphion/ckpts/tts"
+    "log_dir": "ckpts/tts",
+    "preprocess": {
+        // TODO: Fill in the output data path. The default value is "Amphion/data"
+        "processed_dir": "data",
+        ...
+    },
+```
+
+### Run
+
+Run the `run.sh` as the preproces stage (set  `--stage 1`):
+
+```bash
+sh egs/tts/VITS/run.sh --stage 1
+```
+
+> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "1"`.
+
+## 3. Training
+
+### Configuration
+
+We provide the default hyparameters in the `exp_config.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on your GPU machines.
+
+```
+"train": {
+        "batch_size": 16,
+    }
+```
+
+### Run
+
+Run the `run.sh` as the training stage (set  `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `Amphion/ckpts/tts/[YourExptName]`.
+
+```bash
+sh egs/tts/VITS/run.sh --stage 2 --name [YourExptName]
+```
+
+> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "0,1,2,3"`.
+
+
+## 4. Inference
+
+### Configuration
+
+For inference, you need to specify the following configurations when running `run.sh`:
+
+
+| Parameters            | Description                                                                            | Example                                                                                                                                                                         |
+| --------------------- | -------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `--infer_expt_dir`    | The experimental directory which contains `checkpoint`                                 | `Amphion/ckpts/tts/[YourExptName]`                                                                                                                                              |
+| `--infer_output_dir`  | The output directory to save inferred audios.                                          | `Amphion/ckpts/tts/[YourExptName]/result`                                                                                                                                       |
+| `--infer_mode`        | The inference mode, e.g., "`single`", "`batch`".                                       | "`single`" to generate a clip of speech, "`batch`" to generate a batch of speech at a time.                                                                                     |
+| `--infer_dataset`     | The dataset used for inference.                                                        | For LJSpeech dataset, the inference dataset would be `LJSpeech`.                                                                                                                |
+| `--infer_testing_set` | The subset of the inference dataset used for inference, e.g., train, test, golden_test | For LJSpeech dataset, the testing set would be  "`test`" split from LJSpeech at the feature extraction, or "`golden_test`" cherry-picked from test set as template testing set. |
+| `--infer_text`        | The text to be synthesized.                                                            | "`This is a clip of generated speech with the given text from a TTS model.`"                                                                                                    |
+
+### Run
+For example, if you want to generate speech of all testing set split from LJSpeech, just run:
+
+```bash
+sh egs/tts/VITS/run.sh --stage 3 --gpu "0" \
+    --infer_expt_dir Amphion/ckpts/tts/[YourExptName] \
+    --infer_output_dir Amphion/ckpts/tts/[YourExptName]/result \
+    --infer_mode "batch" \
+    --infer_dataset "LJSpeech" \
+    --infer_testing_set "test"
+```
+
+Or, if you want to generate a single clip of speech from a given text, just run:
+
+```bash
+sh egs/tts/VITS/run.sh --stage 3 --gpu "0" \
+    --infer_expt_dir Amphion/ckpts/tts/[YourExptName] \
+    --infer_output_dir Amphion/ckpts/tts/[YourExptName]/result \
+    --infer_mode "single" \
+    --infer_text "This is a clip of generated speech with the given text from a TTS model."
+```
+
+We released a pre-trained Amphion VITS model trained on LJSpeech. So you can download the pre-trained model [here](https://huggingface.co/amphion/vits-ljspeech) and generate speech following the above inference instruction.
+
+
+```bibtex
+@inproceedings{kim2021conditional,
+  title={Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech},
+  author={Kim, Jaehyeon and Kong, Jungil and Son, Juhee},
+  booktitle={International Conference on Machine Learning},
+  pages={5530--5540},
+  year={2021},
+}
+```
\ No newline at end of file
diff --git a/egs/tts/VITS/exp_config.json b/egs/tts/VITS/exp_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..b210a2656186844fe1d0fc88e515fdc6cc207dcb
--- /dev/null
+++ b/egs/tts/VITS/exp_config.json
@@ -0,0 +1,27 @@
+{
+  "base_config": "config/vits.json",
+  "model_type": "VITS",
+  "dataset": [
+    "LJSpeech"
+  ],
+  "dataset_path": {
+    // TODO: Fill in your dataset path
+    "LJSpeech": "[LJSpeech dataset path]"
+  },
+  // TODO: Fill in the output log path. The default value is "Amphion/ckpts/tts"
+  "log_dir": "ckpts/tts",
+  "preprocess": {
+    "use_phone": true,
+    // linguistic features
+    "extract_phone": true,
+    "phone_extractor": "lexicon", // "espeak, pypinyin, pypinyin_initials_finals, lexicon (only for language=en-us right now)"
+    // TODO: Fill in the output data path. The default value is "Amphion/data"
+    "processed_dir": "data",
+    
+    "sample_rate": 22050,
+    "valid_file": "test.json", // validattion set
+  },
+  "train": {
+    "batch_size": 16,
+  }
+}
\ No newline at end of file
diff --git a/egs/tts/VITS/run.sh b/egs/tts/VITS/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..8f330d32bca818b92f1f2c71ed78a6c43b703aea
--- /dev/null
+++ b/egs/tts/VITS/run.sh
@@ -0,0 +1,142 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+######## Build Experiment Environment ###########
+exp_dir=$(cd `dirname $0`; pwd)
+work_dir=$(dirname $(dirname $(dirname $exp_dir)))
+
+export WORK_DIR=$work_dir
+export PYTHONPATH=$work_dir
+export PYTHONIOENCODING=UTF-8
+
+cd $work_dir/modules/monotonic_align
+mkdir -p monotonic_align
+python setup.py build_ext --inplace
+cd $work_dir
+
+######## Parse the Given Parameters from the Commond ###########
+# options=$(getopt -o c:n:s --long gpu:,config:,infer_expt_dir:,infer_output_dir:,infer_source_file:,infer_source_audio_dir:,infer_target_speaker:,infer_key_shift:,infer_vocoder_dir:,name:,stage: -- "$@")
+options=$(getopt -o c:n:s --long gpu:,config:,infer_expt_dir:,infer_output_dir:,infer_mode:,infer_dataset:,infer_testing_set:,infer_text:,name:,stage: -- "$@")
+eval set -- "$options"
+
+while true; do
+  case $1 in
+    # Experimental Configuration File
+    -c | --config) shift; exp_config=$1 ; shift ;;
+    # Experimental Name
+    -n | --name) shift; exp_name=$1 ; shift ;;
+    # Running Stage
+    -s | --stage) shift; running_stage=$1 ; shift ;;
+    # Visible GPU machines. The default value is "0".
+    --gpu) shift; gpu=$1 ; shift ;;
+
+    # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]"
+    --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;;
+    # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result"
+    --infer_output_dir) shift; infer_output_dir=$1 ; shift ;;
+    # [Only for Inference] The inference mode. It can be "batch" to generate speech by batch, or "single" to generage a single clip of speech.
+    --infer_mode) shift; infer_mode=$1 ; shift ;;
+    # [Only for Inference] The inference dataset. It is only used when the inference model is "batch".
+    --infer_dataset) shift; infer_dataset=$1 ; shift ;;
+    # [Only for Inference] The inference testing set. It is only used when the inference model is "batch". It can be "test" set split from the dataset, or "golden_test" carefully selected from the testing set.
+    --infer_testing_set) shift; infer_testing_set=$1 ; shift ;;
+    # [Only for Inference] The text to be synthesized from. It is only used when the inference model is "single". 
+    --infer_text) shift; infer_text=$1 ; shift ;;
+
+    --) shift ; break ;;
+    *) echo "Invalid option: $1" exit 1 ;;
+  esac
+done
+
+
+### Value check ###
+if [ -z "$running_stage" ]; then
+    echo "[Error] Please specify the running stage"
+    exit 1
+fi
+
+if [ -z "$exp_config" ]; then
+    exp_config="${exp_dir}"/exp_config.json
+fi
+echo "Exprimental Configuration File: $exp_config"
+
+if [ -z "$gpu" ]; then
+    gpu="0"
+fi
+
+######## Features Extraction ###########
+if [ $running_stage -eq 1 ]; then
+    CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/tts/preprocess.py \
+        --config=$exp_config \
+        --num_workers=4
+fi
+
+######## Training ###########
+if [ $running_stage -eq 2 ]; then
+    if [ -z "$exp_name" ]; then
+        echo "[Error] Please specify the experiments name"
+        exit 1
+    fi
+    echo "Exprimental Name: $exp_name"
+
+    CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/tts/train.py \
+        --config $exp_config \
+        --exp_name $exp_name \
+        --log_level debug
+fi
+
+######## Inference ###########
+if [ $running_stage -eq 3 ]; then
+    if [ -z "$infer_expt_dir" ]; then
+        echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]"
+        exit 1
+    fi
+
+    if [ -z "$infer_output_dir" ]; then
+        infer_output_dir="$expt_dir/result"
+    fi
+
+    if [ -z "$infer_mode" ]; then
+        echo "[Error] Please specify the inference mode, e.g., "batch", "single""
+        exit 1
+    fi
+
+    if [ "$infer_mode" = "batch" ] && [ -z "$infer_dataset" ]; then
+        echo "[Error] Please specify the dataset used in inference when the inference mode is batch"
+        exit 1
+    fi
+
+    if [ "$infer_mode" = "batch" ] && [ -z "$infer_testing_set" ]; then
+        echo "[Error] Please specify the testing set used in inference when the inference mode is batch"
+        exit 1
+    fi
+ 
+    if [ "$infer_mode" = "single" ] && [ -z "$infer_text" ]; then
+        echo "[Error] Please specify the text to be synthesized when the inference mode is single"
+        exit 1
+    fi
+
+    if [ "$infer_mode" = "single" ]; then
+        echo 'Text: ' ${infer_text}
+        infer_dataset=None
+        infer_testing_set=None
+    elif [ "$infer_mode" = "batch" ]; then
+        infer_text=''
+    fi
+
+
+    CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/tts/inference.py \
+        --config $exp_config \
+        --acoustics_dir $infer_expt_dir \
+        --output_dir $infer_output_dir  \
+        --mode $infer_mode \
+        --dataset $infer_dataset \
+        --testing_set $infer_testing_set \
+        --text "$infer_text" \
+        --log_level debug
+
+
+
+fi
diff --git a/egs/vocoder/README.md b/egs/vocoder/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..213253570b19b5c1d3746ee17911c7eb464ecc73
--- /dev/null
+++ b/egs/vocoder/README.md
@@ -0,0 +1,23 @@
+# Amphion Vocoder Recipe
+
+## Quick Start
+
+We provide a [**beginner recipe**](gan/tfr_enhanced_hifigan/README.md) to demonstrate how to train a high quality HiFi-GAN speech vocoder. Specially, it is also an official implementation of our paper "[Multi-Scale Sub-Band Constant-Q Transform Discriminator for High-Fidelity Vocoder](https://arxiv.org/abs/2311.14957)". Some demos can be seen [here](https://vocodexelysium.github.io/MS-SB-CQTD/).
+
+## Supported Models
+
+Neural vocoder generates audible waveforms from acoustic representations, which is one of the key parts for current audio generation systems. Until now, Amphion has supported various widely-used vocoders according to different vocoder types, including:
+
+- **GAN-based vocoders**, which we have provided [**a unified recipe**](gan/README.md) :
+  - [MelGAN](https://arxiv.org/abs/1910.06711)
+  - [HiFi-GAN](https://arxiv.org/abs/2010.05646)
+  - [NSF-HiFiGAN](https://github.com/nii-yamagishilab/project-NN-Pytorch-scripts)
+  - [BigVGAN](https://arxiv.org/abs/2206.04658)
+  - [APNet](https://arxiv.org/abs/2305.07952)
+- **Flow-based vocoders** (👨‍💻 developing):
+  - [WaveGlow](https://arxiv.org/abs/1811.00002)
+- **Diffusion-based vocoders** (👨‍💻 developing):
+  - [Diffwave](https://arxiv.org/abs/2009.09761)
+- **Auto-regressive based vocoders** (👨‍💻 developing):
+  - [WaveNet](https://arxiv.org/abs/1609.03499)
+  - [WaveRNN](https://arxiv.org/abs/1802.08435v1)
\ No newline at end of file
diff --git a/egs/vocoder/diffusion/README.md b/egs/vocoder/diffusion/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/egs/vocoder/diffusion/exp_config_base.json b/egs/vocoder/diffusion/exp_config_base.json
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/egs/vocoder/gan/README.md b/egs/vocoder/gan/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..dcefd84d6b9c02223efac119d29dcfd88cd0c026
--- /dev/null
+++ b/egs/vocoder/gan/README.md
@@ -0,0 +1,234 @@
+# Amphion GAN-based Vocoder Recipe
+
+## Supported Model Architectures
+
+GAN-based Vocoder consists of a generator and multiple discriminators, as illustrated below:
+
+<br>
+<div align="center">
+  <img src="../../../imgs/vocoder/gan/pipeline.png" width="40%">
+</div>
+<br>
+
+Until now, Amphion GAN-based Vocoder has supported the following generators and discriminators.
+
+- **Generators**
+    - [MelGAN](https://arxiv.org/abs/1910.06711)
+    - [HiFi-GAN](https://arxiv.org/abs/2010.05646)
+    - [NSF-HiFiGAN](https://github.com/nii-yamagishilab/project-NN-Pytorch-scripts)
+    - [BigVGAN](https://arxiv.org/abs/2206.04658)
+    - [APNet](https://arxiv.org/abs/2305.07952)
+- **Discriminators**
+    - [Multi-Scale Discriminator](https://arxiv.org/abs/2010.05646) 
+    - [Multi-Period Discriminator](https://arxiv.org/abs/2010.05646) 
+    - [Multi-Resolution Discriminator](https://arxiv.org/abs/2011.09631)
+    - [Multi-Scale Short-Time Fourier Transform Discriminator](https://arxiv.org/abs/2210.13438)
+    - [**Multi-Scale Constant-Q Transfrom Discriminator (ours)**](https://arxiv.org/abs/2311.14957)
+
+You can use any vocoder architecture with any dataset you want. There are four steps in total:
+
+1. Data preparation
+2. Feature extraction
+3. Training
+4. Inference
+
+> **NOTE:** You need to run every command of this recipe in the `Amphion` root path:
+> ```bash
+> cd Amphion
+> ```
+
+## 1. Data Preparation
+
+You can train the vocoder with any datasets. Amphion's supported open-source datasets are detailed [here](../../../datasets/README.md).
+
+### Configuration
+
+Specify the dataset path in  `exp_config_base.json`. Note that you can change the `dataset` list to use your preferred datasets.
+
+```json
+"dataset": [
+    "csd",
+    "kising",
+    "m4singer",
+    "nus48e",
+    "opencpop",
+    "opensinger",
+    "opera",
+    "pjs",
+    "popbutfy",
+    "popcs",
+    "ljspeech",
+    "vctk",
+    "libritts",
+],
+"dataset_path": {
+    // TODO: Fill in your dataset path
+    "csd": "[dataset path]",
+    "kising": "[dataset path]",
+    "m4singer": "[dataset path]",
+    "nus48e": "[dataset path]",
+    "opencpop": "[dataset path]",
+    "opensinger": "[dataset path]",
+    "opera": "[dataset path]",
+    "pjs": "[dataset path]",
+    "popbutfy": "[dataset path]",
+    "popcs": "[dataset path]",
+    "ljspeech": "[dataset path]",
+    "vctk": "[dataset path]",
+    "libritts": "[dataset path]",
+},
+```
+
+### 2. Feature Extraction
+
+The needed features are speficied in the individual vocoder direction so it doesn't require any modification.
+
+### Configuration
+
+Specify the dataset path and the output path for saving the processed data and the training model in `exp_config_base.json`:
+
+```json
+    // TODO: Fill in the output log path. The default value is "Amphion/ckpts/vocoder"
+    "log_dir": "ckpts/vocoder",
+    "preprocess": {
+        // TODO: Fill in the output data path. The default value is "Amphion/data"
+        "processed_dir": "data",
+        ...
+    },
+```
+
+### Run
+
+Run the `run.sh` as the preproces stage (set  `--stage 1`).
+
+```bash
+sh egs/vocoder/gan/{vocoder_name}/run.sh --stage 1
+```
+
+> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "1"`.
+
+## 3. Training
+
+### Configuration
+
+We provide the default hyparameters in the `exp_config_base.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on you GPU machines.
+
+```json
+"train": {
+    "batch_size": 32,
+    "max_epoch": 1000000,
+    "save_checkpoint_stride": [20],
+    "adamw": {
+        "lr": 2.0e-4,
+        "adam_b1": 0.8,
+        "adam_b2": 0.99
+    },
+    "exponential_lr": {
+        "lr_decay": 0.999
+    },
+}
+```
+
+You can also choose any amount of prefered discriminators for training in the `exp_config_base.json`.
+
+```json
+"discriminators": [
+    "msd",
+    "mpd",
+    "msstftd",
+    "mssbcqtd",
+],
+```
+
+### Run
+
+Run the `run.sh` as the training stage (set  `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `Amphion/ckpts/vocoder/[YourExptName]`.
+
+```bash
+sh egs/vocoder/gan/{vocoder_name}/run.sh --stage 2 --name [YourExptName]
+```
+
+> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "0,1,2,3"`.
+
+If you want to resume or finetune from a pretrained model, run:
+
+```bash
+sh egs/vocoder/gan/{vocoder_name}/run.sh --stage 2 \
+	--name [YourExptName] \
+	--resume_type ["resume" for resuming training and "finetune" for loading parameters only] \
+	--checkpoint Amphion/ckpts/vocoder/[YourExptName]/checkpoint \
+```
+
+> **NOTE:** For multi-gpu training, the `main_process_port` is set as `29500` in default. You can change it when running `run.sh` by specifying such as `--main_process_port 29501`.
+
+## 4. Inference
+
+### Run
+
+Run the `run.sh` as the training stage (set  `--stage 3`), we provide three different inference modes, including `infer_from_dataset`, `infer_from_feature`, `and infer_from_audio`. 
+
+```bash
+sh egs/vocoder/gan/{vocoder_name}/run.sh --stage 3 \
+	--infer_mode [Your chosen inference mode] \
+	--infer_datasets [Datasets you want to inference, needed when infer_from_dataset] \
+	--infer_feature_dir [Your path to your predicted acoustic features, needed when infer_from_feature] \
+	--infer_audio_dir [Your path to your audio files, needed when infer_form_audio] \
+	--infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \
+	--infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \
+```
+
+#### a. Inference from Dataset
+
+Run the `run.sh` with specified datasets, here is an example.
+
+```bash
+sh egs/vocoder/gan/{vocoder_name}/run.sh --stage 3 \
+	--infer_mode infer_from_dataset \
+	--infer_datasets "libritts vctk ljspeech" \
+	--infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \
+	--infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \
+```
+
+#### b. Inference from Features
+
+If you want to inference from your generated acoustic features, you should first prepare your acoustic features into the following structure:
+
+```plaintext
+ ┣ {infer_feature_dir}
+ ┃ ┣ mels
+ ┃ ┃ ┣ sample1.npy
+ ┃ ┃ ┣ sample2.npy
+ ┃ ┣ f0s (required if you use NSF-HiFiGAN)
+ ┃ ┃ ┣ sample1.npy
+ ┃ ┃ ┣ sample2.npy
+```
+
+Then run the `run.sh` with specificed folder direction, here is an example.
+
+```bash
+sh egs/vocoder/gan/{vocoder_name}/run.sh --stage 3 \
+	--infer_mode infer_from_feature \
+	--infer_feature_dir [Your path to your predicted acoustic features] \
+	--infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \
+	--infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \
+```
+
+#### c. Inference from Audios
+
+If you want to inference from audios for quick analysis synthesis, you should first prepare your audios into the following structure:
+
+```plaintext
+ ┣ audios
+ ┃ ┣ sample1.wav
+ ┃ ┣ sample2.wav
+```
+
+Then run the `run.sh` with specificed folder direction, here is an example.
+
+```bash
+sh egs/vocoder/gan/{vocoder_name}/run.sh --stage 3 \
+	--infer_mode infer_from_audio \
+	--infer_audio_dir [Your path to your audio files] \
+	--infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \
+	--infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \
+```
diff --git a/egs/vocoder/gan/_template/run.sh b/egs/vocoder/gan/_template/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..9d7a29276b2ea648f62b079f4d7d5daa871484f9
--- /dev/null
+++ b/egs/vocoder/gan/_template/run.sh
@@ -0,0 +1,141 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+######## Build Experiment Environment ###########
+exp_dir=$(cd `dirname $0`; pwd)
+work_dir=$(dirname $(dirname $(dirname $(dirname $exp_dir))))
+
+export WORK_DIR=$work_dir
+export PYTHONPATH=$work_dir
+export PYTHONIOENCODING=UTF-8
+
+######## Parse the Given Parameters from the Commond ###########
+options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,checkpoint:,resume_type:,main_process_port:,infer_mode:,infer_datasets:,infer_feature_dir:,infer_audio_dir:,infer_expt_dir:,infer_output_dir: -- "$@")
+eval set -- "$options"
+
+while true; do
+  case $1 in
+    # Experimental Configuration File
+    -c | --config) shift; exp_config=$1 ; shift ;;
+    # Experimental Name
+    -n | --name) shift; exp_name=$1 ; shift ;;
+    # Running Stage
+    -s | --stage) shift; running_stage=$1 ; shift ;;
+    # Visible GPU machines. The default value is "0".
+    --gpu) shift; gpu=$1 ; shift ;;
+
+    # [Only for Training] The specific checkpoint path that you want to resume from.
+    --checkpoint) shift; checkpoint=$1 ; shift ;;
+    # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights.
+    --resume_type) shift; resume_type=$1 ; shift ;;
+    # [Only for Traiing] `main_process_port` for multi gpu training
+    --main_process_port) shift; main_process_port=$1 ; shift ;;
+
+    # [Only for Inference] The inference mode
+    --infer_mode) shift; infer_mode=$1 ; shift ;;
+    # [Only for Inference] The inferenced datasets
+    --infer_datasets) shift; infer_datasets=$1 ; shift ;;
+    # [Only for Inference] The feature dir for inference
+    --infer_feature_dir) shift; infer_feature_dir=$1 ; shift ;;
+    # [Only for Inference] The audio dir for inference
+    --infer_audio_dir) shift; infer_audio_dir=$1 ; shift ;;
+    # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]"
+    --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;;
+    # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result"
+    --infer_output_dir) shift; infer_output_dir=$1 ; shift ;;
+
+    --) shift ; break ;;
+    *) echo "Invalid option: $1" exit 1 ;;
+  esac
+done
+
+
+### Value check ###
+if [ -z "$running_stage" ]; then
+    echo "[Error] Please specify the running stage"
+    exit 1
+fi
+
+if [ -z "$exp_config" ]; then
+    exp_config="${exp_dir}"/exp_config.json
+fi
+echo "Exprimental Configuration File: $exp_config"
+
+if [ -z "$gpu" ]; then
+    gpu="0"
+fi
+
+if [ -z "$main_process_port" ]; then
+    main_process_port=29500
+fi
+echo "Main Process Port: $main_process_port"
+
+######## Features Extraction ###########
+if [ $running_stage -eq 1 ]; then
+    CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/vocoder/preprocess.py \
+        --config $exp_config \
+        --num_workers 8
+fi
+
+######## Training ###########
+if [ $running_stage -eq 2 ]; then
+    if [ -z "$exp_name" ]; then
+        echo "[Error] Please specify the experiments name"
+        exit 1
+    fi
+    echo "Exprimental Name: $exp_name"
+
+    CUDA_VISIBLE_DEVICES=$gpu accelerate launch \
+        --main_process_port "$main_process_port" \
+        "${work_dir}"/bins/vocoder/train.py \
+        --config "$exp_config" \
+        --exp_name "$exp_name" \
+        --log_level info \
+        --checkpoint "$checkpoint" \
+        --resume_type "$resume_type"
+fi
+
+######## Inference/Conversion ###########
+if [ $running_stage -eq 3 ]; then
+    if [ -z "$infer_expt_dir" ]; then
+        echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]"
+        exit 1
+    fi
+
+    if [ -z "$infer_output_dir" ]; then
+        infer_output_dir="$infer_expt_dir/result"
+    fi
+
+    if [ $infer_mode = "infer_from_dataset" ]; then
+        CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \
+            --config $exp_config \
+            --infer_mode $infer_mode \
+            --infer_datasets $infer_datasets \
+            --vocoder_dir $infer_expt_dir \
+            --output_dir $infer_output_dir  \
+            --log_level debug
+    fi
+
+    if [ $infer_mode = "infer_from_feature" ]; then
+        CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \
+            --config $exp_config \
+            --infer_mode $infer_mode \
+            --feature_folder $infer_feature_dir \
+            --vocoder_dir $infer_expt_dir \
+            --output_dir $infer_output_dir  \
+            --log_level debug
+    fi
+
+    if [ $infer_mode = "infer_from_audio" ]; then
+        CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \
+            --config $exp_config \
+            --infer_mode $infer_mode \
+            --audio_folder $infer_audio_dir \
+            --vocoder_dir $infer_expt_dir \
+            --output_dir $infer_output_dir  \
+            --log_level debug
+    fi
+
+fi
\ No newline at end of file
diff --git a/egs/vocoder/gan/apnet/exp_config.json b/egs/vocoder/gan/apnet/exp_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..520d3da3ea36201b8fa71e0fb1fd0d1ef9bbfdc0
--- /dev/null
+++ b/egs/vocoder/gan/apnet/exp_config.json
@@ -0,0 +1,45 @@
+{
+  "base_config": "egs/vocoder/gan/exp_config_base.json",
+  "preprocess": {
+    // acoustic features
+    "extract_mel": true,
+    "extract_audio": true,
+    "extract_amplitude_phase": true,
+
+    // Features used for model training
+    "use_mel": true,
+    "use_audio": true,
+    "use_amplitude_phase": true
+  },
+  "model": {
+    "generator": "apnet",
+    "apnet": {
+      "ASP_channel": 512,
+      "ASP_resblock_kernel_sizes": [3,7,11],
+      "ASP_resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+      "ASP_input_conv_kernel_size": 7,
+      "ASP_output_conv_kernel_size": 7,
+
+      "PSP_channel": 512,
+      "PSP_resblock_kernel_sizes": [3,7,11],
+      "PSP_resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]], 
+      "PSP_input_conv_kernel_size": 7,
+      "PSP_output_R_conv_kernel_size": 7,
+      "PSP_output_I_conv_kernel_size": 7,
+    }
+  },
+  "train": {
+    "criterions": [
+        "feature",
+        "discriminator",
+        "generator",
+        "mel",
+        "phase",
+        "amplitude",
+        "consistency"
+    ]
+  },
+  "inference": {
+    "batch_size": 1,
+  }
+}
diff --git a/egs/vocoder/gan/apnet/run.sh b/egs/vocoder/gan/apnet/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..9d7a29276b2ea648f62b079f4d7d5daa871484f9
--- /dev/null
+++ b/egs/vocoder/gan/apnet/run.sh
@@ -0,0 +1,141 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+######## Build Experiment Environment ###########
+exp_dir=$(cd `dirname $0`; pwd)
+work_dir=$(dirname $(dirname $(dirname $(dirname $exp_dir))))
+
+export WORK_DIR=$work_dir
+export PYTHONPATH=$work_dir
+export PYTHONIOENCODING=UTF-8
+
+######## Parse the Given Parameters from the Commond ###########
+options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,checkpoint:,resume_type:,main_process_port:,infer_mode:,infer_datasets:,infer_feature_dir:,infer_audio_dir:,infer_expt_dir:,infer_output_dir: -- "$@")
+eval set -- "$options"
+
+while true; do
+  case $1 in
+    # Experimental Configuration File
+    -c | --config) shift; exp_config=$1 ; shift ;;
+    # Experimental Name
+    -n | --name) shift; exp_name=$1 ; shift ;;
+    # Running Stage
+    -s | --stage) shift; running_stage=$1 ; shift ;;
+    # Visible GPU machines. The default value is "0".
+    --gpu) shift; gpu=$1 ; shift ;;
+
+    # [Only for Training] The specific checkpoint path that you want to resume from.
+    --checkpoint) shift; checkpoint=$1 ; shift ;;
+    # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights.
+    --resume_type) shift; resume_type=$1 ; shift ;;
+    # [Only for Traiing] `main_process_port` for multi gpu training
+    --main_process_port) shift; main_process_port=$1 ; shift ;;
+
+    # [Only for Inference] The inference mode
+    --infer_mode) shift; infer_mode=$1 ; shift ;;
+    # [Only for Inference] The inferenced datasets
+    --infer_datasets) shift; infer_datasets=$1 ; shift ;;
+    # [Only for Inference] The feature dir for inference
+    --infer_feature_dir) shift; infer_feature_dir=$1 ; shift ;;
+    # [Only for Inference] The audio dir for inference
+    --infer_audio_dir) shift; infer_audio_dir=$1 ; shift ;;
+    # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]"
+    --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;;
+    # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result"
+    --infer_output_dir) shift; infer_output_dir=$1 ; shift ;;
+
+    --) shift ; break ;;
+    *) echo "Invalid option: $1" exit 1 ;;
+  esac
+done
+
+
+### Value check ###
+if [ -z "$running_stage" ]; then
+    echo "[Error] Please specify the running stage"
+    exit 1
+fi
+
+if [ -z "$exp_config" ]; then
+    exp_config="${exp_dir}"/exp_config.json
+fi
+echo "Exprimental Configuration File: $exp_config"
+
+if [ -z "$gpu" ]; then
+    gpu="0"
+fi
+
+if [ -z "$main_process_port" ]; then
+    main_process_port=29500
+fi
+echo "Main Process Port: $main_process_port"
+
+######## Features Extraction ###########
+if [ $running_stage -eq 1 ]; then
+    CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/vocoder/preprocess.py \
+        --config $exp_config \
+        --num_workers 8
+fi
+
+######## Training ###########
+if [ $running_stage -eq 2 ]; then
+    if [ -z "$exp_name" ]; then
+        echo "[Error] Please specify the experiments name"
+        exit 1
+    fi
+    echo "Exprimental Name: $exp_name"
+
+    CUDA_VISIBLE_DEVICES=$gpu accelerate launch \
+        --main_process_port "$main_process_port" \
+        "${work_dir}"/bins/vocoder/train.py \
+        --config "$exp_config" \
+        --exp_name "$exp_name" \
+        --log_level info \
+        --checkpoint "$checkpoint" \
+        --resume_type "$resume_type"
+fi
+
+######## Inference/Conversion ###########
+if [ $running_stage -eq 3 ]; then
+    if [ -z "$infer_expt_dir" ]; then
+        echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]"
+        exit 1
+    fi
+
+    if [ -z "$infer_output_dir" ]; then
+        infer_output_dir="$infer_expt_dir/result"
+    fi
+
+    if [ $infer_mode = "infer_from_dataset" ]; then
+        CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \
+            --config $exp_config \
+            --infer_mode $infer_mode \
+            --infer_datasets $infer_datasets \
+            --vocoder_dir $infer_expt_dir \
+            --output_dir $infer_output_dir  \
+            --log_level debug
+    fi
+
+    if [ $infer_mode = "infer_from_feature" ]; then
+        CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \
+            --config $exp_config \
+            --infer_mode $infer_mode \
+            --feature_folder $infer_feature_dir \
+            --vocoder_dir $infer_expt_dir \
+            --output_dir $infer_output_dir  \
+            --log_level debug
+    fi
+
+    if [ $infer_mode = "infer_from_audio" ]; then
+        CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \
+            --config $exp_config \
+            --infer_mode $infer_mode \
+            --audio_folder $infer_audio_dir \
+            --vocoder_dir $infer_expt_dir \
+            --output_dir $infer_output_dir  \
+            --log_level debug
+    fi
+
+fi
\ No newline at end of file
diff --git a/egs/vocoder/gan/bigvgan/exp_config.json b/egs/vocoder/gan/bigvgan/exp_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..cbed8a4bcd082f8a4cfa368a372f35c7e1a94973
--- /dev/null
+++ b/egs/vocoder/gan/bigvgan/exp_config.json
@@ -0,0 +1,66 @@
+{
+  "base_config": "egs/vocoder/gan/exp_config_base.json",
+  "preprocess": {
+    // acoustic features
+    "extract_mel": true,
+    "extract_audio": true,
+
+    // Features used for model training
+    "use_mel": true,
+    "use_audio": true
+  },
+  "model": {
+    "generator": "bigvgan",
+    "bigvgan": {
+      "resblock": "1",
+      "activation": "snakebeta",
+      "snake_logscale": true,
+      "upsample_rates": [
+        8,
+        8,
+        2,
+        2,
+      ],
+      "upsample_kernel_sizes": [
+        16,
+        16,
+        4,
+        4
+      ],
+      "upsample_initial_channel": 512,
+      "resblock_kernel_sizes": [
+        3,
+        7,
+        11
+      ],
+      "resblock_dilation_sizes": [
+        [
+          1,
+          3,
+          5
+        ],
+        [
+          1,
+          3,
+          5
+        ],
+        [
+          1,
+          3,
+          5
+        ]
+      ]
+    }
+  },
+  "train": {
+    "criterions": [
+        "feature",
+        "discriminator",
+        "generator",
+        "mel",
+    ]
+  },
+  "inference": {
+    "batch_size": 1,
+  }
+}
diff --git a/egs/vocoder/gan/bigvgan/run.sh b/egs/vocoder/gan/bigvgan/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..9d7a29276b2ea648f62b079f4d7d5daa871484f9
--- /dev/null
+++ b/egs/vocoder/gan/bigvgan/run.sh
@@ -0,0 +1,141 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+######## Build Experiment Environment ###########
+exp_dir=$(cd `dirname $0`; pwd)
+work_dir=$(dirname $(dirname $(dirname $(dirname $exp_dir))))
+
+export WORK_DIR=$work_dir
+export PYTHONPATH=$work_dir
+export PYTHONIOENCODING=UTF-8
+
+######## Parse the Given Parameters from the Commond ###########
+options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,checkpoint:,resume_type:,main_process_port:,infer_mode:,infer_datasets:,infer_feature_dir:,infer_audio_dir:,infer_expt_dir:,infer_output_dir: -- "$@")
+eval set -- "$options"
+
+while true; do
+  case $1 in
+    # Experimental Configuration File
+    -c | --config) shift; exp_config=$1 ; shift ;;
+    # Experimental Name
+    -n | --name) shift; exp_name=$1 ; shift ;;
+    # Running Stage
+    -s | --stage) shift; running_stage=$1 ; shift ;;
+    # Visible GPU machines. The default value is "0".
+    --gpu) shift; gpu=$1 ; shift ;;
+
+    # [Only for Training] The specific checkpoint path that you want to resume from.
+    --checkpoint) shift; checkpoint=$1 ; shift ;;
+    # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights.
+    --resume_type) shift; resume_type=$1 ; shift ;;
+    # [Only for Traiing] `main_process_port` for multi gpu training
+    --main_process_port) shift; main_process_port=$1 ; shift ;;
+
+    # [Only for Inference] The inference mode
+    --infer_mode) shift; infer_mode=$1 ; shift ;;
+    # [Only for Inference] The inferenced datasets
+    --infer_datasets) shift; infer_datasets=$1 ; shift ;;
+    # [Only for Inference] The feature dir for inference
+    --infer_feature_dir) shift; infer_feature_dir=$1 ; shift ;;
+    # [Only for Inference] The audio dir for inference
+    --infer_audio_dir) shift; infer_audio_dir=$1 ; shift ;;
+    # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]"
+    --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;;
+    # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result"
+    --infer_output_dir) shift; infer_output_dir=$1 ; shift ;;
+
+    --) shift ; break ;;
+    *) echo "Invalid option: $1" exit 1 ;;
+  esac
+done
+
+
+### Value check ###
+if [ -z "$running_stage" ]; then
+    echo "[Error] Please specify the running stage"
+    exit 1
+fi
+
+if [ -z "$exp_config" ]; then
+    exp_config="${exp_dir}"/exp_config.json
+fi
+echo "Exprimental Configuration File: $exp_config"
+
+if [ -z "$gpu" ]; then
+    gpu="0"
+fi
+
+if [ -z "$main_process_port" ]; then
+    main_process_port=29500
+fi
+echo "Main Process Port: $main_process_port"
+
+######## Features Extraction ###########
+if [ $running_stage -eq 1 ]; then
+    CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/vocoder/preprocess.py \
+        --config $exp_config \
+        --num_workers 8
+fi
+
+######## Training ###########
+if [ $running_stage -eq 2 ]; then
+    if [ -z "$exp_name" ]; then
+        echo "[Error] Please specify the experiments name"
+        exit 1
+    fi
+    echo "Exprimental Name: $exp_name"
+
+    CUDA_VISIBLE_DEVICES=$gpu accelerate launch \
+        --main_process_port "$main_process_port" \
+        "${work_dir}"/bins/vocoder/train.py \
+        --config "$exp_config" \
+        --exp_name "$exp_name" \
+        --log_level info \
+        --checkpoint "$checkpoint" \
+        --resume_type "$resume_type"
+fi
+
+######## Inference/Conversion ###########
+if [ $running_stage -eq 3 ]; then
+    if [ -z "$infer_expt_dir" ]; then
+        echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]"
+        exit 1
+    fi
+
+    if [ -z "$infer_output_dir" ]; then
+        infer_output_dir="$infer_expt_dir/result"
+    fi
+
+    if [ $infer_mode = "infer_from_dataset" ]; then
+        CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \
+            --config $exp_config \
+            --infer_mode $infer_mode \
+            --infer_datasets $infer_datasets \
+            --vocoder_dir $infer_expt_dir \
+            --output_dir $infer_output_dir  \
+            --log_level debug
+    fi
+
+    if [ $infer_mode = "infer_from_feature" ]; then
+        CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \
+            --config $exp_config \
+            --infer_mode $infer_mode \
+            --feature_folder $infer_feature_dir \
+            --vocoder_dir $infer_expt_dir \
+            --output_dir $infer_output_dir  \
+            --log_level debug
+    fi
+
+    if [ $infer_mode = "infer_from_audio" ]; then
+        CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \
+            --config $exp_config \
+            --infer_mode $infer_mode \
+            --audio_folder $infer_audio_dir \
+            --vocoder_dir $infer_expt_dir \
+            --output_dir $infer_output_dir  \
+            --log_level debug
+    fi
+
+fi
\ No newline at end of file
diff --git a/egs/vocoder/gan/bigvgan_large/exp_config.json b/egs/vocoder/gan/bigvgan_large/exp_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..a15dc3573839cd05085ac4540b651b98f94a1bf4
--- /dev/null
+++ b/egs/vocoder/gan/bigvgan_large/exp_config.json
@@ -0,0 +1,70 @@
+{
+  "base_config": "egs/vocoder/gan/exp_config_base.json",
+  "preprocess": {
+    // acoustic features
+    "extract_mel": true,
+    "extract_audio": true,
+
+    // Features used for model training
+    "use_mel": true,
+    "use_audio": true
+  },
+  "model": {
+    "generator": "bigvgan",
+    "bigvgan": {
+      "resblock": "1",
+      "activation": "snakebeta",
+      "snake_logscale": true,
+      "upsample_rates": [
+        4,
+        4,
+        2,
+        2,
+        2,
+        2
+      ],
+      "upsample_kernel_sizes": [
+        8,
+        8,
+        4,
+        4,
+        4,
+        4
+      ],
+      "upsample_initial_channel": 1536,
+      "resblock_kernel_sizes": [
+        3,
+        7,
+        11
+      ],
+      "resblock_dilation_sizes": [
+        [
+          1,
+          3,
+          5
+        ],
+        [
+          1,
+          3,
+          5
+        ],
+        [
+          1,
+          3,
+          5
+        ]
+      ]
+    },
+  },
+  "train": {
+    "criterions": [
+        "feature",
+        "discriminator",
+        "generator",
+        "mel",
+    ]
+  },
+  "inference": {
+    "batch_size": 1,
+  }
+}
diff --git a/egs/vocoder/gan/bigvgan_large/run.sh b/egs/vocoder/gan/bigvgan_large/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..9d7a29276b2ea648f62b079f4d7d5daa871484f9
--- /dev/null
+++ b/egs/vocoder/gan/bigvgan_large/run.sh
@@ -0,0 +1,141 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+######## Build Experiment Environment ###########
+exp_dir=$(cd `dirname $0`; pwd)
+work_dir=$(dirname $(dirname $(dirname $(dirname $exp_dir))))
+
+export WORK_DIR=$work_dir
+export PYTHONPATH=$work_dir
+export PYTHONIOENCODING=UTF-8
+
+######## Parse the Given Parameters from the Commond ###########
+options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,checkpoint:,resume_type:,main_process_port:,infer_mode:,infer_datasets:,infer_feature_dir:,infer_audio_dir:,infer_expt_dir:,infer_output_dir: -- "$@")
+eval set -- "$options"
+
+while true; do
+  case $1 in
+    # Experimental Configuration File
+    -c | --config) shift; exp_config=$1 ; shift ;;
+    # Experimental Name
+    -n | --name) shift; exp_name=$1 ; shift ;;
+    # Running Stage
+    -s | --stage) shift; running_stage=$1 ; shift ;;
+    # Visible GPU machines. The default value is "0".
+    --gpu) shift; gpu=$1 ; shift ;;
+
+    # [Only for Training] The specific checkpoint path that you want to resume from.
+    --checkpoint) shift; checkpoint=$1 ; shift ;;
+    # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights.
+    --resume_type) shift; resume_type=$1 ; shift ;;
+    # [Only for Traiing] `main_process_port` for multi gpu training
+    --main_process_port) shift; main_process_port=$1 ; shift ;;
+
+    # [Only for Inference] The inference mode
+    --infer_mode) shift; infer_mode=$1 ; shift ;;
+    # [Only for Inference] The inferenced datasets
+    --infer_datasets) shift; infer_datasets=$1 ; shift ;;
+    # [Only for Inference] The feature dir for inference
+    --infer_feature_dir) shift; infer_feature_dir=$1 ; shift ;;
+    # [Only for Inference] The audio dir for inference
+    --infer_audio_dir) shift; infer_audio_dir=$1 ; shift ;;
+    # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]"
+    --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;;
+    # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result"
+    --infer_output_dir) shift; infer_output_dir=$1 ; shift ;;
+
+    --) shift ; break ;;
+    *) echo "Invalid option: $1" exit 1 ;;
+  esac
+done
+
+
+### Value check ###
+if [ -z "$running_stage" ]; then
+    echo "[Error] Please specify the running stage"
+    exit 1
+fi
+
+if [ -z "$exp_config" ]; then
+    exp_config="${exp_dir}"/exp_config.json
+fi
+echo "Exprimental Configuration File: $exp_config"
+
+if [ -z "$gpu" ]; then
+    gpu="0"
+fi
+
+if [ -z "$main_process_port" ]; then
+    main_process_port=29500
+fi
+echo "Main Process Port: $main_process_port"
+
+######## Features Extraction ###########
+if [ $running_stage -eq 1 ]; then
+    CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/vocoder/preprocess.py \
+        --config $exp_config \
+        --num_workers 8
+fi
+
+######## Training ###########
+if [ $running_stage -eq 2 ]; then
+    if [ -z "$exp_name" ]; then
+        echo "[Error] Please specify the experiments name"
+        exit 1
+    fi
+    echo "Exprimental Name: $exp_name"
+
+    CUDA_VISIBLE_DEVICES=$gpu accelerate launch \
+        --main_process_port "$main_process_port" \
+        "${work_dir}"/bins/vocoder/train.py \
+        --config "$exp_config" \
+        --exp_name "$exp_name" \
+        --log_level info \
+        --checkpoint "$checkpoint" \
+        --resume_type "$resume_type"
+fi
+
+######## Inference/Conversion ###########
+if [ $running_stage -eq 3 ]; then
+    if [ -z "$infer_expt_dir" ]; then
+        echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]"
+        exit 1
+    fi
+
+    if [ -z "$infer_output_dir" ]; then
+        infer_output_dir="$infer_expt_dir/result"
+    fi
+
+    if [ $infer_mode = "infer_from_dataset" ]; then
+        CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \
+            --config $exp_config \
+            --infer_mode $infer_mode \
+            --infer_datasets $infer_datasets \
+            --vocoder_dir $infer_expt_dir \
+            --output_dir $infer_output_dir  \
+            --log_level debug
+    fi
+
+    if [ $infer_mode = "infer_from_feature" ]; then
+        CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \
+            --config $exp_config \
+            --infer_mode $infer_mode \
+            --feature_folder $infer_feature_dir \
+            --vocoder_dir $infer_expt_dir \
+            --output_dir $infer_output_dir  \
+            --log_level debug
+    fi
+
+    if [ $infer_mode = "infer_from_audio" ]; then
+        CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \
+            --config $exp_config \
+            --infer_mode $infer_mode \
+            --audio_folder $infer_audio_dir \
+            --vocoder_dir $infer_expt_dir \
+            --output_dir $infer_output_dir  \
+            --log_level debug
+    fi
+
+fi
\ No newline at end of file
diff --git a/egs/vocoder/gan/exp_config_base.json b/egs/vocoder/gan/exp_config_base.json
new file mode 100644
index 0000000000000000000000000000000000000000..84dbf7bcc0f5044c1e1f1d67b59cf26a338dff3a
--- /dev/null
+++ b/egs/vocoder/gan/exp_config_base.json
@@ -0,0 +1,111 @@
+{
+  "base_config": "config/vocoder.json",
+  "model_type": "GANVocoder",
+  // TODO: Choose your needed datasets
+  "dataset": [
+    "csd",
+    "kising",
+    "m4singer",
+    "nus48e",
+    "opencpop",
+    "opensinger",
+    "opera",
+    "pjs",
+    "popbutfy",
+    "popcs",
+    "ljspeech",
+    "vctk",
+    "libritts",
+  ],
+  "dataset_path": {
+    // TODO: Fill in your dataset path
+    "csd": "[dataset path]",
+    "kising": "[dataset path]",
+    "m4singer": "[dataset path]",
+    "nus48e": "[dataset path]",
+    "opencpop": "[dataset path]",
+    "opensinger": "[dataset path]",
+    "opera": "[dataset path]",
+    "pjs": "[dataset path]",
+    "popbutfy": "[dataset path]",
+    "popcs": "[dataset path]",
+    "ljspeech": "[dataset path]",
+    "vctk": "[dataset path]",
+    "libritts": "[dataset path]",
+  },
+  // TODO: Fill in the output log path
+  "log_dir": "ckpts/vocoder",
+  "preprocess": {
+    // Acoustic features
+    "extract_mel": true,
+    "extract_audio": true,
+    "extract_pitch": false,
+    "extract_uv": false,
+    "pitch_extractor": "parselmouth",
+
+    // Features used for model training
+    "use_mel": true,
+    "use_frame_pitch": false,
+    "use_uv": false,
+    "use_audio": true,
+
+    // TODO: Fill in the output data path
+    "processed_dir": "data/",
+    "n_mel": 100,
+    "sample_rate": 24000
+  },
+  "model": {
+    // TODO: Choose your needed discriminators
+    "discriminators": [
+      "msd",
+      "mpd",
+      "msstftd",
+      "mssbcqtd",
+    ],
+    "mpd": {
+      "mpd_reshapes": [
+        2,
+        3,
+        5,
+        7,
+        11
+      ],
+      "use_spectral_norm": false,
+      "discriminator_channel_mult_factor": 1
+    },
+    "mrd": {
+      "resolutions": [[1024, 120, 600], [2048, 240, 1200], [512, 50, 240]],
+      "use_spectral_norm": false,
+      "discriminator_channel_mult_factor": 1,
+      "mrd_override": false
+    },
+    "msstftd": {
+        "filters": 32
+    },
+    "mssbcqtd": {
+      hop_lengths: [512, 256, 256],
+      filters: 32,
+      max_filters: 1024,
+      filters_scale: 1,
+      dilations: [1, 2, 4],
+      in_channels: 1,
+      out_channels: 1,
+      n_octaves: [9, 9, 9],
+      bins_per_octaves: [24, 36, 48]
+    },
+  },
+  "train": {
+    // TODO: Choose a suitable batch size, training epoch, and save stride
+    "batch_size": 32,
+    "max_epoch": 1000000,
+    "save_checkpoint_stride": [20],
+    "adamw": {
+        "lr": 2.0e-4,
+        "adam_b1": 0.8,
+        "adam_b2": 0.99
+    },
+    "exponential_lr": {
+        "lr_decay": 0.999
+    },
+  }
+}
\ No newline at end of file
diff --git a/egs/vocoder/gan/hifigan/exp_config.json b/egs/vocoder/gan/hifigan/exp_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..b06712eac472a8452d7cfbfcf8bdf9bf3b232514
--- /dev/null
+++ b/egs/vocoder/gan/hifigan/exp_config.json
@@ -0,0 +1,59 @@
+{
+  "base_config": "egs/vocoder/gan/exp_config_base.json",
+  "preprocess": {
+    // acoustic features
+    "extract_mel": true,
+    "extract_audio": true,
+
+    // Features used for model training
+    "use_mel": true,
+    "use_audio": true
+  },
+  "model": {
+    "generator": "hifigan",
+    "hifigan": {
+      "resblock": "2",
+      "upsample_rates": [
+        8,
+        8,
+        4
+      ],
+      "upsample_kernel_sizes": [
+        16,
+        16,
+        8
+      ],
+      "upsample_initial_channel": 256,
+      "resblock_kernel_sizes": [
+        3,
+        5,
+        7
+      ],
+      "resblock_dilation_sizes": [
+        [
+          1,
+          2
+        ],
+        [
+          2,
+          6
+        ],
+        [
+          3,
+          12
+        ]
+      ]
+    }
+  },
+  "train": {
+    "criterions": [
+        "feature",
+        "discriminator",
+        "generator",
+        "mel",
+    ]
+  },
+  "inference": {
+    "batch_size": 1,
+  }
+}
diff --git a/egs/vocoder/gan/hifigan/run.sh b/egs/vocoder/gan/hifigan/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..9d7a29276b2ea648f62b079f4d7d5daa871484f9
--- /dev/null
+++ b/egs/vocoder/gan/hifigan/run.sh
@@ -0,0 +1,141 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+######## Build Experiment Environment ###########
+exp_dir=$(cd `dirname $0`; pwd)
+work_dir=$(dirname $(dirname $(dirname $(dirname $exp_dir))))
+
+export WORK_DIR=$work_dir
+export PYTHONPATH=$work_dir
+export PYTHONIOENCODING=UTF-8
+
+######## Parse the Given Parameters from the Commond ###########
+options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,checkpoint:,resume_type:,main_process_port:,infer_mode:,infer_datasets:,infer_feature_dir:,infer_audio_dir:,infer_expt_dir:,infer_output_dir: -- "$@")
+eval set -- "$options"
+
+while true; do
+  case $1 in
+    # Experimental Configuration File
+    -c | --config) shift; exp_config=$1 ; shift ;;
+    # Experimental Name
+    -n | --name) shift; exp_name=$1 ; shift ;;
+    # Running Stage
+    -s | --stage) shift; running_stage=$1 ; shift ;;
+    # Visible GPU machines. The default value is "0".
+    --gpu) shift; gpu=$1 ; shift ;;
+
+    # [Only for Training] The specific checkpoint path that you want to resume from.
+    --checkpoint) shift; checkpoint=$1 ; shift ;;
+    # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights.
+    --resume_type) shift; resume_type=$1 ; shift ;;
+    # [Only for Traiing] `main_process_port` for multi gpu training
+    --main_process_port) shift; main_process_port=$1 ; shift ;;
+
+    # [Only for Inference] The inference mode
+    --infer_mode) shift; infer_mode=$1 ; shift ;;
+    # [Only for Inference] The inferenced datasets
+    --infer_datasets) shift; infer_datasets=$1 ; shift ;;
+    # [Only for Inference] The feature dir for inference
+    --infer_feature_dir) shift; infer_feature_dir=$1 ; shift ;;
+    # [Only for Inference] The audio dir for inference
+    --infer_audio_dir) shift; infer_audio_dir=$1 ; shift ;;
+    # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]"
+    --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;;
+    # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result"
+    --infer_output_dir) shift; infer_output_dir=$1 ; shift ;;
+
+    --) shift ; break ;;
+    *) echo "Invalid option: $1" exit 1 ;;
+  esac
+done
+
+
+### Value check ###
+if [ -z "$running_stage" ]; then
+    echo "[Error] Please specify the running stage"
+    exit 1
+fi
+
+if [ -z "$exp_config" ]; then
+    exp_config="${exp_dir}"/exp_config.json
+fi
+echo "Exprimental Configuration File: $exp_config"
+
+if [ -z "$gpu" ]; then
+    gpu="0"
+fi
+
+if [ -z "$main_process_port" ]; then
+    main_process_port=29500
+fi
+echo "Main Process Port: $main_process_port"
+
+######## Features Extraction ###########
+if [ $running_stage -eq 1 ]; then
+    CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/vocoder/preprocess.py \
+        --config $exp_config \
+        --num_workers 8
+fi
+
+######## Training ###########
+if [ $running_stage -eq 2 ]; then
+    if [ -z "$exp_name" ]; then
+        echo "[Error] Please specify the experiments name"
+        exit 1
+    fi
+    echo "Exprimental Name: $exp_name"
+
+    CUDA_VISIBLE_DEVICES=$gpu accelerate launch \
+        --main_process_port "$main_process_port" \
+        "${work_dir}"/bins/vocoder/train.py \
+        --config "$exp_config" \
+        --exp_name "$exp_name" \
+        --log_level info \
+        --checkpoint "$checkpoint" \
+        --resume_type "$resume_type"
+fi
+
+######## Inference/Conversion ###########
+if [ $running_stage -eq 3 ]; then
+    if [ -z "$infer_expt_dir" ]; then
+        echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]"
+        exit 1
+    fi
+
+    if [ -z "$infer_output_dir" ]; then
+        infer_output_dir="$infer_expt_dir/result"
+    fi
+
+    if [ $infer_mode = "infer_from_dataset" ]; then
+        CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \
+            --config $exp_config \
+            --infer_mode $infer_mode \
+            --infer_datasets $infer_datasets \
+            --vocoder_dir $infer_expt_dir \
+            --output_dir $infer_output_dir  \
+            --log_level debug
+    fi
+
+    if [ $infer_mode = "infer_from_feature" ]; then
+        CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \
+            --config $exp_config \
+            --infer_mode $infer_mode \
+            --feature_folder $infer_feature_dir \
+            --vocoder_dir $infer_expt_dir \
+            --output_dir $infer_output_dir  \
+            --log_level debug
+    fi
+
+    if [ $infer_mode = "infer_from_audio" ]; then
+        CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \
+            --config $exp_config \
+            --infer_mode $infer_mode \
+            --audio_folder $infer_audio_dir \
+            --vocoder_dir $infer_expt_dir \
+            --output_dir $infer_output_dir  \
+            --log_level debug
+    fi
+
+fi
\ No newline at end of file
diff --git a/egs/vocoder/gan/melgan/exp_config.json b/egs/vocoder/gan/melgan/exp_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..9470600bc0d15afd07107f816995e8c67ad52d9a
--- /dev/null
+++ b/egs/vocoder/gan/melgan/exp_config.json
@@ -0,0 +1,34 @@
+{
+  "base_config": "egs/vocoder/gan/exp_config_base.json",
+  "preprocess": {
+    // acoustic features
+    "extract_mel": true,
+    "extract_audio": true,
+
+    // Features used for model training
+    "use_mel": true,
+    "use_audio": true
+  },
+  "model": {
+    "generator": "melgan",
+    "melgan": {
+      "ratios": [8, 8, 2, 2],
+      "ngf": 32,
+      "n_residual_layers": 3,
+      "num_D": 3,
+      "ndf": 16,
+      "n_layers": 4,
+      "downsampling_factor": 4
+    },
+  },
+  "train": {
+    "criterions": [
+        "feature",
+        "discriminator",
+        "generator",
+    ]
+  },
+  "inference": {
+    "batch_size": 1,
+  }
+}
diff --git a/egs/vocoder/gan/melgan/run.sh b/egs/vocoder/gan/melgan/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..9d7a29276b2ea648f62b079f4d7d5daa871484f9
--- /dev/null
+++ b/egs/vocoder/gan/melgan/run.sh
@@ -0,0 +1,141 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+######## Build Experiment Environment ###########
+exp_dir=$(cd `dirname $0`; pwd)
+work_dir=$(dirname $(dirname $(dirname $(dirname $exp_dir))))
+
+export WORK_DIR=$work_dir
+export PYTHONPATH=$work_dir
+export PYTHONIOENCODING=UTF-8
+
+######## Parse the Given Parameters from the Commond ###########
+options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,checkpoint:,resume_type:,main_process_port:,infer_mode:,infer_datasets:,infer_feature_dir:,infer_audio_dir:,infer_expt_dir:,infer_output_dir: -- "$@")
+eval set -- "$options"
+
+while true; do
+  case $1 in
+    # Experimental Configuration File
+    -c | --config) shift; exp_config=$1 ; shift ;;
+    # Experimental Name
+    -n | --name) shift; exp_name=$1 ; shift ;;
+    # Running Stage
+    -s | --stage) shift; running_stage=$1 ; shift ;;
+    # Visible GPU machines. The default value is "0".
+    --gpu) shift; gpu=$1 ; shift ;;
+
+    # [Only for Training] The specific checkpoint path that you want to resume from.
+    --checkpoint) shift; checkpoint=$1 ; shift ;;
+    # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights.
+    --resume_type) shift; resume_type=$1 ; shift ;;
+    # [Only for Traiing] `main_process_port` for multi gpu training
+    --main_process_port) shift; main_process_port=$1 ; shift ;;
+
+    # [Only for Inference] The inference mode
+    --infer_mode) shift; infer_mode=$1 ; shift ;;
+    # [Only for Inference] The inferenced datasets
+    --infer_datasets) shift; infer_datasets=$1 ; shift ;;
+    # [Only for Inference] The feature dir for inference
+    --infer_feature_dir) shift; infer_feature_dir=$1 ; shift ;;
+    # [Only for Inference] The audio dir for inference
+    --infer_audio_dir) shift; infer_audio_dir=$1 ; shift ;;
+    # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]"
+    --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;;
+    # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result"
+    --infer_output_dir) shift; infer_output_dir=$1 ; shift ;;
+
+    --) shift ; break ;;
+    *) echo "Invalid option: $1" exit 1 ;;
+  esac
+done
+
+
+### Value check ###
+if [ -z "$running_stage" ]; then
+    echo "[Error] Please specify the running stage"
+    exit 1
+fi
+
+if [ -z "$exp_config" ]; then
+    exp_config="${exp_dir}"/exp_config.json
+fi
+echo "Exprimental Configuration File: $exp_config"
+
+if [ -z "$gpu" ]; then
+    gpu="0"
+fi
+
+if [ -z "$main_process_port" ]; then
+    main_process_port=29500
+fi
+echo "Main Process Port: $main_process_port"
+
+######## Features Extraction ###########
+if [ $running_stage -eq 1 ]; then
+    CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/vocoder/preprocess.py \
+        --config $exp_config \
+        --num_workers 8
+fi
+
+######## Training ###########
+if [ $running_stage -eq 2 ]; then
+    if [ -z "$exp_name" ]; then
+        echo "[Error] Please specify the experiments name"
+        exit 1
+    fi
+    echo "Exprimental Name: $exp_name"
+
+    CUDA_VISIBLE_DEVICES=$gpu accelerate launch \
+        --main_process_port "$main_process_port" \
+        "${work_dir}"/bins/vocoder/train.py \
+        --config "$exp_config" \
+        --exp_name "$exp_name" \
+        --log_level info \
+        --checkpoint "$checkpoint" \
+        --resume_type "$resume_type"
+fi
+
+######## Inference/Conversion ###########
+if [ $running_stage -eq 3 ]; then
+    if [ -z "$infer_expt_dir" ]; then
+        echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]"
+        exit 1
+    fi
+
+    if [ -z "$infer_output_dir" ]; then
+        infer_output_dir="$infer_expt_dir/result"
+    fi
+
+    if [ $infer_mode = "infer_from_dataset" ]; then
+        CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \
+            --config $exp_config \
+            --infer_mode $infer_mode \
+            --infer_datasets $infer_datasets \
+            --vocoder_dir $infer_expt_dir \
+            --output_dir $infer_output_dir  \
+            --log_level debug
+    fi
+
+    if [ $infer_mode = "infer_from_feature" ]; then
+        CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \
+            --config $exp_config \
+            --infer_mode $infer_mode \
+            --feature_folder $infer_feature_dir \
+            --vocoder_dir $infer_expt_dir \
+            --output_dir $infer_output_dir  \
+            --log_level debug
+    fi
+
+    if [ $infer_mode = "infer_from_audio" ]; then
+        CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \
+            --config $exp_config \
+            --infer_mode $infer_mode \
+            --audio_folder $infer_audio_dir \
+            --vocoder_dir $infer_expt_dir \
+            --output_dir $infer_output_dir  \
+            --log_level debug
+    fi
+
+fi
\ No newline at end of file
diff --git a/egs/vocoder/gan/nsfhifigan/exp_config.json b/egs/vocoder/gan/nsfhifigan/exp_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..ead9dc8909b6a5283dd12b721cfb4519cc3953ca
--- /dev/null
+++ b/egs/vocoder/gan/nsfhifigan/exp_config.json
@@ -0,0 +1,83 @@
+{
+  "base_config": "egs/vocoder/gan/exp_config_base.json",
+  "preprocess": {
+    // acoustic features
+    "extract_mel": true,
+    "extract_audio": true,
+    "extract_pitch": true,
+
+    // Features used for model training
+    "use_mel": true,
+    "use_audio": true,
+    "use_frame_pitch": true
+  },
+  "model": {
+    "generator": "nsfhifigan",
+    "nsfhifigan": {
+      "resblock": "1",
+      "harmonic_num": 8,
+      "upsample_rates": [
+        8,
+        4,
+        2,
+        2,
+        2
+      ],
+      "upsample_kernel_sizes": [
+        16,
+        8,
+        4,
+        4,
+        4
+      ],
+      "upsample_initial_channel": 768,
+      "resblock_kernel_sizes": [
+        3,
+        7,
+        11
+      ],
+      "resblock_dilation_sizes": [
+        [
+          1,
+          3,
+          5
+        ],
+        [
+          1,
+          3,
+          5
+        ],
+        [
+          1,
+          3,
+          5
+        ]
+      ]
+    },
+    "mpd": {
+      "mpd_reshapes": [
+        2,
+        3,
+        5,
+        7,
+        11,
+        17,
+        23,
+        37
+      ],
+      "use_spectral_norm": false,
+      "discriminator_channel_multi": 1
+    }
+  },
+  "train": {
+    "criterions": [
+        "feature",
+        "discriminator",
+        "generator",
+        "mel",
+    ]
+  },
+  "inference": {
+    "batch_size": 1,
+  }
+}
diff --git a/egs/vocoder/gan/nsfhifigan/run.sh b/egs/vocoder/gan/nsfhifigan/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..9d7a29276b2ea648f62b079f4d7d5daa871484f9
--- /dev/null
+++ b/egs/vocoder/gan/nsfhifigan/run.sh
@@ -0,0 +1,141 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+######## Build Experiment Environment ###########
+exp_dir=$(cd `dirname $0`; pwd)
+work_dir=$(dirname $(dirname $(dirname $(dirname $exp_dir))))
+
+export WORK_DIR=$work_dir
+export PYTHONPATH=$work_dir
+export PYTHONIOENCODING=UTF-8
+
+######## Parse the Given Parameters from the Commond ###########
+options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,checkpoint:,resume_type:,main_process_port:,infer_mode:,infer_datasets:,infer_feature_dir:,infer_audio_dir:,infer_expt_dir:,infer_output_dir: -- "$@")
+eval set -- "$options"
+
+while true; do
+  case $1 in
+    # Experimental Configuration File
+    -c | --config) shift; exp_config=$1 ; shift ;;
+    # Experimental Name
+    -n | --name) shift; exp_name=$1 ; shift ;;
+    # Running Stage
+    -s | --stage) shift; running_stage=$1 ; shift ;;
+    # Visible GPU machines. The default value is "0".
+    --gpu) shift; gpu=$1 ; shift ;;
+
+    # [Only for Training] The specific checkpoint path that you want to resume from.
+    --checkpoint) shift; checkpoint=$1 ; shift ;;
+    # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights.
+    --resume_type) shift; resume_type=$1 ; shift ;;
+    # [Only for Traiing] `main_process_port` for multi gpu training
+    --main_process_port) shift; main_process_port=$1 ; shift ;;
+
+    # [Only for Inference] The inference mode
+    --infer_mode) shift; infer_mode=$1 ; shift ;;
+    # [Only for Inference] The inferenced datasets
+    --infer_datasets) shift; infer_datasets=$1 ; shift ;;
+    # [Only for Inference] The feature dir for inference
+    --infer_feature_dir) shift; infer_feature_dir=$1 ; shift ;;
+    # [Only for Inference] The audio dir for inference
+    --infer_audio_dir) shift; infer_audio_dir=$1 ; shift ;;
+    # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]"
+    --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;;
+    # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result"
+    --infer_output_dir) shift; infer_output_dir=$1 ; shift ;;
+
+    --) shift ; break ;;
+    *) echo "Invalid option: $1" exit 1 ;;
+  esac
+done
+
+
+### Value check ###
+if [ -z "$running_stage" ]; then
+    echo "[Error] Please specify the running stage"
+    exit 1
+fi
+
+if [ -z "$exp_config" ]; then
+    exp_config="${exp_dir}"/exp_config.json
+fi
+echo "Exprimental Configuration File: $exp_config"
+
+if [ -z "$gpu" ]; then
+    gpu="0"
+fi
+
+if [ -z "$main_process_port" ]; then
+    main_process_port=29500
+fi
+echo "Main Process Port: $main_process_port"
+
+######## Features Extraction ###########
+if [ $running_stage -eq 1 ]; then
+    CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/vocoder/preprocess.py \
+        --config $exp_config \
+        --num_workers 8
+fi
+
+######## Training ###########
+if [ $running_stage -eq 2 ]; then
+    if [ -z "$exp_name" ]; then
+        echo "[Error] Please specify the experiments name"
+        exit 1
+    fi
+    echo "Exprimental Name: $exp_name"
+
+    CUDA_VISIBLE_DEVICES=$gpu accelerate launch \
+        --main_process_port "$main_process_port" \
+        "${work_dir}"/bins/vocoder/train.py \
+        --config "$exp_config" \
+        --exp_name "$exp_name" \
+        --log_level info \
+        --checkpoint "$checkpoint" \
+        --resume_type "$resume_type"
+fi
+
+######## Inference/Conversion ###########
+if [ $running_stage -eq 3 ]; then
+    if [ -z "$infer_expt_dir" ]; then
+        echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]"
+        exit 1
+    fi
+
+    if [ -z "$infer_output_dir" ]; then
+        infer_output_dir="$infer_expt_dir/result"
+    fi
+
+    if [ $infer_mode = "infer_from_dataset" ]; then
+        CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \
+            --config $exp_config \
+            --infer_mode $infer_mode \
+            --infer_datasets $infer_datasets \
+            --vocoder_dir $infer_expt_dir \
+            --output_dir $infer_output_dir  \
+            --log_level debug
+    fi
+
+    if [ $infer_mode = "infer_from_feature" ]; then
+        CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \
+            --config $exp_config \
+            --infer_mode $infer_mode \
+            --feature_folder $infer_feature_dir \
+            --vocoder_dir $infer_expt_dir \
+            --output_dir $infer_output_dir  \
+            --log_level debug
+    fi
+
+    if [ $infer_mode = "infer_from_audio" ]; then
+        CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \
+            --config $exp_config \
+            --infer_mode $infer_mode \
+            --audio_folder $infer_audio_dir \
+            --vocoder_dir $infer_expt_dir \
+            --output_dir $infer_output_dir  \
+            --log_level debug
+    fi
+
+fi
\ No newline at end of file
diff --git a/egs/vocoder/gan/tfr_enhanced_hifigan/README.md b/egs/vocoder/gan/tfr_enhanced_hifigan/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..363a47603a87304e308bef0e66198ab69a20d92a
--- /dev/null
+++ b/egs/vocoder/gan/tfr_enhanced_hifigan/README.md
@@ -0,0 +1,196 @@
+# Multi-Scale Sub-Band Constant-Q Transform Discriminator for High-Fedility Vocoder
+
+[![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2311.14957)
+[![demo](https://img.shields.io/badge/Vocoder-Demo-red)](https://vocodexelysium.github.io/MS-SB-CQTD/)
+
+<br>
+<div align="center">
+<img src="../../../../imgs/vocoder/gan/MSSBCQTD.png" width="80%">
+</div>
+<br>
+
+This is the official implementation of the paper "[Multi-Scale Sub-Band Constant-Q Transform Discriminator for High-Fidelity Vocoder](https://arxiv.org/abs/2311.14957)". In this recipe, we will illustrate how to train a high quality HiFi-GAN on LibriTTS, VCTK and LJSpeech via utilizing multiple Time-Frequency-Representation-based Discriminators.
+
+There are four stages in total:
+
+1. Data preparation
+2. Feature extraction
+3. Training
+4. Inference
+
+> **NOTE:** You need to run every command of this recipe in the `Amphion` root path:
+> ```bash
+> cd Amphion
+> ```
+
+## 1. Data Preparation
+
+### Dataset Download
+
+By default, we utilize the three datasets for training: LibriTTS, VCTK and LJSpeech. How to download them is detailed in [here](../../../datasets/README.md).
+
+### Configuration
+
+Specify the dataset path in  `exp_config.json`. Note that you can change the `dataset` list to use your preferred datasets.
+
+```json
+"dataset": [
+    "ljspeech",
+    "vctk",
+    "libritts",
+],
+"dataset_path": {
+    // TODO: Fill in your dataset path
+    "ljspeech": "[LJSpeech dataset path]",
+    "vctk": "[VCTK dataset path]",
+    "libritts": "[LibriTTS dataset path]",
+},
+```
+
+## 2. Features Extraction
+
+For HiFiGAN, only the Mel-Spectrogram and the Output Audio are needed for training.
+
+### Configuration
+
+Specify the dataset path and the output path for saving the processed data and the training model in `exp_config.json`:
+
+```json
+    // TODO: Fill in the output log path. The default value is "Amphion/ckpts/vocoder"
+    "log_dir": "ckpts/vocoder",
+    "preprocess": {
+        // TODO: Fill in the output data path. The default value is "Amphion/data"
+        "processed_dir": "data",
+        ...
+    },
+```
+
+### Run
+
+Run the `run.sh` as the preproces stage (set  `--stage 1`).
+
+```bash
+sh egs/vocoder/gan/tfr_enhanced_hifigan/run.sh --stage 1
+```
+
+> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "1"`.
+
+## 3. Training
+
+### Configuration
+
+We provide the default hyparameters in the `exp_config.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on you GPU machines.
+
+```json
+"train": {
+    "batch_size": 32,
+    ...
+}
+```
+
+### Run
+
+Run the `run.sh` as the training stage (set  `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `Amphion/ckpts/vocoder/[YourExptName]`.
+
+```bash
+sh egs/vocoder/gan/tfr_enhanced_hifigan/run.sh --stage 2 --name [YourExptName]
+```
+
+> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "0,1,2,3"`.
+
+If you want to resume or finetune from a pretrained model, run:
+
+```bash
+sh egs/vocoder/gan/tfr_enhanced_hifigan/run.sh --stage 2 \
+	--name [YourExptName] \
+	--resume_type ["resume" for resuming training and "finetune" for loading parameters only] \
+	--checkpoint Amphion/ckpts/vocoder/[YourExptName]/checkpoint \
+```
+
+> **NOTE:** For multi-gpu training, the `main_process_port` is set as `29500` in default. You can change it when running `run.sh` by specifying such as `--main_process_port 29501`.
+
+## 4. Inference
+
+### Pretrained Vocoder Download
+
+We trained a HiFiGAN checkpoint with around 685 hours Speech data. The final pretrained checkpoint is released [here](../../../../pretrained/hifigan/README.md).
+
+### Run
+
+Run the `run.sh` as the training stage (set  `--stage 3`), we provide three different inference modes, including `infer_from_dataset`, `infer_from_feature`, `and infer_from audio`. 
+
+```bash
+sh egs/vocoder/gan/tfr_enhanced_hifigan/run.sh --stage 3 \
+	--infer_mode [Your chosen inference mode] \
+	--infer_datasets [Datasets you want to inference, needed when infer_from_dataset] \
+	--infer_feature_dir [Your path to your predicted acoustic features, needed when infer_from_feature] \
+	--infer_audio_dir [Your path to your audio files, needed when infer_form_audio] \
+	--infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \
+	--infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \
+```
+
+#### a. Inference from Dataset
+
+Run the `run.sh` with specified datasets, here is an example.
+
+```bash
+sh egs/vocoder/gan/tfr_enhanced_hifigan/run.sh --stage 3 \
+	--infer_mode infer_from_dataset \
+	--infer_datasets "libritts vctk ljspeech" \
+	--infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \
+	--infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \
+```
+
+#### b. Inference from Features
+
+If you want to inference from your generated acoustic features, you should first prepare your acoustic features into the following structure:
+
+```plaintext
+ ┣ {infer_feature_dir}
+ ┃ ┣ mels
+ ┃ ┃ ┣ sample1.npy
+ ┃ ┃ ┣ sample2.npy
+```
+
+Then run the `run.sh` with specificed folder direction, here is an example.
+
+```bash
+sh egs/vocoder/gan/tfr_enhanced_hifigan/run.sh --stage 3 \
+	--infer_mode infer_from_feature \
+	--infer_feature_dir [Your path to your predicted acoustic features] \
+	--infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \
+	--infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \
+```
+
+#### c. Inference from Audios
+
+If you want to inference from audios for quick analysis synthesis, you should first prepare your audios into the following structure:
+
+```plaintext
+ ┣ audios
+ ┃ ┣ sample1.wav
+ ┃ ┣ sample2.wav
+```
+
+Then run the `run.sh` with specificed folder direction, here is an example.
+
+```bash
+sh egs/vocoder/gan/tfr_enhanced_hifigan/run.sh --stage 3 \
+	--infer_mode infer_from_audio \
+	--infer_audio_dir [Your path to your audio files] \
+	--infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \
+	--infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \
+```
+
+## Citations
+
+```bibtex
+@misc{gu2023cqt,
+      title={Multi-Scale Sub-Band Constant-Q Transform Discriminator for High-Fidelity Vocoder}, 
+      author={Yicheng Gu and Xueyao Zhang and Liumeng Xue and Zhizheng Wu},
+      year={2023},
+      eprint={2311.14957},
+      archivePrefix={arXiv},
+      primaryClass={cs.SD}
+}
+```
\ No newline at end of file
diff --git a/egs/vocoder/gan/tfr_enhanced_hifigan/exp_config.json b/egs/vocoder/gan/tfr_enhanced_hifigan/exp_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..854c575aace19e0da5991e2a29321fb8c07a76ca
--- /dev/null
+++ b/egs/vocoder/gan/tfr_enhanced_hifigan/exp_config.json
@@ -0,0 +1,118 @@
+{
+  "base_config": "egs/vocoder/gan/exp_config_base.json",
+  "model_type": "GANVocoder",
+  "dataset": [
+    "ljspeech",
+    "vctk",
+    "libritts",
+  ],
+  "dataset_path": {
+    // TODO: Fill in your dataset path
+    "ljspeech": "[dataset path]",
+    "vctk": "[dataset path]",
+    "libritts": "[dataset path]",
+  },
+  // TODO: Fill in the output log path. The default value is "Amphion/ckpts/vocoder"
+  "log_dir": "ckpts/vocoder",
+  "preprocess": {
+    // TODO: Fill in the output data path. The default value is "Amphion/data"
+    "processed_dir": "data",
+    // acoustic features
+    "extract_mel": true,
+    "extract_audio": true,
+    "extract_pitch": false,
+    "extract_uv": false,
+    "extract_amplitude_phase": false,
+    "pitch_extractor": "parselmouth",
+    // Features used for model training
+    "use_mel": true,
+    "use_frame_pitch": false,
+    "use_uv": false,
+    "use_audio": true,
+    "n_mel": 100,
+    "sample_rate": 24000
+  },
+  "model": {
+    "generator": "hifigan",
+    "discriminators": [
+      "msd",
+      "mpd",
+      "mssbcqtd",
+      "msstftd",
+    ],
+    "hifigan": {
+      "resblock": "1",
+      "upsample_rates": [
+        8,
+        4,
+        2,
+        2,
+        2
+      ],
+      "upsample_kernel_sizes": [
+        16,
+        8,
+        4,
+        4,
+        4
+      ],
+      "upsample_initial_channel": 768,
+      "resblock_kernel_sizes": [
+        3,
+        5,
+        7
+      ],
+      "resblock_dilation_sizes": [
+        [
+          1,
+          3,
+          5
+        ],
+        [
+          1,
+          3,
+          5
+        ],
+        [
+          1,
+          3,
+          5
+        ]
+      ]
+    },
+    "mpd": {
+      "mpd_reshapes": [
+        2,
+        3,
+        5,
+        7,
+        11,
+        17,
+        23,
+        37
+      ],
+      "use_spectral_norm": false,
+      "discriminator_channel_multi": 1
+    }
+  },
+  "train": {
+    "batch_size": 32,
+    "adamw": {
+      "lr": 2.0e-4,
+      "adam_b1": 0.8,
+      "adam_b2": 0.99
+    },
+    "exponential_lr": {
+      "lr_decay": 0.999
+    },
+    "criterions": [
+      "feature",
+      "discriminator",
+      "generator",
+      "mel",
+    ]
+  },
+  "inference": {
+    "batch_size": 1,
+  }
+}
\ No newline at end of file
diff --git a/egs/vocoder/gan/tfr_enhanced_hifigan/run.sh b/egs/vocoder/gan/tfr_enhanced_hifigan/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..9d7a29276b2ea648f62b079f4d7d5daa871484f9
--- /dev/null
+++ b/egs/vocoder/gan/tfr_enhanced_hifigan/run.sh
@@ -0,0 +1,141 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+######## Build Experiment Environment ###########
+exp_dir=$(cd `dirname $0`; pwd)
+work_dir=$(dirname $(dirname $(dirname $(dirname $exp_dir))))
+
+export WORK_DIR=$work_dir
+export PYTHONPATH=$work_dir
+export PYTHONIOENCODING=UTF-8
+
+######## Parse the Given Parameters from the Commond ###########
+options=$(getopt -o c:n:s --long gpu:,config:,name:,stage:,checkpoint:,resume_type:,main_process_port:,infer_mode:,infer_datasets:,infer_feature_dir:,infer_audio_dir:,infer_expt_dir:,infer_output_dir: -- "$@")
+eval set -- "$options"
+
+while true; do
+  case $1 in
+    # Experimental Configuration File
+    -c | --config) shift; exp_config=$1 ; shift ;;
+    # Experimental Name
+    -n | --name) shift; exp_name=$1 ; shift ;;
+    # Running Stage
+    -s | --stage) shift; running_stage=$1 ; shift ;;
+    # Visible GPU machines. The default value is "0".
+    --gpu) shift; gpu=$1 ; shift ;;
+
+    # [Only for Training] The specific checkpoint path that you want to resume from.
+    --checkpoint) shift; checkpoint=$1 ; shift ;;
+    # [Only for Training] `resume` for loading all the things (including model weights, optimizer, scheduler, and random states). `finetune` for loading only the model weights.
+    --resume_type) shift; resume_type=$1 ; shift ;;
+    # [Only for Traiing] `main_process_port` for multi gpu training
+    --main_process_port) shift; main_process_port=$1 ; shift ;;
+
+    # [Only for Inference] The inference mode
+    --infer_mode) shift; infer_mode=$1 ; shift ;;
+    # [Only for Inference] The inferenced datasets
+    --infer_datasets) shift; infer_datasets=$1 ; shift ;;
+    # [Only for Inference] The feature dir for inference
+    --infer_feature_dir) shift; infer_feature_dir=$1 ; shift ;;
+    # [Only for Inference] The audio dir for inference
+    --infer_audio_dir) shift; infer_audio_dir=$1 ; shift ;;
+    # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]"
+    --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;;
+    # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result"
+    --infer_output_dir) shift; infer_output_dir=$1 ; shift ;;
+
+    --) shift ; break ;;
+    *) echo "Invalid option: $1" exit 1 ;;
+  esac
+done
+
+
+### Value check ###
+if [ -z "$running_stage" ]; then
+    echo "[Error] Please specify the running stage"
+    exit 1
+fi
+
+if [ -z "$exp_config" ]; then
+    exp_config="${exp_dir}"/exp_config.json
+fi
+echo "Exprimental Configuration File: $exp_config"
+
+if [ -z "$gpu" ]; then
+    gpu="0"
+fi
+
+if [ -z "$main_process_port" ]; then
+    main_process_port=29500
+fi
+echo "Main Process Port: $main_process_port"
+
+######## Features Extraction ###########
+if [ $running_stage -eq 1 ]; then
+    CUDA_VISIBLE_DEVICES=$gpu python "${work_dir}"/bins/vocoder/preprocess.py \
+        --config $exp_config \
+        --num_workers 8
+fi
+
+######## Training ###########
+if [ $running_stage -eq 2 ]; then
+    if [ -z "$exp_name" ]; then
+        echo "[Error] Please specify the experiments name"
+        exit 1
+    fi
+    echo "Exprimental Name: $exp_name"
+
+    CUDA_VISIBLE_DEVICES=$gpu accelerate launch \
+        --main_process_port "$main_process_port" \
+        "${work_dir}"/bins/vocoder/train.py \
+        --config "$exp_config" \
+        --exp_name "$exp_name" \
+        --log_level info \
+        --checkpoint "$checkpoint" \
+        --resume_type "$resume_type"
+fi
+
+######## Inference/Conversion ###########
+if [ $running_stage -eq 3 ]; then
+    if [ -z "$infer_expt_dir" ]; then
+        echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]"
+        exit 1
+    fi
+
+    if [ -z "$infer_output_dir" ]; then
+        infer_output_dir="$infer_expt_dir/result"
+    fi
+
+    if [ $infer_mode = "infer_from_dataset" ]; then
+        CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \
+            --config $exp_config \
+            --infer_mode $infer_mode \
+            --infer_datasets $infer_datasets \
+            --vocoder_dir $infer_expt_dir \
+            --output_dir $infer_output_dir  \
+            --log_level debug
+    fi
+
+    if [ $infer_mode = "infer_from_feature" ]; then
+        CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \
+            --config $exp_config \
+            --infer_mode $infer_mode \
+            --feature_folder $infer_feature_dir \
+            --vocoder_dir $infer_expt_dir \
+            --output_dir $infer_output_dir  \
+            --log_level debug
+    fi
+
+    if [ $infer_mode = "infer_from_audio" ]; then
+        CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/vocoder/inference.py \
+            --config $exp_config \
+            --infer_mode $infer_mode \
+            --audio_folder $infer_audio_dir \
+            --vocoder_dir $infer_expt_dir \
+            --output_dir $infer_output_dir  \
+            --log_level debug
+    fi
+
+fi
\ No newline at end of file
diff --git a/evaluation/__init__.py b/evaluation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/evaluation/features/__init__.py b/evaluation/features/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/evaluation/features/long_term_average_spectrum.py b/evaluation/features/long_term_average_spectrum.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6724eecf0748a25e1b9afeae853064e2f26fa1e
--- /dev/null
+++ b/evaluation/features/long_term_average_spectrum.py
@@ -0,0 +1,19 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import librosa
+from scipy import signal
+
+
+def extract_ltas(audio, fs=None, n_fft=1024, hop_length=256):
+    """Extract Long-Term Average Spectrum for a given audio."""
+    if fs != None:
+        y, _ = librosa.load(audio, sr=fs)
+    else:
+        y, fs = librosa.load(audio)
+    frequency, density = signal.welch(
+        x=y, fs=fs, window="hann", nperseg=hop_length, nfft=n_fft
+    )
+    return frequency, density
diff --git a/evaluation/features/signal_to_noise_ratio.py b/evaluation/features/signal_to_noise_ratio.py
new file mode 100644
index 0000000000000000000000000000000000000000..42abf0e1e721a680029fc57c743dfe06c3629fe9
--- /dev/null
+++ b/evaluation/features/signal_to_noise_ratio.py
@@ -0,0 +1,133 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+import scipy.signal as sig
+import copy
+import librosa
+
+
+def bandpower(ps, mode="time"):
+    """
+    estimate bandpower, see https://de.mathworks.com/help/signal/ref/bandpower.html
+    """
+    if mode == "time":
+        x = ps
+        l2norm = np.linalg.norm(x) ** 2.0 / len(x)
+        return l2norm
+    elif mode == "psd":
+        return sum(ps)
+
+
+def getIndizesAroundPeak(arr, peakIndex, searchWidth=1000):
+    peakBins = []
+    magMax = arr[peakIndex]
+    curVal = magMax
+    for i in range(searchWidth):
+        newBin = peakIndex + i
+        if newBin >= len(arr):
+            break
+        newVal = arr[newBin]
+        if newVal > curVal:
+            break
+        else:
+            peakBins.append(int(newBin))
+            curVal = newVal
+    curVal = magMax
+    for i in range(searchWidth):
+        newBin = peakIndex - i
+        if newBin < 0:
+            break
+        newVal = arr[newBin]
+        if newVal > curVal:
+            break
+        else:
+            peakBins.append(int(newBin))
+            curVal = newVal
+    return np.array(list(set(peakBins)))
+
+
+def freqToBin(fAxis, Freq):
+    return np.argmin(abs(fAxis - Freq))
+
+
+def getPeakInArea(psd, faxis, estimation, searchWidthHz=10):
+    """
+    returns bin and frequency of the maximum in an area
+    """
+    binLow = freqToBin(faxis, estimation - searchWidthHz)
+    binHi = freqToBin(faxis, estimation + searchWidthHz)
+    peakbin = binLow + np.argmax(psd[binLow : binHi + 1])
+    return peakbin, faxis[peakbin]
+
+
+def getHarmonics(fund, sr, nHarmonics=6, aliased=False):
+    harmonicMultipliers = np.arange(2, nHarmonics + 2)
+    harmonicFs = fund * harmonicMultipliers
+    if not aliased:
+        harmonicFs[harmonicFs > sr / 2] = -1
+        harmonicFs = np.delete(harmonicFs, harmonicFs == -1)
+    else:
+        nyqZone = np.floor(harmonicFs / (sr / 2))
+        oddEvenNyq = nyqZone % 2
+        harmonicFs = np.mod(harmonicFs, sr / 2)
+        harmonicFs[oddEvenNyq == 1] = (sr / 2) - harmonicFs[oddEvenNyq == 1]
+    return harmonicFs
+
+
+def extract_snr(audio, sr=None):
+    """Extract Signal-to-Noise Ratio for a given audio."""
+    if sr != None:
+        audio, _ = librosa.load(audio, sr=sr)
+    else:
+        audio, sr = librosa.load(audio, sr=sr)
+    faxis, ps = sig.periodogram(
+        audio, fs=sr, window=("kaiser", 38)
+    )  # get periodogram, parametrized like in matlab
+    fundBin = np.argmax(
+        ps
+    )  # estimate fundamental at maximum amplitude, get the bin number
+    fundIndizes = getIndizesAroundPeak(
+        ps, fundBin
+    )  # get bin numbers around fundamental peak
+    fundFrequency = faxis[fundBin]  # frequency of fundamental
+
+    nHarmonics = 18
+    harmonicFs = getHarmonics(
+        fundFrequency, sr, nHarmonics=nHarmonics, aliased=True
+    )  # get harmonic frequencies
+
+    harmonicBorders = np.zeros([2, nHarmonics], dtype=np.int16).T
+    fullHarmonicBins = np.array([], dtype=np.int16)
+    fullHarmonicBinList = []
+    harmPeakFreqs = []
+    harmPeaks = []
+    for i, harmonic in enumerate(harmonicFs):
+        searcharea = 0.1 * fundFrequency
+        estimation = harmonic
+
+        binNum, freq = getPeakInArea(ps, faxis, estimation, searcharea)
+        harmPeakFreqs.append(freq)
+        harmPeaks.append(ps[binNum])
+        allBins = getIndizesAroundPeak(ps, binNum, searchWidth=1000)
+        fullHarmonicBins = np.append(fullHarmonicBins, allBins)
+        fullHarmonicBinList.append(allBins)
+        harmonicBorders[i, :] = [allBins[0], allBins[-1]]
+
+    fundIndizes.sort()
+    pFund = bandpower(ps[fundIndizes[0] : fundIndizes[-1]])  # get power of fundamental
+
+    noisePrepared = copy.copy(ps)
+    noisePrepared[fundIndizes] = 0
+    noisePrepared[fullHarmonicBins] = 0
+    noiseMean = np.median(noisePrepared[noisePrepared != 0])
+    noisePrepared[fundIndizes] = noiseMean
+    noisePrepared[fullHarmonicBins] = noiseMean
+
+    noisePower = bandpower(noisePrepared)
+
+    r = 10 * np.log10(pFund / noisePower)
+
+    return r, 10 * np.log10(noisePower)
diff --git a/evaluation/features/singing_power_ratio.py b/evaluation/features/singing_power_ratio.py
new file mode 100644
index 0000000000000000000000000000000000000000..18ad86b6110ea42dab808fe69be8dd4b84cd841b
--- /dev/null
+++ b/evaluation/features/singing_power_ratio.py
@@ -0,0 +1,108 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import librosa
+
+from utils.util import JsonHParams
+from utils.f0 import get_f0_features_using_parselmouth, get_pitch_sub_median
+from utils.mel import extract_mel_features
+
+
+def extract_spr(
+    audio,
+    fs=None,
+    hop_length=256,
+    win_length=1024,
+    n_fft=1024,
+    n_mels=128,
+    f0_min=37,
+    f0_max=1000,
+    pitch_bin=256,
+    pitch_max=1100.0,
+    pitch_min=50.0,
+):
+    """Compute Singing Power Ratio (SPR) from a given audio.
+    audio: path to the audio.
+    fs: sampling rate.
+    hop_length: hop length.
+    win_length: window length.
+    n_mels: number of mel filters.
+    f0_min: lower limit for f0.
+    f0_max: upper limit for f0.
+    pitch_bin: number of bins for f0 quantization.
+    pitch_max: upper limit for f0 quantization.
+    pitch_min: lower limit for f0 quantization.
+    """
+    # Load audio
+    if fs != None:
+        audio, _ = librosa.load(audio, sr=fs)
+    else:
+        audio, fs = librosa.load(audio)
+    audio = torch.from_numpy(audio)
+
+    # Initialize config
+    cfg = JsonHParams()
+    cfg.sample_rate = fs
+    cfg.hop_size = hop_length
+    cfg.win_size = win_length
+    cfg.n_fft = n_fft
+    cfg.n_mel = n_mels
+    cfg.f0_min = f0_min
+    cfg.f0_max = f0_max
+    cfg.pitch_bin = pitch_bin
+    cfg.pitch_max = pitch_max
+    cfg.pitch_min = pitch_min
+
+    # Extract mel spectrograms
+
+    cfg.fmin = 2000
+    cfg.fmax = 4000
+
+    mel1 = extract_mel_features(
+        y=audio.unsqueeze(0),
+        cfg=cfg,
+    ).squeeze(0)
+
+    cfg.fmin = 0
+    cfg.fmax = 2000
+
+    mel2 = extract_mel_features(
+        y=audio.unsqueeze(0),
+        cfg=cfg,
+    ).squeeze(0)
+
+    f0 = get_f0_features_using_parselmouth(
+        audio,
+        cfg,
+    )[0]
+
+    # Mel length alignment
+    length = min(len(f0), mel1.shape[-1])
+    f0 = f0[:length]
+    mel1 = mel1[:, :length]
+    mel2 = mel2[:, :length]
+
+    # Compute SPR
+    res = []
+
+    for i in range(mel1.shape[-1]):
+        if f0[i] <= 1:
+            continue
+
+        chunk1 = mel1[:, i]
+        chunk2 = mel2[:, i]
+
+        max1 = max(chunk1.numpy())
+        max2 = max(chunk2.numpy())
+
+        tmp_res = max2 - max1
+
+        res.append(tmp_res)
+
+    if len(res) == 0:
+        return False
+    else:
+        return sum(res) / len(res)
diff --git a/evaluation/metrics/__init__.py b/evaluation/metrics/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/evaluation/metrics/energy/__init__.py b/evaluation/metrics/energy/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/evaluation/metrics/energy/energy_pearson_coefficients.py b/evaluation/metrics/energy/energy_pearson_coefficients.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c97c8cb348edec6a6952d6df7f18f13fc0b01ac
--- /dev/null
+++ b/evaluation/metrics/energy/energy_pearson_coefficients.py
@@ -0,0 +1,91 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+import librosa
+import torch
+
+import numpy as np
+from numpy import linalg as LA
+
+from torchmetrics import PearsonCorrCoef
+
+
+def extract_energy_pearson_coeffcients(
+    audio_ref,
+    audio_deg,
+    fs=None,
+    n_fft=1024,
+    hop_length=256,
+    win_length=1024,
+    method="dtw",
+    db_scale=True,
+):
+    """Compute Energy Pearson Coefficients between the predicted and the ground truth audio.
+    audio_ref: path to the ground truth audio.
+    audio_deg: path to the predicted audio.
+    fs: sampling rate.
+    n_fft: fft size.
+    hop_length: hop length.
+    win_length: window length.
+    method: "dtw" will use dtw algorithm to align the length of the ground truth and predicted audio.
+            "cut" will cut both audios into a same length according to the one with the shorter length.
+    db_scale: the ground truth and predicted audio will be converted to db_scale if "True".
+    """
+    # Initialize method
+    pearson = PearsonCorrCoef()
+
+    # Load audio
+    if fs != None:
+        audio_ref, _ = librosa.load(audio_ref, sr=fs)
+        audio_deg, _ = librosa.load(audio_deg, sr=fs)
+    else:
+        audio_ref, fs = librosa.load(audio_ref)
+        audio_deg, fs = librosa.load(audio_deg)
+
+    # STFT
+    spec_ref = librosa.stft(
+        y=audio_ref, n_fft=n_fft, hop_length=hop_length, win_length=win_length
+    )
+    spec_deg = librosa.stft(
+        y=audio_deg, n_fft=n_fft, hop_length=hop_length, win_length=win_length
+    )
+
+    # Get magnitudes
+    mag_ref = np.abs(spec_ref).T
+    mag_deg = np.abs(spec_deg).T
+
+    # Convert spectrogram to energy
+    energy_ref = LA.norm(mag_ref, axis=1)
+    energy_deg = LA.norm(mag_deg, axis=1)
+
+    # Convert to db_scale
+    if db_scale:
+        energy_ref = 20 * np.log10(energy_ref)
+        energy_deg = 20 * np.log10(energy_deg)
+
+    # Audio length alignment
+    if method == "cut":
+        length = min(len(energy_ref), len(energy_deg))
+        energy_ref = energy_ref[:length]
+        energy_deg = energy_deg[:length]
+    elif method == "dtw":
+        _, wp = librosa.sequence.dtw(energy_ref, energy_deg, backtrack=True)
+        energy_gt_new = []
+        energy_pred_new = []
+        for i in range(wp.shape[0]):
+            gt_index = wp[i][0]
+            pred_index = wp[i][1]
+            energy_gt_new.append(energy_ref[gt_index])
+            energy_pred_new.append(energy_deg[pred_index])
+        energy_ref = np.array(energy_gt_new)
+        energy_deg = np.array(energy_pred_new)
+        assert len(energy_ref) == len(energy_deg)
+
+    # Convert to tensor
+    energy_ref = torch.from_numpy(energy_ref)
+    energy_deg = torch.from_numpy(energy_deg)
+
+    return pearson(energy_ref, energy_deg).numpy().tolist()
diff --git a/evaluation/metrics/energy/energy_rmse.py b/evaluation/metrics/energy/energy_rmse.py
new file mode 100644
index 0000000000000000000000000000000000000000..557ae879c9b16ca30257abf290d5a14f7e8728d1
--- /dev/null
+++ b/evaluation/metrics/energy/energy_rmse.py
@@ -0,0 +1,86 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+import librosa
+import torch
+
+import numpy as np
+from numpy import linalg as LA
+
+
+def extract_energy_rmse(
+    audio_ref,
+    audio_deg,
+    fs=None,
+    n_fft=1024,
+    hop_length=256,
+    win_length=1024,
+    method="dtw",
+    db_scale=True,
+):
+    """Compute Energy Root Mean Square Error (RMSE) between the predicted and the ground truth audio.
+    audio_ref: path to the ground truth audio.
+    audio_deg: path to the predicted audio.
+    fs: sampling rate.
+    n_fft: fft size.
+    hop_length: hop length.
+    win_length: window length.
+    method: "dtw" will use dtw algorithm to align the length of the ground truth and predicted audio.
+            "cut" will cut both audios into a same length according to the one with the shorter length.
+    db_scale: the ground truth and predicted audio will be converted to db_scale if "True".
+    """
+    # Load audio
+    if fs != None:
+        audio_ref, _ = librosa.load(audio_ref, sr=fs)
+        audio_deg, _ = librosa.load(audio_deg, sr=fs)
+    else:
+        audio_ref, fs = librosa.load(audio_ref)
+        audio_deg, fs = librosa.load(audio_deg)
+
+    # STFT
+    spec_ref = librosa.stft(
+        y=audio_ref, n_fft=n_fft, hop_length=hop_length, win_length=win_length
+    )
+    spec_deg = librosa.stft(
+        y=audio_deg, n_fft=n_fft, hop_length=hop_length, win_length=win_length
+    )
+
+    # Get magnitudes
+    mag_ref = np.abs(spec_ref).T
+    mag_deg = np.abs(spec_deg).T
+
+    # Convert spectrogram to energy
+    energy_ref = LA.norm(mag_ref, axis=1)
+    energy_deg = LA.norm(mag_deg, axis=1)
+
+    # Convert to db_scale
+    if db_scale:
+        energy_ref = 20 * np.log10(energy_ref)
+        energy_deg = 20 * np.log10(energy_deg)
+
+    # Audio length alignment
+    if method == "cut":
+        length = min(len(energy_ref), len(energy_deg))
+        energy_ref = energy_ref[:length]
+        energy_deg = energy_deg[:length]
+    elif method == "dtw":
+        _, wp = librosa.sequence.dtw(energy_ref, energy_deg, backtrack=True)
+        energy_gt_new = []
+        energy_pred_new = []
+        for i in range(wp.shape[0]):
+            gt_index = wp[i][0]
+            pred_index = wp[i][1]
+            energy_gt_new.append(energy_ref[gt_index])
+            energy_pred_new.append(energy_deg[pred_index])
+        energy_ref = np.array(energy_gt_new)
+        energy_deg = np.array(energy_pred_new)
+        assert len(energy_ref) == len(energy_deg)
+
+    # Compute RMSE
+    energy_mse = np.square(np.subtract(energy_ref, energy_deg)).mean()
+    energy_rmse = math.sqrt(energy_mse)
+
+    return energy_rmse
diff --git a/evaluation/metrics/f0/__init__.py b/evaluation/metrics/f0/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/evaluation/metrics/f0/f0_pearson_coefficients.py b/evaluation/metrics/f0/f0_pearson_coefficients.py
new file mode 100644
index 0000000000000000000000000000000000000000..8aad28359e99add54603bd7f1bf1fb0eae1e725e
--- /dev/null
+++ b/evaluation/metrics/f0/f0_pearson_coefficients.py
@@ -0,0 +1,111 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import librosa
+
+import numpy as np
+
+from torchmetrics import PearsonCorrCoef
+
+from utils.util import JsonHParams
+from utils.f0 import get_f0_features_using_parselmouth, get_pitch_sub_median
+
+
+def extract_fpc(
+    audio_ref,
+    audio_deg,
+    fs=None,
+    hop_length=256,
+    f0_min=50,
+    f0_max=1100,
+    pitch_bin=256,
+    pitch_min=50,
+    pitch_max=1100,
+    need_mean=True,
+    method="dtw",
+):
+    """Compute F0 Pearson Distance (FPC) between the predicted and the ground truth audio.
+    audio_ref: path to the ground truth audio.
+    audio_deg: path to the predicted audio.
+    fs: sampling rate.
+    hop_length: hop length.
+    f0_min: lower limit for f0.
+    f0_max: upper limit for f0.
+    pitch_bin: number of bins for f0 quantization.
+    pitch_max: upper limit for f0 quantization.
+    pitch_min: lower limit for f0 quantization.
+    need_mean: subtract the mean value from f0 if "True".
+    method: "dtw" will use dtw algorithm to align the length of the ground truth and predicted audio.
+            "cut" will cut both audios into a same length according to the one with the shorter length.
+    """
+    # Initialize method
+    pearson = PearsonCorrCoef()
+
+    # Load audio
+    if fs != None:
+        audio_ref, _ = librosa.load(audio_ref, sr=fs)
+        audio_deg, _ = librosa.load(audio_deg, sr=fs)
+    else:
+        audio_ref, fs = librosa.load(audio_ref)
+        audio_deg, fs = librosa.load(audio_deg)
+
+    # Initialize config
+    cfg = JsonHParams()
+    cfg.sample_rate = fs
+    cfg.hop_size = hop_length
+    cfg.f0_min = f0_min
+    cfg.f0_max = f0_max
+    cfg.pitch_bin = pitch_bin
+    cfg.pitch_max = pitch_max
+    cfg.pitch_min = pitch_min
+
+    # Compute f0
+    f0_ref = get_f0_features_using_parselmouth(
+        audio_ref,
+        cfg,
+    )[0]
+
+    f0_deg = get_f0_features_using_parselmouth(
+        audio_deg,
+        cfg,
+    )[0]
+
+    # Subtract mean value from f0
+    if need_mean:
+        f0_ref = torch.from_numpy(f0_ref)
+        f0_deg = torch.from_numpy(f0_deg)
+
+        f0_ref = get_pitch_sub_median(f0_ref).numpy()
+        f0_deg = get_pitch_sub_median(f0_deg).numpy()
+
+    # Avoid silence
+    min_length = min(len(f0_ref), len(f0_deg))
+    if min_length <= 1:
+        return 1
+
+    # F0 length alignment
+    if method == "cut":
+        length = min(len(f0_ref), len(f0_deg))
+        f0_ref = f0_ref[:length]
+        f0_deg = f0_deg[:length]
+    elif method == "dtw":
+        _, wp = librosa.sequence.dtw(f0_ref, f0_deg, backtrack=True)
+        f0_gt_new = []
+        f0_pred_new = []
+        for i in range(wp.shape[0]):
+            gt_index = wp[i][0]
+            pred_index = wp[i][1]
+            f0_gt_new.append(f0_ref[gt_index])
+            f0_pred_new.append(f0_deg[pred_index])
+        f0_ref = np.array(f0_gt_new)
+        f0_deg = np.array(f0_pred_new)
+        assert len(f0_ref) == len(f0_deg)
+
+    # Convert to tensor
+    f0_ref = torch.from_numpy(f0_ref)
+    f0_deg = torch.from_numpy(f0_deg)
+
+    return pearson(f0_ref, f0_deg).numpy().tolist()
diff --git a/evaluation/metrics/f0/f0_periodicity_rmse.py b/evaluation/metrics/f0/f0_periodicity_rmse.py
new file mode 100644
index 0000000000000000000000000000000000000000..f25e68d316d44a44c88f0fc53fb2ec3fbe713e97
--- /dev/null
+++ b/evaluation/metrics/f0/f0_periodicity_rmse.py
@@ -0,0 +1,112 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torchcrepe
+import math
+import librosa
+import torch
+
+import numpy as np
+
+
+def extract_f0_periodicity_rmse(
+    audio_ref,
+    audio_deg,
+    fs=None,
+    hop_length=256,
+    method="dtw",
+):
+    """Compute f0 periodicity Root Mean Square Error (RMSE) between the predicted and the ground truth audio.
+    audio_ref: path to the ground truth audio.
+    audio_deg: path to the predicted audio.
+    fs: sampling rate.
+    hop_length: hop length.
+    method: "dtw" will use dtw algorithm to align the length of the ground truth and predicted audio.
+            "cut" will cut both audios into a same length according to the one with the shorter length.
+    """
+    # Load audio
+    if fs != None:
+        audio_ref, _ = librosa.load(audio_ref, sr=fs)
+        audio_deg, _ = librosa.load(audio_deg, sr=fs)
+    else:
+        audio_ref, fs = librosa.load(audio_ref)
+        audio_deg, fs = librosa.load(audio_deg)
+
+    # Convert to torch
+    audio_ref = torch.from_numpy(audio_ref).unsqueeze(0)
+    audio_deg = torch.from_numpy(audio_deg).unsqueeze(0)
+
+    # Get periodicity
+    pitch_ref, periodicity_ref = torchcrepe.predict(
+        audio_ref,
+        sample_rate=fs,
+        hop_length=hop_length,
+        fmin=0,
+        fmax=1500,
+        model="full",
+        return_periodicity=True,
+        device="cuda:0",
+    )
+    pitch_deg, periodicity_deg = torchcrepe.predict(
+        audio_deg,
+        sample_rate=fs,
+        hop_length=hop_length,
+        fmin=0,
+        fmax=1500,
+        model="full",
+        return_periodicity=True,
+        device="cuda:0",
+    )
+
+    # Cut silence
+    periodicity_ref = (
+        torchcrepe.threshold.Silence()(
+            periodicity_ref,
+            audio_ref,
+            fs,
+            hop_length=hop_length,
+        )
+        .squeeze(0)
+        .numpy()
+    )
+    periodicity_deg = (
+        torchcrepe.threshold.Silence()(
+            periodicity_deg,
+            audio_deg,
+            fs,
+            hop_length=hop_length,
+        )
+        .squeeze(0)
+        .numpy()
+    )
+
+    # Avoid silence audio
+    min_length = min(len(periodicity_ref), len(periodicity_deg))
+    if min_length <= 1:
+        return 0
+
+    # Periodicity length alignment
+    if method == "cut":
+        length = min(len(periodicity_ref), len(periodicity_deg))
+        periodicity_ref = periodicity_ref[:length]
+        periodicity_deg = periodicity_deg[:length]
+    elif method == "dtw":
+        _, wp = librosa.sequence.dtw(periodicity_ref, periodicity_deg, backtrack=True)
+        periodicity_ref_new = []
+        periodicity_deg_new = []
+        for i in range(wp.shape[0]):
+            ref_index = wp[i][0]
+            deg_index = wp[i][1]
+            periodicity_ref_new.append(periodicity_ref[ref_index])
+            periodicity_deg_new.append(periodicity_deg[deg_index])
+        periodicity_ref = np.array(periodicity_ref_new)
+        periodicity_deg = np.array(periodicity_deg_new)
+        assert len(periodicity_ref) == len(periodicity_deg)
+
+    # Compute RMSE
+    periodicity_mse = np.square(np.subtract(periodicity_ref, periodicity_deg)).mean()
+    periodicity_rmse = math.sqrt(periodicity_mse)
+
+    return periodicity_rmse
diff --git a/evaluation/metrics/f0/f0_rmse.py b/evaluation/metrics/f0/f0_rmse.py
new file mode 100644
index 0000000000000000000000000000000000000000..af072ef2d5fa9b887b9b39ab2da922dcb3cf3453
--- /dev/null
+++ b/evaluation/metrics/f0/f0_rmse.py
@@ -0,0 +1,110 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+import librosa
+import torch
+
+import numpy as np
+
+from utils.util import JsonHParams
+from utils.f0 import get_f0_features_using_parselmouth, get_pitch_sub_median
+
+
+ZERO = 1e-8
+
+
+def extract_f0rmse(
+    audio_ref,
+    audio_deg,
+    fs=None,
+    hop_length=256,
+    f0_min=37,
+    f0_max=1000,
+    pitch_bin=256,
+    pitch_max=1100.0,
+    pitch_min=50.0,
+    need_mean=True,
+    method="dtw",
+):
+    """Compute F0 Root Mean Square Error (RMSE) between the predicted and the ground truth audio.
+    audio_ref: path to the ground truth audio.
+    audio_deg: path to the predicted audio.
+    fs: sampling rate.
+    hop_length: hop length.
+    f0_min: lower limit for f0.
+    f0_max: upper limit for f0.
+    pitch_bin: number of bins for f0 quantization.
+    pitch_max: upper limit for f0 quantization.
+    pitch_min: lower limit for f0 quantization.
+    need_mean: subtract the mean value from f0 if "True".
+    method: "dtw" will use dtw algorithm to align the length of the ground truth and predicted audio.
+            "cut" will cut both audios into a same length according to the one with the shorter length.
+    """
+    # Load audio
+    if fs != None:
+        audio_ref, _ = librosa.load(audio_ref, sr=fs)
+        audio_deg, _ = librosa.load(audio_deg, sr=fs)
+    else:
+        audio_ref, fs = librosa.load(audio_ref)
+        audio_deg, fs = librosa.load(audio_deg)
+
+    # Initialize config for f0 extraction
+    cfg = JsonHParams()
+    cfg.sample_rate = fs
+    cfg.hop_size = hop_length
+    cfg.f0_min = f0_min
+    cfg.f0_max = f0_max
+    cfg.pitch_bin = pitch_bin
+    cfg.pitch_max = pitch_max
+    cfg.pitch_min = pitch_min
+
+    # Extract f0
+    f0_ref = get_f0_features_using_parselmouth(
+        audio_ref,
+        cfg,
+    )[0]
+
+    f0_deg = get_f0_features_using_parselmouth(
+        audio_deg,
+        cfg,
+    )[0]
+
+    # Subtract mean value from f0
+    if need_mean:
+        f0_ref = torch.from_numpy(f0_ref)
+        f0_deg = torch.from_numpy(f0_deg)
+
+        f0_ref = get_pitch_sub_median(f0_ref).numpy()
+        f0_deg = get_pitch_sub_median(f0_deg).numpy()
+
+    # Avoid silence
+    min_length = min(len(f0_ref), len(f0_deg))
+    if min_length <= 1:
+        return 0
+
+    # F0 length alignment
+    if method == "cut":
+        length = min(len(f0_ref), len(f0_deg))
+        f0_ref = f0_ref[:length]
+        f0_deg = f0_deg[:length]
+    elif method == "dtw":
+        _, wp = librosa.sequence.dtw(f0_ref, f0_deg, backtrack=True)
+        f0_gt_new = []
+        f0_pred_new = []
+        for i in range(wp.shape[0]):
+            gt_index = wp[i][0]
+            pred_index = wp[i][1]
+            f0_gt_new.append(f0_ref[gt_index])
+            f0_pred_new.append(f0_deg[pred_index])
+        f0_ref = np.array(f0_gt_new)
+        f0_deg = np.array(f0_pred_new)
+        assert len(f0_ref) == len(f0_deg)
+
+    # Compute RMSE
+    f0_mse = np.square(np.subtract(f0_ref, f0_deg)).mean()
+    f0_rmse = math.sqrt(f0_mse)
+
+    return f0_rmse
diff --git a/evaluation/metrics/f0/v_uv_f1.py b/evaluation/metrics/f0/v_uv_f1.py
new file mode 100644
index 0000000000000000000000000000000000000000..24e73156bd531789256564959bf37f66e6400f29
--- /dev/null
+++ b/evaluation/metrics/f0/v_uv_f1.py
@@ -0,0 +1,110 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+import librosa
+import torch
+
+import numpy as np
+
+from utils.util import JsonHParams
+from utils.f0 import get_f0_features_using_parselmouth
+
+
+ZERO = 1e-8
+
+
+def extract_f1_v_uv(
+    audio_ref,
+    audio_deg,
+    fs=None,
+    hop_length=256,
+    f0_min=37,
+    f0_max=1000,
+    pitch_bin=256,
+    pitch_max=1100.0,
+    pitch_min=50.0,
+    method="dtw",
+):
+    """Compute F1 socre of voiced/unvoiced accuracy between the predicted and the ground truth audio.
+    audio_ref: path to the ground truth audio.
+    audio_deg: path to the predicted audio.
+    fs: sampling rate.
+    hop_length: hop length.
+    f0_min: lower limit for f0.
+    f0_max: upper limit for f0.
+    pitch_bin: number of bins for f0 quantization.
+    pitch_max: upper limit for f0 quantization.
+    pitch_min: lower limit for f0 quantization.
+    need_mean: subtract the mean value from f0 if "True".
+    method: "dtw" will use dtw algorithm to align the length of the ground truth and predicted audio.
+            "cut" will cut both audios into a same length according to the one with the shorter length.
+    """
+    # Load audio
+    if fs != None:
+        audio_ref, _ = librosa.load(audio_ref, sr=fs)
+        audio_deg, _ = librosa.load(audio_deg, sr=fs)
+    else:
+        audio_ref, fs = librosa.load(audio_ref)
+        audio_deg, fs = librosa.load(audio_deg)
+
+    # Initialize config
+    cfg = JsonHParams()
+    cfg.sample_rate = fs
+    cfg.hop_size = hop_length
+    cfg.f0_min = f0_min
+    cfg.f0_max = f0_max
+    cfg.pitch_bin = pitch_bin
+    cfg.pitch_max = pitch_max
+    cfg.pitch_min = pitch_min
+
+    # Compute f0
+    f0_ref = get_f0_features_using_parselmouth(
+        audio_ref,
+        cfg,
+    )[0]
+
+    f0_deg = get_f0_features_using_parselmouth(
+        audio_deg,
+        cfg,
+    )[0]
+
+    # Avoid silence
+    min_length = min(len(f0_ref), len(f0_deg))
+    if min_length <= 1:
+        return 0, 0, 0
+
+    # F0 length alignment
+    if method == "cut":
+        length = min(len(f0_ref), len(f0_deg))
+        f0_ref = f0_ref[:length]
+        f0_deg = f0_deg[:length]
+    elif method == "dtw":
+        _, wp = librosa.sequence.dtw(f0_ref, f0_deg, backtrack=True)
+        f0_gt_new = []
+        f0_pred_new = []
+        for i in range(wp.shape[0]):
+            gt_index = wp[i][0]
+            pred_index = wp[i][1]
+            f0_gt_new.append(f0_ref[gt_index])
+            f0_pred_new.append(f0_deg[pred_index])
+        f0_ref = np.array(f0_gt_new)
+        f0_deg = np.array(f0_pred_new)
+        assert len(f0_ref) == len(f0_deg)
+
+    # Get voiced/unvoiced parts
+    ref_voiced = torch.Tensor([f0_ref != 0]).bool()
+    deg_voiced = torch.Tensor([f0_deg != 0]).bool()
+
+    # Compute TP, FP, FN
+    true_postives = (ref_voiced & deg_voiced).sum()
+    false_postives = (~ref_voiced & deg_voiced).sum()
+    false_negatives = (ref_voiced & ~deg_voiced).sum()
+
+    return (
+        true_postives.numpy().tolist(),
+        false_postives.numpy().tolist(),
+        false_negatives.numpy().tolist(),
+    )
diff --git a/evaluation/metrics/intelligibility/__init__.py b/evaluation/metrics/intelligibility/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/evaluation/metrics/intelligibility/character_error_rate.py b/evaluation/metrics/intelligibility/character_error_rate.py
new file mode 100644
index 0000000000000000000000000000000000000000..9440d0897d27351a4a03ca761dcec5b621c7fa81
--- /dev/null
+++ b/evaluation/metrics/intelligibility/character_error_rate.py
@@ -0,0 +1,81 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import whisper
+
+from torchmetrics import CharErrorRate
+
+
+def extract_cer(
+    content_gt=None,
+    audio_ref=None,
+    audio_deg=None,
+    fs=None,
+    language="chinese",
+    remove_space=True,
+    remove_punctuation=True,
+    mode="gt_audio",
+):
+    """Compute Character Error Rate (CER) between the predicted and the ground truth audio.
+    content_gt: the ground truth content.
+    audio_ref: path to the ground truth audio.
+    audio_deg: path to the predicted audio.
+    mode: "gt_content" computes the CER between the predicted content obtained from the whisper model and the ground truth content.
+          both content_gt and audio_deg are needed.
+          "gt_audio" computes the CER between the extracted ground truth and predicted contents obtained from the whisper model.
+          both audio_ref and audio_deg are needed.
+    """
+    # Get ground truth content
+    if mode == "gt_content":
+        assert content_gt != None
+        if language == "chinese":
+            prompt = "以下是普通话的句子"
+            model = whisper.load_model("large").cuda()
+            result_deg = model.transcribe(
+                audio_deg, language="zh", verbose=True, initial_prompt=prompt
+            )
+        elif language == "english":
+            model = whisper.load_model("large").cuda()
+            result_deg = model.transcribe(audio_deg, language="en", verbose=True)
+    elif mode == "gt_audio":
+        assert audio_ref != None
+        if language == "chinese":
+            prompt = "以下是普通话的句子"
+            model = whisper.load_model("large").cuda()
+            result_ref = model.transcribe(
+                audio_ref, language="zh", verbose=True, initial_prompt=prompt
+            )
+            result_deg = model.transcribe(
+                audio_deg, language="zh", verbose=True, initial_prompt=prompt
+            )
+        elif language == "english":
+            model = whisper.load_model("large").cuda()
+            result_ref = model.transcribe(audio_deg, language="en", verbose=True)
+            result_deg = model.transcribe(audio_deg, language="en", verbose=True)
+        content_gt = result_ref["text"]
+        if remove_space:
+            content_gt = content_gt.replace(" ", "")
+        if remove_punctuation:
+            content_gt = content_gt.replace(".", "")
+            content_gt = content_gt.replace("'", "")
+            content_gt = content_gt.replace("-", "")
+            content_gt = content_gt.replace(",", "")
+            content_gt = content_gt.replace("!", "")
+            content_gt = content_gt.lower()
+
+    # Get predicted truth content
+    content_pred = result_deg["text"]
+    if remove_space:
+        content_pred = content_pred.replace(" ", "")
+    if remove_punctuation:
+        content_pred = content_pred.replace(".", "")
+        content_pred = content_pred.replace("'", "")
+        content_pred = content_pred.replace("-", "")
+        content_pred = content_pred.replace(",", "")
+        content_pred = content_pred.replace("!", "")
+        content_pred = content_pred.lower()
+    cer = CharErrorRate()
+
+    return cer(content_pred, content_gt).numpy().tolist()
diff --git a/evaluation/metrics/intelligibility/word_error_rate.py b/evaluation/metrics/intelligibility/word_error_rate.py
new file mode 100644
index 0000000000000000000000000000000000000000..337efb9b6a1fc3bef48fdb7ac47e168272c8f9ec
--- /dev/null
+++ b/evaluation/metrics/intelligibility/word_error_rate.py
@@ -0,0 +1,81 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import whisper
+
+from torchmetrics import WordErrorRate
+
+
+def extract_wer(
+    content_gt=None,
+    audio_ref=None,
+    audio_deg=None,
+    fs=None,
+    language="chinese",
+    remove_space=True,
+    remove_punctuation=True,
+    mode="gt_audio",
+):
+    """Compute Word Error Rate (WER) between the predicted and the ground truth audio.
+    content_gt: the ground truth content.
+    audio_ref: path to the ground truth audio.
+    audio_deg: path to the predicted audio.
+    mode: "gt_content" computes the WER between the predicted content obtained from the whisper model and the ground truth content.
+          both content_gt and audio_deg are needed.
+          "gt_audio" computes the WER between the extracted ground truth and predicted contents obtained from the whisper model.
+          both audio_ref and audio_deg are needed.
+    """
+    # Get ground truth content
+    if mode == "gt_content":
+        assert content_gt != None
+        if language == "chinese":
+            prompt = "以下是普通话的句子"
+            model = whisper.load_model("large").cuda()
+            result_deg = model.transcribe(
+                audio_deg, language="zh", verbose=True, initial_prompt=prompt
+            )
+        elif language == "english":
+            model = whisper.load_model("large").cuda()
+            result_deg = model.transcribe(audio_deg, language="en", verbose=True)
+    elif mode == "gt_audio":
+        assert audio_ref != None
+        if language == "chinese":
+            prompt = "以下是普通话的句子"
+            model = whisper.load_model("large").cuda()
+            result_ref = model.transcribe(
+                audio_ref, language="zh", verbose=True, initial_prompt=prompt
+            )
+            result_deg = model.transcribe(
+                audio_deg, language="zh", verbose=True, initial_prompt=prompt
+            )
+        elif language == "english":
+            model = whisper.load_model("large").cuda()
+            result_ref = model.transcribe(audio_deg, language="en", verbose=True)
+            result_deg = model.transcribe(audio_deg, language="en", verbose=True)
+        content_gt = result_ref["text"]
+        if remove_space:
+            content_gt = content_gt.replace(" ", "")
+        if remove_punctuation:
+            content_gt = content_gt.replace(".", "")
+            content_gt = content_gt.replace("'", "")
+            content_gt = content_gt.replace("-", "")
+            content_gt = content_gt.replace(",", "")
+            content_gt = content_gt.replace("!", "")
+            content_gt = content_gt.lower()
+
+    # Get predicted content
+    content_pred = result_deg["text"]
+    if remove_space:
+        content_pred = content_pred.replace(" ", "")
+    if remove_punctuation:
+        content_pred = content_pred.replace(".", "")
+        content_pred = content_pred.replace("'", "")
+        content_pred = content_pred.replace("-", "")
+        content_pred = content_pred.replace(",", "")
+        content_pred = content_pred.replace("!", "")
+        content_pred = content_pred.lower()
+    wer = WordErrorRate()
+
+    return wer(content_pred, content_gt).numpy().tolist()
diff --git a/evaluation/metrics/similarity/__init__.py b/evaluation/metrics/similarity/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/evaluation/metrics/similarity/models/RawNetBasicBlock.py b/evaluation/metrics/similarity/models/RawNetBasicBlock.py
new file mode 100755
index 0000000000000000000000000000000000000000..c52c50c226604ee688a5208e19cf6dcd649a3420
--- /dev/null
+++ b/evaluation/metrics/similarity/models/RawNetBasicBlock.py
@@ -0,0 +1,146 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class PreEmphasis(torch.nn.Module):
+    def __init__(self, coef: float = 0.97) -> None:
+        super().__init__()
+        self.coef = coef
+        # make kernel
+        # In pytorch, the convolution operation uses cross-correlation. So, filter is flipped.
+        self.register_buffer(
+            "flipped_filter",
+            torch.FloatTensor([-self.coef, 1.0]).unsqueeze(0).unsqueeze(0),
+        )
+
+    def forward(self, input: torch.tensor) -> torch.tensor:
+        assert (
+            len(input.size()) == 2
+        ), "The number of dimensions of input tensor must be 2!"
+        # reflect padding to match lengths of in/out
+        input = input.unsqueeze(1)
+        input = F.pad(input, (1, 0), "reflect")
+        return F.conv1d(input, self.flipped_filter)
+
+
+class AFMS(nn.Module):
+    """
+    Alpha-Feature map scaling, added to the output of each residual block[1,2].
+
+    Reference:
+    [1] RawNet2 : https://www.isca-speech.org/archive/Interspeech_2020/pdfs/1011.pdf
+    [2] AMFS    : https://www.koreascience.or.kr/article/JAKO202029757857763.page
+    """
+
+    def __init__(self, nb_dim: int) -> None:
+        super().__init__()
+        self.alpha = nn.Parameter(torch.ones((nb_dim, 1)))
+        self.fc = nn.Linear(nb_dim, nb_dim)
+        self.sig = nn.Sigmoid()
+
+    def forward(self, x):
+        y = F.adaptive_avg_pool1d(x, 1).view(x.size(0), -1)
+        y = self.sig(self.fc(y)).view(x.size(0), x.size(1), -1)
+
+        x = x + self.alpha
+        x = x * y
+        return x
+
+
+class Bottle2neck(nn.Module):
+    def __init__(
+        self,
+        inplanes,
+        planes,
+        kernel_size=None,
+        dilation=None,
+        scale=4,
+        pool=False,
+    ):
+        super().__init__()
+
+        width = int(math.floor(planes / scale))
+
+        self.conv1 = nn.Conv1d(inplanes, width * scale, kernel_size=1)
+        self.bn1 = nn.BatchNorm1d(width * scale)
+
+        self.nums = scale - 1
+
+        convs = []
+        bns = []
+
+        num_pad = math.floor(kernel_size / 2) * dilation
+
+        for i in range(self.nums):
+            convs.append(
+                nn.Conv1d(
+                    width,
+                    width,
+                    kernel_size=kernel_size,
+                    dilation=dilation,
+                    padding=num_pad,
+                )
+            )
+            bns.append(nn.BatchNorm1d(width))
+
+        self.convs = nn.ModuleList(convs)
+        self.bns = nn.ModuleList(bns)
+
+        self.conv3 = nn.Conv1d(width * scale, planes, kernel_size=1)
+        self.bn3 = nn.BatchNorm1d(planes)
+
+        self.relu = nn.ReLU()
+
+        self.width = width
+
+        self.mp = nn.MaxPool1d(pool) if pool else False
+        self.afms = AFMS(planes)
+
+        if inplanes != planes:  # if change in number of filters
+            self.residual = nn.Sequential(
+                nn.Conv1d(inplanes, planes, kernel_size=1, stride=1, bias=False)
+            )
+        else:
+            self.residual = nn.Identity()
+
+    def forward(self, x):
+        residual = self.residual(x)
+
+        out = self.conv1(x)
+        out = self.relu(out)
+        out = self.bn1(out)
+
+        spx = torch.split(out, self.width, 1)
+        for i in range(self.nums):
+            if i == 0:
+                sp = spx[i]
+            else:
+                sp = sp + spx[i]
+            sp = self.convs[i](sp)
+            sp = self.relu(sp)
+            sp = self.bns[i](sp)
+            if i == 0:
+                out = sp
+            else:
+                out = torch.cat((out, sp), 1)
+
+        out = torch.cat((out, spx[self.nums]), 1)
+
+        out = self.conv3(out)
+        out = self.relu(out)
+        out = self.bn3(out)
+
+        out += residual
+        if self.mp:
+            out = self.mp(out)
+        out = self.afms(out)
+
+        return out
diff --git a/evaluation/metrics/similarity/models/RawNetModel.py b/evaluation/metrics/similarity/models/RawNetModel.py
new file mode 100755
index 0000000000000000000000000000000000000000..3e687cf77557b89e2f4b938d202e69f0affb1eb9
--- /dev/null
+++ b/evaluation/metrics/similarity/models/RawNetModel.py
@@ -0,0 +1,142 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+# -*- encoding: utf-8 -*-
+
+import torch
+import torch.nn as nn
+from asteroid_filterbanks import Encoder, ParamSincFB
+
+from .RawNetBasicBlock import Bottle2neck, PreEmphasis
+
+
+class RawNet3(nn.Module):
+    def __init__(self, block, model_scale, context, summed, C=1024, **kwargs):
+        super().__init__()
+
+        nOut = kwargs["nOut"]
+
+        self.context = context
+        self.encoder_type = kwargs["encoder_type"]
+        self.log_sinc = kwargs["log_sinc"]
+        self.norm_sinc = kwargs["norm_sinc"]
+        self.out_bn = kwargs["out_bn"]
+        self.summed = summed
+
+        self.preprocess = nn.Sequential(
+            PreEmphasis(), nn.InstanceNorm1d(1, eps=1e-4, affine=True)
+        )
+        self.conv1 = Encoder(
+            ParamSincFB(
+                C // 4,
+                251,
+                stride=kwargs["sinc_stride"],
+            )
+        )
+        self.relu = nn.ReLU()
+        self.bn1 = nn.BatchNorm1d(C // 4)
+
+        self.layer1 = block(
+            C // 4, C, kernel_size=3, dilation=2, scale=model_scale, pool=5
+        )
+        self.layer2 = block(C, C, kernel_size=3, dilation=3, scale=model_scale, pool=3)
+        self.layer3 = block(C, C, kernel_size=3, dilation=4, scale=model_scale)
+        self.layer4 = nn.Conv1d(3 * C, 1536, kernel_size=1)
+
+        if self.context:
+            attn_input = 1536 * 3
+        else:
+            attn_input = 1536
+        print("self.encoder_type", self.encoder_type)
+        if self.encoder_type == "ECA":
+            attn_output = 1536
+        elif self.encoder_type == "ASP":
+            attn_output = 1
+        else:
+            raise ValueError("Undefined encoder")
+
+        self.attention = nn.Sequential(
+            nn.Conv1d(attn_input, 128, kernel_size=1),
+            nn.ReLU(),
+            nn.BatchNorm1d(128),
+            nn.Conv1d(128, attn_output, kernel_size=1),
+            nn.Softmax(dim=2),
+        )
+
+        self.bn5 = nn.BatchNorm1d(3072)
+
+        self.fc6 = nn.Linear(3072, nOut)
+        self.bn6 = nn.BatchNorm1d(nOut)
+
+        self.mp3 = nn.MaxPool1d(3)
+
+    def forward(self, x):
+        """
+        :param x: input mini-batch (bs, samp)
+        """
+
+        with torch.cuda.amp.autocast(enabled=False):
+            x = self.preprocess(x)
+            x = torch.abs(self.conv1(x))
+            if self.log_sinc:
+                x = torch.log(x + 1e-6)
+            if self.norm_sinc == "mean":
+                x = x - torch.mean(x, dim=-1, keepdim=True)
+            elif self.norm_sinc == "mean_std":
+                m = torch.mean(x, dim=-1, keepdim=True)
+                s = torch.std(x, dim=-1, keepdim=True)
+                s[s < 0.001] = 0.001
+                x = (x - m) / s
+
+        if self.summed:
+            x1 = self.layer1(x)
+            x2 = self.layer2(x1)
+            x3 = self.layer3(self.mp3(x1) + x2)
+        else:
+            x1 = self.layer1(x)
+            x2 = self.layer2(x1)
+            x3 = self.layer3(x2)
+
+        x = self.layer4(torch.cat((self.mp3(x1), x2, x3), dim=1))
+        x = self.relu(x)
+
+        t = x.size()[-1]
+
+        if self.context:
+            global_x = torch.cat(
+                (
+                    x,
+                    torch.mean(x, dim=2, keepdim=True).repeat(1, 1, t),
+                    torch.sqrt(
+                        torch.var(x, dim=2, keepdim=True).clamp(min=1e-4, max=1e4)
+                    ).repeat(1, 1, t),
+                ),
+                dim=1,
+            )
+        else:
+            global_x = x
+
+        w = self.attention(global_x)
+
+        mu = torch.sum(x * w, dim=2)
+        sg = torch.sqrt(
+            (torch.sum((x**2) * w, dim=2) - mu**2).clamp(min=1e-4, max=1e4)
+        )
+
+        x = torch.cat((mu, sg), 1)
+
+        x = self.bn5(x)
+
+        x = self.fc6(x)
+
+        if self.out_bn:
+            x = self.bn6(x)
+
+        return x
+
+
+def MainModel(**kwargs):
+    model = RawNet3(Bottle2neck, model_scale=8, context=True, summed=True, **kwargs)
+    return model
diff --git a/evaluation/metrics/similarity/models/__init__.py b/evaluation/metrics/similarity/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/evaluation/metrics/similarity/speaker_similarity.py b/evaluation/metrics/similarity/speaker_similarity.py
new file mode 100644
index 0000000000000000000000000000000000000000..82e3968da2ba945707382f4f8627b7902edf8a96
--- /dev/null
+++ b/evaluation/metrics/similarity/speaker_similarity.py
@@ -0,0 +1,119 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+
+import numpy as np
+import soundfile as sf
+import torch
+import torch.nn.functional as F
+from tqdm import tqdm
+import librosa
+
+from .models.RawNetModel import RawNet3
+from .models.RawNetBasicBlock import Bottle2neck
+
+
+def extract_speaker_embd(
+    model, fn: str, n_samples: int, n_segments: int = 10, gpu: bool = False
+) -> np.ndarray:
+    audio, sample_rate = sf.read(fn)
+    if len(audio.shape) > 1:
+        raise ValueError(
+            f"RawNet3 supports mono input only. Input data has a shape of {audio.shape}."
+        )
+
+    if sample_rate != 16000:
+        # resample to 16000kHz
+        audio = librosa.resample(audio, orig_sr=sample_rate, target_sr=16000)
+        # print("resample to 16000kHz!")
+    if len(audio) < n_samples:  # RawNet3 was trained using utterances of 3 seconds
+        shortage = n_samples - len(audio) + 1
+        audio = np.pad(audio, (0, shortage), "wrap")
+
+    audios = []
+    startframe = np.linspace(0, len(audio) - n_samples, num=n_segments)
+    for asf in startframe:
+        audios.append(audio[int(asf) : int(asf) + n_samples])
+
+    audios = torch.from_numpy(np.stack(audios, axis=0).astype(np.float32))
+    if gpu:
+        audios = audios.to("cuda")
+    with torch.no_grad():
+        output = model(audios)
+
+    return output
+
+
+def extract_speaker_similarity(target_path, reference_path):
+    model = RawNet3(
+        Bottle2neck,
+        model_scale=8,
+        context=True,
+        summed=True,
+        encoder_type="ECA",
+        nOut=256,
+        out_bn=False,
+        sinc_stride=10,
+        log_sinc=True,
+        norm_sinc="mean",
+        grad_mult=1,
+    )
+
+    gpu = False
+    model.load_state_dict(
+        torch.load(
+            "pretrained/rawnet3/model.pt",
+            map_location=lambda storage, loc: storage,
+        )["model"]
+    )
+    model.eval()
+    print("RawNet3 initialised & weights loaded!")
+
+    if torch.cuda.is_available():
+        print("Cuda available, conducting inference on GPU")
+        model = model.to("cuda")
+        gpu = True
+    # for target_path, reference_path in zip(target_paths, ref_paths):
+    # print(f"Extracting embeddings for target singers...")
+
+    target_embeddings = []
+    for file in tqdm(os.listdir(target_path)):
+        output = extract_speaker_embd(
+            model,
+            fn=os.path.join(target_path, file),
+            n_samples=48000,
+            n_segments=10,
+            gpu=gpu,
+        ).mean(0)
+        target_embeddings.append(output.detach().cpu().numpy())
+    target_embeddings = np.array(target_embeddings)
+    target_embedding = np.mean(target_embeddings, axis=0)
+
+    # print(f"Extracting embeddings for reference singer...")
+
+    reference_embeddings = []
+    for file in tqdm(os.listdir(reference_path)):
+        output = extract_speaker_embd(
+            model,
+            fn=os.path.join(reference_path, file),
+            n_samples=48000,
+            n_segments=10,
+            gpu=gpu,
+        ).mean(0)
+        reference_embeddings.append(output.detach().cpu().numpy())
+    reference_embeddings = np.array(reference_embeddings)
+
+    # print("Calculating cosine similarity...")
+
+    cos_sim = F.cosine_similarity(
+        torch.from_numpy(np.mean(target_embeddings, axis=0)).unsqueeze(0),
+        torch.from_numpy(np.mean(reference_embeddings, axis=0)).unsqueeze(0),
+        dim=1,
+    )
+
+    # print(f"Mean cosine similarity: {cos_sim.item()}")
+
+    return cos_sim.item()
diff --git a/evaluation/metrics/spectrogram/__init__.py b/evaluation/metrics/spectrogram/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/evaluation/metrics/spectrogram/frechet_distance.py b/evaluation/metrics/spectrogram/frechet_distance.py
new file mode 100644
index 0000000000000000000000000000000000000000..81ee41329ef82e42a56bca7ccffb19755ad1d938
--- /dev/null
+++ b/evaluation/metrics/spectrogram/frechet_distance.py
@@ -0,0 +1,31 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from frechet_audio_distance import FrechetAudioDistance
+
+
+def extract_fad(
+    audio_dir1,
+    audio_dir2,
+    mode="vggish",
+    use_pca=False,
+    use_activation=False,
+    verbose=False,
+):
+    """Extract Frechet Audio Distance for two given audio folders.
+    audio_dir1: path to the ground truth audio folder.
+    audio_dir2: path to the predicted audio folder.
+    mode: "vggish", "pann", "clap" for different models.
+    """
+    frechet = FrechetAudioDistance(
+        model_name=mode,
+        use_pca=use_pca,
+        use_activation=use_activation,
+        verbose=verbose,
+    )
+
+    fad_score = frechet.score(audio_dir1, audio_dir2)
+
+    return fad_score
diff --git a/evaluation/metrics/spectrogram/mel_cepstral_distortion.py b/evaluation/metrics/spectrogram/mel_cepstral_distortion.py
new file mode 100644
index 0000000000000000000000000000000000000000..35487c4bceac42617f3fbf6266e306da33778e29
--- /dev/null
+++ b/evaluation/metrics/spectrogram/mel_cepstral_distortion.py
@@ -0,0 +1,21 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from pymcd.mcd import Calculate_MCD
+
+
+def extract_mcd(audio_ref, audio_deg, fs=None, mode="dtw_sl"):
+    """Extract Mel-Cepstral Distance for a two given audio.
+    Args:
+        audio_ref: The given reference audio. It is an audio path.
+        audio_deg: The given synthesized audio. It is an audio path.
+        mode: "plain", "dtw" and "dtw_sl".
+    """
+    mcd_toolbox = Calculate_MCD(MCD_mode=mode)
+    if fs != None:
+        mcd_toolbox.SAMPLING_RATE = fs
+    mcd_value = mcd_toolbox.calculate_mcd(audio_ref, audio_deg)
+
+    return mcd_value
diff --git a/evaluation/metrics/spectrogram/multi_resolution_stft_distance.py b/evaluation/metrics/spectrogram/multi_resolution_stft_distance.py
new file mode 100644
index 0000000000000000000000000000000000000000..4dc5c35aa9c9efb91bdabe09e0cf3d279fb5a7b6
--- /dev/null
+++ b/evaluation/metrics/spectrogram/multi_resolution_stft_distance.py
@@ -0,0 +1,225 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import librosa
+import torch
+
+import numpy as np
+
+
+def extract_mstft(
+    audio_ref,
+    audio_deg,
+    fs=None,
+    mid_freq=None,
+    high_freq=None,
+    method="cut",
+    version="pwg",
+):
+    """Compute Multi-Scale STFT Distance (mstft) between the predicted and the ground truth audio.
+    audio_ref: path to the ground truth audio.
+    audio_deg: path to the predicted audio.
+    fs: sampling rate.
+    med_freq: division frequency for mid frequency parts.
+    high_freq: division frequency for high frequency parts.
+    method: "dtw" will use dtw algorithm to align the length of the ground truth and predicted audio.
+            "cut" will cut both audios into a same length according to the one with the shorter length.
+    version: "pwg" will use the computational version provided by ParallelWaveGAN.
+             "encodec" will use the computational version provided by Encodec.
+    """
+    # Load audio
+    if fs != None:
+        audio_ref, _ = librosa.load(audio_ref, sr=fs)
+        audio_deg, _ = librosa.load(audio_deg, sr=fs)
+    else:
+        audio_ref, fs = librosa.load(audio_ref)
+        audio_deg, fs = librosa.load(audio_deg)
+
+    # Automatically choose mid_freq and high_freq if they are not given
+    if mid_freq == None:
+        mid_freq = fs // 6
+    if high_freq == None:
+        high_freq = fs // 3
+
+    # Audio length alignment
+    if len(audio_ref) != len(audio_deg):
+        if method == "cut":
+            length = min(len(audio_ref), len(audio_deg))
+            audio_ref = audio_ref[:length]
+            audio_deg = audio_deg[:length]
+        elif method == "dtw":
+            _, wp = librosa.sequence.dtw(audio_ref, audio_deg, backtrack=True)
+            audio_ref_new = []
+            audio_deg_new = []
+            for i in range(wp.shape[0]):
+                ref_index = wp[i][0]
+                deg_index = wp[i][1]
+                audio_ref_new.append(audio_ref[ref_index])
+                audio_deg_new.append(audio_deg[deg_index])
+            audio_ref = np.array(audio_ref_new)
+            audio_deg = np.array(audio_deg_new)
+            assert len(audio_ref) == len(audio_deg)
+
+    # Define loss function
+    l1Loss = torch.nn.L1Loss(reduction="mean")
+    l2Loss = torch.nn.MSELoss(reduction="mean")
+
+    # Compute distance
+    if version == "encodec":
+        n_fft = 1024
+
+        mstft = 0
+        mstft_low = 0
+        mstft_mid = 0
+        mstft_high = 0
+
+        freq_resolution = fs / n_fft
+        mid_freq_index = 1 + int(np.floor(mid_freq / freq_resolution))
+        high_freq_index = 1 + int(np.floor(high_freq / freq_resolution))
+
+        for i in range(5, 11):
+            hop_length = 2**i // 4
+            win_length = 2**i
+
+            spec_ref = librosa.stft(
+                y=audio_ref, n_fft=n_fft, hop_length=hop_length, win_length=win_length
+            )
+            spec_deg = librosa.stft(
+                y=audio_deg, n_fft=n_fft, hop_length=hop_length, win_length=win_length
+            )
+
+            mag_ref = np.abs(spec_ref)
+            mag_deg = np.abs(spec_deg)
+
+            mag_ref = torch.from_numpy(mag_ref)
+            mag_deg = torch.from_numpy(mag_deg)
+            mstft += l1Loss(mag_ref, mag_deg) + l2Loss(mag_ref, mag_deg)
+
+            mag_ref_low = mag_ref[:mid_freq_index, :]
+            mag_deg_low = mag_deg[:mid_freq_index, :]
+            mstft_low += l1Loss(mag_ref_low, mag_deg_low) + l2Loss(
+                mag_ref_low, mag_deg_low
+            )
+
+            mag_ref_mid = mag_ref[mid_freq_index:high_freq_index, :]
+            mag_deg_mid = mag_deg[mid_freq_index:high_freq_index, :]
+            mstft_mid += l1Loss(mag_ref_mid, mag_deg_mid) + l2Loss(
+                mag_ref_mid, mag_deg_mid
+            )
+
+            mag_ref_high = mag_ref[high_freq_index:, :]
+            mag_deg_high = mag_deg[high_freq_index:, :]
+            mstft_high += l1Loss(mag_ref_high, mag_deg_high) + l2Loss(
+                mag_ref_high, mag_deg_high
+            )
+
+        mstft /= 6
+        mstft_low /= 6
+        mstft_mid /= 6
+        mstft_high /= 6
+
+        return mstft
+    elif version == "pwg":
+        fft_sizes = [1024, 2048, 512]
+        hop_sizes = [120, 240, 50]
+        win_sizes = [600, 1200, 240]
+
+        audio_ref = torch.from_numpy(audio_ref)
+        audio_deg = torch.from_numpy(audio_deg)
+
+        mstft_sc = 0
+        mstft_sc_low = 0
+        mstft_sc_mid = 0
+        mstft_sc_high = 0
+
+        mstft_mag = 0
+        mstft_mag_low = 0
+        mstft_mag_mid = 0
+        mstft_mag_high = 0
+
+        for n_fft, hop_length, win_length in zip(fft_sizes, hop_sizes, win_sizes):
+            spec_ref = torch.stft(
+                audio_ref, n_fft, hop_length, win_length, return_complex=False
+            )
+            spec_deg = torch.stft(
+                audio_deg, n_fft, hop_length, win_length, return_complex=False
+            )
+
+            real_ref = spec_ref[..., 0]
+            imag_ref = spec_ref[..., 1]
+            real_deg = spec_deg[..., 0]
+            imag_deg = spec_deg[..., 1]
+
+            mag_ref = torch.sqrt(
+                torch.clamp(real_ref**2 + imag_ref**2, min=1e-7)
+            ).transpose(1, 0)
+            mag_deg = torch.sqrt(
+                torch.clamp(real_deg**2 + imag_deg**2, min=1e-7)
+            ).transpose(1, 0)
+            sc_loss = torch.norm(mag_ref - mag_deg, p="fro") / torch.norm(
+                mag_ref, p="fro"
+            )
+            mag_loss = l1Loss(torch.log(mag_ref), torch.log(mag_deg))
+
+            mstft_sc += sc_loss
+            mstft_mag += mag_loss
+
+            freq_resolution = fs / n_fft
+            mid_freq_index = 1 + int(np.floor(mid_freq / freq_resolution))
+            high_freq_index = 1 + int(np.floor(high_freq / freq_resolution))
+
+            mag_ref_low = mag_ref[:, :mid_freq_index]
+            mag_deg_low = mag_deg[:, :mid_freq_index]
+            sc_loss_low = torch.norm(mag_ref_low - mag_deg_low, p="fro") / torch.norm(
+                mag_ref_low, p="fro"
+            )
+            mag_loss_low = l1Loss(torch.log(mag_ref_low), torch.log(mag_deg_low))
+
+            mstft_sc_low += sc_loss_low
+            mstft_mag_low += mag_loss_low
+
+            mag_ref_mid = mag_ref[:, mid_freq_index:high_freq_index]
+            mag_deg_mid = mag_deg[:, mid_freq_index:high_freq_index]
+            sc_loss_mid = torch.norm(mag_ref_mid - mag_deg_mid, p="fro") / torch.norm(
+                mag_ref_mid, p="fro"
+            )
+            mag_loss_mid = l1Loss(torch.log(mag_ref_mid), torch.log(mag_deg_mid))
+
+            mstft_sc_mid += sc_loss_mid
+            mstft_mag_mid += mag_loss_mid
+
+            mag_ref_high = mag_ref[:, high_freq_index:]
+            mag_deg_high = mag_deg[:, high_freq_index:]
+            sc_loss_high = torch.norm(
+                mag_ref_high - mag_deg_high, p="fro"
+            ) / torch.norm(mag_ref_high, p="fro")
+            mag_loss_high = l1Loss(torch.log(mag_ref_high), torch.log(mag_deg_high))
+
+            mstft_sc_high += sc_loss_high
+            mstft_mag_high += mag_loss_high
+
+        # Normalize distances
+        mstft_sc /= len(fft_sizes)
+        mstft_sc_low /= len(fft_sizes)
+        mstft_sc_mid /= len(fft_sizes)
+        mstft_sc_high /= len(fft_sizes)
+
+        mstft_mag /= len(fft_sizes)
+        mstft_mag_low /= len(fft_sizes)
+        mstft_mag_mid /= len(fft_sizes)
+        mstft_mag_high /= len(fft_sizes)
+
+        # return (
+        #     mstft_sc.numpy().tolist(),
+        #     mstft_sc_low.numpy().tolist(),
+        #     mstft_sc_mid.numpy().tolist(),
+        #     mstft_sc_high.numpy().tolist(),
+        #     mstft_mag.numpy().tolist(),
+        #     mstft_mag_low.numpy().tolist(),
+        #     mstft_mag_mid.numpy().tolist(),
+        #     mstft_mag_high.numpy().tolist(),
+        # )
+
+        return mstft_sc.numpy().tolist() + mstft_mag.numpy().tolist()
diff --git a/evaluation/metrics/spectrogram/pesq.py b/evaluation/metrics/spectrogram/pesq.py
new file mode 100644
index 0000000000000000000000000000000000000000..919cb23a4e8f88bb94138cbd4134640cc0a510c9
--- /dev/null
+++ b/evaluation/metrics/spectrogram/pesq.py
@@ -0,0 +1,56 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import librosa
+
+import numpy as np
+
+from pypesq import pesq
+
+
+def extract_pesq(audio_ref, audio_deg, fs=None, method="cut"):
+    """Extract PESQ for a two given audio.
+    audio1: the given reference audio. It is a numpy array.
+    audio2: the given synthesized audio. It is a numpy array.
+    fs: sampling rate.
+    method: "dtw" will use dtw algorithm to align the length of the ground truth and predicted audio.
+            "cut" will cut both audios into a same length according to the one with the shorter length.
+    """
+    # Load audio
+    if fs != None:
+        audio_ref, _ = librosa.load(audio_ref, sr=fs)
+        audio_deg, _ = librosa.load(audio_deg, sr=fs)
+    else:
+        audio_ref, fs = librosa.load(audio_ref)
+        audio_deg, fs = librosa.load(audio_deg)
+
+    # Resample
+    if fs != 16000:
+        audio_ref = librosa.resample(audio_ref, orig_sr=fs, target_sr=16000)
+        audio_deg = librosa.resample(audio_deg, orig_sr=fs, target_sr=16000)
+        fs = 16000
+
+    # Audio length alignment
+    if len(audio_ref) != len(audio_deg):
+        if method == "cut":
+            length = min(len(audio_ref), len(audio_deg))
+            audio_ref = audio_ref[:length]
+            audio_deg = audio_deg[:length]
+        elif method == "dtw":
+            _, wp = librosa.sequence.dtw(audio_ref, audio_deg, backtrack=True)
+            audio_ref_new = []
+            audio_deg_new = []
+            for i in range(wp.shape[0]):
+                ref_index = wp[i][0]
+                deg_index = wp[i][1]
+                audio_ref_new.append(audio_ref[ref_index])
+                audio_deg_new.append(audio_deg[deg_index])
+            audio_ref = np.array(audio_ref_new)
+            audio_deg = np.array(audio_deg_new)
+            assert len(audio_ref) == len(audio_deg)
+
+    # Compute pesq
+    score = pesq(audio_ref, audio_deg, fs)
+    return score
diff --git a/evaluation/metrics/spectrogram/scale_invariant_signal_to_distortion_ratio.py b/evaluation/metrics/spectrogram/scale_invariant_signal_to_distortion_ratio.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7f8254eabaccd66fab45f86cd1dc08ac571eb57
--- /dev/null
+++ b/evaluation/metrics/spectrogram/scale_invariant_signal_to_distortion_ratio.py
@@ -0,0 +1,45 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import librosa
+
+import numpy as np
+
+from torchmetrics import ScaleInvariantSignalDistortionRatio
+
+
+def extract_si_sdr(audio_ref, audio_deg, fs=None, method="cut"):
+    si_sdr = ScaleInvariantSignalDistortionRatio()
+
+    if fs != None:
+        audio_ref, _ = librosa.load(audio_ref, sr=fs)
+        audio_deg, _ = librosa.load(audio_deg, sr=fs)
+    else:
+        audio_ref, fs = librosa.load(audio_ref)
+        audio_deg, fs = librosa.load(audio_deg)
+
+    if len(audio_ref) != len(audio_deg):
+        if method == "cut":
+            length = min(len(audio_ref), len(audio_deg))
+            audio_ref = audio_ref[:length]
+            audio_deg = audio_deg[:length]
+        elif method == "dtw":
+            _, wp = librosa.sequence.dtw(audio_ref, audio_deg, backtrack=True)
+            audio_ref_new = []
+            audio_deg_new = []
+            for i in range(wp.shape[0]):
+                ref_index = wp[i][0]
+                deg_index = wp[i][1]
+                audio_ref_new.append(audio_ref[ref_index])
+                audio_deg_new.append(audio_deg[deg_index])
+            audio_ref = np.array(audio_ref_new)
+            audio_deg = np.array(audio_deg_new)
+            assert len(audio_ref) == len(audio_deg)
+
+    audio_ref = torch.from_numpy(audio_ref)
+    audio_deg = torch.from_numpy(audio_deg)
+
+    return si_sdr(audio_deg, audio_ref)
diff --git a/evaluation/metrics/spectrogram/scale_invariant_signal_to_noise_ratio.py b/evaluation/metrics/spectrogram/scale_invariant_signal_to_noise_ratio.py
new file mode 100644
index 0000000000000000000000000000000000000000..2cd1d66a48bd1d68d95d6cfdbbe73e6046896cfd
--- /dev/null
+++ b/evaluation/metrics/spectrogram/scale_invariant_signal_to_noise_ratio.py
@@ -0,0 +1,45 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import librosa
+
+import numpy as np
+
+from torchmetrics import ScaleInvariantSignalNoiseRatio
+
+
+def extract_si_snr(audio_ref, audio_deg, fs=None, method="cut"):
+    si_snr = ScaleInvariantSignalNoiseRatio()
+
+    if fs != None:
+        audio_ref, _ = librosa.load(audio_ref, sr=fs)
+        audio_deg, _ = librosa.load(audio_deg, sr=fs)
+    else:
+        audio_ref, fs = librosa.load(audio_ref)
+        audio_deg, fs = librosa.load(audio_deg)
+
+    if len(audio_ref) != len(audio_deg):
+        if method == "cut":
+            length = min(len(audio_ref), len(audio_deg))
+            audio_ref = audio_ref[:length]
+            audio_deg = audio_deg[:length]
+        elif method == "dtw":
+            _, wp = librosa.sequence.dtw(audio_ref, audio_deg, backtrack=True)
+            audio_ref_new = []
+            audio_deg_new = []
+            for i in range(wp.shape[0]):
+                ref_index = wp[i][0]
+                deg_index = wp[i][1]
+                audio_ref_new.append(audio_ref[ref_index])
+                audio_deg_new.append(audio_deg[deg_index])
+            audio_ref = np.array(audio_ref_new)
+            audio_deg = np.array(audio_deg_new)
+            assert len(audio_ref) == len(audio_deg)
+
+    audio_ref = torch.from_numpy(audio_ref)
+    audio_deg = torch.from_numpy(audio_deg)
+
+    return si_snr(audio_deg, audio_ref)
diff --git a/evaluation/metrics/spectrogram/short_time_objective_intelligibility.py b/evaluation/metrics/spectrogram/short_time_objective_intelligibility.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab447a444e2303a28e5af7a89b872f242e92c227
--- /dev/null
+++ b/evaluation/metrics/spectrogram/short_time_objective_intelligibility.py
@@ -0,0 +1,56 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import librosa
+
+import numpy as np
+
+from torchmetrics.audio.stoi import ShortTimeObjectiveIntelligibility
+
+
+def extract_stoi(audio_ref, audio_deg, fs=None, extended=False, method="cut"):
+    """Compute Short-Time Objective Intelligibility between the predicted and the ground truth audio.
+    audio_ref: path to the ground truth audio.
+    audio_deg: path to the predicted audio.
+    fs: sampling rate.
+    method: "dtw" will use dtw algorithm to align the length of the ground truth and predicted audio.
+            "cut" will cut both audios into a same length according to the one with the shorter length.
+    """
+    # Load audio
+    if fs != None:
+        audio_ref, _ = librosa.load(audio_ref, sr=fs)
+        audio_deg, _ = librosa.load(audio_deg, sr=fs)
+    else:
+        audio_ref, fs = librosa.load(audio_ref)
+        audio_deg, fs = librosa.load(audio_deg)
+
+    # Initialize method
+    stoi = ShortTimeObjectiveIntelligibility(fs, extended)
+
+    # Audio length alignment
+    if len(audio_ref) != len(audio_deg):
+        if method == "cut":
+            length = min(len(audio_ref), len(audio_deg))
+            audio_ref = audio_ref[:length]
+            audio_deg = audio_deg[:length]
+        elif method == "dtw":
+            _, wp = librosa.sequence.dtw(audio_ref, audio_deg, backtrack=True)
+            audio_ref_new = []
+            audio_deg_new = []
+            for i in range(wp.shape[0]):
+                ref_index = wp[i][0]
+                deg_index = wp[i][1]
+                audio_ref_new.append(audio_ref[ref_index])
+                audio_deg_new.append(audio_deg[deg_index])
+            audio_ref = np.array(audio_ref_new)
+            audio_deg = np.array(audio_deg_new)
+            assert len(audio_ref) == len(audio_deg)
+
+    # Convert to tensor
+    audio_ref = torch.from_numpy(audio_ref)
+    audio_deg = torch.from_numpy(audio_deg)
+
+    return stoi(audio_deg, audio_ref).numpy().tolist()
diff --git a/models/tts/base/__init__.py b/models/tts/base/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..268a381ec731d684418beab0d60cad84b22c4533
--- /dev/null
+++ b/models/tts/base/__init__.py
@@ -0,0 +1,7 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+# from .tts_inferece import TTSInference
+from .tts_trainer import TTSTrainer
diff --git a/models/tts/base/tts_dataset.py b/models/tts/base/tts_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3f6ac7c74d7d563fcee682a35d3e8e7891ca55a
--- /dev/null
+++ b/models/tts/base/tts_dataset.py
@@ -0,0 +1,389 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import json
+import os
+import torchaudio
+import numpy as np
+import torch
+from utils.data_utils import *
+from torch.nn.utils.rnn import pad_sequence
+from text import text_to_sequence
+from text.text_token_collation import phoneIDCollation
+from processors.acoustic_extractor import cal_normalized_mel
+
+from models.base.base_dataset import (
+    BaseDataset,
+    BaseCollator,
+    BaseTestDataset,
+    BaseTestCollator,
+)
+
+from processors.content_extractor import (
+    ContentvecExtractor,
+    WenetExtractor,
+    WhisperExtractor,
+)
+
+
+class TTSDataset(BaseDataset):
+    def __init__(self, cfg, dataset, is_valid=False):
+        """
+        Args:
+            cfg: config
+            dataset: dataset name
+            is_valid: whether to use train or valid dataset
+        """
+
+        assert isinstance(dataset, str)
+
+        self.cfg = cfg
+
+        processed_data_dir = os.path.join(cfg.preprocess.processed_dir, dataset)
+        meta_file = cfg.preprocess.valid_file if is_valid else cfg.preprocess.train_file
+        self.metafile_path = os.path.join(processed_data_dir, meta_file)
+        self.metadata = self.get_metadata()
+
+        """
+        load spk2id and utt2spk from json file
+            spk2id: {spk1: 0, spk2: 1, ...}
+            utt2spk: {dataset_uid: spk1, ...}
+        """
+        if cfg.preprocess.use_spkid:
+            dataset = self.metadata[0]["Dataset"]
+
+            spk2id_path = os.path.join(processed_data_dir, cfg.preprocess.spk2id)
+            with open(spk2id_path, "r") as f:
+                self.spk2id = json.load(f)
+
+            utt2spk_path = os.path.join(processed_data_dir, cfg.preprocess.utt2spk)
+            self.utt2spk = dict()
+            with open(utt2spk_path, "r") as f:
+                for line in f.readlines():
+                    utt, spk = line.strip().split("\t")
+                    self.utt2spk[utt] = spk
+
+        if cfg.preprocess.use_uv:
+            self.utt2uv_path = {}
+            for utt_info in self.metadata:
+                dataset = utt_info["Dataset"]
+                uid = utt_info["Uid"]
+                utt = "{}_{}".format(dataset, uid)
+                self.utt2uv_path[utt] = os.path.join(
+                    cfg.preprocess.processed_dir,
+                    dataset,
+                    cfg.preprocess.uv_dir,
+                    uid + ".npy",
+                )
+
+        if cfg.preprocess.use_frame_pitch:
+            self.utt2frame_pitch_path = {}
+            for utt_info in self.metadata:
+                dataset = utt_info["Dataset"]
+                uid = utt_info["Uid"]
+                utt = "{}_{}".format(dataset, uid)
+
+                self.utt2frame_pitch_path[utt] = os.path.join(
+                    cfg.preprocess.processed_dir,
+                    dataset,
+                    cfg.preprocess.pitch_dir,
+                    uid + ".npy",
+                )
+
+        if cfg.preprocess.use_frame_energy:
+            self.utt2frame_energy_path = {}
+            for utt_info in self.metadata:
+                dataset = utt_info["Dataset"]
+                uid = utt_info["Uid"]
+                utt = "{}_{}".format(dataset, uid)
+
+                self.utt2frame_energy_path[utt] = os.path.join(
+                    cfg.preprocess.processed_dir,
+                    dataset,
+                    cfg.preprocess.energy_dir,
+                    uid + ".npy",
+                )
+
+        if cfg.preprocess.use_mel:
+            self.utt2mel_path = {}
+            for utt_info in self.metadata:
+                dataset = utt_info["Dataset"]
+                uid = utt_info["Uid"]
+                utt = "{}_{}".format(dataset, uid)
+
+                self.utt2mel_path[utt] = os.path.join(
+                    cfg.preprocess.processed_dir,
+                    dataset,
+                    cfg.preprocess.mel_dir,
+                    uid + ".npy",
+                )
+
+        if cfg.preprocess.use_linear:
+            self.utt2linear_path = {}
+            for utt_info in self.metadata:
+                dataset = utt_info["Dataset"]
+                uid = utt_info["Uid"]
+                utt = "{}_{}".format(dataset, uid)
+
+                self.utt2linear_path[utt] = os.path.join(
+                    cfg.preprocess.processed_dir,
+                    dataset,
+                    cfg.preprocess.linear_dir,
+                    uid + ".npy",
+                )
+
+        if cfg.preprocess.use_audio:
+            self.utt2audio_path = {}
+            for utt_info in self.metadata:
+                dataset = utt_info["Dataset"]
+                uid = utt_info["Uid"]
+                utt = "{}_{}".format(dataset, uid)
+
+                if cfg.preprocess.extract_audio:
+                    self.utt2audio_path[utt] = os.path.join(
+                        cfg.preprocess.processed_dir,
+                        dataset,
+                        cfg.preprocess.audio_dir,
+                        uid + ".wav",
+                    )
+                else:
+                    self.utt2audio_path[utt] = utt_info["Path"]
+
+                    # self.utt2audio_path[utt] = os.path.join(
+                    #     cfg.preprocess.processed_dir,
+                    #     dataset,
+                    #     cfg.preprocess.audio_dir,
+                    #     uid + ".numpy",
+                    # )
+
+        elif cfg.preprocess.use_label:
+            self.utt2label_path = {}
+            for utt_info in self.metadata:
+                dataset = utt_info["Dataset"]
+                uid = utt_info["Uid"]
+                utt = "{}_{}".format(dataset, uid)
+
+                self.utt2label_path[utt] = os.path.join(
+                    cfg.preprocess.processed_dir,
+                    dataset,
+                    cfg.preprocess.label_dir,
+                    uid + ".npy",
+                )
+        elif cfg.preprocess.use_one_hot:
+            self.utt2one_hot_path = {}
+            for utt_info in self.metadata:
+                dataset = utt_info["Dataset"]
+                uid = utt_info["Uid"]
+                utt = "{}_{}".format(dataset, uid)
+
+                self.utt2one_hot_path[utt] = os.path.join(
+                    cfg.preprocess.processed_dir,
+                    dataset,
+                    cfg.preprocess.one_hot_dir,
+                    uid + ".npy",
+                )
+
+        if cfg.preprocess.use_text or cfg.preprocess.use_phone:
+            self.utt2seq = {}
+            for utt_info in self.metadata:
+                dataset = utt_info["Dataset"]
+                uid = utt_info["Uid"]
+                utt = "{}_{}".format(dataset, uid)
+
+                if cfg.preprocess.use_text:
+                    text = utt_info["Text"]
+                    sequence = text_to_sequence(text, cfg.preprocess.text_cleaners)
+                elif cfg.preprocess.use_phone:
+                    # load phoneme squence from phone file
+                    phone_path = os.path.join(
+                        processed_data_dir, cfg.preprocess.phone_dir, uid + ".phone"
+                    )
+                    with open(phone_path, "r") as fin:
+                        phones = fin.readlines()
+                        assert len(phones) == 1
+                        phones = phones[0].strip()
+                    phones_seq = phones.split(" ")
+
+                    phon_id_collator = phoneIDCollation(cfg, dataset=dataset)
+                    sequence = phon_id_collator.get_phone_id_sequence(cfg, phones_seq)
+
+                self.utt2seq[utt] = sequence
+
+    def __getitem__(self, index):
+        utt_info = self.metadata[index]
+
+        dataset = utt_info["Dataset"]
+        uid = utt_info["Uid"]
+        utt = "{}_{}".format(dataset, uid)
+
+        single_feature = dict()
+
+        if self.cfg.preprocess.use_spkid:
+            single_feature["spk_id"] = np.array(
+                [self.spk2id[self.utt2spk[utt]]], dtype=np.int32
+            )
+
+        if self.cfg.preprocess.use_mel:
+            mel = np.load(self.utt2mel_path[utt])
+            assert mel.shape[0] == self.cfg.preprocess.n_mel  # [n_mels, T]
+            if self.cfg.preprocess.use_min_max_norm_mel:
+                # do mel norm
+                mel = cal_normalized_mel(mel, utt_info["Dataset"], self.cfg.preprocess)
+
+            if "target_len" not in single_feature.keys():
+                single_feature["target_len"] = mel.shape[1]
+            single_feature["mel"] = mel.T  # [T, n_mels]
+
+        if self.cfg.preprocess.use_linear:
+            linear = np.load(self.utt2linear_path[utt])
+            if "target_len" not in single_feature.keys():
+                single_feature["target_len"] = linear.shape[1]
+            single_feature["linear"] = linear.T  # [T, n_linear]
+
+        if self.cfg.preprocess.use_frame_pitch:
+            frame_pitch_path = self.utt2frame_pitch_path[utt]
+            frame_pitch = np.load(frame_pitch_path)
+            if "target_len" not in single_feature.keys():
+                single_feature["target_len"] = len(frame_pitch)
+            aligned_frame_pitch = align_length(
+                frame_pitch, single_feature["target_len"]
+            )
+            single_feature["frame_pitch"] = aligned_frame_pitch
+
+            if self.cfg.preprocess.use_uv:
+                frame_uv_path = self.utt2uv_path[utt]
+                frame_uv = np.load(frame_uv_path)
+                aligned_frame_uv = align_length(frame_uv, single_feature["target_len"])
+                aligned_frame_uv = [
+                    0 if frame_uv else 1 for frame_uv in aligned_frame_uv
+                ]
+                aligned_frame_uv = np.array(aligned_frame_uv)
+                single_feature["frame_uv"] = aligned_frame_uv
+
+        if self.cfg.preprocess.use_frame_energy:
+            frame_energy_path = self.utt2frame_energy_path[utt]
+            frame_energy = np.load(frame_energy_path)
+            if "target_len" not in single_feature.keys():
+                single_feature["target_len"] = len(frame_energy)
+            aligned_frame_energy = align_length(
+                frame_energy, single_feature["target_len"]
+            )
+            single_feature["frame_energy"] = aligned_frame_energy
+
+        if self.cfg.preprocess.use_audio:
+            audio, sr = torchaudio.load(self.utt2audio_path[utt])
+            audio = audio.cpu().numpy().squeeze()
+            single_feature["audio"] = audio
+            single_feature["audio_len"] = audio.shape[0]
+
+        if self.cfg.preprocess.use_phone or self.cfg.preprocess.use_text:
+            single_feature["phone_seq"] = np.array(self.utt2seq[utt])
+            single_feature["phone_len"] = len(self.utt2seq[utt])
+
+        return single_feature
+
+    def __len__(self):
+        return super().__len__()
+
+    def get_metadata(self):
+        return super().get_metadata()
+
+
+class TTSCollator(BaseCollator):
+    """Zero-pads model inputs and targets based on number of frames per step"""
+
+    def __init__(self, cfg):
+        super().__init__(cfg)
+
+    def __call__(self, batch):
+        parsed_batch_features = super().__call__(batch)
+        return parsed_batch_features
+
+
+class TTSTestDataset(BaseTestDataset):
+    def __init__(self, args, cfg):
+        self.cfg = cfg
+
+        # inference from test list file
+        if args.test_list_file is not None:
+            # construst metadata
+            self.metadata = []
+
+            with open(args.test_list_file, "r") as fin:
+                for idx, line in enumerate(fin.readlines()):
+                    utt_info = {}
+
+                    utt_info["Dataset"] = "test"
+                    utt_info["Text"] = line.strip()
+                    utt_info["Uid"] = str(idx)
+                    self.metadata.append(utt_info)
+
+        else:
+            assert args.testing_set
+            self.metafile_path = os.path.join(
+                cfg.preprocess.processed_dir,
+                args.dataset,
+                "{}.json".format(args.testing_set),
+            )
+            self.metadata = self.get_metadata()
+
+    def __getitem__(self, index):
+        single_feature = {}
+
+        return single_feature
+
+    def __len__(self):
+        return len(self.metadata)
+
+
+class TTSTestCollator(BaseTestCollator):
+    """Zero-pads model inputs and targets based on number of frames per step"""
+
+    def __init__(self, cfg):
+        self.cfg = cfg
+
+    def __call__(self, batch):
+        packed_batch_features = dict()
+
+        # mel: [b, T, n_mels]
+        # frame_pitch, frame_energy: [1, T]
+        # target_len: [1]
+        # spk_id: [b, 1]
+        # mask: [b, T, 1]
+
+        for key in batch[0].keys():
+            if key == "target_len":
+                packed_batch_features["target_len"] = torch.LongTensor(
+                    [b["target_len"] for b in batch]
+                )
+                masks = [
+                    torch.ones((b["target_len"], 1), dtype=torch.long) for b in batch
+                ]
+                packed_batch_features["mask"] = pad_sequence(
+                    masks, batch_first=True, padding_value=0
+                )
+            elif key == "phone_len":
+                packed_batch_features["phone_len"] = torch.LongTensor(
+                    [b["phone_len"] for b in batch]
+                )
+                masks = [
+                    torch.ones((b["phone_len"], 1), dtype=torch.long) for b in batch
+                ]
+                packed_batch_features["phn_mask"] = pad_sequence(
+                    masks, batch_first=True, padding_value=0
+                )
+            elif key == "audio_len":
+                packed_batch_features["audio_len"] = torch.LongTensor(
+                    [b["audio_len"] for b in batch]
+                )
+                masks = [
+                    torch.ones((b["audio_len"], 1), dtype=torch.long) for b in batch
+                ]
+            else:
+                values = [torch.from_numpy(b[key]) for b in batch]
+                packed_batch_features[key] = pad_sequence(
+                    values, batch_first=True, padding_value=0
+                )
+        return packed_batch_features
diff --git a/models/tts/base/tts_inferece.py b/models/tts/base/tts_inferece.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb09a4d47f7252a43df4702c40fe4b76423841b4
--- /dev/null
+++ b/models/tts/base/tts_inferece.py
@@ -0,0 +1,268 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import torch
+import time
+import accelerate
+import random
+import numpy as np
+from tqdm import tqdm
+from accelerate.logging import get_logger
+from torch.utils.data import DataLoader
+
+
+from abc import abstractmethod
+from pathlib import Path
+from utils.io import save_audio
+from utils.util import load_config
+from models.vocoders.vocoder_inference import synthesis
+
+
+class TTSInference(object):
+    def __init__(self, args=None, cfg=None):
+        super().__init__()
+
+        start = time.monotonic_ns()
+        self.args = args
+        self.cfg = cfg
+        self.infer_type = args.mode
+
+        # get exp_dir
+        if self.args.acoustics_dir is not None:
+            self.exp_dir = self.args.acoustics_dir
+        elif self.args.checkpoint_path is not None:
+            self.exp_dir = os.path.dirname(os.path.dirname(self.args.checkpoint_path))
+
+        # Init accelerator
+        self.accelerator = accelerate.Accelerator()
+        self.accelerator.wait_for_everyone()
+        self.device = self.accelerator.device
+
+        # Get logger
+        with self.accelerator.main_process_first():
+            self.logger = get_logger("inference", log_level=args.log_level)
+
+        # Log some info
+        self.logger.info("=" * 56)
+        self.logger.info("||\t\t" + "New inference process started." + "\t\t||")
+        self.logger.info("=" * 56)
+        self.logger.info("\n")
+
+        self.acoustic_model_dir = args.acoustics_dir
+        self.logger.debug(f"Acoustic model dir: {args.acoustics_dir}")
+
+        if args.vocoder_dir is not None:
+            self.vocoder_dir = args.vocoder_dir
+            self.logger.debug(f"Vocoder dir: {args.vocoder_dir}")
+
+        os.makedirs(args.output_dir, exist_ok=True)
+
+        # Set random seed
+        with self.accelerator.main_process_first():
+            start = time.monotonic_ns()
+            self._set_random_seed(self.cfg.train.random_seed)
+            end = time.monotonic_ns()
+            self.logger.debug(
+                f"Setting random seed done in {(end - start) / 1e6:.2f}ms"
+            )
+            self.logger.debug(f"Random seed: {self.cfg.train.random_seed}")
+
+        # Setup data loader
+        if self.infer_type == "batch":
+            with self.accelerator.main_process_first():
+                self.logger.info("Building dataset...")
+                start = time.monotonic_ns()
+                self.test_dataloader = self._build_test_dataloader()
+                end = time.monotonic_ns()
+                self.logger.info(
+                    f"Building dataset done in {(end - start) / 1e6:.2f}ms"
+                )
+
+        # Build model
+        with self.accelerator.main_process_first():
+            self.logger.info("Building model...")
+            start = time.monotonic_ns()
+            self.model = self._build_model()
+            end = time.monotonic_ns()
+            self.logger.info(f"Building model done in {(end - start) / 1e6:.3f}ms")
+
+        # Init with accelerate
+        self.logger.info("Initializing accelerate...")
+        start = time.monotonic_ns()
+        self.accelerator = accelerate.Accelerator()
+        self.model = self.accelerator.prepare(self.model)
+        if self.infer_type == "batch":
+            self.test_dataloader = self.accelerator.prepare(self.test_dataloader)
+        end = time.monotonic_ns()
+        self.accelerator.wait_for_everyone()
+        self.logger.info(f"Initializing accelerate done in {(end - start) / 1e6:.3f}ms")
+
+        with self.accelerator.main_process_first():
+            self.logger.info("Loading checkpoint...")
+            start = time.monotonic_ns()
+            if args.acoustics_dir is not None:
+                self._load_model(
+                    checkpoint_dir=os.path.join(args.acoustics_dir, "checkpoint")
+                )
+            elif args.checkpoint_path is not None:
+                self._load_model(checkpoint_path=args.checkpoint_path)
+            else:
+                print("Either checkpoint dir or checkpoint path should be provided.")
+
+            end = time.monotonic_ns()
+            self.logger.info(f"Loading checkpoint done in {(end - start) / 1e6:.3f}ms")
+
+        self.model.eval()
+        self.accelerator.wait_for_everyone()
+
+    def _build_test_dataset(self):
+        pass
+
+    def _build_model(self):
+        pass
+
+    # TODO: LEGACY CODE
+    def _build_test_dataloader(self):
+        datasets, collate = self._build_test_dataset()
+        self.test_dataset = datasets(self.args, self.cfg)
+        self.test_collate = collate(self.cfg)
+        self.test_batch_size = min(
+            self.cfg.train.batch_size, len(self.test_dataset.metadata)
+        )
+        test_dataloader = DataLoader(
+            self.test_dataset,
+            collate_fn=self.test_collate,
+            num_workers=1,
+            batch_size=self.test_batch_size,
+            shuffle=False,
+        )
+        return test_dataloader
+
+    def _load_model(
+        self,
+        checkpoint_dir: str = None,
+        checkpoint_path: str = None,
+        old_mode: bool = False,
+    ):
+        r"""Load model from checkpoint. If checkpoint_path is None, it will
+        load the latest checkpoint in checkpoint_dir. If checkpoint_path is not
+        None, it will load the checkpoint specified by checkpoint_path. **Only use this
+        method after** ``accelerator.prepare()``.
+        """
+
+        if checkpoint_path is None:
+            assert checkpoint_dir is not None
+            # Load the latest accelerator state dicts
+            ls = [
+                str(i) for i in Path(checkpoint_dir).glob("*") if not "audio" in str(i)
+            ]
+            ls.sort(key=lambda x: int(x.split("_")[-3].split("-")[-1]), reverse=True)
+            checkpoint_path = ls[0]
+
+        self.accelerator.load_state(str(checkpoint_path))
+        return str(checkpoint_path)
+
+    def inference(self):
+        if self.infer_type == "single":
+            out_dir = os.path.join(self.args.output_dir, "single")
+            os.makedirs(out_dir, exist_ok=True)
+
+            pred_audio = self.inference_for_single_utterance()
+            save_path = os.path.join(out_dir, "test_pred.wav")
+            save_audio(save_path, pred_audio, self.cfg.preprocess.sample_rate)
+
+        elif self.infer_type == "batch":
+            out_dir = os.path.join(self.args.output_dir, "batch")
+            os.makedirs(out_dir, exist_ok=True)
+
+            pred_audio_list = self.inference_for_batches()
+            for it, wav in zip(self.test_dataset.metadata, pred_audio_list):
+                uid = it["Uid"]
+                save_audio(
+                    os.path.join(out_dir, f"{uid}.wav"),
+                    wav.numpy(),
+                    self.cfg.preprocess.sample_rate,
+                    add_silence=True,
+                    turn_up=True,
+                )
+                tmp_file = os.path.join(out_dir, f"{uid}.pt")
+                if os.path.exists(tmp_file):
+                    os.remove(tmp_file)
+        print("Saved to: ", out_dir)
+
+    @torch.inference_mode()
+    def inference_for_batches(self):
+        y_pred = []
+        for i, batch in tqdm(enumerate(self.test_dataloader)):
+            y_pred, mel_lens, _ = self._inference_each_batch(batch)
+            y_ls = y_pred.chunk(self.test_batch_size)
+            tgt_ls = mel_lens.chunk(self.test_batch_size)
+            j = 0
+            for it, l in zip(y_ls, tgt_ls):
+                l = l.item()
+                it = it.squeeze(0)[:l].detach().cpu()
+
+                uid = self.test_dataset.metadata[i * self.test_batch_size + j]["Uid"]
+                torch.save(it, os.path.join(self.args.output_dir, f"{uid}.pt"))
+                j += 1
+
+        vocoder_cfg, vocoder_ckpt = self._parse_vocoder(self.args.vocoder_dir)
+        res = synthesis(
+            cfg=vocoder_cfg,
+            vocoder_weight_file=vocoder_ckpt,
+            n_samples=None,
+            pred=[
+                torch.load(
+                    os.path.join(self.args.output_dir, "{}.pt".format(item["Uid"]))
+                ).numpy()
+                for item in self.test_dataset.metadata
+            ],
+        )
+        for it, wav in zip(self.test_dataset.metadata, res):
+            uid = it["Uid"]
+            save_audio(
+                os.path.join(self.args.output_dir, f"{uid}.wav"),
+                wav.numpy(),
+                22050,
+                add_silence=True,
+                turn_up=True,
+            )
+
+    @abstractmethod
+    @torch.inference_mode()
+    def _inference_each_batch(self, batch_data):
+        pass
+
+    def inference_for_single_utterance(self, text):
+        pass
+
+    def synthesis_by_vocoder(self, pred):
+        audios_pred = synthesis(
+            self.vocoder_cfg,
+            self.checkpoint_dir_vocoder,
+            len(pred),
+            pred,
+        )
+
+        return audios_pred
+
+    @staticmethod
+    def _parse_vocoder(vocoder_dir):
+        r"""Parse vocoder config"""
+        vocoder_dir = os.path.abspath(vocoder_dir)
+        ckpt_list = [ckpt for ckpt in Path(vocoder_dir).glob("*.pt")]
+        ckpt_list.sort(key=lambda x: int(x.stem), reverse=True)
+        ckpt_path = str(ckpt_list[0])
+        vocoder_cfg = load_config(
+            os.path.join(vocoder_dir, "args.json"), lowercase=True
+        )
+        return vocoder_cfg, ckpt_path
+
+    def _set_random_seed(self, seed):
+        """Set random seed for all possible random modules."""
+        random.seed(seed)
+        np.random.seed(seed)
+        torch.random.manual_seed(seed)
diff --git a/models/tts/base/tts_trainer.py b/models/tts/base/tts_trainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5d70621f8f6ed5614e83eb2940abc19d0efadbd
--- /dev/null
+++ b/models/tts/base/tts_trainer.py
@@ -0,0 +1,699 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import json
+import os
+import shutil
+import torch
+import time
+from pathlib import Path
+import torch
+from tqdm import tqdm
+import re
+import logging
+import json5
+import accelerate
+from accelerate.logging import get_logger
+from accelerate.utils import ProjectConfiguration
+from torch.utils.data import ConcatDataset, DataLoader
+from accelerate import DistributedDataParallelKwargs
+from schedulers.scheduler import Eden
+from models.base.base_sampler import build_samplers
+from models.base.new_trainer import BaseTrainer
+
+
+class TTSTrainer(BaseTrainer):
+    r"""The base trainer for all TTS models. It inherits from BaseTrainer and implements
+    ``build_criterion``, ``_build_dataset`` and ``_build_singer_lut`` methods. You can inherit from this
+    class, and implement ``_build_model``, ``_forward_step``.
+    """
+
+    def __init__(self, args=None, cfg=None):
+        self.args = args
+        self.cfg = cfg
+
+        cfg.exp_name = args.exp_name
+
+        # init with accelerate
+        self._init_accelerator()
+        self.accelerator.wait_for_everyone()
+
+        with self.accelerator.main_process_first():
+            self.logger = get_logger(args.exp_name, log_level="INFO")
+
+        # Log some info
+        self.logger.info("=" * 56)
+        self.logger.info("||\t\t" + "New training process started." + "\t\t||")
+        self.logger.info("=" * 56)
+        self.logger.info("\n")
+        self.logger.debug(f"Using {args.log_level.upper()} logging level.")
+        self.logger.info(f"Experiment name: {args.exp_name}")
+        self.logger.info(f"Experiment directory: {self.exp_dir}")
+        self.checkpoint_dir = os.path.join(self.exp_dir, "checkpoint")
+        if self.accelerator.is_main_process:
+            os.makedirs(self.checkpoint_dir, exist_ok=True)
+        self.logger.debug(f"Checkpoint directory: {self.checkpoint_dir}")
+
+        # init counts
+        self.batch_count: int = 0
+        self.step: int = 0
+        self.epoch: int = 0
+        self.max_epoch = (
+            self.cfg.train.max_epoch if self.cfg.train.max_epoch > 0 else float("inf")
+        )
+        self.logger.info(
+            "Max epoch: {}".format(
+                self.max_epoch if self.max_epoch < float("inf") else "Unlimited"
+            )
+        )
+
+        # Check values
+        if self.accelerator.is_main_process:
+            self.__check_basic_configs()
+            # Set runtime configs
+            self.save_checkpoint_stride = self.cfg.train.save_checkpoint_stride
+            self.checkpoints_path = [
+                [] for _ in range(len(self.save_checkpoint_stride))
+            ]
+            self.keep_last = [
+                i if i > 0 else float("inf") for i in self.cfg.train.keep_last
+            ]
+            self.run_eval = self.cfg.train.run_eval
+
+        # set random seed
+        with self.accelerator.main_process_first():
+            start = time.monotonic_ns()
+            self._set_random_seed(self.cfg.train.random_seed)
+            end = time.monotonic_ns()
+            self.logger.debug(
+                f"Setting random seed done in {(end - start) / 1e6:.2f}ms"
+            )
+            self.logger.debug(f"Random seed: {self.cfg.train.random_seed}")
+
+        # setup data_loader
+        with self.accelerator.main_process_first():
+            self.logger.info("Building dataset...")
+            start = time.monotonic_ns()
+            self.train_dataloader, self.valid_dataloader = self._build_dataloader()
+            end = time.monotonic_ns()
+            self.logger.info(f"Building dataset done in {(end - start) / 1e6:.2f}ms")
+
+        # save phone table to exp dir. Should be done before building model due to loading phone table in model
+        if cfg.preprocess.use_phone and cfg.preprocess.phone_extractor != "lexicon":
+            self._save_phone_symbols_file_to_exp_path()
+
+        # setup model
+        with self.accelerator.main_process_first():
+            self.logger.info("Building model...")
+            start = time.monotonic_ns()
+            self.model = self._build_model()
+            end = time.monotonic_ns()
+            self.logger.debug(self.model)
+            self.logger.info(f"Building model done in {(end - start) / 1e6:.2f}ms")
+            self.logger.info(
+                f"Model parameters: {self.__count_parameters(self.model)/1e6:.2f}M"
+            )
+
+        # optimizer & scheduler
+        with self.accelerator.main_process_first():
+            self.logger.info("Building optimizer and scheduler...")
+            start = time.monotonic_ns()
+            self.optimizer = self._build_optimizer()
+            self.scheduler = self._build_scheduler()
+            end = time.monotonic_ns()
+            self.logger.info(
+                f"Building optimizer and scheduler done in {(end - start) / 1e6:.2f}ms"
+            )
+
+        # create criterion
+        with self.accelerator.main_process_first():
+            self.logger.info("Building criterion...")
+            start = time.monotonic_ns()
+            self.criterion = self._build_criterion()
+            end = time.monotonic_ns()
+            self.logger.info(f"Building criterion done in {(end - start) / 1e6:.2f}ms")
+
+        # Resume or Finetune
+        with self.accelerator.main_process_first():
+            self._check_resume()
+
+        # accelerate prepare
+        self.logger.info("Initializing accelerate...")
+        start = time.monotonic_ns()
+        self._accelerator_prepare()
+        end = time.monotonic_ns()
+        self.logger.info(f"Initializing accelerate done in {(end - start) / 1e6:.2f}ms")
+
+        # save config file path
+        self.config_save_path = os.path.join(self.exp_dir, "args.json")
+        self.device = self.accelerator.device
+
+        if cfg.preprocess.use_spkid and cfg.train.multi_speaker_training:
+            self.speakers = self._build_speaker_lut()
+            self.utt2spk_dict = self._build_utt2spk_dict()
+
+        # Only for TTS tasks
+        self.task_type = "TTS"
+        self.logger.info("Task type: {}".format(self.task_type))
+
+    def _check_resume(self):
+        # if args.resume:
+        if self.args.resume or (
+            self.cfg.model_type == "VALLE" and self.args.train_stage == 2
+        ):
+            if self.cfg.model_type == "VALLE" and self.args.train_stage == 2:
+                self.args.resume_type = "finetune"
+
+            self.logger.info("Resuming from checkpoint...")
+            start = time.monotonic_ns()
+            self.ckpt_path = self._load_model(
+                self.checkpoint_dir, self.args.checkpoint_path, self.args.resume_type
+            )
+            end = time.monotonic_ns()
+            self.logger.info(
+                f"Resuming from checkpoint done in {(end - start) / 1e6:.2f}ms"
+            )
+            self.checkpoints_path = json.load(
+                open(os.path.join(self.ckpt_path, "ckpts.json"), "r")
+            )
+
+        self.checkpoint_dir = os.path.join(self.exp_dir, "checkpoint")
+        if self.accelerator.is_main_process:
+            os.makedirs(self.checkpoint_dir, exist_ok=True)
+        self.logger.debug(f"Checkpoint directory: {self.checkpoint_dir}")
+
+    def _init_accelerator(self):
+        self.exp_dir = os.path.join(
+            os.path.abspath(self.cfg.log_dir), self.args.exp_name
+        )
+        project_config = ProjectConfiguration(
+            project_dir=self.exp_dir,
+            logging_dir=os.path.join(self.exp_dir, "log"),
+        )
+        kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
+        self.accelerator = accelerate.Accelerator(
+            gradient_accumulation_steps=self.cfg.train.gradient_accumulation_step,
+            log_with=self.cfg.train.tracker,
+            project_config=project_config,
+            kwargs_handlers=[kwargs],
+        )
+        if self.accelerator.is_main_process:
+            os.makedirs(project_config.project_dir, exist_ok=True)
+            os.makedirs(project_config.logging_dir, exist_ok=True)
+        with self.accelerator.main_process_first():
+            self.accelerator.init_trackers(self.args.exp_name)
+
+    def _accelerator_prepare(self):
+        (
+            self.train_dataloader,
+            self.valid_dataloader,
+        ) = self.accelerator.prepare(
+            self.train_dataloader,
+            self.valid_dataloader,
+        )
+
+        if isinstance(self.model, dict):
+            for key in self.model.keys():
+                self.model[key] = self.accelerator.prepare(self.model[key])
+        else:
+            self.model = self.accelerator.prepare(self.model)
+
+        if isinstance(self.optimizer, dict):
+            for key in self.optimizer.keys():
+                self.optimizer[key] = self.accelerator.prepare(self.optimizer[key])
+        else:
+            self.optimizer = self.accelerator.prepare(self.optimizer)
+
+        if isinstance(self.scheduler, dict):
+            for key in self.scheduler.keys():
+                self.scheduler[key] = self.accelerator.prepare(self.scheduler[key])
+        else:
+            self.scheduler = self.accelerator.prepare(self.scheduler)
+
+    ### Following are methods only for TTS tasks ###
+    def _build_dataset(self):
+        pass
+
+    def _build_criterion(self):
+        pass
+
+    def _build_model(self):
+        pass
+
+    def _build_dataloader(self):
+        """Build dataloader which merges a series of datasets."""
+        # Build dataset instance for each dataset and combine them by ConcatDataset
+        Dataset, Collator = self._build_dataset()
+
+        # Build train set
+        datasets_list = []
+        for dataset in self.cfg.dataset:
+            subdataset = Dataset(self.cfg, dataset, is_valid=False)
+            datasets_list.append(subdataset)
+        train_dataset = ConcatDataset(datasets_list)
+        train_collate = Collator(self.cfg)
+        _, batch_sampler = build_samplers(train_dataset, self.cfg, self.logger, "train")
+        train_loader = DataLoader(
+            train_dataset,
+            collate_fn=train_collate,
+            batch_sampler=batch_sampler,
+            num_workers=self.cfg.train.dataloader.num_worker,
+            pin_memory=self.cfg.train.dataloader.pin_memory,
+        )
+
+        # Build test set
+        datasets_list = []
+        for dataset in self.cfg.dataset:
+            subdataset = Dataset(self.cfg, dataset, is_valid=True)
+            datasets_list.append(subdataset)
+        valid_dataset = ConcatDataset(datasets_list)
+        valid_collate = Collator(self.cfg)
+        _, batch_sampler = build_samplers(valid_dataset, self.cfg, self.logger, "valid")
+        valid_loader = DataLoader(
+            valid_dataset,
+            collate_fn=valid_collate,
+            batch_sampler=batch_sampler,
+            num_workers=self.cfg.train.dataloader.num_worker,
+            pin_memory=self.cfg.train.dataloader.pin_memory,
+        )
+        return train_loader, valid_loader
+
+    def _build_optimizer(self):
+        pass
+
+    def _build_scheduler(self):
+        pass
+
+    def _load_model(self, checkpoint_dir, checkpoint_path=None, resume_type="resume"):
+        """Load model from checkpoint. If a folder is given, it will
+        load the latest checkpoint in checkpoint_dir. If a path is given
+        it will load the checkpoint specified by checkpoint_path.
+        **Only use this method after** ``accelerator.prepare()``.
+        """
+        if checkpoint_path is None:
+            ls = [str(i) for i in Path(checkpoint_dir).glob("*")]
+            ls.sort(key=lambda x: int(x.split("_")[-3].split("-")[-1]), reverse=True)
+            checkpoint_path = ls[0]
+        self.logger.info("Load model from {}".format(checkpoint_path))
+        print("Load model from {}".format(checkpoint_path))
+        if resume_type == "resume":
+            self.accelerator.load_state(checkpoint_path)
+            self.epoch = int(checkpoint_path.split("_")[-3].split("-")[-1]) + 1
+            self.step = int(checkpoint_path.split("_")[-2].split("-")[-1]) + 1
+        elif resume_type == "finetune":
+            self.model.load_state_dict(
+                torch.load(os.path.join(checkpoint_path, "pytorch_model.bin"))
+            )
+            self.model.cuda(self.accelerator.device)
+            self.logger.info("Load model weights for finetune SUCCESS!")
+        else:
+            raise ValueError("Unsupported resume type: {}".format(resume_type))
+
+        return checkpoint_path
+
+    ### THIS IS MAIN ENTRY ###
+    def train_loop(self):
+        r"""Training loop. The public entry of training process."""
+        # Wait everyone to prepare before we move on
+        self.accelerator.wait_for_everyone()
+        # dump config file
+        if self.accelerator.is_main_process:
+            self.__dump_cfg(self.config_save_path)
+
+        # self.optimizer.zero_grad()
+        # Wait to ensure good to go
+
+        self.accelerator.wait_for_everyone()
+        while self.epoch < self.max_epoch:
+            self.logger.info("\n")
+            self.logger.info("-" * 32)
+            self.logger.info("Epoch {}: ".format(self.epoch))
+
+            # Do training & validating epoch
+            train_total_loss, train_losses = self._train_epoch()
+            if isinstance(train_losses, dict):
+                for key, loss in train_losses.items():
+                    self.logger.info("  |- Train/{} Loss: {:.6f}".format(key, loss))
+                    self.accelerator.log(
+                        {"Epoch/Train {} Loss".format(key): loss},
+                        step=self.epoch,
+                    )
+
+            valid_total_loss, valid_losses = self._valid_epoch()
+            if isinstance(valid_losses, dict):
+                for key, loss in valid_losses.items():
+                    self.logger.info("  |- Valid/{} Loss: {:.6f}".format(key, loss))
+                    self.accelerator.log(
+                        {"Epoch/Train {} Loss".format(key): loss},
+                        step=self.epoch,
+                    )
+
+            self.logger.info("  |- Train/Loss: {:.6f}".format(train_total_loss))
+            self.logger.info("  |- Valid/Loss: {:.6f}".format(valid_total_loss))
+            self.accelerator.log(
+                {
+                    "Epoch/Train Loss": train_total_loss,
+                    "Epoch/Valid Loss": valid_total_loss,
+                },
+                step=self.epoch,
+            )
+
+            self.accelerator.wait_for_everyone()
+
+            # Check if hit save_checkpoint_stride and run_eval
+            run_eval = False
+            if self.accelerator.is_main_process:
+                save_checkpoint = False
+                hit_dix = []
+                for i, num in enumerate(self.save_checkpoint_stride):
+                    if self.epoch % num == 0:
+                        save_checkpoint = True
+                        hit_dix.append(i)
+                        run_eval |= self.run_eval[i]
+
+            self.accelerator.wait_for_everyone()
+            if self.accelerator.is_main_process and save_checkpoint:
+                path = os.path.join(
+                    self.checkpoint_dir,
+                    "epoch-{:04d}_step-{:07d}_loss-{:.6f}".format(
+                        self.epoch, self.step, train_total_loss
+                    ),
+                )
+                self.accelerator.save_state(path)
+
+                json.dump(
+                    self.checkpoints_path,
+                    open(os.path.join(path, "ckpts.json"), "w"),
+                    ensure_ascii=False,
+                    indent=4,
+                )
+
+                # Remove old checkpoints
+                to_remove = []
+                for idx in hit_dix:
+                    self.checkpoints_path[idx].append(path)
+                    while len(self.checkpoints_path[idx]) > self.keep_last[idx]:
+                        to_remove.append((idx, self.checkpoints_path[idx].pop(0)))
+
+                # Search conflicts
+                total = set()
+                for i in self.checkpoints_path:
+                    total |= set(i)
+                do_remove = set()
+                for idx, path in to_remove[::-1]:
+                    if path in total:
+                        self.checkpoints_path[idx].insert(0, path)
+                    else:
+                        do_remove.add(path)
+
+                # Remove old checkpoints
+                for path in do_remove:
+                    shutil.rmtree(path, ignore_errors=True)
+                    self.logger.debug(f"Remove old checkpoint: {path}")
+
+            self.accelerator.wait_for_everyone()
+            if run_eval:
+                # TODO: run evaluation
+                pass
+
+            # Update info for each epoch
+            self.epoch += 1
+
+        # Finish training and save final checkpoint
+        self.accelerator.wait_for_everyone()
+        if self.accelerator.is_main_process:
+            path = os.path.join(
+                self.checkpoint_dir,
+                "final_epoch-{:04d}_step-{:07d}_loss-{:.6f}".format(
+                    self.epoch, self.step, valid_total_loss
+                ),
+            )
+            self.accelerator.save_state(
+                os.path.join(
+                    self.checkpoint_dir,
+                    "final_epoch-{:04d}_step-{:07d}_loss-{:.6f}".format(
+                        self.epoch, self.step, valid_total_loss
+                    ),
+                )
+            )
+
+            json.dump(
+                self.checkpoints_path,
+                open(os.path.join(path, "ckpts.json"), "w"),
+                ensure_ascii=False,
+                indent=4,
+            )
+
+        self.accelerator.end_training()
+
+    ### Following are methods that can be used directly in child classes ###
+    def _train_epoch(self):
+        r"""Training epoch. Should return average loss of a batch (sample) over
+        one epoch. See ``train_loop`` for usage.
+        """
+        if isinstance(self.model, dict):
+            for key in self.model.keys():
+                self.model[key].train()
+        else:
+            self.model.train()
+
+        epoch_sum_loss: float = 0.0
+        epoch_losses: dict = {}
+        epoch_step: int = 0
+        for batch in tqdm(
+            self.train_dataloader,
+            desc=f"Training Epoch {self.epoch}",
+            unit="batch",
+            colour="GREEN",
+            leave=False,
+            dynamic_ncols=True,
+            smoothing=0.04,
+            disable=not self.accelerator.is_main_process,
+        ):
+            # Do training step and BP
+            with self.accelerator.accumulate(self.model):
+                total_loss, train_losses, _ = self._train_step(batch)
+            self.batch_count += 1
+
+            # Update info for each step
+            # TODO: step means BP counts or batch counts?
+            if self.batch_count % self.cfg.train.gradient_accumulation_step == 0:
+                if isinstance(self.scheduler, dict):
+                    for key in self.scheduler.keys():
+                        self.scheduler[key].step()
+                else:
+                    if isinstance(self.scheduler, Eden):
+                        self.scheduler.step_batch(self.step)
+                    else:
+                        self.scheduler.step()
+
+                epoch_sum_loss += total_loss
+
+                if isinstance(train_losses, dict):
+                    for key, value in train_losses.items():
+                        epoch_losses[key] += value
+
+                if isinstance(train_losses, dict):
+                    for key, loss in train_losses.items():
+                        self.accelerator.log(
+                            {"Epoch/Train {} Loss".format(key): loss},
+                            step=self.step,
+                        )
+
+                self.step += 1
+                epoch_step += 1
+
+        self.accelerator.wait_for_everyone()
+
+        epoch_sum_loss = (
+            epoch_sum_loss
+            / len(self.train_dataloader)
+            * self.cfg.train.gradient_accumulation_step
+        )
+
+        for key in epoch_losses.keys():
+            epoch_losses[key] = (
+                epoch_losses[key]
+                / len(self.train_dataloader)
+                * self.cfg.train.gradient_accumulation_step
+            )
+
+        return epoch_sum_loss, epoch_losses
+
+    @torch.inference_mode()
+    def _valid_epoch(self):
+        r"""Testing epoch. Should return average loss of a batch (sample) over
+        one epoch. See ``train_loop`` for usage.
+        """
+        if isinstance(self.model, dict):
+            for key in self.model.keys():
+                self.model[key].eval()
+        else:
+            self.model.eval()
+
+        epoch_sum_loss = 0.0
+        epoch_losses = dict()
+        for batch in tqdm(
+            self.valid_dataloader,
+            desc=f"Validating Epoch {self.epoch}",
+            unit="batch",
+            colour="GREEN",
+            leave=False,
+            dynamic_ncols=True,
+            smoothing=0.04,
+            disable=not self.accelerator.is_main_process,
+        ):
+            total_loss, valid_losses, valid_stats = self._valid_step(batch)
+            epoch_sum_loss += total_loss
+            if isinstance(valid_losses, dict):
+                for key, value in valid_losses.items():
+                    if key not in epoch_losses.keys():
+                        epoch_losses[key] = value
+                    else:
+                        epoch_losses[key] += value
+
+        epoch_sum_loss = epoch_sum_loss / len(self.valid_dataloader)
+        for key in epoch_losses.keys():
+            epoch_losses[key] = epoch_losses[key] / len(self.valid_dataloader)
+
+        self.accelerator.wait_for_everyone()
+
+        return epoch_sum_loss, epoch_losses
+
+    def _train_step(self):
+        pass
+
+    def _valid_step(self, batch):
+        pass
+
+    def _inference(self):
+        pass
+
+    def _is_valid_pattern(self, directory_name):
+        directory_name = str(directory_name)
+        pattern = r"^epoch-\d{4}_step-\d{7}_loss-\d{1}\.\d{6}"
+        return re.match(pattern, directory_name) is not None
+
+    def _check_basic_configs(self):
+        if self.cfg.train.gradient_accumulation_step <= 0:
+            self.logger.fatal("Invalid gradient_accumulation_step value!")
+            self.logger.error(
+                f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive."
+            )
+            self.accelerator.end_training()
+            raise ValueError(
+                f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive."
+            )
+
+    def __dump_cfg(self, path):
+        os.makedirs(os.path.dirname(path), exist_ok=True)
+        json5.dump(
+            self.cfg,
+            open(path, "w"),
+            indent=4,
+            sort_keys=True,
+            ensure_ascii=False,
+            quote_keys=True,
+        )
+
+    def __check_basic_configs(self):
+        if self.cfg.train.gradient_accumulation_step <= 0:
+            self.logger.fatal("Invalid gradient_accumulation_step value!")
+            self.logger.error(
+                f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive."
+            )
+            self.accelerator.end_training()
+            raise ValueError(
+                f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive."
+            )
+        # TODO: check other values
+
+    @staticmethod
+    def __count_parameters(model):
+        model_param = 0.0
+        if isinstance(model, dict):
+            for key, value in model.items():
+                model_param += sum(p.numel() for p in model[key].parameters())
+        else:
+            model_param = sum(p.numel() for p in model.parameters())
+        return model_param
+
+    def _build_speaker_lut(self):
+        # combine speakers
+        if not os.path.exists(os.path.join(self.exp_dir, self.cfg.preprocess.spk2id)):
+            speakers = {}
+        else:
+            with open(
+                os.path.join(self.exp_dir, self.cfg.preprocess.spk2id), "r"
+            ) as speaker_file:
+                speakers = json.load(speaker_file)
+        for dataset in self.cfg.dataset:
+            speaker_lut_path = os.path.join(
+                self.cfg.preprocess.processed_dir, dataset, self.cfg.preprocess.spk2id
+            )
+            with open(speaker_lut_path, "r") as speaker_lut_path:
+                singer_lut = json.load(speaker_lut_path)
+            for singer in singer_lut.keys():
+                if singer not in speakers:
+                    speakers[singer] = len(speakers)
+        with open(
+            os.path.join(self.exp_dir, self.cfg.preprocess.spk2id), "w"
+        ) as speaker_file:
+            json.dump(speakers, speaker_file, indent=4, ensure_ascii=False)
+        print(
+            "speakers have been dumped to {}".format(
+                os.path.join(self.exp_dir, self.cfg.preprocess.spk2id)
+            )
+        )
+        return speakers
+
+    def _build_utt2spk_dict(self):
+        # combine speakers
+        utt2spk = {}
+        if not os.path.exists(os.path.join(self.exp_dir, self.cfg.preprocess.utt2spk)):
+            utt2spk = {}
+        else:
+            with open(
+                os.path.join(self.exp_dir, self.cfg.preprocess.utt2spk), "r"
+            ) as utt2spk_file:
+                for line in utt2spk_file.readlines():
+                    utt, spk = line.strip().split("\t")
+                    utt2spk[utt] = spk
+        for dataset in self.cfg.dataset:
+            utt2spk_dict_path = os.path.join(
+                self.cfg.preprocess.processed_dir, dataset, self.cfg.preprocess.utt2spk
+            )
+            with open(utt2spk_dict_path, "r") as utt2spk_dict:
+                for line in utt2spk_dict.readlines():
+                    utt, spk = line.strip().split("\t")
+                    if utt not in utt2spk.keys():
+                        utt2spk[utt] = spk
+        with open(
+            os.path.join(self.exp_dir, self.cfg.preprocess.utt2spk), "w"
+        ) as utt2spk_file:
+            for utt, spk in utt2spk.items():
+                utt2spk_file.write(utt + "\t" + spk + "\n")
+        print(
+            "utterance and speaker mapper have been dumped to {}".format(
+                os.path.join(self.exp_dir, self.cfg.preprocess.utt2spk)
+            )
+        )
+        return utt2spk
+
+    def _save_phone_symbols_file_to_exp_path(self):
+        phone_symbols_file = os.path.join(
+            self.cfg.preprocess.processed_dir,
+            self.cfg.dataset[0],
+            self.cfg.preprocess.symbols_dict,
+        )
+        phone_symbols_file_to_exp_path = os.path.join(
+            self.exp_dir, self.cfg.preprocess.symbols_dict
+        )
+        shutil.copy(phone_symbols_file, phone_symbols_file_to_exp_path)
+        print(
+            "phone symbols been dumped to {}".format(
+                os.path.join(self.exp_dir, self.cfg.preprocess.symbols_dict)
+            )
+        )
diff --git a/models/tts/fastspeech2/__init__.py b/models/tts/fastspeech2/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/models/tts/fastspeech2/fs2.py b/models/tts/fastspeech2/fs2.py
new file mode 100644
index 0000000000000000000000000000000000000000..464179f7e34fdf5f6f2388c67ed7ab042fb8a774
--- /dev/null
+++ b/models/tts/fastspeech2/fs2.py
@@ -0,0 +1,548 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+#  This code is modified from https://github.com/ming024/FastSpeech2/blob/master/model/fastspeech2.py
+import torch
+import torch.nn as nn
+import numpy as np
+import torch.nn.functional as F
+
+from modules.transformer.Models import Encoder, Decoder
+from modules.transformer.Layers import PostNet
+from collections import OrderedDict
+
+import os
+import json
+
+
+def get_mask_from_lengths(lengths, max_len=None):
+    device = lengths.device
+    batch_size = lengths.shape[0]
+    if max_len is None:
+        max_len = torch.max(lengths).item()
+
+    ids = torch.arange(0, max_len).unsqueeze(0).expand(batch_size, -1).to(device)
+    mask = ids >= lengths.unsqueeze(1).expand(-1, max_len)
+
+    return mask
+
+
+def pad(input_ele, mel_max_length=None):
+    if mel_max_length:
+        max_len = mel_max_length
+    else:
+        max_len = max([input_ele[i].size(0) for i in range(len(input_ele))])
+
+    out_list = list()
+    for i, batch in enumerate(input_ele):
+        if len(batch.shape) == 1:
+            one_batch_padded = F.pad(
+                batch, (0, max_len - batch.size(0)), "constant", 0.0
+            )
+        elif len(batch.shape) == 2:
+            one_batch_padded = F.pad(
+                batch, (0, 0, 0, max_len - batch.size(0)), "constant", 0.0
+            )
+        out_list.append(one_batch_padded)
+    out_padded = torch.stack(out_list)
+    return out_padded
+
+
+class VarianceAdaptor(nn.Module):
+    """Variance Adaptor"""
+
+    def __init__(self, cfg):
+        super(VarianceAdaptor, self).__init__()
+        self.duration_predictor = VariancePredictor(cfg)
+        self.length_regulator = LengthRegulator()
+        self.pitch_predictor = VariancePredictor(cfg)
+        self.energy_predictor = VariancePredictor(cfg)
+
+        # assign the pitch/energy feature level
+        if cfg.preprocess.use_frame_pitch:
+            self.pitch_feature_level = "frame_level"
+            self.pitch_dir = cfg.preprocess.pitch_dir
+        else:
+            self.pitch_feature_level = "phoneme_level"
+            self.pitch_dir = cfg.preprocess.phone_pitch_dir
+
+        if cfg.preprocess.use_frame_energy:
+            self.energy_feature_level = "frame_level"
+            self.energy_dir = cfg.preprocess.energy_dir
+        else:
+            self.energy_feature_level = "phoneme_level"
+            self.energy_dir = cfg.preprocess.phone_energy_dir
+
+        assert self.pitch_feature_level in ["phoneme_level", "frame_level"]
+        assert self.energy_feature_level in ["phoneme_level", "frame_level"]
+
+        pitch_quantization = cfg.model.variance_embedding.pitch_quantization
+        energy_quantization = cfg.model.variance_embedding.energy_quantization
+        n_bins = cfg.model.variance_embedding.n_bins
+        assert pitch_quantization in ["linear", "log"]
+        assert energy_quantization in ["linear", "log"]
+
+        with open(
+            os.path.join(
+                cfg.preprocess.processed_dir,
+                cfg.dataset[0],
+                self.energy_dir,
+                "statistics.json",
+            )
+        ) as f:
+            stats = json.load(f)
+            stats = stats[cfg.dataset[0] + "_" + cfg.dataset[0]]
+            mean, std = (
+                stats["voiced_positions"]["mean"],
+                stats["voiced_positions"]["std"],
+            )
+            energy_min = (stats["total_positions"]["min"] - mean) / std
+            energy_max = (stats["total_positions"]["max"] - mean) / std
+
+        with open(
+            os.path.join(
+                cfg.preprocess.processed_dir,
+                cfg.dataset[0],
+                self.pitch_dir,
+                "statistics.json",
+            )
+        ) as f:
+            stats = json.load(f)
+            stats = stats[cfg.dataset[0] + "_" + cfg.dataset[0]]
+            mean, std = (
+                stats["voiced_positions"]["mean"],
+                stats["voiced_positions"]["std"],
+            )
+            pitch_min = (stats["total_positions"]["min"] - mean) / std
+            pitch_max = (stats["total_positions"]["max"] - mean) / std
+
+        if pitch_quantization == "log":
+            self.pitch_bins = nn.Parameter(
+                torch.exp(
+                    torch.linspace(np.log(pitch_min), np.log(pitch_max), n_bins - 1)
+                ),
+                requires_grad=False,
+            )
+        else:
+            self.pitch_bins = nn.Parameter(
+                torch.linspace(pitch_min, pitch_max, n_bins - 1),
+                requires_grad=False,
+            )
+        if energy_quantization == "log":
+            self.energy_bins = nn.Parameter(
+                torch.exp(
+                    torch.linspace(np.log(energy_min), np.log(energy_max), n_bins - 1)
+                ),
+                requires_grad=False,
+            )
+        else:
+            self.energy_bins = nn.Parameter(
+                torch.linspace(energy_min, energy_max, n_bins - 1),
+                requires_grad=False,
+            )
+
+        self.pitch_embedding = nn.Embedding(
+            n_bins, cfg.model.transformer.encoder_hidden
+        )
+        self.energy_embedding = nn.Embedding(
+            n_bins, cfg.model.transformer.encoder_hidden
+        )
+
+    def get_pitch_embedding(self, x, target, mask, control):
+        prediction = self.pitch_predictor(x, mask)
+        if target is not None:
+            embedding = self.pitch_embedding(torch.bucketize(target, self.pitch_bins))
+        else:
+            prediction = prediction * control
+            embedding = self.pitch_embedding(
+                torch.bucketize(prediction, self.pitch_bins)
+            )
+        return prediction, embedding
+
+    def get_energy_embedding(self, x, target, mask, control):
+        prediction = self.energy_predictor(x, mask)
+        if target is not None:
+            embedding = self.energy_embedding(torch.bucketize(target, self.energy_bins))
+        else:
+            prediction = prediction * control
+            embedding = self.energy_embedding(
+                torch.bucketize(prediction, self.energy_bins)
+            )
+        return prediction, embedding
+
+    def forward(
+        self,
+        x,
+        src_mask,
+        mel_mask=None,
+        max_len=None,
+        pitch_target=None,
+        energy_target=None,
+        duration_target=None,
+        p_control=1.0,
+        e_control=1.0,
+        d_control=1.0,
+    ):
+        log_duration_prediction = self.duration_predictor(x, src_mask)
+        if self.pitch_feature_level == "phoneme_level":
+            pitch_prediction, pitch_embedding = self.get_pitch_embedding(
+                x, pitch_target, src_mask, p_control
+            )
+            x = x + pitch_embedding
+        if self.energy_feature_level == "phoneme_level":
+            energy_prediction, energy_embedding = self.get_energy_embedding(
+                x, energy_target, src_mask, e_control
+            )
+            x = x + energy_embedding
+
+        if duration_target is not None:
+            x, mel_len = self.length_regulator(x, duration_target, max_len)
+            duration_rounded = duration_target
+        else:
+            duration_rounded = torch.clamp(
+                (torch.round(torch.exp(log_duration_prediction) - 1) * d_control),
+                min=0,
+            )
+            x, mel_len = self.length_regulator(x, duration_rounded, max_len)
+            mel_mask = get_mask_from_lengths(mel_len)
+
+        if self.pitch_feature_level == "frame_level":
+            pitch_prediction, pitch_embedding = self.get_pitch_embedding(
+                x, pitch_target, mel_mask, p_control
+            )
+            x = x + pitch_embedding
+        if self.energy_feature_level == "frame_level":
+            energy_prediction, energy_embedding = self.get_energy_embedding(
+                x, energy_target, mel_mask, p_control
+            )
+            x = x + energy_embedding
+
+        return (
+            x,
+            pitch_prediction,
+            energy_prediction,
+            log_duration_prediction,
+            duration_rounded,
+            mel_len,
+            mel_mask,
+        )
+
+
+class LengthRegulator(nn.Module):
+    """Length Regulator"""
+
+    def __init__(self):
+        super(LengthRegulator, self).__init__()
+
+    def LR(self, x, duration, max_len):
+        device = x.device
+        output = list()
+        mel_len = list()
+        for batch, expand_target in zip(x, duration):
+            expanded = self.expand(batch, expand_target)
+            output.append(expanded)
+            mel_len.append(expanded.shape[0])
+
+        if max_len is not None:
+            output = pad(output, max_len)
+        else:
+            output = pad(output)
+
+        return output, torch.LongTensor(mel_len).to(device)
+
+    def expand(self, batch, predicted):
+        out = list()
+
+        for i, vec in enumerate(batch):
+            expand_size = predicted[i].item()
+            out.append(vec.expand(max(int(expand_size), 0), -1))
+        out = torch.cat(out, 0)
+
+        return out
+
+    def forward(self, x, duration, max_len):
+        output, mel_len = self.LR(x, duration, max_len)
+        return output, mel_len
+
+
+class VariancePredictor(nn.Module):
+    """Duration, Pitch and Energy Predictor"""
+
+    def __init__(self, cfg):
+        super(VariancePredictor, self).__init__()
+
+        self.input_size = cfg.model.transformer.encoder_hidden
+        self.filter_size = cfg.model.variance_predictor.filter_size
+        self.kernel = cfg.model.variance_predictor.kernel_size
+        self.conv_output_size = cfg.model.variance_predictor.filter_size
+        self.dropout = cfg.model.variance_predictor.dropout
+
+        self.conv_layer = nn.Sequential(
+            OrderedDict(
+                [
+                    (
+                        "conv1d_1",
+                        Conv(
+                            self.input_size,
+                            self.filter_size,
+                            kernel_size=self.kernel,
+                            padding=(self.kernel - 1) // 2,
+                        ),
+                    ),
+                    ("relu_1", nn.ReLU()),
+                    ("layer_norm_1", nn.LayerNorm(self.filter_size)),
+                    ("dropout_1", nn.Dropout(self.dropout)),
+                    (
+                        "conv1d_2",
+                        Conv(
+                            self.filter_size,
+                            self.filter_size,
+                            kernel_size=self.kernel,
+                            padding=1,
+                        ),
+                    ),
+                    ("relu_2", nn.ReLU()),
+                    ("layer_norm_2", nn.LayerNorm(self.filter_size)),
+                    ("dropout_2", nn.Dropout(self.dropout)),
+                ]
+            )
+        )
+
+        self.linear_layer = nn.Linear(self.conv_output_size, 1)
+
+    def forward(self, encoder_output, mask):
+        out = self.conv_layer(encoder_output)
+        out = self.linear_layer(out)
+        out = out.squeeze(-1)
+
+        if mask is not None:
+            out = out.masked_fill(mask, 0.0)
+
+        return out
+
+
+class Conv(nn.Module):
+    """
+    Convolution Module
+    """
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size=1,
+        stride=1,
+        padding=0,
+        dilation=1,
+        bias=True,
+        w_init="linear",
+    ):
+        """
+        :param in_channels: dimension of input
+        :param out_channels: dimension of output
+        :param kernel_size: size of kernel
+        :param stride: size of stride
+        :param padding: size of padding
+        :param dilation: dilation rate
+        :param bias: boolean. if True, bias is included.
+        :param w_init: str. weight inits with xavier initialization.
+        """
+        super(Conv, self).__init__()
+
+        self.conv = nn.Conv1d(
+            in_channels,
+            out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            bias=bias,
+        )
+
+    def forward(self, x):
+        x = x.contiguous().transpose(1, 2)
+        x = self.conv(x)
+        x = x.contiguous().transpose(1, 2)
+
+        return x
+
+
+class FastSpeech2(nn.Module):
+    def __init__(self, cfg) -> None:
+        super(FastSpeech2, self).__init__()
+        self.cfg = cfg
+        self.encoder = Encoder(cfg.model)
+        self.variance_adaptor = VarianceAdaptor(cfg)
+        self.decoder = Decoder(cfg.model)
+        self.mel_linear = nn.Linear(
+            cfg.model.transformer.decoder_hidden,
+            cfg.preprocess.n_mel,
+        )
+        self.postnet = PostNet(n_mel_channels=cfg.preprocess.n_mel)
+
+        self.speaker_emb = None
+        if cfg.train.multi_speaker_training:
+            with open(
+                os.path.join(
+                    cfg.preprocess.processed_dir, cfg.dataset[0], "spk2id.json"
+                ),
+                "r",
+            ) as f:
+                n_speaker = len(json.load(f))
+            self.speaker_emb = nn.Embedding(
+                n_speaker,
+                cfg.model.transformer.encoder_hidden,
+            )
+
+    def forward(self, data, p_control=1.0, e_control=1.0, d_control=1.0):
+        speakers = data["spk_id"]
+        texts = data["texts"]
+        src_lens = data["text_len"]
+        max_src_len = max(src_lens)
+        mel_lens = data["target_len"] if "target_len" in data else None
+        max_mel_len = max(mel_lens) if "target_len" in data else None
+        p_targets = data["pitch"] if "pitch" in data else None
+        e_targets = data["energy"] if "energy" in data else None
+        d_targets = data["durations"] if "durations" in data else None
+        src_masks = get_mask_from_lengths(src_lens, max_src_len)
+        mel_masks = (
+            get_mask_from_lengths(mel_lens, max_mel_len)
+            if mel_lens is not None
+            else None
+        )
+
+        output = self.encoder(texts, src_masks)
+
+        if self.speaker_emb is not None:
+            output = output + self.speaker_emb(speakers).unsqueeze(1).expand(
+                -1, max_src_len, -1
+            )
+
+        (
+            output,
+            p_predictions,
+            e_predictions,
+            log_d_predictions,
+            d_rounded,
+            mel_lens,
+            mel_masks,
+        ) = self.variance_adaptor(
+            output,
+            src_masks,
+            mel_masks,
+            max_mel_len,
+            p_targets,
+            e_targets,
+            d_targets,
+            p_control,
+            e_control,
+            d_control,
+        )
+
+        output, mel_masks = self.decoder(output, mel_masks)
+        output = self.mel_linear(output)
+
+        postnet_output = self.postnet(output) + output
+
+        return {
+            "output": output,
+            "postnet_output": postnet_output,
+            "p_predictions": p_predictions,
+            "e_predictions": e_predictions,
+            "log_d_predictions": log_d_predictions,
+            "d_rounded": d_rounded,
+            "src_masks": src_masks,
+            "mel_masks": mel_masks,
+            "src_lens": src_lens,
+            "mel_lens": mel_lens,
+        }
+
+
+class FastSpeech2Loss(nn.Module):
+    """FastSpeech2 Loss"""
+
+    def __init__(self, cfg):
+        super(FastSpeech2Loss, self).__init__()
+        if cfg.preprocess.use_frame_pitch:
+            self.pitch_feature_level = "frame_level"
+        else:
+            self.pitch_feature_level = "phoneme_level"
+
+        if cfg.preprocess.use_frame_energy:
+            self.energy_feature_level = "frame_level"
+        else:
+            self.energy_feature_level = "phoneme_level"
+
+        self.mse_loss = nn.MSELoss()
+        self.mae_loss = nn.L1Loss()
+
+    def forward(self, data, predictions):
+        mel_targets = data["mel"]
+        pitch_targets = data["pitch"].float()
+        energy_targets = data["energy"].float()
+        duration_targets = data["durations"]
+
+        mel_predictions = predictions["output"]
+        postnet_mel_predictions = predictions["postnet_output"]
+        pitch_predictions = predictions["p_predictions"]
+        energy_predictions = predictions["e_predictions"]
+        log_duration_predictions = predictions["log_d_predictions"]
+        src_masks = predictions["src_masks"]
+        mel_masks = predictions["mel_masks"]
+
+        src_masks = ~src_masks
+        mel_masks = ~mel_masks
+
+        log_duration_targets = torch.log(duration_targets.float() + 1)
+        mel_targets = mel_targets[:, : mel_masks.shape[1], :]
+        mel_masks = mel_masks[:, : mel_masks.shape[1]]
+
+        log_duration_targets.requires_grad = False
+        pitch_targets.requires_grad = False
+        energy_targets.requires_grad = False
+        mel_targets.requires_grad = False
+
+        if self.pitch_feature_level == "phoneme_level":
+            pitch_predictions = pitch_predictions.masked_select(src_masks)
+            pitch_targets = pitch_targets.masked_select(src_masks)
+        elif self.pitch_feature_level == "frame_level":
+            pitch_predictions = pitch_predictions.masked_select(mel_masks)
+            pitch_targets = pitch_targets.masked_select(mel_masks)
+
+        if self.energy_feature_level == "phoneme_level":
+            energy_predictions = energy_predictions.masked_select(src_masks)
+            energy_targets = energy_targets.masked_select(src_masks)
+        if self.energy_feature_level == "frame_level":
+            energy_predictions = energy_predictions.masked_select(mel_masks)
+            energy_targets = energy_targets.masked_select(mel_masks)
+
+        log_duration_predictions = log_duration_predictions.masked_select(src_masks)
+        log_duration_targets = log_duration_targets.masked_select(src_masks)
+
+        mel_predictions = mel_predictions.masked_select(mel_masks.unsqueeze(-1))
+        postnet_mel_predictions = postnet_mel_predictions.masked_select(
+            mel_masks.unsqueeze(-1)
+        )
+        mel_targets = mel_targets.masked_select(mel_masks.unsqueeze(-1))
+
+        mel_loss = self.mae_loss(mel_predictions, mel_targets)
+        postnet_mel_loss = self.mae_loss(postnet_mel_predictions, mel_targets)
+
+        pitch_loss = self.mse_loss(pitch_predictions, pitch_targets)
+        energy_loss = self.mse_loss(energy_predictions, energy_targets)
+        duration_loss = self.mse_loss(log_duration_predictions, log_duration_targets)
+
+        total_loss = (
+            mel_loss + postnet_mel_loss + duration_loss + pitch_loss + energy_loss
+        )
+
+        return {
+            "loss": total_loss,
+            "mel_loss": mel_loss,
+            "postnet_mel_loss": postnet_mel_loss,
+            "pitch_loss": pitch_loss,
+            "energy_loss": energy_loss,
+            "duration_loss": duration_loss,
+        }
diff --git a/models/tts/fastspeech2/fs2_dataset.py b/models/tts/fastspeech2/fs2_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d019aee76766d19ed6b72b2e3345b36d84ed04d
--- /dev/null
+++ b/models/tts/fastspeech2/fs2_dataset.py
@@ -0,0 +1,424 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import random
+import torch
+from torch.nn.utils.rnn import pad_sequence
+from utils.data_utils import *
+from models.base.base_dataset import (
+    BaseCollator,
+    BaseDataset,
+    BaseTestDataset,
+    BaseTestCollator,
+)
+from text import text_to_sequence
+
+
+class FS2Dataset(BaseDataset):
+    def __init__(self, cfg, dataset, is_valid=False):
+        BaseDataset.__init__(self, cfg, dataset, is_valid=is_valid)
+        self.batch_size = cfg.train.batch_size
+        cfg = cfg.preprocess
+        # utt2duration
+        self.utt2duration_path = {}
+        for utt_info in self.metadata:
+            dataset = utt_info["Dataset"]
+            uid = utt_info["Uid"]
+            utt = "{}_{}".format(dataset, uid)
+
+            self.utt2duration_path[utt] = os.path.join(
+                cfg.processed_dir,
+                dataset,
+                cfg.duration_dir,
+                uid + ".npy",
+            )
+        self.utt2dur = self.read_duration()
+
+        if cfg.use_frame_energy:
+            self.frame_utt2energy, self.energy_statistic = load_energy(
+                self.metadata,
+                cfg.processed_dir,
+                cfg.energy_dir,
+                use_log_scale=cfg.use_log_scale_energy,
+                utt2spk=self.preprocess.utt2spk if cfg.use_spkid else None,
+                return_norm=True,
+            )
+        elif cfg.use_phone_energy:
+            self.phone_utt2energy, self.energy_statistic = load_energy(
+                self.metadata,
+                cfg.processed_dir,
+                cfg.phone_energy_dir,
+                use_log_scale=cfg.use_log_scale_energy,
+                utt2spk=self.utt2spk if cfg.use_spkid else None,
+                return_norm=True,
+            )
+
+        if cfg.use_frame_pitch:
+            self.frame_utt2pitch, self.pitch_statistic = load_energy(
+                self.metadata,
+                cfg.processed_dir,
+                cfg.pitch_dir,
+                use_log_scale=cfg.energy_extract_mode,
+                utt2spk=self.utt2spk if cfg.use_spkid else None,
+                return_norm=True,
+            )
+
+        elif cfg.use_phone_pitch:
+            self.phone_utt2pitch, self.pitch_statistic = load_energy(
+                self.metadata,
+                cfg.processed_dir,
+                cfg.phone_pitch_dir,
+                use_log_scale=cfg.use_log_scale_pitch,
+                utt2spk=self.utt2spk if cfg.use_spkid else None,
+                return_norm=True,
+            )
+
+        # utt2lab
+        self.utt2lab_path = {}
+        for utt_info in self.metadata:
+            dataset = utt_info["Dataset"]
+            uid = utt_info["Uid"]
+            utt = "{}_{}".format(dataset, uid)
+
+            self.utt2lab_path[utt] = os.path.join(
+                cfg.processed_dir,
+                dataset,
+                cfg.lab_dir,
+                uid + ".txt",
+            )
+
+        self.speaker_map = {}
+        if os.path.exists(os.path.join(cfg.processed_dir, "spk2id.json")):
+            with open(
+                os.path.exists(os.path.join(cfg.processed_dir, "spk2id.json"))
+            ) as f:
+                self.speaker_map = json.load(f)
+
+        self.metadata = self.check_metadata()
+
+    def __getitem__(self, index):
+        single_feature = BaseDataset.__getitem__(self, index)
+
+        utt_info = self.metadata[index]
+        dataset = utt_info["Dataset"]
+        uid = utt_info["Uid"]
+        utt = "{}_{}".format(dataset, uid)
+
+        duration = self.utt2dur[utt]
+
+        # text
+        f = open(self.utt2lab_path[utt], "r")
+        phones = f.readlines()[0].strip()
+        f.close()
+        # todo: add cleaner(chenxi)
+        phones_ids = np.array(text_to_sequence(phones, ["english_cleaners"]))
+        text_len = len(phones_ids)
+
+        if self.cfg.preprocess.use_frame_pitch:
+            pitch = self.frame_utt2pitch[utt]
+        elif self.cfg.preprocess.use_phone_pitch:
+            pitch = self.phone_utt2pitch[utt]
+
+        if self.cfg.preprocess.use_frame_energy:
+            energy = self.frame_utt2energy[utt]
+        elif self.cfg.preprocess.use_phone_energy:
+            energy = self.phone_utt2energy[utt]
+
+        # speaker
+        if len(self.speaker_map) > 0:
+            speaker_id = self.speaker_map[utt_info["Singer"]]
+        else:
+            speaker_id = 0
+
+        single_feature.update(
+            {
+                "durations": duration,
+                "texts": phones_ids,
+                "spk_id": speaker_id,
+                "text_len": text_len,
+                "pitch": pitch,
+                "energy": energy,
+                "uid": uid,
+            }
+        )
+        return self.clip_if_too_long(single_feature)
+
+    def read_duration(self):
+        # read duration
+        utt2dur = {}
+        for index in range(len(self.metadata)):
+            utt_info = self.metadata[index]
+            dataset = utt_info["Dataset"]
+            uid = utt_info["Uid"]
+            utt = "{}_{}".format(dataset, uid)
+
+            if not os.path.exists(self.utt2mel_path[utt]) or not os.path.exists(
+                self.utt2duration_path[utt]
+            ):
+                continue
+
+            mel = np.load(self.utt2mel_path[utt]).transpose(1, 0)
+            duration = np.load(self.utt2duration_path[utt])
+            assert mel.shape[0] == sum(
+                duration
+            ), f"{utt}: mismatch length between mel {mel.shape[0]} and sum(duration) {sum(duration)}"
+            utt2dur[utt] = duration
+        return utt2dur
+
+    def __len__(self):
+        return len(self.metadata)
+
+    def random_select(self, feature_seq_len, max_seq_len, ending_ts=2812):
+        """
+        ending_ts: to avoid invalid whisper features for over 30s audios
+            2812 = 30 * 24000 // 256
+        """
+        ts = max(feature_seq_len - max_seq_len, 0)
+        ts = min(ts, ending_ts - max_seq_len)
+
+        start = random.randint(0, ts)
+        end = start + max_seq_len
+        return start, end
+
+    def clip_if_too_long(self, sample, max_seq_len=1000):
+        """
+        sample :
+            {
+                'spk_id': (1,),
+                'target_len': int
+                'mel': (seq_len, dim),
+                'frame_pitch': (seq_len,)
+                'frame_energy': (seq_len,)
+                'content_vector_feat': (seq_len, dim)
+            }
+        """
+        if sample["target_len"] <= max_seq_len:
+            return sample
+
+        start, end = self.random_select(sample["target_len"], max_seq_len)
+        sample["target_len"] = end - start
+
+        for k in sample.keys():
+            if k not in ["spk_id", "target_len"]:
+                sample[k] = sample[k][start:end]
+
+        return sample
+
+    def check_metadata(self):
+        new_metadata = []
+        for utt_info in self.metadata:
+            dataset = utt_info["Dataset"]
+            uid = utt_info["Uid"]
+            utt = "{}_{}".format(dataset, uid)
+            if not os.path.exists(self.utt2duration_path[utt]) or not os.path.exists(
+                self.utt2mel_path[utt]
+            ):
+                continue
+            else:
+                new_metadata.append(utt_info)
+        return new_metadata
+
+
+class FS2Collator(BaseCollator):
+    """Zero-pads model inputs and targets based on number of frames per step"""
+
+    def __init__(self, cfg):
+        BaseCollator.__init__(self, cfg)
+        self.sort = cfg.train.sort_sample
+        self.batch_size = cfg.train.batch_size
+        self.drop_last = cfg.train.drop_last
+
+    def __call__(self, batch):
+        # mel: [b, T, n_mels]
+        # frame_pitch, frame_energy: [1, T]
+        # target_len: [1]
+        # spk_id: [b, 1]
+        # mask: [b, T, 1]
+        packed_batch_features = dict()
+
+        for key in batch[0].keys():
+            if key == "target_len":
+                packed_batch_features["target_len"] = torch.LongTensor(
+                    [b["target_len"] for b in batch]
+                )
+                masks = [
+                    torch.ones((b["target_len"], 1), dtype=torch.long) for b in batch
+                ]
+                packed_batch_features["mask"] = pad_sequence(
+                    masks, batch_first=True, padding_value=0
+                )
+            elif key == "text_len":
+                packed_batch_features["text_len"] = torch.LongTensor(
+                    [b["text_len"] for b in batch]
+                )
+                masks = [
+                    torch.ones((b["text_len"], 1), dtype=torch.long) for b in batch
+                ]
+                packed_batch_features["text_mask"] = pad_sequence(
+                    masks, batch_first=True, padding_value=0
+                )
+            elif key == "spk_id":
+                packed_batch_features["spk_id"] = torch.LongTensor(
+                    [b["spk_id"] for b in batch]
+                )
+            elif key == "uid":
+                packed_batch_features[key] = [b["uid"] for b in batch]
+            else:
+                values = [torch.from_numpy(b[key]) for b in batch]
+                packed_batch_features[key] = pad_sequence(
+                    values, batch_first=True, padding_value=0
+                )
+        return packed_batch_features
+
+
+class FS2TestDataset(BaseTestDataset):
+    def __init__(self, args, cfg, infer_type=None):
+        datasets = cfg.dataset
+        cfg = cfg.preprocess
+        is_bigdata = False
+
+        assert len(datasets) >= 1
+        if len(datasets) > 1:
+            datasets.sort()
+            bigdata_version = "_".join(datasets)
+            processed_data_dir = os.path.join(cfg.processed_dir, bigdata_version)
+            is_bigdata = True
+        else:
+            processed_data_dir = os.path.join(cfg.processed_dir, args.dataset)
+
+        if args.test_list_file:
+            self.metafile_path = args.test_list_file
+            self.metadata = self.get_metadata()
+        else:
+            assert args.testing_set
+            source_metafile_path = os.path.join(
+                cfg.processed_dir,
+                args.dataset,
+                "{}.json".format(args.testing_set),
+            )
+            with open(source_metafile_path, "r") as f:
+                self.metadata = json.load(f)
+
+        self.cfg = cfg
+        self.datasets = datasets
+        self.data_root = processed_data_dir
+        self.is_bigdata = is_bigdata
+        self.source_dataset = args.dataset
+
+        ######### Load source acoustic features #########
+        if cfg.use_spkid:
+            spk2id_path = os.path.join(self.data_root, cfg.spk2id)
+            utt2sp_path = os.path.join(self.data_root, cfg.utt2spk)
+            self.spk2id, self.utt2spk = get_spk_map(spk2id_path, utt2sp_path, datasets)
+
+        # utt2lab
+        self.utt2lab_path = {}
+        for utt_info in self.metadata:
+            dataset = utt_info["Dataset"]
+            uid = utt_info["Uid"]
+            utt = "{}_{}".format(dataset, uid)
+            self.utt2lab_path[utt] = os.path.join(
+                cfg.processed_dir,
+                dataset,
+                cfg.lab_dir,
+                uid + ".txt",
+            )
+
+        self.speaker_map = {}
+        if os.path.exists(os.path.join(cfg.processed_dir, "spk2id.json")):
+            with open(
+                os.path.exists(os.path.join(cfg.processed_dir, "spk2id.json"))
+            ) as f:
+                self.speaker_map = json.load(f)
+
+    def __getitem__(self, index):
+        single_feature = {}
+
+        utt_info = self.metadata[index]
+        dataset = utt_info["Dataset"]
+        uid = utt_info["Uid"]
+        utt = "{}_{}".format(dataset, uid)
+
+        # text
+        f = open(self.utt2lab_path[utt], "r")
+        phones = f.readlines()[0].strip()
+        f.close()
+
+        phones_ids = np.array(text_to_sequence(phones, self.cfg.text_cleaners))
+        text_len = len(phones_ids)
+
+        # speaker
+        if len(self.speaker_map) > 0:
+            speaker_id = self.speaker_map[utt_info["Singer"]]
+        else:
+            speaker_id = 0
+
+        single_feature.update(
+            {
+                "texts": phones_ids,
+                "spk_id": speaker_id,
+                "text_len": text_len,
+            }
+        )
+
+        return single_feature
+
+    def __len__(self):
+        return len(self.metadata)
+
+    def get_metadata(self):
+        with open(self.metafile_path, "r", encoding="utf-8") as f:
+            metadata = json.load(f)
+
+        return metadata
+
+
+class FS2TestCollator(BaseTestCollator):
+    """Zero-pads model inputs and targets based on number of frames per step"""
+
+    def __init__(self, cfg):
+        self.cfg = cfg
+
+    def __call__(self, batch):
+        packed_batch_features = dict()
+
+        # mel: [b, T, n_mels]
+        # frame_pitch, frame_energy: [1, T]
+        # target_len: [1]
+        # spk_id: [b, 1]
+        # mask: [b, T, 1]
+
+        for key in batch[0].keys():
+            if key == "target_len":
+                packed_batch_features["target_len"] = torch.LongTensor(
+                    [b["target_len"] for b in batch]
+                )
+                masks = [
+                    torch.ones((b["target_len"], 1), dtype=torch.long) for b in batch
+                ]
+                packed_batch_features["mask"] = pad_sequence(
+                    masks, batch_first=True, padding_value=0
+                )
+            elif key == "text_len":
+                packed_batch_features["text_len"] = torch.LongTensor(
+                    [b["text_len"] for b in batch]
+                )
+                masks = [
+                    torch.ones((b["text_len"], 1), dtype=torch.long) for b in batch
+                ]
+                packed_batch_features["text_mask"] = pad_sequence(
+                    masks, batch_first=True, padding_value=0
+                )
+            elif key == "spk_id":
+                packed_batch_features["spk_id"] = torch.LongTensor(
+                    [b["spk_id"] for b in batch]
+                )
+            else:
+                values = [torch.from_numpy(b[key]) for b in batch]
+                packed_batch_features[key] = pad_sequence(
+                    values, batch_first=True, padding_value=0
+                )
+
+        return packed_batch_features
diff --git a/models/tts/fastspeech2/fs2_inference.py b/models/tts/fastspeech2/fs2_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..64d1d387e11209378905ae65f3dd6fd11faaa44e
--- /dev/null
+++ b/models/tts/fastspeech2/fs2_inference.py
@@ -0,0 +1,192 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import torch
+from tqdm import tqdm
+from collections import OrderedDict
+
+from models.tts.base.tts_inferece import TTSInference
+from models.tts.fastspeech2.fs2_dataset import FS2TestDataset, FS2TestCollator
+from utils.util import load_config
+from utils.io import save_audio
+from models.tts.fastspeech2.fs2 import FastSpeech2
+from models.vocoders.vocoder_inference import synthesis
+from pathlib import Path
+from processors.phone_extractor import phoneExtractor
+from text.text_token_collation import phoneIDCollation
+import numpy as np
+import json
+
+
+class FastSpeech2Inference(TTSInference):
+    def __init__(self, args, cfg):
+        TTSInference.__init__(self, args, cfg)
+        self.args = args
+        self.cfg = cfg
+        self.infer_type = args.mode
+
+    def _build_model(self):
+        self.model = FastSpeech2(self.cfg)
+        return self.model
+
+    def load_model(self, state_dict):
+        raw_dict = state_dict["model"]
+        clean_dict = OrderedDict()
+        for k, v in raw_dict.items():
+            if k.startswith("module."):
+                clean_dict[k[7:]] = v
+            else:
+                clean_dict[k] = v
+
+        self.model.load_state_dict(clean_dict)
+
+    def _build_test_dataset(self):
+        return FS2TestDataset, FS2TestCollator
+
+    @staticmethod
+    def _parse_vocoder(vocoder_dir):
+        r"""Parse vocoder config"""
+        vocoder_dir = os.path.abspath(vocoder_dir)
+        ckpt_list = [ckpt for ckpt in Path(vocoder_dir).glob("*.pt")]
+        # last step (different from the base *int(x.stem)*)
+        ckpt_list.sort(
+            key=lambda x: int(x.stem.split("_")[-2].split("-")[-1]), reverse=True
+        )
+        ckpt_path = str(ckpt_list[0])
+        vocoder_cfg = load_config(
+            os.path.join(vocoder_dir, "args.json"), lowercase=True
+        )
+        return vocoder_cfg, ckpt_path
+
+    @torch.inference_mode()
+    def inference_for_batches(self):
+        y_pred = []
+        for i, batch in tqdm(enumerate(self.test_dataloader)):
+            y_pred, mel_lens, _ = self._inference_each_batch(batch)
+            y_ls = y_pred.chunk(self.test_batch_size)
+            tgt_ls = mel_lens.chunk(self.test_batch_size)
+            j = 0
+            for it, l in zip(y_ls, tgt_ls):
+                l = l.item()
+                it = it.squeeze(0)[:l].detach().cpu()
+
+                uid = self.test_dataset.metadata[i * self.test_batch_size + j]["Uid"]
+                torch.save(it, os.path.join(self.args.output_dir, f"{uid}.pt"))
+                j += 1
+
+        vocoder_cfg, vocoder_ckpt = self._parse_vocoder(self.args.vocoder_dir)
+        res = synthesis(
+            cfg=vocoder_cfg,
+            vocoder_weight_file=vocoder_ckpt,
+            n_samples=None,
+            pred=[
+                torch.load(
+                    os.path.join(self.args.output_dir, "{}.pt".format(item["Uid"]))
+                ).numpy()
+                for item in self.test_dataset.metadata
+            ],
+        )
+        for it, wav in zip(self.test_dataset.metadata, res):
+            uid = it["Uid"]
+            save_audio(
+                os.path.join(self.args.output_dir, f"{uid}.wav"),
+                wav.numpy(),
+                self.cfg.preprocess.sample_rate,
+                add_silence=True,
+                turn_up=True,
+            )
+            os.remove(os.path.join(self.args.output_dir, f"{uid}.pt"))
+
+    def _inference_each_batch(self, batch_data):
+        device = self.accelerator.device
+        control_values = (
+            self.args.pitch_control,
+            self.args.energy_control,
+            self.args.duration_control,
+        )
+        for k, v in batch_data.items():
+            batch_data[k] = v.to(device)
+
+        pitch_control, energy_control, duration_control = control_values
+
+        output = self.model(
+            batch_data,
+            p_control=pitch_control,
+            e_control=energy_control,
+            d_control=duration_control,
+        )
+        pred_res = output["postnet_output"]
+        mel_lens = output["mel_lens"].cpu()
+        return pred_res, mel_lens, 0
+
+    def inference_for_single_utterance(self):
+        text = self.args.text
+        control_values = (
+            self.args.pitch_control,
+            self.args.energy_control,
+            self.args.duration_control,
+        )
+        pitch_control, energy_control, duration_control = control_values
+
+        # get phone symbol file
+        phone_symbol_file = None
+        if self.cfg.preprocess.phone_extractor != "lexicon":
+            phone_symbol_file = os.path.join(
+                self.exp_dir, self.cfg.preprocess.symbols_dict
+            )
+            assert os.path.exists(phone_symbol_file)
+        # convert text to phone sequence
+        phone_extractor = phoneExtractor(self.cfg)
+
+        phone_seq = phone_extractor.extract_phone(text)  # phone_seq: list
+        # convert phone sequence to phone id sequence
+        phon_id_collator = phoneIDCollation(
+            self.cfg, symbols_dict_file=phone_symbol_file
+        )
+        phone_seq = ["{"] + phone_seq + ["}"]
+        phone_id_seq = phon_id_collator.get_phone_id_sequence(self.cfg, phone_seq)
+
+        # convert phone sequence to phone id sequence
+        phone_id_seq = np.array(phone_id_seq)
+        phone_id_seq = torch.from_numpy(phone_id_seq)
+
+        # get speaker id if multi-speaker training and use speaker id
+        speaker_id = None
+        if self.cfg.preprocess.use_spkid and self.cfg.train.multi_speaker_training:
+            spk2id_file = os.path.join(self.exp_dir, self.cfg.preprocess.spk2id)
+            with open(spk2id_file, "r") as f:
+                spk2id = json.load(f)
+                speaker_id = spk2id[self.args.speaker_name]
+                speaker_id = torch.from_numpy(np.array([speaker_id], dtype=np.int32))
+        else:
+            speaker_id = torch.Tensor(0).view(-1)
+
+        with torch.no_grad():
+            x_tst = phone_id_seq.to(self.device).unsqueeze(0)
+            x_tst_lengths = torch.LongTensor([phone_id_seq.size(0)]).to(self.device)
+            if speaker_id is not None:
+                speaker_id = speaker_id.to(self.device)
+
+            data = {}
+            data["texts"] = x_tst
+            data["text_len"] = x_tst_lengths
+            data["spk_id"] = speaker_id
+
+            output = self.model(
+                data,
+                p_control=pitch_control,
+                e_control=energy_control,
+                d_control=duration_control,
+            )
+            pred_res = output["postnet_output"]
+            vocoder_cfg, vocoder_ckpt = self._parse_vocoder(self.args.vocoder_dir)
+            audio = synthesis(
+                cfg=vocoder_cfg,
+                vocoder_weight_file=vocoder_ckpt,
+                n_samples=None,
+                pred=pred_res,
+            )
+        return audio[0]
diff --git a/models/tts/fastspeech2/fs2_trainer.py b/models/tts/fastspeech2/fs2_trainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..f0bc1201b192e30339c8f6e877467cc6b1f7a938
--- /dev/null
+++ b/models/tts/fastspeech2/fs2_trainer.py
@@ -0,0 +1,155 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+from tqdm import tqdm
+from models.tts.base import TTSTrainer
+from models.tts.fastspeech2.fs2 import FastSpeech2, FastSpeech2Loss
+from models.tts.fastspeech2.fs2_dataset import FS2Dataset, FS2Collator
+from optimizer.optimizers import NoamLR
+
+
+class FastSpeech2Trainer(TTSTrainer):
+    def __init__(self, args, cfg):
+        TTSTrainer.__init__(self, args, cfg)
+        self.cfg = cfg
+
+    def _build_dataset(self):
+        return FS2Dataset, FS2Collator
+
+    def __build_scheduler(self):
+        return NoamLR(self.optimizer, **self.cfg.train.lr_scheduler)
+
+    def _write_summary(self, losses, stats):
+        for key, value in losses.items():
+            self.sw.add_scalar("train/" + key, value, self.step)
+        lr = self.optimizer.state_dict()["param_groups"][0]["lr"]
+        self.sw.add_scalar("learning_rate", lr, self.step)
+
+    def _write_valid_summary(self, losses, stats):
+        for key, value in losses.items():
+            self.sw.add_scalar("val/" + key, value, self.step)
+
+    def _build_criterion(self):
+        return FastSpeech2Loss(self.cfg)
+
+    def get_state_dict(self):
+        state_dict = {
+            "model": self.model.state_dict(),
+            "optimizer": self.optimizer.state_dict(),
+            "scheduler": self.scheduler.state_dict(),
+            "step": self.step,
+            "epoch": self.epoch,
+            "batch_size": self.cfg.train.batch_size,
+        }
+        return state_dict
+
+    def _build_optimizer(self):
+        optimizer = torch.optim.Adam(self.model.parameters(), **self.cfg.train.adam)
+        return optimizer
+
+    def _build_scheduler(self):
+        scheduler = NoamLR(self.optimizer, **self.cfg.train.lr_scheduler)
+        return scheduler
+
+    def _build_model(self):
+        self.model = FastSpeech2(self.cfg)
+        return self.model
+
+    def _train_epoch(self):
+        r"""Training epoch. Should return average loss of a batch (sample) over
+        one epoch. See ``train_loop`` for usage.
+        """
+        self.model.train()
+        epoch_sum_loss: float = 0.0
+        epoch_step: int = 0
+        epoch_losses: dict = {}
+        for batch in tqdm(
+            self.train_dataloader,
+            desc=f"Training Epoch {self.epoch}",
+            unit="batch",
+            colour="GREEN",
+            leave=False,
+            dynamic_ncols=True,
+            smoothing=0.04,
+            disable=not self.accelerator.is_main_process,
+        ):
+            # Do training step and BP
+            with self.accelerator.accumulate(self.model):
+                loss, train_losses = self._train_step(batch)
+                self.accelerator.backward(loss)
+                grad_clip_thresh = self.cfg.train.grad_clip_thresh
+                nn.utils.clip_grad_norm_(self.model.parameters(), grad_clip_thresh)
+                self.optimizer.step()
+                self.scheduler.step()
+                self.optimizer.zero_grad()
+            self.batch_count += 1
+
+            # Update info for each step
+            if self.batch_count % self.cfg.train.gradient_accumulation_step == 0:
+                epoch_sum_loss += loss
+                for key, value in train_losses.items():
+                    if key not in epoch_losses.keys():
+                        epoch_losses[key] = value
+                    else:
+                        epoch_losses[key] += value
+
+                self.accelerator.log(
+                    {
+                        "Step/Train Loss": loss,
+                        "Step/Learning Rate": self.optimizer.param_groups[0]["lr"],
+                    },
+                    step=self.step,
+                )
+                self.step += 1
+                epoch_step += 1
+
+        self.accelerator.wait_for_everyone()
+
+        epoch_sum_loss = (
+            epoch_sum_loss
+            / len(self.train_dataloader)
+            * self.cfg.train.gradient_accumulation_step
+        )
+
+        for key in epoch_losses.keys():
+            epoch_losses[key] = (
+                epoch_losses[key]
+                / len(self.train_dataloader)
+                * self.cfg.train.gradient_accumulation_step
+            )
+        return epoch_sum_loss, epoch_losses
+
+    def _train_step(self, data):
+        train_losses = {}
+        total_loss = 0
+        train_stats = {}
+
+        preds = self.model(data)
+
+        train_losses = self.criterion(data, preds)
+
+        total_loss = train_losses["loss"]
+        for key, value in train_losses.items():
+            train_losses[key] = value.item()
+
+        return total_loss, train_losses
+
+    @torch.no_grad()
+    def _valid_step(self, data):
+        valid_loss = {}
+        total_valid_loss = 0
+        valid_stats = {}
+
+        preds = self.model(data)
+
+        valid_losses = self.criterion(data, preds)
+
+        total_valid_loss = valid_losses["loss"]
+        for key, value in valid_losses.items():
+            valid_losses[key] = value.item()
+
+        return total_valid_loss, valid_losses, valid_stats
diff --git a/models/tts/naturalspeech2/__init__.py b/models/tts/naturalspeech2/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/models/tts/naturalspeech2/base_trainer.py b/models/tts/naturalspeech2/base_trainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..498f170013438efbabc9c1cc37735eced89f27f7
--- /dev/null
+++ b/models/tts/naturalspeech2/base_trainer.py
@@ -0,0 +1,602 @@
+import json
+import os
+import random
+import shutil
+import torch
+import time
+from abc import abstractmethod
+from pathlib import Path
+import torch
+import torch.nn as nn
+from tqdm import tqdm
+import re
+from utils.util import Logger, ValueWindow
+
+import accelerate
+import json5
+import numpy as np
+from accelerate.logging import get_logger
+from accelerate.utils import ProjectConfiguration
+from torch.utils.data import ConcatDataset, DataLoader
+from accelerate import DistributedDataParallelKwargs
+
+from models.base.base_sampler import build_samplers
+
+
+class TTSTrainer:
+    r"""The base trainer for all TTS models. It inherits from BaseTrainer and implements
+    ``build_criterion``, ``_build_dataset`` and ``_build_singer_lut`` methods. You can inherit from this
+    class, and implement ``_build_model``, ``_forward_step``.
+    """
+
+    def __init__(self, args=None, cfg=None):
+        super().__init__()
+
+        self.args = args
+        self.cfg = cfg
+
+        cfg.exp_name = args.exp_name
+
+        self._init_accelerator()
+        self.accelerator.wait_for_everyone()
+
+        # Init logger
+        with self.accelerator.main_process_first():
+            if self.accelerator.is_main_process:
+                os.makedirs(os.path.join(self.exp_dir, "checkpoint"), exist_ok=True)
+                self.log_file = os.path.join(
+                    os.path.join(self.exp_dir, "checkpoint"), "train.log"
+                )
+                self.logger = Logger(self.log_file, level=self.args.log_level).logger
+
+        self.time_window = ValueWindow(50)
+
+        if self.accelerator.is_main_process:
+            # Log some info
+            self.logger.info("=" * 56)
+            self.logger.info("||\t\t" + "New training process started." + "\t\t||")
+            self.logger.info("=" * 56)
+            self.logger.info("\n")
+            self.logger.debug(f"Using {args.log_level.upper()} logging level.")
+            self.logger.info(f"Experiment name: {args.exp_name}")
+            self.logger.info(f"Experiment directory: {self.exp_dir}")
+
+        self.checkpoint_dir = os.path.join(self.exp_dir, "checkpoint")
+        if self.accelerator.is_main_process:
+            os.makedirs(self.checkpoint_dir, exist_ok=True)
+
+        if self.accelerator.is_main_process:
+            self.logger.debug(f"Checkpoint directory: {self.checkpoint_dir}")
+
+        # init counts
+        self.batch_count: int = 0
+        self.step: int = 0
+        self.epoch: int = 0
+        self.max_epoch = (
+            self.cfg.train.max_epoch if self.cfg.train.max_epoch > 0 else float("inf")
+        )
+        if self.accelerator.is_main_process:
+            self.logger.info(
+                "Max epoch: {}".format(
+                    self.max_epoch if self.max_epoch < float("inf") else "Unlimited"
+                )
+            )
+
+        # Check values
+        if self.accelerator.is_main_process:
+            self._check_basic_configs()
+            # Set runtime configs
+            self.save_checkpoint_stride = self.cfg.train.save_checkpoint_stride
+            self.checkpoints_path = [
+                [] for _ in range(len(self.save_checkpoint_stride))
+            ]
+            self.keep_last = [
+                i if i > 0 else float("inf") for i in self.cfg.train.keep_last
+            ]
+            self.run_eval = self.cfg.train.run_eval
+
+        # set random seed
+        with self.accelerator.main_process_first():
+            start = time.monotonic_ns()
+            self._set_random_seed(self.cfg.train.random_seed)
+            end = time.monotonic_ns()
+            if self.accelerator.is_main_process:
+                self.logger.debug(
+                    f"Setting random seed done in {(end - start) / 1e6:.2f}ms"
+                )
+                self.logger.debug(f"Random seed: {self.cfg.train.random_seed}")
+
+        # setup data_loader
+        with self.accelerator.main_process_first():
+            if self.accelerator.is_main_process:
+                self.logger.info("Building dataset...")
+            start = time.monotonic_ns()
+            self.train_dataloader, self.valid_dataloader = self._build_dataloader()
+            end = time.monotonic_ns()
+            if self.accelerator.is_main_process:
+                self.logger.info(
+                    f"Building dataset done in {(end - start) / 1e6:.2f}ms"
+                )
+
+        # setup model
+        with self.accelerator.main_process_first():
+            if self.accelerator.is_main_process:
+                self.logger.info("Building model...")
+            start = time.monotonic_ns()
+            self.model = self._build_model()
+            end = time.monotonic_ns()
+            if self.accelerator.is_main_process:
+                self.logger.debug(self.model)
+                self.logger.info(f"Building model done in {(end - start) / 1e6:.2f}ms")
+                self.logger.info(
+                    f"Model parameters: {self._count_parameters(self.model)/1e6:.2f}M"
+                )
+
+        # optimizer & scheduler
+        with self.accelerator.main_process_first():
+            if self.accelerator.is_main_process:
+                self.logger.info("Building optimizer and scheduler...")
+            start = time.monotonic_ns()
+            self.optimizer = self._build_optimizer()
+            self.scheduler = self._build_scheduler()
+            end = time.monotonic_ns()
+            if self.accelerator.is_main_process:
+                self.logger.info(
+                    f"Building optimizer and scheduler done in {(end - start) / 1e6:.2f}ms"
+                )
+
+        # accelerate prepare
+        if not self.cfg.train.use_dynamic_batchsize:
+            if self.accelerator.is_main_process:
+                self.logger.info("Initializing accelerate...")
+            start = time.monotonic_ns()
+            (
+                self.train_dataloader,
+                self.valid_dataloader,
+            ) = self.accelerator.prepare(
+                self.train_dataloader,
+                self.valid_dataloader,
+            )
+
+        if isinstance(self.model, dict):
+            for key in self.model.keys():
+                self.model[key] = self.accelerator.prepare(self.model[key])
+        else:
+            self.model = self.accelerator.prepare(self.model)
+
+        if isinstance(self.optimizer, dict):
+            for key in self.optimizer.keys():
+                self.optimizer[key] = self.accelerator.prepare(self.optimizer[key])
+        else:
+            self.optimizer = self.accelerator.prepare(self.optimizer)
+
+        if isinstance(self.scheduler, dict):
+            for key in self.scheduler.keys():
+                self.scheduler[key] = self.accelerator.prepare(self.scheduler[key])
+        else:
+            self.scheduler = self.accelerator.prepare(self.scheduler)
+
+        end = time.monotonic_ns()
+        if self.accelerator.is_main_process:
+            self.logger.info(
+                f"Initializing accelerate done in {(end - start) / 1e6:.2f}ms"
+            )
+
+        # create criterion
+        with self.accelerator.main_process_first():
+            if self.accelerator.is_main_process:
+                self.logger.info("Building criterion...")
+            start = time.monotonic_ns()
+            self.criterion = self._build_criterion()
+            end = time.monotonic_ns()
+            if self.accelerator.is_main_process:
+                self.logger.info(
+                    f"Building criterion done in {(end - start) / 1e6:.2f}ms"
+                )
+
+        # TODO: Resume from ckpt need test/debug
+        with self.accelerator.main_process_first():
+            if args.resume:
+                if self.accelerator.is_main_process:
+                    self.logger.info("Resuming from checkpoint...")
+                start = time.monotonic_ns()
+                ckpt_path = self._load_model(
+                    self.checkpoint_dir,
+                    args.checkpoint_path,
+                    resume_type=args.resume_type,
+                )
+                end = time.monotonic_ns()
+                if self.accelerator.is_main_process:
+                    self.logger.info(
+                        f"Resuming from checkpoint done in {(end - start) / 1e6:.2f}ms"
+                    )
+                self.checkpoints_path = json.load(
+                    open(os.path.join(ckpt_path, "ckpts.json"), "r")
+                )
+
+            self.checkpoint_dir = os.path.join(self.exp_dir, "checkpoint")
+            if self.accelerator.is_main_process:
+                os.makedirs(self.checkpoint_dir, exist_ok=True)
+            if self.accelerator.is_main_process:
+                self.logger.debug(f"Checkpoint directory: {self.checkpoint_dir}")
+
+        # save config file path
+        self.config_save_path = os.path.join(self.exp_dir, "args.json")
+
+        # Only for TTS tasks
+        self.task_type = "TTS"
+        if self.accelerator.is_main_process:
+            self.logger.info("Task type: {}".format(self.task_type))
+
+    def _init_accelerator(self):
+        self.exp_dir = os.path.join(
+            os.path.abspath(self.cfg.log_dir), self.args.exp_name
+        )
+        project_config = ProjectConfiguration(
+            project_dir=self.exp_dir,
+            logging_dir=os.path.join(self.exp_dir, "log"),
+        )
+        # ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
+        self.accelerator = accelerate.Accelerator(
+            gradient_accumulation_steps=self.cfg.train.gradient_accumulation_step,
+            log_with=self.cfg.train.tracker,
+            project_config=project_config,
+            # kwargs_handlers=[ddp_kwargs]
+        )
+        if self.accelerator.is_main_process:
+            os.makedirs(project_config.project_dir, exist_ok=True)
+            os.makedirs(project_config.logging_dir, exist_ok=True)
+        with self.accelerator.main_process_first():
+            self.accelerator.init_trackers(self.args.exp_name)
+
+    ### Following are methods only for TTS tasks ###
+    # TODO: LEGACY CODE, NEED TO BE REFACTORED
+    def _build_dataset(self):
+        pass
+
+    def _build_criterion():
+        pass
+
+    def _build_model(self):
+        pass
+
+    def _build_dataloader(self):
+        pass
+
+    def _build_optimizer(self):
+        pass
+
+    def _build_scheduler(self):
+        pass
+
+    def _load_model(self, checkpoint_dir, checkpoint_path=None, resume_type="resume"):
+        """Load model from checkpoint. If a folder is given, it will
+        load the latest checkpoint in checkpoint_dir. If a path is given
+        it will load the checkpoint specified by checkpoint_path.
+        **Only use this method after** ``accelerator.prepare()``.
+        """
+        if checkpoint_path is None:
+            ls = [str(i) for i in Path(checkpoint_dir).glob("*")]
+            ls.sort(key=lambda x: int(x.split("_")[-3].split("-")[-1]), reverse=True)
+            checkpoint_path = ls[0]
+        if resume_type == "resume":
+            self.accelerator.load_state(checkpoint_path)
+        elif resume_type == "finetune":
+            accelerate.load_checkpoint_and_dispatch(
+                self.accelerator.unwrap_model(self.model),
+                os.path.join(checkpoint_path, "pytorch_model.bin"),
+            )
+            if self.accelerator.is_main_process:
+                self.logger.info("Load model weights for finetune SUCCESS!")
+        else:
+            raise ValueError("Unsupported resume type: {}".format(resume_type))
+        self.epoch = int(checkpoint_path.split("_")[-3].split("-")[-1]) + 1
+        self.step = int(checkpoint_path.split("_")[-2].split("-")[-1]) + 1
+        return checkpoint_path
+
+    ### THIS IS MAIN ENTRY ###
+    def train_loop(self):
+        r"""Training loop. The public entry of training process."""
+        # Wait everyone to prepare before we move on
+        self.accelerator.wait_for_everyone()
+        # dump config file
+        if self.accelerator.is_main_process:
+            self._dump_cfg(self.config_save_path)
+
+        # self.optimizer.zero_grad()
+
+        # Wait to ensure good to go
+        self.accelerator.wait_for_everyone()
+        while self.epoch < self.max_epoch:
+            if self.accelerator.is_main_process:
+                self.logger.info("\n")
+                self.logger.info("-" * 32)
+                self.logger.info("Epoch {}: ".format(self.epoch))
+
+            # Do training & validating epoch
+            train_total_loss, train_losses = self._train_epoch()
+            if isinstance(train_losses, dict):
+                for key, loss in train_losses.items():
+                    if self.accelerator.is_main_process:
+                        self.logger.info("  |- Train/{} Loss: {:.6f}".format(key, loss))
+                    self.accelerator.log(
+                        {"Epoch/Train {} Loss".format(key): loss},
+                        step=self.epoch,
+                    )
+
+            valid_total_loss, valid_losses = self._valid_epoch()
+            if isinstance(valid_losses, dict):
+                for key, loss in valid_losses.items():
+                    if self.accelerator.is_main_process:
+                        self.logger.info("  |- Valid/{} Loss: {:.6f}".format(key, loss))
+                    self.accelerator.log(
+                        {"Epoch/Train {} Loss".format(key): loss},
+                        step=self.epoch,
+                    )
+
+            if self.accelerator.is_main_process:
+                self.logger.info("  |- Train/Loss: {:.6f}".format(train_total_loss))
+                self.logger.info("  |- Valid/Loss: {:.6f}".format(valid_total_loss))
+            self.accelerator.log(
+                {
+                    "Epoch/Train Loss": train_total_loss,
+                    "Epoch/Valid Loss": valid_total_loss,
+                },
+                step=self.epoch,
+            )
+
+            self.accelerator.wait_for_everyone()
+            if isinstance(self.scheduler, dict):
+                for key in self.scheduler.keys():
+                    self.scheduler[key].step()
+            else:
+                self.scheduler.step()
+
+            # Check if hit save_checkpoint_stride and run_eval
+            run_eval = False
+            if self.accelerator.is_main_process:
+                save_checkpoint = False
+                hit_dix = []
+                for i, num in enumerate(self.save_checkpoint_stride):
+                    if self.epoch % num == 0:
+                        save_checkpoint = True
+                        hit_dix.append(i)
+                        run_eval |= self.run_eval[i]
+
+            self.accelerator.wait_for_everyone()
+            if self.accelerator.is_main_process and save_checkpoint:
+                path = os.path.join(
+                    self.checkpoint_dir,
+                    "epoch-{:04d}_step-{:07d}_loss-{:.6f}".format(
+                        self.epoch, self.step, train_total_loss
+                    ),
+                )
+                print("save state......")
+                self.accelerator.save_state(path)
+                print("finish saving state......")
+                json.dump(
+                    self.checkpoints_path,
+                    open(os.path.join(path, "ckpts.json"), "w"),
+                    ensure_ascii=False,
+                    indent=4,
+                )
+                # Remove old checkpoints
+                to_remove = []
+                for idx in hit_dix:
+                    self.checkpoints_path[idx].append(path)
+                    while len(self.checkpoints_path[idx]) > self.keep_last[idx]:
+                        to_remove.append((idx, self.checkpoints_path[idx].pop(0)))
+
+                # Search conflicts
+                total = set()
+                for i in self.checkpoints_path:
+                    total |= set(i)
+                do_remove = set()
+                for idx, path in to_remove[::-1]:
+                    if path in total:
+                        self.checkpoints_path[idx].insert(0, path)
+                    else:
+                        do_remove.add(path)
+
+                # Remove old checkpoints
+                for path in do_remove:
+                    shutil.rmtree(path, ignore_errors=True)
+                    if self.accelerator.is_main_process:
+                        self.logger.debug(f"Remove old checkpoint: {path}")
+
+            self.accelerator.wait_for_everyone()
+            if run_eval:
+                # TODO: run evaluation
+                pass
+
+            # Update info for each epoch
+            self.epoch += 1
+
+        # Finish training and save final checkpoint
+        self.accelerator.wait_for_everyone()
+        if self.accelerator.is_main_process:
+            self.accelerator.save_state(
+                os.path.join(
+                    self.checkpoint_dir,
+                    "final_epoch-{:04d}_step-{:07d}_loss-{:.6f}".format(
+                        self.epoch, self.step, valid_total_loss
+                    ),
+                )
+            )
+        self.accelerator.end_training()
+
+    ### Following are methods that can be used directly in child classes ###
+    def _train_epoch(self):
+        r"""Training epoch. Should return average loss of a batch (sample) over
+        one epoch. See ``train_loop`` for usage.
+        """
+        if isinstance(self.model, dict):
+            for key in self.model.keys():
+                self.model[key].train()
+        else:
+            self.model.train()
+
+        epoch_sum_loss: float = 0.0
+        epoch_losses: dict = {}
+        epoch_step: int = 0
+
+        for batch in self.train_dataloader:
+            # Put the data to cuda device
+            device = self.accelerator.device
+            for k, v in batch.items():
+                if isinstance(v, torch.Tensor):
+                    batch[k] = v.to(device)
+
+            # Do training step and BP
+            with self.accelerator.accumulate(self.model):
+                total_loss, train_losses, training_stats = self._train_step(batch)
+            self.batch_count += 1
+
+            # Update info for each step
+            # TODO: step means BP counts or batch counts?
+            if self.batch_count % self.cfg.train.gradient_accumulation_step == 0:
+                epoch_sum_loss = total_loss
+                for key, value in train_losses.items():
+                    epoch_losses[key] = value
+
+                if isinstance(train_losses, dict):
+                    for key, loss in train_losses.items():
+                        self.accelerator.log(
+                            {"Epoch/Train {} Loss".format(key): loss},
+                            step=self.step,
+                        )
+
+                if (
+                    self.accelerator.is_main_process
+                    and self.batch_count
+                    % (1 * self.cfg.train.gradient_accumulation_step)
+                    == 0
+                ):
+                    self.echo_log(train_losses, mode="Training")
+
+                self.step += 1
+                epoch_step += 1
+
+        self.accelerator.wait_for_everyone()
+
+        return epoch_sum_loss, epoch_losses
+
+    @torch.inference_mode()
+    def _valid_epoch(self):
+        r"""Testing epoch. Should return average loss of a batch (sample) over
+        one epoch. See ``train_loop`` for usage.
+        """
+        if isinstance(self.model, dict):
+            for key in self.model.keys():
+                self.model[key].eval()
+        else:
+            self.model.eval()
+
+        epoch_sum_loss = 0.0
+        epoch_losses = dict()
+
+        for batch in self.valid_dataloader:
+            # Put the data to cuda device
+            device = self.accelerator.device
+            for k, v in batch.items():
+                if isinstance(v, torch.Tensor):
+                    batch[k] = v.to(device)
+
+            total_loss, valid_losses, valid_stats = self._valid_step(batch)
+            epoch_sum_loss = total_loss
+            for key, value in valid_losses.items():
+                epoch_losses[key] = value
+
+        self.accelerator.wait_for_everyone()
+
+        return epoch_sum_loss, epoch_losses
+
+    def _train_step(self):
+        pass
+
+    def _valid_step(self):
+        pass
+
+    def _inference(self):
+        pass
+
+    def _set_random_seed(self, seed):
+        """Set random seed for all possible random modules."""
+        random.seed(seed)
+        np.random.seed(seed)
+        torch.random.manual_seed(seed)
+
+    def _check_nan(self, loss):
+        if torch.any(torch.isnan(loss)):
+            if self.accelerator.is_main_process:
+                self.logger.fatal("Fatal Error: NaN!")
+                self.logger.error("loss = {:.6f}".format(loss.item()), in_order=True)
+
+    def _check_basic_configs(self):
+        if self.cfg.train.gradient_accumulation_step <= 0:
+            if self.accelerator.is_main_process:
+                self.logger.fatal("Invalid gradient_accumulation_step value!")
+                self.logger.error(
+                    f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive."
+                )
+            self.accelerator.end_training()
+            raise ValueError(
+                f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive."
+            )
+
+    @staticmethod
+    def _count_parameters(model):
+        model_param = 0.0
+        if isinstance(model, dict):
+            for key, value in model.items():
+                model_param += sum(p.numel() for p in model[key].parameters())
+        else:
+            model_param = sum(p.numel() for p in model.parameters())
+        return model_param
+
+    def _dump_cfg(self, path):
+        os.makedirs(os.path.dirname(path), exist_ok=True)
+        json5.dump(
+            self.cfg,
+            open(path, "w"),
+            indent=4,
+            sort_keys=True,
+            ensure_ascii=False,
+            quote_keys=True,
+        )
+
+    def _is_valid_pattern(self, directory_name):
+        directory_name = str(directory_name)
+        pattern = r"^epoch-\d{4}_step-\d{7}_loss-\d{1}\.\d{6}"
+        return re.match(pattern, directory_name) is not None
+
+    def _check_basic_configs(self):
+        if self.cfg.train.gradient_accumulation_step <= 0:
+            if self.accelerator.is_main_process:
+                self.logger.fatal("Invalid gradient_accumulation_step value!")
+                self.logger.error(
+                    f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive."
+                )
+            self.accelerator.end_training()
+            raise ValueError(
+                f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive."
+            )
+
+    def echo_log(self, losses, mode="Training"):
+        message = [
+            "{} - Epoch {} Step {}: [{:.3f} s/step]".format(
+                mode, self.epoch + 1, self.step, self.time_window.average
+            )
+        ]
+
+        for key in sorted(losses.keys()):
+            if isinstance(losses[key], dict):
+                for k, v in losses[key].items():
+                    message.append(
+                        str(k).split("/")[-1] + "=" + str(round(float(v), 5))
+                    )
+            else:
+                message.append(
+                    str(key).split("/")[-1] + "=" + str(round(float(losses[key]), 5))
+                )
+        self.logger.info(", ".join(message))
diff --git a/models/tts/naturalspeech2/diffusion.py b/models/tts/naturalspeech2/diffusion.py
new file mode 100644
index 0000000000000000000000000000000000000000..379326f5e9817fc91fa3b2288bf603fcdaf936b1
--- /dev/null
+++ b/models/tts/naturalspeech2/diffusion.py
@@ -0,0 +1,119 @@
+import torch
+import torch.nn as nn
+import numpy as np
+import torch.nn.functional as F
+from models.tts.naturalspeech2.wavenet import WaveNet
+
+
+class Diffusion(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+
+        self.cfg = cfg
+
+        self.diff_estimator = WaveNet(cfg.wavenet)
+        self.beta_min = cfg.beta_min
+        self.beta_max = cfg.beta_max
+        self.sigma = cfg.sigma
+        self.noise_factor = cfg.noise_factor
+
+    def forward(self, x, x_mask, cond, spk_query_emb, offset=1e-5):
+        """
+        x: (B, 128, T)
+        x_mask: (B, T), mask is 0
+        cond: (B, T, 512)
+        spk_query_emb: (B, 32, 512)
+        """
+        diffusion_step = torch.rand(
+            x.shape[0], dtype=x.dtype, device=x.device, requires_grad=False
+        )
+        diffusion_step = torch.clamp(diffusion_step, offset, 1.0 - offset)
+        xt, z = self.forward_diffusion(x0=x, diffusion_step=diffusion_step)
+
+        cum_beta = self.get_cum_beta(diffusion_step.unsqueeze(-1).unsqueeze(-1))
+        x0_pred = self.diff_estimator(xt, x_mask, cond, diffusion_step, spk_query_emb)
+        mean_pred = x0_pred * torch.exp(-0.5 * cum_beta / (self.sigma**2))
+        variance = (self.sigma**2) * (1.0 - torch.exp(-cum_beta / (self.sigma**2)))
+        noise_pred = (xt - mean_pred) / (torch.sqrt(variance) * self.noise_factor)
+        noise = z
+        diff_out = {"x0_pred": x0_pred, "noise_pred": noise_pred, "noise": noise}
+        return diff_out
+
+    @torch.no_grad()
+    def get_cum_beta(self, time_step):
+        return self.beta_min * time_step + 0.5 * (self.beta_max - self.beta_min) * (
+            time_step**2
+        )
+
+    @torch.no_grad()
+    def get_beta_t(self, time_step):
+        return self.beta_min + (self.beta_max - self.beta_min) * time_step
+
+    @torch.no_grad()
+    def forward_diffusion(self, x0, diffusion_step):
+        """
+        x0: (B, 128, T)
+        time_step: (B,)
+        """
+        time_step = diffusion_step.unsqueeze(-1).unsqueeze(-1)
+        cum_beta = self.get_cum_beta(time_step)
+        mean = x0 * torch.exp(-0.5 * cum_beta / (self.sigma**2))
+        variance = (self.sigma**2) * (1 - torch.exp(-cum_beta / (self.sigma**2)))
+        z = torch.randn(x0.shape, dtype=x0.dtype, device=x0.device, requires_grad=False)
+        xt = mean + z * torch.sqrt(variance) * self.noise_factor
+        return xt, z
+
+    @torch.no_grad()
+    def cal_dxt(self, xt, x_mask, cond, spk_query_emb, diffusion_step, h):
+        time_step = diffusion_step.unsqueeze(-1).unsqueeze(-1)
+        cum_beta = self.get_cum_beta(time_step=time_step)
+        beta_t = self.get_beta_t(time_step=time_step)
+        x0_pred = self.diff_estimator(xt, x_mask, cond, diffusion_step, spk_query_emb)
+        mean_pred = x0_pred * torch.exp(-0.5 * cum_beta / (self.sigma**2))
+        noise_pred = xt - mean_pred
+        variance = (self.sigma**2) * (1.0 - torch.exp(-cum_beta / (self.sigma**2)))
+        logp = -noise_pred / (variance + 1e-8)
+        dxt = -0.5 * h * beta_t * (logp + xt / (self.sigma**2))
+        return dxt
+
+    @torch.no_grad()
+    def reverse_diffusion(self, z, x_mask, cond, n_timesteps, spk_query_emb):
+        h = 1.0 / max(n_timesteps, 1)
+        xt = z
+        for i in range(n_timesteps):
+            t = (1.0 - (i + 0.5) * h) * torch.ones(
+                z.shape[0], dtype=z.dtype, device=z.device
+            )
+            dxt = self.cal_dxt(xt, x_mask, cond, spk_query_emb, diffusion_step=t, h=h)
+            xt_ = xt - dxt
+            if self.cfg.ode_solver == "midpoint":
+                x_mid = 0.5 * (xt_ + xt)
+                dxt = self.cal_dxt(
+                    x_mid, x_mask, cond, spk_query_emb, diffusion_step=t + 0.5 * h, h=h
+                )
+                xt = xt - dxt
+            elif self.cfg.ode_solver == "euler":
+                xt = xt_
+        return xt
+
+    @torch.no_grad()
+    def reverse_diffusion_from_t(
+        self, z, x_mask, cond, n_timesteps, spk_query_emb, t_start
+    ):
+        h = t_start / max(n_timesteps, 1)
+        xt = z
+        for i in range(n_timesteps):
+            t = (t_start - (i + 0.5) * h) * torch.ones(
+                z.shape[0], dtype=z.dtype, device=z.device
+            )
+            dxt = self.cal_dxt(xt, x_mask, cond, spk_query_emb, diffusion_step=t, h=h)
+            xt_ = xt - dxt
+            if self.cfg.ode_solver == "midpoint":
+                x_mid = 0.5 * (xt_ + xt)
+                dxt = self.cal_dxt(
+                    x_mid, x_mask, cond, spk_query_emb, diffusion_step=t + 0.5 * h, h=h
+                )
+                xt = xt - dxt
+            elif self.cfg.ode_solver == "euler":
+                xt = xt_
+        return xt
diff --git a/models/tts/naturalspeech2/diffusion_flow.py b/models/tts/naturalspeech2/diffusion_flow.py
new file mode 100644
index 0000000000000000000000000000000000000000..c6669d32863e141489fff9665d9a01351c6b5356
--- /dev/null
+++ b/models/tts/naturalspeech2/diffusion_flow.py
@@ -0,0 +1,74 @@
+import torch
+import torch.nn as nn
+import numpy as np
+import torch.nn.functional as F
+from models.tts.naturalspeech2.wavenet import WaveNet
+
+
+class DiffusionFlow(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+
+        self.diff_estimator = WaveNet(cfg.wavenet)
+        self.beta_min = cfg.beta_min
+        self.beta_max = cfg.beta_max
+        self.sigma = cfg.sigma
+        self.noise_factor = cfg.noise_factor
+
+    def forward(self, x, x_mask, cond, spk_query_emb, offset=1e-5):
+        """
+        x: (B, 128, T)
+        x_mask: (B, T), mask is 0
+        cond: (B, T, 512)
+        spk_query_emb: (B, 32, 512)
+        """
+        diffusion_step = torch.rand(
+            x.shape[0], dtype=x.dtype, device=x.device, requires_grad=False
+        )
+        diffusion_step = torch.clamp(diffusion_step, offset, 1.0 - offset)
+        xt, z = self.forward_diffusion(x0=x, diffusion_step=diffusion_step)
+
+        flow_pred = self.diff_estimator(
+            xt, x_mask, cond, diffusion_step, spk_query_emb
+        )  # noise - x0_pred, noise_pred - x0
+        noise = z
+        x0_pred = noise - flow_pred
+        noise_pred = x + flow_pred
+        diff_out = {
+            "x0_pred": x0_pred,
+            "noise_pred": noise_pred,
+            "noise": noise,
+            "flow_pred": flow_pred,
+        }
+        return diff_out
+
+    @torch.no_grad()
+    def forward_diffusion(self, x0, diffusion_step):
+        """
+        x0: (B, 128, T)
+        time_step: (B,)
+        """
+        time_step = diffusion_step.unsqueeze(-1).unsqueeze(-1)
+        z = torch.randn(x0.shape, dtype=x0.dtype, device=x0.device, requires_grad=False)
+        xt = (1 - time_step) * x0 + time_step * z
+        return xt, z
+
+    @torch.no_grad()
+    def cal_dxt(self, xt, x_mask, cond, spk_query_emb, diffusion_step, h):
+        flow_pred = self.diff_estimator(
+            xt, x_mask, cond, diffusion_step, spk_query_emb
+        )  # z - x0 = x1 - x0
+        dxt = h * flow_pred
+        return dxt
+
+    @torch.no_grad()
+    def reverse_diffusion(self, z, x_mask, cond, n_timesteps, spk_query_emb):
+        h = 1.0 / n_timesteps
+        xt = z
+        for i in range(n_timesteps):
+            t = (1.0 - (i + 0.5) * h) * torch.ones(
+                z.shape[0], dtype=z.dtype, device=z.device
+            )
+            dxt = self.cal_dxt(xt, x_mask, cond, spk_query_emb, diffusion_step=t, h=h)
+            xt = xt - dxt
+        return xt
diff --git a/models/tts/naturalspeech2/ns2.py b/models/tts/naturalspeech2/ns2.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce3e418672baa6977df3ffa95acc6aabff40c56d
--- /dev/null
+++ b/models/tts/naturalspeech2/ns2.py
@@ -0,0 +1,254 @@
+import torch
+import torch.nn as nn
+import numpy as np
+import torch.nn.functional as F
+from models.tts.naturalspeech2.diffusion import Diffusion
+from models.tts.naturalspeech2.diffusion_flow import DiffusionFlow
+from models.tts.naturalspeech2.wavenet import WaveNet
+from models.tts.naturalspeech2.prior_encoder import PriorEncoder
+from modules.naturalpseech2.transformers import TransformerEncoder
+from encodec import EncodecModel
+from einops import rearrange, repeat
+
+import os
+import json
+
+
+class NaturalSpeech2(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        self.cfg = cfg
+
+        self.latent_dim = cfg.latent_dim
+        self.query_emb_num = cfg.query_emb.query_token_num
+
+        self.prior_encoder = PriorEncoder(cfg.prior_encoder)
+        if cfg.diffusion.diffusion_type == "diffusion":
+            self.diffusion = Diffusion(cfg.diffusion)
+        elif cfg.diffusion.diffusion_type == "flow":
+            self.diffusion = DiffusionFlow(cfg.diffusion)
+
+        self.prompt_encoder = TransformerEncoder(cfg=cfg.prompt_encoder)
+        if self.latent_dim != cfg.prompt_encoder.encoder_hidden:
+            self.prompt_lin = nn.Linear(
+                self.latent_dim, cfg.prompt_encoder.encoder_hidden
+            )
+            self.prompt_lin.weight.data.normal_(0.0, 0.02)
+        else:
+            self.prompt_lin = None
+
+        self.query_emb = nn.Embedding(self.query_emb_num, cfg.query_emb.hidden_size)
+        self.query_attn = nn.MultiheadAttention(
+            cfg.query_emb.hidden_size, cfg.query_emb.head_num, batch_first=True
+        )
+
+        codec_model = EncodecModel.encodec_model_24khz()
+        codec_model.set_target_bandwidth(12.0)
+        codec_model.requires_grad_(False)
+        self.quantizer = codec_model.quantizer
+
+    @torch.no_grad()
+    def code_to_latent(self, code):
+        latent = self.quantizer.decode(code.transpose(0, 1))
+        return latent
+
+    def latent_to_code(self, latent, nq=16):
+        residual = latent
+        all_indices = []
+        all_dist = []
+        for i in range(nq):
+            layer = self.quantizer.vq.layers[i]
+            x = rearrange(residual, "b d n -> b n d")
+            x = layer.project_in(x)
+            shape = x.shape
+            x = layer._codebook.preprocess(x)
+            embed = layer._codebook.embed.t()
+            dist = -(
+                x.pow(2).sum(1, keepdim=True)
+                - 2 * x @ embed
+                + embed.pow(2).sum(0, keepdim=True)
+            )
+            indices = dist.max(dim=-1).indices
+            indices = layer._codebook.postprocess_emb(indices, shape)
+            dist = dist.reshape(*shape[:-1], dist.shape[-1])
+            quantized = layer.decode(indices)
+            residual = residual - quantized
+            all_indices.append(indices)
+            all_dist.append(dist)
+
+        out_indices = torch.stack(all_indices)
+        out_dist = torch.stack(all_dist)
+
+        return out_indices, out_dist  # (nq, B, T); (nq, B, T, 1024)
+
+    @torch.no_grad()
+    def latent_to_latent(self, latent, nq=16):
+        codes, _ = self.latent_to_code(latent, nq)
+        latent = self.quantizer.vq.decode(codes)
+        return latent
+
+    def forward(
+        self,
+        code=None,
+        pitch=None,
+        duration=None,
+        phone_id=None,
+        phone_id_frame=None,
+        frame_nums=None,
+        ref_code=None,
+        ref_frame_nums=None,
+        phone_mask=None,
+        mask=None,
+        ref_mask=None,
+    ):
+        ref_latent = self.code_to_latent(ref_code)
+        latent = self.code_to_latent(code)
+
+        if self.latent_dim is not None:
+            ref_latent = self.prompt_lin(ref_latent.transpose(1, 2))
+
+        ref_latent = self.prompt_encoder(ref_latent, ref_mask, condition=None)
+        spk_emb = ref_latent.transpose(1, 2)  # (B, d, T')
+
+        spk_query_emb = self.query_emb(
+            torch.arange(self.query_emb_num).to(latent.device)
+        ).repeat(
+            latent.shape[0], 1, 1
+        )  # (B, query_emb_num, d)
+        spk_query_emb, _ = self.query_attn(
+            spk_query_emb,
+            spk_emb.transpose(1, 2),
+            spk_emb.transpose(1, 2),
+            key_padding_mask=~(ref_mask.bool()),
+        )  # (B, query_emb_num, d)
+
+        prior_out = self.prior_encoder(
+            phone_id=phone_id,
+            duration=duration,
+            pitch=pitch,
+            phone_mask=phone_mask,
+            mask=mask,
+            ref_emb=spk_emb,
+            ref_mask=ref_mask,
+            is_inference=False,
+        )
+        prior_condition = prior_out["prior_out"]  # (B, T, d)
+
+        diff_out = self.diffusion(latent, mask, prior_condition, spk_query_emb)
+
+        return diff_out, prior_out
+
+    @torch.no_grad()
+    def inference(
+        self, ref_code=None, phone_id=None, ref_mask=None, inference_steps=1000
+    ):
+        ref_latent = self.code_to_latent(ref_code)
+
+        if self.latent_dim is not None:
+            ref_latent = self.prompt_lin(ref_latent.transpose(1, 2))
+
+        ref_latent = self.prompt_encoder(ref_latent, ref_mask, condition=None)
+        spk_emb = ref_latent.transpose(1, 2)  # (B, d, T')
+
+        spk_query_emb = self.query_emb(
+            torch.arange(self.query_emb_num).to(ref_latent.device)
+        ).repeat(
+            ref_latent.shape[0], 1, 1
+        )  # (B, query_emb_num, d)
+        spk_query_emb, _ = self.query_attn(
+            spk_query_emb,
+            spk_emb.transpose(1, 2),
+            spk_emb.transpose(1, 2),
+            key_padding_mask=~(ref_mask.bool()),
+        )  # (B, query_emb_num, d)
+
+        prior_out = self.prior_encoder(
+            phone_id=phone_id,
+            duration=None,
+            pitch=None,
+            phone_mask=None,
+            mask=None,
+            ref_emb=spk_emb,
+            ref_mask=ref_mask,
+            is_inference=True,
+        )
+        prior_condition = prior_out["prior_out"]  # (B, T, d)
+
+        z = torch.randn(
+            prior_condition.shape[0], self.latent_dim, prior_condition.shape[1]
+        ).to(ref_latent.device) / (1.20)
+        x0 = self.diffusion.reverse_diffusion(
+            z, None, prior_condition, inference_steps, spk_query_emb
+        )
+
+        return x0, prior_out
+
+    @torch.no_grad()
+    def reverse_diffusion_from_t(
+        self,
+        code=None,
+        pitch=None,
+        duration=None,
+        phone_id=None,
+        ref_code=None,
+        phone_mask=None,
+        mask=None,
+        ref_mask=None,
+        n_timesteps=None,
+        t=None,
+    ):
+        # o Only for debug
+
+        ref_latent = self.code_to_latent(ref_code)
+        latent = self.code_to_latent(code)
+
+        if self.latent_dim is not None:
+            ref_latent = self.prompt_lin(ref_latent.transpose(1, 2))
+
+        ref_latent = self.prompt_encoder(ref_latent, ref_mask, condition=None)
+        spk_emb = ref_latent.transpose(1, 2)  # (B, d, T')
+
+        spk_query_emb = self.query_emb(
+            torch.arange(self.query_emb_num).to(latent.device)
+        ).repeat(
+            latent.shape[0], 1, 1
+        )  # (B, query_emb_num, d)
+        spk_query_emb, _ = self.query_attn(
+            spk_query_emb,
+            spk_emb.transpose(1, 2),
+            spk_emb.transpose(1, 2),
+            key_padding_mask=~(ref_mask.bool()),
+        )  # (B, query_emb_num, d)
+
+        prior_out = self.prior_encoder(
+            phone_id=phone_id,
+            duration=duration,
+            pitch=pitch,
+            phone_mask=phone_mask,
+            mask=mask,
+            ref_emb=spk_emb,
+            ref_mask=ref_mask,
+            is_inference=False,
+        )
+        prior_condition = prior_out["prior_out"]  # (B, T, d)
+
+        diffusion_step = (
+            torch.ones(
+                latent.shape[0],
+                dtype=latent.dtype,
+                device=latent.device,
+                requires_grad=False,
+            )
+            * t
+        )
+        diffusion_step = torch.clamp(diffusion_step, 1e-5, 1.0 - 1e-5)
+        xt, _ = self.diffusion.forward_diffusion(
+            x0=latent, diffusion_step=diffusion_step
+        )
+        # print(torch.abs(xt-latent).max(), torch.abs(xt-latent).mean(), torch.abs(xt-latent).std())
+
+        x0 = self.diffusion.reverse_diffusion_from_t(
+            xt, mask, prior_condition, n_timesteps, spk_query_emb, t_start=t
+        )
+
+        return x0, prior_out, xt
diff --git a/models/tts/naturalspeech2/ns2_dataset.py b/models/tts/naturalspeech2/ns2_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..009176ce692f74921353492efe7f2207ab345b40
--- /dev/null
+++ b/models/tts/naturalspeech2/ns2_dataset.py
@@ -0,0 +1,521 @@
+import random
+import torch
+from torch.nn.utils.rnn import pad_sequence
+from utils.data_utils import *
+from processors.acoustic_extractor import cal_normalized_mel
+from processors.acoustic_extractor import load_normalized
+from models.base.base_dataset import (
+    BaseCollator,
+    BaseDataset,
+    BaseTestDataset,
+    BaseTestCollator,
+)
+from text import text_to_sequence
+from text.cmudict import valid_symbols
+from tqdm import tqdm
+import pickle
+
+
+class NS2Dataset(torch.utils.data.Dataset):
+    def __init__(self, cfg, dataset, is_valid=False):
+        assert isinstance(dataset, str)
+
+        processed_data_dir = os.path.join(cfg.preprocess.processed_dir, dataset)
+        # for example: /home/v-detaixin/LibriTTS/processed_data; train-full
+
+        meta_file = cfg.preprocess.valid_file if is_valid else cfg.preprocess.train_file
+        # train.json
+
+        self.metafile_path = os.path.join(processed_data_dir, meta_file)
+        # /home/v-detaixin/LibriTTS/processed_data/train-full/train.json
+
+        self.metadata = self.get_metadata()
+
+        self.cfg = cfg
+
+        assert cfg.preprocess.use_mel == False
+        if cfg.preprocess.use_mel:
+            self.utt2melspec_path = {}
+            for utt_info in self.metadata:
+                dataset = utt_info["Dataset"]
+                uid = utt_info["Uid"]
+                utt = "{}_{}".format(dataset, uid)
+
+                self.utt2melspec_path[utt] = os.path.join(
+                    cfg.preprocess.processed_dir,
+                    dataset,
+                    cfg.preprocess.melspec_dir,  # mel
+                    utt_info["speaker"],
+                    uid + ".npy",
+                )
+
+        assert cfg.preprocess.use_code == True
+        if cfg.preprocess.use_code:
+            self.utt2code_path = {}
+            for utt_info in self.metadata:
+                dataset = utt_info["Dataset"]
+                uid = utt_info["Uid"]
+                utt = "{}_{}".format(dataset, uid)
+
+                self.utt2code_path[utt] = os.path.join(
+                    cfg.preprocess.processed_dir,
+                    dataset,
+                    cfg.preprocess.code_dir,  # code
+                    utt_info["speaker"],
+                    uid + ".npy",
+                )
+
+        assert cfg.preprocess.use_spkid == True
+        if cfg.preprocess.use_spkid:
+            self.utt2spkid = {}
+            for utt_info in self.metadata:
+                dataset = utt_info["Dataset"]
+                uid = utt_info["Uid"]
+                utt = "{}_{}".format(dataset, uid)
+
+                self.utt2spkid[utt] = utt_info["speaker"]
+
+        assert cfg.preprocess.use_pitch == True
+        if cfg.preprocess.use_pitch:
+            self.utt2pitch_path = {}
+            for utt_info in self.metadata:
+                dataset = utt_info["Dataset"]
+                uid = utt_info["Uid"]
+                utt = "{}_{}".format(dataset, uid)
+
+                self.utt2pitch_path[utt] = os.path.join(
+                    cfg.preprocess.processed_dir,
+                    dataset,
+                    cfg.preprocess.pitch_dir,  # pitch
+                    utt_info["speaker"],
+                    uid + ".npy",
+                )
+
+        assert cfg.preprocess.use_duration == True
+        if cfg.preprocess.use_duration:
+            self.utt2duration_path = {}
+            for utt_info in self.metadata:
+                dataset = utt_info["Dataset"]
+                uid = utt_info["Uid"]
+                utt = "{}_{}".format(dataset, uid)
+
+                self.utt2duration_path[utt] = os.path.join(
+                    cfg.preprocess.processed_dir,
+                    dataset,
+                    cfg.preprocess.duration_dir,  # duration
+                    utt_info["speaker"],
+                    uid + ".npy",
+                )
+
+        assert cfg.preprocess.use_phone == True
+        if cfg.preprocess.use_phone:
+            self.utt2phone = {}
+            for utt_info in self.metadata:
+                dataset = utt_info["Dataset"]
+                uid = utt_info["Uid"]
+                utt = "{}_{}".format(dataset, uid)
+
+                self.utt2phone[utt] = utt_info["phones"]
+
+        assert cfg.preprocess.use_len == True
+        if cfg.preprocess.use_len:
+            self.utt2len = {}
+            for utt_info in self.metadata:
+                dataset = utt_info["Dataset"]
+                uid = utt_info["Uid"]
+                utt = "{}_{}".format(dataset, uid)
+
+                self.utt2len[utt] = utt_info["num_frames"]
+
+        # for cross reference
+        if cfg.preprocess.use_cross_reference:
+            self.spkid2utt = {}
+            for utt_info in self.metadata:
+                dataset = utt_info["Dataset"]
+                uid = utt_info["Uid"]
+                utt = "{}_{}".format(dataset, uid)
+                spkid = utt_info["speaker"]
+                if spkid not in self.spkid2utt:
+                    self.spkid2utt[spkid] = []
+                self.spkid2utt[spkid].append(utt)
+
+        # get phone to id / id to phone map
+        self.phone2id, self.id2phone = self.get_phone_map()
+
+        self.all_num_frames = []
+        for i in range(len(self.metadata)):
+            self.all_num_frames.append(self.metadata[i]["num_frames"])
+        self.num_frame_sorted = np.array(sorted(self.all_num_frames))
+        self.num_frame_indices = np.array(
+            sorted(
+                range(len(self.all_num_frames)), key=lambda k: self.all_num_frames[k]
+            )
+        )
+
+    def __len__(self):
+        return len(self.metadata)
+
+    def get_dataset_name(self):
+        return self.metadata[0]["Dataset"]
+
+    def get_metadata(self):
+        with open(self.metafile_path, "r", encoding="utf-8") as f:
+            metadata = json.load(f)
+
+        print("metadata len: ", len(metadata))
+
+        return metadata
+
+    def get_phone_map(self):
+        symbols = valid_symbols + ["sp", "spn", "sil"] + ["<s>", "</s>"]
+        phone2id = {s: i for i, s in enumerate(symbols)}
+        id2phone = {i: s for s, i in phone2id.items()}
+        return phone2id, id2phone
+
+    def __getitem__(self, index):
+        utt_info = self.metadata[index]
+
+        dataset = utt_info["Dataset"]
+        uid = utt_info["Uid"]
+        utt = "{}_{}".format(dataset, uid)
+
+        single_feature = dict()
+
+        if self.cfg.preprocess.read_metadata:
+            metadata_uid_path = os.path.join(
+                self.cfg.preprocess.processed_dir,
+                self.cfg.preprocess.metadata_dir,
+                dataset,
+                # utt_info["speaker"],
+                uid + ".pkl",
+            )
+            with open(metadata_uid_path, "rb") as f:
+                metadata_uid = pickle.load(f)
+            # code
+            code = metadata_uid["code"]
+            # frame_nums
+            frame_nums = code.shape[1]
+            # pitch
+            pitch = metadata_uid["pitch"]
+            # duration
+            duration = metadata_uid["duration"]
+            # phone_id
+            phone_id = np.array(
+                [
+                    *map(
+                        self.phone2id.get,
+                        self.utt2phone[utt].replace("{", "").replace("}", "").split(),
+                    )
+                ]
+            )
+
+        else:
+            # code
+            code = np.load(self.utt2code_path[utt])
+            # frame_nums
+            frame_nums = code.shape[1]
+            # pitch
+            pitch = np.load(self.utt2pitch_path[utt])
+            # duration
+            duration = np.load(self.utt2duration_path[utt])
+            # phone_id
+            phone_id = np.array(
+                [
+                    *map(
+                        self.phone2id.get,
+                        self.utt2phone[utt].replace("{", "").replace("}", "").split(),
+                    )
+                ]
+            )
+
+        # align length
+        code, pitch, duration, phone_id, frame_nums = self.align_length(
+            code, pitch, duration, phone_id, frame_nums
+        )
+
+        # spkid
+        spkid = self.utt2spkid[utt]
+
+        # get target and reference
+        out = self.get_target_and_reference(code, pitch, duration, phone_id, frame_nums)
+        code, ref_code = out["code"], out["ref_code"]
+        pitch, ref_pitch = out["pitch"], out["ref_pitch"]
+        duration, ref_duration = out["duration"], out["ref_duration"]
+        phone_id, ref_phone_id = out["phone_id"], out["ref_phone_id"]
+        frame_nums, ref_frame_nums = out["frame_nums"], out["ref_frame_nums"]
+
+        # phone_id_frame
+        assert len(phone_id) == len(duration)
+        phone_id_frame = []
+        for i in range(len(phone_id)):
+            phone_id_frame.extend([phone_id[i] for _ in range(duration[i])])
+        phone_id_frame = np.array(phone_id_frame)
+
+        # ref_phone_id_frame
+        assert len(ref_phone_id) == len(ref_duration)
+        ref_phone_id_frame = []
+        for i in range(len(ref_phone_id)):
+            ref_phone_id_frame.extend([ref_phone_id[i] for _ in range(ref_duration[i])])
+        ref_phone_id_frame = np.array(ref_phone_id_frame)
+
+        single_feature.update(
+            {
+                "code": code,
+                "frame_nums": frame_nums,
+                "pitch": pitch,
+                "duration": duration,
+                "phone_id": phone_id,
+                "phone_id_frame": phone_id_frame,
+                "ref_code": ref_code,
+                "ref_frame_nums": ref_frame_nums,
+                "ref_pitch": ref_pitch,
+                "ref_duration": ref_duration,
+                "ref_phone_id": ref_phone_id,
+                "ref_phone_id_frame": ref_phone_id_frame,
+                "spkid": spkid,
+            }
+        )
+
+        return single_feature
+
+    def get_num_frames(self, index):
+        utt_info = self.metadata[index]
+        return utt_info["num_frames"]
+
+    def align_length(self, code, pitch, duration, phone_id, frame_nums):
+        # aligh lenght of code, pitch, duration, phone_id, and frame nums
+        code_len = code.shape[1]
+        pitch_len = len(pitch)
+        dur_sum = sum(duration)
+        min_len = min(code_len, dur_sum)
+        code = code[:, :min_len]
+        if pitch_len >= min_len:
+            pitch = pitch[:min_len]
+        else:
+            pitch = np.pad(pitch, (0, min_len - pitch_len), mode="edge")
+        frame_nums = min_len
+        if dur_sum > min_len:
+            assert (duration[-1] - (dur_sum - min_len)) >= 0
+            duration[-1] = duration[-1] - (dur_sum - min_len)
+            assert duration[-1] >= 0
+
+        return code, pitch, duration, phone_id, frame_nums
+
+    def get_target_and_reference(self, code, pitch, duration, phone_id, frame_nums):
+        phone_nums = len(phone_id)
+        clip_phone_nums = np.random.randint(
+            int(phone_nums * 0.1), int(phone_nums * 0.5) + 1
+        )
+        clip_phone_nums = max(clip_phone_nums, 1)
+        assert clip_phone_nums < phone_nums and clip_phone_nums >= 1
+        if self.cfg.preprocess.clip_mode == "mid":
+            start_idx = np.random.randint(0, phone_nums - clip_phone_nums)
+        elif self.cfg.preprocess.clip_mode == "start":
+            if duration[0] == 0 and clip_phone_nums == 1:
+                start_idx = 1
+            else:
+                start_idx = 0
+        else:
+            assert self.cfg.preprocess.clip_mode in ["mid", "start"]
+        end_idx = start_idx + clip_phone_nums
+        start_frames = sum(duration[:start_idx])
+        end_frames = sum(duration[:end_idx])
+
+        new_code = np.concatenate(
+            (code[:, :start_frames], code[:, end_frames:]), axis=1
+        )
+        ref_code = code[:, start_frames:end_frames]
+
+        new_pitch = np.append(pitch[:start_frames], pitch[end_frames:])
+        ref_pitch = pitch[start_frames:end_frames]
+
+        new_duration = np.append(duration[:start_idx], duration[end_idx:])
+        ref_duration = duration[start_idx:end_idx]
+
+        new_phone_id = np.append(phone_id[:start_idx], phone_id[end_idx:])
+        ref_phone_id = phone_id[start_idx:end_idx]
+
+        new_frame_nums = frame_nums - (end_frames - start_frames)
+        ref_frame_nums = end_frames - start_frames
+
+        return {
+            "code": new_code,
+            "ref_code": ref_code,
+            "pitch": new_pitch,
+            "ref_pitch": ref_pitch,
+            "duration": new_duration,
+            "ref_duration": ref_duration,
+            "phone_id": new_phone_id,
+            "ref_phone_id": ref_phone_id,
+            "frame_nums": new_frame_nums,
+            "ref_frame_nums": ref_frame_nums,
+        }
+
+
+class NS2Collator(BaseCollator):
+    def __init__(self, cfg):
+        BaseCollator.__init__(self, cfg)
+
+    def __call__(self, batch):
+        packed_batch_features = dict()
+
+        # code: (B, 16, T)
+        # frame_nums: (B,)   not used
+        # pitch: (B, T)
+        # duration: (B, N)
+        # phone_id: (B, N)
+        # phone_id_frame: (B, T)
+        # ref_code: (B, 16, T')
+        # ref_frame_nums: (B,)   not used
+        # ref_pitch: (B, T)   not used
+        # ref_duration: (B, N')   not used
+        # ref_phone_id: (B, N')   not used
+        # ref_phone_frame: (B, T')   not used
+        # spkid: (B,)   not used
+        # phone_mask: (B, N)
+        # mask: (B, T)
+        # ref_mask: (B, T')
+
+        for key in batch[0].keys():
+            if key == "phone_id":
+                phone_ids = [torch.LongTensor(b["phone_id"]) for b in batch]
+                phone_masks = [torch.ones(len(b["phone_id"])) for b in batch]
+                packed_batch_features["phone_id"] = pad_sequence(
+                    phone_ids,
+                    batch_first=True,
+                    padding_value=0,
+                )
+                packed_batch_features["phone_mask"] = pad_sequence(
+                    phone_masks,
+                    batch_first=True,
+                    padding_value=0,
+                )
+            elif key == "phone_id_frame":
+                phone_id_frames = [torch.LongTensor(b["phone_id_frame"]) for b in batch]
+                masks = [torch.ones(len(b["phone_id_frame"])) for b in batch]
+                packed_batch_features["phone_id_frame"] = pad_sequence(
+                    phone_id_frames,
+                    batch_first=True,
+                    padding_value=0,
+                )
+                packed_batch_features["mask"] = pad_sequence(
+                    masks,
+                    batch_first=True,
+                    padding_value=0,
+                )
+            elif key == "ref_code":
+                ref_codes = [
+                    torch.from_numpy(b["ref_code"]).transpose(0, 1) for b in batch
+                ]
+                ref_masks = [torch.ones(max(b["ref_code"].shape[1], 1)) for b in batch]
+                packed_batch_features["ref_code"] = pad_sequence(
+                    ref_codes,
+                    batch_first=True,
+                    padding_value=0,
+                ).transpose(1, 2)
+                packed_batch_features["ref_mask"] = pad_sequence(
+                    ref_masks,
+                    batch_first=True,
+                    padding_value=0,
+                )
+            elif key == "code":
+                codes = [torch.from_numpy(b["code"]).transpose(0, 1) for b in batch]
+                masks = [torch.ones(max(b["code"].shape[1], 1)) for b in batch]
+                packed_batch_features["code"] = pad_sequence(
+                    codes,
+                    batch_first=True,
+                    padding_value=0,
+                ).transpose(1, 2)
+                packed_batch_features["mask"] = pad_sequence(
+                    masks,
+                    batch_first=True,
+                    padding_value=0,
+                )
+            elif key == "pitch":
+                values = [torch.from_numpy(b[key]) for b in batch]
+                packed_batch_features[key] = pad_sequence(
+                    values, batch_first=True, padding_value=50.0
+                )
+            elif key == "duration":
+                values = [torch.from_numpy(b[key]) for b in batch]
+                packed_batch_features[key] = pad_sequence(
+                    values, batch_first=True, padding_value=0
+                )
+            elif key == "frame_nums":
+                packed_batch_features["frame_nums"] = torch.LongTensor(
+                    [b["frame_nums"] for b in batch]
+                )
+            elif key == "ref_frame_nums":
+                packed_batch_features["ref_frame_nums"] = torch.LongTensor(
+                    [b["ref_frame_nums"] for b in batch]
+                )
+            else:
+                pass
+
+        return packed_batch_features
+
+
+def _is_batch_full(batch, num_tokens, max_tokens, max_sentences):
+    if len(batch) == 0:
+        return 0
+    if len(batch) == max_sentences:
+        return 1
+    if num_tokens > max_tokens:
+        return 1
+    return 0
+
+
+def batch_by_size(
+    indices,
+    num_tokens_fn,
+    max_tokens=None,
+    max_sentences=None,
+    required_batch_size_multiple=1,
+):
+    """
+    Yield mini-batches of indices bucketed by size. Batches may contain
+    sequences of different lengths.
+
+    Args:
+        indices (List[int]): ordered list of dataset indices
+        num_tokens_fn (callable): function that returns the number of tokens at
+            a given index
+        max_tokens (int, optional): max number of tokens in each batch
+            (default: None).
+        max_sentences (int, optional): max number of sentences in each
+            batch (default: None).
+        required_batch_size_multiple (int, optional): require batch size to
+            be a multiple of N (default: 1).
+    """
+    bsz_mult = required_batch_size_multiple
+
+    sample_len = 0
+    sample_lens = []
+    batch = []
+    batches = []
+    for i in range(len(indices)):
+        idx = indices[i]
+        num_tokens = num_tokens_fn(idx)
+        sample_lens.append(num_tokens)
+        sample_len = max(sample_len, num_tokens)
+
+        assert (
+            sample_len <= max_tokens
+        ), "sentence at index {} of size {} exceeds max_tokens " "limit of {}!".format(
+            idx, sample_len, max_tokens
+        )
+        num_tokens = (len(batch) + 1) * sample_len
+
+        if _is_batch_full(batch, num_tokens, max_tokens, max_sentences):
+            mod_len = max(
+                bsz_mult * (len(batch) // bsz_mult),
+                len(batch) % bsz_mult,
+            )
+            batches.append(batch[:mod_len])
+            batch = batch[mod_len:]
+            sample_lens = sample_lens[mod_len:]
+            sample_len = max(sample_lens) if len(sample_lens) > 0 else 0
+        batch.append(idx)
+    if len(batch) > 0:
+        batches.append(batch)
+    return batches
diff --git a/models/tts/naturalspeech2/ns2_inference.py b/models/tts/naturalspeech2/ns2_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a38beec9ea7d0f340537dc64c9ab99e602922ea
--- /dev/null
+++ b/models/tts/naturalspeech2/ns2_inference.py
@@ -0,0 +1,123 @@
+import argparse
+import os
+import torch
+import soundfile as sf
+import numpy as np
+
+from models.tts.naturalspeech2.ns2 import NaturalSpeech2
+from encodec import EncodecModel
+from encodec.utils import convert_audio
+from utils.util import load_config
+
+from text import text_to_sequence
+from text.cmudict import valid_symbols
+from text.g2p import preprocess_english, read_lexicon
+
+import torchaudio
+
+
+class NS2Inference:
+    def __init__(self, args, cfg):
+        self.cfg = cfg
+        self.args = args
+
+        self.model = self.build_model()
+        self.codec = self.build_codec()
+
+        self.symbols = valid_symbols + ["sp", "spn", "sil"] + ["<s>", "</s>"]
+        self.phone2id = {s: i for i, s in enumerate(self.symbols)}
+        self.id2phone = {i: s for s, i in self.phone2id.items()}
+
+    def build_model(self):
+        model = NaturalSpeech2(self.cfg.model)
+        model.load_state_dict(
+            torch.load(
+                os.path.join(self.args.checkpoint_path, "pytorch_model.bin"),
+                map_location="cpu",
+            )
+        )
+        model = model.to(self.args.device)
+        return model
+
+    def build_codec(self):
+        encodec_model = EncodecModel.encodec_model_24khz()
+        encodec_model = encodec_model.to(device=self.args.device)
+        encodec_model.set_target_bandwidth(12.0)
+        return encodec_model
+
+    def get_ref_code(self):
+        ref_wav_path = self.args.ref_audio
+        ref_wav, sr = torchaudio.load(ref_wav_path)
+        ref_wav = convert_audio(
+            ref_wav, sr, self.codec.sample_rate, self.codec.channels
+        )
+        ref_wav = ref_wav.unsqueeze(0).to(device=self.args.device)
+
+        with torch.no_grad():
+            encoded_frames = self.codec.encode(ref_wav)
+            ref_code = torch.cat([encoded[0] for encoded in encoded_frames], dim=-1)
+        # print(ref_code.shape)
+
+        ref_mask = torch.ones(ref_code.shape[0], ref_code.shape[-1]).to(ref_code.device)
+        # print(ref_mask.shape)
+
+        return ref_code, ref_mask
+
+    def inference(self):
+        ref_code, ref_mask = self.get_ref_code()
+
+        lexicon = read_lexicon(self.cfg.preprocess.lexicon_path)
+        phone_seq = preprocess_english(self.args.text, lexicon)
+        print(phone_seq)
+
+        phone_id = np.array(
+            [
+                *map(
+                    self.phone2id.get,
+                    phone_seq.replace("{", "").replace("}", "").split(),
+                )
+            ]
+        )
+        phone_id = torch.from_numpy(phone_id).unsqueeze(0).to(device=self.args.device)
+        print(phone_id)
+
+        x0, prior_out = self.model.inference(
+            ref_code, phone_id, ref_mask, self.args.inference_step
+        )
+        print(prior_out["dur_pred"])
+        print(prior_out["dur_pred_round"])
+        print(torch.sum(prior_out["dur_pred_round"]))
+
+        latent_ref = self.codec.quantizer.vq.decode(ref_code.transpose(0, 1))
+
+        rec_wav = self.codec.decoder(x0)
+        # ref_wav = self.codec.decoder(latent_ref)
+
+        os.makedirs(self.args.output_dir, exist_ok=True)
+
+        sf.write(
+            "{}/{}.wav".format(
+                self.args.output_dir, self.args.text.replace(" ", "_", 100)
+            ),
+            rec_wav[0, 0].detach().cpu().numpy(),
+            samplerate=24000,
+        )
+
+    def add_arguments(parser: argparse.ArgumentParser):
+        parser.add_argument(
+            "--ref_audio",
+            type=str,
+            default="",
+            help="Reference audio path",
+        )
+        parser.add_argument(
+            "--device",
+            type=str,
+            default="cuda",
+        )
+        parser.add_argument(
+            "--inference_step",
+            type=int,
+            default=200,
+            help="Total inference steps for the diffusion model",
+        )
diff --git a/models/tts/naturalspeech2/ns2_loss.py b/models/tts/naturalspeech2/ns2_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..af61fbd2b2ff7f853906753969fa74f7cbff018a
--- /dev/null
+++ b/models/tts/naturalspeech2/ns2_loss.py
@@ -0,0 +1,70 @@
+import torch
+import torch.nn as nn
+import numpy as np
+import torch.nn.functional as F
+
+
+def log_dur_loss(dur_pred_log, dur_target, mask, loss_type="l1"):
+    # dur_pred_log: (B, N)
+    # dur_target: (B, N)
+    # mask: (B, N) mask is 0
+    dur_target_log = torch.log(1 + dur_target)
+    if loss_type == "l1":
+        loss = F.l1_loss(
+            dur_pred_log, dur_target_log, reduction="none"
+        ).float() * mask.to(dur_target.dtype)
+    elif loss_type == "l2":
+        loss = F.mse_loss(
+            dur_pred_log, dur_target_log, reduction="none"
+        ).float() * mask.to(dur_target.dtype)
+    else:
+        raise NotImplementedError()
+    loss = loss.sum() / (mask.to(dur_target.dtype).sum())
+    return loss
+
+
+def log_pitch_loss(pitch_pred_log, pitch_target, mask, loss_type="l1"):
+    pitch_target_log = torch.log(pitch_target)
+    if loss_type == "l1":
+        loss = F.l1_loss(
+            pitch_pred_log, pitch_target_log, reduction="none"
+        ).float() * mask.to(pitch_target.dtype)
+    elif loss_type == "l2":
+        loss = F.mse_loss(
+            pitch_pred_log, pitch_target_log, reduction="none"
+        ).float() * mask.to(pitch_target.dtype)
+    else:
+        raise NotImplementedError()
+    loss = loss.sum() / (mask.to(pitch_target.dtype).sum() + 1e-8)
+    return loss
+
+
+def diff_loss(pred, target, mask, loss_type="l1"):
+    # pred: (B, d, T)
+    # target: (B, d, T)
+    # mask: (B, T)
+    if loss_type == "l1":
+        loss = F.l1_loss(pred, target, reduction="none").float() * (
+            mask.to(pred.dtype).unsqueeze(1)
+        )
+    elif loss_type == "l2":
+        loss = F.mse_loss(pred, target, reduction="none").float() * (
+            mask.to(pred.dtype).unsqueeze(1)
+        )
+    else:
+        raise NotImplementedError()
+    loss = (torch.mean(loss, dim=1)).sum() / (mask.to(pred.dtype).sum())
+    return loss
+
+
+def diff_ce_loss(pred_dist, gt_indices, mask):
+    # pred_dist: (nq, B, T, 1024)
+    # gt_indices: (nq, B, T)
+    pred_dist = pred_dist.permute(1, 3, 0, 2)  # (B, 1024, nq, T)
+    gt_indices = gt_indices.permute(1, 0, 2).long()  # (B, nq, T)
+    loss = F.cross_entropy(
+        pred_dist, gt_indices, reduction="none"
+    ).float()  # (B, nq, T)
+    loss = loss * mask.to(loss.dtype).unsqueeze(1)
+    loss = (torch.mean(loss, dim=1)).sum() / (mask.to(loss.dtype).sum())
+    return loss
diff --git a/models/tts/naturalspeech2/ns2_trainer.py b/models/tts/naturalspeech2/ns2_trainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..d41452dfb909c212cc5dd81984f2a33481e887c2
--- /dev/null
+++ b/models/tts/naturalspeech2/ns2_trainer.py
@@ -0,0 +1,357 @@
+import torch
+import numpy as np
+from torch.utils.data import ConcatDataset, DataLoader
+from models.tts.naturalspeech2.base_trainer import TTSTrainer
+from models.base.base_trainer import BaseTrainer
+from models.base.base_sampler import VariableSampler
+from models.tts.naturalspeech2.ns2_dataset import NS2Dataset, NS2Collator, batch_by_size
+from models.tts.naturalspeech2.ns2_loss import (
+    log_pitch_loss,
+    log_dur_loss,
+    diff_loss,
+    diff_ce_loss,
+)
+from torch.utils.data.sampler import BatchSampler, SequentialSampler
+from models.tts.naturalspeech2.ns2 import NaturalSpeech2
+from torch.optim import Adam, AdamW
+from torch.nn import MSELoss, L1Loss
+import torch.nn.functional as F
+from diffusers import get_scheduler
+
+
+class NS2Trainer(TTSTrainer):
+    def __init__(self, args, cfg):
+        TTSTrainer.__init__(self, args, cfg)
+
+    def _build_model(self):
+        model = NaturalSpeech2(cfg=self.cfg.model)
+        return model
+
+    def _build_dataset(self):
+        return NS2Dataset, NS2Collator
+
+    def _build_dataloader(self):
+        if self.cfg.train.use_dynamic_batchsize:
+            print("Use Dynamic Batchsize......")
+            Dataset, Collator = self._build_dataset()
+            train_dataset = Dataset(self.cfg, self.cfg.dataset[0], is_valid=False)
+            train_collate = Collator(self.cfg)
+            batch_sampler = batch_by_size(
+                train_dataset.num_frame_indices,
+                train_dataset.get_num_frames,
+                max_tokens=self.cfg.train.max_tokens * self.accelerator.num_processes,
+                max_sentences=self.cfg.train.max_sentences
+                * self.accelerator.num_processes,
+                required_batch_size_multiple=self.accelerator.num_processes,
+            )
+            np.random.seed(980205)
+            np.random.shuffle(batch_sampler)
+            print(batch_sampler[:1])
+            batches = [
+                x[
+                    self.accelerator.local_process_index :: self.accelerator.num_processes
+                ]
+                for x in batch_sampler
+                if len(x) % self.accelerator.num_processes == 0
+            ]
+
+            train_loader = DataLoader(
+                train_dataset,
+                collate_fn=train_collate,
+                num_workers=self.cfg.train.dataloader.num_worker,
+                batch_sampler=VariableSampler(
+                    batches, drop_last=False, use_random_sampler=True
+                ),
+                pin_memory=self.cfg.train.dataloader.pin_memory,
+            )
+            self.accelerator.wait_for_everyone()
+
+            valid_dataset = Dataset(self.cfg, self.cfg.dataset[0], is_valid=True)
+            valid_collate = Collator(self.cfg)
+            batch_sampler = batch_by_size(
+                valid_dataset.num_frame_indices,
+                valid_dataset.get_num_frames,
+                max_tokens=self.cfg.train.max_tokens * self.accelerator.num_processes,
+                max_sentences=self.cfg.train.max_sentences
+                * self.accelerator.num_processes,
+                required_batch_size_multiple=self.accelerator.num_processes,
+            )
+            batches = [
+                x[
+                    self.accelerator.local_process_index :: self.accelerator.num_processes
+                ]
+                for x in batch_sampler
+                if len(x) % self.accelerator.num_processes == 0
+            ]
+            valid_loader = DataLoader(
+                valid_dataset,
+                collate_fn=valid_collate,
+                num_workers=self.cfg.train.dataloader.num_worker,
+                batch_sampler=VariableSampler(batches, drop_last=False),
+                pin_memory=self.cfg.train.dataloader.pin_memory,
+            )
+            self.accelerator.wait_for_everyone()
+
+        else:
+            print("Use Normal Batchsize......")
+            Dataset, Collator = self._build_dataset()
+            train_dataset = Dataset(self.cfg, self.cfg.dataset[0], is_valid=False)
+            train_collate = Collator(self.cfg)
+
+            train_loader = DataLoader(
+                train_dataset,
+                shuffle=True,
+                collate_fn=train_collate,
+                batch_size=self.cfg.train.batch_size,
+                num_workers=self.cfg.train.dataloader.num_worker,
+                pin_memory=self.cfg.train.dataloader.pin_memory,
+            )
+
+            valid_dataset = Dataset(self.cfg, self.cfg.dataset[0], is_valid=True)
+            valid_collate = Collator(self.cfg)
+
+            valid_loader = DataLoader(
+                valid_dataset,
+                shuffle=True,
+                collate_fn=valid_collate,
+                batch_size=self.cfg.train.batch_size,
+                num_workers=self.cfg.train.dataloader.num_worker,
+                pin_memory=self.cfg.train.dataloader.pin_memory,
+            )
+            self.accelerator.wait_for_everyone()
+
+        return train_loader, valid_loader
+
+    def _build_optimizer(self):
+        optimizer = torch.optim.AdamW(
+            filter(lambda p: p.requires_grad, self.model.parameters()),
+            **self.cfg.train.adam
+        )
+        return optimizer
+
+    def _build_scheduler(self):
+        lr_scheduler = get_scheduler(
+            self.cfg.train.lr_scheduler,
+            optimizer=self.optimizer,
+            num_warmup_steps=self.cfg.train.lr_warmup_steps,
+            num_training_steps=self.cfg.train.num_train_steps,
+        )
+        return lr_scheduler
+
+    def _build_criterion(self):
+        criterion = torch.nn.L1Loss(reduction="mean")
+        return criterion
+
+    def write_summary(self, losses, stats):
+        for key, value in losses.items():
+            self.sw.add_scalar(key, value, self.step)
+
+    def write_valid_summary(self, losses, stats):
+        for key, value in losses.items():
+            self.sw.add_scalar(key, value, self.step)
+
+    def get_state_dict(self):
+        state_dict = {
+            "model": self.model.state_dict(),
+            "optimizer": self.optimizer.state_dict(),
+            "scheduler": self.scheduler.state_dict(),
+            "step": self.step,
+            "epoch": self.epoch,
+            "batch_size": self.cfg.train.batch_size,
+        }
+        return state_dict
+
+    def load_model(self, checkpoint):
+        self.step = checkpoint["step"]
+        self.epoch = checkpoint["epoch"]
+
+        self.model.load_state_dict(checkpoint["model"])
+        self.optimizer.load_state_dict(checkpoint["optimizer"])
+        self.scheduler.load_state_dict(checkpoint["scheduler"])
+
+    def _train_step(self, batch):
+        train_losses = {}
+        total_loss = 0
+        train_stats = {}
+
+        code = batch["code"]  # (B, 16, T)
+        pitch = batch["pitch"]  # (B, T)
+        duration = batch["duration"]  # (B, N)
+        phone_id = batch["phone_id"]  # (B, N)
+        ref_code = batch["ref_code"]  # (B, 16, T')
+        phone_mask = batch["phone_mask"]  # (B, N)
+        mask = batch["mask"]  # (B, T)
+        ref_mask = batch["ref_mask"]  # (B, T')
+
+        diff_out, prior_out = self.model(
+            code=code,
+            pitch=pitch,
+            duration=duration,
+            phone_id=phone_id,
+            ref_code=ref_code,
+            phone_mask=phone_mask,
+            mask=mask,
+            ref_mask=ref_mask,
+        )
+
+        # pitch loss
+        pitch_loss = log_pitch_loss(prior_out["pitch_pred_log"], pitch, mask=mask)
+        total_loss += pitch_loss
+        train_losses["pitch_loss"] = pitch_loss
+
+        # duration loss
+        dur_loss = log_dur_loss(prior_out["dur_pred_log"], duration, mask=phone_mask)
+        total_loss += dur_loss
+        train_losses["dur_loss"] = dur_loss
+
+        x0 = self.model.module.code_to_latent(code)
+        if self.cfg.model.diffusion.diffusion_type == "diffusion":
+            # diff loss x0
+            diff_loss_x0 = diff_loss(diff_out["x0_pred"], x0, mask=mask)
+            total_loss += diff_loss_x0
+            train_losses["diff_loss_x0"] = diff_loss_x0
+
+            # diff loss noise
+            diff_loss_noise = diff_loss(
+                diff_out["noise_pred"], diff_out["noise"], mask=mask
+            )
+            total_loss += diff_loss_noise * self.cfg.train.diff_noise_loss_lambda
+            train_losses["diff_loss_noise"] = diff_loss_noise
+
+        elif self.cfg.model.diffusion.diffusion_type == "flow":
+            # diff flow matching loss
+            flow_gt = diff_out["noise"] - x0
+            diff_loss_flow = diff_loss(diff_out["flow_pred"], flow_gt, mask=mask)
+            total_loss += diff_loss_flow
+            train_losses["diff_loss_flow"] = diff_loss_flow
+
+        # diff loss ce
+
+        # (nq, B, T); (nq, B, T, 1024)
+        if self.cfg.train.diff_ce_loss_lambda > 0:
+            pred_indices, pred_dist = self.model.module.latent_to_code(
+                diff_out["x0_pred"], nq=code.shape[1]
+            )
+            gt_indices, _ = self.model.module.latent_to_code(x0, nq=code.shape[1])
+            diff_loss_ce = diff_ce_loss(pred_dist, gt_indices, mask=mask)
+            total_loss += diff_loss_ce * self.cfg.train.diff_ce_loss_lambda
+            train_losses["diff_loss_ce"] = diff_loss_ce
+
+        self.optimizer.zero_grad()
+        # total_loss.backward()
+        self.accelerator.backward(total_loss)
+        if self.accelerator.sync_gradients:
+            self.accelerator.clip_grad_norm_(
+                filter(lambda p: p.requires_grad, self.model.parameters()), 0.5
+            )
+        self.optimizer.step()
+        self.scheduler.step()
+
+        for item in train_losses:
+            train_losses[item] = train_losses[item].item()
+
+        if self.cfg.train.diff_ce_loss_lambda > 0:
+            pred_indices_list = pred_indices.long().detach().cpu().numpy()
+            gt_indices_list = gt_indices.long().detach().cpu().numpy()
+            mask_list = batch["mask"].detach().cpu().numpy()
+
+            for i in range(pred_indices_list.shape[0]):
+                pred_acc = np.sum(
+                    (pred_indices_list[i] == gt_indices_list[i]) * mask_list
+                ) / np.sum(mask_list)
+                train_losses["pred_acc_{}".format(str(i))] = pred_acc
+
+        train_losses["batch_size"] = code.shape[0]
+        train_losses["max_frame_nums"] = np.max(
+            batch["frame_nums"].detach().cpu().numpy()
+        )
+
+        return (total_loss.item(), train_losses, train_stats)
+
+    @torch.inference_mode()
+    def _valid_step(self, batch):
+        valid_losses = {}
+        total_loss = 0
+        valid_stats = {}
+
+        code = batch["code"]  # (B, 16, T)
+        pitch = batch["pitch"]  # (B, T)
+        duration = batch["duration"]  # (B, N)
+        phone_id = batch["phone_id"]  # (B, N)
+        ref_code = batch["ref_code"]  # (B, 16, T')
+        phone_mask = batch["phone_mask"]  # (B, N)
+        mask = batch["mask"]  # (B, T)
+        ref_mask = batch["ref_mask"]  # (B, T')
+
+        diff_out, prior_out = self.model(
+            code=code,
+            pitch=pitch,
+            duration=duration,
+            phone_id=phone_id,
+            ref_code=ref_code,
+            phone_mask=phone_mask,
+            mask=mask,
+            ref_mask=ref_mask,
+        )
+
+        # pitch loss
+        pitch_loss = log_pitch_loss(prior_out["pitch_pred_log"], pitch, mask=mask)
+        total_loss += pitch_loss
+        valid_losses["pitch_loss"] = pitch_loss
+
+        # duration loss
+        dur_loss = log_dur_loss(prior_out["dur_pred_log"], duration, mask=phone_mask)
+        total_loss += dur_loss
+        valid_losses["dur_loss"] = dur_loss
+
+        x0 = self.model.module.code_to_latent(code)
+        if self.cfg.model.diffusion.diffusion_type == "diffusion":
+            # diff loss x0
+            diff_loss_x0 = diff_loss(diff_out["x0_pred"], x0, mask=mask)
+            total_loss += diff_loss_x0
+            valid_losses["diff_loss_x0"] = diff_loss_x0
+
+            # diff loss noise
+            diff_loss_noise = diff_loss(
+                diff_out["noise_pred"], diff_out["noise"], mask=mask
+            )
+            total_loss += diff_loss_noise * self.cfg.train.diff_noise_loss_lambda
+            valid_losses["diff_loss_noise"] = diff_loss_noise
+
+        elif self.cfg.model.diffusion.diffusion_type == "flow":
+            # diff flow matching loss
+            flow_gt = diff_out["noise"] - x0
+            diff_loss_flow = diff_loss(diff_out["flow_pred"], flow_gt, mask=mask)
+            total_loss += diff_loss_flow
+            valid_losses["diff_loss_flow"] = diff_loss_flow
+
+        # diff loss ce
+
+        # (nq, B, T); (nq, B, T, 1024)
+        if self.cfg.train.diff_ce_loss_lambda > 0:
+            pred_indices, pred_dist = self.model.module.latent_to_code(
+                diff_out["x0_pred"], nq=code.shape[1]
+            )
+            gt_indices, _ = self.model.module.latent_to_code(x0, nq=code.shape[1])
+            diff_loss_ce = diff_ce_loss(pred_dist, gt_indices, mask=mask)
+            total_loss += diff_loss_ce * self.cfg.train.diff_ce_loss_lambda
+            valid_losses["diff_loss_ce"] = diff_loss_ce
+
+        for item in valid_losses:
+            valid_losses[item] = valid_losses[item].item()
+
+        if self.cfg.train.diff_ce_loss_lambda > 0:
+            pred_indices_list = pred_indices.long().detach().cpu().numpy()
+            gt_indices_list = gt_indices.long().detach().cpu().numpy()
+            mask_list = batch["mask"].detach().cpu().numpy()
+
+            for i in range(pred_indices_list.shape[0]):
+                pred_acc = np.sum(
+                    (pred_indices_list[i] == gt_indices_list[i]) * mask_list
+                ) / np.sum(mask_list)
+                valid_losses["pred_acc_{}".format(str(i))] = pred_acc
+
+        return (total_loss.item(), valid_losses, valid_stats)
+
+    # def _train_epoch(self):
+    #     ...
diff --git a/models/tts/naturalspeech2/prior_encoder.py b/models/tts/naturalspeech2/prior_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..732102f5a3264151c2216053530730c74c0e6927
--- /dev/null
+++ b/models/tts/naturalspeech2/prior_encoder.py
@@ -0,0 +1,109 @@
+import torch
+import torch.nn as nn
+import numpy as np
+import torch.nn.functional as F
+from modules.naturalpseech2.transformers import (
+    TransformerEncoder,
+    DurationPredictor,
+    PitchPredictor,
+    LengthRegulator,
+)
+
+
+class PriorEncoder(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        self.cfg = cfg
+        self.enc_emb_tokens = nn.Embedding(
+            cfg.vocab_size, cfg.encoder.encoder_hidden, padding_idx=0
+        )
+        self.enc_emb_tokens.weight.data.normal_(mean=0.0, std=1e-5)
+        self.encoder = TransformerEncoder(
+            enc_emb_tokens=self.enc_emb_tokens, cfg=cfg.encoder
+        )
+
+        self.duration_predictor = DurationPredictor(cfg.duration_predictor)
+        self.pitch_predictor = PitchPredictor(cfg.pitch_predictor)
+        self.length_regulator = LengthRegulator()
+
+        self.pitch_min = cfg.pitch_min
+        self.pitch_max = cfg.pitch_max
+        self.pitch_bins_num = cfg.pitch_bins_num
+
+        pitch_bins = torch.exp(
+            torch.linspace(
+                np.log(self.pitch_min), np.log(self.pitch_max), self.pitch_bins_num - 1
+            )
+        )
+        self.register_buffer("pitch_bins", pitch_bins)
+
+        self.pitch_embedding = nn.Embedding(
+            self.pitch_bins_num, cfg.encoder.encoder_hidden
+        )
+
+    def forward(
+        self,
+        phone_id,
+        duration=None,
+        pitch=None,
+        phone_mask=None,
+        mask=None,
+        ref_emb=None,
+        ref_mask=None,
+        is_inference=False,
+    ):
+        """
+        input:
+        phone_id: (B, N)
+        duration: (B, N)
+        pitch: (B, T)
+        phone_mask: (B, N); mask is 0
+        mask: (B, T); mask is 0
+        ref_emb: (B, d, T')
+        ref_mask: (B, T'); mask is 0
+
+        output:
+        prior_embedding: (B, d, T)
+        pred_dur: (B, N)
+        pred_pitch: (B, T)
+        """
+
+        x = self.encoder(phone_id, phone_mask, ref_emb.transpose(1, 2))
+        # print(torch.min(x), torch.max(x))
+        dur_pred_out = self.duration_predictor(x, phone_mask, ref_emb, ref_mask)
+        # dur_pred_out: {dur_pred_log, dur_pred, dur_pred_round}
+
+        if is_inference or duration is None:
+            x, mel_len = self.length_regulator(
+                x,
+                dur_pred_out["dur_pred_round"],
+                max_len=torch.max(torch.sum(dur_pred_out["dur_pred_round"], dim=1)),
+            )
+        else:
+            x, mel_len = self.length_regulator(x, duration, max_len=pitch.shape[1])
+
+        pitch_pred_log = self.pitch_predictor(x, mask, ref_emb, ref_mask)
+
+        if is_inference or pitch is None:
+            pitch_tokens = torch.bucketize(pitch_pred_log.exp(), self.pitch_bins)
+            pitch_embedding = self.pitch_embedding(pitch_tokens)
+        else:
+            pitch_tokens = torch.bucketize(pitch, self.pitch_bins)
+            pitch_embedding = self.pitch_embedding(pitch_tokens)
+
+        x = x + pitch_embedding
+
+        if (not is_inference) and (mask is not None):
+            x = x * mask.to(x.dtype)[:, :, None]
+
+        prior_out = {
+            "dur_pred_round": dur_pred_out["dur_pred_round"],
+            "dur_pred_log": dur_pred_out["dur_pred_log"],
+            "dur_pred": dur_pred_out["dur_pred"],
+            "pitch_pred_log": pitch_pred_log,
+            "pitch_token": pitch_tokens,
+            "mel_len": mel_len,
+            "prior_out": x,
+        }
+
+        return prior_out
diff --git a/models/tts/naturalspeech2/wavenet.py b/models/tts/naturalspeech2/wavenet.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc8be2f694e02c426c10b096bd8135b67eadc6a0
--- /dev/null
+++ b/models/tts/naturalspeech2/wavenet.py
@@ -0,0 +1,201 @@
+import torch
+import torch.nn as nn
+import numpy as np
+import torch.nn.functional as F
+import math
+
+
+class FiLM(nn.Module):
+    def __init__(self, in_dim, cond_dim):
+        super().__init__()
+
+        self.gain = Linear(cond_dim, in_dim)
+        self.bias = Linear(cond_dim, in_dim)
+
+        nn.init.xavier_uniform_(self.gain.weight)
+        nn.init.constant_(self.gain.bias, 1)
+
+        nn.init.xavier_uniform_(self.bias.weight)
+        nn.init.constant_(self.bias.bias, 0)
+
+    def forward(self, x, condition):
+        gain = self.gain(condition)
+        bias = self.bias(condition)
+        if gain.dim() == 2:
+            gain = gain.unsqueeze(-1)
+        if bias.dim() == 2:
+            bias = bias.unsqueeze(-1)
+        return x * gain + bias
+
+
+class Mish(nn.Module):
+    def forward(self, x):
+        return x * torch.tanh(F.softplus(x))
+
+
+def Conv1d(*args, **kwargs):
+    layer = nn.Conv1d(*args, **kwargs)
+    nn.init.kaiming_normal_(layer.weight)
+    return layer
+
+
+def Linear(*args, **kwargs):
+    layer = nn.Linear(*args, **kwargs)
+    layer.weight.data.normal_(0.0, 0.02)
+    return layer
+
+
+class SinusoidalPosEmb(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.dim = dim
+
+    def forward(self, x):
+        device = x.device
+        half_dim = self.dim // 2
+        emb = math.log(10000) / (half_dim - 1)
+        emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
+        emb = x[:, None] * emb[None, :]
+        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
+        return emb
+
+
+class ResidualBlock(nn.Module):
+    def __init__(self, hidden_dim, attn_head, dilation, drop_out, has_cattn=False):
+        super().__init__()
+
+        self.hidden_dim = hidden_dim
+        self.dilation = dilation
+        self.has_cattn = has_cattn
+        self.attn_head = attn_head
+        self.drop_out = drop_out
+
+        self.dilated_conv = Conv1d(
+            hidden_dim, 2 * hidden_dim, 3, padding=dilation, dilation=dilation
+        )
+        self.diffusion_proj = Linear(hidden_dim, hidden_dim)
+
+        self.cond_proj = Conv1d(hidden_dim, hidden_dim * 2, 1)
+        self.out_proj = Conv1d(hidden_dim, hidden_dim * 2, 1)
+
+        if self.has_cattn:
+            self.attn = nn.MultiheadAttention(
+                hidden_dim, attn_head, 0.1, batch_first=True
+            )
+            self.film = FiLM(hidden_dim * 2, hidden_dim)
+
+            self.ln = nn.LayerNorm(hidden_dim)
+
+        self.dropout = nn.Dropout(self.drop_out)
+
+    def forward(self, x, x_mask, cond, diffusion_step, spk_query_emb):
+        diffusion_step = self.diffusion_proj(diffusion_step).unsqueeze(-1)  # (B, d, 1)
+        cond = self.cond_proj(cond)  # (B, 2*d, T)
+
+        y = x + diffusion_step
+        if x_mask != None:
+            y = y * x_mask.to(y.dtype)[:, None, :]  # (B, 2*d, T)
+
+        if self.has_cattn:
+            y_ = y.transpose(1, 2)
+            y_ = self.ln(y_)
+
+            y_, _ = self.attn(y_, spk_query_emb, spk_query_emb)  # (B, T, d)
+
+        y = self.dilated_conv(y) + cond  # (B, 2*d, T)
+
+        if self.has_cattn:
+            y = self.film(y.transpose(1, 2), y_)  # (B, T, 2*d)
+            y = y.transpose(1, 2)  # (B, 2*d, T)
+
+        gate, filter_ = torch.chunk(y, 2, dim=1)
+        y = torch.sigmoid(gate) * torch.tanh(filter_)
+
+        y = self.out_proj(y)
+
+        residual, skip = torch.chunk(y, 2, dim=1)
+
+        if x_mask != None:
+            residual = residual * x_mask.to(y.dtype)[:, None, :]
+            skip = skip * x_mask.to(y.dtype)[:, None, :]
+
+        return (x + residual) / math.sqrt(2.0), skip
+
+
+class WaveNet(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+
+        self.cfg = cfg
+        self.in_dim = cfg.input_size
+        self.hidden_dim = cfg.hidden_size
+        self.out_dim = cfg.out_size
+        self.num_layers = cfg.num_layers
+        self.cross_attn_per_layer = cfg.cross_attn_per_layer
+        self.dilation_cycle = cfg.dilation_cycle
+        self.attn_head = cfg.attn_head
+        self.drop_out = cfg.drop_out
+
+        self.in_proj = Conv1d(self.in_dim, self.hidden_dim, 1)
+        self.diffusion_embedding = SinusoidalPosEmb(self.hidden_dim)
+
+        self.mlp = nn.Sequential(
+            Linear(self.hidden_dim, self.hidden_dim * 4),
+            Mish(),
+            Linear(self.hidden_dim * 4, self.hidden_dim),
+        )
+
+        self.cond_ln = nn.LayerNorm(self.hidden_dim)
+
+        self.layers = nn.ModuleList(
+            [
+                ResidualBlock(
+                    self.hidden_dim,
+                    self.attn_head,
+                    2 ** (i % self.dilation_cycle),
+                    self.drop_out,
+                    has_cattn=(i % self.cross_attn_per_layer == 0),
+                )
+                for i in range(self.num_layers)
+            ]
+        )
+
+        self.skip_proj = Conv1d(self.hidden_dim, self.hidden_dim, 1)
+        self.out_proj = Conv1d(self.hidden_dim, self.out_dim, 1)
+
+        nn.init.zeros_(self.out_proj.weight)
+
+    def forward(self, x, x_mask, cond, diffusion_step, spk_query_emb):
+        """
+        x: (B, 128, T)
+        x_mask: (B, T), mask is 0
+        cond: (B, T, 512)
+        diffusion_step: (B,)
+        spk_query_emb: (B, 32, 512)
+        """
+        cond = self.cond_ln(cond)
+        cond_input = cond.transpose(1, 2)
+
+        x_input = self.in_proj(x)
+
+        x_input = F.relu(x_input)
+
+        diffusion_step = self.diffusion_embedding(diffusion_step).to(x.dtype)
+        diffusion_step = self.mlp(diffusion_step)
+
+        skip = []
+        for _, layer in enumerate(self.layers):
+            x_input, skip_connection = layer(
+                x_input, x_mask, cond_input, diffusion_step, spk_query_emb
+            )
+            skip.append(skip_connection)
+
+        x_input = torch.sum(torch.stack(skip), dim=0) / math.sqrt(self.num_layers)
+
+        x_out = self.skip_proj(x_input)
+
+        x_out = F.relu(x_out)
+
+        x_out = self.out_proj(x_out)  # (B, 128, T)
+
+        return x_out
diff --git a/models/tts/valle/__init__.py b/models/tts/valle/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/models/tts/valle/valle.py b/models/tts/valle/valle.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ba81dc57439c7050eaf2832257a8b98b7904414
--- /dev/null
+++ b/models/tts/valle/valle.py
@@ -0,0 +1,792 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+# This code is modified from https://github.com/lifeiteng/vall-e/blob/main/valle/models/valle.py
+
+import random
+from typing import Dict, Iterator, List, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torchmetrics.classification import MulticlassAccuracy
+from utils.util import make_pad_mask
+from utils.topk_sampling import topk_sampling
+from modules.general import Transpose
+from modules.encoder import TokenEmbedding
+from modules.general import PromptedFeatures
+from modules.transformer import SinePositionalEmbedding
+from modules.norms import AdaptiveLayerNorm, LayerNorm
+from modules.transformer.transformer import TransformerEncoder, TransformerEncoderLayer
+
+
+class VALLE(nn.Module):
+    def __init__(
+        self,
+        cfg,
+        decoder_cls=TransformerEncoder,
+        decoder_layer_cls=TransformerEncoderLayer,
+    ):
+        super().__init__()
+        decoder_dim = cfg.decoder_dim
+        nhead = cfg.nhead
+        nar_scale_factor = cfg.nar_scale_factor
+        num_quantizers = cfg.num_quantizers
+        num_decoder_layers = cfg.num_decoder_layers
+        nar_decoder_dim = int(decoder_dim * nar_scale_factor)
+
+        self.ar_text_embedding = TokenEmbedding(decoder_dim, cfg.text_token_num)
+        self.nar_text_embedding = TokenEmbedding(nar_decoder_dim, cfg.text_token_num)
+
+        self.ar_audio_prepend_bos = cfg.prepend_bos
+        self.ar_audio_embedding = TokenEmbedding(
+            decoder_dim, cfg.audio_token_num + 1 + int(cfg.prepend_bos)
+        )
+        self.audio_token_num = cfg.audio_token_num
+
+        # PreNet of AR
+        if cfg.add_prenet:
+            self.ar_text_prenet = nn.Sequential(
+                Transpose(),
+                nn.Conv1d(decoder_dim, decoder_dim, kernel_size=5, padding="same"),
+                nn.BatchNorm1d(decoder_dim),
+                nn.ReLU(),
+                nn.Dropout(0.5),
+                nn.Conv1d(decoder_dim, decoder_dim, kernel_size=5, padding="same"),
+                nn.BatchNorm1d(decoder_dim),
+                nn.ReLU(),
+                nn.Dropout(0.5),
+                nn.Conv1d(decoder_dim, decoder_dim, kernel_size=5, padding="same"),
+                nn.BatchNorm1d(decoder_dim),
+                nn.ReLU(),
+                nn.Dropout(0.5),
+                Transpose(),
+                nn.Linear(decoder_dim, decoder_dim),
+            )
+
+            self.ar_audio_prenet = nn.Sequential(
+                nn.Linear(decoder_dim, 256),
+                nn.ReLU(),
+                nn.Dropout(0.25),
+                nn.Linear(256, 256),
+                nn.ReLU(),
+                nn.Dropout(0.25),
+                nn.Linear(256, decoder_dim),
+            )
+        else:
+            self.ar_text_prenet = nn.Identity()
+            self.ar_audio_prenet = nn.Identity()
+
+        self.ar_text_position = SinePositionalEmbedding(
+            decoder_dim,
+            dropout=0.1,
+            scale=False,
+            alpha=True,
+        )
+        self.ar_audio_position = SinePositionalEmbedding(
+            decoder_dim,
+            dropout=0.1,
+            scale=False,
+            alpha=True,
+        )
+
+        self.ar_decoder = decoder_cls(
+            decoder_layer_cls(
+                decoder_dim,
+                nhead,
+                dim_feedforward=decoder_dim * 4,  # *4?
+                dropout=0.1,
+                batch_first=True,
+                norm_first=cfg.norm_first,
+            ),
+            num_layers=num_decoder_layers,
+            norm=LayerNorm(decoder_dim) if cfg.norm_first else None,
+        )
+        self.ar_predict_layer = nn.Linear(
+            decoder_dim, cfg.audio_token_num + 1, bias=False
+        )
+
+        self.ar_accuracy_metric = MulticlassAccuracy(
+            cfg.audio_token_num + 1,
+            top_k=10,
+            average="micro",
+            multidim_average="global",
+            ignore_index=cfg.audio_token_num,
+        )
+
+        self.rng = random.Random(0)
+        self.num_heads = nhead
+        self.prefix_mode = cfg.prefix_mode
+        self.num_quantizers = num_quantizers
+
+        assert num_quantizers >= 1
+        if num_quantizers > 1:
+            self.nar_audio_embeddings = nn.ModuleList(
+                [
+                    TokenEmbedding(nar_decoder_dim, cfg.audio_token_num + 1)
+                ]  # Why the first layer is audio_token_num + 1?
+                + [
+                    TokenEmbedding(nar_decoder_dim, cfg.audio_token_num)
+                    for i in range(num_quantizers - 1)
+                ]
+            )
+
+            if cfg.add_prenet:
+                self.nar_text_prenet = nn.Sequential(
+                    Transpose(),
+                    nn.Conv1d(
+                        nar_decoder_dim, nar_decoder_dim, kernel_size=5, padding="same"
+                    ),
+                    nn.BatchNorm1d(nar_decoder_dim),
+                    nn.ReLU(),
+                    nn.Dropout(0.5),
+                    nn.Conv1d(
+                        nar_decoder_dim, nar_decoder_dim, kernel_size=5, padding="same"
+                    ),
+                    nn.BatchNorm1d(nar_decoder_dim),
+                    nn.ReLU(),
+                    nn.Dropout(0.5),
+                    nn.Conv1d(
+                        nar_decoder_dim, nar_decoder_dim, kernel_size=5, padding="same"
+                    ),
+                    nn.BatchNorm1d(nar_decoder_dim),
+                    nn.ReLU(),
+                    nn.Dropout(0.5),
+                    Transpose(),
+                    nn.Linear(nar_decoder_dim, nar_decoder_dim),
+                )
+                self.nar_audio_prenet = nn.Sequential(
+                    nn.Linear(nar_decoder_dim, 256),
+                    nn.ReLU(),
+                    nn.Dropout(0.25),
+                    nn.Linear(256, 256),
+                    nn.ReLU(),
+                    nn.Dropout(0.25),
+                    nn.Linear(256, nar_decoder_dim),
+                )
+            else:
+                self.nar_text_prenet = nn.Identity()
+                self.nar_audio_prenet = nn.Identity()
+
+            self.nar_text_position = SinePositionalEmbedding(
+                nar_decoder_dim,
+                dropout=0.0,
+                scale=False,
+                alpha=False,
+            )
+            self.nar_audio_position = SinePositionalEmbedding(
+                nar_decoder_dim,
+                dropout=0.1,
+                scale=False,
+                alpha=False,
+            )
+
+            self.nar_decoder = decoder_cls(
+                decoder_layer_cls(
+                    nar_decoder_dim,
+                    int(nhead * nar_scale_factor),
+                    dim_feedforward=nar_decoder_dim * 4,
+                    dropout=0.1,
+                    batch_first=True,
+                    norm_first=cfg.norm_first,
+                    adaptive_layer_norm=True,
+                ),
+                num_layers=int(num_decoder_layers * nar_scale_factor),
+                norm=AdaptiveLayerNorm(
+                    nar_decoder_dim, norm=nn.LayerNorm(nar_decoder_dim)
+                )
+                if cfg.norm_first
+                else None,
+            )
+            self.nar_predict_layers = nn.ModuleList(
+                [
+                    nn.Linear(nar_decoder_dim, cfg.audio_token_num, bias=False)
+                    for i in range(num_quantizers - 1)
+                ]
+            )
+            self.nar_stage_embeddings = nn.ModuleList(
+                [TokenEmbedding(nar_decoder_dim, 1) for i in range(num_quantizers - 1)]
+            )
+
+            if cfg.share_embedding:
+                for j in range(0, num_quantizers - 2):
+                    self.nar_predict_layers[j].weight = self.nar_audio_embeddings[
+                        j + 2
+                    ].weight
+
+            self.nar_accuracy_metric = MulticlassAccuracy(
+                cfg.audio_token_num + 1,
+                top_k=10,
+                average="micro",
+                multidim_average="global",
+                ignore_index=cfg.audio_token_num,
+            )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_lens: torch.Tensor,
+        y: Union[torch.Tensor, PromptedFeatures],
+        y_lens: Union[torch.Tensor, PromptedFeatures],
+        reduction: str = "sum",
+        train_stage: int = 0,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Union[torch.Tensor, None]]:
+        """
+        Args:
+          x:
+            A 2-D tensor of shape (N, S).
+          x_lens:
+            A 1-D tensor of shape (N,). It contains the number of tokens in `x`
+            before padding.
+          y:
+            A 3-D tensor of shape (N, T, 8).
+          y_lens:
+            A 1-D tensor of shape (N,). It contains the number of tokens in `x`
+            before padding.
+          train_stage:
+            0: AR & NAR modules, 1: AR modules, 2: NAR modules
+        Returns:
+          Return the predicted audio code matrix, cross-entropy loss and Top-10 accuracy.
+        """
+        assert x.ndim == 2, x.shape
+        assert x_lens.ndim == 1, x_lens.shape
+
+        y_prompts_codes = None
+        if isinstance(y, PromptedFeatures):
+            y_prompts_codes, y = y.data
+            prompts_len, y_lens = y_lens.data
+            assert prompts_len.min() == prompts_len.max()
+            assert self.prefix_mode == 4
+            y_prompts_codes = y_prompts_codes.type(torch.int64)
+
+        assert y.ndim == 3, y.shape
+        assert y_lens.ndim == 1, y_lens.shape
+
+        x_mask = make_pad_mask(x_lens).to(x.device)
+        y_mask = make_pad_mask(y_lens).to(y.device)
+        y_mask_int = y_mask.type(torch.int64)
+
+        text = x
+        codes = y.type(torch.int64) * (1 - y_mask_int.unsqueeze(dim=-1))
+
+        y, targets = self.pad_y_eos(
+            codes[..., 0], y_mask_int, eos_id=self.audio_token_num
+        )
+        self.y_mask_int = y_mask_int
+
+        metrics = {}
+        total_loss = 0.0
+
+        xy_padding_mask = torch.concat([x_mask, y_mask], dim=1)
+        if self.ar_audio_prepend_bos:
+            ar_xy_padding_mask = torch.concat(
+                [x_mask, F.pad(y_mask, (1, 0), value=False)], dim=1
+            )
+        else:
+            ar_xy_padding_mask = xy_padding_mask
+        self.xy_padding_mask = xy_padding_mask
+        self.ar_xy_padding_mask = ar_xy_padding_mask
+
+        # AR Decoder
+        if train_stage in [0, 1]:
+            ar_loss, ar_metrics = self._forward_ar_decoder(
+                text, x_lens.max(), y, y_lens.max(), targets, x_mask, y_mask, reduction
+            )
+            total_loss += ar_loss
+            metrics["AR_Top100Acc"] = ar_metrics
+
+        # NAR Decoder
+        if self.ar_audio_prepend_bos:
+            y = y[:, 1:]
+
+        if self.num_quantizers > 1 and train_stage in [0, 2]:
+            nar_loss, nar_metrics = self._forward_nar_decoder(
+                text,
+                x_lens,
+                y,
+                y_lens,
+                codes,
+                y_prompts_codes,
+                x_mask,
+                y_mask,
+                reduction,
+            )
+            total_loss += nar_loss
+            metrics["NAR_Top100Acc"] = nar_metrics
+
+        if train_stage == 0:
+            total_loss = total_loss / 2.0
+
+        return total_loss, metrics
+
+    def _forward_ar_decoder(
+        self, x, x_len, y, y_lens, targets, x_mask, y_mask, reduction
+    ):
+        x = self.ar_text_embedding(x)
+        x = self.ar_text_prenet(x)
+        x = self.ar_text_position(x)
+
+        y_len = y_lens.max() + int(self.ar_audio_prepend_bos)
+
+        x_attn_mask = F.pad(
+            torch.zeros((x_len, x_len), dtype=torch.bool, device=x.device),
+            (0, y_len),
+            value=True,
+        )
+        y_attn_mask = F.pad(
+            torch.triu(
+                torch.ones(y_len, y_len, dtype=torch.bool, device=x.device),
+                diagonal=1,
+            ),
+            (x_len, 0),
+            value=False,
+        )
+        xy_attn_mask = torch.concat([x_attn_mask, y_attn_mask], dim=0)
+
+        bsz, src_len = x.shape[0], x_len + y_len
+        _xy_padding_mask = (
+            self.ar_xy_padding_mask.view(bsz, 1, 1, src_len)
+            .expand(-1, self.num_heads, -1, -1)
+            .reshape(bsz * self.num_heads, 1, src_len)
+        )
+        xy_attn_mask = xy_attn_mask.logical_or(_xy_padding_mask)
+
+        new_attn_mask = torch.zeros_like(xy_attn_mask, dtype=x.dtype)
+        new_attn_mask.masked_fill_(xy_attn_mask, float("-inf"))
+        xy_attn_mask = new_attn_mask
+
+        y_emb = self.ar_audio_embedding(y)
+        y_emb = self.ar_audio_prenet(y_emb)
+        y_pos = self.ar_audio_position(y_emb)
+
+        xy_pos = torch.concat([x, y_pos], dim=1)
+
+        xy_dec, _ = self.ar_decoder(
+            (xy_pos, None),
+            mask=xy_attn_mask,
+        )
+        logits = self.ar_predict_layer(xy_dec[:, x_len:]).permute(0, 2, 1)
+        ar_loss = F.cross_entropy(logits, targets, reduction=reduction)
+
+        ar_metrics = self.ar_accuracy_metric(
+            logits.detach(), targets
+        ).item() * y_lens.sum().type(torch.float32)
+
+        return ar_loss, ar_metrics
+
+    def _forward_nar_decoder(
+        self, x, x_lens, y, y_lens, codes, y_prompts_codes, x_mask, y_mask, reduction
+    ):
+        num_nar_layers = self.num_quantizers - 1
+        nar_stage = self.rng.choices(
+            [_k for _k in range(1, self.num_quantizers)],
+            weights=[1.0 / num_nar_layers] * num_nar_layers,
+            k=1,
+        )[0]
+
+        x = self.nar_text_embedding(x)
+        x = self.nar_text_prenet(x)
+        x = self.nar_text_position(x)
+
+        y_emb, prefix_len = self._prepare_prompts(
+            y, y_lens, codes, nar_stage, y_prompts_codes
+        )
+
+        y_len = y_lens.max()
+        targets = codes[..., nar_stage] + self.audio_token_num * self.y_mask_int
+        if self.prefix_mode in [2, 4]:
+            xy_padding_mask = torch.concat(
+                [
+                    x_mask,
+                    F.pad(y_mask, (y_emb.shape[1] - y_len, 0), value=False),
+                ],
+                dim=1,
+            )
+        elif self.prefix_mode == 1:
+            targets = targets[:, prefix_len:]
+
+        y_pos = self.nar_audio_prenet(y_emb)
+        y_pos = self.nar_audio_position(y_pos)
+        xy_pos = torch.concat([x, y_pos], dim=1)
+        xy_dec, _ = self.nar_decoder(
+            (xy_pos, self.nar_stage_embeddings[nar_stage - 1].weight),
+            src_key_padding_mask=self.xy_padding_mask,
+        )
+        xy_dec = xy_dec[:, x_lens.max() + prefix_len :]
+        if self.prefix_mode == 4:
+            prefix_len = 0
+        logits = self.nar_predict_layers[nar_stage - 1](xy_dec).permute(0, 2, 1)
+
+        total_length = (y_lens).sum().type(torch.float32)
+        nar_loss = F.cross_entropy(
+            logits,
+            targets,
+            ignore_index=self.audio_token_num,
+            reduction=reduction,
+        ) * (total_length / (total_length - prefix_len * x.shape[0]))
+        nar_metrics = (
+            self.nar_accuracy_metric(
+                F.pad(
+                    logits.detach(),
+                    (0, 0, 0, 1, 0, 0),
+                    value=logits.min().cpu().item(),
+                ),
+                targets,
+            ).item()
+            * total_length
+        )
+        return nar_loss, nar_metrics
+
+    def inference(
+        self,
+        x: torch.Tensor,
+        x_lens: torch.Tensor,
+        y: torch.Tensor,
+        enroll_x_lens: torch.Tensor,
+        top_k: int = -100,
+        temperature: float = 1.0,
+    ) -> torch.Tensor:
+        """
+        Args:
+          x:
+            A 2-D tensor of shape (1, S).
+          x_lens:
+            A 1-D tensor of shape (1,). It contains the number of tokens in `x`
+            before padding.
+          y:
+            A 3-D tensor of shape (1, T, 8).
+          top_k: (`optional`) int
+            The number of highest probability tokens to keep for top-k-filtering. Default to -100.
+          temperature: (`optional`) float
+            The value used to module the next token probabilities. Must be strictly positive. Default to 1.0.
+        Returns:
+          Return the predicted audio code matrix.
+        """
+        assert x.ndim == 2, x.shape
+        assert x_lens.ndim == 1, x_lens.shape
+        assert y.ndim == 3, y.shape
+        assert y.shape[0] == 1, y.shape
+
+        assert torch.all(x_lens > 0)
+
+        text = x
+        x = self.ar_text_embedding(text)
+        x = self.ar_text_prenet(x)
+        x = self.ar_text_position(x)
+
+        text_len = x_lens.max()
+        prompts = y
+        prefix_len = y.shape[1]
+
+        # AR Decoder
+        y = prompts[..., 0]
+        if self.ar_audio_prepend_bos:
+            y = F.pad(y, (1, 0), value=self.audio_token_num + 1)
+
+        x_len = x_lens.max()
+        x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool)
+
+        while True:
+            y_emb = self.ar_audio_embedding(y)
+            y_emb = self.ar_audio_prenet(y_emb)
+            y_pos = self.ar_audio_position(y_emb)
+            xy_pos = torch.concat([x, y_pos], dim=1)
+
+            y_len = y.shape[1]
+            x_attn_mask_pad = F.pad(
+                x_attn_mask,
+                (0, y_len),
+                value=True,
+            )
+            y_attn_mask = F.pad(
+                torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1),
+                (x_len, 0),
+                value=False,
+            )
+            xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0).to(
+                y.device
+            )
+
+            xy_dec, _ = self.ar_decoder(
+                (xy_pos, None),
+                mask=xy_attn_mask,
+            )
+            logits = self.ar_predict_layer(xy_dec[:, -1])
+            samples = topk_sampling(
+                logits, top_k=top_k, top_p=1.0, temperature=temperature
+            )
+
+            if (
+                torch.argmax(logits, dim=-1)[0] == self.audio_token_num
+                or samples[0, 0] == self.audio_token_num
+                or (y.shape[1] - prompts.shape[1]) > x_lens.max() * 16
+            ):
+                if prompts.shape[1] == y.shape[1]:
+                    raise SyntaxError("well trained model shouldn't reach here.")
+
+                break
+
+            y = torch.concat([y, samples], dim=1)
+
+        codes = [y[:, prefix_len + int(self.ar_audio_prepend_bos) :]]
+        if self.num_quantizers == 1:
+            return torch.stack(codes, dim=-1)
+
+        # Non-AR Decoders
+        y_emb = self.nar_audio_embeddings[0](y[:, int(self.ar_audio_prepend_bos) :])
+
+        if self.prefix_mode in [2, 4]:
+            enrolled_len = enroll_x_lens.max().item()
+            # SOS + Synthesis Text + EOS
+            text = torch.concat(
+                [
+                    text[:, :1],
+                    text[:, enrolled_len - 1 :],
+                ],
+                dim=1,
+            )
+            text_len = text_len - (enrolled_len - 2)
+            assert text.shape[0] == 1
+
+        x = self.nar_text_embedding(text)
+        x = self.nar_text_prenet(x)
+        x = self.nar_text_position(x)
+
+        if self.prefix_mode == 0:
+            for i, (predict_layer, embedding_layer) in enumerate(
+                zip(
+                    self.nar_predict_layers,
+                    self.nar_audio_embeddings[1:],
+                )
+            ):
+                y_pos = self.nar_audio_prenet(y_emb)
+                y_pos = self.nar_audio_position(y_pos)
+                xy_pos = torch.concat([x, y_pos], dim=1)
+
+                xy_dec, _ = self.nar_decoder(
+                    (xy_pos, self.nar_stage_embeddings[i].weight)
+                )
+                logits = predict_layer(xy_dec[:, text_len + prefix_len :])
+
+                samples = torch.argmax(logits, dim=-1)
+                codes.append(samples)
+
+                if i < self.num_quantizers - 2:
+                    y_emb[:, :prefix_len] += embedding_layer(prompts[..., i + 1])
+                    y_emb[:, prefix_len:] += embedding_layer(samples)
+        else:
+            for j in range(1, self.num_quantizers):
+                y_emb[:, :prefix_len] += self.nar_audio_embeddings[j](prompts[..., j])
+
+            for i, (predict_layer, embedding_layer) in enumerate(
+                zip(
+                    self.nar_predict_layers,
+                    self.nar_audio_embeddings[1:],
+                )
+            ):
+                y_pos = self.nar_audio_prenet(y_emb)
+                y_pos = self.nar_audio_position(y_pos)
+                xy_pos = torch.concat([x, y_pos], dim=1)
+
+                xy_dec, _ = self.nar_decoder(
+                    (xy_pos, self.nar_stage_embeddings[i].weight)
+                )
+                logits = predict_layer(xy_dec[:, text_len + prefix_len :])
+
+                samples = torch.argmax(logits, dim=-1)
+                codes.append(samples)
+
+                if i < self.num_quantizers - 2:
+                    y_emb[:, prefix_len:] += embedding_layer(samples)
+
+        assert len(codes) == self.num_quantizers
+        return torch.stack(codes, dim=-1)
+
+    def continual(
+        self,
+        x: torch.Tensor,
+        x_lens: torch.Tensor,
+        y: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Args:
+          x:
+            A 2-D tensor of shape (1, S).
+          x_lens:
+            A 1-D tensor of shape (1,). It contains the number of tokens in `x`
+            before padding.
+          y:
+            A 3-D tensor of shape (1, T, 8).
+        Returns:
+          Return the predicted audio code matrix.
+        """
+        assert x.ndim == 2, x.shape
+        assert x_lens.ndim == 1, x_lens.shape
+        assert y.ndim == 3, y.shape
+        assert y.shape[0] == 1, y.shape
+
+        assert torch.all(x_lens > 0)
+        assert self.num_quantizers == 8
+
+        text = x
+        x = self.ar_text_embedding(text)
+        x = self.ar_text_prenet(x)
+        x = self.ar_text_position(x)
+
+        text_len = x_lens.max()
+
+        prefix_len = min(int(y.shape[1] * 0.5), 3 * 75)
+
+        # AR Decoder
+        prompts = y[:, :prefix_len]
+
+        codes = [y[:, prefix_len:, 0]]
+        # Non-AR Decoders
+        x = self.nar_text_embedding(text)
+        x = self.nar_text_prenet(x)
+        x = self.nar_text_position(x)
+
+        y_emb = self.nar_audio_embeddings[0](y[..., 0])
+
+        if self.prefix_mode == 0:
+            for i, (predict_layer, embedding_layer) in enumerate(
+                zip(
+                    self.nar_predict_layers,
+                    self.nar_audio_embeddings[1:],
+                )
+            ):
+                y_pos = self.nar_audio_position(y_emb)
+                y_pos = self.nar_audio_prenet(y_pos)
+                xy_pos = torch.concat([x, y_pos], dim=1)
+
+                xy_dec, _ = self.nar_decoder(
+                    (xy_pos, self.nar_stage_embeddings[i].weight)
+                )
+                logits = predict_layer(xy_dec[:, text_len + prefix_len :])
+
+                samples = torch.argmax(logits, dim=-1)
+                codes.append(samples)
+
+                if i < 6:
+                    y_emb[:, :prefix_len] += embedding_layer(prompts[..., i + 1])
+                    y_emb[:, prefix_len:] += embedding_layer(samples)
+        else:
+            for j in range(1, 8):
+                y_emb[:, :prefix_len] += self.nar_audio_embeddings[j](prompts[..., j])
+
+            for i, (predict_layer, embedding_layer) in enumerate(
+                zip(
+                    self.nar_predict_layers,
+                    self.nar_audio_embeddings[1:],
+                )
+            ):
+                y_pos = self.nar_audio_prenet(y_emb)
+                y_pos = self.nar_audio_position(y_pos)
+                xy_pos = torch.concat([x, y_pos], dim=1)
+
+                xy_dec, _ = self.nar_decoder(
+                    (xy_pos, self.nar_stage_embeddings[i].weight)
+                )
+                logits = predict_layer(xy_dec[:, text_len + prefix_len :])
+
+                samples = torch.argmax(logits, dim=-1)
+                codes.append(samples)
+
+                if i < 6:
+                    y_emb[:, prefix_len:] += embedding_layer(samples)
+
+        assert len(codes) == 8
+        return torch.stack(codes, dim=-1)
+
+    def stage_parameters(self, stage: int = 1) -> Iterator[nn.Parameter]:
+        assert stage > 0
+        if stage == 1:
+            for name, param in self.named_parameters():
+                if name.startswith("ar_"):
+                    yield param
+
+        if stage == 2:
+            for name, param in self.named_parameters():
+                if name.startswith("nar_"):
+                    yield param
+
+    def stage_named_parameters(
+        self, stage: int = 1
+    ) -> Iterator[Tuple[str, nn.Parameter]]:
+        assert stage > 0
+        if stage == 1:
+            for pair in self.named_parameters():
+                if pair[0].startswith("ar_"):
+                    yield pair
+
+        if stage == 2:
+            for pair in self.named_parameters():
+                if pair[0].startswith("nar_"):
+                    yield pair
+
+    def pad_y_eos(self, y, y_mask_int, eos_id):
+        targets = F.pad(y, (0, 1), value=0) + eos_id * F.pad(
+            y_mask_int, (0, 1), value=1
+        )
+        if self.ar_audio_prepend_bos:
+            return (
+                F.pad(targets[:, :-1], (1, 0), value=self.audio_token_num + 1),
+                targets,
+            )
+
+        return targets[:, :-1], targets[:, 1:]
+
+    def _prepare_prompts(self, y, y_lens, codes, nar_stage, y_prompts_codes):
+        # 5.1 For the NAR acoustic prompt tokens, we select a random segment waveform of 3 seconds
+        # from the same utterance.
+        # We implement this differently.
+        if self.prefix_mode == 0:
+            # no prefix
+            prefix_len = 0
+            y_emb = self.nar_audio_embeddings[0](y)
+            for j in range(1, nar_stage):
+                # Formula (4) (5)
+                y_emb = y_emb + self.nar_audio_embeddings[j](codes[..., j])
+        elif self.prefix_mode == 1:
+            # prefix at begining
+            int_low = (0.25 * y_lens.min()).type(torch.int64).item()
+            prefix_len = torch.randint(int_low, int_low * 2, size=()).item()
+            prefix_len = min(prefix_len, 225)  # 24000/320 * 3s = 225 frames
+
+            y_prompts = self.nar_audio_embeddings[0](y[:, :prefix_len])
+            y_emb = self.nar_audio_embeddings[0](y[:, prefix_len:])
+            for j in range(1, self.num_quantizers):
+                y_prompts += self.nar_audio_embeddings[j](codes[:, :prefix_len, j])
+                if j < nar_stage:
+                    y_emb += self.nar_audio_embeddings[j](codes[:, prefix_len:, j])
+            y_emb = torch.concat([y_prompts, y_emb], axis=1)
+        elif self.prefix_mode in [2, 4]:
+            if self.prefix_mode == 2:
+                # random prefix
+                prefix_len = min(225, int(0.25 * y_lens.min().item()))
+
+                y_prompts_codes = []
+                for b in range(codes.shape[0]):
+                    start = self.rng.randint(0, y_lens[b].item() - prefix_len)
+                    y_prompts_codes.append(
+                        torch.clone(codes[b, start : start + prefix_len])
+                    )
+                    codes[b, start : start + prefix_len, nar_stage] = NUM_AUDIO_TOKENS
+                y_prompts_codes = torch.stack(y_prompts_codes, dim=0)
+            else:
+                prefix_len = y_prompts_codes.shape[1]
+
+            y_prompts = self.nar_audio_embeddings[0](y_prompts_codes[..., 0])
+            y_emb = self.nar_audio_embeddings[0](y)
+            for j in range(1, self.num_quantizers):
+                y_prompts += self.nar_audio_embeddings[j](y_prompts_codes[..., j])
+                if j < nar_stage:
+                    y_emb += self.nar_audio_embeddings[j](codes[..., j])
+            y_emb = torch.concat([y_prompts, y_emb], axis=1)
+        else:
+            raise ValueError
+
+        return y_emb, prefix_len
diff --git a/models/tts/valle/valle_dataset.py b/models/tts/valle/valle_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f3b3fa0f730bb5955ea556d871c530f2b04e007
--- /dev/null
+++ b/models/tts/valle/valle_dataset.py
@@ -0,0 +1,312 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+from torch.nn.utils.rnn import pad_sequence
+from utils.data_utils import *
+from models.tts.base.tts_dataset import (
+    TTSDataset,
+    TTSCollator,
+    TTSTestDataset,
+    TTSTestCollator,
+)
+
+from torch.utils.data.sampler import (
+    BatchSampler,
+    RandomSampler,
+    SequentialSampler,
+)
+
+from utils.tokenizer import tokenize_audio
+
+
+class VALLEDataset(TTSDataset):
+    def __init__(self, cfg, dataset, is_valid=False):
+        super().__init__(cfg, dataset, is_valid=is_valid)
+
+        """
+        Args:
+            cfg: config
+            dataset: dataset name
+            is_valid: whether to use train or valid dataset
+        """
+
+        assert isinstance(dataset, str)
+
+        assert cfg.preprocess.use_acoustic_token == True
+        if cfg.preprocess.use_acoustic_token:
+            self.utt2acousticToken_path = {}
+            for utt_info in self.metadata:
+                dataset = utt_info["Dataset"]
+                uid = utt_info["Uid"]
+                utt = "{}_{}".format(dataset, uid)
+
+                self.utt2acousticToken_path[utt] = os.path.join(
+                    cfg.preprocess.processed_dir,
+                    dataset,
+                    cfg.preprocess.acoustic_token_dir,  # code
+                    uid + ".npy",
+                )
+
+        self.all_num_frames = []
+        for i in range(len(self.metadata)):
+            self.all_num_frames.append(self.metadata[i]["Duration"])
+        self.num_frame_sorted = np.array(sorted(self.all_num_frames))
+        self.num_frame_indices = np.array(
+            sorted(
+                range(len(self.all_num_frames)), key=lambda k: self.all_num_frames[k]
+            )
+        )
+
+    def __len__(self):
+        return super().__len__()
+
+    def get_metadata(self):
+        metadata_filter = []
+        with open(self.metafile_path, "r", encoding="utf-8") as f:
+            metadata = json.load(f)
+        for utt_info in metadata:
+            duration = utt_info["Duration"]
+            if (
+                duration >= self.cfg.preprocess.max_duration
+                or duration <= self.cfg.preprocess.min_duration
+            ):
+                continue
+            metadata_filter.append(utt_info)
+
+        return metadata_filter
+
+    def get_dur(self, idx):
+        utt_info = self.metadata[idx]
+        return utt_info["Duration"]
+
+    def __getitem__(self, index):
+        single_feature = super().__getitem__(index)
+
+        utt_info = self.metadata[index]
+        dataset = utt_info["Dataset"]
+        uid = utt_info["Uid"]
+        utt = "{}_{}".format(dataset, uid)
+
+        # acoustic token
+        if self.cfg.preprocess.use_acoustic_token:
+            acoustic_token = np.load(self.utt2acousticToken_path[utt])
+            if "target_len" not in single_feature.keys():
+                single_feature["target_len"] = acoustic_token.shape[0]
+            single_feature["acoustic_token"] = acoustic_token  # [T, 8]
+
+        return single_feature
+
+    def get_num_frames(self, index):
+        utt_info = self.metadata[index]
+        return int(
+            utt_info["Duration"]
+            * (self.cfg.preprocess.sample_rate // self.cfg.preprocess.codec_hop_size)
+        )
+
+
+class VALLECollator(TTSCollator):
+    def __init__(self, cfg):
+        super().__init__(cfg)
+
+    def __call__(self, batch):
+        parsed_batch_features = super().__call__(batch)
+        return parsed_batch_features
+
+
+class VALLETestDataset(TTSTestDataset):
+    def __init__(self, args, cfg):
+        super().__init__(args, cfg)
+
+        # prepare data
+        assert cfg.preprocess.use_acoustic_token == True
+        if cfg.preprocess.use_acoustic_token:
+            self.utt2acousticToken = {}
+            for utt_info in self.metadata:
+                dataset = utt_info["Dataset"]
+                uid = utt_info["Uid"]
+                utt = "{}_{}".format(dataset, uid)
+
+                # extract acoustic token
+                audio_file = utt_info["Audio_pormpt_path"]
+                encoded_frames = tokenize_audio(self.audio_tokenizer, audio_file)
+                audio_prompt_token = (
+                    encoded_frames[0][0].transpose(2, 1).squeeze(0).cpu().numpy()
+                )
+                self.utt2acousticToken[utt] = audio_prompt_token
+
+    def __getitem__(self, index):
+        utt_info = self.metadata[index]
+
+        dataset = utt_info["Dataset"]
+        uid = utt_info["Uid"]
+        utt = "{}_{}".format(dataset, uid)
+
+        single_feature = dict()
+
+        # acoustic token
+        if self.cfg.preprocess.use_acoustic_token:
+            acoustic_token = self.utt2acousticToken[utt]
+            if "target_len" not in single_feature.keys():
+                single_feature["target_len"] = acoustic_token.shape[0]
+            single_feature["acoustic_token"] = acoustic_token  # [T, 8]
+
+        # phone sequence todo
+        if self.cfg.preprocess.use_phone:
+            single_feature["phone_seq"] = np.array(self.utt2seq[utt])
+            single_feature["phone_len"] = len(self.utt2seq[utt])
+            single_feature["pmt_phone_seq"] = np.array(self.utt2pmtseq[utt])
+            single_feature["pmt_phone_len"] = len(self.utt2pmtseq[utt])
+
+        return single_feature
+
+    def get_metadata(self):
+        with open(self.metafile_path, "r", encoding="utf-8") as f:
+            metadata = json.load(f)
+        return metadata
+
+    def __len__(self):
+        return len(self.metadata)
+
+
+class VALLETestCollator(TTSTestCollator):
+    def __init__(self, cfg):
+        self.cfg = cfg
+
+    def __call__(self, batch):
+        packed_batch_features = dict()
+
+        for key in batch[0].keys():
+            if key == "target_len":
+                packed_batch_features["target_len"] = torch.LongTensor(
+                    [b["target_len"] for b in batch]
+                )
+                masks = [
+                    torch.ones((b["target_len"], 1), dtype=torch.long) for b in batch
+                ]
+                packed_batch_features["mask"] = pad_sequence(
+                    masks, batch_first=True, padding_value=0
+                )
+            elif key == "phone_len":
+                packed_batch_features["phone_len"] = torch.LongTensor(
+                    [b["phone_len"] for b in batch]
+                )
+                masks = [
+                    torch.ones((b["phone_len"], 1), dtype=torch.long) for b in batch
+                ]
+                packed_batch_features["phn_mask"] = pad_sequence(
+                    masks, batch_first=True, padding_value=0
+                )
+            elif key == "pmt_phone_len":
+                packed_batch_features["pmt_phone_len"] = torch.LongTensor(
+                    [b["pmt_phone_len"] for b in batch]
+                )
+                masks = [
+                    torch.ones((b["pmt_phone_len"], 1), dtype=torch.long) for b in batch
+                ]
+                packed_batch_features["pmt_phone_len_mask"] = pad_sequence(
+                    masks, batch_first=True, padding_value=0
+                )
+            elif key == "audio_len":
+                packed_batch_features["audio_len"] = torch.LongTensor(
+                    [b["audio_len"] for b in batch]
+                )
+                masks = [
+                    torch.ones((b["audio_len"], 1), dtype=torch.long) for b in batch
+                ]
+            else:
+                values = [torch.from_numpy(b[key]) for b in batch]
+                packed_batch_features[key] = pad_sequence(
+                    values, batch_first=True, padding_value=0
+                )
+
+        return packed_batch_features
+
+
+def _is_batch_full(batch, num_tokens, max_tokens, max_sentences):
+    if len(batch) == 0:
+        return 0
+    if len(batch) == max_sentences:
+        return 1
+    if num_tokens > max_tokens:
+        return 1
+    return 0
+
+
+def batch_by_size(
+    indices,
+    num_tokens_fn,
+    max_tokens=None,
+    max_sentences=None,
+    required_batch_size_multiple=1,
+):
+    """
+    Yield mini-batches of indices bucketed by size. Batches may contain
+    sequences of different lengths.
+
+    Args:
+        indices (List[int]): ordered list of dataset indices
+        num_tokens_fn (callable): function that returns the number of tokens at
+            a given index
+        max_tokens (int, optional): max number of tokens in each batch
+            (default: None).
+        max_sentences (int, optional): max number of sentences in each
+            batch (default: None).
+        required_batch_size_multiple (int, optional): require batch size to
+            be a multiple of N (default: 1).
+    """
+    bsz_mult = required_batch_size_multiple
+
+    sample_len = 0
+    sample_lens = []
+    batch = []
+    batches = []
+    for i in range(len(indices)):
+        idx = indices[i]
+        num_tokens = num_tokens_fn(idx)
+        sample_lens.append(num_tokens)
+        sample_len = max(sample_len, num_tokens)
+
+        assert (
+            sample_len <= max_tokens
+        ), "sentence at index {} of size {} exceeds max_tokens " "limit of {}!".format(
+            idx, sample_len, max_tokens
+        )
+        num_tokens = (len(batch) + 1) * sample_len
+
+        if _is_batch_full(batch, num_tokens, max_tokens, max_sentences):
+            mod_len = max(
+                bsz_mult * (len(batch) // bsz_mult),
+                len(batch) % bsz_mult,
+            )
+            batches.append(batch[:mod_len])
+            batch = batch[mod_len:]
+            sample_lens = sample_lens[mod_len:]
+            sample_len = max(sample_lens) if len(sample_lens) > 0 else 0
+        batch.append(idx)
+    if len(batch) > 0:
+        batches.append(batch)
+    return batches
+
+
+class VariableSampler(BatchSampler):
+    def __init__(self, sampler, drop_last: bool, use_random_sampler=False):
+        self.data_list = sampler
+        if use_random_sampler:
+            self.sampler = RandomSampler(sampler)
+        else:
+            self.sampler = SequentialSampler(sampler)
+
+        super().__init__(self.sampler, 1, drop_last)
+
+    def __iter__(self):
+        for batch_ids in self.data_list:
+            yield batch_ids
+
+    def __len__(self):
+        if self.drop_last:
+            return len(self.sampler) // self.batch_size
+        else:
+            return (len(self.sampler) + self.batch_size - 1) // self.batch_size
diff --git a/models/tts/valle/valle_inference.py b/models/tts/valle/valle_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..2735057b87302c2132b5ef94c67d4df45ac3fcbb
--- /dev/null
+++ b/models/tts/valle/valle_inference.py
@@ -0,0 +1,237 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import numpy as np
+import torch
+import torchaudio
+import argparse
+
+
+from text.g2p_module import G2PModule
+from utils.tokenizer import AudioTokenizer, tokenize_audio
+from models.tts.valle.valle import VALLE
+from models.tts.base.tts_inferece import TTSInference
+from models.tts.valle.valle_dataset import VALLETestDataset, VALLETestCollator
+from processors.phone_extractor import phoneExtractor
+from text.text_token_collation import phoneIDCollation
+
+
+class VALLEInference(TTSInference):
+    def __init__(self, args=None, cfg=None):
+        TTSInference.__init__(self, args, cfg)
+
+        self.g2p_module = G2PModule(backend=self.cfg.preprocess.phone_extractor)
+        text_token_path = os.path.join(
+            cfg.preprocess.processed_dir, cfg.dataset[0], cfg.preprocess.symbols_dict
+        )
+        self.audio_tokenizer = AudioTokenizer()
+
+    def _build_model(self):
+        model = VALLE(self.cfg.model)
+        return model
+
+    def _build_test_dataset(self):
+        return VALLETestDataset, VALLETestCollator
+
+    def inference_one_clip(self, text, text_prompt, audio_file, save_name="pred"):
+        # get phone symbol file
+        phone_symbol_file = None
+        if self.cfg.preprocess.phone_extractor != "lexicon":
+            phone_symbol_file = os.path.join(
+                self.exp_dir, self.cfg.preprocess.symbols_dict
+            )
+            assert os.path.exists(phone_symbol_file)
+        # convert text to phone sequence
+        phone_extractor = phoneExtractor(self.cfg)
+        # convert phone sequence to phone id sequence
+        phon_id_collator = phoneIDCollation(
+            self.cfg, symbols_dict_file=phone_symbol_file
+        )
+
+        text = f"{text_prompt} {text}".strip()
+        phone_seq = phone_extractor.extract_phone(text)  # phone_seq: list
+        phone_id_seq = phon_id_collator.get_phone_id_sequence(self.cfg, phone_seq)
+        phone_id_seq_len = torch.IntTensor([len(phone_id_seq)]).to(self.device)
+
+        # convert phone sequence to phone id sequence
+        phone_id_seq = np.array([phone_id_seq])
+        phone_id_seq = torch.from_numpy(phone_id_seq).to(self.device)
+
+        # extract acoustic token
+        encoded_frames = tokenize_audio(self.audio_tokenizer, audio_file)
+        audio_prompt_token = encoded_frames[0][0].transpose(2, 1).to(self.device)
+
+        # copysyn
+        if self.args.copysyn:
+            samples = self.audio_tokenizer.decode(encoded_frames)
+            audio_copysyn = samples[0].cpu().detach()
+
+            out_path = os.path.join(
+                self.args.output_dir, self.infer_type, f"{save_name}_copysyn.wav"
+            )
+            torchaudio.save(out_path, audio_copysyn, self.cfg.preprocess.sampling_rate)
+
+        if self.args.continual:
+            encoded_frames = self.model.continual(
+                phone_id_seq,
+                phone_id_seq_len,
+                audio_prompt_token,
+            )
+        else:
+            enroll_x_lens = None
+            if text_prompt:
+                # prompt_phone_seq = tokenize_text(self.g2p_module, text=f"{text_prompt}".strip())
+                # _, enroll_x_lens = self.text_tokenizer.get_token_id_seq(prompt_phone_seq)
+
+                text = f"{text_prompt}".strip()
+                prompt_phone_seq = phone_extractor.extract_phone(
+                    text
+                )  # phone_seq: list
+                prompt_phone_id_seq = phon_id_collator.get_phone_id_sequence(
+                    self.cfg, prompt_phone_seq
+                )
+                prompt_phone_id_seq_len = torch.IntTensor(
+                    [len(prompt_phone_id_seq)]
+                ).to(self.device)
+
+            encoded_frames = self.model.inference(
+                phone_id_seq,
+                phone_id_seq_len,
+                audio_prompt_token,
+                enroll_x_lens=prompt_phone_id_seq_len,
+                top_k=self.args.top_k,
+                temperature=self.args.temperature,
+            )
+
+        samples = self.audio_tokenizer.decode([(encoded_frames.transpose(2, 1), None)])
+
+        audio = samples[0].squeeze(0).cpu().detach()
+
+        return audio
+
+    def inference_for_single_utterance(self):
+        text = self.args.text
+        text_prompt = self.args.text_prompt
+        audio_file = self.args.audio_prompt
+
+        if not self.args.continual:
+            assert text != ""
+        else:
+            text = ""
+        assert text_prompt != ""
+        assert audio_file != ""
+
+        audio = self.inference_one_clip(text, text_prompt, audio_file)
+
+        return audio
+
+    def inference_for_batches(self):
+        test_list_file = self.args.test_list_file
+        assert test_list_file is not None
+
+        pred_res = []
+        with open(test_list_file, "r") as fin:
+            for idx, line in enumerate(fin.readlines()):
+                fields = line.strip().split("|")
+                if self.args.continual:
+                    assert len(fields) == 2
+                    text_prompt, audio_prompt_path = fields
+                    text = ""
+                else:
+                    assert len(fields) == 3
+                    text_prompt, audio_prompt_path, text = fields
+
+                audio = self.inference_one_clip(
+                    text, text_prompt, audio_prompt_path, str(idx)
+                )
+                pred_res.append(audio)
+
+        return pred_res
+
+        """
+        TODO: batch inference 
+        ###### Construct test_batch ######
+        n_batch = len(self.test_dataloader)
+        now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time()))
+        print(
+            "Model eval time: {}, batch_size = {}, n_batch = {}".format(
+                now, self.test_batch_size, n_batch
+            )
+        )       
+        
+        ###### Inference for each batch ######
+        pred_res = []
+        with torch.no_grad():
+            for i, batch_data in enumerate(
+                self.test_dataloader if n_batch == 1 else tqdm(self.test_dataloader)
+            ):
+                if self.args.continual:
+                    encoded_frames = self.model.continual(
+                        batch_data["phone_seq"],
+                        batch_data["phone_len"],
+                        batch_data["acoustic_token"],
+                    )
+                else:
+                    encoded_frames = self.model.inference(
+                        batch_data["phone_seq"],
+                        batch_data["phone_len"],
+                        batch_data["acoustic_token"],
+                        enroll_x_lens=batch_data["pmt_phone_len"],
+                        top_k=self.args.top_k,
+                        temperature=self.args.temperature
+                    )
+                    
+                samples = self.audio_tokenizer.decode(
+                    [(encoded_frames.transpose(2, 1), None)]
+                )
+                
+                            
+                for idx in range(samples.size(0)):
+                    audio = samples[idx].cpu()
+                    pred_res.append(audio)
+                    
+        return pred_res
+        """
+
+    def add_arguments(parser: argparse.ArgumentParser):
+        parser.add_argument(
+            "--text_prompt",
+            type=str,
+            default="",
+            help="Text prompt that should be aligned with --audio_prompt.",
+        )
+
+        parser.add_argument(
+            "--audio_prompt",
+            type=str,
+            default="",
+            help="Audio prompt that should be aligned with --text_prompt.",
+        )
+        parser.add_argument(
+            "--top-k",
+            type=int,
+            default=-100,
+            help="Whether AR Decoder do top_k(if > 0) sampling.",
+        )
+
+        parser.add_argument(
+            "--temperature",
+            type=float,
+            default=1.0,
+            help="The temperature of AR Decoder top_k sampling.",
+        )
+
+        parser.add_argument(
+            "--continual",
+            action="store_true",
+            help="Inference for continual task.",
+        )
+
+        parser.add_argument(
+            "--copysyn",
+            action="store_true",
+            help="Copysyn: generate audio by decoder of the original audio tokenizer.",
+        )
diff --git a/models/tts/valle/valle_trainer.py b/models/tts/valle/valle_trainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..14ae28b63161f33098af0925259149a86fc8c368
--- /dev/null
+++ b/models/tts/valle/valle_trainer.py
@@ -0,0 +1,356 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import argparse
+from tqdm import tqdm
+import torch
+import numpy as np
+from torch.utils.data import DataLoader
+from torch.nn.parallel import DistributedDataParallel
+from optimizer.optimizers import Eve, ScaledAdam
+from schedulers.scheduler import NoamScheduler, Eden
+from models.tts.valle.valle_dataset import (
+    VALLEDataset,
+    VALLECollator,
+    VariableSampler,
+    batch_by_size,
+)
+from models.tts.base import TTSTrainer
+from models.tts.valle.valle import VALLE
+
+
+class VALLETrainer(TTSTrainer):
+    def __init__(self, args, cfg):
+        TTSTrainer.__init__(self, args, cfg)
+
+    def _build_model(self):
+        model = VALLE(self.cfg.model)
+
+        return model
+
+    def _build_dataset(self):
+        return VALLEDataset, VALLECollator
+
+    def _build_optimizer(self):
+        if self.args.train_stage:
+            if isinstance(self.model, DistributedDataParallel):
+                model = self.model.module
+            else:
+                model = self.model
+            model_parameters = model.stage_parameters(self.args.train_stage)
+        else:
+            model_parameters = self.model.parameters()
+
+        if self.cfg.train.optimizer == "ScaledAdam":
+            parameters_names = []
+            if self.args.train_stage != 0:
+                parameters_names.append(
+                    [
+                        name_param_pair[0]
+                        for name_param_pair in model.stage_named_parameters(
+                            self.args.train_stage
+                        )
+                    ]
+                )
+            else:
+                parameters_names.append(
+                    [name_param_pair[0] for name_param_pair in model.named_parameters()]
+                )
+
+            optimizer = ScaledAdam(
+                model_parameters,
+                lr=self.cfg.train.base_lr,
+                betas=(0.9, 0.95),
+                clipping_scale=2.0,
+                parameters_names=parameters_names,
+                show_dominant_parameters=False,
+                clipping_update_period=1000,
+            )
+        elif self.cfg.train.optimizer == "Eve":
+            optimizer = Eve(
+                model_parameters,
+                lr=self.cfg.train.base_lr,
+                betas=(0.9, 0.98),
+                target_rms=0.1,
+            )
+        elif self.cfg.train.optimizer == "AdamW":
+            optimizer = torch.optim.AdamW(
+                model_parameters,
+                lr=self.cfg.train.base_lr,
+                betas=(0.9, 0.95),
+                weight_decay=1e-2,
+                eps=1e-8,
+            )
+        elif self.cfg.train.optimizer == "Adam":
+            optimizer = torch.optim.Adam(
+                model_parameters,
+                lr=self.cfg.train.base_lr,
+                betas=(0.9, 0.95),
+                eps=1e-8,
+            )
+        else:
+            raise NotImplementedError()
+
+        return optimizer
+
+    def _build_scheduler(self):
+        if self.cfg.train.scheduler.lower() == "eden":
+            scheduler = Eden(
+                self.optimizer, 5000, 4, warmup_batches=self.cfg.train.warmup_steps
+            )
+        elif self.cfg.train.scheduler.lower() == "noam":
+            scheduler = NoamScheduler(
+                self.cfg.train.base_lr,
+                self.optimizer,
+                self.cfg.model.decoder_dim,
+                warmup_steps=self.cfg.train.warmup_steps,
+            )
+        elif self.cfg.train.scheduler.lower() == "cosine":
+            scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
+                self.cfg.train.warmup_steps,
+                self.optimizer,
+                eta_min=self.cfg.train.base_lr,
+            )
+        else:
+            raise NotImplementedError(f"{self.cfg.train.scheduler}")
+
+        return scheduler
+
+    def _train_epoch(self):
+        r"""Training epoch. Should return average loss of a batch (sample) over
+        one epoch. See ``train_loop`` for usage.
+        """
+        if isinstance(self.model, dict):
+            for key in self.model.keys():
+                self.model[key].train()
+        else:
+            self.model.train()
+
+        epoch_sum_loss: float = 0.0
+        epoch_losses: dict = {}
+        epoch_step: int = 0
+        for batch in tqdm(
+            self.train_dataloader,
+            desc=f"Training Epoch {self.epoch}",
+            unit="batch",
+            colour="GREEN",
+            leave=False,
+            dynamic_ncols=True,
+            smoothing=0.04,
+            disable=not self.accelerator.is_main_process,
+        ):
+            # Do training step and BP
+            with self.accelerator.accumulate(self.model):
+                total_loss, train_losses = self._train_step(batch)
+                self.accelerator.backward(total_loss)
+                self.optimizer.step()
+                self.optimizer.zero_grad()
+            self.batch_count += 1
+
+            if self.batch_count % self.cfg.train.gradient_accumulation_step == 0:
+                if self.cfg.train.optimizer not in ["ScaledAdam", "Eve"]:
+                    torch.nn.utils.clip_grad_norm_(self.model.parameters(), 1.0)
+
+                for k in range(self.cfg.train.gradient_accumulation_step):
+                    if isinstance(self.scheduler, Eden):
+                        self.scheduler.step_batch(self.step)
+                    else:
+                        self.scheduler.step()
+
+                epoch_sum_loss += total_loss.detach().cpu().item()
+
+                if isinstance(train_losses, dict):
+                    for key, value in train_losses.items():
+                        if key not in epoch_losses.keys():
+                            epoch_losses[key] = value
+                        else:
+                            epoch_losses[key] += value
+
+                if isinstance(train_losses, dict):
+                    for key, loss in train_losses.items():
+                        self.accelerator.log(
+                            {"Step/Train {}".format(key): "{:.6f}".format(loss)},
+                            step=self.step,
+                        )
+                else:
+                    self.accelerator.log(
+                        {"Step/Train Loss": loss},
+                        step=self.step,
+                    )
+
+                self.accelerator.log(
+                    {"Step/lr": self.scheduler.get_last_lr()[0]},
+                    step=self.step,
+                )
+
+                # print loss every log_epoch_step steps
+                # if epoch_step % self.cfg.train.log_epoch_step == 0:
+                #     for key, loss in train_losses.items():
+                #         self.logger.info("Step/Train {}: {:.6f}".format(key, loss))
+                #         print("Step/Train {}: {:.6f}".format(key, loss))
+
+                self.step += 1
+                epoch_step += 1
+
+        self.accelerator.wait_for_everyone()
+
+        epoch_sum_loss = (
+            epoch_sum_loss
+            / len(self.train_dataloader)
+            * self.cfg.train.gradient_accumulation_step
+        )
+
+        for key in epoch_losses.keys():
+            epoch_losses[key] = (
+                epoch_losses[key]
+                / len(self.train_dataloader)
+                * self.cfg.train.gradient_accumulation_step
+            )
+
+        return epoch_sum_loss, epoch_losses
+
+    def _train_step(self, batch, is_training=True):
+        text_tokens = batch["phone_seq"].to(self.device)
+        text_tokens_lens = batch["phone_len"].to(self.device)
+        assert text_tokens.ndim == 2
+
+        audio_features = batch["acoustic_token"].to(self.device)
+        audio_features_lens = batch["target_len"].to(self.device)
+        assert audio_features.ndim == 3
+
+        with torch.set_grad_enabled(is_training):
+            loss, losses = self.model(
+                x=text_tokens,
+                x_lens=text_tokens_lens,
+                y=audio_features,
+                y_lens=audio_features_lens,
+                train_stage=self.args.train_stage,
+            )
+
+        assert loss.requires_grad == is_training
+
+        loss_dict = {}
+        frames_sum = (audio_features_lens).sum()
+
+        avg_loss = loss / frames_sum
+
+        loss_dict["loss"] = avg_loss.detach().cpu().item()
+        for l in losses:
+            loss_dict[l] = losses[l].detach().cpu().item() / frames_sum.item()
+
+        return avg_loss, loss_dict
+
+    def _valid_step(self, batch):
+        valid_losses = {}
+        total_loss = 0
+        valid_stats = {}
+
+        total_loss, valid_losses = self._train_step(
+            batch=batch,
+            is_training=False,
+        )
+        assert total_loss.requires_grad is False
+
+        total_loss = total_loss.detach().cpu().item()
+
+        return total_loss, valid_losses, valid_stats
+
+    def add_arguments(parser: argparse.ArgumentParser):
+        parser.add_argument(
+            "--train_stage",
+            type=int,
+            default="1",
+            help="0: train all modules, 1: AR Decoder, 2: NAR Decoder",
+        )
+
+    def _build_dataloader(self):
+        if not self.cfg.train.use_dynamic_batchsize:
+            return super()._build_dataloader()
+        if len(self.cfg.dataset) > 1:
+            raise Exception("use_dynamic_batchsize only supports single dataset now.")
+        Dataset, Collator = self._build_dataset()
+        train_dataset = Dataset(
+            self.cfg, self.cfg.dataset[0], is_valid=False
+        )  # TODO: support use_dynamic_batchsize for more than one datasets.
+        train_collate = Collator(self.cfg)
+        batch_sampler = batch_by_size(
+            train_dataset.num_frame_indices,
+            train_dataset.get_num_frames,
+            max_tokens=self.cfg.train.max_tokens * self.accelerator.num_processes,
+            max_sentences=self.cfg.train.max_sentences * self.accelerator.num_processes,
+            required_batch_size_multiple=self.accelerator.num_processes,
+        )
+        np.random.seed(1234)
+        np.random.shuffle(batch_sampler)
+        print(batch_sampler[:1])
+        batches = [
+            x[self.accelerator.local_process_index :: self.accelerator.num_processes]
+            for x in batch_sampler
+            if len(x) % self.accelerator.num_processes == 0
+        ]
+
+        train_loader = DataLoader(
+            train_dataset,
+            collate_fn=train_collate,
+            num_workers=self.cfg.train.dataloader.num_worker,
+            batch_sampler=VariableSampler(
+                batches, drop_last=False, use_random_sampler=True
+            ),
+            pin_memory=False,
+        )
+        self.accelerator.wait_for_everyone()
+
+        valid_dataset = Dataset(self.cfg, self.cfg.dataset[0], is_valid=True)
+        valid_collate = Collator(self.cfg)
+        batch_sampler = batch_by_size(
+            valid_dataset.num_frame_indices,
+            valid_dataset.get_num_frames,
+            max_tokens=self.cfg.train.max_tokens * self.accelerator.num_processes,
+            max_sentences=self.cfg.train.max_sentences * self.accelerator.num_processes,
+            required_batch_size_multiple=self.accelerator.num_processes,
+        )
+        batches = [
+            x[self.accelerator.local_process_index :: self.accelerator.num_processes]
+            for x in batch_sampler
+            if len(x) % self.accelerator.num_processes == 0
+        ]
+        valid_loader = DataLoader(
+            valid_dataset,
+            collate_fn=valid_collate,
+            num_workers=self.cfg.train.dataloader.num_worker,
+            batch_sampler=VariableSampler(batches, drop_last=False),
+            pin_memory=False,
+        )
+        self.accelerator.wait_for_everyone()
+
+        return train_loader, valid_loader
+
+    def _accelerator_prepare(self):
+        if not self.cfg.train.use_dynamic_batchsize:
+            (
+                self.train_dataloader,
+                self.valid_dataloader,
+            ) = self.accelerator.prepare(
+                self.train_dataloader,
+                self.valid_dataloader,
+            )
+
+        if isinstance(self.model, dict):
+            for key in self.model.keys():
+                self.model[key] = self.accelerator.prepare(self.model[key])
+        else:
+            self.model = self.accelerator.prepare(self.model)
+
+        if isinstance(self.optimizer, dict):
+            for key in self.optimizer.keys():
+                self.optimizer[key] = self.accelerator.prepare(self.optimizer[key])
+        else:
+            self.optimizer = self.accelerator.prepare(self.optimizer)
+
+        if isinstance(self.scheduler, dict):
+            for key in self.scheduler.keys():
+                self.scheduler[key] = self.accelerator.prepare(self.scheduler[key])
+        else:
+            self.scheduler = self.accelerator.prepare(self.scheduler)
diff --git a/models/tts/vits/__init__.py b/models/tts/vits/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/models/tts/vits/vits.py b/models/tts/vits/vits.py
new file mode 100644
index 0000000000000000000000000000000000000000..61aff4b24caa7652e818b623bd89be3ee8eff3c7
--- /dev/null
+++ b/models/tts/vits/vits.py
@@ -0,0 +1,379 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+# This code is modified from https://github.com/jaywalnut310/vits/blob/main/models.py
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from utils.util import *
+from modules.flow.modules import *
+from modules.base.base_module import *
+from modules.transformer.attentions import Encoder
+from modules.duration_predictor.standard_duration_predictor import DurationPredictor
+from modules.duration_predictor.stochastic_duration_predictor import (
+    StochasticDurationPredictor,
+)
+from models.vocoders.gan.generator.hifigan import HiFiGAN_vits as Generator
+
+try:
+    from modules import monotonic_align
+except ImportError:
+    print("Monotonic align not found. Please make sure you have compiled it.")
+
+
+class TextEncoder(nn.Module):
+    def __init__(
+        self,
+        n_vocab,
+        out_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+    ):
+        super().__init__()
+        self.n_vocab = n_vocab
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+
+        self.emb = nn.Embedding(n_vocab, hidden_channels)
+        nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
+
+        self.encoder = Encoder(
+            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, x, x_lengths):
+        x = self.emb(x) * math.sqrt(self.hidden_channels)  # [b, t, h]
+        x = torch.transpose(x, 1, -1)  # [b, h, t]
+        x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
+
+        x = self.encoder(x * x_mask, x_mask)
+        stats = self.proj(x) * x_mask
+
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        return x, m, logs, x_mask
+
+
+class ResidualCouplingBlock(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        n_flows=4,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+        for i in range(n_flows):
+            self.flows.append(
+                ResidualCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    dilation_rate,
+                    n_layers,
+                    gin_channels=gin_channels,
+                    mean_only=True,
+                )
+            )
+            self.flows.append(Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+
+class PosteriorEncoder(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.gin_channels = gin_channels
+
+        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+        self.enc = WN(
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            gin_channels=gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, x, x_lengths, g=None):
+        x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
+        x = self.pre(x) * x_mask
+        x = self.enc(x, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+        return z, m, logs, x_mask
+
+
+class SynthesizerTrn(nn.Module):
+    """
+    Synthesizer for Training
+    """
+
+    def __init__(
+        self,
+        n_vocab,
+        spec_channels,
+        segment_size,
+        inter_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        n_speakers=0,
+        gin_channels=0,
+        use_sdp=True,
+        **kwargs,
+    ):
+        super().__init__()
+        self.n_vocab = n_vocab
+        self.spec_channels = spec_channels
+        self.inter_channels = inter_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.resblock = resblock
+        self.resblock_kernel_sizes = resblock_kernel_sizes
+        self.resblock_dilation_sizes = resblock_dilation_sizes
+        self.upsample_rates = upsample_rates
+        self.upsample_initial_channel = upsample_initial_channel
+        self.upsample_kernel_sizes = upsample_kernel_sizes
+        self.segment_size = segment_size
+        self.n_speakers = n_speakers
+        self.gin_channels = gin_channels
+
+        self.use_sdp = use_sdp
+
+        self.enc_p = TextEncoder(
+            n_vocab,
+            inter_channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+        )
+        self.dec = Generator(
+            inter_channels,
+            resblock,
+            resblock_kernel_sizes,
+            resblock_dilation_sizes,
+            upsample_rates,
+            upsample_initial_channel,
+            upsample_kernel_sizes,
+            gin_channels=gin_channels,
+        )
+        self.enc_q = PosteriorEncoder(
+            spec_channels,
+            inter_channels,
+            hidden_channels,
+            5,
+            1,
+            16,
+            gin_channels=gin_channels,
+        )
+        self.flow = ResidualCouplingBlock(
+            inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels
+        )
+
+        if use_sdp:
+            self.dp = StochasticDurationPredictor(
+                hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels
+            )
+        else:
+            self.dp = DurationPredictor(
+                hidden_channels, 256, 3, 0.5, gin_channels=gin_channels
+            )
+
+        if n_speakers >= 1:
+            self.emb_g = nn.Embedding(n_speakers, gin_channels)
+
+    def forward(self, data):
+        x = data["phone_seq"]
+        x_lengths = data["phone_len"]
+        y = data["linear"]
+        y_lengths = data["target_len"]
+
+        x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
+        if self.n_speakers > 0:
+            g = self.emb_g(data["spk_id"].squeeze(-1)).unsqueeze(-1)  # [b, h, 1]
+        else:
+            g = None
+
+        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
+        z_p = self.flow(z, y_mask, g=g)
+
+        with torch.no_grad():
+            # negative cross-entropy
+            s_p_sq_r = torch.exp(-2 * logs_p)  # [b, d, t]
+            neg_cent1 = torch.sum(
+                -0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True
+            )  # [b, 1, t_s]
+            neg_cent2 = torch.matmul(
+                -0.5 * (z_p**2).transpose(1, 2), s_p_sq_r
+            )  # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
+            neg_cent3 = torch.matmul(
+                z_p.transpose(1, 2), (m_p * s_p_sq_r)
+            )  # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
+            neg_cent4 = torch.sum(
+                -0.5 * (m_p**2) * s_p_sq_r, [1], keepdim=True
+            )  # [b, 1, t_s]
+            neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
+
+            attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+            attn = (
+                monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1))
+                .unsqueeze(1)
+                .detach()
+            )
+
+        w = attn.sum(2)
+        if self.use_sdp:
+            l_length = self.dp(x, x_mask, w, g=g)
+            l_length = l_length / torch.sum(x_mask)
+        else:
+            logw_ = torch.log(w + 1e-6) * x_mask
+            logw = self.dp(x, x_mask, g=g)
+            l_length = torch.sum((logw - logw_) ** 2, [1, 2]) / torch.sum(x_mask)
+
+        # expand prior
+        m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
+        logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
+
+        z_slice, ids_slice = rand_slice_segments(z, y_lengths, self.segment_size)
+        o = self.dec(z_slice, g=g)
+        outputs = {
+            "y_hat": o,
+            "l_length": l_length,
+            "attn": attn,
+            "ids_slice": ids_slice,
+            "x_mask": x_mask,
+            "z_mask": y_mask,
+            "z": z,
+            "z_p": z_p,
+            "m_p": m_p,
+            "logs_p": logs_p,
+            "m_q": m_q,
+            "logs_q": logs_q,
+        }
+        return outputs
+
+    def infer(
+        self,
+        x,
+        x_lengths,
+        sid=None,
+        noise_scale=1,
+        length_scale=1,
+        noise_scale_w=1.0,
+        max_len=None,
+    ):
+        x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
+        if self.n_speakers > 0:
+            sid = sid.squeeze(-1)
+            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
+        else:
+            g = None
+
+        if self.use_sdp:
+            logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
+        else:
+            logw = self.dp(x, x_mask, g=g)
+        w = torch.exp(logw) * x_mask * length_scale
+        w_ceil = torch.ceil(w)
+        y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
+        y_mask = torch.unsqueeze(sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
+        attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+        attn = generate_path(w_ceil, attn_mask)
+
+        m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(
+            1, 2
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+        logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(
+            1, 2
+        )  # [b, t', t], [b, t, d] -> [b, d, t']
+
+        z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
+        z = self.flow(z_p, y_mask, g=g, reverse=True)
+        o = self.dec((z * y_mask)[:, :, :max_len], g=g)
+
+        outputs = {
+            "y_hat": o,
+            "attn": attn,
+            "mask": y_mask,
+            "z": z,
+            "z_p": z_p,
+            "m_p": m_p,
+            "logs_p": logs_p,
+        }
+
+        return outputs
+
+    def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
+        assert self.n_speakers > 0, "n_speakers have to be larger than 0."
+        g_src = self.emb_g(sid_src).unsqueeze(-1)
+        g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
+        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
+        z_p = self.flow(z, y_mask, g=g_src)
+        z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
+        o_hat = self.dec(z_hat * y_mask, g=g_tgt)
+        return o_hat, y_mask, (z, z_p, z_hat)
diff --git a/models/tts/vits/vits_dataset.py b/models/tts/vits/vits_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd596894f1e519d8564f366ae5d0aee986f33e30
--- /dev/null
+++ b/models/tts/vits/vits_dataset.py
@@ -0,0 +1,123 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import json
+import numpy as np
+from text import text_to_sequence
+from text.text_token_collation import phoneIDCollation
+from models.tts.base.tts_dataset import (
+    TTSDataset,
+    TTSCollator,
+    TTSTestDataset,
+    TTSTestCollator,
+)
+
+
+class VITSDataset(TTSDataset):
+    def __init__(self, cfg, dataset, is_valid):
+        super().__init__(cfg, dataset, is_valid=is_valid)
+
+    def __getitem__(self, index):
+        single_feature = super().__getitem__(index)
+        return single_feature
+
+    def __len__(self):
+        return super().__len__()
+
+
+class VITSCollator(TTSCollator):
+    """Zero-pads model inputs and targets based on number of frames per step"""
+
+    def __init__(self, cfg):
+        super().__init__(cfg)
+
+    def __call__(self, batch):
+        parsed_batch_features = super().__call__(batch)
+        return parsed_batch_features
+
+
+class VITSTestDataset(TTSTestDataset):
+    def __init__(self, args, cfg):
+        super().__init__(args, cfg)
+
+        if cfg.preprocess.use_spkid:
+            processed_data_dir = os.path.join(
+                cfg.preprocess.processed_dir, args.dataset
+            )
+            spk2id_path = os.path.join(processed_data_dir, cfg.preprocess.spk2id)
+            with open(spk2id_path, "r") as f:
+                self.spk2id = json.load(f)
+
+            utt2spk_path = os.path.join(processed_data_dir, cfg.preprocess.utt2spk)
+            self.utt2spk = dict()
+            with open(utt2spk_path, "r") as f:
+                for line in f.readlines():
+                    utt, spk = line.strip().split("\t")
+                    self.utt2spk[utt] = spk
+
+        if cfg.preprocess.use_text or cfg.preprocess.use_phone:
+            self.utt2seq = {}
+            for utt_info in self.metadata:
+                dataset = utt_info["Dataset"]
+                uid = utt_info["Uid"]
+                utt = "{}_{}".format(dataset, uid)
+
+                if cfg.preprocess.use_text:
+                    text = utt_info["Text"]
+                    sequence = text_to_sequence(text, cfg.preprocess.text_cleaners)
+                elif cfg.preprocess.use_phone:
+                    # load phoneme squence from phone file
+                    phone_path = os.path.join(
+                        processed_data_dir, cfg.preprocess.phone_dir, uid + ".phone"
+                    )
+                    with open(phone_path, "r") as fin:
+                        phones = fin.readlines()
+                        assert len(phones) == 1
+                        phones = phones[0].strip()
+                    phones_seq = phones.split(" ")
+
+                    phon_id_collator = phoneIDCollation(cfg, dataset=dataset)
+                    sequence = phon_id_collator.get_phone_id_sequence(cfg, phones_seq)
+
+                self.utt2seq[utt] = sequence
+
+    def __getitem__(self, index):
+        utt_info = self.metadata[index]
+
+        dataset = utt_info["Dataset"]
+        uid = utt_info["Uid"]
+        utt = "{}_{}".format(dataset, uid)
+
+        single_feature = dict()
+
+        if self.cfg.preprocess.use_spkid:
+            single_feature["spk_id"] = np.array(
+                [self.spk2id[self.utt2spk[utt]]], dtype=np.int32
+            )
+
+        if self.cfg.preprocess.use_phone or self.cfg.preprocess.use_text:
+            single_feature["phone_seq"] = np.array(self.utt2seq[utt])
+            single_feature["phone_len"] = len(self.utt2seq[utt])
+
+        return single_feature
+
+    def get_metadata(self):
+        with open(self.metafile_path, "r", encoding="utf-8") as f:
+            metadata = json.load(f)
+        return metadata
+
+    def __len__(self):
+        return len(self.metadata)
+
+
+class VITSTestCollator(TTSTestCollator):
+    """Zero-pads model inputs and targets based on number of frames per step"""
+
+    def __init__(self, cfg):
+        self.cfg = cfg
+
+    def __call__(self, batch):
+        return super().__call__(batch)
diff --git a/models/tts/vits/vits_inference.py b/models/tts/vits/vits_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c3d385a053ee59e6cc2285f06a8c5f10a929827
--- /dev/null
+++ b/models/tts/vits/vits_inference.py
@@ -0,0 +1,152 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import time
+import numpy as np
+from tqdm import tqdm
+import torch
+import json
+from models.tts.base.tts_inferece import TTSInference
+from models.tts.vits.vits_dataset import VITSTestDataset, VITSTestCollator
+from models.tts.vits.vits import SynthesizerTrn
+from processors.phone_extractor import phoneExtractor
+from text.text_token_collation import phoneIDCollation
+
+
+class VitsInference(TTSInference):
+    def __init__(self, args=None, cfg=None):
+        TTSInference.__init__(self, args, cfg)
+
+    def _build_model(self):
+        net_g = SynthesizerTrn(
+            self.cfg.model.text_token_num,
+            self.cfg.preprocess.n_fft // 2 + 1,
+            self.cfg.preprocess.segment_size // self.cfg.preprocess.hop_size,
+            **self.cfg.model,
+        )
+
+        return net_g
+
+    def _build_test_dataset(sefl):
+        return VITSTestDataset, VITSTestCollator
+
+    def build_save_dir(self, dataset, speaker):
+        save_dir = os.path.join(
+            self.args.output_dir,
+            "tts_am_step-{}_{}".format(self.am_restore_step, self.args.mode),
+        )
+        if dataset is not None:
+            save_dir = os.path.join(save_dir, "data_{}".format(dataset))
+        if speaker != -1:
+            save_dir = os.path.join(
+                save_dir,
+                "spk_{}".format(speaker),
+            )
+        os.makedirs(save_dir, exist_ok=True)
+        print("Saving to ", save_dir)
+        return save_dir
+
+    def inference_for_batches(
+        self, noise_scale=0.667, noise_scale_w=0.8, length_scale=1
+    ):
+        ###### Construct test_batch ######
+        n_batch = len(self.test_dataloader)
+        now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time()))
+        print(
+            "Model eval time: {}, batch_size = {}, n_batch = {}".format(
+                now, self.test_batch_size, n_batch
+            )
+        )
+        self.model.eval()
+
+        ###### Inference for each batch ######
+        pred_res = []
+        with torch.no_grad():
+            for i, batch_data in enumerate(
+                self.test_dataloader if n_batch == 1 else tqdm(self.test_dataloader)
+            ):
+                spk_id = None
+                if (
+                    self.cfg.preprocess.use_spkid
+                    and self.cfg.train.multi_speaker_training
+                ):
+                    spk_id = batch_data["spk_id"]
+
+                outputs = self.model.infer(
+                    batch_data["phone_seq"],
+                    batch_data["phone_len"],
+                    spk_id,
+                    noise_scale=noise_scale,
+                    noise_scale_w=noise_scale_w,
+                    length_scale=length_scale,
+                )
+
+                audios = outputs["y_hat"]
+                masks = outputs["mask"]
+
+                for idx in range(audios.size(0)):
+                    audio = audios[idx, 0, :].data.cpu().float()
+                    mask = masks[idx, :, :]
+                    audio_length = (
+                        mask.sum([0, 1]).long() * self.cfg.preprocess.hop_size
+                    )
+                    audio_length = audio_length.cpu().numpy()
+                    audio = audio[:audio_length]
+                    pred_res.append(audio)
+
+        return pred_res
+
+    def inference_for_single_utterance(
+        self, noise_scale=0.667, noise_scale_w=0.8, length_scale=1
+    ):
+        text = self.args.text
+
+        # get phone symbol file
+        phone_symbol_file = None
+        if self.cfg.preprocess.phone_extractor != "lexicon":
+            phone_symbol_file = os.path.join(
+                self.exp_dir, self.cfg.preprocess.symbols_dict
+            )
+            assert os.path.exists(phone_symbol_file)
+        # convert text to phone sequence
+        phone_extractor = phoneExtractor(self.cfg)
+        phone_seq = phone_extractor.extract_phone(text)  # phone_seq: list
+        # convert phone sequence to phone id sequence
+        phon_id_collator = phoneIDCollation(
+            self.cfg, symbols_dict_file=phone_symbol_file
+        )
+        phone_id_seq = phon_id_collator.get_phone_id_sequence(self.cfg, phone_seq)
+
+        # convert phone sequence to phone id sequence
+        phone_id_seq = np.array(phone_id_seq)
+        phone_id_seq = torch.from_numpy(phone_id_seq)
+
+        # get speaker id if multi-speaker training and use speaker id
+        speaker_id = None
+        if self.cfg.preprocess.use_spkid and self.cfg.train.multi_speaker_training:
+            spk2id_file = os.path.join(self.exp_dir, self.cfg.preprocess.spk2id)
+            with open(spk2id_file, "r") as f:
+                spk2id = json.load(f)
+                speaker_id = spk2id[self.args.speaker_name]
+                speaker_id = torch.from_numpy(np.array([speaker_id], dtype=np.int32))
+
+        with torch.no_grad():
+            x_tst = phone_id_seq.to(self.device).unsqueeze(0)
+            x_tst_lengths = torch.LongTensor([phone_id_seq.size(0)]).to(self.device)
+            if speaker_id is not None:
+                speaker_id = speaker_id.to(self.device)
+            outputs = self.model.infer(
+                x_tst,
+                x_tst_lengths,
+                sid=speaker_id,
+                noise_scale=noise_scale,
+                noise_scale_w=noise_scale_w,
+                length_scale=length_scale,
+            )
+
+            audio = outputs["y_hat"][0, 0].data.cpu().float().numpy()
+
+        return audio
diff --git a/models/tts/vits/vits_trainer.py b/models/tts/vits/vits_trainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..e799f9173887db01745901abee736df7e2ba9070
--- /dev/null
+++ b/models/tts/vits/vits_trainer.py
@@ -0,0 +1,439 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+from torch.optim.lr_scheduler import ExponentialLR
+
+from tqdm import tqdm
+
+from utils.util import *
+from utils.mel import mel_spectrogram_torch
+from models.tts.base import TTSTrainer
+from models.tts.vits.vits import SynthesizerTrn
+from models.tts.vits.vits_dataset import VITSDataset, VITSCollator
+from models.vocoders.gan.discriminator.mpd import (
+    MultiPeriodDiscriminator_vits as MultiPeriodDiscriminator,
+)
+
+
+class VITSTrainer(TTSTrainer):
+    def __init__(self, args, cfg):
+        TTSTrainer.__init__(self, args, cfg)
+
+        if cfg.preprocess.use_spkid and cfg.train.multi_speaker_training:
+            if cfg.model.n_speakers == 0:
+                cfg.model.n_speaker = len(self.speakers)
+
+    def _build_model(self):
+        net_g = SynthesizerTrn(
+            self.cfg.model.text_token_num,
+            self.cfg.preprocess.n_fft // 2 + 1,
+            self.cfg.preprocess.segment_size // self.cfg.preprocess.hop_size,
+            **self.cfg.model,
+        )
+        net_d = MultiPeriodDiscriminator(self.cfg.model.use_spectral_norm)
+        model = {"generator": net_g, "discriminator": net_d}
+
+        return model
+
+    def _build_dataset(self):
+        return VITSDataset, VITSCollator
+
+    def _build_optimizer(self):
+        optimizer_g = torch.optim.AdamW(
+            self.model["generator"].parameters(),
+            self.cfg.train.learning_rate,
+            betas=self.cfg.train.AdamW.betas,
+            eps=self.cfg.train.AdamW.eps,
+        )
+        optimizer_d = torch.optim.AdamW(
+            self.model["discriminator"].parameters(),
+            self.cfg.train.learning_rate,
+            betas=self.cfg.train.AdamW.betas,
+            eps=self.cfg.train.AdamW.eps,
+        )
+        optimizer = {"optimizer_g": optimizer_g, "optimizer_d": optimizer_d}
+
+        return optimizer
+
+    def _build_scheduler(self):
+        scheduler_g = ExponentialLR(
+            self.optimizer["optimizer_g"],
+            gamma=self.cfg.train.lr_decay,
+            last_epoch=self.epoch - 1,
+        )
+        scheduler_d = ExponentialLR(
+            self.optimizer["optimizer_d"],
+            gamma=self.cfg.train.lr_decay,
+            last_epoch=self.epoch - 1,
+        )
+
+        scheduler = {"scheduler_g": scheduler_g, "scheduler_d": scheduler_d}
+        return scheduler
+
+    def _build_criterion(self):
+        class GeneratorLoss(nn.Module):
+            def __init__(self, cfg):
+                super(GeneratorLoss, self).__init__()
+                self.cfg = cfg
+                self.l1_loss = nn.L1Loss()
+
+            def generator_loss(self, disc_outputs):
+                loss = 0
+                gen_losses = []
+                for dg in disc_outputs:
+                    dg = dg.float()
+                    l = torch.mean((1 - dg) ** 2)
+                    gen_losses.append(l)
+                    loss += l
+
+                return loss, gen_losses
+
+            def feature_loss(self, fmap_r, fmap_g):
+                loss = 0
+                for dr, dg in zip(fmap_r, fmap_g):
+                    for rl, gl in zip(dr, dg):
+                        rl = rl.float().detach()
+                        gl = gl.float()
+                        loss += torch.mean(torch.abs(rl - gl))
+
+                return loss * 2
+
+            def kl_loss(self, z_p, logs_q, m_p, logs_p, z_mask):
+                """
+                z_p, logs_q: [b, h, t_t]
+                m_p, logs_p: [b, h, t_t]
+                """
+                z_p = z_p.float()
+                logs_q = logs_q.float()
+                m_p = m_p.float()
+                logs_p = logs_p.float()
+                z_mask = z_mask.float()
+
+                kl = logs_p - logs_q - 0.5
+                kl += 0.5 * ((z_p - m_p) ** 2) * torch.exp(-2.0 * logs_p)
+                kl = torch.sum(kl * z_mask)
+                l = kl / torch.sum(z_mask)
+                return l
+
+            def forward(
+                self,
+                outputs_g,
+                outputs_d,
+                y_mel,
+                y_hat_mel,
+            ):
+                loss_g = {}
+
+                # duration loss
+                loss_dur = torch.sum(outputs_g["l_length"].float())
+                loss_g["loss_dur"] = loss_dur
+
+                # mel loss
+                loss_mel = self.l1_loss(y_mel, y_hat_mel) * self.cfg.train.c_mel
+                loss_g["loss_mel"] = loss_mel
+
+                # kl loss
+                loss_kl = (
+                    self.kl_loss(
+                        outputs_g["z_p"],
+                        outputs_g["logs_q"],
+                        outputs_g["m_p"],
+                        outputs_g["logs_p"],
+                        outputs_g["z_mask"],
+                    )
+                    * self.cfg.train.c_kl
+                )
+                loss_g["loss_kl"] = loss_kl
+
+                # feature loss
+                loss_fm = self.feature_loss(outputs_d["fmap_rs"], outputs_d["fmap_gs"])
+                loss_g["loss_fm"] = loss_fm
+
+                # gan loss
+                loss_gen, losses_gen = self.generator_loss(outputs_d["y_d_hat_g"])
+                loss_g["loss_gen"] = loss_gen
+                loss_g["loss_gen_all"] = (
+                    loss_dur + loss_mel + loss_kl + loss_fm + loss_gen
+                )
+
+                return loss_g
+
+        class DiscriminatorLoss(nn.Module):
+            def __init__(self, cfg):
+                super(DiscriminatorLoss, self).__init__()
+                self.cfg = cfg
+                self.l1Loss = torch.nn.L1Loss(reduction="mean")
+
+            def __call__(self, disc_real_outputs, disc_generated_outputs):
+                loss_d = {}
+
+                loss = 0
+                r_losses = []
+                g_losses = []
+                for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
+                    dr = dr.float()
+                    dg = dg.float()
+                    r_loss = torch.mean((1 - dr) ** 2)
+                    g_loss = torch.mean(dg**2)
+                    loss += r_loss + g_loss
+                    r_losses.append(r_loss.item())
+                    g_losses.append(g_loss.item())
+
+                loss_d["loss_disc_all"] = loss
+
+                return loss_d
+
+        criterion = {
+            "generator": GeneratorLoss(self.cfg),
+            "discriminator": DiscriminatorLoss(self.cfg),
+        }
+        return criterion
+
+    def write_summary(
+        self,
+        losses,
+        stats,
+        images={},
+        audios={},
+        audio_sampling_rate=24000,
+        tag="train",
+    ):
+        for key, value in losses.items():
+            self.sw.add_scalar(tag + "/" + key, value, self.step)
+        self.sw.add_scalar(
+            "learning_rate",
+            self.optimizer["optimizer_g"].param_groups[0]["lr"],
+            self.step,
+        )
+
+        if len(images) != 0:
+            for key, value in images.items():
+                self.sw.add_image(key, value, self.global_step, batchformats="HWC")
+        if len(audios) != 0:
+            for key, value in audios.items():
+                self.sw.add_audio(key, value, self.global_step, audio_sampling_rate)
+
+    def write_valid_summary(
+        self, losses, stats, images={}, audios={}, audio_sampling_rate=24000, tag="val"
+    ):
+        for key, value in losses.items():
+            self.sw.add_scalar(tag + "/" + key, value, self.step)
+
+        if len(images) != 0:
+            for key, value in images.items():
+                self.sw.add_image(key, value, self.global_step, batchformats="HWC")
+        if len(audios) != 0:
+            for key, value in audios.items():
+                self.sw.add_audio(key, value, self.global_step, audio_sampling_rate)
+
+    def get_state_dict(self):
+        state_dict = {
+            "generator": self.model["generator"].state_dict(),
+            "discriminator": self.model["discriminator"].state_dict(),
+            "optimizer_g": self.optimizer["optimizer_g"].state_dict(),
+            "optimizer_d": self.optimizer["optimizer_d"].state_dict(),
+            "scheduler_g": self.scheduler["scheduler_g"].state_dict(),
+            "scheduler_d": self.scheduler["scheduler_d"].state_dict(),
+            "step": self.step,
+            "epoch": self.epoch,
+            "batch_size": self.cfg.train.batch_size,
+        }
+        return state_dict
+
+    def load_model(self, checkpoint):
+        self.step = checkpoint["step"]
+        self.epoch = checkpoint["epoch"]
+        self.model["generator"].load_state_dict(checkpoint["generator"])
+        self.model["discriminator"].load_state_dict(checkpoint["discriminator"])
+        self.optimizer["optimizer_g"].load_state_dict(checkpoint["optimizer_g"])
+        self.optimizer["optimizer_d"].load_state_dict(checkpoint["optimizer_d"])
+        self.scheduler["scheduler_g"].load_state_dict(checkpoint["scheduler_g"])
+        self.scheduler["scheduler_d"].load_state_dict(checkpoint["scheduler_d"])
+
+    @torch.inference_mode()
+    def _valid_step(self, batch):
+        r"""Testing forward step. Should return average loss of a sample over
+        one batch. Provoke ``_forward_step`` is recommended except for special case.
+        See ``_test_epoch`` for usage.
+        """
+
+        valid_losses = {}
+        total_loss = 0
+        valid_stats = {}
+
+        batch["linear"] = batch["linear"].transpose(2, 1)  # [b, d, t]
+        batch["mel"] = batch["mel"].transpose(2, 1)  # [b, d, t]
+        batch["audio"] = batch["audio"].unsqueeze(1)  # [b, d, t]
+
+        #  Discriminator
+        # Generator output
+        outputs_g = self.model["generator"](batch)
+
+        y_mel = slice_segments(
+            batch["mel"],
+            outputs_g["ids_slice"],
+            self.cfg.preprocess.segment_size // self.cfg.preprocess.hop_size,
+        )
+        y_hat_mel = mel_spectrogram_torch(
+            outputs_g["y_hat"].squeeze(1), self.cfg.preprocess
+        )
+        y = slice_segments(
+            batch["audio"],
+            outputs_g["ids_slice"] * self.cfg.preprocess.hop_size,
+            self.cfg.preprocess.segment_size,
+        )
+
+        # Discriminator output
+        outputs_d = self.model["discriminator"](y, outputs_g["y_hat"].detach())
+        ##  Discriminator loss
+        loss_d = self.criterion["discriminator"](
+            outputs_d["y_d_hat_r"], outputs_d["y_d_hat_g"]
+        )
+        valid_losses.update(loss_d)
+
+        ##  Generator
+        outputs_d = self.model["discriminator"](y, outputs_g["y_hat"])
+        loss_g = self.criterion["generator"](outputs_g, outputs_d, y_mel, y_hat_mel)
+        valid_losses.update(loss_g)
+
+        for item in valid_losses:
+            valid_losses[item] = valid_losses[item].item()
+
+        total_loss = loss_g["loss_gen_all"] + loss_d["loss_disc_all"]
+
+        return (
+            total_loss.item(),
+            valid_losses,
+            valid_stats,
+        )
+
+    def _train_step(self, batch):
+        r"""Forward step for training and inference. This function is called
+        in ``_train_step`` & ``_test_step`` function.
+        """
+
+        train_losses = {}
+        total_loss = 0
+        training_stats = {}
+
+        batch["linear"] = batch["linear"].transpose(2, 1)  # [b, d, t]
+        batch["mel"] = batch["mel"].transpose(2, 1)  # [b, d, t]
+        batch["audio"] = batch["audio"].unsqueeze(1)  # [b, d, t]
+
+        # Train Discriminator
+        # Generator output
+        outputs_g = self.model["generator"](batch)
+
+        y_mel = slice_segments(
+            batch["mel"],
+            outputs_g["ids_slice"],
+            self.cfg.preprocess.segment_size // self.cfg.preprocess.hop_size,
+        )
+        y_hat_mel = mel_spectrogram_torch(
+            outputs_g["y_hat"].squeeze(1), self.cfg.preprocess
+        )
+        y = slice_segments(
+            batch["audio"],
+            outputs_g["ids_slice"] * self.cfg.preprocess.hop_size,
+            self.cfg.preprocess.segment_size,
+        )
+
+        # Discriminator output
+        outputs_d = self.model["discriminator"](y, outputs_g["y_hat"].detach())
+        ##  Discriminator loss
+        loss_d = self.criterion["discriminator"](
+            outputs_d["y_d_hat_r"], outputs_d["y_d_hat_g"]
+        )
+        train_losses.update(loss_d)
+
+        # BP and Grad Updated
+        self.optimizer["optimizer_d"].zero_grad()
+        self.accelerator.backward(loss_d["loss_disc_all"])
+        self.optimizer["optimizer_d"].step()
+
+        ## Train Generator
+        outputs_d = self.model["discriminator"](y, outputs_g["y_hat"])
+        loss_g = self.criterion["generator"](outputs_g, outputs_d, y_mel, y_hat_mel)
+        train_losses.update(loss_g)
+
+        # BP and Grad Updated
+        self.optimizer["optimizer_g"].zero_grad()
+        self.accelerator.backward(loss_g["loss_gen_all"])
+        self.optimizer["optimizer_g"].step()
+
+        for item in train_losses:
+            train_losses[item] = train_losses[item].item()
+
+        total_loss = loss_g["loss_gen_all"] + loss_d["loss_disc_all"]
+
+        return (
+            total_loss.item(),
+            train_losses,
+            training_stats,
+        )
+
+    def _train_epoch(self):
+        r"""Training epoch. Should return average loss of a batch (sample) over
+        one epoch. See ``train_loop`` for usage.
+        """
+        epoch_sum_loss: float = 0.0
+        epoch_losses: dict = {}
+        epoch_step: int = 0
+        for batch in tqdm(
+            self.train_dataloader,
+            desc=f"Training Epoch {self.epoch}",
+            unit="batch",
+            colour="GREEN",
+            leave=False,
+            dynamic_ncols=True,
+            smoothing=0.04,
+            disable=not self.accelerator.is_main_process,
+        ):
+            with self.accelerator.accumulate(self.model):
+                total_loss, train_losses, training_stats = self._train_step(batch)
+            self.batch_count += 1
+
+            if self.batch_count % self.cfg.train.gradient_accumulation_step == 0:
+                epoch_sum_loss += total_loss
+                for key, value in train_losses.items():
+                    if key not in epoch_losses.keys():
+                        epoch_losses[key] = value
+                    else:
+                        epoch_losses[key] += value
+
+                self.accelerator.log(
+                    {
+                        "Step/Generator Loss": train_losses["loss_gen_all"],
+                        "Step/Discriminator Loss": train_losses["loss_disc_all"],
+                        "Step/Generator Learning Rate": self.optimizer[
+                            "optimizer_d"
+                        ].param_groups[0]["lr"],
+                        "Step/Discriminator Learning Rate": self.optimizer[
+                            "optimizer_g"
+                        ].param_groups[0]["lr"],
+                    },
+                    step=self.step,
+                )
+                self.step += 1
+                epoch_step += 1
+
+        self.accelerator.wait_for_everyone()
+
+        epoch_sum_loss = (
+            epoch_sum_loss
+            / len(self.train_dataloader)
+            * self.cfg.train.gradient_accumulation_step
+        )
+
+        for key in epoch_losses.keys():
+            epoch_losses[key] = (
+                epoch_losses[key]
+                / len(self.train_dataloader)
+                * self.cfg.train.gradient_accumulation_step
+            )
+
+        return epoch_sum_loss, epoch_losses
diff --git a/modules/__init__.py b/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/modules/activation_functions/__init__.py b/modules/activation_functions/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d35dfbc27a825ea8ed286074d40971764b890468
--- /dev/null
+++ b/modules/activation_functions/__init__.py
@@ -0,0 +1,7 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .gated_activation_unit import GaU
+from .snake import Snake, SnakeBeta
diff --git a/modules/activation_functions/gated_activation_unit.py b/modules/activation_functions/gated_activation_unit.py
new file mode 100644
index 0000000000000000000000000000000000000000..e55956981fa99299059dd1c02236011fbadac049
--- /dev/null
+++ b/modules/activation_functions/gated_activation_unit.py
@@ -0,0 +1,61 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+
+from modules.general.utils import Conv1d
+
+
+class GaU(nn.Module):
+    r"""Gated Activation Unit (GaU) proposed in `Gated Activation Units for Neural
+    Networks <https://arxiv.org/pdf/1606.05328.pdf>`_.
+
+    Args:
+        channels: number of input channels.
+        kernel_size: kernel size of the convolution.
+        dilation: dilation rate of the convolution.
+        d_context: dimension of context tensor, None if don't use context.
+    """
+
+    def __init__(
+        self,
+        channels: int,
+        kernel_size: int = 3,
+        dilation: int = 1,
+        d_context: int = None,
+    ):
+        super().__init__()
+
+        self.context = d_context
+
+        self.conv = Conv1d(
+            channels,
+            channels * 2,
+            kernel_size,
+            dilation=dilation,
+            padding=dilation * (kernel_size - 1) // 2,
+        )
+
+        if self.context:
+            self.context_proj = Conv1d(d_context, channels * 2, 1)
+
+    def forward(self, x: torch.Tensor, context: torch.Tensor = None):
+        r"""Calculate forward propagation.
+
+        Args:
+            x: input tensor with shape [B, C, T].
+            context: context tensor with shape [B, ``d_context``, T], default to None.
+        """
+
+        h = self.conv(x)
+
+        if self.context:
+            h = h + self.context_proj(context)
+
+        h1, h2 = h.chunk(2, 1)
+        h = torch.tanh(h1) * torch.sigmoid(h2)
+
+        return h
diff --git a/modules/activation_functions/snake.py b/modules/activation_functions/snake.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5398819f2e7c836e02189321cbf048bedcd9e91
--- /dev/null
+++ b/modules/activation_functions/snake.py
@@ -0,0 +1,122 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+from torch import nn, pow, sin
+from torch.nn import Parameter
+
+
+class Snake(nn.Module):
+    r"""Implementation of a sine-based periodic activation function.
+    Alpha is initialized to 1 by default, higher values means higher frequency.
+    It will be trained along with the rest of your model.
+
+    Args:
+        in_features: shape of the input
+        alpha: trainable parameter
+
+    Shape:
+        - Input: (B, C, T)
+        - Output: (B, C, T), same shape as the input
+
+    References:
+        This activation function is from this paper by Liu Ziyin, Tilman Hartwig,
+        Masahito Ueda: https://arxiv.org/abs/2006.08195
+
+    Examples:
+        >>> a1 = Snake(256)
+        >>> x = torch.randn(256)
+        >>> x = a1(x)
+    """
+
+    def __init__(
+        self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False
+    ):
+        super(Snake, self).__init__()
+        self.in_features = in_features
+
+        # initialize alpha
+        self.alpha_logscale = alpha_logscale
+        if self.alpha_logscale:  # log scale alphas initialized to zeros
+            self.alpha = Parameter(torch.zeros(in_features) * alpha)
+        else:  # linear scale alphas initialized to ones
+            self.alpha = Parameter(torch.ones(in_features) * alpha)
+
+        self.alpha.requires_grad = alpha_trainable
+
+        self.no_div_by_zero = 0.000000001
+
+    def forward(self, x):
+        r"""Forward pass of the function. Applies the function to the input elementwise.
+        Snake ∶= x + 1/a * sin^2 (ax)
+        """
+
+        alpha = self.alpha.unsqueeze(0).unsqueeze(-1)  # line up with x to [B, C, T]
+        if self.alpha_logscale:
+            alpha = torch.exp(alpha)
+        x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
+
+        return x
+
+
+class SnakeBeta(nn.Module):
+    r"""A modified Snake function which uses separate parameters for the magnitude
+    of the periodic components. Alpha is initialized to 1 by default,
+    higher values means higher frequency. Beta is initialized to 1 by default,
+    higher values means higher magnitude. Both will be trained along with the
+    rest of your model.
+
+    Args:
+        in_features: shape of the input
+        alpha: trainable parameter that controls frequency
+        beta: trainable parameter that controls magnitude
+
+    Shape:
+        - Input: (B, C, T)
+        - Output: (B, C, T), same shape as the input
+
+    References:
+        This activation function is a modified version based on this paper by Liu Ziyin,
+        Tilman Hartwig, Masahito Ueda: https://arxiv.org/abs/2006.08195
+
+    Examples:
+        >>> a1 = SnakeBeta(256)
+        >>> x = torch.randn(256)
+        >>> x = a1(x)
+    """
+
+    def __init__(
+        self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False
+    ):
+        super(SnakeBeta, self).__init__()
+        self.in_features = in_features
+
+        # initialize alpha
+        self.alpha_logscale = alpha_logscale
+        if self.alpha_logscale:  # log scale alphas initialized to zeros
+            self.alpha = Parameter(torch.zeros(in_features) * alpha)
+            self.beta = Parameter(torch.zeros(in_features) * alpha)
+        else:  # linear scale alphas initialized to ones
+            self.alpha = Parameter(torch.ones(in_features) * alpha)
+            self.beta = Parameter(torch.ones(in_features) * alpha)
+
+        self.alpha.requires_grad = alpha_trainable
+        self.beta.requires_grad = alpha_trainable
+
+        self.no_div_by_zero = 0.000000001
+
+    def forward(self, x):
+        r"""Forward pass of the function. Applies the function to the input elementwise.
+        SnakeBeta ∶= x + 1/b * sin^2 (xa)
+        """
+
+        alpha = self.alpha.unsqueeze(0).unsqueeze(-1)  # line up with x to [B, C, T]
+        beta = self.beta.unsqueeze(0).unsqueeze(-1)
+        if self.alpha_logscale:
+            alpha = torch.exp(alpha)
+            beta = torch.exp(beta)
+        x = x + (1.0 / (beta + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
+
+        return x
diff --git a/modules/anti_aliasing/__init__.py b/modules/anti_aliasing/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..193756bfa04218d10f65fbed8665a8a0abad1cd4
--- /dev/null
+++ b/modules/anti_aliasing/__init__.py
@@ -0,0 +1,8 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .act import *
+from .filter import *
+from .resample import *
diff --git a/modules/anti_aliasing/act.py b/modules/anti_aliasing/act.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b62c29467ad33899dd699437236f9b808a4dbf5
--- /dev/null
+++ b/modules/anti_aliasing/act.py
@@ -0,0 +1,36 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch.nn as nn
+
+from .resample import *
+
+# This code is adopted from BigVGAN under the MIT License
+# https://github.com/NVIDIA/BigVGAN
+
+
+class Activation1d(nn.Module):
+    def __init__(
+        self,
+        activation,
+        up_ratio: int = 2,
+        down_ratio: int = 2,
+        up_kernel_size: int = 12,
+        down_kernel_size: int = 12,
+    ):
+        super().__init__()
+        self.up_ratio = up_ratio
+        self.down_ratio = down_ratio
+        self.act = activation
+        self.upsample = UpSample1d(up_ratio, up_kernel_size)
+        self.downsample = DownSample1d(down_ratio, down_kernel_size)
+
+    # x: [B,C,T]
+    def forward(self, x):
+        x = self.upsample(x)
+        x = self.act(x)
+        x = self.downsample(x)
+
+        return x
diff --git a/modules/anti_aliasing/filter.py b/modules/anti_aliasing/filter.py
new file mode 100644
index 0000000000000000000000000000000000000000..b91688866bd073c7ce268004791ec1263a6d76d7
--- /dev/null
+++ b/modules/anti_aliasing/filter.py
@@ -0,0 +1,99 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+
+if "sinc" in dir(torch):
+    sinc = torch.sinc
+else:
+    # This code is adopted from adefossez's julius.core.sinc under the MIT License
+    # https://adefossez.github.io/julius/julius/core.html
+    def sinc(x: torch.Tensor):
+        """
+        Implementation of sinc, i.e. sin(pi * x) / (pi * x)
+        __Warning__: Different to julius.sinc, the input is multiplied by `pi`!
+        """
+        return torch.where(
+            x == 0,
+            torch.tensor(1.0, device=x.device, dtype=x.dtype),
+            torch.sin(math.pi * x) / math.pi / x,
+        )
+
+
+# This code is adopted from adefossez's julius.lowpass.LowPassFilters under the MIT License
+# https://adefossez.github.io/julius/julius/lowpass.html
+def kaiser_sinc_filter1d(
+    cutoff, half_width, kernel_size
+):  # return filter [1,1,kernel_size]
+    even = kernel_size % 2 == 0
+    half_size = kernel_size // 2
+
+    # For kaiser window
+    delta_f = 4 * half_width
+    A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95
+    if A > 50.0:
+        beta = 0.1102 * (A - 8.7)
+    elif A >= 21.0:
+        beta = 0.5842 * (A - 21) ** 0.4 + 0.07886 * (A - 21.0)
+    else:
+        beta = 0.0
+    window = torch.kaiser_window(kernel_size, beta=beta, periodic=False)
+
+    # ratio = 0.5/cutoff -> 2 * cutoff = 1 / ratio
+    if even:
+        time = torch.arange(-half_size, half_size) + 0.5
+    else:
+        time = torch.arange(kernel_size) - half_size
+    if cutoff == 0:
+        filter_ = torch.zeros_like(time)
+    else:
+        filter_ = 2 * cutoff * window * sinc(2 * cutoff * time)
+        # Normalize filter to have sum = 1, otherwise we will have a small leakage
+        # of the constant component in the input signal.
+        filter_ /= filter_.sum()
+        filter = filter_.view(1, 1, kernel_size)
+
+    return filter
+
+
+class LowPassFilter1d(nn.Module):
+    def __init__(
+        self,
+        cutoff=0.5,
+        half_width=0.6,
+        stride: int = 1,
+        padding: bool = True,
+        padding_mode: str = "replicate",
+        kernel_size: int = 12,
+    ):
+        # kernel_size should be even number for stylegan3 setup,
+        # in this implementation, odd number is also possible.
+        super().__init__()
+        if cutoff < -0.0:
+            raise ValueError("Minimum cutoff must be larger than zero.")
+        if cutoff > 0.5:
+            raise ValueError("A cutoff above 0.5 does not make sense.")
+        self.kernel_size = kernel_size
+        self.even = kernel_size % 2 == 0
+        self.pad_left = kernel_size // 2 - int(self.even)
+        self.pad_right = kernel_size // 2
+        self.stride = stride
+        self.padding = padding
+        self.padding_mode = padding_mode
+        filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size)
+        self.register_buffer("filter", filter)
+
+    # input [B, C, T]
+    def forward(self, x):
+        _, C, _ = x.shape
+
+        if self.padding:
+            x = F.pad(x, (self.pad_left, self.pad_right), mode=self.padding_mode)
+        out = F.conv1d(x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C)
+
+        return out
diff --git a/modules/anti_aliasing/resample.py b/modules/anti_aliasing/resample.py
new file mode 100644
index 0000000000000000000000000000000000000000..462c8a42380bd8f6d26e19350599bbd5820f79a1
--- /dev/null
+++ b/modules/anti_aliasing/resample.py
@@ -0,0 +1,65 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+#################### Anti-aliasing ####################
+
+import torch.nn as nn
+from torch.nn import functional as F
+
+from .filter import *
+
+# This code is adopted from BigVGAN under the MIT License
+# https://github.com/NVIDIA/BigVGAN
+
+
+class UpSample1d(nn.Module):
+    def __init__(self, ratio=2, kernel_size=None):
+        super().__init__()
+        self.ratio = ratio
+        self.kernel_size = (
+            int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
+        )
+        self.stride = ratio
+        self.pad = self.kernel_size // ratio - 1
+        self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2
+        self.pad_right = (
+            self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2
+        )
+        filter = kaiser_sinc_filter1d(
+            cutoff=0.5 / ratio, half_width=0.6 / ratio, kernel_size=self.kernel_size
+        )
+        self.register_buffer("filter", filter)
+
+    # x: [B, C, T]
+    def forward(self, x):
+        _, C, _ = x.shape
+
+        x = F.pad(x, (self.pad, self.pad), mode="replicate")
+        x = self.ratio * F.conv_transpose1d(
+            x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C
+        )
+        x = x[..., self.pad_left : -self.pad_right]
+
+        return x
+
+
+class DownSample1d(nn.Module):
+    def __init__(self, ratio=2, kernel_size=None):
+        super().__init__()
+        self.ratio = ratio
+        self.kernel_size = (
+            int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
+        )
+        self.lowpass = LowPassFilter1d(
+            cutoff=0.5 / ratio,
+            half_width=0.6 / ratio,
+            stride=ratio,
+            kernel_size=self.kernel_size,
+        )
+
+    def forward(self, x):
+        xx = self.lowpass(x)
+
+        return xx
diff --git a/modules/base/base_module.py b/modules/base/base_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3c81206f4f130b706fffd45e93a054ce1231ddd
--- /dev/null
+++ b/modules/base/base_module.py
@@ -0,0 +1,75 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, channels, eps=1e-5):
+        super().__init__()
+        self.channels = channels
+        self.eps = eps
+
+        self.gamma = nn.Parameter(torch.ones(channels))
+        self.beta = nn.Parameter(torch.zeros(channels))
+
+    def forward(self, x):
+        x = x.transpose(1, -1)
+        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+        return x.transpose(1, -1)
+
+
+class ConvReluNorm(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        hidden_channels,
+        out_channels,
+        kernel_size,
+        n_layers,
+        p_dropout,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.hidden_channels = hidden_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.p_dropout = p_dropout
+        assert n_layers > 1, "Number of layers should be larger than 0."
+
+        self.conv_layers = nn.ModuleList()
+        self.norm_layers = nn.ModuleList()
+        self.conv_layers.append(
+            nn.Conv1d(
+                in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
+            )
+        )
+        self.norm_layers.append(LayerNorm(hidden_channels))
+        self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
+        for _ in range(n_layers - 1):
+            self.conv_layers.append(
+                nn.Conv1d(
+                    hidden_channels,
+                    hidden_channels,
+                    kernel_size,
+                    padding=kernel_size // 2,
+                )
+            )
+            self.norm_layers.append(LayerNorm(hidden_channels))
+        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
+        self.proj.weight.data.zero_()
+        self.proj.bias.data.zero_()
+
+    def forward(self, x, x_mask):
+        x_org = x
+        for i in range(self.n_layers):
+            x = self.conv_layers[i](x * x_mask)
+            x = self.norm_layers[i](x)
+            x = self.relu_drop(x)
+        x = x_org + self.proj(x)
+        return x * x_mask
diff --git a/modules/diffusion/__init__.py b/modules/diffusion/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf1e4f9e71ef3b0ef2d677a9c0db8e03b022eaea
--- /dev/null
+++ b/modules/diffusion/__init__.py
@@ -0,0 +1,7 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .bidilconv.bidilated_conv import BiDilConv
+from .unet.unet import UNet
diff --git a/modules/diffusion/bidilconv/bidilated_conv.py b/modules/diffusion/bidilconv/bidilated_conv.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac75522a3da8a40ae1b76656dd2a78097d6d94f3
--- /dev/null
+++ b/modules/diffusion/bidilconv/bidilated_conv.py
@@ -0,0 +1,102 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+
+import torch.nn as nn
+
+from modules.general.utils import Conv1d, zero_module
+from .residual_block import ResidualBlock
+
+
+class BiDilConv(nn.Module):
+    r"""Dilated CNN architecture with residual connections, default diffusion decoder.
+
+    Args:
+        input_channel: The number of input channels.
+        base_channel: The number of base channels.
+        n_res_block: The number of residual blocks.
+        conv_kernel_size: The kernel size of convolutional layers.
+        dilation_cycle_length: The cycle length of dilation.
+        conditioner_size: The size of conditioner.
+    """
+
+    def __init__(
+        self,
+        input_channel,
+        base_channel,
+        n_res_block,
+        conv_kernel_size,
+        dilation_cycle_length,
+        conditioner_size,
+        output_channel: int = -1,
+    ):
+        super().__init__()
+
+        self.input_channel = input_channel
+        self.base_channel = base_channel
+        self.n_res_block = n_res_block
+        self.conv_kernel_size = conv_kernel_size
+        self.dilation_cycle_length = dilation_cycle_length
+        self.conditioner_size = conditioner_size
+        self.output_channel = output_channel if output_channel > 0 else input_channel
+
+        self.input = nn.Sequential(
+            Conv1d(
+                input_channel,
+                base_channel,
+                1,
+            ),
+            nn.ReLU(),
+        )
+
+        self.residual_blocks = nn.ModuleList(
+            [
+                ResidualBlock(
+                    channels=base_channel,
+                    kernel_size=conv_kernel_size,
+                    dilation=2 ** (i % dilation_cycle_length),
+                    d_context=conditioner_size,
+                )
+                for i in range(n_res_block)
+            ]
+        )
+
+        self.out_proj = nn.Sequential(
+            Conv1d(
+                base_channel,
+                base_channel,
+                1,
+            ),
+            nn.ReLU(),
+            zero_module(
+                Conv1d(
+                    base_channel,
+                    self.output_channel,
+                    1,
+                ),
+            ),
+        )
+
+    def forward(self, x, y, context=None):
+        """
+        Args:
+            x: Noisy mel-spectrogram [B x ``n_mel`` x L]
+            y: FILM embeddings with the shape of (B, ``base_channel``)
+            context: Context with the shape of [B x ``d_context`` x L], default to None.
+        """
+
+        h = self.input(x)
+
+        skip = None
+        for i in range(self.n_res_block):
+            h, skip_connection = self.residual_blocks[i](h, y, context)
+            skip = skip_connection if skip is None else skip_connection + skip
+
+        out = skip / math.sqrt(self.n_res_block)
+
+        out = self.out_proj(out)
+
+        return out
diff --git a/modules/diffusion/bidilconv/residual_block.py b/modules/diffusion/bidilconv/residual_block.py
new file mode 100644
index 0000000000000000000000000000000000000000..df75d4fcb307af58fafe7258ba6e4a7a8cedd2f6
--- /dev/null
+++ b/modules/diffusion/bidilconv/residual_block.py
@@ -0,0 +1,73 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+
+import torch
+import torch.nn as nn
+
+from modules.activation_functions import GaU
+from modules.general.utils import Conv1d
+
+
+class ResidualBlock(nn.Module):
+    r"""Residual block with dilated convolution, main portion of ``BiDilConv``.
+
+    Args:
+        channels: The number of channels of input and output.
+        kernel_size: The kernel size of dilated convolution.
+        dilation: The dilation rate of dilated convolution.
+        d_context: The dimension of content encoder output, None if don't use context.
+    """
+
+    def __init__(
+        self,
+        channels: int = 256,
+        kernel_size: int = 3,
+        dilation: int = 1,
+        d_context: int = None,
+    ):
+        super().__init__()
+
+        self.context = d_context
+
+        self.gau = GaU(
+            channels,
+            kernel_size,
+            dilation,
+            d_context,
+        )
+
+        self.out_proj = Conv1d(
+            channels,
+            channels * 2,
+            1,
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        y_emb: torch.Tensor,
+        context: torch.Tensor = None,
+    ):
+        """
+        Args:
+            x: Latent representation inherited from previous residual block
+                with the shape of [B x C x T].
+            y_emb: Embeddings with the shape of [B x C], which will be FILM on the x.
+            context: Context with the shape of [B x ``d_context`` x T], default to None.
+        """
+
+        h = x + y_emb[..., None]
+
+        if self.context:
+            h = self.gau(h, context)
+        else:
+            h = self.gau(h)
+
+        h = self.out_proj(h)
+        res, skip = h.chunk(2, 1)
+
+        return (res + x) / math.sqrt(2.0), skip
diff --git a/modules/diffusion/karras/karras_diffusion.py b/modules/diffusion/karras/karras_diffusion.py
new file mode 100644
index 0000000000000000000000000000000000000000..f590c25a04a3db9cc16308de4e43f31d77e13f11
--- /dev/null
+++ b/modules/diffusion/karras/karras_diffusion.py
@@ -0,0 +1,979 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Based on: https://github.com/crowsonkb/k-diffusion
+"""
+import random
+
+import numpy as np
+import torch as th
+import torch.nn as nn
+import torch.nn.functional as F
+
+# from piq import LPIPS
+from utils.ssim import SSIM
+
+from modules.diffusion.karras.random_utils import get_generator
+
+
+def mean_flat(tensor):
+    """
+    Take the mean over all non-batch dimensions.
+    """
+    return tensor.mean(dim=list(range(1, len(tensor.shape))))
+
+
+def append_dims(x, target_dims):
+    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
+    dims_to_append = target_dims - x.ndim
+    if dims_to_append < 0:
+        raise ValueError(
+            f"input has {x.ndim} dims but target_dims is {target_dims}, which is less"
+        )
+    return x[(...,) + (None,) * dims_to_append]
+
+
+def append_zero(x):
+    return th.cat([x, x.new_zeros([1])])
+
+
+def get_weightings(weight_schedule, snrs, sigma_data):
+    if weight_schedule == "snr":
+        weightings = snrs
+    elif weight_schedule == "snr+1":
+        weightings = snrs + 1
+    elif weight_schedule == "karras":
+        weightings = snrs + 1.0 / sigma_data**2
+    elif weight_schedule == "truncated-snr":
+        weightings = th.clamp(snrs, min=1.0)
+    elif weight_schedule == "uniform":
+        weightings = th.ones_like(snrs)
+    else:
+        raise NotImplementedError()
+    return weightings
+
+
+class KarrasDenoiser:
+    def __init__(
+        self,
+        sigma_data: float = 0.5,
+        sigma_max=80.0,
+        sigma_min=0.002,
+        rho=7.0,
+        weight_schedule="karras",
+        distillation=False,
+        loss_norm="l2",
+    ):
+        self.sigma_data = sigma_data
+        self.sigma_max = sigma_max
+        self.sigma_min = sigma_min
+        self.weight_schedule = weight_schedule
+        self.distillation = distillation
+        self.loss_norm = loss_norm
+        # if loss_norm == "lpips":
+        #     self.lpips_loss = LPIPS(replace_pooling=True, reduction="none")
+        if loss_norm == "ssim":
+            self.ssim_loss = SSIM()
+        self.rho = rho
+        self.num_timesteps = 40
+
+    def get_snr(self, sigmas):
+        return sigmas**-2
+
+    def get_sigmas(self, sigmas):
+        return sigmas
+
+    def get_scalings(self, sigma):
+        c_skip = self.sigma_data**2 / (sigma**2 + self.sigma_data**2)
+        c_out = sigma * self.sigma_data / (sigma**2 + self.sigma_data**2) ** 0.5
+        c_in = 1 / (sigma**2 + self.sigma_data**2) ** 0.5
+        return c_skip, c_out, c_in
+
+    def get_scalings_for_boundary_condition(self, sigma):
+        c_skip = self.sigma_data**2 / (
+            (sigma - self.sigma_min) ** 2 + self.sigma_data**2
+        )
+        c_out = (
+            (sigma - self.sigma_min)
+            * self.sigma_data
+            / (sigma**2 + self.sigma_data**2) ** 0.5
+        )
+        c_in = 1 / (sigma**2 + self.sigma_data**2) ** 0.5
+        return c_skip, c_out, c_in
+
+    def training_losses(self, model, x_start, sigmas, condition=None, noise=None):
+        if noise is None:
+            noise = th.randn_like(x_start)
+
+        terms = {}
+
+        dims = x_start.ndim
+        x_t = x_start + noise * append_dims(sigmas, dims)
+        model_output, denoised = self.denoise(model, x_t, sigmas, condition)
+
+        snrs = self.get_snr(sigmas)
+        weights = append_dims(
+            get_weightings(self.weight_schedule, snrs, self.sigma_data), dims
+        )
+        # terms["xs_mse"] = mean_flat((denoised - x_start) ** 2)
+        terms["mse"] = mean_flat(weights * (denoised - x_start) ** 2)
+        # terms["mae"] = mean_flat(weights * th.abs(denoised - x_start))
+        # terms["mse"] = nn.MSELoss(reduction="none")(denoised, x_start)
+
+        # if "vb" in terms:
+        #     terms["loss"] = terms["mse"] + terms["vb"]
+        # else:
+        terms["loss"] = terms["mse"]
+
+        return terms
+
+    def consistency_losses(
+        self,
+        model,
+        x_start,
+        num_scales,
+        # model_kwargs=None,
+        condition=None,
+        target_model=None,
+        teacher_model=None,
+        teacher_diffusion=None,
+        noise=None,
+    ):
+        if noise is None:
+            noise = th.randn_like(x_start)
+
+        dims = x_start.ndim
+
+        def denoise_fn(x, t):
+            return self.denoise(model, x, t, condition)[1]
+
+        if target_model:
+
+            @th.no_grad()
+            def target_denoise_fn(x, t):
+                return self.denoise(target_model, x, t, condition)[1]
+
+        else:
+            raise NotImplementedError("Must have a target model")
+
+        if teacher_model:
+
+            @th.no_grad()
+            def teacher_denoise_fn(x, t):
+                return teacher_diffusion.denoise(teacher_model, x, t, condition)[1]
+
+        @th.no_grad()
+        def heun_solver(samples, t, next_t, x0):
+            x = samples
+            if teacher_model is None:
+                denoiser = x0
+            else:
+                denoiser = teacher_denoise_fn(x, t)
+
+            d = (x - denoiser) / append_dims(t, dims)
+            samples = x + d * append_dims(next_t - t, dims)
+            if teacher_model is None:
+                denoiser = x0
+            else:
+                denoiser = teacher_denoise_fn(samples, next_t)
+
+            next_d = (samples - denoiser) / append_dims(next_t, dims)
+            samples = x + (d + next_d) * append_dims((next_t - t) / 2, dims)
+
+            return samples
+
+        @th.no_grad()
+        def euler_solver(samples, t, next_t, x0):
+            x = samples
+            if teacher_model is None:
+                denoiser = x0
+            else:
+                denoiser = teacher_denoise_fn(x, t)
+            d = (x - denoiser) / append_dims(t, dims)
+            samples = x + d * append_dims(next_t - t, dims)
+
+            return samples
+
+        indices = th.randint(
+            0, num_scales - 1, (x_start.shape[0],), device=x_start.device
+        )
+
+        t = self.sigma_max ** (1 / self.rho) + indices / (num_scales - 1) * (
+            self.sigma_min ** (1 / self.rho) - self.sigma_max ** (1 / self.rho)
+        )
+        t = t**self.rho
+
+        t2 = self.sigma_max ** (1 / self.rho) + (indices + 1) / (num_scales - 1) * (
+            self.sigma_min ** (1 / self.rho) - self.sigma_max ** (1 / self.rho)
+        )
+        t2 = t2**self.rho
+
+        x_t = x_start + noise * append_dims(t, dims)
+
+        dropout_state = th.get_rng_state()
+        distiller = denoise_fn(x_t, t)
+
+        if teacher_model is None:
+            x_t2 = euler_solver(x_t, t, t2, x_start).detach()
+        else:
+            x_t2 = heun_solver(x_t, t, t2, x_start).detach()
+
+        th.set_rng_state(dropout_state)
+        distiller_target = target_denoise_fn(x_t2, t2)
+        distiller_target = distiller_target.detach()
+
+        snrs = self.get_snr(t)
+        weights = get_weightings(self.weight_schedule, snrs, self.sigma_data)
+        if self.loss_norm == "l1":
+            diffs = th.abs(distiller - distiller_target)
+            loss = mean_flat(diffs) * weights
+        elif self.loss_norm == "l2":
+            # diffs = (distiller - distiller_target) ** 2
+            loss = F.mse_loss(distiller, distiller_target)
+            # loss = mean_flat(diffs) * weights
+        elif self.loss_norm == "ssim":
+            loss = self.ssim_loss(distiller, distiller_target) * weights
+        # elif self.loss_norm == "l2-32":
+        #     distiller = F.interpolate(distiller, size=32, mode="bilinear")
+        #     distiller_target = F.interpolate(
+        #         distiller_target,
+        #         size=32,
+        #         mode="bilinear",
+        #     )
+        #     diffs = (distiller - distiller_target) ** 2
+        #     loss = mean_flat(diffs) * weights
+        # elif self.loss_norm == "lpips":
+        #     if x_start.shape[-1] < 256:
+        #         distiller = F.interpolate(distiller, size=224, mode="bilinear")
+        #         distiller_target = F.interpolate(
+        #             distiller_target, size=224, mode="bilinear"
+        #         )
+
+        #     loss = (
+        #         self.lpips_loss(
+        #             (distiller + 1) / 2.0,
+        #             (distiller_target + 1) / 2.0,
+        #         )
+        #         * weights
+        #     )
+        else:
+            raise ValueError(f"Unknown loss norm {self.loss_norm}")
+
+        terms = {}
+        terms["loss"] = loss
+
+        return terms
+
+    # def progdist_losses(
+    #     self,
+    #     model,
+    #     x_start,
+    #     num_scales,
+    #     model_kwargs=None,
+    #     teacher_model=None,
+    #     teacher_diffusion=None,
+    #     noise=None,
+    # ):
+    #     if model_kwargs is None:
+    #         model_kwargs = {}
+    #     if noise is None:
+    #         noise = th.randn_like(x_start)
+
+    #     dims = x_start.ndim
+
+    #     def denoise_fn(x, t):
+    #         return self.denoise(model, x, t, **model_kwargs)[1]
+
+    #     @th.no_grad()
+    #     def teacher_denoise_fn(x, t):
+    #         return teacher_diffusion.denoise(teacher_model, x, t, **model_kwargs)[1]
+
+    #     @th.no_grad()
+    #     def euler_solver(samples, t, next_t):
+    #         x = samples
+    #         denoiser = teacher_denoise_fn(x, t)
+    #         d = (x - denoiser) / append_dims(t, dims)
+    #         samples = x + d * append_dims(next_t - t, dims)
+
+    #         return samples
+
+    #     @th.no_grad()
+    #     def euler_to_denoiser(x_t, t, x_next_t, next_t):
+    #         denoiser = x_t - append_dims(t, dims) * (x_next_t - x_t) / append_dims(
+    #             next_t - t, dims
+    #         )
+    #         return denoiser
+
+    #     indices = th.randint(0, num_scales, (x_start.shape[0],), device=x_start.device)
+
+    #     t = self.sigma_max ** (1 / self.rho) + indices / num_scales * (
+    #         self.sigma_min ** (1 / self.rho) - self.sigma_max ** (1 / self.rho)
+    #     )
+    #     t = t**self.rho
+
+    #     t2 = self.sigma_max ** (1 / self.rho) + (indices + 0.5) / num_scales * (
+    #         self.sigma_min ** (1 / self.rho) - self.sigma_max ** (1 / self.rho)
+    #     )
+    #     t2 = t2**self.rho
+
+    #     t3 = self.sigma_max ** (1 / self.rho) + (indices + 1) / num_scales * (
+    #         self.sigma_min ** (1 / self.rho) - self.sigma_max ** (1 / self.rho)
+    #     )
+    #     t3 = t3**self.rho
+
+    #     x_t = x_start + noise * append_dims(t, dims)
+
+    #     denoised_x = denoise_fn(x_t, t)
+
+    #     x_t2 = euler_solver(x_t, t, t2).detach()
+    #     x_t3 = euler_solver(x_t2, t2, t3).detach()
+
+    #     target_x = euler_to_denoiser(x_t, t, x_t3, t3).detach()
+
+    #     snrs = self.get_snr(t)
+    #     weights = get_weightings(self.weight_schedule, snrs, self.sigma_data)
+    #     if self.loss_norm == "l1":
+    #         diffs = th.abs(denoised_x - target_x)
+    #         loss = mean_flat(diffs) * weights
+    #     elif self.loss_norm == "l2":
+    #         diffs = (denoised_x - target_x) ** 2
+    #         loss = mean_flat(diffs) * weights
+    #     elif self.loss_norm == "lpips":
+    #         if x_start.shape[-1] < 256:
+    #             denoised_x = F.interpolate(denoised_x, size=224, mode="bilinear")
+    #             target_x = F.interpolate(target_x, size=224, mode="bilinear")
+    #         loss = (
+    #             self.lpips_loss(
+    #                 (denoised_x + 1) / 2.0,
+    #                 (target_x + 1) / 2.0,
+    #             )
+    #             * weights
+    #         )
+    #     else:
+    #         raise ValueError(f"Unknown loss norm {self.loss_norm}")
+
+    #     terms = {}
+    #     terms["loss"] = loss
+
+    #     return terms
+
+    def denoise(self, model, x_t, sigmas, condition):
+        if not self.distillation:
+            c_skip, c_out, c_in = [
+                append_dims(x, x_t.ndim) for x in self.get_scalings(sigmas)
+            ]
+        else:
+            c_skip, c_out, c_in = [
+                append_dims(x, x_t.ndim)
+                for x in self.get_scalings_for_boundary_condition(sigmas)
+            ]
+        rescaled_t = 1000 * 0.25 * th.log(sigmas + 1e-44)
+        # rescaled_t = rescaled_t[:, None]
+        model_output = model(c_in * x_t, rescaled_t, condition)
+        denoised = c_out * model_output + c_skip * x_t
+        return model_output, denoised
+
+
+def karras_sample(
+    diffusion,
+    model,
+    shape,
+    steps,
+    clip_denoised=True,
+    progress=True,
+    callback=None,
+    # model_kwargs=None,
+    condition=None,
+    device=None,
+    sigma_min=0.002,
+    sigma_max=80,  # higher for highres?
+    rho=7.0,
+    sampler="heun",
+    s_churn=0.0,
+    s_tmin=0.0,
+    s_tmax=float("inf"),
+    s_noise=1.0,
+    generator=None,
+    ts=None,
+):
+    if generator is None:
+        generator = get_generator("dummy")
+
+    if sampler == "progdist":
+        sigmas = get_sigmas_karras(steps + 1, sigma_min, sigma_max, rho, device=device)
+    else:
+        sigmas = get_sigmas_karras(steps, sigma_min, sigma_max, rho, device=device)
+    th.manual_seed(42)
+    x_T = generator.randn(*shape, device=device) * sigma_max
+    sigmas = sigmas.unsqueeze(-1)
+    sample_fn = {
+        "heun": sample_heun,
+        "dpm": sample_dpm,
+        "ancestral": sample_euler_ancestral,
+        "onestep": sample_onestep,
+        "progdist": sample_progdist,
+        "euler": sample_euler,
+        "multistep": stochastic_iterative_sampler,
+    }[sampler]
+
+    if sampler in ["heun", "dpm"]:
+        sampler_args = dict(
+            s_churn=s_churn, s_tmin=s_tmin, s_tmax=s_tmax, s_noise=s_noise
+        )
+    elif sampler == "multistep":
+        sampler_args = dict(
+            ts=ts, t_min=sigma_min, t_max=sigma_max, rho=diffusion.rho, steps=steps
+        )
+    else:
+        sampler_args = {}
+
+    def denoiser(x_t, sigma):
+        _, denoised = diffusion.denoise(model, x_t, sigma, condition)
+        if clip_denoised:
+            denoised = denoised.clamp(-1, 1)
+        return denoised
+
+    x_0 = sample_fn(
+        denoiser,
+        x_T,
+        sigmas,
+        generator,
+        progress=progress,
+        callback=callback,
+        **sampler_args,
+    )
+    return x_0.clamp(-1, 1)
+
+
+def get_sigmas_karras(n, sigma_min, sigma_max, rho=7.0, device="cpu"):
+    """Constructs the noise schedule of Karras et al. (2022)."""
+    ramp = th.linspace(0, 1, n)
+    min_inv_rho = sigma_min ** (1 / rho)
+    max_inv_rho = sigma_max ** (1 / rho)
+    sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
+    return append_zero(sigmas).to(device)
+
+
+def to_d(x, sigma, denoised):
+    """Converts a denoiser output to a Karras ODE derivative."""
+    return (x - denoised) / append_dims(sigma, x.ndim)
+
+
+def get_ancestral_step(sigma_from, sigma_to):
+    """Calculates the noise level (sigma_down) to step down to and the amount
+    of noise to add (sigma_up) when doing an ancestral sampling step."""
+    sigma_up = (
+        sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2
+    ) ** 0.5
+    sigma_down = (sigma_to**2 - sigma_up**2) ** 0.5
+    return sigma_down, sigma_up
+
+
+@th.no_grad()
+def sample_euler_ancestral(model, x, sigmas, generator, progress=False, callback=None):
+    """Ancestral sampling with Euler method steps."""
+    s_in = x.new_ones([x.shape[0]])
+    indices = range(len(sigmas) - 1)
+    if progress:
+        from tqdm.auto import tqdm
+
+        indices = tqdm(indices)
+
+    for i in indices:
+        denoised = model(x, sigmas[i] * s_in)
+        sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1])
+        if callback is not None:
+            callback(
+                {
+                    "x": x,
+                    "i": i,
+                    "sigma": sigmas[i],
+                    "sigma_hat": sigmas[i],
+                    "denoised": denoised,
+                }
+            )
+        d = to_d(x, sigmas[i], denoised)
+        # Euler method
+        dt = sigma_down - sigmas[i]
+        x = x + d * dt
+        x = x + generator.randn_like(x) * sigma_up
+    return x
+
+
+@th.no_grad()
+def sample_midpoint_ancestral(model, x, ts, generator, progress=False, callback=None):
+    """Ancestral sampling with midpoint method steps."""
+    s_in = x.new_ones([x.shape[0]])
+    step_size = 1 / len(ts)
+    if progress:
+        from tqdm.auto import tqdm
+
+        ts = tqdm(ts)
+
+    for tn in ts:
+        dn = model(x, tn * s_in)
+        dn_2 = model(x + (step_size / 2) * dn, (tn + step_size / 2) * s_in)
+        x = x + step_size * dn_2
+        if callback is not None:
+            callback({"x": x, "tn": tn, "dn": dn, "dn_2": dn_2})
+    return x
+
+
+@th.no_grad()
+def sample_heun(
+    denoiser,
+    x,
+    sigmas,
+    generator,
+    progress=False,
+    callback=None,
+    s_churn=0.0,
+    s_tmin=0.0,
+    s_tmax=float("inf"),
+    s_noise=1.0,
+):
+    """Implements Algorithm 2 (Heun steps) from Karras et al. (2022)."""
+    s_in = x.new_ones([x.shape[0]])
+    indices = range(len(sigmas) - 1)
+    if progress:
+        from tqdm.auto import tqdm
+
+        indices = tqdm(indices)
+
+    for i in indices:
+        gamma = (
+            min(s_churn / (len(sigmas) - 1), 2**0.5 - 1)
+            if s_tmin <= sigmas[i] <= s_tmax
+            else 0.0
+        )
+        eps = generator.randn_like(x) * s_noise
+        sigma_hat = sigmas[i] * (gamma + 1)
+        if gamma > 0:
+            x = x + eps * (sigma_hat**2 - sigmas[i] ** 2) ** 0.5
+        denoised = denoiser(x, sigma_hat * s_in)
+        d = to_d(x, sigma_hat, denoised)
+        if callback is not None:
+            callback(
+                {
+                    "x": x,
+                    "i": i,
+                    "sigma": sigmas[i],
+                    "sigma_hat": sigma_hat,
+                    "denoised": denoised,
+                }
+            )
+        dt = sigmas[i + 1] - sigma_hat
+        if sigmas[i + 1] == 0:
+            # Euler method
+            x = x + d * dt
+        else:
+            # Heun's method
+            x_2 = x + d * dt
+            denoised_2 = denoiser(x_2, sigmas[i + 1] * s_in)
+            d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
+            d_prime = (d + d_2) / 2
+            x = x + d_prime * dt
+    return x
+
+
+@th.no_grad()
+def sample_euler(
+    denoiser,
+    x,
+    sigmas,
+    generator,
+    progress=False,
+    callback=None,
+):
+    """Implements Algorithm 2 (Heun steps) from Karras et al. (2022)."""
+    s_in = x.new_ones([x.shape[0]])
+    indices = range(len(sigmas) - 1)
+    if progress:
+        from tqdm.auto import tqdm
+
+        indices = tqdm(indices)
+
+    for i in indices:
+        sigma = sigmas[i]
+        denoised = denoiser(x, sigma * s_in)
+        d = to_d(x, sigma, denoised)
+        if callback is not None:
+            callback(
+                {
+                    "x": x,
+                    "i": i,
+                    "sigma": sigmas[i],
+                    "denoised": denoised,
+                }
+            )
+        dt = sigmas[i + 1] - sigma
+        x = x + d * dt
+    return x
+
+
+@th.no_grad()
+def sample_dpm(
+    denoiser,
+    x,
+    sigmas,
+    generator,
+    progress=False,
+    callback=None,
+    s_churn=0.0,
+    s_tmin=0.0,
+    s_tmax=float("inf"),
+    s_noise=1.0,
+):
+    """A sampler inspired by DPM-Solver-2 and Algorithm 2 from Karras et al. (2022)."""
+    s_in = x.new_ones([x.shape[0]])
+    indices = range(len(sigmas) - 1)
+    if progress:
+        from tqdm.auto import tqdm
+
+        indices = tqdm(indices)
+
+    for i in indices:
+        gamma = (
+            min(s_churn / (len(sigmas) - 1), 2**0.5 - 1)
+            if s_tmin <= sigmas[i] <= s_tmax
+            else 0.0
+        )
+        eps = generator.randn_like(x) * s_noise
+        sigma_hat = sigmas[i] * (gamma + 1)
+        if gamma > 0:
+            x = x + eps * (sigma_hat**2 - sigmas[i] ** 2) ** 0.5
+        denoised = denoiser(x, sigma_hat * s_in)
+        d = to_d(x, sigma_hat, denoised)
+        if callback is not None:
+            callback(
+                {
+                    "x": x,
+                    "i": i,
+                    "sigma": sigmas[i],
+                    "sigma_hat": sigma_hat,
+                    "denoised": denoised,
+                }
+            )
+        # Midpoint method, where the midpoint is chosen according to a rho=3 Karras schedule
+        sigma_mid = ((sigma_hat ** (1 / 3) + sigmas[i + 1] ** (1 / 3)) / 2) ** 3
+        dt_1 = sigma_mid - sigma_hat
+        dt_2 = sigmas[i + 1] - sigma_hat
+        x_2 = x + d * dt_1
+        denoised_2 = denoiser(x_2, sigma_mid * s_in)
+        d_2 = to_d(x_2, sigma_mid, denoised_2)
+        x = x + d_2 * dt_2
+    return x
+
+
+@th.no_grad()
+def sample_onestep(
+    distiller,
+    x,
+    sigmas,
+    generator=None,
+    progress=False,
+    callback=None,
+):
+    """Single-step generation from a distilled model."""
+    s_in = x.new_ones([x.shape[0]])
+    return distiller(x, sigmas[0] * s_in)
+
+
+@th.no_grad()
+def stochastic_iterative_sampler(
+    distiller,
+    x,
+    sigmas,
+    generator,
+    ts,
+    progress=False,
+    callback=None,
+    t_min=0.002,
+    t_max=80.0,
+    rho=7.0,
+    steps=40,
+):
+    t_max_rho = t_max ** (1 / rho)
+    t_min_rho = t_min ** (1 / rho)
+    s_in = x.new_ones([x.shape[0]])
+
+    for i in range(len(ts) - 1):
+        t = (t_max_rho + ts[i] / (steps - 1) * (t_min_rho - t_max_rho)) ** rho
+        x0 = distiller(x, t * s_in)
+        next_t = (t_max_rho + ts[i + 1] / (steps - 1) * (t_min_rho - t_max_rho)) ** rho
+        next_t = np.clip(next_t, t_min, t_max)
+        x = x0 + generator.randn_like(x) * np.sqrt(next_t**2 - t_min**2)
+
+    return x
+
+
+@th.no_grad()
+def sample_progdist(
+    denoiser,
+    x,
+    sigmas,
+    generator=None,
+    progress=False,
+    callback=None,
+):
+    s_in = x.new_ones([x.shape[0]])
+    sigmas = sigmas[:-1]  # skip the zero sigma
+
+    indices = range(len(sigmas) - 1)
+    if progress:
+        from tqdm.auto import tqdm
+
+        indices = tqdm(indices)
+
+    for i in indices:
+        sigma = sigmas[i]
+        denoised = denoiser(x, sigma * s_in)
+        d = to_d(x, sigma, denoised)
+        if callback is not None:
+            callback(
+                {
+                    "x": x,
+                    "i": i,
+                    "sigma": sigma,
+                    "denoised": denoised,
+                }
+            )
+        dt = sigmas[i + 1] - sigma
+        x = x + d * dt
+
+    return x
+
+
+# @th.no_grad()
+# def iterative_colorization(
+#     distiller,
+#     images,
+#     x,
+#     ts,
+#     t_min=0.002,
+#     t_max=80.0,
+#     rho=7.0,
+#     steps=40,
+#     generator=None,
+# ):
+#     def obtain_orthogonal_matrix():
+#         vector = np.asarray([0.2989, 0.5870, 0.1140])
+#         vector = vector / np.linalg.norm(vector)
+#         matrix = np.eye(3)
+#         matrix[:, 0] = vector
+#         matrix = np.linalg.qr(matrix)[0]
+#         if np.sum(matrix[:, 0]) < 0:
+#             matrix = -matrix
+#         return matrix
+
+#     Q = th.from_numpy(obtain_orthogonal_matrix()).to(dist_util.dev()).to(th.float32)
+#     mask = th.zeros(*x.shape[1:], device=dist_util.dev())
+#     mask[0, ...] = 1.0
+
+#     def replacement(x0, x1):
+#         x0 = th.einsum("bchw,cd->bdhw", x0, Q)
+#         x1 = th.einsum("bchw,cd->bdhw", x1, Q)
+
+#         x_mix = x0 * mask + x1 * (1.0 - mask)
+#         x_mix = th.einsum("bdhw,cd->bchw", x_mix, Q)
+#         return x_mix
+
+#     t_max_rho = t_max ** (1 / rho)
+#     t_min_rho = t_min ** (1 / rho)
+#     s_in = x.new_ones([x.shape[0]])
+#     images = replacement(images, th.zeros_like(images))
+
+#     for i in range(len(ts) - 1):
+#         t = (t_max_rho + ts[i] / (steps - 1) * (t_min_rho - t_max_rho)) ** rho
+#         x0 = distiller(x, t * s_in)
+#         x0 = th.clamp(x0, -1.0, 1.0)
+#         x0 = replacement(images, x0)
+#         next_t = (t_max_rho + ts[i + 1] / (steps - 1) * (t_min_rho - t_max_rho)) ** rho
+#         next_t = np.clip(next_t, t_min, t_max)
+#         x = x0 + generator.randn_like(x) * np.sqrt(next_t**2 - t_min**2)
+
+#     return x, images
+
+
+# @th.no_grad()
+# def iterative_inpainting(
+#     distiller,
+#     images,
+#     x,
+#     ts,
+#     t_min=0.002,
+#     t_max=80.0,
+#     rho=7.0,
+#     steps=40,
+#     generator=None,
+# ):
+#     from PIL import Image, ImageDraw, ImageFont
+
+#     image_size = x.shape[-1]
+
+#     # create a blank image with a white background
+#     img = Image.new("RGB", (image_size, image_size), color="white")
+
+#     # get a drawing context for the image
+#     draw = ImageDraw.Draw(img)
+
+#     # load a font
+#     font = ImageFont.truetype("arial.ttf", 250)
+
+#     # draw the letter "C" in black
+#     draw.text((50, 0), "S", font=font, fill=(0, 0, 0))
+
+#     # convert the image to a numpy array
+#     img_np = np.array(img)
+#     img_np = img_np.transpose(2, 0, 1)
+#     img_th = th.from_numpy(img_np).to(dist_util.dev())
+
+#     mask = th.zeros(*x.shape, device=dist_util.dev())
+#     mask = mask.reshape(-1, 7, 3, image_size, image_size)
+
+#     mask[::2, :, img_th > 0.5] = 1.0
+#     mask[1::2, :, img_th < 0.5] = 1.0
+#     mask = mask.reshape(-1, 3, image_size, image_size)
+
+#     def replacement(x0, x1):
+#         x_mix = x0 * mask + x1 * (1 - mask)
+#         return x_mix
+
+#     t_max_rho = t_max ** (1 / rho)
+#     t_min_rho = t_min ** (1 / rho)
+#     s_in = x.new_ones([x.shape[0]])
+#     images = replacement(images, -th.ones_like(images))
+
+#     for i in range(len(ts) - 1):
+#         t = (t_max_rho + ts[i] / (steps - 1) * (t_min_rho - t_max_rho)) ** rho
+#         x0 = distiller(x, t * s_in)
+#         x0 = th.clamp(x0, -1.0, 1.0)
+#         x0 = replacement(images, x0)
+#         next_t = (t_max_rho + ts[i + 1] / (steps - 1) * (t_min_rho - t_max_rho)) ** rho
+#         next_t = np.clip(next_t, t_min, t_max)
+#         x = x0 + generator.randn_like(x) * np.sqrt(next_t**2 - t_min**2)
+
+#     return x, images
+
+
+# @th.no_grad()
+# def iterative_superres(
+#     distiller,
+#     images,
+#     x,
+#     ts,
+#     t_min=0.002,
+#     t_max=80.0,
+#     rho=7.0,
+#     steps=40,
+#     generator=None,
+# ):
+#     patch_size = 8
+
+#     def obtain_orthogonal_matrix():
+#         vector = np.asarray([1] * patch_size**2)
+#         vector = vector / np.linalg.norm(vector)
+#         matrix = np.eye(patch_size**2)
+#         matrix[:, 0] = vector
+#         matrix = np.linalg.qr(matrix)[0]
+#         if np.sum(matrix[:, 0]) < 0:
+#             matrix = -matrix
+#         return matrix
+
+#     Q = th.from_numpy(obtain_orthogonal_matrix()).to(dist_util.dev()).to(th.float32)
+
+#     image_size = x.shape[-1]
+
+#     def replacement(x0, x1):
+#         x0_flatten = (
+#             x0.reshape(-1, 3, image_size, image_size)
+#             .reshape(
+#                 -1,
+#                 3,
+#                 image_size // patch_size,
+#                 patch_size,
+#                 image_size // patch_size,
+#                 patch_size,
+#             )
+#             .permute(0, 1, 2, 4, 3, 5)
+#             .reshape(-1, 3, image_size**2 // patch_size**2, patch_size**2)
+#         )
+#         x1_flatten = (
+#             x1.reshape(-1, 3, image_size, image_size)
+#             .reshape(
+#                 -1,
+#                 3,
+#                 image_size // patch_size,
+#                 patch_size,
+#                 image_size // patch_size,
+#                 patch_size,
+#             )
+#             .permute(0, 1, 2, 4, 3, 5)
+#             .reshape(-1, 3, image_size**2 // patch_size**2, patch_size**2)
+#         )
+#         x0 = th.einsum("bcnd,de->bcne", x0_flatten, Q)
+#         x1 = th.einsum("bcnd,de->bcne", x1_flatten, Q)
+#         x_mix = x0.new_zeros(x0.shape)
+#         x_mix[..., 0] = x0[..., 0]
+#         x_mix[..., 1:] = x1[..., 1:]
+#         x_mix = th.einsum("bcne,de->bcnd", x_mix, Q)
+#         x_mix = (
+#             x_mix.reshape(
+#                 -1,
+#                 3,
+#                 image_size // patch_size,
+#                 image_size // patch_size,
+#                 patch_size,
+#                 patch_size,
+#             )
+#             .permute(0, 1, 2, 4, 3, 5)
+#             .reshape(-1, 3, image_size, image_size)
+#         )
+#         return x_mix
+
+#     def average_image_patches(x):
+#         x_flatten = (
+#             x.reshape(-1, 3, image_size, image_size)
+#             .reshape(
+#                 -1,
+#                 3,
+#                 image_size // patch_size,
+#                 patch_size,
+#                 image_size // patch_size,
+#                 patch_size,
+#             )
+#             .permute(0, 1, 2, 4, 3, 5)
+#             .reshape(-1, 3, image_size**2 // patch_size**2, patch_size**2)
+#         )
+#         x_flatten[..., :] = x_flatten.mean(dim=-1, keepdim=True)
+#         return (
+#             x_flatten.reshape(
+#                 -1,
+#                 3,
+#                 image_size // patch_size,
+#                 image_size // patch_size,
+#                 patch_size,
+#                 patch_size,
+#             )
+#             .permute(0, 1, 2, 4, 3, 5)
+#             .reshape(-1, 3, image_size, image_size)
+#         )
+
+#     t_max_rho = t_max ** (1 / rho)
+#     t_min_rho = t_min ** (1 / rho)
+#     s_in = x.new_ones([x.shape[0]])
+#     images = average_image_patches(images)
+
+#     for i in range(len(ts) - 1):
+#         t = (t_max_rho + ts[i] / (steps - 1) * (t_min_rho - t_max_rho)) ** rho
+#         x0 = distiller(x, t * s_in)
+#         x0 = th.clamp(x0, -1.0, 1.0)
+#         x0 = replacement(images, x0)
+#         next_t = (t_max_rho + ts[i + 1] / (steps - 1) * (t_min_rho - t_max_rho)) ** rho
+#         next_t = np.clip(next_t, t_min, t_max)
+#         x = x0 + generator.randn_like(x) * np.sqrt(next_t**2 - t_min**2)
+
+#     return x, images
diff --git a/modules/diffusion/karras/random_utils.py b/modules/diffusion/karras/random_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ebf4265934edad153adaf83777731afa313d000
--- /dev/null
+++ b/modules/diffusion/karras/random_utils.py
@@ -0,0 +1,177 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch as th
+
+
+def get_generator(generator, num_samples=0, seed=0):
+    if generator == "dummy":
+        return DummyGenerator()
+    elif generator == "determ":
+        return DeterministicGenerator(num_samples, seed)
+    elif generator == "determ-indiv":
+        return DeterministicIndividualGenerator(num_samples, seed)
+    else:
+        raise NotImplementedError
+
+
+class DummyGenerator:
+    def randn(self, *args, **kwargs):
+        return th.randn(*args, **kwargs)
+
+    def randint(self, *args, **kwargs):
+        return th.randint(*args, **kwargs)
+
+    def randn_like(self, *args, **kwargs):
+        return th.randn_like(*args, **kwargs)
+
+
+class DeterministicGenerator:
+    """
+    RNG to deterministically sample num_samples samples that does not depend on batch_size or mpi_machines
+    Uses a single rng and samples num_samples sized randomness and subsamples the current indices
+    """
+
+    def __init__(self, num_samples, seed=0):
+        print("Warning: Distributed not initialised, using single rank")
+        self.rank = 0
+        self.world_size = 1
+        self.num_samples = num_samples
+        self.done_samples = 0
+        self.seed = seed
+        self.rng_cpu = th.Generator()
+        if th.cuda.is_available():
+            self.rng_cuda = th.Generator(dist_util.dev())
+        self.set_seed(seed)
+
+    def get_global_size_and_indices(self, size):
+        global_size = (self.num_samples, *size[1:])
+        indices = th.arange(
+            self.done_samples + self.rank,
+            self.done_samples + self.world_size * int(size[0]),
+            self.world_size,
+        )
+        indices = th.clamp(indices, 0, self.num_samples - 1)
+        assert (
+            len(indices) == size[0]
+        ), f"rank={self.rank}, ws={self.world_size}, l={len(indices)}, bs={size[0]}"
+        return global_size, indices
+
+    def get_generator(self, device):
+        return self.rng_cpu if th.device(device).type == "cpu" else self.rng_cuda
+
+    def randn(self, *size, dtype=th.float, device="cpu"):
+        global_size, indices = self.get_global_size_and_indices(size)
+        generator = self.get_generator(device)
+        return th.randn(*global_size, generator=generator, dtype=dtype, device=device)[
+            indices
+        ]
+
+    def randint(self, low, high, size, dtype=th.long, device="cpu"):
+        global_size, indices = self.get_global_size_and_indices(size)
+        generator = self.get_generator(device)
+        return th.randint(
+            low, high, generator=generator, size=global_size, dtype=dtype, device=device
+        )[indices]
+
+    def randn_like(self, tensor):
+        size, dtype, device = tensor.size(), tensor.dtype, tensor.device
+        return self.randn(*size, dtype=dtype, device=device)
+
+    def set_done_samples(self, done_samples):
+        self.done_samples = done_samples
+        self.set_seed(self.seed)
+
+    def get_seed(self):
+        return self.seed
+
+    def set_seed(self, seed):
+        self.rng_cpu.manual_seed(seed)
+        if th.cuda.is_available():
+            self.rng_cuda.manual_seed(seed)
+
+
+class DeterministicIndividualGenerator:
+    """
+    RNG to deterministically sample num_samples samples that does not depend on batch_size or mpi_machines
+    Uses a separate rng for each sample to reduce memoery usage
+    """
+
+    def __init__(self, num_samples, seed=0):
+        print("Warning: Distributed not initialised, using single rank")
+        self.rank = 0
+        self.world_size = 1
+        self.num_samples = num_samples
+        self.done_samples = 0
+        self.seed = seed
+        self.rng_cpu = [th.Generator() for _ in range(num_samples)]
+        if th.cuda.is_available():
+            self.rng_cuda = [th.Generator(dist_util.dev()) for _ in range(num_samples)]
+        self.set_seed(seed)
+
+    def get_size_and_indices(self, size):
+        indices = th.arange(
+            self.done_samples + self.rank,
+            self.done_samples + self.world_size * int(size[0]),
+            self.world_size,
+        )
+        indices = th.clamp(indices, 0, self.num_samples - 1)
+        assert (
+            len(indices) == size[0]
+        ), f"rank={self.rank}, ws={self.world_size}, l={len(indices)}, bs={size[0]}"
+        return (1, *size[1:]), indices
+
+    def get_generator(self, device):
+        return self.rng_cpu if th.device(device).type == "cpu" else self.rng_cuda
+
+    def randn(self, *size, dtype=th.float, device="cpu"):
+        size, indices = self.get_size_and_indices(size)
+        generator = self.get_generator(device)
+        return th.cat(
+            [
+                th.randn(*size, generator=generator[i], dtype=dtype, device=device)
+                for i in indices
+            ],
+            dim=0,
+        )
+
+    def randint(self, low, high, size, dtype=th.long, device="cpu"):
+        size, indices = self.get_size_and_indices(size)
+        generator = self.get_generator(device)
+        return th.cat(
+            [
+                th.randint(
+                    low,
+                    high,
+                    generator=generator[i],
+                    size=size,
+                    dtype=dtype,
+                    device=device,
+                )
+                for i in indices
+            ],
+            dim=0,
+        )
+
+    def randn_like(self, tensor):
+        size, dtype, device = tensor.size(), tensor.dtype, tensor.device
+        return self.randn(*size, dtype=dtype, device=device)
+
+    def set_done_samples(self, done_samples):
+        self.done_samples = done_samples
+
+    def get_seed(self):
+        return self.seed
+
+    def set_seed(self, seed):
+        [
+            rng_cpu.manual_seed(i + self.num_samples * seed)
+            for i, rng_cpu in enumerate(self.rng_cpu)
+        ]
+        if th.cuda.is_available():
+            [
+                rng_cuda.manual_seed(i + self.num_samples * seed)
+                for i, rng_cuda in enumerate(self.rng_cuda)
+            ]
diff --git a/modules/diffusion/karras/sample.py b/modules/diffusion/karras/sample.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d7472a37342c8e8eb06ccd6e292a28b80e8dfef
--- /dev/null
+++ b/modules/diffusion/karras/sample.py
@@ -0,0 +1,185 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from abc import ABC, abstractmethod
+
+import numpy as np
+import torch as th
+from scipy.stats import norm
+import torch.distributed as dist
+
+
+def create_named_schedule_sampler(name, diffusion):
+    """
+    Create a ScheduleSampler from a library of pre-defined samplers.
+
+    :param name: the name of the sampler.
+    :param diffusion: the diffusion object to sample for.
+    """
+    if name == "uniform":
+        return UniformSampler(diffusion)
+    elif name == "loss-second-moment":
+        return LossSecondMomentResampler(diffusion)
+    elif name == "lognormal":
+        return LogNormalSampler()
+    else:
+        raise NotImplementedError(f"unknown schedule sampler: {name}")
+
+
+class ScheduleSampler(ABC):
+    """
+    A distribution over timesteps in the diffusion process, intended to reduce
+    variance of the objective.
+
+    By default, samplers perform unbiased importance sampling, in which the
+    objective's mean is unchanged.
+    However, subclasses may override sample() to change how the resampled
+    terms are reweighted, allowing for actual changes in the objective.
+    """
+
+    @abstractmethod
+    def weights(self):
+        """
+        Get a numpy array of weights, one per diffusion step.
+
+        The weights needn't be normalized, but must be positive.
+        """
+
+    def sample(self, batch_size, device):
+        """
+        Importance-sample timesteps for a batch.
+
+        :param batch_size: the number of timesteps.
+        :param device: the torch device to save to.
+        :return: a tuple (timesteps, weights):
+                 - timesteps: a tensor of timestep indices.
+                 - weights: a tensor of weights to scale the resulting losses.
+        """
+        w = self.weights()
+        p = w / np.sum(w)
+        indices_np = np.random.choice(len(p), size=(batch_size,), p=p)
+        indices = th.from_numpy(indices_np).long().to(device)
+        weights_np = 1 / (len(p) * p[indices_np])
+        weights = th.from_numpy(weights_np).float().to(device)
+        return indices, weights
+
+
+class UniformSampler(ScheduleSampler):
+    def __init__(self, diffusion):
+        self.diffusion = diffusion
+        self._weights = np.ones([diffusion.num_timesteps])
+
+    def weights(self):
+        return self._weights
+
+
+class LossAwareSampler(ScheduleSampler):
+    def update_with_local_losses(self, local_ts, local_losses):
+        """
+        Update the reweighting using losses from a model.
+
+        Call this method from each rank with a batch of timesteps and the
+        corresponding losses for each of those timesteps.
+        This method will perform synchronization to make sure all of the ranks
+        maintain the exact same reweighting.
+
+        :param local_ts: an integer Tensor of timesteps.
+        :param local_losses: a 1D Tensor of losses.
+        """
+        batch_sizes = [
+            th.tensor([0], dtype=th.int32, device=local_ts.device)
+            for _ in range(dist.get_world_size())
+        ]
+        dist.all_gather(
+            batch_sizes,
+            th.tensor([len(local_ts)], dtype=th.int32, device=local_ts.device),
+        )
+
+        # Pad all_gather batches to be the maximum batch size.
+        batch_sizes = [x.item() for x in batch_sizes]
+        max_bs = max(batch_sizes)
+
+        timestep_batches = [th.zeros(max_bs).to(local_ts) for bs in batch_sizes]
+        loss_batches = [th.zeros(max_bs).to(local_losses) for bs in batch_sizes]
+        dist.all_gather(timestep_batches, local_ts)
+        dist.all_gather(loss_batches, local_losses)
+        timesteps = [
+            x.item() for y, bs in zip(timestep_batches, batch_sizes) for x in y[:bs]
+        ]
+        losses = [x.item() for y, bs in zip(loss_batches, batch_sizes) for x in y[:bs]]
+        self.update_with_all_losses(timesteps, losses)
+
+    @abstractmethod
+    def update_with_all_losses(self, ts, losses):
+        """
+        Update the reweighting using losses from a model.
+
+        Sub-classes should override this method to update the reweighting
+        using losses from the model.
+
+        This method directly updates the reweighting without synchronizing
+        between workers. It is called by update_with_local_losses from all
+        ranks with identical arguments. Thus, it should have deterministic
+        behavior to maintain state across workers.
+
+        :param ts: a list of int timesteps.
+        :param losses: a list of float losses, one per timestep.
+        """
+
+
+class LossSecondMomentResampler(LossAwareSampler):
+    def __init__(self, diffusion, history_per_term=10, uniform_prob=0.001):
+        self.diffusion = diffusion
+        self.history_per_term = history_per_term
+        self.uniform_prob = uniform_prob
+        self._loss_history = np.zeros(
+            [diffusion.num_timesteps, history_per_term], dtype=np.float64
+        )
+        self._loss_counts = np.zeros([diffusion.num_timesteps], dtype=np.int)
+
+    def weights(self):
+        if not self._warmed_up():
+            return np.ones([self.diffusion.num_timesteps], dtype=np.float64)
+        weights = np.sqrt(np.mean(self._loss_history**2, axis=-1))
+        weights /= np.sum(weights)
+        weights *= 1 - self.uniform_prob
+        weights += self.uniform_prob / len(weights)
+        return weights
+
+    def update_with_all_losses(self, ts, losses):
+        for t, loss in zip(ts, losses):
+            if self._loss_counts[t] == self.history_per_term:
+                # Shift out the oldest loss term.
+                self._loss_history[t, :-1] = self._loss_history[t, 1:]
+                self._loss_history[t, -1] = loss
+            else:
+                self._loss_history[t, self._loss_counts[t]] = loss
+                self._loss_counts[t] += 1
+
+    def _warmed_up(self):
+        return (self._loss_counts == self.history_per_term).all()
+
+
+class LogNormalSampler:
+    def __init__(self, p_mean=-1.2, p_std=1.2, even=False):
+        self.p_mean = p_mean
+        self.p_std = p_std
+        self.even = even
+        if self.even:
+            self.inv_cdf = lambda x: norm.ppf(x, loc=p_mean, scale=p_std)
+            self.rank, self.size = dist.get_rank(), dist.get_world_size()
+
+    def sample(self, bs, device):
+        if self.even:
+            # buckets = [1/G]
+            start_i, end_i = self.rank * bs, (self.rank + 1) * bs
+            global_batch_size = self.size * bs
+            locs = (th.arange(start_i, end_i) + th.rand(bs)) / global_batch_size
+            log_sigmas = th.tensor(self.inv_cdf(locs), dtype=th.float32, device=device)
+        else:
+            log_sigmas = self.p_mean + self.p_std * th.randn(bs, device=device)
+        sigmas = th.exp(log_sigmas)
+        weights = th.ones_like(sigmas)
+        return sigmas, weights
diff --git a/modules/diffusion/unet/attention.py b/modules/diffusion/unet/attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5a210cb392e3e1beb9f8c2884978f6b0b71ced4
--- /dev/null
+++ b/modules/diffusion/unet/attention.py
@@ -0,0 +1,241 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from modules.general.utils import Conv1d, normalization, zero_module
+from .basic import UNetBlock
+
+
+class AttentionBlock(UNetBlock):
+    r"""A spatial transformer encoder block that allows spatial positions to attend
+    to each other. Reference from `latent diffusion repo
+    <https://github.com/Stability-AI/generative-models/blob/main/sgm/modules/attention.py#L531>`_.
+
+    Args:
+        channels: Number of channels in the input.
+        num_head_channels: Number of channels per attention head.
+        num_heads: Number of attention heads. Overrides ``num_head_channels`` if set.
+        encoder_channels: Number of channels in the encoder output for cross-attention.
+            If ``None``, then self-attention is performed.
+        use_self_attention: Whether to use self-attention before cross-attention, only applicable if encoder_channels is set.
+        dims: Number of spatial dimensions, i.e. 1 for temporal signals, 2 for images.
+        h_dim: The dimension of the height, would be applied if ``dims`` is 2.
+        encoder_hdim: The dimension of the height of the encoder output, would be applied if ``dims`` is 2.
+        p_dropout: Dropout probability.
+    """
+
+    def __init__(
+        self,
+        channels: int,
+        num_head_channels: int = 32,
+        num_heads: int = -1,
+        encoder_channels: int = None,
+        use_self_attention: bool = False,
+        dims: int = 1,
+        h_dim: int = 100,
+        encoder_hdim: int = 384,
+        p_dropout: float = 0.0,
+    ):
+        super().__init__()
+
+        self.channels = channels
+        self.p_dropout = p_dropout
+        self.dims = dims
+
+        if dims == 1:
+            self.channels = channels
+        elif dims == 2:
+            # We consider the channel as product of channel and height, i.e. C x H
+            # This is because we want to apply attention on the audio signal, which is 1D
+            self.channels = channels * h_dim
+        else:
+            raise ValueError(f"invalid number of dimensions: {dims}")
+
+        if num_head_channels == -1:
+            assert (
+                self.channels % num_heads == 0
+            ), f"q,k,v channels {self.channels} is not divisible by num_heads {num_heads}"
+            self.num_heads = num_heads
+            self.num_head_channels = self.channels // num_heads
+        else:
+            assert (
+                self.channels % num_head_channels == 0
+            ), f"q,k,v channels {self.channels} is not divisible by num_head_channels {num_head_channels}"
+            self.num_heads = self.channels // num_head_channels
+            self.num_head_channels = num_head_channels
+
+        if encoder_channels is not None:
+            self.use_self_attention = use_self_attention
+
+            if dims == 1:
+                self.encoder_channels = encoder_channels
+            elif dims == 2:
+                self.encoder_channels = encoder_channels * encoder_hdim
+            else:
+                raise ValueError(f"invalid number of dimensions: {dims}")
+
+            if use_self_attention:
+                self.self_attention = BasicAttentionBlock(
+                    self.channels,
+                    self.num_head_channels,
+                    self.num_heads,
+                    p_dropout=self.p_dropout,
+                )
+            self.cross_attention = BasicAttentionBlock(
+                self.channels,
+                self.num_head_channels,
+                self.num_heads,
+                self.encoder_channels,
+                p_dropout=self.p_dropout,
+            )
+        else:
+            self.encoder_channels = None
+            self.self_attention = BasicAttentionBlock(
+                self.channels,
+                self.num_head_channels,
+                self.num_heads,
+                p_dropout=self.p_dropout,
+            )
+
+    def forward(self, x: torch.Tensor, encoder_output: torch.Tensor = None):
+        r"""
+        Args:
+            x: input tensor with shape [B x ``channels`` x ...]
+            encoder_output: feature tensor with shape [B x ``encoder_channels`` x ...], if ``None``, then self-attention is performed.
+
+        Returns:
+            output tensor with shape [B x ``channels`` x ...]
+        """
+        shape = x.size()
+        x = x.reshape(shape[0], self.channels, -1).contiguous()
+
+        if self.encoder_channels is None:
+            assert (
+                encoder_output is None
+            ), "encoder_output must be None for self-attention."
+            h = self.self_attention(x)
+
+        else:
+            assert (
+                encoder_output is not None
+            ), "encoder_output must be given for cross-attention."
+            encoder_output = encoder_output.reshape(
+                shape[0], self.encoder_channels, -1
+            ).contiguous()
+
+            if self.use_self_attention:
+                x = self.self_attention(x)
+            h = self.cross_attention(x, encoder_output)
+
+        return h.reshape(*shape).contiguous()
+
+
+class BasicAttentionBlock(nn.Module):
+    def __init__(
+        self,
+        channels: int,
+        num_head_channels: int = 32,
+        num_heads: int = -1,
+        context_channels: int = None,
+        p_dropout: float = 0.0,
+    ):
+        super().__init__()
+
+        self.channels = channels
+        self.p_dropout = p_dropout
+        self.context_channels = context_channels
+
+        if num_head_channels == -1:
+            assert (
+                self.channels % num_heads == 0
+            ), f"q,k,v channels {self.channels} is not divisible by num_heads {num_heads}"
+            self.num_heads = num_heads
+            self.num_head_channels = self.channels // num_heads
+        else:
+            assert (
+                self.channels % num_head_channels == 0
+            ), f"q,k,v channels {self.channels} is not divisible by num_head_channels {num_head_channels}"
+            self.num_heads = self.channels // num_head_channels
+            self.num_head_channels = num_head_channels
+
+        if context_channels is not None:
+            self.to_q = nn.Sequential(
+                normalization(self.channels),
+                Conv1d(self.channels, self.channels, 1),
+            )
+            self.to_kv = Conv1d(context_channels, 2 * self.channels, 1)
+        else:
+            self.to_qkv = nn.Sequential(
+                normalization(self.channels),
+                Conv1d(self.channels, 3 * self.channels, 1),
+            )
+
+        self.linear = Conv1d(self.channels, self.channels)
+
+        self.proj_out = nn.Sequential(
+            normalization(self.channels),
+            Conv1d(self.channels, self.channels, 1),
+            nn.GELU(),
+            nn.Dropout(p=self.p_dropout),
+            zero_module(Conv1d(self.channels, self.channels, 1)),
+        )
+
+    def forward(self, q: torch.Tensor, kv: torch.Tensor = None):
+        r"""
+        Args:
+            q: input tensor with shape [B, ``channels``, L]
+            kv: feature tensor with shape [B, ``context_channels``, T], if ``None``, then self-attention is performed.
+
+        Returns:
+            output tensor with shape [B, ``channels``, L]
+        """
+        N, C, L = q.size()
+
+        if self.context_channels is not None:
+            assert kv is not None, "kv must be given for cross-attention."
+
+            q = (
+                self.to_q(q)
+                .reshape(self.num_heads, self.num_head_channels, -1)
+                .transpose(-1, -2)
+                .contiguous()
+            )
+            kv = (
+                self.to_kv(kv)
+                .reshape(2, self.num_heads, self.num_head_channels, -1)
+                .transpose(-1, -2)
+                .chunk(2)
+            )
+            k, v = (
+                kv[0].squeeze(0).contiguous(),
+                kv[1].squeeze(0).contiguous(),
+            )
+
+        else:
+            qkv = (
+                self.to_qkv(q)
+                .reshape(3, self.num_heads, self.num_head_channels, -1)
+                .transpose(-1, -2)
+                .chunk(3)
+            )
+            q, k, v = (
+                qkv[0].squeeze(0).contiguous(),
+                qkv[1].squeeze(0).contiguous(),
+                qkv[2].squeeze(0).contiguous(),
+            )
+
+        h = F.scaled_dot_product_attention(q, k, v, dropout_p=self.p_dropout).transpose(
+            -1, -2
+        )
+        h = h.reshape(N, -1, L).contiguous()
+        h = self.linear(h)
+
+        x = q + h
+        h = self.proj_out(x)
+
+        return x + h
diff --git a/modules/diffusion/unet/basic.py b/modules/diffusion/unet/basic.py
new file mode 100644
index 0000000000000000000000000000000000000000..9cd4bc55ab58b3ebb7f9b2c0ec8dd37ad6d43721
--- /dev/null
+++ b/modules/diffusion/unet/basic.py
@@ -0,0 +1,15 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch.nn as nn
+from abc import abstractmethod
+
+
+class UNetBlock(nn.Module):
+    r"""Any module where forward() takes timestep embeddings as a second argument."""
+
+    @abstractmethod
+    def forward(self, x, emb):
+        r"""Apply the module to `x` given `emb` timestep embeddings."""
diff --git a/modules/diffusion/unet/resblock.py b/modules/diffusion/unet/resblock.py
new file mode 100644
index 0000000000000000000000000000000000000000..144c867785bbf76fa877ea6e24dfbdcffb78008e
--- /dev/null
+++ b/modules/diffusion/unet/resblock.py
@@ -0,0 +1,178 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from .basic import UNetBlock
+from modules.general.utils import (
+    append_dims,
+    ConvNd,
+    normalization,
+    zero_module,
+)
+
+
+class ResBlock(UNetBlock):
+    r"""A residual block that can optionally change the number of channels.
+
+    Args:
+        channels: the number of input channels.
+        emb_channels: the number of timestep embedding channels.
+        dropout: the rate of dropout.
+        out_channels: if specified, the number of out channels.
+        use_conv: if True and out_channels is specified, use a spatial
+            convolution instead of a smaller 1x1 convolution to change the
+            channels in the skip connection.
+        dims: determines if the signal is 1D, 2D, or 3D.
+        up: if True, use this block for upsampling.
+        down: if True, use this block for downsampling.
+    """
+
+    def __init__(
+        self,
+        channels,
+        emb_channels,
+        dropout: float = 0.0,
+        out_channels=None,
+        use_conv=False,
+        use_scale_shift_norm=False,
+        dims=2,
+        up=False,
+        down=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.emb_channels = emb_channels
+        self.dropout = dropout
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.use_scale_shift_norm = use_scale_shift_norm
+
+        self.in_layers = nn.Sequential(
+            normalization(channels),
+            nn.SiLU(),
+            ConvNd(dims, channels, self.out_channels, 3, padding=1),
+        )
+
+        self.updown = up or down
+
+        if up:
+            self.h_upd = Upsample(channels, False, dims)
+            self.x_upd = Upsample(channels, False, dims)
+        elif down:
+            self.h_upd = Downsample(channels, False, dims)
+            self.x_upd = Downsample(channels, False, dims)
+        else:
+            self.h_upd = self.x_upd = nn.Identity()
+
+        self.emb_layers = nn.Sequential(
+            nn.SiLU(),
+            ConvNd(
+                dims,
+                emb_channels,
+                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
+                1,
+            ),
+        )
+        self.out_layers = nn.Sequential(
+            normalization(self.out_channels),
+            nn.SiLU(),
+            nn.Dropout(p=dropout),
+            zero_module(
+                ConvNd(dims, self.out_channels, self.out_channels, 3, padding=1)
+            ),
+        )
+
+        if self.out_channels == channels:
+            self.skip_connection = nn.Identity()
+        elif use_conv:
+            self.skip_connection = ConvNd(
+                dims, channels, self.out_channels, 3, padding=1
+            )
+        else:
+            self.skip_connection = ConvNd(dims, channels, self.out_channels, 1)
+
+    def forward(self, x, emb):
+        """
+        Apply the block to a Tensor, conditioned on a timestep embedding.
+
+            x: an [N x C x ...] Tensor of features.
+            emb: an [N x emb_channels x ...] Tensor of timestep embeddings.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        if self.updown:
+            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
+            h = in_rest(x)
+            h = self.h_upd(h)
+            x = self.x_upd(x)
+            h = in_conv(h)
+        else:
+            h = self.in_layers(x)
+        emb_out = self.emb_layers(emb)
+        emb_out = append_dims(emb_out, h.dim())
+        if self.use_scale_shift_norm:
+            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
+            scale, shift = torch.chunk(emb_out, 2, dim=1)
+            h = out_norm(h) * (1 + scale) + shift
+            h = out_rest(h)
+        else:
+            h = h + emb_out
+            h = self.out_layers(h)
+        return self.skip_connection(x) + h
+
+
+class Upsample(nn.Module):
+    r"""An upsampling layer with an optional convolution.
+
+    Args:
+        channels: channels in the inputs and outputs.
+        dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+            upsampling occurs in the inner-two dimensions.
+        out_channels: if specified, the number of out channels.
+    """
+
+    def __init__(self, channels, dims=2, out_channels=None):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.dims = dims
+        self.conv = ConvNd(dims, self.channels, self.out_channels, 3, padding=1)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        if self.dims == 3:
+            x = F.interpolate(
+                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
+            )
+        else:
+            x = F.interpolate(x, scale_factor=2, mode="nearest")
+        x = self.conv(x)
+        return x
+
+
+class Downsample(nn.Module):
+    r"""A downsampling layer with an optional convolution.
+
+    Args:
+        channels: channels in the inputs and outputs.
+        dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+            downsampling occurs in the inner-two dimensions.
+        out_channels: if specified, the number of output channels.
+    """
+
+    def __init__(self, channels, dims=2, out_channels=None):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.dims = dims
+        stride = 2 if dims != 3 else (1, 2, 2)
+        self.op = ConvNd(
+            dims, self.channels, self.out_channels, 3, stride=stride, padding=1
+        )
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        return self.op(x)
diff --git a/modules/diffusion/unet/unet.py b/modules/diffusion/unet/unet.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d39f5d1e07f64012ba08268a0a4a5e71ad01e88
--- /dev/null
+++ b/modules/diffusion/unet/unet.py
@@ -0,0 +1,310 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+
+from modules.encoder.position_encoder import PositionEncoder
+from modules.general.utils import append_dims, ConvNd, normalization, zero_module
+from .attention import AttentionBlock
+from .resblock import Downsample, ResBlock, Upsample
+
+
+class UNet(nn.Module):
+    r"""The full UNet model with attention and timestep embedding.
+
+    Args:
+        dims: determines if the signal is 1D (temporal), 2D(spatial).
+        in_channels: channels in the input Tensor.
+        model_channels: base channel count for the model.
+        out_channels: channels in the output Tensor.
+        num_res_blocks: number of residual blocks per downsample.
+        channel_mult: channel multiplier for each level of the UNet.
+        num_attn_blocks: number of attention blocks at place.
+        attention_resolutions: a collection of downsample rates at which attention will
+            take place. May be a set, list, or tuple. For example, if this contains 4,
+            then at 4x downsampling, attention will be used.
+        num_heads: the number of attention heads in each attention layer.
+        num_head_channels: if specified, ignore num_heads and instead use a fixed
+            channel width per attention head.
+        d_context: if specified, use for cross-attention channel project.
+        p_dropout: the dropout probability.
+        use_self_attention: Apply self attention before cross attention.
+        num_classes: if specified (as an int), then this model will be class-conditional
+            with ``num_classes`` classes.
+        use_extra_film: if specified, use an extra FiLM-like conditioning mechanism.
+        d_emb: if specified, use for FiLM-like conditioning.
+        use_scale_shift_norm: use a FiLM-like conditioning mechanism.
+        resblock_updown: use residual blocks for up/downsampling.
+    """
+
+    def __init__(
+        self,
+        dims: int = 1,
+        in_channels: int = 100,
+        model_channels: int = 128,
+        out_channels: int = 100,
+        h_dim: int = 128,
+        num_res_blocks: int = 1,
+        channel_mult: tuple = (1, 2, 4),
+        num_attn_blocks: int = 1,
+        attention_resolutions: tuple = (1, 2, 4),
+        num_heads: int = 1,
+        num_head_channels: int = -1,
+        d_context: int = None,
+        context_hdim: int = 128,
+        p_dropout: float = 0.0,
+        num_classes: int = -1,
+        use_extra_film: str = None,
+        d_emb: int = None,
+        use_scale_shift_norm: bool = True,
+        resblock_updown: bool = False,
+    ):
+        super().__init__()
+
+        self.dims = dims
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.out_channels = out_channels
+        self.num_res_blocks = num_res_blocks
+        self.channel_mult = channel_mult
+        self.num_attn_blocks = num_attn_blocks
+        self.attention_resolutions = attention_resolutions
+        self.num_heads = num_heads
+        self.num_head_channels = num_head_channels
+        self.d_context = d_context
+        self.p_dropout = p_dropout
+        self.num_classes = num_classes
+        self.use_extra_film = use_extra_film
+        self.d_emb = d_emb
+        self.use_scale_shift_norm = use_scale_shift_norm
+        self.resblock_updown = resblock_updown
+
+        time_embed_dim = model_channels * 4
+        self.pos_enc = PositionEncoder(model_channels, time_embed_dim)
+
+        assert (
+            num_classes == -1 or use_extra_film is None
+        ), "You cannot set both num_classes and use_extra_film."
+
+        if self.num_classes > 0:
+            # TODO: if used for singer, norm should be 1, correct?
+            self.label_emb = nn.Embedding(num_classes, time_embed_dim, max_norm=1.0)
+        elif use_extra_film is not None:
+            assert (
+                d_emb is not None
+            ), "d_emb must be specified if use_extra_film is not None"
+            assert use_extra_film in [
+                "add",
+                "concat",
+            ], f"use_extra_film only supported by add or concat. Your input is {use_extra_film}"
+            self.use_extra_film = use_extra_film
+            self.film_emb = ConvNd(dims, d_emb, time_embed_dim, 1)
+            if use_extra_film == "concat":
+                time_embed_dim *= 2
+
+        # Input blocks
+        ch = input_ch = int(channel_mult[0] * model_channels)
+        self.input_blocks = nn.ModuleList(
+            [UNetSequential(ConvNd(dims, in_channels, ch, 3, padding=1))]
+        )
+        self._feature_size = ch
+        input_block_chans = [ch]
+        ds = 1
+        for level, mult in enumerate(channel_mult):
+            for _ in range(num_res_blocks):
+                layers = [
+                    ResBlock(
+                        ch,
+                        time_embed_dim,
+                        p_dropout,
+                        out_channels=int(mult * model_channels),
+                        dims=dims,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = int(mult * model_channels)
+                if ds in attention_resolutions:
+                    for _ in range(num_attn_blocks):
+                        layers.append(
+                            AttentionBlock(
+                                ch,
+                                num_heads=num_heads,
+                                num_head_channels=num_head_channels,
+                                encoder_channels=d_context,
+                                dims=dims,
+                                h_dim=h_dim // (level + 1),
+                                encoder_hdim=context_hdim,
+                                p_dropout=p_dropout,
+                            )
+                        )
+                self.input_blocks.append(UNetSequential(*layers))
+                self._feature_size += ch
+                input_block_chans.append(ch)
+            if level != len(channel_mult) - 1:
+                out_ch = ch
+                self.input_blocks.append(
+                    UNetSequential(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            p_dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            down=True,
+                        )
+                        if resblock_updown
+                        else Downsample(ch, dims=dims, out_channels=out_ch)
+                    )
+                )
+                ch = out_ch
+                input_block_chans.append(ch)
+                ds *= 2
+                self._feature_size += ch
+
+        # Middle blocks
+        self.middle_block = UNetSequential(
+            ResBlock(
+                ch,
+                time_embed_dim,
+                p_dropout,
+                dims=dims,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+            AttentionBlock(
+                ch,
+                num_heads=num_heads,
+                num_head_channels=num_head_channels,
+                encoder_channels=d_context,
+                dims=dims,
+                h_dim=h_dim // (level + 1),
+                encoder_hdim=context_hdim,
+                p_dropout=p_dropout,
+            ),
+            ResBlock(
+                ch,
+                time_embed_dim,
+                p_dropout,
+                dims=dims,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+        )
+        self._feature_size += ch
+
+        # Output blocks
+        self.output_blocks = nn.ModuleList([])
+        for level, mult in tuple(enumerate(channel_mult))[::-1]:
+            for i in range(num_res_blocks + 1):
+                ich = input_block_chans.pop()
+                layers = [
+                    ResBlock(
+                        ch + ich,
+                        time_embed_dim,
+                        p_dropout,
+                        out_channels=int(model_channels * mult),
+                        dims=dims,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = int(model_channels * mult)
+                if ds in attention_resolutions:
+                    for _ in range(num_attn_blocks):
+                        layers.append(
+                            AttentionBlock(
+                                ch,
+                                num_heads=num_heads,
+                                num_head_channels=num_head_channels,
+                                encoder_channels=d_context,
+                                dims=dims,
+                                h_dim=h_dim // (level + 1),
+                                encoder_hdim=context_hdim,
+                                p_dropout=p_dropout,
+                            )
+                        )
+                if level and i == num_res_blocks:
+                    out_ch = ch
+                    layers.append(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            p_dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            up=True,
+                        )
+                        if resblock_updown
+                        else Upsample(ch, dims=dims, out_channels=out_ch)
+                    )
+                    ds //= 2
+                self.output_blocks.append(UNetSequential(*layers))
+                self._feature_size += ch
+
+        # Final proj out
+        self.out = nn.Sequential(
+            normalization(ch),
+            nn.SiLU(),
+            zero_module(ConvNd(dims, input_ch, out_channels, 3, padding=1)),
+        )
+
+    def forward(self, x, timesteps=None, context=None, y=None, **kwargs):
+        r"""Apply the model to an input batch.
+
+        Args:
+            x: an [N x C x ...] Tensor of inputs.
+            timesteps: a 1-D batch of timesteps, i.e. [N].
+            context: conditioning Tensor with shape of [N x ``d_context`` x ...] plugged
+            in via cross attention.
+            y: an [N] Tensor of labels, if **class-conditional**.
+            an [N x ``d_emb`` x ...] Tensor if **film-embed conditional**.
+
+        Returns:
+            an [N x C x ...] Tensor of outputs.
+        """
+        assert (y is None) or (
+            (y is not None)
+            and ((self.num_classes > 0) or (self.use_extra_film is not None))
+        ), f"y must be specified if num_classes or use_extra_film is not None. \nGot num_classes: {self.num_classes}\t\nuse_extra_film: {self.use_extra_film}\t\n"
+
+        hs = []
+        emb = self.pos_enc(timesteps)
+        emb = append_dims(emb, x.dim())
+
+        if self.num_classes > 0:
+            assert y.size() == (x.size(0),)
+            emb = emb + self.label_emb(y)
+        elif self.use_extra_film is not None:
+            assert y.size() == (x.size(0), self.d_emb, *x.size()[2:])
+            y = self.film_emb(y)
+            if self.use_extra_film == "add":
+                emb = emb + y
+            elif self.use_extra_film == "concat":
+                emb = torch.cat([emb, y], dim=1)
+
+        h = x
+        for module in self.input_blocks:
+            h = module(h, emb, context)
+            hs.append(h)
+        h = self.middle_block(h, emb, context)
+        for module in self.output_blocks:
+            h = torch.cat([h, hs.pop()], dim=1)
+            h = module(h, emb, context)
+
+        return self.out(h)
+
+
+class UNetSequential(nn.Sequential):
+    r"""A sequential module that passes embeddings to the children that support it."""
+
+    def forward(self, x, emb=None, context=None):
+        for layer in self:
+            if isinstance(layer, ResBlock):
+                x = layer(x, emb)
+            elif isinstance(layer, AttentionBlock):
+                x = layer(x, context)
+            else:
+                x = layer(x)
+        return x
diff --git a/modules/distributions/__init__.py b/modules/distributions/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/modules/distributions/distributions.py b/modules/distributions/distributions.py
new file mode 100644
index 0000000000000000000000000000000000000000..0287d76c7ff2d0c1436c8e8b24ccd43513d38714
--- /dev/null
+++ b/modules/distributions/distributions.py
@@ -0,0 +1,107 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import numpy as np
+
+
+class AbstractDistribution:
+    def sample(self):
+        raise NotImplementedError()
+
+    def mode(self):
+        raise NotImplementedError()
+
+
+class DiracDistribution(AbstractDistribution):
+    def __init__(self, value):
+        self.value = value
+
+    def sample(self):
+        return self.value
+
+    def mode(self):
+        return self.value
+
+
+class DiagonalGaussianDistribution(object):
+    def __init__(self, parameters, deterministic=False):
+        self.parameters = parameters
+        self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
+        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
+        self.deterministic = deterministic
+        self.std = torch.exp(0.5 * self.logvar)
+        self.var = torch.exp(self.logvar)
+        if self.deterministic:
+            self.var = self.std = torch.zeros_like(self.mean).to(
+                device=self.parameters.device
+            )
+
+    def sample(self):
+        x = self.mean + self.std * torch.randn(self.mean.shape).to(
+            device=self.parameters.device
+        )
+        return x
+
+    def kl(self, other=None):
+        if self.deterministic:
+            return torch.Tensor([0.0])
+        else:
+            if other is None:
+                return 0.5 * torch.sum(
+                    torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,
+                    dim=[1, 2, 3],
+                )
+            else:
+                return 0.5 * torch.sum(
+                    torch.pow(self.mean - other.mean, 2) / other.var
+                    + self.var / other.var
+                    - 1.0
+                    - self.logvar
+                    + other.logvar,
+                    dim=[1, 2, 3],
+                )
+
+    def nll(self, sample, dims=[1, 2, 3]):
+        if self.deterministic:
+            return torch.Tensor([0.0])
+        logtwopi = np.log(2.0 * np.pi)
+        return 0.5 * torch.sum(
+            logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
+            dim=dims,
+        )
+
+    def mode(self):
+        return self.mean
+
+
+def normal_kl(mean1, logvar1, mean2, logvar2):
+    """
+    source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
+    Compute the KL divergence between two gaussians.
+    Shapes are automatically broadcasted, so batches can be compared to
+    scalars, among other use cases.
+    """
+    tensor = None
+    for obj in (mean1, logvar1, mean2, logvar2):
+        if isinstance(obj, torch.Tensor):
+            tensor = obj
+            break
+    assert tensor is not None, "at least one argument must be a Tensor"
+
+    # Force variances to be Tensors. Broadcasting helps convert scalars to
+    # Tensors, but it does not work for torch.exp().
+    logvar1, logvar2 = [
+        x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
+        for x in (logvar1, logvar2)
+    ]
+
+    return 0.5 * (
+        -1.0
+        + logvar2
+        - logvar1
+        + torch.exp(logvar1 - logvar2)
+        + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
+    )
diff --git a/modules/duration_predictor/__init__.py b/modules/duration_predictor/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/modules/duration_predictor/standard_duration_predictor.py b/modules/duration_predictor/standard_duration_predictor.py
new file mode 100644
index 0000000000000000000000000000000000000000..9924e70a7571cef0359019d6d083db7d9bd6e9cf
--- /dev/null
+++ b/modules/duration_predictor/standard_duration_predictor.py
@@ -0,0 +1,53 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+# This code is modified from https://github.com/jaywalnut310/vits/blob/main/models.py
+
+import torch
+from torch import nn
+from modules.base.base_module import LayerNorm
+
+
+class DurationPredictor(nn.Module):
+    def __init__(
+        self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+
+        self.drop = nn.Dropout(p_dropout)
+        self.conv_1 = nn.Conv1d(
+            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_1 = LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_2 = LayerNorm(filter_channels)
+        self.proj = nn.Conv1d(filter_channels, 1, 1)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+    def forward(self, x, x_mask, g=None):
+        x = torch.detach(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_2(x)
+        x = self.drop(x)
+        x = self.proj(x * x_mask)
+        return x * x_mask
diff --git a/modules/duration_predictor/stochastic_duration_predictor.py b/modules/duration_predictor/stochastic_duration_predictor.py
new file mode 100644
index 0000000000000000000000000000000000000000..60bbb95547bb89a24bb8852c57a462a2d2326a32
--- /dev/null
+++ b/modules/duration_predictor/stochastic_duration_predictor.py
@@ -0,0 +1,120 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+# This code is modified from https://github.com/jaywalnut310/vits/blob/main/models.pyimport torch
+
+from torch import nn
+from torch.nn import functional as F
+import math
+from modules.flow.modules import *
+
+
+class StochasticDurationPredictor(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        filter_channels,
+        kernel_size,
+        p_dropout,
+        n_flows=4,
+        gin_channels=0,
+    ):
+        super().__init__()
+        filter_channels = in_channels
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.log_flow = Log()
+        self.flows = nn.ModuleList()
+        self.flows.append(ElementwiseAffine(2))
+        for i in range(n_flows):
+            self.flows.append(ConvFlow(2, filter_channels, kernel_size, n_layers=3))
+            self.flows.append(Flip())
+
+        self.post_pre = nn.Conv1d(1, filter_channels, 1)
+        self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.post_convs = DDSConv(
+            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
+        )
+        self.post_flows = nn.ModuleList()
+        self.post_flows.append(ElementwiseAffine(2))
+        for i in range(4):
+            self.post_flows.append(
+                ConvFlow(2, filter_channels, kernel_size, n_layers=3)
+            )
+            self.post_flows.append(Flip())
+
+        self.pre = nn.Conv1d(in_channels, filter_channels, 1)
+        self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.convs = DDSConv(
+            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
+        )
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
+
+    def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
+        x = torch.detach(x)
+        x = self.pre(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.convs(x, x_mask)
+        x = self.proj(x) * x_mask
+
+        if not reverse:
+            flows = self.flows
+            assert w is not None
+
+            logdet_tot_q = 0
+            h_w = self.post_pre(w)
+            h_w = self.post_convs(h_w, x_mask)
+            h_w = self.post_proj(h_w) * x_mask
+            e_q = (
+                torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype)
+                * x_mask
+            )
+            z_q = e_q
+            for flow in self.post_flows:
+                z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
+                logdet_tot_q += logdet_q
+            z_u, z1 = torch.split(z_q, [1, 1], 1)
+            u = torch.sigmoid(z_u) * x_mask
+            z0 = (w - u) * x_mask
+            logdet_tot_q += torch.sum(
+                (F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2]
+            )
+            logq = (
+                torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q**2)) * x_mask, [1, 2])
+                - logdet_tot_q
+            )
+
+            logdet_tot = 0
+            z0, logdet = self.log_flow(z0, x_mask)
+            logdet_tot += logdet
+            z = torch.cat([z0, z1], 1)
+            for flow in flows:
+                z, logdet = flow(z, x_mask, g=x, reverse=reverse)
+                logdet_tot = logdet_tot + logdet
+            nll = (
+                torch.sum(0.5 * (math.log(2 * math.pi) + (z**2)) * x_mask, [1, 2])
+                - logdet_tot
+            )
+            return nll + logq
+        else:
+            flows = list(reversed(self.flows))
+            flows = flows[:-2] + [flows[-1]]
+            z = (
+                torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype)
+                * noise_scale
+            )
+            for flow in flows:
+                z = flow(z, x_mask, g=x, reverse=reverse)
+            z0, z1 = torch.split(z, [1, 1], 1)
+            logw = z0
+            return logw
diff --git a/modules/encoder/__init__.py b/modules/encoder/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d704d2a703dac50edb49cec355c7a0b1ce92b5fa
--- /dev/null
+++ b/modules/encoder/__init__.py
@@ -0,0 +1 @@
+from .token_encoder import TokenEmbedding
diff --git a/modules/encoder/condition_encoder.py b/modules/encoder/condition_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..88ee892efcdfb7538eb1dca8ffc84cd452b073bc
--- /dev/null
+++ b/modules/encoder/condition_encoder.py
@@ -0,0 +1,251 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+import torch
+import torch.nn as nn
+from torchaudio.models import Conformer
+from models.svc.transformer.transformer import PositionalEncoding
+
+from utils.f0 import f0_to_coarse
+
+
+class ContentEncoder(nn.Module):
+    def __init__(self, cfg, input_dim, output_dim):
+        super().__init__()
+        self.cfg = cfg
+
+        assert input_dim != 0
+        self.nn = nn.Linear(input_dim, output_dim)
+
+        # Introduce conformer or not
+        if (
+            "use_conformer_for_content_features" in cfg
+            and cfg.use_conformer_for_content_features
+        ):
+            self.pos_encoder = PositionalEncoding(input_dim)
+            self.conformer = Conformer(
+                input_dim=input_dim,
+                num_heads=2,
+                ffn_dim=256,
+                num_layers=6,
+                depthwise_conv_kernel_size=3,
+            )
+        else:
+            self.conformer = None
+
+    def forward(self, x, length=None):
+        # x: (N, seq_len, input_dim) -> (N, seq_len, output_dim)
+        if self.conformer:
+            x = self.pos_encoder(x)
+            x, _ = self.conformer(x, length)
+        return self.nn(x)
+
+
+class MelodyEncoder(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        self.cfg = cfg
+
+        self.input_dim = self.cfg.input_melody_dim
+        self.output_dim = self.cfg.output_melody_dim
+        self.n_bins = self.cfg.n_bins_melody
+        self.pitch_min = self.cfg.pitch_min
+        self.pitch_max = self.cfg.pitch_max
+
+        if self.input_dim != 0:
+            if self.n_bins == 0:
+                # Not use quantization
+                self.nn = nn.Linear(self.input_dim, self.output_dim)
+            else:
+                self.f0_min = cfg.f0_min
+                self.f0_max = cfg.f0_max
+
+                self.nn = nn.Embedding(
+                    num_embeddings=self.n_bins,
+                    embedding_dim=self.output_dim,
+                    padding_idx=None,
+                )
+                self.uv_embedding = nn.Embedding(2, self.output_dim)
+                # self.conformer = Conformer(
+                #     input_dim=self.output_dim,
+                #     num_heads=4,
+                #     ffn_dim=128,
+                #     num_layers=4,
+                #     depthwise_conv_kernel_size=3,
+                # )
+
+    def forward(self, x, uv=None, length=None):
+        # x: (N, frame_len)
+        # print(x.shape)
+        if self.n_bins == 0:
+            x = x.unsqueeze(-1)
+        else:
+            x = f0_to_coarse(x, self.n_bins, self.f0_min, self.f0_max)
+            x = self.nn(x)
+            if uv is not None:
+                uv = self.uv_embedding(uv)
+                x = x + uv
+            # x, _ = self.conformer(x, length)
+        return x
+
+
+class LoudnessEncoder(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        self.cfg = cfg
+
+        self.input_dim = self.cfg.input_loudness_dim
+        self.output_dim = self.cfg.output_loudness_dim
+        self.n_bins = self.cfg.n_bins_loudness
+
+        if self.input_dim != 0:
+            if self.n_bins == 0:
+                # Not use quantization
+                self.nn = nn.Linear(self.input_dim, self.output_dim)
+            else:
+                # TODO: set trivially now
+                self.loudness_min = 1e-30
+                self.loudness_max = 1.5
+
+                if cfg.use_log_loudness:
+                    self.energy_bins = nn.Parameter(
+                        torch.exp(
+                            torch.linspace(
+                                np.log(self.loudness_min),
+                                np.log(self.loudness_max),
+                                self.n_bins - 1,
+                            )
+                        ),
+                        requires_grad=False,
+                    )
+
+                self.nn = nn.Embedding(
+                    num_embeddings=self.n_bins,
+                    embedding_dim=self.output_dim,
+                    padding_idx=None,
+                )
+
+    def forward(self, x):
+        # x: (N, frame_len)
+        if self.n_bins == 0:
+            x = x.unsqueeze(-1)
+        else:
+            x = torch.bucketize(x, self.energy_bins)
+        return self.nn(x)
+
+
+class SingerEncoder(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        self.cfg = cfg
+
+        self.input_dim = 1
+        self.output_dim = self.cfg.output_singer_dim
+
+        self.nn = nn.Embedding(
+            num_embeddings=cfg.singer_table_size,
+            embedding_dim=self.output_dim,
+            padding_idx=None,
+        )
+
+    def forward(self, x):
+        # x: (N, 1) -> (N, 1, output_dim)
+        return self.nn(x)
+
+
+class ConditionEncoder(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        self.cfg = cfg
+
+        self.merge_mode = cfg.merge_mode
+
+        if cfg.use_whisper:
+            self.whisper_encoder = ContentEncoder(
+                self.cfg, self.cfg.whisper_dim, self.cfg.content_encoder_dim
+            )
+
+        if cfg.use_contentvec:
+            self.contentvec_encoder = ContentEncoder(
+                self.cfg, self.cfg.contentvec_dim, self.cfg.content_encoder_dim
+            )
+
+        if cfg.use_mert:
+            self.mert_encoder = ContentEncoder(
+                self.cfg, self.cfg.mert_dim, self.cfg.content_encoder_dim
+            )
+
+        if cfg.use_wenet:
+            self.wenet_encoder = ContentEncoder(
+                self.cfg, self.cfg.wenet_dim, self.cfg.content_encoder_dim
+            )
+
+        self.melody_encoder = MelodyEncoder(self.cfg)
+        self.loudness_encoder = LoudnessEncoder(self.cfg)
+        if cfg.use_spkid:
+            self.singer_encoder = SingerEncoder(self.cfg)
+
+    def forward(self, x):
+        outputs = []
+
+        if "frame_pitch" in x.keys():
+            if "frame_uv" not in x.keys():
+                x["frame_uv"] = None
+            pitch_enc_out = self.melody_encoder(
+                x["frame_pitch"], uv=x["frame_uv"], length=x["target_len"]
+            )
+            outputs.append(pitch_enc_out)
+
+        if "frame_energy" in x.keys():
+            loudness_enc_out = self.loudness_encoder(x["frame_energy"])
+            outputs.append(loudness_enc_out)
+
+        if "whisper_feat" in x.keys():
+            # whisper_feat: [b, T, 1024]
+            whiser_enc_out = self.whisper_encoder(
+                x["whisper_feat"], length=x["target_len"]
+            )
+            outputs.append(whiser_enc_out)
+            seq_len = whiser_enc_out.shape[1]
+
+        if "contentvec_feat" in x.keys():
+            contentvec_enc_out = self.contentvec_encoder(
+                x["contentvec_feat"], length=x["target_len"]
+            )
+            outputs.append(contentvec_enc_out)
+            seq_len = contentvec_enc_out.shape[1]
+
+        if "mert_feat" in x.keys():
+            mert_enc_out = self.mert_encoder(x["mert_feat"], length=x["target_len"])
+            outputs.append(mert_enc_out)
+            seq_len = mert_enc_out.shape[1]
+
+        if "wenet_feat" in x.keys():
+            wenet_enc_out = self.wenet_encoder(x["wenet_feat"], length=x["target_len"])
+            outputs.append(wenet_enc_out)
+            seq_len = wenet_enc_out.shape[1]
+
+        if "spk_id" in x.keys():
+            speaker_enc_out = self.singer_encoder(x["spk_id"])  # [b, 1, 384]
+            assert (
+                "whisper_feat" in x.keys()
+                or "contentvec_feat" in x.keys()
+                or "mert_feat" in x.keys()
+                or "wenet_feat" in x.keys()
+            )
+            singer_info = speaker_enc_out.expand(-1, seq_len, -1)
+            outputs.append(singer_info)
+
+        encoder_output = None
+        if self.merge_mode == "concat":
+            encoder_output = torch.cat(outputs, dim=-1)
+        if self.merge_mode == "add":
+            # (#modules, N, seq_len, output_dim)
+            outputs = torch.cat([out[None, :, :, :] for out in outputs], dim=0)
+            # (N, seq_len, output_dim)
+            encoder_output = torch.sum(outputs, dim=0)
+
+        return encoder_output
diff --git a/modules/encoder/conv_encoder.py b/modules/encoder/conv_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..cfbd2e290a678e1e5ec1332f73d654c7f13e9fc5
--- /dev/null
+++ b/modules/encoder/conv_encoder.py
@@ -0,0 +1,103 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.utils import spectral_norm
+from modules.generic.conv import Conv1d
+
+
+class ConvEncoder(nn.Module):
+    def __init__(self, in_channels, z_channels, spk_channels, num_dilation_layer=10):
+        super(ConvEncoder, self).__init__()
+
+        self.in_channels = in_channels
+        self.z_channels = z_channels
+        self.spk_channels = spk_channels
+
+        self.pre_process = Conv1d(in_channels, 512, kernel_size=3)
+
+        self.dilated_conv_layers = nn.ModuleList()
+        for i in range(num_dilation_layer):
+            dilation = 2**i
+            self.dilated_conv_layers.append(
+                DilatedConvBlock(512, 512, z_channels, spk_channels, dilation)
+            )
+
+    def forward(self, inputs, z, s):
+        inputs = inputs.transpose(1, 2)
+        outputs = self.pre_process(inputs)
+        print(inputs.shape)
+        for layer in self.dilated_conv_layers:
+            outputs = layer(outputs, z, s)
+
+        encoder_outputs = outputs.transpose(1, 2)
+        return encoder_outputs
+
+
+class DilatedConvBlock(nn.Module):
+    """A stack of dilated convolutions interspersed
+    with batch normalisation and ReLU activations"""
+
+    def __init__(self, in_channels, out_channels, z_channels, s_channels, dilation):
+        super(DilatedConvBlock, self).__init__()
+
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.z_channels = z_channels
+        self.s_channels = s_channels
+
+        self.conv1d = Conv1d(
+            in_channels, out_channels, kernel_size=3, dilation=dilation
+        )
+        self.batch_layer = BatchNorm1dLayer(out_channels, s_channels, z_channels)
+
+    def forward(self, inputs, z, s):
+        outputs = self.conv1d(inputs)
+        outputs = self.batch_layer(outputs, z, s)
+        return F.relu(outputs)
+
+
+class BatchNorm1dLayer(nn.Module):
+    """The latents z and speaker embedding s modulate the scale and
+    shift parameters of the batch normalisation layers"""
+
+    def __init__(self, num_features, s_channels=128, z_channels=128):
+        super().__init__()
+
+        self.num_features = num_features
+        self.s_channels = s_channels
+        self.z_channels = z_channels
+        self.batch_nrom = nn.BatchNorm1d(num_features, affine=False)
+
+        self.scale_layer = spectral_norm(nn.Linear(z_channels, num_features))
+        self.scale_layer.weight.data.normal_(1, 0.02)  # Initialise scale at N(1, 0.02)
+        self.scale_layer.bias.data.zero_()  # Initialise bias at 0
+
+        self.shift_layer = spectral_norm(nn.Linear(s_channels, num_features))
+        self.shift_layer.weight.data.normal_(1, 0.02)  # Initialise scale at N(1, 0.02)
+        self.shift_layer.bias.data.zero_()  # Initialise bias at 0
+
+    def forward(self, inputs, z, s):
+        outputs = self.batch_nrom(inputs)
+        scale = self.scale_layer(z)
+        scale = scale.view(-1, self.num_features, 1)
+
+        shift = self.shift_layer(s)
+        shift = shift.view(-1, self.num_features, 1)
+
+        outputs = scale * outputs + shift
+
+        return outputs
+
+
+if __name__ == "__main__":
+    model = ConvEncoder(256, 64, 64)
+    encoder_inputs = torch.randn(2, 256, 10)
+    z = torch.randn(2, 64)
+    speaker = torch.randn(1, 64)
+    outputs, duration = model(encoder_inputs, z, speaker)
+    print(outputs.shape, duration.shape)
diff --git a/modules/encoder/position_encoder.py b/modules/encoder/position_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f67c688a2b55390c75142ba57d159aca4a31c91
--- /dev/null
+++ b/modules/encoder/position_encoder.py
@@ -0,0 +1,85 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+
+import torch
+import torch.nn as nn
+
+from modules.general.utils import Linear
+
+
+class PositionEncoder(nn.Module):
+    r"""Encoder of positional embedding, generates PE and then
+    feed into 2 full-connected layers with ``SiLU``.
+
+    Args:
+        d_raw_emb: The dimension of raw embedding vectors.
+        d_out: The dimension of output embedding vectors, default to ``d_raw_emb``.
+        d_mlp: The dimension of hidden layer in MLP, default to ``d_raw_emb`` * 4.
+        activation_function: The activation function used in MLP, default to ``SiLU``.
+        n_layer: The number of layers in MLP, default to 2.
+        max_period: controls the minimum frequency of the embeddings.
+    """
+
+    def __init__(
+        self,
+        d_raw_emb: int = 128,
+        d_out: int = None,
+        d_mlp: int = None,
+        activation_function: str = "SiLU",
+        n_layer: int = 2,
+        max_period: int = 10000,
+    ):
+        super().__init__()
+
+        self.d_raw_emb = d_raw_emb
+        self.d_out = d_raw_emb if d_out is None else d_out
+        self.d_mlp = d_raw_emb * 4 if d_mlp is None else d_mlp
+        self.n_layer = n_layer
+        self.max_period = max_period
+
+        if activation_function.lower() == "silu":
+            self.activation_function = "SiLU"
+        elif activation_function.lower() == "relu":
+            self.activation_function = "ReLU"
+        elif activation_function.lower() == "gelu":
+            self.activation_function = "GELU"
+        else:
+            raise ValueError("activation_function must be one of SiLU, ReLU, GELU")
+        self.activation_function = activation_function
+
+        tmp = [Linear(self.d_raw_emb, self.d_mlp), getattr(nn, activation_function)()]
+        for _ in range(self.n_layer - 1):
+            tmp.append(Linear(self.d_mlp, self.d_mlp))
+            tmp.append(getattr(nn, activation_function)())
+        tmp.append(Linear(self.d_mlp, self.d_out))
+
+        self.out = nn.Sequential(*tmp)
+
+    def forward(self, steps: torch.Tensor) -> torch.Tensor:
+        r"""Create and return sinusoidal timestep embeddings directly.
+
+        Args:
+            steps: a 1D Tensor of N indices, one per batch element.
+                These may be fractional.
+
+        Returns:
+            an [N x ``d_out``] Tensor of positional embeddings.
+        """
+
+        half = self.d_raw_emb // 2
+        freqs = torch.exp(
+            -math.log(self.max_period)
+            / half
+            * torch.arange(half, dtype=torch.float32, device=steps.device)
+        )
+        args = steps[:, None].float() * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if self.d_raw_emb % 2:
+            embedding = torch.cat(
+                [embedding, torch.zeros_like(embedding[:, :1])], dim=-1
+            )
+        return self.out(embedding)
diff --git a/modules/encoder/token_encoder.py b/modules/encoder/token_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7594c173fd9127d543ff9e1e5fb6471704c6468
--- /dev/null
+++ b/modules/encoder/token_encoder.py
@@ -0,0 +1,25 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+# This code is modified from https://github.com/lifeiteng/vall-e
+
+import torch
+import torch.nn as nn
+
+
+class TokenEmbedding(nn.Module):
+    def __init__(self, dim_model: int, vocab_size: int, dropout: float = 0.0):
+        super().__init__()
+        self.dropout = nn.Dropout(p=dropout)
+        self.word_embeddings = nn.Embedding(vocab_size, dim_model)
+
+    @property
+    def weight(self) -> torch.Tensor:
+        return self.word_embeddings.weight
+
+    def forward(self, x: torch.Tensor):
+        x = self.word_embeddings(x)
+        x = self.dropout(x)
+        return x
diff --git a/modules/flow/modules.py b/modules/flow/modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4d897a4ae151a90d4a1fb886108903511ff14b7
--- /dev/null
+++ b/modules/flow/modules.py
@@ -0,0 +1,457 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+# This code is modified from https://github.com/jaywalnut310/vits/
+
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from torch.nn import Conv1d
+from torch.nn.utils import weight_norm, remove_weight_norm
+
+from utils.util import *
+from modules.transformer.transforms import (
+    piecewise_rational_quadratic_transform,
+)
+from modules.base.base_module import LayerNorm
+
+LRELU_SLOPE = 0.1
+
+
+class DDSConv(nn.Module):
+    """
+    Dialted and Depth-Separable Convolution
+    """
+
+    def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
+        super().__init__()
+        self.channels = channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.p_dropout = p_dropout
+
+        self.drop = nn.Dropout(p_dropout)
+        self.convs_sep = nn.ModuleList()
+        self.convs_1x1 = nn.ModuleList()
+        self.norms_1 = nn.ModuleList()
+        self.norms_2 = nn.ModuleList()
+        for i in range(n_layers):
+            dilation = kernel_size**i
+            padding = (kernel_size * dilation - dilation) // 2
+            self.convs_sep.append(
+                nn.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    groups=channels,
+                    dilation=dilation,
+                    padding=padding,
+                )
+            )
+            self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
+            self.norms_1.append(LayerNorm(channels))
+            self.norms_2.append(LayerNorm(channels))
+
+    def forward(self, x, x_mask, g=None):
+        if g is not None:
+            x = x + g
+        for i in range(self.n_layers):
+            y = self.convs_sep[i](x * x_mask)
+            y = self.norms_1[i](y)
+            y = F.gelu(y)
+            y = self.convs_1x1[i](y)
+            y = self.norms_2[i](y)
+            y = F.gelu(y)
+            y = self.drop(y)
+            x = x + y
+        return x * x_mask
+
+
+class WN(torch.nn.Module):
+    def __init__(
+        self,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        gin_channels=0,
+        p_dropout=0,
+    ):
+        super(WN, self).__init__()
+        assert kernel_size % 2 == 1
+        self.hidden_channels = hidden_channels
+        self.kernel_size = (kernel_size,)
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.gin_channels = gin_channels
+        self.p_dropout = p_dropout
+
+        self.in_layers = torch.nn.ModuleList()
+        self.res_skip_layers = torch.nn.ModuleList()
+        self.drop = nn.Dropout(p_dropout)
+
+        if gin_channels != 0:
+            cond_layer = torch.nn.Conv1d(
+                gin_channels, 2 * hidden_channels * n_layers, 1
+            )
+            self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
+
+        for i in range(n_layers):
+            dilation = dilation_rate**i
+            padding = int((kernel_size * dilation - dilation) / 2)
+            in_layer = torch.nn.Conv1d(
+                hidden_channels,
+                2 * hidden_channels,
+                kernel_size,
+                dilation=dilation,
+                padding=padding,
+            )
+            in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
+            self.in_layers.append(in_layer)
+
+            # last one is not necessary
+            if i < n_layers - 1:
+                res_skip_channels = 2 * hidden_channels
+            else:
+                res_skip_channels = hidden_channels
+
+            res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
+            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
+            self.res_skip_layers.append(res_skip_layer)
+
+    def forward(self, x, x_mask, g=None, **kwargs):
+        output = torch.zeros_like(x)
+        n_channels_tensor = torch.IntTensor([self.hidden_channels])
+
+        if g is not None:
+            g = self.cond_layer(g)
+
+        for i in range(self.n_layers):
+            x_in = self.in_layers[i](x)
+            if g is not None:
+                cond_offset = i * 2 * self.hidden_channels
+                g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
+            else:
+                g_l = torch.zeros_like(x_in)
+
+            acts = fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
+            acts = self.drop(acts)
+
+            res_skip_acts = self.res_skip_layers[i](acts)
+            if i < self.n_layers - 1:
+                res_acts = res_skip_acts[:, : self.hidden_channels, :]
+                x = (x + res_acts) * x_mask
+                output = output + res_skip_acts[:, self.hidden_channels :, :]
+            else:
+                output = output + res_skip_acts
+        return output * x_mask
+
+    def remove_weight_norm(self):
+        if self.gin_channels != 0:
+            torch.nn.utils.remove_weight_norm(self.cond_layer)
+        for l in self.in_layers:
+            torch.nn.utils.remove_weight_norm(l)
+        for l in self.res_skip_layers:
+            torch.nn.utils.remove_weight_norm(l)
+
+
+class ResBlock1(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
+        super(ResBlock1, self).__init__()
+        self.convs1 = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[0],
+                        padding=get_padding(kernel_size, dilation[0]),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[1],
+                        padding=get_padding(kernel_size, dilation[1]),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[2],
+                        padding=get_padding(kernel_size, dilation[2]),
+                    )
+                ),
+            ]
+        )
+        self.convs1.apply(init_weights)
+
+        self.convs2 = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        padding=get_padding(kernel_size, 1),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        padding=get_padding(kernel_size, 1),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        padding=get_padding(kernel_size, 1),
+                    )
+                ),
+            ]
+        )
+        self.convs2.apply(init_weights)
+
+    def forward(self, x, x_mask=None):
+        for c1, c2 in zip(self.convs1, self.convs2):
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c1(xt)
+            xt = F.leaky_relu(xt, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c2(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs1:
+            remove_weight_norm(l)
+        for l in self.convs2:
+            remove_weight_norm(l)
+
+
+class ResBlock2(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
+        super(ResBlock2, self).__init__()
+        self.convs = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[0],
+                        padding=get_padding(kernel_size, dilation[0]),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[1],
+                        padding=get_padding(kernel_size, dilation[1]),
+                    )
+                ),
+            ]
+        )
+        self.convs.apply(init_weights)
+
+    def forward(self, x, x_mask=None):
+        for c in self.convs:
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs:
+            remove_weight_norm(l)
+
+
+class Log(nn.Module):
+    def forward(self, x, x_mask, reverse=False, **kwargs):
+        if not reverse:
+            y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
+            logdet = torch.sum(-y, [1, 2])
+            return y, logdet
+        else:
+            x = torch.exp(x) * x_mask
+            return x
+
+
+class Flip(nn.Module):
+    def forward(self, x, *args, reverse=False, **kwargs):
+        x = torch.flip(x, [1])
+        if not reverse:
+            logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
+            return x, logdet
+        else:
+            return x
+
+
+class ElementwiseAffine(nn.Module):
+    def __init__(self, channels):
+        super().__init__()
+        self.channels = channels
+        self.m = nn.Parameter(torch.zeros(channels, 1))
+        self.logs = nn.Parameter(torch.zeros(channels, 1))
+
+    def forward(self, x, x_mask, reverse=False, **kwargs):
+        if not reverse:
+            y = self.m + torch.exp(self.logs) * x
+            y = y * x_mask
+            logdet = torch.sum(self.logs * x_mask, [1, 2])
+            return y, logdet
+        else:
+            x = (x - self.m) * torch.exp(-self.logs) * x_mask
+            return x
+
+
+class ResidualCouplingLayer(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        p_dropout=0,
+        gin_channels=0,
+        mean_only=False,
+    ):
+        assert channels % 2 == 0, "channels should be divisible by 2"
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.half_channels = channels // 2
+        self.mean_only = mean_only
+
+        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
+        self.enc = WN(
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            p_dropout=p_dropout,
+            gin_channels=gin_channels,
+        )
+        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
+        self.post.weight.data.zero_()
+        self.post.bias.data.zero_()
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
+        h = self.pre(x0) * x_mask
+        h = self.enc(h, x_mask, g=g)
+        stats = self.post(h) * x_mask
+        if not self.mean_only:
+            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
+        else:
+            m = stats
+            logs = torch.zeros_like(m)
+
+        if not reverse:
+            x1 = m + x1 * torch.exp(logs) * x_mask
+            x = torch.cat([x0, x1], 1)
+            logdet = torch.sum(logs, [1, 2])
+            return x, logdet
+        else:
+            x1 = (x1 - m) * torch.exp(-logs) * x_mask
+            x = torch.cat([x0, x1], 1)
+            return x
+
+
+class ConvFlow(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        filter_channels,
+        kernel_size,
+        n_layers,
+        num_bins=10,
+        tail_bound=5.0,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.num_bins = num_bins
+        self.tail_bound = tail_bound
+        self.half_channels = in_channels // 2
+
+        self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
+        self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
+        self.proj = nn.Conv1d(
+            filter_channels, self.half_channels * (num_bins * 3 - 1), 1
+        )
+        self.proj.weight.data.zero_()
+        self.proj.bias.data.zero_()
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
+        h = self.pre(x0)
+        h = self.convs(h, x_mask, g=g)
+        h = self.proj(h) * x_mask
+
+        b, c, t = x0.shape
+        h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2)  # [b, cx?, t] -> [b, c, t, ?]
+
+        unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
+        unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
+            self.filter_channels
+        )
+        unnormalized_derivatives = h[..., 2 * self.num_bins :]
+
+        x1, logabsdet = piecewise_rational_quadratic_transform(
+            x1,
+            unnormalized_widths,
+            unnormalized_heights,
+            unnormalized_derivatives,
+            inverse=reverse,
+            tails="linear",
+            tail_bound=self.tail_bound,
+        )
+
+        x = torch.cat([x0, x1], 1) * x_mask
+        logdet = torch.sum(logabsdet * x_mask, [1, 2])
+        if not reverse:
+            return x, logdet
+        else:
+            return x
diff --git a/modules/general/__init__.py b/modules/general/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f68ee3911c2b31c32ad0bd0fcfb017d3743370f
--- /dev/null
+++ b/modules/general/__init__.py
@@ -0,0 +1,3 @@
+from .input_strategies import PromptedFeatures, PromptedPrecomputedFeatures
+from .scaling import BalancedDoubleSwish
+from .utils import Transpose
diff --git a/modules/general/input_strategies.py b/modules/general/input_strategies.py
new file mode 100644
index 0000000000000000000000000000000000000000..2edefd9ec9224a650c1002a416062c2ece37f553
--- /dev/null
+++ b/modules/general/input_strategies.py
@@ -0,0 +1,130 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+# This code is modified from
+# https://github.com/lifeiteng/vall-e/blob/9c69096d603ce13174fb5cb025f185e2e9b36ac7/valle/data/input_strategies.py
+import random
+from collections import defaultdict
+from concurrent.futures import ThreadPoolExecutor
+from typing import Tuple, Type
+
+from lhotse import CutSet
+from lhotse.dataset.collation import collate_features
+from lhotse.dataset.input_strategies import (
+    ExecutorType,
+    PrecomputedFeatures,
+    _get_executor,
+)
+from lhotse.utils import fastcopy
+
+
+class PromptedFeatures:
+    def __init__(self, prompts, features):
+        self.prompts = prompts
+        self.features = features
+
+    def to(self, device):
+        return PromptedFeatures(self.prompts.to(device), self.features.to(device))
+
+    def sum(self):
+        return self.features.sum()
+
+    @property
+    def ndim(self):
+        return self.features.ndim
+
+    @property
+    def data(self):
+        return (self.prompts, self.features)
+
+
+class PromptedPrecomputedFeatures(PrecomputedFeatures):
+    def __init__(
+        self,
+        dataset: str,
+        cuts: CutSet,
+        num_workers: int = 0,
+        executor_type: Type[ExecutorType] = ThreadPoolExecutor,
+    ) -> None:
+        super().__init__(num_workers, executor_type)
+        self.utt2neighbors = self._create_utt2neighbors(dataset, cuts)
+
+    def __call__(self, cuts: CutSet) -> Tuple[PromptedFeatures, PromptedFeatures]:
+        features, features_lens = self._collate_features(cuts)
+        prompts, prompts_lens = self._collate_prompts(cuts)
+        return PromptedFeatures(prompts, features), PromptedFeatures(
+            prompts_lens, features_lens
+        )
+
+    def _create_utt2neighbors(self, dataset, cuts):
+        utt2neighbors = defaultdict(lambda: [])
+        utt2cut = {cut.id: cut for cut in cuts}
+        if dataset.lower() == "libritts":
+            self._process_libritts_dataset(utt2neighbors, utt2cut, cuts)
+        elif dataset.lower() == "ljspeech":
+            self._process_ljspeech_dataset(utt2neighbors, utt2cut, cuts)
+        else:
+            raise ValueError("Unsupported dataset")
+        return utt2neighbors
+
+    def _process_libritts_dataset(self, utt2neighbors, utt2cut, cuts):
+        speaker2utts = defaultdict(lambda: [])
+        for cut in cuts:
+            speaker = cut.supervisions[0].speaker
+            speaker2utts[speaker].append(cut.id)
+
+        for spk, uttids in speaker2utts.items():
+            sorted_uttids = sorted(uttids)
+            if len(sorted_uttids) == 1:
+                utt2neighbors[sorted_uttids[0]].append(utt2cut[sorted_uttids[0]])
+                continue
+
+            utt2prevutt = dict(
+                zip(sorted_uttids, [sorted_uttids[1]] + sorted_uttids[:-1])
+            )
+            utt2postutt = dict(zip(sorted_uttids[:-1], sorted_uttids[1:]))
+            for utt in sorted_uttids:
+                if utt in utt2prevutt:
+                    utt2neighbors[utt].append(utt2cut[utt2prevutt[utt]])
+                if utt in utt2postutt:
+                    utt2neighbors[utt].append(utt2cut[utt2postutt[utt]])
+
+    def _process_ljspeech_dataset(self, utt2neighbors, utt2cut, cuts):
+        uttids = [cut.id for cut in cuts]
+        if len(uttids) == 1:
+            utt2neighbors[uttids[0]].append(utt2cut[uttids[0]])
+            return
+
+        utt2prevutt = dict(zip(uttids, [uttids[1]] + uttids[:-1]))
+        utt2postutt = dict(zip(uttids[:-1], uttids[1:]))
+        for utt in uttids:
+            prevutt, postutt = utt2prevutt.get(utt), utt2postutt.get(utt)
+            if prevutt and utt[:5] == prevutt[:5]:
+                utt2neighbors[utt].append(utt2cut[prevutt])
+            if postutt and utt[:5] == postutt[:5]:
+                utt2neighbors[utt].append(utt2cut[postutt])
+
+    def _collate_features(self, cuts):
+        return collate_features(
+            cuts,
+            executor=_get_executor(self.num_workers, executor_type=self._executor_type),
+        )
+
+    def _collate_prompts(self, cuts):
+        prompts_cuts = []
+        for k, cut in enumerate(cuts):
+            prompts_cut = random.choice(self.utt2neighbors[cut.id])
+            prompts_cuts.append(fastcopy(prompts_cut, id=f"{cut.id}-{str(k)}"))
+
+        mini_duration = min([cut.duration for cut in prompts_cuts] + [3.0])
+        prompts_cuts = CutSet(
+            cuts={k: cut for k, cut in enumerate(prompts_cuts)}
+        ).truncate(max_duration=mini_duration, offset_type="random", preserve_id=False)
+
+        return collate_features(
+            prompts_cuts,
+            executor=_get_executor(self.num_workers, executor_type=self._executor_type),
+        )
diff --git a/modules/general/scaling.py b/modules/general/scaling.py
new file mode 100644
index 0000000000000000000000000000000000000000..bef858ffc65f7bcefedc0040f3377965f9eb3992
--- /dev/null
+++ b/modules/general/scaling.py
@@ -0,0 +1,1349 @@
+# This module is modified from https://github.com/Plachtaa/VALL-E-X/blob/3faaf8ccadb154d63b38070caf518ce9309ea0f4/modules/scaling.py
+
+
+import logging
+import random
+import math
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+from torch import Tensor
+
+
+class Transpose(nn.Identity):
+    """(N, T, D) -> (N, D, T)"""
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        return input.transpose(1, 2)
+
+
+class ActivationBalancerFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx,
+        x: Tensor,
+        scale_factor: Tensor,
+        sign_factor: Optional[Tensor],
+        channel_dim: int,
+    ) -> Tensor:
+        if channel_dim < 0:
+            channel_dim += x.ndim
+        ctx.channel_dim = channel_dim
+        xgt0 = x > 0
+        if sign_factor is None:
+            ctx.save_for_backward(xgt0, scale_factor)
+        else:
+            ctx.save_for_backward(xgt0, scale_factor, sign_factor)
+        return x
+
+    @staticmethod
+    def backward(ctx, x_grad: Tensor) -> Tuple[Tensor, None, None, None]:
+        if len(ctx.saved_tensors) == 3:
+            xgt0, scale_factor, sign_factor = ctx.saved_tensors
+            for _ in range(ctx.channel_dim, x_grad.ndim - 1):
+                scale_factor = scale_factor.unsqueeze(-1)
+                sign_factor = sign_factor.unsqueeze(-1)
+            factor = sign_factor + scale_factor * (xgt0.to(x_grad.dtype) - 0.5)
+        else:
+            xgt0, scale_factor = ctx.saved_tensors
+            for _ in range(ctx.channel_dim, x_grad.ndim - 1):
+                scale_factor = scale_factor.unsqueeze(-1)
+            factor = scale_factor * (xgt0.to(x_grad.dtype) - 0.5)
+        neg_delta_grad = x_grad.abs() * factor
+        return (
+            x_grad - neg_delta_grad,
+            None,
+            None,
+            None,
+        )
+
+
+def _compute_scale_factor(
+    x: Tensor,
+    channel_dim: int,
+    min_abs: float,
+    max_abs: float,
+    gain_factor: float,
+    max_factor: float,
+) -> Tensor:
+    if channel_dim < 0:
+        channel_dim += x.ndim
+    sum_dims = [d for d in range(x.ndim) if d != channel_dim]
+    x_abs_mean = torch.mean(x.abs(), dim=sum_dims).to(torch.float32)
+
+    if min_abs == 0.0:
+        below_threshold = 0.0
+    else:
+        # below_threshold is 0 if x_abs_mean > min_abs, can be at most max_factor if
+        # x_abs)_mean , min_abs.
+        below_threshold = ((min_abs - x_abs_mean) * (gain_factor / min_abs)).clamp(
+            min=0, max=max_factor
+        )
+
+    above_threshold = ((x_abs_mean - max_abs) * (gain_factor / max_abs)).clamp(
+        min=0, max=max_factor
+    )
+
+    return below_threshold - above_threshold
+
+
+def _compute_sign_factor(
+    x: Tensor,
+    channel_dim: int,
+    min_positive: float,
+    max_positive: float,
+    gain_factor: float,
+    max_factor: float,
+) -> Tensor:
+    if channel_dim < 0:
+        channel_dim += x.ndim
+    sum_dims = [d for d in range(x.ndim) if d != channel_dim]
+    proportion_positive = torch.mean((x > 0).to(torch.float32), dim=sum_dims)
+    if min_positive == 0.0:
+        factor1 = 0.0
+    else:
+        # 0 if proportion_positive >= min_positive, else can be
+        # as large as max_factor.
+        factor1 = (
+            (min_positive - proportion_positive) * (gain_factor / min_positive)
+        ).clamp_(min=0, max=max_factor)
+
+    if max_positive == 1.0:
+        factor2 = 0.0
+    else:
+        # 0 if self.proportion_positive <= max_positive, else can be
+        # as large as -max_factor.
+        factor2 = (
+            (proportion_positive - max_positive) * (gain_factor / (1.0 - max_positive))
+        ).clamp_(min=0, max=max_factor)
+    sign_factor = factor1 - factor2
+    # require min_positive != 0 or max_positive != 1:
+    assert not isinstance(sign_factor, float)
+    return sign_factor
+
+
+class ActivationScaleBalancerFunction(torch.autograd.Function):
+    """
+    This object is used in class ActivationBalancer when the user specified
+    min_positive=0, max_positive=1, so there are no constraints on the signs
+    of the activations and only the absolute value has a constraint.
+    """
+
+    @staticmethod
+    def forward(
+        ctx,
+        x: Tensor,
+        sign_factor: Tensor,
+        scale_factor: Tensor,
+        channel_dim: int,
+    ) -> Tensor:
+        if channel_dim < 0:
+            channel_dim += x.ndim
+        ctx.channel_dim = channel_dim
+        xgt0 = x > 0
+        ctx.save_for_backward(xgt0, sign_factor, scale_factor)
+        return x
+
+    @staticmethod
+    def backward(ctx, x_grad: Tensor) -> Tuple[Tensor, None, None, None]:
+        xgt0, sign_factor, scale_factor = ctx.saved_tensors
+        for _ in range(ctx.channel_dim, x_grad.ndim - 1):
+            sign_factor = sign_factor.unsqueeze(-1)
+            scale_factor = scale_factor.unsqueeze(-1)
+
+        factor = sign_factor + scale_factor * (xgt0.to(x_grad.dtype) - 0.5)
+        neg_delta_grad = x_grad.abs() * factor
+        return (
+            x_grad - neg_delta_grad,
+            None,
+            None,
+            None,
+        )
+
+
+class RandomClampFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx,
+        x: Tensor,
+        min: Optional[float],
+        max: Optional[float],
+        prob: float,
+        reflect: float,
+    ) -> Tensor:
+        x_clamped = torch.clamp(x, min=min, max=max)
+        mask = torch.rand_like(x) < prob
+        ans = torch.where(mask, x_clamped, x)
+        if x.requires_grad:
+            ctx.save_for_backward(ans == x)
+            ctx.reflect = reflect
+        if reflect != 0.0:
+            ans = ans * (1.0 + reflect) - (x * reflect)
+        return ans
+
+    @staticmethod
+    def backward(ctx, ans_grad: Tensor) -> Tuple[Tensor, None, None, None, None]:
+        (is_same,) = ctx.saved_tensors
+        x_grad = ans_grad * is_same.to(ans_grad.dtype)
+        reflect = ctx.reflect
+        if reflect != 0.0:
+            x_grad = x_grad * (1.0 + reflect) - (ans_grad * reflect)
+        return x_grad, None, None, None, None
+
+
+def random_clamp(
+    x: Tensor,
+    min: Optional[float] = None,
+    max: Optional[float] = None,
+    prob: float = 0.5,
+    reflect: float = 0.0,
+):
+    return RandomClampFunction.apply(x, min, max, prob, reflect)
+
+
+def random_cast_to_half(x: Tensor, min_abs: float = 5.0e-06) -> Tensor:
+    """
+    A randomized way of casting a floating point value to half precision.
+    """
+    if x.dtype == torch.float16:
+        return x
+    x_abs = x.abs()
+    is_too_small = x_abs < min_abs
+    # for elements where is_too_small is true, random_val will contain +-min_abs with
+    # probability (x.abs() / min_abs), and 0.0 otherwise.  [so this preserves expectations,
+    # for those elements].
+    random_val = min_abs * x.sign() * (torch.rand_like(x) * min_abs < x_abs)
+    return torch.where(is_too_small, random_val, x).to(torch.float16)
+
+
+class RandomGradFunction(torch.autograd.Function):
+    """
+    Does nothing in forward pass; in backward pass, gets rid of very small grads using
+    randomized approach that preserves expectations (intended to reduce roundoff).
+    """
+
+    @staticmethod
+    def forward(ctx, x: Tensor, min_abs: float) -> Tensor:
+        ctx.min_abs = min_abs
+        return x
+
+    @staticmethod
+    def backward(ctx, ans_grad: Tensor) -> Tuple[Tensor, None]:
+        if ans_grad.dtype == torch.float16:
+            return (
+                random_cast_to_half(ans_grad.to(torch.float32), min_abs=ctx.min_abs),
+                None,
+            )
+        else:
+            return ans_grad, None
+
+
+class RandomGrad(torch.nn.Module):
+    """
+    Gets rid of very small gradients using an expectation-preserving method, intended to increase
+    accuracy of training when using amp (automatic mixed precision)
+    """
+
+    def __init__(self, min_abs: float = 5.0e-06):
+        super(RandomGrad, self).__init__()
+        self.min_abs = min_abs
+
+    def forward(self, x: Tensor):
+        if torch.jit.is_scripting() or not self.training or torch.jit.is_tracing():
+            return x
+        else:
+            return RandomGradFunction.apply(x, self.min_abs)
+
+
+class SoftmaxFunction(torch.autograd.Function):
+    """
+    Tries to handle half-precision derivatives in a randomized way that should
+    be more accurate for training than the default behavior.
+    """
+
+    @staticmethod
+    def forward(ctx, x: Tensor, dim: int):
+        ans = x.softmax(dim=dim)
+        # if x dtype is float16, x.softmax() returns a float32 because
+        # (presumably) that op does not support float16, and autocast
+        # is enabled.
+        if torch.is_autocast_enabled():
+            ans = ans.to(torch.float16)
+        ctx.save_for_backward(ans)
+        ctx.x_dtype = x.dtype
+        ctx.dim = dim
+        return ans
+
+    @staticmethod
+    def backward(ctx, ans_grad: Tensor):
+        (ans,) = ctx.saved_tensors
+        with torch.cuda.amp.autocast(enabled=False):
+            ans_grad = ans_grad.to(torch.float32)
+            ans = ans.to(torch.float32)
+            x_grad = ans_grad * ans
+            x_grad = x_grad - ans * x_grad.sum(dim=ctx.dim, keepdim=True)
+            return x_grad, None
+
+
+def softmax(x: Tensor, dim: int):
+    if torch.jit.is_scripting() or torch.jit.is_tracing():
+        return x.softmax(dim)
+
+    return SoftmaxFunction.apply(x, dim)
+
+
+class MaxEigLimiterFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx,
+        x: Tensor,
+        coeffs: Tensor,
+        direction: Tensor,
+        channel_dim: int,
+        grad_scale: float,
+    ) -> Tensor:
+        ctx.channel_dim = channel_dim
+        ctx.grad_scale = grad_scale
+        ctx.save_for_backward(x.detach(), coeffs.detach(), direction.detach())
+        return x
+
+    @staticmethod
+    def backward(ctx, x_grad, *args):
+        with torch.enable_grad():
+            (x_orig, coeffs, new_direction) = ctx.saved_tensors
+            x_orig.requires_grad = True
+            num_channels = x_orig.shape[ctx.channel_dim]
+            x = x_orig.transpose(ctx.channel_dim, -1).reshape(-1, num_channels)
+            new_direction.requires_grad = False
+            x = x - x.mean(dim=0)
+            x_var = (x**2).mean()
+            x_residual = x - coeffs * new_direction
+            x_residual_var = (x_residual**2).mean()
+            # `variance_proportion` is the proportion of the variance accounted for
+            # by the top eigen-direction.  This is to be minimized.
+            variance_proportion = (x_var - x_residual_var) / (x_var + 1.0e-20)
+            variance_proportion.backward()
+        x_orig_grad = x_orig.grad
+        x_extra_grad = (
+            x_orig.grad
+            * ctx.grad_scale
+            * x_grad.norm()
+            / (x_orig_grad.norm() + 1.0e-20)
+        )
+        return x_grad + x_extra_grad.detach(), None, None, None, None
+
+
+class BasicNorm(torch.nn.Module):
+    """
+    This is intended to be a simpler, and hopefully cheaper, replacement for
+    LayerNorm.  The observation this is based on, is that Transformer-type
+    networks, especially with pre-norm, sometimes seem to set one of the
+    feature dimensions to a large constant value (e.g. 50), which "defeats"
+    the LayerNorm because the output magnitude is then not strongly dependent
+    on the other (useful) features.  Presumably the weight and bias of the
+    LayerNorm are required to allow it to do this.
+
+    So the idea is to introduce this large constant value as an explicit
+    parameter, that takes the role of the "eps" in LayerNorm, so the network
+    doesn't have to do this trick.  We make the "eps" learnable.
+
+    Args:
+       num_channels: the number of channels, e.g. 512.
+      channel_dim: the axis/dimension corresponding to the channel,
+        interprted as an offset from the input's ndim if negative.
+        shis is NOT the num_channels; it should typically be one of
+        {-2, -1, 0, 1, 2, 3}.
+       eps: the initial "epsilon" that we add as ballast in:
+             scale = ((input_vec**2).mean() + epsilon)**-0.5
+          Note: our epsilon is actually large, but we keep the name
+          to indicate the connection with conventional LayerNorm.
+       learn_eps: if true, we learn epsilon; if false, we keep it
+         at the initial value.
+    eps_min: float
+    eps_max: float
+    """
+
+    def __init__(
+        self,
+        num_channels: int,
+        channel_dim: int = -1,  # CAUTION: see documentation.
+        eps: float = 0.25,
+        learn_eps: bool = True,
+        eps_min: float = -3.0,
+        eps_max: float = 3.0,
+    ) -> None:
+        super(BasicNorm, self).__init__()
+        self.num_channels = num_channels
+        self.channel_dim = channel_dim
+        if learn_eps:
+            self.eps = nn.Parameter(torch.tensor(eps).log().detach())
+        else:
+            self.register_buffer("eps", torch.tensor(eps).log().detach())
+        self.eps_min = eps_min
+        self.eps_max = eps_max
+
+    def forward(self, x: Tensor) -> Tensor:
+        assert x.shape[self.channel_dim] == self.num_channels
+        eps = self.eps
+        if self.training and random.random() < 0.25:
+            # with probability 0.25, in training mode, clamp eps between the min
+            # and max; this will encourage it to learn parameters within the
+            # allowed range by making parameters that are outside the allowed
+            # range noisy.
+
+            # gradients to allow the parameter to get back into the allowed
+            # region if it happens to exit it.
+            eps = eps.clamp(min=self.eps_min, max=self.eps_max)
+        scales = (
+            torch.mean(x**2, dim=self.channel_dim, keepdim=True) + eps.exp()
+        ) ** -0.5
+        return x * scales
+
+
+def ScaledLinear(*args, initial_scale: float = 1.0, **kwargs) -> nn.Linear:
+    """
+    Behaves like a constructor of a modified version of nn.Linear
+    that gives an easy way to set the default initial parameter scale.
+
+    Args:
+        Accepts the standard args and kwargs that nn.Linear accepts
+        e.g. in_features, out_features, bias=False.
+
+        initial_scale: you can override this if you want to increase
+           or decrease the initial magnitude of the module's output
+           (affects the initialization of weight_scale and bias_scale).
+           Another option, if you want to do something like this, is
+           to re-initialize the parameters.
+    """
+    ans = nn.Linear(*args, **kwargs)
+    with torch.no_grad():
+        ans.weight[:] *= initial_scale
+        if ans.bias is not None:
+            torch.nn.init.uniform_(ans.bias, -0.1 * initial_scale, 0.1 * initial_scale)
+    return ans
+
+
+def ScaledConv1d(
+    *args,
+    initial_scale: float = 1.0,
+    kernel_size: int = 3,
+    padding: str = "same",
+    **kwargs,
+) -> nn.Conv1d:
+    """
+    Behaves like a constructor of a modified version of nn.Conv1d
+    that gives an easy way to set the default initial parameter scale.
+
+    Args:
+        Accepts the standard args and kwargs that nn.Linear accepts
+        e.g. in_features, out_features, bias=False.
+
+        initial_scale: you can override this if you want to increase
+           or decrease the initial magnitude of the module's output
+           (affects the initialization of weight_scale and bias_scale).
+           Another option, if you want to do something like this, is
+           to re-initialize the parameters.
+    """
+    ans = nn.Conv1d(*args, kernel_size=kernel_size, padding=padding, **kwargs)
+    with torch.no_grad():
+        ans.weight[:] *= initial_scale
+        if ans.bias is not None:
+            torch.nn.init.uniform_(ans.bias, -0.1 * initial_scale, 0.1 * initial_scale)
+    return ans
+
+
+def TransposeScaledConv1d(
+    *args,
+    initial_scale: float = 1.0,
+    kernel_size: int = 3,
+    padding: str = "same",
+    **kwargs,
+) -> nn.Sequential:
+    """
+    Transpose -> ScaledConv1d
+    """
+    return nn.Sequential(
+        Transpose(),
+        ScaledConv1d(
+            *args,
+            initial_scale=initial_scale,
+            kernel_size=kernel_size,
+            padding=padding,
+            **kwargs,
+        ),
+    )
+
+
+def ScaledConv1dTranspose(
+    *args,
+    initial_scale: float = 1.0,
+    kernel_size: int = 3,
+    padding: str = "same",
+    **kwargs,
+) -> nn.Sequential:
+    """
+    Transpose -> ScaledConv1d
+    """
+    return nn.Sequential(
+        ScaledConv1d(
+            *args,
+            initial_scale=initial_scale,
+            kernel_size=kernel_size,
+            padding=padding,
+            **kwargs,
+        ),
+        Transpose(),
+    )
+
+
+def TransposeConv1d(
+    *args, kernel_size: int = 3, padding: str = "same", **kwargs
+) -> nn.Sequential:
+    """
+    Transpose -> Conv1d
+    """
+    return nn.Sequential(
+        Transpose(),
+        nn.Conv1d(*args, kernel_size=kernel_size, padding=padding, **kwargs),
+    )
+
+
+def Conv1dTranspose(
+    *args, kernel_size: int = 3, padding: str = "same", **kwargs
+) -> nn.Sequential:
+    """
+    ScaledConv1d -> Transpose
+    """
+    return nn.Sequential(
+        nn.Conv1d(*args, kernel_size=kernel_size, padding=padding, **kwargs),
+        Transpose(),
+    )
+
+
+class SRLinear(nn.Linear):
+    """https://arxiv.org/abs/2303.06296
+    Stabilizing Transformer Training by Preventing Attention Entropy Collapse
+    """
+
+    def __init__(self, in_features, out_features, bias=True, **kwargs):
+        super().__init__(in_features, out_features, bias=bias, **kwargs)
+        self.register_buffer(
+            "u", nn.functional.normalize(torch.randn(in_features), dim=0)
+        )
+        with torch.no_grad():
+            sigma = self.get_sigma()
+        self.register_buffer("spectral_norm", sigma)
+        self.sigma = nn.Parameter(torch.ones(1))
+
+    def get_sigma(self):
+        with torch.no_grad():
+            u = self.u
+            v = self.weight.mv(u)
+            v = nn.functional.normalize(v, dim=0)
+            u = self.weight.T.mv(v)
+            u = nn.functional.normalize(u, dim=0)
+            self.u.data.copy_(u)
+        return torch.einsum("c,cd,d->", v, self.weight, u)
+
+    def get_weight(self):
+        sigma = self.get_sigma()
+        if self.training:
+            self.spectral_norm.data.copy_(sigma)
+        weight = (self.sigma / sigma) * self.weight
+        return weight
+
+    def forward(self, x):
+        return nn.functional.linear(x, self.get_weight(), self.bias)
+
+
+class SRConv1d(SRLinear):
+    def __init__(
+        self,
+        in_features,
+        out_features,
+        kernel_size,
+        stride: int = 1,
+        padding: str = "same",
+        bias: bool = True,
+        **kwargs,
+    ):
+        in_features = in_features * kernel_size
+        super().__init__(in_features, out_features, bias=bias, **kwargs)
+        nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.padding = padding
+
+    def forward(self, x):
+        in_features = self.in_features // self.kernel_size
+        weight = self.get_weight().view(
+            self.out_features, in_features, self.kernel_size
+        )
+        return nn.functional.conv1d(
+            x, weight, bias=self.bias, stride=self.stride, padding=self.padding
+        )
+
+
+def TransposeSRConv1d(
+    *args, kernel_size: int = 3, padding: str = "same", **kwargs
+) -> nn.Sequential:
+    """
+    Transpose -> SRConv1d
+    """
+    return nn.Sequential(
+        Transpose(),
+        SRConv1d(*args, kernel_size=kernel_size, padding=padding, **kwargs),
+    )
+
+
+def SRConv1dTranspose(
+    *args, kernel_size: int = 3, padding: str = "same", **kwargs
+) -> nn.Sequential:
+    """
+    SRConv1d -> Transpose
+    """
+    return nn.Sequential(
+        SRConv1d(*args, kernel_size=kernel_size, padding=padding, **kwargs),
+        Transpose(),
+    )
+
+
+class ActivationBalancer(torch.nn.Module):
+    """
+    Modifies the backpropped derivatives of a function to try to encourage, for
+    each channel, that it is positive at least a proportion `threshold` of the
+    time.  It does this by multiplying negative derivative values by up to
+    (1+max_factor), and positive derivative values by up to (1-max_factor),
+    interpolated from 1 at the threshold to those extremal values when none
+    of the inputs are positive.
+
+    Args:
+           num_channels: the number of channels
+           channel_dim: the dimension/axis corresponding to the channel, e.g.
+               -1, 0, 1, 2; will be interpreted as an offset from x.ndim if negative.
+           min_positive: the minimum, per channel, of the proportion of the time
+               that (x > 0), below which we start to modify the derivatives.
+           max_positive: the maximum, per channel, of the proportion of the time
+               that (x > 0), above which we start to modify the derivatives.
+           max_factor: the maximum factor by which we modify the derivatives for
+              either the sign constraint or the magnitude constraint;
+              e.g. with max_factor=0.02, the the derivatives would be multiplied by
+              values in the range [0.98..1.02].
+           sign_gain_factor: determines the 'gain' with which we increase the
+              change in gradient once the constraints on min_positive and max_positive
+              are violated.
+           scale_gain_factor: determines the 'gain' with which we increase the
+              change in gradient once the constraints on min_abs and max_abs
+              are violated.
+           min_abs:  the minimum average-absolute-value difference from the mean
+               value per channel, which we allow, before we start to modify
+               the derivatives to prevent this.
+           max_abs:  the maximum average-absolute-value difference from the mean
+               value per channel, which we allow, before we start to modify
+               the derivatives to prevent this.
+          min_prob: determines the minimum probability with which we modify the
+             gradients for the {min,max}_positive and {min,max}_abs constraints,
+             on each forward().  This is done randomly to prevent all layers
+             from doing it at the same time.  Early in training we may use
+             higher probabilities than this; it will decay to this value.
+    """
+
+    def __init__(
+        self,
+        num_channels: int,
+        channel_dim: int,
+        min_positive: float = 0.05,
+        max_positive: float = 0.95,
+        max_factor: float = 0.04,
+        sign_gain_factor: float = 0.01,
+        scale_gain_factor: float = 0.02,
+        min_abs: float = 0.2,
+        max_abs: float = 100.0,
+        min_prob: float = 0.1,
+    ):
+        super(ActivationBalancer, self).__init__()
+        self.num_channels = num_channels
+        self.channel_dim = channel_dim
+        self.min_positive = min_positive
+        self.max_positive = max_positive
+        self.max_factor = max_factor
+        self.min_abs = min_abs
+        self.max_abs = max_abs
+        self.min_prob = min_prob
+        self.sign_gain_factor = sign_gain_factor
+        self.scale_gain_factor = scale_gain_factor
+
+        # count measures how many times the forward() function has been called.
+        # We occasionally sync this to a tensor called `count`, that exists to
+        # make sure it is synced to disk when we load and save the model.
+        self.cpu_count = 0
+        self.register_buffer("count", torch.tensor(0, dtype=torch.int64))
+
+    def forward(self, x: Tensor) -> Tensor:
+        if torch.jit.is_scripting() or not x.requires_grad or torch.jit.is_tracing():
+            return _no_op(x)
+
+        count = self.cpu_count
+        self.cpu_count += 1
+
+        if random.random() < 0.01:
+            # Occasionally sync self.cpu_count with self.count.
+            # count affects the decay of 'prob'.  don't do this on every iter,
+            # because syncing with the GPU is slow.
+            self.cpu_count = max(self.cpu_count, self.count.item())
+            self.count.fill_(self.cpu_count)
+
+        # the prob of doing some work exponentially decreases from 0.5 till it hits
+        # a floor at min_prob (==0.1, by default)
+        prob = max(self.min_prob, 0.5 ** (1 + (count / 4000.0)))
+
+        if random.random() < prob:
+            sign_gain_factor = 0.5
+            if self.min_positive != 0.0 or self.max_positive != 1.0:
+                sign_factor = _compute_sign_factor(
+                    x,
+                    self.channel_dim,
+                    self.min_positive,
+                    self.max_positive,
+                    gain_factor=self.sign_gain_factor / prob,
+                    max_factor=self.max_factor,
+                )
+            else:
+                sign_factor = None
+
+            scale_factor = _compute_scale_factor(
+                x.detach(),
+                self.channel_dim,
+                min_abs=self.min_abs,
+                max_abs=self.max_abs,
+                gain_factor=self.scale_gain_factor / prob,
+                max_factor=self.max_factor,
+            )
+            return ActivationBalancerFunction.apply(
+                x,
+                scale_factor,
+                sign_factor,
+                self.channel_dim,
+            )
+        else:
+            return _no_op(x)
+
+
+def penalize_abs_values_gt(x: Tensor, limit: float, penalty: float) -> Tensor:
+    """
+    Returns x unmodified, but in backprop will put a penalty for the excess of
+    the absolute values of elements of x over the limit "limit".  E.g. if
+    limit == 10.0, then if x has any values over 10 it will get a penalty.
+
+    Caution: the value of this penalty will be affected by grad scaling used
+    in automatic mixed precision training.  For this reasons we use this,
+    it shouldn't really matter, or may even be helpful; we just use this
+    to disallow really implausible values of scores to be given to softmax.
+    """
+    x_sign = x.sign()
+    over_limit = (x.abs() - limit) > 0
+    # The following is a memory efficient way to penalize the absolute values of
+    # x that's over the limit.  (The memory efficiency comes when you think
+    # about which items torch needs to cache for the autograd, and which ones it
+    # can throw away).  The numerical value of aux_loss as computed here will
+    # actually be larger than it should be, by limit * over_limit.sum(), but it
+    # has the same derivative as the real aux_loss which is penalty * (x.abs() -
+    # limit).relu().
+    aux_loss = penalty * ((x_sign * over_limit).to(torch.int8) * x)
+    # note: we don't do sum() here on aux)_loss, but it's as if we had done
+    # sum() due to how with_loss() works.
+    x = with_loss(x, aux_loss)
+    # you must use x for something, or this will be ineffective.
+    return x
+
+
+def _diag(x: Tensor):  # like .diag(), but works for tensors with 3 dims.
+    if x.ndim == 2:
+        return x.diag()
+    else:
+        (batch, dim, dim) = x.shape
+        x = x.reshape(batch, dim * dim)
+        x = x[:, :: dim + 1]
+        assert x.shape == (batch, dim)
+        return x
+
+
+def _whitening_metric(x: Tensor, num_groups: int):
+    """
+    Computes the "whitening metric", a value which will be 1.0 if all the eigenvalues of
+    of the centered feature covariance are the same within each group's covariance matrix
+    and also between groups.
+    Args:
+        x: a Tensor of shape (*, num_channels)
+     num_groups:  the number of groups of channels, a number >=1 that divides num_channels
+    Returns:
+        Returns a scalar Tensor that will be 1.0 if the data is "perfectly white" and
+    greater than 1.0 otherwise.
+    """
+    assert x.dtype != torch.float16
+    x = x.reshape(-1, x.shape[-1])
+    (num_frames, num_channels) = x.shape
+    assert num_channels % num_groups == 0
+    channels_per_group = num_channels // num_groups
+    x = x.reshape(num_frames, num_groups, channels_per_group).transpose(0, 1)
+    # x now has shape (num_groups, num_frames, channels_per_group)
+    # subtract the mean so we use the centered, not uncentered, covariance.
+    # My experience has been that when we "mess with the gradients" like this,
+    # it's better not do anything that tries to move the mean around, because
+    # that can easily cause instability.
+    x = x - x.mean(dim=1, keepdim=True)
+    # x_covar: (num_groups, channels_per_group, channels_per_group)
+    x_covar = torch.matmul(x.transpose(1, 2), x)
+    x_covar_mean_diag = _diag(x_covar).mean()
+    # the following expression is what we'd get if we took the matrix product
+    # of each covariance and measured the mean of its trace, i.e.
+    # the same as _diag(torch.matmul(x_covar, x_covar)).mean().
+    x_covarsq_mean_diag = (x_covar**2).sum() / (num_groups * channels_per_group)
+    # this metric will be >= 1.0; the larger it is, the less 'white' the data was.
+    metric = x_covarsq_mean_diag / (x_covar_mean_diag**2 + 1.0e-20)
+    return metric
+
+
+class WhiteningPenaltyFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx,
+        x: Tensor,
+        num_groups: int,
+        whitening_limit: float,
+        grad_scale: float,
+    ) -> Tensor:
+        ctx.save_for_backward(x)
+        ctx.num_groups = num_groups
+        ctx.whitening_limit = whitening_limit
+        ctx.grad_scale = grad_scale
+        return x
+
+    @staticmethod
+    def backward(ctx, x_grad: Tensor):
+        (x_orig,) = ctx.saved_tensors
+        with torch.enable_grad():
+            with torch.cuda.amp.autocast(enabled=False):
+                x_detached = x_orig.to(torch.float32).detach()
+                x_detached.requires_grad = True
+
+                metric = _whitening_metric(x_detached, ctx.num_groups)
+
+                if random.random() < 0.005 or __name__ == "__main__":
+                    logging.info(
+                        f"Whitening: num_groups={ctx.num_groups}, num_channels={x_orig.shape[-1]}, "
+                        f"metric={metric.item():.2f} vs. limit={ctx.whitening_limit}"
+                    )
+
+                (metric - ctx.whitening_limit).relu().backward()
+                penalty_grad = x_detached.grad
+                scale = ctx.grad_scale * (
+                    x_grad.to(torch.float32).norm() / (penalty_grad.norm() + 1.0e-20)
+                )
+                penalty_grad = penalty_grad * scale
+        return x_grad + penalty_grad.to(x_grad.dtype), None, None, None
+
+
+class Whiten(nn.Module):
+    def __init__(
+        self,
+        num_groups: int,
+        whitening_limit: float,
+        prob: Union[float, Tuple[float, float]],
+        grad_scale: float,
+    ):
+        """
+        Args:
+          num_groups: the number of groups to divide the channel dim into before
+            whitening.  We will attempt to make the feature covariance
+            within each group, after mean subtraction, as "white" as possible,
+            while having the same trace across all groups.
+         whitening_limit: a value greater than 1.0, that dictates how much
+           freedom we have to violate the constraints.  1.0 would mean perfectly
+           white, with exactly the same trace across groups; larger values
+           give more freedom.  E.g. 2.0.
+         prob: the probability with which we apply the gradient modification
+           (also affects the grad scale).  May be supplied as a float,
+           or as a pair (min_prob, max_prob)
+
+          grad_scale: determines the scale on the gradient term from this object,
+            relative to the rest of the gradient on the attention weights.
+            E.g. 0.02 (you may want to use smaller values than this if prob is large)
+        """
+        super(Whiten, self).__init__()
+        assert num_groups >= 1
+        assert whitening_limit >= 1
+        assert grad_scale >= 0
+        self.num_groups = num_groups
+        self.whitening_limit = whitening_limit
+        if isinstance(prob, float):
+            assert 0 < prob <= 1
+            self.prob = prob
+        else:
+            (self.min_prob, self.max_prob) = prob
+            assert 0 < self.min_prob < self.max_prob <= 1
+            self.prob = self.max_prob
+
+        self.grad_scale = grad_scale
+
+    def forward(self, x: Tensor) -> Tensor:
+        """
+        In the forward pass, this function just returns the input unmodified.
+        In the backward pass, it will modify the gradients to ensure that the
+        distribution in each group has close to (lambda times I) as the covariance
+        after mean subtraction, with the same lambda across groups.
+        For whitening_limit > 1, there will be more freedom to violate this
+        constraint.
+
+        Args:
+           x: the input of shape (*, num_channels)
+
+        Returns:
+            x, unmodified.   You should make sure
+        you use the returned value, or the graph will be freed
+        and nothing will happen in backprop.
+        """
+        if not x.requires_grad or random.random() > self.prob or self.grad_scale == 0:
+            return _no_op(x)
+        else:
+            if hasattr(self, "min_prob") and random.random() < 0.25:
+                # occasionally switch between min_prob and max_prob, based on whether
+                # we are above or below the threshold.
+                if (
+                    _whitening_metric(x.to(torch.float32), self.num_groups)
+                    > self.whitening_limit
+                ):
+                    # there would be a change to the grad.
+                    self.prob = self.max_prob
+                else:
+                    self.prob = self.min_prob
+
+            return WhiteningPenaltyFunction.apply(
+                x, self.num_groups, self.whitening_limit, self.grad_scale
+            )
+
+
+class WithLoss(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x: Tensor, y: Tensor):
+        ctx.y_shape = y.shape
+        return x
+
+    @staticmethod
+    def backward(ctx, ans_grad: Tensor):
+        return ans_grad, torch.ones(
+            ctx.y_shape, dtype=ans_grad.dtype, device=ans_grad.device
+        )
+
+
+def with_loss(x, y):
+    if torch.jit.is_scripting() or torch.jit.is_tracing():
+        return x
+    # returns x but adds y.sum() to the loss function.
+    return WithLoss.apply(x, y)
+
+
+def _no_op(x: Tensor) -> Tensor:
+    if torch.jit.is_scripting() or torch.jit.is_tracing():
+        return x
+    else:
+        # a no-op function that will have a node in the autograd graph,
+        # to avoid certain bugs relating to backward hooks
+        return x.chunk(1, dim=-1)[0]
+
+
+class Identity(torch.nn.Module):
+    def __init__(self):
+        super(Identity, self).__init__()
+
+    def forward(self, x):
+        return _no_op(x)
+
+
+class MaxEig(torch.nn.Module):
+    """
+    Modifies the backpropped derivatives of a function to try to discourage
+    that any given direction in activation space accounts for more than
+    a specified proportion of the covariance (e.g. 0.2).
+
+
+    Args:
+           num_channels: the number of channels
+           channel_dim: the dimension/axis corresponding to the channel, e.g.
+               -1, 0, 1, 2; will be interpreted as an offset from x.ndim if negative.
+           max_var_per_eig:  the maximum proportion of the variance of the
+               features/channels, after mean subtraction, that can come from
+               any given eigenvalue.
+           min_prob: the minimum probability with which we apply this during any invocation
+               of forward(), assuming last time we applied the constraint it was
+               not active; supplied for speed.
+           scale: determines the scale with which we modify the gradients, relative
+               to the existing / unmodified gradients
+    """
+
+    def __init__(
+        self,
+        num_channels: int,
+        channel_dim: int,
+        max_var_per_eig: float = 0.2,
+        min_prob: float = 0.01,
+        scale: float = 0.01,
+    ):
+        super(MaxEig, self).__init__()
+        self.num_channels = num_channels
+        self.channel_dim = channel_dim
+        self.scale = scale
+        assert max_var_per_eig == 0.0 or max_var_per_eig > 1.0 / num_channels
+        self.max_var_per_eig = max_var_per_eig
+
+        # we figure out the dominant direction using the power method: starting with
+        # a random vector, keep multiplying by the covariance and renormalizing.
+        with torch.no_grad():
+            # arbitrary.. would use randn() but want to leave the rest of the model's
+            # random parameters unchanged for comparison
+            direction = torch.arange(num_channels).to(torch.float)
+            direction = direction / direction.norm()
+            self.register_buffer("max_eig_direction", direction)
+
+        self.min_prob = min_prob
+        # cur_prob is the current probability we'll use to apply the ActivationBalancer.
+        # We'll regress this towards prob, each tiem we try to apply it and it is not
+        # active.
+        self.cur_prob = 1.0
+
+    def forward(self, x: Tensor) -> Tensor:
+        if (
+            torch.jit.is_scripting()
+            or self.max_var_per_eig <= 0
+            or random.random() > self.cur_prob
+            or torch.jit.is_tracing()
+        ):
+            return _no_op(x)
+
+        with torch.cuda.amp.autocast(enabled=False):
+            eps = 1.0e-20
+            orig_x = x
+            x = x.to(torch.float32)
+            with torch.no_grad():
+                x = x.transpose(self.channel_dim, -1).reshape(-1, self.num_channels)
+                x = x - x.mean(dim=0)
+                new_direction, coeffs = self._find_direction_coeffs(
+                    x, self.max_eig_direction
+                )
+                x_var = (x**2).mean()
+                x_residual = x - coeffs * new_direction
+                x_residual_var = (x_residual**2).mean()
+
+                # `variance_proportion` is the proportion of the variance accounted for
+                # by the top eigen-direction.
+                variance_proportion = (x_var - x_residual_var) / (x_var + 1.0e-20)
+
+                # ensure new direction is nonzero even if x == 0, by including `direction`.
+                self._set_direction(0.1 * self.max_eig_direction + new_direction)
+
+            if random.random() < 0.01 or __name__ == "__main__":
+                logging.info(
+                    f"variance_proportion = {variance_proportion.item()}, shape={tuple(orig_x.shape)}, cur_prob={self.cur_prob}"
+                )
+
+            if variance_proportion >= self.max_var_per_eig:
+                # The constraint is active.  Note, we should quite rarely
+                # reach here, only near the beginning of training if we are
+                # starting to diverge, should this constraint be active.
+                cur_prob = self.cur_prob
+                self.cur_prob = 1.0  # next time, do the update with probability 1.0.
+                return MaxEigLimiterFunction.apply(
+                    orig_x, coeffs, new_direction, self.channel_dim, self.scale
+                )
+            else:
+                # let self.cur_prob exponentially approach self.min_prob, as
+                # long as the constraint is inactive.
+                self.cur_prob = 0.75 * self.cur_prob + 0.25 * self.min_prob
+                return orig_x
+
+    def _set_direction(self, direction: Tensor):
+        """
+        Sets self.max_eig_direction to a normalized version of `direction`
+        """
+        direction = direction.detach()
+        direction = direction / direction.norm()
+        direction_sum = direction.sum().item()
+        if direction_sum - direction_sum == 0:  # no inf/nan
+            self.max_eig_direction[:] = direction
+        else:
+            logging.info(
+                f"Warning: sum of direction in MaxEig is {direction_sum}, "
+                "num_channels={self.num_channels}, channel_dim={self.channel_dim}"
+            )
+
+    def _find_direction_coeffs(
+        self, x: Tensor, prev_direction: Tensor
+    ) -> Tuple[Tensor, Tensor, Tensor]:
+        """
+            Figure out (an approximation to) the proportion of the variance of a set of
+            feature vectors that can be attributed to the top eigen-direction.
+            Args:
+             x: a Tensor of shape (num_frames, num_channels), with num_frames > 1.
+          prev_direction:  a Tensor of shape (num_channels,), that is our previous estimate
+                   of the top eigen-direction, or a random direction if this is the first
+                   iteration.  Does not have to be normalized, but should be nonzero.
+
+        Returns: (cur_direction, coeffs), where:
+             cur_direction: a Tensor of shape (num_channels,) that is the current
+                estimate of the top eigen-direction.
+             coeffs: a Tensor of shape (num_frames, 1) that minimizes, or
+                approximately minimizes, (x - coeffs * cur_direction).norm()
+        """
+        (num_frames, num_channels) = x.shape
+        assert num_channels > 1 and num_frames > 1
+        assert prev_direction.shape == (num_channels,)
+        # `coeffs` are the coefficients of `prev_direction` in x.
+        # actually represent the coeffs up to a constant positive factor.
+        coeffs = (x * prev_direction).sum(dim=1, keepdim=True) + 1.0e-10
+        cur_direction = (x * coeffs).sum(dim=0) / ((coeffs**2).sum() + 1.0e-20)
+        return cur_direction, coeffs
+
+
+class DoubleSwishFunction(torch.autograd.Function):
+    """
+      double_swish(x) = x * torch.sigmoid(x-1)
+    This is a definition, originally motivated by its close numerical
+    similarity to swish(swish(x)), where swish(x) =  x * sigmoid(x).
+
+    Memory-efficient derivative computation:
+     double_swish(x) = x * s, where s(x) = torch.sigmoid(x-1)
+     double_swish'(x) = d/dx double_swish(x) =  x * s'(x) + x' * s(x) = x * s'(x) + s(x).
+     Now, s'(x) = s(x) * (1-s(x)).
+     double_swish'(x) =  x * s'(x) + s(x).
+                      =  x * s(x) * (1-s(x)) + s(x).
+                     = double_swish(x) * (1-s(x)) + s(x)
+     ... so we just need to remember s(x) but not x itself.
+    """
+
+    @staticmethod
+    def forward(ctx, x: Tensor) -> Tensor:
+        requires_grad = x.requires_grad
+        x_dtype = x.dtype
+        if x.dtype == torch.float16:
+            x = x.to(torch.float32)
+
+        s = torch.sigmoid(x - 1.0)
+        y = x * s
+
+        if requires_grad:
+            deriv = y * (1 - s) + s
+            # notes on derivative of x * sigmoid(x - 1):
+            # https://www.wolframalpha.com/input?i=d%2Fdx+%28x+*+sigmoid%28x-1%29%29
+            # min \simeq -0.043638.  Take floor as -0.043637 so it's a lower bund
+            # max \simeq 1.1990.   Take ceil to be 1.2 so it's an upper bound.
+            # the combination of "+ torch.rand_like(deriv)" and casting to torch.uint8 (which
+            # floors), should be expectation-preserving.
+            floor = -0.043637
+            ceil = 1.2
+            d_scaled = (deriv - floor) * (255.0 / (ceil - floor)) + torch.rand_like(
+                deriv
+            )
+            if __name__ == "__main__":
+                # for self-testing only.
+                assert d_scaled.min() >= 0.0
+                assert d_scaled.max() < 256.0
+            d_int = d_scaled.to(torch.uint8)
+            ctx.save_for_backward(d_int)
+        if x.dtype == torch.float16 or torch.is_autocast_enabled():
+            y = y.to(torch.float16)
+        return y
+
+    @staticmethod
+    def backward(ctx, y_grad: Tensor) -> Tensor:
+        (d,) = ctx.saved_tensors
+        # the same constants as used in forward pass.
+        floor = -0.043637
+        ceil = 1.2
+        d = d * ((ceil - floor) / 255.0) + floor
+        return y_grad * d
+
+
+class DoubleSwish(torch.nn.Module):
+    def forward(self, x: Tensor) -> Tensor:
+        """Return double-swish activation function which is an approximation to Swish(Swish(x)),
+        that we approximate closely with x * sigmoid(x-1).
+        """
+        if torch.jit.is_scripting() or torch.jit.is_tracing():
+            return x * torch.sigmoid(x - 1.0)
+        return DoubleSwishFunction.apply(x)
+
+
+def BalancedDoubleSwish(
+    d_model, channel_dim=-1, max_abs=10.0, min_prob=0.25
+) -> nn.Sequential:
+    """
+    ActivationBalancer -> DoubleSwish
+    """
+    balancer = ActivationBalancer(
+        d_model, channel_dim=channel_dim, max_abs=max_abs, min_prob=min_prob
+    )
+    return nn.Sequential(
+        balancer,
+        DoubleSwish(),
+    )
+
+
+def _test_max_eig():
+    for proportion in [0.1, 0.5, 10.0]:
+        logging.info(f"proportion = {proportion}")
+        x = torch.randn(100, 128)
+        direction = torch.randn(128)
+        coeffs = torch.randn(100, 1)
+        x += proportion * direction * coeffs
+
+        x.requires_grad = True
+
+        num_channels = 128
+        m = MaxEig(
+            num_channels, 1, 0.5, scale=0.1  # channel_dim  # max_var_per_eig
+        )  # grad_scale
+
+        for _ in range(4):
+            y = m(x)
+
+        y_grad = torch.randn_like(x)
+        y.backward(gradient=y_grad)
+
+        if proportion < 0.2:
+            assert torch.allclose(x.grad, y_grad, atol=1.0e-02)
+        elif proportion > 1.0:
+            assert not torch.allclose(x.grad, y_grad)
+
+
+def _test_whiten():
+    for proportion in [0.1, 0.5, 10.0]:
+        logging.info(f"_test_whiten(): proportion = {proportion}")
+        x = torch.randn(100, 128)
+        direction = torch.randn(128)
+        coeffs = torch.randn(100, 1)
+        x += proportion * direction * coeffs
+
+        x.requires_grad = True
+
+        num_channels = 128
+        m = Whiten(
+            1, 5.0, prob=1.0, grad_scale=0.1  # num_groups  # whitening_limit,
+        )  # grad_scale
+
+        for _ in range(4):
+            y = m(x)
+
+        y_grad = torch.randn_like(x)
+        y.backward(gradient=y_grad)
+
+        if proportion < 0.2:
+            assert torch.allclose(x.grad, y_grad)
+        elif proportion > 1.0:
+            assert not torch.allclose(x.grad, y_grad)
+
+
+def _test_activation_balancer_sign():
+    probs = torch.arange(0, 1, 0.01)
+    N = 1000
+    x = 1.0 * ((2.0 * (torch.rand(probs.numel(), N) < probs.unsqueeze(-1))) - 1.0)
+    x = x.detach()
+    x.requires_grad = True
+    m = ActivationBalancer(
+        probs.numel(),
+        channel_dim=0,
+        min_positive=0.05,
+        max_positive=0.95,
+        max_factor=0.2,
+        min_abs=0.0,
+    )
+
+    y_grad = torch.sign(torch.randn(probs.numel(), N))
+
+    y = m(x)
+    y.backward(gradient=y_grad)
+    print("_test_activation_balancer_sign: x = ", x)
+    print("_test_activation_balancer_sign: y grad = ", y_grad)
+    print("_test_activation_balancer_sign: x grad = ", x.grad)
+
+
+def _test_activation_balancer_magnitude():
+    magnitudes = torch.arange(0, 1, 0.01)
+    N = 1000
+    x = torch.sign(torch.randn(magnitudes.numel(), N)) * magnitudes.unsqueeze(-1)
+    x = x.detach()
+    x.requires_grad = True
+    m = ActivationBalancer(
+        magnitudes.numel(),
+        channel_dim=0,
+        min_positive=0.0,
+        max_positive=1.0,
+        max_factor=0.2,
+        min_abs=0.2,
+        max_abs=0.8,
+        min_prob=1.0,
+    )
+
+    y_grad = torch.sign(torch.randn(magnitudes.numel(), N))
+
+    y = m(x)
+    y.backward(gradient=y_grad)
+    print("_test_activation_balancer_magnitude: x = ", x)
+    print("_test_activation_balancer_magnitude: y grad = ", y_grad)
+    print("_test_activation_balancer_magnitude: x grad = ", x.grad)
+
+
+def _test_basic_norm():
+    num_channels = 128
+    m = BasicNorm(num_channels=num_channels, channel_dim=1)
+
+    x = torch.randn(500, num_channels)
+
+    y = m(x)
+
+    assert y.shape == x.shape
+    x_rms = (x**2).mean().sqrt()
+    y_rms = (y**2).mean().sqrt()
+    print("x rms = ", x_rms)
+    print("y rms = ", y_rms)
+    assert y_rms < x_rms
+    assert y_rms > 0.5 * x_rms
+
+
+def _test_double_swish_deriv():
+    x = torch.randn(10, 12, dtype=torch.double) * 3.0
+    x.requires_grad = True
+    m = DoubleSwish()
+
+    tol = (1.2 - (-0.043637)) / 255.0
+    torch.autograd.gradcheck(m, x, atol=tol)
+
+    # for self-test.
+    x = torch.randn(1000, 1000, dtype=torch.double) * 3.0
+    x.requires_grad = True
+    y = m(x)
+
+
+def _test_softmax():
+    a = torch.randn(2, 10, dtype=torch.float64)
+    b = a.clone()
+    a.requires_grad = True
+    b.requires_grad = True
+    a.softmax(dim=1)[:, 0].sum().backward()
+    print("a grad = ", a.grad)
+    softmax(b, dim=1)[:, 0].sum().backward()
+    print("b grad = ", b.grad)
+    assert torch.allclose(a.grad, b.grad)
+
+
+if __name__ == "__main__":
+    logging.getLogger().setLevel(logging.INFO)
+    torch.set_num_threads(1)
+    torch.set_num_interop_threads(1)
+    _test_softmax()
+    _test_whiten()
+    _test_max_eig()
+    _test_activation_balancer_sign()
+    _test_activation_balancer_magnitude()
+    _test_basic_norm()
+    _test_double_swish_deriv()
diff --git a/modules/general/utils.py b/modules/general/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..38c94dfbb9859661322f3d2e8204b423fffae736
--- /dev/null
+++ b/modules/general/utils.py
@@ -0,0 +1,100 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+
+
+def normalization(channels: int, groups: int = 32):
+    r"""Make a standard normalization layer, i.e. GroupNorm.
+
+    Args:
+        channels: number of input channels.
+        groups: number of groups for group normalization.
+
+    Returns:
+        a ``nn.Module`` for normalization.
+    """
+    assert groups > 0, f"invalid number of groups: {groups}"
+    return nn.GroupNorm(groups, channels)
+
+
+def Linear(*args, **kwargs):
+    r"""Wrapper of ``nn.Linear`` with kaiming_normal_ initialization."""
+    layer = nn.Linear(*args, **kwargs)
+    nn.init.kaiming_normal_(layer.weight)
+    return layer
+
+
+def Conv1d(*args, **kwargs):
+    r"""Wrapper of ``nn.Conv1d`` with kaiming_normal_ initialization."""
+    layer = nn.Conv1d(*args, **kwargs)
+    nn.init.kaiming_normal_(layer.weight)
+    return layer
+
+
+def Conv2d(*args, **kwargs):
+    r"""Wrapper of ``nn.Conv2d`` with kaiming_normal_ initialization."""
+    layer = nn.Conv2d(*args, **kwargs)
+    nn.init.kaiming_normal_(layer.weight)
+    return layer
+
+
+def ConvNd(dims: int = 1, *args, **kwargs):
+    r"""Wrapper of N-dimension convolution with kaiming_normal_ initialization.
+
+    Args:
+        dims: number of dimensions of the convolution.
+    """
+    if dims == 1:
+        return Conv1d(*args, **kwargs)
+    elif dims == 2:
+        return Conv2d(*args, **kwargs)
+    else:
+        raise ValueError(f"invalid number of dimensions: {dims}")
+
+
+def zero_module(module: nn.Module):
+    r"""Zero out the parameters of a module and return it."""
+    nn.init.zeros_(module.weight)
+    nn.init.zeros_(module.bias)
+    return module
+
+
+def scale_module(module: nn.Module, scale):
+    r"""Scale the parameters of a module and return it."""
+    for p in module.parameters():
+        p.detach().mul_(scale)
+    return module
+
+
+def mean_flat(tensor: torch.Tensor):
+    r"""Take the mean over all non-batch dimensions."""
+    return tensor.mean(dim=tuple(range(1, tensor.dim())))
+
+
+def append_dims(x, target_dims):
+    r"""Appends dimensions to the end of a tensor until
+    it has target_dims dimensions.
+    """
+    dims_to_append = target_dims - x.dim()
+    if dims_to_append < 0:
+        raise ValueError(
+            f"input has {x.dim()} dims but target_dims is {target_dims}, which is less"
+        )
+    return x[(...,) + (None,) * dims_to_append]
+
+
+def append_zero(x, count=1):
+    r"""Appends ``count`` zeros to the end of a tensor along the last dimension."""
+    assert count > 0, f"invalid count: {count}"
+    return torch.cat([x, x.new_zeros((*x.size()[:-1], count))], dim=-1)
+
+
+class Transpose(nn.Identity):
+    """(N, T, D) -> (N, D, T)"""
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        return input.transpose(1, 2)
diff --git a/modules/monotonic_align/__init__.py b/modules/monotonic_align/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..784d9081ca1aa1355b893dbd91d159d6f911cb4b
--- /dev/null
+++ b/modules/monotonic_align/__init__.py
@@ -0,0 +1,21 @@
+# This code from https://github.com/jaywalnut310/vits/
+
+import numpy as np
+import torch
+from .monotonic_align.core import maximum_path_c
+
+
+def maximum_path(neg_cent, mask):
+    """Cython optimized version.
+    neg_cent: [b, t_t, t_s]
+    mask: [b, t_t, t_s]
+    """
+    device = neg_cent.device
+    dtype = neg_cent.dtype
+    neg_cent = neg_cent.data.cpu().numpy().astype(np.float32)
+    path = np.zeros(neg_cent.shape, dtype=np.int32)
+
+    t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(np.int32)
+    t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(np.int32)
+    maximum_path_c(path, neg_cent, t_t_max, t_s_max)
+    return torch.from_numpy(path).to(device=device, dtype=dtype)
diff --git a/modules/monotonic_align/core.pyx b/modules/monotonic_align/core.pyx
new file mode 100644
index 0000000000000000000000000000000000000000..bfaabd4d21c2299cdd978f0cc0caefa20ad186e5
--- /dev/null
+++ b/modules/monotonic_align/core.pyx
@@ -0,0 +1,42 @@
+cimport cython
+from cython.parallel import prange
+
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_y, int t_x, float max_neg_val=-1e9) nogil:
+  cdef int x
+  cdef int y
+  cdef float v_prev
+  cdef float v_cur
+  cdef float tmp
+  cdef int index = t_x - 1
+
+  for y in range(t_y):
+    for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
+      if x == y:
+        v_cur = max_neg_val
+      else:
+        v_cur = value[y-1, x]
+      if x == 0:
+        if y == 0:
+          v_prev = 0.
+        else:
+          v_prev = max_neg_val
+      else:
+        v_prev = value[y-1, x-1]
+      value[y, x] += max(v_prev, v_cur)
+
+  for y in range(t_y - 1, -1, -1):
+    path[y, index] = 1
+    if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]):
+      index = index - 1
+
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_ys, int[::1] t_xs) nogil:
+  cdef int b = paths.shape[0]
+  cdef int i
+  for i in prange(b, nogil=True):
+    maximum_path_each(paths[i], values[i], t_ys[i], t_xs[i])
diff --git a/modules/monotonic_align/setup.py b/modules/monotonic_align/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..773fafd96fb0f1bac88037187542c84eccb26ef5
--- /dev/null
+++ b/modules/monotonic_align/setup.py
@@ -0,0 +1,22 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+# This code is modified from https://github.com/jaywalnut310/vits/
+
+from distutils.core import setup, Extension
+from Cython.Build import cythonize
+import numpy
+
+extension = Extension(
+    name="monotonic_align.core",
+    sources=["core.pyx"],
+    include_dirs=[numpy.get_include()],
+    # Define additional arguments if needed
+)
+
+setup(
+    name="monotonic_align",
+    ext_modules=cythonize([extension]),
+)
diff --git a/modules/naturalpseech2/transformers.py b/modules/naturalpseech2/transformers.py
new file mode 100644
index 0000000000000000000000000000000000000000..c646bf960a9db78ce7f95d3f36fce464ce3a6ec6
--- /dev/null
+++ b/modules/naturalpseech2/transformers.py
@@ -0,0 +1,509 @@
+import math
+import torch
+from torch import nn
+from torch.nn import Parameter
+import torch.nn.functional as F
+import numpy as np
+
+
+class StyleAdaptiveLayerNorm(nn.Module):
+    def __init__(self, normalized_shape, eps=1e-5):
+        super().__init__()
+        self.in_dim = normalized_shape
+        self.norm = nn.LayerNorm(self.in_dim, eps=eps, elementwise_affine=False)
+        self.style = nn.Linear(self.in_dim, self.in_dim * 2)
+        self.style.bias.data[: self.in_dim] = 1
+        self.style.bias.data[self.in_dim :] = 0
+
+    def forward(self, x, condition):
+        # x: (B, T, d); condition: (B, T, d)
+
+        style = self.style(torch.mean(condition, dim=1, keepdim=True))
+
+        gamma, beta = style.chunk(2, -1)
+
+        out = self.norm(x)
+
+        out = gamma * out + beta
+        return out
+
+
+class PositionalEncoding(nn.Module):
+    def __init__(self, d_model, dropout, max_len=5000):
+        super().__init__()
+
+        self.dropout = dropout
+        position = torch.arange(max_len).unsqueeze(1)
+        div_term = torch.exp(
+            torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model)
+        )
+        pe = torch.zeros(max_len, 1, d_model)
+        pe[:, 0, 0::2] = torch.sin(position * div_term)
+        pe[:, 0, 1::2] = torch.cos(position * div_term)
+        self.register_buffer("pe", pe)
+
+    def forward(self, x):
+        x = x + self.pe[: x.size(0)]
+        return F.dropout(x, self.dropout, training=self.training)
+
+
+class TransformerFFNLayer(nn.Module):
+    def __init__(
+        self, encoder_hidden, conv_filter_size, conv_kernel_size, encoder_dropout
+    ):
+        super().__init__()
+
+        self.encoder_hidden = encoder_hidden
+        self.conv_filter_size = conv_filter_size
+        self.conv_kernel_size = conv_kernel_size
+        self.encoder_dropout = encoder_dropout
+
+        self.ffn_1 = nn.Conv1d(
+            self.encoder_hidden,
+            self.conv_filter_size,
+            self.conv_kernel_size,
+            padding=self.conv_kernel_size // 2,
+        )
+        self.ffn_1.weight.data.normal_(0.0, 0.02)
+        self.ffn_2 = nn.Linear(self.conv_filter_size, self.encoder_hidden)
+        self.ffn_2.weight.data.normal_(0.0, 0.02)
+
+    def forward(self, x):
+        # x: (B, T, d)
+        x = self.ffn_1(x.permute(0, 2, 1)).permute(
+            0, 2, 1
+        )  # (B, T, d) -> (B, d, T) -> (B, T, d)
+        x = F.relu(x)
+        x = F.dropout(x, self.encoder_dropout, training=self.training)
+        x = self.ffn_2(x)
+        return x
+
+
+class TransformerEncoderLayer(nn.Module):
+    def __init__(
+        self,
+        encoder_hidden,
+        encoder_head,
+        conv_filter_size,
+        conv_kernel_size,
+        encoder_dropout,
+        use_cln,
+    ):
+        super().__init__()
+        self.encoder_hidden = encoder_hidden
+        self.encoder_head = encoder_head
+        self.conv_filter_size = conv_filter_size
+        self.conv_kernel_size = conv_kernel_size
+        self.encoder_dropout = encoder_dropout
+        self.use_cln = use_cln
+
+        if not self.use_cln:
+            self.ln_1 = nn.LayerNorm(self.encoder_hidden)
+            self.ln_2 = nn.LayerNorm(self.encoder_hidden)
+        else:
+            self.ln_1 = StyleAdaptiveLayerNorm(self.encoder_hidden)
+            self.ln_2 = StyleAdaptiveLayerNorm(self.encoder_hidden)
+
+        self.self_attn = nn.MultiheadAttention(
+            self.encoder_hidden, self.encoder_head, batch_first=True
+        )
+
+        self.ffn = TransformerFFNLayer(
+            self.encoder_hidden,
+            self.conv_filter_size,
+            self.conv_kernel_size,
+            self.encoder_dropout,
+        )
+
+    def forward(self, x, key_padding_mask, conditon=None):
+        # x: (B, T, d); key_padding_mask: (B, T), mask is 0; condition: (B, T, d)
+
+        # self attention
+        residual = x
+        if self.use_cln:
+            x = self.ln_1(x, conditon)
+        else:
+            x = self.ln_1(x)
+
+        if key_padding_mask != None:
+            key_padding_mask_input = ~(key_padding_mask.bool())
+        else:
+            key_padding_mask_input = None
+        x, _ = self.self_attn(
+            query=x, key=x, value=x, key_padding_mask=key_padding_mask_input
+        )
+        x = F.dropout(x, self.encoder_dropout, training=self.training)
+        x = residual + x
+
+        # ffn
+        residual = x
+        if self.use_cln:
+            x = self.ln_2(x, conditon)
+        else:
+            x = self.ln_2(x)
+        x = self.ffn(x)
+        x = residual + x
+
+        return x
+
+
+class TransformerEncoder(nn.Module):
+    def __init__(
+        self,
+        enc_emb_tokens=None,
+        encoder_layer=None,
+        encoder_hidden=None,
+        encoder_head=None,
+        conv_filter_size=None,
+        conv_kernel_size=None,
+        encoder_dropout=None,
+        use_cln=None,
+        cfg=None,
+    ):
+        super().__init__()
+
+        self.encoder_layer = (
+            encoder_layer if encoder_layer is not None else cfg.encoder_layer
+        )
+        self.encoder_hidden = (
+            encoder_hidden if encoder_hidden is not None else cfg.encoder_hidden
+        )
+        self.encoder_head = (
+            encoder_head if encoder_head is not None else cfg.encoder_head
+        )
+        self.conv_filter_size = (
+            conv_filter_size if conv_filter_size is not None else cfg.conv_filter_size
+        )
+        self.conv_kernel_size = (
+            conv_kernel_size if conv_kernel_size is not None else cfg.conv_kernel_size
+        )
+        self.encoder_dropout = (
+            encoder_dropout if encoder_dropout is not None else cfg.encoder_dropout
+        )
+        self.use_cln = use_cln if use_cln is not None else cfg.use_cln
+
+        if enc_emb_tokens != None:
+            self.use_enc_emb = True
+            self.enc_emb_tokens = enc_emb_tokens
+        else:
+            self.use_enc_emb = False
+
+        self.position_emb = PositionalEncoding(
+            self.encoder_hidden, self.encoder_dropout
+        )
+
+        self.layers = nn.ModuleList([])
+        self.layers.extend(
+            [
+                TransformerEncoderLayer(
+                    self.encoder_hidden,
+                    self.encoder_head,
+                    self.conv_filter_size,
+                    self.conv_kernel_size,
+                    self.encoder_dropout,
+                    self.use_cln,
+                )
+                for i in range(self.encoder_layer)
+            ]
+        )
+
+        if self.use_cln:
+            self.last_ln = StyleAdaptiveLayerNorm(self.encoder_hidden)
+        else:
+            self.last_ln = nn.LayerNorm(self.encoder_hidden)
+
+    def forward(self, x, key_padding_mask, condition=None):
+        if len(x.shape) == 2 and self.use_enc_emb:
+            x = self.enc_emb_tokens(x)
+            x = self.position_emb(x)
+        else:
+            x = self.position_emb(x)  # (B, T, d)
+
+        for layer in self.layers:
+            x = layer(x, key_padding_mask, condition)
+
+        if self.use_cln:
+            x = self.last_ln(x, condition)
+        else:
+            x = self.last_ln(x)
+
+        return x
+
+
+class DurationPredictor(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        self.cfg = cfg
+        self.input_size = cfg.input_size
+        self.filter_size = cfg.filter_size
+        self.kernel_size = cfg.kernel_size
+        self.conv_layers = cfg.conv_layers
+        self.cross_attn_per_layer = cfg.cross_attn_per_layer
+        self.attn_head = cfg.attn_head
+        self.drop_out = cfg.drop_out
+
+        self.conv = nn.ModuleList()
+        self.cattn = nn.ModuleList()
+
+        for idx in range(self.conv_layers):
+            in_dim = self.input_size if idx == 0 else self.filter_size
+            self.conv += [
+                nn.Sequential(
+                    nn.Conv1d(
+                        in_dim,
+                        self.filter_size,
+                        self.kernel_size,
+                        padding=self.kernel_size // 2,
+                    ),
+                    nn.ReLU(),
+                    nn.LayerNorm(self.filter_size),
+                    nn.Dropout(self.drop_out),
+                )
+            ]
+            if idx % self.cross_attn_per_layer == 0:
+                self.cattn.append(
+                    torch.nn.Sequential(
+                        nn.MultiheadAttention(
+                            self.filter_size,
+                            self.attn_head,
+                            batch_first=True,
+                            kdim=self.filter_size,
+                            vdim=self.filter_size,
+                        ),
+                        nn.LayerNorm(self.filter_size),
+                        nn.Dropout(0.2),
+                    )
+                )
+
+        self.linear = nn.Linear(self.filter_size, 1)
+        self.linear.weight.data.normal_(0.0, 0.02)
+
+    def forward(self, x, mask, ref_emb, ref_mask):
+        """
+        input:
+        x: (B, N, d)
+        mask: (B, N), mask is 0
+        ref_emb: (B, d, T')
+        ref_mask: (B, T'), mask is 0
+
+        output:
+        dur_pred: (B, N)
+        dur_pred_log: (B, N)
+        dur_pred_round: (B, N)
+        """
+
+        input_ref_mask = ~(ref_mask.bool())  # (B, T')
+        # print(input_ref_mask)
+
+        x = x.transpose(1, -1)  # (B, N, d) -> (B, d, N)
+
+        for idx, (conv, act, ln, dropout) in enumerate(self.conv):
+            res = x
+            # print(torch.min(x), torch.max(x))
+            if idx % self.cross_attn_per_layer == 0:
+                attn_idx = idx // self.cross_attn_per_layer
+                attn, attn_ln, attn_drop = self.cattn[attn_idx]
+
+                attn_res = y_ = x.transpose(1, 2)  # (B, d, N) -> (B, N, d)
+
+                y_ = attn_ln(y_)
+                # print(torch.min(y_), torch.min(y_))
+                # print(torch.min(ref_emb), torch.max(ref_emb))
+                y_, _ = attn(
+                    y_,
+                    ref_emb.transpose(1, 2),
+                    ref_emb.transpose(1, 2),
+                    key_padding_mask=input_ref_mask,
+                )
+                # y_, _ = attn(y_, ref_emb.transpose(1, 2), ref_emb.transpose(1, 2))
+                # print(torch.min(y_), torch.min(y_))
+                y_ = attn_drop(y_)
+                y_ = (y_ + attn_res) / math.sqrt(2.0)
+
+                x = y_.transpose(1, 2)
+
+            x = conv(x)
+            # print(torch.min(x), torch.max(x))
+            x = act(x)
+            x = ln(x.transpose(1, 2))
+            # print(torch.min(x), torch.max(x))
+            x = x.transpose(1, 2)
+
+            x = dropout(x)
+
+            if idx != 0:
+                x += res
+
+            if mask is not None:
+                x = x * mask.to(x.dtype)[:, None, :]
+
+        x = self.linear(x.transpose(1, 2))
+        x = torch.squeeze(x, -1)
+
+        dur_pred = x.exp() - 1
+        dur_pred_round = torch.clamp(torch.round(x.exp() - 1), min=0).long()
+
+        return {
+            "dur_pred_log": x,
+            "dur_pred": dur_pred,
+            "dur_pred_round": dur_pred_round,
+        }
+
+
+class PitchPredictor(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        self.cfg = cfg
+        self.input_size = cfg.input_size
+        self.filter_size = cfg.filter_size
+        self.kernel_size = cfg.kernel_size
+        self.conv_layers = cfg.conv_layers
+        self.cross_attn_per_layer = cfg.cross_attn_per_layer
+        self.attn_head = cfg.attn_head
+        self.drop_out = cfg.drop_out
+
+        self.conv = nn.ModuleList()
+        self.cattn = nn.ModuleList()
+
+        for idx in range(self.conv_layers):
+            in_dim = self.input_size if idx == 0 else self.filter_size
+            self.conv += [
+                nn.Sequential(
+                    nn.Conv1d(
+                        in_dim,
+                        self.filter_size,
+                        self.kernel_size,
+                        padding=self.kernel_size // 2,
+                    ),
+                    nn.ReLU(),
+                    nn.LayerNorm(self.filter_size),
+                    nn.Dropout(self.drop_out),
+                )
+            ]
+            if idx % self.cross_attn_per_layer == 0:
+                self.cattn.append(
+                    torch.nn.Sequential(
+                        nn.MultiheadAttention(
+                            self.filter_size,
+                            self.attn_head,
+                            batch_first=True,
+                            kdim=self.filter_size,
+                            vdim=self.filter_size,
+                        ),
+                        nn.LayerNorm(self.filter_size),
+                        nn.Dropout(0.2),
+                    )
+                )
+
+        self.linear = nn.Linear(self.filter_size, 1)
+        self.linear.weight.data.normal_(0.0, 0.02)
+
+    def forward(self, x, mask, ref_emb, ref_mask):
+        """
+        input:
+        x: (B, N, d)
+        mask: (B, N), mask is 0
+        ref_emb: (B, d, T')
+        ref_mask: (B, T'), mask is 0
+
+        output:
+        pitch_pred: (B, T)
+        """
+
+        input_ref_mask = ~(ref_mask.bool())  # (B, T')
+
+        x = x.transpose(1, -1)  # (B, N, d) -> (B, d, N)
+
+        for idx, (conv, act, ln, dropout) in enumerate(self.conv):
+            res = x
+            if idx % self.cross_attn_per_layer == 0:
+                attn_idx = idx // self.cross_attn_per_layer
+                attn, attn_ln, attn_drop = self.cattn[attn_idx]
+
+                attn_res = y_ = x.transpose(1, 2)  # (B, d, N) -> (B, N, d)
+
+                y_ = attn_ln(y_)
+                y_, _ = attn(
+                    y_,
+                    ref_emb.transpose(1, 2),
+                    ref_emb.transpose(1, 2),
+                    key_padding_mask=input_ref_mask,
+                )
+                # y_, _ = attn(y_, ref_emb.transpose(1, 2), ref_emb.transpose(1, 2))
+                y_ = attn_drop(y_)
+                y_ = (y_ + attn_res) / math.sqrt(2.0)
+
+                x = y_.transpose(1, 2)
+
+            x = conv(x)
+            x = act(x)
+            x = ln(x.transpose(1, 2))
+            x = x.transpose(1, 2)
+
+            x = dropout(x)
+
+            if idx != 0:
+                x += res
+
+        x = self.linear(x.transpose(1, 2))
+        x = torch.squeeze(x, -1)
+
+        return x
+
+
+def pad(input_ele, mel_max_length=None):
+    if mel_max_length:
+        max_len = mel_max_length
+    else:
+        max_len = max([input_ele[i].size(0) for i in range(len(input_ele))])
+
+    out_list = list()
+    for i, batch in enumerate(input_ele):
+        if len(batch.shape) == 1:
+            one_batch_padded = F.pad(
+                batch, (0, max_len - batch.size(0)), "constant", 0.0
+            )
+        elif len(batch.shape) == 2:
+            one_batch_padded = F.pad(
+                batch, (0, 0, 0, max_len - batch.size(0)), "constant", 0.0
+            )
+        out_list.append(one_batch_padded)
+    out_padded = torch.stack(out_list)
+    return out_padded
+
+
+class LengthRegulator(nn.Module):
+    """Length Regulator"""
+
+    def __init__(self):
+        super(LengthRegulator, self).__init__()
+
+    def LR(self, x, duration, max_len):
+        device = x.device
+        output = list()
+        mel_len = list()
+        for batch, expand_target in zip(x, duration):
+            expanded = self.expand(batch, expand_target)
+            output.append(expanded)
+            mel_len.append(expanded.shape[0])
+
+        if max_len is not None:
+            output = pad(output, max_len)
+        else:
+            output = pad(output)
+
+        return output, torch.LongTensor(mel_len).to(device)
+
+    def expand(self, batch, predicted):
+        out = list()
+
+        for i, vec in enumerate(batch):
+            expand_size = predicted[i].item()
+            out.append(vec.expand(max(int(expand_size), 0), -1))
+        out = torch.cat(out, 0)
+
+        return out
+
+    def forward(self, x, duration, max_len):
+        output, mel_len = self.LR(x, duration, max_len)
+        return output, mel_len
diff --git a/modules/neural_source_filter/__init__.py b/modules/neural_source_filter/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa2749078427c312fa046f354ded858bae82f666
--- /dev/null
+++ b/modules/neural_source_filter/__init__.py
@@ -0,0 +1,6 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .sine_excitation import *
diff --git a/modules/neural_source_filter/sine_excitation.py b/modules/neural_source_filter/sine_excitation.py
new file mode 100644
index 0000000000000000000000000000000000000000..08b72b2fc4f5e3065e32b2891bdd7f510c727e70
--- /dev/null
+++ b/modules/neural_source_filter/sine_excitation.py
@@ -0,0 +1,82 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+#################### NSF ####################
+
+import torch
+
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+
+# This code is adopted from Xin Wang's NSF under the MIT License
+# https://github.com/nii-yamagishilab/project-NN-Pytorch-scripts
+
+
+class SineGen(nn.Module):
+    def __init__(
+        self, fs, harmonic_num=0, amp=0.1, noise_std=0.003, voiced_threshold=0
+    ):
+        super(SineGen, self).__init__()
+        self.amp = amp
+        self.noise_std = noise_std
+        self.harmonic_num = harmonic_num
+        self.dim = harmonic_num + 1
+        self.fs = fs
+        self.voice_threshold = voiced_threshold
+
+    def _f0toUnvoiced(self, f0):
+        uv = torch.ones_like(f0)
+        uv = uv * (f0 > self.voice_threshold)
+        return uv
+
+    @torch.no_grad()
+    def forward(self, f0, upp):
+        f0 = f0.unsqueeze(-1)
+        fn = torch.multiply(
+            f0, torch.arange(1, self.dim + 1, device=f0.device).reshape(1, 1, -1)
+        )
+        rad_values = (fn / self.fs) % 1
+        rand_ini = torch.rand(fn.shape[0], fn.shape[2], device=fn.device)
+        rand_ini[:, 0] = 0
+        rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
+        is_half = rad_values.dtype is not torch.float32
+        tmp_over_one = torch.cumsum(rad_values.double(), 1)
+        if is_half:
+            tmp_over_one = tmp_over_one.half()
+        else:
+            tmp_over_one = tmp_over_one.float()
+        tmp_over_one *= upp
+        tmp_over_one = F.interpolate(
+            tmp_over_one.transpose(2, 1),
+            scale_factor=upp,
+            mode="linear",
+            align_corners=True,
+        ).transpose(2, 1)
+        rad_values = F.interpolate(
+            rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
+        ).transpose(2, 1)
+        tmp_over_one %= 1
+        tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
+        cumsum_shift = torch.zeros_like(rad_values)
+        cumsum_shift[:, 1:, :] = tmp_over_one_idx * (-1.0)
+        rad_values = rad_values.double()
+        cumsum_shift = cumsum_shift.double()
+        sine_waves = torch.sin(
+            torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
+        )
+        if is_half:
+            sine_waves = sine_waves.half()
+        else:
+            sine_waves = sine_waves.float()
+        sine_waves = sine_waves * self.amp
+        uv = self._f0toUnvoiced(f0)
+        uv = F.interpolate(
+            uv.transpose(2, 1), scale_factor=upp, mode="nearest"
+        ).transpose(2, 1)
+        noise_amp = uv * self.noise_std + (1 - uv) * self.amp / 3
+        noise = noise_amp * torch.randn_like(sine_waves)
+        sine_waves = sine_waves * uv + noise
+        return sine_waves, uv, noise
diff --git a/modules/norms/__init__.py b/modules/norms/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..16c8ce8395657c349c2cf41a4d64eb20ceda7e53
--- /dev/null
+++ b/modules/norms/__init__.py
@@ -0,0 +1 @@
+from .norm import AdaptiveLayerNorm, LayerNorm, BalancedBasicNorm, IdentityNorm
diff --git a/modules/norms/norm.py b/modules/norms/norm.py
new file mode 100644
index 0000000000000000000000000000000000000000..adf6992f1a70250406ccd48ff132718f2af884a6
--- /dev/null
+++ b/modules/norms/norm.py
@@ -0,0 +1,173 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+import copy
+import numbers
+from typing import Any, List, Tuple, Union
+
+import torch
+from torch import Tensor, nn
+from torch.nn import functional as F
+
+from modules.general.scaling import ActivationBalancer
+from modules.general.scaling import BasicNorm as _BasicNorm
+
+
+_shape_t = Union[int, List[int], torch.Size]
+
+
+class LayerNorm(nn.Module):
+    __constants__ = ["normalized_shape", "eps", "elementwise_affine"]
+    normalized_shape: Tuple[int, ...]
+    eps: float
+    elementwise_affine: bool
+
+    def __init__(
+        self,
+        normalized_shape: _shape_t,
+        eps: float = 1e-5,
+        elementwise_affine: bool = True,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super(LayerNorm, self).__init__()
+        if isinstance(normalized_shape, numbers.Integral):
+            normalized_shape = (normalized_shape,)
+        self.normalized_shape = tuple(normalized_shape)
+        self.eps = eps
+        self.elementwise_affine = elementwise_affine
+        if self.elementwise_affine:
+            self.weight = nn.Parameter(
+                torch.empty(self.normalized_shape, **factory_kwargs)
+            )
+            self.bias = nn.Parameter(
+                torch.empty(self.normalized_shape, **factory_kwargs)
+            )
+        else:
+            self.register_parameter("weight", None)
+            self.register_parameter("bias", None)
+
+        self.reset_parameters()
+
+    def reset_parameters(self) -> None:
+        if self.elementwise_affine:
+            nn.init.ones_(self.weight)
+            nn.init.zeros_(self.bias)
+
+    def forward(self, input: Tensor, embedding: Any = None) -> Tensor:
+        if isinstance(input, tuple):
+            input, embedding = input
+            output = F.layer_norm(
+                input, self.normalized_shape, self.weight, self.bias, self.eps
+            )
+            return output, embedding
+
+        assert embedding is None
+        return F.layer_norm(
+            input, self.normalized_shape, self.weight, self.bias, self.eps
+        )
+
+    def extra_repr(self) -> str:
+        return (
+            "{normalized_shape}, eps={eps}, "
+            "elementwise_affine={elementwise_affine}".format(**self.__dict__)
+        )
+
+
+class AdaptiveLayerNorm(nn.Module):
+    r"""Adaptive Layer Normalization"""
+
+    def __init__(self, d_model, norm) -> None:
+        super(AdaptiveLayerNorm, self).__init__()
+        self.project_layer = nn.Linear(d_model, 2 * d_model)
+        self.norm = norm
+        self.d_model = d_model
+        self.eps = self.norm.eps
+
+    def forward(self, input: Tensor, embedding: Tensor = None) -> Tensor:
+        if isinstance(input, tuple):
+            input, embedding = input
+            weight, bias = torch.split(
+                self.project_layer(embedding),
+                split_size_or_sections=self.d_model,
+                dim=-1,
+            )
+            return (weight * self.norm(input) + bias, embedding)
+
+        weight, bias = torch.split(
+            self.project_layer(embedding),
+            split_size_or_sections=self.d_model,
+            dim=-1,
+        )
+        return weight * self.norm(input) + bias
+
+
+class BasicNorm(_BasicNorm):
+    def __init__(
+        self,
+        d_model: int,
+        eps: float = 1e-5,
+        device=None,
+        dtype=None,
+    ):
+        super(BasicNorm, self).__init__(d_model, eps=eps)
+
+    def forward(self, input: Tensor, embedding: Any = None) -> Tensor:
+        if isinstance(input, tuple):
+            input, embedding = input
+            return (
+                super(BasicNorm, self).forward(input),
+                embedding,
+            )
+
+        assert embedding is None
+        return super(BasicNorm, self).forward(input)
+
+
+class BalancedBasicNorm(nn.Module):
+    def __init__(
+        self,
+        d_model: int,
+        eps: float = 1e-5,
+        device=None,
+        dtype=None,
+    ):
+        super(BalancedBasicNorm, self).__init__()
+        self.balancer = ActivationBalancer(
+            d_model,
+            channel_dim=-1,
+            min_positive=0.45,
+            max_positive=0.55,
+            max_abs=6.0,
+        )
+        self.norm = BasicNorm(d_model, eps, device=device, dtype=dtype)
+
+    def forward(self, input: Tensor, embedding: Any = None) -> Tensor:
+        if isinstance(input, tuple):
+            input, embedding = input
+            return self.norm((self.balancer(input), embedding))
+
+        assert embedding is None
+        return self.norm(self.balancer(input))
+
+
+class IdentityNorm(nn.Module):
+    def __init__(
+        self,
+        d_model: int,
+        eps: float = 1e-5,
+        device=None,
+        dtype=None,
+    ) -> None:
+        super(IdentityNorm, self).__init__()
+
+    def forward(self, input: Tensor, embedding: Any = None) -> Tensor:
+        if isinstance(input, tuple):
+            return input
+
+        assert embedding is None
+        return input
diff --git a/modules/transformer/Constants.py b/modules/transformer/Constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..5bd586f3185114b7ab22f9a2479e93088d74ced0
--- /dev/null
+++ b/modules/transformer/Constants.py
@@ -0,0 +1,14 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+PAD = 0
+UNK = 1
+BOS = 2
+EOS = 3
+
+PAD_WORD = "<blank>"
+UNK_WORD = "<unk>"
+BOS_WORD = "<s>"
+EOS_WORD = "</s>"
diff --git a/modules/transformer/Layers.py b/modules/transformer/Layers.py
new file mode 100644
index 0000000000000000000000000000000000000000..915b86a7f434456f24643e28fd93813bd6e13d52
--- /dev/null
+++ b/modules/transformer/Layers.py
@@ -0,0 +1,137 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from .SubLayers import MultiHeadAttention, PositionwiseFeedForward
+
+
+class FFTBlock(torch.nn.Module):
+    """FFT Block"""
+
+    def __init__(self, d_model, n_head, d_k, d_v, d_inner, kernel_size, dropout=0.1):
+        super(FFTBlock, self).__init__()
+        self.slf_attn = MultiHeadAttention(n_head, d_model, d_k, d_v, dropout=dropout)
+        self.pos_ffn = PositionwiseFeedForward(
+            d_model, d_inner, kernel_size, dropout=dropout
+        )
+
+    def forward(self, enc_input, mask=None, slf_attn_mask=None):
+        enc_output, enc_slf_attn = self.slf_attn(
+            enc_input, enc_input, enc_input, mask=slf_attn_mask
+        )
+        enc_output = enc_output.masked_fill(mask.unsqueeze(-1), 0)
+
+        enc_output = self.pos_ffn(enc_output)
+        enc_output = enc_output.masked_fill(mask.unsqueeze(-1), 0)
+
+        return enc_output, enc_slf_attn
+
+
+class ConvNorm(torch.nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size=1,
+        stride=1,
+        padding=None,
+        dilation=1,
+        bias=True,
+        w_init_gain="linear",
+    ):
+        super(ConvNorm, self).__init__()
+
+        if padding is None:
+            assert kernel_size % 2 == 1
+            padding = int(dilation * (kernel_size - 1) / 2)
+
+        self.conv = torch.nn.Conv1d(
+            in_channels,
+            out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            bias=bias,
+        )
+
+    def forward(self, signal):
+        conv_signal = self.conv(signal)
+
+        return conv_signal
+
+
+class PostNet(nn.Module):
+    """
+    PostNet: Five 1-d convolution with 512 channels and kernel size 5
+    """
+
+    def __init__(
+        self,
+        n_mel_channels=80,
+        postnet_embedding_dim=512,
+        postnet_kernel_size=5,
+        postnet_n_convolutions=5,
+    ):
+        super(PostNet, self).__init__()
+        self.convolutions = nn.ModuleList()
+
+        self.convolutions.append(
+            nn.Sequential(
+                ConvNorm(
+                    n_mel_channels,
+                    postnet_embedding_dim,
+                    kernel_size=postnet_kernel_size,
+                    stride=1,
+                    padding=int((postnet_kernel_size - 1) / 2),
+                    dilation=1,
+                    w_init_gain="tanh",
+                ),
+                nn.BatchNorm1d(postnet_embedding_dim),
+            )
+        )
+
+        for i in range(1, postnet_n_convolutions - 1):
+            self.convolutions.append(
+                nn.Sequential(
+                    ConvNorm(
+                        postnet_embedding_dim,
+                        postnet_embedding_dim,
+                        kernel_size=postnet_kernel_size,
+                        stride=1,
+                        padding=int((postnet_kernel_size - 1) / 2),
+                        dilation=1,
+                        w_init_gain="tanh",
+                    ),
+                    nn.BatchNorm1d(postnet_embedding_dim),
+                )
+            )
+
+        self.convolutions.append(
+            nn.Sequential(
+                ConvNorm(
+                    postnet_embedding_dim,
+                    n_mel_channels,
+                    kernel_size=postnet_kernel_size,
+                    stride=1,
+                    padding=int((postnet_kernel_size - 1) / 2),
+                    dilation=1,
+                    w_init_gain="linear",
+                ),
+                nn.BatchNorm1d(n_mel_channels),
+            )
+        )
+
+    def forward(self, x):
+        x = x.contiguous().transpose(1, 2)
+
+        for i in range(len(self.convolutions) - 1):
+            x = F.dropout(torch.tanh(self.convolutions[i](x)), 0.5, self.training)
+        x = F.dropout(self.convolutions[-1](x), 0.5, self.training)
+
+        x = x.contiguous().transpose(1, 2)
+        return x
diff --git a/modules/transformer/Models.py b/modules/transformer/Models.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f2bf3796da5324dbdacd50297563d135e08af6c
--- /dev/null
+++ b/modules/transformer/Models.py
@@ -0,0 +1,181 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+import numpy as np
+
+from .Layers import FFTBlock
+from text.symbols import symbols
+
+PAD = 0
+UNK = 1
+BOS = 2
+EOS = 3
+
+PAD_WORD = "<blank>"
+UNK_WORD = "<unk>"
+BOS_WORD = "<s>"
+EOS_WORD = "</s>"
+
+
+def get_sinusoid_encoding_table(n_position, d_hid, padding_idx=None):
+    """Sinusoid position encoding table"""
+
+    def cal_angle(position, hid_idx):
+        return position / np.power(10000, 2 * (hid_idx // 2) / d_hid)
+
+    def get_posi_angle_vec(position):
+        return [cal_angle(position, hid_j) for hid_j in range(d_hid)]
+
+    sinusoid_table = np.array(
+        [get_posi_angle_vec(pos_i) for pos_i in range(n_position)]
+    )
+
+    sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2])  # dim 2i
+    sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2])  # dim 2i+1
+
+    if padding_idx is not None:
+        # zero vector for padding dimension
+        sinusoid_table[padding_idx] = 0.0
+
+    return torch.FloatTensor(sinusoid_table)
+
+
+class Encoder(nn.Module):
+    """Encoder"""
+
+    def __init__(self, config):
+        super(Encoder, self).__init__()
+
+        n_position = config["max_seq_len"] + 1
+        n_src_vocab = len(symbols) + 1
+        d_word_vec = config["transformer"]["encoder_hidden"]
+        n_layers = config["transformer"]["encoder_layer"]
+        n_head = config["transformer"]["encoder_head"]
+        d_k = d_v = (
+            config["transformer"]["encoder_hidden"]
+            // config["transformer"]["encoder_head"]
+        )
+        d_model = config["transformer"]["encoder_hidden"]
+        d_inner = config["transformer"]["conv_filter_size"]
+        kernel_size = config["transformer"]["conv_kernel_size"]
+        dropout = config["transformer"]["encoder_dropout"]
+
+        self.max_seq_len = config["max_seq_len"]
+        self.d_model = d_model
+
+        self.src_word_emb = nn.Embedding(n_src_vocab, d_word_vec, padding_idx=PAD)
+        self.position_enc = nn.Parameter(
+            get_sinusoid_encoding_table(n_position, d_word_vec).unsqueeze(0),
+            requires_grad=False,
+        )
+
+        self.layer_stack = nn.ModuleList(
+            [
+                FFTBlock(
+                    d_model, n_head, d_k, d_v, d_inner, kernel_size, dropout=dropout
+                )
+                for _ in range(n_layers)
+            ]
+        )
+
+    def forward(self, src_seq, mask, return_attns=False):
+        enc_slf_attn_list = []
+        batch_size, max_len = src_seq.shape[0], src_seq.shape[1]
+
+        # -- Prepare masks
+        slf_attn_mask = mask.unsqueeze(1).expand(-1, max_len, -1)
+
+        # -- Forward
+        if not self.training and src_seq.shape[1] > self.max_seq_len:
+            enc_output = self.src_word_emb(src_seq) + get_sinusoid_encoding_table(
+                src_seq.shape[1], self.d_model
+            )[: src_seq.shape[1], :].unsqueeze(0).expand(batch_size, -1, -1).to(
+                src_seq.device
+            )
+        else:
+            enc_output = self.src_word_emb(src_seq) + self.position_enc[
+                :, :max_len, :
+            ].expand(batch_size, -1, -1)
+
+        for enc_layer in self.layer_stack:
+            enc_output, enc_slf_attn = enc_layer(
+                enc_output, mask=mask, slf_attn_mask=slf_attn_mask
+            )
+            if return_attns:
+                enc_slf_attn_list += [enc_slf_attn]
+
+        return enc_output
+
+
+class Decoder(nn.Module):
+    """Decoder"""
+
+    def __init__(self, config):
+        super(Decoder, self).__init__()
+
+        n_position = config["max_seq_len"] + 1
+        d_word_vec = config["transformer"]["decoder_hidden"]
+        n_layers = config["transformer"]["decoder_layer"]
+        n_head = config["transformer"]["decoder_head"]
+        d_k = d_v = (
+            config["transformer"]["decoder_hidden"]
+            // config["transformer"]["decoder_head"]
+        )
+        d_model = config["transformer"]["decoder_hidden"]
+        d_inner = config["transformer"]["conv_filter_size"]
+        kernel_size = config["transformer"]["conv_kernel_size"]
+        dropout = config["transformer"]["decoder_dropout"]
+
+        self.max_seq_len = config["max_seq_len"]
+        self.d_model = d_model
+
+        self.position_enc = nn.Parameter(
+            get_sinusoid_encoding_table(n_position, d_word_vec).unsqueeze(0),
+            requires_grad=False,
+        )
+
+        self.layer_stack = nn.ModuleList(
+            [
+                FFTBlock(
+                    d_model, n_head, d_k, d_v, d_inner, kernel_size, dropout=dropout
+                )
+                for _ in range(n_layers)
+            ]
+        )
+
+    def forward(self, enc_seq, mask, return_attns=False):
+        dec_slf_attn_list = []
+        batch_size, max_len = enc_seq.shape[0], enc_seq.shape[1]
+
+        # -- Forward
+        if not self.training and enc_seq.shape[1] > self.max_seq_len:
+            # -- Prepare masks
+            slf_attn_mask = mask.unsqueeze(1).expand(-1, max_len, -1)
+            dec_output = enc_seq + get_sinusoid_encoding_table(
+                enc_seq.shape[1], self.d_model
+            )[: enc_seq.shape[1], :].unsqueeze(0).expand(batch_size, -1, -1).to(
+                enc_seq.device
+            )
+        else:
+            max_len = min(max_len, self.max_seq_len)
+
+            # -- Prepare masks
+            slf_attn_mask = mask.unsqueeze(1).expand(-1, max_len, -1)
+            dec_output = enc_seq[:, :max_len, :] + self.position_enc[
+                :, :max_len, :
+            ].expand(batch_size, -1, -1)
+            mask = mask[:, :max_len]
+            slf_attn_mask = slf_attn_mask[:, :, :max_len]
+
+        for dec_layer in self.layer_stack:
+            dec_output, dec_slf_attn = dec_layer(
+                dec_output, mask=mask, slf_attn_mask=slf_attn_mask
+            )
+            if return_attns:
+                dec_slf_attn_list += [dec_slf_attn]
+
+        return dec_output, mask
diff --git a/modules/transformer/Modules.py b/modules/transformer/Modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..339aedd0cd3ec603724204e98aced5372cd0a334
--- /dev/null
+++ b/modules/transformer/Modules.py
@@ -0,0 +1,29 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+import numpy as np
+
+
+class ScaledDotProductAttention(nn.Module):
+    """Scaled Dot-Product Attention"""
+
+    def __init__(self, temperature):
+        super().__init__()
+        self.temperature = temperature
+        self.softmax = nn.Softmax(dim=2)
+
+    def forward(self, q, k, v, mask=None):
+        attn = torch.bmm(q, k.transpose(1, 2))
+        attn = attn / self.temperature
+
+        if mask is not None:
+            attn = attn.masked_fill(mask, -np.inf)
+
+        attn = self.softmax(attn)
+        output = torch.bmm(attn, v)
+
+        return output, attn
diff --git a/modules/transformer/SubLayers.py b/modules/transformer/SubLayers.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1d1b88dfee5eaaec4cc9bfd2017d88847b18b60
--- /dev/null
+++ b/modules/transformer/SubLayers.py
@@ -0,0 +1,97 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+
+from .Modules import ScaledDotProductAttention
+
+
+class MultiHeadAttention(nn.Module):
+    """Multi-Head Attention module"""
+
+    def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):
+        super().__init__()
+
+        self.n_head = n_head
+        self.d_k = d_k
+        self.d_v = d_v
+
+        self.w_qs = nn.Linear(d_model, n_head * d_k)
+        self.w_ks = nn.Linear(d_model, n_head * d_k)
+        self.w_vs = nn.Linear(d_model, n_head * d_v)
+
+        self.attention = ScaledDotProductAttention(temperature=np.power(d_k, 0.5))
+        self.layer_norm = nn.LayerNorm(d_model)
+
+        self.fc = nn.Linear(n_head * d_v, d_model)
+
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, q, k, v, mask=None):
+        d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
+
+        sz_b, len_q, _ = q.size()
+        sz_b, len_k, _ = k.size()
+        sz_b, len_v, _ = v.size()
+
+        residual = q
+
+        q = self.w_qs(q).view(sz_b, len_q, n_head, d_k)
+        k = self.w_ks(k).view(sz_b, len_k, n_head, d_k)
+        v = self.w_vs(v).view(sz_b, len_v, n_head, d_v)
+        q = q.permute(2, 0, 1, 3).contiguous().view(-1, len_q, d_k)  # (n*b) x lq x dk
+        k = k.permute(2, 0, 1, 3).contiguous().view(-1, len_k, d_k)  # (n*b) x lk x dk
+        v = v.permute(2, 0, 1, 3).contiguous().view(-1, len_v, d_v)  # (n*b) x lv x dv
+
+        mask = mask.repeat(n_head, 1, 1)  # (n*b) x .. x ..
+        output, attn = self.attention(q, k, v, mask=mask)
+
+        output = output.view(n_head, sz_b, len_q, d_v)
+        output = (
+            output.permute(1, 2, 0, 3).contiguous().view(sz_b, len_q, -1)
+        )  # b x lq x (n*dv)
+
+        output = self.dropout(self.fc(output))
+        output = self.layer_norm(output + residual)
+
+        return output, attn
+
+
+class PositionwiseFeedForward(nn.Module):
+    """A two-feed-forward-layer module"""
+
+    def __init__(self, d_in, d_hid, kernel_size, dropout=0.1):
+        super().__init__()
+
+        # Use Conv1D
+        # position-wise
+        self.w_1 = nn.Conv1d(
+            d_in,
+            d_hid,
+            kernel_size=kernel_size[0],
+            padding=(kernel_size[0] - 1) // 2,
+        )
+        # position-wise
+        self.w_2 = nn.Conv1d(
+            d_hid,
+            d_in,
+            kernel_size=kernel_size[1],
+            padding=(kernel_size[1] - 1) // 2,
+        )
+
+        self.layer_norm = nn.LayerNorm(d_in)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        residual = x
+        output = x.transpose(1, 2)
+        output = self.w_2(F.relu(self.w_1(output)))
+        output = output.transpose(1, 2)
+        output = self.dropout(output)
+        output = self.layer_norm(output + residual)
+
+        return output
diff --git a/modules/transformer/__init__.py b/modules/transformer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b790531c24500929331a498856196b3254e14e4c
--- /dev/null
+++ b/modules/transformer/__init__.py
@@ -0,0 +1,2 @@
+from .mh_attention import MultiheadAttention
+from .position_embedding import SinePositionalEmbedding
diff --git a/modules/transformer/attentions.py b/modules/transformer/attentions.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b0cfc54ae038d4ca35ba32732dc69c776f4c4f0
--- /dev/null
+++ b/modules/transformer/attentions.py
@@ -0,0 +1,416 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from utils.util import *
+from modules.base.base_module import *
+from modules.base.base_module import LayerNorm
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size=1,
+        p_dropout=0.0,
+        window_size=4,
+        **kwargs,
+    ):
+        super().__init__()
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+
+        self.drop = nn.Dropout(p_dropout)
+        self.attn_layers = nn.ModuleList()
+        self.norm_layers_1 = nn.ModuleList()
+        self.ffn_layers = nn.ModuleList()
+        self.norm_layers_2 = nn.ModuleList()
+        for i in range(self.n_layers):
+            self.attn_layers.append(
+                MultiHeadAttention(
+                    hidden_channels,
+                    hidden_channels,
+                    n_heads,
+                    p_dropout=p_dropout,
+                    window_size=window_size,
+                )
+            )
+            self.norm_layers_1.append(LayerNorm(hidden_channels))
+            self.ffn_layers.append(
+                FFN(
+                    hidden_channels,
+                    hidden_channels,
+                    filter_channels,
+                    kernel_size,
+                    p_dropout=p_dropout,
+                )
+            )
+            self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+    def forward(self, x, x_mask):
+        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+        x = x * x_mask
+        for i in range(self.n_layers):
+            y = self.attn_layers[i](x, x, attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_1[i](x + y)
+
+            y = self.ffn_layers[i](x, x_mask)
+            y = self.drop(y)
+            x = self.norm_layers_2[i](x + y)
+        x = x * x_mask
+        return x
+
+
+class Decoder(nn.Module):
+    def __init__(
+        self,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size=1,
+        p_dropout=0.0,
+        proximal_bias=False,
+        proximal_init=True,
+        **kwargs,
+    ):
+        super().__init__()
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.proximal_bias = proximal_bias
+        self.proximal_init = proximal_init
+
+        self.drop = nn.Dropout(p_dropout)
+        self.self_attn_layers = nn.ModuleList()
+        self.norm_layers_0 = nn.ModuleList()
+        self.encdec_attn_layers = nn.ModuleList()
+        self.norm_layers_1 = nn.ModuleList()
+        self.ffn_layers = nn.ModuleList()
+        self.norm_layers_2 = nn.ModuleList()
+        for i in range(self.n_layers):
+            self.self_attn_layers.append(
+                MultiHeadAttention(
+                    hidden_channels,
+                    hidden_channels,
+                    n_heads,
+                    p_dropout=p_dropout,
+                    proximal_bias=proximal_bias,
+                    proximal_init=proximal_init,
+                )
+            )
+            self.norm_layers_0.append(LayerNorm(hidden_channels))
+            self.encdec_attn_layers.append(
+                MultiHeadAttention(
+                    hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
+                )
+            )
+            self.norm_layers_1.append(LayerNorm(hidden_channels))
+            self.ffn_layers.append(
+                FFN(
+                    hidden_channels,
+                    hidden_channels,
+                    filter_channels,
+                    kernel_size,
+                    p_dropout=p_dropout,
+                    causal=True,
+                )
+            )
+            self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+    def forward(self, x, x_mask, h, h_mask):
+        """
+        x: decoder input
+        h: encoder output
+        """
+        self_attn_mask = subsequent_mask(x_mask.size(2)).to(
+            device=x.device, dtype=x.dtype
+        )
+        encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+        x = x * x_mask
+        for i in range(self.n_layers):
+            y = self.self_attn_layers[i](x, x, self_attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_0[i](x + y)
+
+            y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_1[i](x + y)
+
+            y = self.ffn_layers[i](x, x_mask)
+            y = self.drop(y)
+            x = self.norm_layers_2[i](x + y)
+        x = x * x_mask
+        return x
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(
+        self,
+        channels,
+        out_channels,
+        n_heads,
+        p_dropout=0.0,
+        window_size=None,
+        heads_share=True,
+        block_length=None,
+        proximal_bias=False,
+        proximal_init=False,
+    ):
+        super().__init__()
+        assert channels % n_heads == 0
+
+        self.channels = channels
+        self.out_channels = out_channels
+        self.n_heads = n_heads
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+        self.heads_share = heads_share
+        self.block_length = block_length
+        self.proximal_bias = proximal_bias
+        self.proximal_init = proximal_init
+        self.attn = None
+
+        self.k_channels = channels // n_heads
+        self.conv_q = nn.Conv1d(channels, channels, 1)
+        self.conv_k = nn.Conv1d(channels, channels, 1)
+        self.conv_v = nn.Conv1d(channels, channels, 1)
+        self.conv_o = nn.Conv1d(channels, out_channels, 1)
+        self.drop = nn.Dropout(p_dropout)
+
+        if window_size is not None:
+            n_heads_rel = 1 if heads_share else n_heads
+            rel_stddev = self.k_channels**-0.5
+            self.emb_rel_k = nn.Parameter(
+                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
+                * rel_stddev
+            )
+            self.emb_rel_v = nn.Parameter(
+                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
+                * rel_stddev
+            )
+
+        nn.init.xavier_uniform_(self.conv_q.weight)
+        nn.init.xavier_uniform_(self.conv_k.weight)
+        nn.init.xavier_uniform_(self.conv_v.weight)
+        if proximal_init:
+            with torch.no_grad():
+                self.conv_k.weight.copy_(self.conv_q.weight)
+                self.conv_k.bias.copy_(self.conv_q.bias)
+
+    def forward(self, x, c, attn_mask=None):
+        q = self.conv_q(x)
+        k = self.conv_k(c)
+        v = self.conv_v(c)
+
+        x, self.attn = self.attention(q, k, v, mask=attn_mask)
+
+        x = self.conv_o(x)
+        return x
+
+    def attention(self, query, key, value, mask=None):
+        # reshape [b, d, t] -> [b, n_h, t, d_k]
+        b, d, t_s, t_t = (*key.size(), query.size(2))
+        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
+        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+
+        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
+        if self.window_size is not None:
+            assert (
+                t_s == t_t
+            ), "Relative attention is only available for self-attention."
+            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
+            rel_logits = self._matmul_with_relative_keys(
+                query / math.sqrt(self.k_channels), key_relative_embeddings
+            )
+            scores_local = self._relative_position_to_absolute_position(rel_logits)
+            scores = scores + scores_local
+        if self.proximal_bias:
+            assert t_s == t_t, "Proximal bias is only available for self-attention."
+            scores = scores + self._attention_bias_proximal(t_s).to(
+                device=scores.device, dtype=scores.dtype
+            )
+        if mask is not None:
+            scores = scores.masked_fill(mask == 0, -1e4)
+            if self.block_length is not None:
+                assert (
+                    t_s == t_t
+                ), "Local attention is only available for self-attention."
+                block_mask = (
+                    torch.ones_like(scores)
+                    .triu(-self.block_length)
+                    .tril(self.block_length)
+                )
+                scores = scores.masked_fill(block_mask == 0, -1e4)
+        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
+        p_attn = self.drop(p_attn)
+        output = torch.matmul(p_attn, value)
+        if self.window_size is not None:
+            relative_weights = self._absolute_position_to_relative_position(p_attn)
+            value_relative_embeddings = self._get_relative_embeddings(
+                self.emb_rel_v, t_s
+            )
+            output = output + self._matmul_with_relative_values(
+                relative_weights, value_relative_embeddings
+            )
+        output = (
+            output.transpose(2, 3).contiguous().view(b, d, t_t)
+        )  # [b, n_h, t_t, d_k] -> [b, d, t_t]
+        return output, p_attn
+
+    def _matmul_with_relative_values(self, x, y):
+        """
+        x: [b, h, l, m]
+        y: [h or 1, m, d]
+        ret: [b, h, l, d]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0))
+        return ret
+
+    def _matmul_with_relative_keys(self, x, y):
+        """
+        x: [b, h, l, d]
+        y: [h or 1, m, d]
+        ret: [b, h, l, m]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
+        return ret
+
+    def _get_relative_embeddings(self, relative_embeddings, length):
+        max_relative_position = 2 * self.window_size + 1
+        # Pad first before slice to avoid using cond ops.
+        pad_length = max(length - (self.window_size + 1), 0)
+        slice_start_position = max((self.window_size + 1) - length, 0)
+        slice_end_position = slice_start_position + 2 * length - 1
+        if pad_length > 0:
+            padded_relative_embeddings = F.pad(
+                relative_embeddings,
+                convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
+            )
+        else:
+            padded_relative_embeddings = relative_embeddings
+        used_relative_embeddings = padded_relative_embeddings[
+            :, slice_start_position:slice_end_position
+        ]
+        return used_relative_embeddings
+
+    def _relative_position_to_absolute_position(self, x):
+        """
+        x: [b, h, l, 2*l-1]
+        ret: [b, h, l, l]
+        """
+        batch, heads, length, _ = x.size()
+        # Concat columns of pad to shift from relative to absolute indexing.
+        x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
+
+        # Concat extra elements so to add up to shape (len+1, 2*len-1).
+        x_flat = x.view([batch, heads, length * 2 * length])
+        x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [0, length - 1]]))
+
+        # Reshape and slice out the padded elements.
+        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
+            :, :, :length, length - 1 :
+        ]
+        return x_final
+
+    def _absolute_position_to_relative_position(self, x):
+        """
+        x: [b, h, l, l]
+        ret: [b, h, l, 2*l-1]
+        """
+        batch, heads, length, _ = x.size()
+        # padd along column
+        x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]]))
+        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
+        # add 0's in the beginning that will skew the elements after reshape
+        x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
+        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
+        return x_final
+
+    def _attention_bias_proximal(self, length):
+        """Bias for self-attention to encourage attention to close positions.
+        Args:
+          length: an integer scalar.
+        Returns:
+          a Tensor with shape [1, 1, length, length]
+        """
+        r = torch.arange(length, dtype=torch.float32)
+        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
+        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
+
+
+class FFN(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        filter_channels,
+        kernel_size,
+        p_dropout=0.0,
+        activation=None,
+        causal=False,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.activation = activation
+        self.causal = causal
+
+        if causal:
+            self.padding = self._causal_padding
+        else:
+            self.padding = self._same_padding
+
+        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
+        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
+        self.drop = nn.Dropout(p_dropout)
+
+    def forward(self, x, x_mask):
+        x = self.conv_1(self.padding(x * x_mask))
+        if self.activation == "gelu":
+            x = x * torch.sigmoid(1.702 * x)
+        else:
+            x = torch.relu(x)
+        x = self.drop(x)
+        x = self.conv_2(self.padding(x * x_mask))
+        return x * x_mask
+
+    def _causal_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = self.kernel_size - 1
+        pad_r = 0
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, convert_pad_shape(padding))
+        return x
+
+    def _same_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = (self.kernel_size - 1) // 2
+        pad_r = self.kernel_size // 2
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, convert_pad_shape(padding))
+        return x
diff --git a/modules/transformer/mh_attention.py b/modules/transformer/mh_attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..55ced7b673f48723bcb20256c661ba73f88d5420
--- /dev/null
+++ b/modules/transformer/mh_attention.py
@@ -0,0 +1,416 @@
+from typing import Optional, Tuple
+
+import torch
+from torch import Tensor
+from torch.nn import Linear, Module
+from torch.nn import functional as F
+from torch.nn.init import constant_, xavier_normal_, xavier_uniform_
+from torch.nn.modules.linear import NonDynamicallyQuantizableLinear
+from torch.nn.parameter import Parameter
+
+
+class MultiheadAttention(Module):
+    r"""Allows the model to jointly attend to information
+    from different representation subspaces as described in the paper:
+    `Attention Is All You Need <https://arxiv.org/abs/1706.03762>`_.
+
+    Multi-Head Attention is defined as:
+
+    .. math::
+        \text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O
+
+    where :math:`head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)`.
+
+    ``forward()`` will use a special optimized implementation if all of the following
+    conditions are met:
+
+    - self attention is being computed (i.e., ``query``, ``key``, and ``value`` are the same tensor. This
+      restriction will be loosened in the future.)
+    - Either autograd is disabled (using ``torch.inference_mode`` or ``torch.no_grad``) or no tensor argument ``requires_grad``
+    - training is disabled (using ``.eval()``)
+    - dropout is 0
+    - ``add_bias_kv`` is ``False``
+    - ``add_zero_attn`` is ``False``
+    - ``batch_first`` is ``True`` and the input is batched
+    - ``kdim`` and ``vdim`` are equal to ``embed_dim``
+    - at most one of ``key_padding_mask`` or ``attn_mask`` is passed
+    - if a `NestedTensor <https://pytorch.org/docs/stable/nested.html>`_ is passed, neither ``key_padding_mask``
+      nor ``attn_mask`` is passed
+
+    If the optimized implementation is in use, a
+    `NestedTensor <https://pytorch.org/docs/stable/nested.html>`_ can be passed for
+    ``query``/``key``/``value`` to represent padding more efficiently than using a
+    padding mask. In this case, a `NestedTensor <https://pytorch.org/docs/stable/nested.html>`_
+    will be returned, and an additional speedup proportional to the fraction of the input
+    that is padding can be expected.
+
+    Args:
+        embed_dim: Total dimension of the model.
+        num_heads: Number of parallel attention heads. Note that ``embed_dim`` will be split
+            across ``num_heads`` (i.e. each head will have dimension ``embed_dim // num_heads``).
+        dropout: Dropout probability on ``attn_output_weights``. Default: ``0.0`` (no dropout).
+        bias: If specified, adds bias to input / output projection layers. Default: ``True``.
+        add_bias_kv: If specified, adds bias to the key and value sequences at dim=0. Default: ``False``.
+        add_zero_attn: If specified, adds a new batch of zeros to the key and value sequences at dim=1.
+            Default: ``False``.
+        kdim: Total number of features for keys. Default: ``None`` (uses ``kdim=embed_dim``).
+        vdim: Total number of features for values. Default: ``None`` (uses ``vdim=embed_dim``).
+        batch_first: If ``True``, then the input and output tensors are provided
+            as (batch, seq, feature). Default: ``False`` (seq, batch, feature).
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> multihead_attn = nn.MultiheadAttention(embed_dim, num_heads)
+        >>> attn_output, attn_output_weights = multihead_attn(query, key, value)
+
+    """
+    __constants__ = ["batch_first"]
+    bias_k: Optional[torch.Tensor]
+    bias_v: Optional[torch.Tensor]
+
+    def __init__(
+        self,
+        embed_dim,
+        num_heads,
+        dropout=0.0,
+        bias=True,
+        add_bias_kv=False,
+        add_zero_attn=False,
+        kdim=None,
+        vdim=None,
+        batch_first=False,
+        linear1_cls=Linear,
+        linear2_cls=Linear,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super(MultiheadAttention, self).__init__()
+        self.embed_dim = embed_dim
+        self.kdim = kdim if kdim is not None else embed_dim
+        self.vdim = vdim if vdim is not None else embed_dim
+        self._qkv_same_embed_dim = self.kdim == embed_dim and self.vdim == embed_dim
+
+        self.num_heads = num_heads
+        self.dropout = dropout
+        self.batch_first = batch_first
+        self.head_dim = embed_dim // num_heads
+        assert (
+            self.head_dim * num_heads == self.embed_dim
+        ), "embed_dim must be divisible by num_heads"
+
+        if add_bias_kv:
+            self.bias_k = Parameter(torch.empty((1, 1, embed_dim), **factory_kwargs))
+            self.bias_v = Parameter(torch.empty((1, 1, embed_dim), **factory_kwargs))
+        else:
+            self.bias_k = self.bias_v = None
+
+        if linear1_cls == Linear:
+            if not self._qkv_same_embed_dim:
+                self.q_proj_weight = Parameter(
+                    torch.empty((embed_dim, embed_dim), **factory_kwargs)
+                )
+                self.k_proj_weight = Parameter(
+                    torch.empty((embed_dim, self.kdim), **factory_kwargs)
+                )
+                self.v_proj_weight = Parameter(
+                    torch.empty((embed_dim, self.vdim), **factory_kwargs)
+                )
+                self.register_parameter("in_proj_weight", None)
+            else:
+                self.in_proj_weight = Parameter(
+                    torch.empty((3 * embed_dim, embed_dim), **factory_kwargs)
+                )
+                self.register_parameter("q_proj_weight", None)
+                self.register_parameter("k_proj_weight", None)
+                self.register_parameter("v_proj_weight", None)
+
+            if bias:
+                self.in_proj_bias = Parameter(
+                    torch.empty(3 * embed_dim, **factory_kwargs)
+                )
+            else:
+                self.register_parameter("in_proj_bias", None)
+            self.out_proj = NonDynamicallyQuantizableLinear(
+                embed_dim, embed_dim, bias=bias, **factory_kwargs
+            )
+
+            self._reset_parameters()
+        else:
+            if not self._qkv_same_embed_dim:
+                raise NotImplementedError
+            else:
+                self.in_proj_linear = linear1_cls(
+                    embed_dim, 3 * embed_dim, bias=bias, **factory_kwargs
+                )
+                self.in_proj_weight = self.in_proj_linear.weight
+
+                self.register_parameter("q_proj_weight", None)
+                self.register_parameter("k_proj_weight", None)
+                self.register_parameter("v_proj_weight", None)
+
+                if bias:
+                    self.in_proj_bias = self.in_proj_linear.bias
+                else:
+                    self.register_parameter("in_proj_bias", None)
+
+            self.out_proj = linear2_cls(
+                embed_dim, embed_dim, bias=bias, **factory_kwargs
+            )
+
+            if self.bias_k is not None:
+                xavier_normal_(self.bias_k)
+            if self.bias_v is not None:
+                xavier_normal_(self.bias_v)
+
+        self.add_zero_attn = add_zero_attn
+
+    def _reset_parameters(self):
+        if self._qkv_same_embed_dim:
+            xavier_uniform_(self.in_proj_weight)
+        else:
+            xavier_uniform_(self.q_proj_weight)
+            xavier_uniform_(self.k_proj_weight)
+            xavier_uniform_(self.v_proj_weight)
+
+        if self.in_proj_bias is not None:
+            constant_(self.in_proj_bias, 0.0)
+            constant_(self.out_proj.bias, 0.0)
+
+        if self.bias_k is not None:
+            xavier_normal_(self.bias_k)
+        if self.bias_v is not None:
+            xavier_normal_(self.bias_v)
+
+    def __setstate__(self, state):
+        if "_qkv_same_embed_dim" not in state:
+            state["_qkv_same_embed_dim"] = True
+
+        super(MultiheadAttention, self).__setstate__(state)
+
+    def forward(
+        self,
+        query: Tensor,
+        key: Tensor,
+        value: Tensor,
+        key_padding_mask: Optional[Tensor] = None,
+        need_weights: bool = True,
+        attn_mask: Optional[Tensor] = None,
+        average_attn_weights: bool = True,
+    ) -> Tuple[Tensor, Optional[Tensor]]:
+        r"""
+        Args:
+            query: Query embeddings of shape :math:`(L, E_q)` for unbatched input, :math:`(L, N, E_q)` when ``batch_first=False``
+                or :math:`(N, L, E_q)` when ``batch_first=True``, where :math:`L` is the target sequence length,
+                :math:`N` is the batch size, and :math:`E_q` is the query embedding dimension ``embed_dim``.
+                Queries are compared against key-value pairs to produce the output.
+                See "Attention Is All You Need" for more details.
+            key: Key embeddings of shape :math:`(S, E_k)` for unbatched input, :math:`(S, N, E_k)` when ``batch_first=False``
+                or :math:`(N, S, E_k)` when ``batch_first=True``, where :math:`S` is the source sequence length,
+                :math:`N` is the batch size, and :math:`E_k` is the key embedding dimension ``kdim``.
+                See "Attention Is All You Need" for more details.
+            value: Value embeddings of shape :math:`(S, E_v)` for unbatched input, :math:`(S, N, E_v)` when
+                ``batch_first=False`` or :math:`(N, S, E_v)` when ``batch_first=True``, where :math:`S` is the source
+                sequence length, :math:`N` is the batch size, and :math:`E_v` is the value embedding dimension ``vdim``.
+                See "Attention Is All You Need" for more details.
+            key_padding_mask: If specified, a mask of shape :math:`(N, S)` indicating which elements within ``key``
+                to ignore for the purpose of attention (i.e. treat as "padding"). For unbatched `query`, shape should be :math:`(S)`.
+                Binary and byte masks are supported.
+                For a binary mask, a ``True`` value indicates that the corresponding ``key`` value will be ignored for
+                the purpose of attention. For a float mask, it will be directly added to the corresponding ``key`` value.
+            need_weights: If specified, returns ``attn_output_weights`` in addition to ``attn_outputs``.
+                Default: ``True``.
+            attn_mask: If specified, a 2D or 3D mask preventing attention to certain positions. Must be of shape
+                :math:`(L, S)` or :math:`(N\cdot\text{num\_heads}, L, S)`, where :math:`N` is the batch size,
+                :math:`L` is the target sequence length, and :math:`S` is the source sequence length. A 2D mask will be
+                broadcasted across the batch while a 3D mask allows for a different mask for each entry in the batch.
+                Binary, byte, and float masks are supported. For a binary mask, a ``True`` value indicates that the
+                corresponding position is not allowed to attend. For a byte mask, a non-zero value indicates that the
+                corresponding position is not allowed to attend. For a float mask, the mask values will be added to
+                the attention weight.
+            average_attn_weights: If true, indicates that the returned ``attn_weights`` should be averaged across
+                heads. Otherwise, ``attn_weights`` are provided separately per head. Note that this flag only has an
+                effect when ``need_weights=True``. Default: ``True`` (i.e. average weights across heads)
+
+        Outputs:
+            - **attn_output** - Attention outputs of shape :math:`(L, E)` when input is unbatched,
+              :math:`(L, N, E)` when ``batch_first=False`` or :math:`(N, L, E)` when ``batch_first=True``,
+              where :math:`L` is the target sequence length, :math:`N` is the batch size, and :math:`E` is the
+              embedding dimension ``embed_dim``.
+            - **attn_output_weights** - Only returned when ``need_weights=True``. If ``average_attn_weights=True``,
+              returns attention weights averaged across heads of shape :math:`(L, S)` when input is unbatched or
+              :math:`(N, L, S)`, where :math:`N` is the batch size, :math:`L` is the target sequence length, and
+              :math:`S` is the source sequence length. If ``average_attn_weights=False``, returns attention weights per
+              head of shape :math:`(\text{num\_heads}, L, S)` when input is unbatched or :math:`(N, \text{num\_heads}, L, S)`.
+
+            .. note::
+                `batch_first` argument is ignored for unbatched inputs.
+        """
+        is_batched = query.dim() == 3
+        if key_padding_mask is not None:
+            _kpm_dtype = key_padding_mask.dtype
+            if _kpm_dtype != torch.bool and not torch.is_floating_point(
+                key_padding_mask
+            ):
+                raise AssertionError(
+                    "only bool and floating types of key_padding_mask are supported"
+                )
+        why_not_fast_path = ""
+        if not is_batched:
+            why_not_fast_path = (
+                f"input not batched; expected query.dim() of 3 but got {query.dim()}"
+            )
+        elif query is not key or key is not value:
+            # When lifting this restriction, don't forget to either
+            # enforce that the dtypes all match or test cases where
+            # they don't!
+            why_not_fast_path = "non-self attention was used (query, key, and value are not the same Tensor)"
+        elif self.in_proj_bias is not None and query.dtype != self.in_proj_bias.dtype:
+            why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_bias ({self.in_proj_bias.dtype}) don't match"
+        elif (
+            self.in_proj_weight is not None and query.dtype != self.in_proj_weight.dtype
+        ):
+            # this case will fail anyway, but at least they'll get a useful error message.
+            why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_weight ({self.in_proj_weight.dtype}) don't match"
+        elif self.training:
+            why_not_fast_path = "training is enabled"
+        elif not self.batch_first:
+            why_not_fast_path = "batch_first was not True"
+        elif self.bias_k is not None:
+            why_not_fast_path = "self.bias_k was not None"
+        elif self.bias_v is not None:
+            why_not_fast_path = "self.bias_v was not None"
+        elif self.dropout:
+            why_not_fast_path = f"dropout was {self.dropout}, required zero"
+        elif self.add_zero_attn:
+            why_not_fast_path = "add_zero_attn was enabled"
+        elif not self._qkv_same_embed_dim:
+            why_not_fast_path = "_qkv_same_embed_dim was not True"
+        elif attn_mask is not None:
+            why_not_fast_path = "attn_mask was not None"
+        elif query.is_nested and key_padding_mask is not None:
+            why_not_fast_path = (
+                "key_padding_mask is not supported with NestedTensor input"
+            )
+        elif self.num_heads % 2 == 1:
+            why_not_fast_path = "num_heads is odd"
+        elif torch.is_autocast_enabled():
+            why_not_fast_path = "autocast is enabled"
+
+        if not why_not_fast_path:
+            tensor_args = (
+                query,
+                key,
+                value,
+                self.in_proj_weight,
+                self.in_proj_bias,
+                self.out_proj.weight,
+                self.out_proj.bias,
+            )
+            # We have to use list comprehensions below because TorchScript does not support
+            # generator expressions.
+            if torch.overrides.has_torch_function(tensor_args):
+                why_not_fast_path = "some Tensor argument has_torch_function"
+            elif not all(
+                [
+                    (x is None or x.is_cuda or "cpu" in str(x.device))
+                    for x in tensor_args
+                ]
+            ):
+                why_not_fast_path = "some Tensor argument is neither CUDA nor CPU"
+            elif torch.is_grad_enabled() and any(
+                [x is not None and x.requires_grad for x in tensor_args]
+            ):
+                why_not_fast_path = (
+                    "grad is enabled and at least one of query or the "
+                    "input/output projection weights or biases requires_grad"
+                )
+            if not why_not_fast_path:
+                return torch._native_multi_head_attention(
+                    query,
+                    key,
+                    value,
+                    self.embed_dim,
+                    self.num_heads,
+                    self.in_proj_weight,
+                    self.in_proj_bias,
+                    self.out_proj.weight,
+                    self.out_proj.bias,
+                    key_padding_mask if key_padding_mask is not None else attn_mask,
+                    need_weights,
+                    average_attn_weights,
+                    1
+                    if key_padding_mask is not None
+                    else 0
+                    if attn_mask is not None
+                    else None,
+                )
+
+        any_nested = query.is_nested or key.is_nested or value.is_nested
+        assert not any_nested, (
+            "MultiheadAttention does not support NestedTensor outside of its fast path. "
+            + f"The fast path was not hit because {why_not_fast_path}"
+        )
+
+        if self.batch_first and is_batched:
+            # make sure that the transpose op does not affect the "is" property
+            if key is value:
+                if query is key:
+                    query = key = value = query.transpose(1, 0)
+                else:
+                    query, key = [x.transpose(1, 0) for x in (query, key)]
+                    value = key
+            else:
+                query, key, value = [x.transpose(1, 0) for x in (query, key, value)]
+
+        if not self._qkv_same_embed_dim:
+            attn_output, attn_output_weights = F.multi_head_attention_forward(
+                query,
+                key,
+                value,
+                self.embed_dim,
+                self.num_heads,
+                self.in_proj_weight,
+                self.in_proj_bias,
+                self.bias_k,
+                self.bias_v,
+                self.add_zero_attn,
+                self.dropout,
+                self.out_proj.weight,
+                self.out_proj.bias,
+                training=self.training,
+                key_padding_mask=key_padding_mask,
+                need_weights=need_weights,
+                attn_mask=attn_mask,
+                use_separate_proj_weight=True,
+                q_proj_weight=self.q_proj_weight,
+                k_proj_weight=self.k_proj_weight,
+                v_proj_weight=self.v_proj_weight,
+                average_attn_weights=average_attn_weights,
+            )
+        else:
+            attn_output, attn_output_weights = F.multi_head_attention_forward(
+                query,
+                key,
+                value,
+                self.embed_dim,
+                self.num_heads,
+                self.in_proj_weight,
+                self.in_proj_bias,
+                self.bias_k,
+                self.bias_v,
+                self.add_zero_attn,
+                self.dropout,
+                self.out_proj.weight,
+                self.out_proj.bias,
+                training=self.training,
+                key_padding_mask=key_padding_mask,
+                need_weights=need_weights,
+                attn_mask=attn_mask,
+                average_attn_weights=average_attn_weights,
+            )
+        if self.batch_first and is_batched:
+            return attn_output.transpose(1, 0), attn_output_weights
+        else:
+            return attn_output, attn_output_weights
diff --git a/modules/transformer/position_embedding.py b/modules/transformer/position_embedding.py
new file mode 100644
index 0000000000000000000000000000000000000000..aca97ff35fccb0ab84a7f5556c62be2337dda6c0
--- /dev/null
+++ b/modules/transformer/position_embedding.py
@@ -0,0 +1,108 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+import math
+
+
+class SinePositionalEmbedding(nn.Module):
+    def __init__(
+        self,
+        dim_model: int,
+        dropout: float = 0.0,
+        scale: bool = False,
+        alpha: bool = False,
+    ):
+        super().__init__()
+        self.dim_model = dim_model
+        self.x_scale = math.sqrt(dim_model) if scale else 1.0
+        self.alpha = nn.Parameter(torch.ones(1), requires_grad=alpha)
+        self.dropout = torch.nn.Dropout(p=dropout)
+
+        self.reverse = False
+        self.pe = None
+        self.extend_pe(torch.tensor(0.0).expand(1, 4000))
+
+    def extend_pe(self, x):
+        """Reset the positional encodings."""
+        if self.pe is not None:
+            if self.pe.size(1) >= x.size(1):
+                if self.pe.dtype != x.dtype or self.pe.device != x.device:
+                    self.pe = self.pe.to(dtype=x.dtype, device=x.device)
+                return
+        pe = torch.zeros(x.size(1), self.dim_model)
+        if self.reverse:
+            position = torch.arange(
+                x.size(1) - 1, -1, -1.0, dtype=torch.float32
+            ).unsqueeze(1)
+        else:
+            position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
+        div_term = torch.exp(
+            torch.arange(0, self.dim_model, 2, dtype=torch.float32)
+            * -(math.log(10000.0) / self.dim_model)
+        )
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        pe = pe.unsqueeze(0)
+        self.pe = pe.to(device=x.device, dtype=x.dtype).detach()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        self.extend_pe(x)
+        output = x.unsqueeze(-1) if x.ndim == 2 else x
+        output = output * self.x_scale + self.alpha * self.pe[:, : x.size(1)]
+        return self.dropout(output)
+
+
+# import torch
+# import torch.nn as nn
+# import math
+
+# class SinePositionalEmbedding(nn.Module):
+#     def __init__(
+#         self,
+#         dim_model: int,
+#         dropout: float = 0.0,
+#         scale: bool = False,
+#         alpha: bool = False,
+#     ):
+#         super().__init__()
+#         self.dim_model = dim_model
+#         self.x_scale = math.sqrt(dim_model) if scale else 1.0
+#         self.alpha = nn.Parameter(torch.ones(1), requires_grad=alpha)
+#         self.dropout = torch.nn.Dropout(p=dropout)
+
+#         self.reverse = False
+#         self.pe = None
+#         self.extend_pe(torch.zeros(1, 4000))
+
+#     def extend_pe(self, x):
+#         """Reset the positional encodings."""
+#         if self._pe_needs_extension(x):
+#             self.pe = self._generate_positional_encodings(x)
+
+#     def _pe_needs_extension(self, x):
+#         return self.pe is None or self.pe.size(1) < x.size(1) or self.pe.dtype != x.dtype or self.pe.device != x.device
+
+#     def _generate_positional_encodings(self, x):
+#         pe = torch.zeros(x.size(1), self.dim_model)
+#         position = self._get_position_tensor(x)
+#         div_term = self._get_div_term()
+#         pe[:, 0::2] = torch.sin(position * div_term)
+#         pe[:, 1::2] = torch.cos(position * div_term)
+#         return pe.unsqueeze(0).to(device=x.device, dtype=x.dtype).detach()
+
+#     def _get_position_tensor(self, x):
+#         position = torch.arange(x.size(1), dtype=torch.float32).unsqueeze(1)
+#         return position.flip(0) if self.reverse else position
+
+#     def _get_div_term(self):
+#         return torch.exp(torch.arange(0, self.dim_model, 2, dtype=torch.float32) * -(math.log(10000.0) / self.dim_model))
+
+#     def forward(self, x: torch.Tensor) -> torch.Tensor:
+#         self.extend_pe(x)
+#         output = x.unsqueeze(-1) if x.ndim == 2 else x
+#         output = output * self.x_scale + self.alpha * self.pe[:, : x.size(1)]
+#         return self.dropout(output)
diff --git a/modules/transformer/transformer.py b/modules/transformer/transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..e64e705482b56b7f69eef1d39bcac47359ffbe7f
--- /dev/null
+++ b/modules/transformer/transformer.py
@@ -0,0 +1,411 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import copy
+from functools import partial
+from typing import Any, Callable, List, Optional, Union
+
+import torch
+from torch import Tensor, nn
+from torch.nn import functional as F
+
+from modules.norms import AdaptiveLayerNorm, LayerNorm, BalancedBasicNorm, IdentityNorm
+from modules.transformer import MultiheadAttention
+from modules.general.scaling import BalancedDoubleSwish
+
+
+class TransformerEncoderLayer(nn.Module):
+    __constants__ = ["batch_first", "norm_first"]
+
+    def __init__(
+        self,
+        d_model: int,
+        nhead: int,
+        dim_feedforward: int = 2048,
+        dropout: float = 0.1,
+        activation: Union[str, Callable[[Tensor], Tensor]] = F.relu,
+        batch_first: bool = False,
+        norm_first: bool = False,
+        device=None,
+        dtype=None,
+        linear1_self_attention_cls: nn.Module = nn.Linear,
+        linear2_self_attention_cls: nn.Module = nn.Linear,
+        linear1_feedforward_cls: nn.Module = nn.Linear,
+        linear2_feedforward_cls: nn.Module = nn.Linear,
+        layer_norm_cls: nn.Module = LayerNorm,
+        layer_norm_eps: float = 1e-5,
+        adaptive_layer_norm=False,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super(TransformerEncoderLayer, self).__init__()
+        self.self_attn = MultiheadAttention(
+            d_model,
+            nhead,
+            dropout=dropout,
+            batch_first=batch_first,
+            linear1_cls=linear1_self_attention_cls,
+            linear2_cls=linear2_self_attention_cls,
+            **factory_kwargs,
+        )
+
+        # Implementation of Feedforward model
+        self.linear1 = linear1_feedforward_cls(
+            d_model, dim_feedforward, **factory_kwargs
+        )
+        self.dropout = nn.Dropout(dropout)
+        self.linear2 = linear2_feedforward_cls(
+            dim_feedforward, d_model, **factory_kwargs
+        )
+
+        self.norm_first = norm_first
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(dropout)
+
+        if isinstance(activation, str):
+            activation = _get_activation_fn(activation)
+        elif isinstance(activation, partial):
+            activation = activation(d_model)
+        elif activation == BalancedDoubleSwish:
+            activation = BalancedDoubleSwish(d_model)
+
+        self.activation = activation
+
+        norm1 = layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs)
+        if layer_norm_cls == IdentityNorm:
+            norm2 = BalancedBasicNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
+        else:
+            norm2 = layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs)
+
+        if adaptive_layer_norm:
+            self.norm1 = AdaptiveLayerNorm(d_model, norm1)
+            self.norm2 = AdaptiveLayerNorm(d_model, norm2)
+        else:
+            self.norm1 = norm1
+            self.norm2 = norm2
+
+    def __setstate__(self, state):
+        super(TransformerEncoderLayer, self).__setstate__(state)
+        if not hasattr(self, "activation"):
+            self.activation = F.relu
+
+    def forward(
+        self,
+        src: Tensor,
+        src_mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        r"""Pass the input through the encoder layer.
+
+        Args:
+            src: the sequence to the encoder layer (required).
+            src_mask: the mask for the src sequence (optional).
+            src_key_padding_mask: the mask for the src keys per batch (optional).
+
+        Shape:
+            see the docs in Transformer class.
+        """
+        x, stage_embedding = src, None
+        is_src_tuple = False
+        if isinstance(src, tuple):
+            x, stage_embedding = src
+            is_src_tuple = True
+
+        if src_key_padding_mask is not None:
+            _skpm_dtype = src_key_padding_mask.dtype
+            if _skpm_dtype != torch.bool and not torch.is_floating_point(
+                src_key_padding_mask
+            ):
+                raise AssertionError(
+                    "only bool and floating types of key_padding_mask are supported"
+                )
+
+        if self.norm_first:
+            x = x + self._sa_block(
+                self.norm1(x, stage_embedding),
+                src_mask,
+                src_key_padding_mask,
+            )
+            x = x + self._ff_block(self.norm2(x, stage_embedding))
+        else:
+            x = self.norm1(
+                x + self._sa_block(x, src_mask, src_key_padding_mask),
+                stage_embedding,
+            )
+            x = self.norm2(x + self._ff_block(x), stage_embedding)
+
+        if is_src_tuple:
+            return (x, stage_embedding)
+        return x
+
+    def _sa_block(
+        self,
+        x: Tensor,
+        attn_mask: Optional[Tensor],
+        key_padding_mask: Optional[Tensor],
+    ) -> Tensor:
+        x = self.self_attn(
+            x,
+            x,
+            x,
+            attn_mask=attn_mask,
+            key_padding_mask=key_padding_mask,
+            need_weights=False,
+        )[0]
+        return self.dropout1(x)
+
+    def _ff_block(self, x: Tensor) -> Tensor:
+        x = self.linear2(self.dropout(self.activation(self.linear1(x))))
+        return self.dropout2(x)
+
+
+class TransformerEncoder(nn.Module):
+    """TransformerEncoder is a stack of N encoder layers."""
+
+    def __init__(self, encoder_layer, num_layers, norm=None):
+        super(TransformerEncoder, self).__init__()
+        self.layers = _get_clones(encoder_layer, num_layers)
+        self.num_layers = num_layers
+        self.norm = norm
+
+    def forward(
+        self,
+        src: Tensor,
+        mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+        return_layer_states: bool = False,
+    ) -> Tensor:
+        # Pass the input through the encoder layers
+        output = src
+        layer_states = [] if return_layer_states else None
+
+        for mod in self.layers:
+            output = self._apply_module(
+                mod, output, mask, src_key_padding_mask, layer_states
+            )
+
+        if self.norm is not None:
+            output = self.norm(output)
+
+        return (layer_states, output) if return_layer_states else output
+
+    def _apply_module(self, module, output, mask, key_padding_mask, layer_states):
+        # Apply a single transformer module
+        output = module(output, src_mask=mask, src_key_padding_mask=key_padding_mask)
+        if layer_states is not None:
+            layer_states.append(output)
+        return output
+
+
+class TransformerDecoderLayer(nn.Module):
+    __constants__ = ["batch_first", "norm_first"]
+
+    def __init__(
+        self,
+        d_model: int,
+        nhead: int,
+        dim_feedforward: int = 2048,
+        dropout: float = 0.1,
+        activation: Union[str, Callable[[Tensor], Tensor]] = F.relu,
+        linear1_self_attention_cls: nn.Module = nn.Linear,
+        linear2_self_attention_cls: nn.Module = nn.Linear,
+        linear1_feedforward_cls: nn.Module = nn.Linear,
+        linear2_feedforward_cls: nn.Module = nn.Linear,
+        batch_first: bool = False,
+        norm_first: bool = False,
+        device=None,
+        dtype=None,
+        layer_norm_cls: nn.Module = LayerNorm,
+        layer_norm_eps: float = 1e-5,
+        adaptive_layer_norm=False,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super(TransformerDecoderLayer, self).__init__()
+        self.self_attn = MultiheadAttention(
+            d_model,
+            nhead,
+            dropout=dropout,
+            batch_first=batch_first,
+            linear1_cls=linear1_self_attention_cls,
+            linear2_cls=linear2_self_attention_cls,
+            **factory_kwargs,
+        )
+        self.multihead_attn = MultiheadAttention(
+            d_model,
+            nhead,
+            dropout=dropout,
+            batch_first=batch_first,
+            linear1_cls=linear1_self_attention_cls,
+            linear2_cls=linear2_self_attention_cls,
+            **factory_kwargs,
+        )
+        self.linear1 = linear1_feedforward_cls(
+            d_model, dim_feedforward, **factory_kwargs
+        )
+        self.dropout = nn.Dropout(dropout)
+        self.linear2 = linear2_feedforward_cls(
+            dim_feedforward, d_model, **factory_kwargs
+        )
+
+        self.norm_first = norm_first
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(dropout)
+        self.dropout3 = nn.Dropout(dropout)
+
+        self.activation = self._get_activation_fn(activation)
+        self.norm1, self.norm2, self.norm3 = self._init_norm_layers(
+            d_model, layer_norm_cls, layer_norm_eps, adaptive_layer_norm, factory_kwargs
+        )
+
+    def forward(
+        self,
+        tgt: Tensor,
+        memory: Tensor,
+        tgt_mask: Optional[Tensor] = None,
+        memory_mask: Optional[Tensor] = None,
+        tgt_key_padding_mask: Optional[Tensor] = None,
+        memory_key_padding_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        r"""Pass the inputs (and mask) through the decoder layer.
+
+        Args:
+            tgt: the sequence to the decoder layer (required).
+            memory: the sequence from the last layer of the encoder (required).
+            tgt_mask: the mask for the tgt sequence (optional).
+            memory_mask: the mask for the memory sequence (optional).
+            tgt_key_padding_mask: the mask for the tgt keys per batch (optional).
+            memory_key_padding_mask: the mask for the memory keys per batch (optional).
+
+        Shape:
+            see the docs in Transformer class.
+        """
+        tgt_is_tuple = False
+        if isinstance(tgt, tuple):
+            x, stage_embedding = tgt
+            tgt_is_tuple = True
+        else:
+            x, stage_embedding = tgt, None
+
+        if self.norm_first:
+            x = x + self._sa_block(
+                self.norm1(x, stage_embedding), tgt_mask, tgt_key_padding_mask
+            )
+            x = x + self._mha_block(
+                self.norm2(x, stage_embedding),
+                memory,
+                memory_mask,
+                memory_key_padding_mask,
+            )
+            x = x + self._ff_block(self.norm3(x, stage_embedding))
+        else:
+            x = self.norm1(
+                x + self._sa_block(x, tgt_mask, tgt_key_padding_mask),
+                stage_embedding,
+            )
+            x = self.norm2(
+                x + self._mha_block(x, memory, memory_mask, memory_key_padding_mask),
+                stage_embedding,
+            )
+            x = self.norm3(x + self._ff_block(x), stage_embedding)
+
+        if tgt_is_tuple:
+            return (x, stage_embedding)
+        return x
+
+    def _sa_block(
+        self,
+        x: Tensor,
+        attn_mask: Optional[Tensor],
+        key_padding_mask: Optional[Tensor],
+    ) -> Tensor:
+        x = self.self_attn(
+            x,
+            x,
+            x,
+            attn_mask=attn_mask,
+            key_padding_mask=key_padding_mask,
+            need_weights=False,
+        )[0]
+        return self.dropout1(x)
+
+    def _mha_block(
+        self,
+        x: Tensor,
+        mem: Tensor,
+        attn_mask: Optional[Tensor],
+        key_padding_mask: Optional[Tensor],
+    ) -> Tensor:
+        x = self.multihead_attn(
+            x,
+            mem,
+            mem,
+            attn_mask=attn_mask,
+            key_padding_mask=key_padding_mask,
+            need_weights=False,
+        )[0]
+        return self.dropout2(x)
+
+    def _ff_block(self, x: Tensor) -> Tensor:
+        x = self.linear2(self.dropout(self.activation(self.linear1(x))))
+        return self.dropout3(x)
+
+    def _get_activation_fn(self, activation):
+        if isinstance(activation, str):
+            return _get_activation_fn(activation)
+        elif callable(activation):
+            return activation
+        else:
+            raise ValueError("Unsupported activation type")
+
+    def _init_norm_layers(
+        self,
+        d_model,
+        layer_norm_cls,
+        layer_norm_eps,
+        adaptive_layer_norm,
+        factory_kwargs,
+    ):
+        if adaptive_layer_norm:
+            return (
+                AdaptiveLayerNorm(
+                    d_model,
+                    layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs),
+                ),
+                AdaptiveLayerNorm(
+                    d_model,
+                    layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs),
+                ),
+                AdaptiveLayerNorm(
+                    d_model,
+                    layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs),
+                ),
+            )
+        else:
+            return (
+                layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs),
+                layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs),
+                layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs)
+                if layer_norm_cls != IdentityNorm
+                else BalancedBasicNorm(d_model, eps=layer_norm_eps, **factory_kwargs),
+            )
+
+
+def _get_clones(module, N):
+    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
+
+
+def _get_activation_fn(activation: str) -> Callable[[Tensor], Tensor]:
+    if activation == "relu":
+        return F.relu
+    elif activation == "gelu":
+        return F.gelu
+
+    raise RuntimeError("activation should be relu/gelu, not {}".format(activation))
+
+
+class Transpose(nn.Identity):
+    """(N, T, D) -> (N, D, T)"""
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        return input.transpose(1, 2)
diff --git a/modules/transformer/transforms.py b/modules/transformer/transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..d28b2e037e80f81ab75665c1f397c930bd0eca20
--- /dev/null
+++ b/modules/transformer/transforms.py
@@ -0,0 +1,214 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+from torch.nn import functional as F
+
+import numpy as np
+
+
+DEFAULT_MIN_BIN_WIDTH = 1e-3
+DEFAULT_MIN_BIN_HEIGHT = 1e-3
+DEFAULT_MIN_DERIVATIVE = 1e-3
+
+
+def piecewise_rational_quadratic_transform(
+    inputs,
+    unnormalized_widths,
+    unnormalized_heights,
+    unnormalized_derivatives,
+    inverse=False,
+    tails=None,
+    tail_bound=1.0,
+    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
+    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
+    min_derivative=DEFAULT_MIN_DERIVATIVE,
+):
+    if tails is None:
+        spline_fn = rational_quadratic_spline
+        spline_kwargs = {}
+    else:
+        spline_fn = unconstrained_rational_quadratic_spline
+        spline_kwargs = {"tails": tails, "tail_bound": tail_bound}
+
+    outputs, logabsdet = spline_fn(
+        inputs=inputs,
+        unnormalized_widths=unnormalized_widths,
+        unnormalized_heights=unnormalized_heights,
+        unnormalized_derivatives=unnormalized_derivatives,
+        inverse=inverse,
+        min_bin_width=min_bin_width,
+        min_bin_height=min_bin_height,
+        min_derivative=min_derivative,
+        **spline_kwargs,
+    )
+    return outputs, logabsdet
+
+
+def searchsorted(bin_locations, inputs, eps=1e-6):
+    bin_locations[..., -1] += eps
+    return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1
+
+
+def unconstrained_rational_quadratic_spline(
+    inputs,
+    unnormalized_widths,
+    unnormalized_heights,
+    unnormalized_derivatives,
+    inverse=False,
+    tails="linear",
+    tail_bound=1.0,
+    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
+    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
+    min_derivative=DEFAULT_MIN_DERIVATIVE,
+):
+    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
+    outside_interval_mask = ~inside_interval_mask
+
+    outputs = torch.zeros_like(inputs)
+    logabsdet = torch.zeros_like(inputs)
+
+    if tails == "linear":
+        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
+        constant = np.log(np.exp(1 - min_derivative) - 1)
+        unnormalized_derivatives[..., 0] = constant
+        unnormalized_derivatives[..., -1] = constant
+
+        outputs[outside_interval_mask] = inputs[outside_interval_mask]
+        logabsdet[outside_interval_mask] = 0
+    else:
+        raise RuntimeError("{} tails are not implemented.".format(tails))
+
+    (
+        outputs[inside_interval_mask],
+        logabsdet[inside_interval_mask],
+    ) = rational_quadratic_spline(
+        inputs=inputs[inside_interval_mask],
+        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
+        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
+        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
+        inverse=inverse,
+        left=-tail_bound,
+        right=tail_bound,
+        bottom=-tail_bound,
+        top=tail_bound,
+        min_bin_width=min_bin_width,
+        min_bin_height=min_bin_height,
+        min_derivative=min_derivative,
+    )
+
+    return outputs, logabsdet
+
+
+def rational_quadratic_spline(
+    inputs,
+    unnormalized_widths,
+    unnormalized_heights,
+    unnormalized_derivatives,
+    inverse=False,
+    left=0.0,
+    right=1.0,
+    bottom=0.0,
+    top=1.0,
+    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
+    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
+    min_derivative=DEFAULT_MIN_DERIVATIVE,
+):
+    if torch.min(inputs) < left or torch.max(inputs) > right:
+        raise ValueError("Input to a transform is not within its domain")
+
+    num_bins = unnormalized_widths.shape[-1]
+
+    if min_bin_width * num_bins > 1.0:
+        raise ValueError("Minimal bin width too large for the number of bins")
+    if min_bin_height * num_bins > 1.0:
+        raise ValueError("Minimal bin height too large for the number of bins")
+
+    widths = F.softmax(unnormalized_widths, dim=-1)
+    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
+    cumwidths = torch.cumsum(widths, dim=-1)
+    cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0)
+    cumwidths = (right - left) * cumwidths + left
+    cumwidths[..., 0] = left
+    cumwidths[..., -1] = right
+    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
+
+    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
+
+    heights = F.softmax(unnormalized_heights, dim=-1)
+    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
+    cumheights = torch.cumsum(heights, dim=-1)
+    cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0)
+    cumheights = (top - bottom) * cumheights + bottom
+    cumheights[..., 0] = bottom
+    cumheights[..., -1] = top
+    heights = cumheights[..., 1:] - cumheights[..., :-1]
+
+    if inverse:
+        bin_idx = searchsorted(cumheights, inputs)[..., None]
+    else:
+        bin_idx = searchsorted(cumwidths, inputs)[..., None]
+
+    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
+    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
+
+    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
+    delta = heights / widths
+    input_delta = delta.gather(-1, bin_idx)[..., 0]
+
+    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
+    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
+
+    input_heights = heights.gather(-1, bin_idx)[..., 0]
+
+    if inverse:
+        a = (inputs - input_cumheights) * (
+            input_derivatives + input_derivatives_plus_one - 2 * input_delta
+        ) + input_heights * (input_delta - input_derivatives)
+        b = input_heights * input_derivatives - (inputs - input_cumheights) * (
+            input_derivatives + input_derivatives_plus_one - 2 * input_delta
+        )
+        c = -input_delta * (inputs - input_cumheights)
+
+        discriminant = b.pow(2) - 4 * a * c
+        assert (discriminant >= 0).all()
+
+        root = (2 * c) / (-b - torch.sqrt(discriminant))
+        outputs = root * input_bin_widths + input_cumwidths
+
+        theta_one_minus_theta = root * (1 - root)
+        denominator = input_delta + (
+            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
+            * theta_one_minus_theta
+        )
+        derivative_numerator = input_delta.pow(2) * (
+            input_derivatives_plus_one * root.pow(2)
+            + 2 * input_delta * theta_one_minus_theta
+            + input_derivatives * (1 - root).pow(2)
+        )
+        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
+
+        return outputs, -logabsdet
+    else:
+        theta = (inputs - input_cumwidths) / input_bin_widths
+        theta_one_minus_theta = theta * (1 - theta)
+
+        numerator = input_heights * (
+            input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta
+        )
+        denominator = input_delta + (
+            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
+            * theta_one_minus_theta
+        )
+        outputs = input_cumheights + numerator / denominator
+
+        derivative_numerator = input_delta.pow(2) * (
+            input_derivatives_plus_one * theta.pow(2)
+            + 2 * input_delta * theta_one_minus_theta
+            + input_derivatives * (1 - theta).pow(2)
+        )
+        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
+
+        return outputs, logabsdet
diff --git a/modules/vocoder_blocks/__init__.py b/modules/vocoder_blocks/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b678da73bf7b15cf9c42a1f4f36379f62fc43896
--- /dev/null
+++ b/modules/vocoder_blocks/__init__.py
@@ -0,0 +1,7 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .gan_utils import *
+from .norm2d import *
diff --git a/modules/vocoder_blocks/gan_utils.py b/modules/vocoder_blocks/gan_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e6fc38cb3c047ea856f8d63b0f3a755644e98ee
--- /dev/null
+++ b/modules/vocoder_blocks/gan_utils.py
@@ -0,0 +1,28 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+#################### GAN utils ####################
+
+
+import typing as tp
+
+
+def get_padding(kernel_size, dilation=1):
+    return int((kernel_size * dilation - dilation) / 2)
+
+
+def get_2d_padding(
+    kernel_size: tp.Tuple[int, int], dilation: tp.Tuple[int, int] = (1, 1)
+):
+    return (
+        ((kernel_size[0] - 1) * dilation[0]) // 2,
+        ((kernel_size[1] - 1) * dilation[1]) // 2,
+    )
+
+
+def init_weights(m, mean=0.0, std=0.01):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        m.weight.data.normal_(mean, std)
diff --git a/modules/vocoder_blocks/norm2d.py b/modules/vocoder_blocks/norm2d.py
new file mode 100644
index 0000000000000000000000000000000000000000..a907f907faae2ce9cf9e695b4a8b103ab39773ce
--- /dev/null
+++ b/modules/vocoder_blocks/norm2d.py
@@ -0,0 +1,92 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+#################### Norm2D for Discriminators ####################
+
+import torch
+import torch.nn as nn
+import einops
+from torch.nn.utils import spectral_norm, weight_norm
+
+CONV_NORMALIZATIONS = frozenset(
+    [
+        "none",
+        "weight_norm",
+        "spectral_norm",
+        "time_layer_norm",
+        "layer_norm",
+        "time_group_norm",
+    ]
+)
+
+
+class ConvLayerNorm(nn.LayerNorm):
+    """
+    Convolution-friendly LayerNorm that moves channels to last dimensions
+    before running the normalization and moves them back to original position right after.
+    """
+
+    def __init__(self, normalized_shape, **kwargs):
+        super().__init__(normalized_shape, **kwargs)
+
+    def forward(self, x):
+        x = einops.rearrange(x, "b ... t -> b t ...")
+        x = super().forward(x)
+        x = einops.rearrange(x, "b t ... -> b ... t")
+        return
+
+
+def apply_parametrization_norm(module: nn.Module, norm: str = "none") -> nn.Module:
+    assert norm in CONV_NORMALIZATIONS
+    if norm == "weight_norm":
+        return weight_norm(module)
+    elif norm == "spectral_norm":
+        return spectral_norm(module)
+    else:
+        # We already check was in CONV_NORMALIZATION, so any other choice
+        # doesn't need reparametrization.
+        return module
+
+
+def get_norm_module(
+    module: nn.Module, causal: bool = False, norm: str = "none", **norm_kwargs
+) -> nn.Module:
+    """Return the proper normalization module. If causal is True, this will ensure the returned
+    module is causal, or return an error if the normalization doesn't support causal evaluation.
+    """
+    assert norm in CONV_NORMALIZATIONS
+    if norm == "layer_norm":
+        assert isinstance(module, nn.modules.conv._ConvNd)
+        return ConvLayerNorm(module.out_channels, **norm_kwargs)
+    elif norm == "time_group_norm":
+        if causal:
+            raise ValueError("GroupNorm doesn't support causal evaluation.")
+        assert isinstance(module, nn.modules.conv._ConvNd)
+        return nn.GroupNorm(1, module.out_channels, **norm_kwargs)
+    else:
+        return nn.Identity()
+
+
+class NormConv2d(nn.Module):
+    """Wrapper around Conv2d and normalization applied to this conv
+    to provide a uniform interface across normalization approaches.
+    """
+
+    def __init__(
+        self,
+        *args,
+        norm: str = "none",
+        norm_kwargs={},
+        **kwargs,
+    ):
+        super().__init__()
+        self.conv = apply_parametrization_norm(nn.Conv2d(*args, **kwargs), norm)
+        self.norm = get_norm_module(self.conv, causal=False, norm=norm, **norm_kwargs)
+        self.norm_type = norm
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.norm(x)
+        return x
diff --git a/modules/wenet_extractor/README.md b/modules/wenet_extractor/README.md
new file mode 100755
index 0000000000000000000000000000000000000000..b8f0abc4d5d9fda42edba488d01bd40222a78304
--- /dev/null
+++ b/modules/wenet_extractor/README.md
@@ -0,0 +1,23 @@
+## Acknowledgement
+
+This module borrows some codes from [WeNet](https://github.com/wenet-e2e/wenet).
+
+## Citations
+
+```bibtex
+@inproceedings{yao2021wenet,
+  title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+  author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+  booktitle={Proc. Interspeech},
+  year={2021},
+  address={Brno, Czech Republic },
+  organization={IEEE}
+}
+
+@article{zhang2022wenet,
+  title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+  author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+  journal={arXiv preprint arXiv:2203.15455},
+  year={2022}
+}
+```
\ No newline at end of file
diff --git a/modules/wenet_extractor/__init__.py b/modules/wenet_extractor/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/modules/wenet_extractor/cif/predictor.py b/modules/wenet_extractor/cif/predictor.py
new file mode 100755
index 0000000000000000000000000000000000000000..91428c4c418da3026e6865f334e4b4cdafd02900
--- /dev/null
+++ b/modules/wenet_extractor/cif/predictor.py
@@ -0,0 +1,274 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+from typing import Optional
+
+import torch
+from torch import nn
+from modules.wenet_extractor.utils.mask import make_pad_mask
+
+
+class Predictor(nn.Module):
+    def __init__(
+        self,
+        idim,
+        l_order,
+        r_order,
+        threshold=1.0,
+        dropout=0.1,
+        smooth_factor=1.0,
+        noise_threshold=0,
+        tail_threshold=0.45,
+    ):
+        super().__init__()
+
+        self.pad = nn.ConstantPad1d((l_order, r_order), 0.0)
+        self.cif_conv1d = nn.Conv1d(idim, idim, l_order + r_order + 1, groups=idim)
+        self.cif_output = nn.Linear(idim, 1)
+        self.dropout = torch.nn.Dropout(p=dropout)
+        self.threshold = threshold
+        self.smooth_factor = smooth_factor
+        self.noise_threshold = noise_threshold
+        self.tail_threshold = tail_threshold
+
+    def forward(
+        self,
+        hidden,
+        target_label: Optional[torch.Tensor] = None,
+        mask: torch.Tensor = torch.tensor(0),
+        ignore_id: int = -1,
+        mask_chunk_predictor: Optional[torch.Tensor] = None,
+        target_label_length: Optional[torch.Tensor] = None,
+    ):
+        h = hidden
+        context = h.transpose(1, 2)
+        queries = self.pad(context)
+        memory = self.cif_conv1d(queries)
+        output = memory + context
+        output = self.dropout(output)
+        output = output.transpose(1, 2)
+        output = torch.relu(output)
+        output = self.cif_output(output)
+        alphas = torch.sigmoid(output)
+        alphas = torch.nn.functional.relu(
+            alphas * self.smooth_factor - self.noise_threshold
+        )
+        if mask is not None:
+            mask = mask.transpose(-1, -2).float()
+            alphas = alphas * mask
+        if mask_chunk_predictor is not None:
+            alphas = alphas * mask_chunk_predictor
+        alphas = alphas.squeeze(-1)
+        mask = mask.squeeze(-1)
+        if target_label_length is not None:
+            target_length = target_label_length
+        elif target_label is not None:
+            target_length = (target_label != ignore_id).float().sum(-1)
+        else:
+            target_length = None
+        token_num = alphas.sum(-1)
+        if target_length is not None:
+            alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1))
+        elif self.tail_threshold > 0.0:
+            hidden, alphas, token_num = self.tail_process_fn(
+                hidden, alphas, token_num, mask=mask
+            )
+
+        acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)
+
+        if target_length is None and self.tail_threshold > 0.0:
+            token_num_int = torch.max(token_num).type(torch.int32).item()
+            acoustic_embeds = acoustic_embeds[:, :token_num_int, :]
+
+        return acoustic_embeds, token_num, alphas, cif_peak
+
+    def tail_process_fn(
+        self,
+        hidden,
+        alphas,
+        token_num: Optional[torch.Tensor] = None,
+        mask: Optional[torch.Tensor] = None,
+    ):
+        b, t, d = hidden.size()
+        tail_threshold = self.tail_threshold
+        if mask is not None:
+            zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device)
+            ones_t = torch.ones_like(zeros_t)
+            mask_1 = torch.cat([mask, zeros_t], dim=1)
+            mask_2 = torch.cat([ones_t, mask], dim=1)
+            mask = mask_2 - mask_1
+            tail_threshold = mask * tail_threshold
+            alphas = torch.cat([alphas, zeros_t], dim=1)
+            alphas = torch.add(alphas, tail_threshold)
+        else:
+            tail_threshold_tensor = torch.tensor(
+                [tail_threshold], dtype=alphas.dtype
+            ).to(alphas.device)
+            tail_threshold_tensor = torch.reshape(tail_threshold_tensor, (1, 1))
+            alphas = torch.cat([alphas, tail_threshold_tensor], dim=1)
+        zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device)
+        hidden = torch.cat([hidden, zeros], dim=1)
+        token_num = alphas.sum(dim=-1)
+        token_num_floor = torch.floor(token_num)
+
+        return hidden, alphas, token_num_floor
+
+    def gen_frame_alignments(
+        self, alphas: torch.Tensor = None, encoder_sequence_length: torch.Tensor = None
+    ):
+        batch_size, maximum_length = alphas.size()
+        int_type = torch.int32
+
+        is_training = self.training
+        if is_training:
+            token_num = torch.round(torch.sum(alphas, dim=1)).type(int_type)
+        else:
+            token_num = torch.floor(torch.sum(alphas, dim=1)).type(int_type)
+
+        max_token_num = torch.max(token_num).item()
+
+        alphas_cumsum = torch.cumsum(alphas, dim=1)
+        alphas_cumsum = torch.floor(alphas_cumsum).type(int_type)
+        alphas_cumsum = alphas_cumsum[:, None, :].repeat(1, max_token_num, 1)
+
+        index = torch.ones([batch_size, max_token_num], dtype=int_type)
+        index = torch.cumsum(index, dim=1)
+        index = index[:, :, None].repeat(1, 1, maximum_length).to(alphas_cumsum.device)
+
+        index_div = torch.floor(torch.true_divide(alphas_cumsum, index)).type(int_type)
+        index_div_bool_zeros = index_div.eq(0)
+        index_div_bool_zeros_count = torch.sum(index_div_bool_zeros, dim=-1) + 1
+        index_div_bool_zeros_count = torch.clamp(
+            index_div_bool_zeros_count, 0, encoder_sequence_length.max()
+        )
+        token_num_mask = (~make_pad_mask(token_num, max_len=max_token_num)).to(
+            token_num.device
+        )
+        index_div_bool_zeros_count *= token_num_mask
+
+        index_div_bool_zeros_count_tile = index_div_bool_zeros_count[:, :, None].repeat(
+            1, 1, maximum_length
+        )
+        ones = torch.ones_like(index_div_bool_zeros_count_tile)
+        zeros = torch.zeros_like(index_div_bool_zeros_count_tile)
+        ones = torch.cumsum(ones, dim=2)
+        cond = index_div_bool_zeros_count_tile == ones
+        index_div_bool_zeros_count_tile = torch.where(cond, zeros, ones)
+
+        index_div_bool_zeros_count_tile_bool = index_div_bool_zeros_count_tile.type(
+            torch.bool
+        )
+        index_div_bool_zeros_count_tile = 1 - index_div_bool_zeros_count_tile_bool.type(
+            int_type
+        )
+        index_div_bool_zeros_count_tile_out = torch.sum(
+            index_div_bool_zeros_count_tile, dim=1
+        )
+        index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out.type(
+            int_type
+        )
+        predictor_mask = (
+            (
+                ~make_pad_mask(
+                    encoder_sequence_length, max_len=encoder_sequence_length.max()
+                )
+            )
+            .type(int_type)
+            .to(encoder_sequence_length.device)
+        )
+        index_div_bool_zeros_count_tile_out = (
+            index_div_bool_zeros_count_tile_out * predictor_mask
+        )
+
+        predictor_alignments = index_div_bool_zeros_count_tile_out
+        predictor_alignments_length = predictor_alignments.sum(-1).type(
+            encoder_sequence_length.dtype
+        )
+        return predictor_alignments.detach(), predictor_alignments_length.detach()
+
+
+class MAELoss(nn.Module):
+    def __init__(self, normalize_length=False):
+        super(MAELoss, self).__init__()
+        self.normalize_length = normalize_length
+        self.criterion = torch.nn.L1Loss(reduction="sum")
+
+    def forward(self, token_length, pre_token_length):
+        loss_token_normalizer = token_length.size(0)
+        if self.normalize_length:
+            loss_token_normalizer = token_length.sum().type(torch.float32)
+        loss = self.criterion(token_length, pre_token_length)
+        loss = loss / loss_token_normalizer
+        return loss
+
+
+def cif(hidden: torch.Tensor, alphas: torch.Tensor, threshold: float):
+    batch_size, len_time, hidden_size = hidden.size()
+
+    # loop varss
+    integrate = torch.zeros([batch_size], device=hidden.device)
+    frame = torch.zeros([batch_size, hidden_size], device=hidden.device)
+    # intermediate vars along time
+    list_fires = []
+    list_frames = []
+
+    for t in range(len_time):
+        alpha = alphas[:, t]
+        distribution_completion = (
+            torch.ones([batch_size], device=hidden.device) - integrate
+        )
+
+        integrate += alpha
+        list_fires.append(integrate)
+
+        fire_place = integrate >= threshold
+        integrate = torch.where(
+            fire_place,
+            integrate - torch.ones([batch_size], device=hidden.device),
+            integrate,
+        )
+        cur = torch.where(fire_place, distribution_completion, alpha)
+        remainds = alpha - cur
+
+        frame += cur[:, None] * hidden[:, t, :]
+        list_frames.append(frame)
+        frame = torch.where(
+            fire_place[:, None].repeat(1, hidden_size),
+            remainds[:, None] * hidden[:, t, :],
+            frame,
+        )
+
+    fires = torch.stack(list_fires, 1)
+    frames = torch.stack(list_frames, 1)
+    list_ls = []
+    len_labels = torch.round(alphas.sum(-1)).int()
+    max_label_len = len_labels.max()
+    for b in range(batch_size):
+        fire = fires[b, :]
+        l = torch.index_select(
+            frames[b, :, :], 0, torch.nonzero(fire >= threshold).squeeze()
+        )
+        pad_l = torch.zeros(
+            [int(max_label_len - l.size(0)), hidden_size], device=hidden.device
+        )
+        list_ls.append(torch.cat([l, pad_l], 0))
+    return torch.stack(list_ls, 0), fires
diff --git a/modules/wenet_extractor/efficient_conformer/__init__.py b/modules/wenet_extractor/efficient_conformer/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/modules/wenet_extractor/efficient_conformer/attention.py b/modules/wenet_extractor/efficient_conformer/attention.py
new file mode 100755
index 0000000000000000000000000000000000000000..96f5ac10d56e44c0873e6d659ed501fdedf43192
--- /dev/null
+++ b/modules/wenet_extractor/efficient_conformer/attention.py
@@ -0,0 +1,273 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+"""Multi-Head Attention layer definition."""
+
+import math
+from typing import Tuple, Optional
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+from modules.wenet_extractor.transformer.attention import MultiHeadedAttention
+
+
+class GroupedRelPositionMultiHeadedAttention(MultiHeadedAttention):
+    """Multi-Head Attention layer with relative position encoding.
+    Paper:
+        https://arxiv.org/abs/1901.02860
+        https://arxiv.org/abs/2109.01163
+    Args:
+        n_head (int): The number of heads.
+        n_feat (int): The number of features.
+        dropout_rate (float): Dropout rate.
+    """
+
+    def __init__(self, n_head, n_feat, dropout_rate, group_size=3):
+        """Construct an RelPositionMultiHeadedAttention object."""
+        super().__init__(n_head, n_feat, dropout_rate)
+        # linear transformation for positional encoding
+        self.linear_pos = nn.Linear(n_feat, n_feat, bias=False)
+        self.group_size = group_size
+        self.d_k = n_feat // n_head  # for GroupedAttention
+        self.n_feat = n_feat
+        # these two learnable bias are used in matrix c and matrix d
+        # as described in https://arxiv.org/abs/1901.02860 Section 3.3
+        self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k * self.group_size))
+        self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k * self.group_size))
+        torch.nn.init.xavier_uniform_(self.pos_bias_u)
+        torch.nn.init.xavier_uniform_(self.pos_bias_v)
+
+    def rel_shift(self, x, zero_triu: bool = False):
+        """Compute relative positinal encoding.
+        Args:
+            x (torch.Tensor): Input tensor (batch, time, size).
+            zero_triu (bool): If true, return the lower triangular part of
+                the matrix.
+        Returns:
+            torch.Tensor: Output tensor.
+        """
+
+        zero_pad = torch.zeros(
+            (x.size()[0], x.size()[1], x.size()[2], 1), device=x.device, dtype=x.dtype
+        )
+        x_padded = torch.cat([zero_pad, x], dim=-1)
+
+        x_padded = x_padded.view(x.size()[0], x.size()[1], x.size(3) + 1, x.size(2))
+        x = x_padded[:, :, 1:].view_as(x)
+
+        if zero_triu:
+            ones = torch.ones((x.size(2), x.size(3)))
+            x = x * torch.tril(ones, x.size(3) - x.size(2))[None, None, :, :]
+
+        return x
+
+    def pad4group(self, Q, K, V, P, mask, group_size: int = 3):
+        """
+        q: (#batch, time1, size) -> (#batch, head, time1, size/head)
+        k,v: (#batch, time2, size) -> (#batch, head, time2, size/head)
+        p: (#batch, time2, size)
+        """
+        # Compute Overflows
+        overflow_Q = Q.size(2) % group_size
+        overflow_KV = K.size(2) % group_size
+
+        # if-else for ONNX export
+        #   0 // 0.00000000000000001 = 0
+        #   1 // 1.00000000000000001 = 1
+        padding_Q = (group_size - overflow_Q) * int(
+            overflow_Q // (overflow_Q + 0.00000000000000001)
+        )
+        padding_KV = (group_size - overflow_KV) * int(
+            overflow_KV // (overflow_KV + 0.00000000000000001)
+        )
+
+        batch_size, _, seq_len_KV, _ = K.size()
+
+        # Input Padding (B, T, D) -> (B, T + P, D)
+        Q = F.pad(Q, (0, 0, 0, padding_Q), value=0.0)
+        K = F.pad(K, (0, 0, 0, padding_KV), value=0.0)
+        V = F.pad(V, (0, 0, 0, padding_KV), value=0.0)
+
+        if mask is not None and mask.size(2) > 0:  # time2 > 0:
+            mask = mask[:, ::group_size, ::group_size]
+
+        Q = (
+            Q.transpose(1, 2)
+            .contiguous()
+            .view(batch_size, -1, self.h, self.d_k * group_size)
+            .transpose(1, 2)
+        )
+        K = (
+            K.transpose(1, 2)
+            .contiguous()
+            .view(batch_size, -1, self.h, self.d_k * group_size)
+            .transpose(1, 2)
+        )
+        V = (
+            V.transpose(1, 2)
+            .contiguous()
+            .view(batch_size, -1, self.h, self.d_k * group_size)
+            .transpose(1, 2)
+        )
+
+        # process pos_emb
+        P_batch_size = P.size(0)
+        overflow_P = P.size(1) % group_size
+        padding_P = group_size - overflow_P if overflow_P else 0
+        P = F.pad(P, (0, 0, 0, padding_P), value=0.0)
+        P = P.view(P_batch_size, -1, self.h, self.d_k * group_size).transpose(1, 2)
+
+        return Q, K, V, P, mask, padding_Q
+
+    def forward_attention(
+        self,
+        value: torch.Tensor,
+        scores: torch.Tensor,
+        mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+        padding_q: Optional[int] = None,
+    ) -> torch.Tensor:
+        """Compute attention context vector.
+
+        Args:
+            value (torch.Tensor): Transformed value, size
+                (#batch, n_head, time2, d_k).
+            scores (torch.Tensor): Attention score, size
+                (#batch, n_head, time1, time2).
+            mask (torch.Tensor): Mask, size (#batch, 1, time2) or
+                (#batch, time1, time2), (0, 0, 0) means fake mask.
+            padding_q : for GroupedAttention in efficent conformer
+
+        Returns:
+            torch.Tensor: Transformed value (#batch, time1, d_model)
+                weighted by the attention score (#batch, time1, time2).
+
+        """
+        n_batch = value.size(0)
+        # NOTE(xcsong): When will `if mask.size(2) > 0` be True?
+        #   1. onnx(16/4) [WHY? Because we feed real cache & real mask for the
+        #           1st chunk to ease the onnx export.]
+        #   2. pytorch training
+        if mask.size(2) > 0:  # time2 > 0
+            mask = mask.unsqueeze(1).eq(0)  # (batch, 1, *, time2)
+            # For last chunk, time2 might be larger than scores.size(-1)
+            mask = mask[:, :, :, : scores.size(-1)]  # (batch, 1, *, time2)
+            scores = scores.masked_fill(mask, -float("inf"))
+            attn = torch.softmax(scores, dim=-1).masked_fill(
+                mask, 0.0
+            )  # (batch, head, time1, time2)
+        # NOTE(xcsong): When will `if mask.size(2) > 0` be False?
+        #   1. onnx(16/-1, -1/-1, 16/0)
+        #   2. jit (16/-1, -1/-1, 16/0, 16/4)
+        else:
+            attn = torch.softmax(scores, dim=-1)  # (batch, head, time1, time2)
+
+        p_attn = self.dropout(attn)
+        x = torch.matmul(p_attn, value)  # (batch, head, time1, d_k)
+
+        # n_feat!=h*d_k may be happened in GroupAttention
+        x = (
+            x.transpose(1, 2).contiguous().view(n_batch, -1, self.n_feat)
+        )  # (batch, time1, d_model)
+        if padding_q is not None:
+            # for GroupedAttention in efficent conformer
+            x = x[:, : x.size(1) - padding_q]
+
+        return self.linear_out(x)  # (batch, time1, d_model)
+
+    def forward(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+        pos_emb: torch.Tensor = torch.empty(0),
+        cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Compute 'Scaled Dot Product Attention' with rel. positional encoding.
+        Args:
+            query (torch.Tensor): Query tensor (#batch, time1, size).
+            key (torch.Tensor): Key tensor (#batch, time2, size).
+            value (torch.Tensor): Value tensor (#batch, time2, size).
+            mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
+                (#batch, time1, time2).
+            pos_emb (torch.Tensor): Positional embedding tensor
+                (#batch, time2, size).
+            cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2),
+                where `cache_t == chunk_size * num_decoding_left_chunks`
+                and `head * d_k == size`
+        Returns:
+            torch.Tensor: Output tensor (#batch, time1, d_model).
+            torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2)
+                where `cache_t == chunk_size * num_decoding_left_chunks`
+                and `head * d_k == size`
+        """
+        q = self.linear_q(query)
+        k = self.linear_k(key)  # (#batch, time2, size)
+        v = self.linear_v(value)
+        p = self.linear_pos(pos_emb)  # (#batch, time2, size)
+
+        batch_size, seq_len_KV, _ = k.size()  # seq_len_KV = time2
+
+        # (#batch, time2, size) -> (#batch, head, time2, size/head)
+        q = q.view(batch_size, -1, self.h, self.d_k).transpose(1, 2)
+        k = k.view(batch_size, -1, self.h, self.d_k).transpose(1, 2)
+        v = v.view(batch_size, -1, self.h, self.d_k).transpose(1, 2)
+        if cache.size(0) > 0:
+            # use attention cache
+            key_cache, value_cache = torch.split(cache, cache.size(-1) // 2, dim=-1)
+            k = torch.cat([key_cache, k], dim=2)
+            v = torch.cat([value_cache, v], dim=2)
+        new_cache = torch.cat((k, v), dim=-1)
+
+        # May be k and p does not match.  eg. time2=18+18/2=27 > mask=36/2=18
+        if mask is not None and mask.size(2) > 0:
+            time2 = mask.size(2)
+            k = k[:, :, -time2:, :]
+            v = v[:, :, -time2:, :]
+
+        # q k v p: (batch, head, time1, d_k)
+        q, k, v, p, mask, padding_q = self.pad4group(q, k, v, p, mask, self.group_size)
+
+        # q_with_bias_u & q_with_bias_v = (batch, head, time1, d_k)
+        q = q.transpose(1, 2)  # (batch, time1, head, d_k)
+        q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2)
+        q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2)
+
+        # compute attention score
+        # first compute matrix a and matrix c
+        # as described in https://arxiv.org/abs/1901.02860 Section 3.3
+        # (batch, head, time1, time2)
+        matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))
+
+        # compute matrix b and matrix d
+        # (batch, head, time1, time2)
+        matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
+        # Remove rel_shift since it is useless in speech recognition,
+        # and it requires special attention for streaming.
+        # matrix_bd = self.rel_shift(matrix_bd)
+
+        scores = (matrix_ac + matrix_bd) / math.sqrt(
+            self.d_k * self.group_size
+        )  # (batch, head, time1, time2)
+
+        return self.forward_attention(v, scores, mask, padding_q), new_cache
diff --git a/modules/wenet_extractor/efficient_conformer/convolution.py b/modules/wenet_extractor/efficient_conformer/convolution.py
new file mode 100755
index 0000000000000000000000000000000000000000..88ca82bffbd6ed5fd48a64d71f61bc281594efa2
--- /dev/null
+++ b/modules/wenet_extractor/efficient_conformer/convolution.py
@@ -0,0 +1,163 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+"""ConvolutionModule definition."""
+from typing import Tuple
+
+import torch
+from torch import nn
+
+
+class ConvolutionModule(nn.Module):
+    """ConvolutionModule in Conformer model."""
+
+    def __init__(
+        self,
+        channels: int,
+        kernel_size: int = 15,
+        activation: nn.Module = nn.ReLU(),
+        norm: str = "batch_norm",
+        causal: bool = False,
+        bias: bool = True,
+        stride: int = 1,
+    ):
+        """Construct an ConvolutionModule object.
+        Args:
+            channels (int): The number of channels of conv layers.
+            kernel_size (int): Kernel size of conv layers.
+            causal (int): Whether use causal convolution or not
+            stride (int): Stride Convolution, for efficient Conformer
+        """
+        super().__init__()
+
+        self.pointwise_conv1 = nn.Conv1d(
+            channels,
+            2 * channels,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            bias=bias,
+        )
+        # self.lorder is used to distinguish if it's a causal convolution,
+        # if self.lorder > 0: it's a causal convolution, the input will be
+        #    padded with self.lorder frames on the left in forward.
+        # else: it's a symmetrical convolution
+        if causal:
+            padding = 0
+            self.lorder = kernel_size - 1
+        else:
+            # kernel_size should be an odd number for none causal convolution
+            assert (kernel_size - 1) % 2 == 0
+            padding = (kernel_size - 1) // 2
+            self.lorder = 0
+
+        self.depthwise_conv = nn.Conv1d(
+            channels,
+            channels,
+            kernel_size,
+            stride=stride,  # for depthwise_conv in StrideConv
+            padding=padding,
+            groups=channels,
+            bias=bias,
+        )
+
+        assert norm in ["batch_norm", "layer_norm"]
+        if norm == "batch_norm":
+            self.use_layer_norm = False
+            self.norm = nn.BatchNorm1d(channels)
+        else:
+            self.use_layer_norm = True
+            self.norm = nn.LayerNorm(channels)
+
+        self.pointwise_conv2 = nn.Conv1d(
+            channels,
+            channels,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            bias=bias,
+        )
+        self.activation = activation
+        self.stride = stride
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+        cache: torch.Tensor = torch.zeros((0, 0, 0)),
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Compute convolution module.
+        Args:
+            x (torch.Tensor): Input tensor (#batch, time, channels).
+            mask_pad (torch.Tensor): used for batch padding (#batch, 1, time),
+                (0, 0, 0) means fake mask.
+            cache (torch.Tensor): left context cache, it is only
+                used in causal convolution (#batch, channels, cache_t),
+                (0, 0, 0) meas fake cache.
+        Returns:
+            torch.Tensor: Output tensor (#batch, time, channels).
+        """
+        # exchange the temporal dimension and the feature dimension
+        x = x.transpose(1, 2)  # (#batch, channels, time)
+
+        # mask batch padding
+        if mask_pad.size(2) > 0:  # time > 0
+            x.masked_fill_(~mask_pad, 0.0)
+
+        if self.lorder > 0:
+            if cache.size(2) == 0:  # cache_t == 0
+                x = nn.functional.pad(x, (self.lorder, 0), "constant", 0.0)
+            else:
+                # When export ONNX，the first cache is not None but all-zero,
+                # cause shape error in residual block,
+                #   eg. cache14 + x9 = 23, 23-7+1=17 != 9
+                cache = cache[:, :, -self.lorder :]
+                assert cache.size(0) == x.size(0)  # equal batch
+                assert cache.size(1) == x.size(1)  # equal channel
+                x = torch.cat((cache, x), dim=2)
+            assert x.size(2) > self.lorder
+            new_cache = x[:, :, -self.lorder :]
+        else:
+            # It's better we just return None if no cache is requried,
+            # However, for JIT export, here we just fake one tensor instead of
+            # None.
+            new_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device)
+
+        # GLU mechanism
+        x = self.pointwise_conv1(x)  # (batch, 2*channel, dim)
+        x = nn.functional.glu(x, dim=1)  # (batch, channel, dim)
+
+        # 1D Depthwise Conv
+        x = self.depthwise_conv(x)
+        if self.use_layer_norm:
+            x = x.transpose(1, 2)
+        x = self.activation(self.norm(x))
+        if self.use_layer_norm:
+            x = x.transpose(1, 2)
+        x = self.pointwise_conv2(x)
+        # mask batch padding
+        if mask_pad.size(2) > 0:  # time > 0
+            if mask_pad.size(2) != x.size(2):
+                mask_pad = mask_pad[:, :, :: self.stride]
+            x.masked_fill_(~mask_pad, 0.0)
+
+        return x.transpose(1, 2), new_cache
diff --git a/modules/wenet_extractor/efficient_conformer/encoder.py b/modules/wenet_extractor/efficient_conformer/encoder.py
new file mode 100755
index 0000000000000000000000000000000000000000..e23c50763f4fddf610371e116e478fcf27f65ea8
--- /dev/null
+++ b/modules/wenet_extractor/efficient_conformer/encoder.py
@@ -0,0 +1,627 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+"""Encoder definition."""
+from typing import Tuple, Optional, List, Union
+
+import torch
+import logging
+import torch.nn.functional as F
+
+from modules.wenet_extractor.transformer.positionwise_feed_forward import (
+    PositionwiseFeedForward,
+)
+from modules.wenet_extractor.transformer.embedding import PositionalEncoding
+from modules.wenet_extractor.transformer.embedding import RelPositionalEncoding
+from modules.wenet_extractor.transformer.embedding import NoPositionalEncoding
+from modules.wenet_extractor.transformer.subsampling import Conv2dSubsampling4
+from modules.wenet_extractor.transformer.subsampling import Conv2dSubsampling6
+from modules.wenet_extractor.transformer.subsampling import Conv2dSubsampling8
+from modules.wenet_extractor.transformer.subsampling import LinearNoSubsampling
+from modules.wenet_extractor.transformer.attention import MultiHeadedAttention
+from modules.wenet_extractor.transformer.attention import (
+    RelPositionMultiHeadedAttention,
+)
+from modules.wenet_extractor.transformer.encoder_layer import ConformerEncoderLayer
+
+from modules.wenet_extractor.efficient_conformer.subsampling import Conv2dSubsampling2
+from modules.wenet_extractor.efficient_conformer.convolution import ConvolutionModule
+from modules.wenet_extractor.efficient_conformer.attention import (
+    GroupedRelPositionMultiHeadedAttention,
+)
+from modules.wenet_extractor.efficient_conformer.encoder_layer import (
+    StrideConformerEncoderLayer,
+)
+
+from modules.wenet_extractor.utils.common import get_activation
+from modules.wenet_extractor.utils.mask import make_pad_mask
+from modules.wenet_extractor.utils.mask import add_optional_chunk_mask
+
+
+class EfficientConformerEncoder(torch.nn.Module):
+    """Conformer encoder module."""
+
+    def __init__(
+        self,
+        input_size: int,
+        output_size: int = 256,
+        attention_heads: int = 4,
+        linear_units: int = 2048,
+        num_blocks: int = 6,
+        dropout_rate: float = 0.1,
+        positional_dropout_rate: float = 0.1,
+        attention_dropout_rate: float = 0.0,
+        input_layer: str = "conv2d",
+        pos_enc_layer_type: str = "rel_pos",
+        normalize_before: bool = True,
+        static_chunk_size: int = 0,
+        use_dynamic_chunk: bool = False,
+        global_cmvn: torch.nn.Module = None,
+        use_dynamic_left_chunk: bool = False,
+        macaron_style: bool = True,
+        activation_type: str = "swish",
+        use_cnn_module: bool = True,
+        cnn_module_kernel: int = 15,
+        causal: bool = False,
+        cnn_module_norm: str = "batch_norm",
+        stride_layer_idx: Optional[Union[int, List[int]]] = 3,
+        stride: Optional[Union[int, List[int]]] = 2,
+        group_layer_idx: Optional[Union[int, List[int], tuple]] = (0, 1, 2, 3),
+        group_size: int = 3,
+        stride_kernel: bool = True,
+        **kwargs,
+    ):
+        """Construct Efficient Conformer Encoder
+
+        Args:
+            input_size to use_dynamic_chunk, see in BaseEncoder
+            macaron_style (bool): Whether to use macaron style for
+                positionwise layer.
+            activation_type (str): Encoder activation function type.
+            use_cnn_module (bool): Whether to use convolution module.
+            cnn_module_kernel (int): Kernel size of convolution module.
+            causal (bool): whether to use causal convolution or not.
+            stride_layer_idx (list): layer id with StrideConv, start from 0
+            stride (list): stride size of each StrideConv in efficient conformer
+            group_layer_idx (list): layer id with GroupedAttention, start from 0
+            group_size (int): group size of every GroupedAttention layer
+            stride_kernel (bool): default True. True: recompute cnn kernels with stride.
+        """
+        super().__init__()
+        self._output_size = output_size
+
+        if pos_enc_layer_type == "abs_pos":
+            pos_enc_class = PositionalEncoding
+        elif pos_enc_layer_type == "rel_pos":
+            pos_enc_class = RelPositionalEncoding
+        elif pos_enc_layer_type == "no_pos":
+            pos_enc_class = NoPositionalEncoding
+        else:
+            raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type)
+
+        if input_layer == "linear":
+            subsampling_class = LinearNoSubsampling
+        elif input_layer == "conv2d2":
+            subsampling_class = Conv2dSubsampling2
+        elif input_layer == "conv2d":
+            subsampling_class = Conv2dSubsampling4
+        elif input_layer == "conv2d6":
+            subsampling_class = Conv2dSubsampling6
+        elif input_layer == "conv2d8":
+            subsampling_class = Conv2dSubsampling8
+        else:
+            raise ValueError("unknown input_layer: " + input_layer)
+
+        logging.info(
+            f"input_layer = {input_layer}, " f"subsampling_class = {subsampling_class}"
+        )
+
+        self.global_cmvn = global_cmvn
+        self.embed = subsampling_class(
+            input_size,
+            output_size,
+            dropout_rate,
+            pos_enc_class(output_size, positional_dropout_rate),
+        )
+        self.input_layer = input_layer
+        self.normalize_before = normalize_before
+        self.after_norm = torch.nn.LayerNorm(output_size, eps=1e-5)
+        self.static_chunk_size = static_chunk_size
+        self.use_dynamic_chunk = use_dynamic_chunk
+        self.use_dynamic_left_chunk = use_dynamic_left_chunk
+
+        activation = get_activation(activation_type)
+        self.num_blocks = num_blocks
+        self.attention_heads = attention_heads
+        self.cnn_module_kernel = cnn_module_kernel
+        self.global_chunk_size = 0
+        self.chunk_feature_map = 0
+
+        # efficient conformer configs
+        self.stride_layer_idx = (
+            [stride_layer_idx] if type(stride_layer_idx) == int else stride_layer_idx
+        )
+        self.stride = [stride] if type(stride) == int else stride
+        self.group_layer_idx = (
+            [group_layer_idx] if type(group_layer_idx) == int else group_layer_idx
+        )
+        self.grouped_size = group_size  # group size of every GroupedAttention layer
+
+        assert len(self.stride) == len(self.stride_layer_idx)
+        self.cnn_module_kernels = [cnn_module_kernel]  # kernel size of each StridedConv
+        for i in self.stride:
+            if stride_kernel:
+                self.cnn_module_kernels.append(self.cnn_module_kernels[-1] // i)
+            else:
+                self.cnn_module_kernels.append(self.cnn_module_kernels[-1])
+
+        logging.info(
+            f"stride_layer_idx= {self.stride_layer_idx}, "
+            f"stride = {self.stride}, "
+            f"cnn_module_kernel = {self.cnn_module_kernels}, "
+            f"group_layer_idx = {self.group_layer_idx}, "
+            f"grouped_size = {self.grouped_size}"
+        )
+
+        # feed-forward module definition
+        positionwise_layer = PositionwiseFeedForward
+        positionwise_layer_args = (
+            output_size,
+            linear_units,
+            dropout_rate,
+            activation,
+        )
+        # convolution module definition
+        convolution_layer = ConvolutionModule
+
+        # encoder definition
+        index = 0
+        layers = []
+        for i in range(num_blocks):
+            # self-attention module definition
+            if i in self.group_layer_idx:
+                encoder_selfattn_layer = GroupedRelPositionMultiHeadedAttention
+                encoder_selfattn_layer_args = (
+                    attention_heads,
+                    output_size,
+                    attention_dropout_rate,
+                    self.grouped_size,
+                )
+            else:
+                if pos_enc_layer_type == "no_pos":
+                    encoder_selfattn_layer = MultiHeadedAttention
+                else:
+                    encoder_selfattn_layer = RelPositionMultiHeadedAttention
+                encoder_selfattn_layer_args = (
+                    attention_heads,
+                    output_size,
+                    attention_dropout_rate,
+                )
+
+            # conformer module definition
+            if i in self.stride_layer_idx:
+                # conformer block with downsampling
+                convolution_layer_args_stride = (
+                    output_size,
+                    self.cnn_module_kernels[index],
+                    activation,
+                    cnn_module_norm,
+                    causal,
+                    True,
+                    self.stride[index],
+                )
+                layers.append(
+                    StrideConformerEncoderLayer(
+                        output_size,
+                        encoder_selfattn_layer(*encoder_selfattn_layer_args),
+                        positionwise_layer(*positionwise_layer_args),
+                        positionwise_layer(*positionwise_layer_args)
+                        if macaron_style
+                        else None,
+                        convolution_layer(*convolution_layer_args_stride)
+                        if use_cnn_module
+                        else None,
+                        torch.nn.AvgPool1d(
+                            kernel_size=self.stride[index],
+                            stride=self.stride[index],
+                            padding=0,
+                            ceil_mode=True,
+                            count_include_pad=False,
+                        ),  # pointwise_conv_layer
+                        dropout_rate,
+                        normalize_before,
+                    )
+                )
+                index = index + 1
+            else:
+                # conformer block
+                convolution_layer_args_normal = (
+                    output_size,
+                    self.cnn_module_kernels[index],
+                    activation,
+                    cnn_module_norm,
+                    causal,
+                )
+                layers.append(
+                    ConformerEncoderLayer(
+                        output_size,
+                        encoder_selfattn_layer(*encoder_selfattn_layer_args),
+                        positionwise_layer(*positionwise_layer_args),
+                        positionwise_layer(*positionwise_layer_args)
+                        if macaron_style
+                        else None,
+                        convolution_layer(*convolution_layer_args_normal)
+                        if use_cnn_module
+                        else None,
+                        dropout_rate,
+                        normalize_before,
+                    )
+                )
+
+        self.encoders = torch.nn.ModuleList(layers)
+
+    def set_global_chunk_size(self, chunk_size):
+        """Used in ONNX export."""
+        logging.info(f"set global chunk size: {chunk_size}, default is 0.")
+        self.global_chunk_size = chunk_size
+        if self.embed.subsampling_rate == 2:
+            self.chunk_feature_map = 2 * self.global_chunk_size + 1
+        elif self.embed.subsampling_rate == 6:
+            self.chunk_feature_map = 6 * self.global_chunk_size + 5
+        elif self.embed.subsampling_rate == 8:
+            self.chunk_feature_map = 8 * self.global_chunk_size + 7
+        else:
+            self.chunk_feature_map = 4 * self.global_chunk_size + 3
+
+    def output_size(self) -> int:
+        return self._output_size
+
+    def calculate_downsampling_factor(self, i: int) -> int:
+        factor = 1
+        for idx, stride_idx in enumerate(self.stride_layer_idx):
+            if i > stride_idx:
+                factor *= self.stride[idx]
+        return factor
+
+    def forward(
+        self,
+        xs: torch.Tensor,
+        xs_lens: torch.Tensor,
+        decoding_chunk_size: int = 0,
+        num_decoding_left_chunks: int = -1,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Embed positions in tensor.
+        Args:
+            xs: padded input tensor (B, T, D)
+            xs_lens: input length (B)
+            decoding_chunk_size: decoding chunk size for dynamic chunk
+                0: default for training, use random dynamic chunk.
+                <0: for decoding, use full chunk.
+                >0: for decoding, use fixed chunk size as set.
+            num_decoding_left_chunks: number of left chunks, this is for decoding,
+            the chunk size is decoding_chunk_size.
+                >=0: use num_decoding_left_chunks
+                <0: use all left chunks
+        Returns:
+            encoder output tensor xs, and subsampled masks
+            xs: padded output tensor (B, T' ~= T/subsample_rate, D)
+            masks: torch.Tensor batch padding mask after subsample
+                (B, 1, T' ~= T/subsample_rate)
+        """
+        T = xs.size(1)
+        masks = ~make_pad_mask(xs_lens, T).unsqueeze(1)  # (B, 1, T)
+        if self.global_cmvn is not None:
+            xs = self.global_cmvn(xs)
+        xs, pos_emb, masks = self.embed(xs, masks)
+        mask_pad = masks  # (B, 1, T/subsample_rate)
+        chunk_masks = add_optional_chunk_mask(
+            xs,
+            masks,
+            self.use_dynamic_chunk,
+            self.use_dynamic_left_chunk,
+            decoding_chunk_size,
+            self.static_chunk_size,
+            num_decoding_left_chunks,
+        )
+        index = 0  # traverse stride
+        for i, layer in enumerate(self.encoders):
+            # layer return : x, mask, new_att_cache, new_cnn_cache
+            xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad)
+            if i in self.stride_layer_idx:
+                masks = masks[:, :, :: self.stride[index]]
+                chunk_masks = chunk_masks[
+                    :, :: self.stride[index], :: self.stride[index]
+                ]
+                mask_pad = masks
+                pos_emb = pos_emb[:, :: self.stride[index], :]
+                index = index + 1
+
+        if self.normalize_before:
+            xs = self.after_norm(xs)
+        # Here we assume the mask is not changed in encoder layers, so just
+        # return the masks before encoder layers, and the masks will be used
+        # for cross attention with decoder later
+        return xs, masks
+
+    def forward_chunk(
+        self,
+        xs: torch.Tensor,
+        offset: int,
+        required_cache_size: int,
+        att_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
+        cnn_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
+        att_mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Forward just one chunk
+
+        Args:
+            xs (torch.Tensor): chunk input
+            offset (int): current offset in encoder output time stamp
+            required_cache_size (int): cache size required for next chunk
+                compuation
+                >=0: actual cache size
+                <0: means all history cache is required
+            att_cache (torch.Tensor): cache tensor for KEY & VALUE in
+                transformer/conformer attention, with shape
+                (elayers, head, cache_t1, d_k * 2), where
+                `head * d_k == hidden-dim` and
+                `cache_t1 == chunk_size * num_decoding_left_chunks`.
+            cnn_cache (torch.Tensor): cache tensor for cnn_module in conformer,
+                (elayers, b=1, hidden-dim, cache_t2), where
+                `cache_t2 == cnn.lorder - 1`
+            att_mask : mask matrix of self attention
+
+        Returns:
+            torch.Tensor: output of current input xs
+            torch.Tensor: subsampling cache required for next chunk computation
+            List[torch.Tensor]: encoder layers output cache required for next
+                chunk computation
+            List[torch.Tensor]: conformer cnn cache
+
+        """
+        assert xs.size(0) == 1
+
+        # using downsampling factor to recover offset
+        offset *= self.calculate_downsampling_factor(self.num_blocks + 1)
+
+        chunk_masks = torch.ones(1, xs.size(1), device=xs.device, dtype=torch.bool)
+        chunk_masks = chunk_masks.unsqueeze(1)  # (1, 1, xs-time)
+
+        real_len = 0
+        if self.global_chunk_size > 0:
+            # for ONNX decode simulation， padding xs to chunk_size
+            real_len = xs.size(1)
+            pad_len = self.chunk_feature_map - real_len
+            xs = F.pad(xs, (0, 0, 0, pad_len), value=0.0)
+            chunk_masks = F.pad(chunk_masks, (0, pad_len), value=0.0)
+
+        if self.global_cmvn is not None:
+            xs = self.global_cmvn(xs)
+
+        # NOTE(xcsong): Before embed, shape(xs) is (b=1, time, mel-dim)
+        xs, pos_emb, chunk_masks = self.embed(xs, chunk_masks, offset)
+        elayers, cache_t1 = att_cache.size(0), att_cache.size(2)
+        chunk_size = xs.size(1)
+        attention_key_size = cache_t1 + chunk_size
+        # NOTE(xcsong): After  embed, shape(xs) is (b=1, chunk_size, hidden-dim)
+        # shape(pos_emb) = (b=1, chunk_size, emb_size=output_size=hidden-dim)
+
+        if required_cache_size < 0:
+            next_cache_start = 0
+        elif required_cache_size == 0:
+            next_cache_start = attention_key_size
+        else:
+            next_cache_start = max(attention_key_size - required_cache_size, 0)
+
+        r_att_cache = []
+        r_cnn_cache = []
+        mask_pad = torch.ones(1, xs.size(1), device=xs.device, dtype=torch.bool)
+        mask_pad = mask_pad.unsqueeze(1)  # batchPad (b=1, 1, time=chunk_size)
+
+        if self.global_chunk_size > 0:
+            # for ONNX decode simulation
+            pos_emb = self.embed.position_encoding(
+                offset=max(offset - cache_t1, 0), size=cache_t1 + self.global_chunk_size
+            )
+            att_mask[:, :, -self.global_chunk_size :] = chunk_masks
+            mask_pad = chunk_masks.to(torch.bool)
+        else:
+            pos_emb = self.embed.position_encoding(
+                offset=offset - cache_t1, size=attention_key_size
+            )
+
+        max_att_len, max_cnn_len = 0, 0  # for repeat_interleave of new_att_cache
+        for i, layer in enumerate(self.encoders):
+            factor = self.calculate_downsampling_factor(i)
+            # NOTE(xcsong): Before layer.forward
+            #   shape(att_cache[i:i + 1]) is (1, head, cache_t1, d_k * 2),
+            #   shape(cnn_cache[i])       is (b=1, hidden-dim, cache_t2)
+            # shape(new_att_cache) = [ batch, head, time2, outdim//head * 2 ]
+            att_cache_trunc = 0
+            if xs.size(1) + att_cache.size(2) / factor > pos_emb.size(1):
+                # The time step is not divisible by the downsampling multiple
+                att_cache_trunc = (
+                    xs.size(1) + att_cache.size(2) // factor - pos_emb.size(1) + 1
+                )
+            xs, _, new_att_cache, new_cnn_cache = layer(
+                xs,
+                att_mask,
+                pos_emb,
+                mask_pad=mask_pad,
+                att_cache=att_cache[i : i + 1, :, ::factor, :][
+                    :, :, att_cache_trunc:, :
+                ],
+                cnn_cache=cnn_cache[i, :, :, :] if cnn_cache.size(0) > 0 else cnn_cache,
+            )
+
+            if i in self.stride_layer_idx:
+                # compute time dimension for next block
+                efficient_index = self.stride_layer_idx.index(i)
+                att_mask = att_mask[
+                    :, :: self.stride[efficient_index], :: self.stride[efficient_index]
+                ]
+                mask_pad = mask_pad[
+                    :, :: self.stride[efficient_index], :: self.stride[efficient_index]
+                ]
+                pos_emb = pos_emb[:, :: self.stride[efficient_index], :]
+
+            # shape(new_att_cache) = [batch, head, time2, outdim]
+            new_att_cache = new_att_cache[:, :, next_cache_start // factor :, :]
+            # shape(new_cnn_cache) = [1, batch, outdim, cache_t2]
+            new_cnn_cache = new_cnn_cache.unsqueeze(0)
+
+            # use repeat_interleave to new_att_cache
+            new_att_cache = new_att_cache.repeat_interleave(repeats=factor, dim=2)
+            # padding new_cnn_cache to cnn.lorder for casual convolution
+            new_cnn_cache = F.pad(
+                new_cnn_cache, (self.cnn_module_kernel - 1 - new_cnn_cache.size(3), 0)
+            )
+
+            if i == 0:
+                # record length for the first block as max length
+                max_att_len = new_att_cache.size(2)
+                max_cnn_len = new_cnn_cache.size(3)
+
+            # update real shape of att_cache and cnn_cache
+            r_att_cache.append(new_att_cache[:, :, -max_att_len:, :])
+            r_cnn_cache.append(new_cnn_cache[:, :, :, -max_cnn_len:])
+
+        if self.normalize_before:
+            xs = self.after_norm(xs)
+
+        # NOTE(xcsong): shape(r_att_cache) is (elayers, head, ?, d_k * 2),
+        #   ? may be larger than cache_t1, it depends on required_cache_size
+        r_att_cache = torch.cat(r_att_cache, dim=0)
+        # NOTE(xcsong): shape(r_cnn_cache) is (e, b=1, hidden-dim, cache_t2)
+        r_cnn_cache = torch.cat(r_cnn_cache, dim=0)
+
+        if self.global_chunk_size > 0 and real_len:
+            chunk_real_len = (
+                real_len
+                // self.embed.subsampling_rate
+                // self.calculate_downsampling_factor(self.num_blocks + 1)
+            )
+            # Keeping 1 more timestep can mitigate information leakage
+            #   from the encoder caused by the padding
+            xs = xs[:, : chunk_real_len + 1, :]
+
+        return xs, r_att_cache, r_cnn_cache
+
+    def forward_chunk_by_chunk(
+        self,
+        xs: torch.Tensor,
+        decoding_chunk_size: int,
+        num_decoding_left_chunks: int = -1,
+        use_onnx=False,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Forward input chunk by chunk with chunk_size like a streaming
+            fashion
+
+        Here we should pay special attention to computation cache in the
+        streaming style forward chunk by chunk. Three things should be taken
+        into account for computation in the current network:
+            1. transformer/conformer encoder layers output cache
+            2. convolution in conformer
+            3. convolution in subsampling
+
+        However, we don't implement subsampling cache for:
+            1. We can control subsampling module to output the right result by
+               overlapping input instead of cache left context, even though it
+               wastes some computation, but subsampling only takes a very
+               small fraction of computation in the whole model.
+            2. Typically, there are several covolution layers with subsampling
+               in subsampling module, it is tricky and complicated to do cache
+               with different convolution layers with different subsampling
+               rate.
+            3. Currently, nn.Sequential is used to stack all the convolution
+               layers in subsampling, we need to rewrite it to make it work
+               with cache, which is not prefered.
+        Args:
+            xs (torch.Tensor): (1, max_len, dim)
+            decoding_chunk_size (int): decoding chunk size
+            num_decoding_left_chunks (int):
+            use_onnx (bool): True for simulating ONNX model inference.
+        """
+        assert decoding_chunk_size > 0
+        # The model is trained by static or dynamic chunk
+        assert self.static_chunk_size > 0 or self.use_dynamic_chunk
+        subsampling = self.embed.subsampling_rate
+        context = self.embed.right_context + 1  # Add current frame
+        stride = subsampling * decoding_chunk_size
+        decoding_window = (decoding_chunk_size - 1) * subsampling + context
+        num_frames = xs.size(1)
+
+        outputs = []
+        offset = 0
+        required_cache_size = decoding_chunk_size * num_decoding_left_chunks
+        if use_onnx:
+            logging.info("Simulating for ONNX runtime ...")
+            att_cache: torch.Tensor = torch.zeros(
+                (
+                    self.num_blocks,
+                    self.attention_heads,
+                    required_cache_size,
+                    self.output_size() // self.attention_heads * 2,
+                ),
+                device=xs.device,
+            )
+            cnn_cache: torch.Tensor = torch.zeros(
+                (self.num_blocks, 1, self.output_size(), self.cnn_module_kernel - 1),
+                device=xs.device,
+            )
+            self.set_global_chunk_size(chunk_size=decoding_chunk_size)
+        else:
+            logging.info("Simulating for JIT runtime ...")
+            att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device)
+            cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device)
+
+        # Feed forward overlap input step by step
+        for cur in range(0, num_frames - context + 1, stride):
+            end = min(cur + decoding_window, num_frames)
+            logging.info(
+                f"-->> frame chunk msg: cur={cur}, "
+                f"end={end}, num_frames={end-cur}, "
+                f"decoding_window={decoding_window}"
+            )
+            if use_onnx:
+                att_mask: torch.Tensor = torch.ones(
+                    (1, 1, required_cache_size + decoding_chunk_size),
+                    dtype=torch.bool,
+                    device=xs.device,
+                )
+                if cur == 0:
+                    att_mask[:, :, :required_cache_size] = 0
+            else:
+                att_mask: torch.Tensor = torch.ones(
+                    (0, 0, 0), dtype=torch.bool, device=xs.device
+                )
+
+            chunk_xs = xs[:, cur:end, :]
+            (y, att_cache, cnn_cache) = self.forward_chunk(
+                chunk_xs, offset, required_cache_size, att_cache, cnn_cache, att_mask
+            )
+            outputs.append(y)
+            offset += y.size(1)
+
+        ys = torch.cat(outputs, 1)
+        masks = torch.ones(1, 1, ys.size(1), device=ys.device, dtype=torch.bool)
+        return ys, masks
diff --git a/modules/wenet_extractor/efficient_conformer/encoder_layer.py b/modules/wenet_extractor/efficient_conformer/encoder_layer.py
new file mode 100755
index 0000000000000000000000000000000000000000..9156c0edf9f79df6425accd542369e139591ed42
--- /dev/null
+++ b/modules/wenet_extractor/efficient_conformer/encoder_layer.py
@@ -0,0 +1,172 @@
+## This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+"""Encoder self-attention layer definition."""
+
+from typing import Optional, Tuple
+import torch
+from torch import nn
+
+
+class StrideConformerEncoderLayer(nn.Module):
+    """Encoder layer module.
+    Args:
+        size (int): Input dimension.
+        self_attn (torch.nn.Module): Self-attention module instance.
+            `MultiHeadedAttention` or `RelPositionMultiHeadedAttention`
+            instance can be used as the argument.
+        feed_forward (torch.nn.Module): Feed-forward module instance.
+            `PositionwiseFeedForward` instance can be used as the argument.
+        feed_forward_macaron (torch.nn.Module): Additional feed-forward module
+             instance.
+            `PositionwiseFeedForward` instance can be used as the argument.
+        conv_module (torch.nn.Module): Convolution module instance.
+            `ConvlutionModule` instance can be used as the argument.
+        dropout_rate (float): Dropout rate.
+        normalize_before (bool):
+            True: use layer_norm before each sub-block.
+            False: use layer_norm after each sub-block.
+    """
+
+    def __init__(
+        self,
+        size: int,
+        self_attn: torch.nn.Module,
+        feed_forward: Optional[nn.Module] = None,
+        feed_forward_macaron: Optional[nn.Module] = None,
+        conv_module: Optional[nn.Module] = None,
+        pointwise_conv_layer: Optional[nn.Module] = None,
+        dropout_rate: float = 0.1,
+        normalize_before: bool = True,
+    ):
+        """Construct an EncoderLayer object."""
+        super().__init__()
+        self.self_attn = self_attn
+        self.feed_forward = feed_forward
+        self.feed_forward_macaron = feed_forward_macaron
+        self.conv_module = conv_module
+        self.pointwise_conv_layer = pointwise_conv_layer
+        self.norm_ff = nn.LayerNorm(size, eps=1e-5)  # for the FNN module
+        self.norm_mha = nn.LayerNorm(size, eps=1e-5)  # for the MHA module
+        if feed_forward_macaron is not None:
+            self.norm_ff_macaron = nn.LayerNorm(size, eps=1e-5)
+            self.ff_scale = 0.5
+        else:
+            self.ff_scale = 1.0
+        if self.conv_module is not None:
+            self.norm_conv = nn.LayerNorm(size, eps=1e-5)  # for the CNN module
+            self.norm_final = nn.LayerNorm(
+                size, eps=1e-5
+            )  # for the final output of the block
+        self.dropout = nn.Dropout(dropout_rate)
+        self.size = size
+        self.normalize_before = normalize_before
+        self.concat_linear = nn.Linear(size + size, size)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        mask: torch.Tensor,
+        pos_emb: torch.Tensor,
+        mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+        att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
+        cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Compute encoded features.
+
+        Args:
+            x (torch.Tensor): (#batch, time, size)
+            mask (torch.Tensor): Mask tensor for the input (#batch, time，time),
+                (0, 0, 0) means fake mask.
+            pos_emb (torch.Tensor): positional encoding, must not be None
+                for ConformerEncoderLayer.
+            mask_pad (torch.Tensor): batch padding mask used for conv module.
+                (#batch, 1，time), (0, 0, 0) means fake mask.
+            att_cache (torch.Tensor): Cache tensor of the KEY & VALUE
+                (#batch=1, head, cache_t1, d_k * 2), head * d_k == size.
+            cnn_cache (torch.Tensor): Convolution cache in conformer layer
+                (#batch=1, size, cache_t2)
+        Returns:
+            torch.Tensor: Output tensor (#batch, time, size).
+            torch.Tensor: Mask tensor (#batch, time, time).
+            torch.Tensor: att_cache tensor,
+                (#batch=1, head, cache_t1 + time, d_k * 2).
+            torch.Tensor: cnn_cahce tensor (#batch, size, cache_t2).
+        """
+
+        # whether to use macaron style
+        if self.feed_forward_macaron is not None:
+            residual = x
+            if self.normalize_before:
+                x = self.norm_ff_macaron(x)
+            x = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(x))
+            if not self.normalize_before:
+                x = self.norm_ff_macaron(x)
+
+        # multi-headed self-attention module
+        residual = x
+        if self.normalize_before:
+            x = self.norm_mha(x)
+
+        x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb, att_cache)
+
+        x = residual + self.dropout(x_att)
+        if not self.normalize_before:
+            x = self.norm_mha(x)
+
+        # convolution module
+        # Fake new cnn cache here, and then change it in conv_module
+        new_cnn_cache = torch.tensor([0.0], dtype=x.dtype, device=x.device)
+        if self.conv_module is not None:
+            residual = x
+            if self.normalize_before:
+                x = self.norm_conv(x)
+            x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache)
+
+            # add pointwise_conv for efficient conformer
+            #   pointwise_conv_layer does not change shape
+            if self.pointwise_conv_layer is not None:
+                residual = residual.transpose(1, 2)
+                residual = self.pointwise_conv_layer(residual)
+                residual = residual.transpose(1, 2)
+                assert residual.size(0) == x.size(0)
+                assert residual.size(1) == x.size(1)
+                assert residual.size(2) == x.size(2)
+
+            x = residual + self.dropout(x)
+
+            if not self.normalize_before:
+                x = self.norm_conv(x)
+
+        # feed forward module
+        residual = x
+        if self.normalize_before:
+            x = self.norm_ff(x)
+
+        x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
+        if not self.normalize_before:
+            x = self.norm_ff(x)
+
+        if self.conv_module is not None:
+            x = self.norm_final(x)
+
+        return x, mask, new_att_cache, new_cnn_cache
diff --git a/modules/wenet_extractor/efficient_conformer/subsampling.py b/modules/wenet_extractor/efficient_conformer/subsampling.py
new file mode 100755
index 0000000000000000000000000000000000000000..6017084c2accb40c86be16d798e587b4f32c26d4
--- /dev/null
+++ b/modules/wenet_extractor/efficient_conformer/subsampling.py
@@ -0,0 +1,81 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+
+"""Subsampling layer definition."""
+
+from typing import Tuple, Union
+
+import torch
+from modules.wenet_extractor.transformer.subsampling import BaseSubsampling
+
+
+class Conv2dSubsampling2(BaseSubsampling):
+    """Convolutional 2D subsampling (to 1/4 length).
+
+    Args:
+        idim (int): Input dimension.
+        odim (int): Output dimension.
+        dropout_rate (float): Dropout rate.
+
+    """
+
+    def __init__(
+        self, idim: int, odim: int, dropout_rate: float, pos_enc_class: torch.nn.Module
+    ):
+        """Construct an Conv2dSubsampling4 object."""
+        super().__init__()
+        self.conv = torch.nn.Sequential(torch.nn.Conv2d(1, odim, 3, 2), torch.nn.ReLU())
+        self.out = torch.nn.Sequential(torch.nn.Linear(odim * ((idim - 1) // 2), odim))
+        self.pos_enc = pos_enc_class
+        # The right context for every conv layer is computed by:
+        # (kernel_size - 1) * frame_rate_of_this_layer
+        self.subsampling_rate = 2
+        # 2 = (3 - 1) * 1
+        self.right_context = 2
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_mask: torch.Tensor,
+        offset: Union[int, torch.Tensor] = 0,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Subsample x.
+
+        Args:
+            x (torch.Tensor): Input tensor (#batch, time, idim).
+            x_mask (torch.Tensor): Input mask (#batch, 1, time).
+
+        Returns:
+            torch.Tensor: Subsampled tensor (#batch, time', odim),
+                where time' = time // 2.
+            torch.Tensor: Subsampled mask (#batch, 1, time'),
+                where time' = time // 2.
+            torch.Tensor: positional encoding
+
+        """
+        x = x.unsqueeze(1)  # (b, c=1, t, f)
+        x = self.conv(x)
+        b, c, t, f = x.size()
+        x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
+        x, pos_emb = self.pos_enc(x, offset)
+        return x, pos_emb, x_mask[:, :, :-2:2]
diff --git a/modules/wenet_extractor/paraformer/paraformer.py b/modules/wenet_extractor/paraformer/paraformer.py
new file mode 100755
index 0000000000000000000000000000000000000000..8f144baa2a5df2292fa4616952338c8f937bfcba
--- /dev/null
+++ b/modules/wenet_extractor/paraformer/paraformer.py
@@ -0,0 +1,366 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+from typing import Dict, Optional, Tuple
+
+import torch
+
+from modules.wenet_extractor.cif.predictor import MAELoss
+from modules.wenet_extractor.paraformer.search.beam_search import Hypothesis
+from modules.wenet_extractor.transformer.asr_model import ASRModel
+from modules.wenet_extractor.transformer.ctc import CTC
+from modules.wenet_extractor.transformer.decoder import TransformerDecoder
+from modules.wenet_extractor.transformer.encoder import TransformerEncoder
+from modules.wenet_extractor.utils.common import IGNORE_ID, add_sos_eos, th_accuracy
+from modules.wenet_extractor.utils.mask import make_pad_mask
+
+
+class Paraformer(ASRModel):
+    """Paraformer: Fast and Accurate Parallel Transformer for
+    Non-autoregressive End-to-End Speech Recognition
+    see https://arxiv.org/pdf/2206.08317.pdf
+    """
+
+    def __init__(
+        self,
+        vocab_size: int,
+        encoder: TransformerEncoder,
+        decoder: TransformerDecoder,
+        ctc: CTC,
+        predictor,
+        ctc_weight: float = 0.5,
+        predictor_weight: float = 1.0,
+        predictor_bias: int = 0,
+        ignore_id: int = IGNORE_ID,
+        reverse_weight: float = 0.0,
+        lsm_weight: float = 0.0,
+        length_normalized_loss: bool = False,
+    ):
+        assert 0.0 <= ctc_weight <= 1.0, ctc_weight
+        assert 0.0 <= predictor_weight <= 1.0, predictor_weight
+
+        super().__init__(
+            vocab_size,
+            encoder,
+            decoder,
+            ctc,
+            ctc_weight,
+            ignore_id,
+            reverse_weight,
+            lsm_weight,
+            length_normalized_loss,
+        )
+        self.predictor = predictor
+        self.predictor_weight = predictor_weight
+        self.predictor_bias = predictor_bias
+        self.criterion_pre = MAELoss(normalize_length=length_normalized_loss)
+
+    def forward(
+        self,
+        speech: torch.Tensor,
+        speech_lengths: torch.Tensor,
+        text: torch.Tensor,
+        text_lengths: torch.Tensor,
+    ) -> Dict[str, Optional[torch.Tensor]]:
+        """Frontend + Encoder + Decoder + Calc loss
+
+        Args:
+            speech: (Batch, Length, ...)
+            speech_lengths: (Batch, )
+            text: (Batch, Length)
+            text_lengths: (Batch,)
+        """
+        assert text_lengths.dim() == 1, text_lengths.shape
+        # Check that batch_size is unified
+        assert (
+            speech.shape[0]
+            == speech_lengths.shape[0]
+            == text.shape[0]
+            == text_lengths.shape[0]
+        ), (speech.shape, speech_lengths.shape, text.shape, text_lengths.shape)
+        # 1. Encoder
+        encoder_out, encoder_mask = self.encoder(speech, speech_lengths)
+        encoder_out_lens = encoder_mask.squeeze(1).sum(1)
+
+        # 2a. Attention-decoder branch
+        if self.ctc_weight != 1.0:
+            loss_att, acc_att, loss_pre = self._calc_att_loss(
+                encoder_out, encoder_mask, text, text_lengths
+            )
+        else:
+            # loss_att = None
+            # loss_pre = None
+            loss_att: torch.Tensor = torch.tensor(0)
+            loss_pre: torch.Tensor = torch.tensor(0)
+
+        # 2b. CTC branch
+        if self.ctc_weight != 0.0:
+            loss_ctc = self.ctc(encoder_out, encoder_out_lens, text, text_lengths)
+        else:
+            loss_ctc = None
+
+        if loss_ctc is None:
+            loss = loss_att + self.predictor_weight * loss_pre
+        # elif loss_att is None:
+        elif loss_att == torch.tensor(0):
+            loss = loss_ctc
+        else:
+            loss = (
+                self.ctc_weight * loss_ctc
+                + (1 - self.ctc_weight) * loss_att
+                + self.predictor_weight * loss_pre
+            )
+        return {
+            "loss": loss,
+            "loss_att": loss_att,
+            "loss_ctc": loss_ctc,
+            "loss_pre": loss_pre,
+        }
+
+    def _calc_att_loss(
+        self,
+        encoder_out: torch.Tensor,
+        encoder_mask: torch.Tensor,
+        ys_pad: torch.Tensor,
+        ys_pad_lens: torch.Tensor,
+    ) -> Tuple[torch.Tensor, float, torch.Tensor]:
+        if self.predictor_bias == 1:
+            _, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
+            ys_pad_lens = ys_pad_lens + self.predictor_bias
+        pre_acoustic_embeds, pre_token_length, _, pre_peak_index = self.predictor(
+            encoder_out, ys_pad, encoder_mask, ignore_id=self.ignore_id
+        )
+        # 1. Forward decoder
+        decoder_out, _, _ = self.decoder(
+            encoder_out, encoder_mask, pre_acoustic_embeds, ys_pad_lens
+        )
+
+        # 2. Compute attention loss
+        loss_att = self.criterion_att(decoder_out, ys_pad)
+        acc_att = th_accuracy(
+            decoder_out.view(-1, self.vocab_size),
+            ys_pad,
+            ignore_label=self.ignore_id,
+        )
+        loss_pre: torch.Tensor = self.criterion_pre(
+            ys_pad_lens.type_as(pre_token_length), pre_token_length
+        )
+
+        return loss_att, acc_att, loss_pre
+
+    def calc_predictor(self, encoder_out, encoder_mask):
+        encoder_mask = (
+            ~make_pad_mask(encoder_mask, max_len=encoder_out.size(1))[:, None, :]
+        ).to(encoder_out.device)
+        pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = self.predictor(
+            encoder_out, None, encoder_mask, ignore_id=self.ignore_id
+        )
+        return pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index
+
+    def cal_decoder_with_predictor(
+        self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens
+    ):
+        decoder_out, _, _ = self.decoder(
+            encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens
+        )
+        decoder_out = torch.log_softmax(decoder_out, dim=-1)
+        return decoder_out, ys_pad_lens
+
+    def recognize(self):
+        raise NotImplementedError
+
+    def paraformer_greedy_search(
+        self,
+        speech: torch.Tensor,
+        speech_lengths: torch.Tensor,
+        decoding_chunk_size: int = -1,
+        num_decoding_left_chunks: int = -1,
+        simulate_streaming: bool = False,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Apply beam search on attention decoder
+
+        Args:
+            speech (torch.Tensor): (batch, max_len, feat_dim)
+            speech_length (torch.Tensor): (batch, )
+            decoding_chunk_size (int): decoding chunk for dynamic chunk
+                trained model.
+                <0: for decoding, use full chunk.
+                >0: for decoding, use fixed chunk size as set.
+                0: used for training, it's prohibited here
+            simulate_streaming (bool): whether do encoder forward in a
+                streaming fashion
+
+        Returns:
+            torch.Tensor: decoding result, (batch, max_result_len)
+        """
+        assert speech.shape[0] == speech_lengths.shape[0]
+        assert decoding_chunk_size != 0
+        device = speech.device
+        batch_size = speech.shape[0]
+
+        # Let's assume B = batch_size and N = beam_size
+        # 1. Encoder
+        encoder_out, encoder_mask = self._forward_encoder(
+            speech,
+            speech_lengths,
+            decoding_chunk_size,
+            num_decoding_left_chunks,
+            simulate_streaming,
+        )  # (B, maxlen, encoder_dim)
+        encoder_out_lens = encoder_mask.squeeze(1).sum(1)
+        # 2. Predictor
+        predictor_outs = self.calc_predictor(encoder_out, encoder_mask)
+        pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = (
+            predictor_outs[0],
+            predictor_outs[1],
+            predictor_outs[2],
+            predictor_outs[3],
+        )
+        pre_token_length = pre_token_length.round().long()
+        if torch.max(pre_token_length) < 1:
+            return torch.tensor([]), torch.tensor([])
+        # 2. Decoder forward
+        decoder_outs = self.cal_decoder_with_predictor(
+            encoder_out, encoder_out_lens, pre_acoustic_embeds, pre_token_length
+        )
+        decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
+        hyps = []
+        b, n, d = decoder_out.size()
+        for i in range(b):
+            x = encoder_out[i, : encoder_out_lens[i], :]
+            am_scores = decoder_out[i, : pre_token_length[i], :]
+            yseq = am_scores.argmax(dim=-1)
+            score = am_scores.max(dim=-1)[0]
+            score = torch.sum(score, dim=-1)
+            # pad with mask tokens to ensure compatibility with sos/eos tokens
+            yseq = torch.tensor(
+                [self.sos] + yseq.tolist() + [self.eos], device=yseq.device
+            )
+            nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
+
+            for hyp in nbest_hyps:
+                assert isinstance(hyp, (Hypothesis)), type(hyp)
+
+                # remove sos/eos and get hyps
+                last_pos = -1
+                if isinstance(hyp.yseq, list):
+                    token_int = hyp.yseq[1:last_pos]
+                else:
+                    token_int = hyp.yseq[1:last_pos].tolist()
+
+                # remove blank symbol id and unk id, which is assumed to be 0
+                # and 1
+                token_int = list(filter(lambda x: x != 0 and x != 1, token_int))
+                hyps.append(token_int)
+        return hyps
+
+    def paraformer_beam_search(
+        self,
+        speech: torch.Tensor,
+        speech_lengths: torch.Tensor,
+        beam_search: torch.nn.Module = None,
+        decoding_chunk_size: int = -1,
+        num_decoding_left_chunks: int = -1,
+        simulate_streaming: bool = False,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Apply beam search on attention decoder
+
+        Args:
+            speech (torch.Tensor): (batch, max_len, feat_dim)
+            speech_lengths (torch.Tensor): (batch, )
+            beam_search (torch.nn.Moudle): beam search module
+            decoding_chunk_size (int): decoding chunk for dynamic chunk
+                trained model.
+                <0: for decoding, use full chunk.
+                >0: for decoding, use fixed chunk size as set.
+                0: used for training, it's prohibited here
+            simulate_streaming (bool): whether do encoder forward in a
+                streaming fashion
+
+        Returns:
+            torch.Tensor: decoding result, (batch, max_result_len)
+        """
+        assert speech.shape[0] == speech_lengths.shape[0]
+        assert decoding_chunk_size != 0
+        device = speech.device
+        batch_size = speech.shape[0]
+
+        # Let's assume B = batch_size and N = beam_size
+        # 1. Encoder
+        encoder_out, encoder_mask = self._forward_encoder(
+            speech,
+            speech_lengths,
+            decoding_chunk_size,
+            num_decoding_left_chunks,
+            simulate_streaming,
+        )  # (B, maxlen, encoder_dim)
+        encoder_out_lens = encoder_mask.squeeze(1).sum(1)
+        # 2. Predictor
+        predictor_outs = self.calc_predictor(encoder_out, encoder_mask)
+        pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = (
+            predictor_outs[0],
+            predictor_outs[1],
+            predictor_outs[2],
+            predictor_outs[3],
+        )
+        pre_token_length = pre_token_length.round().long()
+        if torch.max(pre_token_length) < 1:
+            return torch.tensor([]), torch.tensor([])
+        # 2. Decoder forward
+        decoder_outs = self.cal_decoder_with_predictor(
+            encoder_out, encoder_out_lens, pre_acoustic_embeds, pre_token_length
+        )
+        decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
+        hyps = []
+        b, n, d = decoder_out.size()
+        for i in range(b):
+            x = encoder_out[i, : encoder_out_lens[i], :]
+            am_scores = decoder_out[i, : pre_token_length[i], :]
+            if beam_search is not None:
+                nbest_hyps = beam_search(x=x, am_scores=am_scores)
+                nbest_hyps = nbest_hyps[:1]
+            else:
+                yseq = am_scores.argmax(dim=-1)
+                score = am_scores.max(dim=-1)[0]
+                score = torch.sum(score, dim=-1)
+                # pad with mask tokens to ensure compatibility with sos/eos
+                # tokens
+                yseq = torch.tensor(
+                    [self.sos] + yseq.tolist() + [self.eos], device=yseq.device
+                )
+                nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
+
+            for hyp in nbest_hyps:
+                assert isinstance(hyp, (Hypothesis)), type(hyp)
+
+                # remove sos/eos and get hyps
+                last_pos = -1
+                if isinstance(hyp.yseq, list):
+                    token_int = hyp.yseq[1:last_pos]
+                else:
+                    token_int = hyp.yseq[1:last_pos].tolist()
+
+                # remove blank symbol id and unk id, which is assumed to be 0
+                # and 1
+                token_int = list(filter(lambda x: x != 0 and x != 1, token_int))
+                hyps.append(token_int)
+        return hyps
diff --git a/modules/wenet_extractor/paraformer/search/beam_search.py b/modules/wenet_extractor/paraformer/search/beam_search.py
new file mode 100755
index 0000000000000000000000000000000000000000..5389ce4ac7f0026ab1dcf042c62b9c790bd61085
--- /dev/null
+++ b/modules/wenet_extractor/paraformer/search/beam_search.py
@@ -0,0 +1,479 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+from itertools import chain
+from typing import Any
+from typing import Dict
+from typing import List
+from typing import Tuple
+from typing import Union
+from typing import NamedTuple
+
+import torch
+
+from modules.wenet_extractor.paraformer.utils import end_detect
+from modules.wenet_extractor.paraformer.search.ctc import CTCPrefixScorer
+from modules.wenet_extractor.paraformer.search.scorer_interface import (
+    ScorerInterface,
+    PartialScorerInterface,
+)
+
+
+class Hypothesis(NamedTuple):
+    """Hypothesis data type."""
+
+    yseq: torch.Tensor
+    score: Union[float, torch.Tensor] = 0
+    scores: Dict[str, Union[float, torch.Tensor]] = dict()
+    states: Dict[str, Any] = dict()
+
+    def asdict(self) -> dict:
+        """Convert data to JSON-friendly dict."""
+        return self._replace(
+            yseq=self.yseq.tolist(),
+            score=float(self.score),
+            scores={k: float(v) for k, v in self.scores.items()},
+        )._asdict()
+
+
+class BeamSearchCIF(torch.nn.Module):
+    """Beam search implementation."""
+
+    def __init__(
+        self,
+        scorers: Dict[str, ScorerInterface],
+        weights: Dict[str, float],
+        beam_size: int,
+        vocab_size: int,
+        sos: int,
+        eos: int,
+        pre_beam_ratio: float = 1.5,
+        pre_beam_score_key: str = None,
+    ):
+        """Initialize beam search.
+
+        Args:
+            scorers (dict[str, ScorerInterface]): Dict of decoder modules
+                e.g., Decoder, CTCPrefixScorer, LM
+                The scorer will be ignored if it is `None`
+            weights (dict[str, float]): Dict of weights for each scorers
+                The scorer will be ignored if its weight is 0
+            beam_size (int): The number of hypotheses kept during search
+            vocab_size (int): The number of vocabulary
+            sos (int): Start of sequence id
+            eos (int): End of sequence id
+            pre_beam_score_key (str): key of scores to perform pre-beam search
+            pre_beam_ratio (float): beam size in the pre-beam search
+                will be `int(pre_beam_ratio * beam_size)`
+
+        """
+        super().__init__()
+        # set scorers
+        self.weights = weights
+        self.scorers = dict()
+        self.full_scorers = dict()
+        self.part_scorers = dict()
+        # this module dict is required for recursive cast
+        # `self.to(device, dtype)` in `recog.py`
+        self.nn_dict = torch.nn.ModuleDict()
+        for k, v in scorers.items():
+            w = weights.get(k, 0)
+            if w == 0 or v is None:
+                continue
+            assert isinstance(
+                v, ScorerInterface
+            ), f"{k} ({type(v)}) does not implement ScorerInterface"
+            self.scorers[k] = v
+            if isinstance(v, PartialScorerInterface):
+                self.part_scorers[k] = v
+            else:
+                self.full_scorers[k] = v
+            if isinstance(v, torch.nn.Module):
+                self.nn_dict[k] = v
+
+        # set configurations
+        self.sos = sos
+        self.eos = eos
+        self.pre_beam_size = int(pre_beam_ratio * beam_size)
+        self.beam_size = beam_size
+        self.n_vocab = vocab_size
+        if (
+            pre_beam_score_key is not None
+            and pre_beam_score_key != "full"
+            and pre_beam_score_key not in self.full_scorers
+        ):
+            raise KeyError(
+                f"{pre_beam_score_key} is not found in " f"{self.full_scorers}"
+            )
+        self.pre_beam_score_key = pre_beam_score_key
+        self.do_pre_beam = (
+            self.pre_beam_score_key is not None
+            and self.pre_beam_size < self.n_vocab
+            and len(self.part_scorers) > 0
+        )
+
+    def init_hyp(self, x: torch.Tensor) -> List[Hypothesis]:
+        """Get an initial hypothesis data.
+
+        Args:
+            x (torch.Tensor): The encoder output feature
+
+        Returns:
+            Hypothesis: The initial hypothesis.
+
+        """
+        init_states = dict()
+        init_scores = dict()
+        for k, d in self.scorers.items():
+            init_states[k] = d.init_state(x)
+            init_scores[k] = 0.0
+        return [
+            Hypothesis(
+                score=0.0,
+                scores=init_scores,
+                states=init_states,
+                yseq=torch.tensor([self.sos], device=x.device),
+            )
+        ]
+
+    @staticmethod
+    def append_token(xs: torch.Tensor, x: int) -> torch.Tensor:
+        """Append new token to prefix tokens.
+
+        Args:
+            xs (torch.Tensor): The prefix token
+            x (int): The new token to append
+
+        Returns:
+            torch.Tensor: New tensor contains: xs + [x] with xs.dtype and
+            xs.device
+
+        """
+        x = torch.tensor([x], dtype=xs.dtype, device=xs.device)
+        return torch.cat((xs, x))
+
+    def score_full(
+        self, hyp: Hypothesis, x: torch.Tensor
+    ) -> Tuple[Dict[str, torch.Tensor], Dict[str, Any]]:
+        """Score new hypothesis by `self.full_scorers`.
+
+        Args:
+            hyp (Hypothesis): Hypothesis with prefix tokens to score
+            x (torch.Tensor): Corresponding input feature
+
+        Returns:
+            Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: Tuple of
+                score dict of `hyp` that has string keys of `self.full_scorers`
+                and tensor score values of shape: `(self.n_vocab,)`,
+                and state dict that has string keys
+                and state values of `self.full_scorers`
+
+        """
+        scores = dict()
+        states = dict()
+        for k, d in self.full_scorers.items():
+            scores[k], states[k] = d.score(hyp.yseq, hyp.states[k], x)
+        return scores, states
+
+    def score_partial(
+        self, hyp: Hypothesis, ids: torch.Tensor, x: torch.Tensor
+    ) -> Tuple[Dict[str, torch.Tensor], Dict[str, Any]]:
+        """Score new hypothesis by `self.part_scorers`.
+
+        Args:
+            hyp (Hypothesis): Hypothesis with prefix tokens to score
+            ids (torch.Tensor): 1D tensor of new partial tokens to score
+            x (torch.Tensor): Corresponding input feature
+
+        Returns:
+            Tuple[Dict[str, torch.Tensor], Dict[str, Any]]: Tuple of
+                score dict of `hyp` that has string keys of `self.part_scorers`
+                and tensor score values of shape: `(len(ids),)`,
+                and state dict that has string keys
+                and state values of `self.part_scorers`
+
+        """
+        scores = dict()
+        states = dict()
+        for k, d in self.part_scorers.items():
+            scores[k], states[k] = d.score_partial(hyp.yseq, ids, hyp.states[k], x)
+        return scores, states
+
+    def beam(
+        self, weighted_scores: torch.Tensor, ids: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Compute topk full token ids and partial token ids.
+
+        Args:
+            weighted_scores (torch.Tensor): The weighted sum scores for each
+            tokens.
+            Its shape is `(self.n_vocab,)`.
+            ids (torch.Tensor): The partial token ids to compute topk
+
+        Returns:
+            Tuple[torch.Tensor, torch.Tensor]:
+                The topk full token ids and partial token ids.
+                Their shapes are `(self.beam_size,)`
+
+        """
+        # no pre beam performed
+        if weighted_scores.size(0) == ids.size(0):
+            top_ids = weighted_scores.topk(self.beam_size)[1]
+            return top_ids, top_ids
+
+        # mask pruned in pre-beam not to select in topk
+        tmp = weighted_scores[ids]
+        weighted_scores[:] = -float("inf")
+        weighted_scores[ids] = tmp
+        top_ids = weighted_scores.topk(self.beam_size)[1]
+        local_ids = weighted_scores[ids].topk(self.beam_size)[1]
+        return top_ids, local_ids
+
+    @staticmethod
+    def merge_scores(
+        prev_scores: Dict[str, float],
+        next_full_scores: Dict[str, torch.Tensor],
+        full_idx: int,
+        next_part_scores: Dict[str, torch.Tensor],
+        part_idx: int,
+    ) -> Dict[str, torch.Tensor]:
+        """Merge scores for new hypothesis.
+
+        Args:
+            prev_scores (Dict[str, float]):
+                The previous hypothesis scores by `self.scorers`
+            next_full_scores (Dict[str, torch.Tensor]): scores by
+            `self.full_scorers`
+            full_idx (int): The next token id for `next_full_scores`
+            next_part_scores (Dict[str, torch.Tensor]):
+                scores of partial tokens by `self.part_scorers`
+            part_idx (int): The new token id for `next_part_scores`
+
+        Returns:
+            Dict[str, torch.Tensor]: The new score dict.
+                Its keys are names of `self.full_scorers` and
+                `self.part_scorers`.
+                Its values are scalar tensors by the scorers.
+
+        """
+        new_scores = dict()
+        for k, v in next_full_scores.items():
+            new_scores[k] = prev_scores[k] + v[full_idx]
+        for k, v in next_part_scores.items():
+            new_scores[k] = prev_scores[k] + v[part_idx]
+        return new_scores
+
+    def merge_states(self, states: Any, part_states: Any, part_idx: int) -> Any:
+        """Merge states for new hypothesis.
+
+        Args:
+            states: states of `self.full_scorers`
+            part_states: states of `self.part_scorers`
+            part_idx (int): The new token id for `part_scores`
+
+        Returns:
+            Dict[str, torch.Tensor]: The new score dict.
+                Its keys are names of `self.full_scorers` and
+                `self.part_scorers`.
+                Its values are states of the scorers.
+
+        """
+        new_states = dict()
+        for k, v in states.items():
+            new_states[k] = v
+        for k, d in self.part_scorers.items():
+            new_states[k] = d.select_state(part_states[k], part_idx)
+        return new_states
+
+    def search(
+        self, running_hyps: List[Hypothesis], x: torch.Tensor, am_score: torch.Tensor
+    ) -> List[Hypothesis]:
+        """Search new tokens for running hypotheses and encoded speech x.
+
+        Args:
+            running_hyps (List[Hypothesis]): Running hypotheses on beam
+            x (torch.Tensor): Encoded speech feature (T, D)
+
+        Returns:
+            List[Hypotheses]: Best sorted hypotheses
+
+        """
+        best_hyps = []
+        part_ids = torch.arange(self.n_vocab, device=x.device)  # no pre-beam
+        for hyp in running_hyps:
+            # scoring
+            weighted_scores = torch.zeros(self.n_vocab, dtype=x.dtype, device=x.device)
+            weighted_scores += am_score
+            scores, states = self.score_full(hyp, x)
+            for k in self.full_scorers:
+                weighted_scores += self.weights[k] * scores[k]
+            # partial scoring
+            if self.do_pre_beam:
+                pre_beam_scores = (
+                    weighted_scores
+                    if self.pre_beam_score_key == "full"
+                    else scores[self.pre_beam_score_key]
+                )
+                part_ids = torch.topk(pre_beam_scores, self.pre_beam_size)[1]
+            part_scores, part_states = self.score_partial(hyp, part_ids, x)
+            for k in self.part_scorers:
+                weighted_scores[part_ids] += self.weights[k] * part_scores[k]
+            # add previous hyp score
+            weighted_scores += hyp.score
+
+            # update hyps
+            for j, part_j in zip(*self.beam(weighted_scores, part_ids)):
+                # will be (2 x beam at most)
+                best_hyps.append(
+                    Hypothesis(
+                        score=weighted_scores[j],
+                        yseq=self.append_token(hyp.yseq, j),
+                        scores=self.merge_scores(
+                            hyp.scores, scores, j, part_scores, part_j
+                        ),
+                        states=self.merge_states(states, part_states, part_j),
+                    )
+                )
+
+            # sort and prune 2 x beam -> beam
+            best_hyps = sorted(best_hyps, key=lambda x: x.score, reverse=True)[
+                : min(len(best_hyps), self.beam_size)
+            ]
+        return best_hyps
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        am_scores: torch.Tensor,
+        maxlenratio: float = 0.0,
+        minlenratio: float = 0.0,
+    ) -> List[Hypothesis]:
+        """Perform beam search.
+
+        Args:
+            x (torch.Tensor): Encoded speech feature (T, D)
+            maxlenratio (float): Input length ratio to obtain max output length.
+                If maxlenratio=0.0 (default), it uses a end-detect function
+                to automatically find maximum hypothesis lengths
+                If maxlenratio<0.0, its absolute value is interpreted
+                as a constant max output length.
+            minlenratio (float): Input length ratio to obtain min output length.
+
+        Returns:
+            list[Hypothesis]: N-best decoding results
+
+        """
+        # set length bounds
+        maxlen = am_scores.shape[0]
+
+        # main loop of prefix search
+        running_hyps = self.init_hyp(x)
+        ended_hyps = []
+        for i in range(maxlen):
+            best = self.search(running_hyps, x, am_scores[i])
+            # post process of one iteration
+            running_hyps = self.post_process(i, maxlen, maxlenratio, best, ended_hyps)
+            # end detection
+            if maxlenratio == 0.0 and end_detect([h.asdict() for h in ended_hyps], i):
+                break
+
+        nbest_hyps = sorted(ended_hyps, key=lambda x: x.score, reverse=True)
+        # check the number of hypotheses reaching to eos
+        if len(nbest_hyps) == 0:
+            return (
+                []
+                if minlenratio < 0.1
+                else self.forward(x, maxlenratio, max(0.0, minlenratio - 0.1))
+            )
+
+        best = nbest_hyps[0]
+        return nbest_hyps
+
+    def post_process(
+        self,
+        i: int,
+        maxlen: int,
+        maxlenratio: float,
+        running_hyps: List[Hypothesis],
+        ended_hyps: List[Hypothesis],
+    ) -> List[Hypothesis]:
+        """Perform post-processing of beam search iterations.
+
+        Args:
+            i (int): The length of hypothesis tokens.
+            maxlen (int): The maximum length of tokens in beam search.
+            maxlenratio (int): The maximum length ratio in beam search.
+            running_hyps (List[Hypothesis]): The running hypotheses in beam
+                search.
+            ended_hyps (List[Hypothesis]): The ended hypotheses in beam search.
+
+        Returns:
+            List[Hypothesis]: The new running hypotheses.
+
+        """
+
+        # add eos in the final loop to avoid that there are no ended hyps
+        if i == maxlen - 1:
+            # logging.info("adding <eos> in the last position in the loop")
+            running_hyps = [
+                h._replace(yseq=self.append_token(h.yseq, self.eos))
+                for h in running_hyps
+            ]
+
+        # add ended hypotheses to a final list, and removed them from current
+        # hypotheses
+        # (this will be a problem, number of hyps < beam)
+        remained_hyps = []
+        for hyp in running_hyps:
+            if hyp.yseq[-1] == self.eos:
+                # e.g., Word LM needs to add final <eos> score
+                for k, d in chain(self.full_scorers.items(), self.part_scorers.items()):
+                    s = d.final_score(hyp.states[k])
+                    hyp.scores[k] += s
+                    hyp = hyp._replace(score=hyp.score + self.weights[k] * s)
+                ended_hyps.append(hyp)
+            else:
+                remained_hyps.append(hyp)
+        return remained_hyps
+
+
+def build_beam_search(model, args, device):
+    scorers = {}
+    if model.ctc is not None:
+        ctc = CTCPrefixScorer(ctc=model.ctc, eos=model.eos)
+        scorers.update(ctc=ctc)
+    weights = dict(
+        decoder=1.0 - args.ctc_weight,
+        ctc=args.ctc_weight,
+        length_bonus=args.penalty,
+    )
+    beam_search = BeamSearchCIF(
+        beam_size=args.beam_size,
+        weights=weights,
+        scorers=scorers,
+        sos=model.sos,
+        eos=model.eos,
+        vocab_size=model.vocab_size,
+        pre_beam_score_key=None if args.ctc_weight == 1.0 else "full",
+    )
+    beam_search.to(device=device, dtype=torch.float32).eval()
+    return beam_search
diff --git a/modules/wenet_extractor/paraformer/search/ctc.py b/modules/wenet_extractor/paraformer/search/ctc.py
new file mode 100755
index 0000000000000000000000000000000000000000..a40c337ed3ce69051292096a9648b6675f3b8049
--- /dev/null
+++ b/modules/wenet_extractor/paraformer/search/ctc.py
@@ -0,0 +1,181 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+"""ScorerInterface implementation for CTC."""
+import numpy as np
+import torch
+
+from modules.wenet_extractor.paraformer.search.ctc_prefix_score import CTCPrefixScore
+from modules.wenet_extractor.paraformer.search.ctc_prefix_score import CTCPrefixScoreTH
+from modules.wenet_extractor.paraformer.search.scorer_interface import (
+    BatchPartialScorerInterface,
+)
+
+
+class CTCPrefixScorer(BatchPartialScorerInterface):
+    """Decoder interface wrapper for CTCPrefixScore."""
+
+    def __init__(self, ctc: torch.nn.Module, eos: int):
+        """Initialize class.
+
+        Args:
+            ctc (torch.nn.Module): The CTC implementation.
+                For example, :class:`espnet.nets.pytorch_backend.ctc.CTC`
+            eos (int): The end-of-sequence id.
+
+        """
+        self.ctc = ctc
+        self.eos = eos
+        self.impl = None
+
+    def init_state(self, x: torch.Tensor):
+        """Get an initial state for decoding.
+
+        Args:
+            x (torch.Tensor): The encoded feature tensor
+
+        Returns: initial state
+
+        """
+        logp = self.ctc.log_softmax(x.unsqueeze(0)).detach().squeeze(0).cpu().numpy()
+        # TODO(karita): use CTCPrefixScoreTH
+        self.impl = CTCPrefixScore(logp, 0, self.eos, np)
+        return 0, self.impl.initial_state()
+
+    def select_state(self, state, i, new_id=None):
+        """Select state with relative ids in the main beam search.
+
+        Args:
+            state: Decoder state for prefix tokens
+            i (int): Index to select a state in the main beam search
+            new_id (int): New label id to select a state if necessary
+
+        Returns:
+            state: pruned state
+
+        """
+        if type(state) == tuple:
+            if len(state) == 2:  # for CTCPrefixScore
+                sc, st = state
+                return sc[i], st[i]
+            else:  # for CTCPrefixScoreTH (need new_id > 0)
+                r, log_psi, f_min, f_max, scoring_idmap = state
+                s = log_psi[i, new_id].expand(log_psi.size(1))
+                if scoring_idmap is not None:
+                    return r[:, :, i, scoring_idmap[i, new_id]], s, f_min, f_max
+                else:
+                    return r[:, :, i, new_id], s, f_min, f_max
+        return None if state is None else state[i]
+
+    def score_partial(self, y, ids, state, x):
+        """Score new token.
+
+        Args:
+            y (torch.Tensor): 1D prefix token
+            next_tokens (torch.Tensor): torch.int64 next token to score
+            state: decoder state for prefix tokens
+            x (torch.Tensor): 2D encoder feature that generates ys
+
+        Returns:
+            tuple[torch.Tensor, Any]:
+                Tuple of a score tensor for y that has a shape
+                `(len(next_tokens),)` and next state for ys
+
+        """
+        prev_score, state = state
+        presub_score, new_st = self.impl(y.cpu(), ids.cpu(), state)
+        tscore = torch.as_tensor(
+            presub_score - prev_score, device=x.device, dtype=x.dtype
+        )
+        return tscore, (presub_score, new_st)
+
+    def batch_init_state(self, x: torch.Tensor):
+        """Get an initial state for decoding.
+
+        Args:
+            x (torch.Tensor): The encoded feature tensor
+
+        Returns: initial state
+
+        """
+        logp = self.ctc.log_softmax(x.unsqueeze(0))  # assuming batch_size = 1
+        xlen = torch.tensor([logp.size(1)])
+        self.impl = CTCPrefixScoreTH(logp, xlen, 0, self.eos)
+        return None
+
+    def batch_score_partial(self, y, ids, state, x):
+        """Score new token.
+
+        Args:
+            y (torch.Tensor): 1D prefix token
+            ids (torch.Tensor): torch.int64 next token to score
+            state: decoder state for prefix tokens
+            x (torch.Tensor): 2D encoder feature that generates ys
+
+        Returns:
+            tuple[torch.Tensor, Any]:
+                Tuple of a score tensor for y that has a shape
+                `(len(next_tokens),)` and next state for ys
+
+        """
+        batch_state = (
+            (
+                torch.stack([s[0] for s in state], dim=2),
+                torch.stack([s[1] for s in state]),
+                state[0][2],
+                state[0][3],
+            )
+            if state[0] is not None
+            else None
+        )
+        return self.impl(y, batch_state, ids)
+
+    def extend_prob(self, x: torch.Tensor):
+        """Extend probs for decoding.
+
+        This extension is for streaming decoding
+        as in Eq (14) in https://arxiv.org/abs/2006.14941
+
+        Args:
+            x (torch.Tensor): The encoded feature tensor
+
+        """
+        logp = self.ctc.log_softmax(x.unsqueeze(0))
+        self.impl.extend_prob(logp)
+
+    def extend_state(self, state):
+        """Extend state for decoding.
+
+        This extension is for streaming decoding
+        as in Eq (14) in https://arxiv.org/abs/2006.14941
+
+        Args:
+            state: The states of hyps
+
+        Returns: exteded state
+
+        """
+        new_state = []
+        for s in state:
+            new_state.append(self.impl.extend_state(s))
+
+        return new_state
diff --git a/modules/wenet_extractor/paraformer/search/ctc_prefix_score.py b/modules/wenet_extractor/paraformer/search/ctc_prefix_score.py
new file mode 100755
index 0000000000000000000000000000000000000000..b4a4eb50b7556f6a463aef5428dba3ebc4f49875
--- /dev/null
+++ b/modules/wenet_extractor/paraformer/search/ctc_prefix_score.py
@@ -0,0 +1,377 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+import torch
+import numpy as np
+
+import six
+
+
+class CTCPrefixScore(object):
+    """Compute CTC label sequence scores
+
+    which is based on Algorithm 2 in WATANABE et al.
+    "HYBRID CTC/ATTENTION ARCHITECTURE FOR END-TO-END SPEECH RECOGNITION,"
+    but extended to efficiently compute the probablities of multiple labels
+    simultaneously
+    """
+
+    def __init__(self, x, blank, eos, xp):
+        self.xp = xp
+        self.logzero = -10000000000.0
+        self.blank = blank
+        self.eos = eos
+        self.input_length = len(x)
+        self.x = x
+
+    def initial_state(self):
+        """Obtain an initial CTC state
+
+        :return: CTC state
+        """
+        # initial CTC state is made of a frame x 2 tensor that corresponds to
+        # r_t^n(<sos>) and r_t^b(<sos>), where 0 and 1 of axis=1 represent
+        # superscripts n and b (non-blank and blank), respectively.
+        r = self.xp.full((self.input_length, 2), self.logzero, dtype=np.float32)
+        r[0, 1] = self.x[0, self.blank]
+        for i in six.moves.range(1, self.input_length):
+            r[i, 1] = r[i - 1, 1] + self.x[i, self.blank]
+        return r
+
+    def __call__(self, y, cs, r_prev):
+        """Compute CTC prefix scores for next labels
+
+        :param y     : prefix label sequence
+        :param cs    : array of next labels
+        :param r_prev: previous CTC state
+        :return ctc_scores, ctc_states
+        """
+        # initialize CTC states
+        output_length = len(y) - 1  # ignore sos
+        # new CTC states are prepared as a frame x (n or b) x n_labels tensor
+        # that corresponds to r_t^n(h) and r_t^b(h).
+        r = self.xp.ndarray((self.input_length, 2, len(cs)), dtype=np.float32)
+        xs = self.x[:, cs]
+        if output_length == 0:
+            r[0, 0] = xs[0]
+            r[0, 1] = self.logzero
+        else:
+            r[output_length - 1] = self.logzero
+
+        # prepare forward probabilities for the last label
+        r_sum = self.xp.logaddexp(
+            r_prev[:, 0], r_prev[:, 1]
+        )  # log(r_t^n(g) + r_t^b(g))
+        last = y[-1]
+        if output_length > 0 and last in cs:
+            log_phi = self.xp.ndarray((self.input_length, len(cs)), dtype=np.float32)
+            for i in six.moves.range(len(cs)):
+                log_phi[:, i] = r_sum if cs[i] != last else r_prev[:, 1]
+        else:
+            log_phi = r_sum
+
+        # compute forward probabilities log(r_t^n(h)), log(r_t^b(h)),
+        # and log prefix probabilities log(psi)
+        start = max(output_length, 1)
+        log_psi = r[start - 1, 0]
+        for t in six.moves.range(start, self.input_length):
+            r[t, 0] = self.xp.logaddexp(r[t - 1, 0], log_phi[t - 1]) + xs[t]
+            r[t, 1] = (
+                self.xp.logaddexp(r[t - 1, 0], r[t - 1, 1]) + self.x[t, self.blank]
+            )
+            log_psi = self.xp.logaddexp(log_psi, log_phi[t - 1] + xs[t])
+
+        # get P(...eos|X) that ends with the prefix itself
+        eos_pos = self.xp.where(cs == self.eos)[0]
+        if len(eos_pos) > 0:
+            log_psi[eos_pos] = r_sum[-1]  # log(r_T^n(g) + r_T^b(g))
+
+        # exclude blank probs
+        blank_pos = self.xp.where(cs == self.blank)[0]
+        if len(blank_pos) > 0:
+            log_psi[blank_pos] = self.logzero
+
+        # return the log prefix probability and CTC states, where the label axis
+        # of the CTC states is moved to the first axis to slice it easily
+        return log_psi, self.xp.rollaxis(r, 2)
+
+
+class CTCPrefixScoreTH(object):
+    """Batch processing of CTCPrefixScore
+
+    which is based on Algorithm 2 in WATANABE et al.
+    "HYBRID CTC/ATTENTION ARCHITECTURE FOR END-TO-END SPEECH RECOGNITION,"
+    but extended to efficiently compute the label probablities for multiple
+    hypotheses simultaneously
+    See also Seki et al. "Vectorized Beam Search for CTC-Attention-Based
+    Speech Recognition," In INTERSPEECH (pp. 3825-3829), 2019.
+    """
+
+    def __init__(self, x, xlens, blank, eos, margin=0):
+        """Construct CTC prefix scorer
+
+        :param torch.Tensor x: input label posterior sequences (B, T, O)
+        :param torch.Tensor xlens: input lengths (B,)
+        :param int blank: blank label id
+        :param int eos: end-of-sequence id
+        :param int margin: margin parameter for windowing (0 means no windowing)
+        """
+        # In the comment lines,
+        # we assume T: input_length, B: batch size, W: beam width, O: output dim
+        self.logzero = -10000000000.0
+        self.blank = blank
+        self.eos = eos
+        self.batch = x.size(0)
+        self.input_length = x.size(1)
+        self.odim = x.size(2)
+        self.dtype = x.dtype
+        self.device = (
+            torch.device("cuda:%d" % x.get_device())
+            if x.is_cuda
+            else torch.device("cpu")
+        )
+        # Pad the rest of posteriors in the batch
+        # TODO(takaaki-hori): need a better way without for-loops
+        for i, l in enumerate(xlens):
+            if l < self.input_length:
+                x[i, l:, :] = self.logzero
+                x[i, l:, blank] = 0
+        # Reshape input x
+        xn = x.transpose(0, 1)  # (B, T, O) -> (T, B, O)
+        xb = xn[:, :, self.blank].unsqueeze(2).expand(-1, -1, self.odim)
+        self.x = torch.stack([xn, xb])  # (2, T, B, O)
+        self.end_frames = torch.as_tensor(xlens) - 1
+
+        # Setup CTC windowing
+        self.margin = margin
+        if margin > 0:
+            self.frame_ids = torch.arange(
+                self.input_length, dtype=self.dtype, device=self.device
+            )
+        # Base indices for index conversion
+        self.idx_bh = None
+        self.idx_b = torch.arange(self.batch, device=self.device)
+        self.idx_bo = (self.idx_b * self.odim).unsqueeze(1)
+
+    def __call__(self, y, state, scoring_ids=None, att_w=None):
+        """Compute CTC prefix scores for next labels
+
+        :param list y: prefix label sequences
+        :param tuple state: previous CTC state
+        :param torch.Tensor pre_scores: scores for pre-selection of hypotheses
+            (BW, O)
+        :param torch.Tensor att_w: attention weights to decide CTC window
+        :return new_state, ctc_local_scores (BW, O)
+        """
+        output_length = len(y[0]) - 1  # ignore sos
+        last_ids = [yi[-1] for yi in y]  # last output label ids
+        n_bh = len(last_ids)  # batch * hyps
+        n_hyps = n_bh // self.batch  # assuming each utterance has the same
+        self.scoring_num = scoring_ids.size(-1) if scoring_ids is not None else 0
+        # prepare state info
+        if state is None:
+            r_prev = torch.full(
+                (self.input_length, 2, self.batch, n_hyps),
+                self.logzero,
+                dtype=self.dtype,
+                device=self.device,
+            )
+            r_prev[:, 1] = torch.cumsum(self.x[0, :, :, self.blank], 0).unsqueeze(2)
+            r_prev = r_prev.view(-1, 2, n_bh)
+            s_prev = 0.0
+            f_min_prev = 0
+            f_max_prev = 1
+        else:
+            r_prev, s_prev, f_min_prev, f_max_prev = state
+
+        # select input dimensions for scoring
+        if self.scoring_num > 0:
+            scoring_idmap = torch.full(
+                (n_bh, self.odim), -1, dtype=torch.long, device=self.device
+            )
+            snum = self.scoring_num
+            if self.idx_bh is None or n_bh > len(self.idx_bh):
+                self.idx_bh = torch.arange(n_bh, device=self.device).view(-1, 1)
+            scoring_idmap[self.idx_bh[:n_bh], scoring_ids] = torch.arange(
+                snum, device=self.device
+            )
+            scoring_idx = (
+                scoring_ids + self.idx_bo.repeat(1, n_hyps).view(-1, 1)
+            ).view(-1)
+            x_ = torch.index_select(
+                self.x.view(2, -1, self.batch * self.odim), 2, scoring_idx
+            ).view(2, -1, n_bh, snum)
+        else:
+            scoring_ids = None
+            scoring_idmap = None
+            snum = self.odim
+            x_ = self.x.unsqueeze(3).repeat(1, 1, 1, n_hyps, 1).view(2, -1, n_bh, snum)
+
+        # new CTC forward probs are prepared as a (T x 2 x BW x S) tensor
+        # that corresponds to r_t^n(h) and r_t^b(h) in a batch.
+        r = torch.full(
+            (self.input_length, 2, n_bh, snum),
+            self.logzero,
+            dtype=self.dtype,
+            device=self.device,
+        )
+        if output_length == 0:
+            r[0, 0] = x_[0, 0]
+
+        r_sum = torch.logsumexp(r_prev, 1)
+        log_phi = r_sum.unsqueeze(2).repeat(1, 1, snum)
+        if scoring_ids is not None:
+            for idx in range(n_bh):
+                pos = scoring_idmap[idx, last_ids[idx]]
+                if pos >= 0:
+                    log_phi[:, idx, pos] = r_prev[:, 1, idx]
+        else:
+            for idx in range(n_bh):
+                log_phi[:, idx, last_ids[idx]] = r_prev[:, 1, idx]
+
+        # decide start and end frames based on attention weights
+        if att_w is not None and self.margin > 0:
+            f_arg = torch.matmul(att_w, self.frame_ids)
+            f_min = max(int(f_arg.min().cpu()), f_min_prev)
+            f_max = max(int(f_arg.max().cpu()), f_max_prev)
+            start = min(f_max_prev, max(f_min - self.margin, output_length, 1))
+            end = min(f_max + self.margin, self.input_length)
+        else:
+            f_min = f_max = 0
+            start = max(output_length, 1)
+            end = self.input_length
+
+        # compute forward probabilities log(r_t^n(h)) and log(r_t^b(h))
+        for t in range(start, end):
+            rp = r[t - 1]
+            rr = torch.stack([rp[0], log_phi[t - 1], rp[0], rp[1]]).view(
+                2, 2, n_bh, snum
+            )
+            r[t] = torch.logsumexp(rr, 1) + x_[:, t]
+
+        # compute log prefix probabilities log(psi)
+        log_phi_x = torch.cat((log_phi[0].unsqueeze(0), log_phi[:-1]), dim=0) + x_[0]
+        if scoring_ids is not None:
+            log_psi = torch.full(
+                (n_bh, self.odim), self.logzero, dtype=self.dtype, device=self.device
+            )
+            log_psi_ = torch.logsumexp(
+                torch.cat((log_phi_x[start:end], r[start - 1, 0].unsqueeze(0)), dim=0),
+                dim=0,
+            )
+            for si in range(n_bh):
+                log_psi[si, scoring_ids[si]] = log_psi_[si]
+        else:
+            log_psi = torch.logsumexp(
+                torch.cat((log_phi_x[start:end], r[start - 1, 0].unsqueeze(0)), dim=0),
+                dim=0,
+            )
+
+        for si in range(n_bh):
+            log_psi[si, self.eos] = r_sum[self.end_frames[si // n_hyps], si]
+
+        # exclude blank probs
+        log_psi[:, self.blank] = self.logzero
+
+        return (log_psi - s_prev), (r, log_psi, f_min, f_max, scoring_idmap)
+
+    def index_select_state(self, state, best_ids):
+        """Select CTC states according to best ids
+
+        :param state    : CTC state
+        :param best_ids : index numbers selected by beam pruning (B, W)
+        :return selected_state
+        """
+        r, s, f_min, f_max, scoring_idmap = state
+        # convert ids to BHO space
+        n_bh = len(s)
+        n_hyps = n_bh // self.batch
+        vidx = (best_ids + (self.idx_b * (n_hyps * self.odim)).view(-1, 1)).view(-1)
+        # select hypothesis scores
+        s_new = torch.index_select(s.view(-1), 0, vidx)
+        s_new = s_new.view(-1, 1).repeat(1, self.odim).view(n_bh, self.odim)
+        # convert ids to BHS space (S: scoring_num)
+        if scoring_idmap is not None:
+            snum = self.scoring_num
+            hyp_idx = (best_ids // self.odim + (self.idx_b * n_hyps).view(-1, 1)).view(
+                -1
+            )
+            label_ids = torch.fmod(best_ids, self.odim).view(-1)
+            score_idx = scoring_idmap[hyp_idx, label_ids]
+            score_idx[score_idx == -1] = 0
+            vidx = score_idx + hyp_idx * snum
+        else:
+            snum = self.odim
+        # select forward probabilities
+        r_new = torch.index_select(r.view(-1, 2, n_bh * snum), 2, vidx).view(
+            -1, 2, n_bh
+        )
+        return r_new, s_new, f_min, f_max
+
+    def extend_prob(self, x):
+        """Extend CTC prob.
+
+        :param torch.Tensor x: input label posterior sequences (B, T, O)
+        """
+
+        if self.x.shape[1] < x.shape[1]:  # self.x (2,T,B,O); x (B,T,O)
+            # Pad the rest of posteriors in the batch
+            # TODO(takaaki-hori): need a better way without for-loops
+            xlens = [x.size(1)]
+            for i, l in enumerate(xlens):
+                if l < self.input_length:
+                    x[i, l:, :] = self.logzero
+                    x[i, l:, self.blank] = 0
+            tmp_x = self.x
+            xn = x.transpose(0, 1)  # (B, T, O) -> (T, B, O)
+            xb = xn[:, :, self.blank].unsqueeze(2).expand(-1, -1, self.odim)
+            self.x = torch.stack([xn, xb])  # (2, T, B, O)
+            self.x[:, : tmp_x.shape[1], :, :] = tmp_x
+            self.input_length = x.size(1)
+            self.end_frames = torch.as_tensor(xlens) - 1
+
+    def extend_state(self, state):
+        """Compute CTC prefix state.
+
+
+        :param state    : CTC state
+        :return ctc_state
+        """
+
+        if state is None:
+            # nothing to do
+            return state
+        else:
+            r_prev, s_prev, f_min_prev, f_max_prev = state
+
+            r_prev_new = torch.full(
+                (self.input_length, 2),
+                self.logzero,
+                dtype=self.dtype,
+                device=self.device,
+            )
+            start = max(r_prev.shape[0], 1)
+            r_prev_new[0:start] = r_prev
+            for t in six.moves.range(start, self.input_length):
+                r_prev_new[t, 1] = r_prev_new[t - 1, 1] + self.x[0, t, :, self.blank]
+
+            return r_prev_new, s_prev, f_min_prev, f_max_prev
diff --git a/modules/wenet_extractor/paraformer/search/scorer_interface.py b/modules/wenet_extractor/paraformer/search/scorer_interface.py
new file mode 100755
index 0000000000000000000000000000000000000000..ed021726f3a93dddd9b00eab23bc6fbe7583a04b
--- /dev/null
+++ b/modules/wenet_extractor/paraformer/search/scorer_interface.py
@@ -0,0 +1,208 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+"""Scorer interface module."""
+from abc import ABC
+from typing import Any
+from typing import List
+from typing import Tuple
+
+import torch
+
+
+class ScorerInterface:
+    """Scorer interface for beam search.
+
+    The scorer performs scoring of the all tokens in vocabulary.
+
+    Examples:
+        * Search heuristics
+            * :class:`espnet.nets.scorers.length_bonus.LengthBonus`
+        * Decoder networks of the sequence-to-sequence models
+            * :class:`espnet.nets.pytorch_backend.nets.transformer.decoder
+                .Decoder`
+            * :class:`espnet.nets.pytorch_backend.nets.rnn.decoders.Decoder`
+        * Neural language models
+            * :class:`espnet.nets.pytorch_backend.lm.transformer.TransformerLM`
+            * :class:`espnet.nets.pytorch_backend.lm.default.DefaultRNNLM`
+            * :class:`espnet.nets.pytorch_backend.lm.seq_rnn.SequentialRNNLM`
+
+    """
+
+    def init_state(self, x: torch.Tensor) -> Any:
+        """Get an initial state for decoding (optional).
+
+        Args:
+            x (torch.Tensor): The encoded feature tensor
+
+        Returns: initial state
+
+        """
+        return None
+
+    def select_state(self, state: Any, i: int, new_id: int = None) -> Any:
+        """Select state with relative ids in the main beam search.
+
+        Args:
+            state: Decoder state for prefix tokens
+            i (int): Index to select a state in the main beam search
+            new_id (int): New label index to select a state if necessary
+
+        Returns:
+            state: pruned state
+
+        """
+        return None if state is None else state[i]
+
+    def score(
+        self, y: torch.Tensor, state: Any, x: torch.Tensor
+    ) -> Tuple[torch.Tensor, Any]:
+        """Score new token (required).
+
+        Args:
+            y (torch.Tensor): 1D torch.int64 prefix tokens.
+            state: Scorer state for prefix tokens
+            x (torch.Tensor): The encoder feature that generates ys.
+
+        Returns:
+            tuple[torch.Tensor, Any]: Tuple of
+                scores for next token that has a shape of `(n_vocab)`
+                and next state for ys
+
+        """
+        raise NotImplementedError
+
+    def final_score(self, state: Any) -> float:
+        """Score eos (optional).
+
+        Args:
+            state: Scorer state for prefix tokens
+
+        Returns:
+            float: final score
+
+        """
+        return 0.0
+
+
+class BatchScorerInterface(ScorerInterface, ABC):
+    """Batch scorer interface."""
+
+    def batch_init_state(self, x: torch.Tensor) -> Any:
+        """Get an initial state for decoding (optional).
+
+        Args:
+            x (torch.Tensor): The encoded feature tensor
+
+        Returns: initial state
+
+        """
+        return self.init_state(x)
+
+    def batch_score(
+        self, ys: torch.Tensor, states: List[Any], xs: torch.Tensor
+    ) -> Tuple[torch.Tensor, List[Any]]:
+        """Score new token batch (required).
+
+        Args:
+            ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen).
+            states (List[Any]): Scorer states for prefix tokens.
+            xs (torch.Tensor):
+                The encoder feature that generates ys (n_batch, xlen, n_feat).
+
+        Returns:
+            tuple[torch.Tensor, List[Any]]: Tuple of
+                batchfied scores for next token with shape of `(n_batch,
+                    n_vocab)`
+                and next state list for ys.
+
+        """
+        scores = list()
+        outstates = list()
+        for i, (y, state, x) in enumerate(zip(ys, states, xs)):
+            score, outstate = self.score(y, state, x)
+            outstates.append(outstate)
+            scores.append(score)
+        scores = torch.cat(scores, 0).view(ys.shape[0], -1)
+        return scores, outstates
+
+
+class PartialScorerInterface(ScorerInterface, ABC):
+    """Partial scorer interface for beam search.
+
+    The partial scorer performs scoring when non-partial scorer finished scoring
+    and receives pre-pruned next tokens to score because it is too heavy to
+    score all the tokens.
+
+    Examples:
+         * Prefix search for connectionist-temporal-classification models
+             * :class:`espnet.nets.scorers.ctc.CTCPrefixScorer`
+
+    """
+
+    def score_partial(
+        self, y: torch.Tensor, next_tokens: torch.Tensor, state: Any, x: torch.Tensor
+    ) -> Tuple[torch.Tensor, Any]:
+        """Score new token (required).
+
+        Args:
+            y (torch.Tensor): 1D prefix token
+            next_tokens (torch.Tensor): torch.int64 next token to score
+            state: decoder state for prefix tokens
+            x (torch.Tensor): The encoder feature that generates ys
+
+        Returns:
+            tuple[torch.Tensor, Any]:
+                Tuple of a score tensor for y that has a shape
+                `(len(next_tokens),)` and next state for ys
+
+        """
+        raise NotImplementedError
+
+
+class BatchPartialScorerInterface(BatchScorerInterface, PartialScorerInterface, ABC):
+    """Batch partial scorer interface for beam search."""
+
+    def batch_score_partial(
+        self,
+        ys: torch.Tensor,
+        next_tokens: torch.Tensor,
+        states: List[Any],
+        xs: torch.Tensor,
+    ) -> Tuple[torch.Tensor, Any]:
+        """Score new token (required).
+
+        Args:
+            ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen).
+            next_tokens (torch.Tensor): torch.int64 tokens to score (n_batch,
+                n_token).
+            states (List[Any]): Scorer states for prefix tokens.
+            xs (torch.Tensor):
+                The encoder feature that generates ys (n_batch, xlen, n_feat).
+
+        Returns:
+            tuple[torch.Tensor, Any]:
+                Tuple of a score tensor for ys that has a shape `(n_batch,
+                    n_vocab)`
+                and next states for ys
+        """
+        raise NotImplementedError
diff --git a/modules/wenet_extractor/paraformer/utils.py b/modules/wenet_extractor/paraformer/utils.py
new file mode 100755
index 0000000000000000000000000000000000000000..83d644f7643f3f0c213a54ab4b634e89e51a1c40
--- /dev/null
+++ b/modules/wenet_extractor/paraformer/utils.py
@@ -0,0 +1,76 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+from typing import Optional
+
+import six
+import torch
+import numpy as np
+
+
+def sequence_mask(
+    lengths,
+    maxlen: Optional[int] = None,
+    dtype: torch.dtype = torch.float32,
+    device: Optional[torch.device] = None,
+) -> torch.Tensor:
+    if maxlen is None:
+        maxlen = lengths.max()
+    row_vector = torch.arange(0, maxlen, 1).to(lengths.device)
+    matrix = torch.unsqueeze(lengths, dim=-1)
+    mask = row_vector < matrix
+    mask = mask.detach()
+
+    return mask.type(dtype).to(device) if device is not None else mask.type(dtype)
+
+
+def end_detect(ended_hyps, i, M=3, d_end=np.log(1 * np.exp(-10))):
+    """End detection.
+
+    described in Eq. (50) of S. Watanabe et al
+    "Hybrid CTC/Attention Architecture for End-to-End Speech Recognition"
+
+    :param ended_hyps:
+    :param i:
+    :param M:
+    :param d_end:
+    :return:
+    """
+    if len(ended_hyps) == 0:
+        return False
+    count = 0
+    best_hyp = sorted(ended_hyps, key=lambda x: x["score"], reverse=True)[0]
+    for m in six.moves.range(M):
+        # get ended_hyps with their length is i - m
+        hyp_length = i - m
+        hyps_same_length = [x for x in ended_hyps if len(x["yseq"]) == hyp_length]
+        if len(hyps_same_length) > 0:
+            best_hyp_same_length = sorted(
+                hyps_same_length, key=lambda x: x["score"], reverse=True
+            )[0]
+            if best_hyp_same_length["score"] - best_hyp["score"] < d_end:
+                count += 1
+
+    if count == M:
+        return True
+    else:
+        return False
diff --git a/modules/wenet_extractor/squeezeformer/__init__.py b/modules/wenet_extractor/squeezeformer/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/modules/wenet_extractor/squeezeformer/attention.py b/modules/wenet_extractor/squeezeformer/attention.py
new file mode 100755
index 0000000000000000000000000000000000000000..830da3dccc0257afc36747c7f572455746ef652e
--- /dev/null
+++ b/modules/wenet_extractor/squeezeformer/attention.py
@@ -0,0 +1,239 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+"""Multi-Head Attention layer definition."""
+
+import math
+import torch
+import torch.nn as nn
+from modules.wenet_extractor.transformer.attention import MultiHeadedAttention
+from typing import Tuple
+
+
+class RelPositionMultiHeadedAttention(MultiHeadedAttention):
+    """Multi-Head Attention layer with relative position encoding.
+    Paper: https://arxiv.org/abs/1901.02860
+    Args:
+        n_head (int): The number of heads.
+        n_feat (int): The number of features.
+        dropout_rate (float): Dropout rate.
+    """
+
+    def __init__(
+        self,
+        n_head,
+        n_feat,
+        dropout_rate,
+        do_rel_shift=False,
+        adaptive_scale=False,
+        init_weights=False,
+    ):
+        """Construct an RelPositionMultiHeadedAttention object."""
+        super().__init__(n_head, n_feat, dropout_rate)
+        # linear transformation for positional encoding
+        self.linear_pos = nn.Linear(n_feat, n_feat, bias=False)
+        # these two learnable bias are used in matrix c and matrix d
+        # as described in https://arxiv.org/abs/1901.02860 Section 3.3
+        self.do_rel_shift = do_rel_shift
+        self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k))
+        self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k))
+        torch.nn.init.xavier_uniform_(self.pos_bias_u)
+        torch.nn.init.xavier_uniform_(self.pos_bias_v)
+        self.adaptive_scale = adaptive_scale
+        self.ada_scale = nn.Parameter(
+            torch.ones([1, 1, n_feat]), requires_grad=adaptive_scale
+        )
+        self.ada_bias = nn.Parameter(
+            torch.zeros([1, 1, n_feat]), requires_grad=adaptive_scale
+        )
+        if init_weights:
+            self.init_weights()
+
+    def init_weights(self):
+        input_max = (self.h * self.d_k) ** -0.5
+        torch.nn.init.uniform_(self.linear_q.weight, -input_max, input_max)
+        torch.nn.init.uniform_(self.linear_q.bias, -input_max, input_max)
+        torch.nn.init.uniform_(self.linear_k.weight, -input_max, input_max)
+        torch.nn.init.uniform_(self.linear_k.bias, -input_max, input_max)
+        torch.nn.init.uniform_(self.linear_v.weight, -input_max, input_max)
+        torch.nn.init.uniform_(self.linear_v.bias, -input_max, input_max)
+        torch.nn.init.uniform_(self.linear_pos.weight, -input_max, input_max)
+        torch.nn.init.uniform_(self.linear_out.weight, -input_max, input_max)
+        torch.nn.init.uniform_(self.linear_out.bias, -input_max, input_max)
+
+    def rel_shift(self, x, zero_triu: bool = False):
+        """Compute relative positinal encoding.
+        Args:
+            x (torch.Tensor): Input tensor (batch, time, size).
+            zero_triu (bool): If true, return the lower triangular part of
+                the matrix.
+        Returns:
+            torch.Tensor: Output tensor.
+        """
+
+        zero_pad = torch.zeros(
+            (x.size()[0], x.size()[1], x.size()[2], 1), device=x.device, dtype=x.dtype
+        )
+        x_padded = torch.cat([zero_pad, x], dim=-1)
+
+        x_padded = x_padded.view(x.size()[0], x.size()[1], x.size(3) + 1, x.size(2))
+        x = x_padded[:, :, 1:].view_as(x)
+
+        if zero_triu:
+            ones = torch.ones((x.size(2), x.size(3)))
+            x = x * torch.tril(ones, x.size(3) - x.size(2))[None, None, :, :]
+
+        return x
+
+    def forward_attention(
+        self,
+        value: torch.Tensor,
+        scores: torch.Tensor,
+        mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+    ) -> torch.Tensor:
+        """Compute attention context vector.
+
+        Args:
+            value (torch.Tensor): Transformed value, size
+                (#batch, n_head, time2, d_k).
+            scores (torch.Tensor): Attention score, size
+                (#batch, n_head, time1, time2).
+            mask (torch.Tensor): Mask, size (#batch, 1, time2) or
+                (#batch, time1, time2), (0, 0, 0) means fake mask.
+
+        Returns:
+            torch.Tensor: Transformed value (#batch, time1, d_model)
+                weighted by the attention score (#batch, time1, time2).
+
+        """
+        n_batch = value.size(0)
+        # NOTE(xcsong): When will `if mask.size(2) > 0` be True?
+        #   1. onnx(16/4) [WHY? Because we feed real cache & real mask for the
+        #           1st chunk to ease the onnx export.]
+        #   2. pytorch training
+        if mask.size(2) > 0:  # time2 > 0
+            mask = mask.unsqueeze(1).eq(0)  # (batch, 1, *, time2)
+            # For last chunk, time2 might be larger than scores.size(-1)
+            mask = mask[:, :, :, : scores.size(-1)]  # (batch, 1, *, time2)
+            scores = scores.masked_fill(mask, -float("inf"))
+            # (batch, head, time1, time2)
+            attn = torch.softmax(scores, dim=-1).masked_fill(mask, 0.0)
+        # NOTE(xcsong): When will `if mask.size(2) > 0` be False?
+        #   1. onnx(16/-1, -1/-1, 16/0)
+        #   2. jit (16/-1, -1/-1, 16/0, 16/4)
+        else:
+            attn = torch.softmax(scores, dim=-1)  # (batch, head, time1, time2)
+
+        p_attn = self.dropout(attn)
+        x = torch.matmul(p_attn, value)  # (batch, head, time1, d_k)
+        x = (
+            x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k)
+        )  # (batch, time1, d_model)
+
+        return self.linear_out(x)  # (batch, time1, d_model)
+
+    def forward(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+        pos_emb: torch.Tensor = torch.empty(0),
+        cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Compute 'Scaled Dot Product Attention' with rel. positional encoding.
+        Args:
+            query (torch.Tensor): Query tensor (#batch, time1, size).
+            key (torch.Tensor): Key tensor (#batch, time2, size).
+            value (torch.Tensor): Value tensor (#batch, time2, size).
+            mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
+                (#batch, time1, time2), (0, 0, 0) means fake mask.
+            pos_emb (torch.Tensor): Positional embedding tensor
+                (#batch, time2, size).
+            cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2),
+                where `cache_t == chunk_size * num_decoding_left_chunks`
+                and `head * d_k == size`
+        Returns:
+            torch.Tensor: Output tensor (#batch, time1, d_model).
+            torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2)
+                where `cache_t == chunk_size * num_decoding_left_chunks`
+                and `head * d_k == size`
+        """
+        if self.adaptive_scale:
+            query = self.ada_scale * query + self.ada_bias
+            key = self.ada_scale * key + self.ada_bias
+            value = self.ada_scale * value + self.ada_bias
+        q, k, v = self.forward_qkv(query, key, value)
+        q = q.transpose(1, 2)  # (batch, time1, head, d_k)
+
+        # NOTE(xcsong):
+        #   when export onnx model, for 1st chunk, we feed
+        #       cache(1, head, 0, d_k * 2) (16/-1, -1/-1, 16/0 mode)
+        #       or cache(1, head, real_cache_t, d_k * 2) (16/4 mode).
+        #       In all modes, `if cache.size(0) > 0` will alwayse be `True`
+        #       and we will always do splitting and
+        #       concatnation(this will simplify onnx export). Note that
+        #       it's OK to concat & split zero-shaped tensors(see code below).
+        #   when export jit  model, for 1st chunk, we always feed
+        #       cache(0, 0, 0, 0) since jit supports dynamic if-branch.
+        # >>> a = torch.ones((1, 2, 0, 4))
+        # >>> b = torch.ones((1, 2, 3, 4))
+        # >>> c = torch.cat((a, b), dim=2)
+        # >>> torch.equal(b, c)        # True
+        # >>> d = torch.split(a, 2, dim=-1)
+        # >>> torch.equal(d[0], d[1])  # True
+        if cache.size(0) > 0:
+            key_cache, value_cache = torch.split(cache, cache.size(-1) // 2, dim=-1)
+            k = torch.cat([key_cache, k], dim=2)
+            v = torch.cat([value_cache, v], dim=2)
+        # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's
+        #   non-trivial to calculate `next_cache_start` here.
+        new_cache = torch.cat((k, v), dim=-1)
+
+        n_batch_pos = pos_emb.size(0)
+        p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k)
+        p = p.transpose(1, 2)  # (batch, head, time1, d_k)
+
+        # (batch, head, time1, d_k)
+        q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2)
+        # (batch, head, time1, d_k)
+        q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2)
+
+        # compute attention score
+        # first compute matrix a and matrix c
+        # as described in https://arxiv.org/abs/1901.02860 Section 3.3
+        # (batch, head, time1, time2)
+        matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))
+
+        # compute matrix b and matrix d
+        # (batch, head, time1, time2)
+        matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
+        # Remove rel_shift since it is useless in speech recognition,
+        # and it requires special attention for streaming.
+        if self.do_rel_shift:
+            matrix_bd = self.rel_shift(matrix_bd)
+
+        scores = (matrix_ac + matrix_bd) / math.sqrt(
+            self.d_k
+        )  # (batch, head, time1, time2)
+
+        return self.forward_attention(v, scores, mask), new_cache
diff --git a/modules/wenet_extractor/squeezeformer/conv2d.py b/modules/wenet_extractor/squeezeformer/conv2d.py
new file mode 100755
index 0000000000000000000000000000000000000000..01d2981e4b6853013ce1a629496e03b04517d0c0
--- /dev/null
+++ b/modules/wenet_extractor/squeezeformer/conv2d.py
@@ -0,0 +1,93 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+"""Conv2d Module with Valid Padding"""
+
+import torch.nn.functional as F
+from torch.nn.modules.conv import _ConvNd, _size_2_t, Union, _pair, Tensor, Optional
+
+
+class Conv2dValid(_ConvNd):
+    """
+    Conv2d operator for VALID mode padding.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: _size_2_t,
+        stride: _size_2_t = 1,
+        padding: Union[str, _size_2_t] = 0,
+        dilation: _size_2_t = 1,
+        groups: int = 1,
+        bias: bool = True,
+        padding_mode: str = "zeros",  # TODO: refine this type
+        device=None,
+        dtype=None,
+        valid_trigx: bool = False,
+        valid_trigy: bool = False,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        kernel_size_ = _pair(kernel_size)
+        stride_ = _pair(stride)
+        padding_ = padding if isinstance(padding, str) else _pair(padding)
+        dilation_ = _pair(dilation)
+        super(Conv2dValid, self).__init__(
+            in_channels,
+            out_channels,
+            kernel_size_,
+            stride_,
+            padding_,
+            dilation_,
+            False,
+            _pair(0),
+            groups,
+            bias,
+            padding_mode,
+            **factory_kwargs,
+        )
+        self.valid_trigx = valid_trigx
+        self.valid_trigy = valid_trigy
+
+    def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor]):
+        validx, validy = 0, 0
+        if self.valid_trigx:
+            validx = (
+                input.size(-2) * (self.stride[-2] - 1) - 1 + self.kernel_size[-2]
+            ) // 2
+        if self.valid_trigy:
+            validy = (
+                input.size(-1) * (self.stride[-1] - 1) - 1 + self.kernel_size[-1]
+            ) // 2
+        return F.conv2d(
+            input,
+            weight,
+            bias,
+            self.stride,
+            (validx, validy),
+            self.dilation,
+            self.groups,
+        )
+
+    def forward(self, input: Tensor) -> Tensor:
+        return self._conv_forward(input, self.weight, self.bias)
diff --git a/modules/wenet_extractor/squeezeformer/convolution.py b/modules/wenet_extractor/squeezeformer/convolution.py
new file mode 100755
index 0000000000000000000000000000000000000000..f0ec43dadcd22aff53f79c459888344def57ef12
--- /dev/null
+++ b/modules/wenet_extractor/squeezeformer/convolution.py
@@ -0,0 +1,182 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+"""ConvolutionModule definition."""
+
+from typing import Tuple
+
+import torch
+from torch import nn
+
+
+class ConvolutionModule(nn.Module):
+    """ConvolutionModule in Conformer model."""
+
+    def __init__(
+        self,
+        channels: int,
+        kernel_size: int = 15,
+        activation: nn.Module = nn.ReLU(),
+        norm: str = "batch_norm",
+        causal: bool = False,
+        bias: bool = True,
+        adaptive_scale: bool = False,
+        init_weights: bool = False,
+    ):
+        """Construct an ConvolutionModule object.
+        Args:
+            channels (int): The number of channels of conv layers.
+            kernel_size (int): Kernel size of conv layers.
+            causal (int): Whether use causal convolution or not
+        """
+        super().__init__()
+        self.bias = bias
+        self.channels = channels
+        self.kernel_size = kernel_size
+        self.adaptive_scale = adaptive_scale
+        self.ada_scale = torch.nn.Parameter(
+            torch.ones([1, 1, channels]), requires_grad=adaptive_scale
+        )
+        self.ada_bias = torch.nn.Parameter(
+            torch.zeros([1, 1, channels]), requires_grad=adaptive_scale
+        )
+
+        self.pointwise_conv1 = nn.Conv1d(
+            channels,
+            2 * channels,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            bias=bias,
+        )
+        # self.lorder is used to distinguish if it's a causal convolution,
+        # if self.lorder > 0: it's a causal convolution, the input will be
+        #    padded with self.lorder frames on the left in forward.
+        # else: it's a symmetrical convolution
+        if causal:
+            padding = 0
+            self.lorder = kernel_size - 1
+        else:
+            # kernel_size should be an odd number for none causal convolution
+            assert (kernel_size - 1) % 2 == 0
+            padding = (kernel_size - 1) // 2
+            self.lorder = 0
+        self.depthwise_conv = nn.Conv1d(
+            channels,
+            channels,
+            kernel_size,
+            stride=1,
+            padding=padding,
+            groups=channels,
+            bias=bias,
+        )
+
+        assert norm in ["batch_norm", "layer_norm"]
+        if norm == "batch_norm":
+            self.use_layer_norm = False
+            self.norm = nn.BatchNorm1d(channels)
+        else:
+            self.use_layer_norm = True
+            self.norm = nn.LayerNorm(channels)
+
+        self.pointwise_conv2 = nn.Conv1d(
+            channels,
+            channels,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            bias=bias,
+        )
+        self.activation = activation
+        if init_weights:
+            self.init_weights()
+
+    def init_weights(self):
+        pw_max = self.channels**-0.5
+        dw_max = self.kernel_size**-0.5
+        torch.nn.init.uniform_(self.pointwise_conv1.weight.data, -pw_max, pw_max)
+        if self.bias:
+            torch.nn.init.uniform_(self.pointwise_conv1.bias.data, -pw_max, pw_max)
+        torch.nn.init.uniform_(self.depthwise_conv.weight.data, -dw_max, dw_max)
+        if self.bias:
+            torch.nn.init.uniform_(self.depthwise_conv.bias.data, -dw_max, dw_max)
+        torch.nn.init.uniform_(self.pointwise_conv2.weight.data, -pw_max, pw_max)
+        if self.bias:
+            torch.nn.init.uniform_(self.pointwise_conv2.bias.data, -pw_max, pw_max)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+        cache: torch.Tensor = torch.zeros((0, 0, 0)),
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Compute convolution module.
+        Args:
+            x (torch.Tensor): Input tensor (#batch, time, channels).
+            mask_pad (torch.Tensor): used for batch padding (#batch, 1, time),
+                (0, 0, 0) means fake mask.
+            cache (torch.Tensor): left context cache, it is only
+                used in causal convolution (#batch, channels, cache_t),
+                (0, 0, 0) meas fake cache.
+        Returns:
+            torch.Tensor: Output tensor (#batch, time, channels).
+        """
+        if self.adaptive_scale:
+            x = self.ada_scale * x + self.ada_bias
+        # exchange the temporal dimension and the feature dimension
+        x = x.transpose(1, 2)  # (#batch, channels, time)
+        # mask batch padding
+        if mask_pad.size(2) > 0:  # time > 0
+            x.masked_fill_(~mask_pad, 0.0)
+
+        if self.lorder > 0:
+            if cache.size(2) == 0:  # cache_t == 0
+                x = nn.functional.pad(x, (self.lorder, 0), "constant", 0.0)
+            else:
+                assert cache.size(0) == x.size(0)  # equal batch
+                assert cache.size(1) == x.size(1)  # equal channel
+                x = torch.cat((cache, x), dim=2)
+            assert x.size(2) > self.lorder
+            new_cache = x[:, :, -self.lorder :]
+        else:
+            # It's better we just return None if no cache is required,
+            # However, for JIT export, here we just fake one tensor instead of
+            # None.
+            new_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device)
+
+        # GLU mechanism
+        x = self.pointwise_conv1(x)  # (batch, 2*channel, dim)
+        x = nn.functional.glu(x, dim=1)  # (batch, channel, dim)
+
+        # 1D Depthwise Conv
+        x = self.depthwise_conv(x)
+        if self.use_layer_norm:
+            x = x.transpose(1, 2)
+        x = self.activation(self.norm(x))
+        if self.use_layer_norm:
+            x = x.transpose(1, 2)
+        x = self.pointwise_conv2(x)
+        # mask batch padding
+        if mask_pad.size(2) > 0:  # time > 0
+            x.masked_fill_(~mask_pad, 0.0)
+
+        return x.transpose(1, 2), new_cache
diff --git a/modules/wenet_extractor/squeezeformer/encoder.py b/modules/wenet_extractor/squeezeformer/encoder.py
new file mode 100755
index 0000000000000000000000000000000000000000..4d4fbe9f615c8d9889a6c2ea821afe392bfab5db
--- /dev/null
+++ b/modules/wenet_extractor/squeezeformer/encoder.py
@@ -0,0 +1,514 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+import torch
+import torch.nn as nn
+from typing import Tuple, Union, Optional, List
+from modules.wenet_extractor.squeezeformer.subsampling import (
+    DepthwiseConv2dSubsampling4,
+    TimeReductionLayer1D,
+    TimeReductionLayer2D,
+    TimeReductionLayerStream,
+)
+from modules.wenet_extractor.squeezeformer.encoder_layer import (
+    SqueezeformerEncoderLayer,
+)
+from modules.wenet_extractor.transformer.embedding import RelPositionalEncoding
+from modules.wenet_extractor.transformer.attention import MultiHeadedAttention
+from modules.wenet_extractor.squeezeformer.attention import (
+    RelPositionMultiHeadedAttention,
+)
+from modules.wenet_extractor.squeezeformer.positionwise_feed_forward import (
+    PositionwiseFeedForward,
+)
+from modules.wenet_extractor.squeezeformer.convolution import ConvolutionModule
+from modules.wenet_extractor.utils.mask import make_pad_mask, add_optional_chunk_mask
+from modules.wenet_extractor.utils.common import get_activation
+
+
+class SqueezeformerEncoder(nn.Module):
+    def __init__(
+        self,
+        input_size: int = 80,
+        encoder_dim: int = 256,
+        output_size: int = 256,
+        attention_heads: int = 4,
+        num_blocks: int = 12,
+        reduce_idx: Optional[Union[int, List[int]]] = 5,
+        recover_idx: Optional[Union[int, List[int]]] = 11,
+        feed_forward_expansion_factor: int = 4,
+        dw_stride: bool = False,
+        input_dropout_rate: float = 0.1,
+        pos_enc_layer_type: str = "rel_pos",
+        time_reduction_layer_type: str = "conv1d",
+        do_rel_shift: bool = True,
+        feed_forward_dropout_rate: float = 0.1,
+        attention_dropout_rate: float = 0.1,
+        cnn_module_kernel: int = 31,
+        cnn_norm_type: str = "batch_norm",
+        dropout: float = 0.1,
+        causal: bool = False,
+        adaptive_scale: bool = True,
+        activation_type: str = "swish",
+        init_weights: bool = True,
+        global_cmvn: torch.nn.Module = None,
+        normalize_before: bool = False,
+        use_dynamic_chunk: bool = False,
+        concat_after: bool = False,
+        static_chunk_size: int = 0,
+        use_dynamic_left_chunk: bool = False,
+    ):
+        """Construct SqueezeformerEncoder
+
+        Args:
+            input_size to use_dynamic_chunk, see in Transformer BaseEncoder.
+            encoder_dim (int): The hidden dimension of encoder layer.
+            output_size (int): The output dimension of final projection layer.
+            attention_heads (int): Num of attention head in attention module.
+            num_blocks (int): Num of encoder layers.
+            reduce_idx Optional[Union[int, List[int]]]:
+                reduce layer index, from 40ms to 80ms per frame.
+            recover_idx Optional[Union[int, List[int]]]:
+                recover layer index, from 80ms to 40ms per frame.
+            feed_forward_expansion_factor (int): Enlarge coefficient of FFN.
+            dw_stride (bool): Whether do depthwise convolution
+                              on subsampling module.
+            input_dropout_rate (float): Dropout rate of input projection layer.
+            pos_enc_layer_type (str): Self attention type.
+            time_reduction_layer_type (str): Conv1d or Conv2d reduction layer.
+            do_rel_shift (bool): Whether to do relative shift
+                                 operation on rel-attention module.
+            cnn_module_kernel (int): Kernel size of CNN module.
+            activation_type (str): Encoder activation function type.
+            use_cnn_module (bool): Whether to use convolution module.
+            cnn_module_kernel (int): Kernel size of convolution module.
+            adaptive_scale (bool): Whether to use adaptive scale.
+            init_weights (bool): Whether to initialize weights.
+            causal (bool): whether to use causal convolution or not.
+        """
+        super(SqueezeformerEncoder, self).__init__()
+        self.global_cmvn = global_cmvn
+        self.reduce_idx: Optional[Union[int, List[int]]] = (
+            [reduce_idx] if type(reduce_idx) == int else reduce_idx
+        )
+        self.recover_idx: Optional[Union[int, List[int]]] = (
+            [recover_idx] if type(recover_idx) == int else recover_idx
+        )
+        self.check_ascending_list()
+        if reduce_idx is None:
+            self.time_reduce = None
+        else:
+            if recover_idx is None:
+                self.time_reduce = "normal"  # no recovery at the end
+            else:
+                self.time_reduce = "recover"  # recovery at the end
+                assert len(self.reduce_idx) == len(self.recover_idx)
+            self.reduce_stride = 2
+        self._output_size = output_size
+        self.normalize_before = normalize_before
+        self.static_chunk_size = static_chunk_size
+        self.use_dynamic_chunk = use_dynamic_chunk
+        self.use_dynamic_left_chunk = use_dynamic_left_chunk
+        self.pos_enc_layer_type = pos_enc_layer_type
+        activation = get_activation(activation_type)
+
+        # self-attention module definition
+        if pos_enc_layer_type != "rel_pos":
+            encoder_selfattn_layer = MultiHeadedAttention
+            encoder_selfattn_layer_args = (
+                attention_heads,
+                output_size,
+                attention_dropout_rate,
+            )
+        else:
+            encoder_selfattn_layer = RelPositionMultiHeadedAttention
+            encoder_selfattn_layer_args = (
+                attention_heads,
+                encoder_dim,
+                attention_dropout_rate,
+                do_rel_shift,
+                adaptive_scale,
+                init_weights,
+            )
+
+        # feed-forward module definition
+        positionwise_layer = PositionwiseFeedForward
+        positionwise_layer_args = (
+            encoder_dim,
+            encoder_dim * feed_forward_expansion_factor,
+            feed_forward_dropout_rate,
+            activation,
+            adaptive_scale,
+            init_weights,
+        )
+
+        # convolution module definition
+        convolution_layer = ConvolutionModule
+        convolution_layer_args = (
+            encoder_dim,
+            cnn_module_kernel,
+            activation,
+            cnn_norm_type,
+            causal,
+            True,
+            adaptive_scale,
+            init_weights,
+        )
+
+        self.embed = DepthwiseConv2dSubsampling4(
+            1,
+            encoder_dim,
+            RelPositionalEncoding(encoder_dim, dropout_rate=0.1),
+            dw_stride,
+            input_size,
+            input_dropout_rate,
+            init_weights,
+        )
+
+        self.preln = nn.LayerNorm(encoder_dim)
+        self.encoders = torch.nn.ModuleList(
+            [
+                SqueezeformerEncoderLayer(
+                    encoder_dim,
+                    encoder_selfattn_layer(*encoder_selfattn_layer_args),
+                    positionwise_layer(*positionwise_layer_args),
+                    convolution_layer(*convolution_layer_args),
+                    positionwise_layer(*positionwise_layer_args),
+                    normalize_before,
+                    dropout,
+                    concat_after,
+                )
+                for _ in range(num_blocks)
+            ]
+        )
+        if time_reduction_layer_type == "conv1d":
+            time_reduction_layer = TimeReductionLayer1D
+            time_reduction_layer_args = {
+                "channel": encoder_dim,
+                "out_dim": encoder_dim,
+            }
+        elif time_reduction_layer_type == "stream":
+            time_reduction_layer = TimeReductionLayerStream
+            time_reduction_layer_args = {
+                "channel": encoder_dim,
+                "out_dim": encoder_dim,
+            }
+        else:
+            time_reduction_layer = TimeReductionLayer2D
+            time_reduction_layer_args = {"encoder_dim": encoder_dim}
+
+        self.time_reduction_layer = time_reduction_layer(**time_reduction_layer_args)
+        self.time_recover_layer = nn.Linear(encoder_dim, encoder_dim)
+        self.final_proj = None
+        if output_size != encoder_dim:
+            self.final_proj = nn.Linear(encoder_dim, output_size)
+
+    def output_size(self) -> int:
+        return self._output_size
+
+    def forward(
+        self,
+        xs: torch.Tensor,
+        xs_lens: torch.Tensor,
+        decoding_chunk_size: int = 0,
+        num_decoding_left_chunks: int = -1,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        T = xs.size(1)
+        masks = ~make_pad_mask(xs_lens, T).unsqueeze(1)  # (B, 1, T)
+        if self.global_cmvn is not None:
+            xs = self.global_cmvn(xs)
+        xs, pos_emb, masks = self.embed(xs, masks)
+        mask_pad = masks  # (B, 1, T/subsample_rate)
+        chunk_masks = add_optional_chunk_mask(
+            xs,
+            masks,
+            self.use_dynamic_chunk,
+            self.use_dynamic_left_chunk,
+            decoding_chunk_size,
+            self.static_chunk_size,
+            num_decoding_left_chunks,
+        )
+        xs_lens = mask_pad.squeeze(1).sum(1)
+        xs = self.preln(xs)
+        recover_activations: List[
+            Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]
+        ] = []
+        index = 0
+        for i, layer in enumerate(self.encoders):
+            if self.reduce_idx is not None:
+                if self.time_reduce is not None and i in self.reduce_idx:
+                    recover_activations.append((xs, chunk_masks, pos_emb, mask_pad))
+                    xs, xs_lens, chunk_masks, mask_pad = self.time_reduction_layer(
+                        xs, xs_lens, chunk_masks, mask_pad
+                    )
+                    pos_emb = pos_emb[:, ::2, :]
+                    index += 1
+
+            if self.recover_idx is not None:
+                if self.time_reduce == "recover" and i in self.recover_idx:
+                    index -= 1
+                    (
+                        recover_tensor,
+                        recover_chunk_masks,
+                        recover_pos_emb,
+                        recover_mask_pad,
+                    ) = recover_activations[index]
+                    # recover output length for ctc decode
+                    xs = xs.unsqueeze(2).repeat(1, 1, 2, 1).flatten(1, 2)
+                    xs = self.time_recover_layer(xs)
+                    recoverd_t = recover_tensor.size(1)
+                    xs = recover_tensor + xs[:, :recoverd_t, :].contiguous()
+                    chunk_masks = recover_chunk_masks
+                    pos_emb = recover_pos_emb
+                    mask_pad = recover_mask_pad
+                    xs = xs.masked_fill(~mask_pad[:, 0, :].unsqueeze(-1), 0.0)
+
+            xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad)
+
+        if self.final_proj is not None:
+            xs = self.final_proj(xs)
+        return xs, masks
+
+    def check_ascending_list(self):
+        if self.reduce_idx is not None:
+            assert self.reduce_idx == sorted(
+                self.reduce_idx
+            ), "reduce_idx should be int or ascending list"
+        if self.recover_idx is not None:
+            assert self.recover_idx == sorted(
+                self.recover_idx
+            ), "recover_idx should be int or ascending list"
+
+    def calculate_downsampling_factor(self, i: int) -> int:
+        if self.reduce_idx is None:
+            return 1
+        else:
+            reduce_exp, recover_exp = 0, 0
+            for exp, rd_idx in enumerate(self.reduce_idx):
+                if i >= rd_idx:
+                    reduce_exp = exp + 1
+            if self.recover_idx is not None:
+                for exp, rc_idx in enumerate(self.recover_idx):
+                    if i >= rc_idx:
+                        recover_exp = exp + 1
+            return int(2 ** (reduce_exp - recover_exp))
+
+    def forward_chunk(
+        self,
+        xs: torch.Tensor,
+        offset: int,
+        required_cache_size: int,
+        att_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
+        cnn_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
+        att_mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """ Forward just one chunk
+
+        Args:
+            xs (torch.Tensor): chunk input, with shape (b=1, time, mel-dim),
+                where `time == (chunk_size - 1) * subsample_rate + \
+                        subsample.right_context + 1`
+            offset (int): current offset in encoder output time stamp
+            required_cache_size (int): cache size required for next chunk
+                compuation
+                >=0: actual cache size
+                <0: means all history cache is required
+            att_cache (torch.Tensor): cache tensor for KEY & VALUE in
+                transformer/conformer attention, with shape
+                (elayers, head, cache_t1, d_k * 2), where
+                `head * d_k == hidden-dim` and
+                `cache_t1 == chunk_size * num_decoding_left_chunks`.
+            cnn_cache (torch.Tensor): cache tensor for cnn_module in conformer,
+                (elayers, b=1, hidden-dim, cache_t2), where
+                `cache_t2 == cnn.lorder - 1`
+
+        Returns:
+            torch.Tensor: output of current input xs,
+                with shape (b=1, chunk_size, hidden-dim).
+            torch.Tensor: new attention cache required for next chunk, with
+                dynamic shape (elayers, head, ?, d_k * 2)
+                depending on required_cache_size.
+            torch.Tensor: new conformer cnn cache required for next chunk, with
+                same shape as the original cnn_cache.
+
+        """
+        assert xs.size(0) == 1
+        # tmp_masks is just for interface compatibility
+        tmp_masks = torch.ones(1, xs.size(1), device=xs.device, dtype=torch.bool)
+        tmp_masks = tmp_masks.unsqueeze(1)
+        if self.global_cmvn is not None:
+            xs = self.global_cmvn(xs)
+        # NOTE(xcsong): Before embed, shape(xs) is (b=1, time, mel-dim)
+        xs, pos_emb, _ = self.embed(xs, tmp_masks, offset)
+        # NOTE(xcsong): After  embed, shape(xs) is (b=1, chunk_size, hidden-dim)
+        elayers, cache_t1 = att_cache.size(0), att_cache.size(2)
+        chunk_size = xs.size(1)
+        attention_key_size = cache_t1 + chunk_size
+        pos_emb = self.embed.position_encoding(
+            offset=offset - cache_t1, size=attention_key_size
+        )
+        if required_cache_size < 0:
+            next_cache_start = 0
+        elif required_cache_size == 0:
+            next_cache_start = attention_key_size
+        else:
+            next_cache_start = max(attention_key_size - required_cache_size, 0)
+
+        r_att_cache = []
+        r_cnn_cache = []
+
+        mask_pad = torch.ones(1, xs.size(1), device=xs.device, dtype=torch.bool)
+        mask_pad = mask_pad.unsqueeze(1)
+        max_att_len: int = 0
+        recover_activations: List[
+            Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]
+        ] = []
+        index = 0
+        xs_lens = torch.tensor([xs.size(1)], device=xs.device, dtype=torch.int)
+        xs = self.preln(xs)
+        for i, layer in enumerate(self.encoders):
+            # NOTE(xcsong): Before layer.forward
+            #   shape(att_cache[i:i + 1]) is (1, head, cache_t1, d_k * 2),
+            #   shape(cnn_cache[i])       is (b=1, hidden-dim, cache_t2)
+            if self.reduce_idx is not None:
+                if self.time_reduce is not None and i in self.reduce_idx:
+                    recover_activations.append((xs, att_mask, pos_emb, mask_pad))
+                    xs, xs_lens, att_mask, mask_pad = self.time_reduction_layer(
+                        xs, xs_lens, att_mask, mask_pad
+                    )
+                    pos_emb = pos_emb[:, ::2, :]
+                    index += 1
+
+            if self.recover_idx is not None:
+                if self.time_reduce == "recover" and i in self.recover_idx:
+                    index -= 1
+                    (
+                        recover_tensor,
+                        recover_att_mask,
+                        recover_pos_emb,
+                        recover_mask_pad,
+                    ) = recover_activations[index]
+                    # recover output length for ctc decode
+                    xs = xs.unsqueeze(2).repeat(1, 1, 2, 1).flatten(1, 2)
+                    xs = self.time_recover_layer(xs)
+                    recoverd_t = recover_tensor.size(1)
+                    xs = recover_tensor + xs[:, :recoverd_t, :].contiguous()
+                    att_mask = recover_att_mask
+                    pos_emb = recover_pos_emb
+                    mask_pad = recover_mask_pad
+                    if att_mask.size(1) != 0:
+                        xs = xs.masked_fill(~att_mask[:, 0, :].unsqueeze(-1), 0.0)
+
+            factor = self.calculate_downsampling_factor(i)
+
+            xs, _, new_att_cache, new_cnn_cache = layer(
+                xs,
+                att_mask,
+                pos_emb,
+                att_cache=att_cache[i : i + 1][:, :, ::factor, :][
+                    :, :, : pos_emb.size(1) - xs.size(1), :
+                ]
+                if elayers > 0
+                else att_cache[:, :, ::factor, :],
+                cnn_cache=cnn_cache[i] if cnn_cache.size(0) > 0 else cnn_cache,
+            )
+            # NOTE(xcsong): After layer.forward
+            #   shape(new_att_cache) is (1, head, attention_key_size, d_k * 2),
+            #   shape(new_cnn_cache) is (b=1, hidden-dim, cache_t2)
+            cached_att = new_att_cache[:, :, next_cache_start // factor :, :]
+            cached_cnn = new_cnn_cache.unsqueeze(0)
+            cached_att = (
+                cached_att.unsqueeze(3).repeat(1, 1, 1, factor, 1).flatten(2, 3)
+            )
+            if i == 0:
+                # record length for the first block as max length
+                max_att_len = cached_att.size(2)
+            r_att_cache.append(cached_att[:, :, :max_att_len, :])
+            r_cnn_cache.append(cached_cnn)
+        # NOTE(xcsong): shape(r_att_cache) is (elayers, head, ?, d_k * 2),
+        #   ? may be larger than cache_t1, it depends on required_cache_size
+        r_att_cache = torch.cat(r_att_cache, dim=0)
+        # NOTE(xcsong): shape(r_cnn_cache) is (e, b=1, hidden-dim, cache_t2)
+        r_cnn_cache = torch.cat(r_cnn_cache, dim=0)
+
+        if self.final_proj is not None:
+            xs = self.final_proj(xs)
+        return (xs, r_att_cache, r_cnn_cache)
+
+    def forward_chunk_by_chunk(
+        self,
+        xs: torch.Tensor,
+        decoding_chunk_size: int,
+        num_decoding_left_chunks: int = -1,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Forward input chunk by chunk with chunk_size like a streaming
+            fashion
+
+        Here we should pay special attention to computation cache in the
+        streaming style forward chunk by chunk. Three things should be taken
+        into account for computation in the current network:
+            1. transformer/conformer encoder layers output cache
+            2. convolution in conformer
+            3. convolution in subsampling
+
+        However, we don't implement subsampling cache for:
+            1. We can control subsampling module to output the right result by
+               overlapping input instead of cache left context, even though it
+               wastes some computation, but subsampling only takes a very
+               small fraction of computation in the whole model.
+            2. Typically, there are several covolution layers with subsampling
+               in subsampling module, it is tricky and complicated to do cache
+               with different convolution layers with different subsampling
+               rate.
+            3. Currently, nn.Sequential is used to stack all the convolution
+               layers in subsampling, we need to rewrite it to make it work
+               with cache, which is not prefered.
+        Args:
+            xs (torch.Tensor): (1, max_len, dim)
+            chunk_size (int): decoding chunk size
+        """
+        assert decoding_chunk_size > 0
+        # The model is trained by static or dynamic chunk
+        assert self.static_chunk_size > 0 or self.use_dynamic_chunk
+        subsampling = self.embed.subsampling_rate
+        context = self.embed.right_context + 1  # Add current frame
+        stride = subsampling * decoding_chunk_size
+        decoding_window = (decoding_chunk_size - 1) * subsampling + context
+        num_frames = xs.size(1)
+        att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device)
+        cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device)
+        outputs = []
+        offset = 0
+        required_cache_size = decoding_chunk_size * num_decoding_left_chunks
+
+        # Feed forward overlap input step by step
+        for cur in range(0, num_frames - context + 1, stride):
+            end = min(cur + decoding_window, num_frames)
+            chunk_xs = xs[:, cur:end, :]
+            (y, att_cache, cnn_cache) = self.forward_chunk(
+                chunk_xs, offset, required_cache_size, att_cache, cnn_cache
+            )
+            outputs.append(y)
+            offset += y.size(1)
+        ys = torch.cat(outputs, 1)
+        masks = torch.ones((1, 1, ys.size(1)), device=ys.device, dtype=torch.bool)
+        return ys, masks
diff --git a/modules/wenet_extractor/squeezeformer/encoder_layer.py b/modules/wenet_extractor/squeezeformer/encoder_layer.py
new file mode 100755
index 0000000000000000000000000000000000000000..74e7c0fdeee6f7156468b14416d268702cf4d40a
--- /dev/null
+++ b/modules/wenet_extractor/squeezeformer/encoder_layer.py
@@ -0,0 +1,129 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+"""SqueezeformerEncoderLayer definition."""
+
+import torch
+import torch.nn as nn
+from typing import Optional, Tuple
+
+
+class SqueezeformerEncoderLayer(nn.Module):
+    """Encoder layer module.
+    Args:
+        size (int): Input dimension.
+        self_attn (torch.nn.Module): Self-attention module instance.
+            `MultiHeadedAttention` or `RelPositionMultiHeadedAttention`
+            instance can be used as the argument.
+        feed_forward1 (torch.nn.Module): Feed-forward module instance.
+            `PositionwiseFeedForward` instance can be used as the argument.
+        conv_module (torch.nn.Module): Convolution module instance.
+            `ConvlutionModule` instance can be used as the argument.
+        feed_forward2 (torch.nn.Module): Feed-forward module instance.
+            `PositionwiseFeedForward` instance can be used as the argument.
+        dropout_rate (float): Dropout rate.
+        normalize_before (bool):
+            True: use layer_norm before each sub-block.
+            False: use layer_norm after each sub-block.
+    """
+
+    def __init__(
+        self,
+        size: int,
+        self_attn: torch.nn.Module,
+        feed_forward1: Optional[nn.Module] = None,
+        conv_module: Optional[nn.Module] = None,
+        feed_forward2: Optional[nn.Module] = None,
+        normalize_before: bool = False,
+        dropout_rate: float = 0.1,
+        concat_after: bool = False,
+    ):
+        super(SqueezeformerEncoderLayer, self).__init__()
+        self.size = size
+        self.self_attn = self_attn
+        self.layer_norm1 = nn.LayerNorm(size)
+        self.ffn1 = feed_forward1
+        self.layer_norm2 = nn.LayerNorm(size)
+        self.conv_module = conv_module
+        self.layer_norm3 = nn.LayerNorm(size)
+        self.ffn2 = feed_forward2
+        self.layer_norm4 = nn.LayerNorm(size)
+        self.normalize_before = normalize_before
+        self.dropout = nn.Dropout(dropout_rate)
+        self.concat_after = concat_after
+        if concat_after:
+            self.concat_linear = nn.Linear(size + size, size)
+        else:
+            self.concat_linear = nn.Identity()
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        mask: torch.Tensor,
+        pos_emb: torch.Tensor,
+        mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+        att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
+        cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        # self attention module
+        residual = x
+        if self.normalize_before:
+            x = self.layer_norm1(x)
+        x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb, att_cache)
+        if self.concat_after:
+            x_concat = torch.cat((x, x_att), dim=-1)
+            x = residual + self.concat_linear(x_concat)
+        else:
+            x = residual + self.dropout(x_att)
+        if not self.normalize_before:
+            x = self.layer_norm1(x)
+
+        # ffn module
+        residual = x
+        if self.normalize_before:
+            x = self.layer_norm2(x)
+        x = self.ffn1(x)
+        x = residual + self.dropout(x)
+        if not self.normalize_before:
+            x = self.layer_norm2(x)
+
+        # conv module
+        new_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device)
+        residual = x
+        if self.normalize_before:
+            x = self.layer_norm3(x)
+        x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache)
+        x = residual + self.dropout(x)
+        if not self.normalize_before:
+            x = self.layer_norm3(x)
+
+        # ffn module
+        residual = x
+        if self.normalize_before:
+            x = self.layer_norm4(x)
+        x = self.ffn2(x)
+        # we do not use dropout here since it is inside feed forward function
+        x = residual + self.dropout(x)
+        if not self.normalize_before:
+            x = self.layer_norm4(x)
+
+        return x, mask, new_att_cache, new_cnn_cache
diff --git a/modules/wenet_extractor/squeezeformer/positionwise_feed_forward.py b/modules/wenet_extractor/squeezeformer/positionwise_feed_forward.py
new file mode 100755
index 0000000000000000000000000000000000000000..bfd4aabf9a7e246243d9792616bbe995c2a8a6ba
--- /dev/null
+++ b/modules/wenet_extractor/squeezeformer/positionwise_feed_forward.py
@@ -0,0 +1,88 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+"""Positionwise feed forward layer definition."""
+
+import torch
+
+
+class PositionwiseFeedForward(torch.nn.Module):
+    """Positionwise feed forward layer.
+
+    FeedForward are appied on each position of the sequence.
+    The output dim is same with the input dim.
+
+    Args:
+        idim (int): Input dimenstion.
+        hidden_units (int): The number of hidden units.
+        dropout_rate (float): Dropout rate.
+        activation (torch.nn.Module): Activation function
+    """
+
+    def __init__(
+        self,
+        idim: int,
+        hidden_units: int,
+        dropout_rate: float,
+        activation: torch.nn.Module = torch.nn.ReLU(),
+        adaptive_scale: bool = False,
+        init_weights: bool = False,
+    ):
+        """Construct a PositionwiseFeedForward object."""
+        super(PositionwiseFeedForward, self).__init__()
+        self.idim = idim
+        self.hidden_units = hidden_units
+        self.w_1 = torch.nn.Linear(idim, hidden_units)
+        self.activation = activation
+        self.dropout = torch.nn.Dropout(dropout_rate)
+        self.w_2 = torch.nn.Linear(hidden_units, idim)
+        self.ada_scale = None
+        self.ada_bias = None
+        self.adaptive_scale = adaptive_scale
+        self.ada_scale = torch.nn.Parameter(
+            torch.ones([1, 1, idim]), requires_grad=adaptive_scale
+        )
+        self.ada_bias = torch.nn.Parameter(
+            torch.zeros([1, 1, idim]), requires_grad=adaptive_scale
+        )
+        if init_weights:
+            self.init_weights()
+
+    def init_weights(self):
+        ffn1_max = self.idim**-0.5
+        ffn2_max = self.hidden_units**-0.5
+        torch.nn.init.uniform_(self.w_1.weight.data, -ffn1_max, ffn1_max)
+        torch.nn.init.uniform_(self.w_1.bias.data, -ffn1_max, ffn1_max)
+        torch.nn.init.uniform_(self.w_2.weight.data, -ffn2_max, ffn2_max)
+        torch.nn.init.uniform_(self.w_2.bias.data, -ffn2_max, ffn2_max)
+
+    def forward(self, xs: torch.Tensor) -> torch.Tensor:
+        """Forward function.
+
+        Args:
+            xs: input tensor (B, L, D)
+        Returns:
+            output tensor, (B, L, D)
+        """
+        if self.adaptive_scale:
+            xs = self.ada_scale * xs + self.ada_bias
+        return self.w_2(self.dropout(self.activation(self.w_1(xs))))
diff --git a/modules/wenet_extractor/squeezeformer/subsampling.py b/modules/wenet_extractor/squeezeformer/subsampling.py
new file mode 100755
index 0000000000000000000000000000000000000000..329dd6c51f1719d73307d636dd625cf98a34cd34
--- /dev/null
+++ b/modules/wenet_extractor/squeezeformer/subsampling.py
@@ -0,0 +1,321 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+"""DepthwiseConv2dSubsampling4 and TimeReductionLayer definition."""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from modules.wenet_extractor.transformer.subsampling import BaseSubsampling
+from typing import Tuple
+from modules.wenet_extractor.squeezeformer.conv2d import Conv2dValid
+
+
+class DepthwiseConv2dSubsampling4(BaseSubsampling):
+    """Depthwise Convolutional 2D subsampling (to 1/4 length).
+
+    Args:
+        idim (int): Input dimension.
+        odim (int): Output dimension.
+        pos_enc_class (nn.Module): position encoding class.
+        dw_stride (int): Whether do depthwise convolution.
+        input_size (int): filter bank dimension.
+
+    """
+
+    def __init__(
+        self,
+        idim: int,
+        odim: int,
+        pos_enc_class: torch.nn.Module,
+        dw_stride: bool = False,
+        input_size: int = 80,
+        input_dropout_rate: float = 0.1,
+        init_weights: bool = True,
+    ):
+        super(DepthwiseConv2dSubsampling4, self).__init__()
+        self.idim = idim
+        self.odim = odim
+        self.pw_conv = nn.Conv2d(
+            in_channels=idim, out_channels=odim, kernel_size=3, stride=2
+        )
+        self.act1 = nn.ReLU()
+        self.dw_conv = nn.Conv2d(
+            in_channels=odim,
+            out_channels=odim,
+            kernel_size=3,
+            stride=2,
+            groups=odim if dw_stride else 1,
+        )
+        self.act2 = nn.ReLU()
+        self.pos_enc = pos_enc_class
+        self.input_proj = nn.Sequential(
+            nn.Linear(odim * (((input_size - 1) // 2 - 1) // 2), odim),
+            nn.Dropout(p=input_dropout_rate),
+        )
+        if init_weights:
+            linear_max = (odim * input_size / 4) ** -0.5
+            torch.nn.init.uniform_(
+                self.input_proj.state_dict()["0.weight"], -linear_max, linear_max
+            )
+            torch.nn.init.uniform_(
+                self.input_proj.state_dict()["0.bias"], -linear_max, linear_max
+            )
+        self.subsampling_rate = 4
+        # 6 = (3 - 1) * 1 + (3 - 1) * 2
+        self.right_context = 6
+
+    def forward(
+        self, x: torch.Tensor, x_mask: torch.Tensor, offset: int = 0
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        x = x.unsqueeze(1)  # (b, c=1, t, f)
+        x = self.pw_conv(x)
+        x = self.act1(x)
+        x = self.dw_conv(x)
+        x = self.act2(x)
+        b, c, t, f = x.size()
+        x = x.permute(0, 2, 1, 3)
+        x = x.contiguous().view(b, t, c * f)
+        x, pos_emb = self.pos_enc(x, offset)
+        x = self.input_proj(x)
+        return x, pos_emb, x_mask[:, :, :-2:2][:, :, :-2:2]
+
+
+class TimeReductionLayer1D(nn.Module):
+    """
+    Modified NeMo,
+    Squeezeformer Time Reduction procedure.
+    Downsamples the audio by `stride` in the time dimension.
+    Args:
+        channel (int): input dimension of
+                       MultiheadAttentionMechanism and PositionwiseFeedForward
+        out_dim (int): Output dimension of the module.
+        kernel_size (int): Conv kernel size for
+                           depthwise convolution in convolution module
+        stride (int): Downsampling factor in time dimension.
+    """
+
+    def __init__(
+        self, channel: int, out_dim: int, kernel_size: int = 5, stride: int = 2
+    ):
+        super(TimeReductionLayer1D, self).__init__()
+
+        self.channel = channel
+        self.out_dim = out_dim
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.padding = max(0, self.kernel_size - self.stride)
+
+        self.dw_conv = nn.Conv1d(
+            in_channels=channel,
+            out_channels=channel,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=self.padding,
+            groups=channel,
+        )
+
+        self.pw_conv = nn.Conv1d(
+            in_channels=channel,
+            out_channels=out_dim,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            groups=1,
+        )
+
+        self.init_weights()
+
+    def init_weights(self):
+        dw_max = self.kernel_size**-0.5
+        pw_max = self.channel**-0.5
+        torch.nn.init.uniform_(self.dw_conv.weight, -dw_max, dw_max)
+        torch.nn.init.uniform_(self.dw_conv.bias, -dw_max, dw_max)
+        torch.nn.init.uniform_(self.pw_conv.weight, -pw_max, pw_max)
+        torch.nn.init.uniform_(self.pw_conv.bias, -pw_max, pw_max)
+
+    def forward(
+        self,
+        xs,
+        xs_lens: torch.Tensor,
+        mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+        mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+    ):
+        xs = xs.transpose(1, 2)  # [B, C, T]
+        xs = xs.masked_fill(mask_pad.eq(0), 0.0)
+
+        xs = self.dw_conv(xs)
+        xs = self.pw_conv(xs)
+
+        xs = xs.transpose(1, 2)  # [B, T, C]
+
+        B, T, D = xs.size()
+        mask = mask[:, :: self.stride, :: self.stride]
+        mask_pad = mask_pad[:, :, :: self.stride]
+        L = mask_pad.size(-1)
+        # For JIT exporting, we remove F.pad operator.
+        if L - T < 0:
+            xs = xs[:, : L - T, :].contiguous()
+        else:
+            dummy_pad = torch.zeros(B, L - T, D, device=xs.device)
+            xs = torch.cat([xs, dummy_pad], dim=1)
+
+        xs_lens = torch.div(xs_lens + 1, 2, rounding_mode="trunc")
+        return xs, xs_lens, mask, mask_pad
+
+
+class TimeReductionLayer2D(nn.Module):
+    def __init__(self, kernel_size: int = 5, stride: int = 2, encoder_dim: int = 256):
+        super(TimeReductionLayer2D, self).__init__()
+        self.encoder_dim = encoder_dim
+        self.kernel_size = kernel_size
+        self.dw_conv = Conv2dValid(
+            in_channels=encoder_dim,
+            out_channels=encoder_dim,
+            kernel_size=(kernel_size, 1),
+            stride=stride,
+            valid_trigy=True,
+        )
+        self.pw_conv = Conv2dValid(
+            in_channels=encoder_dim,
+            out_channels=encoder_dim,
+            kernel_size=1,
+            stride=1,
+            valid_trigx=False,
+            valid_trigy=False,
+        )
+
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.init_weights()
+
+    def init_weights(self):
+        dw_max = self.kernel_size**-0.5
+        pw_max = self.encoder_dim**-0.5
+        torch.nn.init.uniform_(self.dw_conv.weight, -dw_max, dw_max)
+        torch.nn.init.uniform_(self.dw_conv.bias, -dw_max, dw_max)
+        torch.nn.init.uniform_(self.pw_conv.weight, -pw_max, pw_max)
+        torch.nn.init.uniform_(self.pw_conv.bias, -pw_max, pw_max)
+
+    def forward(
+        self,
+        xs: torch.Tensor,
+        xs_lens: torch.Tensor,
+        mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+        mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        xs = xs.masked_fill(mask_pad.transpose(1, 2).eq(0), 0.0)
+        xs = xs.unsqueeze(2)
+        padding1 = self.kernel_size - self.stride
+        xs = F.pad(xs, (0, 0, 0, 0, 0, padding1, 0, 0), mode="constant", value=0.0)
+        xs = self.dw_conv(xs.permute(0, 3, 1, 2))
+        xs = self.pw_conv(xs).permute(0, 3, 2, 1).squeeze(1).contiguous()
+        tmp_length = xs.size(1)
+        xs_lens = torch.div(xs_lens + 1, 2, rounding_mode="trunc")
+        padding2 = max(0, (xs_lens.max() - tmp_length).data.item())
+        batch_size, hidden = xs.size(0), xs.size(-1)
+        dummy_pad = torch.zeros(batch_size, padding2, hidden, device=xs.device)
+        xs = torch.cat([xs, dummy_pad], dim=1)
+        mask = mask[:, ::2, ::2]
+        mask_pad = mask_pad[:, :, ::2]
+        return xs, xs_lens, mask, mask_pad
+
+
+class TimeReductionLayerStream(nn.Module):
+    """
+    Squeezeformer Time Reduction procedure.
+    Downsamples the audio by `stride` in the time dimension.
+    Args:
+        channel (int): input dimension of
+            MultiheadAttentionMechanism and PositionwiseFeedForward
+        out_dim (int): Output dimension of the module.
+        kernel_size (int): Conv kernel size for
+            depthwise convolution in convolution module
+        stride (int): Downsampling factor in time dimension.
+    """
+
+    def __init__(
+        self, channel: int, out_dim: int, kernel_size: int = 1, stride: int = 2
+    ):
+        super(TimeReductionLayerStream, self).__init__()
+
+        self.channel = channel
+        self.out_dim = out_dim
+        self.kernel_size = kernel_size
+        self.stride = stride
+
+        self.dw_conv = nn.Conv1d(
+            in_channels=channel,
+            out_channels=channel,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=0,
+            groups=channel,
+        )
+
+        self.pw_conv = nn.Conv1d(
+            in_channels=channel,
+            out_channels=out_dim,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            groups=1,
+        )
+
+        self.init_weights()
+
+    def init_weights(self):
+        dw_max = self.kernel_size**-0.5
+        pw_max = self.channel**-0.5
+        torch.nn.init.uniform_(self.dw_conv.weight, -dw_max, dw_max)
+        torch.nn.init.uniform_(self.dw_conv.bias, -dw_max, dw_max)
+        torch.nn.init.uniform_(self.pw_conv.weight, -pw_max, pw_max)
+        torch.nn.init.uniform_(self.pw_conv.bias, -pw_max, pw_max)
+
+    def forward(
+        self,
+        xs,
+        xs_lens: torch.Tensor,
+        mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+        mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+    ):
+        xs = xs.transpose(1, 2)  # [B, C, T]
+        xs = xs.masked_fill(mask_pad.eq(0), 0.0)
+
+        xs = self.dw_conv(xs)
+        xs = self.pw_conv(xs)
+
+        xs = xs.transpose(1, 2)  # [B, T, C]
+
+        B, T, D = xs.size()
+        mask = mask[:, :: self.stride, :: self.stride]
+        mask_pad = mask_pad[:, :, :: self.stride]
+        L = mask_pad.size(-1)
+        # For JIT exporting, we remove F.pad operator.
+        if L - T < 0:
+            xs = xs[:, : L - T, :].contiguous()
+        else:
+            dummy_pad = torch.zeros(B, L - T, D, device=xs.device)
+            xs = torch.cat([xs, dummy_pad], dim=1)
+
+        xs_lens = torch.div(xs_lens + 1, 2, rounding_mode="trunc")
+        return xs, xs_lens, mask, mask_pad
diff --git a/modules/wenet_extractor/transducer/__init__.py b/modules/wenet_extractor/transducer/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/modules/wenet_extractor/transducer/joint.py b/modules/wenet_extractor/transducer/joint.py
new file mode 100755
index 0000000000000000000000000000000000000000..8592136c9f33902942fb01ee42af82761b7ad26b
--- /dev/null
+++ b/modules/wenet_extractor/transducer/joint.py
@@ -0,0 +1,72 @@
+from typing import Optional
+
+import torch
+from torch import nn
+from modules.wenet_extractor.utils.common import get_activation
+
+
+class TransducerJoint(torch.nn.Module):
+    def __init__(
+        self,
+        voca_size: int,
+        enc_output_size: int,
+        pred_output_size: int,
+        join_dim: int,
+        prejoin_linear: bool = True,
+        postjoin_linear: bool = False,
+        joint_mode: str = "add",
+        activation: str = "tanh",
+    ):
+        # TODO(Mddct): concat in future
+        assert joint_mode in ["add"]
+        super().__init__()
+
+        self.activatoin = get_activation(activation)
+        self.prejoin_linear = prejoin_linear
+        self.postjoin_linear = postjoin_linear
+        self.joint_mode = joint_mode
+
+        if not self.prejoin_linear and not self.postjoin_linear:
+            assert enc_output_size == pred_output_size == join_dim
+        # torchscript compatibility
+        self.enc_ffn: Optional[nn.Linear] = None
+        self.pred_ffn: Optional[nn.Linear] = None
+        if self.prejoin_linear:
+            self.enc_ffn = nn.Linear(enc_output_size, join_dim)
+            self.pred_ffn = nn.Linear(pred_output_size, join_dim)
+        # torchscript compatibility
+        self.post_ffn: Optional[nn.Linear] = None
+        if self.postjoin_linear:
+            self.post_ffn = nn.Linear(join_dim, join_dim)
+
+        self.ffn_out = nn.Linear(join_dim, voca_size)
+
+    def forward(self, enc_out: torch.Tensor, pred_out: torch.Tensor):
+        """
+        Args:
+            enc_out (torch.Tensor): [B, T, E]
+            pred_out (torch.Tensor): [B, T, P]
+        Return:
+            [B,T,U,V]
+        """
+        if (
+            self.prejoin_linear
+            and self.enc_ffn is not None
+            and self.pred_ffn is not None
+        ):
+            enc_out = self.enc_ffn(enc_out)  # [B,T,E] -> [B,T,V]
+            pred_out = self.pred_ffn(pred_out)
+
+        enc_out = enc_out.unsqueeze(2)  # [B,T,V] -> [B,T,1,V]
+        pred_out = pred_out.unsqueeze(1)  # [B,U,V] -> [B,1 U, V]
+
+        # TODO(Mddct): concat joint
+        _ = self.joint_mode
+        out = enc_out + pred_out  # [B,T,U,V]
+
+        if self.postjoin_linear and self.post_ffn is not None:
+            out = self.post_ffn(out)
+
+        out = self.activatoin(out)
+        out = self.ffn_out(out)
+        return out
diff --git a/modules/wenet_extractor/transducer/predictor.py b/modules/wenet_extractor/transducer/predictor.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c1ffdc7b30c23147ba80ae928eb3bd18f602932
--- /dev/null
+++ b/modules/wenet_extractor/transducer/predictor.py
@@ -0,0 +1,477 @@
+from typing import List, Optional, Tuple
+
+import torch
+from torch import nn
+from modules.wenet_extractor.utils.common import get_activation, get_rnn
+
+
+def ApplyPadding(input, padding, pad_value) -> torch.Tensor:
+    """
+    Args:
+        input:   [bs, max_time_step, dim]
+        padding: [bs, max_time_step]
+    """
+    return padding * pad_value + input * (1 - padding)
+
+
+class PredictorBase(torch.nn.Module):
+    # NOTE(Mddct): We can use ABC abstract here, but
+    # keep this class simple enough for now
+    def __init__(self) -> None:
+        super().__init__()
+
+    def init_state(
+        self, batch_size: int, device: torch.device, method: str = "zero"
+    ) -> List[torch.Tensor]:
+        _, _, _ = batch_size, method, device
+        raise NotImplementedError("this is a base precictor")
+
+    def batch_to_cache(self, cache: List[torch.Tensor]) -> List[List[torch.Tensor]]:
+        _ = cache
+        raise NotImplementedError("this is a base precictor")
+
+    def cache_to_batch(self, cache: List[List[torch.Tensor]]) -> List[torch.Tensor]:
+        _ = cache
+        raise NotImplementedError("this is a base precictor")
+
+    def forward(
+        self,
+        input: torch.Tensor,
+        cache: Optional[List[torch.Tensor]] = None,
+    ):
+        (
+            _,
+            _,
+        ) = (
+            input,
+            cache,
+        )
+        raise NotImplementedError("this is a base precictor")
+
+    def forward_step(
+        self, input: torch.Tensor, padding: torch.Tensor, cache: List[torch.Tensor]
+    ) -> Tuple[torch.Tensor, List[torch.Tensor]]:
+        (
+            _,
+            _,
+            _,
+        ) = (
+            input,
+            padding,
+            cache,
+        )
+        raise NotImplementedError("this is a base precictor")
+
+
+class RNNPredictor(PredictorBase):
+    def __init__(
+        self,
+        voca_size: int,
+        embed_size: int,
+        output_size: int,
+        embed_dropout: float,
+        hidden_size: int,
+        num_layers: int,
+        bias: bool = True,
+        rnn_type: str = "lstm",
+        dropout: float = 0.1,
+    ) -> None:
+        super().__init__()
+        self.n_layers = num_layers
+        self.hidden_size = hidden_size
+        # disable rnn base out projection
+        self.embed = nn.Embedding(voca_size, embed_size)
+        self.dropout = nn.Dropout(embed_dropout)
+        # NOTE(Mddct): rnn base from torch not support layer norm
+        # will add layer norm and prune value in cell and layer
+        # ref: https://github.com/Mddct/neural-lm/blob/main/models/gru_cell.py
+        self.rnn = get_rnn(rnn_type=rnn_type)(
+            input_size=embed_size,
+            hidden_size=hidden_size,
+            num_layers=num_layers,
+            bias=bias,
+            batch_first=True,
+            dropout=dropout,
+        )
+        self.projection = nn.Linear(hidden_size, output_size)
+
+    def forward(
+        self,
+        input: torch.Tensor,
+        cache: Optional[List[torch.Tensor]] = None,
+    ) -> torch.Tensor:
+        """
+        Args:
+            input (torch.Tensor): [batch, max_time).
+            padding (torch.Tensor): [batch, max_time]
+            cache : rnn predictor cache[0] == state_m
+                    cache[1] == state_c
+        Returns:
+            output: [batch, max_time, output_size]
+        """
+
+        # NOTE(Mddct): we don't use pack input format
+        embed = self.embed(input)  # [batch, max_time, emb_size]
+        embed = self.dropout(embed)
+        states: Optional[Tuple[torch.Tensor, torch.Tensor]] = None
+        if cache is None:
+            state = self.init_state(batch_size=input.size(0), device=input.device)
+            states = (state[0], state[1])
+        else:
+            assert len(cache) == 2
+            states = (cache[0], cache[1])
+        out, (m, c) = self.rnn(embed, states)
+        out = self.projection(out)
+
+        # NOTE(Mddct): Although we don't use staate in transducer
+        # training forward, we need make it right for padding value
+        # so we create forward_step for infering, forward for training
+        _, _ = m, c
+        return out
+
+    def batch_to_cache(self, cache: List[torch.Tensor]) -> List[List[torch.Tensor]]:
+        """
+        Args:
+           cache: [state_m, state_c]
+               state_ms: [1*n_layers, bs, ...]
+               state_cs: [1*n_layers, bs, ...]
+        Returns:
+           new_cache: [[state_m_1, state_c_1], [state_m_2, state_c_2]...]
+        """
+        assert len(cache) == 2
+        state_ms = cache[0]
+        state_cs = cache[1]
+
+        assert state_ms.size(1) == state_cs.size(1)
+
+        new_cache: List[List[torch.Tensor]] = []
+        for state_m, state_c in zip(
+            torch.split(state_ms, 1, dim=1), torch.split(state_cs, 1, dim=1)
+        ):
+            new_cache.append([state_m, state_c])
+        return new_cache
+
+    def cache_to_batch(self, cache: List[List[torch.Tensor]]) -> List[torch.Tensor]:
+        """
+        Args:
+            cache : [[state_m_1, state_c_1], [state_m_1, state_c_1]...]
+
+        Returns:
+            new_caceh: [state_ms, state_cs],
+                state_ms: [1*n_layers, bs, ...]
+                state_cs: [1*n_layers, bs, ...]
+        """
+        state_ms = torch.cat([states[0] for states in cache], dim=1)
+        state_cs = torch.cat([states[1] for states in cache], dim=1)
+        return [state_ms, state_cs]
+
+    def init_state(
+        self,
+        batch_size: int,
+        device: torch.device,
+        method: str = "zero",
+    ) -> List[torch.Tensor]:
+        assert batch_size > 0
+        # TODO(Mddct): xavier init method
+        _ = method
+        return [
+            torch.zeros(1 * self.n_layers, batch_size, self.hidden_size, device=device),
+            torch.zeros(1 * self.n_layers, batch_size, self.hidden_size, device=device),
+        ]
+
+    def forward_step(
+        self, input: torch.Tensor, padding: torch.Tensor, cache: List[torch.Tensor]
+    ) -> Tuple[torch.Tensor, List[torch.Tensor]]:
+        """
+        Args:
+            input (torch.Tensor): [batch_size, time_step=1]
+            padding (torch.Tensor): [batch_size,1], 1 is padding value
+            cache : rnn predictor cache[0] == state_m
+                    cache[1] == state_c
+        """
+        assert len(cache) == 2
+        state_m, state_c = cache[0], cache[1]
+        embed = self.embed(input)  # [batch, 1, emb_size]
+        embed = self.dropout(embed)
+        out, (m, c) = self.rnn(embed, (state_m, state_c))
+
+        out = self.projection(out)
+        m = ApplyPadding(m, padding.unsqueeze(0), state_m)
+        c = ApplyPadding(c, padding.unsqueeze(0), state_c)
+
+        return (out, [m, c])
+
+
+class EmbeddingPredictor(PredictorBase):
+    """Embedding predictor
+
+    Described in:
+    https://arxiv.org/pdf/2109.07513.pdf
+
+    embed-> proj -> layer norm -> swish
+    """
+
+    def __init__(
+        self,
+        voca_size: int,
+        embed_size: int,
+        embed_dropout: float,
+        n_head: int,
+        history_size: int = 2,
+        activation: str = "swish",
+        bias: bool = False,
+        layer_norm_epsilon: float = 1e-5,
+    ) -> None:
+        super().__init__()
+        # multi head
+        self.num_heads = n_head
+        self.embed_size = embed_size
+        self.context_size = history_size + 1
+        self.pos_embed = torch.nn.Linear(
+            embed_size * self.context_size, self.num_heads, bias=bias
+        )
+        self.embed = nn.Embedding(voca_size, self.embed_size)
+        self.embed_dropout = nn.Dropout(p=embed_dropout)
+        self.ffn = nn.Linear(self.embed_size, self.embed_size)
+        self.norm = nn.LayerNorm(self.embed_size, eps=layer_norm_epsilon)
+        self.activatoin = get_activation(activation)
+
+    def init_state(
+        self, batch_size: int, device: torch.device, method: str = "zero"
+    ) -> List[torch.Tensor]:
+        assert batch_size > 0
+        _ = method
+        return [
+            torch.zeros(
+                batch_size, self.context_size - 1, self.embed_size, device=device
+            ),
+        ]
+
+    def batch_to_cache(self, cache: List[torch.Tensor]) -> List[List[torch.Tensor]]:
+        """
+        Args:
+            cache : [history]
+                history: [bs, ...]
+        Returns:
+            new_ache : [[history_1], [history_2], [history_3]...]
+        """
+        assert len(cache) == 1
+        cache_0 = cache[0]
+        history: List[List[torch.Tensor]] = []
+        for h in torch.split(cache_0, 1, dim=0):
+            history.append([h])
+        return history
+
+    def cache_to_batch(self, cache: List[List[torch.Tensor]]) -> List[torch.Tensor]:
+        """
+        Args:
+            cache : [[history_1], [history_2], [history3]...]
+
+        Returns:
+            new_caceh: [history],
+                history: [bs, ...]
+        """
+        history = torch.cat([h[0] for h in cache], dim=0)
+        return [history]
+
+    def forward(self, input: torch.Tensor, cache: Optional[List[torch.Tensor]] = None):
+        """forward for training"""
+        input = self.embed(input)  # [bs, seq_len, embed]
+        input = self.embed_dropout(input)
+        if cache is None:
+            zeros = self.init_state(input.size(0), device=input.device)[0]
+        else:
+            assert len(cache) == 1
+            zeros = cache[0]
+
+        input = torch.cat(
+            (zeros, input), dim=1
+        )  # [bs, context_size-1 + seq_len, embed]
+
+        input = input.unfold(1, self.context_size, 1).permute(
+            0, 1, 3, 2
+        )  # [bs, seq_len, context_size, embed]
+        # multi head pos: [n_head, embed, context_size]
+        multi_head_pos = self.pos_embed.weight.view(
+            self.num_heads, self.embed_size, self.context_size
+        )
+
+        # broadcast dot attenton
+        input_expand = input.unsqueeze(2)  # [bs, seq_len, 1, context_size, embed]
+        multi_head_pos = multi_head_pos.permute(
+            0, 2, 1
+        )  # [num_heads, context_size, embed]
+
+        # [bs, seq_len, num_heads, context_size, embed]
+        weight = input_expand * multi_head_pos
+        weight = weight.sum(dim=-1, keepdim=False).unsqueeze(
+            3
+        )  # [bs, seq_len, num_heads, 1, context_size]
+        output = weight.matmul(input_expand).squeeze(
+            dim=3
+        )  # [bs, seq_len, num_heads, embed]
+        output = output.sum(dim=2)  # [bs, seq_len, embed]
+        output = output / (self.num_heads * self.context_size)
+
+        output = self.ffn(output)
+        output = self.norm(output)
+        output = self.activatoin(output)
+        return output
+
+    def forward_step(
+        self,
+        input: torch.Tensor,
+        padding: torch.Tensor,
+        cache: List[torch.Tensor],
+    ) -> Tuple[torch.Tensor, List[torch.Tensor]]:
+        """forward step for inference
+        Args:
+            input (torch.Tensor): [batch_size, time_step=1]
+            padding (torch.Tensor): [batch_size,1], 1 is padding value
+            cache: for embedding predictor, cache[0] == history
+        """
+        assert input.size(1) == 1
+        assert len(cache) == 1
+        history = cache[0]
+        assert history.size(1) == self.context_size - 1
+        input = self.embed(input)  # [bs, 1, embed]
+        input = self.embed_dropout(input)
+        context_input = torch.cat((history, input), dim=1)
+        input_expand = context_input.unsqueeze(1).unsqueeze(
+            2
+        )  # [bs, 1, 1, context_size, embed]
+
+        # multi head pos: [n_head, embed, context_size]
+        multi_head_pos = self.pos_embed.weight.view(
+            self.num_heads, self.embed_size, self.context_size
+        )
+
+        multi_head_pos = multi_head_pos.permute(
+            0, 2, 1
+        )  # [num_heads, context_size, embed]
+        # [bs, 1, num_heads, context_size, embed]
+        weight = input_expand * multi_head_pos
+        weight = weight.sum(dim=-1, keepdim=False).unsqueeze(
+            3
+        )  # [bs, 1, num_heads, 1, context_size]
+        output = weight.matmul(input_expand).squeeze(dim=3)  # [bs, 1, num_heads, embed]
+        output = output.sum(dim=2)  # [bs, 1, embed]
+        output = output / (self.num_heads * self.context_size)
+
+        output = self.ffn(output)
+        output = self.norm(output)
+        output = self.activatoin(output)
+        new_cache = context_input[:, 1:, :]
+        # TODO(Mddct): we need padding new_cache in future
+        # new_cache = ApplyPadding(history, padding, new_cache)
+        return (output, [new_cache])
+
+
+class ConvPredictor(PredictorBase):
+    def __init__(
+        self,
+        voca_size: int,
+        embed_size: int,
+        embed_dropout: float,
+        history_size: int = 2,
+        activation: str = "relu",
+        bias: bool = False,
+        layer_norm_epsilon: float = 1e-5,
+    ) -> None:
+        super().__init__()
+
+        assert history_size >= 0
+        self.embed_size = embed_size
+        self.context_size = history_size + 1
+        self.embed = nn.Embedding(voca_size, self.embed_size)
+        self.embed_dropout = nn.Dropout(p=embed_dropout)
+        self.conv = nn.Conv1d(
+            in_channels=embed_size,
+            out_channels=embed_size,
+            kernel_size=self.context_size,
+            padding=0,
+            groups=embed_size,
+            bias=bias,
+        )
+        self.norm = nn.LayerNorm(embed_size, eps=layer_norm_epsilon)
+        self.activatoin = get_activation(activation)
+
+    def init_state(
+        self, batch_size: int, device: torch.device, method: str = "zero"
+    ) -> List[torch.Tensor]:
+        assert batch_size > 0
+        assert method == "zero"
+        return [
+            torch.zeros(
+                batch_size, self.context_size - 1, self.embed_size, device=device
+            )
+        ]
+
+    def cache_to_batch(self, cache: List[List[torch.Tensor]]) -> List[torch.Tensor]:
+        """
+        Args:
+            cache : [[history_1], [history_2], [history3]...]
+
+        Returns:
+            new_caceh: [history],
+                history: [bs, ...]
+        """
+        history = torch.cat([h[0] for h in cache], dim=0)
+        return [history]
+
+    def batch_to_cache(self, cache: List[torch.Tensor]) -> List[List[torch.Tensor]]:
+        """
+        Args:
+            cache : [history]
+                history: [bs, ...]
+        Returns:
+            new_ache : [[history_1], [history_2], [history_3]...]
+        """
+        assert len(cache) == 1
+        cache_0 = cache[0]
+        history: List[List[torch.Tensor]] = []
+        for h in torch.split(cache_0, 1, dim=0):
+            history.append([h])
+        return history
+
+    def forward(self, input: torch.Tensor, cache: Optional[List[torch.Tensor]] = None):
+        """forward for training"""
+        input = self.embed(input)  # [bs, seq_len, embed]
+        input = self.embed_dropout(input)
+        if cache is None:
+            zeros = self.init_state(input.size(0), device=input.device)[0]
+        else:
+            assert len(cache) == 1
+            zeros = cache[0]
+
+        input = torch.cat(
+            (zeros, input), dim=1
+        )  # [bs, context_size-1 + seq_len, embed]
+        input = input.permute(0, 2, 1)
+        out = self.conv(input).permute(0, 2, 1)
+        out = self.activatoin(self.norm(out))
+        return out
+
+    def forward_step(
+        self, input: torch.Tensor, padding: torch.Tensor, cache: List[torch.Tensor]
+    ) -> Tuple[torch.Tensor, List[torch.Tensor]]:
+        """forward step for inference
+        Args:
+            input (torch.Tensor): [batch_size, time_step=1]
+            padding (torch.Tensor): [batch_size,1], 1 is padding value
+            cache: for embedding predictor, cache[0] == history
+        """
+        assert input.size(1) == 1
+        assert len(cache) == 1
+        history = cache[0]
+        assert history.size(1) == self.context_size - 1
+        input = self.embed(input)  # [bs, 1, embed]
+        input = self.embed_dropout(input)
+        context_input = torch.cat((history, input), dim=1)
+        input = context_input.permute(0, 2, 1)
+        out = self.conv(input).permute(0, 2, 1)
+        out = self.activatoin(self.norm(out))
+
+        new_cache = context_input[:, 1:, :]
+        # TODO(Mddct): apply padding in future
+        return (out, [new_cache])
diff --git a/modules/wenet_extractor/transducer/search/greedy_search.py b/modules/wenet_extractor/transducer/search/greedy_search.py
new file mode 100755
index 0000000000000000000000000000000000000000..df4cfea28902aac0005b0b72e930c790a2d79de4
--- /dev/null
+++ b/modules/wenet_extractor/transducer/search/greedy_search.py
@@ -0,0 +1,52 @@
+from typing import List
+
+import torch
+
+
+def basic_greedy_search(
+    model: torch.nn.Module,
+    encoder_out: torch.Tensor,
+    encoder_out_lens: torch.Tensor,
+    n_steps: int = 64,
+) -> List[List[int]]:
+    # fake padding
+    padding = torch.zeros(1, 1).to(encoder_out.device)
+    # sos
+    pred_input_step = torch.tensor([model.blank]).reshape(1, 1)
+    cache = model.predictor.init_state(1, method="zero", device=encoder_out.device)
+    new_cache: List[torch.Tensor] = []
+    t = 0
+    hyps = []
+    prev_out_nblk = True
+    pred_out_step = None
+    per_frame_max_noblk = n_steps
+    per_frame_noblk = 0
+    while t < encoder_out_lens:
+        encoder_out_step = encoder_out[:, t : t + 1, :]  # [1, 1, E]
+        if prev_out_nblk:
+            step_outs = model.predictor.forward_step(
+                pred_input_step, padding, cache
+            )  # [1, 1, P]
+            pred_out_step, new_cache = step_outs[0], step_outs[1]
+
+        joint_out_step = model.joint(encoder_out_step, pred_out_step)  # [1,1,v]
+        joint_out_probs = joint_out_step.log_softmax(dim=-1)
+
+        joint_out_max = joint_out_probs.argmax(dim=-1).squeeze()  # []
+        if joint_out_max != model.blank:
+            hyps.append(joint_out_max.item())
+            prev_out_nblk = True
+            per_frame_noblk = per_frame_noblk + 1
+            pred_input_step = joint_out_max.reshape(1, 1)
+            # state_m, state_c =  clstate_out_m, state_out_c
+            cache = new_cache
+
+        if joint_out_max == model.blank or per_frame_noblk >= per_frame_max_noblk:
+            if joint_out_max == model.blank:
+                prev_out_nblk = False
+            # TODO(Mddct): make t in chunk for streamming
+            # or t should't be too lang to predict none blank
+            t = t + 1
+            per_frame_noblk = 0
+
+    return [hyps]
diff --git a/modules/wenet_extractor/transducer/search/prefix_beam_search.py b/modules/wenet_extractor/transducer/search/prefix_beam_search.py
new file mode 100755
index 0000000000000000000000000000000000000000..b4b4f4ea46f24f75f43f7cef27d9a12b57a7452d
--- /dev/null
+++ b/modules/wenet_extractor/transducer/search/prefix_beam_search.py
@@ -0,0 +1,149 @@
+from typing import List, Tuple
+
+import torch
+from modules.wenet_extractor.utils.common import log_add
+
+
+class Sequence:
+    __slots__ = {"hyp", "score", "cache"}
+
+    def __init__(
+        self,
+        hyp: List[torch.Tensor],
+        score,
+        cache: List[torch.Tensor],
+    ):
+        self.hyp = hyp
+        self.score = score
+        self.cache = cache
+
+
+class PrefixBeamSearch:
+    def __init__(self, encoder, predictor, joint, ctc, blank):
+        self.encoder = encoder
+        self.predictor = predictor
+        self.joint = joint
+        self.ctc = ctc
+        self.blank = blank
+
+    def forward_decoder_one_step(
+        self, encoder_x: torch.Tensor, pre_t: torch.Tensor, cache: List[torch.Tensor]
+    ) -> Tuple[torch.Tensor, List[torch.Tensor]]:
+        padding = torch.zeros(pre_t.size(0), 1, device=encoder_x.device)
+        pre_t, new_cache = self.predictor.forward_step(
+            pre_t.unsqueeze(-1), padding, cache
+        )
+        x = self.joint(encoder_x, pre_t)  # [beam, 1, 1, vocab]
+        x = x.log_softmax(dim=-1)
+        return x, new_cache
+
+    def prefix_beam_search(
+        self,
+        speech: torch.Tensor,
+        speech_lengths: torch.Tensor,
+        decoding_chunk_size: int = -1,
+        beam_size: int = 5,
+        num_decoding_left_chunks: int = -1,
+        simulate_streaming: bool = False,
+        ctc_weight: float = 0.3,
+        transducer_weight: float = 0.7,
+    ):
+        """prefix beam search
+        also see wenet.transducer.transducer.beam_search
+        """
+        assert speech.shape[0] == speech_lengths.shape[0]
+        assert decoding_chunk_size != 0
+        device = speech.device
+        batch_size = speech.shape[0]
+        assert batch_size == 1
+
+        # 1. Encoder
+        encoder_out, _ = self.encoder(
+            speech, speech_lengths, decoding_chunk_size, num_decoding_left_chunks
+        )  # (B, maxlen, encoder_dim)
+        maxlen = encoder_out.size(1)
+
+        ctc_probs = self.ctc.log_softmax(encoder_out).squeeze(0)
+        beam_init: List[Sequence] = []
+
+        # 2. init beam using Sequence to save beam unit
+        cache = self.predictor.init_state(1, method="zero", device=device)
+        beam_init.append(Sequence(hyp=[self.blank], score=0.0, cache=cache))
+        # 3. start decoding (notice: we use breathwise first searching)
+        # !!!! In this decoding method: one frame do not output multi units. !!!!
+        # !!!!    Experiments show that this strategy has little impact      !!!!
+        for i in range(maxlen):
+            # 3.1 building input
+            # decoder taking the last token to predict the next token
+            input_hyp = [s.hyp[-1] for s in beam_init]
+            input_hyp_tensor = torch.tensor(input_hyp, dtype=torch.int, device=device)
+            # building statement from beam
+            cache_batch = self.predictor.cache_to_batch([s.cache for s in beam_init])
+            # build score tensor to do torch.add() function
+            scores = torch.tensor([s.score for s in beam_init]).to(device)
+
+            # 3.2 forward decoder
+            logp, new_cache = self.forward_decoder_one_step(
+                encoder_out[:, i, :].unsqueeze(1),
+                input_hyp_tensor,
+                cache_batch,
+            )  # logp: (N, 1, 1, vocab_size)
+            logp = logp.squeeze(1).squeeze(1)  # logp: (N, vocab_size)
+            new_cache = self.predictor.batch_to_cache(new_cache)
+
+            # 3.3 shallow fusion for transducer score
+            #     and ctc score where we can also add the LM score
+            logp = torch.log(
+                torch.add(
+                    transducer_weight * torch.exp(logp),
+                    ctc_weight * torch.exp(ctc_probs[i].unsqueeze(0)),
+                )
+            )
+
+            # 3.4 first beam prune
+            top_k_logp, top_k_index = logp.topk(beam_size)  # (N, N)
+            scores = torch.add(scores.unsqueeze(1), top_k_logp)
+
+            # 3.5 generate new beam (N*N)
+            beam_A = []
+            for j in range(len(beam_init)):
+                # update seq
+                base_seq = beam_init[j]
+                for t in range(beam_size):
+                    # blank: only update the score
+                    if top_k_index[j, t] == self.blank:
+                        new_seq = Sequence(
+                            hyp=base_seq.hyp.copy(),
+                            score=scores[j, t].item(),
+                            cache=base_seq.cache,
+                        )
+
+                        beam_A.append(new_seq)
+                    # other unit: update hyp score statement and last
+                    else:
+                        hyp_new = base_seq.hyp.copy()
+                        hyp_new.append(top_k_index[j, t].item())
+                        new_seq = Sequence(
+                            hyp=hyp_new, score=scores[j, t].item(), cache=new_cache[j]
+                        )
+                        beam_A.append(new_seq)
+
+            # 3.6 prefix fusion
+            fusion_A = [beam_A[0]]
+            for j in range(1, len(beam_A)):
+                s1 = beam_A[j]
+                if_do_append = True
+                for t in range(len(fusion_A)):
+                    # notice: A_ can not fusion with A
+                    if s1.hyp == fusion_A[t].hyp:
+                        fusion_A[t].score = log_add([fusion_A[t].score, s1.score])
+                        if_do_append = False
+                        break
+                if if_do_append:
+                    fusion_A.append(s1)
+
+            # 4. second pruned
+            fusion_A.sort(key=lambda x: x.score, reverse=True)
+            beam_init = fusion_A[:beam_size]
+
+        return beam_init, encoder_out
diff --git a/modules/wenet_extractor/transducer/transducer.py b/modules/wenet_extractor/transducer/transducer.py
new file mode 100755
index 0000000000000000000000000000000000000000..3618eeb2e355955a606d59e3a91f6253872843e0
--- /dev/null
+++ b/modules/wenet_extractor/transducer/transducer.py
@@ -0,0 +1,486 @@
+from typing import Dict, List, Optional, Tuple, Union
+
+import torch
+import torchaudio
+from torch import nn
+from torch.nn.utils.rnn import pad_sequence
+
+from modules.wenet_extractor.transducer.predictor import PredictorBase
+from modules.wenet_extractor.transducer.search.greedy_search import basic_greedy_search
+from modules.wenet_extractor.transducer.search.prefix_beam_search import (
+    PrefixBeamSearch,
+)
+from modules.wenet_extractor.transformer.asr_model import ASRModel
+from modules.wenet_extractor.transformer.ctc import CTC
+from modules.wenet_extractor.transformer.decoder import (
+    BiTransformerDecoder,
+    TransformerDecoder,
+)
+from modules.wenet_extractor.transformer.label_smoothing_loss import LabelSmoothingLoss
+from modules.wenet_extractor.utils.common import (
+    IGNORE_ID,
+    add_blank,
+    add_sos_eos,
+    reverse_pad_list,
+)
+
+
+class Transducer(ASRModel):
+    """Transducer-ctc-attention hybrid Encoder-Predictor-Decoder model"""
+
+    def __init__(
+        self,
+        vocab_size: int,
+        blank: int,
+        encoder: nn.Module,
+        predictor: PredictorBase,
+        joint: nn.Module,
+        attention_decoder: Optional[
+            Union[TransformerDecoder, BiTransformerDecoder]
+        ] = None,
+        ctc: Optional[CTC] = None,
+        ctc_weight: float = 0,
+        ignore_id: int = IGNORE_ID,
+        reverse_weight: float = 0.0,
+        lsm_weight: float = 0.0,
+        length_normalized_loss: bool = False,
+        transducer_weight: float = 1.0,
+        attention_weight: float = 0.0,
+    ) -> None:
+        assert attention_weight + ctc_weight + transducer_weight == 1.0
+        super().__init__(
+            vocab_size,
+            encoder,
+            attention_decoder,
+            ctc,
+            ctc_weight,
+            ignore_id,
+            reverse_weight,
+            lsm_weight,
+            length_normalized_loss,
+        )
+
+        self.blank = blank
+        self.transducer_weight = transducer_weight
+        self.attention_decoder_weight = 1 - self.transducer_weight - self.ctc_weight
+
+        self.predictor = predictor
+        self.joint = joint
+        self.bs = None
+
+        # Note(Mddct): decoder also means predictor in transducer,
+        # but here decoder is attention decoder
+        del self.criterion_att
+        if attention_decoder is not None:
+            self.criterion_att = LabelSmoothingLoss(
+                size=vocab_size,
+                padding_idx=ignore_id,
+                smoothing=lsm_weight,
+                normalize_length=length_normalized_loss,
+            )
+
+    def forward(
+        self,
+        speech: torch.Tensor,
+        speech_lengths: torch.Tensor,
+        text: torch.Tensor,
+        text_lengths: torch.Tensor,
+    ) -> Dict[str, Optional[torch.Tensor]]:
+        """Frontend + Encoder + predictor + joint + loss
+
+        Args:
+            speech: (Batch, Length, ...)
+            speech_lengths: (Batch, )
+            text: (Batch, Length)
+            text_lengths: (Batch,)
+        """
+        assert text_lengths.dim() == 1, text_lengths.shape
+        # Check that batch_size is unified
+        assert (
+            speech.shape[0]
+            == speech_lengths.shape[0]
+            == text.shape[0]
+            == text_lengths.shape[0]
+        ), (speech.shape, speech_lengths.shape, text.shape, text_lengths.shape)
+
+        # Encoder
+        encoder_out, encoder_mask = self.encoder(speech, speech_lengths)
+        encoder_out_lens = encoder_mask.squeeze(1).sum(1)
+        # predictor
+        ys_in_pad = add_blank(text, self.blank, self.ignore_id)
+        predictor_out = self.predictor(ys_in_pad)
+        # joint
+        joint_out = self.joint(encoder_out, predictor_out)
+        # NOTE(Mddct): some loss implementation require pad valid is zero
+        # torch.int32 rnnt_loss required
+        rnnt_text = text.to(torch.int64)
+        rnnt_text = torch.where(rnnt_text == self.ignore_id, 0, rnnt_text).to(
+            torch.int32
+        )
+        rnnt_text_lengths = text_lengths.to(torch.int32)
+        encoder_out_lens = encoder_out_lens.to(torch.int32)
+        loss = torchaudio.functional.rnnt_loss(
+            joint_out,
+            rnnt_text,
+            encoder_out_lens,
+            rnnt_text_lengths,
+            blank=self.blank,
+            reduction="mean",
+        )
+        loss_rnnt = loss
+
+        loss = self.transducer_weight * loss
+        # optional attention decoder
+        loss_att: Optional[torch.Tensor] = None
+        if self.attention_decoder_weight != 0.0 and self.decoder is not None:
+            loss_att, _ = self._calc_att_loss(
+                encoder_out, encoder_mask, text, text_lengths
+            )
+
+        # optional ctc
+        loss_ctc: Optional[torch.Tensor] = None
+        if self.ctc_weight != 0.0 and self.ctc is not None:
+            loss_ctc = self.ctc(encoder_out, encoder_out_lens, text, text_lengths)
+        else:
+            loss_ctc = None
+
+        if loss_ctc is not None:
+            loss = loss + self.ctc_weight * loss_ctc.sum()
+        if loss_att is not None:
+            loss = loss + self.attention_decoder_weight * loss_att.sum()
+        # NOTE: 'loss' must be in dict
+        return {
+            "loss": loss,
+            "loss_att": loss_att,
+            "loss_ctc": loss_ctc,
+            "loss_rnnt": loss_rnnt,
+        }
+
+    def init_bs(self):
+        if self.bs is None:
+            self.bs = PrefixBeamSearch(
+                self.encoder, self.predictor, self.joint, self.ctc, self.blank
+            )
+
+    def _cal_transducer_score(
+        self,
+        encoder_out: torch.Tensor,
+        encoder_mask: torch.Tensor,
+        hyps_lens: torch.Tensor,
+        hyps_pad: torch.Tensor,
+    ):
+        # ignore id -> blank, add blank at head
+        hyps_pad_blank = add_blank(hyps_pad, self.blank, self.ignore_id)
+        xs_in_lens = encoder_mask.squeeze(1).sum(1).int()
+
+        # 1. Forward predictor
+        predictor_out = self.predictor(hyps_pad_blank)
+        # 2. Forward joint
+        joint_out = self.joint(encoder_out, predictor_out)
+        rnnt_text = hyps_pad.to(torch.int64)
+        rnnt_text = torch.where(rnnt_text == self.ignore_id, 0, rnnt_text).to(
+            torch.int32
+        )
+        # 3. Compute transducer loss
+        loss_td = torchaudio.functional.rnnt_loss(
+            joint_out,
+            rnnt_text,
+            xs_in_lens,
+            hyps_lens.int(),
+            blank=self.blank,
+            reduction="none",
+        )
+        return loss_td * -1
+
+    def _cal_attn_score(
+        self,
+        encoder_out: torch.Tensor,
+        encoder_mask: torch.Tensor,
+        hyps_pad: torch.Tensor,
+        hyps_lens: torch.Tensor,
+    ):
+        # (beam_size, max_hyps_len)
+        ori_hyps_pad = hyps_pad
+
+        # td_score = loss_td * -1
+        hyps_pad, _ = add_sos_eos(hyps_pad, self.sos, self.eos, self.ignore_id)
+        hyps_lens = hyps_lens + 1  # Add <sos> at begining
+        # used for right to left decoder
+        r_hyps_pad = reverse_pad_list(ori_hyps_pad, hyps_lens, self.ignore_id)
+        r_hyps_pad, _ = add_sos_eos(r_hyps_pad, self.sos, self.eos, self.ignore_id)
+        decoder_out, r_decoder_out, _ = self.decoder(
+            encoder_out,
+            encoder_mask,
+            hyps_pad,
+            hyps_lens,
+            r_hyps_pad,
+            self.reverse_weight,
+        )  # (beam_size, max_hyps_len, vocab_size)
+        decoder_out = torch.nn.functional.log_softmax(decoder_out, dim=-1)
+        decoder_out = decoder_out.cpu().numpy()
+        # r_decoder_out will be 0.0, if reverse_weight is 0.0 or decoder is a
+        # conventional transformer decoder.
+        r_decoder_out = torch.nn.functional.log_softmax(r_decoder_out, dim=-1)
+        r_decoder_out = r_decoder_out.cpu().numpy()
+        return decoder_out, r_decoder_out
+
+    def beam_search(
+        self,
+        speech: torch.Tensor,
+        speech_lengths: torch.Tensor,
+        decoding_chunk_size: int = -1,
+        beam_size: int = 5,
+        num_decoding_left_chunks: int = -1,
+        simulate_streaming: bool = False,
+        ctc_weight: float = 0.3,
+        transducer_weight: float = 0.7,
+    ):
+        """beam search
+
+        Args:
+            speech (torch.Tensor): (batch=1, max_len, feat_dim)
+            speech_length (torch.Tensor): (batch, )
+            beam_size (int): beam size for beam search
+            decoding_chunk_size (int): decoding chunk for dynamic chunk
+                trained model.
+                <0: for decoding, use full chunk.
+                >0: for decoding, use fixed chunk size as set.
+                0: used for training, it's prohibited here
+            simulate_streaming (bool): whether do encoder forward in a
+                streaming fashion
+            ctc_weight (float): ctc probability weight in transducer
+                prefix beam search.
+                final_prob = ctc_weight * ctc_prob + transducer_weight * transducer_prob
+            transducer_weight (float): transducer probability weight in
+                prefix beam search
+        Returns:
+            List[List[int]]: best path result
+
+        """
+        self.init_bs()
+        beam, _ = self.bs.prefix_beam_search(
+            speech,
+            speech_lengths,
+            decoding_chunk_size,
+            beam_size,
+            num_decoding_left_chunks,
+            simulate_streaming,
+            ctc_weight,
+            transducer_weight,
+        )
+        return beam[0].hyp[1:], beam[0].score
+
+    def transducer_attention_rescoring(
+        self,
+        speech: torch.Tensor,
+        speech_lengths: torch.Tensor,
+        beam_size: int,
+        decoding_chunk_size: int = -1,
+        num_decoding_left_chunks: int = -1,
+        simulate_streaming: bool = False,
+        reverse_weight: float = 0.0,
+        ctc_weight: float = 0.0,
+        attn_weight: float = 0.0,
+        transducer_weight: float = 0.0,
+        search_ctc_weight: float = 1.0,
+        search_transducer_weight: float = 0.0,
+        beam_search_type: str = "transducer",
+    ) -> List[List[int]]:
+        """beam search
+
+        Args:
+            speech (torch.Tensor): (batch=1, max_len, feat_dim)
+            speech_length (torch.Tensor): (batch, )
+            beam_size (int): beam size for beam search
+            decoding_chunk_size (int): decoding chunk for dynamic chunk
+                trained model.
+                <0: for decoding, use full chunk.
+                >0: for decoding, use fixed chunk size as set.
+                0: used for training, it's prohibited here
+            simulate_streaming (bool): whether do encoder forward in a
+                streaming fashion
+            ctc_weight (float): ctc probability weight using in rescoring.
+                rescore_prob = ctc_weight * ctc_prob +
+                               transducer_weight * (transducer_loss * -1) +
+                               attn_weight * attn_prob
+            attn_weight (float): attn probability weight using in rescoring.
+            transducer_weight (float): transducer probability weight using in
+                rescoring
+            search_ctc_weight (float): ctc weight using
+                               in rnnt beam search (seeing in self.beam_search)
+            search_transducer_weight (float): transducer weight using
+                               in rnnt beam search (seeing in self.beam_search)
+        Returns:
+            List[List[int]]: best path result
+
+        """
+
+        assert speech.shape[0] == speech_lengths.shape[0]
+        assert decoding_chunk_size != 0
+        if reverse_weight > 0.0:
+            # decoder should be a bitransformer decoder if reverse_weight > 0.0
+            assert hasattr(self.decoder, "right_decoder")
+        device = speech.device
+        batch_size = speech.shape[0]
+        # For attention rescoring we only support batch_size=1
+        assert batch_size == 1
+        # encoder_out: (1, maxlen, encoder_dim), len(hyps) = beam_size
+        self.init_bs()
+        if beam_search_type == "transducer":
+            beam, encoder_out = self.bs.prefix_beam_search(
+                speech,
+                speech_lengths,
+                decoding_chunk_size=decoding_chunk_size,
+                beam_size=beam_size,
+                num_decoding_left_chunks=num_decoding_left_chunks,
+                ctc_weight=search_ctc_weight,
+                transducer_weight=search_transducer_weight,
+            )
+            beam_score = [s.score for s in beam]
+            hyps = [s.hyp[1:] for s in beam]
+
+        elif beam_search_type == "ctc":
+            hyps, encoder_out = self._ctc_prefix_beam_search(
+                speech,
+                speech_lengths,
+                beam_size=beam_size,
+                decoding_chunk_size=decoding_chunk_size,
+                num_decoding_left_chunks=num_decoding_left_chunks,
+                simulate_streaming=simulate_streaming,
+            )
+            beam_score = [hyp[1] for hyp in hyps]
+            hyps = [hyp[0] for hyp in hyps]
+        assert len(hyps) == beam_size
+
+        # build hyps and encoder output
+        hyps_pad = pad_sequence(
+            [torch.tensor(hyp, device=device, dtype=torch.long) for hyp in hyps],
+            True,
+            self.ignore_id,
+        )  # (beam_size, max_hyps_len)
+        hyps_lens = torch.tensor(
+            [len(hyp) for hyp in hyps], device=device, dtype=torch.long
+        )  # (beam_size,)
+
+        encoder_out = encoder_out.repeat(beam_size, 1, 1)
+        encoder_mask = torch.ones(
+            beam_size, 1, encoder_out.size(1), dtype=torch.bool, device=device
+        )
+
+        # 2.1 calculate transducer score
+        td_score = self._cal_transducer_score(
+            encoder_out,
+            encoder_mask,
+            hyps_lens,
+            hyps_pad,
+        )
+        # 2.2 calculate attention score
+        decoder_out, r_decoder_out = self._cal_attn_score(
+            encoder_out,
+            encoder_mask,
+            hyps_pad,
+            hyps_lens,
+        )
+
+        # Only use decoder score for rescoring
+        best_score = -float("inf")
+        best_index = 0
+        for i, hyp in enumerate(hyps):
+            score = 0.0
+            for j, w in enumerate(hyp):
+                score += decoder_out[i][j][w]
+            score += decoder_out[i][len(hyp)][self.eos]
+            td_s = td_score[i]
+            # add right to left decoder score
+            if reverse_weight > 0:
+                r_score = 0.0
+                for j, w in enumerate(hyp):
+                    r_score += r_decoder_out[i][len(hyp) - j - 1][w]
+                r_score += r_decoder_out[i][len(hyp)][self.eos]
+                score = score * (1 - reverse_weight) + r_score * reverse_weight
+            # add ctc score
+            score = (
+                score * attn_weight
+                + beam_score[i] * ctc_weight
+                + td_s * transducer_weight
+            )
+            if score > best_score:
+                best_score = score
+                best_index = i
+
+        return hyps[best_index], best_score
+
+    def greedy_search(
+        self,
+        speech: torch.Tensor,
+        speech_lengths: torch.Tensor,
+        decoding_chunk_size: int = -1,
+        num_decoding_left_chunks: int = -1,
+        simulate_streaming: bool = False,
+        n_steps: int = 64,
+    ) -> List[List[int]]:
+        """greedy search
+
+        Args:
+            speech (torch.Tensor): (batch=1, max_len, feat_dim)
+            speech_length (torch.Tensor): (batch, )
+            beam_size (int): beam size for beam search
+            decoding_chunk_size (int): decoding chunk for dynamic chunk
+                trained model.
+                <0: for decoding, use full chunk.
+                >0: for decoding, use fixed chunk size as set.
+                0: used for training, it's prohibited here
+            simulate_streaming (bool): whether do encoder forward in a
+                streaming fashion
+        Returns:
+            List[List[int]]: best path result
+        """
+        # TODO(Mddct): batch decode
+        assert speech.size(0) == 1
+        assert speech.shape[0] == speech_lengths.shape[0]
+        assert decoding_chunk_size != 0
+        # TODO(Mddct): forward chunk by chunk
+        _ = simulate_streaming
+        # Let's assume B = batch_size
+        encoder_out, encoder_mask = self.encoder(
+            speech,
+            speech_lengths,
+            decoding_chunk_size,
+            num_decoding_left_chunks,
+        )
+        encoder_out_lens = encoder_mask.squeeze(1).sum()
+        hyps = basic_greedy_search(self, encoder_out, encoder_out_lens, n_steps=n_steps)
+
+        return hyps
+
+    @torch.jit.export
+    def forward_encoder_chunk(
+        self,
+        xs: torch.Tensor,
+        offset: int,
+        required_cache_size: int,
+        att_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
+        cnn_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        return self.encoder.forward_chunk(
+            xs, offset, required_cache_size, att_cache, cnn_cache
+        )
+
+    @torch.jit.export
+    def forward_predictor_step(
+        self, xs: torch.Tensor, cache: List[torch.Tensor]
+    ) -> Tuple[torch.Tensor, List[torch.Tensor]]:
+        assert len(cache) == 2
+        # fake padding
+        padding = torch.zeros(1, 1)
+        return self.predictor.forward_step(xs, padding, cache)
+
+    @torch.jit.export
+    def forward_joint_step(
+        self, enc_out: torch.Tensor, pred_out: torch.Tensor
+    ) -> torch.Tensor:
+        return self.joint(enc_out, pred_out)
+
+    @torch.jit.export
+    def forward_predictor_init_state(self) -> List[torch.Tensor]:
+        return self.predictor.init_state(1, device=torch.device("cpu"))
diff --git a/modules/wenet_extractor/transformer/__init__.py b/modules/wenet_extractor/transformer/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/modules/wenet_extractor/transformer/asr_model.py b/modules/wenet_extractor/transformer/asr_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ad7869679ee15fdcf6a759a7c39762d7f166ff2
--- /dev/null
+++ b/modules/wenet_extractor/transformer/asr_model.py
@@ -0,0 +1,1056 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+from collections import defaultdict
+from typing import Dict, List, Optional, Tuple
+
+import torch
+import torch.nn.functional as F
+from torch.nn.utils.rnn import pad_sequence
+
+from modules.wenet_extractor.transformer.ctc import CTC
+from modules.wenet_extractor.transformer.decoder import TransformerDecoder
+from modules.wenet_extractor.transformer.encoder import TransformerEncoder
+from modules.wenet_extractor.transformer.label_smoothing_loss import LabelSmoothingLoss
+from modules.wenet_extractor.utils.common import (
+    IGNORE_ID,
+    add_sos_eos,
+    log_add,
+    remove_duplicates_and_blank,
+    th_accuracy,
+    reverse_pad_list,
+)
+from modules.wenet_extractor.utils.mask import (
+    make_pad_mask,
+    mask_finished_preds,
+    mask_finished_scores,
+    subsequent_mask,
+)
+
+
+class ASRModel(torch.nn.Module):
+    """CTC-attention hybrid Encoder-Decoder model"""
+
+    def __init__(
+        self,
+        vocab_size: int,
+        encoder: TransformerEncoder,
+        decoder: TransformerDecoder,
+        ctc: CTC,
+        ctc_weight: float = 0.5,
+        ignore_id: int = IGNORE_ID,
+        reverse_weight: float = 0.0,
+        lsm_weight: float = 0.0,
+        length_normalized_loss: bool = False,
+        lfmmi_dir: str = "",
+    ):
+        assert 0.0 <= ctc_weight <= 1.0, ctc_weight
+
+        super().__init__()
+        # note that eos is the same as sos (equivalent ID)
+        self.sos = vocab_size - 1
+        self.eos = vocab_size - 1
+        self.vocab_size = vocab_size
+        self.ignore_id = ignore_id
+        self.ctc_weight = ctc_weight
+        self.reverse_weight = reverse_weight
+
+        self.encoder = encoder
+        self.decoder = decoder
+        self.ctc = ctc
+        self.criterion_att = LabelSmoothingLoss(
+            size=vocab_size,
+            padding_idx=ignore_id,
+            smoothing=lsm_weight,
+            normalize_length=length_normalized_loss,
+        )
+        self.lfmmi_dir = lfmmi_dir
+        if self.lfmmi_dir != "":
+            self.load_lfmmi_resource()
+
+    def forward(
+        self,
+        speech: torch.Tensor,
+        speech_lengths: torch.Tensor,
+        text: torch.Tensor,
+        text_lengths: torch.Tensor,
+    ) -> Dict[str, Optional[torch.Tensor]]:
+        """Frontend + Encoder + Decoder + Calc loss
+
+        Args:
+            speech: (Batch, Length, ...)
+            speech_lengths: (Batch, )
+            text: (Batch, Length)
+            text_lengths: (Batch,)
+        """
+
+        assert text_lengths.dim() == 1, text_lengths.shape
+        # Check that batch_size is unified
+        assert (
+            speech.shape[0]
+            == speech_lengths.shape[0]
+            == text.shape[0]
+            == text_lengths.shape[0]
+        ), (speech.shape, speech_lengths.shape, text.shape, text_lengths.shape)
+        # 1. Encoder
+        encoder_out, encoder_mask = self.encoder(speech, speech_lengths)
+        encoder_out_lens = encoder_mask.squeeze(1).sum(1)
+
+        # 2a. Attention-decoder branch
+        if self.ctc_weight != 1.0:
+            loss_att, acc_att = self._calc_att_loss(
+                encoder_out, encoder_mask, text, text_lengths
+            )
+        else:
+            loss_att = None
+
+        # 2b. CTC branch or LF-MMI loss
+        if self.ctc_weight != 0.0:
+            if self.lfmmi_dir != "":
+                loss_ctc = self._calc_lfmmi_loss(encoder_out, encoder_mask, text)
+            else:
+                loss_ctc = self.ctc(encoder_out, encoder_out_lens, text, text_lengths)
+        else:
+            loss_ctc = None
+
+        if loss_ctc is None:
+            loss = loss_att
+        elif loss_att is None:
+            loss = loss_ctc
+        else:
+            loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att
+        return {"loss": loss, "loss_att": loss_att, "loss_ctc": loss_ctc}
+
+    def _calc_att_loss(
+        self,
+        encoder_out: torch.Tensor,
+        encoder_mask: torch.Tensor,
+        ys_pad: torch.Tensor,
+        ys_pad_lens: torch.Tensor,
+    ) -> Tuple[torch.Tensor, float]:
+        ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
+        ys_in_lens = ys_pad_lens + 1
+
+        # reverse the seq, used for right to left decoder
+        r_ys_pad = reverse_pad_list(ys_pad, ys_pad_lens, float(self.ignore_id))
+        r_ys_in_pad, r_ys_out_pad = add_sos_eos(
+            r_ys_pad, self.sos, self.eos, self.ignore_id
+        )
+        # 1. Forward decoder
+        decoder_out, r_decoder_out, _ = self.decoder(
+            encoder_out,
+            encoder_mask,
+            ys_in_pad,
+            ys_in_lens,
+            r_ys_in_pad,
+            self.reverse_weight,
+        )
+        # 2. Compute attention loss
+        loss_att = self.criterion_att(decoder_out, ys_out_pad)
+        r_loss_att = torch.tensor(0.0)
+        if self.reverse_weight > 0.0:
+            r_loss_att = self.criterion_att(r_decoder_out, r_ys_out_pad)
+        loss_att = (
+            loss_att * (1 - self.reverse_weight) + r_loss_att * self.reverse_weight
+        )
+        acc_att = th_accuracy(
+            decoder_out.view(-1, self.vocab_size),
+            ys_out_pad,
+            ignore_label=self.ignore_id,
+        )
+        return loss_att, acc_att
+
+    def _forward_encoder(
+        self,
+        speech: torch.Tensor,
+        speech_lengths: torch.Tensor,
+        decoding_chunk_size: int = -1,
+        num_decoding_left_chunks: int = -1,
+        simulate_streaming: bool = False,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # Let's assume B = batch_size
+        # 1. Encoder
+        if simulate_streaming and decoding_chunk_size > 0:
+            encoder_out, encoder_mask = self.encoder.forward_chunk_by_chunk(
+                speech,
+                decoding_chunk_size=decoding_chunk_size,
+                num_decoding_left_chunks=num_decoding_left_chunks,
+            )  # (B, maxlen, encoder_dim)
+        else:
+            encoder_out, encoder_mask = self.encoder(
+                speech,
+                speech_lengths,
+                decoding_chunk_size=decoding_chunk_size,
+                num_decoding_left_chunks=num_decoding_left_chunks,
+            )  # (B, maxlen, encoder_dim)
+        return encoder_out, encoder_mask
+
+    def encoder_extractor(
+        self,
+        speech: torch.Tensor,
+        speech_lengths: torch.Tensor,
+        decoding_chunk_size: int = -1,
+        num_decoding_left_chunks: int = -1,
+        simulate_streaming: bool = False,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # assert speech.shape[0] == speech_lengths[0]
+        assert decoding_chunk_size != 0
+        batch_size = speech.shape[0]
+
+        encoder_out, encoder_mask = self._forward_encoder(
+            speech,
+            speech_lengths,
+            decoding_chunk_size,
+            num_decoding_left_chunks,
+            simulate_streaming,
+        )  # (B, maxlen, encoder_dim)
+
+        return encoder_out
+
+    def recognize(
+        self,
+        speech: torch.Tensor,
+        speech_lengths: torch.Tensor,
+        beam_size: int = 10,
+        decoding_chunk_size: int = -1,
+        num_decoding_left_chunks: int = -1,
+        simulate_streaming: bool = False,
+    ) -> torch.Tensor:
+        """Apply beam search on attention decoder
+
+        Args:
+            speech (torch.Tensor): (batch, max_len, feat_dim)
+            speech_length (torch.Tensor): (batch, )
+            beam_size (int): beam size for beam search
+            decoding_chunk_size (int): decoding chunk for dynamic chunk
+                trained model.
+                <0: for decoding, use full chunk.
+                >0: for decoding, use fixed chunk size as set.
+                0: used for training, it's prohibited here
+            simulate_streaming (bool): whether do encoder forward in a
+                streaming fashion
+
+        Returns:
+            torch.Tensor: decoding result, (batch, max_result_len)
+        """
+        assert speech.shape[0] == speech_lengths.shape[0]
+        assert decoding_chunk_size != 0
+        device = speech.device
+        batch_size = speech.shape[0]
+
+        # Let's assume B = batch_size and N = beam_size
+        # 1. Encoder
+        encoder_out, encoder_mask = self._forward_encoder(
+            speech,
+            speech_lengths,
+            decoding_chunk_size,
+            num_decoding_left_chunks,
+            simulate_streaming,
+        )  # (B, maxlen, encoder_dim)
+        maxlen = encoder_out.size(1)
+        encoder_dim = encoder_out.size(2)
+        running_size = batch_size * beam_size
+        encoder_out = (
+            encoder_out.unsqueeze(1)
+            .repeat(1, beam_size, 1, 1)
+            .view(running_size, maxlen, encoder_dim)
+        )  # (B*N, maxlen, encoder_dim)
+        encoder_mask = (
+            encoder_mask.unsqueeze(1)
+            .repeat(1, beam_size, 1, 1)
+            .view(running_size, 1, maxlen)
+        )  # (B*N, 1, max_len)
+
+        hyps = torch.ones([running_size, 1], dtype=torch.long, device=device).fill_(
+            self.sos
+        )  # (B*N, 1)
+        scores = torch.tensor(
+            [0.0] + [-float("inf")] * (beam_size - 1), dtype=torch.float
+        )
+        scores = (
+            scores.to(device).repeat([batch_size]).unsqueeze(1).to(device)
+        )  # (B*N, 1)
+        end_flag = torch.zeros_like(scores, dtype=torch.bool, device=device)
+        cache: Optional[List[torch.Tensor]] = None
+        # 2. Decoder forward step by step
+        for i in range(1, maxlen + 1):
+            # Stop if all batch and all beam produce eos
+            if end_flag.sum() == running_size:
+                break
+            # 2.1 Forward decoder step
+            hyps_mask = (
+                subsequent_mask(i).unsqueeze(0).repeat(running_size, 1, 1).to(device)
+            )  # (B*N, i, i)
+            # logp: (B*N, vocab)
+            logp, cache = self.decoder.forward_one_step(
+                encoder_out, encoder_mask, hyps, hyps_mask, cache
+            )
+            # 2.2 First beam prune: select topk best prob at current time
+            top_k_logp, top_k_index = logp.topk(beam_size)  # (B*N, N)
+            top_k_logp = mask_finished_scores(top_k_logp, end_flag)
+            top_k_index = mask_finished_preds(top_k_index, end_flag, self.eos)
+            # 2.3 Second beam prune: select topk score with history
+            scores = scores + top_k_logp  # (B*N, N), broadcast add
+            scores = scores.view(batch_size, beam_size * beam_size)  # (B, N*N)
+            scores, offset_k_index = scores.topk(k=beam_size)  # (B, N)
+            # Update cache to be consistent with new topk scores / hyps
+            cache_index = (offset_k_index // beam_size).view(-1)  # (B*N)
+            base_cache_index = (
+                torch.arange(batch_size, device=device)
+                .view(-1, 1)
+                .repeat([1, beam_size])
+                * beam_size
+            ).view(
+                -1
+            )  # (B*N)
+            cache_index = base_cache_index + cache_index
+            cache = [torch.index_select(c, dim=0, index=cache_index) for c in cache]
+            scores = scores.view(-1, 1)  # (B*N, 1)
+            # 2.4. Compute base index in top_k_index,
+            # regard top_k_index as (B*N*N),regard offset_k_index as (B*N),
+            # then find offset_k_index in top_k_index
+            base_k_index = (
+                torch.arange(batch_size, device=device)
+                .view(-1, 1)
+                .repeat([1, beam_size])
+            )  # (B, N)
+            base_k_index = base_k_index * beam_size * beam_size
+            best_k_index = base_k_index.view(-1) + offset_k_index.view(-1)  # (B*N)
+
+            # 2.5 Update best hyps
+            best_k_pred = torch.index_select(
+                top_k_index.view(-1), dim=-1, index=best_k_index
+            )  # (B*N)
+            best_hyps_index = best_k_index // beam_size
+            last_best_k_hyps = torch.index_select(
+                hyps, dim=0, index=best_hyps_index
+            )  # (B*N, i)
+            hyps = torch.cat(
+                (last_best_k_hyps, best_k_pred.view(-1, 1)), dim=1
+            )  # (B*N, i+1)
+
+            # 2.6 Update end flag
+            end_flag = torch.eq(hyps[:, -1], self.eos).view(-1, 1)
+
+        # 3. Select best of best
+        scores = scores.view(batch_size, beam_size)
+        # TODO: length normalization
+        best_scores, best_index = scores.max(dim=-1)
+        best_hyps_index = (
+            best_index
+            + torch.arange(batch_size, dtype=torch.long, device=device) * beam_size
+        )
+        best_hyps = torch.index_select(hyps, dim=0, index=best_hyps_index)
+        best_hyps = best_hyps[:, 1:]
+        return best_hyps, best_scores
+
+    def ctc_greedy_search(
+        self,
+        speech: torch.Tensor,
+        speech_lengths: torch.Tensor,
+        decoding_chunk_size: int = -1,
+        num_decoding_left_chunks: int = -1,
+        simulate_streaming: bool = False,
+    ) -> List[List[int]]:
+        """Apply CTC greedy search
+
+        Args:
+            speech (torch.Tensor): (batch, max_len, feat_dim)
+            speech_length (torch.Tensor): (batch, )
+            beam_size (int): beam size for beam search
+            decoding_chunk_size (int): decoding chunk for dynamic chunk
+                trained model.
+                <0: for decoding, use full chunk.
+                >0: for decoding, use fixed chunk size as set.
+                0: used for training, it's prohibited here
+            simulate_streaming (bool): whether do encoder forward in a
+                streaming fashion
+        Returns:
+            List[List[int]]: best path result
+        """
+        assert speech.shape[0] == speech_lengths.shape[0]
+        assert decoding_chunk_size != 0
+        batch_size = speech.shape[0]
+        # Let's assume B = batch_size
+        encoder_out, encoder_mask = self._forward_encoder(
+            speech,
+            speech_lengths,
+            decoding_chunk_size,
+            num_decoding_left_chunks,
+            simulate_streaming,
+        )  # (B, maxlen, encoder_dim)
+        maxlen = encoder_out.size(1)
+        encoder_out_lens = encoder_mask.squeeze(1).sum(1)
+        ctc_probs = self.ctc.log_softmax(encoder_out)  # (B, maxlen, vocab_size)
+        topk_prob, topk_index = ctc_probs.topk(1, dim=2)  # (B, maxlen, 1)
+        topk_index = topk_index.view(batch_size, maxlen)  # (B, maxlen)
+        mask = make_pad_mask(encoder_out_lens, maxlen)  # (B, maxlen)
+        topk_index = topk_index.masked_fill_(mask, self.eos)  # (B, maxlen)
+        hyps = [hyp.tolist() for hyp in topk_index]
+        scores = topk_prob.max(1)
+        hyps = [remove_duplicates_and_blank(hyp) for hyp in hyps]
+        return hyps, scores
+
+    def _ctc_prefix_beam_search(
+        self,
+        speech: torch.Tensor,
+        speech_lengths: torch.Tensor,
+        beam_size: int,
+        decoding_chunk_size: int = -1,
+        num_decoding_left_chunks: int = -1,
+        simulate_streaming: bool = False,
+    ) -> Tuple[List[List[int]], torch.Tensor]:
+        """CTC prefix beam search inner implementation
+
+        Args:
+            speech (torch.Tensor): (batch, max_len, feat_dim)
+            speech_length (torch.Tensor): (batch, )
+            beam_size (int): beam size for beam search
+            decoding_chunk_size (int): decoding chunk for dynamic chunk
+                trained model.
+                <0: for decoding, use full chunk.
+                >0: for decoding, use fixed chunk size as set.
+                0: used for training, it's prohibited here
+            simulate_streaming (bool): whether do encoder forward in a
+                streaming fashion
+
+        Returns:
+            List[List[int]]: nbest results
+            torch.Tensor: encoder output, (1, max_len, encoder_dim),
+                it will be used for rescoring in attention rescoring mode
+        """
+        assert speech.shape[0] == speech_lengths.shape[0]
+        assert decoding_chunk_size != 0
+        batch_size = speech.shape[0]
+        # For CTC prefix beam search, we only support batch_size=1
+        assert batch_size == 1
+        # Let's assume B = batch_size and N = beam_size
+        # 1. Encoder forward and get CTC score
+        encoder_out, encoder_mask = self._forward_encoder(
+            speech,
+            speech_lengths,
+            decoding_chunk_size,
+            num_decoding_left_chunks,
+            simulate_streaming,
+        )  # (B, maxlen, encoder_dim)
+        maxlen = encoder_out.size(1)
+        ctc_probs = self.ctc.log_softmax(encoder_out)  # (1, maxlen, vocab_size)
+        ctc_probs = ctc_probs.squeeze(0)
+        # cur_hyps: (prefix, (blank_ending_score, none_blank_ending_score))
+        cur_hyps = [(tuple(), (0.0, -float("inf")))]
+        # 2. CTC beam search step by step
+        for t in range(0, maxlen):
+            logp = ctc_probs[t]  # (vocab_size,)
+            # key: prefix, value (pb, pnb), default value(-inf, -inf)
+            next_hyps = defaultdict(lambda: (-float("inf"), -float("inf")))
+            # 2.1 First beam prune: select topk best
+            top_k_logp, top_k_index = logp.topk(beam_size)  # (beam_size,)
+            for s in top_k_index:
+                s = s.item()
+                ps = logp[s].item()
+                for prefix, (pb, pnb) in cur_hyps:
+                    last = prefix[-1] if len(prefix) > 0 else None
+                    if s == 0:  # blank
+                        n_pb, n_pnb = next_hyps[prefix]
+                        n_pb = log_add([n_pb, pb + ps, pnb + ps])
+                        next_hyps[prefix] = (n_pb, n_pnb)
+                    elif s == last:
+                        #  Update *ss -> *s;
+                        n_pb, n_pnb = next_hyps[prefix]
+                        n_pnb = log_add([n_pnb, pnb + ps])
+                        next_hyps[prefix] = (n_pb, n_pnb)
+                        # Update *s-s -> *ss, - is for blank
+                        n_prefix = prefix + (s,)
+                        n_pb, n_pnb = next_hyps[n_prefix]
+                        n_pnb = log_add([n_pnb, pb + ps])
+                        next_hyps[n_prefix] = (n_pb, n_pnb)
+                    else:
+                        n_prefix = prefix + (s,)
+                        n_pb, n_pnb = next_hyps[n_prefix]
+                        n_pnb = log_add([n_pnb, pb + ps, pnb + ps])
+                        next_hyps[n_prefix] = (n_pb, n_pnb)
+
+            # 2.2 Second beam prune
+            next_hyps = sorted(
+                next_hyps.items(), key=lambda x: log_add(list(x[1])), reverse=True
+            )
+            cur_hyps = next_hyps[:beam_size]
+        hyps = [(y[0], log_add([y[1][0], y[1][1]])) for y in cur_hyps]
+        return hyps, encoder_out
+
+    def ctc_prefix_beam_search(
+        self,
+        speech: torch.Tensor,
+        speech_lengths: torch.Tensor,
+        beam_size: int,
+        decoding_chunk_size: int = -1,
+        num_decoding_left_chunks: int = -1,
+        simulate_streaming: bool = False,
+    ) -> List[int]:
+        """Apply CTC prefix beam search
+
+        Args:
+            speech (torch.Tensor): (batch, max_len, feat_dim)
+            speech_length (torch.Tensor): (batch, )
+            beam_size (int): beam size for beam search
+            decoding_chunk_size (int): decoding chunk for dynamic chunk
+                trained model.
+                <0: for decoding, use full chunk.
+                >0: for decoding, use fixed chunk size as set.
+                0: used for training, it's prohibited here
+            simulate_streaming (bool): whether do encoder forward in a
+                streaming fashion
+
+        Returns:
+            List[int]: CTC prefix beam search nbest results
+        """
+        hyps, _ = self._ctc_prefix_beam_search(
+            speech,
+            speech_lengths,
+            beam_size,
+            decoding_chunk_size,
+            num_decoding_left_chunks,
+            simulate_streaming,
+        )
+        return hyps[0]
+
+    def attention_rescoring(
+        self,
+        speech: torch.Tensor,
+        speech_lengths: torch.Tensor,
+        beam_size: int,
+        decoding_chunk_size: int = -1,
+        num_decoding_left_chunks: int = -1,
+        ctc_weight: float = 0.0,
+        simulate_streaming: bool = False,
+        reverse_weight: float = 0.0,
+    ) -> List[int]:
+        """Apply attention rescoring decoding, CTC prefix beam search
+            is applied first to get nbest, then we resoring the nbest on
+            attention decoder with corresponding encoder out
+
+        Args:
+            speech (torch.Tensor): (batch, max_len, feat_dim)
+            speech_length (torch.Tensor): (batch, )
+            beam_size (int): beam size for beam search
+            decoding_chunk_size (int): decoding chunk for dynamic chunk
+                trained model.
+                <0: for decoding, use full chunk.
+                >0: for decoding, use fixed chunk size as set.
+                0: used for training, it's prohibited here
+            simulate_streaming (bool): whether do encoder forward in a
+                streaming fashion
+            reverse_weight (float): right to left decoder weight
+            ctc_weight (float): ctc score weight
+
+        Returns:
+            List[int]: Attention rescoring result
+        """
+        assert speech.shape[0] == speech_lengths.shape[0]
+        assert decoding_chunk_size != 0
+        if reverse_weight > 0.0:
+            # decoder should be a bitransformer decoder if reverse_weight > 0.0
+            assert hasattr(self.decoder, "right_decoder")
+        device = speech.device
+        batch_size = speech.shape[0]
+        # For attention rescoring we only support batch_size=1
+        assert batch_size == 1
+        # encoder_out: (1, maxlen, encoder_dim), len(hyps) = beam_size
+        hyps, encoder_out = self._ctc_prefix_beam_search(
+            speech,
+            speech_lengths,
+            beam_size,
+            decoding_chunk_size,
+            num_decoding_left_chunks,
+            simulate_streaming,
+        )
+
+        assert len(hyps) == beam_size
+        hyps_pad = pad_sequence(
+            [torch.tensor(hyp[0], device=device, dtype=torch.long) for hyp in hyps],
+            True,
+            self.ignore_id,
+        )  # (beam_size, max_hyps_len)
+        ori_hyps_pad = hyps_pad
+        hyps_lens = torch.tensor(
+            [len(hyp[0]) for hyp in hyps], device=device, dtype=torch.long
+        )  # (beam_size,)
+        hyps_pad, _ = add_sos_eos(hyps_pad, self.sos, self.eos, self.ignore_id)
+        hyps_lens = hyps_lens + 1  # Add <sos> at begining
+        encoder_out = encoder_out.repeat(beam_size, 1, 1)
+        encoder_mask = torch.ones(
+            beam_size, 1, encoder_out.size(1), dtype=torch.bool, device=device
+        )
+        # used for right to left decoder
+        r_hyps_pad = reverse_pad_list(ori_hyps_pad, hyps_lens, self.ignore_id)
+        r_hyps_pad, _ = add_sos_eos(r_hyps_pad, self.sos, self.eos, self.ignore_id)
+        decoder_out, r_decoder_out, _ = self.decoder(
+            encoder_out, encoder_mask, hyps_pad, hyps_lens, r_hyps_pad, reverse_weight
+        )  # (beam_size, max_hyps_len, vocab_size)
+        decoder_out = torch.nn.functional.log_softmax(decoder_out, dim=-1)
+        decoder_out = decoder_out.cpu().numpy()
+        # r_decoder_out will be 0.0, if reverse_weight is 0.0 or decoder is a
+        # conventional transformer decoder.
+        r_decoder_out = torch.nn.functional.log_softmax(r_decoder_out, dim=-1)
+        r_decoder_out = r_decoder_out.cpu().numpy()
+        # Only use decoder score for rescoring
+        best_score = -float("inf")
+        best_index = 0
+        for i, hyp in enumerate(hyps):
+            score = 0.0
+            for j, w in enumerate(hyp[0]):
+                score += decoder_out[i][j][w]
+            score += decoder_out[i][len(hyp[0])][self.eos]
+            # add right to left decoder score
+            if reverse_weight > 0:
+                r_score = 0.0
+                for j, w in enumerate(hyp[0]):
+                    r_score += r_decoder_out[i][len(hyp[0]) - j - 1][w]
+                r_score += r_decoder_out[i][len(hyp[0])][self.eos]
+                score = score * (1 - reverse_weight) + r_score * reverse_weight
+            # add ctc score
+            score += hyp[1] * ctc_weight
+            if score > best_score:
+                best_score = score
+                best_index = i
+        return hyps[best_index][0], best_score
+
+    @torch.jit.unused
+    def load_lfmmi_resource(self):
+        with open("{}/tokens.txt".format(self.lfmmi_dir), "r") as fin:
+            for line in fin:
+                arr = line.strip().split()
+                if arr[0] == "<sos/eos>":
+                    self.sos_eos_id = int(arr[1])
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.graph_compiler = MmiTrainingGraphCompiler(
+            self.lfmmi_dir,
+            device=device,
+            oov="<UNK>",
+            sos_id=self.sos_eos_id,
+            eos_id=self.sos_eos_id,
+        )
+        self.lfmmi = LFMMILoss(
+            graph_compiler=self.graph_compiler,
+            den_scale=1,
+            use_pruned_intersect=False,
+        )
+        self.word_table = {}
+        with open("{}/words.txt".format(self.lfmmi_dir), "r") as fin:
+            for line in fin:
+                arr = line.strip().split()
+                assert len(arr) == 2
+                self.word_table[int(arr[1])] = arr[0]
+
+    @torch.jit.unused
+    def _calc_lfmmi_loss(self, encoder_out, encoder_mask, text):
+        ctc_probs = self.ctc.log_softmax(encoder_out)
+        supervision_segments = torch.stack(
+            (
+                torch.arange(len(encoder_mask)),
+                torch.zeros(len(encoder_mask)),
+                encoder_mask.squeeze(dim=1).sum(dim=1).to("cpu"),
+            ),
+            1,
+        ).to(torch.int32)
+        dense_fsa_vec = k2.DenseFsaVec(
+            ctc_probs,
+            supervision_segments,
+            allow_truncate=3,
+        )
+        text = [
+            " ".join([self.word_table[j.item()] for j in i if j != -1]) for i in text
+        ]
+        loss = self.lfmmi(dense_fsa_vec=dense_fsa_vec, texts=text) / len(text)
+        return loss
+
+    def load_hlg_resource_if_necessary(self, hlg, word):
+        if not hasattr(self, "hlg"):
+            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+            self.hlg = k2.Fsa.from_dict(torch.load(hlg, map_location=device))
+        if not hasattr(self.hlg, "lm_scores"):
+            self.hlg.lm_scores = self.hlg.scores.clone()
+        if not hasattr(self, "word_table"):
+            self.word_table = {}
+            with open(word, "r") as fin:
+                for line in fin:
+                    arr = line.strip().split()
+                    assert len(arr) == 2
+                    self.word_table[int(arr[1])] = arr[0]
+
+    @torch.no_grad()
+    def hlg_onebest(
+        self,
+        speech: torch.Tensor,
+        speech_lengths: torch.Tensor,
+        decoding_chunk_size: int = -1,
+        num_decoding_left_chunks: int = -1,
+        simulate_streaming: bool = False,
+        hlg: str = "",
+        word: str = "",
+        symbol_table: Dict[str, int] = None,
+    ) -> List[int]:
+        self.load_hlg_resource_if_necessary(hlg, word)
+        encoder_out, encoder_mask = self._forward_encoder(
+            speech,
+            speech_lengths,
+            decoding_chunk_size,
+            num_decoding_left_chunks,
+            simulate_streaming,
+        )  # (B, maxlen, encoder_dim)
+        ctc_probs = self.ctc.log_softmax(encoder_out)  # (1, maxlen, vocab_size)
+        supervision_segments = torch.stack(
+            (
+                torch.arange(len(encoder_mask)),
+                torch.zeros(len(encoder_mask)),
+                encoder_mask.squeeze(dim=1).sum(dim=1).cpu(),
+            ),
+            1,
+        ).to(torch.int32)
+        lattice = get_lattice(
+            nnet_output=ctc_probs,
+            decoding_graph=self.hlg,
+            supervision_segments=supervision_segments,
+            search_beam=20,
+            output_beam=7,
+            min_active_states=30,
+            max_active_states=10000,
+            subsampling_factor=4,
+        )
+        best_path = one_best_decoding(lattice=lattice, use_double_scores=True)
+        hyps = get_texts(best_path)
+        hyps = [[symbol_table[k] for j in i for k in self.word_table[j]] for i in hyps]
+        return hyps
+
+    @torch.no_grad()
+    def hlg_rescore(
+        self,
+        speech: torch.Tensor,
+        speech_lengths: torch.Tensor,
+        decoding_chunk_size: int = -1,
+        num_decoding_left_chunks: int = -1,
+        simulate_streaming: bool = False,
+        lm_scale: float = 0,
+        decoder_scale: float = 0,
+        r_decoder_scale: float = 0,
+        hlg: str = "",
+        word: str = "",
+        symbol_table: Dict[str, int] = None,
+    ) -> List[int]:
+        self.load_hlg_resource_if_necessary(hlg, word)
+        device = speech.device
+        encoder_out, encoder_mask = self._forward_encoder(
+            speech,
+            speech_lengths,
+            decoding_chunk_size,
+            num_decoding_left_chunks,
+            simulate_streaming,
+        )  # (B, maxlen, encoder_dim)
+        ctc_probs = self.ctc.log_softmax(encoder_out)  # (1, maxlen, vocab_size)
+        supervision_segments = torch.stack(
+            (
+                torch.arange(len(encoder_mask)),
+                torch.zeros(len(encoder_mask)),
+                encoder_mask.squeeze(dim=1).sum(dim=1).cpu(),
+            ),
+            1,
+        ).to(torch.int32)
+        lattice = get_lattice(
+            nnet_output=ctc_probs,
+            decoding_graph=self.hlg,
+            supervision_segments=supervision_segments,
+            search_beam=20,
+            output_beam=7,
+            min_active_states=30,
+            max_active_states=10000,
+            subsampling_factor=4,
+        )
+        nbest = Nbest.from_lattice(
+            lattice=lattice,
+            num_paths=100,
+            use_double_scores=True,
+            nbest_scale=0.5,
+        )
+        nbest = nbest.intersect(lattice)
+        assert hasattr(nbest.fsa, "lm_scores")
+        assert hasattr(nbest.fsa, "tokens")
+        assert isinstance(nbest.fsa.tokens, torch.Tensor)
+
+        tokens_shape = nbest.fsa.arcs.shape().remove_axis(1)
+        tokens = k2.RaggedTensor(tokens_shape, nbest.fsa.tokens)
+        tokens = tokens.remove_values_leq(0)
+        hyps = tokens.tolist()
+
+        # cal attention_score
+        hyps_pad = pad_sequence(
+            [torch.tensor(hyp, device=device, dtype=torch.long) for hyp in hyps],
+            True,
+            self.ignore_id,
+        )  # (beam_size, max_hyps_len)
+        ori_hyps_pad = hyps_pad
+        hyps_lens = torch.tensor(
+            [len(hyp) for hyp in hyps], device=device, dtype=torch.long
+        )  # (beam_size,)
+        hyps_pad, _ = add_sos_eos(hyps_pad, self.sos, self.eos, self.ignore_id)
+        hyps_lens = hyps_lens + 1  # Add <sos> at begining
+        encoder_out_repeat = []
+        tot_scores = nbest.tot_scores()
+        repeats = [tot_scores[i].shape[0] for i in range(tot_scores.dim0)]
+        for i in range(len(encoder_out)):
+            encoder_out_repeat.append(encoder_out[i : i + 1].repeat(repeats[i], 1, 1))
+        encoder_out = torch.concat(encoder_out_repeat, dim=0)
+        encoder_mask = torch.ones(
+            encoder_out.size(0), 1, encoder_out.size(1), dtype=torch.bool, device=device
+        )
+        # used for right to left decoder
+        r_hyps_pad = reverse_pad_list(ori_hyps_pad, hyps_lens, self.ignore_id)
+        r_hyps_pad, _ = add_sos_eos(r_hyps_pad, self.sos, self.eos, self.ignore_id)
+        reverse_weight = 0.5
+        decoder_out, r_decoder_out, _ = self.decoder(
+            encoder_out, encoder_mask, hyps_pad, hyps_lens, r_hyps_pad, reverse_weight
+        )  # (beam_size, max_hyps_len, vocab_size)
+        decoder_out = torch.nn.functional.log_softmax(decoder_out, dim=-1)
+        decoder_out = decoder_out
+        # r_decoder_out will be 0.0, if reverse_weight is 0.0 or decoder is a
+        # conventional transformer decoder.
+        r_decoder_out = torch.nn.functional.log_softmax(r_decoder_out, dim=-1)
+        r_decoder_out = r_decoder_out
+
+        decoder_scores = torch.tensor(
+            [
+                sum([decoder_out[i, j, hyps[i][j]] for j in range(len(hyps[i]))])
+                for i in range(len(hyps))
+            ],
+            device=device,
+        )
+        r_decoder_scores = []
+        for i in range(len(hyps)):
+            score = 0
+            for j in range(len(hyps[i])):
+                score += r_decoder_out[i, len(hyps[i]) - j - 1, hyps[i][j]]
+            score += r_decoder_out[i, len(hyps[i]), self.eos]
+            r_decoder_scores.append(score)
+        r_decoder_scores = torch.tensor(r_decoder_scores, device=device)
+
+        am_scores = nbest.compute_am_scores()
+        ngram_lm_scores = nbest.compute_lm_scores()
+        tot_scores = (
+            am_scores.values
+            + lm_scale * ngram_lm_scores.values
+            + decoder_scale * decoder_scores
+            + r_decoder_scale * r_decoder_scores
+        )
+        ragged_tot_scores = k2.RaggedTensor(nbest.shape, tot_scores)
+        max_indexes = ragged_tot_scores.argmax()
+        best_path = k2.index_fsa(nbest.fsa, max_indexes)
+        hyps = get_texts(best_path)
+        hyps = [[symbol_table[k] for j in i for k in self.word_table[j]] for i in hyps]
+        return hyps
+
+    @torch.jit.export
+    def subsampling_rate(self) -> int:
+        """Export interface for c++ call, return subsampling_rate of the
+        model
+        """
+        return self.encoder.embed.subsampling_rate
+
+    @torch.jit.export
+    def right_context(self) -> int:
+        """Export interface for c++ call, return right_context of the model"""
+        return self.encoder.embed.right_context
+
+    @torch.jit.export
+    def sos_symbol(self) -> int:
+        """Export interface for c++ call, return sos symbol id of the model"""
+        return self.sos
+
+    @torch.jit.export
+    def eos_symbol(self) -> int:
+        """Export interface for c++ call, return eos symbol id of the model"""
+        return self.eos
+
+    @torch.jit.export
+    def forward_encoder_chunk(
+        self,
+        xs: torch.Tensor,
+        offset: int,
+        required_cache_size: int,
+        att_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
+        cnn_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """ Export interface for c++ call, give input chunk xs, and return
+            output from time 0 to current chunk.
+
+        Args:
+            xs (torch.Tensor): chunk input, with shape (b=1, time, mel-dim),
+                where `time == (chunk_size - 1) * subsample_rate + \
+                        subsample.right_context + 1`
+            offset (int): current offset in encoder output time stamp
+            required_cache_size (int): cache size required for next chunk
+                compuation
+                >=0: actual cache size
+                <0: means all history cache is required
+            att_cache (torch.Tensor): cache tensor for KEY & VALUE in
+                transformer/conformer attention, with shape
+                (elayers, head, cache_t1, d_k * 2), where
+                `head * d_k == hidden-dim` and
+                `cache_t1 == chunk_size * num_decoding_left_chunks`.
+            cnn_cache (torch.Tensor): cache tensor for cnn_module in conformer,
+                (elayers, b=1, hidden-dim, cache_t2), where
+                `cache_t2 == cnn.lorder - 1`
+
+        Returns:
+            torch.Tensor: output of current input xs,
+                with shape (b=1, chunk_size, hidden-dim).
+            torch.Tensor: new attention cache required for next chunk, with
+                dynamic shape (elayers, head, ?, d_k * 2)
+                depending on required_cache_size.
+            torch.Tensor: new conformer cnn cache required for next chunk, with
+                same shape as the original cnn_cache.
+
+        """
+        return self.encoder.forward_chunk(
+            xs, offset, required_cache_size, att_cache, cnn_cache
+        )
+
+    @torch.jit.export
+    def ctc_activation(self, xs: torch.Tensor) -> torch.Tensor:
+        """Export interface for c++ call, apply linear transform and log
+            softmax before ctc
+        Args:
+            xs (torch.Tensor): encoder output
+
+        Returns:
+            torch.Tensor: activation before ctc
+
+        """
+        return self.ctc.log_softmax(xs)
+
+    @torch.jit.export
+    def is_bidirectional_decoder(self) -> bool:
+        """
+        Returns:
+            torch.Tensor: decoder output
+        """
+        if hasattr(self.decoder, "right_decoder"):
+            return True
+        else:
+            return False
+
+    @torch.jit.export
+    def forward_attention_decoder(
+        self,
+        hyps: torch.Tensor,
+        hyps_lens: torch.Tensor,
+        encoder_out: torch.Tensor,
+        reverse_weight: float = 0,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Export interface for c++ call, forward decoder with multiple
+            hypothesis from ctc prefix beam search and one encoder output
+        Args:
+            hyps (torch.Tensor): hyps from ctc prefix beam search, already
+                pad sos at the begining
+            hyps_lens (torch.Tensor): length of each hyp in hyps
+            encoder_out (torch.Tensor): corresponding encoder output
+            r_hyps (torch.Tensor): hyps from ctc prefix beam search, already
+                pad eos at the begining which is used fo right to left decoder
+            reverse_weight: used for verfing whether used right to left decoder,
+            > 0 will use.
+
+        Returns:
+            torch.Tensor: decoder output
+        """
+        assert encoder_out.size(0) == 1
+        num_hyps = hyps.size(0)
+        assert hyps_lens.size(0) == num_hyps
+        encoder_out = encoder_out.repeat(num_hyps, 1, 1)
+        encoder_mask = torch.ones(
+            num_hyps,
+            1,
+            encoder_out.size(1),
+            dtype=torch.bool,
+            device=encoder_out.device,
+        )
+
+        # input for right to left decoder
+        # this hyps_lens has count <sos> token, we need minus it.
+        r_hyps_lens = hyps_lens - 1
+        # this hyps has included <sos> token, so it should be
+        # convert the original hyps.
+        r_hyps = hyps[:, 1:]
+        #   >>> r_hyps
+        #   >>> tensor([[ 1,  2,  3],
+        #   >>>         [ 9,  8,  4],
+        #   >>>         [ 2, -1, -1]])
+        #   >>> r_hyps_lens
+        #   >>> tensor([3, 3, 1])
+
+        # NOTE(Mddct): `pad_sequence` is not supported by ONNX, it is used
+        #   in `reverse_pad_list` thus we have to refine the below code.
+        #   Issue: https://github.com/wenet-e2e/wenet/issues/1113
+        # Equal to:
+        #   >>> r_hyps = reverse_pad_list(r_hyps, r_hyps_lens, float(self.ignore_id))
+        #   >>> r_hyps, _ = add_sos_eos(r_hyps, self.sos, self.eos, self.ignore_id)
+        max_len = torch.max(r_hyps_lens)
+        index_range = torch.arange(0, max_len, 1).to(encoder_out.device)
+        seq_len_expand = r_hyps_lens.unsqueeze(1)
+        seq_mask = seq_len_expand > index_range  # (beam, max_len)
+        #   >>> seq_mask
+        #   >>> tensor([[ True,  True,  True],
+        #   >>>         [ True,  True,  True],
+        #   >>>         [ True, False, False]])
+        index = (seq_len_expand - 1) - index_range  # (beam, max_len)
+        #   >>> index
+        #   >>> tensor([[ 2,  1,  0],
+        #   >>>         [ 2,  1,  0],
+        #   >>>         [ 0, -1, -2]])
+        index = index * seq_mask
+        #   >>> index
+        #   >>> tensor([[2, 1, 0],
+        #   >>>         [2, 1, 0],
+        #   >>>         [0, 0, 0]])
+        r_hyps = torch.gather(r_hyps, 1, index)
+        #   >>> r_hyps
+        #   >>> tensor([[3, 2, 1],
+        #   >>>         [4, 8, 9],
+        #   >>>         [2, 2, 2]])
+        r_hyps = torch.where(seq_mask, r_hyps, self.eos)
+        #   >>> r_hyps
+        #   >>> tensor([[3, 2, 1],
+        #   >>>         [4, 8, 9],
+        #   >>>         [2, eos, eos]])
+        r_hyps = torch.cat([hyps[:, 0:1], r_hyps], dim=1)
+        #   >>> r_hyps
+        #   >>> tensor([[sos, 3, 2, 1],
+        #   >>>         [sos, 4, 8, 9],
+        #   >>>         [sos, 2, eos, eos]])
+
+        decoder_out, r_decoder_out, _ = self.decoder(
+            encoder_out, encoder_mask, hyps, hyps_lens, r_hyps, reverse_weight
+        )  # (num_hyps, max_hyps_len, vocab_size)
+        decoder_out = torch.nn.functional.log_softmax(decoder_out, dim=-1)
+
+        # right to left decoder may be not used during decoding process,
+        # which depends on reverse_weight param.
+        # r_dccoder_out will be 0.0, if reverse_weight is 0.0
+        r_decoder_out = torch.nn.functional.log_softmax(r_decoder_out, dim=-1)
+        return decoder_out, r_decoder_out
diff --git a/modules/wenet_extractor/transformer/attention.py b/modules/wenet_extractor/transformer/attention.py
new file mode 100755
index 0000000000000000000000000000000000000000..a13c0ce0aa408166a990fba119ba2e167f2ee868
--- /dev/null
+++ b/modules/wenet_extractor/transformer/attention.py
@@ -0,0 +1,326 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+"""Multi-Head Attention layer definition."""
+
+import math
+from typing import Tuple
+
+import torch
+from torch import nn
+
+
+class MultiHeadedAttention(nn.Module):
+    """Multi-Head Attention layer.
+
+    Args:
+        n_head (int): The number of heads.
+        n_feat (int): The number of features.
+        dropout_rate (float): Dropout rate.
+
+    """
+
+    def __init__(self, n_head: int, n_feat: int, dropout_rate: float):
+        """Construct an MultiHeadedAttention object."""
+        super().__init__()
+        assert n_feat % n_head == 0
+        # We assume d_v always equals d_k
+        self.d_k = n_feat // n_head
+        self.h = n_head
+        self.linear_q = nn.Linear(n_feat, n_feat)
+        self.linear_k = nn.Linear(n_feat, n_feat)
+        self.linear_v = nn.Linear(n_feat, n_feat)
+        self.linear_out = nn.Linear(n_feat, n_feat)
+        self.dropout = nn.Dropout(p=dropout_rate)
+
+    def forward_qkv(
+        self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Transform query, key and value.
+
+        Args:
+            query (torch.Tensor): Query tensor (#batch, time1, size).
+            key (torch.Tensor): Key tensor (#batch, time2, size).
+            value (torch.Tensor): Value tensor (#batch, time2, size).
+
+        Returns:
+            torch.Tensor: Transformed query tensor, size
+                (#batch, n_head, time1, d_k).
+            torch.Tensor: Transformed key tensor, size
+                (#batch, n_head, time2, d_k).
+            torch.Tensor: Transformed value tensor, size
+                (#batch, n_head, time2, d_k).
+
+        """
+        n_batch = query.size(0)
+        q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k)
+        k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k)
+        v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k)
+        q = q.transpose(1, 2)  # (batch, head, time1, d_k)
+        k = k.transpose(1, 2)  # (batch, head, time2, d_k)
+        v = v.transpose(1, 2)  # (batch, head, time2, d_k)
+
+        return q, k, v
+
+    def forward_attention(
+        self,
+        value: torch.Tensor,
+        scores: torch.Tensor,
+        mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+    ) -> torch.Tensor:
+        """Compute attention context vector.
+
+        Args:
+            value (torch.Tensor): Transformed value, size
+                (#batch, n_head, time2, d_k).
+            scores (torch.Tensor): Attention score, size
+                (#batch, n_head, time1, time2).
+            mask (torch.Tensor): Mask, size (#batch, 1, time2) or
+                (#batch, time1, time2), (0, 0, 0) means fake mask.
+
+        Returns:
+            torch.Tensor: Transformed value (#batch, time1, d_model)
+                weighted by the attention score (#batch, time1, time2).
+
+        """
+        n_batch = value.size(0)
+        # NOTE(xcsong): When will `if mask.size(2) > 0` be True?
+        #   1. onnx(16/4) [WHY? Because we feed real cache & real mask for the
+        #           1st chunk to ease the onnx export.]
+        #   2. pytorch training
+        if mask.size(2) > 0:  # time2 > 0
+            mask = mask.unsqueeze(1).eq(0)  # (batch, 1, *, time2)
+            # For last chunk, time2 might be larger than scores.size(-1)
+            mask = mask[:, :, :, : scores.size(-1)]  # (batch, 1, *, time2)
+            scores = scores.masked_fill(mask, -float("inf"))
+            attn = torch.softmax(scores, dim=-1).masked_fill(
+                mask, 0.0
+            )  # (batch, head, time1, time2)
+        # NOTE(xcsong): When will `if mask.size(2) > 0` be False?
+        #   1. onnx(16/-1, -1/-1, 16/0)
+        #   2. jit (16/-1, -1/-1, 16/0, 16/4)
+        else:
+            attn = torch.softmax(scores, dim=-1)  # (batch, head, time1, time2)
+
+        p_attn = self.dropout(attn)
+        x = torch.matmul(p_attn, value)  # (batch, head, time1, d_k)
+        x = (
+            x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k)
+        )  # (batch, time1, d_model)
+
+        return self.linear_out(x)  # (batch, time1, d_model)
+
+    def forward(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+        pos_emb: torch.Tensor = torch.empty(0),
+        cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Compute scaled dot product attention.
+
+        Args:
+            query (torch.Tensor): Query tensor (#batch, time1, size).
+            key (torch.Tensor): Key tensor (#batch, time2, size).
+            value (torch.Tensor): Value tensor (#batch, time2, size).
+            mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
+                (#batch, time1, time2).
+                1.When applying cross attention between decoder and encoder,
+                the batch padding mask for input is in (#batch, 1, T) shape.
+                2.When applying self attention of encoder,
+                the mask is in (#batch, T, T)  shape.
+                3.When applying self attention of decoder,
+                the mask is in (#batch, L, L)  shape.
+                4.If the different position in decoder see different block
+                of the encoder, such as Mocha, the passed in mask could be
+                in (#batch, L, T) shape. But there is no such case in current
+                Wenet.
+            cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2),
+                where `cache_t == chunk_size * num_decoding_left_chunks`
+                and `head * d_k == size`
+
+
+        Returns:
+            torch.Tensor: Output tensor (#batch, time1, d_model).
+            torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2)
+                where `cache_t == chunk_size * num_decoding_left_chunks`
+                and `head * d_k == size`
+
+        """
+        q, k, v = self.forward_qkv(query, key, value)
+
+        # NOTE(xcsong):
+        #   when export onnx model, for 1st chunk, we feed
+        #       cache(1, head, 0, d_k * 2) (16/-1, -1/-1, 16/0 mode)
+        #       or cache(1, head, real_cache_t, d_k * 2) (16/4 mode).
+        #       In all modes, `if cache.size(0) > 0` will alwayse be `True`
+        #       and we will always do splitting and
+        #       concatnation(this will simplify onnx export). Note that
+        #       it's OK to concat & split zero-shaped tensors(see code below).
+        #   when export jit  model, for 1st chunk, we always feed
+        #       cache(0, 0, 0, 0) since jit supports dynamic if-branch.
+        # >>> a = torch.ones((1, 2, 0, 4))
+        # >>> b = torch.ones((1, 2, 3, 4))
+        # >>> c = torch.cat((a, b), dim=2)
+        # >>> torch.equal(b, c)        # True
+        # >>> d = torch.split(a, 2, dim=-1)
+        # >>> torch.equal(d[0], d[1])  # True
+        if cache.size(0) > 0:
+            key_cache, value_cache = torch.split(cache, cache.size(-1) // 2, dim=-1)
+            k = torch.cat([key_cache, k], dim=2)
+            v = torch.cat([value_cache, v], dim=2)
+        # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's
+        #   non-trivial to calculate `next_cache_start` here.
+        new_cache = torch.cat((k, v), dim=-1)
+
+        scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k)
+        return self.forward_attention(v, scores, mask), new_cache
+
+
+class RelPositionMultiHeadedAttention(MultiHeadedAttention):
+    """Multi-Head Attention layer with relative position encoding.
+    Paper: https://arxiv.org/abs/1901.02860
+    Args:
+        n_head (int): The number of heads.
+        n_feat (int): The number of features.
+        dropout_rate (float): Dropout rate.
+    """
+
+    def __init__(self, n_head, n_feat, dropout_rate):
+        """Construct an RelPositionMultiHeadedAttention object."""
+        super().__init__(n_head, n_feat, dropout_rate)
+        # linear transformation for positional encoding
+        self.linear_pos = nn.Linear(n_feat, n_feat, bias=False)
+        # these two learnable bias are used in matrix c and matrix d
+        # as described in https://arxiv.org/abs/1901.02860 Section 3.3
+        self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k))
+        self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k))
+        torch.nn.init.xavier_uniform_(self.pos_bias_u)
+        torch.nn.init.xavier_uniform_(self.pos_bias_v)
+
+    def rel_shift(self, x, zero_triu: bool = False):
+        """Compute relative positinal encoding.
+        Args:
+            x (torch.Tensor): Input tensor (batch, time, size).
+            zero_triu (bool): If true, return the lower triangular part of
+                the matrix.
+        Returns:
+            torch.Tensor: Output tensor.
+        """
+
+        zero_pad = torch.zeros(
+            (x.size()[0], x.size()[1], x.size()[2], 1), device=x.device, dtype=x.dtype
+        )
+        x_padded = torch.cat([zero_pad, x], dim=-1)
+
+        x_padded = x_padded.view(x.size()[0], x.size()[1], x.size(3) + 1, x.size(2))
+        x = x_padded[:, :, 1:].view_as(x)
+
+        if zero_triu:
+            ones = torch.ones((x.size(2), x.size(3)))
+            x = x * torch.tril(ones, x.size(3) - x.size(2))[None, None, :, :]
+
+        return x
+
+    def forward(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+        pos_emb: torch.Tensor = torch.empty(0),
+        cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Compute 'Scaled Dot Product Attention' with rel. positional encoding.
+        Args:
+            query (torch.Tensor): Query tensor (#batch, time1, size).
+            key (torch.Tensor): Key tensor (#batch, time2, size).
+            value (torch.Tensor): Value tensor (#batch, time2, size).
+            mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
+                (#batch, time1, time2), (0, 0, 0) means fake mask.
+            pos_emb (torch.Tensor): Positional embedding tensor
+                (#batch, time2, size).
+            cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2),
+                where `cache_t == chunk_size * num_decoding_left_chunks`
+                and `head * d_k == size`
+        Returns:
+            torch.Tensor: Output tensor (#batch, time1, d_model).
+            torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2)
+                where `cache_t == chunk_size * num_decoding_left_chunks`
+                and `head * d_k == size`
+        """
+        q, k, v = self.forward_qkv(query, key, value)
+        q = q.transpose(1, 2)  # (batch, time1, head, d_k)
+
+        # NOTE(xcsong):
+        #   when export onnx model, for 1st chunk, we feed
+        #       cache(1, head, 0, d_k * 2) (16/-1, -1/-1, 16/0 mode)
+        #       or cache(1, head, real_cache_t, d_k * 2) (16/4 mode).
+        #       In all modes, `if cache.size(0) > 0` will alwayse be `True`
+        #       and we will always do splitting and
+        #       concatnation(this will simplify onnx export). Note that
+        #       it's OK to concat & split zero-shaped tensors(see code below).
+        #   when export jit  model, for 1st chunk, we always feed
+        #       cache(0, 0, 0, 0) since jit supports dynamic if-branch.
+        # >>> a = torch.ones((1, 2, 0, 4))
+        # >>> b = torch.ones((1, 2, 3, 4))
+        # >>> c = torch.cat((a, b), dim=2)
+        # >>> torch.equal(b, c)        # True
+        # >>> d = torch.split(a, 2, dim=-1)
+        # >>> torch.equal(d[0], d[1])  # True
+        if cache.size(0) > 0:
+            key_cache, value_cache = torch.split(cache, cache.size(-1) // 2, dim=-1)
+            k = torch.cat([key_cache, k], dim=2)
+            v = torch.cat([value_cache, v], dim=2)
+        # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's
+        #   non-trivial to calculate `next_cache_start` here.
+        new_cache = torch.cat((k, v), dim=-1)
+
+        n_batch_pos = pos_emb.size(0)
+        p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k)
+        p = p.transpose(1, 2)  # (batch, head, time1, d_k)
+
+        # (batch, head, time1, d_k)
+        q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2)
+        # (batch, head, time1, d_k)
+        q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2)
+
+        # compute attention score
+        # first compute matrix a and matrix c
+        # as described in https://arxiv.org/abs/1901.02860 Section 3.3
+        # (batch, head, time1, time2)
+        matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))
+
+        # compute matrix b and matrix d
+        # (batch, head, time1, time2)
+        matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
+        # Remove rel_shift since it is useless in speech recognition,
+        # and it requires special attention for streaming.
+        # matrix_bd = self.rel_shift(matrix_bd)
+
+        scores = (matrix_ac + matrix_bd) / math.sqrt(
+            self.d_k
+        )  # (batch, head, time1, time2)
+
+        return self.forward_attention(v, scores, mask), new_cache
diff --git a/modules/wenet_extractor/transformer/cmvn.py b/modules/wenet_extractor/transformer/cmvn.py
new file mode 100755
index 0000000000000000000000000000000000000000..d97f8924e7bf0db25d475be79256086682aa9f6a
--- /dev/null
+++ b/modules/wenet_extractor/transformer/cmvn.py
@@ -0,0 +1,51 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+import torch
+
+
+class GlobalCMVN(torch.nn.Module):
+    def __init__(self, mean: torch.Tensor, istd: torch.Tensor, norm_var: bool = True):
+        """
+        Args:
+            mean (torch.Tensor): mean stats
+            istd (torch.Tensor): inverse std, std which is 1.0 / std
+        """
+        super().__init__()
+        assert mean.shape == istd.shape
+        self.norm_var = norm_var
+        # The buffer can be accessed from this module using self.mean
+        self.register_buffer("mean", mean)
+        self.register_buffer("istd", istd)
+
+    def forward(self, x: torch.Tensor):
+        """
+        Args:
+            x (torch.Tensor): (batch, max_len, feat_dim)
+
+        Returns:
+            (torch.Tensor): normalized feature
+        """
+        x = x - self.mean
+        if self.norm_var:
+            x = x * self.istd
+        return x
diff --git a/modules/wenet_extractor/transformer/convolution.py b/modules/wenet_extractor/transformer/convolution.py
new file mode 100755
index 0000000000000000000000000000000000000000..be1148386f8aab6c1b01244f4a84c7e90aba7952
--- /dev/null
+++ b/modules/wenet_extractor/transformer/convolution.py
@@ -0,0 +1,154 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+"""ConvolutionModule definition."""
+
+from typing import Tuple
+
+import torch
+from torch import nn
+
+
+class ConvolutionModule(nn.Module):
+    """ConvolutionModule in Conformer model."""
+
+    def __init__(
+        self,
+        channels: int,
+        kernel_size: int = 15,
+        activation: nn.Module = nn.ReLU(),
+        norm: str = "batch_norm",
+        causal: bool = False,
+        bias: bool = True,
+    ):
+        """Construct an ConvolutionModule object.
+        Args:
+            channels (int): The number of channels of conv layers.
+            kernel_size (int): Kernel size of conv layers.
+            causal (int): Whether use causal convolution or not
+        """
+        super().__init__()
+
+        self.pointwise_conv1 = nn.Conv1d(
+            channels,
+            2 * channels,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            bias=bias,
+        )
+        # self.lorder is used to distinguish if it's a causal convolution,
+        # if self.lorder > 0: it's a causal convolution, the input will be
+        #    padded with self.lorder frames on the left in forward.
+        # else: it's a symmetrical convolution
+        if causal:
+            padding = 0
+            self.lorder = kernel_size - 1
+        else:
+            # kernel_size should be an odd number for none causal convolution
+            assert (kernel_size - 1) % 2 == 0
+            padding = (kernel_size - 1) // 2
+            self.lorder = 0
+        self.depthwise_conv = nn.Conv1d(
+            channels,
+            channels,
+            kernel_size,
+            stride=1,
+            padding=padding,
+            groups=channels,
+            bias=bias,
+        )
+
+        assert norm in ["batch_norm", "layer_norm"]
+        if norm == "batch_norm":
+            self.use_layer_norm = False
+            self.norm = nn.BatchNorm1d(channels)
+        else:
+            self.use_layer_norm = True
+            self.norm = nn.LayerNorm(channels)
+
+        self.pointwise_conv2 = nn.Conv1d(
+            channels,
+            channels,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            bias=bias,
+        )
+        self.activation = activation
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+        cache: torch.Tensor = torch.zeros((0, 0, 0)),
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Compute convolution module.
+        Args:
+            x (torch.Tensor): Input tensor (#batch, time, channels).
+            mask_pad (torch.Tensor): used for batch padding (#batch, 1, time),
+                (0, 0, 0) means fake mask.
+            cache (torch.Tensor): left context cache, it is only
+                used in causal convolution (#batch, channels, cache_t),
+                (0, 0, 0) meas fake cache.
+        Returns:
+            torch.Tensor: Output tensor (#batch, time, channels).
+        """
+        # exchange the temporal dimension and the feature dimension
+        x = x.transpose(1, 2)  # (#batch, channels, time)
+
+        # mask batch padding
+        if mask_pad.size(2) > 0:  # time > 0
+            x.masked_fill_(~mask_pad, 0.0)
+
+        if self.lorder > 0:
+            if cache.size(2) == 0:  # cache_t == 0
+                x = nn.functional.pad(x, (self.lorder, 0), "constant", 0.0)
+            else:
+                assert cache.size(0) == x.size(0)  # equal batch
+                assert cache.size(1) == x.size(1)  # equal channel
+                x = torch.cat((cache, x), dim=2)
+            assert x.size(2) > self.lorder
+            new_cache = x[:, :, -self.lorder :]
+        else:
+            # It's better we just return None if no cache is required,
+            # However, for JIT export, here we just fake one tensor instead of
+            # None.
+            new_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device)
+
+        # GLU mechanism
+        x = self.pointwise_conv1(x)  # (batch, 2*channel, dim)
+        x = nn.functional.glu(x, dim=1)  # (batch, channel, dim)
+
+        # 1D Depthwise Conv
+        x = self.depthwise_conv(x)
+        if self.use_layer_norm:
+            x = x.transpose(1, 2)
+        x = self.activation(self.norm(x))
+        if self.use_layer_norm:
+            x = x.transpose(1, 2)
+        x = self.pointwise_conv2(x)
+        # mask batch padding
+        if mask_pad.size(2) > 0:  # time > 0
+            x.masked_fill_(~mask_pad, 0.0)
+
+        return x.transpose(1, 2), new_cache
diff --git a/modules/wenet_extractor/transformer/ctc.py b/modules/wenet_extractor/transformer/ctc.py
new file mode 100755
index 0000000000000000000000000000000000000000..3cbf13a83137642a61b2c46033907ca22d9d6498
--- /dev/null
+++ b/modules/wenet_extractor/transformer/ctc.py
@@ -0,0 +1,95 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+import torch
+import torch.nn.functional as F
+
+
+class CTC(torch.nn.Module):
+    """CTC module"""
+
+    def __init__(
+        self,
+        odim: int,
+        encoder_output_size: int,
+        dropout_rate: float = 0.0,
+        reduce: bool = True,
+    ):
+        """Construct CTC module
+        Args:
+            odim: dimension of outputs
+            encoder_output_size: number of encoder projection units
+            dropout_rate: dropout rate (0.0 ~ 1.0)
+            reduce: reduce the CTC loss into a scalar
+        """
+        super().__init__()
+        eprojs = encoder_output_size
+        self.dropout_rate = dropout_rate
+        self.ctc_lo = torch.nn.Linear(eprojs, odim)
+
+        reduction_type = "sum" if reduce else "none"
+        self.ctc_loss = torch.nn.CTCLoss(reduction=reduction_type)
+
+    def forward(
+        self,
+        hs_pad: torch.Tensor,
+        hlens: torch.Tensor,
+        ys_pad: torch.Tensor,
+        ys_lens: torch.Tensor,
+    ) -> torch.Tensor:
+        """Calculate CTC loss.
+
+        Args:
+            hs_pad: batch of padded hidden state sequences (B, Tmax, D)
+            hlens: batch of lengths of hidden state sequences (B)
+            ys_pad: batch of padded character id sequence tensor (B, Lmax)
+            ys_lens: batch of lengths of character sequence (B)
+        """
+        # hs_pad: (B, L, NProj) -> ys_hat: (B, L, Nvocab)
+        ys_hat = self.ctc_lo(F.dropout(hs_pad, p=self.dropout_rate))
+        # ys_hat: (B, L, D) -> (L, B, D)
+        ys_hat = ys_hat.transpose(0, 1)
+        ys_hat = ys_hat.log_softmax(2)
+        loss = self.ctc_loss(ys_hat, ys_pad, hlens, ys_lens)
+        # Batch-size average
+        loss = loss / ys_hat.size(1)
+        return loss
+
+    def log_softmax(self, hs_pad: torch.Tensor) -> torch.Tensor:
+        """log_softmax of frame activations
+
+        Args:
+            Tensor hs_pad: 3d tensor (B, Tmax, eprojs)
+        Returns:
+            torch.Tensor: log softmax applied 3d tensor (B, Tmax, odim)
+        """
+        return F.log_softmax(self.ctc_lo(hs_pad), dim=2)
+
+    def argmax(self, hs_pad: torch.Tensor) -> torch.Tensor:
+        """argmax of frame activations
+
+        Args:
+            torch.Tensor hs_pad: 3d tensor (B, Tmax, eprojs)
+        Returns:
+            torch.Tensor: argmax applied 2d tensor (B, Tmax)
+        """
+        return torch.argmax(self.ctc_lo(hs_pad), dim=2)
diff --git a/modules/wenet_extractor/transformer/decoder.py b/modules/wenet_extractor/transformer/decoder.py
new file mode 100755
index 0000000000000000000000000000000000000000..8312769ad80d5ff06132bc6c5f440ad63b2d53d4
--- /dev/null
+++ b/modules/wenet_extractor/transformer/decoder.py
@@ -0,0 +1,323 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+"""Decoder definition."""
+from typing import Tuple, List, Optional
+
+import torch
+
+from modules.wenet_extractor.transformer.attention import MultiHeadedAttention
+from modules.wenet_extractor.transformer.decoder_layer import DecoderLayer
+from modules.wenet_extractor.transformer.embedding import PositionalEncoding
+from modules.wenet_extractor.transformer.embedding import NoPositionalEncoding
+from modules.wenet_extractor.transformer.positionwise_feed_forward import (
+    PositionwiseFeedForward,
+)
+from modules.wenet_extractor.utils.mask import subsequent_mask, make_pad_mask
+
+
+class TransformerDecoder(torch.nn.Module):
+    """Base class of Transfomer decoder module.
+    Args:
+        vocab_size: output dim
+        encoder_output_size: dimension of attention
+        attention_heads: the number of heads of multi head attention
+        linear_units: the hidden units number of position-wise feedforward
+        num_blocks: the number of decoder blocks
+        dropout_rate: dropout rate
+        self_attention_dropout_rate: dropout rate for attention
+        input_layer: input layer type
+        use_output_layer: whether to use output layer
+        pos_enc_class: PositionalEncoding or ScaledPositionalEncoding
+        normalize_before:
+            True: use layer_norm before each sub-block of a layer.
+            False: use layer_norm after each sub-block of a layer.
+        src_attention: if false, encoder-decoder cross attention is not
+                       applied, such as CIF model
+    """
+
+    def __init__(
+        self,
+        vocab_size: int,
+        encoder_output_size: int,
+        attention_heads: int = 4,
+        linear_units: int = 2048,
+        num_blocks: int = 6,
+        dropout_rate: float = 0.1,
+        positional_dropout_rate: float = 0.1,
+        self_attention_dropout_rate: float = 0.0,
+        src_attention_dropout_rate: float = 0.0,
+        input_layer: str = "embed",
+        use_output_layer: bool = True,
+        normalize_before: bool = True,
+        src_attention: bool = True,
+    ):
+        super().__init__()
+        attention_dim = encoder_output_size
+
+        if input_layer == "embed":
+            self.embed = torch.nn.Sequential(
+                torch.nn.Embedding(vocab_size, attention_dim),
+                PositionalEncoding(attention_dim, positional_dropout_rate),
+            )
+        elif input_layer == "none":
+            self.embed = NoPositionalEncoding(attention_dim, positional_dropout_rate)
+        else:
+            raise ValueError(f"only 'embed' is supported: {input_layer}")
+
+        self.normalize_before = normalize_before
+        self.after_norm = torch.nn.LayerNorm(attention_dim, eps=1e-5)
+        self.use_output_layer = use_output_layer
+        self.output_layer = torch.nn.Linear(attention_dim, vocab_size)
+        self.num_blocks = num_blocks
+        self.decoders = torch.nn.ModuleList(
+            [
+                DecoderLayer(
+                    attention_dim,
+                    MultiHeadedAttention(
+                        attention_heads, attention_dim, self_attention_dropout_rate
+                    ),
+                    MultiHeadedAttention(
+                        attention_heads, attention_dim, src_attention_dropout_rate
+                    )
+                    if src_attention
+                    else None,
+                    PositionwiseFeedForward(attention_dim, linear_units, dropout_rate),
+                    dropout_rate,
+                    normalize_before,
+                )
+                for _ in range(self.num_blocks)
+            ]
+        )
+
+    def forward(
+        self,
+        memory: torch.Tensor,
+        memory_mask: torch.Tensor,
+        ys_in_pad: torch.Tensor,
+        ys_in_lens: torch.Tensor,
+        r_ys_in_pad: torch.Tensor = torch.empty(0),
+        reverse_weight: float = 0.0,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Forward decoder.
+        Args:
+            memory: encoded memory, float32  (batch, maxlen_in, feat)
+            memory_mask: encoder memory mask, (batch, 1, maxlen_in)
+            ys_in_pad: padded input token ids, int64 (batch, maxlen_out)
+            ys_in_lens: input lengths of this batch (batch)
+            r_ys_in_pad: not used in transformer decoder, in order to unify api
+                with bidirectional decoder
+            reverse_weight: not used in transformer decoder, in order to unify
+                api with bidirectional decode
+        Returns:
+            (tuple): tuple containing:
+                x: decoded token score before softmax (batch, maxlen_out,
+                    vocab_size) if use_output_layer is True,
+                torch.tensor(0.0), in order to unify api with bidirectional decoder
+                olens: (batch, )
+        """
+        tgt = ys_in_pad
+        maxlen = tgt.size(1)
+        # tgt_mask: (B, 1, L)
+        tgt_mask = ~make_pad_mask(ys_in_lens, maxlen).unsqueeze(1)
+        tgt_mask = tgt_mask.to(tgt.device)
+        # m: (1, L, L)
+        m = subsequent_mask(tgt_mask.size(-1), device=tgt_mask.device).unsqueeze(0)
+        # tgt_mask: (B, L, L)
+        tgt_mask = tgt_mask & m
+        x, _ = self.embed(tgt)
+        for layer in self.decoders:
+            x, tgt_mask, memory, memory_mask = layer(x, tgt_mask, memory, memory_mask)
+        if self.normalize_before:
+            x = self.after_norm(x)
+        if self.use_output_layer:
+            x = self.output_layer(x)
+        olens = tgt_mask.sum(1)
+        return x, torch.tensor(0.0), olens
+
+    def forward_one_step(
+        self,
+        memory: torch.Tensor,
+        memory_mask: torch.Tensor,
+        tgt: torch.Tensor,
+        tgt_mask: torch.Tensor,
+        cache: Optional[List[torch.Tensor]] = None,
+    ) -> Tuple[torch.Tensor, List[torch.Tensor]]:
+        """Forward one step.
+            This is only used for decoding.
+        Args:
+            memory: encoded memory, float32  (batch, maxlen_in, feat)
+            memory_mask: encoded memory mask, (batch, 1, maxlen_in)
+            tgt: input token ids, int64 (batch, maxlen_out)
+            tgt_mask: input token mask,  (batch, maxlen_out)
+                      dtype=torch.uint8 in PyTorch 1.2-
+                      dtype=torch.bool in PyTorch 1.2+ (include 1.2)
+            cache: cached output list of (batch, max_time_out-1, size)
+        Returns:
+            y, cache: NN output value and cache per `self.decoders`.
+            y.shape` is (batch, maxlen_out, token)
+        """
+        x, _ = self.embed(tgt)
+        new_cache = []
+        for i, decoder in enumerate(self.decoders):
+            if cache is None:
+                c = None
+            else:
+                c = cache[i]
+            x, tgt_mask, memory, memory_mask = decoder(
+                x, tgt_mask, memory, memory_mask, cache=c
+            )
+            new_cache.append(x)
+        if self.normalize_before:
+            y = self.after_norm(x[:, -1])
+        else:
+            y = x[:, -1]
+        if self.use_output_layer:
+            y = torch.log_softmax(self.output_layer(y), dim=-1)
+        return y, new_cache
+
+
+class BiTransformerDecoder(torch.nn.Module):
+    """Base class of Transfomer decoder module.
+    Args:
+        vocab_size: output dim
+        encoder_output_size: dimension of attention
+        attention_heads: the number of heads of multi head attention
+        linear_units: the hidden units number of position-wise feedforward
+        num_blocks: the number of decoder blocks
+        r_num_blocks: the number of right to left decoder blocks
+        dropout_rate: dropout rate
+        self_attention_dropout_rate: dropout rate for attention
+        input_layer: input layer type
+        use_output_layer: whether to use output layer
+        pos_enc_class: PositionalEncoding or ScaledPositionalEncoding
+        normalize_before:
+            True: use layer_norm before each sub-block of a layer.
+            False: use layer_norm after each sub-block of a layer.
+    """
+
+    def __init__(
+        self,
+        vocab_size: int,
+        encoder_output_size: int,
+        attention_heads: int = 4,
+        linear_units: int = 2048,
+        num_blocks: int = 6,
+        r_num_blocks: int = 0,
+        dropout_rate: float = 0.1,
+        positional_dropout_rate: float = 0.1,
+        self_attention_dropout_rate: float = 0.0,
+        src_attention_dropout_rate: float = 0.0,
+        input_layer: str = "embed",
+        use_output_layer: bool = True,
+        normalize_before: bool = True,
+    ):
+        super().__init__()
+        self.left_decoder = TransformerDecoder(
+            vocab_size,
+            encoder_output_size,
+            attention_heads,
+            linear_units,
+            num_blocks,
+            dropout_rate,
+            positional_dropout_rate,
+            self_attention_dropout_rate,
+            src_attention_dropout_rate,
+            input_layer,
+            use_output_layer,
+            normalize_before,
+        )
+
+        self.right_decoder = TransformerDecoder(
+            vocab_size,
+            encoder_output_size,
+            attention_heads,
+            linear_units,
+            r_num_blocks,
+            dropout_rate,
+            positional_dropout_rate,
+            self_attention_dropout_rate,
+            src_attention_dropout_rate,
+            input_layer,
+            use_output_layer,
+            normalize_before,
+        )
+
+    def forward(
+        self,
+        memory: torch.Tensor,
+        memory_mask: torch.Tensor,
+        ys_in_pad: torch.Tensor,
+        ys_in_lens: torch.Tensor,
+        r_ys_in_pad: torch.Tensor,
+        reverse_weight: float = 0.0,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Forward decoder.
+        Args:
+            memory: encoded memory, float32  (batch, maxlen_in, feat)
+            memory_mask: encoder memory mask, (batch, 1, maxlen_in)
+            ys_in_pad: padded input token ids, int64 (batch, maxlen_out)
+            ys_in_lens: input lengths of this batch (batch)
+            r_ys_in_pad: padded input token ids, int64 (batch, maxlen_out),
+                used for right to left decoder
+            reverse_weight: used for right to left decoder
+        Returns:
+            (tuple): tuple containing:
+                x: decoded token score before softmax (batch, maxlen_out,
+                    vocab_size) if use_output_layer is True,
+                r_x: x: decoded token score (right to left decoder)
+                    before softmax (batch, maxlen_out, vocab_size)
+                    if use_output_layer is True,
+                olens: (batch, )
+        """
+        l_x, _, olens = self.left_decoder(memory, memory_mask, ys_in_pad, ys_in_lens)
+        r_x = torch.tensor(0.0)
+        if reverse_weight > 0.0:
+            r_x, _, olens = self.right_decoder(
+                memory, memory_mask, r_ys_in_pad, ys_in_lens
+            )
+        return l_x, r_x, olens
+
+    def forward_one_step(
+        self,
+        memory: torch.Tensor,
+        memory_mask: torch.Tensor,
+        tgt: torch.Tensor,
+        tgt_mask: torch.Tensor,
+        cache: Optional[List[torch.Tensor]] = None,
+    ) -> Tuple[torch.Tensor, List[torch.Tensor]]:
+        """Forward one step.
+            This is only used for decoding.
+        Args:
+            memory: encoded memory, float32  (batch, maxlen_in, feat)
+            memory_mask: encoded memory mask, (batch, 1, maxlen_in)
+            tgt: input token ids, int64 (batch, maxlen_out)
+            tgt_mask: input token mask,  (batch, maxlen_out)
+                      dtype=torch.uint8 in PyTorch 1.2-
+                      dtype=torch.bool in PyTorch 1.2+ (include 1.2)
+            cache: cached output list of (batch, max_time_out-1, size)
+        Returns:
+            y, cache: NN output value and cache per `self.decoders`.
+            y.shape` is (batch, maxlen_out, token)
+        """
+        return self.left_decoder.forward_one_step(
+            memory, memory_mask, tgt, tgt_mask, cache
+        )
diff --git a/modules/wenet_extractor/transformer/decoder_layer.py b/modules/wenet_extractor/transformer/decoder_layer.py
new file mode 100755
index 0000000000000000000000000000000000000000..fc4171c47d4cccddade323f04ee1730b98feff76
--- /dev/null
+++ b/modules/wenet_extractor/transformer/decoder_layer.py
@@ -0,0 +1,140 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+"""Decoder self-attention layer definition."""
+from typing import Optional, Tuple
+
+import torch
+from torch import nn
+
+
+class DecoderLayer(nn.Module):
+    """Single decoder layer module.
+
+    Args:
+        size (int): Input dimension.
+        self_attn (torch.nn.Module): Self-attention module instance.
+            `MultiHeadedAttention` instance can be used as the argument.
+        src_attn (torch.nn.Module): Inter-attention module instance.
+            `MultiHeadedAttention` instance can be used as the argument.
+            If `None` is passed, Inter-attention is not used, such as
+            CIF, GPT, and other decoder only model.
+        feed_forward (torch.nn.Module): Feed-forward module instance.
+            `PositionwiseFeedForward` instance can be used as the argument.
+        dropout_rate (float): Dropout rate.
+        normalize_before (bool):
+            True: use layer_norm before each sub-block.
+            False: to use layer_norm after each sub-block.
+    """
+
+    def __init__(
+        self,
+        size: int,
+        self_attn: nn.Module,
+        src_attn: Optional[nn.Module],
+        feed_forward: nn.Module,
+        dropout_rate: float,
+        normalize_before: bool = True,
+    ):
+        """Construct an DecoderLayer object."""
+        super().__init__()
+        self.size = size
+        self.self_attn = self_attn
+        self.src_attn = src_attn
+        self.feed_forward = feed_forward
+        self.norm1 = nn.LayerNorm(size, eps=1e-5)
+        self.norm2 = nn.LayerNorm(size, eps=1e-5)
+        self.norm3 = nn.LayerNorm(size, eps=1e-5)
+        self.dropout = nn.Dropout(dropout_rate)
+        self.normalize_before = normalize_before
+
+    def forward(
+        self,
+        tgt: torch.Tensor,
+        tgt_mask: torch.Tensor,
+        memory: torch.Tensor,
+        memory_mask: torch.Tensor,
+        cache: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Compute decoded features.
+
+        Args:
+            tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size).
+            tgt_mask (torch.Tensor): Mask for input tensor
+                (#batch, maxlen_out).
+            memory (torch.Tensor): Encoded memory
+                (#batch, maxlen_in, size).
+            memory_mask (torch.Tensor): Encoded memory mask
+                (#batch, maxlen_in).
+            cache (torch.Tensor): cached tensors.
+                (#batch, maxlen_out - 1, size).
+
+        Returns:
+            torch.Tensor: Output tensor (#batch, maxlen_out, size).
+            torch.Tensor: Mask for output tensor (#batch, maxlen_out).
+            torch.Tensor: Encoded memory (#batch, maxlen_in, size).
+            torch.Tensor: Encoded memory mask (#batch, maxlen_in).
+
+        """
+        residual = tgt
+        if self.normalize_before:
+            tgt = self.norm1(tgt)
+
+        if cache is None:
+            tgt_q = tgt
+            tgt_q_mask = tgt_mask
+        else:
+            # compute only the last frame query keeping dim: max_time_out -> 1
+            assert cache.shape == (
+                tgt.shape[0],
+                tgt.shape[1] - 1,
+                self.size,
+            ), "{cache.shape} == {(tgt.shape[0], tgt.shape[1] - 1, self.size)}"
+            tgt_q = tgt[:, -1:, :]
+            residual = residual[:, -1:, :]
+            tgt_q_mask = tgt_mask[:, -1:, :]
+
+        x = residual + self.dropout(self.self_attn(tgt_q, tgt, tgt, tgt_q_mask)[0])
+        if not self.normalize_before:
+            x = self.norm1(x)
+
+        if self.src_attn is not None:
+            residual = x
+            if self.normalize_before:
+                x = self.norm2(x)
+            x = residual + self.dropout(
+                self.src_attn(x, memory, memory, memory_mask)[0]
+            )
+            if not self.normalize_before:
+                x = self.norm2(x)
+
+        residual = x
+        if self.normalize_before:
+            x = self.norm3(x)
+        x = residual + self.dropout(self.feed_forward(x))
+        if not self.normalize_before:
+            x = self.norm3(x)
+
+        if cache is not None:
+            x = torch.cat([cache, x], dim=1)
+
+        return x, tgt_mask, memory, memory_mask
diff --git a/modules/wenet_extractor/transformer/embedding.py b/modules/wenet_extractor/transformer/embedding.py
new file mode 100755
index 0000000000000000000000000000000000000000..55ee113f8d267875f147a55326883c207cc0c646
--- /dev/null
+++ b/modules/wenet_extractor/transformer/embedding.py
@@ -0,0 +1,174 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+"""Positonal Encoding Module."""
+
+import math
+from typing import Tuple, Union
+
+import torch
+import torch.nn.functional as F
+
+
+class PositionalEncoding(torch.nn.Module):
+    """Positional encoding.
+
+    :param int d_model: embedding dim
+    :param float dropout_rate: dropout rate
+    :param int max_len: maximum input length
+
+    PE(pos, 2i)   = sin(pos/(10000^(2i/dmodel)))
+    PE(pos, 2i+1) = cos(pos/(10000^(2i/dmodel)))
+    """
+
+    def __init__(
+        self,
+        d_model: int,
+        dropout_rate: float,
+        max_len: int = 5000,
+        reverse: bool = False,
+    ):
+        """Construct an PositionalEncoding object."""
+        super().__init__()
+        self.d_model = d_model
+        self.xscale = math.sqrt(self.d_model)
+        self.dropout = torch.nn.Dropout(p=dropout_rate)
+        self.max_len = max_len
+
+        self.pe = torch.zeros(self.max_len, self.d_model)
+        position = torch.arange(0, self.max_len, dtype=torch.float32).unsqueeze(1)
+        div_term = torch.exp(
+            torch.arange(0, self.d_model, 2, dtype=torch.float32)
+            * -(math.log(10000.0) / self.d_model)
+        )
+        self.pe[:, 0::2] = torch.sin(position * div_term)
+        self.pe[:, 1::2] = torch.cos(position * div_term)
+        self.pe = self.pe.unsqueeze(0)
+
+    def forward(
+        self, x: torch.Tensor, offset: Union[int, torch.Tensor] = 0
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Add positional encoding.
+
+        Args:
+            x (torch.Tensor): Input. Its shape is (batch, time, ...)
+            offset (int, torch.tensor): position offset
+
+        Returns:
+            torch.Tensor: Encoded tensor. Its shape is (batch, time, ...)
+            torch.Tensor: for compatibility to RelPositionalEncoding
+        """
+
+        self.pe = self.pe.to(x.device)
+        pos_emb = self.position_encoding(offset, x.size(1), False)
+        x = x * self.xscale + pos_emb
+        return self.dropout(x), self.dropout(pos_emb)
+
+    def position_encoding(
+        self, offset: Union[int, torch.Tensor], size: int, apply_dropout: bool = True
+    ) -> torch.Tensor:
+        """For getting encoding in a streaming fashion
+
+        Attention!!!!!
+        we apply dropout only once at the whole utterance level in a none
+        streaming way, but will call this function several times with
+        increasing input size in a streaming scenario, so the dropout will
+        be applied several times.
+
+        Args:
+            offset (int or torch.tensor): start offset
+            size (int): required size of position encoding
+
+        Returns:
+            torch.Tensor: Corresponding encoding
+        """
+        # How to subscript a Union type:
+        #   https://github.com/pytorch/pytorch/issues/69434
+        if isinstance(offset, int):
+            assert offset + size < self.max_len
+            pos_emb = self.pe[:, offset : offset + size]
+        elif isinstance(offset, torch.Tensor) and offset.dim() == 0:  # scalar
+            assert offset + size < self.max_len
+            pos_emb = self.pe[:, offset : offset + size]
+        else:  # for batched streaming decoding on GPU
+            assert torch.max(offset) + size < self.max_len
+            index = offset.unsqueeze(1) + torch.arange(0, size).to(
+                offset.device
+            )  # B X T
+            flag = index > 0
+            # remove negative offset
+            index = index * flag
+            pos_emb = F.embedding(index, self.pe[0])  # B X T X d_model
+
+        if apply_dropout:
+            pos_emb = self.dropout(pos_emb)
+        return pos_emb
+
+
+class RelPositionalEncoding(PositionalEncoding):
+    """Relative positional encoding module.
+    See : Appendix B in https://arxiv.org/abs/1901.02860
+    Args:
+        d_model (int): Embedding dimension.
+        dropout_rate (float): Dropout rate.
+        max_len (int): Maximum input length.
+    """
+
+    def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000):
+        """Initialize class."""
+        super().__init__(d_model, dropout_rate, max_len, reverse=True)
+
+    def forward(
+        self, x: torch.Tensor, offset: Union[int, torch.Tensor] = 0
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Compute positional encoding.
+        Args:
+            x (torch.Tensor): Input tensor (batch, time, `*`).
+        Returns:
+            torch.Tensor: Encoded tensor (batch, time, `*`).
+            torch.Tensor: Positional embedding tensor (1, time, `*`).
+        """
+        self.pe = self.pe.to(x.device)
+        x = x * self.xscale
+        pos_emb = self.position_encoding(offset, x.size(1), False)
+        return self.dropout(x), self.dropout(pos_emb)
+
+
+class NoPositionalEncoding(torch.nn.Module):
+    """No position encoding"""
+
+    def __init__(self, d_model: int, dropout_rate: float):
+        super().__init__()
+        self.d_model = d_model
+        self.dropout = torch.nn.Dropout(p=dropout_rate)
+
+    def forward(
+        self, x: torch.Tensor, offset: Union[int, torch.Tensor] = 0
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Just return zero vector for interface compatibility"""
+        pos_emb = torch.zeros(1, x.size(1), self.d_model).to(x.device)
+        return self.dropout(x), pos_emb
+
+    def position_encoding(
+        self, offset: Union[int, torch.Tensor], size: int
+    ) -> torch.Tensor:
+        return torch.zeros(1, size, self.d_model)
diff --git a/modules/wenet_extractor/transformer/encoder.py b/modules/wenet_extractor/transformer/encoder.py
new file mode 100755
index 0000000000000000000000000000000000000000..c0563262afec7d74e6a1d26a46c3f03376f290fd
--- /dev/null
+++ b/modules/wenet_extractor/transformer/encoder.py
@@ -0,0 +1,503 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+"""Encoder definition."""
+from typing import Tuple
+
+import torch
+
+from modules.wenet_extractor.transformer.attention import MultiHeadedAttention
+from modules.wenet_extractor.transformer.attention import (
+    RelPositionMultiHeadedAttention,
+)
+from modules.wenet_extractor.transformer.convolution import ConvolutionModule
+from modules.wenet_extractor.transformer.embedding import PositionalEncoding
+from modules.wenet_extractor.transformer.embedding import RelPositionalEncoding
+from modules.wenet_extractor.transformer.embedding import NoPositionalEncoding
+from modules.wenet_extractor.transformer.encoder_layer import TransformerEncoderLayer
+from modules.wenet_extractor.transformer.encoder_layer import ConformerEncoderLayer
+from modules.wenet_extractor.transformer.positionwise_feed_forward import (
+    PositionwiseFeedForward,
+)
+from modules.wenet_extractor.transformer.subsampling import Conv2dSubsampling4
+from modules.wenet_extractor.transformer.subsampling import Conv2dSubsampling6
+from modules.wenet_extractor.transformer.subsampling import Conv2dSubsampling8
+from modules.wenet_extractor.transformer.subsampling import LinearNoSubsampling
+from modules.wenet_extractor.utils.common import get_activation
+from modules.wenet_extractor.utils.mask import make_pad_mask
+from modules.wenet_extractor.utils.mask import add_optional_chunk_mask
+
+
+class BaseEncoder(torch.nn.Module):
+    def __init__(
+        self,
+        input_size: int,
+        output_size: int = 256,
+        attention_heads: int = 4,
+        linear_units: int = 2048,
+        num_blocks: int = 6,
+        dropout_rate: float = 0.1,
+        positional_dropout_rate: float = 0.1,
+        attention_dropout_rate: float = 0.0,
+        input_layer: str = "conv2d",
+        pos_enc_layer_type: str = "abs_pos",
+        normalize_before: bool = True,
+        static_chunk_size: int = 0,
+        use_dynamic_chunk: bool = False,
+        global_cmvn: torch.nn.Module = None,
+        use_dynamic_left_chunk: bool = False,
+    ):
+        """
+        Args:
+            input_size (int): input dim
+            output_size (int): dimension of attention
+            attention_heads (int): the number of heads of multi head attention
+            linear_units (int): the hidden units number of position-wise feed
+                forward
+            num_blocks (int): the number of decoder blocks
+            dropout_rate (float): dropout rate
+            attention_dropout_rate (float): dropout rate in attention
+            positional_dropout_rate (float): dropout rate after adding
+                positional encoding
+            input_layer (str): input layer type.
+                optional [linear, conv2d, conv2d6, conv2d8]
+            pos_enc_layer_type (str): Encoder positional encoding layer type.
+                opitonal [abs_pos, scaled_abs_pos, rel_pos, no_pos]
+            normalize_before (bool):
+                True: use layer_norm before each sub-block of a layer.
+                False: use layer_norm after each sub-block of a layer.
+            static_chunk_size (int): chunk size for static chunk training and
+                decoding
+            use_dynamic_chunk (bool): whether use dynamic chunk size for
+                training or not, You can only use fixed chunk(chunk_size > 0)
+                or dyanmic chunk size(use_dynamic_chunk = True)
+            global_cmvn (Optional[torch.nn.Module]): Optional GlobalCMVN module
+            use_dynamic_left_chunk (bool): whether use dynamic left chunk in
+                dynamic chunk training
+        """
+        super().__init__()
+        self._output_size = output_size
+
+        if pos_enc_layer_type == "abs_pos":
+            pos_enc_class = PositionalEncoding
+        elif pos_enc_layer_type == "rel_pos":
+            pos_enc_class = RelPositionalEncoding
+        elif pos_enc_layer_type == "no_pos":
+            pos_enc_class = NoPositionalEncoding
+        else:
+            raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type)
+
+        if input_layer == "linear":
+            subsampling_class = LinearNoSubsampling
+        elif input_layer == "conv2d":
+            subsampling_class = Conv2dSubsampling4
+        elif input_layer == "conv2d6":
+            subsampling_class = Conv2dSubsampling6
+        elif input_layer == "conv2d8":
+            subsampling_class = Conv2dSubsampling8
+        else:
+            raise ValueError("unknown input_layer: " + input_layer)
+
+        self.global_cmvn = global_cmvn
+        self.embed = subsampling_class(
+            input_size,
+            output_size,
+            dropout_rate,
+            pos_enc_class(output_size, positional_dropout_rate),
+        )
+
+        self.normalize_before = normalize_before
+        self.after_norm = torch.nn.LayerNorm(output_size, eps=1e-5)
+        self.static_chunk_size = static_chunk_size
+        self.use_dynamic_chunk = use_dynamic_chunk
+        self.use_dynamic_left_chunk = use_dynamic_left_chunk
+
+    def output_size(self) -> int:
+        return self._output_size
+
+    def forward(
+        self,
+        xs: torch.Tensor,
+        xs_lens: torch.Tensor,
+        decoding_chunk_size: int = 0,
+        num_decoding_left_chunks: int = -1,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Embed positions in tensor.
+
+        Args:
+            xs: padded input tensor (B, T, D)
+            xs_lens: input length (B)
+            decoding_chunk_size: decoding chunk size for dynamic chunk
+                0: default for training, use random dynamic chunk.
+                <0: for decoding, use full chunk.
+                >0: for decoding, use fixed chunk size as set.
+            num_decoding_left_chunks: number of left chunks, this is for decoding,
+            the chunk size is decoding_chunk_size.
+                >=0: use num_decoding_left_chunks
+                <0: use all left chunks
+        Returns:
+            encoder output tensor xs, and subsampled masks
+            xs: padded output tensor (B, T' ~= T/subsample_rate, D)
+            masks: torch.Tensor batch padding mask after subsample
+                (B, 1, T' ~= T/subsample_rate)
+        """
+        T = xs.size(1)
+        masks = ~make_pad_mask(xs_lens, T).unsqueeze(1)  # (B, 1, T)
+        if self.global_cmvn is not None:
+            xs = self.global_cmvn(xs)
+        xs, pos_emb, masks = self.embed(xs, masks)
+        mask_pad = masks  # (B, 1, T/subsample_rate)
+        chunk_masks = add_optional_chunk_mask(
+            xs,
+            masks,
+            self.use_dynamic_chunk,
+            self.use_dynamic_left_chunk,
+            decoding_chunk_size,
+            self.static_chunk_size,
+            num_decoding_left_chunks,
+        )
+        for layer in self.encoders:
+            xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad)
+        if self.normalize_before:
+            xs = self.after_norm(xs)
+        # Here we assume the mask is not changed in encoder layers, so just
+        # return the masks before encoder layers, and the masks will be used
+        # for cross attention with decoder later
+        return xs, masks
+
+    def forward_chunk(
+        self,
+        xs: torch.Tensor,
+        offset: int,
+        required_cache_size: int,
+        att_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
+        cnn_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
+        att_mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """ Forward just one chunk
+
+        Args:
+            xs (torch.Tensor): chunk input, with shape (b=1, time, mel-dim),
+                where `time == (chunk_size - 1) * subsample_rate + \
+                        subsample.right_context + 1`
+            offset (int): current offset in encoder output time stamp
+            required_cache_size (int): cache size required for next chunk
+                compuation
+                >=0: actual cache size
+                <0: means all history cache is required
+            att_cache (torch.Tensor): cache tensor for KEY & VALUE in
+                transformer/conformer attention, with shape
+                (elayers, head, cache_t1, d_k * 2), where
+                `head * d_k == hidden-dim` and
+                `cache_t1 == chunk_size * num_decoding_left_chunks`.
+            cnn_cache (torch.Tensor): cache tensor for cnn_module in conformer,
+                (elayers, b=1, hidden-dim, cache_t2), where
+                `cache_t2 == cnn.lorder - 1`
+
+        Returns:
+            torch.Tensor: output of current input xs,
+                with shape (b=1, chunk_size, hidden-dim).
+            torch.Tensor: new attention cache required for next chunk, with
+                dynamic shape (elayers, head, ?, d_k * 2)
+                depending on required_cache_size.
+            torch.Tensor: new conformer cnn cache required for next chunk, with
+                same shape as the original cnn_cache.
+
+        """
+        assert xs.size(0) == 1
+        # tmp_masks is just for interface compatibility
+        tmp_masks = torch.ones(1, xs.size(1), device=xs.device, dtype=torch.bool)
+        tmp_masks = tmp_masks.unsqueeze(1)
+        if self.global_cmvn is not None:
+            xs = self.global_cmvn(xs)
+        # NOTE(xcsong): Before embed, shape(xs) is (b=1, time, mel-dim)
+        xs, pos_emb, _ = self.embed(xs, tmp_masks, offset)
+        # NOTE(xcsong): After  embed, shape(xs) is (b=1, chunk_size, hidden-dim)
+        elayers, cache_t1 = att_cache.size(0), att_cache.size(2)
+        chunk_size = xs.size(1)
+        attention_key_size = cache_t1 + chunk_size
+        pos_emb = self.embed.position_encoding(
+            offset=offset - cache_t1, size=attention_key_size
+        )
+        if required_cache_size < 0:
+            next_cache_start = 0
+        elif required_cache_size == 0:
+            next_cache_start = attention_key_size
+        else:
+            next_cache_start = max(attention_key_size - required_cache_size, 0)
+        r_att_cache = []
+        r_cnn_cache = []
+        for i, layer in enumerate(self.encoders):
+            # NOTE(xcsong): Before layer.forward
+            #   shape(att_cache[i:i + 1]) is (1, head, cache_t1, d_k * 2),
+            #   shape(cnn_cache[i])       is (b=1, hidden-dim, cache_t2)
+            xs, _, new_att_cache, new_cnn_cache = layer(
+                xs,
+                att_mask,
+                pos_emb,
+                att_cache=att_cache[i : i + 1] if elayers > 0 else att_cache,
+                cnn_cache=cnn_cache[i] if cnn_cache.size(0) > 0 else cnn_cache,
+            )
+            # NOTE(xcsong): After layer.forward
+            #   shape(new_att_cache) is (1, head, attention_key_size, d_k * 2),
+            #   shape(new_cnn_cache) is (b=1, hidden-dim, cache_t2)
+            r_att_cache.append(new_att_cache[:, :, next_cache_start:, :])
+            r_cnn_cache.append(new_cnn_cache.unsqueeze(0))
+        if self.normalize_before:
+            xs = self.after_norm(xs)
+
+        # NOTE(xcsong): shape(r_att_cache) is (elayers, head, ?, d_k * 2),
+        #   ? may be larger than cache_t1, it depends on required_cache_size
+        r_att_cache = torch.cat(r_att_cache, dim=0)
+        # NOTE(xcsong): shape(r_cnn_cache) is (e, b=1, hidden-dim, cache_t2)
+        r_cnn_cache = torch.cat(r_cnn_cache, dim=0)
+
+        return (xs, r_att_cache, r_cnn_cache)
+
+    def forward_chunk_by_chunk(
+        self,
+        xs: torch.Tensor,
+        decoding_chunk_size: int,
+        num_decoding_left_chunks: int = -1,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Forward input chunk by chunk with chunk_size like a streaming
+            fashion
+
+        Here we should pay special attention to computation cache in the
+        streaming style forward chunk by chunk. Three things should be taken
+        into account for computation in the current network:
+            1. transformer/conformer encoder layers output cache
+            2. convolution in conformer
+            3. convolution in subsampling
+
+        However, we don't implement subsampling cache for:
+            1. We can control subsampling module to output the right result by
+               overlapping input instead of cache left context, even though it
+               wastes some computation, but subsampling only takes a very
+               small fraction of computation in the whole model.
+            2. Typically, there are several covolution layers with subsampling
+               in subsampling module, it is tricky and complicated to do cache
+               with different convolution layers with different subsampling
+               rate.
+            3. Currently, nn.Sequential is used to stack all the convolution
+               layers in subsampling, we need to rewrite it to make it work
+               with cache, which is not prefered.
+        Args:
+            xs (torch.Tensor): (1, max_len, dim)
+            chunk_size (int): decoding chunk size
+        """
+        assert decoding_chunk_size > 0
+        # The model is trained by static or dynamic chunk
+        assert self.static_chunk_size > 0 or self.use_dynamic_chunk
+        subsampling = self.embed.subsampling_rate
+        context = self.embed.right_context + 1  # Add current frame
+        stride = subsampling * decoding_chunk_size
+        decoding_window = (decoding_chunk_size - 1) * subsampling + context
+        num_frames = xs.size(1)
+        att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device)
+        cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device)
+        outputs = []
+        offset = 0
+        required_cache_size = decoding_chunk_size * num_decoding_left_chunks
+
+        # Feed forward overlap input step by step
+        for cur in range(0, num_frames - context + 1, stride):
+            end = min(cur + decoding_window, num_frames)
+            chunk_xs = xs[:, cur:end, :]
+            (y, att_cache, cnn_cache) = self.forward_chunk(
+                chunk_xs, offset, required_cache_size, att_cache, cnn_cache
+            )
+            outputs.append(y)
+            offset += y.size(1)
+        ys = torch.cat(outputs, 1)
+        masks = torch.ones((1, 1, ys.size(1)), device=ys.device, dtype=torch.bool)
+        return ys, masks
+
+
+class TransformerEncoder(BaseEncoder):
+    """Transformer encoder module."""
+
+    def __init__(
+        self,
+        input_size: int,
+        output_size: int = 256,
+        attention_heads: int = 4,
+        linear_units: int = 2048,
+        num_blocks: int = 6,
+        dropout_rate: float = 0.1,
+        positional_dropout_rate: float = 0.1,
+        attention_dropout_rate: float = 0.0,
+        input_layer: str = "conv2d",
+        pos_enc_layer_type: str = "abs_pos",
+        normalize_before: bool = True,
+        static_chunk_size: int = 0,
+        use_dynamic_chunk: bool = False,
+        global_cmvn: torch.nn.Module = None,
+        use_dynamic_left_chunk: bool = False,
+    ):
+        """Construct TransformerEncoder
+
+        See Encoder for the meaning of each parameter.
+        """
+        super().__init__(
+            input_size,
+            output_size,
+            attention_heads,
+            linear_units,
+            num_blocks,
+            dropout_rate,
+            positional_dropout_rate,
+            attention_dropout_rate,
+            input_layer,
+            pos_enc_layer_type,
+            normalize_before,
+            static_chunk_size,
+            use_dynamic_chunk,
+            global_cmvn,
+            use_dynamic_left_chunk,
+        )
+        self.encoders = torch.nn.ModuleList(
+            [
+                TransformerEncoderLayer(
+                    output_size,
+                    MultiHeadedAttention(
+                        attention_heads, output_size, attention_dropout_rate
+                    ),
+                    PositionwiseFeedForward(output_size, linear_units, dropout_rate),
+                    dropout_rate,
+                    normalize_before,
+                )
+                for _ in range(num_blocks)
+            ]
+        )
+
+
+class ConformerEncoder(BaseEncoder):
+    """Conformer encoder module."""
+
+    def __init__(
+        self,
+        input_size: int,
+        output_size: int = 256,
+        attention_heads: int = 4,
+        linear_units: int = 2048,
+        num_blocks: int = 6,
+        dropout_rate: float = 0.1,
+        positional_dropout_rate: float = 0.1,
+        attention_dropout_rate: float = 0.0,
+        input_layer: str = "conv2d",
+        pos_enc_layer_type: str = "rel_pos",
+        normalize_before: bool = True,
+        static_chunk_size: int = 0,
+        use_dynamic_chunk: bool = False,
+        global_cmvn: torch.nn.Module = None,
+        use_dynamic_left_chunk: bool = False,
+        positionwise_conv_kernel_size: int = 1,
+        macaron_style: bool = True,
+        selfattention_layer_type: str = "rel_selfattn",
+        activation_type: str = "swish",
+        use_cnn_module: bool = True,
+        cnn_module_kernel: int = 15,
+        causal: bool = False,
+        cnn_module_norm: str = "batch_norm",
+    ):
+        """Construct ConformerEncoder
+
+        Args:
+            input_size to use_dynamic_chunk, see in BaseEncoder
+            positionwise_conv_kernel_size (int): Kernel size of positionwise
+                conv1d layer.
+            macaron_style (bool): Whether to use macaron style for
+                positionwise layer.
+            selfattention_layer_type (str): Encoder attention layer type,
+                the parameter has no effect now, it's just for configure
+                compatibility.
+            activation_type (str): Encoder activation function type.
+            use_cnn_module (bool): Whether to use convolution module.
+            cnn_module_kernel (int): Kernel size of convolution module.
+            causal (bool): whether to use causal convolution or not.
+        """
+        super().__init__(
+            input_size,
+            output_size,
+            attention_heads,
+            linear_units,
+            num_blocks,
+            dropout_rate,
+            positional_dropout_rate,
+            attention_dropout_rate,
+            input_layer,
+            pos_enc_layer_type,
+            normalize_before,
+            static_chunk_size,
+            use_dynamic_chunk,
+            global_cmvn,
+            use_dynamic_left_chunk,
+        )
+        activation = get_activation(activation_type)
+
+        # self-attention module definition
+        if pos_enc_layer_type != "rel_pos":
+            encoder_selfattn_layer = MultiHeadedAttention
+        else:
+            encoder_selfattn_layer = RelPositionMultiHeadedAttention
+        encoder_selfattn_layer_args = (
+            attention_heads,
+            output_size,
+            attention_dropout_rate,
+        )
+        # feed-forward module definition
+        positionwise_layer = PositionwiseFeedForward
+        positionwise_layer_args = (
+            output_size,
+            linear_units,
+            dropout_rate,
+            activation,
+        )
+        # convolution module definition
+        convolution_layer = ConvolutionModule
+        convolution_layer_args = (
+            output_size,
+            cnn_module_kernel,
+            activation,
+            cnn_module_norm,
+            causal,
+        )
+
+        self.encoders = torch.nn.ModuleList(
+            [
+                ConformerEncoderLayer(
+                    output_size,
+                    encoder_selfattn_layer(*encoder_selfattn_layer_args),
+                    positionwise_layer(*positionwise_layer_args),
+                    positionwise_layer(*positionwise_layer_args)
+                    if macaron_style
+                    else None,
+                    convolution_layer(*convolution_layer_args)
+                    if use_cnn_module
+                    else None,
+                    dropout_rate,
+                    normalize_before,
+                )
+                for _ in range(num_blocks)
+            ]
+        )
diff --git a/modules/wenet_extractor/transformer/encoder_layer.py b/modules/wenet_extractor/transformer/encoder_layer.py
new file mode 100755
index 0000000000000000000000000000000000000000..6b2ebb2f4fc9cdb5f6cd6c2401597638aa27fa4c
--- /dev/null
+++ b/modules/wenet_extractor/transformer/encoder_layer.py
@@ -0,0 +1,242 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+"""Encoder self-attention layer definition."""
+
+from typing import Optional, Tuple
+
+import torch
+from torch import nn
+
+
+class TransformerEncoderLayer(nn.Module):
+    """Encoder layer module.
+
+    Args:
+        size (int): Input dimension.
+        self_attn (torch.nn.Module): Self-attention module instance.
+            `MultiHeadedAttention` or `RelPositionMultiHeadedAttention`
+            instance can be used as the argument.
+        feed_forward (torch.nn.Module): Feed-forward module instance.
+            `PositionwiseFeedForward`, instance can be used as the argument.
+        dropout_rate (float): Dropout rate.
+        normalize_before (bool):
+            True: use layer_norm before each sub-block.
+            False: to use layer_norm after each sub-block.
+    """
+
+    def __init__(
+        self,
+        size: int,
+        self_attn: torch.nn.Module,
+        feed_forward: torch.nn.Module,
+        dropout_rate: float,
+        normalize_before: bool = True,
+    ):
+        """Construct an EncoderLayer object."""
+        super().__init__()
+        self.self_attn = self_attn
+        self.feed_forward = feed_forward
+        self.norm1 = nn.LayerNorm(size, eps=1e-5)
+        self.norm2 = nn.LayerNorm(size, eps=1e-5)
+        self.dropout = nn.Dropout(dropout_rate)
+        self.size = size
+        self.normalize_before = normalize_before
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        mask: torch.Tensor,
+        pos_emb: torch.Tensor,
+        mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+        att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
+        cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Compute encoded features.
+
+        Args:
+            x (torch.Tensor): (#batch, time, size)
+            mask (torch.Tensor): Mask tensor for the input (#batch, time，time),
+                (0, 0, 0) means fake mask.
+            pos_emb (torch.Tensor): just for interface compatibility
+                to ConformerEncoderLayer
+            mask_pad (torch.Tensor): does not used in transformer layer,
+                just for unified api with conformer.
+            att_cache (torch.Tensor): Cache tensor of the KEY & VALUE
+                (#batch=1, head, cache_t1, d_k * 2), head * d_k == size.
+            cnn_cache (torch.Tensor): Convolution cache in conformer layer
+                (#batch=1, size, cache_t2), not used here, it's for interface
+                compatibility to ConformerEncoderLayer.
+        Returns:
+            torch.Tensor: Output tensor (#batch, time, size).
+            torch.Tensor: Mask tensor (#batch, time, time).
+            torch.Tensor: att_cache tensor,
+                (#batch=1, head, cache_t1 + time, d_k * 2).
+            torch.Tensor: cnn_cahce tensor (#batch=1, size, cache_t2).
+
+        """
+        residual = x
+        if self.normalize_before:
+            x = self.norm1(x)
+        x_att, new_att_cache = self.self_attn(x, x, x, mask, cache=att_cache)
+        x = residual + self.dropout(x_att)
+        if not self.normalize_before:
+            x = self.norm1(x)
+
+        residual = x
+        if self.normalize_before:
+            x = self.norm2(x)
+        x = residual + self.dropout(self.feed_forward(x))
+        if not self.normalize_before:
+            x = self.norm2(x)
+
+        fake_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device)
+        return x, mask, new_att_cache, fake_cnn_cache
+
+
+class ConformerEncoderLayer(nn.Module):
+    """Encoder layer module.
+    Args:
+        size (int): Input dimension.
+        self_attn (torch.nn.Module): Self-attention module instance.
+            `MultiHeadedAttention` or `RelPositionMultiHeadedAttention`
+            instance can be used as the argument.
+        feed_forward (torch.nn.Module): Feed-forward module instance.
+            `PositionwiseFeedForward` instance can be used as the argument.
+        feed_forward_macaron (torch.nn.Module): Additional feed-forward module
+             instance.
+            `PositionwiseFeedForward` instance can be used as the argument.
+        conv_module (torch.nn.Module): Convolution module instance.
+            `ConvlutionModule` instance can be used as the argument.
+        dropout_rate (float): Dropout rate.
+        normalize_before (bool):
+            True: use layer_norm before each sub-block.
+            False: use layer_norm after each sub-block.
+    """
+
+    def __init__(
+        self,
+        size: int,
+        self_attn: torch.nn.Module,
+        feed_forward: Optional[nn.Module] = None,
+        feed_forward_macaron: Optional[nn.Module] = None,
+        conv_module: Optional[nn.Module] = None,
+        dropout_rate: float = 0.1,
+        normalize_before: bool = True,
+    ):
+        """Construct an EncoderLayer object."""
+        super().__init__()
+        self.self_attn = self_attn
+        self.feed_forward = feed_forward
+        self.feed_forward_macaron = feed_forward_macaron
+        self.conv_module = conv_module
+        self.norm_ff = nn.LayerNorm(size, eps=1e-5)  # for the FNN module
+        self.norm_mha = nn.LayerNorm(size, eps=1e-5)  # for the MHA module
+        if feed_forward_macaron is not None:
+            self.norm_ff_macaron = nn.LayerNorm(size, eps=1e-5)
+            self.ff_scale = 0.5
+        else:
+            self.ff_scale = 1.0
+        if self.conv_module is not None:
+            self.norm_conv = nn.LayerNorm(size, eps=1e-5)  # for the CNN module
+            self.norm_final = nn.LayerNorm(
+                size, eps=1e-5
+            )  # for the final output of the block
+        self.dropout = nn.Dropout(dropout_rate)
+        self.size = size
+        self.normalize_before = normalize_before
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        mask: torch.Tensor,
+        pos_emb: torch.Tensor,
+        mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+        att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
+        cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Compute encoded features.
+
+        Args:
+            x (torch.Tensor): (#batch, time, size)
+            mask (torch.Tensor): Mask tensor for the input (#batch, time，time),
+                (0, 0, 0) means fake mask.
+            pos_emb (torch.Tensor): positional encoding, must not be None
+                for ConformerEncoderLayer.
+            mask_pad (torch.Tensor): batch padding mask used for conv module.
+                (#batch, 1，time), (0, 0, 0) means fake mask.
+            att_cache (torch.Tensor): Cache tensor of the KEY & VALUE
+                (#batch=1, head, cache_t1, d_k * 2), head * d_k == size.
+            cnn_cache (torch.Tensor): Convolution cache in conformer layer
+                (#batch=1, size, cache_t2)
+        Returns:
+            torch.Tensor: Output tensor (#batch, time, size).
+            torch.Tensor: Mask tensor (#batch, time, time).
+            torch.Tensor: att_cache tensor,
+                (#batch=1, head, cache_t1 + time, d_k * 2).
+            torch.Tensor: cnn_cahce tensor (#batch, size, cache_t2).
+        """
+
+        # whether to use macaron style
+        if self.feed_forward_macaron is not None:
+            residual = x
+            if self.normalize_before:
+                x = self.norm_ff_macaron(x)
+            x = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(x))
+            if not self.normalize_before:
+                x = self.norm_ff_macaron(x)
+
+        # multi-headed self-attention module
+        residual = x
+        if self.normalize_before:
+            x = self.norm_mha(x)
+        x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb, att_cache)
+        x = residual + self.dropout(x_att)
+        if not self.normalize_before:
+            x = self.norm_mha(x)
+
+        # convolution module
+        # Fake new cnn cache here, and then change it in conv_module
+        new_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device)
+        if self.conv_module is not None:
+            residual = x
+            if self.normalize_before:
+                x = self.norm_conv(x)
+            x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache)
+            x = residual + self.dropout(x)
+
+            if not self.normalize_before:
+                x = self.norm_conv(x)
+
+        # feed forward module
+        residual = x
+        if self.normalize_before:
+            x = self.norm_ff(x)
+
+        x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
+        if not self.normalize_before:
+            x = self.norm_ff(x)
+
+        if self.conv_module is not None:
+            x = self.norm_final(x)
+
+        return x, mask, new_att_cache, new_cnn_cache
diff --git a/modules/wenet_extractor/transformer/label_smoothing_loss.py b/modules/wenet_extractor/transformer/label_smoothing_loss.py
new file mode 100755
index 0000000000000000000000000000000000000000..5c2b1cae5923fef66665adf9f84604af03c1ea9a
--- /dev/null
+++ b/modules/wenet_extractor/transformer/label_smoothing_loss.py
@@ -0,0 +1,106 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+"""Label smoothing module."""
+
+import torch
+from torch import nn
+
+
+class LabelSmoothingLoss(nn.Module):
+    """Label-smoothing loss.
+
+    In a standard CE loss, the label's data distribution is:
+    [0,1,2] ->
+    [
+        [1.0, 0.0, 0.0],
+        [0.0, 1.0, 0.0],
+        [0.0, 0.0, 1.0],
+    ]
+
+    In the smoothing version CE Loss,some probabilities
+    are taken from the true label prob (1.0) and are divided
+    among other labels.
+
+    e.g.
+    smoothing=0.1
+    [0,1,2] ->
+    [
+        [0.9, 0.05, 0.05],
+        [0.05, 0.9, 0.05],
+        [0.05, 0.05, 0.9],
+    ]
+
+    Args:
+        size (int): the number of class
+        padding_idx (int): padding class id which will be ignored for loss
+        smoothing (float): smoothing rate (0.0 means the conventional CE)
+        normalize_length (bool):
+            normalize loss by sequence length if True
+            normalize loss by batch size if False
+    """
+
+    def __init__(
+        self,
+        size: int,
+        padding_idx: int,
+        smoothing: float,
+        normalize_length: bool = False,
+    ):
+        """Construct an LabelSmoothingLoss object."""
+        super(LabelSmoothingLoss, self).__init__()
+        self.criterion = nn.KLDivLoss(reduction="none")
+        self.padding_idx = padding_idx
+        self.confidence = 1.0 - smoothing
+        self.smoothing = smoothing
+        self.size = size
+        self.normalize_length = normalize_length
+
+    def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
+        """Compute loss between x and target.
+
+        The model outputs and data labels tensors are flatten to
+        (batch*seqlen, class) shape and a mask is applied to the
+        padding part which should not be calculated for loss.
+
+        Args:
+            x (torch.Tensor): prediction (batch, seqlen, class)
+            target (torch.Tensor):
+                target signal masked with self.padding_id (batch, seqlen)
+        Returns:
+            loss (torch.Tensor) : The KL loss, scalar float value
+        """
+        assert x.size(2) == self.size
+        batch_size = x.size(0)
+        x = x.view(-1, self.size)
+        target = target.view(-1)
+        # use zeros_like instead of torch.no_grad() for true_dist,
+        # since no_grad() can not be exported by JIT
+        true_dist = torch.zeros_like(x)
+        true_dist.fill_(self.smoothing / (self.size - 1))
+        ignore = target == self.padding_idx  # (B,)
+        total = len(target) - ignore.sum().item()
+        target = target.masked_fill(ignore, 0)  # avoid -1 index
+        true_dist.scatter_(1, target.unsqueeze(1), self.confidence)
+        kl = self.criterion(torch.log_softmax(x, dim=1), true_dist)
+        denom = total if self.normalize_length else batch_size
+        return kl.masked_fill(ignore.unsqueeze(1), 0).sum() / denom
diff --git a/modules/wenet_extractor/transformer/positionwise_feed_forward.py b/modules/wenet_extractor/transformer/positionwise_feed_forward.py
new file mode 100755
index 0000000000000000000000000000000000000000..eaaefdc61d392d44e503458deb490690b3183a03
--- /dev/null
+++ b/modules/wenet_extractor/transformer/positionwise_feed_forward.py
@@ -0,0 +1,63 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+"""Positionwise feed forward layer definition."""
+
+import torch
+
+
+class PositionwiseFeedForward(torch.nn.Module):
+    """Positionwise feed forward layer.
+
+    FeedForward are appied on each position of the sequence.
+    The output dim is same with the input dim.
+
+    Args:
+        idim (int): Input dimenstion.
+        hidden_units (int): The number of hidden units.
+        dropout_rate (float): Dropout rate.
+        activation (torch.nn.Module): Activation function
+    """
+
+    def __init__(
+        self,
+        idim: int,
+        hidden_units: int,
+        dropout_rate: float,
+        activation: torch.nn.Module = torch.nn.ReLU(),
+    ):
+        """Construct a PositionwiseFeedForward object."""
+        super(PositionwiseFeedForward, self).__init__()
+        self.w_1 = torch.nn.Linear(idim, hidden_units)
+        self.activation = activation
+        self.dropout = torch.nn.Dropout(dropout_rate)
+        self.w_2 = torch.nn.Linear(hidden_units, idim)
+
+    def forward(self, xs: torch.Tensor) -> torch.Tensor:
+        """Forward function.
+
+        Args:
+            xs: input tensor (B, L, D)
+        Returns:
+            output tensor, (B, L, D)
+        """
+        return self.w_2(self.dropout(self.activation(self.w_1(xs))))
diff --git a/modules/wenet_extractor/transformer/subsampling.py b/modules/wenet_extractor/transformer/subsampling.py
new file mode 100755
index 0000000000000000000000000000000000000000..ffbb5dae0dd013c916cc4a4266cdc838ba48a84f
--- /dev/null
+++ b/modules/wenet_extractor/transformer/subsampling.py
@@ -0,0 +1,257 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+
+"""Subsampling layer definition."""
+
+from typing import Tuple, Union
+
+import torch
+
+
+class BaseSubsampling(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.right_context = 0
+        self.subsampling_rate = 1
+
+    def position_encoding(
+        self, offset: Union[int, torch.Tensor], size: int
+    ) -> torch.Tensor:
+        return self.pos_enc.position_encoding(offset, size)
+
+
+class LinearNoSubsampling(BaseSubsampling):
+    """Linear transform the input without subsampling
+
+    Args:
+        idim (int): Input dimension.
+        odim (int): Output dimension.
+        dropout_rate (float): Dropout rate.
+
+    """
+
+    def __init__(
+        self, idim: int, odim: int, dropout_rate: float, pos_enc_class: torch.nn.Module
+    ):
+        """Construct an linear object."""
+        super().__init__()
+        self.out = torch.nn.Sequential(
+            torch.nn.Linear(idim, odim),
+            torch.nn.LayerNorm(odim, eps=1e-5),
+            torch.nn.Dropout(dropout_rate),
+        )
+        self.pos_enc = pos_enc_class
+        self.right_context = 0
+        self.subsampling_rate = 1
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_mask: torch.Tensor,
+        offset: Union[int, torch.Tensor] = 0,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Input x.
+
+        Args:
+            x (torch.Tensor): Input tensor (#batch, time, idim).
+            x_mask (torch.Tensor): Input mask (#batch, 1, time).
+
+        Returns:
+            torch.Tensor: linear input tensor (#batch, time', odim),
+                where time' = time .
+            torch.Tensor: linear input mask (#batch, 1, time'),
+                where time' = time .
+
+        """
+        x = self.out(x)
+        x, pos_emb = self.pos_enc(x, offset)
+        return x, pos_emb, x_mask
+
+
+class Conv2dSubsampling4(BaseSubsampling):
+    """Convolutional 2D subsampling (to 1/4 length).
+
+    Args:
+        idim (int): Input dimension.
+        odim (int): Output dimension.
+        dropout_rate (float): Dropout rate.
+
+    """
+
+    def __init__(
+        self, idim: int, odim: int, dropout_rate: float, pos_enc_class: torch.nn.Module
+    ):
+        """Construct an Conv2dSubsampling4 object."""
+        super().__init__()
+        self.conv = torch.nn.Sequential(
+            torch.nn.Conv2d(1, odim, 3, 2),
+            torch.nn.ReLU(),
+            torch.nn.Conv2d(odim, odim, 3, 2),
+            torch.nn.ReLU(),
+        )
+        self.out = torch.nn.Sequential(
+            torch.nn.Linear(odim * (((idim - 1) // 2 - 1) // 2), odim)
+        )
+        self.pos_enc = pos_enc_class
+        # The right context for every conv layer is computed by:
+        # (kernel_size - 1) * frame_rate_of_this_layer
+        self.subsampling_rate = 4
+        # 6 = (3 - 1) * 1 + (3 - 1) * 2
+        self.right_context = 6
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_mask: torch.Tensor,
+        offset: Union[int, torch.Tensor] = 0,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Subsample x.
+
+        Args:
+            x (torch.Tensor): Input tensor (#batch, time, idim).
+            x_mask (torch.Tensor): Input mask (#batch, 1, time).
+
+        Returns:
+            torch.Tensor: Subsampled tensor (#batch, time', odim),
+                where time' = time // 4.
+            torch.Tensor: Subsampled mask (#batch, 1, time'),
+                where time' = time // 4.
+            torch.Tensor: positional encoding
+
+        """
+        x = x.unsqueeze(1)  # (b, c=1, t, f)
+        x = self.conv(x)
+        b, c, t, f = x.size()
+        x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
+        x, pos_emb = self.pos_enc(x, offset)
+        return x, pos_emb, x_mask[:, :, 2::2][:, :, 2::2]
+
+
+class Conv2dSubsampling6(BaseSubsampling):
+    """Convolutional 2D subsampling (to 1/6 length).
+    Args:
+        idim (int): Input dimension.
+        odim (int): Output dimension.
+        dropout_rate (float): Dropout rate.
+        pos_enc (torch.nn.Module): Custom position encoding layer.
+    """
+
+    def __init__(
+        self, idim: int, odim: int, dropout_rate: float, pos_enc_class: torch.nn.Module
+    ):
+        """Construct an Conv2dSubsampling6 object."""
+        super().__init__()
+        self.conv = torch.nn.Sequential(
+            torch.nn.Conv2d(1, odim, 3, 2),
+            torch.nn.ReLU(),
+            torch.nn.Conv2d(odim, odim, 5, 3),
+            torch.nn.ReLU(),
+        )
+        self.linear = torch.nn.Linear(odim * (((idim - 1) // 2 - 2) // 3), odim)
+        self.pos_enc = pos_enc_class
+        # 10 = (3 - 1) * 1 + (5 - 1) * 2
+        self.subsampling_rate = 6
+        self.right_context = 10
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_mask: torch.Tensor,
+        offset: Union[int, torch.Tensor] = 0,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Subsample x.
+        Args:
+            x (torch.Tensor): Input tensor (#batch, time, idim).
+            x_mask (torch.Tensor): Input mask (#batch, 1, time).
+
+        Returns:
+            torch.Tensor: Subsampled tensor (#batch, time', odim),
+                where time' = time // 6.
+            torch.Tensor: Subsampled mask (#batch, 1, time'),
+                where time' = time // 6.
+            torch.Tensor: positional encoding
+        """
+        x = x.unsqueeze(1)  # (b, c, t, f)
+        x = self.conv(x)
+        b, c, t, f = x.size()
+        x = self.linear(x.transpose(1, 2).contiguous().view(b, t, c * f))
+        x, pos_emb = self.pos_enc(x, offset)
+        return x, pos_emb, x_mask[:, :, 2::2][:, :, 4::3]
+
+
+class Conv2dSubsampling8(BaseSubsampling):
+    """Convolutional 2D subsampling (to 1/8 length).
+
+    Args:
+        idim (int): Input dimension.
+        odim (int): Output dimension.
+        dropout_rate (float): Dropout rate.
+
+    """
+
+    def __init__(
+        self, idim: int, odim: int, dropout_rate: float, pos_enc_class: torch.nn.Module
+    ):
+        """Construct an Conv2dSubsampling8 object."""
+        super().__init__()
+        self.conv = torch.nn.Sequential(
+            torch.nn.Conv2d(1, odim, 3, 2),
+            torch.nn.ReLU(),
+            torch.nn.Conv2d(odim, odim, 3, 2),
+            torch.nn.ReLU(),
+            torch.nn.Conv2d(odim, odim, 3, 2),
+            torch.nn.ReLU(),
+        )
+        self.linear = torch.nn.Linear(
+            odim * ((((idim - 1) // 2 - 1) // 2 - 1) // 2), odim
+        )
+        self.pos_enc = pos_enc_class
+        self.subsampling_rate = 8
+        # 14 = (3 - 1) * 1 + (3 - 1) * 2 + (3 - 1) * 4
+        self.right_context = 14
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_mask: torch.Tensor,
+        offset: Union[int, torch.Tensor] = 0,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Subsample x.
+
+        Args:
+            x (torch.Tensor): Input tensor (#batch, time, idim).
+            x_mask (torch.Tensor): Input mask (#batch, 1, time).
+
+        Returns:
+            torch.Tensor: Subsampled tensor (#batch, time', odim),
+                where time' = time // 8.
+            torch.Tensor: Subsampled mask (#batch, 1, time'),
+                where time' = time // 8.
+            torch.Tensor: positional encoding
+        """
+        x = x.unsqueeze(1)  # (b, c, t, f)
+        x = self.conv(x)
+        b, c, t, f = x.size()
+        x = self.linear(x.transpose(1, 2).contiguous().view(b, t, c * f))
+        x, pos_emb = self.pos_enc(x, offset)
+        return x, pos_emb, x_mask[:, :, 2::2][:, :, 2::2][:, :, 2::2]
diff --git a/modules/wenet_extractor/transformer/swish.py b/modules/wenet_extractor/transformer/swish.py
new file mode 100755
index 0000000000000000000000000000000000000000..8728089430d0c20fa1575d12e3b6573417b4448c
--- /dev/null
+++ b/modules/wenet_extractor/transformer/swish.py
@@ -0,0 +1,33 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+"""Swish() activation function for Conformer."""
+
+import torch
+
+
+class Swish(torch.nn.Module):
+    """Construct an Swish object."""
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Return Swish activation function."""
+        return x * torch.sigmoid(x)
diff --git a/modules/wenet_extractor/utils/__init__.py b/modules/wenet_extractor/utils/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/modules/wenet_extractor/utils/checkpoint.py b/modules/wenet_extractor/utils/checkpoint.py
new file mode 100755
index 0000000000000000000000000000000000000000..f17784b1e1b41284191b0caff7570dd2066f7e91
--- /dev/null
+++ b/modules/wenet_extractor/utils/checkpoint.py
@@ -0,0 +1,113 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+import logging
+import os
+import re
+
+import yaml
+import torch
+from collections import OrderedDict
+
+import datetime
+
+
+def load_checkpoint(model: torch.nn.Module, path: str) -> dict:
+    if torch.cuda.is_available():
+        logging.info("Checkpoint: loading from checkpoint %s for GPU" % path)
+        checkpoint = torch.load(path)
+    else:
+        logging.info("Checkpoint: loading from checkpoint %s for CPU" % path)
+        checkpoint = torch.load(path, map_location="cpu")
+    model.load_state_dict(checkpoint, strict=False)
+    info_path = re.sub(".pt$", ".yaml", path)
+    configs = {}
+    if os.path.exists(info_path):
+        with open(info_path, "r") as fin:
+            configs = yaml.load(fin, Loader=yaml.FullLoader)
+    return configs
+
+
+def save_checkpoint(model: torch.nn.Module, path: str, infos=None):
+    """
+    Args:
+        infos (dict or None): any info you want to save.
+    """
+    logging.info("Checkpoint: save to checkpoint %s" % path)
+    if isinstance(model, torch.nn.DataParallel):
+        state_dict = model.module.state_dict()
+    elif isinstance(model, torch.nn.parallel.DistributedDataParallel):
+        state_dict = model.module.state_dict()
+    else:
+        state_dict = model.state_dict()
+    torch.save(state_dict, path)
+    info_path = re.sub(".pt$", ".yaml", path)
+    if infos is None:
+        infos = {}
+    infos["save_time"] = datetime.datetime.now().strftime("%d/%m/%Y %H:%M:%S")
+    with open(info_path, "w") as fout:
+        data = yaml.dump(infos)
+        fout.write(data)
+
+
+def filter_modules(model_state_dict, modules):
+    new_mods = []
+    incorrect_mods = []
+    mods_model = model_state_dict.keys()
+    for mod in modules:
+        if any(key.startswith(mod) for key in mods_model):
+            new_mods += [mod]
+        else:
+            incorrect_mods += [mod]
+    if incorrect_mods:
+        logging.warning(
+            "module(s) %s don't match or (partially match) "
+            "available modules in model.",
+            incorrect_mods,
+        )
+        logging.warning("for information, the existing modules in model are:")
+        logging.warning("%s", mods_model)
+
+    return new_mods
+
+
+def load_trained_modules(model: torch.nn.Module, args: None):
+    # Load encoder modules with pre-trained model(s).
+    enc_model_path = args.enc_init
+    enc_modules = args.enc_init_mods
+    main_state_dict = model.state_dict()
+    logging.warning("model(s) found for pre-initialization")
+    if os.path.isfile(enc_model_path):
+        logging.info("Checkpoint: loading from checkpoint %s for CPU" % enc_model_path)
+        model_state_dict = torch.load(enc_model_path, map_location="cpu")
+        modules = filter_modules(model_state_dict, enc_modules)
+        partial_state_dict = OrderedDict()
+        for key, value in model_state_dict.items():
+            if any(key.startswith(m) for m in modules):
+                partial_state_dict[key] = value
+        main_state_dict.update(partial_state_dict)
+    else:
+        logging.warning("model was not found : %s", enc_model_path)
+
+    model.load_state_dict(main_state_dict)
+    configs = {}
+    return configs
diff --git a/modules/wenet_extractor/utils/cmvn.py b/modules/wenet_extractor/utils/cmvn.py
new file mode 100755
index 0000000000000000000000000000000000000000..483adc1f709786ae83f570faf10ee1a1ded85e52
--- /dev/null
+++ b/modules/wenet_extractor/utils/cmvn.py
@@ -0,0 +1,103 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+import json
+import math
+
+import numpy as np
+
+
+def _load_json_cmvn(json_cmvn_file):
+    """Load the json format cmvn stats file and calculate cmvn
+
+    Args:
+        json_cmvn_file: cmvn stats file in json format
+
+    Returns:
+        a numpy array of [means, vars]
+    """
+    with open(json_cmvn_file) as f:
+        cmvn_stats = json.load(f)
+
+    means = cmvn_stats["mean_stat"]
+    variance = cmvn_stats["var_stat"]
+    count = cmvn_stats["frame_num"]
+    for i in range(len(means)):
+        means[i] /= count
+        variance[i] = variance[i] / count - means[i] * means[i]
+        if variance[i] < 1.0e-20:
+            variance[i] = 1.0e-20
+        variance[i] = 1.0 / math.sqrt(variance[i])
+    cmvn = np.array([means, variance])
+    return cmvn
+
+
+def _load_kaldi_cmvn(kaldi_cmvn_file):
+    """Load the kaldi format cmvn stats file and calculate cmvn
+
+    Args:
+        kaldi_cmvn_file:  kaldi text style global cmvn file, which
+           is generated by:
+           compute-cmvn-stats --binary=false scp:feats.scp global_cmvn
+
+    Returns:
+        a numpy array of [means, vars]
+    """
+    means = []
+    variance = []
+    with open(kaldi_cmvn_file, "r") as fid:
+        # kaldi binary file start with '\0B'
+        if fid.read(2) == "\0B":
+            logging.error(
+                "kaldi cmvn binary file is not supported, please "
+                "recompute it by: compute-cmvn-stats --binary=false "
+                " scp:feats.scp global_cmvn"
+            )
+            sys.exit(1)
+        fid.seek(0)
+        arr = fid.read().split()
+        assert arr[0] == "["
+        assert arr[-2] == "0"
+        assert arr[-1] == "]"
+        feat_dim = int((len(arr) - 2 - 2) / 2)
+        for i in range(1, feat_dim + 1):
+            means.append(float(arr[i]))
+        count = float(arr[feat_dim + 1])
+        for i in range(feat_dim + 2, 2 * feat_dim + 2):
+            variance.append(float(arr[i]))
+
+    for i in range(len(means)):
+        means[i] /= count
+        variance[i] = variance[i] / count - means[i] * means[i]
+        if variance[i] < 1.0e-20:
+            variance[i] = 1.0e-20
+        variance[i] = 1.0 / math.sqrt(variance[i])
+    cmvn = np.array([means, variance])
+    return cmvn
+
+
+def load_cmvn(cmvn_file, is_json):
+    if is_json:
+        cmvn = _load_json_cmvn(cmvn_file)
+    else:
+        cmvn = _load_kaldi_cmvn(cmvn_file)
+    return cmvn[0], cmvn[1]
diff --git a/modules/wenet_extractor/utils/common.py b/modules/wenet_extractor/utils/common.py
new file mode 100755
index 0000000000000000000000000000000000000000..8900ce4cd279e87829807c14a14e2ca38841b89d
--- /dev/null
+++ b/modules/wenet_extractor/utils/common.py
@@ -0,0 +1,266 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+"""Unility functions for Transformer."""
+
+import math
+from typing import List, Tuple
+
+import torch
+from torch.nn.utils.rnn import pad_sequence
+
+IGNORE_ID = -1
+
+
+def pad_list(xs: List[torch.Tensor], pad_value: int):
+    """Perform padding for the list of tensors.
+
+    Args:
+        xs (List): List of Tensors [(T_1, `*`), (T_2, `*`), ..., (T_B, `*`)].
+        pad_value (float): Value for padding.
+
+    Returns:
+        Tensor: Padded tensor (B, Tmax, `*`).
+
+    Examples:
+        >>> x = [torch.ones(4), torch.ones(2), torch.ones(1)]
+        >>> x
+        [tensor([1., 1., 1., 1.]), tensor([1., 1.]), tensor([1.])]
+        >>> pad_list(x, 0)
+        tensor([[1., 1., 1., 1.],
+                [1., 1., 0., 0.],
+                [1., 0., 0., 0.]])
+
+    """
+    n_batch = len(xs)
+    max_len = max([x.size(0) for x in xs])
+    pad = torch.zeros(n_batch, max_len, dtype=xs[0].dtype, device=xs[0].device)
+    pad = pad.fill_(pad_value)
+    for i in range(n_batch):
+        pad[i, : xs[i].size(0)] = xs[i]
+
+    return pad
+
+
+def add_blank(ys_pad: torch.Tensor, blank: int, ignore_id: int) -> torch.Tensor:
+    """Prepad blank for transducer predictor
+
+    Args:
+        ys_pad (torch.Tensor): batch of padded target sequences (B, Lmax)
+        blank (int): index of <blank>
+
+    Returns:
+        ys_in (torch.Tensor) : (B, Lmax + 1)
+
+    Examples:
+        >>> blank = 0
+        >>> ignore_id = -1
+        >>> ys_pad
+        tensor([[ 1,  2,  3,   4,   5],
+                [ 4,  5,  6,  -1,  -1],
+                [ 7,  8,  9,  -1,  -1]], dtype=torch.int32)
+        >>> ys_in = add_blank(ys_pad, 0, -1)
+        >>> ys_in
+        tensor([[0,  1,  2,  3,  4,  5],
+                [0,  4,  5,  6,  0,  0],
+                [0,  7,  8,  9,  0,  0]])
+    """
+    bs = ys_pad.size(0)
+    _blank = torch.tensor(
+        [blank], dtype=torch.long, requires_grad=False, device=ys_pad.device
+    )
+    _blank = _blank.repeat(bs).unsqueeze(1)  # [bs,1]
+    out = torch.cat([_blank, ys_pad], dim=1)  # [bs, Lmax+1]
+    return torch.where(out == ignore_id, blank, out)
+
+
+def add_sos_eos(
+    ys_pad: torch.Tensor, sos: int, eos: int, ignore_id: int
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Add <sos> and <eos> labels.
+
+    Args:
+        ys_pad (torch.Tensor): batch of padded target sequences (B, Lmax)
+        sos (int): index of <sos>
+        eos (int): index of <eeos>
+        ignore_id (int): index of padding
+
+    Returns:
+        ys_in (torch.Tensor) : (B, Lmax + 1)
+        ys_out (torch.Tensor) : (B, Lmax + 1)
+
+    Examples:
+        >>> sos_id = 10
+        >>> eos_id = 11
+        >>> ignore_id = -1
+        >>> ys_pad
+        tensor([[ 1,  2,  3,  4,  5],
+                [ 4,  5,  6, -1, -1],
+                [ 7,  8,  9, -1, -1]], dtype=torch.int32)
+        >>> ys_in,ys_out=add_sos_eos(ys_pad, sos_id , eos_id, ignore_id)
+        >>> ys_in
+        tensor([[10,  1,  2,  3,  4,  5],
+                [10,  4,  5,  6, 11, 11],
+                [10,  7,  8,  9, 11, 11]])
+        >>> ys_out
+        tensor([[ 1,  2,  3,  4,  5, 11],
+                [ 4,  5,  6, 11, -1, -1],
+                [ 7,  8,  9, 11, -1, -1]])
+    """
+    _sos = torch.tensor(
+        [sos], dtype=torch.long, requires_grad=False, device=ys_pad.device
+    )
+    _eos = torch.tensor(
+        [eos], dtype=torch.long, requires_grad=False, device=ys_pad.device
+    )
+    ys = [y[y != ignore_id] for y in ys_pad]  # parse padded ys
+    ys_in = [torch.cat([_sos, y], dim=0) for y in ys]
+    ys_out = [torch.cat([y, _eos], dim=0) for y in ys]
+    return pad_list(ys_in, eos), pad_list(ys_out, ignore_id)
+
+
+def reverse_pad_list(
+    ys_pad: torch.Tensor, ys_lens: torch.Tensor, pad_value: float = -1.0
+) -> torch.Tensor:
+    """Reverse padding for the list of tensors.
+
+    Args:
+        ys_pad (tensor): The padded tensor (B, Tokenmax).
+        ys_lens (tensor): The lens of token seqs (B)
+        pad_value (int): Value for padding.
+
+    Returns:
+        Tensor: Padded tensor (B, Tokenmax).
+
+    Examples:
+        >>> x
+        tensor([[1, 2, 3, 4], [5, 6, 7, 0], [8, 9, 0, 0]])
+        >>> pad_list(x, 0)
+        tensor([[4, 3, 2, 1],
+                [7, 6, 5, 0],
+                [9, 8, 0, 0]])
+
+    """
+    r_ys_pad = pad_sequence(
+        [(torch.flip(y.int()[:i], [0])) for y, i in zip(ys_pad, ys_lens)],
+        True,
+        pad_value,
+    )
+    return r_ys_pad
+
+
+def th_accuracy(
+    pad_outputs: torch.Tensor, pad_targets: torch.Tensor, ignore_label: int
+) -> float:
+    """Calculate accuracy.
+
+    Args:
+        pad_outputs (Tensor): Prediction tensors (B * Lmax, D).
+        pad_targets (LongTensor): Target label tensors (B, Lmax).
+        ignore_label (int): Ignore label id.
+
+    Returns:
+        float: Accuracy value (0.0 - 1.0).
+
+    """
+    pad_pred = pad_outputs.view(
+        pad_targets.size(0), pad_targets.size(1), pad_outputs.size(1)
+    ).argmax(2)
+    mask = pad_targets != ignore_label
+    numerator = torch.sum(
+        pad_pred.masked_select(mask) == pad_targets.masked_select(mask)
+    )
+    denominator = torch.sum(mask)
+    return float(numerator) / float(denominator)
+
+
+def get_rnn(rnn_type: str) -> torch.nn.Module:
+    assert rnn_type in ["rnn", "lstm", "gru"]
+    if rnn_type == "rnn":
+        return torch.nn.RNN
+    elif rnn_type == "lstm":
+        return torch.nn.LSTM
+    else:
+        return torch.nn.GRU
+
+
+def get_activation(act):
+    """Return activation function."""
+    # Lazy load to avoid unused import
+    from modules.wenet_extractor.transformer.swish import Swish
+
+    activation_funcs = {
+        "hardtanh": torch.nn.Hardtanh,
+        "tanh": torch.nn.Tanh,
+        "relu": torch.nn.ReLU,
+        "selu": torch.nn.SELU,
+        "swish": getattr(torch.nn, "SiLU", Swish),
+        "gelu": torch.nn.GELU,
+    }
+
+    return activation_funcs[act]()
+
+
+def get_subsample(config):
+    input_layer = config["encoder_conf"]["input_layer"]
+    assert input_layer in ["conv2d", "conv2d6", "conv2d8"]
+    if input_layer == "conv2d":
+        return 4
+    elif input_layer == "conv2d6":
+        return 6
+    elif input_layer == "conv2d8":
+        return 8
+
+
+def remove_duplicates_and_blank(hyp: List[int]) -> List[int]:
+    new_hyp: List[int] = []
+    cur = 0
+    while cur < len(hyp):
+        if hyp[cur] != 0:
+            new_hyp.append(hyp[cur])
+        prev = cur
+        while cur < len(hyp) and hyp[cur] == hyp[prev]:
+            cur += 1
+    return new_hyp
+
+
+def replace_duplicates_with_blank(hyp: List[int]) -> List[int]:
+    new_hyp: List[int] = []
+    cur = 0
+    while cur < len(hyp):
+        new_hyp.append(hyp[cur])
+        prev = cur
+        cur += 1
+        while cur < len(hyp) and hyp[cur] == hyp[prev] and hyp[cur] != 0:
+            new_hyp.append(0)
+            cur += 1
+    return new_hyp
+
+
+def log_add(args: List[int]) -> float:
+    """
+    Stable log add
+    """
+    if all(a == -float("inf") for a in args):
+        return -float("inf")
+    a_max = max(args)
+    lsp = math.log(sum(math.exp(a - a_max) for a in args))
+    return a_max + lsp
diff --git a/modules/wenet_extractor/utils/config.py b/modules/wenet_extractor/utils/config.py
new file mode 100755
index 0000000000000000000000000000000000000000..5f7ab928eec9d18d1af46655f9f324a7ac1eb9dc
--- /dev/null
+++ b/modules/wenet_extractor/utils/config.py
@@ -0,0 +1,48 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+
+import copy
+
+
+def override_config(configs, override_list):
+    new_configs = copy.deepcopy(configs)
+    for item in override_list:
+        arr = item.split()
+        if len(arr) != 2:
+            print(f"the overrive {item} format not correct, skip it")
+            continue
+        keys = arr[0].split(".")
+        s_configs = new_configs
+        for i, key in enumerate(keys):
+            if key not in s_configs:
+                print(f"the overrive {item} format not correct, skip it")
+            if i == len(keys) - 1:
+                param_type = type(s_configs[key])
+                if param_type != bool:
+                    s_configs[key] = param_type(arr[1])
+                else:
+                    s_configs[key] = arr[1] in ["true", "True"]
+                print(f"override {arr[0]} with {arr[1]}")
+            else:
+                s_configs = s_configs[key]
+    return new_configs
diff --git a/modules/wenet_extractor/utils/ctc_util.py b/modules/wenet_extractor/utils/ctc_util.py
new file mode 100755
index 0000000000000000000000000000000000000000..03154248bb087d930a9cc579e73d782756cd6ea0
--- /dev/null
+++ b/modules/wenet_extractor/utils/ctc_util.py
@@ -0,0 +1,96 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+import numpy as np
+import torch
+
+
+def insert_blank(label, blank_id=0):
+    """Insert blank token between every two label token."""
+    label = np.expand_dims(label, 1)
+    blanks = np.zeros((label.shape[0], 1), dtype=np.int64) + blank_id
+    label = np.concatenate([blanks, label], axis=1)
+    label = label.reshape(-1)
+    label = np.append(label, label[0])
+    return label
+
+
+def forced_align(ctc_probs: torch.Tensor, y: torch.Tensor, blank_id=0) -> list:
+    """ctc forced alignment.
+
+    Args:
+        torch.Tensor ctc_probs: hidden state sequence, 2d tensor (T, D)
+        torch.Tensor y: id sequence tensor 1d tensor (L)
+        int blank_id: blank symbol index
+    Returns:
+        torch.Tensor: alignment result
+    """
+    y_insert_blank = insert_blank(y, blank_id)
+
+    log_alpha = torch.zeros((ctc_probs.size(0), len(y_insert_blank)))
+    log_alpha = log_alpha - float("inf")  # log of zero
+    state_path = (
+        torch.zeros((ctc_probs.size(0), len(y_insert_blank)), dtype=torch.int16) - 1
+    )  # state path
+
+    # init start state
+    log_alpha[0, 0] = ctc_probs[0][y_insert_blank[0]]
+    log_alpha[0, 1] = ctc_probs[0][y_insert_blank[1]]
+
+    for t in range(1, ctc_probs.size(0)):
+        for s in range(len(y_insert_blank)):
+            if (
+                y_insert_blank[s] == blank_id
+                or s < 2
+                or y_insert_blank[s] == y_insert_blank[s - 2]
+            ):
+                candidates = torch.tensor(
+                    [log_alpha[t - 1, s], log_alpha[t - 1, s - 1]]
+                )
+                prev_state = [s, s - 1]
+            else:
+                candidates = torch.tensor(
+                    [
+                        log_alpha[t - 1, s],
+                        log_alpha[t - 1, s - 1],
+                        log_alpha[t - 1, s - 2],
+                    ]
+                )
+                prev_state = [s, s - 1, s - 2]
+            log_alpha[t, s] = torch.max(candidates) + ctc_probs[t][y_insert_blank[s]]
+            state_path[t, s] = prev_state[torch.argmax(candidates)]
+
+    state_seq = -1 * torch.ones((ctc_probs.size(0), 1), dtype=torch.int16)
+
+    candidates = torch.tensor(
+        [log_alpha[-1, len(y_insert_blank) - 1], log_alpha[-1, len(y_insert_blank) - 2]]
+    )
+    final_state = [len(y_insert_blank) - 1, len(y_insert_blank) - 2]
+    state_seq[-1] = final_state[torch.argmax(candidates)]
+    for t in range(ctc_probs.size(0) - 2, -1, -1):
+        state_seq[t] = state_path[t + 1, state_seq[t + 1, 0]]
+
+    output_alignment = []
+    for t in range(0, ctc_probs.size(0)):
+        output_alignment.append(y_insert_blank[state_seq[t, 0]])
+
+    return output_alignment
diff --git a/modules/wenet_extractor/utils/executor.py b/modules/wenet_extractor/utils/executor.py
new file mode 100755
index 0000000000000000000000000000000000000000..8d55549fec4f4d350e56093f3995ac93a31ff029
--- /dev/null
+++ b/modules/wenet_extractor/utils/executor.py
@@ -0,0 +1,163 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+import logging
+from contextlib import nullcontext
+
+# if your python version < 3.7 use the below one
+# from contextlib import suppress as nullcontext
+import torch
+from torch.nn.utils import clip_grad_norm_
+
+
+class Executor:
+    def __init__(self):
+        self.step = 0
+
+    def train(
+        self, model, optimizer, scheduler, data_loader, device, writer, args, scaler
+    ):
+        """Train one epoch"""
+        model.train()
+        clip = args.get("grad_clip", 50.0)
+        log_interval = args.get("log_interval", 10)
+        rank = args.get("rank", 0)
+        epoch = args.get("epoch", 0)
+        accum_grad = args.get("accum_grad", 1)
+        is_distributed = args.get("is_distributed", True)
+        use_amp = args.get("use_amp", False)
+        logging.info(
+            "using accumulate grad, new batch size is {} times"
+            " larger than before".format(accum_grad)
+        )
+        if use_amp:
+            assert scaler is not None
+        # A context manager to be used in conjunction with an instance of
+        # torch.nn.parallel.DistributedDataParallel to be able to train
+        # with uneven inputs across participating processes.
+        if isinstance(model, torch.nn.parallel.DistributedDataParallel):
+            model_context = model.join
+        else:
+            model_context = nullcontext
+        num_seen_utts = 0
+        with model_context():
+            for batch_idx, batch in enumerate(data_loader):
+                key, feats, target, feats_lengths, target_lengths = batch
+                feats = feats.to(device)
+                target = target.to(device)
+                feats_lengths = feats_lengths.to(device)
+                target_lengths = target_lengths.to(device)
+                num_utts = target_lengths.size(0)
+                if num_utts == 0:
+                    continue
+                context = None
+                # Disable gradient synchronizations across DDP processes.
+                # Within this context, gradients will be accumulated on module
+                # variables, which will later be synchronized.
+                if is_distributed and batch_idx % accum_grad != 0:
+                    context = model.no_sync
+                # Used for single gpu training and DDP gradient synchronization
+                # processes.
+                else:
+                    context = nullcontext
+                with context():
+                    # autocast context
+                    # The more details about amp can be found in
+                    # https://pytorch.org/docs/stable/notes/amp_examples.html
+                    with torch.cuda.amp.autocast(scaler is not None):
+                        loss_dict = model(feats, feats_lengths, target, target_lengths)
+                        loss = loss_dict["loss"] / accum_grad
+                    if use_amp:
+                        scaler.scale(loss).backward()
+                    else:
+                        loss.backward()
+
+                num_seen_utts += num_utts
+                if batch_idx % accum_grad == 0:
+                    if rank == 0 and writer is not None:
+                        writer.add_scalar("train_loss", loss, self.step)
+                    # Use mixed precision training
+                    if use_amp:
+                        scaler.unscale_(optimizer)
+                        grad_norm = clip_grad_norm_(model.parameters(), clip)
+                        # Must invoke scaler.update() if unscale_() is used in
+                        # the iteration to avoid the following error:
+                        #   RuntimeError: unscale_() has already been called
+                        #   on this optimizer since the last update().
+                        # We don't check grad here since that if the gradient
+                        # has inf/nan values, scaler.step will skip
+                        # optimizer.step().
+                        scaler.step(optimizer)
+                        scaler.update()
+                    else:
+                        grad_norm = clip_grad_norm_(model.parameters(), clip)
+                        if torch.isfinite(grad_norm):
+                            optimizer.step()
+                    optimizer.zero_grad()
+                    scheduler.step()
+                    self.step += 1
+                if batch_idx % log_interval == 0:
+                    lr = optimizer.param_groups[0]["lr"]
+                    log_str = "TRAIN Batch {}/{} loss {:.6f} ".format(
+                        epoch, batch_idx, loss.item() * accum_grad
+                    )
+                    for name, value in loss_dict.items():
+                        if name != "loss" and value is not None:
+                            log_str += "{} {:.6f} ".format(name, value.item())
+                    log_str += "lr {:.8f} rank {}".format(lr, rank)
+                    logging.debug(log_str)
+
+    def cv(self, model, data_loader, device, args):
+        """Cross validation on"""
+        model.eval()
+        rank = args.get("rank", 0)
+        epoch = args.get("epoch", 0)
+        log_interval = args.get("log_interval", 10)
+        # in order to avoid division by 0
+        num_seen_utts = 1
+        total_loss = 0.0
+        with torch.no_grad():
+            for batch_idx, batch in enumerate(data_loader):
+                key, feats, target, feats_lengths, target_lengths = batch
+                feats = feats.to(device)
+                target = target.to(device)
+                feats_lengths = feats_lengths.to(device)
+                target_lengths = target_lengths.to(device)
+                num_utts = target_lengths.size(0)
+                if num_utts == 0:
+                    continue
+                loss_dict = model(feats, feats_lengths, target, target_lengths)
+                loss = loss_dict["loss"]
+                if torch.isfinite(loss):
+                    num_seen_utts += num_utts
+                    total_loss += loss.item() * num_utts
+                if batch_idx % log_interval == 0:
+                    log_str = "CV Batch {}/{} loss {:.6f} ".format(
+                        epoch, batch_idx, loss.item()
+                    )
+                    for name, value in loss_dict.items():
+                        if name != "loss" and value is not None:
+                            log_str += "{} {:.6f} ".format(name, value.item())
+                    log_str += "history loss {:.6f}".format(total_loss / num_seen_utts)
+                    log_str += " rank {}".format(rank)
+                    logging.debug(log_str)
+        return total_loss, num_seen_utts
diff --git a/modules/wenet_extractor/utils/file_utils.py b/modules/wenet_extractor/utils/file_utils.py
new file mode 100755
index 0000000000000000000000000000000000000000..443759bd4da9d7f85d48f0534205f9e5cd7048df
--- /dev/null
+++ b/modules/wenet_extractor/utils/file_utils.py
@@ -0,0 +1,77 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+import re
+
+
+def read_lists(list_file):
+    lists = []
+    with open(list_file, "r", encoding="utf8") as fin:
+        for line in fin:
+            lists.append(line.strip())
+    return lists
+
+
+def read_non_lang_symbols(non_lang_sym_path):
+    """read non-linguistic symbol from file.
+
+    The file format is like below:
+
+    {NOISE}\n
+    {BRK}\n
+    ...
+
+
+    Args:
+        non_lang_sym_path: non-linguistic symbol file path, None means no any
+        syms.
+
+    """
+    if non_lang_sym_path is None:
+        return None
+    else:
+        syms = read_lists(non_lang_sym_path)
+        non_lang_syms_pattern = re.compile(r"(\[[^\[\]]+\]|<[^<>]+>|{[^{}]+})")
+        for sym in syms:
+            if non_lang_syms_pattern.fullmatch(sym) is None:
+
+                class BadSymbolFormat(Exception):
+                    pass
+
+                raise BadSymbolFormat(
+                    "Non-linguistic symbols should be "
+                    "formatted in {xxx}/<xxx>/[xxx], consider"
+                    " modify '%s' to meet the requirment. "
+                    "More details can be found in discussions here : "
+                    "https://github.com/wenet-e2e/wenet/pull/819" % (sym)
+                )
+        return syms
+
+
+def read_symbol_table(symbol_table_file):
+    symbol_table = {}
+    with open(symbol_table_file, "r", encoding="utf8") as fin:
+        for line in fin:
+            arr = line.strip().split()
+            assert len(arr) == 2
+            symbol_table[arr[0]] = int(arr[1])
+    return symbol_table
diff --git a/modules/wenet_extractor/utils/init_model.py b/modules/wenet_extractor/utils/init_model.py
new file mode 100755
index 0000000000000000000000000000000000000000..82a391efec400163935c387e1134f179ea48e2cb
--- /dev/null
+++ b/modules/wenet_extractor/utils/init_model.py
@@ -0,0 +1,152 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+import torch
+from modules.wenet_extractor.transducer.joint import TransducerJoint
+from modules.wenet_extractor.transducer.predictor import (
+    ConvPredictor,
+    EmbeddingPredictor,
+    RNNPredictor,
+)
+from modules.wenet_extractor.transducer.transducer import Transducer
+from modules.wenet_extractor.transformer.asr_model import ASRModel
+from modules.wenet_extractor.transformer.cmvn import GlobalCMVN
+from modules.wenet_extractor.transformer.ctc import CTC
+from modules.wenet_extractor.transformer.decoder import (
+    BiTransformerDecoder,
+    TransformerDecoder,
+)
+from modules.wenet_extractor.transformer.encoder import (
+    ConformerEncoder,
+    TransformerEncoder,
+)
+from modules.wenet_extractor.squeezeformer.encoder import SqueezeformerEncoder
+from modules.wenet_extractor.efficient_conformer.encoder import (
+    EfficientConformerEncoder,
+)
+from modules.wenet_extractor.paraformer.paraformer import Paraformer
+from modules.wenet_extractor.cif.predictor import Predictor
+from modules.wenet_extractor.utils.cmvn import load_cmvn
+
+
+def init_model(configs):
+    if configs["cmvn_file"] is not None:
+        mean, istd = load_cmvn(configs["cmvn_file"], configs["is_json_cmvn"])
+        global_cmvn = GlobalCMVN(
+            torch.from_numpy(mean).float(), torch.from_numpy(istd).float()
+        )
+    else:
+        global_cmvn = None
+
+    input_dim = configs["input_dim"]
+    vocab_size = configs["output_dim"]
+
+    encoder_type = configs.get("encoder", "conformer")
+    decoder_type = configs.get("decoder", "bitransformer")
+
+    if encoder_type == "conformer":
+        encoder = ConformerEncoder(
+            input_dim, global_cmvn=global_cmvn, **configs["encoder_conf"]
+        )
+    elif encoder_type == "squeezeformer":
+        encoder = SqueezeformerEncoder(
+            input_dim, global_cmvn=global_cmvn, **configs["encoder_conf"]
+        )
+    elif encoder_type == "efficientConformer":
+        encoder = EfficientConformerEncoder(
+            input_dim,
+            global_cmvn=global_cmvn,
+            **configs["encoder_conf"],
+            **configs["encoder_conf"]["efficient_conf"]
+            if "efficient_conf" in configs["encoder_conf"]
+            else {},
+        )
+    else:
+        encoder = TransformerEncoder(
+            input_dim, global_cmvn=global_cmvn, **configs["encoder_conf"]
+        )
+    if decoder_type == "transformer":
+        decoder = TransformerDecoder(
+            vocab_size, encoder.output_size(), **configs["decoder_conf"]
+        )
+    else:
+        assert 0.0 < configs["model_conf"]["reverse_weight"] < 1.0
+        assert configs["decoder_conf"]["r_num_blocks"] > 0
+        decoder = BiTransformerDecoder(
+            vocab_size, encoder.output_size(), **configs["decoder_conf"]
+        )
+    ctc = CTC(vocab_size, encoder.output_size())
+
+    # Init joint CTC/Attention or Transducer model
+    if "predictor" in configs:
+        predictor_type = configs.get("predictor", "rnn")
+        if predictor_type == "rnn":
+            predictor = RNNPredictor(vocab_size, **configs["predictor_conf"])
+        elif predictor_type == "embedding":
+            predictor = EmbeddingPredictor(vocab_size, **configs["predictor_conf"])
+            configs["predictor_conf"]["output_size"] = configs["predictor_conf"][
+                "embed_size"
+            ]
+        elif predictor_type == "conv":
+            predictor = ConvPredictor(vocab_size, **configs["predictor_conf"])
+            configs["predictor_conf"]["output_size"] = configs["predictor_conf"][
+                "embed_size"
+            ]
+        else:
+            raise NotImplementedError("only rnn, embedding and conv type support now")
+        configs["joint_conf"]["enc_output_size"] = configs["encoder_conf"][
+            "output_size"
+        ]
+        configs["joint_conf"]["pred_output_size"] = configs["predictor_conf"][
+            "output_size"
+        ]
+        joint = TransducerJoint(vocab_size, **configs["joint_conf"])
+        model = Transducer(
+            vocab_size=vocab_size,
+            blank=0,
+            predictor=predictor,
+            encoder=encoder,
+            attention_decoder=decoder,
+            joint=joint,
+            ctc=ctc,
+            **configs["model_conf"],
+        )
+    elif "paraformer" in configs:
+        predictor = Predictor(**configs["cif_predictor_conf"])
+        model = Paraformer(
+            vocab_size=vocab_size,
+            encoder=encoder,
+            decoder=decoder,
+            ctc=ctc,
+            predictor=predictor,
+            **configs["model_conf"],
+        )
+    else:
+        model = ASRModel(
+            vocab_size=vocab_size,
+            encoder=encoder,
+            decoder=decoder,
+            ctc=ctc,
+            lfmmi_dir=configs.get("lfmmi_dir", ""),
+            **configs["model_conf"],
+        )
+    return model
diff --git a/modules/wenet_extractor/utils/mask.py b/modules/wenet_extractor/utils/mask.py
new file mode 100755
index 0000000000000000000000000000000000000000..97b6149781893da3fb37e225ef47338a79ef601f
--- /dev/null
+++ b/modules/wenet_extractor/utils/mask.py
@@ -0,0 +1,304 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+
+import torch
+
+'''
+def subsequent_mask(
+        size: int,
+        device: torch.device = torch.device("cpu"),
+) -> torch.Tensor:
+    """Create mask for subsequent steps (size, size).
+
+    This mask is used only in decoder which works in an auto-regressive mode.
+    This means the current step could only do attention with its left steps.
+
+    In encoder, fully attention is used when streaming is not necessary and
+    the sequence is not long. In this  case, no attention mask is needed.
+
+    When streaming is need, chunk-based attention is used in encoder. See
+    subsequent_chunk_mask for the chunk-based attention mask.
+
+    Args:
+        size (int): size of mask
+        str device (str): "cpu" or "cuda" or torch.Tensor.device
+        dtype (torch.device): result dtype
+
+    Returns:
+        torch.Tensor: mask
+
+    Examples:
+        >>> subsequent_mask(3)
+        [[1, 0, 0],
+         [1, 1, 0],
+         [1, 1, 1]]
+    """
+    ret = torch.ones(size, size, device=device, dtype=torch.bool)
+    return torch.tril(ret)
+'''
+
+
+def subsequent_mask(
+    size: int,
+    device: torch.device = torch.device("cpu"),
+) -> torch.Tensor:
+    """Create mask for subsequent steps (size, size).
+
+    This mask is used only in decoder which works in an auto-regressive mode.
+    This means the current step could only do attention with its left steps.
+
+    In encoder, fully attention is used when streaming is not necessary and
+    the sequence is not long. In this  case, no attention mask is needed.
+
+    When streaming is need, chunk-based attention is used in encoder. See
+    subsequent_chunk_mask for the chunk-based attention mask.
+
+    Args:
+        size (int): size of mask
+        str device (str): "cpu" or "cuda" or torch.Tensor.device
+        dtype (torch.device): result dtype
+
+    Returns:
+        torch.Tensor: mask
+
+    Examples:
+        >>> subsequent_mask(3)
+        [[1, 0, 0],
+         [1, 1, 0],
+         [1, 1, 1]]
+    """
+    arange = torch.arange(size, device=device)
+    mask = arange.expand(size, size)
+    arange = arange.unsqueeze(-1)
+    mask = mask <= arange
+    return mask
+
+
+def subsequent_chunk_mask(
+    size: int,
+    chunk_size: int,
+    num_left_chunks: int = -1,
+    device: torch.device = torch.device("cpu"),
+) -> torch.Tensor:
+    """Create mask for subsequent steps (size, size) with chunk size,
+       this is for streaming encoder
+
+    Args:
+        size (int): size of mask
+        chunk_size (int): size of chunk
+        num_left_chunks (int): number of left chunks
+            <0: use full chunk
+            >=0: use num_left_chunks
+        device (torch.device): "cpu" or "cuda" or torch.Tensor.device
+
+    Returns:
+        torch.Tensor: mask
+
+    Examples:
+        >>> subsequent_chunk_mask(4, 2)
+        [[1, 1, 0, 0],
+         [1, 1, 0, 0],
+         [1, 1, 1, 1],
+         [1, 1, 1, 1]]
+    """
+    ret = torch.zeros(size, size, device=device, dtype=torch.bool)
+    for i in range(size):
+        if num_left_chunks < 0:
+            start = 0
+        else:
+            start = max((i // chunk_size - num_left_chunks) * chunk_size, 0)
+        ending = min((i // chunk_size + 1) * chunk_size, size)
+        ret[i, start:ending] = True
+    return ret
+
+
+def add_optional_chunk_mask(
+    xs: torch.Tensor,
+    masks: torch.Tensor,
+    use_dynamic_chunk: bool,
+    use_dynamic_left_chunk: bool,
+    decoding_chunk_size: int,
+    static_chunk_size: int,
+    num_decoding_left_chunks: int,
+):
+    """Apply optional mask for encoder.
+
+    Args:
+        xs (torch.Tensor): padded input, (B, L, D), L for max length
+        mask (torch.Tensor): mask for xs, (B, 1, L)
+        use_dynamic_chunk (bool): whether to use dynamic chunk or not
+        use_dynamic_left_chunk (bool): whether to use dynamic left chunk for
+            training.
+        decoding_chunk_size (int): decoding chunk size for dynamic chunk, it's
+            0: default for training, use random dynamic chunk.
+            <0: for decoding, use full chunk.
+            >0: for decoding, use fixed chunk size as set.
+        static_chunk_size (int): chunk size for static chunk training/decoding
+            if it's greater than 0, if use_dynamic_chunk is true,
+            this parameter will be ignored
+        num_decoding_left_chunks: number of left chunks, this is for decoding,
+            the chunk size is decoding_chunk_size.
+            >=0: use num_decoding_left_chunks
+            <0: use all left chunks
+
+    Returns:
+        torch.Tensor: chunk mask of the input xs.
+    """
+    # Whether to use chunk mask or not
+    if use_dynamic_chunk:
+        max_len = xs.size(1)
+        if decoding_chunk_size < 0:
+            chunk_size = max_len
+            num_left_chunks = -1
+        elif decoding_chunk_size > 0:
+            chunk_size = decoding_chunk_size
+            num_left_chunks = num_decoding_left_chunks
+        else:
+            # chunk size is either [1, 25] or full context(max_len).
+            # Since we use 4 times subsampling and allow up to 1s(100 frames)
+            # delay, the maximum frame is 100 / 4 = 25.
+            chunk_size = torch.randint(1, max_len, (1,)).item()
+            num_left_chunks = -1
+            if chunk_size > max_len // 2:
+                chunk_size = max_len
+            else:
+                chunk_size = chunk_size % 25 + 1
+                if use_dynamic_left_chunk:
+                    max_left_chunks = (max_len - 1) // chunk_size
+                    num_left_chunks = torch.randint(0, max_left_chunks, (1,)).item()
+        chunk_masks = subsequent_chunk_mask(
+            xs.size(1), chunk_size, num_left_chunks, xs.device
+        )  # (L, L)
+        chunk_masks = chunk_masks.unsqueeze(0)  # (1, L, L)
+        chunk_masks = masks & chunk_masks  # (B, L, L)
+    elif static_chunk_size > 0:
+        num_left_chunks = num_decoding_left_chunks
+        chunk_masks = subsequent_chunk_mask(
+            xs.size(1), static_chunk_size, num_left_chunks, xs.device
+        )  # (L, L)
+        chunk_masks = chunk_masks.unsqueeze(0)  # (1, L, L)
+        chunk_masks = masks & chunk_masks  # (B, L, L)
+    else:
+        chunk_masks = masks
+    return chunk_masks
+
+
+def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor:
+    """Make mask tensor containing indices of padded part.
+
+    See description of make_non_pad_mask.
+
+    Args:
+        lengths (torch.Tensor): Batch of lengths (B,).
+    Returns:
+        torch.Tensor: Mask tensor containing indices of padded part.
+
+    Examples:
+        >>> lengths = [5, 3, 2]
+        >>> make_pad_mask(lengths)
+        masks = [[0, 0, 0, 0 ,0],
+                 [0, 0, 0, 1, 1],
+                 [0, 0, 1, 1, 1]]
+    """
+    batch_size = lengths.size(0)
+    max_len = max_len if max_len > 0 else lengths.max().item()
+    seq_range = torch.arange(0, max_len, dtype=torch.int64, device=lengths.device)
+    seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)
+    seq_length_expand = lengths.unsqueeze(-1)
+    mask = seq_range_expand >= seq_length_expand
+    return mask
+
+
+def make_non_pad_mask(lengths: torch.Tensor) -> torch.Tensor:
+    """Make mask tensor containing indices of non-padded part.
+
+    The sequences in a batch may have different lengths. To enable
+    batch computing, padding is need to make all sequence in same
+    size. To avoid the padding part pass value to context dependent
+    block such as attention or convolution , this padding part is
+    masked.
+
+    This pad_mask is used in both encoder and decoder.
+
+    1 for non-padded part and 0 for padded part.
+
+    Args:
+        lengths (torch.Tensor): Batch of lengths (B,).
+    Returns:
+        torch.Tensor: mask tensor containing indices of padded part.
+
+    Examples:
+        >>> lengths = [5, 3, 2]
+        >>> make_non_pad_mask(lengths)
+        masks = [[1, 1, 1, 1 ,1],
+                 [1, 1, 1, 0, 0],
+                 [1, 1, 0, 0, 0]]
+    """
+    return ~make_pad_mask(lengths)
+
+
+def mask_finished_scores(score: torch.Tensor, flag: torch.Tensor) -> torch.Tensor:
+    """
+    If a sequence is finished, we only allow one alive branch. This function
+    aims to give one branch a zero score and the rest -inf score.
+
+    Args:
+        score (torch.Tensor): A real value array with shape
+            (batch_size * beam_size, beam_size).
+        flag (torch.Tensor): A bool array with shape
+            (batch_size * beam_size, 1).
+
+    Returns:
+        torch.Tensor: (batch_size * beam_size, beam_size).
+    """
+    beam_size = score.size(-1)
+    zero_mask = torch.zeros_like(flag, dtype=torch.bool)
+    if beam_size > 1:
+        unfinished = torch.cat((zero_mask, flag.repeat([1, beam_size - 1])), dim=1)
+        finished = torch.cat((flag, zero_mask.repeat([1, beam_size - 1])), dim=1)
+    else:
+        unfinished = zero_mask
+        finished = flag
+    score.masked_fill_(unfinished, -float("inf"))
+    score.masked_fill_(finished, 0)
+    return score
+
+
+def mask_finished_preds(
+    pred: torch.Tensor, flag: torch.Tensor, eos: int
+) -> torch.Tensor:
+    """
+    If a sequence is finished, all of its branch should be <eos>
+
+    Args:
+        pred (torch.Tensor): A int array with shape
+            (batch_size * beam_size, beam_size).
+        flag (torch.Tensor): A bool array with shape
+            (batch_size * beam_size, 1).
+
+    Returns:
+        torch.Tensor: (batch_size * beam_size).
+    """
+    beam_size = pred.size(-1)
+    finished = flag.repeat([1, beam_size])
+    return pred.masked_fill_(finished, eos)
diff --git a/modules/wenet_extractor/utils/scheduler.py b/modules/wenet_extractor/utils/scheduler.py
new file mode 100755
index 0000000000000000000000000000000000000000..a5885dbb1faca52272f8825df2da7aa7533d1540
--- /dev/null
+++ b/modules/wenet_extractor/utils/scheduler.py
@@ -0,0 +1,738 @@
+# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
+
+# ## Citations
+
+# ```bibtex
+# @inproceedings{yao2021wenet,
+#   title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
+#   author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
+#   booktitle={Proc. Interspeech},
+#   year={2021},
+#   address={Brno, Czech Republic },
+#   organization={IEEE}
+# }
+
+# @article{zhang2022wenet,
+#   title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
+#   author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
+#   journal={arXiv preprint arXiv:2203.15455},
+#   year={2022}
+# }
+#
+
+from typing import Union
+
+import math
+import warnings
+import torch
+from torch.optim.lr_scheduler import _LRScheduler
+
+
+class WarmupLR(_LRScheduler):
+    """The WarmupLR scheduler
+
+    This scheduler is almost same as NoamLR Scheduler except for following
+    difference:
+
+    NoamLR:
+        lr = optimizer.lr * model_size ** -0.5
+             * min(step ** -0.5, step * warmup_step ** -1.5)
+    WarmupLR:
+        lr = optimizer.lr * warmup_step ** 0.5
+             * min(step ** -0.5, step * warmup_step ** -1.5)
+
+    Note that the maximum lr equals to optimizer.lr in this scheduler.
+
+    """
+
+    def __init__(
+        self,
+        optimizer: torch.optim.Optimizer,
+        warmup_steps: Union[int, float] = 25000,
+        last_epoch: int = -1,
+    ):
+        self.warmup_steps = warmup_steps
+
+        # __init__() must be invoked before setting field
+        # because step() is also invoked in __init__()
+        super().__init__(optimizer, last_epoch)
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}(warmup_steps={self.warmup_steps})"
+
+    def get_lr(self):
+        step_num = self.last_epoch + 1
+        if self.warmup_steps == 0:
+            return [lr * step_num**-0.5 for lr in self.base_lrs]
+        else:
+            return [
+                lr
+                * self.warmup_steps**0.5
+                * min(step_num**-0.5, step_num * self.warmup_steps**-1.5)
+                for lr in self.base_lrs
+            ]
+
+    def set_step(self, step: int):
+        self.last_epoch = step
+
+
+class WarmupPolicy(_LRScheduler):
+    """Adds warmup kwargs and warmup logic to lr policy.
+    All arguments should be passed as kwargs for clarity,
+    Args:
+        warmup_steps: Number of training steps in warmup stage
+        warmup_ratio: Ratio of warmup steps to total steps
+        max_steps: Total number of steps while training or `None` for
+            infinite training
+    """
+
+    def __init__(
+        self,
+        optimizer,
+        *,
+        warmup_steps=None,
+        warmup_ratio=None,
+        max_steps=None,
+        min_lr=0.0,
+        last_epoch=-1,
+    ):
+        assert not (
+            warmup_steps is not None and warmup_ratio is not None
+        ), "Either use particular number of step or ratio"
+        assert (
+            warmup_ratio is None or max_steps is not None
+        ), "If there is a ratio, there should be a total steps"
+
+        # It is necessary to assign all attributes *before* __init__,
+        # as class is wrapped by an inner class.
+        self.max_steps = max_steps
+        if warmup_steps is not None:
+            self.warmup_steps = warmup_steps
+        elif warmup_ratio is not None:
+            self.warmup_steps = int(warmup_ratio * max_steps)
+        else:
+            self.warmup_steps = 0
+
+        self.min_lr = min_lr
+        super().__init__(optimizer, last_epoch)
+
+    def get_lr(self):
+        if not self._get_lr_called_within_step:
+            warnings.warn(
+                "To get the last learning rate computed "
+                "by the scheduler, please use `get_last_lr()`.",
+                UserWarning,
+                stacklevel=2,
+            )
+
+        step = self.last_epoch
+
+        if step <= self.warmup_steps and self.warmup_steps > 0:
+            return self._get_warmup_lr(step)
+
+        if step > self.max_steps:
+            return [self.min_lr for _ in self.base_lrs]
+
+        return self._get_lr(step)
+
+    def _get_warmup_lr(self, step):
+        lr_val = (step + 1) / (self.warmup_steps + 1)
+        return [initial_lr * lr_val for initial_lr in self.base_lrs]
+
+    def _get_lr(self, step):
+        """Simple const lr policy"""
+        return self.base_lrs
+
+
+class SquareRootConstantPolicy(_LRScheduler):
+    """Adds warmup kwargs and warmup logic to lr policy.
+    All arguments should be passed as kwargs for clarity,
+    Args:
+        warmup_steps: Number of training steps in warmup stage
+        warmup_ratio: Ratio of warmup steps to total steps
+        max_steps: Total number of steps while training or `None` for
+            infinite training
+    """
+
+    def __init__(
+        self,
+        optimizer,
+        *,
+        constant_steps=None,
+        constant_ratio=None,
+        max_steps=None,
+        min_lr=0.0,
+        last_epoch=-1,
+    ):
+        assert not (
+            constant_steps is not None and constant_ratio is not None
+        ), "Either use particular number of step or ratio"
+        assert (
+            constant_ratio is None or max_steps is not None
+        ), "If there is a ratio, there should be a total steps"
+
+        # It is necessary to assign all attributes *before* __init__,
+        # as class is wrapped by an inner class.
+        self.max_steps = max_steps
+        if constant_steps is not None:
+            self.constant_steps = constant_steps
+        elif constant_ratio is not None:
+            self.constant_steps = int(constant_ratio * max_steps)
+        else:
+            self.constant_steps = 0
+
+        self.constant_lr = 1 / (constant_steps**0.5)
+        self.min_lr = min_lr
+        super().__init__(optimizer, last_epoch)
+
+    def get_lr(self):
+        if not self._get_lr_called_within_step:
+            warnings.warn(
+                "To get the last learning rate computed "
+                "by the scheduler, please use `get_last_lr()`.",
+                UserWarning,
+                stacklevel=2,
+            )
+
+        step = self.last_epoch
+
+        if step <= self.constant_steps:
+            return [self.constant_lr for _ in self.base_lrs]
+
+        if step > self.max_steps:
+            return [self.min_lr for _ in self.base_lrs]
+
+        return self._get_lr(step)
+
+    def _get_lr(self, step):
+        """Simple const lr policy"""
+        return self.base_lrs
+
+
+class WarmupHoldPolicy(WarmupPolicy):
+    """Variant of WarmupPolicy which maintains high
+       learning rate for a defined number of steps.
+    All arguments should be passed as kwargs for clarity,
+    Args:
+        warmup_steps: Number of training steps in warmup stage
+        warmup_ratio: Ratio of warmup steps to total steps
+        hold_steps: Number of training steps to
+                    hold the learning rate after warm up
+        hold_ratio: Ratio of hold steps to total steps
+        max_steps: Total number of steps while training or `None` for
+            infinite training
+    """
+
+    def __init__(
+        self,
+        optimizer,
+        *,
+        warmup_steps=None,
+        warmup_ratio=None,
+        hold_steps=None,
+        hold_ratio=None,
+        max_steps=None,
+        min_lr=0.0,
+        last_epoch=-1,
+    ):
+        assert not (
+            hold_steps is not None and hold_ratio is not None
+        ), "Either use particular number of step or ratio"
+        assert (
+            hold_ratio is None or max_steps is not None
+        ), "If there is a ratio, there should be a total steps"
+
+        self.min_lr = min_lr
+        self._last_warmup_lr = 0.0
+
+        # Necessary to duplicate as class attributes are hidden in inner class
+        self.max_steps = max_steps
+        if warmup_steps is not None:
+            self.warmup_steps = warmup_steps
+        elif warmup_ratio is not None:
+            self.warmup_steps = int(warmup_ratio * max_steps)
+        else:
+            self.warmup_steps = 0
+
+        if hold_steps is not None:
+            self.hold_steps = hold_steps + self.warmup_steps
+        elif hold_ratio is not None:
+            self.hold_steps = int(hold_ratio * max_steps) + self.warmup_steps
+        else:
+            self.hold_steps = 0
+
+        super().__init__(
+            optimizer,
+            warmup_steps=warmup_steps,
+            warmup_ratio=warmup_ratio,
+            max_steps=max_steps,
+            last_epoch=last_epoch,
+            min_lr=min_lr,
+        )
+
+    def get_lr(self):
+        if not self._get_lr_called_within_step:
+            warnings.warn(
+                "To get the last learning rate computed by the scheduler,"
+                " "
+                "please use `get_last_lr()`.",
+                UserWarning,
+                stacklevel=2,
+            )
+
+        step = self.last_epoch
+
+        # Warmup phase
+        if step <= self.warmup_steps and self.warmup_steps > 0:
+            return self._get_warmup_lr(step)
+
+        # Hold phase
+        if (step >= self.warmup_steps) and (step < self.hold_steps):
+            return self.base_lrs
+
+        if step > self.max_steps:
+            return [self.min_lr for _ in self.base_lrs]
+
+        return self._get_lr(step)
+
+
+class WarmupAnnealHoldPolicy(_LRScheduler):
+    """Adds warmup kwargs and warmup logic to lr policy.
+    All arguments should be passed as kwargs for clarity,
+    Args:
+        warmup_steps: Number of training steps in warmup stage
+        warmup_ratio: Ratio of warmup steps to total steps
+        max_steps: Total number of steps while training or `None` for
+            infinite training
+        min_lr: Minimum lr to hold the learning rate after decay at.
+        constant_steps: Number of steps to keep lr constant at.
+        constant_ratio: Ratio of steps to keep lr constant.
+    """
+
+    def __init__(
+        self,
+        optimizer,
+        *,
+        warmup_steps=None,
+        warmup_ratio=None,
+        constant_steps=None,
+        constant_ratio=None,
+        max_steps=None,
+        min_lr=0.0,
+        last_epoch=-1,
+    ):
+        assert not (
+            warmup_steps is not None and warmup_ratio is not None
+        ), "Either use particular number of step or ratio"
+        assert not (
+            constant_steps is not None and constant_ratio is not None
+        ), "Either use constant_steps or constant_ratio"
+        assert (
+            warmup_ratio is None or max_steps is not None
+        ), "If there is a ratio, there should be a total steps"
+
+        # It is necessary to assign all attributes *before* __init__,
+        # as class is wrapped by an inner class.
+        self.max_steps = max_steps
+
+        if warmup_steps is not None:
+            self.warmup_steps = warmup_steps
+        elif warmup_ratio is not None:
+            self.warmup_steps = int(warmup_ratio * max_steps)
+        else:
+            self.warmup_steps = 0
+
+        if constant_steps is not None:
+            self.constant_steps = constant_steps
+        elif constant_ratio is not None:
+            self.constant_steps = int(constant_ratio * max_steps)
+        else:
+            self.constant_steps = 0
+
+        self.decay_steps = max_steps - (self.constant_steps + self.warmup_steps)
+
+        self.min_lr = min_lr
+        super().__init__(optimizer, last_epoch)
+
+    def get_lr(self):
+        if not self._get_lr_called_within_step:
+            warnings.warn(
+                "To get the last learning rate computed "
+                "by the scheduler, please use `get_last_lr()`.",
+                UserWarning,
+                stacklevel=2,
+            )
+
+        step = self.last_epoch
+
+        # Warmup steps
+        if self.warmup_steps > 0 and step <= self.warmup_steps:
+            return self._get_warmup_lr(step)
+
+        # Constant steps after warmup and decay
+        if (
+            self.constant_steps > 0
+            and (self.warmup_steps + self.decay_steps) < step <= self.max_steps
+        ):
+            return self._get_constant_lr(step)
+
+        # Min lr after max steps of updates
+        if step > self.max_steps:
+            return [self.min_lr for _ in self.base_lrs]
+
+        return self._get_lr(step)
+
+    def _get_warmup_lr(self, step):
+        lr_val = (step + 1) / (self.warmup_steps + 1)
+        return [initial_lr * lr_val for initial_lr in self.base_lrs]
+
+    def _get_constant_lr(self, step):
+        return [self.min_lr for _ in self.base_lrs]
+
+    def _get_lr(self, step):
+        """Simple const lr policy"""
+        return self.base_lrs
+
+
+def _squareroot_annealing(initial_lr, step, max_steps, min_lr):
+    mult = ((max_steps - step) / max_steps) ** 0.5
+    out_lr = initial_lr * mult
+    out_lr = max(out_lr, min_lr)
+    return out_lr
+
+
+def _square_annealing(initial_lr, step, max_steps, min_lr):
+    mult = ((max_steps - step) / max_steps) ** 2
+    out_lr = initial_lr * mult
+    out_lr = max(out_lr, min_lr)
+    return out_lr
+
+
+def _cosine_annealing(initial_lr, step, max_steps, min_lr):
+    mult = 0.5 * (1 + math.cos(math.pi * step / max_steps))
+    out_lr = (initial_lr - min_lr) * mult + min_lr
+    return out_lr
+
+
+def _linear_warmup_with_cosine_annealing(
+    max_lr, warmup_steps, step, decay_steps, min_lr
+):
+    assert max_lr > min_lr
+    # Use linear warmup for the initial part.
+    if warmup_steps > 0 and step <= warmup_steps:
+        return max_lr * float(step) / float(warmup_steps)
+
+    # For any steps larger than `decay_steps`, use `min_lr`.
+    if step > warmup_steps + decay_steps:
+        return min_lr
+
+    # If we are done with the warmup period, use the decay style.
+    num_steps_ = step - warmup_steps
+    decay_steps_ = decay_steps
+    decay_ratio = float(num_steps_) / float(decay_steps_)
+    assert decay_ratio >= 0.0
+    assert decay_ratio <= 1.0
+    delta_lr = max_lr - min_lr
+
+    coeff = 0.5 * (math.cos(math.pi * decay_ratio) + 1.0)
+
+    return min_lr + coeff * delta_lr
+
+
+def _poly_decay(initial_lr, step, decay_steps, power, min_lr, cycle):
+    if cycle:
+        multiplier = 1.0 if step == 0 else math.ceil(step / decay_steps)
+        decay_steps *= multiplier
+    else:
+        step = min(step, decay_steps)
+    p = step / decay_steps
+    lr = (initial_lr - min_lr) * math.pow(1.0 - p, power)
+    lr += min_lr
+    return lr
+
+
+def _noam_hold_annealing(
+    initial_lr, step, warmup_steps, hold_steps, decay_rate, min_lr
+):
+    # hold_steps = total number of steps
+    # to hold the LR, not the warmup + hold steps.
+    T_warmup_decay = max(1, warmup_steps**decay_rate)
+    T_hold_decay = max(1, (step - hold_steps) ** decay_rate)
+    lr = (initial_lr * T_warmup_decay) / T_hold_decay
+    lr = max(lr, min_lr)
+    return lr
+
+
+class SquareAnnealing(WarmupPolicy):
+    def __init__(self, optimizer, *, max_steps, min_lr=1e-5, last_epoch=-1, **kwargs):
+        super().__init__(
+            optimizer=optimizer,
+            max_steps=max_steps,
+            last_epoch=last_epoch,
+            min_lr=min_lr,
+            **kwargs,
+        )
+
+    def _get_lr(self, step):
+        new_lrs = [
+            _square_annealing(
+                initial_lr=initial_lr,
+                step=step - self.warmup_steps,
+                max_steps=self.max_steps - self.warmup_steps,
+                min_lr=self.min_lr,
+            )
+            for initial_lr in self.base_lrs
+        ]
+        return new_lrs
+
+
+class SquareRootAnnealing(WarmupPolicy):
+    def __init__(self, optimizer, *, max_steps, min_lr=0, last_epoch=-1, **kwargs):
+        super().__init__(
+            optimizer=optimizer,
+            max_steps=max_steps,
+            last_epoch=last_epoch,
+            min_lr=min_lr,
+            **kwargs,
+        )
+
+    def _get_lr(self, step):
+        new_lrs = [
+            _squareroot_annealing(
+                initial_lr=initial_lr,
+                step=step,
+                max_steps=self.max_steps,
+                min_lr=self.min_lr,
+            )
+            for initial_lr in self.base_lrs
+        ]
+        return new_lrs
+
+
+class CosineAnnealing(WarmupAnnealHoldPolicy):
+    def __init__(self, optimizer, *, max_steps, min_lr=0, last_epoch=-1, **kwargs):
+        super().__init__(
+            optimizer=optimizer,
+            max_steps=max_steps,
+            last_epoch=last_epoch,
+            min_lr=min_lr,
+            **kwargs,
+        )
+
+    def _get_lr(self, step):
+        for initial_lr in self.base_lrs:
+            if initial_lr < self.min_lr:
+                raise ValueError(
+                    f"{self} received an initial learning rate "
+                    f"that was lower than the minimum learning rate."
+                )
+
+        if self.constant_steps is None or self.constant_steps == 0:
+            new_lrs = [
+                _cosine_annealing(
+                    initial_lr=initial_lr,
+                    step=step - self.warmup_steps,
+                    max_steps=self.max_steps - self.warmup_steps,
+                    min_lr=self.min_lr,
+                )
+                for initial_lr in self.base_lrs
+            ]
+        else:
+            new_lrs = self._get_linear_warmup_with_cosine_annealing_lr(step)
+        return new_lrs
+
+    def _get_warmup_lr(self, step):
+        if self.constant_steps is None or self.constant_steps == 0:
+            return super()._get_warmup_lr(step)
+        else:
+            # Use linear warmup for the initial part.
+            return self._get_linear_warmup_with_cosine_annealing_lr(step)
+
+    def _get_constant_lr(self, step):
+        # Only called when `constant_steps` > 0.
+        return self._get_linear_warmup_with_cosine_annealing_lr(step)
+
+    def _get_linear_warmup_with_cosine_annealing_lr(self, step):
+        # Cosine Schedule for Megatron LM,
+        # slightly different warmup schedule + constant LR at the end.
+        new_lrs = [
+            _linear_warmup_with_cosine_annealing(
+                max_lr=self.base_lrs[0],
+                warmup_steps=self.warmup_steps,
+                step=step,
+                decay_steps=self.decay_steps,
+                min_lr=self.min_lr,
+            )
+            for _ in self.base_lrs
+        ]
+        return new_lrs
+
+
+class NoamAnnealing(_LRScheduler):
+    def __init__(
+        self,
+        optimizer,
+        *,
+        d_model,
+        warmup_steps=None,
+        warmup_ratio=None,
+        max_steps=None,
+        min_lr=0.0,
+        last_epoch=-1,
+    ):
+        self._normalize = d_model ** (-0.5)
+        assert not (
+            warmup_steps is not None and warmup_ratio is not None
+        ), "Either use particular number of step or ratio"
+        assert (
+            warmup_ratio is None or max_steps is not None
+        ), "If there is a ratio, there should be a total steps"
+
+        # It is necessary to assign all attributes *before* __init__,
+        # as class is wrapped by an inner class.
+        self.max_steps = max_steps
+        if warmup_steps is not None:
+            self.warmup_steps = warmup_steps
+        elif warmup_ratio is not None:
+            self.warmup_steps = int(warmup_ratio * max_steps)
+        else:
+            self.warmup_steps = 0
+
+        self.min_lr = min_lr
+        super().__init__(optimizer, last_epoch)
+
+    def get_lr(self):
+        if not self._get_lr_called_within_step:
+            warnings.warn(
+                "To get the last learning rate computed "
+                "by the scheduler, please use `get_last_lr()`.",
+                UserWarning,
+                stacklevel=2,
+            )
+
+        step = max(1, self.last_epoch)
+
+        for initial_lr in self.base_lrs:
+            if initial_lr < self.min_lr:
+                raise ValueError(
+                    f"{self} received an initial learning rate "
+                    f"that was lower than the minimum learning rate."
+                )
+
+        new_lrs = [
+            self._noam_annealing(initial_lr=initial_lr, step=step)
+            for initial_lr in self.base_lrs
+        ]
+        return new_lrs
+
+    def _noam_annealing(self, initial_lr, step):
+        if self.warmup_steps > 0:
+            mult = self._normalize * min(
+                step ** (-0.5), step * (self.warmup_steps ** (-1.5))
+            )
+        else:
+            mult = self._normalize * step ** (-0.5)
+
+        out_lr = initial_lr * mult
+        if step > self.warmup_steps:
+            out_lr = max(out_lr, self.min_lr)
+        return out_lr
+
+
+class NoamHoldAnnealing(WarmupHoldPolicy):
+    def __init__(
+        self,
+        optimizer,
+        *,
+        max_steps,
+        decay_rate=0.5,
+        min_lr=0.0,
+        last_epoch=-1,
+        **kwargs,
+    ):
+        """
+        From Nemo:
+        Implementation of the Noam Hold Annealing policy
+        from the SqueezeFormer paper.
+
+        Unlike NoamAnnealing, the peak learning rate
+        can be explicitly set for this scheduler.
+        The schedule first performs linear warmup,
+        then holds the peak LR, then decays with some schedule for
+        the remainder of the steps.
+        Therefore the min-lr is still dependent
+        on the hyper parameters selected.
+
+        It's schedule is determined by three factors-
+
+        Warmup Steps: Initial stage, where linear warmup
+            occurs uptil the peak LR is reached. Unlike NoamAnnealing,
+            the peak LR is explicitly stated here instead of a scaling factor.
+
+        Hold Steps: Intermediate stage, where the peak LR
+            is maintained for some number of steps. In this region,
+            the high peak LR allows the model to converge faster
+            if training is stable. However the high LR
+            may also cause instability during training.
+            Should usually be a significant fraction of training
+            steps (around 30-40% of the entire training steps).
+
+        Decay Steps: Final stage, where the LR rapidly decays
+            with some scaling rate (set by decay rate).
+            To attain Noam decay, use 0.5,
+            for Squeezeformer recommended decay, use 1.0.
+            The fast decay after prolonged high LR during
+            hold phase allows for rapid convergence.
+
+        References:
+            - [Squeezeformer:
+            An Efficient Transformer for Automatic Speech Recognition]
+            (https://arxiv.org/abs/2206.00888)
+
+        Args:
+            optimizer: Pytorch compatible Optimizer object.
+            warmup_steps: Number of training steps in warmup stage
+            warmup_ratio: Ratio of warmup steps to total steps
+            hold_steps: Number of training steps to
+                        hold the learning rate after warm up
+            hold_ratio: Ratio of hold steps to total steps
+            max_steps: Total number of steps while training or `None` for
+                infinite training
+            decay_rate: Float value describing the polynomial decay
+                        after the hold period. Default value
+                        of 0.5 corresponds to Noam decay.
+            min_lr: Minimum learning rate.
+        """
+        self.decay_rate = decay_rate
+        super().__init__(
+            optimizer=optimizer,
+            max_steps=max_steps,
+            last_epoch=last_epoch,
+            min_lr=min_lr,
+            **kwargs,
+        )
+
+    def _get_lr(self, step):
+        if self.warmup_steps is None or self.warmup_steps == 0:
+            raise ValueError("Noam scheduler cannot be used without warmup steps")
+
+        if self.hold_steps > 0:
+            hold_steps = self.hold_steps - self.warmup_steps
+        else:
+            hold_steps = 0
+
+        new_lrs = [
+            _noam_hold_annealing(
+                initial_lr,
+                step=step,
+                warmup_steps=self.warmup_steps,
+                hold_steps=hold_steps,
+                decay_rate=self.decay_rate,
+                min_lr=self.min_lr,
+            )
+            for initial_lr in self.base_lrs
+        ]
+        return new_lrs
+
+    def set_step(self, step: int):
+        self.last_epoch = step
diff --git a/optimizer/__init__.py b/optimizer/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/optimizer/optimizers.py b/optimizer/optimizers.py
new file mode 100644
index 0000000000000000000000000000000000000000..180689e02571fa7901dfe556950bfca31dccbb3b
--- /dev/null
+++ b/optimizer/optimizers.py
@@ -0,0 +1,776 @@
+# This module is modified from https://github.com/Plachtaa/VALL-E-X/blob/3faaf8ccadb154d63b38070caf518ce9309ea0f4/modules/optim.py#L836
+
+import logging
+import contextlib
+import torch
+from torch import Tensor
+from torch.optim.lr_scheduler import _LRScheduler
+from torch.optim import Optimizer
+from typing import List, Tuple
+from collections import defaultdict
+
+
+class NoamLR(_LRScheduler):
+    """
+    Implements the Noam Learning rate schedule. This corresponds to increasing the learning rate
+    linearly for the first ``num_warmup`` training steps, and decreasing it thereafter proportionally
+    to the inverse square root of the step number, scaled by the inverse square root of the
+    dimensionality of the model. Time will tell if this is just madness or it's actually important.
+    Parameters
+    ----------
+    num_warmup: ``int``, required.
+        The number of steps to linearly increase the learning rate.
+    """
+
+    def __init__(self, optimizer, num_warmup):
+        self.num_warmup = num_warmup
+        self.base_lr = optimizer.param_groups[0]["lr"]
+        super().__init__(optimizer)
+
+    def get_lr(self):
+        last_epoch = max(1, self.last_epoch)
+        scale = min(last_epoch ** (-0.5), last_epoch * self.num_warmup ** (-1.5))
+        return [scale * self.base_lr]
+
+
+class Eve(Optimizer):
+    """
+    Implements Eve algorithm.  This is a modified version of AdamW with a special
+    way of setting the weight-decay / shrinkage-factor, which is designed to make the
+    rms of the parameters approach a particular target_rms (default: 0.1).  This is
+    for use with networks with 'scaled' versions of modules (see scaling.py), which
+    will be close to invariant to the absolute scale on the parameter matrix.
+
+    The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_.
+    The AdamW variant was proposed in `Decoupled Weight Decay Regularization`_.
+    Eve is unpublished so far.
+
+    Arguments:
+        params (iterable): iterable of parameters to optimize or dicts defining
+            parameter groups
+        lr (float, optional): learning rate (default: 1e-3)
+        betas (Tuple[float, float], optional): coefficients used for computing
+            running averages of gradient and its square (default: (0.9, 0.999))
+        eps (float, optional): term added to the denominator to improve
+            numerical stability (default: 1e-8)
+        weight_decay (float, optional): weight decay coefficient (default: 3e-4;
+            this value means that the weight would decay significantly after
+            about 3k minibatches.  Is not multiplied by learning rate, but
+            is conditional on RMS-value of parameter being > target_rms.
+        target_rms (float, optional): target root-mean-square value of
+           parameters, if they fall below this we will stop applying weight decay.
+
+
+    .. _Adam: A Method for Stochastic Optimization:
+        https://arxiv.org/abs/1412.6980
+    .. _Decoupled Weight Decay Regularization:
+        https://arxiv.org/abs/1711.05101
+    .. _On the Convergence of Adam and Beyond:
+        https://openreview.net/forum?id=ryQu7f-RZ
+    """
+
+    def __init__(
+        self,
+        params,
+        lr=1e-3,
+        betas=(0.9, 0.98),
+        eps=1e-8,
+        weight_decay=1e-3,
+        target_rms=0.1,
+    ):
+        if not 0.0 <= lr:
+            raise ValueError("Invalid learning rate: {}".format(lr))
+        if not 0.0 <= eps:
+            raise ValueError("Invalid epsilon value: {}".format(eps))
+        if not 0.0 <= betas[0] < 1.0:
+            raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
+        if not 0.0 <= betas[1] < 1.0:
+            raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
+        if not 0 <= weight_decay <= 0.1:
+            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
+        if not 0 < target_rms <= 10.0:
+            raise ValueError("Invalid target_rms value: {}".format(target_rms))
+        defaults = dict(
+            lr=lr,
+            betas=betas,
+            eps=eps,
+            weight_decay=weight_decay,
+            target_rms=target_rms,
+        )
+        super(Eve, self).__init__(params, defaults)
+
+    def __setstate__(self, state):
+        super(Eve, self).__setstate__(state)
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        """Performs a single optimization step.
+
+        Arguments:
+            closure (callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            for p in group["params"]:
+                if p.grad is None:
+                    continue
+
+                # Perform optimization step
+                grad = p.grad
+                if grad.is_sparse:
+                    raise RuntimeError("AdamW does not support sparse gradients")
+
+                state = self.state[p]
+
+                # State initialization
+                if len(state) == 0:
+                    state["step"] = 0
+                    # Exponential moving average of gradient values
+                    state["exp_avg"] = torch.zeros_like(
+                        p, memory_format=torch.preserve_format
+                    )
+                    # Exponential moving average of squared gradient values
+                    state["exp_avg_sq"] = torch.zeros_like(
+                        p, memory_format=torch.preserve_format
+                    )
+
+                exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
+
+                beta1, beta2 = group["betas"]
+
+                state["step"] += 1
+                bias_correction1 = 1 - beta1 ** state["step"]
+                bias_correction2 = 1 - beta2 ** state["step"]
+
+                # Decay the first and second moment running average coefficient
+                exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
+                exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
+                denom = (exp_avg_sq.sqrt() * (bias_correction2**-0.5)).add_(
+                    group["eps"]
+                )
+
+                step_size = group["lr"] / bias_correction1
+                target_rms = group["target_rms"]
+                weight_decay = group["weight_decay"]
+
+                if p.numel() > 1:
+                    # avoid applying this weight-decay on "scaling factors"
+                    # (which are scalar).
+                    is_above_target_rms = p.norm() > (target_rms * (p.numel() ** 0.5))
+                    p.mul_(1 - (weight_decay * is_above_target_rms))
+
+                p.addcdiv_(exp_avg, denom, value=-step_size)
+
+                # if random.random() < 0.0005:
+                #     step = (exp_avg / denom) * step_size
+                #     logging.info(
+                #         f"Delta rms = {(step**2).mean().item()}, shape = {step.shape}"
+                #     )
+
+        return loss
+
+
+class BatchedOptimizer(Optimizer):
+    """
+    This class adds to class Optimizer the capability to optimize parameters in batches:
+    it will stack the parameters and their grads for you so the optimizer can work
+    on tensors with an extra leading dimension.  This is intended for speed with GPUs,
+    as it reduces the number of kernels launched in the optimizer.
+
+    Args:
+      params:
+    """
+
+    def __init__(self, params, defaults):
+        super(BatchedOptimizer, self).__init__(params, defaults)
+
+    @contextlib.contextmanager
+    def batched_params(self, param_group, group_params_names):
+        """
+        This function returns (technically, yields) a list of
+          of tuples (p, state), where
+        p is a `fake` parameter that is stacked (over axis 0) from real parameters
+        that share the same shape, and its gradient is also stacked;
+        `state` is the state corresponding to this batch of parameters
+        (it will be physically located in the "state" for one of the real
+        parameters, the last one that has any particular shape and dtype).
+
+        This function is decorated as a context manager so that it can
+        write parameters back to their "real" locations.
+
+        The idea is, instead of doing:
+        <code>
+          for p in group["params"]:
+             state = self.state[p]
+             ...
+        </code>
+        you can do:
+        <code>
+          with self.batched_params(group["params"]) as batches:
+             for p, state, p_names in batches:
+                 ...
+        </code>
+
+        Args:
+          group: a parameter group, which is a list of parameters; should be
+                one of self.param_groups.
+          group_params_names: name for each parameter in group,
+                which is List[str].
+        """
+        batches = defaultdict(
+            list
+        )  # `batches` maps from tuple (dtype_as_str,*shape) to list of nn.Parameter
+        batches_names = defaultdict(
+            list
+        )  # `batches` maps from tuple (dtype_as_str,*shape) to list of str
+
+        assert len(param_group) == len(group_params_names)
+        for p, named_p in zip(param_group, group_params_names):
+            key = (str(p.dtype), *p.shape)
+            batches[key].append(p)
+            batches_names[key].append(named_p)
+
+        batches_names_keys = list(batches_names.keys())
+        sorted_idx = sorted(
+            range(len(batches_names)), key=lambda i: batches_names_keys[i]
+        )
+        batches_names = [batches_names[batches_names_keys[idx]] for idx in sorted_idx]
+        batches = [batches[batches_names_keys[idx]] for idx in sorted_idx]
+
+        stacked_params_dict = dict()
+
+        # turn batches into a list, in deterministic order.
+        # tuples will contain tuples of (stacked_param, state, stacked_params_names),
+        # one for each batch in `batches`.
+        tuples = []
+
+        for batch, batch_names in zip(batches, batches_names):
+            p = batch[0]
+            # we arbitrarily store the state in the
+            # state corresponding to the 1st parameter in the
+            # group.  class Optimizer will take care of saving/loading state.
+            state = self.state[p]
+            p_stacked = torch.stack(batch)
+            grad = torch.stack(
+                [torch.zeros_like(p) if p.grad is None else p.grad for p in batch]
+            )
+            p_stacked.grad = grad
+            stacked_params_dict[key] = p_stacked
+            tuples.append((p_stacked, state, batch_names))
+
+        yield tuples
+
+        for (stacked_params, _state, _names), batch in zip(tuples, batches):
+            for i, p in enumerate(batch):
+                p.copy_(stacked_params[i])
+
+
+class ScaledAdam(BatchedOptimizer):
+    """
+     Implements 'Scaled Adam', a variant of Adam where we scale each parameter's update
+     proportional to the norm of that parameter; and also learn the scale of the parameter,
+     in log space, subject to upper and lower limits (as if we had factored each parameter as
+     param = underlying_param * log_scale.exp())
+
+
+     Args:
+          params:  The parameters or param_groups to optimize (like other Optimizer subclasses)
+              lr:  The learning rate.  We will typically use a learning rate schedule that starts
+                   at 0.03 and decreases over time, i.e. much higher than other common
+                   optimizers.
+     clipping_scale: (e.g. 2.0)
+                   A scale for gradient-clipping: if specified, the normalized gradients
+                   over the whole model will be clipped to have 2-norm equal to
+                   `clipping_scale` times the median 2-norm over the most recent period
+                   of `clipping_update_period` minibatches.  By "normalized gradients",
+                   we mean after multiplying by the rms parameter value for this tensor
+                   [for non-scalars]; this is appropriate because our update is scaled
+                   by this quantity.
+            betas: beta1,beta2 are momentum constants for regular momentum, and moving sum-sq grad.
+                   Must satisfy 0 < beta <= beta2 < 1.
+     scalar_lr_scale: A scaling factor on the learning rate, that we use to update the
+                   scale of each parameter tensor and scalar parameters of the mode..
+                   If each parameter were decomposed
+                   as p * p_scale.exp(), where (p**2).mean().sqrt() == 1.0, scalar_lr_scale
+                   would be a the scaling factor on the learning rate of p_scale.
+              eps:  A general-purpose epsilon to prevent division by zero
+    param_min_rms: Minimum root-mean-square value of parameter tensor, for purposes of
+                   learning the scale on the parameters (we'll constrain the rms of each non-scalar
+                   parameter tensor to be >= this value)
+    param_max_rms: Maximum root-mean-square value of parameter tensor, for purposes of
+                   learning the scale on the parameters (we'll constrain the rms of each non-scalar
+                   parameter tensor to be <= this value)
+       scalar_max: Maximum absolute value for scalar parameters (applicable if your
+                   model has any parameters with numel() == 1).
+    size_update_period: The periodicity, in steps, with which we update the size (scale)
+                   of the parameter tensor.  This is provided to save a little time
+                   in the update.
+     clipping_update_period: if clipping_scale is specified, this is the period
+    """
+
+    def __init__(
+        self,
+        params,
+        lr=3e-02,
+        clipping_scale=None,
+        betas=(0.9, 0.98),
+        scalar_lr_scale=0.1,
+        eps=1.0e-08,
+        param_min_rms=1.0e-05,
+        param_max_rms=3.0,
+        scalar_max=10.0,
+        size_update_period=4,
+        clipping_update_period=100,
+        parameters_names=None,
+        show_dominant_parameters=True,
+    ):
+        assert parameters_names is not None, (
+            "Please prepare parameters_names,"
+            "which is a List[List[str]]. Each List[str] is for a group"
+            "and each str is for a parameter"
+        )
+        defaults = dict(
+            lr=lr,
+            clipping_scale=clipping_scale,
+            betas=betas,
+            scalar_lr_scale=scalar_lr_scale,
+            eps=eps,
+            param_min_rms=param_min_rms,
+            param_max_rms=param_max_rms,
+            scalar_max=scalar_max,
+            size_update_period=size_update_period,
+            clipping_update_period=clipping_update_period,
+        )
+
+        super(ScaledAdam, self).__init__(params, defaults)
+        assert len(self.param_groups) == len(parameters_names)
+        self.parameters_names = parameters_names
+        self.show_dominant_parameters = show_dominant_parameters
+
+    def __setstate__(self, state):
+        super(ScaledAdam, self).__setstate__(state)
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        """Performs a single optimization step.
+
+        Arguments:
+            closure (callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        batch = True
+
+        for group, group_params_names in zip(self.param_groups, self.parameters_names):
+            with self.batched_params(group["params"], group_params_names) as batches:
+                # batches is list of pairs (stacked_param, state).  stacked_param is like
+                # a regular parameter, and will have a .grad, but the 1st dim corresponds to
+                # a stacking dim, it is not a real dim.
+
+                if len(batches[0][1]) == 0:
+                    clipping_scale = 1
+                else:
+                    clipping_scale = self._get_clipping_scale(group, batches)
+
+                for p, state, _ in batches:
+                    # Perform optimization step.
+                    # grad is not going to be None, we handled that when creating the batches.
+                    grad = p.grad
+                    if grad.is_sparse:
+                        raise RuntimeError(
+                            "ScaledAdam optimizer does not support sparse gradients"
+                        )
+                    # State initialization
+                    if len(state) == 0:
+                        self._init_state(group, p, state)
+
+                    self._step_one_batch(group, p, state, clipping_scale)
+
+        return loss
+
+    def _init_state(self, group: dict, p: Tensor, state: dict):
+        """
+        Initializes state dict for parameter 'p'.  Assumes that dim 0 of tensor p
+        is actually the batch dimension, corresponding to batched-together
+        parameters of a given shape.
+
+
+        Args:
+           group:   Dict to look up configuration values.
+               p: The parameter that we are initializing the state for
+           state: Dict from string to whatever state we are initializing
+        """
+        size_update_period = group["size_update_period"]
+
+        state["step"] = 0
+
+        kwargs = {"device": p.device, "dtype": p.dtype}
+
+        # 'delta' implements conventional momentum.  There are
+        # several different kinds of update going on, so rather than
+        # compute "exp_avg" like in Adam, we store and decay a
+        # parameter-change "delta", which combines all forms of
+        # update.  this is equivalent to how it's done in Adam,
+        # except for the first few steps.
+        state["delta"] = torch.zeros_like(p, memory_format=torch.preserve_format)
+
+        batch_size = p.shape[0]
+        numel = p.numel() // batch_size
+        numel = p.numel()
+
+        if numel > 1:
+            # "param_rms" just periodically records the scalar root-mean-square value of
+            # the parameter tensor.
+            # it has a shape like (batch_size, 1, 1, 1, 1)
+            param_rms = (p**2).mean(dim=list(range(1, p.ndim)), keepdim=True).sqrt()
+            state["param_rms"] = param_rms
+
+            state["scale_exp_avg_sq"] = torch.zeros_like(param_rms)
+            state["scale_grads"] = torch.zeros(
+                size_update_period, *param_rms.shape, **kwargs
+            )
+
+        # exp_avg_sq is the weighted sum of scaled gradients. as in Adam.
+        state["exp_avg_sq"] = torch.zeros_like(p, memory_format=torch.preserve_format)
+
+    def _get_clipping_scale(
+        self, group: dict, tuples: List[Tuple[Tensor, dict, List[str]]]
+    ) -> float:
+        """
+        Returns a scalar factor <= 1.0 that dictates gradient clipping, i.e. we will scale the gradients
+        by this amount before applying the rest of the update.
+
+        Args:
+           group: the parameter group, an item in self.param_groups
+           tuples: a list of tuples of (param, state, param_names)
+                where param is a batched set of parameters,
+                with a .grad (1st dim is batch dim)
+                and state is the state-dict where optimization parameters are kept.
+                param_names is a List[str] while each str is name for a parameter
+                in batched set of parameters "param".
+        """
+        assert len(tuples) >= 1
+        clipping_scale = group["clipping_scale"]
+        (first_p, first_state, _) = tuples[0]
+        step = first_state["step"]
+        if clipping_scale is None or step == 0:
+            # no clipping.  return early on step == 0 because the other
+            # parameters' state won't have been initialized yet.
+            return 1.0
+        clipping_update_period = group["clipping_update_period"]
+
+        tot_sumsq = torch.tensor(0.0, device=first_p.device)
+        for p, state, param_names in tuples:
+            grad = p.grad
+            if grad.is_sparse:
+                raise RuntimeError(
+                    "ScaledAdam optimizer does not support sparse gradients"
+                )
+            if p.numel() == p.shape[0]:  # a batch of scalars
+                tot_sumsq += (grad**2).sum()  # sum() to change shape [1] to []
+            else:
+                tot_sumsq += ((grad * state["param_rms"]) ** 2).sum()
+
+        tot_norm = tot_sumsq.sqrt()
+        if "model_norms" not in first_state:
+            first_state["model_norms"] = torch.zeros(
+                clipping_update_period, device=p.device
+            )
+        first_state["model_norms"][step % clipping_update_period] = tot_norm
+
+        if step % clipping_update_period == 0:
+            # Print some stats.
+            # We don't reach here if step == 0 because we would have returned
+            # above.
+            sorted_norms = first_state["model_norms"].sort()[0].to("cpu")
+            quartiles = []
+            for n in range(0, 5):
+                index = min(
+                    clipping_update_period - 1,
+                    (clipping_update_period // 4) * n,
+                )
+                quartiles.append(sorted_norms[index].item())
+
+            median = quartiles[2]
+            threshold = clipping_scale * median
+            first_state["model_norm_threshold"] = threshold
+            percent_clipped = (
+                first_state["num_clipped"] * 100.0 / clipping_update_period
+                if "num_clipped" in first_state
+                else 0.0
+            )
+            first_state["num_clipped"] = 0
+            quartiles = " ".join(["%.3e" % x for x in quartiles])
+            logging.info(
+                f"Clipping_scale={clipping_scale}, grad-norm quartiles {quartiles}, "
+                f"threshold={threshold:.3e}, percent-clipped={percent_clipped:.1f}"
+            )
+
+        if step < clipping_update_period:
+            return 1.0  # We have not yet estimated a norm to clip to.
+        else:
+            try:
+                model_norm_threshold = first_state["model_norm_threshold"]
+            except KeyError:
+                logging.info(
+                    "Warning: model_norm_threshold not in state: possibly "
+                    "you changed config when restarting, adding clipping_scale option?"
+                )
+                return 1.0
+            ans = min(1.0, (model_norm_threshold / (tot_norm + 1.0e-20)).item())
+            if ans < 1.0:
+                first_state["num_clipped"] += 1
+            if ans < 0.1:
+                logging.warn(
+                    f"Scaling gradients by {ans}, model_norm_threshold={model_norm_threshold}"
+                )
+                if self.show_dominant_parameters:
+                    assert p.shape[0] == len(param_names)
+                    self._show_gradient_dominating_parameter(tuples, tot_sumsq)
+            return ans
+
+    def _show_gradient_dominating_parameter(
+        self, tuples: List[Tuple[Tensor, dict, List[str]]], tot_sumsq: Tensor
+    ):
+        """
+        Show information of parameter wihch dominanting tot_sumsq.
+
+        Args:
+           tuples: a list of tuples of (param, state, param_names)
+                where param is a batched set of parameters,
+                with a .grad (1st dim is batch dim)
+                and state is the state-dict where optimization parameters are kept.
+                param_names is a List[str] while each str is name for a parameter
+                in batched set of parameters "param".
+            tot_sumsq: sumsq of all parameters. Though it's could be calculated
+                from tuples, we still pass it to save some time.
+        """
+        all_sumsq_orig = {}
+        for p, state, batch_param_names in tuples:
+            # p is a stacked batch parameters.
+            batch_grad = p.grad
+            if p.numel() == p.shape[0]:  # a batch of scalars
+                batch_sumsq_orig = batch_grad**2
+                # Dummpy values used by following `zip` statement.
+                batch_rms_orig = torch.ones(p.shape[0])
+            else:
+                batch_rms_orig = state["param_rms"]
+                batch_sumsq_orig = ((batch_grad * batch_rms_orig) ** 2).sum(
+                    dim=list(range(1, batch_grad.ndim))
+                )
+
+            for name, sumsq_orig, rms, grad in zip(
+                batch_param_names, batch_sumsq_orig, batch_rms_orig, batch_grad
+            ):
+                proportion_orig = sumsq_orig / tot_sumsq
+                all_sumsq_orig[name] = (proportion_orig, sumsq_orig, rms, grad)
+
+        assert torch.isclose(
+            sum([value[0] for value in all_sumsq_orig.values()]).cpu(),
+            torch.tensor(1.0),
+        )
+        sorted_by_proportion = {
+            k: v
+            for k, v in sorted(
+                all_sumsq_orig.items(),
+                key=lambda item: item[1][0],
+                reverse=True,
+            )
+        }
+        dominant_param_name = next(iter(sorted_by_proportion))
+        (
+            dominant_proportion,
+            dominant_sumsq,
+            dominant_rms,
+            dominant_grad,
+        ) = sorted_by_proportion[dominant_param_name]
+        logging.info(
+            f"Parameter Dominanting tot_sumsq {dominant_param_name}"
+            f" with proportion {dominant_proportion:.2f},"
+            f" where dominant_sumsq=(grad_sumsq*orig_rms_sq)"
+            f"={dominant_sumsq:.3e},"
+            f" grad_sumsq = {(dominant_grad**2).sum():.3e},"
+            f" orig_rms_sq={(dominant_rms**2).item():.3e}"
+        )
+
+    def _step_one_batch(
+        self, group: dict, p: Tensor, state: dict, clipping_scale: float
+    ):
+        """
+        Do the step for one parameter, which is actually going to be a batch of
+        `real` parameters, with dim 0 as the batch dim.
+        Args:
+                  group:  dict to look up configuration values
+                    p: parameter to update (actually multiple parameters stacked together
+                       as a batch)
+                  state: state-dict for p, to look up the optimizer state
+        """
+        lr = group["lr"]
+        size_update_period = group["size_update_period"]
+        beta1 = group["betas"][0]
+
+        grad = p.grad
+        if clipping_scale != 1.0:
+            grad = grad * clipping_scale
+        step = state["step"]
+        delta = state["delta"]
+
+        delta.mul_(beta1)
+        batch_size = p.shape[0]
+        numel = p.numel() // batch_size
+        if numel > 1:
+            # Update the size/scale of p, and set param_rms
+            scale_grads = state["scale_grads"]
+            scale_grads[step % size_update_period] = (p * grad).sum(
+                dim=list(range(1, p.ndim)), keepdim=True
+            )
+            if step % size_update_period == size_update_period - 1:
+                param_rms = state["param_rms"]  # shape: (batch_size, 1, 1, ..)
+                param_rms.copy_(
+                    (p**2).mean(dim=list(range(1, p.ndim)), keepdim=True).sqrt()
+                )
+                if step > 0:
+                    # self._size_update() learns the overall scale on the
+                    # parameter, by shrinking or expanding it.
+                    self._size_update(group, scale_grads, p, state)
+
+        if numel == 1:
+            # For parameters with 1 element we just use regular Adam.
+            # Updates delta.
+            self._step_scalar(group, p, state)
+        else:
+            self._step(group, p, state)
+
+        state["step"] = step + 1
+
+    def _size_update(
+        self, group: dict, scale_grads: Tensor, p: Tensor, state: dict
+    ) -> None:
+        """
+               Called only where p.numel() > 1, this updates the scale of the parameter.
+               If we imagine: p =  underlying_param * scale.exp(), and we are doing
+               gradient descent on underlying param and on scale, this function does the update
+               on `scale`.
+
+               Args:
+              group: dict to look up configuration values
+        scale_grads: a tensor of shape (size_update_period, batch_size, 1, 1,...) containing
+                      grads w.r.t. the scales.
+                  p:  The parameter to update
+               state: The state-dict of p
+        """
+
+        param_rms = state["param_rms"]
+        beta1, beta2 = group["betas"]
+        size_lr = group["lr"] * group["scalar_lr_scale"]
+        param_min_rms = group["param_min_rms"]
+        param_max_rms = group["param_max_rms"]
+        eps = group["eps"]
+        step = state["step"]
+        batch_size = p.shape[0]
+
+        size_update_period = scale_grads.shape[0]
+        # correct beta2 for the size update period: we will have
+        # faster decay at this level.
+        beta2_corr = beta2**size_update_period
+
+        scale_exp_avg_sq = state["scale_exp_avg_sq"]  # shape: (batch_size, 1, 1, ..)
+        scale_exp_avg_sq.mul_(beta2_corr).add_(
+            (scale_grads**2).mean(dim=0),  # mean over dim `size_update_period`
+            alpha=1 - beta2_corr,
+        )  # shape is (batch_size, 1, 1, ...)
+
+        # The 1st time we reach here is when size_step == 1.
+        size_step = (step + 1) // size_update_period
+        bias_correction2 = 1 - beta2_corr**size_step
+        # we don't bother with bias_correction1; this will help prevent divergence
+        # at the start of training.
+
+        denom = scale_exp_avg_sq.sqrt() + eps
+
+        scale_step = (
+            -size_lr * (bias_correction2**0.5) * scale_grads.sum(dim=0) / denom
+        )
+
+        is_too_small = param_rms < param_min_rms
+        is_too_large = param_rms > param_max_rms
+
+        # when the param gets too small, just don't shrink it any further.
+        scale_step.masked_fill_(is_too_small, 0.0)
+        # when it gets too large, stop it from getting any larger.
+        scale_step.masked_fill_(is_too_large, -size_lr * size_update_period)
+        delta = state["delta"]
+        # the factor of (1-beta1) relates to momentum.
+        delta.add_(p * scale_step, alpha=(1 - beta1))
+
+    def _step(self, group: dict, p: Tensor, state: dict):
+        """
+        This function does the core update of self.step(), in the case where the members of
+        the batch have more than 1 element.
+
+        Args:
+            group: A dict which will be used to look up configuration values
+                p: The parameter to be updated
+             grad: The grad of p
+            state: The state-dict corresponding to parameter p
+
+        This function modifies p.
+        """
+        grad = p.grad
+        lr = group["lr"]
+        beta1, beta2 = group["betas"]
+        eps = group["eps"]
+        param_min_rms = group["param_min_rms"]
+        step = state["step"]
+
+        exp_avg_sq = state["exp_avg_sq"]
+        exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=(1 - beta2))
+
+        this_step = state["step"] - (state["zero_step"] if "zero_step" in state else 0)
+        bias_correction2 = 1 - beta2 ** (this_step + 1)
+        if bias_correction2 < 0.99:
+            # note: not in-place.
+            exp_avg_sq = exp_avg_sq * (1.0 / bias_correction2)
+
+        denom = exp_avg_sq.sqrt()
+        denom += eps
+        grad = grad / denom
+
+        alpha = -lr * (1 - beta1) * state["param_rms"].clamp(min=param_min_rms)
+
+        delta = state["delta"]
+        delta.add_(grad * alpha)
+        p.add_(delta)
+
+    def _step_scalar(self, group: dict, p: Tensor, state: dict):
+        """
+        A simplified form of the core update for scalar tensors, where we cannot get a good
+        estimate of the parameter rms.
+        """
+        beta1, beta2 = group["betas"]
+        scalar_max = group["scalar_max"]
+        eps = group["eps"]
+        lr = group["lr"] * group["scalar_lr_scale"]
+        grad = p.grad
+
+        exp_avg_sq = state["exp_avg_sq"]  # shape: (batch_size,)
+        exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
+
+        # bias_correction2 is like in Adam.  Don't bother with bias_correction1;
+        # slower update at the start will help stability anyway.
+        bias_correction2 = 1 - beta2 ** (state["step"] + 1)
+        denom = (exp_avg_sq / bias_correction2).sqrt() + eps
+
+        delta = state["delta"]
+        delta.add_(grad / denom, alpha=-lr * (1 - beta1))
+        p.clamp_(min=-scalar_max, max=scalar_max)
+        p.add_(delta)
diff --git a/pre-requirements.txt b/pre-requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d01d84495bdcfe607dbf349cea3b2c2164d6293b
--- /dev/null
+++ b/pre-requirements.txt
@@ -0,0 +1,46 @@
+setuptools
+ruamel.yaml
+tqdm
+colorama
+easydict
+tabulate
+loguru
+json5
+Cython
+unidecode
+inflect
+argparse
+g2p_en
+tgt
+librosa
+matplotlib
+typeguard
+einops
+omegaconf
+hydra-core
+humanfriendly
+pandas
+tensorboard
+tensorboardX
+torch==2.0.1
+torchaudio==2.0.2
+torchvision==0.15.2
+transformers==4.30.2
+diffusers==0.19.3
+accelerate
+praat-parselmouth
+audiomentations
+pedalboard
+ffmpeg-python==0.2.0
+pyworld
+diffsptk
+nnAudio
+unidecode
+inflect
+ptwt
+torchmetrics
+pymcd
+openai-whisper
+frechet_audio_distance
+asteroid
+https://github.com/vBaiCai/python-pesq/archive/master.zip
\ No newline at end of file
diff --git a/preprocessors/__init__.py b/preprocessors/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..92514cbbafea25f61bcc4f4bbdce13bfa58b5fd8
--- /dev/null
+++ b/preprocessors/__init__.py
@@ -0,0 +1,189 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+For source datasets' standard samples
+"""
+
+from collections import defaultdict
+import os
+import json
+
+SPEECH_DATASETS = ["vctk", "vctksample"]
+
+GOLDEN_TEST_SAMPLES = defaultdict(list)
+GOLDEN_TEST_SAMPLES["m4singer"] = [
+    "Alto-1_美错_0014",
+    "Bass-1_十年_0008",
+    "Soprano-2_同桌的你_0018",
+    "Tenor-5_爱笑的眼睛_0010",
+]
+GOLDEN_TEST_SAMPLES["svcc"] = [
+    # IDF1
+    "IDF1_10030",
+    "IDF1_10120",
+    "IDF1_10140",
+    # IDM1
+    "IDM1_10001",
+    "IDM1_10030",
+    "IDM1_10120",
+    # CDF1
+    "CDF1_10030",
+    "CDF1_10120",
+    "CDF1_10140",
+    # CDM1
+    "CDM1_10001",
+    "CDM1_10030",
+    "CDM1_10120",
+]
+GOLDEN_TEST_SAMPLES["svcceval"] = [
+    # SF1
+    "SF1_30001",
+    "SF1_30002",
+    "SF1_30003",
+    # SM1
+    "SM1_30001",
+    "SM1_30002",
+    "SM1_30003",
+]
+GOLDEN_TEST_SAMPLES["popbutfy"] = [
+    "Female1#you_are_my_sunshine_Professional#0",
+    "Female4#Someone_Like_You_Professional#10",
+    "Male2#Lemon_Tree_Professional#12",
+    "Male5#can_you_feel_the_love_tonight_Professional#20",
+]
+GOLDEN_TEST_SAMPLES["opensinger"] = [
+    "Man_0_大鱼_10",
+    "Man_21_丑八怪_14",
+    "Woman_39_mojito_22",
+    "Woman_40_易燃易爆炸_12",
+]
+GOLDEN_TEST_SAMPLES["nus48e"] = [
+    "ADIZ_read#01#0000",
+    "MCUR_sing#10#0000",
+    "JLEE_read#08#0001",
+    "SAMF_sing#18#0001",
+]
+GOLDEN_TEST_SAMPLES["popcs"] = [
+    "明天会更好_0004",
+    "欧若拉_0005",
+    "虫儿飞_0006",
+    "隐形的翅膀_0008",
+]
+GOLDEN_TEST_SAMPLES["kising"] = [
+    "421_0040",
+    "424_0013",
+    "431_0026",
+]
+GOLDEN_TEST_SAMPLES["csd"] = [
+    "en_004a_0001",
+    "en_042b_0006",
+    "kr_013a_0006",
+    "kr_045b_0004",
+]
+GOLDEN_TEST_SAMPLES["opera"] = [
+    "fem_01#neg_1#0000",
+    "fem_12#pos_3#0003",
+    "male_02#neg_1#0002",
+    "male_11#pos_2#0001",
+]
+GOLDEN_TEST_SAMPLES["lijian"] = [
+    "058矜持_0000",
+    "079绒花_0000",
+    "120遥远的天空底下_0000",
+]
+GOLDEN_TEST_SAMPLES["cdmusiceval"] = ["陶喆_普通朋友", "蔡琴_给电影人的情书"]
+
+GOLDEN_TRAIN_SAMPLES = defaultdict(list)
+
+
+def get_golden_samples_indexes(
+    dataset_name,
+    dataset_dir=None,
+    cfg=None,
+    split=None,
+    min_samples=5,
+):
+    """
+    # Get Standard samples' indexes
+    """
+    if dataset_dir is None:
+        assert cfg is not None
+        dataset_dir = os.path.join(
+            cfg.OUTPUT_PATH,
+            "preprocess/{}_version".format(cfg.PREPROCESS_VERSION),
+            dataset_name,
+        )
+
+    assert split is not None
+    utt_file = os.path.join(dataset_dir, "{}.json".format(split))
+    with open(utt_file, "r", encoding="utf-8") as f:
+        samples = json.load(f)
+
+    if "train" in split:
+        golden_samples = GOLDEN_TRAIN_SAMPLES[dataset_name]
+    if "test" in split:
+        golden_samples = GOLDEN_TEST_SAMPLES[dataset_name]
+
+    res = []
+    for idx, utt in enumerate(samples):
+        if utt["Uid"] in golden_samples:
+            res.append(idx)
+
+        if dataset_name == "cdmusiceval":
+            if "_".join(utt["Uid"].split("_")[:2]) in golden_samples:
+                res.append(idx)
+
+    if len(res) == 0:
+        res = [i for i in range(min_samples)]
+
+    return res
+
+
+def get_specific_singer_indexes(dataset_dir, singer_name, split):
+    utt_file = os.path.join(dataset_dir, "{}.json".format(split))
+    with open(utt_file, "r", encoding="utf-8") as f:
+        samples = json.load(f)
+
+    res = []
+    for idx, utt in enumerate(samples):
+        if utt["Singer"] == singer_name:
+            res.append(idx)
+
+    assert len(res) != 0
+    return res
+
+
+def get_uids_and_wav_paths(
+    cfg, dataset, dataset_type="train", only_specific_singer=None, return_singers=False
+):
+    dataset_dir = os.path.join(
+        cfg.OUTPUT_PATH, "preprocess/{}_version".format(cfg.PREPROCESS_VERSION), dataset
+    )
+    dataset_file = os.path.join(
+        dataset_dir, "{}.json".format(dataset_type.split("_")[-1])
+    )
+    with open(dataset_file, "r") as f:
+        utterances = json.load(f)
+
+    indexes = range(len(utterances))
+    if "golden" in dataset_type:
+        # golden_train or golden_test
+        indexes = get_golden_samples_indexes(
+            dataset, dataset_dir, split=dataset_type.split("_")[-1]
+        )
+    if only_specific_singer is not None:
+        indexes = get_specific_singer_indexes(
+            dataset_dir, only_specific_singer, dataset_type
+        )
+
+    uids = [utterances[i]["Uid"] for i in indexes]
+    wav_paths = [utterances[i]["Path"] for i in indexes]
+    singers = [utterances[i]["Singer"] for i in indexes]
+
+    if not return_singers:
+        return uids, wav_paths
+    else:
+        return uids, wav_paths, singers
diff --git a/preprocessors/bigdata.py b/preprocessors/bigdata.py
new file mode 100644
index 0000000000000000000000000000000000000000..da541191259e337933ce469503c0d057808ebb85
--- /dev/null
+++ b/preprocessors/bigdata.py
@@ -0,0 +1,145 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import json
+import os
+from collections import defaultdict
+from tqdm import tqdm
+
+
+def get_uids_and_wav_paths(cfg, dataset, dataset_type):
+    assert dataset == "bigdata"
+    dataset_dir = os.path.join(
+        cfg.OUTPUT_PATH,
+        "preprocess/{}_version".format(cfg.PREPROCESS_VERSION),
+        "bigdata/{}".format(cfg.BIGDATA_VERSION),
+    )
+    dataset_file = os.path.join(
+        dataset_dir, "{}.json".format(dataset_type.split("_")[-1])
+    )
+    with open(dataset_file, "r") as f:
+        utterances = json.load(f)
+
+    # Uids
+    uids = [u["Uid"] for u in utterances]
+
+    # Wav paths
+    wav_paths = [u["Path"] for u in utterances]
+
+    return uids, wav_paths
+
+
+def take_duration(utt):
+    return utt["Duration"]
+
+
+def main(output_path, cfg):
+    datasets = cfg.dataset
+
+    print("-" * 10)
+    print("Preparing samples for bigdata...")
+    print("Including: \n{}\n".format("\n".join(datasets)))
+
+    datasets.sort()
+    bigdata_version = "_".join(datasets)
+
+    save_dir = os.path.join(output_path, bigdata_version)
+    os.makedirs(save_dir, exist_ok=True)
+
+    train_output_file = os.path.join(save_dir, "train.json")
+    test_output_file = os.path.join(save_dir, "test.json")
+    singer_dict_file = os.path.join(save_dir, cfg.preprocess.spk2id)
+    utt2singer_file = os.path.join(save_dir, cfg.preprocess.utt2spk)
+    utt2singer = open(utt2singer_file, "a+")
+    # We select songs of standard samples as test songs
+    train = []
+    test = []
+
+    train_total_duration = 0
+    test_total_duration = 0
+
+    # Singer unique names
+    singer_names = set()
+
+    for dataset in datasets:
+        dataset_path = os.path.join(output_path, dataset)
+        train_json = os.path.join(dataset_path, "train.json")
+        test_json = os.path.join(dataset_path, "test.json")
+
+        with open(train_json, "r", encoding="utf-8") as f:
+            train_utterances = json.load(f)
+
+        with open(test_json, "r", encoding="utf-8") as f:
+            test_utterances = json.load(f)
+
+        for utt in tqdm(train_utterances):
+            train.append(utt)
+            train_total_duration += utt["Duration"]
+            singer_names.add("{}_{}".format(utt["Dataset"], utt["Singer"]))
+            utt2singer.write(
+                "{}_{}\t{}_{}\n".format(
+                    utt["Dataset"], utt["Uid"], utt["Dataset"], utt["Singer"]
+                )
+            )
+
+        for utt in test_utterances:
+            test.append(utt)
+            test_total_duration += utt["Duration"]
+            singer_names.add("{}_{}".format(utt["Dataset"], utt["Singer"]))
+            utt2singer.write(
+                "{}_{}\t{}_{}\n".format(
+                    utt["Dataset"], utt["Uid"], utt["Dataset"], utt["Singer"]
+                )
+            )
+
+    utt2singer.close()
+
+    train.sort(key=take_duration)
+    test.sort(key=take_duration)
+    print("#Train = {}, #Test = {}".format(len(train), len(test)))
+    print(
+        "#Train hours= {}, #Test hours= {}".format(
+            train_total_duration / 3600, test_total_duration / 3600
+        )
+    )
+
+    # Singer Look Up Table
+    singer_names = list(singer_names)
+    singer_names.sort()
+    singer_lut = {name: i for i, name in enumerate(singer_names)}
+    print("#Singers: {}\n".format(len(singer_lut)))
+
+    # Save
+    with open(train_output_file, "w") as f:
+        json.dump(train, f, indent=4, ensure_ascii=False)
+    with open(test_output_file, "w") as f:
+        json.dump(test, f, indent=4, ensure_ascii=False)
+    with open(singer_dict_file, "w") as f:
+        json.dump(singer_lut, f, indent=4, ensure_ascii=False)
+
+    # Save meta info
+    meta_info = {
+        "datasets": datasets,
+        "train": {"size": len(train), "hours": round(train_total_duration / 3600, 4)},
+        "test": {"size": len(test), "hours": round(test_total_duration / 3600, 4)},
+        "singers": {"size": len(singer_lut)},
+    }
+    singer2mins = defaultdict(float)
+    for utt in train:
+        dataset, singer, duration = utt["Dataset"], utt["Singer"], utt["Duration"]
+        singer2mins["{}_{}".format(dataset, singer)] += duration / 60
+    singer2mins = sorted(singer2mins.items(), key=lambda x: x[1], reverse=True)
+    singer2mins = dict(
+        zip([i[0] for i in singer2mins], [round(i[1], 2) for i in singer2mins])
+    )
+    meta_info["singers"]["training_minutes"] = singer2mins
+
+    with open(os.path.join(save_dir, "meta_info.json"), "w") as f:
+        json.dump(meta_info, f, indent=4, ensure_ascii=False)
+
+    for singer, min in singer2mins.items():
+        print("Singer {}: {} mins".format(singer, min))
+    print("-" * 10, "\n")
diff --git a/preprocessors/cdmusiceval.py b/preprocessors/cdmusiceval.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c4af97c17153196501200811f4e96cfcfa2f4f1
--- /dev/null
+++ b/preprocessors/cdmusiceval.py
@@ -0,0 +1,174 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from glob import glob
+import os
+import json
+import torchaudio
+from tqdm import tqdm
+from collections import defaultdict
+
+from utils.util import has_existed, remove_and_create
+from utils.audio_slicer import split_utterances_from_audio
+
+
+def split_to_utterances(input_dir, output_dir):
+    print("Splitting to utterances for {}...".format(input_dir))
+
+    files_list = glob("*", root_dir=input_dir)
+    files_list.sort()
+    for wav_file in tqdm(files_list):
+        # # Load waveform
+        # waveform, fs = torchaudio.load(os.path.join(input_dir, wav_file))
+
+        # Singer name, Song name
+        song_name, singer_name = wav_file.split("_")[2].split("-")
+        save_dir = os.path.join(output_dir, singer_name, song_name)
+
+        split_utterances_from_audio(
+            os.path.join(input_dir, wav_file), save_dir, max_duration_of_utterance=10
+        )
+
+        # # Split
+        # slicer = Slicer(sr=fs, threshold=-30.0, max_sil_kept=3000, min_interval=1000)
+        # chunks = slicer.slice(waveform)
+
+        # for i, chunk in enumerate(chunks):
+        #     save_dir = os.path.join(output_dir, singer_name, song_name)
+        #     os.makedirs(save_dir, exist_ok=True)
+
+        #     output_file = os.path.join(save_dir, "{:04d}.wav".format(i))
+        #     save_audio(output_file, chunk, fs)
+
+
+def _main(dataset_path):
+    """
+    Split to utterances
+    """
+    utterance_dir = os.path.join(dataset_path, "utterances")
+    remove_and_create(utterance_dir)
+    split_to_utterances(os.path.join(dataset_path, "vocal"), utterance_dir)
+
+
+def statistics(utterance_dir):
+    singers = []
+    songs = []
+    singers2songs = defaultdict(lambda: defaultdict(list))
+
+    singer_infos = glob(utterance_dir + "/*")
+
+    for singer_info in singer_infos:
+        singer = singer_info.split("/")[-1]
+
+        song_infos = glob(singer_info + "/*")
+
+        for song_info in song_infos:
+            song = song_info.split("/")[-1]
+
+            singers.append(singer)
+            songs.append(song)
+
+            utts = glob(song_info + "/*.wav")
+
+            for utt in utts:
+                uid = utt.split("/")[-1].split(".")[0]
+                singers2songs[singer][song].append(uid)
+
+    unique_singers = list(set(singers))
+    unique_songs = list(set(songs))
+    unique_singers.sort()
+    unique_songs.sort()
+
+    print(
+        "Statistics: {} singers, {} utterances ({} unique songs)".format(
+            len(unique_singers), len(songs), len(unique_songs)
+        )
+    )
+    print("Singers: \n{}".format("\t".join(unique_singers)))
+    return singers2songs, unique_singers
+
+
+def main(output_path, dataset_path):
+    print("-" * 10)
+    print("Preparing samples for CD Music Eval...\n")
+
+    if not os.path.exists(os.path.join(dataset_path, "utterances")):
+        print("Spliting into utterances...\n")
+        _main(dataset_path)
+
+    save_dir = os.path.join(output_path, "cdmusiceval")
+    os.makedirs(save_dir, exist_ok=True)
+    train_output_file = os.path.join(save_dir, "train.json")
+    test_output_file = os.path.join(save_dir, "test.json")
+    singer_dict_file = os.path.join(save_dir, "singers.json")
+    utt2singer_file = os.path.join(save_dir, "utt2singer")
+    if (
+        has_existed(train_output_file)
+        and has_existed(test_output_file)
+        and has_existed(singer_dict_file)
+        and has_existed(utt2singer_file)
+    ):
+        return
+    utt2singer = open(utt2singer_file, "w")
+
+    # Load
+    utt_path = os.path.join(dataset_path, "utterances")
+    singers2songs, unique_singers = statistics(utt_path)
+
+    # We select songs of standard samples as test songs
+    train = []
+    test = []
+
+    train_index_count = 0
+    test_index_count = 0
+
+    train_total_duration = 0
+    test_total_duration = 0
+
+    for singer, songs in tqdm(singers2songs.items()):
+        song_names = list(songs.keys())
+
+        for chosen_song in song_names:
+            for chosen_uid in songs[chosen_song]:
+                res = {
+                    "Dataset": "cdmusiceval",
+                    "Singer": singer,
+                    "Uid": "{}_{}_{}".format(singer, chosen_song, chosen_uid),
+                }
+                res["Path"] = "{}/{}/{}.wav".format(singer, chosen_song, chosen_uid)
+                res["Path"] = os.path.join(utt_path, res["Path"])
+                assert os.path.exists(res["Path"])
+
+                waveform, sample_rate = torchaudio.load(res["Path"])
+                duration = waveform.size(-1) / sample_rate
+                res["Duration"] = duration
+
+                if duration <= 1e-8:
+                    continue
+
+                res["index"] = test_index_count
+                test_total_duration += duration
+                test.append(res)
+                test_index_count += 1
+
+                utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"]))
+
+    print("#Train = {}, #Test = {}".format(len(train), len(test)))
+    print(
+        "#Train hours= {}, #Test hours= {}".format(
+            train_total_duration / 3600, test_total_duration / 3600
+        )
+    )
+
+    # Save train.json and test.json
+    with open(train_output_file, "w") as f:
+        json.dump(train, f, indent=4, ensure_ascii=False)
+    with open(test_output_file, "w") as f:
+        json.dump(test, f, indent=4, ensure_ascii=False)
+
+    # Save singers.json
+    singer_lut = {name: i for i, name in enumerate(unique_singers)}
+    with open(singer_dict_file, "w") as f:
+        json.dump(singer_lut, f, indent=4, ensure_ascii=False)
diff --git a/preprocessors/coco.py b/preprocessors/coco.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ac462f32d7a58ed245f58d13a44e02b4efef06b
--- /dev/null
+++ b/preprocessors/coco.py
@@ -0,0 +1,100 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import json
+import torchaudio
+from tqdm import tqdm
+from glob import glob
+from collections import defaultdict
+
+from utils.util import has_existed
+from preprocessors import GOLDEN_TEST_SAMPLES
+
+
+def get_test_songs():
+    return ["007Di Da Di"]
+
+
+def coco_statistics(data_dir):
+    song2utts = defaultdict(list)
+
+    song_infos = glob(data_dir + "/*")
+
+    for song in song_infos:
+        song_name = song.split("/")[-1]
+        utts = glob(song + "/*.wav")
+        for utt in utts:
+            uid = utt.split("/")[-1].split(".")[0]
+            song2utts[song_name].append(uid)
+
+    print("Coco: {} songs".format(len(song_infos)))
+    return song2utts
+
+
+def main(output_path, dataset_path):
+    print("-" * 10)
+    print("Preparing datasets for Coco...\n")
+
+    save_dir = os.path.join(output_path, "coco")
+    train_output_file = os.path.join(save_dir, "train.json")
+    test_output_file = os.path.join(save_dir, "test.json")
+    if has_existed(test_output_file):
+        return
+
+    # Load
+    song2utts = coco_statistics(dataset_path)
+    test_songs = get_test_songs()
+
+    # We select songs of standard samples as test songs
+    train = []
+    test = []
+
+    train_index_count = 0
+    test_index_count = 0
+
+    train_total_duration = 0
+    test_total_duration = 0
+
+    for song_name, uids in tqdm(song2utts.items()):
+        for chosen_uid in uids:
+            res = {
+                "Dataset": "coco",
+                "Singer": "coco",
+                "Song": song_name,
+                "Uid": "{}_{}".format(song_name, chosen_uid),
+            }
+            res["Path"] = "{}/{}.wav".format(song_name, chosen_uid)
+            res["Path"] = os.path.join(dataset_path, res["Path"])
+            assert os.path.exists(res["Path"])
+
+            waveform, sample_rate = torchaudio.load(res["Path"])
+            duration = waveform.size(-1) / sample_rate
+            res["Duration"] = duration
+
+            if song_name in test_songs:
+                res["index"] = test_index_count
+                test_total_duration += duration
+                test.append(res)
+                test_index_count += 1
+            else:
+                res["index"] = train_index_count
+                train_total_duration += duration
+                train.append(res)
+                train_index_count += 1
+
+    print("#Train = {}, #Test = {}".format(len(train), len(test)))
+    print(
+        "#Train hours= {}, #Test hours= {}".format(
+            train_total_duration / 3600, test_total_duration / 3600
+        )
+    )
+
+    # Save
+    os.makedirs(save_dir, exist_ok=True)
+    with open(train_output_file, "w") as f:
+        json.dump(train, f, indent=4, ensure_ascii=False)
+    with open(test_output_file, "w") as f:
+        json.dump(test, f, indent=4, ensure_ascii=False)
diff --git a/preprocessors/cocoeval.py b/preprocessors/cocoeval.py
new file mode 100644
index 0000000000000000000000000000000000000000..3cb3604c9d540d4e8f166f459a1ac992a1bd7a17
--- /dev/null
+++ b/preprocessors/cocoeval.py
@@ -0,0 +1,99 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import random
+import os
+import json
+import torchaudio
+from tqdm import tqdm
+from glob import glob
+from collections import defaultdict
+
+from utils.util import has_existed
+from utils.audio_slicer import split_utterances_from_audio
+from preprocessors import GOLDEN_TEST_SAMPLES
+
+
+def _split_utts():
+    raw_dir = "/mnt/chongqinggeminiceph1fs/geminicephfs/wx-mm-spr-xxxx/xueyaozhang/dataset/李玟/cocoeval/raw"
+    output_root = "/mnt/chongqinggeminiceph1fs/geminicephfs/wx-mm-spr-xxxx/xueyaozhang/dataset/李玟/cocoeval/utterances"
+
+    if os.path.exists(output_root):
+        os.system("rm -rf {}".format(output_root))
+
+    vocal_files = glob(os.path.join(raw_dir, "*/vocal.wav"))
+    for vocal_f in tqdm(vocal_files):
+        song_name = vocal_f.split("/")[-2]
+
+        output_dir = os.path.join(output_root, song_name)
+        os.makedirs(output_dir, exist_ok=True)
+
+        split_utterances_from_audio(vocal_f, output_dir, min_interval=300)
+
+
+def cocoeval_statistics(data_dir):
+    song2utts = defaultdict(list)
+
+    song_infos = glob(data_dir + "/*")
+
+    for song in song_infos:
+        song_name = song.split("/")[-1]
+        utts = glob(song + "/*.wav")
+        for utt in utts:
+            uid = utt.split("/")[-1].split(".")[0]
+            song2utts[song_name].append(uid)
+
+    print("Cocoeval: {} songs".format(len(song_infos)))
+    return song2utts
+
+
+def main(output_path, dataset_path):
+    print("-" * 10)
+    print("Preparing datasets for Cocoeval...\n")
+
+    save_dir = os.path.join(output_path, "cocoeval")
+    test_output_file = os.path.join(save_dir, "test.json")
+    if has_existed(test_output_file):
+        return
+
+    # Load
+    song2utts = cocoeval_statistics(dataset_path)
+
+    train, test = [], []
+    train_index_count, test_index_count = 0, 0
+    train_total_duration, test_total_duration = 0.0, 0.0
+
+    for song_name, uids in tqdm(song2utts.items()):
+        for chosen_uid in uids:
+            res = {
+                "Dataset": "cocoeval",
+                "Singer": "TBD",
+                "Song": song_name,
+                "Uid": "{}_{}".format(song_name, chosen_uid),
+            }
+            res["Path"] = "{}/{}.wav".format(song_name, chosen_uid)
+            res["Path"] = os.path.join(dataset_path, res["Path"])
+            assert os.path.exists(res["Path"])
+
+            waveform, sample_rate = torchaudio.load(res["Path"])
+            duration = waveform.size(-1) / sample_rate
+            res["Duration"] = duration
+
+            res["index"] = test_index_count
+            test_total_duration += duration
+            test.append(res)
+            test_index_count += 1
+
+    print("#Train = {}, #Test = {}".format(len(train), len(test)))
+    print(
+        "#Train hours= {}, #Test hours= {}".format(
+            train_total_duration / 3600, test_total_duration / 3600
+        )
+    )
+
+    # Save
+    os.makedirs(save_dir, exist_ok=True)
+    with open(test_output_file, "w") as f:
+        json.dump(test, f, indent=4, ensure_ascii=False)
diff --git a/preprocessors/csd.py b/preprocessors/csd.py
new file mode 100644
index 0000000000000000000000000000000000000000..645a8b3d1e2bc556ffa7ee36d9b889422e500619
--- /dev/null
+++ b/preprocessors/csd.py
@@ -0,0 +1,202 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import json
+import os
+import glob
+from tqdm import tqdm
+import torchaudio
+import pandas as pd
+from glob import glob
+from collections import defaultdict
+
+from utils.io import save_audio
+from utils.util import has_existed
+from preprocessors import GOLDEN_TEST_SAMPLES
+
+
+def save_utterance(output_file, waveform, fs, start, end, overlap=0.1):
+    """
+    waveform: [#channel, audio_len]
+    start, end, overlap: seconds
+    """
+    start = int((start - overlap) * fs)
+    end = int((end + overlap) * fs)
+    utterance = waveform[:, start:end]
+    save_audio(output_file, utterance, fs)
+
+
+def split_to_utterances(language_dir, output_dir):
+    print("Splitting to utterances for {}...".format(language_dir))
+    wav_dir = os.path.join(language_dir, "wav")
+    phoneme_dir = os.path.join(language_dir, "txt")
+    annot_dir = os.path.join(language_dir, "csv")
+
+    pitches = set()
+    for wav_file in tqdm(glob("{}/*.wav".format(wav_dir))):
+        # Load waveform
+        song_name = wav_file.split("/")[-1].split(".")[0]
+        waveform, fs = torchaudio.load(wav_file)
+
+        # Load utterances
+        phoneme_file = os.path.join(phoneme_dir, "{}.txt".format(song_name))
+        with open(phoneme_file, "r") as f:
+            lines = f.readlines()
+        utterances = [l.strip().split() for l in lines]
+        utterances = [utt for utt in utterances if len(utt) > 0]
+
+        # Load annotation
+        annot_file = os.path.join(annot_dir, "{}.csv".format(song_name))
+        annot_df = pd.read_csv(annot_file)
+        pitches = pitches.union(set(annot_df["pitch"]))
+        starts = annot_df["start"].tolist()
+        ends = annot_df["end"].tolist()
+        syllables = annot_df["syllable"].tolist()
+
+        # Split
+        curr = 0
+        for i, phones in enumerate(utterances):
+            sz = len(phones)
+            assert phones[0] == syllables[curr]
+            assert phones[-1] == syllables[curr + sz - 1]
+
+            s = starts[curr]
+            e = ends[curr + sz - 1]
+            curr += sz
+
+            save_dir = os.path.join(output_dir, song_name)
+            os.makedirs(save_dir, exist_ok=True)
+
+            output_file = os.path.join(save_dir, "{:04d}.wav".format(i))
+            save_utterance(output_file, waveform, fs, start=s, end=e)
+
+
+def _main(dataset_path):
+    """
+    Split to utterances
+    """
+    utterance_dir = os.path.join(dataset_path, "utterances")
+
+    for lang in ["english", "korean"]:
+        split_to_utterances(os.path.join(dataset_path, lang), utterance_dir)
+
+
+def get_test_songs():
+    golden_samples = GOLDEN_TEST_SAMPLES["csd"]
+    # every item is a tuple (language, song)
+    golden_songs = [s.split("_")[:2] for s in golden_samples]
+    # language_song, eg: en_001a
+    return golden_songs
+
+
+def csd_statistics(data_dir):
+    languages = []
+    songs = []
+    languages2songs = defaultdict(lambda: defaultdict(list))
+
+    folder_infos = glob(data_dir + "/*")
+
+    for folder_info in folder_infos:
+        folder_info_split = folder_info.split("/")[-1]
+
+        language = folder_info_split[:2]
+        song = folder_info_split[2:]
+
+        languages.append(language)
+        songs.append(song)
+
+        utts = glob(folder_info + "/*")
+
+        for utt in utts:
+            uid = utt.split("/")[-1].split(".")[0]
+            languages2songs[language][song].append(uid)
+
+    unique_languages = list(set(languages))
+    unique_songs = list(set(songs))
+    unique_languages.sort()
+    unique_songs.sort()
+
+    print(
+        "csd: {} languages, {} utterances ({} unique songs)".format(
+            len(unique_languages), len(songs), len(unique_songs)
+        )
+    )
+    print("Languages: \n{}".format("\t".join(unique_languages)))
+    return languages2songs
+
+
+def main(output_path, dataset_path):
+    print("-" * 10)
+    print("Preparing test samples for csd...\n")
+
+    if not os.path.exists(os.path.join(dataset_path, "utterances")):
+        print("Spliting into utterances...\n")
+        _main(dataset_path)
+
+    save_dir = os.path.join(output_path, "csd")
+    train_output_file = os.path.join(save_dir, "train.json")
+    test_output_file = os.path.join(save_dir, "test.json")
+    if has_existed(test_output_file):
+        return
+
+    # Load
+    csd_path = os.path.join(dataset_path, "utterances")
+
+    language2songs = csd_statistics(csd_path)
+    test_songs = get_test_songs()
+
+    # We select songs of standard samples as test songs
+    train = []
+    test = []
+
+    train_index_count = 0
+    test_index_count = 0
+
+    train_total_duration = 0
+    test_total_duration = 0
+
+    for language, songs in tqdm(language2songs.items()):
+        song_names = list(songs.keys())
+
+        for chosen_song in song_names:
+            for chosen_uid in songs[chosen_song]:
+                res = {
+                    "Dataset": "csd",
+                    "Singer": "Female1_{}".format(language),
+                    "Uid": "{}_{}_{}".format(language, chosen_song, chosen_uid),
+                }
+                res["Path"] = "{}{}/{}.wav".format(language, chosen_song, chosen_uid)
+                res["Path"] = os.path.join(csd_path, res["Path"])
+                assert os.path.exists(res["Path"])
+
+                waveform, sample_rate = torchaudio.load(res["Path"])
+                duration = waveform.size(-1) / sample_rate
+                res["Duration"] = duration
+
+                if [language, chosen_song] in test_songs:
+                    res["index"] = test_index_count
+                    test_total_duration += duration
+                    test.append(res)
+                    test_index_count += 1
+                else:
+                    res["index"] = train_index_count
+                    train_total_duration += duration
+                    train.append(res)
+                    train_index_count += 1
+
+    print("#Train = {}, #Test = {}".format(len(train), len(test)))
+    print(
+        "#Train hours= {}, #Test hours= {}".format(
+            train_total_duration / 3600, test_total_duration / 3600
+        )
+    )
+
+    # Save
+    os.makedirs(save_dir, exist_ok=True)
+    with open(train_output_file, "w") as f:
+        json.dump(train, f, indent=4, ensure_ascii=False)
+    with open(test_output_file, "w") as f:
+        json.dump(test, f, indent=4, ensure_ascii=False)
diff --git a/preprocessors/custom.py b/preprocessors/custom.py
new file mode 100644
index 0000000000000000000000000000000000000000..daa457bb84b7aca70fcd631f25a1678e6e84a333
--- /dev/null
+++ b/preprocessors/custom.py
@@ -0,0 +1,143 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from glob import glob
+import os
+import json
+import torchaudio
+from tqdm import tqdm
+from collections import defaultdict
+
+from utils.util import has_existed
+
+
+def statistics(utterance_dir):
+    singers = []
+    songs = []
+    singers2songs = defaultdict(lambda: defaultdict(list))
+
+    singer_infos = glob(utterance_dir + "/*")
+
+    for singer_info in singer_infos:
+        singer = singer_info.split("/")[-1]
+
+        song_infos = glob(singer_info + "/*")
+
+        for song_info in song_infos:
+            song = song_info.split("/")[-1]
+
+            singers.append(singer)
+            songs.append(song)
+
+            utts = glob(song_info + "/*.wav")
+
+            for utt in utts:
+                uid = utt.split("/")[-1].split(".")[0]
+                singers2songs[singer][song].append(uid)
+
+    unique_singers = list(set(singers))
+    unique_songs = list(set(songs))
+    unique_singers.sort()
+    unique_songs.sort()
+
+    print(
+        "Statistics: {} singers, {} utterances ({} unique songs)".format(
+            len(unique_singers), len(songs), len(unique_songs)
+        )
+    )
+    print("Singers: \n{}".format("\t".join(unique_singers)))
+    return singers2songs, unique_singers
+
+
+def main(output_path, dataset_path, dataset_name):
+    print("-" * 10)
+    print("Preparing samples for {}...\n".format(dataset_name))
+
+    save_dir = os.path.join(output_path, dataset_name)
+    os.makedirs(save_dir, exist_ok=True)
+
+    train_output_file = os.path.join(save_dir, "train.json")
+    test_output_file = os.path.join(save_dir, "test.json")
+    singer_dict_file = os.path.join(save_dir, "singers.json")
+    utt2singer_file = os.path.join(save_dir, "utt2singer")
+    if (
+        has_existed(train_output_file)
+        and has_existed(test_output_file)
+        and has_existed(singer_dict_file)
+        and has_existed(utt2singer_file)
+    ):
+        return
+    utt2singer = open(utt2singer_file, "w")
+
+    # Load
+    singers2songs, unique_singers = statistics(dataset_path)
+
+    # We select songs of standard samples as test songs
+    train = []
+    test = []
+    test_songs = set()
+
+    train_index_count = 0
+    test_index_count = 0
+
+    train_total_duration = 0
+    test_total_duration = 0
+
+    for singer, songs in singers2songs.items():
+        song_names = list(songs.keys())
+
+        print("Singer {}...".format(singer))
+        for chosen_song in tqdm(song_names):
+            for chosen_uid in songs[chosen_song]:
+                res = {
+                    "Dataset": dataset_name,
+                    "Singer": singer,
+                    "Uid": "{}_{}_{}".format(singer, chosen_song, chosen_uid),
+                }
+                res["Path"] = "{}/{}/{}.wav".format(singer, chosen_song, chosen_uid)
+                res["Path"] = os.path.join(dataset_path, res["Path"])
+                assert os.path.exists(res["Path"])
+
+                waveform, sample_rate = torchaudio.load(res["Path"])
+                duration = waveform.size(-1) / sample_rate
+                res["Duration"] = duration
+
+                # Remove the utterance whose duration is shorter than 0.1s
+                if duration <= 1e-2:
+                    continue
+
+                # Place into train or test
+                if "{}_{}".format(singer, chosen_song) not in test_songs:
+                    test_songs.add("{}_{}".format(singer, chosen_song))
+
+                    res["index"] = test_index_count
+                    test_total_duration += duration
+                    test.append(res)
+                    test_index_count += 1
+                else:
+                    res["index"] = train_index_count
+                    train_total_duration += duration
+                    train.append(res)
+                    train_index_count += 1
+
+                utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"]))
+
+    print("#Train = {}, #Test = {}".format(len(train), len(test)))
+    print(
+        "#Train hours= {}, #Test hours= {}".format(
+            train_total_duration / 3600, test_total_duration / 3600
+        )
+    )
+
+    # Save train.json and test.json
+    with open(train_output_file, "w") as f:
+        json.dump(train, f, indent=4, ensure_ascii=False)
+    with open(test_output_file, "w") as f:
+        json.dump(test, f, indent=4, ensure_ascii=False)
+
+    # Save singers.json
+    singer_lut = {name: i for i, name in enumerate(unique_singers)}
+    with open(singer_dict_file, "w") as f:
+        json.dump(singer_lut, f, indent=4, ensure_ascii=False)
diff --git a/preprocessors/kising.py b/preprocessors/kising.py
new file mode 100644
index 0000000000000000000000000000000000000000..f81b852aeb05a5d30fe22df07af4c4ab9defda2a
--- /dev/null
+++ b/preprocessors/kising.py
@@ -0,0 +1,116 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import random
+import os
+import json
+import torchaudio
+from tqdm import tqdm
+from glob import glob
+from collections import defaultdict
+
+from utils.util import has_existed
+from preprocessors import GOLDEN_TEST_SAMPLES
+
+
+def get_test_folders():
+    golden_samples = GOLDEN_TEST_SAMPLES["kising"]
+    # every item is a string
+    golden_folders = [s.split("_")[:1] for s in golden_samples]
+    # folder, eg: 422
+    return golden_folders
+
+
+def KiSing_statistics(data_dir):
+    folders = []
+    folders2utts = defaultdict(list)
+
+    folder_infos = glob(data_dir + "/*")
+
+    for folder_info in folder_infos:
+        folder = folder_info.split("/")[-1]
+
+        folders.append(folder)
+
+        utts = glob(folder_info + "/*.wav")
+
+        for utt in utts:
+            uid = utt.split("/")[-1].split(".")[0]
+            folders2utts[folder].append(uid)
+
+    unique_folders = list(set(folders))
+    unique_folders.sort()
+
+    print("KiSing: {} unique songs".format(len(unique_folders)))
+    return folders2utts
+
+
+def main(output_path, dataset_path):
+    print("-" * 10)
+    print("Preparing test samples for KiSing...\n")
+
+    save_dir = os.path.join(output_path, "kising")
+    train_output_file = os.path.join(save_dir, "train.json")
+    test_output_file = os.path.join(save_dir, "test.json")
+    if has_existed(test_output_file):
+        return
+
+    # Load
+    KiSing_dir = dataset_path
+
+    folders2utts = KiSing_statistics(KiSing_dir)
+    test_folders = get_test_folders()
+
+    # We select songs of standard samples as test songs
+    train = []
+    test = []
+
+    train_index_count = 0
+    test_index_count = 0
+
+    train_total_duration = 0
+    test_total_duration = 0
+
+    folder_names = list(folders2utts.keys())
+
+    for chosen_folder in folder_names:
+        for chosen_uid in folders2utts[chosen_folder]:
+            res = {
+                "Dataset": "kising",
+                "Singer": "female1",
+                "Uid": "{}_{}".format(chosen_folder, chosen_uid),
+            }
+            res["Path"] = "{}/{}.wav".format(chosen_folder, chosen_uid)
+            res["Path"] = os.path.join(KiSing_dir, res["Path"])
+            assert os.path.exists(res["Path"])
+
+            waveform, sample_rate = torchaudio.load(res["Path"])
+            duration = waveform.size(-1) / sample_rate
+            res["Duration"] = duration
+
+            if ([chosen_folder]) in test_folders:
+                res["index"] = test_index_count
+                test_total_duration += duration
+                test.append(res)
+                test_index_count += 1
+            else:
+                res["index"] = train_index_count
+                train_total_duration += duration
+                train.append(res)
+                train_index_count += 1
+
+    print("#Train = {}, #Test = {}".format(len(train), len(test)))
+    print(
+        "#Train hours= {}, #Test hours= {}".format(
+            train_total_duration / 3600, test_total_duration / 3600
+        )
+    )
+
+    # Save
+    os.makedirs(save_dir, exist_ok=True)
+    with open(train_output_file, "w") as f:
+        json.dump(train, f, indent=4, ensure_ascii=False)
+    with open(test_output_file, "w") as f:
+        json.dump(test, f, indent=4, ensure_ascii=False)
diff --git a/preprocessors/libritts.py b/preprocessors/libritts.py
new file mode 100644
index 0000000000000000000000000000000000000000..adc86f0cecc8101a191e8a59764edaa845541d53
--- /dev/null
+++ b/preprocessors/libritts.py
@@ -0,0 +1,171 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import json
+import torchaudio
+from tqdm import tqdm
+from glob import glob
+from collections import defaultdict
+
+from utils.util import has_existed
+
+
+def libritts_statistics(data_dir):
+    speakers = []
+    distribution2speakers2pharases2utts = defaultdict(
+        lambda: defaultdict(lambda: defaultdict(list))
+    )
+
+    distribution_infos = glob(data_dir + "/*")
+
+    for distribution_info in distribution_infos:
+        distribution = distribution_info.split("/")[-1]
+        print(distribution)
+
+        speaker_infos = glob(distribution_info + "/*")
+
+        if len(speaker_infos) == 0:
+            continue
+
+        for speaker_info in speaker_infos:
+            speaker = speaker_info.split("/")[-1]
+
+            speakers.append(speaker)
+
+            pharase_infos = glob(speaker_info + "/*")
+
+            for pharase_info in pharase_infos:
+                pharase = pharase_info.split("/")[-1]
+
+                utts = glob(pharase_info + "/*.wav")
+
+                for utt in utts:
+                    uid = utt.split("/")[-1].split(".")[0]
+                    distribution2speakers2pharases2utts[distribution][speaker][
+                        pharase
+                    ].append(uid)
+
+    unique_speakers = list(set(speakers))
+    unique_speakers.sort()
+
+    print("Speakers: \n{}".format("\t".join(unique_speakers)))
+    return distribution2speakers2pharases2utts, unique_speakers
+
+
+def main(output_path, dataset_path):
+    print("-" * 10)
+    print("Preparing samples for libritts...\n")
+
+    save_dir = os.path.join(output_path, "libritts")
+    os.makedirs(save_dir, exist_ok=True)
+    train_output_file = os.path.join(save_dir, "train.json")
+    test_output_file = os.path.join(save_dir, "test.json")
+    valid_output_file = os.path.join(save_dir, "valid.json")
+    singer_dict_file = os.path.join(save_dir, "singers.json")
+    utt2singer_file = os.path.join(save_dir, "utt2singer")
+    if has_existed(train_output_file):
+        return
+    utt2singer = open(utt2singer_file, "w")
+
+    # Load
+    libritts_path = dataset_path
+
+    distribution2speakers2pharases2utts, unique_speakers = libritts_statistics(
+        libritts_path
+    )
+
+    # We select pharases of standard spekaer as test songs
+    train = []
+    test = []
+    valid = []
+
+    train_index_count = 0
+    test_index_count = 0
+    valid_index_count = 0
+
+    train_total_duration = 0
+    test_total_duration = 0
+    valid_total_duration = 0
+
+    for distribution, speakers2pharases2utts in tqdm(
+        distribution2speakers2pharases2utts.items()
+    ):
+        for speaker, pharases2utts in tqdm(speakers2pharases2utts.items()):
+            pharase_names = list(pharases2utts.keys())
+
+            for chosen_pharase in pharase_names:
+                for chosen_uid in pharases2utts[chosen_pharase]:
+                    res = {
+                        "Dataset": "libritts",
+                        "Singer": speaker,
+                        "Uid": "{}#{}#{}#{}".format(
+                            distribution, speaker, chosen_pharase, chosen_uid
+                        ),
+                    }
+                    res["Path"] = "{}/{}/{}/{}.wav".format(
+                        distribution, speaker, chosen_pharase, chosen_uid
+                    )
+                    res["Path"] = os.path.join(libritts_path, res["Path"])
+                    assert os.path.exists(res["Path"])
+
+                    text_file_path = os.path.join(
+                        libritts_path,
+                        distribution,
+                        speaker,
+                        chosen_pharase,
+                        chosen_uid + ".normalized.txt",
+                    )
+                    with open(text_file_path, "r") as f:
+                        lines = f.readlines()
+                        assert len(lines) == 1
+                        text = lines[0].strip()
+                        res["Text"] = text
+
+                    waveform, sample_rate = torchaudio.load(res["Path"])
+                    duration = waveform.size(-1) / sample_rate
+                    res["Duration"] = duration
+
+                    if "test" in distribution:
+                        res["index"] = test_index_count
+                        test_total_duration += duration
+                        test.append(res)
+                        test_index_count += 1
+                    elif "train" in distribution:
+                        res["index"] = train_index_count
+                        train_total_duration += duration
+                        train.append(res)
+                        train_index_count += 1
+                    elif "dev" in distribution:
+                        res["index"] = valid_index_count
+                        valid_total_duration += duration
+                        valid.append(res)
+                        valid_index_count += 1
+
+                    utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"]))
+
+    print(
+        "#Train = {}, #Test = {}, #Valid = {}".format(len(train), len(test), len(valid))
+    )
+    print(
+        "#Train hours= {}, #Test hours= {}, #Valid hours= {}".format(
+            train_total_duration / 3600,
+            test_total_duration / 3600,
+            valid_total_duration / 3600,
+        )
+    )
+
+    # Save train.json and test.json
+    with open(train_output_file, "w") as f:
+        json.dump(train, f, indent=4, ensure_ascii=False)
+    with open(test_output_file, "w") as f:
+        json.dump(test, f, indent=4, ensure_ascii=False)
+    with open(valid_output_file, "w") as f:
+        json.dump(valid, f, indent=4, ensure_ascii=False)
+
+    # Save singers.json
+    singer_lut = {name: i for i, name in enumerate(unique_speakers)}
+    with open(singer_dict_file, "w") as f:
+        json.dump(singer_lut, f, indent=4, ensure_ascii=False)
diff --git a/preprocessors/lijian.py b/preprocessors/lijian.py
new file mode 100644
index 0000000000000000000000000000000000000000..459986a8d68a5b0dc0811f4f8197cd818fdc263f
--- /dev/null
+++ b/preprocessors/lijian.py
@@ -0,0 +1,151 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import glob
+import os
+import json
+import torchaudio
+from tqdm import tqdm
+from collections import defaultdict
+
+
+from utils.io import save_audio
+from utils.util import has_existed, remove_and_create
+from utils.audio_slicer import Slicer
+from preprocessors import GOLDEN_TEST_SAMPLES
+
+
+def split_to_utterances(input_dir, output_dir):
+    print("Splitting to utterances for {}...".format(input_dir))
+
+    files_list = glob.glob("*.flac", root_dir=input_dir)
+    files_list.sort()
+    for wav_file in tqdm(files_list):
+        # Load waveform
+        waveform, fs = torchaudio.load(os.path.join(input_dir, wav_file))
+
+        # Song name
+        filename = wav_file.replace(" ", "")
+        filename = filename.replace("(Live)", "")
+        song_id, filename = filename.split("李健-")
+
+        song_id = song_id.split("_")[0]
+        song_name = "{:03d}".format(int(song_id)) + filename.split("_")[0].split("-")[0]
+
+        # Split
+        slicer = Slicer(sr=fs, threshold=-30.0, max_sil_kept=3000)
+        chunks = slicer.slice(waveform)
+
+        save_dir = os.path.join(output_dir, song_name)
+        remove_and_create(save_dir)
+
+        for i, chunk in enumerate(chunks):
+            output_file = os.path.join(save_dir, "{:04d}.wav".format(i))
+            save_audio(output_file, chunk, fs)
+
+
+def _main(dataset_path):
+    """
+    Split to utterances
+    """
+    utterance_dir = os.path.join(dataset_path, "utterances")
+    split_to_utterances(os.path.join(dataset_path, "vocal_v2"), utterance_dir)
+
+
+def get_test_songs():
+    golden_samples = GOLDEN_TEST_SAMPLES["lijian"]
+    golden_songs = [s.split("_")[0] for s in golden_samples]
+    return golden_songs
+
+
+def statistics(utt_dir):
+    song2utts = defaultdict(list)
+
+    song_infos = glob.glob(utt_dir + "/*")
+    song_infos.sort()
+    for song in song_infos:
+        song_name = song.split("/")[-1]
+        utt_infos = glob.glob(song + "/*.wav")
+        utt_infos.sort()
+        for utt in utt_infos:
+            uid = utt.split("/")[-1].split(".")[0]
+            song2utts[song_name].append(uid)
+
+    utt_sum = sum([len(utts) for utts in song2utts.values()])
+    print("Li Jian: {} unique songs, {} utterances".format(len(song2utts), utt_sum))
+    return song2utts
+
+
+def main(output_path, dataset_path):
+    print("-" * 10)
+    print("Preparing test samples for Li Jian...\n")
+
+    if not os.path.exists(os.path.join(dataset_path, "utterances")):
+        print("Spliting into utterances...\n")
+        _main(dataset_path)
+
+    save_dir = os.path.join(output_path, "lijian")
+    train_output_file = os.path.join(save_dir, "train.json")
+    test_output_file = os.path.join(save_dir, "test.json")
+    if has_existed(test_output_file):
+        return
+
+    # Load
+    lijian_path = os.path.join(dataset_path, "utterances")
+    song2utts = statistics(lijian_path)
+    test_songs = get_test_songs()
+
+    # We select songs of standard samples as test songs
+    train = []
+    test = []
+
+    train_index_count = 0
+    test_index_count = 0
+
+    train_total_duration = 0
+    test_total_duration = 0
+
+    for chosen_song, utts in tqdm(song2utts.items()):
+        for chosen_uid in song2utts[chosen_song]:
+            res = {
+                "Dataset": "lijian",
+                "Singer": "lijian",
+                "Uid": "{}_{}".format(chosen_song, chosen_uid),
+            }
+            res["Path"] = "{}/{}.wav".format(chosen_song, chosen_uid)
+            res["Path"] = os.path.join(lijian_path, res["Path"])
+            assert os.path.exists(res["Path"])
+
+            waveform, sample_rate = torchaudio.load(res["Path"])
+            duration = waveform.size(-1) / sample_rate
+            res["Duration"] = duration
+
+            if duration <= 1e-8:
+                continue
+
+            if chosen_song in test_songs:
+                res["index"] = test_index_count
+                test_total_duration += duration
+                test.append(res)
+                test_index_count += 1
+            else:
+                res["index"] = train_index_count
+                train_total_duration += duration
+                train.append(res)
+                train_index_count += 1
+
+    print("#Train = {}, #Test = {}".format(len(train), len(test)))
+    print(
+        "#Train hours= {}, #Test hours= {}".format(
+            train_total_duration / 3600, test_total_duration / 3600
+        )
+    )
+
+    # Save
+    os.makedirs(save_dir, exist_ok=True)
+    with open(train_output_file, "w") as f:
+        json.dump(train, f, indent=4, ensure_ascii=False)
+    with open(test_output_file, "w") as f:
+        json.dump(test, f, indent=4, ensure_ascii=False)
diff --git a/preprocessors/ljspeech.py b/preprocessors/ljspeech.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7615a708d7d0ea46be046fd8fa1e569b0311d42
--- /dev/null
+++ b/preprocessors/ljspeech.py
@@ -0,0 +1,216 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import json
+from tqdm import tqdm
+import os
+import torchaudio
+from utils import audio
+import csv
+import random
+
+from utils.util import has_existed
+from text import _clean_text
+import librosa
+import soundfile as sf
+from scipy.io import wavfile
+
+from pathlib import Path
+import numpy as np
+
+
+def textgird_extract(
+    corpus_directory,
+    output_directory,
+    mfa_path=os.path.join("mfa", "montreal-forced-aligner", "bin", "mfa_align"),
+    lexicon=os.path.join("mfa", "lexicon", "librispeech-lexicon.txt"),
+    acoustic_model_path=os.path.join(
+        "mfa", "montreal-forced-aligner", "pretrained_models", "english.zip"
+    ),
+    jobs="8",
+):
+    assert os.path.exists(
+        corpus_directory
+    ), "Please check the directionary contains *.wav, *.lab"
+    assert (
+        os.path.exists(mfa_path)
+        and os.path.exists(lexicon)
+        and os.path.exists(acoustic_model_path)
+    ), f"Please download the MFA tools to {mfa_path} firstly"
+    Path(output_directory).mkdir(parents=True, exist_ok=True)
+    print(f"MFA results are save in {output_directory}")
+    os.system(
+        f".{os.path.sep}{mfa_path} {corpus_directory} {lexicon} {acoustic_model_path} {output_directory} -j {jobs} --clean"
+    )
+
+
+def get_lines(file):
+    lines = []
+    with open(file, encoding="utf-8") as f:
+        for line in tqdm(f):
+            lines.append(line.strip())
+    return lines
+
+
+def get_uid2utt(ljspeech_path, dataset, cfg):
+    index_count = 0
+    total_duration = 0
+
+    uid2utt = []
+    for l in tqdm(dataset):
+        items = l.split("|")
+        uid = items[0]
+        text = items[2]
+
+        res = {
+            "Dataset": "LJSpeech",
+            "index": index_count,
+            "Singer": "LJSpeech",
+            "Uid": uid,
+            "Text": text,
+        }
+
+        # Duration in wav files
+        audio_file = os.path.join(ljspeech_path, "wavs/{}.wav".format(uid))
+
+        res["Path"] = audio_file
+
+        waveform, sample_rate = torchaudio.load(audio_file)
+        duration = waveform.size(-1) / sample_rate
+        res["Duration"] = duration
+
+        uid2utt.append(res)
+
+        index_count = index_count + 1
+        total_duration += duration
+
+    return uid2utt, total_duration / 3600
+
+
+def split_dataset(
+    lines, test_rate=0.05, valid_rate=0.05, test_size=None, valid_size=None
+):
+    if test_size == None:
+        test_size = int(len(lines) * test_rate)
+    if valid_size == None:
+        valid_size = int(len(lines) * valid_rate)
+    random.shuffle(lines)
+
+    train_set = []
+    test_set = []
+    valid_set = []
+
+    for line in lines[:test_size]:
+        test_set.append(line)
+    for line in lines[test_size : test_size + valid_size]:
+        valid_set.append(line)
+    for line in lines[test_size + valid_size :]:
+        train_set.append(line)
+    return train_set, test_set, valid_set
+
+
+max_wav_value = 32768.0
+
+
+def prepare_align(dataset, dataset_path, cfg, output_path):
+    in_dir = dataset_path
+    out_dir = os.path.join(output_path, dataset, cfg.raw_data)
+    sampling_rate = cfg.sample_rate
+    cleaners = cfg.text_cleaners
+    speaker = "LJSpeech"
+    with open(os.path.join(dataset_path, "metadata.csv"), encoding="utf-8") as f:
+        for line in tqdm(f):
+            parts = line.strip().split("|")
+            base_name = parts[0]
+            text = parts[2]
+            text = _clean_text(text, cleaners)
+
+            output_wav_path = os.path.join(out_dir, speaker, "{}.wav".format(base_name))
+            output_lab_path = os.path.join(out_dir, speaker, "{}.lab".format(base_name))
+
+            if os.path.exists(output_wav_path) and os.path.exists(output_lab_path):
+                continue
+
+            wav_path = os.path.join(in_dir, "wavs", "{}.wav".format(base_name))
+            if os.path.exists(wav_path):
+                os.makedirs(os.path.join(out_dir, speaker), exist_ok=True)
+                wav, _ = librosa.load(wav_path, sampling_rate)
+                wav = wav / max(abs(wav)) * max_wav_value
+
+                wavfile.write(
+                    os.path.join(out_dir, speaker, "{}.wav".format(base_name)),
+                    sampling_rate,
+                    wav.astype(np.int16),
+                )
+
+                with open(
+                    os.path.join(out_dir, speaker, "{}.lab".format(base_name)),
+                    "w",
+                ) as f1:
+                    f1.write(text)
+    # Extract textgird with MFA
+    textgird_extract(
+        corpus_directory=out_dir,
+        output_directory=os.path.join(output_path, dataset, "TextGrid"),
+    )
+
+
+def main(output_path, dataset_path, cfg):
+    print("-" * 10)
+    print("Dataset splits for {}...\n".format("LJSpeech"))
+
+    dataset = "LJSpeech"
+
+    save_dir = os.path.join(output_path, dataset)
+    os.makedirs(save_dir, exist_ok=True)
+    ljspeech_path = dataset_path
+
+    train_output_file = os.path.join(save_dir, "train.json")
+    test_output_file = os.path.join(save_dir, "test.json")
+    valid_output_file = os.path.join(save_dir, "valid.json")
+    singer_dict_file = os.path.join(save_dir, "singers.json")
+
+    speaker = "LJSpeech"
+    speakers = [dataset + "_" + speaker]
+    singer_lut = {name: i for i, name in enumerate(sorted(speakers))}
+    with open(singer_dict_file, "w") as f:
+        json.dump(singer_lut, f, indent=4, ensure_ascii=False)
+
+    if (
+        has_existed(train_output_file)
+        and has_existed(test_output_file)
+        and has_existed(valid_output_file)
+    ):
+        return
+
+    meta_file = os.path.join(ljspeech_path, "metadata.csv")
+    lines = get_lines(meta_file)
+
+    train_set, test_set, valid_set = split_dataset(lines)
+
+    res, hours = get_uid2utt(ljspeech_path, train_set, cfg)
+
+    # Save train
+    os.makedirs(save_dir, exist_ok=True)
+    with open(train_output_file, "w") as f:
+        json.dump(res, f, indent=4, ensure_ascii=False)
+
+    print("Train_hours= {}".format(hours))
+
+    res, hours = get_uid2utt(ljspeech_path, test_set, cfg)
+
+    # Save test
+    os.makedirs(save_dir, exist_ok=True)
+    with open(test_output_file, "w") as f:
+        json.dump(res, f, indent=4, ensure_ascii=False)
+
+    print("Test_hours= {}".format(hours))
+
+    # Save valid
+    os.makedirs(save_dir, exist_ok=True)
+    with open(valid_output_file, "w") as f:
+        json.dump(res, f, indent=4, ensure_ascii=False)
+
+    print("Valid_hours= {}".format(hours))
diff --git a/preprocessors/ljspeech_vocoder.py b/preprocessors/ljspeech_vocoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6ef4fd2c88d85454a939e9fe3329e02b0be949b
--- /dev/null
+++ b/preprocessors/ljspeech_vocoder.py
@@ -0,0 +1,86 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import json
+import torchaudio
+from tqdm import tqdm
+from glob import glob
+
+from utils.util import has_existed
+
+
+def main(output_path, dataset_path):
+    print("-" * 10)
+    print("Dataset splits for ljspeech...\n")
+
+    save_dir = os.path.join(output_path, "ljspeech")
+    ljspeech_path = dataset_path
+
+    wave_files = glob(ljspeech_path + "/wavs/*.wav")
+
+    train_output_file = os.path.join(save_dir, "train.json")
+    test_output_file = os.path.join(save_dir, "test.json")
+
+    if has_existed(train_output_file):
+        return
+
+    utts = []
+
+    for wave_file in tqdm(wave_files):
+        res = {
+            "Dataset": "ljspeech",
+            "Singer": "female1",
+            "Uid": "{}".format(wave_file.split("/")[-1].split(".")[0]),
+        }
+        res["Path"] = wave_file
+        assert os.path.exists(res["Path"])
+
+        waveform, sample_rate = torchaudio.load(res["Path"])
+        duration = waveform.size(-1) / sample_rate
+        res["Duration"] = duration
+
+        if duration <= 1e-8:
+            continue
+
+        utts.append(res)
+
+    test_length = len(utts) // 20
+
+    train_utts = []
+    train_index_count = 0
+    train_total_duration = 0
+
+    for i in tqdm(range(len(utts) - test_length)):
+        tmp = utts[i]
+        tmp["index"] = train_index_count
+        train_index_count += 1
+        train_total_duration += tmp["Duration"]
+        train_utts.append(tmp)
+
+    test_utts = []
+    test_index_count = 0
+    test_total_duration = 0
+
+    for i in tqdm(range(len(utts) - test_length, len(utts))):
+        tmp = utts[i]
+        tmp["index"] = test_index_count
+        test_index_count += 1
+        test_total_duration += tmp["Duration"]
+        test_utts.append(tmp)
+
+    print("#Train = {}, #Test = {}".format(len(train_utts), len(test_utts)))
+    print(
+        "#Train hours= {}, #Test hours= {}".format(
+            train_total_duration / 3600, test_total_duration / 3600
+        )
+    )
+
+    # Save
+    os.makedirs(save_dir, exist_ok=True)
+    with open(train_output_file, "w") as f:
+        json.dump(train_utts, f, indent=4, ensure_ascii=False)
+    with open(test_output_file, "w") as f:
+        json.dump(test_utts, f, indent=4, ensure_ascii=False)
diff --git a/preprocessors/m4singer.py b/preprocessors/m4singer.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb8eb1715ce2af6d712efd9d93c5983dd58ba257
--- /dev/null
+++ b/preprocessors/m4singer.py
@@ -0,0 +1,138 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import json
+import librosa
+from tqdm import tqdm
+from collections import defaultdict
+
+from utils.util import has_existed
+from preprocessors import GOLDEN_TEST_SAMPLES
+
+
+def get_test_songs():
+    golden_samples = GOLDEN_TEST_SAMPLES["m4singer"]
+    # every item is a tuple (singer, song)
+    golden_songs = [s.split("_")[:2] for s in golden_samples]
+    # singer_song, eg: Alto-1_美错
+    golden_songs = ["_".join(t) for t in golden_songs]
+    return golden_songs
+
+
+def m4singer_statistics(meta):
+    singers = []
+    songs = []
+    singer2songs = defaultdict(lambda: defaultdict(list))
+    for utt in meta:
+        p, s, uid = utt["item_name"].split("#")
+        singers.append(p)
+        songs.append(s)
+        singer2songs[p][s].append(uid)
+
+    unique_singers = list(set(singers))
+    unique_songs = list(set(songs))
+    unique_singers.sort()
+    unique_songs.sort()
+
+    print(
+        "M4Singer: {} singers, {} utterances ({} unique songs)".format(
+            len(unique_singers), len(songs), len(unique_songs)
+        )
+    )
+    print("Singers: \n{}".format("\t".join(unique_singers)))
+    return singer2songs, unique_singers
+
+
+def main(output_path, dataset_path):
+    print("-" * 10)
+    print("Preparing test samples for m4singer...\n")
+
+    save_dir = os.path.join(output_path, "m4singer")
+    os.makedirs(save_dir, exist_ok=True)
+    train_output_file = os.path.join(save_dir, "train.json")
+    test_output_file = os.path.join(save_dir, "test.json")
+    singer_dict_file = os.path.join(save_dir, "singers.json")
+    utt2singer_file = os.path.join(save_dir, "utt2singer")
+    if (
+        has_existed(train_output_file)
+        and has_existed(test_output_file)
+        and has_existed(singer_dict_file)
+        and has_existed(utt2singer_file)
+    ):
+        return
+    utt2singer = open(utt2singer_file, "w")
+
+    # Load
+    m4singer_dir = dataset_path
+    meta_file = os.path.join(m4singer_dir, "meta.json")
+    with open(meta_file, "r", encoding="utf-8") as f:
+        meta = json.load(f)
+
+    singer2songs, unique_singers = m4singer_statistics(meta)
+
+    test_songs = get_test_songs()
+
+    # We select songs of standard samples as test songs
+    train = []
+    test = []
+
+    train_index_count = 0
+    test_index_count = 0
+
+    train_total_duration = 0
+    test_total_duration = 0
+
+    for singer, songs in tqdm(singer2songs.items()):
+        song_names = list(songs.keys())
+
+        for chosen_song in song_names:
+            chosen_song = chosen_song.replace(" ", "-")
+            for chosen_uid in songs[chosen_song]:
+                res = {
+                    "Dataset": "m4singer",
+                    "Singer": singer,
+                    "Song": chosen_song,
+                    "Uid": "{}_{}_{}".format(singer, chosen_song, chosen_uid),
+                }
+
+                res["Path"] = os.path.join(
+                    m4singer_dir, "{}#{}/{}.wav".format(singer, chosen_song, chosen_uid)
+                )
+                assert os.path.exists(res["Path"])
+
+                duration = librosa.get_duration(filename=res["Path"])
+                res["Duration"] = duration
+
+                if "_".join([singer, chosen_song]) in test_songs:
+                    res["index"] = test_index_count
+                    test_total_duration += duration
+                    test.append(res)
+                    test_index_count += 1
+                else:
+                    res["index"] = train_index_count
+                    train_total_duration += duration
+                    train.append(res)
+                    train_index_count += 1
+
+                utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"]))
+
+    print("#Train = {}, #Test = {}".format(len(train), len(test)))
+    print(
+        "#Train hours= {}, #Test hours= {}".format(
+            train_total_duration / 3600, test_total_duration / 3600
+        )
+    )
+
+    # Save train.json and test.json
+    with open(train_output_file, "w") as f:
+        json.dump(train, f, indent=4, ensure_ascii=False)
+    with open(test_output_file, "w") as f:
+        json.dump(test, f, indent=4, ensure_ascii=False)
+
+    # Save singers.json
+    singer_lut = {name: i for i, name in enumerate(unique_singers)}
+    with open(singer_dict_file, "w") as f:
+        json.dump(singer_lut, f, indent=4, ensure_ascii=False)
diff --git a/preprocessors/metadata.py b/preprocessors/metadata.py
new file mode 100644
index 0000000000000000000000000000000000000000..8411ea9ab74883e454b4e9a3927d979bd1a64d1d
--- /dev/null
+++ b/preprocessors/metadata.py
@@ -0,0 +1,137 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import json
+from tqdm import tqdm
+
+
+def cal_metadata(cfg, dataset_types=["train", "test"]):
+    """
+    Dump metadata (singers.json, meta_info.json, utt2singer) for singer dataset or multi-datasets.
+    """
+    from collections import Counter
+
+    datasets = cfg.dataset
+
+    print("-" * 10)
+    print("Preparing metadata...")
+    print("Including: \n{}\n".format("\n".join(datasets)))
+
+    datasets.sort()
+
+    for dataset in tqdm(datasets):
+        save_dir = os.path.join(cfg.preprocess.processed_dir, dataset)
+        assert os.path.exists(save_dir)
+
+        # 'train.json' and 'test.json' and 'valid.json' of target dataset
+        meta_info = dict()
+        utterances_dict = dict()
+        all_utterances = list()
+        duration = dict()
+        total_duration = 0.0
+        for dataset_type in dataset_types:
+            metadata = os.path.join(save_dir, "{}.json".format(dataset_type))
+
+            # Sort the metadata as the duration order
+            with open(metadata, "r", encoding="utf-8") as f:
+                utterances = json.load(f)
+            utterances = sorted(utterances, key=lambda x: x["Duration"])
+            utterances_dict[dataset_type] = utterances
+            all_utterances.extend(utterances)
+
+            # Write back the sorted metadata
+            with open(metadata, "w") as f:
+                json.dump(utterances, f, indent=4, ensure_ascii=False)
+
+            # Get the total duration and singer names for train and test utterances
+            duration[dataset_type] = sum(utt["Duration"] for utt in utterances)
+            total_duration += duration[dataset_type]
+
+        # Paths of metadata needed to be generated
+        singer_dict_file = os.path.join(save_dir, cfg.preprocess.spk2id)
+        utt2singer_file = os.path.join(save_dir, cfg.preprocess.utt2spk)
+
+        singer_names = set(
+            f"{replace_augment_name(utt['Dataset'])}_{utt['Singer']}"
+            for utt in all_utterances
+        )
+
+        # Write the utt2singer file and sort the singer names
+        with open(utt2singer_file, "w", encoding="utf-8") as f:
+            for utt in all_utterances:
+                f.write(
+                    f"{utt['Dataset']}_{utt['Uid']}\t{replace_augment_name(utt['Dataset'])}_{utt['Singer']}\n"
+                )
+
+        singer_names = sorted(singer_names)
+        singer_lut = {name: i for i, name in enumerate(singer_names)}
+
+        # dump singers.json
+        with open(singer_dict_file, "w", encoding="utf-8") as f:
+            json.dump(singer_lut, f, indent=4, ensure_ascii=False)
+
+        meta_info = {
+            "dataset": dataset,
+            "statistics": {
+                "size": len(all_utterances),
+                "hours": round(total_duration / 3600, 4),
+            },
+        }
+
+        for dataset_type in dataset_types:
+            meta_info[dataset_type] = {
+                "size": len(utterances_dict[dataset_type]),
+                "hours": round(duration[dataset_type] / 3600, 4),
+            }
+
+        meta_info["singers"] = {"size": len(singer_lut)}
+
+        # Use Counter to count the minutes for each singer
+        total_singer2mins = Counter()
+        training_singer2mins = Counter()
+        for dataset_type in dataset_types:
+            for utt in utterances_dict[dataset_type]:
+                k = f"{replace_augment_name(utt['Dataset'])}_{utt['Singer']}"
+                if dataset_type == "train":
+                    training_singer2mins[k] += utt["Duration"] / 60
+                total_singer2mins[k] += utt["Duration"] / 60
+
+        training_singer2mins = dict(
+            sorted(training_singer2mins.items(), key=lambda x: x[1], reverse=True)
+        )
+        training_singer2mins = {k: round(v, 2) for k, v in training_singer2mins.items()}
+        meta_info["singers"]["training_minutes"] = training_singer2mins
+
+        total_singer2mins = dict(
+            sorted(total_singer2mins.items(), key=lambda x: x[1], reverse=True)
+        )
+        total_singer2mins = {k: round(v, 2) for k, v in total_singer2mins.items()}
+        meta_info["singers"]["minutes"] = total_singer2mins
+
+        with open(os.path.join(save_dir, "meta_info.json"), "w") as f:
+            json.dump(meta_info, f, indent=4, ensure_ascii=False)
+
+        for singer, min in training_singer2mins.items():
+            print(f"Speaker/Singer {singer}: {min} mins for training")
+        print("-" * 10, "\n")
+
+
+def replace_augment_name(dataset: str) -> str:
+    """Replace the augmented dataset name with the original dataset name.
+    >>> print(replace_augment_name("dataset_equalizer"))
+    dataset
+    """
+    if "equalizer" in dataset:
+        dataset = dataset.replace("_equalizer", "")
+    elif "formant_shift" in dataset:
+        dataset = dataset.replace("_formant_shift", "")
+    elif "pitch_shift" in dataset:
+        dataset = dataset.replace("_pitch_shift", "")
+    elif "time_stretch" in dataset:
+        dataset = dataset.replace("_time_stretch", "")
+    else:
+        pass
+    return dataset
diff --git a/preprocessors/nus48e.py b/preprocessors/nus48e.py
new file mode 100644
index 0000000000000000000000000000000000000000..e780b3265b3921f21ee0efb5fcd9162e5555c529
--- /dev/null
+++ b/preprocessors/nus48e.py
@@ -0,0 +1,203 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import json
+import torchaudio
+from tqdm import tqdm
+from glob import glob
+from collections import defaultdict
+
+
+from utils.io import save_audio
+from utils.util import has_existed
+from utils.audio_slicer import Slicer
+from preprocessors import GOLDEN_TEST_SAMPLES
+
+
+def split_to_utterances(dataset_path, singer, style, output_dir):
+    data_dir = os.path.join(dataset_path, singer, style)
+
+    print("Splitting to utterances for {}...".format(data_dir))
+
+    wave_files = glob(data_dir + "/*.wav")
+
+    for wav_file in tqdm(wave_files):
+        # Load waveform
+        song_name = wav_file.split("/")[-1].split(".")[0]
+        waveform, fs = torchaudio.load(wav_file)
+
+        # Split
+        slicer = Slicer(sr=fs, threshold=-40.0, max_sil_kept=4000)
+        chunks = slicer.slice(waveform)
+
+        for i, chunk in enumerate(chunks):
+            save_dir = os.path.join(output_dir, singer, style, song_name)
+            os.makedirs(save_dir, exist_ok=True)
+
+            output_file = os.path.join(save_dir, "{:04d}.wav".format(i))
+            save_audio(output_file, chunk, fs)
+
+
+def _main(dataset_path):
+    """
+    Split to utterances
+    """
+    utterance_dir = os.path.join(dataset_path, "utterances")
+
+    singer_infos = glob(dataset_path + "/*")
+
+    for singer_info in singer_infos:
+        singer = singer_info.split("/")[-1]
+
+        for style in ["read", "sing"]:
+            split_to_utterances(dataset_path, singer, style, utterance_dir)
+
+
+def get_test_songs():
+    golden_samples = GOLDEN_TEST_SAMPLES["nus48e"]
+    # every item is a tuple (singer, song)
+    golden_songs = [s.split("#")[:2] for s in golden_samples]
+    # singer_song, eg: Female1#Almost_lover_Amateur
+    return golden_songs
+
+
+def nus48e_statistics(data_dir):
+    singers = []
+    songs = []
+    singer2songs = defaultdict(lambda: defaultdict(list))
+
+    singer_infos = glob(data_dir + "/*")
+
+    for singer_info in singer_infos:
+        singer_info_split = singer_info.split("/")[-1]
+
+        style_infos = glob(singer_info + "/*")
+
+        for style_info in style_infos:
+            style_info_split = style_info.split("/")[-1]
+
+            singer = singer_info_split + "_" + style_info_split
+            singers.append(singer)
+
+            song_infos = glob(style_info + "/*")
+
+            for song_info in song_infos:
+                song = song_info.split("/")[-1]
+
+                songs.append(song)
+
+                utts = glob(song_info + "/*.wav")
+
+                for utt in utts:
+                    uid = utt.split("/")[-1].split(".")[0]
+                    singer2songs[singer][song].append(uid)
+
+    unique_singers = list(set(singers))
+    unique_songs = list(set(songs))
+    unique_singers.sort()
+    unique_songs.sort()
+
+    print(
+        "nus_48_e: {} singers, {} utterances ({} unique songs)".format(
+            len(unique_singers), len(songs), len(unique_songs)
+        )
+    )
+    print("Singers: \n{}".format("\t".join(unique_singers)))
+    return singer2songs, unique_singers
+
+
+def main(output_path, dataset_path):
+    print("-" * 10)
+    print("Preparing test samples for nus48e...\n")
+
+    if not os.path.exists(os.path.join(dataset_path, "utterances")):
+        print("Spliting into utterances...\n")
+        _main(dataset_path)
+
+    save_dir = os.path.join(output_path, "nus48e")
+    os.makedirs(save_dir, exist_ok=True)
+    train_output_file = os.path.join(save_dir, "train.json")
+    test_output_file = os.path.join(save_dir, "test.json")
+    singer_dict_file = os.path.join(save_dir, "singers.json")
+    utt2singer_file = os.path.join(save_dir, "utt2singer")
+    if (
+        has_existed(train_output_file)
+        and has_existed(test_output_file)
+        and has_existed(singer_dict_file)
+        and has_existed(utt2singer_file)
+    ):
+        return
+    utt2singer = open(utt2singer_file, "w")
+
+    # Load
+    nus48e_path = os.path.join(dataset_path, "utterances")
+
+    singer2songs, unique_singers = nus48e_statistics(nus48e_path)
+    test_songs = get_test_songs()
+
+    # We select songs of standard samples as test songs
+    train = []
+    test = []
+
+    train_index_count = 0
+    test_index_count = 0
+
+    train_total_duration = 0
+    test_total_duration = 0
+
+    for singer, songs in singer2songs.items():
+        song_names = list(songs.keys())
+
+        for chosen_song in song_names:
+            for chosen_uid in songs[chosen_song]:
+                res = {
+                    "Dataset": "nus48e",
+                    "Singer": singer,
+                    "Uid": "{}#{}#{}".format(singer, chosen_song, chosen_uid),
+                }
+                res["Path"] = "{}/{}/{}/{}.wav".format(
+                    singer.split("_")[0], singer.split("_")[-1], chosen_song, chosen_uid
+                )
+                res["Path"] = os.path.join(nus48e_path, res["Path"])
+                assert os.path.exists(res["Path"])
+
+                waveform, sample_rate = torchaudio.load(res["Path"])
+                duration = waveform.size(-1) / sample_rate
+                res["Duration"] = duration
+
+                if duration <= 1e-8:
+                    continue
+
+                if ([singer, chosen_song]) in test_songs:
+                    res["index"] = test_index_count
+                    test_total_duration += duration
+                    test.append(res)
+                    test_index_count += 1
+                else:
+                    res["index"] = train_index_count
+                    train_total_duration += duration
+                    train.append(res)
+                    train_index_count += 1
+
+                utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"]))
+
+    print("#Train = {}, #Test = {}".format(len(train), len(test)))
+    print(
+        "#Train hours= {}, #Test hours= {}".format(
+            train_total_duration / 3600, test_total_duration / 3600
+        )
+    )
+
+    # Save train.json and test.json
+    with open(train_output_file, "w") as f:
+        json.dump(train, f, indent=4, ensure_ascii=False)
+    with open(test_output_file, "w") as f:
+        json.dump(test, f, indent=4, ensure_ascii=False)
+
+    # Save singers.json
+    singer_lut = {name: i for i, name in enumerate(unique_singers)}
+    with open(singer_dict_file, "w") as f:
+        json.dump(singer_lut, f, indent=4, ensure_ascii=False)
diff --git a/preprocessors/opencpop.py b/preprocessors/opencpop.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1da6e303cbb06370f3215ce5c2cb4dd22e611b5
--- /dev/null
+++ b/preprocessors/opencpop.py
@@ -0,0 +1,73 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import json
+from tqdm import tqdm
+import os
+import librosa
+
+from utils.util import has_existed
+
+
+def get_lines(file):
+    with open(file, "r") as f:
+        lines = f.readlines()
+        lines = [l.strip() for l in lines]
+    return lines
+
+
+def get_uid2utt(opencpop_path, dataset, dataset_type):
+    index_count = 0
+    total_duration = 0
+
+    file = os.path.join(opencpop_path, "segments", "{}.txt".format(dataset_type))
+    lines = get_lines(file)
+
+    uid2utt = []
+    for l in tqdm(lines):
+        items = l.split("|")
+        uid = items[0]
+
+        res = {
+            "Dataset": dataset,
+            "index": index_count,
+            "Singer": "female1",
+            "Uid": uid,
+        }
+
+        # Duration in wav files
+        audio_file = os.path.join(opencpop_path, "segments/wavs/{}.wav".format(uid))
+        res["Path"] = audio_file
+
+        duration = librosa.get_duration(filename=res["Path"])
+        res["Duration"] = duration
+
+        uid2utt.append(res)
+
+        index_count = index_count + 1
+        total_duration += duration
+
+    return uid2utt, total_duration / 3600
+
+
+def main(dataset, output_path, dataset_path):
+    print("-" * 10)
+    print("Dataset splits for {}...\n".format(dataset))
+
+    save_dir = os.path.join(output_path, dataset)
+    opencpop_path = dataset_path
+    for dataset_type in ["train", "test"]:
+        output_file = os.path.join(save_dir, "{}.json".format(dataset_type))
+        if has_existed(output_file):
+            continue
+
+        res, hours = get_uid2utt(opencpop_path, dataset, dataset_type)
+
+        # Save
+        os.makedirs(save_dir, exist_ok=True)
+        with open(output_file, "w") as f:
+            json.dump(res, f, indent=4, ensure_ascii=False)
+
+        print("{}_{}_hours= {}".format(dataset, dataset_type, hours))
diff --git a/preprocessors/opensinger.py b/preprocessors/opensinger.py
new file mode 100644
index 0000000000000000000000000000000000000000..93fc3f648df11a866f6c7017d7c6964c9b36cee6
--- /dev/null
+++ b/preprocessors/opensinger.py
@@ -0,0 +1,169 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import random
+import os
+import json
+import librosa
+from tqdm import tqdm
+from glob import glob
+from collections import defaultdict
+
+from utils.util import has_existed
+from preprocessors import GOLDEN_TEST_SAMPLES
+
+
+def get_test_songs():
+    golden_samples = GOLDEN_TEST_SAMPLES["opensinger"]
+    # every item is a tuple (singer, song)
+    golden_songs = [s.split("_")[:3] for s in golden_samples]
+    # singer_song, eg: Female1#Almost_lover_Amateur
+    return golden_songs
+
+
+def opensinger_statistics(data_dir):
+    singers = []
+    songs = []
+    singer2songs = defaultdict(lambda: defaultdict(list))
+
+    gender_infos = glob(data_dir + "/*")
+
+    for gender_info in gender_infos:
+        gender_info_split = gender_info.split("/")[-1][:-3]
+
+        singer_and_song_infos = glob(gender_info + "/*")
+
+        for singer_and_song_info in singer_and_song_infos:
+            singer_and_song_info_split = singer_and_song_info.split("/")[-1].split("_")
+            singer_id, song = (
+                singer_and_song_info_split[0],
+                singer_and_song_info_split[1],
+            )
+            singer = gender_info_split + "_" + singer_id
+            singers.append(singer)
+            songs.append(song)
+
+            utts = glob(singer_and_song_info + "/*.wav")
+
+            for utt in utts:
+                uid = utt.split("/")[-1].split("_")[-1].split(".")[0]
+                singer2songs[singer][song].append(uid)
+
+    unique_singers = list(set(singers))
+    unique_songs = list(set(songs))
+    unique_singers.sort()
+    unique_songs.sort()
+
+    print(
+        "opensinger: {} singers, {} songs ({} unique songs)".format(
+            len(unique_singers), len(songs), len(unique_songs)
+        )
+    )
+    print("Singers: \n{}".format("\t".join(unique_singers)))
+    return singer2songs, unique_singers
+
+
+def main(output_path, dataset_path):
+    print("-" * 10)
+    print("Preparing test samples for opensinger...\n")
+
+    save_dir = os.path.join(output_path, "opensinger")
+    os.makedirs(save_dir, exist_ok=True)
+    train_output_file = os.path.join(save_dir, "train.json")
+    test_output_file = os.path.join(save_dir, "test.json")
+    singer_dict_file = os.path.join(save_dir, "singers.json")
+    utt2singer_file = os.path.join(save_dir, "utt2singer")
+    if (
+        has_existed(train_output_file)
+        and has_existed(test_output_file)
+        and has_existed(singer_dict_file)
+        and has_existed(utt2singer_file)
+    ):
+        return
+    utt2singer = open(utt2singer_file, "w")
+
+    # Load
+    opensinger_path = dataset_path
+
+    singer2songs, unique_singers = opensinger_statistics(opensinger_path)
+    test_songs = get_test_songs()
+
+    # We select songs of standard samples as test songs
+    train = []
+    test = []
+
+    train_index_count = 0
+    test_index_count = 0
+
+    train_total_duration = 0
+    test_total_duration = 0
+
+    for i, (singer, songs) in enumerate(singer2songs.items()):
+        song_names = list(songs.keys())
+
+        for chosen_song in tqdm(
+            song_names, desc="Singer {}/{}".format(i, len(singer2songs))
+        ):
+            for chosen_uid in songs[chosen_song]:
+                res = {
+                    "Dataset": "opensinger",
+                    "Singer": singer,
+                    "Song": chosen_song,
+                    "Uid": "{}_{}_{}".format(singer, chosen_song, chosen_uid),
+                }
+                res["Path"] = "{}Raw/{}_{}/{}_{}_{}.wav".format(
+                    singer.split("_")[0],
+                    singer.split("_")[1],
+                    chosen_song,
+                    singer.split("_")[1],
+                    chosen_song,
+                    chosen_uid,
+                )
+                res["Path"] = os.path.join(opensinger_path, res["Path"])
+                assert os.path.exists(res["Path"])
+
+                duration = librosa.get_duration(filename=res["Path"])
+                res["Duration"] = duration
+
+                if duration > 30:
+                    print(
+                        "Wav file: {}, the duration = {:.2f}s > 30s, which has been abandoned.".format(
+                            res["Path"], duration
+                        )
+                    )
+                    continue
+
+                if (
+                    [singer.split("_")[0], singer.split("_")[1], chosen_song]
+                ) in test_songs:
+                    res["index"] = test_index_count
+                    test_total_duration += duration
+                    test.append(res)
+                    test_index_count += 1
+                else:
+                    res["index"] = train_index_count
+                    train_total_duration += duration
+                    train.append(res)
+                    train_index_count += 1
+
+                utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"]))
+
+    print("#Train = {}, #Test = {}".format(len(train), len(test)))
+    print(
+        "#Train hours= {}, #Test hours= {}".format(
+            train_total_duration / 3600, test_total_duration / 3600
+        )
+    )
+
+    # Save train.json and test.json
+    with open(train_output_file, "w") as f:
+        json.dump(train, f, indent=4, ensure_ascii=False)
+    with open(test_output_file, "w") as f:
+        json.dump(test, f, indent=4, ensure_ascii=False)
+
+    # Save singers.json
+    singer_lut = {name: i for i, name in enumerate(unique_singers)}
+    with open(singer_dict_file, "w") as f:
+        json.dump(singer_lut, f, indent=4, ensure_ascii=False)
diff --git a/preprocessors/opera.py b/preprocessors/opera.py
new file mode 100644
index 0000000000000000000000000000000000000000..c421fbb8346945c5dec40b1c872cebcb49f1f606
--- /dev/null
+++ b/preprocessors/opera.py
@@ -0,0 +1,186 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import json
+import os
+from tqdm import tqdm
+import torchaudio
+from glob import glob
+from collections import defaultdict
+
+from utils.util import has_existed
+from utils.io import save_audio
+from utils.audio_slicer import Slicer
+from preprocessors import GOLDEN_TEST_SAMPLES
+
+
+def split_to_utterances(language_dir, output_dir):
+    print("Splitting to utterances for {}...".format(language_dir))
+
+    for wav_file in tqdm(glob("{}/*/*".format(language_dir))):
+        # Load waveform
+        singer_name, song_name = wav_file.split("/")[-2:]
+        song_name = song_name.split(".")[0]
+        waveform, fs = torchaudio.load(wav_file)
+
+        # Split
+        slicer = Slicer(sr=fs, threshold=-30.0, max_sil_kept=3000)
+        chunks = slicer.slice(waveform)
+
+        for i, chunk in enumerate(chunks):
+            save_dir = os.path.join(output_dir, singer_name, song_name)
+            os.makedirs(save_dir, exist_ok=True)
+
+            output_file = os.path.join(save_dir, "{:04d}.wav".format(i))
+            save_audio(output_file, chunk, fs)
+
+
+def _main(dataset_path):
+    """
+    Split to utterances
+    """
+    utterance_dir = os.path.join(dataset_path, "utterances")
+
+    for lang in ["chinese", "western"]:
+        split_to_utterances(os.path.join(dataset_path, lang), utterance_dir)
+
+
+def get_test_songs():
+    golden_samples = GOLDEN_TEST_SAMPLES["opera"]
+    # every item is a tuple (singer, song)
+    golden_songs = [s.split("#")[:2] for s in golden_samples]
+    # singer#song, eg:fem_01#neg_01
+    return golden_songs
+
+
+def opera_statistics(data_dir):
+    singers = []
+    songs = []
+    singers2songs = defaultdict(lambda: defaultdict(list))
+
+    singer_infos = glob(data_dir + "/*")
+
+    for singer_info in singer_infos:
+        singer = singer_info.split("/")[-1]
+
+        song_infos = glob(singer_info + "/*")
+
+        for song_info in song_infos:
+            song = song_info.split("/")[-1]
+
+            singers.append(singer)
+            songs.append(song)
+
+            utts = glob(song_info + "/*.wav")
+
+            for utt in utts:
+                uid = utt.split("/")[-1].split(".")[0]
+                singers2songs[singer][song].append(uid)
+
+    unique_singers = list(set(singers))
+    unique_songs = list(set(songs))
+    unique_singers.sort()
+    unique_songs.sort()
+
+    print(
+        "opera: {} singers, {} utterances ({} unique songs)".format(
+            len(unique_singers), len(songs), len(unique_songs)
+        )
+    )
+    print("Singers: \n{}".format("\t".join(unique_singers)))
+    return singers2songs, unique_singers
+
+
+def main(output_path, dataset_path):
+    print("-" * 10)
+    print("Preparing test samples for opera...\n")
+
+    if not os.path.exists(os.path.join(dataset_path, "utterances")):
+        print("Spliting into utterances...\n")
+        _main(dataset_path)
+
+    save_dir = os.path.join(output_path, "opera")
+    os.makedirs(save_dir, exist_ok=True)
+    train_output_file = os.path.join(save_dir, "train.json")
+    test_output_file = os.path.join(save_dir, "test.json")
+    singer_dict_file = os.path.join(save_dir, "singers.json")
+    utt2singer_file = os.path.join(save_dir, "utt2singer")
+    if (
+        has_existed(train_output_file)
+        and has_existed(test_output_file)
+        and has_existed(singer_dict_file)
+        and has_existed(utt2singer_file)
+    ):
+        return
+    utt2singer = open(utt2singer_file, "w")
+
+    # Load
+    opera_path = os.path.join(dataset_path, "utterances")
+
+    singers2songs, unique_singers = opera_statistics(opera_path)
+    test_songs = get_test_songs()
+
+    # We select songs of standard samples as test songs
+    train = []
+    test = []
+
+    train_index_count = 0
+    test_index_count = 0
+
+    train_total_duration = 0
+    test_total_duration = 0
+
+    for singer, songs in tqdm(singers2songs.items()):
+        song_names = list(songs.keys())
+
+        for chosen_song in song_names:
+            for chosen_uid in songs[chosen_song]:
+                res = {
+                    "Dataset": "opera",
+                    "Singer": singer,
+                    "Uid": "{}#{}#{}".format(singer, chosen_song, chosen_uid),
+                }
+                res["Path"] = "{}/{}/{}.wav".format(singer, chosen_song, chosen_uid)
+                res["Path"] = os.path.join(opera_path, res["Path"])
+                assert os.path.exists(res["Path"])
+
+                waveform, sample_rate = torchaudio.load(res["Path"])
+                duration = waveform.size(-1) / sample_rate
+                res["Duration"] = duration
+
+                if duration <= 1e-8:
+                    continue
+
+                if ([singer, chosen_song]) in test_songs:
+                    res["index"] = test_index_count
+                    test_total_duration += duration
+                    test.append(res)
+                    test_index_count += 1
+                else:
+                    res["index"] = train_index_count
+                    train_total_duration += duration
+                    train.append(res)
+                    train_index_count += 1
+
+                utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"]))
+
+    print("#Train = {}, #Test = {}".format(len(train), len(test)))
+    print(
+        "#Train hours= {}, #Test hours= {}".format(
+            train_total_duration / 3600, test_total_duration / 3600
+        )
+    )
+
+    # Save train.json and test.json
+    with open(train_output_file, "w") as f:
+        json.dump(train, f, indent=4, ensure_ascii=False)
+    with open(test_output_file, "w") as f:
+        json.dump(test, f, indent=4, ensure_ascii=False)
+
+    # Save singers.json
+    singer_lut = {name: i for i, name in enumerate(unique_singers)}
+    with open(singer_dict_file, "w") as f:
+        json.dump(singer_lut, f, indent=4, ensure_ascii=False)
diff --git a/preprocessors/pjs.py b/preprocessors/pjs.py
new file mode 100644
index 0000000000000000000000000000000000000000..78d69bc56cae59b3bf512078e365853ccad053ff
--- /dev/null
+++ b/preprocessors/pjs.py
@@ -0,0 +1,135 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+from tqdm import tqdm
+import glob
+import json
+import torchaudio
+
+from utils.util import has_existed
+from utils.io import save_audio
+
+
+def get_splitted_utterances(
+    raw_wav_dir, trimed_wav_dir, n_utterance_splits, overlapping
+):
+    res = []
+    raw_song_files = glob.glob(
+        os.path.join(raw_wav_dir, "**/pjs*_song.wav"), recursive=True
+    )
+    trimed_song_files = glob.glob(
+        os.path.join(trimed_wav_dir, "**/*.wav"), recursive=True
+    )
+
+    if len(raw_song_files) * n_utterance_splits == len(trimed_song_files):
+        print("Splitted done...")
+        for wav_file in tqdm(trimed_song_files):
+            uid = wav_file.split("/")[-1].split(".")[0]
+            utt = {"Dataset": "pjs", "Singer": "male1", "Uid": uid, "Path": wav_file}
+
+            waveform, sample_rate = torchaudio.load(wav_file)
+            duration = waveform.size(-1) / sample_rate
+            utt["Duration"] = duration
+
+            res.append(utt)
+
+    else:
+        for wav_file in tqdm(raw_song_files):
+            song_id = wav_file.split("/")[-1].split(".")[0]
+
+            waveform, sample_rate = torchaudio.load(wav_file)
+            trimed_waveform = torchaudio.functional.vad(waveform, sample_rate)
+            trimed_waveform = torchaudio.functional.vad(
+                trimed_waveform.flip(dims=[1]), sample_rate
+            ).flip(dims=[1])
+
+            audio_len = trimed_waveform.size(-1)
+            lapping_len = overlapping * sample_rate
+
+            for i in range(n_utterance_splits):
+                start = i * audio_len // 3
+                end = start + audio_len // 3 + lapping_len
+                splitted_waveform = trimed_waveform[:, start:end]
+
+                utt = {
+                    "Dataset": "pjs",
+                    "Singer": "male1",
+                    "Uid": "{}_{}".format(song_id, i),
+                }
+
+                # Duration
+                duration = splitted_waveform.size(-1) / sample_rate
+                utt["Duration"] = duration
+
+                # Save trimed wav
+                splitted_waveform_file = os.path.join(
+                    trimed_wav_dir, "{}.wav".format(utt["Uid"])
+                )
+                save_audio(splitted_waveform_file, splitted_waveform, sample_rate)
+
+                # Path
+                utt["Path"] = splitted_waveform_file
+
+                res.append(utt)
+
+    res = sorted(res, key=lambda x: x["Uid"])
+    return res
+
+
+def main(output_path, dataset_path, n_utterance_splits=3, overlapping=1):
+    """
+    1. Split one raw utterance to three splits (since some samples are too long)
+    2. Overlapping of ajacent splits is 1 s
+    """
+    print("-" * 10)
+    print("Preparing training dataset for PJS...")
+
+    save_dir = os.path.join(output_path, "pjs")
+    raw_wav_dir = os.path.join(dataset_path, "PJS_corpus_ver1.1")
+
+    # Trim for silence
+    trimed_wav_dir = os.path.join(dataset_path, "trim")
+    os.makedirs(trimed_wav_dir, exist_ok=True)
+
+    # Total utterances
+    utterances = get_splitted_utterances(
+        raw_wav_dir, trimed_wav_dir, n_utterance_splits, overlapping
+    )
+    total_uids = [utt["Uid"] for utt in utterances]
+
+    # Test uids
+    n_test_songs = 3
+    test_uids = []
+    for i in range(1, n_test_songs + 1):
+        test_uids += [
+            "pjs00{}_song_{}".format(i, split_id)
+            for split_id in range(n_utterance_splits)
+        ]
+
+    # Train uids
+    train_uids = [uid for uid in total_uids if uid not in test_uids]
+
+    for dataset_type in ["train", "test"]:
+        output_file = os.path.join(save_dir, "{}.json".format(dataset_type))
+        if has_existed(output_file):
+            continue
+
+        uids = eval("{}_uids".format(dataset_type))
+        res = [utt for utt in utterances if utt["Uid"] in uids]
+        for i in range(len(res)):
+            res[i]["index"] = i
+
+        time = sum([utt["Duration"] for utt in res])
+        print(
+            "{}, Total size: {}, Total Duraions = {} s = {:.2f} hour\n".format(
+                dataset_type, len(res), time, time / 3600
+            )
+        )
+
+        # Save
+        os.makedirs(save_dir, exist_ok=True)
+        with open(output_file, "w") as f:
+            json.dump(res, f, indent=4, ensure_ascii=False)
diff --git a/preprocessors/popbutfy.py b/preprocessors/popbutfy.py
new file mode 100644
index 0000000000000000000000000000000000000000..72ba7bdd60f6d370a7ff39bbb90d695f1157a4b4
--- /dev/null
+++ b/preprocessors/popbutfy.py
@@ -0,0 +1,153 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import json
+import torchaudio
+import librosa
+from tqdm import tqdm
+from glob import glob
+from collections import defaultdict
+
+from utils.util import has_existed
+from preprocessors import GOLDEN_TEST_SAMPLES
+
+
+def get_test_songs():
+    golden_samples = GOLDEN_TEST_SAMPLES["popbutfy"]
+    # every item is a tuple (singer, song)
+    golden_songs = [s.split("#")[:2] for s in golden_samples]
+    # singer#song, eg: Female1#Almost_lover_Amateur
+    return golden_songs
+
+
+def popbutfy_statistics(data_dir):
+    singers = []
+    songs = []
+    singer2songs = defaultdict(lambda: defaultdict(list))
+
+    data_infos = glob(data_dir + "/*")
+
+    for data_info in data_infos:
+        data_info_split = data_info.split("/")[-1].split("#")
+
+        singer, song = data_info_split[0], data_info_split[-1]
+        singers.append(singer)
+        songs.append(song)
+
+        utts = glob(data_info + "/*")
+
+        for utt in utts:
+            uid = utt.split("/")[-1].split("_")[-1].split(".")[0]
+            singer2songs[singer][song].append(uid)
+
+    unique_singers = list(set(singers))
+    unique_songs = list(set(songs))
+    unique_singers.sort()
+    unique_songs.sort()
+
+    print(
+        "PopBuTFy: {} singers, {} utterances ({} unique songs)".format(
+            len(unique_singers), len(songs), len(unique_songs)
+        )
+    )
+    print("Singers: \n{}".format("\t".join(unique_singers)))
+    return singer2songs, unique_singers
+
+
+def main(output_path, dataset_path):
+    print("-" * 10)
+    print("Preparing test samples for popbutfy...\n")
+
+    save_dir = os.path.join(output_path, "popbutfy")
+    os.makedirs(save_dir, exist_ok=True)
+    train_output_file = os.path.join(save_dir, "train.json")
+    test_output_file = os.path.join(save_dir, "test.json")
+    singer_dict_file = os.path.join(save_dir, "singers.json")
+    utt2singer_file = os.path.join(save_dir, "utt2singer")
+    if (
+        has_existed(train_output_file)
+        and has_existed(test_output_file)
+        and has_existed(singer_dict_file)
+        and has_existed(utt2singer_file)
+    ):
+        return
+    utt2singer = open(utt2singer_file, "w")
+
+    # Load
+    popbutfy_dir = dataset_path
+
+    singer2songs, unique_singers = popbutfy_statistics(popbutfy_dir)
+    test_songs = get_test_songs()
+
+    # We select songs of standard samples as test songs
+    train = []
+    test = []
+
+    train_index_count = 0
+    test_index_count = 0
+
+    train_total_duration = 0
+    test_total_duration = 0
+
+    for singer, songs in tqdm(singer2songs.items()):
+        song_names = list(songs.keys())
+
+        for chosen_song in song_names:
+            for chosen_uid in songs[chosen_song]:
+                res = {
+                    "Dataset": "popbutfy",
+                    "Singer": singer,
+                    "Song": chosen_song,
+                    "Uid": "{}#{}#".format(singer, chosen_song, chosen_uid),
+                }
+                res["Path"] = "{}#singing#{}/{}#singing#{}_{}.mp3".format(
+                    singer, chosen_song, singer, chosen_song, chosen_uid
+                )
+                if not os.path.exists(os.path.join(popbutfy_dir, res["Path"])):
+                    res["Path"] = "{}#singing#{}/{}#singing#{}_{}.wav".format(
+                        singer, chosen_song, singer, chosen_song, chosen_uid
+                    )
+                res["Path"] = os.path.join(popbutfy_dir, res["Path"])
+                assert os.path.exists(res["Path"])
+
+                if res["Path"].split("/")[-1].split(".")[-1] == "wav":
+                    waveform, sample_rate = torchaudio.load(res["Path"])
+                    duration = waveform.size(-1) / sample_rate
+                else:
+                    waveform, sample_rate = librosa.load(res["Path"])
+                    duration = waveform.shape[-1] / sample_rate
+                res["Duration"] = duration
+
+                if ([singer, chosen_song]) in test_songs:
+                    res["index"] = test_index_count
+                    test_total_duration += duration
+                    test.append(res)
+                    test_index_count += 1
+                else:
+                    res["index"] = train_index_count
+                    train_total_duration += duration
+                    train.append(res)
+                    train_index_count += 1
+
+                utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"]))
+
+    print("#Train = {}, #Test = {}".format(len(train), len(test)))
+    print(
+        "#Train hours= {}, #Test hours= {}".format(
+            train_total_duration / 3600, test_total_duration / 3600
+        )
+    )
+
+    # Save train.json and test.json
+    with open(train_output_file, "w") as f:
+        json.dump(train, f, indent=4, ensure_ascii=False)
+    with open(test_output_file, "w") as f:
+        json.dump(test, f, indent=4, ensure_ascii=False)
+
+    # Save singers.json
+    singer_lut = {name: i for i, name in enumerate(unique_singers)}
+    with open(singer_dict_file, "w") as f:
+        json.dump(singer_lut, f, indent=4, ensure_ascii=False)
diff --git a/preprocessors/popcs.py b/preprocessors/popcs.py
new file mode 100644
index 0000000000000000000000000000000000000000..9acf2127ec27f679d715108495f580a30f75d01a
--- /dev/null
+++ b/preprocessors/popcs.py
@@ -0,0 +1,118 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import json
+import torchaudio
+from glob import glob
+from collections import defaultdict
+
+from utils.util import has_existed
+from preprocessors import GOLDEN_TEST_SAMPLES
+
+
+def get_test_songs():
+    golden_samples = GOLDEN_TEST_SAMPLES["popcs"]
+    # every item is a string
+    golden_songs = [s.split("_")[:1] for s in golden_samples]
+    # song, eg: 万有引力
+    return golden_songs
+
+
+def popcs_statistics(data_dir):
+    songs = []
+    songs2utts = defaultdict(list)
+
+    song_infos = glob(data_dir + "/*")
+
+    for song_info in song_infos:
+        song_info_split = song_info.split("/")[-1].split("-")[-1]
+
+        songs.append(song_info_split)
+
+        utts = glob(song_info + "/*.wav")
+
+        for utt in utts:
+            uid = utt.split("/")[-1].split("_")[0]
+            songs2utts[song_info_split].append(uid)
+
+    unique_songs = list(set(songs))
+    unique_songs.sort()
+
+    print(
+        "popcs: {} utterances ({} unique songs)".format(len(songs), len(unique_songs))
+    )
+    print("Songs: \n{}".format("\t".join(unique_songs)))
+    return songs2utts
+
+
+def main(output_path, dataset_path):
+    print("-" * 10)
+    print("Preparing test samples for popcs...\n")
+
+    save_dir = os.path.join(output_path, "popcs")
+    train_output_file = os.path.join(save_dir, "train.json")
+    test_output_file = os.path.join(save_dir, "test.json")
+    if has_existed(test_output_file):
+        return
+
+    # Load
+    popcs_dir = dataset_path
+
+    songs2utts = popcs_statistics(popcs_dir)
+    test_songs = get_test_songs()
+
+    # We select songs of standard samples as test songs
+    train = []
+    test = []
+
+    train_index_count = 0
+    test_index_count = 0
+
+    train_total_duration = 0
+    test_total_duration = 0
+
+    song_names = list(songs2utts.keys())
+
+    for chosen_song in song_names:
+        for chosen_uid in songs2utts[chosen_song]:
+            res = {
+                "Dataset": "popcs",
+                "Singer": "female1",
+                "Song": chosen_song,
+                "Uid": "{}_{}".format(chosen_song, chosen_uid),
+            }
+            res["Path"] = "popcs-{}/{}_wf0.wav".format(chosen_song, chosen_uid)
+            res["Path"] = os.path.join(popcs_dir, res["Path"])
+            assert os.path.exists(res["Path"])
+
+            waveform, sample_rate = torchaudio.load(res["Path"])
+            duration = waveform.size(-1) / sample_rate
+            res["Duration"] = duration
+
+            if ([chosen_song]) in test_songs:
+                res["index"] = test_index_count
+                test_total_duration += duration
+                test.append(res)
+                test_index_count += 1
+            else:
+                res["index"] = train_index_count
+                train_total_duration += duration
+                train.append(res)
+                train_index_count += 1
+
+    print("#Train = {}, #Test = {}".format(len(train), len(test)))
+    print(
+        "#Train hours= {}, #Test hours= {}".format(
+            train_total_duration / 3600, test_total_duration / 3600
+        )
+    )
+
+    # Save
+    os.makedirs(save_dir, exist_ok=True)
+    with open(train_output_file, "w") as f:
+        json.dump(train, f, indent=4, ensure_ascii=False)
+    with open(test_output_file, "w") as f:
+        json.dump(test, f, indent=4, ensure_ascii=False)
diff --git a/preprocessors/processor.py b/preprocessors/processor.py
new file mode 100644
index 0000000000000000000000000000000000000000..5661d74f0d27f9c3c949d82a41b010e6760b3802
--- /dev/null
+++ b/preprocessors/processor.py
@@ -0,0 +1,100 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import re
+from preprocessors import (
+    m4singer,
+    opencpop,
+    svcc,
+    pjs,
+    popbutfy,
+    opensinger,
+    popcs,
+    kising,
+    csd,
+    opera,
+    nus48e,
+    svcceval,
+    vctk,
+    vctksample,
+    libritts,
+    lijian,
+    cdmusiceval,
+    ljspeech,
+    coco,
+    cocoeval,
+    custom,
+    vocalist,
+    ljspeech_vocoder,
+)
+
+
+def preprocess_dataset(
+    dataset, dataset_path, output_path, cfg, is_custom_dataset=False
+):
+    """Call specific function to handle specific dataset
+    Args:
+        dataset (str): name of a dataset, e.g. opencpop, m4singer
+        dataset_path (str): path to dataset
+        output_path (str): path to store preprocessing result files
+    """
+    if is_custom_dataset:
+        custom.main(output_path, dataset_path, dataset_name=dataset)
+        return
+
+    if re.match("opencpop*", dataset):
+        opencpop.main(dataset, output_path, dataset_path)
+    if dataset == "m4singer":
+        m4singer.main(output_path, dataset_path)
+    if dataset == "svcc":
+        svcc.main(output_path, dataset_path)
+    if dataset == "pjs":
+        pjs.main(output_path, dataset_path)
+    if dataset == "popbutfy":
+        popbutfy.main(output_path, dataset_path)
+    if dataset == "opensinger":
+        opensinger.main(output_path, dataset_path)
+    if dataset == "popcs":
+        popcs.main(output_path, dataset_path)
+    if dataset == "kising":
+        kising.main(output_path, dataset_path)
+    if dataset == "csd":
+        csd.main(output_path, dataset_path)
+    if dataset == "opera":
+        opera.main(output_path, dataset_path)
+    if dataset == "nus48e":
+        nus48e.main(output_path, dataset_path)
+    if dataset == "vctk":
+        vctk.main(output_path, dataset_path)
+    if dataset == "svcceval":
+        svcceval.main(output_path, dataset_path)
+    if dataset == "libritts":
+        libritts.main(output_path, dataset_path)
+    if dataset == "lijian":
+        lijian.main(output_path, dataset_path)
+    if dataset == "cdmusiceval":
+        cdmusiceval.main(output_path, dataset_path)
+    if dataset == "LJSpeech":
+        ljspeech.main(output_path, dataset_path, cfg)
+    if dataset == "ljspeech":
+        ljspeech_vocoder.main(output_path, dataset_path)
+    if dataset == "coco":
+        coco.main(output_path, dataset_path)
+    if dataset == "cocoeval":
+        cocoeval.main(output_path, dataset_path)
+    if dataset == "vocalist":
+        vocalist.main(output_path, dataset_path)
+
+
+def prepare_align(dataset, dataset_path, cfg, output_path):
+    """Call specific function to handle specific dataset
+
+    Args:
+        dataset (str): name of a dataset, e.g. ljspeech
+        dataset_path (str): path to dataset
+        output_path (str): path to store preprocessing result files
+    """
+    if dataset == "LJSpeech":
+        ljspeech.prepare_align(dataset, dataset_path, cfg, output_path)
diff --git a/preprocessors/svcc.py b/preprocessors/svcc.py
new file mode 100644
index 0000000000000000000000000000000000000000..6afee6197146eff9464839ea7844edbb64e76211
--- /dev/null
+++ b/preprocessors/svcc.py
@@ -0,0 +1,85 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import glob
+import librosa
+import json
+
+from utils.util import has_existed
+from preprocessors import GOLDEN_TEST_SAMPLES
+
+
+def main(output_path, dataset_path):
+    print("-" * 10)
+    print("Preparing training dataset for svcc...")
+
+    data_dir = os.path.join(dataset_path, "Data")
+    save_dir = os.path.join(output_path, "svcc")
+    os.makedirs(save_dir, exist_ok=True)
+
+    singer_dict_file = os.path.join(save_dir, "singers.json")
+    utt2singer_file = os.path.join(save_dir, "utt2singer")
+    utt2singer = open(utt2singer_file, "w")
+
+    # Load utterances
+    train = []
+    test = []
+    singers = []
+
+    for wav_file in glob.glob(os.path.join(data_dir, "*/*.wav")):
+        singer, filename = wav_file.split("/")[-2:]
+        uid = filename.split(".")[0]
+        utt = {
+            "Dataset": "svcc",
+            "Singer": singer,
+            "Uid": "{}_{}".format(singer, uid),
+            "Path": wav_file,
+        }
+
+        # Duration
+        duration = librosa.get_duration(filename=wav_file)
+        utt["Duration"] = duration
+
+        if utt["Uid"] in GOLDEN_TEST_SAMPLES["svcc"]:
+            test.append(utt)
+        else:
+            train.append(utt)
+
+        singers.append(singer)
+        utt2singer.write("{}\t{}\n".format(utt["Uid"], utt["Singer"]))
+
+    # Save singers.json
+    unique_singers = list(set(singers))
+    unique_singers.sort()
+    singer_lut = {name: i for i, name in enumerate(unique_singers)}
+    with open(singer_dict_file, "w") as f:
+        json.dump(singer_lut, f, indent=4, ensure_ascii=False)
+
+    train_total_duration = sum([utt["Duration"] for utt in train])
+    test_total_duration = sum([utt["Duration"] for utt in test])
+
+    for dataset_type in ["train", "test"]:
+        output_file = os.path.join(save_dir, "{}.json".format(dataset_type))
+        if has_existed(output_file):
+            continue
+
+        utterances = eval(dataset_type)
+        utterances = sorted(utterances, key=lambda x: x["Uid"])
+
+        for i in range(len(utterances)):
+            utterances[i]["index"] = i
+
+        print("{}: Total size: {}\n".format(dataset_type, len(utterances)))
+
+        # Save
+        with open(output_file, "w") as f:
+            json.dump(utterances, f, indent=4, ensure_ascii=False)
+
+    print(
+        "#Train hours= {}, #Test hours= {}".format(
+            train_total_duration / 3600, test_total_duration / 3600
+        )
+    )
diff --git a/preprocessors/svcceval.py b/preprocessors/svcceval.py
new file mode 100644
index 0000000000000000000000000000000000000000..871e78956d8e9d323125aaa79770f02176e2426c
--- /dev/null
+++ b/preprocessors/svcceval.py
@@ -0,0 +1,80 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import glob
+import librosa
+import json
+
+from utils.util import has_existed
+
+
+def main(output_path, dataset_path):
+    print("-" * 10)
+    print("Preparing training dataset for svcceval...")
+
+    data_dir = os.path.join(dataset_path, "Data")
+    save_dir = os.path.join(output_path, "svcceval")
+    os.makedirs(save_dir, exist_ok=True)
+
+    singer_dict_file = os.path.join(save_dir, "singers.json")
+    utt2singer_file = os.path.join(save_dir, "utt2singer")
+    utt2singer = open(utt2singer_file, "w")
+
+    # Load utterances
+    train = []
+    test = []
+    singers = []
+    for wav_file in glob.glob(os.path.join(data_dir, "*/*.wav")):
+        singer, filename = wav_file.split("/")[-2:]
+        uid = filename.split(".")[0]
+        utt = {
+            "Dataset": "svcceval",
+            "Singer": singer,
+            "Uid": "{}_{}".format(singer, uid),
+            "Path": wav_file,
+        }
+
+        # Duration
+        duration = librosa.get_duration(filename=wav_file)
+        utt["Duration"] = duration
+
+        test.append(utt)
+
+        singers.append(singer)
+        utt2singer.write("{}\t{}\n".format(utt["Uid"], utt["Singer"]))
+
+    # Save singers.json
+    unique_singers = list(set(singers))
+    unique_singers.sort()
+    singer_lut = {name: i for i, name in enumerate(unique_singers)}
+    with open(singer_dict_file, "w") as f:
+        json.dump(singer_lut, f, indent=4, ensure_ascii=False)
+
+    train_total_duration = sum([utt["Duration"] for utt in train])
+    test_total_duration = sum([utt["Duration"] for utt in test])
+
+    for dataset_type in ["train", "test"]:
+        output_file = os.path.join(save_dir, "{}.json".format(dataset_type))
+        if has_existed(output_file):
+            continue
+
+        utterances = eval(dataset_type)
+        utterances = sorted(utterances, key=lambda x: x["Uid"])
+
+        for i in range(len(utterances)):
+            utterances[i]["index"] = i
+
+        print("{}: Total size: {}\n".format(dataset_type, len(utterances)))
+
+        # Save
+        with open(output_file, "w") as f:
+            json.dump(utterances, f, indent=4, ensure_ascii=False)
+
+    print(
+        "#Train hours= {}, #Test hours= {}".format(
+            train_total_duration / 3600, test_total_duration / 3600
+        )
+    )
diff --git a/preprocessors/vctk.py b/preprocessors/vctk.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa4c1c69414561f6e40c69acc653630ba783abd1
--- /dev/null
+++ b/preprocessors/vctk.py
@@ -0,0 +1,163 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import json
+import librosa
+from tqdm import tqdm
+from glob import glob
+from collections import defaultdict
+
+from utils.util import has_existed
+
+
+def get_lines(file):
+    with open(file, "r") as f:
+        lines = f.readlines()
+        lines = [l.strip() for l in lines]
+    return lines
+
+
+def vctk_statistics(data_dir):
+    speakers = []
+    speakers2utts = defaultdict(list)
+
+    speaker_infos = glob(data_dir + "/wav48_silence_trimmed" + "/*")
+
+    for speaker_info in speaker_infos:
+        speaker = speaker_info.split("/")[-1]
+
+        if speaker == "log.txt":
+            continue
+
+        speakers.append(speaker)
+
+        utts = glob(speaker_info + "/*")
+
+        for utt in utts:
+            uid = (
+                utt.split("/")[-1].split("_")[1]
+                + "_"
+                + utt.split("/")[-1].split("_")[2].split(".")[0]
+            )
+            speakers2utts[speaker].append(uid)
+
+    unique_speakers = list(set(speakers))
+    unique_speakers.sort()
+
+    print("Speakers: \n{}".format("\t".join(unique_speakers)))
+    return speakers2utts, unique_speakers
+
+
+def vctk_speaker_infos(data_dir):
+    file = os.path.join(data_dir, "speaker-info.txt")
+    lines = get_lines(file)
+
+    ID2speakers = defaultdict()
+    for l in tqdm(lines):
+        items = l.replace(" ", "")
+
+        if items[:2] == "ID":
+            # The header line
+            continue
+
+        if items[0] == "p":
+            id = items[:4]
+            gender = items[6]
+        elif items[0] == "s":
+            id = items[:2]
+            gender = items[4]
+
+        if gender == "F":
+            speaker = "female_{}".format(id)
+        elif gender == "M":
+            speaker = "male_{}".format(id)
+
+        ID2speakers[id] = speaker
+
+    return ID2speakers
+
+
+def main(output_path, dataset_path, TEST_NUM_OF_EVERY_SPEAKER=3):
+    print("-" * 10)
+    print("Preparing test samples for vctk...")
+
+    save_dir = os.path.join(output_path, "vctk")
+    os.makedirs(save_dir, exist_ok=True)
+    train_output_file = os.path.join(save_dir, "train.json")
+    test_output_file = os.path.join(save_dir, "test.json")
+    singer_dict_file = os.path.join(save_dir, "singers.json")
+    utt2singer_file = os.path.join(save_dir, "utt2singer")
+    if has_existed(train_output_file):
+        return
+    utt2singer = open(utt2singer_file, "w")
+
+    # Load
+    vctk_dir = dataset_path
+
+    ID2speakers = vctk_speaker_infos(vctk_dir)
+    speaker2utts, unique_speakers = vctk_statistics(vctk_dir)
+
+    # We select speakers of standard samples as test utts
+    train = []
+    test = []
+
+    train_index_count = 0
+    test_index_count = 0
+    test_speaker_count = defaultdict(int)
+
+    train_total_duration = 0
+    test_total_duration = 0
+
+    for i, speaker in enumerate(speaker2utts.keys()):
+        for chosen_uid in tqdm(
+            speaker2utts[speaker],
+            desc="Speaker {}/{}, #Train = {}, #Test = {}".format(
+                i + 1, len(speaker2utts), train_index_count, test_index_count
+            ),
+        ):
+            res = {
+                "Dataset": "vctk",
+                "Singer": ID2speakers[speaker],
+                "Uid": "{}#{}".format(ID2speakers[speaker], chosen_uid),
+            }
+            res["Path"] = "{}/{}_{}.flac".format(speaker, speaker, chosen_uid)
+            res["Path"] = os.path.join(vctk_dir, "wav48_silence_trimmed", res["Path"])
+            assert os.path.exists(res["Path"])
+
+            duration = librosa.get_duration(filename=res["Path"])
+            res["Duration"] = duration
+
+            if test_speaker_count[speaker] < TEST_NUM_OF_EVERY_SPEAKER:
+                res["index"] = test_index_count
+                test_total_duration += duration
+                test.append(res)
+                test_index_count += 1
+                test_speaker_count[speaker] += 1
+            else:
+                res["index"] = train_index_count
+                train_total_duration += duration
+                train.append(res)
+                train_index_count += 1
+
+            utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"]))
+
+    print("#Train = {}, #Test = {}".format(len(train), len(test)))
+    print(
+        "#Train hours= {}, #Test hours= {}".format(
+            train_total_duration / 3600, test_total_duration / 3600
+        )
+    )
+
+    # Save train.json and test.json
+    with open(train_output_file, "w") as f:
+        json.dump(train, f, indent=4, ensure_ascii=False)
+    with open(test_output_file, "w") as f:
+        json.dump(test, f, indent=4, ensure_ascii=False)
+
+    # Save singers.json
+    singer_lut = {name: i for i, name in enumerate(unique_speakers)}
+    with open(singer_dict_file, "w") as f:
+        json.dump(singer_lut, f, indent=4, ensure_ascii=False)
diff --git a/preprocessors/vctkfewsinger.py b/preprocessors/vctkfewsinger.py
new file mode 100644
index 0000000000000000000000000000000000000000..e4b6adc76597b18ccbbf065ef339ccff77032a52
--- /dev/null
+++ b/preprocessors/vctkfewsinger.py
@@ -0,0 +1,175 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import json
+import pickle
+import glob
+from collections import defaultdict
+from tqdm import tqdm
+
+
+# Train: male 20 hours, female 10 hours
+TRAIN_MALE_MAX_SECONDS = 20 * 3600
+TRAIN_FEMALE_MAX_SECONDS = 10 * 3600
+TEST_MAX_NUM_EVERY_PERSON = 5
+
+
+def select_sample_idxs():
+    chosen_speakers = get_chosen_speakers()
+
+    with open(os.path.join(vctk_dir, "train.json"), "r") as f:
+        raw_train = json.load(f)
+    with open(os.path.join(vctk_dir, "test.json"), "r") as f:
+        raw_test = json.load(f)
+
+    train_idxs, test_idxs = [], []
+
+    # =========== Test ===========
+    test_nums = defaultdict(int)
+    for utt in tqdm(raw_train):
+        idx = utt["index"]
+        singer = utt["Singer"]
+
+        if singer in chosen_speakers and test_nums[singer] < TEST_MAX_NUM_EVERY_PERSON:
+            test_nums[singer] += 1
+            test_idxs.append("train_{}".format(idx))
+
+    for utt in tqdm(raw_test):
+        idx = utt["index"]
+        singer = utt["Singer"]
+
+        if singer in chosen_speakers and test_nums[singer] < TEST_MAX_NUM_EVERY_PERSON:
+            test_nums[singer] += 1
+            test_idxs.append("test_{}".format(idx))
+
+    # =========== Train ===========
+    for utt in tqdm(raw_train):
+        idx = utt["index"]
+        singer = utt["Singer"]
+
+        if singer in chosen_speakers and "train_{}".format(idx) not in test_idxs:
+            train_idxs.append("train_{}".format(idx))
+
+    for utt in tqdm(raw_test):
+        idx = utt["index"]
+        singer = utt["Singer"]
+
+        if singer in chosen_speakers and "test_{}".format(idx) not in test_idxs:
+            train_idxs.append("test_{}".format(idx))
+
+    train_idxs.sort()
+    test_idxs.sort()
+    return train_idxs, test_idxs, raw_train, raw_test
+
+
+def statistics_of_speakers():
+    speaker2time = defaultdict(float)
+    sex2time = defaultdict(float)
+
+    with open(os.path.join(vctk_dir, "train.json"), "r") as f:
+        train = json.load(f)
+    with open(os.path.join(vctk_dir, "test.json"), "r") as f:
+        test = json.load(f)
+
+    for utt in train + test:
+        # minutes
+        speaker2time[utt["Singer"]] += utt["Duration"]
+        # hours
+        sex2time[utt["Singer"].split("_")[0]] += utt["Duration"]
+
+    print(
+        "Female: {:.2f} hours, Male: {:.2f} hours.\n".format(
+            sex2time["female"] / 3600, sex2time["male"] / 3600
+        )
+    )
+
+    speaker2time = sorted(speaker2time.items(), key=lambda x: x[-1], reverse=True)
+    for singer, seconds in speaker2time:
+        print("{}\t{:.2f} mins".format(singer, seconds / 60))
+
+    return speaker2time
+
+
+def get_chosen_speakers():
+    speaker2time = statistics_of_speakers()
+
+    chosen_time = defaultdict(float)
+    chosen_speaker = defaultdict(list)
+    train_constrait = {
+        "male": TRAIN_MALE_MAX_SECONDS,
+        "female": TRAIN_FEMALE_MAX_SECONDS,
+    }
+
+    for speaker, seconds in speaker2time:
+        sex = speaker.split("_")[0]
+        if chosen_time[sex] < train_constrait[sex]:
+            chosen_time[sex] += seconds
+            chosen_speaker[sex].append(speaker)
+
+    speaker2time = dict(speaker2time)
+    chosen_speaker = chosen_speaker["male"] + chosen_speaker["female"]
+    print("\n#Chosen speakers = {}".format(len(chosen_speaker)))
+    for spk in chosen_speaker:
+        print("{}\t{:.2f} mins".format(spk, speaker2time[spk] / 60))
+
+    return chosen_speaker
+
+
+if __name__ == "__main__":
+    root_path = ""
+    vctk_dir = os.path.join(root_path, "vctk")
+    fewspeaker_dir = os.path.join(root_path, "vctkfewspeaker")
+    os.makedirs(fewspeaker_dir, exist_ok=True)
+
+    train_idxs, test_idxs, raw_train, raw_test = select_sample_idxs()
+    print("#Train = {}, #Test = {}".format(len(train_idxs), len(test_idxs)))
+
+    # There are no data leakage
+    assert len(set(train_idxs).intersection(set(test_idxs))) == 0
+    for idx in train_idxs + test_idxs:
+        # No test data of raw vctk
+        assert "test_" not in idx
+
+    for split, chosen_idxs in zip(["train", "test"], [train_idxs, test_idxs]):
+        print("{}: #chosen idx = {}\n".format(split, len(chosen_idxs)))
+
+        # Select features
+        feat_files = glob.glob("**/train.pkl", root_dir=vctk_dir, recursive=True)
+        for file in tqdm(feat_files):
+            raw_file = os.path.join(vctk_dir, file)
+            new_file = os.path.join(
+                fewspeaker_dir, file.replace("train.pkl", "{}.pkl".format(split))
+            )
+
+            new_dir = "/".join(new_file.split("/")[:-1])
+            os.makedirs(new_dir, exist_ok=True)
+
+            if "mel_min" in file or "mel_max" in file:
+                os.system("cp {} {}".format(raw_file, new_file))
+                continue
+
+            with open(raw_file, "rb") as f:
+                raw_feats = pickle.load(f)
+
+            print("file: {}, #raw_feats = {}".format(file, len(raw_feats)))
+            new_feats = []
+            for idx in chosen_idxs:
+                chosen_split_is_train, raw_idx = idx.split("_")
+                assert chosen_split_is_train == "train"
+                new_feats.append(raw_feats[int(raw_idx)])
+
+            with open(new_file, "wb") as f:
+                pickle.dump(new_feats, f)
+            print("New file: {}, #new_feats = {}".format(new_file, len(new_feats)))
+
+        # Utterance re-index
+        news_utts = [raw_train[int(idx.split("_")[-1])] for idx in chosen_idxs]
+        for i, utt in enumerate(news_utts):
+            utt["Dataset"] = "vctkfewsinger"
+            utt["index"] = i
+
+        with open(os.path.join(fewspeaker_dir, "{}.json".format(split)), "w") as f:
+            json.dump(news_utts, f, indent=4)
diff --git a/preprocessors/vctksample.py b/preprocessors/vctksample.py
new file mode 100644
index 0000000000000000000000000000000000000000..476790f3941eb07cfe6ed9051293004b4e588f24
--- /dev/null
+++ b/preprocessors/vctksample.py
@@ -0,0 +1,108 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import json
+import pickle
+import glob
+from collections import defaultdict
+from tqdm import tqdm
+from preprocessors import get_golden_samples_indexes
+
+
+TRAIN_MAX_NUM_EVERY_PERSON = 250
+TEST_MAX_NUM_EVERY_PERSON = 25
+
+
+def select_sample_idxs():
+    # =========== Train ===========
+    with open(os.path.join(vctk_dir, "train.json"), "r") as f:
+        raw_train = json.load(f)
+
+    train_idxs = []
+    train_nums = defaultdict(int)
+    for utt in tqdm(raw_train):
+        idx = utt["index"]
+        singer = utt["Singer"]
+
+        if train_nums[singer] < TRAIN_MAX_NUM_EVERY_PERSON:
+            train_idxs.append(idx)
+            train_nums[singer] += 1
+
+    # =========== Test ===========
+    with open(os.path.join(vctk_dir, "test.json"), "r") as f:
+        raw_test = json.load(f)
+
+    # golden test
+    test_idxs = get_golden_samples_indexes(
+        dataset_name="vctk", split="test", dataset_dir=vctk_dir
+    )
+    test_nums = defaultdict(int)
+    for idx in test_idxs:
+        singer = raw_test[idx]["Singer"]
+        test_nums[singer] += 1
+
+    for utt in tqdm(raw_test):
+        idx = utt["index"]
+        singer = utt["Singer"]
+
+        if test_nums[singer] < TEST_MAX_NUM_EVERY_PERSON:
+            test_idxs.append(idx)
+            test_nums[singer] += 1
+
+    train_idxs.sort()
+    test_idxs.sort()
+    return train_idxs, test_idxs, raw_train, raw_test
+
+
+if __name__ == "__main__":
+    root_path = ""
+    vctk_dir = os.path.join(root_path, "vctk")
+    sample_dir = os.path.join(root_path, "vctksample")
+    os.makedirs(sample_dir, exist_ok=True)
+
+    train_idxs, test_idxs, raw_train, raw_test = select_sample_idxs()
+    print("#Train = {}, #Test = {}".format(len(train_idxs), len(test_idxs)))
+
+    for split, chosen_idxs, utterances in zip(
+        ["train", "test"], [train_idxs, test_idxs], [raw_train, raw_test]
+    ):
+        print(
+            "#{} = {}, #chosen idx = {}\n".format(
+                split, len(utterances), len(chosen_idxs)
+            )
+        )
+
+        # Select features
+        feat_files = glob.glob(
+            "**/{}.pkl".format(split), root_dir=vctk_dir, recursive=True
+        )
+        for file in tqdm(feat_files):
+            raw_file = os.path.join(vctk_dir, file)
+            new_file = os.path.join(sample_dir, file)
+
+            new_dir = "/".join(new_file.split("/")[:-1])
+            os.makedirs(new_dir, exist_ok=True)
+
+            if "mel_min" in file or "mel_max" in file:
+                os.system("cp {} {}".format(raw_file, new_file))
+                continue
+
+            with open(raw_file, "rb") as f:
+                raw_feats = pickle.load(f)
+
+            print("file: {}, #raw_feats = {}".format(file, len(raw_feats)))
+            new_feats = [raw_feats[idx] for idx in chosen_idxs]
+            with open(new_file, "wb") as f:
+                pickle.dump(new_feats, f)
+
+        # Utterance re-index
+        news_utts = [utterances[idx] for idx in chosen_idxs]
+        for i, utt in enumerate(news_utts):
+            utt["Dataset"] = "vctksample"
+            utt["index"] = i
+
+        with open(os.path.join(sample_dir, "{}.json".format(split)), "w") as f:
+            json.dump(news_utts, f, indent=4)
diff --git a/preprocessors/vocalist.py b/preprocessors/vocalist.py
new file mode 100644
index 0000000000000000000000000000000000000000..44de1ac00ebcf10d65e6af37a98afcfe6ae85b89
--- /dev/null
+++ b/preprocessors/vocalist.py
@@ -0,0 +1,137 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import json
+import torchaudio
+from tqdm import tqdm
+from glob import glob
+from collections import defaultdict
+
+from utils.util import has_existed
+
+
+def vocalist_statistics(data_dir):
+    singers = []
+    songs = []
+    global2singer2songs = defaultdict(lambda: defaultdict(lambda: defaultdict(list)))
+
+    global_infos = glob(data_dir + "/*")
+
+    for global_info in global_infos:
+        global_split = global_info.split("/")[-1]
+
+        singer_infos = glob(global_info + "/*")
+
+        for singer_info in singer_infos:
+            singer = singer_info.split("/")[-1]
+
+            singers.append(singer)
+
+            song_infos = glob(singer_info + "/*")
+            for song_info in song_infos:
+                song = song_info.split("/")[-1]
+
+                songs.append(song)
+
+                utts = glob(song_info + "/*.wav")
+
+                for utt in utts:
+                    uid = utt.split("/")[-1].split(".")[0]
+                    global2singer2songs[global_split][singer][song].append(uid)
+
+    unique_singers = list(set(singers))
+    unique_songs = list(set(songs))
+    unique_singers.sort()
+    unique_songs.sort()
+
+    print(
+        "vocalist: {} singers, {} songs ({} unique songs)".format(
+            len(unique_singers), len(songs), len(unique_songs)
+        )
+    )
+    print("Singers: \n{}".format("\t".join(unique_singers)))
+    return global2singer2songs, unique_singers
+
+
+def main(output_path, dataset_path):
+    print("-" * 10)
+    print("Preparing test samples for vocalist...\n")
+
+    save_dir = os.path.join(output_path, "vocalist")
+    os.makedirs(save_dir, exist_ok=True)
+    train_output_file = os.path.join(save_dir, "train.json")
+    test_output_file = os.path.join(save_dir, "test.json")
+    singer_dict_file = os.path.join(save_dir, "singers.json")
+    utt2singer_file = os.path.join(save_dir, "utt2singer")
+    if (
+        has_existed(train_output_file)
+        and has_existed(test_output_file)
+        and has_existed(singer_dict_file)
+        and has_existed(utt2singer_file)
+    ):
+        return
+    utt2singer = open(utt2singer_file, "w")
+
+    # Load
+    vocalist_path = dataset_path
+
+    global2singer2songs, unique_singers = vocalist_statistics(vocalist_path)
+
+    train = []
+    test = []
+
+    train_index_count = 0
+    test_index_count = 0
+
+    train_total_duration = 0
+    test_total_duration = 0
+
+    for global_info, singer2songs in tqdm(global2singer2songs.items()):
+        for singer, songs in tqdm(singer2songs.items()):
+            song_names = list(songs.keys())
+
+            for chosen_song in song_names:
+                for chosen_uid in songs[chosen_song]:
+                    res = {
+                        "Dataset": "opensinger",
+                        "Singer": singer,
+                        "Song": chosen_song,
+                        "Uid": "{}_{}_{}".format(singer, chosen_song, chosen_uid),
+                    }
+                    res["Path"] = "{}/{}/{}/{}.wav".format(
+                        global_info, singer, chosen_song, chosen_uid
+                    )
+                    res["Path"] = os.path.join(vocalist_path, res["Path"])
+                    assert os.path.exists(res["Path"])
+
+                    waveform, sample_rate = torchaudio.load(res["Path"])
+                    duration = waveform.size(-1) / sample_rate
+                    res["Duration"] = duration
+
+                    res["index"] = test_index_count
+                    test_total_duration += duration
+                    test.append(res)
+                    test_index_count += 1
+
+                    utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"]))
+
+    print("#Train = {}, #Test = {}".format(len(train), len(test)))
+    print(
+        "#Train hours= {}, #Test hours= {}".format(
+            train_total_duration / 3600, test_total_duration / 3600
+        )
+    )
+
+    # Save train.json and test.json
+    with open(train_output_file, "w") as f:
+        json.dump(train, f, indent=4, ensure_ascii=False)
+    with open(test_output_file, "w") as f:
+        json.dump(test, f, indent=4, ensure_ascii=False)
+
+    # Save singers.json
+    singer_lut = {name: i for i, name in enumerate(unique_singers)}
+    with open(singer_dict_file, "w") as f:
+        json.dump(singer_lut, f, indent=4, ensure_ascii=False)
diff --git a/pretrained/README.md b/pretrained/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..c2b4c4c8df46c7efea8c243b373eab417a266cef
--- /dev/null
+++ b/pretrained/README.md
@@ -0,0 +1,116 @@
+# Pretrained Models Dependency
+
+The models dependency of Amphion are as follows (sort alphabetically):
+
+- [Pretrained Models Dependency](#pretrained-models-dependency)
+  - [Amphion Singing BigVGAN](#amphion-singing-bigvgan)
+  - [Amphion Speech HiFi-GAN](#amphion-speech-hifi-gan)
+  - [ContentVec](#contentvec)
+  - [WeNet](#wenet)
+  - [Whisper](#whisper)
+  - [RawNet3](#rawnet3)
+
+
+The instructions about how to download them is displayed as follows.
+
+## Amphion Singing BigVGAN
+
+We fine-tune the official BigVGAN pretrained model with over 120 hours singing voice data. The fine-tuned checkpoint can be downloaded [here](https://cuhko365-my.sharepoint.com/:f:/g/personal/222042021_link_cuhk_edu_cn/EtiHh5JZ0_xGlYbyLLSoqBgBe9kI5q3ROY-SvBqefae-IA?e=dk4Pqa). You need to download the `400000.pt` and `args.json` files into `Amphion/pretrained/bigvgan`:
+
+```
+Amphion
+ ┣ pretrained
+ ┃ ┣ bivgan
+ ┃ ┃ ┣ 400000.pt
+ ┃ ┃ ┣ args.json
+```
+
+## Amphion Speech HiFi-GAN
+
+We trained our HiFi-GAN pretrained model with 685 hours speech data. Which can be downloaded [here](https://cuhko365-my.sharepoint.com/:f:/g/personal/xueliumeng_cuhk_edu_cn/Ei24hGJO_PVBopjhKje1uzEBqfhV9h89HoLrOoy9K8tzGg?e=ka7MCO). You need to download the whole folder of `hifigan_speech` into `Amphion/pretrained/hifigan`.
+
+```
+Amphion
+ ┣ pretrained
+ ┃ ┣ hifigan
+ ┃ ┃ ┣ hifigan_speech
+ ┃ ┃ ┃ ┣ log
+ ┃ ┃ ┃ ┣ result
+ ┃ ┃ ┃ ┣ checkpoint
+ ┃ ┃ ┃ ┣ args.json
+```
+
+## ContentVec
+
+You can download the pretrained ContentVec model [here](https://github.com/auspicious3000/contentvec). Note that we use the `ContentVec_legacy-500 classes` checkpoint. Assume that you download the `checkpoint_best_legacy_500.pt` into the `Amphion/pretrained/contentvec`.
+
+```
+Amphion
+ ┣ pretrained
+ ┃ ┣ contentvec
+ ┃ ┃ ┣ checkpoint_best_legacy_500.pt
+```
+
+## WeNet
+
+You can download the pretrained WeNet model [here](https://github.com/wenet-e2e/wenet/blob/main/docs/pretrained_models.md). Take the `wenetspeech` pretrained checkpoint as an example, assume you download the `wenetspeech_u2pp_conformer_exp.tar` into the `Amphion/pretrained/wenet`. Unzip it and modify its configuration file as follows:
+
+```sh
+cd Amphion/pretrained/wenet
+
+### Unzip the expt dir
+tar -xvf wenetspeech_u2pp_conformer_exp.tar.gz
+
+### Specify the updated path in train.yaml
+cd 20220506_u2pp_conformer_exp
+vim train.yaml
+# TODO: Change the value of "cmvn_file" (Line 2) to the absolute path of the `global_cmvn` file. (Eg: [YourPath]/Amphion/pretrained/wenet/20220506_u2pp_conformer_exp/global_cmvn)
+```
+
+The final file struture tree is like:
+
+```
+Amphion
+ ┣ pretrained
+ ┃ ┣ wenet
+ ┃ ┃ ┣ 20220506_u2pp_conformer_exp
+ ┃ ┃ ┃ ┣ final.pt
+ ┃ ┃ ┃ ┣ global_cmvn
+ ┃ ┃ ┃ ┣ train.yaml
+ ┃ ┃ ┃ ┣ units.txt
+```
+
+## Whisper
+
+The official pretrained whisper checkpoints can be available [here](https://github.com/openai/whisper/blob/e58f28804528831904c3b6f2c0e473f346223433/whisper/__init__.py#L17). In Amphion, we use the `medium` whisper model by default. You can download it as follows:
+
+```bash
+cd Amphion/pretrained
+mkdir whisper
+cd whisper
+
+wget https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt
+```
+
+The final file structure tree is like:
+
+```
+Amphion
+ ┣ pretrained
+ ┃ ┣ whisper
+ ┃ ┃ ┣ medium.pt
+```
+
+## RawNet3
+
+The official pretrained RawNet3 checkpoints can be available [here](https://huggingface.co/jungjee/RawNet3). You need to download the `model.pt` file and put it in the folder.
+
+The final file structure tree is like:
+
+```
+Amphion
+ ┣ pretrained
+ ┃ ┣ rawnet3
+ ┃ ┃ ┣ model.pt
+```
+
diff --git a/pretrained/bigvgan/args.json b/pretrained/bigvgan/args.json
new file mode 100644
index 0000000000000000000000000000000000000000..06878c8cc6e7c667b107836a51c1d577c15fa7b1
--- /dev/null
+++ b/pretrained/bigvgan/args.json
@@ -0,0 +1,235 @@
+{
+    "base_config": "egs/vocoder/gan/exp_config_base.json",
+    "exp_name": "bigvgan_large",
+    "inference": {
+        "batch_size": 1,
+    },
+    "model": {
+        "bigvgan": {
+            "activation": "snakebeta",
+            "resblock": "1",
+            "resblock_dilation_sizes": [
+                [
+                    1,
+                    3,
+                    5,
+                ],
+                [
+                    1,
+                    3,
+                    5,
+                ],
+                [
+                    1,
+                    3,
+                    5,
+                ],
+            ],
+            "resblock_kernel_sizes": [
+                3,
+                7,
+                11,
+            ],
+            "snake_logscale": true,
+            "upsample_initial_channel": 1536,
+            "upsample_kernel_sizes": [
+                8,
+                8,
+                4,
+                4,
+                4,
+                4,
+            ],
+            "upsample_rates": [
+                4,
+                4,
+                2,
+                2,
+                2,
+                2,
+            ],
+        },
+        "discriminators": [
+            "mpd",
+            "msstftd",
+        ],
+        "generator": "bigvgan",
+        "mpd": {
+            "discriminator_channel_multi": 1,
+            "mpd_reshapes": [
+                2,
+                3,
+                5,
+                7,
+                11,
+            ],
+            "use_spectral_norm": false,
+        },
+        "mrd": {
+            "discriminator_channel_multi": 1,
+            "mrd_override": false,
+            "resolutions": [
+                [
+                    1024,
+                    120,
+                    600,
+                ],
+                [
+                    2048,
+                    240,
+                    1200,
+                ],
+                [
+                    512,
+                    50,
+                    240,
+                ],
+            ],
+            "use_spectral_norm": false,
+        },
+        "msstftd": {
+            "filters": 32,
+        },
+    },
+    "model_type": "GANVocoder",
+    "preprocess": {
+        "audio_dir": "audios",
+        "bits": 8,
+        "contentvec_dir": "contentvec",
+        "cut_mel_frame": 32,
+        "data_augment": false,
+        "dur_dir": "durs",
+        "duration_dir": "duration",
+        "emo2id": "emo2id.json",
+        "energy_dir": "energys",
+        "energy_extract_mode": "from_mel",
+        "energy_norm": false,
+        "extract_audio": true,
+        "extract_contentvec_feature": false,
+        "extract_duration": false,
+        "extract_energy": false,
+        "extract_label": false,
+        "extract_mcep": false,
+        "extract_mel": true,
+        "extract_mert_feature": false,
+        "extract_one_hot": false,
+        "extract_pitch": false,
+        "extract_uv": false,
+        "extract_wenet_feature": false,
+        "extract_whisper_feature": false,
+        "f0_max": 1100,
+        "f0_min": 50,
+        "file_lst": "file.lst",
+        "fmax": 12000,
+        "fmin": 0,
+        "hop_size": 256,
+        "is_mu_law": false,
+        "lab_dir": "labs",
+        "label_dir": "labels",
+        "mcep_dir": "mcep",
+        "mel_dir": "mels",
+        "mel_min_max_norm": false,
+        "min_level_db": -115,
+        "n_fft": 1024,
+        "n_mel": 100,
+        "num_silent_frames": 8,
+        "phone_seq_file": "phone_seq_file",
+        "pitch_bin": 256,
+        "pitch_dir": "pitches",
+        "pitch_extractor": "parselmouth",
+        "pitch_max": 1100.0,
+        "pitch_min": 50.0,
+        "pitch_norm": false,
+        "processed_dir": "processed_data",
+        "ref_level_db": 20,
+        "sample_rate": 24000,
+        "spk2id": "singers.json",
+        "train_file": "train.json",
+        "trim_fft_size": 512,
+        "trim_hop_size": 128,
+        "trim_silence": false,
+        "trim_top_db": 30,
+        "trimmed_wav_dir": "trimmed_wavs",
+        "use_audio": true,
+        "use_dur": false,
+        "use_emoid": false,
+        "use_frame_duration": false,
+        "use_frame_energy": false,
+        "use_frame_pitch": false,
+        "use_lab": false,
+        "use_label": false,
+        "use_log_scale_energy": false,
+        "use_log_scale_pitch": false,
+        "use_mel": true,
+        "use_one_hot": false,
+        "use_phn_seq": false,
+        "use_phone_duration": false,
+        "use_phone_energy": false,
+        "use_phone_pitch": false,
+        "use_spkid": false,
+        "use_uv": false,
+        "use_wav": false,
+        "use_wenet": false,
+        "utt2emo": "utt2emo",
+        "utt2spk": "utt2spk",
+        "uv_dir": "uvs",
+        "valid_file": "test.json",
+        "wav_dir": "wavs",
+        "wenet_dir": "wenet",
+        "win_size": 1024,
+    },
+    "supported_model_type": [
+        "GANVocoder",
+        "Fastspeech2",
+        "DiffSVC",
+        "Transformer",
+        "EDM",
+        "CD",
+    ],
+    "train": {
+        "adamw": {
+            "adam_b1": 0.8,
+            "adam_b2": 0.99,
+            "lr": 0.0002,
+        },
+        "batch_size": 4,
+        "criterions": [
+            "feature",
+            "discriminator",
+            "generator",
+            "mel",
+        ],
+        "dataloader": {
+            "num_worker": 4,
+            "pin_memory": true,
+        },
+        "ddp": true,
+        "epochs": 50000,
+        "exponential_lr": {
+            "lr_decay": 0.999,
+        },
+        "gradient_accumulation_step": 1,
+        "keep_checkpoint_max": 5,
+        "max_epoch": 1000000,
+        "max_steps": 1000000,
+        "multi_speaker_training": false,
+        "random_seed": 114514,
+        "run_eval": [
+            true,
+        ],
+        "sampler": {
+            "drop_last": true,
+            "holistic_shuffle": true,
+        },
+        "save_checkpoint_stride": [
+            200,
+        ],
+        "save_checkpoints_steps": 10000,
+        "save_summary_steps": 500,
+        "total_training_steps": 50000,
+        "tracker": [
+            "tensorboard",
+        ],
+        "valid_interval": 10000,
+    },
+}
\ No newline at end of file
diff --git a/pretrained/contentvec/README.md b/pretrained/contentvec/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..6ea10938244c7282355be035c9489efa5bf08bdd
--- /dev/null
+++ b/pretrained/contentvec/README.md
@@ -0,0 +1,5 @@
+# Download
+
+- [Link](https://github.com/auspicious3000/contentvec)
+- Model: `ContentVec_legacy`
+- Classes: 500
diff --git a/pretrained/hifigan/README.md b/pretrained/hifigan/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..77365668d3d7d4efb084fc68e7dc79f7539f0def
--- /dev/null
+++ b/pretrained/hifigan/README.md
@@ -0,0 +1,5 @@
+# Download
+
+- [Link](https://cuhko365-my.sharepoint.com/:f:/g/personal/xueliumeng_cuhk_edu_cn/Ei24hGJO_PVBopjhKje1uzEBqfhV9h89HoLrOoy9K8tzGg?e=iDELHE)
+- Model: `hifigan_speech`
+- Datasets: VCTK + LibriTTS + LJSpeech
diff --git a/pretrained/rawnet3/README.md b/pretrained/rawnet3/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..8f4d94a9adb6e83e50f30a9d79c44f4ec2f56d42
--- /dev/null
+++ b/pretrained/rawnet3/README.md
@@ -0,0 +1,4 @@
+# Download
+
+- [Link](https://huggingface.co/jungjee/RawNet3)
+- Pretrained Datasets: `VoxCeleb1`, `VoxCeleb2`
diff --git a/pretrained/wenet/README.md b/pretrained/wenet/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..34c91e30f4b3a3fae0fc89fbc92f59e3824260c1
--- /dev/null
+++ b/pretrained/wenet/README.md
@@ -0,0 +1,4 @@
+# Download
+
+- [Link](https://github.com/wenet-e2e/wenet/blob/main/docs/pretrained_models.md)
+- Pretrained Datasets: `wenetspeech`
diff --git a/processors/__init__.py b/processors/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/processors/acoustic_extractor.py b/processors/acoustic_extractor.py
new file mode 100644
index 0000000000000000000000000000000000000000..2423be596bee39da04898752c05d5c5c9a35f280
--- /dev/null
+++ b/processors/acoustic_extractor.py
@@ -0,0 +1,1033 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import torch
+import numpy as np
+
+import json
+from tqdm import tqdm
+from sklearn.preprocessing import StandardScaler
+from utils.io import save_feature, save_txt, save_torch_audio
+from utils.util import has_existed
+from utils.tokenizer import extract_encodec_token
+from utils.stft import TacotronSTFT
+from utils.dsp import compress, audio_to_label
+from utils.data_utils import remove_outlier
+from preprocessors.metadata import replace_augment_name
+from scipy.interpolate import interp1d
+
+ZERO = 1e-12
+
+
+def extract_utt_acoustic_features_parallel(metadata, dataset_output, cfg, n_workers=1):
+    """Extract acoustic features from utterances using muliprocess
+
+    Args:
+        metadata (dict): dictionary that stores data in train.json and test.json files
+        dataset_output (str): directory to store acoustic features
+        cfg (dict): dictionary that stores configurations
+        n_workers (int, optional): num of processes to extract features in parallel. Defaults to 1.
+
+    Returns:
+        list: acoustic features
+    """
+    for utt in tqdm(metadata):
+        if cfg.task_type == "tts":
+            extract_utt_acoustic_features_tts(dataset_output, cfg, utt)
+        if cfg.task_type == "svc":
+            extract_utt_acoustic_features_svc(dataset_output, cfg, utt)
+        if cfg.task_type == "vocoder":
+            extract_utt_acoustic_features_vocoder(dataset_output, cfg, utt)
+        if cfg.task_type == "tta":
+            extract_utt_acoustic_features_tta(dataset_output, cfg, utt)
+
+
+def avg_phone_feature(feature, duration, interpolation=False):
+    feature = feature[: sum(duration)]
+    if interpolation:
+        nonzero_ids = np.where(feature != 0)[0]
+        interp_fn = interp1d(
+            nonzero_ids,
+            feature[nonzero_ids],
+            fill_value=(feature[nonzero_ids[0]], feature[nonzero_ids[-1]]),
+            bounds_error=False,
+        )
+        feature = interp_fn(np.arange(0, len(feature)))
+
+    # Phoneme-level average
+    pos = 0
+    for i, d in enumerate(duration):
+        if d > 0:
+            feature[i] = np.mean(feature[pos : pos + d])
+        else:
+            feature[i] = 0
+        pos += d
+    feature = feature[: len(duration)]
+    return feature
+
+
+def extract_utt_acoustic_features_serial(metadata, dataset_output, cfg):
+    """Extract acoustic features from utterances (in single process)
+
+    Args:
+        metadata (dict): dictionary that stores data in train.json and test.json files
+        dataset_output (str): directory to store acoustic features
+        cfg (dict): dictionary that stores configurations
+
+    """
+    for utt in tqdm(metadata):
+        if cfg.task_type == "tts":
+            extract_utt_acoustic_features_tts(dataset_output, cfg, utt)
+        if cfg.task_type == "svc":
+            extract_utt_acoustic_features_svc(dataset_output, cfg, utt)
+        if cfg.task_type == "vocoder":
+            extract_utt_acoustic_features_vocoder(dataset_output, cfg, utt)
+        if cfg.task_type == "tta":
+            extract_utt_acoustic_features_tta(dataset_output, cfg, utt)
+
+
+def __extract_utt_acoustic_features(dataset_output, cfg, utt):
+    """Extract acoustic features from utterances (in single process)
+
+    Args:
+        dataset_output (str): directory to store acoustic features
+        cfg (dict): dictionary that stores configurations
+        utt (dict): utterance info including dataset, singer, uid:{singer}_{song}_{index},
+                    path to utternace, duration, utternace index
+
+    """
+    from utils import audio, f0, world, duration
+
+    uid = utt["Uid"]
+    wav_path = utt["Path"]
+    if os.path.exists(os.path.join(dataset_output, cfg.preprocess.raw_data)):
+        wav_path = os.path.join(
+            dataset_output, cfg.preprocess.raw_data, utt["Singer"], uid + ".wav"
+        )
+
+    with torch.no_grad():
+        # Load audio data into tensor with sample rate of the config file
+        wav_torch, _ = audio.load_audio_torch(wav_path, cfg.preprocess.sample_rate)
+        wav = wav_torch.cpu().numpy()
+
+        # extract features
+        if cfg.preprocess.extract_duration:
+            durations, phones, start, end = duration.get_duration(
+                utt, wav, cfg.preprocess
+            )
+            save_feature(dataset_output, cfg.preprocess.duration_dir, uid, durations)
+            save_txt(dataset_output, cfg.preprocess.lab_dir, uid, phones)
+            wav = wav[start:end].astype(np.float32)
+            wav_torch = torch.from_numpy(wav).to(wav_torch.device)
+
+        if cfg.preprocess.extract_linear_spec:
+            from utils.mel import extract_linear_features
+
+            linear = extract_linear_features(wav_torch.unsqueeze(0), cfg.preprocess)
+            save_feature(
+                dataset_output, cfg.preprocess.linear_dir, uid, linear.cpu().numpy()
+            )
+
+        if cfg.preprocess.extract_mel:
+            from utils.mel import extract_mel_features
+
+            if cfg.preprocess.mel_extract_mode == "taco":
+                _stft = TacotronSTFT(
+                    sampling_rate=cfg.preprocess.sample_rate,
+                    win_length=cfg.preprocess.win_size,
+                    hop_length=cfg.preprocess.hop_size,
+                    filter_length=cfg.preprocess.n_fft,
+                    n_mel_channels=cfg.preprocess.n_mel,
+                    mel_fmin=cfg.preprocess.fmin,
+                    mel_fmax=cfg.preprocess.fmax,
+                )
+                mel = extract_mel_features(
+                    wav_torch.unsqueeze(0), cfg.preprocess, taco=True, _stft=_stft
+                )
+                if cfg.preprocess.extract_duration:
+                    mel = mel[:, : sum(durations)]
+            else:
+                mel = extract_mel_features(wav_torch.unsqueeze(0), cfg.preprocess)
+            save_feature(dataset_output, cfg.preprocess.mel_dir, uid, mel.cpu().numpy())
+
+        if cfg.preprocess.extract_energy:
+            if (
+                cfg.preprocess.energy_extract_mode == "from_mel"
+                and cfg.preprocess.extract_mel
+            ):
+                energy = (mel.exp() ** 2).sum(0).sqrt().cpu().numpy()
+            elif cfg.preprocess.energy_extract_mode == "from_waveform":
+                energy = audio.energy(wav, cfg.preprocess)
+            elif cfg.preprocess.energy_extract_mode == "from_tacotron_stft":
+                _stft = TacotronSTFT(
+                    sampling_rate=cfg.preprocess.sample_rate,
+                    win_length=cfg.preprocess.win_size,
+                    hop_length=cfg.preprocess.hop_size,
+                    filter_length=cfg.preprocess.n_fft,
+                    n_mel_channels=cfg.preprocess.n_mel,
+                    mel_fmin=cfg.preprocess.fmin,
+                    mel_fmax=cfg.preprocess.fmax,
+                )
+                _, energy = audio.get_energy_from_tacotron(wav, _stft)
+            else:
+                assert cfg.preprocess.energy_extract_mode in [
+                    "from_mel",
+                    "from_waveform",
+                    "from_tacotron_stft",
+                ], f"{cfg.preprocess.energy_extract_mode} not in supported energy_extract_mode [from_mel, from_waveform, from_tacotron_stft]"
+            if cfg.preprocess.extract_duration:
+                energy = energy[: sum(durations)]
+                phone_energy = avg_phone_feature(energy, durations)
+                save_feature(
+                    dataset_output, cfg.preprocess.phone_energy_dir, uid, phone_energy
+                )
+
+            save_feature(dataset_output, cfg.preprocess.energy_dir, uid, energy)
+
+        if cfg.preprocess.extract_pitch:
+            pitch = f0.get_f0(wav, cfg.preprocess)
+            if cfg.preprocess.extract_duration:
+                pitch = pitch[: sum(durations)]
+                phone_pitch = avg_phone_feature(pitch, durations, interpolation=True)
+                save_feature(
+                    dataset_output, cfg.preprocess.phone_pitch_dir, uid, phone_pitch
+                )
+            save_feature(dataset_output, cfg.preprocess.pitch_dir, uid, pitch)
+
+            if cfg.preprocess.extract_uv:
+                assert isinstance(pitch, np.ndarray)
+                uv = pitch != 0
+                save_feature(dataset_output, cfg.preprocess.uv_dir, uid, uv)
+
+        if cfg.preprocess.extract_audio:
+            save_feature(dataset_output, cfg.preprocess.audio_dir, uid, wav)
+
+        if cfg.preprocess.extract_label:
+            if cfg.preprocess.is_mu_law:
+                # compress audio
+                wav = compress(wav, cfg.preprocess.bits)
+            label = audio_to_label(wav, cfg.preprocess.bits)
+            save_feature(dataset_output, cfg.preprocess.label_dir, uid, label)
+
+        if cfg.preprocess.extract_acoustic_token:
+            if cfg.preprocess.acoustic_token_extractor == "Encodec":
+                codes = extract_encodec_token(wav_path)
+                save_feature(
+                    dataset_output, cfg.preprocess.acoustic_token_dir, uid, codes
+                )
+
+
+# TODO: refactor extract_utt_acoustic_features_task function due to many duplicated code
+def extract_utt_acoustic_features_tts(dataset_output, cfg, utt):
+    """Extract acoustic features from utterances (in single process)
+
+    Args:
+        dataset_output (str): directory to store acoustic features
+        cfg (dict): dictionary that stores configurations
+        utt (dict): utterance info including dataset, singer, uid:{singer}_{song}_{index},
+                    path to utternace, duration, utternace index
+
+    """
+    from utils import audio, f0, world, duration
+
+    uid = utt["Uid"]
+    wav_path = utt["Path"]
+    if os.path.exists(os.path.join(dataset_output, cfg.preprocess.raw_data)):
+        wav_path = os.path.join(
+            dataset_output, cfg.preprocess.raw_data, utt["Singer"], uid + ".wav"
+        )
+        if not os.path.exists(wav_path):
+            wav_path = os.path.join(
+                dataset_output, cfg.preprocess.raw_data, utt["Singer"], uid + ".flac"
+            )
+
+        assert os.path.exists(wav_path)
+
+    with torch.no_grad():
+        # Load audio data into tensor with sample rate of the config file
+        wav_torch, _ = audio.load_audio_torch(wav_path, cfg.preprocess.sample_rate)
+        wav = wav_torch.cpu().numpy()
+
+        # extract features
+        if cfg.preprocess.extract_duration:
+            durations, phones, start, end = duration.get_duration(
+                utt, wav, cfg.preprocess
+            )
+            save_feature(dataset_output, cfg.preprocess.duration_dir, uid, durations)
+            save_txt(dataset_output, cfg.preprocess.lab_dir, uid, phones)
+            wav = wav[start:end].astype(np.float32)
+            wav_torch = torch.from_numpy(wav).to(wav_torch.device)
+
+        if cfg.preprocess.extract_linear_spec:
+            from utils.mel import extract_linear_features
+
+            linear = extract_linear_features(wav_torch.unsqueeze(0), cfg.preprocess)
+            save_feature(
+                dataset_output, cfg.preprocess.linear_dir, uid, linear.cpu().numpy()
+            )
+
+        if cfg.preprocess.extract_mel:
+            from utils.mel import extract_mel_features
+
+            if cfg.preprocess.mel_extract_mode == "taco":
+                _stft = TacotronSTFT(
+                    sampling_rate=cfg.preprocess.sample_rate,
+                    win_length=cfg.preprocess.win_size,
+                    hop_length=cfg.preprocess.hop_size,
+                    filter_length=cfg.preprocess.n_fft,
+                    n_mel_channels=cfg.preprocess.n_mel,
+                    mel_fmin=cfg.preprocess.fmin,
+                    mel_fmax=cfg.preprocess.fmax,
+                )
+                mel = extract_mel_features(
+                    wav_torch.unsqueeze(0), cfg.preprocess, taco=True, _stft=_stft
+                )
+                if cfg.preprocess.extract_duration:
+                    mel = mel[:, : sum(durations)]
+            else:
+                mel = extract_mel_features(wav_torch.unsqueeze(0), cfg.preprocess)
+            save_feature(dataset_output, cfg.preprocess.mel_dir, uid, mel.cpu().numpy())
+
+        if cfg.preprocess.extract_energy:
+            if (
+                cfg.preprocess.energy_extract_mode == "from_mel"
+                and cfg.preprocess.extract_mel
+            ):
+                energy = (mel.exp() ** 2).sum(0).sqrt().cpu().numpy()
+            elif cfg.preprocess.energy_extract_mode == "from_waveform":
+                energy = audio.energy(wav, cfg.preprocess)
+            elif cfg.preprocess.energy_extract_mode == "from_tacotron_stft":
+                _stft = TacotronSTFT(
+                    sampling_rate=cfg.preprocess.sample_rate,
+                    win_length=cfg.preprocess.win_size,
+                    hop_length=cfg.preprocess.hop_size,
+                    filter_length=cfg.preprocess.n_fft,
+                    n_mel_channels=cfg.preprocess.n_mel,
+                    mel_fmin=cfg.preprocess.fmin,
+                    mel_fmax=cfg.preprocess.fmax,
+                )
+                _, energy = audio.get_energy_from_tacotron(wav, _stft)
+            else:
+                assert cfg.preprocess.energy_extract_mode in [
+                    "from_mel",
+                    "from_waveform",
+                    "from_tacotron_stft",
+                ], f"{cfg.preprocess.energy_extract_mode} not in supported energy_extract_mode [from_mel, from_waveform, from_tacotron_stft]"
+            if cfg.preprocess.extract_duration:
+                energy = energy[: sum(durations)]
+                phone_energy = avg_phone_feature(energy, durations)
+                save_feature(
+                    dataset_output, cfg.preprocess.phone_energy_dir, uid, phone_energy
+                )
+
+            save_feature(dataset_output, cfg.preprocess.energy_dir, uid, energy)
+
+        if cfg.preprocess.extract_pitch:
+            pitch = f0.get_f0(wav, cfg.preprocess)
+            if cfg.preprocess.extract_duration:
+                pitch = pitch[: sum(durations)]
+                phone_pitch = avg_phone_feature(pitch, durations, interpolation=True)
+                save_feature(
+                    dataset_output, cfg.preprocess.phone_pitch_dir, uid, phone_pitch
+                )
+            save_feature(dataset_output, cfg.preprocess.pitch_dir, uid, pitch)
+
+            if cfg.preprocess.extract_uv:
+                assert isinstance(pitch, np.ndarray)
+                uv = pitch != 0
+                save_feature(dataset_output, cfg.preprocess.uv_dir, uid, uv)
+
+        if cfg.preprocess.extract_audio:
+            save_torch_audio(
+                dataset_output,
+                cfg.preprocess.audio_dir,
+                uid,
+                wav_torch,
+                cfg.preprocess.sample_rate,
+            )
+
+        if cfg.preprocess.extract_label:
+            if cfg.preprocess.is_mu_law:
+                # compress audio
+                wav = compress(wav, cfg.preprocess.bits)
+            label = audio_to_label(wav, cfg.preprocess.bits)
+            save_feature(dataset_output, cfg.preprocess.label_dir, uid, label)
+
+        if cfg.preprocess.extract_acoustic_token:
+            if cfg.preprocess.acoustic_token_extractor == "Encodec":
+                codes = extract_encodec_token(wav_path)
+                save_feature(
+                    dataset_output, cfg.preprocess.acoustic_token_dir, uid, codes
+                )
+
+
+def extract_utt_acoustic_features_svc(dataset_output, cfg, utt):
+    __extract_utt_acoustic_features(dataset_output, cfg, utt)
+
+
+def extract_utt_acoustic_features_tta(dataset_output, cfg, utt):
+    __extract_utt_acoustic_features(dataset_output, cfg, utt)
+
+
+def extract_utt_acoustic_features_vocoder(dataset_output, cfg, utt):
+    """Extract acoustic features from utterances (in single process)
+
+    Args:
+        dataset_output (str): directory to store acoustic features
+        cfg (dict): dictionary that stores configurations
+        utt (dict): utterance info including dataset, singer, uid:{singer}_{song}_{index},
+                    path to utternace, duration, utternace index
+
+    """
+    from utils import audio, f0, world, duration
+
+    uid = utt["Uid"]
+    wav_path = utt["Path"]
+
+    with torch.no_grad():
+        # Load audio data into tensor with sample rate of the config file
+        wav_torch, _ = audio.load_audio_torch(wav_path, cfg.preprocess.sample_rate)
+        wav = wav_torch.cpu().numpy()
+
+        # extract features
+        if cfg.preprocess.extract_mel:
+            from utils.mel import extract_mel_features
+
+            mel = extract_mel_features(wav_torch.unsqueeze(0), cfg.preprocess)
+            save_feature(dataset_output, cfg.preprocess.mel_dir, uid, mel.cpu().numpy())
+
+        if cfg.preprocess.extract_energy:
+            if (
+                cfg.preprocess.energy_extract_mode == "from_mel"
+                and cfg.preprocess.extract_mel
+            ):
+                energy = (mel.exp() ** 2).sum(0).sqrt().cpu().numpy()
+            elif cfg.preprocess.energy_extract_mode == "from_waveform":
+                energy = audio.energy(wav, cfg.preprocess)
+            else:
+                assert cfg.preprocess.energy_extract_mode in [
+                    "from_mel",
+                    "from_waveform",
+                ], f"{cfg.preprocess.energy_extract_mode} not in supported energy_extract_mode [from_mel, from_waveform, from_tacotron_stft]"
+
+            save_feature(dataset_output, cfg.preprocess.energy_dir, uid, energy)
+
+        if cfg.preprocess.extract_pitch:
+            pitch = f0.get_f0(wav, cfg.preprocess)
+            save_feature(dataset_output, cfg.preprocess.pitch_dir, uid, pitch)
+
+            if cfg.preprocess.extract_uv:
+                assert isinstance(pitch, np.ndarray)
+                uv = pitch != 0
+                save_feature(dataset_output, cfg.preprocess.uv_dir, uid, uv)
+
+        if cfg.preprocess.extract_amplitude_phase:
+            from utils.mel import amplitude_phase_spectrum
+
+            log_amplitude, phase, real, imaginary = amplitude_phase_spectrum(
+                wav_torch.unsqueeze(0), cfg.preprocess
+            )
+            save_feature(
+                dataset_output, cfg.preprocess.log_amplitude_dir, uid, log_amplitude
+            )
+            save_feature(dataset_output, cfg.preprocess.phase_dir, uid, phase)
+            save_feature(dataset_output, cfg.preprocess.real_dir, uid, real)
+            save_feature(dataset_output, cfg.preprocess.imaginary_dir, uid, imaginary)
+
+        if cfg.preprocess.extract_audio:
+            save_feature(dataset_output, cfg.preprocess.audio_dir, uid, wav)
+
+        if cfg.preprocess.extract_label:
+            if cfg.preprocess.is_mu_law:
+                # compress audio
+                wav = compress(wav, cfg.preprocess.bits)
+            label = audio_to_label(wav, cfg.preprocess.bits)
+            save_feature(dataset_output, cfg.preprocess.label_dir, uid, label)
+
+
+def cal_normalized_mel(mel, dataset_name, cfg):
+    mel_min, mel_max = load_mel_extrema(cfg, dataset_name)
+    mel_norm = normalize_mel_channel(mel, mel_min, mel_max)
+    return mel_norm
+
+
+def cal_mel_min_max(dataset, output_path, cfg, metadata=None):
+    dataset_output = os.path.join(output_path, dataset)
+
+    if metadata is None:
+        metadata = []
+        for dataset_type in ["train", "test"] if "eval" not in dataset else ["test"]:
+            dataset_file = os.path.join(dataset_output, "{}.json".format(dataset_type))
+            with open(dataset_file, "r") as f:
+                metadata.extend(json.load(f))
+
+    tmp_mel_min = []
+    tmp_mel_max = []
+    for item in metadata:
+        mel_path = os.path.join(
+            dataset_output, cfg.preprocess.mel_dir, item["Uid"] + ".npy"
+        )
+        if not os.path.exists(mel_path):
+            continue
+        mel = np.load(mel_path)
+        if mel.shape[0] != cfg.preprocess.n_mel:
+            mel = mel.T
+        # mel: (n_mels, T)
+        assert mel.shape[0] == cfg.preprocess.n_mel
+
+        tmp_mel_min.append(np.min(mel, axis=-1))
+        tmp_mel_max.append(np.max(mel, axis=-1))
+
+    mel_min = np.min(tmp_mel_min, axis=0)
+    mel_max = np.max(tmp_mel_max, axis=0)
+
+    ## save mel min max data
+    mel_min_max_dir = os.path.join(dataset_output, cfg.preprocess.mel_min_max_stats_dir)
+    os.makedirs(mel_min_max_dir, exist_ok=True)
+
+    mel_min_path = os.path.join(mel_min_max_dir, "mel_min.npy")
+    mel_max_path = os.path.join(mel_min_max_dir, "mel_max.npy")
+    np.save(mel_min_path, mel_min)
+    np.save(mel_max_path, mel_max)
+
+
+def denorm_for_pred_mels(cfg, dataset_name, split, pred):
+    """
+    Args:
+        pred: a list whose every element is (frame_len, n_mels)
+    Return:
+        similar like pred
+    """
+    mel_min, mel_max = load_mel_extrema(cfg.preprocess, dataset_name)
+    recovered_mels = [
+        denormalize_mel_channel(mel.T, mel_min, mel_max).T for mel in pred
+    ]
+
+    return recovered_mels
+
+
+def load_mel_extrema(cfg, dataset_name):
+    data_dir = os.path.join(cfg.processed_dir, dataset_name, cfg.mel_min_max_stats_dir)
+
+    min_file = os.path.join(data_dir, "mel_min.npy")
+    max_file = os.path.join(data_dir, "mel_max.npy")
+
+    mel_min = np.load(min_file)
+    mel_max = np.load(max_file)
+
+    return mel_min, mel_max
+
+
+def denormalize_mel_channel(mel, mel_min, mel_max):
+    mel_min = np.expand_dims(mel_min, -1)
+    mel_max = np.expand_dims(mel_max, -1)
+    return (mel + 1) / 2 * (mel_max - mel_min + ZERO) + mel_min
+
+
+def normalize_mel_channel(mel, mel_min, mel_max):
+    mel_min = np.expand_dims(mel_min, -1)
+    mel_max = np.expand_dims(mel_max, -1)
+    return (mel - mel_min) / (mel_max - mel_min + ZERO) * 2 - 1
+
+
+def normalize(dataset, feat_dir, cfg):
+    dataset_output = os.path.join(cfg.preprocess.processed_dir, dataset)
+    print(f"normalize {feat_dir}")
+
+    max_value = np.finfo(np.float64).min
+    min_value = np.finfo(np.float64).max
+
+    scaler = StandardScaler()
+    feat_files = os.listdir(os.path.join(dataset_output, feat_dir))
+
+    for feat_file in tqdm(feat_files):
+        feat_file = os.path.join(dataset_output, feat_dir, feat_file)
+        if not feat_file.endswith(".npy"):
+            continue
+        feat = np.load(feat_file)
+        max_value = max(max_value, max(feat))
+        min_value = min(min_value, min(feat))
+        scaler.partial_fit(feat.reshape((-1, 1)))
+    mean = scaler.mean_[0]
+    std = scaler.scale_[0]
+    stat = np.array([min_value, max_value, mean, std])
+    stat_npy = os.path.join(dataset_output, f"{feat_dir}_stat.npy")
+    np.save(stat_npy, stat)
+    return mean, std, min_value, max_value
+
+
+def load_normalized(feat_dir, dataset_name, cfg):
+    dataset_output = os.path.join(cfg.preprocess.processed_dir, dataset_name)
+    stat_npy = os.path.join(dataset_output, f"{feat_dir}_stat.npy")
+    min_value, max_value, mean, std = np.load(stat_npy)
+    return mean, std, min_value, max_value
+
+
+def cal_pitch_statistics_svc(dataset, output_path, cfg, metadata=None):
+    # path of dataset
+    dataset_dir = os.path.join(output_path, dataset)
+    save_dir = os.path.join(dataset_dir, cfg.preprocess.pitch_dir)
+    os.makedirs(save_dir, exist_ok=True)
+    if has_existed(os.path.join(save_dir, "statistics.json")):
+        return
+
+    if metadata is None:
+        # load singers and ids
+        singers = json.load(open(os.path.join(dataset_dir, "singers.json"), "r"))
+
+        # combine train and test metadata
+        metadata = []
+        for dataset_type in ["train", "test"] if "eval" not in dataset else ["test"]:
+            dataset_file = os.path.join(dataset_dir, "{}.json".format(dataset_type))
+            with open(dataset_file, "r") as f:
+                metadata.extend(json.load(f))
+    else:
+        singers = list(set([item["Singer"] for item in metadata]))
+        singers = {
+            "{}_{}".format(dataset, name): idx for idx, name in enumerate(singers)
+        }
+
+    # use different scalers for each singer
+    pitch_scalers = [[] for _ in range(len(singers))]
+    total_pitch_scalers = [[] for _ in range(len(singers))]
+
+    for utt_info in tqdm(metadata, desc="Loading F0..."):
+        # utt = f'{utt_info["Dataset"]}_{utt_info["Uid"]}'
+        singer = utt_info["Singer"]
+        pitch_path = os.path.join(
+            dataset_dir, cfg.preprocess.pitch_dir, utt_info["Uid"] + ".npy"
+        )
+        # total_pitch contains all pitch including unvoiced frames
+        if not os.path.exists(pitch_path):
+            continue
+        total_pitch = np.load(pitch_path)
+        assert len(total_pitch) > 0
+        # pitch contains only voiced frames
+        pitch = total_pitch[total_pitch != 0]
+        spkid = singers[f"{replace_augment_name(dataset)}_{singer}"]
+
+        # update pitch scalers
+        pitch_scalers[spkid].extend(pitch.tolist())
+        # update total pitch scalers
+        total_pitch_scalers[spkid].extend(total_pitch.tolist())
+
+    # save pitch statistics for each singer in dict
+    sta_dict = {}
+    for singer in tqdm(singers, desc="Singers statistics"):
+        spkid = singers[singer]
+        # voiced pitch statistics
+        mean, std, min, max, median = (
+            np.mean(pitch_scalers[spkid]),
+            np.std(pitch_scalers[spkid]),
+            np.min(pitch_scalers[spkid]),
+            np.max(pitch_scalers[spkid]),
+            np.median(pitch_scalers[spkid]),
+        )
+
+        # total pitch statistics
+        mean_t, std_t, min_t, max_t, median_t = (
+            np.mean(total_pitch_scalers[spkid]),
+            np.std(total_pitch_scalers[spkid]),
+            np.min(total_pitch_scalers[spkid]),
+            np.max(total_pitch_scalers[spkid]),
+            np.median(total_pitch_scalers[spkid]),
+        )
+        sta_dict[singer] = {
+            "voiced_positions": {
+                "mean": mean,
+                "std": std,
+                "median": median,
+                "min": min,
+                "max": max,
+            },
+            "total_positions": {
+                "mean": mean_t,
+                "std": std_t,
+                "median": median_t,
+                "min": min_t,
+                "max": max_t,
+            },
+        }
+
+    # save statistics
+    with open(os.path.join(save_dir, "statistics.json"), "w") as f:
+        json.dump(sta_dict, f, indent=4, ensure_ascii=False)
+
+
+def cal_pitch_statistics(dataset, output_path, cfg):
+    # path of dataset
+    dataset_dir = os.path.join(output_path, dataset)
+    if cfg.preprocess.use_phone_pitch:
+        pitch_dir = cfg.preprocess.phone_pitch_dir
+    else:
+        pitch_dir = cfg.preprocess.pitch_dir
+    save_dir = os.path.join(dataset_dir, pitch_dir)
+
+    os.makedirs(save_dir, exist_ok=True)
+    if has_existed(os.path.join(save_dir, "statistics.json")):
+        return
+    # load singers and ids
+    singers = json.load(open(os.path.join(dataset_dir, "singers.json"), "r"))
+
+    # combine train and test metadata
+    metadata = []
+    for dataset_type in ["train", "test"] if "eval" not in dataset else ["test"]:
+        dataset_file = os.path.join(dataset_dir, "{}.json".format(dataset_type))
+        with open(dataset_file, "r") as f:
+            metadata.extend(json.load(f))
+
+    # use different scalers for each singer
+    pitch_scalers = [[] for _ in range(len(singers))]
+    total_pitch_scalers = [[] for _ in range(len(singers))]
+
+    for utt_info in metadata:
+        utt = f'{utt_info["Dataset"]}_{utt_info["Uid"]}'
+        singer = utt_info["Singer"]
+        pitch_path = os.path.join(dataset_dir, pitch_dir, utt_info["Uid"] + ".npy")
+        # total_pitch contains all pitch including unvoiced frames
+        if not os.path.exists(pitch_path):
+            continue
+        total_pitch = np.load(pitch_path)
+        assert len(total_pitch) > 0
+        # pitch contains only voiced frames
+        # pitch = total_pitch[total_pitch != 0]
+        if cfg.preprocess.pitch_remove_outlier:
+            pitch = remove_outlier(total_pitch)
+        spkid = singers[f"{replace_augment_name(dataset)}_{singer}"]
+
+        # update pitch scalers
+        pitch_scalers[spkid].extend(pitch.tolist())
+        # update total pitch scalers
+        total_pitch_scalers[spkid].extend(total_pitch.tolist())
+
+    # save pitch statistics for each singer in dict
+    sta_dict = {}
+    for singer in singers:
+        spkid = singers[singer]
+        # voiced pitch statistics
+        mean, std, min, max, median = (
+            np.mean(pitch_scalers[spkid]),
+            np.std(pitch_scalers[spkid]),
+            np.min(pitch_scalers[spkid]),
+            np.max(pitch_scalers[spkid]),
+            np.median(pitch_scalers[spkid]),
+        )
+
+        # total pitch statistics
+        mean_t, std_t, min_t, max_t, median_t = (
+            np.mean(total_pitch_scalers[spkid]),
+            np.std(total_pitch_scalers[spkid]),
+            np.min(total_pitch_scalers[spkid]),
+            np.max(total_pitch_scalers[spkid]),
+            np.median(total_pitch_scalers[spkid]),
+        )
+        sta_dict[singer] = {
+            "voiced_positions": {
+                "mean": mean,
+                "std": std,
+                "median": median,
+                "min": min,
+                "max": max,
+            },
+            "total_positions": {
+                "mean": mean_t,
+                "std": std_t,
+                "median": median_t,
+                "min": min_t,
+                "max": max_t,
+            },
+        }
+
+    # save statistics
+    with open(os.path.join(save_dir, "statistics.json"), "w") as f:
+        json.dump(sta_dict, f, indent=4, ensure_ascii=False)
+
+
+def cal_energy_statistics(dataset, output_path, cfg):
+    # path of dataset
+    dataset_dir = os.path.join(output_path, dataset)
+    if cfg.preprocess.use_phone_energy:
+        energy_dir = cfg.preprocess.phone_energy_dir
+    else:
+        energy_dir = cfg.preprocess.energy_dir
+    save_dir = os.path.join(dataset_dir, energy_dir)
+    os.makedirs(save_dir, exist_ok=True)
+    print(os.path.join(save_dir, "statistics.json"))
+    if has_existed(os.path.join(save_dir, "statistics.json")):
+        return
+    # load singers and ids
+    singers = json.load(open(os.path.join(dataset_dir, "singers.json"), "r"))
+
+    # combine train and test metadata
+    metadata = []
+    for dataset_type in ["train", "test"] if "eval" not in dataset else ["test"]:
+        dataset_file = os.path.join(dataset_dir, "{}.json".format(dataset_type))
+        with open(dataset_file, "r") as f:
+            metadata.extend(json.load(f))
+
+    # use different scalers for each singer
+    energy_scalers = [[] for _ in range(len(singers))]
+    total_energy_scalers = [[] for _ in range(len(singers))]
+
+    for utt_info in metadata:
+        utt = f'{utt_info["Dataset"]}_{utt_info["Uid"]}'
+        singer = utt_info["Singer"]
+        energy_path = os.path.join(dataset_dir, energy_dir, utt_info["Uid"] + ".npy")
+        # total_energy contains all energy including unvoiced frames
+        if not os.path.exists(energy_path):
+            continue
+        total_energy = np.load(energy_path)
+        assert len(total_energy) > 0
+        # energy contains only voiced frames
+        # energy = total_energy[total_energy != 0]
+        if cfg.preprocess.energy_remove_outlier:
+            energy = remove_outlier(total_energy)
+        spkid = singers[f"{replace_augment_name(dataset)}_{singer}"]
+
+        # update energy scalers
+        energy_scalers[spkid].extend(energy.tolist())
+        # update total energyscalers
+        total_energy_scalers[spkid].extend(total_energy.tolist())
+
+    # save energy statistics for each singer in dict
+    sta_dict = {}
+    for singer in singers:
+        spkid = singers[singer]
+        # voiced energy statistics
+        mean, std, min, max, median = (
+            np.mean(energy_scalers[spkid]),
+            np.std(energy_scalers[spkid]),
+            np.min(energy_scalers[spkid]),
+            np.max(energy_scalers[spkid]),
+            np.median(energy_scalers[spkid]),
+        )
+
+        # total energy statistics
+        mean_t, std_t, min_t, max_t, median_t = (
+            np.mean(total_energy_scalers[spkid]),
+            np.std(total_energy_scalers[spkid]),
+            np.min(total_energy_scalers[spkid]),
+            np.max(total_energy_scalers[spkid]),
+            np.median(total_energy_scalers[spkid]),
+        )
+        sta_dict[singer] = {
+            "voiced_positions": {
+                "mean": mean,
+                "std": std,
+                "median": median,
+                "min": min,
+                "max": max,
+            },
+            "total_positions": {
+                "mean": mean_t,
+                "std": std_t,
+                "median": median_t,
+                "min": min_t,
+                "max": max_t,
+            },
+        }
+
+    # save statistics
+    with open(os.path.join(save_dir, "statistics.json"), "w") as f:
+        json.dump(sta_dict, f, indent=4, ensure_ascii=False)
+
+
+def copy_acoustic_features(metadata, dataset_dir, src_dataset_dir, cfg):
+    """Copy acoustic features from src_dataset_dir to dataset_dir
+
+    Args:
+        metadata (dict): dictionary that stores data in train.json and test.json files
+        dataset_dir (str): directory to store acoustic features
+        src_dataset_dir (str): directory to store acoustic features
+        cfg (dict): dictionary that stores configurations
+
+    """
+
+    if cfg.preprocess.extract_mel:
+        if not has_existed(os.path.join(dataset_dir, cfg.preprocess.mel_dir)):
+            os.makedirs(
+                os.path.join(dataset_dir, cfg.preprocess.mel_dir), exist_ok=True
+            )
+            print(
+                "Copying mel features from {} to {}...".format(
+                    src_dataset_dir, dataset_dir
+                )
+            )
+            for utt_info in tqdm(metadata):
+                src_mel_path = os.path.join(
+                    src_dataset_dir, cfg.preprocess.mel_dir, utt_info["Uid"] + ".npy"
+                )
+                dst_mel_path = os.path.join(
+                    dataset_dir, cfg.preprocess.mel_dir, utt_info["Uid"] + ".npy"
+                )
+                # create soft-links
+                if not os.path.exists(dst_mel_path):
+                    os.symlink(src_mel_path, dst_mel_path)
+    if cfg.preprocess.extract_energy:
+        if not has_existed(os.path.join(dataset_dir, cfg.preprocess.energy_dir)):
+            os.makedirs(
+                os.path.join(dataset_dir, cfg.preprocess.energy_dir), exist_ok=True
+            )
+            print(
+                "Copying energy features from {} to {}...".format(
+                    src_dataset_dir, dataset_dir
+                )
+            )
+            for utt_info in tqdm(metadata):
+                src_energy_path = os.path.join(
+                    src_dataset_dir, cfg.preprocess.energy_dir, utt_info["Uid"] + ".npy"
+                )
+                dst_energy_path = os.path.join(
+                    dataset_dir, cfg.preprocess.energy_dir, utt_info["Uid"] + ".npy"
+                )
+                # create soft-links
+                if not os.path.exists(dst_energy_path):
+                    os.symlink(src_energy_path, dst_energy_path)
+    if cfg.preprocess.extract_pitch:
+        if not has_existed(os.path.join(dataset_dir, cfg.preprocess.pitch_dir)):
+            os.makedirs(
+                os.path.join(dataset_dir, cfg.preprocess.pitch_dir), exist_ok=True
+            )
+            print(
+                "Copying pitch features from {} to {}...".format(
+                    src_dataset_dir, dataset_dir
+                )
+            )
+            for utt_info in tqdm(metadata):
+                src_pitch_path = os.path.join(
+                    src_dataset_dir, cfg.preprocess.pitch_dir, utt_info["Uid"] + ".npy"
+                )
+                dst_pitch_path = os.path.join(
+                    dataset_dir, cfg.preprocess.pitch_dir, utt_info["Uid"] + ".npy"
+                )
+                # create soft-links
+                if not os.path.exists(dst_pitch_path):
+                    os.symlink(src_pitch_path, dst_pitch_path)
+        if cfg.preprocess.extract_uv:
+            if not has_existed(os.path.join(dataset_dir, cfg.preprocess.uv_dir)):
+                os.makedirs(
+                    os.path.join(dataset_dir, cfg.preprocess.uv_dir), exist_ok=True
+                )
+                print(
+                    "Copying uv features from {} to {}...".format(
+                        src_dataset_dir, dataset_dir
+                    )
+                )
+                for utt_info in tqdm(metadata):
+                    src_uv_path = os.path.join(
+                        src_dataset_dir, cfg.preprocess.uv_dir, utt_info["Uid"] + ".npy"
+                    )
+                    dst_uv_path = os.path.join(
+                        dataset_dir, cfg.preprocess.uv_dir, utt_info["Uid"] + ".npy"
+                    )
+                    # create soft-links
+                    if not os.path.exists(dst_uv_path):
+                        os.symlink(src_uv_path, dst_uv_path)
+    if cfg.preprocess.extract_audio:
+        if not has_existed(os.path.join(dataset_dir, cfg.preprocess.audio_dir)):
+            os.makedirs(
+                os.path.join(dataset_dir, cfg.preprocess.audio_dir), exist_ok=True
+            )
+            print(
+                "Copying audio features from {} to {}...".format(
+                    src_dataset_dir, dataset_dir
+                )
+            )
+            for utt_info in tqdm(metadata):
+                if cfg.task_type == "tts":
+                    src_audio_path = os.path.join(
+                        src_dataset_dir,
+                        cfg.preprocess.audio_dir,
+                        utt_info["Uid"] + ".wav",
+                    )
+                else:
+                    src_audio_path = os.path.join(
+                        src_dataset_dir,
+                        cfg.preprocess.audio_dir,
+                        utt_info["Uid"] + ".npy",
+                    )
+                if cfg.task_type == "tts":
+                    dst_audio_path = os.path.join(
+                        dataset_dir, cfg.preprocess.audio_dir, utt_info["Uid"] + ".wav"
+                    )
+                else:
+                    dst_audio_path = os.path.join(
+                        dataset_dir, cfg.preprocess.audio_dir, utt_info["Uid"] + ".npy"
+                    )
+                # create soft-links
+                if not os.path.exists(dst_audio_path):
+                    os.symlink(src_audio_path, dst_audio_path)
+    if cfg.preprocess.extract_label:
+        if not has_existed(os.path.join(dataset_dir, cfg.preprocess.label_dir)):
+            os.makedirs(
+                os.path.join(dataset_dir, cfg.preprocess.label_dir), exist_ok=True
+            )
+            print(
+                "Copying label features from {} to {}...".format(
+                    src_dataset_dir, dataset_dir
+                )
+            )
+            for utt_info in tqdm(metadata):
+                src_label_path = os.path.join(
+                    src_dataset_dir, cfg.preprocess.label_dir, utt_info["Uid"] + ".npy"
+                )
+                dst_label_path = os.path.join(
+                    dataset_dir, cfg.preprocess.label_dir, utt_info["Uid"] + ".npy"
+                )
+                # create soft-links
+                if not os.path.exists(dst_label_path):
+                    os.symlink(src_label_path, dst_label_path)
+
+
+def align_duration_mel(dataset, output_path, cfg):
+    print("align the duration and mel")
+
+    dataset_dir = os.path.join(output_path, dataset)
+    metadata = []
+    for dataset_type in ["train", "test"] if "eval" not in dataset else ["test"]:
+        dataset_file = os.path.join(dataset_dir, "{}.json".format(dataset_type))
+        with open(dataset_file, "r") as f:
+            metadata.extend(json.load(f))
+
+    utt2dur = {}
+    for index in tqdm(range(len(metadata))):
+        utt_info = metadata[index]
+        dataset = utt_info["Dataset"]
+        uid = utt_info["Uid"]
+        utt = "{}_{}".format(dataset, uid)
+
+        mel_path = os.path.join(dataset_dir, cfg.preprocess.mel_dir, uid + ".npy")
+        mel = np.load(mel_path).transpose(1, 0)
+        duration_path = os.path.join(
+            dataset_dir, cfg.preprocess.duration_dir, uid + ".npy"
+        )
+        duration = np.load(duration_path)
+        if sum(duration) != mel.shape[0]:
+            duration_sum = sum(duration)
+            mel_len = mel.shape[0]
+            mismatch = abs(duration_sum - mel_len)
+            assert mismatch <= 5, "duration and mel length mismatch!"
+            cloned = np.array(duration, copy=True)
+            if duration_sum > mel_len:
+                for j in range(1, len(duration) - 1):
+                    if mismatch == 0:
+                        break
+                    dur_val = cloned[-j]
+                    if dur_val >= mismatch:
+                        cloned[-j] -= mismatch
+                        mismatch -= dur_val
+                        break
+                    else:
+                        cloned[-j] = 0
+                        mismatch -= dur_val
+
+            elif duration_sum < mel_len:
+                cloned[-1] += mismatch
+            duration = cloned
+        utt2dur[utt] = duration
+        np.save(duration_path, duration)
+
+    return utt2dur
diff --git a/processors/content_extractor.py b/processors/content_extractor.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec0b395960726f63df4405a978ce8a3951b934a2
--- /dev/null
+++ b/processors/content_extractor.py
@@ -0,0 +1,557 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import torch
+import numpy as np
+import yaml
+import copy
+from tqdm import tqdm
+from torchaudio.compliance import kaldi
+from torch.nn.utils.rnn import pad_sequence
+from torch.utils.data import DataLoader
+from fairseq import checkpoint_utils
+from transformers import AutoModel, Wav2Vec2FeatureExtractor
+
+from utils.io_optim import (
+    TorchaudioDataset,
+    LibrosaDataset,
+    FFmpegDataset,
+    collate_batch,
+)
+import whisper
+from modules.wenet_extractor.utils.init_model import init_model
+from modules.wenet_extractor.utils.checkpoint import load_checkpoint
+
+"""
+    Extractor for content features
+    1. whisper
+    2. contentvec
+    3. wenet
+    4. mert
+
+    Pipeline:
+        in preprocess.py:
+            call extract_utt_content_features() to extract content features for each utterance
+            extract_utt_content_features() envelopes the following steps:
+                1. load the model (whisper, contentvec, wenet)
+                2. extract the content features
+                3. save the content features into files
+        in svc_dataset.py:
+            call offline_align() to align the content features to the given target length
+
+"""
+
+"""
+    Extractor Usage:
+        1. initialize an instance of extractor
+            extractor = WhisperExtractor(cfg)
+        2. load the specified model
+            extractor.load_model()
+        3. extract the content features
+            extractor.extract_content(utt) for single utterance
+            extractor.extract_content_batch(utts) for batch utterances
+        4. save the content features
+            extractor.save_feature(utt, content_feature) for single utterance
+"""
+
+
+class BaseExtractor:
+    def __init__(self, cfg):
+        self.cfg = cfg
+        self.extractor_type = None
+        self.model = None
+
+    def offline_align(self, content, target_len):
+        """
+        args:
+            content: (source_len, dim)
+            target_len: target length
+        return:
+            mapped_feature: (target_len, dim)
+        """
+        target_hop = self.cfg.preprocess.hop_size
+
+        assert self.extractor_type in ["whisper", "contentvec", "wenet"]
+        if self.extractor_type == "whisper":
+            source_hop = (
+                self.cfg.preprocess.whisper_frameshift
+                * self.cfg.preprocess.whisper_downsample_rate
+                * self.cfg.preprocess.sample_rate
+            )
+        elif self.extractor_type == "contentvec":
+            source_hop = (
+                self.cfg.preprocess.contentvec_frameshift
+                * self.cfg.preprocess.sample_rate
+            )
+        elif self.extractor_type == "wenet":
+            source_hop = (
+                self.cfg.preprocess.wenet_frameshift
+                * self.cfg.preprocess.wenet_downsample_rate
+                * self.cfg.preprocess.sample_rate
+            )
+        source_hop = int(source_hop)
+        factor = np.gcd(source_hop, target_hop)
+        source_hop //= factor
+        target_hop //= factor
+
+        # (source_len, 256)
+        _, width = content.shape
+        # slice the content from padded feature
+        source_len = min(target_len * target_hop // source_hop + 1, len(content))
+
+        # const ~= target_len * target_hop
+        const = source_len * source_hop // target_hop * target_hop
+
+        # (source_len * source_hop, dim)
+        up_sampling_feats = np.repeat(content, source_hop, axis=0)
+        # (const, dim) -> (const/target_hop, target_hop, dim) -> (const/target_hop, dim)
+        down_sampling_feats = np.average(
+            up_sampling_feats[:const].reshape(-1, target_hop, width), axis=1
+        )
+
+        err = abs(target_len - len(down_sampling_feats))
+        if err > 8:
+            # err_log_dir is indeterminate
+            err_log_dir = os.path.join(
+                self.cfg.preprocess.processed_dir, "align_max_err.log"
+            )
+            try:
+                with open(err_log_dir, "r") as f:
+                    err_num = int(f.read())
+            except:
+                with open(err_log_dir, "w") as f:
+                    f.write("0")
+                err_num = 0
+            if err > err_num:
+                with open(err_log_dir, "w") as f:
+                    f.write(str(err))
+
+        if len(down_sampling_feats) < target_len:
+            # (1, dim) -> (err, dim)
+            end = down_sampling_feats[-1][None, :].repeat(err, axis=0)
+            down_sampling_feats = np.concatenate([down_sampling_feats, end], axis=0)
+
+        # (target_len, dim)
+        mapped_feature = down_sampling_feats[:target_len]
+
+        return mapped_feature
+
+    def save_feature(self, utt, content_feature):
+        """Save a single utternace to path {cfg.preprocess.processed_dir}
+
+        Args:
+            utt (dict): one item in metadata, containing information for one utterance
+            content_feature (tensor): content feature of one utterance
+        """
+        uid = utt["Uid"]
+        assert self.extractor_type != None
+        out_dir = os.path.join(
+            self.cfg.preprocess.processed_dir, utt["Dataset"], self.extractor_type
+        )
+        os.makedirs(out_dir, exist_ok=True)
+        save_path = os.path.join(out_dir, uid + ".npy")
+        # only keep effective parts
+        duration = utt["Duration"]
+        if self.extractor_type == "whisper":
+            frameshift = (
+                self.cfg.preprocess.whisper_frameshift
+                * self.cfg.preprocess.whisper_downsample_rate
+            )  # 20ms
+        elif self.extractor_type == "contentvec":
+            frameshift = self.cfg.preprocess.contentvec_frameshift  # 20ms
+        elif self.extractor_type == "wenet":
+            frameshift = (
+                self.cfg.preprocess.wenet_frameshift
+                * self.cfg.preprocess.wenet_downsample_rate
+            )  # 40ms
+        elif self.extractor_type == "mert":
+            frameshift = self.cfg.preprocess.mert_frameshift
+        else:
+            raise NotImplementedError
+        # calculate the number of valid frames
+        num_frames = int(np.ceil((duration - frameshift) / frameshift)) + 1
+        # (num_frames, dim) -> (valid_frames, dim)
+        assert (
+            len(content_feature.shape) == 2
+        ), "content feature shape error, it should be (num_frames, dim)"
+        content_feature = content_feature[:num_frames, :]
+        np.save(save_path, content_feature.cpu().detach().numpy())
+
+
+class WhisperExtractor(BaseExtractor):
+    def __init__(self, config):
+        super(WhisperExtractor, self).__init__(config)
+        self.extractor_type = "whisper"
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    def load_model(self):
+        # load whisper checkpoint
+        print("Loading Whisper Model...")
+
+        if "whisper_model_path" in self.cfg.preprocess:
+            if os.path.isfile(self.cfg.preprocess.whisper_model_path):
+                # "pretrained/whisper/medium.pt"
+                download_root = os.path.dirname(self.cfg.preprocess.whisper_model_path)
+            elif os.path.isdir(self.cfg.preprocess.whisper_model_path):
+                # "pretrained/whisper"
+                download_root = self.cfg.preprocess.whisper_model_path
+        else:
+            download_root = None
+
+        model = whisper.load_model(
+            self.cfg.preprocess.whisper_model, self.device, download_root
+        )
+        if torch.cuda.is_available():
+            print("Using GPU...\n")
+            model = model.cuda()
+        else:
+            print("Using CPU...\n")
+
+        self.model = model.eval()
+
+    def extract_content_features(self, wavs, lens):
+        """extract content features from a batch of dataloader
+        Args:
+            wavs: tensor (batch_size, T)
+            lens: list
+        """
+        # wavs: (batch, max_len)
+        wavs = whisper.pad_or_trim(wavs)
+        # batch_mel: (batch, 80, 3000)
+        batch_mel = whisper.log_mel_spectrogram(wavs, device=self.model.device)
+        with torch.no_grad():
+            # (batch, 1500, 1024)
+            features = self.model.embed_audio(batch_mel)
+        return features
+
+
+class ContentvecExtractor(BaseExtractor):
+    def __init__(self, cfg):
+        super(ContentvecExtractor, self).__init__(cfg)
+        self.extractor_type = "contentvec"
+
+    def load_model(self):
+        assert self.model == None
+        # Load model
+        ckpt_path = self.cfg.preprocess.contentvec_file
+        print("Load Contentvec Model...")
+
+        models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
+            [ckpt_path],
+            suffix="",
+        )
+        model = models[0]
+        model.eval()
+
+        if torch.cuda.is_available():
+            # print("Using GPU...\n")
+            model = model.cuda()
+
+        self.model = model
+
+    def extract_content_features(self, wavs, lens):
+        """extract content features from a batch of dataloader
+        Args:
+            wavs: tensor (batch, T)
+            lens: list
+        """
+        device = next(self.model.parameters()).device
+        wavs = wavs.to(device)  # (batch, max_len)
+        padding_mask = torch.eq(wavs, torch.zeros_like(wavs)).to(device)
+        with torch.no_grad():
+            logits = self.model.extract_features(
+                source=wavs, padding_mask=padding_mask, output_layer=12
+            )
+            # feats: (batch, T, 256)
+            feats = self.model.final_proj(logits[0])
+        return feats
+
+
+class WenetExtractor(BaseExtractor):
+    def __init__(self, config):
+        super(WenetExtractor, self).__init__(config)
+        self.extractor_type = "wenet"
+
+    def load_model(self):
+        wenet_cfg = self.cfg.preprocess.wenet_config
+        wenet_model_path = self.cfg.preprocess.wenet_model_path
+        # load Wenet config
+        with open(wenet_cfg, "r") as w:
+            wenet_configs = yaml.load(w, Loader=yaml.FullLoader)
+        self.extract_conf = copy.deepcopy(wenet_configs["dataset_conf"])
+        print("Loading Wenet Model...")
+        self.model = init_model(wenet_configs)
+        load_checkpoint(self.model, wenet_model_path)
+
+        if torch.cuda.is_available():
+            print("Using GPU...\n")
+            self.model = self.model.cuda()
+        else:
+            print("Using CPU...\n")
+
+        self.model = self.model.eval()
+
+    def extract_content_features(self, wavs, lens):
+        """extract content features from a batch of dataloader
+        Args:
+            wavs: tensor
+            lens: list
+        """
+        feats_list = []
+        lengths_list = []
+
+        device = next(self.model.parameters()).device
+        # Extract fbank/mfcc features by kaldi
+        assert self.extract_conf is not None, "load model first!"
+        feats_type = self.extract_conf.get("feats_type", "fbank")
+        assert feats_type in ["fbank", "mfcc"]
+
+        for idx, wav in enumerate(wavs):
+            # wav: (T)
+            wav = wav[: lens[idx]].to(device)
+
+            # pad one frame to compensate for the frame cut off after feature extraction
+            pad_tensor = torch.zeros(160, device=wav.device)
+            wav = torch.cat((wav, pad_tensor), dim=-1)
+            wav *= 1 << 15
+
+            wav = wav.unsqueeze(0)  # (T) -> (1, T)
+            if feats_type == "fbank":
+                fbank_conf = self.extract_conf.get("fbank_conf", {})
+                feat = kaldi.fbank(
+                    wav,
+                    sample_frequency=16000,
+                    num_mel_bins=fbank_conf["num_mel_bins"],
+                    frame_length=fbank_conf["frame_length"],
+                    frame_shift=fbank_conf["frame_shift"],
+                    dither=fbank_conf["dither"],
+                )
+            elif feats_type == "mfcc":
+                mfcc_conf = self.extract_conf.get("mfcc", {})
+                feat = kaldi.mfcc(
+                    wav,
+                    sample_frequency=16000,
+                    num_mel_bins=mfcc_conf["num_mel_bins"],
+                    frame_length=mfcc_conf["frame_length"],
+                    frame_shift=mfcc_conf["frame_shift"],
+                    dither=mfcc_conf["dither"],
+                    num_ceps=mfcc_conf.get("num_ceps", 40),
+                    high_freq=mfcc_conf.get("high_freq", 0.0),
+                    low_freq=mfcc_conf.get("low_freq", 20.0),
+                )
+            feats_list.append(feat)
+            lengths_list.append(feat.shape[0])
+
+        feats_lengths = torch.tensor(lengths_list, dtype=torch.int32).to(device)
+        feats_tensor = pad_sequence(feats_list, batch_first=True).to(
+            device
+        )  # (batch, len, 80)
+
+        features = self.model.encoder_extractor(
+            feats_tensor,
+            feats_lengths,
+            decoding_chunk_size=-1,
+            num_decoding_left_chunks=-1,
+            simulate_streaming=False,
+        )
+        return features
+
+
+class MertExtractor(BaseExtractor):
+    def __init__(self, cfg):
+        super(MertExtractor, self).__init__(cfg)
+        self.extractor_type = "mert"
+        self.preprocessor = None
+
+    def load_model(self):
+        assert self.model == None
+        assert self.preprocessor == None
+
+        print("Loading MERT Model: ...", self.cfg.preprocess.mert_model)
+
+        model_name = self.cfg.preprocess.mert_model
+        model = AutoModel.from_pretrained(model_name, trust_remote_code=True)
+
+        if torch.cuda.is_available():
+            model = model.cuda()
+        preprocessor = Wav2Vec2FeatureExtractor.from_pretrained(
+            model_name, trust_remote_code=True
+        )
+
+        self.model = model
+        self.preprocessor = preprocessor
+
+    def extract_content_features(self, wavs, lens):
+        """extract content features from a batch of dataloader
+        Args:
+            wavs: tensor (batch, T)
+            lens: list
+        """
+        with torch.no_grad():
+            sample_rate = self.preprocessor.sampling_rate
+            device = next(self.model.parameters()).device
+            assert (
+                sample_rate == self.cfg.preprocess.mert_sample_rate
+            ), "mert sample rate mismatch, expected {}, got {}".format(
+                self.cfg.preprocess.mert_sample_rate, sample_rate
+            )
+            mert_features = []
+            # wav: (len)
+            for wav in wavs:
+                # {input_values: tensor, attention_mask: tensor}
+                inputs = self.preprocessor(
+                    wavs, sampling_rate=sample_rate, return_tensors="pt"
+                ).to(device)
+
+                outputs = self.model(**inputs, output_hidden_states=True)
+                # (25 layers, time steps, 1024 feature_dim)
+                all_layer_hidden_states = torch.stack(outputs.hidden_states).squeeze()
+                # (1, frame_len, 1024) -> (frame_len, 1024)
+                feature = outputs.hidden_states[
+                    self.cfg.preprocess.mert_feature_layer
+                ].squeeze(0)
+                mert_features.append(feature)
+
+        return mert_features
+
+
+def extract_utt_content_features_dataloader(cfg, metadata, num_workers):
+    dataset_name = metadata[0]["Dataset"]
+    with torch.no_grad():
+        if cfg.preprocess.extract_whisper_feature:
+            feat_dir = os.path.join(
+                cfg.preprocess.processed_dir, dataset_name, "whisper"
+            )
+            os.makedirs(feat_dir, exist_ok=True)
+            feat_files_num = len(os.listdir(feat_dir))
+
+            if feat_files_num != len(metadata):
+                whisper_waveforms = FFmpegDataset(
+                    cfg,
+                    dataset_name,
+                    cfg.preprocess.whisper_sample_rate,
+                    metadata=metadata,
+                )
+                data_loader = DataLoader(
+                    whisper_waveforms,
+                    num_workers=num_workers,
+                    shuffle=False,
+                    pin_memory=cfg.preprocess.pin_memory,
+                    batch_size=cfg.preprocess.content_feature_batch_size,
+                    collate_fn=collate_batch,
+                    drop_last=False,
+                )
+                extractor = WhisperExtractor(cfg)
+                extractor.load_model()
+                for batch_idx, items in enumerate(tqdm(data_loader)):
+                    _metadata, wavs, lens = items
+
+                    batch_content_features = extractor.extract_content_features(
+                        wavs,
+                        lens,
+                    )
+                    for index, utt in enumerate(_metadata):
+                        extractor.save_feature(utt, batch_content_features[index])
+
+        if cfg.preprocess.extract_contentvec_feature:
+            feat_dir = os.path.join(
+                cfg.preprocess.processed_dir, dataset_name, "contentvec"
+            )
+            os.makedirs(feat_dir, exist_ok=True)
+            feat_files_num = len(os.listdir(feat_dir))
+
+            if feat_files_num != len(metadata):
+                contentvec_waveforms = LibrosaDataset(
+                    cfg,
+                    dataset_name,
+                    cfg.preprocess.contentvec_sample_rate,
+                    metadata=metadata,
+                )
+                data_loader = DataLoader(
+                    contentvec_waveforms,
+                    num_workers=num_workers,
+                    shuffle=False,
+                    pin_memory=cfg.preprocess.pin_memory,
+                    batch_size=cfg.preprocess.content_feature_batch_size,
+                    collate_fn=collate_batch,
+                    drop_last=False,
+                )
+                extractor = ContentvecExtractor(cfg)
+                extractor.load_model()
+                for batch_idx, items in enumerate(tqdm(data_loader)):
+                    _metadata, wavs, lens = items
+
+                    batch_content_features = extractor.extract_content_features(
+                        wavs, lens
+                    )
+                    for index, utt in enumerate(_metadata):
+                        extractor.save_feature(utt, batch_content_features[index])
+
+        if cfg.preprocess.extract_wenet_feature:
+            feat_dir = os.path.join(cfg.preprocess.processed_dir, dataset_name, "wenet")
+            os.makedirs(feat_dir, exist_ok=True)
+            feat_files_num = len(os.listdir(feat_dir))
+
+            if feat_files_num != len(metadata):
+                wenet_waveforms = TorchaudioDataset(
+                    cfg,
+                    dataset_name,
+                    cfg.preprocess.wenet_sample_rate,
+                    metadata=metadata,
+                )
+                data_loader = DataLoader(
+                    wenet_waveforms,
+                    num_workers=num_workers,
+                    shuffle=False,
+                    pin_memory=cfg.preprocess.pin_memory,
+                    batch_size=cfg.preprocess.content_feature_batch_size,
+                    collate_fn=collate_batch,
+                    drop_last=False,
+                )
+                extractor = WenetExtractor(cfg)
+                extractor.load_model()
+                for batch_idx, items in enumerate(tqdm(data_loader)):
+                    _metadata, wavs, lens = items
+
+                    batch_content_features = extractor.extract_content_features(
+                        wavs,
+                        lens,
+                    )
+                    for index, utt in enumerate(_metadata):
+                        extractor.save_feature(utt, batch_content_features[index])
+
+        if cfg.preprocess.extract_mert_feature:
+            feat_dir = os.path.join(cfg.preprocess.processed_dir, dataset_name, "mert")
+            os.makedirs(feat_dir, exist_ok=True)
+            feat_files_num = len(os.listdir(feat_dir))
+
+            if feat_files_num != len(metadata):
+                mert_waveforms = TorchaudioDataset(
+                    cfg,
+                    dataset_name,
+                    cfg.preprocess.mert_sample_rate,
+                    metadata=metadata,
+                )
+                data_loader = DataLoader(
+                    mert_waveforms,
+                    num_workers=num_workers,
+                    shuffle=False,
+                    pin_memory=cfg.preprocess.pin_memory,
+                    batch_size=cfg.preprocess.content_feature_batch_size,
+                    collate_fn=collate_batch,
+                    drop_last=False,
+                )
+                extractor = MertExtractor(cfg)
+                extractor.load_model()
+                for batch_idx, items in enumerate(tqdm(data_loader)):
+                    _metadata, wavs, lens = items
+
+                    batch_content_features = extractor.extract_content_features(
+                        wavs,
+                        lens,
+                    )
+                    for index, utt in enumerate(_metadata):
+                        extractor.save_feature(utt, batch_content_features[index])
diff --git a/processors/data_augment.py b/processors/data_augment.py
new file mode 100644
index 0000000000000000000000000000000000000000..2fc183361d4bcfd454693ee0b7ffdd9758c09312
--- /dev/null
+++ b/processors/data_augment.py
@@ -0,0 +1,378 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+import random
+import os
+import json
+
+import numpy as np
+import parselmouth
+import torch
+import torchaudio
+from tqdm import tqdm
+
+from audiomentations import TimeStretch
+
+from pedalboard import (
+    Pedalboard,
+    HighShelfFilter,
+    LowShelfFilter,
+    PeakFilter,
+    PitchShift,
+)
+
+from utils.util import has_existed
+
+PRAAT_CHANGEGENDER_PITCHMEDIAN_DEFAULT = 0.0
+PRAAT_CHANGEGENDER_FORMANTSHIFTRATIO_DEFAULT = 1.0
+PRAAT_CHANGEGENDER_PITCHSHIFTRATIO_DEFAULT = 1.0
+PRAAT_CHANGEGENDER_PITCHRANGERATIO_DEFAULT = 1.0
+PRAAT_CHANGEGENDER_DURATIONFACTOR_DEFAULT = 1.0
+
+
+def wav_to_Sound(wav, sr: int) -> parselmouth.Sound:
+    """Convert a waveform to a parselmouth.Sound object
+
+    Args:
+        wav (np.ndarray/torch.Tensor): waveform of shape (n_channels, n_samples)
+        sr (int, optional): sampling rate.
+
+    Returns:
+        parselmouth.Sound: a parselmouth.Sound object
+    """
+    assert wav.shape == (1, len(wav[0])), "wav must be of shape (1, n_samples)"
+    sound = None
+    if isinstance(wav, np.ndarray):
+        sound = parselmouth.Sound(wav[0], sampling_frequency=sr)
+    elif isinstance(wav, torch.Tensor):
+        sound = parselmouth.Sound(wav[0].numpy(), sampling_frequency=sr)
+    assert sound is not None, "wav must be either np.ndarray or torch.Tensor"
+    return sound
+
+
+def get_pitch_median(wav, sr: int):
+    """Get the median pitch of a waveform
+
+    Args:
+        wav (np.ndarray/torch.Tensor): waveform of shape (n_channels, n_samples)
+        sr (int, optional): sampling rate.
+
+    Returns:
+        parselmouth.Pitch, float: a parselmouth.Pitch object and the median pitch
+    """
+    if not isinstance(wav, parselmouth.Sound):
+        sound = wav_to_Sound(wav, sr)
+    else:
+        sound = wav
+    pitch_median = PRAAT_CHANGEGENDER_PITCHMEDIAN_DEFAULT
+
+    # To Pitch: Time step(s)(standard value: 0.0), Pitch floor (Hz)(standard value: 75), Pitch ceiling (Hz)(standard value: 600.0)
+    pitch = parselmouth.praat.call(sound, "To Pitch", 0.8 / 75, 75, 600)
+    # Get quantile: From time (s), To time (s), Quantile(0.5 is then the 50% quantile, i.e., the median), Units (Hertz or Bark)
+    pitch_median = parselmouth.praat.call(pitch, "Get quantile", 0.0, 0.0, 0.5, "Hertz")
+
+    return pitch, pitch_median
+
+
+def change_gender(
+    sound,
+    pitch=None,
+    formant_shift_ratio: float = PRAAT_CHANGEGENDER_FORMANTSHIFTRATIO_DEFAULT,
+    new_pitch_median: float = PRAAT_CHANGEGENDER_PITCHMEDIAN_DEFAULT,
+    pitch_range_ratio: float = PRAAT_CHANGEGENDER_PITCHRANGERATIO_DEFAULT,
+    duration_factor: float = PRAAT_CHANGEGENDER_DURATIONFACTOR_DEFAULT,
+) -> parselmouth.Sound:
+    """Invoke change gender function in praat
+
+    Args:
+        sound (parselmouth.Sound): a parselmouth.Sound object
+        pitch (parselmouth.Pitch, optional): a parselmouth.Pitch object. Defaults to None.
+        formant_shift_ratio (float, optional): formant shift ratio. A value of 1.0 means no change. Greater than 1.0 means higher pitch. Less than 1.0 means lower pitch.
+        new_pitch_median (float, optional): new pitch median.
+        pitch_range_ratio (float, optional): pitch range ratio. A value of 1.0 means no change. Greater than 1.0 means higher pitch range. Less than 1.0 means lower pitch range.
+        duration_factor (float, optional): duration factor. A value of 1.0 means no change. Greater than 1.0 means longer duration. Less than 1.0 means shorter duration.
+
+    Returns:
+        parselmouth.Sound: a parselmouth.Sound object
+    """
+    if pitch is None:
+        new_sound = parselmouth.praat.call(
+            sound,
+            "Change gender",
+            75,
+            600,
+            formant_shift_ratio,
+            new_pitch_median,
+            pitch_range_ratio,
+            duration_factor,
+        )
+    else:
+        new_sound = parselmouth.praat.call(
+            (sound, pitch),
+            "Change gender",
+            formant_shift_ratio,
+            new_pitch_median,
+            pitch_range_ratio,
+            duration_factor,
+        )
+    return new_sound
+
+
+def apply_formant_and_pitch_shift(
+    sound: parselmouth.Sound,
+    formant_shift_ratio: float = PRAAT_CHANGEGENDER_FORMANTSHIFTRATIO_DEFAULT,
+    pitch_shift_ratio: float = PRAAT_CHANGEGENDER_PITCHSHIFTRATIO_DEFAULT,
+    pitch_range_ratio: float = PRAAT_CHANGEGENDER_PITCHRANGERATIO_DEFAULT,
+    duration_factor: float = PRAAT_CHANGEGENDER_DURATIONFACTOR_DEFAULT,
+) -> parselmouth.Sound:
+    """use Praat "Changer gender" command to manipulate pitch and formant
+    "Change gender": Praat -> Sound Object -> Convert -> Change gender
+    refer to Help of Praat for more details
+    # https://github.com/YannickJadoul/Parselmouth/issues/25#issuecomment-608632887 might help
+    """
+    pitch = None
+    new_pitch_median = PRAAT_CHANGEGENDER_PITCHMEDIAN_DEFAULT
+    if pitch_shift_ratio != 1.0:
+        pitch, pitch_median = get_pitch_median(sound, sound.sampling_frequency)
+        new_pitch_median = pitch_median * pitch_shift_ratio
+
+        # refer to https://github.com/praat/praat/issues/1926#issuecomment-974909408
+        pitch_minimum = parselmouth.praat.call(
+            pitch, "Get minimum", 0.0, 0.0, "Hertz", "Parabolic"
+        )
+        new_median = pitch_median * pitch_shift_ratio
+        scaled_minimum = pitch_minimum * pitch_shift_ratio
+        result_minimum = new_median + (scaled_minimum - new_median) * pitch_range_ratio
+        if result_minimum < 0:
+            new_pitch_median = PRAAT_CHANGEGENDER_PITCHMEDIAN_DEFAULT
+            pitch_range_ratio = PRAAT_CHANGEGENDER_PITCHRANGERATIO_DEFAULT
+
+        if math.isnan(new_pitch_median):
+            new_pitch_median = PRAAT_CHANGEGENDER_PITCHMEDIAN_DEFAULT
+            pitch_range_ratio = PRAAT_CHANGEGENDER_PITCHRANGERATIO_DEFAULT
+
+    new_sound = change_gender(
+        sound,
+        pitch,
+        formant_shift_ratio,
+        new_pitch_median,
+        pitch_range_ratio,
+        duration_factor,
+    )
+    return new_sound
+
+
+# Function used in EQ
+def pedalboard_equalizer(wav: np.ndarray, sr: int) -> np.ndarray:
+    """Use pedalboard to do equalizer"""
+    board = Pedalboard()
+
+    cutoff_low_freq = 60
+    cutoff_high_freq = 10000
+
+    q_min = 2
+    q_max = 5
+
+    random_all_freq = True
+    num_filters = 10
+    if random_all_freq:
+        key_freqs = [random.uniform(1, 12000) for _ in range(num_filters)]
+    else:
+        key_freqs = [
+            power_ratio(float(z) / (num_filters - 1), cutoff_low_freq, cutoff_high_freq)
+            for z in range(num_filters)
+        ]
+    q_values = [
+        power_ratio(random.uniform(0, 1), q_min, q_max) for _ in range(num_filters)
+    ]
+    gains = [random.uniform(-12, 12) for _ in range(num_filters)]
+    # low-shelving filter
+    board.append(
+        LowShelfFilter(
+            cutoff_frequency_hz=key_freqs[0], gain_db=gains[0], q=q_values[0]
+        )
+    )
+    # peaking filters
+    for i in range(1, 9):
+        board.append(
+            PeakFilter(
+                cutoff_frequency_hz=key_freqs[i], gain_db=gains[i], q=q_values[i]
+            )
+        )
+    # high-shelving filter
+    board.append(
+        HighShelfFilter(
+            cutoff_frequency_hz=key_freqs[9], gain_db=gains[9], q=q_values[9]
+        )
+    )
+
+    # Apply the pedalboard to the audio
+    processed_audio = board(wav, sr)
+    return processed_audio
+
+
+def power_ratio(r: float, a: float, b: float):
+    return a * math.pow((b / a), r)
+
+
+def audiomentations_time_stretch(wav: np.ndarray, sr: int) -> np.ndarray:
+    """Use audiomentations to do time stretch"""
+    transform = TimeStretch(
+        min_rate=0.8, max_rate=1.25, leave_length_unchanged=False, p=1.0
+    )
+    augmented_wav = transform(wav, sample_rate=sr)
+    return augmented_wav
+
+
+def formant_and_pitch_shift(
+    sound: parselmouth.Sound, fs: bool, ps: bool
+) -> parselmouth.Sound:
+    """ """
+    formant_shift_ratio = PRAAT_CHANGEGENDER_FORMANTSHIFTRATIO_DEFAULT
+    pitch_shift_ratio = PRAAT_CHANGEGENDER_PITCHSHIFTRATIO_DEFAULT
+    pitch_range_ratio = PRAAT_CHANGEGENDER_PITCHRANGERATIO_DEFAULT
+
+    assert fs != ps, "fs, ps are mutually exclusive"
+
+    if fs:
+        formant_shift_ratio = random.uniform(1.0, 1.4)
+        use_reciprocal = random.uniform(-1, 1) > 0
+        if use_reciprocal:
+            formant_shift_ratio = 1.0 / formant_shift_ratio
+        # only use praat to change formant
+        new_sound = apply_formant_and_pitch_shift(
+            sound,
+            formant_shift_ratio=formant_shift_ratio,
+        )
+        return new_sound
+
+    if ps:
+        board = Pedalboard()
+        board.append(PitchShift(random.uniform(-12, 12)))
+        wav_numpy = sound.values
+        wav_numpy = board(wav_numpy, sound.sampling_frequency)
+        # use pedalboard to change pitch
+        new_sound = parselmouth.Sound(
+            wav_numpy, sampling_frequency=sound.sampling_frequency
+        )
+        return new_sound
+
+
+def wav_manipulation(
+    wav: torch.Tensor,
+    sr: int,
+    aug_type: str = "None",
+    formant_shift: bool = False,
+    pitch_shift: bool = False,
+    time_stretch: bool = False,
+    equalizer: bool = False,
+) -> torch.Tensor:
+    assert aug_type == "None" or aug_type in [
+        "formant_shift",
+        "pitch_shift",
+        "time_stretch",
+        "equalizer",
+    ], "aug_type must be one of formant_shift, pitch_shift, time_stretch, equalizer"
+
+    assert aug_type == "None" or (
+        formant_shift == False
+        and pitch_shift == False
+        and time_stretch == False
+        and equalizer == False
+    ), "if aug_type is specified, other argument must be False"
+
+    if aug_type != "None":
+        if aug_type == "formant_shift":
+            formant_shift = True
+        if aug_type == "pitch_shift":
+            pitch_shift = True
+        if aug_type == "equalizer":
+            equalizer = True
+        if aug_type == "time_stretch":
+            time_stretch = True
+
+    wav_numpy = wav.numpy()
+
+    if equalizer:
+        wav_numpy = pedalboard_equalizer(wav_numpy, sr)
+
+    if time_stretch:
+        wav_numpy = audiomentations_time_stretch(wav_numpy, sr)
+
+    sound = wav_to_Sound(wav_numpy, sr)
+
+    if formant_shift or pitch_shift:
+        sound = formant_and_pitch_shift(sound, formant_shift, pitch_shift)
+
+    wav = torch.from_numpy(sound.values).float()
+    # shape (1, n_samples)
+    return wav
+
+
+def augment_dataset(cfg, dataset) -> list:
+    """Augment dataset with formant_shift, pitch_shift, time_stretch, equalizer
+
+    Args:
+        cfg (dict): configuration
+        dataset (str): dataset name
+
+    Returns:
+        list: augmented dataset names
+    """
+    # load metadata
+    dataset_path = os.path.join(cfg.preprocess.processed_dir, dataset)
+    split = ["train", "test"] if "eval" not in dataset else ["test"]
+    augment_datasets = []
+    aug_types = [
+        "formant_shift" if cfg.preprocess.use_formant_shift else None,
+        "pitch_shift" if cfg.preprocess.use_pitch_shift else None,
+        "time_stretch" if cfg.preprocess.use_time_stretch else None,
+        "equalizer" if cfg.preprocess.use_equalizer else None,
+    ]
+    aug_types = filter(None, aug_types)
+    for aug_type in aug_types:
+        print("Augmenting {} with {}...".format(dataset, aug_type))
+        new_dataset = dataset + "_" + aug_type
+        augment_datasets.append(new_dataset)
+        new_dataset_path = os.path.join(cfg.preprocess.processed_dir, new_dataset)
+
+        for dataset_type in split:
+            metadata_path = os.path.join(dataset_path, "{}.json".format(dataset_type))
+            augmented_metadata = []
+            new_metadata_path = os.path.join(
+                new_dataset_path, "{}.json".format(dataset_type)
+            )
+            os.makedirs(new_dataset_path, exist_ok=True)
+            new_dataset_wav_dir = os.path.join(new_dataset_path, "wav")
+            os.makedirs(new_dataset_wav_dir, exist_ok=True)
+
+            if has_existed(new_metadata_path):
+                continue
+
+            with open(metadata_path, "r") as f:
+                metadata = json.load(f)
+
+            for utt in tqdm(metadata):
+                original_wav_path = utt["Path"]
+                original_wav, sr = torchaudio.load(original_wav_path)
+                new_wav = wav_manipulation(original_wav, sr, aug_type=aug_type)
+                new_wav_path = os.path.join(new_dataset_wav_dir, utt["Uid"] + ".wav")
+                torchaudio.save(new_wav_path, new_wav, sr)
+                new_utt = {
+                    "Dataset": utt["Dataset"] + "_" + aug_type,
+                    "index": utt["index"],
+                    "Singer": utt["Singer"],
+                    "Uid": utt["Uid"],
+                    "Path": new_wav_path,
+                    "Duration": utt["Duration"],
+                }
+                augmented_metadata.append(new_utt)
+            new_metadata_path = os.path.join(
+                new_dataset_path, "{}.json".format(dataset_type)
+            )
+            with open(new_metadata_path, "w") as f:
+                json.dump(augmented_metadata, f, indent=4, ensure_ascii=False)
+    return augment_datasets
diff --git a/processors/phone_extractor.py b/processors/phone_extractor.py
new file mode 100644
index 0000000000000000000000000000000000000000..26b59d8be4fa83ccfa3022b41dfc082592f3a920
--- /dev/null
+++ b/processors/phone_extractor.py
@@ -0,0 +1,153 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+from tqdm import tqdm
+from text.g2p_module import G2PModule, LexiconModule
+from text.symbol_table import SymbolTable
+
+"""
+    phoneExtractor: extract phone from text
+"""
+
+
+class phoneExtractor:
+    def __init__(self, cfg, dataset_name=None, phone_symbol_file=None):
+        """
+        Args:
+            cfg: config
+            dataset_name: name of dataset
+        """
+        self.cfg = cfg
+
+        #  phone symbols dict
+        self.phone_symbols = set()
+
+        # phone symbols dict file
+        if phone_symbol_file is not None:
+            self.phone_symbols_file = phone_symbol_file
+        elif dataset_name is not None:
+            self.dataset_name = dataset_name
+            self.phone_symbols_file = os.path.join(
+                cfg.preprocess.processed_dir, dataset_name, cfg.preprocess.symbols_dict
+            )
+
+        # initialize g2p module
+        if cfg.preprocess.phone_extractor in [
+            "espeak",
+            "pypinyin",
+            "pypinyin_initials_finals",
+        ]:
+            self.g2p_module = G2PModule(backend=cfg.preprocess.phone_extractor)
+        elif cfg.preprocess.phone_extractor == "lexicon":
+            assert cfg.preprocess.lexicon_path != ""
+            self.g2p_module = LexiconModule(cfg.preprocess.lexicon_path)
+        else:
+            print("No suppert to", cfg.preprocess.phone_extractor)
+            raise
+
+    def extract_phone(self, text):
+        """
+        Extract phone from text
+        Args:
+
+            text:  text of utterance
+
+        Returns:
+            phone_symbols: set of phone symbols
+            phone_seq: list of phone sequence of each utterance
+        """
+
+        if self.cfg.preprocess.phone_extractor in [
+            "espeak",
+            "pypinyin",
+            "pypinyin_initials_finals",
+        ]:
+            text = text.replace("”", '"').replace("“", '"')
+            phone = self.g2p_module.g2p_conversion(text=text)
+            self.phone_symbols.update(phone)
+            phone_seq = [phn for phn in phone]
+
+        elif self.cfg.preprocess.phone_extractor == "lexicon":
+            phone_seq = self.g2p_module.g2p_conversion(text)
+            phone = phone_seq
+            if not isinstance(phone_seq, list):
+                phone_seq = phone_seq.split()
+
+        return phone_seq
+
+    def save_dataset_phone_symbols_to_table(self):
+        # load and merge saved phone symbols
+        if os.path.exists(self.phone_symbols_file):
+            phone_symbol_dict_saved = SymbolTable.from_file(
+                self.phone_symbols_file
+            )._sym2id.keys()
+            self.phone_symbols.update(set(phone_symbol_dict_saved))
+
+        # save phone symbols
+        phone_symbol_dict = SymbolTable()
+        for s in sorted(list(self.phone_symbols)):
+            phone_symbol_dict.add(s)
+        phone_symbol_dict.to_file(self.phone_symbols_file)
+
+
+def extract_utt_phone_sequence(cfg, metadata):
+    """
+    Extract phone sequence from text
+    Args:
+        cfg: config
+        metadata: list of dict, each dict contains "Uid", "Text"
+
+    """
+
+    dataset_name = cfg.dataset[0]
+
+    # output path
+    out_path = os.path.join(
+        cfg.preprocess.processed_dir, dataset_name, cfg.preprocess.phone_dir
+    )
+    os.makedirs(out_path, exist_ok=True)
+
+    phone_extractor = phoneExtractor(cfg, dataset_name)
+
+    for utt in tqdm(metadata):
+        uid = utt["Uid"]
+        text = utt["Text"]
+
+        phone_seq = phone_extractor.extract_phone(text)
+
+        phone_path = os.path.join(out_path, uid + ".phone")
+        with open(phone_path, "w") as fin:
+            fin.write(" ".join(phone_seq))
+
+    if cfg.preprocess.phone_extractor != "lexicon":
+        phone_extractor.save_dataset_phone_symbols_to_table()
+
+
+def save_all_dataset_phone_symbols_to_table(self, cfg, dataset):
+    #  phone symbols dict
+    phone_symbols = set()
+
+    for dataset_name in dataset:
+        phone_symbols_file = os.path.join(
+            cfg.preprocess.processed_dir, dataset_name, cfg.preprocess.symbols_dict
+        )
+
+        # load and merge saved phone symbols
+        assert os.path.exists(phone_symbols_file)
+        phone_symbol_dict_saved = SymbolTable.from_file(
+            phone_symbols_file
+        )._sym2id.keys()
+        phone_symbols.update(set(phone_symbol_dict_saved))
+
+    # save all phone symbols to each dataset
+    phone_symbol_dict = SymbolTable()
+    for s in sorted(list(phone_symbols)):
+        phone_symbol_dict.add(s)
+    for dataset_name in dataset:
+        phone_symbols_file = os.path.join(
+            cfg.preprocess.processed_dir, dataset_name, cfg.preprocess.symbols_dict
+        )
+        phone_symbol_dict.to_file(phone_symbols_file)
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..630a405d4b17d5a2615edee3cfde7a4c52de5a32
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,5 @@
+fairseq
+git+https://github.com/lhotse-speech/lhotse
+encodec
+phonemizer==3.2.1
+pypinyin==0.48.0
\ No newline at end of file
diff --git a/schedulers/__init__.py b/schedulers/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/schedulers/scheduler.py b/schedulers/scheduler.py
new file mode 100644
index 0000000000000000000000000000000000000000..fbde0e53679440b08d968c7ea0a4dc88f3db5ffa
--- /dev/null
+++ b/schedulers/scheduler.py
@@ -0,0 +1,173 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+from torch.optim import Optimizer
+from typing import List, Optional, Tuple, Union
+
+
+def calc_lr(step, dim_embed, warmup_steps):
+    return dim_embed ** (-0.5) * min(step ** (-0.5), step * warmup_steps ** (-1.5))
+
+
+# The function is modified from
+# https://github.com/lifeiteng/vall-e/blob/9c69096d603ce13174fb5cb025f185e2e9b36ac7/valle/modules/scheduler.py
+class NoamScheduler(torch.optim.lr_scheduler._LRScheduler):
+    def __init__(
+        self,
+        base_lr: float,
+        optimizer: torch.optim.Optimizer,
+        dim_embed: int,
+        warmup_steps: int,
+        last_epoch: int = -1,
+        verbose: bool = False,
+    ) -> None:
+        self.dim_embed = dim_embed
+        self.base_lr = base_lr
+        self.warmup_steps = warmup_steps
+        self.num_param_groups = len(optimizer.param_groups)
+
+        super().__init__(optimizer, last_epoch, verbose)
+
+    def get_lr(self) -> float:
+        lr = self.base_lr * calc_lr(self._step_count, self.dim_embed, self.warmup_steps)
+        return [lr] * self.num_param_groups
+
+    def set_step(self, step: int):
+        self._step_count = step
+
+
+class LRScheduler(object):
+    """
+    Base-class for learning rate schedulers where the learning-rate depends on both the
+    batch and the epoch.
+    """
+
+    def __init__(self, optimizer: Optimizer, verbose: bool = False):
+        # Attach optimizer
+        if not isinstance(optimizer, Optimizer):
+            raise TypeError("{} is not an Optimizer".format(type(optimizer).__name__))
+        self.optimizer = optimizer
+        self.verbose = verbose
+
+        for group in optimizer.param_groups:
+            group.setdefault("base_lr", group["lr"])
+
+        self.base_lrs = [group["base_lr"] for group in optimizer.param_groups]
+
+        self.epoch = 0
+        self.batch = 0
+
+    def state_dict(self):
+        """Returns the state of the scheduler as a :class:`dict`.
+
+        It contains an entry for every variable in self.__dict__ which
+        is not the optimizer.
+        """
+        return {
+            "base_lrs": self.base_lrs,
+            "epoch": self.epoch,
+            "batch": self.batch,
+        }
+
+    def load_state_dict(self, state_dict):
+        """Loads the schedulers state.
+
+        Args:
+            state_dict (dict): scheduler state. Should be an object returned
+                from a call to :meth:`state_dict`.
+        """
+        self.__dict__.update(state_dict)
+
+    def get_last_lr(self) -> List[float]:
+        """Return last computed learning rate by current scheduler.  Will be a list of float."""
+        return self._last_lr
+
+    def get_lr(self):
+        # Compute list of learning rates from self.epoch and self.batch and
+        # self.base_lrs; this must be overloaded by the user.
+        # e.g. return [some_formula(self.batch, self.epoch, base_lr) for base_lr in self.base_lrs ]
+        raise NotImplementedError
+
+    def step_batch(self, batch: Optional[int] = None) -> None:
+        # Step the batch index, or just set it.  If `batch` is specified, it
+        # must be the batch index from the start of training, i.e. summed over
+        # all epochs.
+        # You can call this in any order; if you don't provide 'batch', it should
+        # of course be called once per batch.
+        if batch is not None:
+            self.batch = batch
+        else:
+            self.batch = self.batch + 1
+        self._set_lrs()
+
+    def step_epoch(self, epoch: Optional[int] = None):
+        # Step the epoch index, or just set it.  If you provide the 'epoch' arg,
+        # you should call this at the start of the epoch; if you don't provide the 'epoch'
+        # arg, you should call it at the end of the epoch.
+        if epoch is not None:
+            self.epoch = epoch
+        else:
+            self.epoch = self.epoch + 1
+        self._set_lrs()
+
+    def _set_lrs(self):
+        values = self.get_lr()
+        assert len(values) == len(self.optimizer.param_groups)
+
+        for i, data in enumerate(zip(self.optimizer.param_groups, values)):
+            param_group, lr = data
+            param_group["lr"] = lr
+        self._last_lr = [group["lr"] for group in self.optimizer.param_groups]
+
+
+class Eden(LRScheduler):
+    """
+    Eden scheduler.
+    The basic formula (before warmup) is:
+      lr = base_lr * (((batch**2 + lr_batches**2) / lr_batches**2) ** -0.25 *
+                     (((epoch**2 + lr_epochs**2) / lr_epochs**2) ** -0.25)) * warmup
+    where `warmup` increases from linearly 0.5 to 1 over `warmup_batches` batches
+    and then stays constant at 1.
+
+
+     E.g. suggest base_lr = 0.04 (passed to optimizer) if used with ScaledAdam
+
+    Args:
+        optimizer: the optimizer to change the learning rates on
+        lr_batches: the number of batches after which we start significantly
+              decreasing the learning rate, suggest 5000.
+        lr_epochs: the number of epochs after which we start significantly
+              decreasing the learning rate, suggest 6 if you plan to do e.g.
+              20 to 40 epochs, but may need smaller number if dataset is huge
+              and you will do few epochs.
+    """
+
+    def __init__(
+        self,
+        optimizer: Optimizer,
+        lr_batches: Union[int, float],
+        lr_epochs: Union[int, float],
+        warmup_batches: Union[int, float] = 500.0,
+        verbose: bool = False,
+    ):
+        super(Eden, self).__init__(optimizer, verbose)
+        self.lr_batches = lr_batches
+        self.lr_epochs = lr_epochs
+        self.warmup_batches = warmup_batches
+
+    def get_lr(self):
+        factor = (
+            (self.batch**2 + self.lr_batches**2) / self.lr_batches**2
+        ) ** -0.25 * (
+            ((self.epoch**2 + self.lr_epochs**2) / self.lr_epochs**2) ** -0.25
+        )
+        warmup_factor = (
+            1.0
+            if self.batch >= self.warmup_batches
+            else 0.5 + 0.5 * (self.batch / self.warmup_batches)
+        )
+
+        return [x * factor * warmup_factor for x in self.base_lrs]
diff --git a/text/__init__.py b/text/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4db9a6a2c8dbb20fb4188c322a7cc32afe722e2
--- /dev/null
+++ b/text/__init__.py
@@ -0,0 +1,79 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+""" This code is modified from https://github.com/keithito/tacotron """
+import re
+from text import cleaners
+from text.symbols import symbols
+
+
+# Mappings from symbol to numeric ID and vice versa:
+_symbol_to_id = {s: i for i, s in enumerate(symbols)}
+_id_to_symbol = {i: s for i, s in enumerate(symbols)}
+
+# Regular expression matching text enclosed in curly braces:
+_curly_re = re.compile(r"(.*?)\{(.+?)\}(.*)")
+
+
+def text_to_sequence(text, cleaner_names):
+    """Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
+
+    The text can optionally have ARPAbet sequences enclosed in curly braces embedded
+    in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."
+
+    Args:
+      text: string to convert to a sequence
+      cleaner_names: names of the cleaner functions to run the text through
+
+    Returns:
+      List of integers corresponding to the symbols in the text
+    """
+    sequence = []
+
+    # Check for curly braces and treat their contents as ARPAbet:
+    while len(text):
+        m = _curly_re.match(text)
+
+        if not m:
+            sequence += _symbols_to_sequence(_clean_text(text, cleaner_names))
+            break
+        sequence += _symbols_to_sequence(_clean_text(m.group(1), cleaner_names))
+        sequence += _arpabet_to_sequence(m.group(2))
+        text = m.group(3)
+    return sequence
+
+
+def sequence_to_text(sequence):
+    """Converts a sequence of IDs back to a string"""
+    result = ""
+    for symbol_id in sequence:
+        if symbol_id in _id_to_symbol:
+            s = _id_to_symbol[symbol_id]
+            # Enclose ARPAbet back in curly braces:
+            if len(s) > 1 and s[0] == "@":
+                s = "{%s}" % s[1:]
+            result += s
+    return result.replace("}{", " ")
+
+
+def _clean_text(text, cleaner_names):
+    for name in cleaner_names:
+        cleaner = getattr(cleaners, name)
+        if not cleaner:
+            raise Exception("Unknown cleaner: %s" % name)
+        text = cleaner(text)
+    return text
+
+
+def _symbols_to_sequence(symbols):
+    return [_symbol_to_id[s] for s in symbols if _should_keep_symbol(s)]
+
+
+def _arpabet_to_sequence(text):
+    return _symbols_to_sequence(["@" + s for s in text.split()])
+
+
+def _should_keep_symbol(s):
+    return s in _symbol_to_id and s != "_" and s != "~"
diff --git a/text/cleaners.py b/text/cleaners.py
new file mode 100644
index 0000000000000000000000000000000000000000..10df96dd400efa0cceb9283cbab0371196ff3c29
--- /dev/null
+++ b/text/cleaners.py
@@ -0,0 +1,98 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+""" This code is modified from https://github.com/keithito/tacotron """
+
+"""
+Cleaners are transformations that run over the input text at both training and eval time.
+
+Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
+hyperparameter. Some cleaners are English-specific. You'll typically want to use:
+  1. "english_cleaners" for English text
+  2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
+     the Unidecode library (https://pypi.python.org/pypi/Unidecode)
+  3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
+     the symbols in symbols.py to match your data).
+"""
+
+
+# Regular expression matching whitespace:
+import re
+from unidecode import unidecode
+from .numbers import normalize_numbers
+
+_whitespace_re = re.compile(r"\s+")
+
+# List of (regular expression, replacement) pairs for abbreviations:
+_abbreviations = [
+    (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
+    for x in [
+        ("mrs", "misess"),
+        ("mr", "mister"),
+        ("dr", "doctor"),
+        ("st", "saint"),
+        ("co", "company"),
+        ("jr", "junior"),
+        ("maj", "major"),
+        ("gen", "general"),
+        ("drs", "doctors"),
+        ("rev", "reverend"),
+        ("lt", "lieutenant"),
+        ("hon", "honorable"),
+        ("sgt", "sergeant"),
+        ("capt", "captain"),
+        ("esq", "esquire"),
+        ("ltd", "limited"),
+        ("col", "colonel"),
+        ("ft", "fort"),
+    ]
+]
+
+
+def expand_abbreviations(text):
+    for regex, replacement in _abbreviations:
+        text = re.sub(regex, replacement, text)
+    return text
+
+
+def expand_numbers(text):
+    return normalize_numbers(text)
+
+
+def lowercase(text):
+    return text.lower()
+
+
+def collapse_whitespace(text):
+    return re.sub(_whitespace_re, " ", text)
+
+
+def convert_to_ascii(text):
+    return unidecode(text)
+
+
+def basic_cleaners(text):
+    """Basic pipeline that lowercases and collapses whitespace without transliteration."""
+    text = lowercase(text)
+    text = collapse_whitespace(text)
+    return text
+
+
+def transliteration_cleaners(text):
+    """Pipeline for non-English text that transliterates to ASCII."""
+    text = convert_to_ascii(text)
+    text = lowercase(text)
+    text = collapse_whitespace(text)
+    return text
+
+
+def english_cleaners(text):
+    """Pipeline for English text, including number and abbreviation expansion."""
+    text = convert_to_ascii(text)
+    text = lowercase(text)
+    text = expand_numbers(text)
+    text = expand_abbreviations(text)
+    text = collapse_whitespace(text)
+    return text
diff --git a/text/cmudict.py b/text/cmudict.py
new file mode 100644
index 0000000000000000000000000000000000000000..684e63a52f84c4942d5be2a6fac80b6abc004eda
--- /dev/null
+++ b/text/cmudict.py
@@ -0,0 +1,145 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""  This code is modified from https://github.com/keithito/tacotron """
+
+import re
+
+
+valid_symbols = [
+    "AA",
+    "AA0",
+    "AA1",
+    "AA2",
+    "AE",
+    "AE0",
+    "AE1",
+    "AE2",
+    "AH",
+    "AH0",
+    "AH1",
+    "AH2",
+    "AO",
+    "AO0",
+    "AO1",
+    "AO2",
+    "AW",
+    "AW0",
+    "AW1",
+    "AW2",
+    "AY",
+    "AY0",
+    "AY1",
+    "AY2",
+    "B",
+    "CH",
+    "D",
+    "DH",
+    "EH",
+    "EH0",
+    "EH1",
+    "EH2",
+    "ER",
+    "ER0",
+    "ER1",
+    "ER2",
+    "EY",
+    "EY0",
+    "EY1",
+    "EY2",
+    "F",
+    "G",
+    "HH",
+    "IH",
+    "IH0",
+    "IH1",
+    "IH2",
+    "IY",
+    "IY0",
+    "IY1",
+    "IY2",
+    "JH",
+    "K",
+    "L",
+    "M",
+    "N",
+    "NG",
+    "OW",
+    "OW0",
+    "OW1",
+    "OW2",
+    "OY",
+    "OY0",
+    "OY1",
+    "OY2",
+    "P",
+    "R",
+    "S",
+    "SH",
+    "T",
+    "TH",
+    "UH",
+    "UH0",
+    "UH1",
+    "UH2",
+    "UW",
+    "UW0",
+    "UW1",
+    "UW2",
+    "V",
+    "W",
+    "Y",
+    "Z",
+    "ZH",
+]
+
+_valid_symbol_set = set(valid_symbols)
+
+
+class CMUDict:
+    """Thin wrapper around CMUDict data. http://www.speech.cs.cmu.edu/cgi-bin/cmudict"""
+
+    def __init__(self, file_or_path, keep_ambiguous=True):
+        if isinstance(file_or_path, str):
+            with open(file_or_path, encoding="latin-1") as f:
+                entries = _parse_cmudict(f)
+        else:
+            entries = _parse_cmudict(file_or_path)
+        if not keep_ambiguous:
+            entries = {word: pron for word, pron in entries.items() if len(pron) == 1}
+        self._entries = entries
+
+    def __len__(self):
+        return len(self._entries)
+
+    def lookup(self, word):
+        """Returns list of ARPAbet pronunciations of the given word."""
+        return self._entries.get(word.upper())
+
+
+_alt_re = re.compile(r"\([0-9]+\)")
+
+
+def _parse_cmudict(file):
+    cmudict = {}
+    for line in file:
+        if len(line) and (line[0] >= "A" and line[0] <= "Z" or line[0] == "'"):
+            parts = line.split("  ")
+            word = re.sub(_alt_re, "", parts[0])
+            pronunciation = _get_pronunciation(parts[1])
+            if pronunciation:
+                if word in cmudict:
+                    cmudict[word].append(pronunciation)
+                else:
+                    cmudict[word] = [pronunciation]
+    return cmudict
+
+
+def _get_pronunciation(s):
+    parts = s.strip().split(" ")
+    for part in parts:
+        if part not in _valid_symbol_set:
+            return None
+    return " ".join(parts)
diff --git a/text/g2p.py b/text/g2p.py
new file mode 100644
index 0000000000000000000000000000000000000000..2368c1e97b4018abde395b190a9bbec76ba8ede0
--- /dev/null
+++ b/text/g2p.py
@@ -0,0 +1,38 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import re
+from g2p_en import G2p
+from string import punctuation
+
+
+def read_lexicon(lex_path):
+    lexicon = {}
+    with open(lex_path) as f:
+        for line in f:
+            temp = re.split(r"\s+", line.strip("\n"))
+            word = temp[0]
+            phones = temp[1:]
+            if word.lower() not in lexicon:
+                lexicon[word.lower()] = phones
+    return lexicon
+
+
+def preprocess_english(text, lexicon):
+    text = text.rstrip(punctuation)
+
+    g2p = G2p()
+    phones = []
+    words = re.split(r"([,;.\-\?\!\s+])", text)
+    for w in words:
+        if w.lower() in lexicon:
+            phones += lexicon[w.lower()]
+        else:
+            phones += list(filter(lambda p: p != " ", g2p(w)))
+    phones = "}{".join(phones)
+    phones = re.sub(r"\{[^\w\s]?\}", "{sp}", phones)
+    phones = phones.replace("}{", " ")
+
+    return phones
diff --git a/text/g2p_module.py b/text/g2p_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..df304888ce8ba6520b692f95f9d8818818a26b94
--- /dev/null
+++ b/text/g2p_module.py
@@ -0,0 +1,223 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+import re
+from g2p_en import G2p
+from string import punctuation
+from typing import Any, Dict, List, Optional, Pattern, Union
+
+from phonemizer.backend import EspeakBackend
+from phonemizer.backend.espeak.language_switch import LanguageSwitch
+from phonemizer.backend.espeak.words_mismatch import WordMismatch
+from phonemizer.punctuation import Punctuation
+from phonemizer.separator import Separator
+
+try:
+    from pypinyin import Style, pinyin
+    from pypinyin.style._utils import get_finals, get_initials
+except Exception:
+    pass
+
+
+# This code is modified from
+# https://github.com/lifeiteng/vall-e/blob/9c69096d603ce13174fb5cb025f185e2e9b36ac7/valle/data/tokenizer.py
+
+
+class PypinyinBackend:
+    """PypinyinBackend for Chinese. Most codes is referenced from espnet.
+    There are two types pinyin or initials_finals, one is
+    just like "ni1 hao3", the other is like "n i1 h ao3".
+    """
+
+    def __init__(
+        self,
+        backend="initials_finals",
+        punctuation_marks: Union[str, Pattern] = Punctuation.default_marks(),
+    ) -> None:
+        self.backend = backend
+        self.punctuation_marks = punctuation_marks
+
+    def phonemize(
+        self, text: List[str], separator: Separator, strip=True, njobs=1
+    ) -> List[str]:
+        assert isinstance(text, List)
+        phonemized = []
+        for _text in text:
+            _text = re.sub(" +", " ", _text.strip())
+            _text = _text.replace(" ", separator.word)
+            phones = []
+            if self.backend == "pypinyin":
+                for n, py in enumerate(
+                    pinyin(_text, style=Style.TONE3, neutral_tone_with_five=True)
+                ):
+                    if all([c in self.punctuation_marks for c in py[0]]):
+                        if len(phones):
+                            assert phones[-1] == separator.syllable
+                            phones.pop(-1)
+
+                        phones.extend(list(py[0]))
+                    else:
+                        phones.extend([py[0], separator.syllable])
+            elif self.backend == "pypinyin_initials_finals":
+                for n, py in enumerate(
+                    pinyin(_text, style=Style.TONE3, neutral_tone_with_five=True)
+                ):
+                    if all([c in self.punctuation_marks for c in py[0]]):
+                        if len(phones):
+                            assert phones[-1] == separator.syllable
+                            phones.pop(-1)
+                        phones.extend(list(py[0]))
+                    else:
+                        if py[0][-1].isalnum():
+                            initial = get_initials(py[0], strict=False)
+                            if py[0][-1].isdigit():
+                                final = get_finals(py[0][:-1], strict=False) + py[0][-1]
+                            else:
+                                final = get_finals(py[0], strict=False)
+                            phones.extend(
+                                [
+                                    initial,
+                                    separator.phone,
+                                    final,
+                                    separator.syllable,
+                                ]
+                            )
+                        else:
+                            assert ValueError
+            else:
+                raise NotImplementedError
+            phonemized.append(
+                "".join(phones).rstrip(f"{separator.word}{separator.syllable}")
+            )
+        return phonemized
+
+
+class G2PModule:
+    """Phonemize Text."""
+
+    def __init__(
+        self,
+        language="en-us",
+        backend="espeak",
+        separator=Separator(word="_", syllable="-", phone="|"),
+        preserve_punctuation=True,
+        punctuation_marks: Union[str, Pattern] = Punctuation.default_marks(),
+        with_stress: bool = False,
+        tie: Union[bool, str] = False,
+        language_switch: LanguageSwitch = "keep-flags",
+        words_mismatch: WordMismatch = "ignore",
+    ) -> None:
+        self.backend = self._initialize_backend(
+            backend,
+            language,
+            punctuation_marks,
+            preserve_punctuation,
+            with_stress,
+            tie,
+            language_switch,
+            words_mismatch,
+        )
+        self.separator = separator
+
+    def _initialize_backend(
+        self,
+        backend,
+        language,
+        punctuation_marks,
+        preserve_punctuation,
+        with_stress,
+        tie,
+        language_switch,
+        words_mismatch,
+    ):
+        if backend == "espeak":
+            return EspeakBackend(
+                language,
+                punctuation_marks=punctuation_marks,
+                preserve_punctuation=preserve_punctuation,
+                with_stress=with_stress,
+                tie=tie,
+                language_switch=language_switch,
+                words_mismatch=words_mismatch,
+            )
+        elif backend in ["pypinyin", "pypinyin_initials_finals"]:
+            return PypinyinBackend(
+                backend=backend,
+                punctuation_marks=punctuation_marks + self.separator.word,
+            )
+        else:
+            raise NotImplementedError(f"{backend}")
+
+    def to_list(self, phonemized: str) -> List[str]:
+        fields = []
+        for word in phonemized.split(self.separator.word):
+            pp = re.findall(r"\w+|[^\w\s]", word, re.UNICODE)
+            fields.extend(
+                [p for p in pp if p != self.separator.phone] + [self.separator.word]
+            )
+        assert len("".join(fields[:-1])) == len(phonemized) - phonemized.count(
+            self.separator.phone
+        )
+        return fields[:-1]
+
+    def phonemization(self, text, strip=True) -> List[List[str]]:
+        if isinstance(text, str):
+            text = [text]
+
+        phonemized = self.backend.phonemize(
+            text, separator=self.separator, strip=strip, njobs=1
+        )
+        phonemes = [self.to_list(p) for p in phonemized]
+        return phonemes
+
+    def g2p_conversion(self, text: str) -> List[str]:
+        phonemes = self.phonemization([text.strip()])
+        return phonemes[0]
+
+
+class LexiconModule:
+    def __init__(self, lex_path, language="en-us") -> None:
+        # todo: check lexicon derivation, merge with G2PModule?
+        lexicon = {}
+        with open(lex_path) as f:
+            for line in f:
+                temp = re.split(r"\s+", line.strip("\n"))
+                word = temp[0]
+                phones = temp[1:]
+                if word.lower() not in lexicon:
+                    lexicon[word.lower()] = phones
+        self.lexicon = lexicon
+        self.language = language
+        self.lang2g2p = {"en-us": G2p()}
+
+    def g2p_conversion(self, text):
+        phone = None
+
+        # todo: preprocess with other languages
+        if self.language == "en-us":
+            phone = self.preprocess_english(text)
+        else:
+            print("No support to", self.language)
+            raise
+
+        return phone
+
+    def preprocess_english(self, text):
+        text = text.rstrip(punctuation)
+
+        g2p = self.lang2g2p["en-us"]
+        phones = []
+        words = re.split(r"([,;.\-\?\!\s+])", text)
+        for w in words:
+            if w.lower() in self.lexicon:
+                phones += self.lexicon[w.lower()]
+            else:
+                phones += list(filter(lambda p: p != " ", g2p(w)))
+        phones = "}{".join(phones)
+        phones = re.sub(r"\{[^\w\s]?\}", "{sp}", phones)
+        phones = phones.replace("}{", " ")
+
+        return phones
diff --git a/text/lexicon/librispeech-lexicon.txt b/text/lexicon/librispeech-lexicon.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1091e15b860e3ce6dd9c785ba62076fa0ea20b6f
--- /dev/null
+++ b/text/lexicon/librispeech-lexicon.txt
@@ -0,0 +1,206508 @@
+A  AH0
+A  EY1
+A''S	EY1 Z
+A'BODY	EY1 B AA2 D IY0
+A'COURT	EY1 K AO2 R T
+A'D	EY1 D
+A'GHA	EY1 G AH0
+A'GOIN	EY1 G OY1 N
+A'LL	EY1 L
+A'M	EY1 M
+A'MIGHTY	EY1 M AY1 T IY0
+A'MIGHTY'S	EY1 M AY1 T IY0 Z
+A'MOST	EY1 M OW2 S T
+A'N'T	EY1 AH0 N T
+A'PENNY	EY1 P EH2 N IY0
+A'READY	EY1 R IY1 D IY0
+A'RIGHT	EY1 R AY2 T
+A'RONY	EY1 R OW1 N IY0
+A'S  EY1 Z
+A'TER	EY1 T ER0
+A'TERNOON	EY1 T ER0 N UW1 N
+A'TERWARDS	EY1 T ER0 W ER0 D Z
+A'THEGITHER	EY1 DH AH0 JH IH1 DH ER0
+A'THING	EY1 DH IH0 NG
+A'TIM	EY1 T IH2 M
+A'VE	AH0 V
+AA	AA1
+AAANTHOR	T R IH2 P AH0 L EY1 N TH ER0
+AACHEN  AA1 K AH0 N
+AAD	AA1 D
+AAGE	AA1 ZH
+AAGE'S	IH0 JH IH0 Z
+AAGOT	AA1 G AH0 T
+AAGOT'S	AA1 G AH0 T S
+AAH	AA1
+AAHMES	AA1 M Z
+AAKRE	AA1 K ER0
+AAL	AA1 L
+AALBOM	AA1 L B AO2 M
+AALST	AA1 L S T
+AAMASH	AA1 M AE2 SH
+AAN	AA1 N
+AANA	AA1 N AH0
+AANY	EH1 N IY0
+AAR	AA1 R
+AARAAF	AA1 R AA0 F
+AARAU	AA1 R AW1
+AARD	AA0 R D
+AARON  EH1 R AH0 N
+AARON'S  EH1 R AH0 N Z
+AARONIC	EH1 R AH0 N IH2 K
+AARONS  EH1 R AH0 N Z
+AARONSON  AA1 R AH0 N S AH0 N
+AARONSON  EH1 R AH0 N S AH0 N
+AASA	AA1 S AH0
+AASE  AA1 S
+AASVOGEL	AA1 Z V OW2 G AH0 L
+AASVOGELS	AA1 Z V OW2 G AH0 L Z
+AAT	AA1 T
+AB  AE1 B
+AB  EY1 B IY1
+AB'S	AE1 B Z
+ABA	AA1 B AH0
+ABAAT	AH0 B AA1 T
+ABAB'DEH	AH0 B AA1 B D AH0
+ABABA  AA1 B AH0 B AH0
+ABABA  AH0 B AA1 B AH0
+ABABDE	AA1 B AH0 B D
+ABABDEH	AH0 B AE1 B D EH2
+ABACA	AE1 B AH0 K AH0
+ABACK  AH0 B AE1 K
+ABACO  AE1 B AH0 K OW2
+ABACUS  AE1 B AH0 K AH0 S
+ABAD  AH0 B AA1 D
+ABADAN	AH0 B AE1 D AH0 N
+ABADDON	AH0 B AE1 D AH0 N
+ABAFT	AH0 B AE1 F T
+ABAHT	AH0 B AA1 T
+ABAJO	AE1 B AH0 JH OW0
+ABALONE  AE2 B AH0 L OW1 N IY0
+ABALONES	AE2 B AH0 L OW1 N Z
+ABANA	AH0 B AE1 N AH0
+ABANAZAR	AH0 B AE1 N AA0 Z AA1 R
+ABANDON  AH0 B AE1 N D AH0 N
+ABANDON'D	AH0 B AE1 N D AH0 N D
+ABANDONED  AH0 B AE1 N D AH0 N D
+ABANDONEDLY	AH0 B AE1 N D AH0 N D L IY0
+ABANDONING  AH0 B AE1 N D AH0 N IH0 NG
+ABANDONMENT  AH0 B AE1 N D AH0 N M AH0 N T
+ABANDONMENTS  AH0 B AE1 N D AH0 N M AH0 N T S
+ABANDONS  AH0 B AE1 N D AH0 N Z
+ABAOUT	AH0 B EY1 AW2 T
+ABARA	AH0 B AA1 R AH0
+ABARAK	AH0 B AA1 R AH0 K
+ABARIA	AA0 B AA1 R IY0 AH0
+ABARIAN	AH0 B EH1 R IY0 AH0 N
+ABARIANS	AH0 B EH1 R IY0 AH0 N Z
+ABARIS	AA0 B AA1 R IY0 Z
+ABART	AH0 B AA1 R T
+ABAS	AE1 B AH0 S
+ABASE	AH0 B EY2 S
+ABASED	AH0 B EY2 S T
+ABASEMENT	AH0 B EY1 S M AH0 N T
+ABASES	AH0 B EY2 S IH0 Z
+ABASH  AH0 B AE1 SH
+ABASH'D	AH0 B AE1 SH D
+ABASHED  AH0 B AE1 SH T
+ABASHES	AH0 B AE1 SH IH0 Z
+ABASHING	AH0 B AE1 SH IH0 NG
+ABASHMENT	AH0 B AE1 SH M AH0 N T
+ABASING	AH0 B EY1 S IH0 NG
+ABATAGATI	AH0 B EY1 T AH0 G AA1 T IY0
+ABATE  AH0 B EY1 T
+ABATED  AH0 B EY1 T IH0 D
+ABATEMENT  AH0 B EY1 T M AH0 N T
+ABATEMENTS  AH0 B EY1 T M AH0 N T S
+ABATES  AH0 B EY1 T S
+ABATI	AH0 B AA1 T IY0
+ABATING  AH0 B EY1 T IH0 NG
+ABATIS	AH0 B EY1 T IH0 S
+ABATTIS	AH0 B AE1 T IH0 S
+ABATTOIR	AH0 B AE1 T W AA2 R
+ABATTOIRS	AH0 B AE1 T W AA2 R Z
+ABB	AE1 B
+ABB'S	AA0 B Z
+ABBA  AE1 B AH0
+ABBACY	AE1 B AH0 S IY0
+ABBAD	AH0 B AA1 D
+ABBAN	AH0 B AA1 N
+ABBAS  AH0 B AA1 S
+ABBASIDS	AH0 B AA1 S IH0 D Z
+ABBAT	AH0 B AE1 T
+ABBAT'S	AA0 B AA0 T S
+ABBATE  AA1 B EY0 T
+ABBATI	AH0 B AA1 T IY0
+ABBATIAL	AA0 B AA0 T IY0 AH0 L
+ABBAYE	AA1 B EY0
+ABBAZIA	AH0 B AA1 Z IY0 AH0
+ABBE  AE0 B EY1
+ABBE  AE1 B IY0
+ABBE'S	AE1 B IY0 Z
+ABBEOKUTA	AE1 B IY0 AA0 K UW1 T AH0
+ABBES	AE1 B IY0 Z
+ABBESS	AH0 B EH1 S
+ABBESS'S	AE1 B AH0 S IH0 Z
+ABBESSES	AH0 B EH1 S AH0 Z
+ABBEVILLE  AE1 B V IH0 L
+ABBEWAY	AE1 B W EY2
+ABBEY  AE1 B IY0
+ABBEY'S  AE1 B IY0 Z
+ABBEYFIELD	AE1 B IY0 F IY2 L D
+ABBEYS	AE0 B EY1 Z
+ABBIA	AE1 B IY0 AH0
+ABBIE  AE1 B IY0
+ABBIE'S	AE1 B IY0 Z
+ABBITIBBI	AE1 B IH0 T IH1 B IY0
+ABBOT  AE1 B AH0 T
+ABBOT'S	AE1 B AH0 T S
+ABBOTS	AE1 B AH0 T S
+ABBOTSCLIFF	AE1 B AH0 T S K L IH0 F
+ABBOTSFORD	AE1 B AH0 T S F ER0 D
+ABBOTSMEAD	AE1 B AH0 T S M IY1 D
+ABBOTSWORTHY	AE1 B AH0 T S W ER0 DH IY0
+ABBOTT  AE1 B AH0 T
+ABBOTT'S  AE1 B AH0 T S
+ABBOTTS	AE1 B AH0 T S
+ABBREVIATE  AH0 B R IY1 V IY0 EY2 T
+ABBREVIATED  AH0 B R IY1 V IY0 EY2 T AH0 D
+ABBREVIATED  AH0 B R IY1 V IY0 EY2 T IH0 D
+ABBREVIATING  AH0 B R IY1 V IY0 EY2 T IH0 NG
+ABBREVIATION  AH0 B R IY2 V IY0 EY1 SH AH0 N
+ABBREVIATIONS  AH0 B R IY2 V IY0 EY1 SH AH0 N Z
+ABBUT	AA0 B AH0 T
+ABBY  AE1 B IY0
+ABBY'S	AE1 B IY0 Z
+ABCHESTER	AE1 B CH EH2 S T ER0
+ABCHURCH	AE1 B CH ER1 CH
+ABD	AE0 B D
+ABDAL	AE2 B D AE1 L
+ABDALLA  AE2 B D AE1 L AH0
+ABDALLAH  AE2 B D AE1 L AH0
+ABDALLAH'S	AE2 B D AE1 L AH0 Z
+ABDALONIM	AE2 B D AE1 L OW0 N IY1 M
+ABDEL  AE1 B D EH2 L
+ABDELAZIZ	AE1 B D EH2 L AH0 Z IY1 Z
+ABDERA	AE2 B D EH1 R AH0
+ABDERRAHMAN	AE0 B D EH0 R AA1 M AH0 N
+ABDI	AE0 B D IY0
+ABDI'S	AE0 B D IY0 Z
+ABDIAS	AE0 B D IY0 AH0 Z
+ABDICATE  AE1 B D AH0 K EY2 T
+ABDICATED  AE1 B D AH0 K EY2 T AH0 D
+ABDICATES  AE1 B D AH0 K EY2 T S
+ABDICATING  AE1 B D IH0 K EY2 T IH0 NG
+ABDICATION  AE2 B D IH0 K EY1 SH AH0 N
+ABDIEL	AE0 B D IY2 L
+ABDIN	AE1 B D AH0 N
+ABDOMEN  AE0 B D OW1 M AH0 N
+ABDOMEN  AE1 B D AH0 M AH0 N
+ABDOMENS	AE0 B D OW1 M AH0 N Z
+ABDOMINAL  AE0 B D AA1 M AH0 N AH0 L
+ABDOMINAL  AH0 B D AA1 M AH0 N AH0 L
+ABDON	AE1 B D AH0 N
+ABDOOL	AE0 B D UW2 L
+ABDOOLAH	AE0 B D UW1 L AH0
+ABDSLEM	AE0 B D Z L AH0 M
+ABDU	AE0 B D UW1
+ABDU'L	AE0 B D UW2 AH0 L
+ABDUCT  AE0 B D AH1 K T
+ABDUCTED  AE0 B D AH1 K T IH0 D
+ABDUCTED  AH0 B D AH1 K T IH0 D
+ABDUCTING  AE0 B D AH1 K T IH0 NG
+ABDUCTING  AH0 B D AH1 K T IH0 NG
+ABDUCTION  AE0 B D AH1 K SH AH0 N
+ABDUCTION  AH0 B D AH1 K SH AH0 N
+ABDUCTIONS  AE0 B D AH1 K SH AH0 N Z
+ABDUCTIONS  AH0 B D AH1 K SH AH0 N Z
+ABDUCTOR  AE0 B D AH1 K T ER0
+ABDUCTOR  AH0 B D AH1 K T ER0
+ABDUCTORS  AE0 B D AH1 K T ER0 Z
+ABDUCTORS  AH0 B D AH1 K T ER0 Z
+ABDUL  AE0 B D UW1 L
+ABDUL'S	AE0 B D UW2 L Z
+ABDULKADER	AE0 B D UW2 L K EY1 D ER0
+ABDULLA  AA0 B D UW1 L AH0
+ABDULLA'S	AA0 B D UW1 L AH0 Z
+ABDULLAH  AE2 B D AH1 L AH0
+ABDULLAH'S	AE2 B D AH1 L AH0 Z
+ABDULLAHI	AE2 B D AH1 L AA1 HH IY0
+ABDUR	AE0 B D ER0
+ABDURRAHMAN	AE0 B D ER0 R AA1 M AH0 N
+ABDY	AE0 B D IY0
+ABE  EY1 B
+ABE'S	AA1 B EY0 Z
+ABEAM	AH0 B IY2 M
+ABEAR	AH0 B EH2 R
+ABED  AH0 B EH1 D
+ABEDNEGO	AH0 B EH1 D N EY1 G OW0
+ABEILLE	AH0 B EY1 L
+ABEL  EY1 B AH0 L
+ABEL'S	EY1 B AH0 L Z
+ABELA  AA0 B EH1 L AH0
+ABELARD  AE1 B IH0 L ER0 D
+ABELARD'S	AE1 B IH0 L ER0 D Z
+ABELES  AH0 B EH1 L Z
+ABELES  EY1 B AH0 L IY2 Z
+ABELL  EY1 B AH0 L
+ABELLA  AH0 B EH1 L AH0
+ABELLINO	EY1 B EH0 L IY1 N OW0
+ABELUNGU	AE1 B IH0 L AH2 NG UW1
+ABEN	AE1 B AH0 N
+ABENADAR	AH0 B EH1 N AH0 D AA1 R
+ABENAKI	AE1 B EH0 N AA1 K IY0
+ABENAKIS	AE1 B EH0 N AA1 K IH0 S
+ABENALFANGE	AE1 B EH0 N AA1 L F AA0 N JH
+ABENALFARAX	AE1 B EH0 N AA0 L F AA1 R AE0 K S
+ABENALI	AE1 B EH0 N AA1 L IY0
+ABENAMAR	AE1 B EH0 N AA0 M AA1 R
+ABENAQUIS	AE1 B EH0 N AA1 K IY1
+ABENCERRAGE	AH0 B EH1 N S ER0 AA1 ZH
+ABENCERRAGES	AH0 B EH1 N S ER0 AA1 ZH IH0 Z
+ABEND  AE1 B EH0 N D
+ABEND  AH0 B EH1 N D
+ABENDBLATT	AE1 B EH0 N D B L AH0 T
+ABENIAF	AH0 B EH1 N IY0 AE0 F
+ABENOZ	AE1 B IH0 N OW2 Z
+ABEOKUTA	AH0 B IY1 AA0 K UW1 T AH0
+ABEOUT	AH0 B AW2 T
+ABER  EY1 B ER0
+ABERALVA	AE1 B ER0 AA1 L V AH0
+ABERCORN	AE2 B ER0 K AO2 R N
+ABERCROMBIE  AE2 B ER0 K R AA1 M B IY0
+ABERCROMBIE'S	AE2 B ER0 K R AA1 M B IY0 Z
+ABERCROMBIES	AE2 B ER0 K R AA1 M B IY0 Z
+ABERCROMBY	AE2 B ER0 K R AA1 M B IY0
+ABERCROMBY'S	AE2 B ER0 K R AA1 M B IY0 Z
+ABERDARE	AE1 B ER0 D EH2 R
+ABERDEEN  AE1 B ER0 D IY2 N
+ABERDEEN'S	AE1 B ER0 D IY2 N Z
+ABERDEENSHIRE	AE1 B ER0 D IY2 N SH AY2 R
+ABERDONIAN	AE1 B ER0 D OW1 N IY0 AH0 N
+ABERDOUR	AE1 B ER0 D AO0 R
+ABERFILLY	EY1 B ER0 F IH2 L IY0
+ABERFOIL	EY1 B ER0 F OY2 L
+ABERFORD  EY1 B ER0 F ER0 D
+ABERFOYLE	EY1 B ER0 F OY0 L
+ABERGANN	AE1 B ER0 G AE1 N
+ABERGAVENNY	AE1 B ER0 G AA0 V EH1 N IY0
+ABERGELDIE	AE1 B ER0 G EH2 L D IY0
+ABERGLYN	AE1 B ER0 G L IH0 N
+ABERLEIGH	AE1 B ER0 L AH0
+ABERMOUTH	AE1 B ER0 M AH0 TH
+ABERNETHY  AE1 B ER0 N EH2 TH IY0
+ABERNETHY'S	AE1 B ER0 N EH2 TH IY0 Z
+ABERRANT  AE0 B EH1 R AH0 N T
+ABERRATION  AE2 B ER0 EY1 SH AH0 N
+ABERRATIONS  AE2 B ER0 EY1 SH AH0 N Z
+ABERSHAW	AE1 B ER0 SH AO2
+ABERTAFF	AE1 B ER0 T AE2 F
+ABERTEWEY	AE1 B ER0 T UW1 IY0
+ABERYSTWITH	AE1 B ER0 IH1 S T W IH0 TH
+ABERYSTWYTH	AE1 B ER0 IH1 SH T W IH1 TH
+ABEST	EY1 B AH0 S T
+ABET  AH0 B EH1 T
+ABETS	AH0 B EH1 T S
+ABETTED  AH0 B EH1 T IH0 D
+ABETTER	AH0 B EH1 T ER0
+ABETTING  AH0 B EH1 T IH0 NG
+ABETTOR	AH0 B EH1 T ER0
+ABETTORS	AH0 B EH1 T ER0 Z
+ABEUCHAPETA	AH0 B EY1 AH0 SH AH0 P IY1 T AH0
+ABEUNDI	AH0 B UH1 N D IY0
+ABEY	EY1 B IY0
+ABEYANCE  AH0 B EY1 AH0 N S
+ABGARUS	AE0 B G ER0 AH0 S
+ABHA	AH0 B HH AH0
+ABHOR  AE0 B HH AO1 R
+ABHORR'D	AH0 B HH AO1 ER0 D
+ABHORRED  AH0 B HH AO1 R D
+ABHORRENCE  AH0 B HH AO1 R AH0 N S
+ABHORRENT  AE0 B HH AO1 R AH0 N T
+ABHORRENTLY	AH0 B HH AO1 R AH0 N T L IY0
+ABHORRETH	AH0 B HH AO1 R IH0 TH
+ABHORRING	AH0 B HH AO1 R IH0 NG
+ABHORS  AH0 B HH AO1 R Z
+ABI	AA1 B IY0
+ABIAD	AH0 B AY1 AE0 D
+ABIAH	AH0 B AY1 AH0
+ABIATHAR	AH0 B AY1 AH0 TH AA1 R
+ABIB	AH0 B IY1 B
+ABIDE  AH0 B AY1 D
+ABIDED  AH0 B AY1 D IH0 D
+ABIDERS	AH0 B AY1 D ER0 Z
+ABIDES  AH0 B AY1 D Z
+ABIDEST	AH0 B AY1 D AH0 S T
+ABIDETH	AH0 B AY1 D IH0 TH
+ABIDIN	AH0 B AY1 D IH0 N
+ABIDING  AH0 B AY1 D IH0 NG
+ABIDINGLY	AH0 B AY1 D IH0 NG L IY0
+ABIE  AE1 B IY0
+ABIEL	AE1 B IY0 L
+ABIES	EY1 B IY0 Z
+ABIET	AH0 B AY1 IH0 T
+ABIGAIL  AE1 B AH0 G EY2 L
+ABIGAIL'S	AE1 B AH0 G EY2 L Z
+ABIGAILS	AE1 B AH0 G EY2 L Z
+ABIHU	AE1 B IH0 HH UW0
+ABIIT	AH0 B AY1 IH0 T
+ABIJAH	AH0 B IH1 JH AH0
+ABIJAH'S	AH0 B IH1 JH AH0 Z
+ABIL	AE1 B IH0 L
+ABILENE  AE1 B IH0 L IY2 N
+ABILITIES  AH0 B IH1 L AH0 T IY0 Z
+ABILITY  AH0 B IH1 L AH0 T IY0
+ABIMELECH	AE0 B IH0 M EH0 L EH1 K
+ABINADAB	AH0 B AY1 N AH0 D AE1 B
+ABINGDON  AE1 B IH0 NG D AH0 N
+ABINGDON'S	AE1 B IH0 NG D AH0 N Z
+ABINGER	AE1 B IH0 NG ER0
+ABINGER'S	AE1 B IH0 NG ER0 Z
+ABINGTON  AE1 B IH0 NG T AH0 N
+ABIRAM	EY0 B IY1 R AH0 M
+ABISHAG	AE1 B IH0 SH AE1 G
+ABISHAI	AE1 B IH0 SH AY1
+ABIT	AE1 B IH0 T
+ABITIBI  AE2 B IH0 T IY1 B IY0
+ABITS	AE1 B AH0 T S
+ABJECT  AE1 B JH EH0 K T
+ABJECTION	AE1 B JH EH1 K SH AH0 N
+ABJECTLY	AE1 B JH EH0 K T L IY0
+ABJECTNESS	AE1 B JH EH0 K T N AH0 S
+ABJURATION	AE1 B JH ER0 EY1 SH AH0 N
+ABJURATIONS	AE1 B JH ER0 EY1 SH AH0 N Z
+ABJURE	AE1 B JH ER0
+ABJURED	AE1 B JH ER0 D
+ABJURES	AE1 B JH ER0 Z
+ABJURING	AE1 B JH ER0 IH0 NG
+ABLA	AE1 B L AH0
+ABLAGE	AH0 B L EY1 JH
+ABLAIN	EY1 B L EY0 N
+ABLANO	EY1 B L AA1 N OW0
+ABLATIVE	AE1 B L AH0 T IH0 V
+ABLAZE  AH0 B L EY1 Z
+ABLE  EY1 B AH0 L
+ABLED  EY1 B AH0 L D
+ABLER  EY1 B AH0 L ER0
+ABLER  EY1 B L ER0
+ABLEST  EY1 B AH0 L S T
+ABLEST  EY1 B L AH0 S T
+ABLETHORPE	EY1 B AH0 L TH AO0 R P
+ABLETT	EY1 B L IH0 T
+ABLETT'S	EY1 B L AH0 T S
+ABLEWHITE	EY1 B AH0 L W AY2 T
+ABLEWHITE'S	EY1 B AH0 L W AY2 T S
+ABLI	EY1 B L IY0
+ABLOOM  AH0 B L UW1 M
+ABLOW	AE1 B L OW0
+ABLUTION	EY1 B L UW1 SH AH0 N
+ABLUTIONS	EY1 B L UW1 SH AH0 N Z
+ABLY  EY1 B L IY0
+ABNAKIS	AE0 B N AA1 K IH0 S
+ABNEGATE	AE1 B N G EY2 T
+ABNEGATED	EY1 B IY1 EH1 N IY1 G EY0 T IH0 D
+ABNEGATING	AE0 B N IH0 G EY1 T IH0 NG
+ABNEGATION	AE0 B N AH0 G EY1 SH AH0 N
+ABNEGATIONS	AE0 B N AH0 G EY1 SH AH0 N Z
+ABNER  AE1 B N ER0
+ABNER'S	AE1 B N ER0 Z
+ABNEY  AE1 B N IY0
+ABNEY'S	AE1 B N IY0 Z
+ABNORMAL  AE0 B N AO1 R M AH0 L
+ABNORMALITIES  AE2 B N AO0 R M AE1 L AH0 T IY0 Z
+ABNORMALITY  AE2 B N AO0 R M AE1 L AH0 T IY0
+ABNORMALLY  AE0 B N AO1 R M AH0 L IY0
+ABO  AA1 B OW0
+ABOARD  AH0 B AO1 R D
+ABODE  AH0 B OW1 D
+ABODES	AH0 B OW1 D Z
+ABOEGIB	AH0 B OW1 G IH1 B
+ABOEZA	AH0 B EY1 Z AH0
+ABOGAZ	AA1 B OW0 G AA1 Z
+ABOGIN	AH0 B OW1 G AH0 N
+ABOLISH  AH0 B AA1 L IH0 SH
+ABOLISHED  AH0 B AA1 L IH0 SH T
+ABOLISHES  AH0 B AA1 L IH0 SH IH0 Z
+ABOLISHING  AH0 B AA1 L IH0 SH IH0 NG
+ABOLISHMENT	AH0 B AA1 L IH0 SH M AH0 N T
+ABOLITION  AE2 B AH0 L IH1 SH AH0 N
+ABOLITIONISM  AE2 B AH0 L IH1 SH AH0 N IH2 Z AH0 M
+ABOLITIONIST  AE2 B AH0 L IH1 SH AH0 N AH0 S T
+ABOLITIONISTS  AE2 B AH0 L IH1 SH AH0 N AH0 S
+ABOLITIONISTS  AE2 B AH0 L IH1 SH AH0 N AH0 S T S
+ABOMINABLE  AH0 B AA1 M AH0 N AH0 B AH0 L
+ABOMINABLY	AH0 B AA1 M AH0 N AH0 B L IY0
+ABOMINATE	AH0 B AA1 M AH0 N EY2 T
+ABOMINATED	AH0 B AA1 M AH0 N EY2 T AH0 D
+ABOMINATES	AH0 B AA1 M AH0 N EY2 T S
+ABOMINATING	AH0 B AA1 M AH0 N EY2 T IH0 NG
+ABOMINATION  AH0 B AA2 M AH0 N EY1 SH AH0 N
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+DETERMINATION  D IH0 T ER2 M AH0 N EY1 SH AH0 N
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+DETERMINES  D IH0 T ER1 M AH0 N Z
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+DIPPING  D IH1 P IH0 NG
+DIPPINGS	D IH1 P IH0 NG Z
+DIPPY  D IH1 P IY0
+DIPS  D IH1 P S
+DIPSEY	D IH1 P S IY0
+DIPSOMANIA	D IH1 P S OW0 M EY1 N IY0 AH0
+DIPSOMANIAC	D IH1 P S OW0 M EY1 N IY0 AE2 K
+DIPSOMANIACS	D IH1 P S OW0 M EY1 N IY0 AE2 K S
+DIPT	D IH1 P T
+DIPTERA	D IH1 P T EH1 R AH0
+DIPUS	D IH0 P AH0 S
+DIPWELL	D IH1 P W EH2 L
+DIR	D IH0 R
+DIRA	D IH1 R AH0
+DIRAE	D AY0 R EY1
+DIRAI	D IH0 R AA1 IY0
+DIRAIT	D IH0 R EY1 T
+DIRANDA	D ER0 AE1 N D AH0
+DIRCE	D IH0 R S
+DIRCK	D ER1 K
+DIRCK'S	D ER1 K S
+DIRE  D AY1 ER0
+DIRE  D AY1 R
+DIRECKLY	D AY0 R EH1 K L IY0
+DIRECT  D AY0 R EH1 K T
+DIRECT  D ER0 EH1 K T
+DIRECT  D IH0 R EH1 K T
+DIRECTED  D AY0 R EH1 K T IH0 D
+DIRECTED  D ER0 EH1 K T AH0 D
+DIRECTED  D ER0 EH1 K T IH0 D
+DIRECTED  D IH0 R EH1 K T IH0 D
+DIRECTER	D ER0 EH1 K T ER0
+DIRECTEST	D ER0 EH1 K T AH0 S T
+DIRECTETH	D ER0 EH1 K T IH0 TH
+DIRECTEUR	D ER0 EH0 K T UW1 R
+DIRECTIN	D IH0 R EH1 K T AH0 N
+DIRECTING  D AY0 R EH1 K T IH0 NG
+DIRECTING  D ER0 EH1 K T IH0 NG
+DIRECTING  D IH0 R EH1 K T IH0 NG
+DIRECTING  D IY0 R EH1 K T IH0 NG
+DIRECTION  D AY0 R EH1 K SH IH0 N
+DIRECTION  D ER0 EH1 K SH AH0 N
+DIRECTION  D IH0 R EH1 K SH IH0 N
+DIRECTION  D IY0 R EH1 K SH IH0 N
+DIRECTIONAL  D AY0 R EH1 K SH IH0 N AH0 L
+DIRECTIONAL  D ER0 EH1 K SH AH0 N AH0 L
+DIRECTIONAL  D IH0 R EH1 K SH IH0 N AH0 L
+DIRECTIONAL  D IY0 R EH1 K SH IH0 N AH0 L
+DIRECTIONS  D AY0 R EH1 K SH IH0 N Z
+DIRECTIONS  D ER0 EH1 K SH AH0 N Z
+DIRECTIONS  D IH0 R EH1 K SH IH0 N Z
+DIRECTIONS  D IY0 R EH1 K SH IH0 N Z
+DIRECTIVE  D AY0 R EH1 K T IH0 V
+DIRECTIVE  D ER0 EH1 K T IH0 V
+DIRECTIVE  D IH0 R EH1 K T IH0 V
+DIRECTIVE  D IY0 R EH1 K T IH0 V
+DIRECTIVES  D AY0 R EH1 K T IH0 V Z
+DIRECTIVES  D ER0 EH1 K T IH0 V Z
+DIRECTIVES  D IH0 R EH1 K T IH0 V Z
+DIRECTIVES  D IY0 R EH1 K T IH0 V Z
+DIRECTLY  D AY0 R EH1 K L IY0
+DIRECTLY  D ER0 EH1 K T L IY0
+DIRECTLY  D IH0 R EH1 K L IY0
+DIRECTLY  D IY0 R EH1 K L IY0
+DIRECTNESS  D AY0 R EH1 K N AH0 S
+DIRECTNESS  D ER0 EH1 K T N AH0 S
+DIRECTNESS  D IH0 R EH1 K N AH0 S
+DIRECTNESS  D IY0 R EH1 K N AH0 S
+DIRECTOIRE	D ER0 EH1 K T W AA1 R
+DIRECTOR  D AY0 R EH1 K T ER0
+DIRECTOR  D ER0 EH1 K T ER0
+DIRECTOR  D IH0 R EH1 K T ER0
+DIRECTOR  D IY0 R EH1 K T ER0
+DIRECTOR'S  D AY0 R EH1 K T ER0 Z
+DIRECTOR'S  D ER0 EH1 K T ER0 Z
+DIRECTOR'S  D IH0 R EH1 K T ER0 Z
+DIRECTOR'S  D IY0 R EH1 K T ER0 Z
+DIRECTORATE  D AY0 R EH1 K T ER0 AH0 T
+DIRECTORATE  D ER0 EH1 K T ER0 AH0 T
+DIRECTORATE  D IH0 R EH1 K T ER0 AH0 T
+DIRECTORATE  D IY0 R EH1 K T ER0 AH0 T
+DIRECTORATES	D IH0 R EH1 K T ER0 AH0 T S
+DIRECTORCILLO	D IH0 R EH1 K T ER0 CH IH1 L OW0
+DIRECTORIAL  D AY0 R EH0 K T AO1 R IY0 AH0 L
+DIRECTORIAL  D ER0 EH0 K T AO1 R IY0 AH0 L
+DIRECTORIAL  D IH0 R EH0 K T AO1 R IY0 AH0 L
+DIRECTORIAL  D IY0 R EH0 K T AO1 R IY0 AH0 L
+DIRECTORIES  D AY0 R EH1 K T ER0 IY0 Z
+DIRECTORIES  D ER0 EH1 K T ER0 IY0 Z
+DIRECTORIES  D IH0 R EH1 K T ER0 IY0 Z
+DIRECTORIES  D IY0 R EH1 K T ER0 IY0 Z
+DIRECTORIN	D ER0 EH1 K T AH0 R IH0 N
+DIRECTORS  D AY0 R EH1 K T ER0 Z
+DIRECTORS  D ER0 EH1 K T ER0 Z
+DIRECTORS  D IH0 R EH1 K T ER0 Z
+DIRECTORS  D IY0 R EH1 K T ER0 Z
+DIRECTORSHIP  D AY0 R EH1 K T ER0 SH IH2 P
+DIRECTORSHIP  D ER0 EH1 K T ER0 SH IH2 P
+DIRECTORSHIP  D IH0 R EH1 K T ER0 SH IH2 P
+DIRECTORSHIP  D IY0 R EH1 K T ER0 SH IH2 P
+DIRECTORSHIPS  D AY0 R EH1 K T ER0 SH IH2 P S
+DIRECTORSHIPS  D ER0 EH1 K T ER0 SH IH2 P S
+DIRECTORSHIPS  D IH0 R EH1 K T ER0 SH IH2 P S
+DIRECTORSHIPS  D IY0 R EH1 K T ER0 SH IH2 P S
+DIRECTORY  D AY0 R EH1 K T ER0 IY0
+DIRECTORY  D ER0 EH1 K T ER0 IY0
+DIRECTORY  D IH0 R EH1 K T ER0 IY0
+DIRECTORY  D IY0 R EH1 K T ER0 IY0
+DIRECTRESS	D ER0 EH1 K T R AH0 S
+DIRECTRICE	D ER0 EH1 K T R IH0 S
+DIRECTS  D AY0 R EH1 K T S
+DIRECTS  D ER0 EH1 K T S
+DIRECTS  D IH0 R EH1 K T S
+DIRECTS  D IY0 R EH1 K T S
+DIREFUL	D AY0 R F AH0 L
+DIREFULLY	D AY0 R F AH0 L IY0
+DIREGUS	D IY0 R EH0 G AH0 S
+DIREKTOR	D AY0 R EH1 K T ER0
+DIRELY	D AY1 ER0 L IY0
+DIRER	D AY1 ER0 R
+DIREST  D AY1 R AH0 S T
+DIREXIA	D ER0 EH1 K S IY0 AH0
+DIRGE  D ER1 JH
+DIRGES	D ER1 JH IH0 Z
+DIRGING	D ER1 JH IH0 NG
+DIRHAM	D ER1 AH0 M
+DIRHEM	D ER1 AH0 M
+DIRHEMS	D ER1 AH0 M Z
+DIRIGIBLE	D IH0 R AH0 JH AH0 B AH0 L
+DIRIGIBLE'S	D IH0 R AH0 JH AH0 B AH0 L Z
+DIRIGIBLES	D IH0 R AH0 JH AH0 B AH0 L Z
+DIRK  D ER1 K
+DIRK'S	D ER1 K S
+DIRKOVITCH	D ER1 K AH0 V IH0 CH
+DIRKS  D ER1 K S
+DIRL	D ER1 L
+DIROLA	D IH0 R OW1 L AH0
+DIRRIK	D ER1 IH0 K
+DIRRUL	D ER1 AH0 L
+DIRRUL'S	D ER1 AH0 L Z
+DIRT  D ER1 T
+DIRT'S  D ER1 T S
+DIRTHY	D ER1 DH IY0
+DIRTIED	D ER1 T IY0 D
+DIRTIER  D ER1 T IY0 ER0
+DIRTIES	D ER1 T IY0 Z
+DIRTIEST  D ER1 T IY0 AH0 S T
+DIRTILY	D ER1 T IY0 L IY0
+DIRTINESS	D ER1 T IY0 N AH0 S
+DIRTY  D ER1 T IY0
+DIRTYING	D ER1 T IY0 IH0 NG
+DIRZED	D ER1 Z D
+DIRZEE	D ER1 Z IY0
+DIS  D IH1 S
+DISA  D IH1 S AH0
+DISABILITIES  D IH0 S AH0 B IH1 L AH0 T IY0 Z
+DISABILITIES  D IH0 S AH0 B IH1 L IH0 T IY0 Z
+DISABILITY  D IH0 S AH0 B IH1 L IH0 T IY0 Z
+DISABILITY  D IH2 S AH0 B IH1 L IH0 T IY0
+DISABLE  D IH0 S EY1 B AH0 L
+DISABLED  D IH0 S EY1 B AH0 L D
+DISABLEMENT	D IH0 S EY1 B AH0 L M AH0 N T
+DISABLES  D IH0 S EY1 B AH0 L Z
+DISABLING  D IH0 S EY1 B AH0 L IH0 NG
+DISABLING  D IH2 S EY1 B L IH0 NG
+DISABUSE  D IH0 S AH0 B Y UW1 S
+DISABUSE  D IH0 S AH0 B Y UW1 Z
+DISABUSED  D IH0 S AH0 B Y UW1 Z D
+DISABUSING	D IH0 S AH0 B Y UW1 Z IH0 NG
+DISACCORD	D IH2 S AH0 K AO1 R D
+DISADVANTAGE  D IH2 S AH0 D V AE1 N IH0 JH
+DISADVANTAGE  D IH2 S AH0 D V AE1 N T IH0 JH
+DISADVANTAGED  D IH0 S AH0 D V AE1 N T IH0 JH D
+DISADVANTAGED  D IH2 S AH0 D V AE1 N IH0 JH D
+DISADVANTAGEOUS  D IH2 S AE2 D V AE2 N T EY1 JH AH0 S
+DISADVANTAGEOUSLY	D IH2 S AH0 D V AE1 N IH0 JH AH0 S L IY0
+DISADVANTAGES  D IH2 S AH0 D V AE1 N IH0 JH Z
+DISADVANTAGES  D IH2 S AH0 D V AE1 N T IH0 JH IH0 Z
+DISAFFECTED  D IH2 S AH0 F EH1 K T IH0 D
+DISAFFECTION  D IH2 S AH0 F EH1 K SH AH0 N
+DISAGREABLE	D IH0 S AH0 G R IY1 AH0 B AH0 L
+DISAGREE  D IH0 S AH0 G R IY1
+DISAGREEABLE  D IH2 S AH0 G R IY1 AH0 B AH0 L
+DISAGREEABLENESS	D IH0 S AH0 G R IY1 AH0 B AH0 L N AH0 S
+DISAGREEABLES	D IH0 S AH0 G R IY1 AH0 B AH0 L Z
+DISAGREEABLY	D IH0 S AH0 G R IY1 AH0 B L IY0
+DISAGREED  D IH0 S AH0 G R IY1 D
+DISAGREEING  D IH0 S AH0 G R IY1 IH0 NG
+DISAGREEMENT  D IH0 S AH0 G R IY1 M AH0 N T
+DISAGREEMENTS  D IH2 S AH0 G R IY1 M AH0 N T S
+DISAGREES  D IH0 S AH0 G R IY1 Z
+DISAIT	D IH2 S EY1 T
+DISALLOW  D IH2 S AH0 L AW1
+DISALLOWANCE  D IH2 S AH0 L AW1 AH0 N S
+DISALLOWED  D IH2 S AH0 L AW1 D
+DISALLOWING  D IH0 S AH0 L AW1 IH0 NG
+DISALLOWS	D IH2 S AH0 L AW1 Z
+DISANNUL	D IH0 Z AE1 N AH0 L
+DISANT	D IH0 S AE1 N T
+DISAPOINTMENT	D IH2 S AH0 P OY1 N T M AH0 N T
+DISAPP'INTED	D IH2 S AH0 P IH2 N T IH0 D
+DISAPP'INTMENT	D IH2 S AH0 P IH1 N T M AH0 N T
+DISAPPEAR  D IH2 S AH0 P IH1 R
+DISAPPEAR  D IH2 S AH0 P IY1 R
+DISAPPEAR'D	D IH2 S AH0 P IY1 R D
+DISAPPEARANCE  D IH2 S AH0 P IH1 R AH0 N S
+DISAPPEARANCE  D IH2 S AH0 P IY1 R AH0 N S
+DISAPPEARANCES  D IH2 S AH0 P IH1 R AH0 N S IH0 Z
+DISAPPEARANCES  D IH2 S AH0 P IY1 R AH0 N S IH0 Z
+DISAPPEARED  D IH2 S AH0 P IH1 R D
+DISAPPEARED  D IH2 S AH0 P IY1 R D
+DISAPPEARIN	D IH2 S AH0 P IH1 R IH0 N
+DISAPPEARING  D IH2 S AH0 P IH1 R IH0 NG
+DISAPPEARING  D IH2 S AH0 P IY1 R IH0 NG
+DISAPPEARS  D IH2 S AH0 P IH1 R Z
+DISAPPEARS  D IH2 S AH0 P IY1 R Z
+DISAPPINTED	D IH2 S AH0 P IY1 N IH0 D
+DISAPPOINT  D IH2 S AH0 P OY1 N T
+DISAPPOINTED  D IH2 S AH0 P OY1 N IH0 D
+DISAPPOINTED  D IH2 S AH0 P OY1 N T IH0 D
+DISAPPOINTEDLY	D IH2 S AH0 P OY1 N T IH0 D L IY0
+DISAPPOINTING  D IH2 S AH0 P OY1 N IH0 NG
+DISAPPOINTING  D IH2 S AH0 P OY1 N T IH0 NG
+DISAPPOINTINGLY  D IH0 S AH0 P OY1 N IH0 NG L IY0
+DISAPPOINTINGLY  D IH0 S AH0 P OY1 N T IH0 NG L IY0
+DISAPPOINTMENT  D IH2 S AH0 P OY1 N T M AH0 N T
+DISAPPOINTMENT'S	D IH2 S AH0 P OY1 N T M AH0 N T S
+DISAPPOINTMENTS  D IH0 S AH0 P OY1 N T M AH0 N T S
+DISAPPOINTS  D IH2 S AH0 P OY1 N T S
+DISAPPROBATION	D IH2 S AH0 P R AH0 B EY1 SH AH0 N
+DISAPPROVAL  D IH0 S AH0 P R UW1 V AH0 L
+DISAPPROVE  D IH2 S AH0 P R UW1 V
+DISAPPROVED  D IH2 S AH0 P R UW1 V D
+DISAPPROVES  D IH0 S AH0 P R UW1 V Z
+DISAPPROVING  D IH0 S AH0 P R UW1 V IH0 NG
+DISAPPROVINGLY	D IH2 S AH0 P R UW1 V IH0 NG L IY0
+DISARM  D IH0 S AA1 R M
+DISARM'D	D IH0 S AA1 R M D
+DISARMAMENT  D IH0 S AA1 R M AH0 M AH0 N T
+DISARMED  D IH0 S AA1 R M D
+DISARMER	D IH0 S AA1 R M ER0
+DISARMING  D IH0 S AA1 R M IH0 NG
+DISARMINGLY  D IH0 S AA1 R M IH0 NG L IY0
+DISARMS	D IH0 S AA1 R M Z
+DISARRANGE	D IH2 S ER0 EY1 N JH
+DISARRANGED	D IH2 S ER0 EY1 N JH D
+DISARRANGEMENT	D IH2 S ER0 EY1 N JH M AH0 N T
+DISARRANGEMENTS	D IH2 S ER0 EY1 N JH M AH0 N T S
+DISARRANGES	D IH2 S ER0 EY1 N JH IH0 Z
+DISARRANGING	D IH2 S ER0 EY1 N JH IH0 NG
+DISARRAY  D IH2 S AH0 R EY1
+DISARRAY  D IH2 S ER0 EY1
+DISARRAYED	D IH2 S AH0 R EY1 D
+DISASSEMBLED  D IH2 S AH0 S EH1 M B AH0 L D
+DISASSOCIATE  D IH2 S AH0 S OW1 S IY0 EY0 T
+DISASSOCIATE  D IH2 S AH0 S OW1 SH IY0 EY0 T
+DISASSOCIATED  D IH2 S AH0 S OW1 S IY0 EY0 T AH0 D
+DISASSOCIATED  D IH2 S AH0 S OW1 SH IY0 EY0 T AH0 D
+DISASSOCIATION	D IH2 S AH0 S OW1 SH IY0 EY1 SH AH0 N
+DISASTER  D IH0 Z AE1 S T ER0
+DISASTERS  D IH0 Z AE1 S T ER0 Z
+DISASTROUS  D IH0 Z AE1 S T R AH0 S
+DISASTROUSLY  D IH0 Z AE1 S T R AH0 S L IY0
+DISAVOW  D IH2 S AH0 V AW1
+DISAVOWAL	D IH2 S AH0 V AW1 AH0 L
+DISAVOWALS	D IH2 S AH0 V AW1 AH0 L Z
+DISAVOWED  D IH2 S AH0 V AW1 D
+DISAVOWING  D IH0 S AH0 V AW1 IH0 NG
+DISAVOWS	D IH2 S AH0 V AW1 Z
+DISBAND  D IH0 S B AE1 N D
+DISBANDED  D IH0 S B AE1 N D IH0 D
+DISBANDING  D IH0 S B AE1 N D IH0 NG
+DISBANDMENT	D IH0 S B AE1 N D M AH0 N T
+DISBANDS	D IH0 S B AE1 N D Z
+DISBARRED  D IH0 S B AA1 R D
+DISBELIEF  D IH2 S B IH0 L IY1 F
+DISBELIEFS	D IH2 S B IH0 L IY1 F S
+DISBELIEVE  D IH0 S B AH0 L IY1 V
+DISBELIEVED	D IH0 S B AH0 L IY1 V D
+DISBELIEVER	D IH0 S B AH0 L IY1 V ER0
+DISBELIEVERS	D IH0 S B AH0 L IY1 V ER0 Z
+DISBELIEVES	D IH0 S B AH0 L IY1 V Z
+DISBELIEVING  D IH0 S B AH0 L IY1 V IH0 NG
+DISBELIEVINGLY	D IH0 S B AH0 L IY1 V IH0 NG L IY0
+DISBROW  D IH1 S B R AW0
+DISBURDEN	D IH0 S B ER1 D AH0 N
+DISBURDENED	D IH0 S B ER1 D AH0 N D
+DISBURDENING	D IH0 S B ER1 D AH0 N IH0 NG
+DISBURSE  D IH0 S B ER1 S
+DISBURSED  D IH0 S B ER1 S T
+DISBURSEMENT  D IH0 S B ER1 S M AH0 N T
+DISBURSEMENTS  D IH0 S B ER1 S M AH0 N T S
+DISBURSING  D IH0 S B ER1 S IH0 NG
+DISBURTHEN	D IH0 S B ER1 TH AH0 N
+DISBURTHENED	D IH0 S B ER1 TH AH0 N D
+DISC  D IH1 S K
+DISCALCED	D IH0 S K AE1 L S T
+DISCARD  D IH0 S K AA1 R D
+DISCARDED  D IH0 S K AA1 R D IH0 D
+DISCARDING  D IH0 S K AA1 R D IH0 NG
+DISCARDS  D IH0 S K AA1 R D Z
+DISCARNATE	D IH0 S K AA1 R N EY2 T
+DISCARNATED	D IH0 S K AA1 R N EY2 T IH0 D
+DISCARNATION	D IH0 S K AA1 R N EY1 SH AH0 N
+DISCE	D IH0 S
+DISCERN  D IH0 S ER1 N
+DISCERN'D	D IH0 S ER1 N D
+DISCERNABLE	D IH0 S ER1 N AH0 B AH0 L
+DISCERNED  D IH0 S ER1 N D
+DISCERNER	D IH0 S ER1 N ER0
+DISCERNETH	D IH0 S ER1 N IH0 TH
+DISCERNIBLE  D IH0 S ER1 N AH0 B AH0 L
+DISCERNIBLY	D IH0 S ER1 N AH0 B L IY0
+DISCERNING  D IH0 S ER1 N IH0 NG
+DISCERNINGLY	D IH0 S ER1 N IH0 NG L IY0
+DISCERNMENT  D IH0 S ER1 N M AH0 N T
+DISCERNMENTS	D IH0 S ER1 N M AH0 N T S
+DISCERNS  D IH0 S ER1 N Z
+DISCHARG'D	D IH1 S CH AA2 R JH D
+DISCHARGE  D IH0 S CH AA1 R JH
+DISCHARGE  D IH1 S CH AA2 R JH
+DISCHARGED  D IH0 S CH AA1 R JH D
+DISCHARGED  D IH1 S CH AA2 R JH D
+DISCHARGES  D IH0 S CH AA1 R JH AH0 Z
+DISCHARGES  D IH1 S CH AA2 R JH AH0 Z
+DISCHARGETH	D IH0 S CH AA1 R JH IH0 TH
+DISCHARGING  D IH0 S CH AA1 R JH IH0 NG
+DISCHARGING  D IH1 S CH AA2 R JH IH0 NG
+DISCIPLE  D IH0 S AY1 P AH0 L
+DISCIPLE'S	D IH0 S AY1 P AH0 L Z
+DISCIPLES  D IH0 S AY1 P AH0 L Z
+DISCIPLESHIP	D IH0 S AY1 P AH0 L SH IH2 P
+DISCIPLINA	D IH1 S AH0 P L AH0 N AH0
+DISCIPLINARIAN  D IH2 S IH0 P L IH0 N EH1 R IY0 AH0 N
+DISCIPLINARIANS  D IH2 S IH0 P L IH0 N EH1 R IY0 AH0 N Z
+DISCIPLINARY  D IH1 S AH0 P L AH0 N EH2 R IY0
+DISCIPLINE  D IH1 S AH0 P L AH0 N
+DISCIPLINE'S	D IH1 S AH0 P L AH0 N Z
+DISCIPLINED  D IH1 S AH0 P L AH0 N D
+DISCIPLINES  D IH1 S AH0 P L AH0 N Z
+DISCIPLINING  D IH1 S AH0 P L AH0 N IH0 NG
+DISCLAIM  D IH0 S K L EY1 M
+DISCLAIMED  D IH0 S K L EY1 M D
+DISCLAIMER  D IH0 S K L EY1 M ER0
+DISCLAIMERS  D IH0 S K L EY1 M ER0 Z
+DISCLAIMING  D IH0 S K L EY1 M IH0 NG
+DISCLAIMS  D IH0 S K L EY1 M Z
+DISCLOS'D	D IH0 S K L OW1 Z D
+DISCLOSE  D IH0 S K L OW1 Z
+DISCLOSED  D IH0 S K L OW1 Z D
+DISCLOSES  D IH0 S K L OW1 Z IH0 Z
+DISCLOSING  D IH0 S K L OW1 Z IH0 NG
+DISCLOSURE  D IH0 S K L OW1 ZH ER0
+DISCLOSURES  D IH0 S K L OW1 ZH ER0 Z
+DISCO  D IH1 S K OW0
+DISCO'S	D IH1 S K OW0 Z
+DISCOBOLUS	D IH2 S K AH0 B OW1 L AH0 S
+DISCOIDAL	D IH0 S K OY1 D AH0 L
+DISCOLOR  D IH0 S K AH1 L ER0
+DISCOLORATION  D IH0 S K AH2 L ER0 EY1 SH AH0 N
+DISCOLORATIONS  D IH0 S K AH2 L ER0 EY1 SH AH0 N Z
+DISCOLORED  D IH0 S K AH1 L ER0 D
+DISCOLORING	D IH0 S K AH1 L ER0 IH0 NG
+DISCOLORS  D IH0 S K AH1 L ER0 Z
+DISCOLOUR	D IH0 S K AH1 L ER0
+DISCOLOUR'D	D IH0 S K AH1 L ER0 D
+DISCOLOURATION	D IH0 S K AH1 L ER0 EY1 SH AH0 N
+DISCOLOURED	D IH0 S K AH1 L ER0 D
+DISCOLOURING	D IH0 S K AH1 L ER0 IH0 NG
+DISCOLOURS	D IH0 S K AH1 L ER0 Z
+DISCOMETER	D IH2 S K AH0 M IY2 T ER0
+DISCOMFIT	D IH0 S K AH1 M F IH0 T
+DISCOMFITED	D IH0 S K AH1 M F IH2 T IH0 D
+DISCOMFITING	D IH0 S K AH1 M F IH0 T IH0 NG
+DISCOMFITTED	D IH0 S K AH1 M F IH2 T IH0 D
+DISCOMFITURE	D IH0 S K AH1 M F IH0 CH ER0
+DISCOMFITURES	D IH0 S K AH1 M F IH0 CH ER0 Z
+DISCOMFORT  D IH0 S K AH1 M F ER0 T
+DISCOMFORTABLE	D IH0 S K AH1 M F ER0 T AH0 B AH0 L
+DISCOMFORTED	D IH0 S K AH1 M F ER0 T IH0 D
+DISCOMFORTING	D IH0 S K AH1 M F ER0 T IH0 NG
+DISCOMFORTS	D IH0 S K AH1 M F ER0 T S
+DISCOMFORTURE	D IH0 S K AH1 M F ER0 T UH1 R
+DISCOMMODE	D IH2 S K AH0 M OW1 D
+DISCOMMODED	D IH2 S K AH0 M OW1 D IH0 D
+DISCOMMODING	D IH2 S K AH0 M AA1 D IH0 NG
+DISCOMPOSE	D IH2 S K AH0 M P OW1 Z
+DISCOMPOSED	D IH2 S K AH0 M P OW1 Z D
+DISCOMPOSES	D IH2 S K AH0 M P OW1 Z IH0 Z
+DISCOMPOSING	D IH2 S K AH0 M P OW1 Z IH0 NG
+DISCOMPOSURE	D IH2 S K AH0 M P OW1 ZH ER0
+DISCONCERT  D IH2 S K AH0 N S ER1 T
+DISCONCERTED	D IH2 S K AH0 N S ER1 T IH0 D
+DISCONCERTEDLY	D IH2 S K AH0 N S ER1 T IH0 D L IY0
+DISCONCERTING  D IH2 S K AH0 N S ER1 T IH0 NG
+DISCONCERTINGLY	D IH2 S K AH0 N S ER1 T IH0 NG L IY0
+DISCONCERTION	D IH2 S K AH0 N S ER1 SH AH0 N
+DISCONCERTMENT	D IH2 S K AH0 N S ER1 T M AH0 N T
+DISCONCERTS	D IH2 S K AH0 N S ER1 T S
+DISCONNECT  D IH0 S K AH0 N EH1 K T
+DISCONNECTED  D IH2 S K AH0 N EH1 K T IH0 D
+DISCONNECTEDLY	D IH2 S K AH0 N EH1 K T IH0 D L IY0
+DISCONNECTEDNESS	D IH2 S K AH0 N EH1 K T IH0 D N AH0 S
+DISCONNECTING  D IH2 S K AH0 N EH1 K T IH0 NG
+DISCONNECTION  D IH2 S K AH0 N EH1 K SH AH0 N
+DISCONNECTS  D IH0 S K AH0 N EH1 K T S
+DISCONSOLATE	D IH0 S K AA2 N S AH0 L EY2 T
+DISCONSOLATELY	D IH2 S K AH0 N S AA1 L AH0 T L IY0
+DISCONSOLATENESS	D IH2 S K AH0 N S AA1 L AH0 T N AH0 S
+DISCONSOLATION	D IH0 S K AA2 N S AH0 L EY1 SH AH0 N
+DISCONTENT  D IH0 S K AH0 N T EH1 N T
+DISCONTENTED  D IH2 S K AH0 N T EH1 N T IH0 D
+DISCONTENTEDLY  D IH2 S K AH0 N T EH1 N T IH0 D L IY0
+DISCONTENTMENT	D IH2 S K AH0 N T EH1 N T M AH0 N T
+DISCONTENTS  D IH2 S K AH0 N T EH1 N T S
+DISCONTINUANCE  D IH2 S K AH0 N T IH1 N Y UW0 AH0 N S
+DISCONTINUE  D IH0 S K AH0 N T IH1 N Y UW0
+DISCONTINUED  D IH0 S K AH0 N T IH1 N Y UW0 D
+DISCONTINUING  D IH0 S K AH0 N T IH1 N Y UW0 IH0 NG
+DISCONTINUITY  D IH0 S K AA2 N T IH0 N UW1 IH0 T IY0
+DISCONTINUOUS	D IH2 S K AH0 N T IH1 N Y UW0 AH0 S
+DISCOORSE	D IH0 S K UH1 R S
+DISCOORSIN	D IH0 S K UH1 R S AH0 N
+DISCORD  D IH1 S K AO0 R D
+DISCORDANCE	D IH0 S K AO1 R D AH0 N S
+DISCORDANCES	D IH0 S K AO1 R D AH0 N S IH0 Z
+DISCORDANCY	D IH0 S K AO1 R D AH0 N S IY0
+DISCORDANT  D IH0 S K AO1 R D AH0 N T
+DISCORDANTLY	D IH0 S K AO1 R D AH0 N T L IY0
+DISCORDIA	D IH0 S K AO1 R D IY0 AH0
+DISCORDS	D IH0 S K AO1 R D Z
+DISCOUNSELLED	D IH0 S K AW1 N S AH0 L D
+DISCOUNT  D IH0 S K AW1 N T
+DISCOUNT  D IH1 S K AW0 N T
+DISCOUNTED  D IH1 S K AW2 N IH0 D
+DISCOUNTED  D IH1 S K AW2 N T IH0 D
+DISCOUNTENANCE	D IH0 S K AW1 N T AH0 N AH0 N S
+DISCOUNTENANCED	D IH0 S K AW1 N T AH0 N AH0 N S T
+DISCOUNTENANCES	D IH0 S K AW1 N T AH0 N AH0 N S IH0 Z
+DISCOUNTENANCING	D IH0 S K AW1 N T AH0 N AH0 N S IH0 NG
+DISCOUNTER  D IH1 S K AW2 N T ER0
+DISCOUNTERS  D IH0 S K AW1 N T ER0 Z
+DISCOUNTERS  D IH1 S K AW2 N ER0 R Z
+DISCOUNTING  D IH1 S K AW2 N IH0 NG
+DISCOUNTING  D IH1 S K AW2 N T IH0 NG
+DISCOUNTS  D IH0 S K AW1 N T S
+DISCOUNTS  D IH1 S K AW2 N T S
+DISCOURAGE  D IH0 S K ER1 IH0 JH
+DISCOURAGED  D IH0 S K ER1 AH0 JH D
+DISCOURAGED  D IH0 S K ER1 IH0 JH D
+DISCOURAGEDLY	D IH0 S K ER1 AH0 JH D L IY0
+DISCOURAGEMENT  D IH0 S K ER1 IH0 JH M AH0 N T
+DISCOURAGEMENTS	D IH0 S K ER1 IH0 JH M AH0 N T S
+DISCOURAGER	D IH0 S K ER1 IH0 JH ER0
+DISCOURAGES  D IH0 S K ER1 IH0 JH IH0 Z
+DISCOURAGIN	D IH0 S K ER1 AH0 JH IH0 N
+DISCOURAGING  D IH0 S K ER1 AH0 JH IH0 NG
+DISCOURAGING  D IH0 S K ER1 IH0 JH IH0 NG
+DISCOURAGINGLY	D IH0 S K ER1 AH0 JH IH0 NG L IY0
+DISCOURS	D IH0 S K AW1 ER0 Z
+DISCOURSE  D IH1 S K AO0 R S
+DISCOURSED	D IH1 S K AO0 R S T
+DISCOURSES  D IH0 S K AO1 R S IH0 Z
+DISCOURSES  D IH1 S K AO0 R S IH0 Z
+DISCOURSETH	D IH1 S K AO0 R S IH0 TH
+DISCOURSING	D IH0 S K ER1 S IH0 NG
+DISCOURTEOUS	D IH1 S K AO0 R T IY0 IH0 S
+DISCOURTEOUSLY	D IH1 S K AO0 R T IY0 AH0 S L IY0
+DISCOURTESIES	D IH0 S K ER1 T AH0 S IY0 Z
+DISCOURTESY	D IH0 S K ER1 T AH0 S IY0
+DISCOVER  D IH0 S K AH1 V ER0
+DISCOVER'D	D IH0 S K AH1 V R AH0 D
+DISCOVERABLE  D IH0 S K AH1 V ER0 AH0 B AH0 L
+DISCOVERABLE  D IH0 S K AH1 V R AH0 B AH0 L
+DISCOVERED  D IH0 S K AH1 V ER0 D
+DISCOVERER	D IH0 S K AH1 V ER0 ER0
+DISCOVERER'S	D IH0 S K AH1 V ER0 ER0 Z
+DISCOVERERS	D IH0 S K AH1 V ER0 ER0 Z
+DISCOVERETH	D IH0 S K AH1 V R IH0 TH
+DISCOVERIES  D IH0 S K AH1 V ER0 IY0 Z
+DISCOVERIN	D IH0 S K AH1 V ER0 IH0 N
+DISCOVERING  D IH0 S K AH1 V ER0 IH0 NG
+DISCOVERS  D IH0 S K AH1 V ER0 Z
+DISCOVERY  D IH0 S K AH1 V ER0 IY0
+DISCOVERY  D IH0 S K AH1 V R IY0
+DISCOVERY  D IH2 S K AH1 V R IY0
+DISCREDIT  D IH0 S K R EH1 D AH0 T
+DISCREDITABLE	D IH0 S K R EH1 D AH0 T AH0 B AH0 L
+DISCREDITABLY	D IH0 S K R EH1 D AH0 T AH0 B L IY0
+DISCREDITED  D IH0 S K R EH1 D IH0 T IH0 D
+DISCREDITING  D IH0 S K R EH1 D AH0 T IH0 NG
+DISCREDITS  D IH0 S K R EH1 D AH0 T S
+DISCREET  D IH0 S K R IY1 T
+DISCREETER	D IH0 S K R IY1 T ER0
+DISCREETEST	D IH0 S K R IY1 T AH0 S T
+DISCREETLY  D IH0 S K R IY1 T L IY0
+DISCREETNESS	D IH0 S K R IY1 T N AH0 S
+DISCREPANCIES  D IH0 S K R EH1 P AH0 N S IY0 Z
+DISCREPANCY  D IH0 S K R EH1 P AH0 N S IY0
+DISCREPANT	D IH0 S K R EH1 P AH0 N T
+DISCRETE  D IH0 S K R IY1 T
+DISCRETELY	D IH0 S K R IY2 T L IY0
+DISCRETION  D IH0 S K R EH1 SH AH0 N
+DISCRETION'S	D IH0 S K R EH1 SH AH0 N Z
+DISCRETIONARY  D IH0 S K R EH1 SH AH0 N EH2 R IY0
+DISCRETIONS  D IH0 S K R EH1 SH AH0 N Z
+DISCRIBED	D IH0 S K R AY1 B D
+DISCRIMINA	D IH0 S K R IH1 M AH0 N AH0
+DISCRIMINATE  D IH0 S K R IH1 M AH0 N EY2 T
+DISCRIMINATED  D IH0 S K R IH1 M AH0 N EY2 T AH0 D
+DISCRIMINATED  D IH0 S K R IH1 M AH0 N EY2 T IH0 D
+DISCRIMINATELY	D IH0 S K R IH1 M AH0 N AH0 T L IY0
+DISCRIMINATES  D IH0 S K R IH1 M AH0 N EY2 T S
+DISCRIMINATING  D IH0 S K R IH1 M AH0 N EY2 T IH0 NG
+DISCRIMINATINGLY	D IH0 S K R IH1 M AH0 N EY2 T IH0 NG L IY0
+DISCRIMINATION  D IH0 S K R IH2 M AH0 N EY1 SH AH0 N
+DISCRIMINATIONS	D IH0 S K R IH2 M AH0 N EY1 SH AH0 N Z
+DISCRIMINATIVE  D IH0 S K R IH1 M AH0 N AH0 T IH0 V
+DISCRIMINATORY  D IH0 S K R IH1 M AH0 N AH0 T AO2 R IY0
+DISCRIPTION	D IH0 S K R IH1 P SH AH0 N
+DISCROWN	D IH0 S K R OW1 N
+DISCROWNED	D IH0 S K R AW1 N D
+DISCS  D IH1 S K S
+DISCURSIVE  D IH0 S K ER1 S IH0 V
+DISCURSIVELY	D IH0 S K ER1 S IH0 V L IY0
+DISCURSIVENESS	D IH0 S K ER1 S IH0 V N AH0 S
+DISCUS  D IH1 S K AH0 S
+DISCUSS  D IH0 S K AH1 S
+DISCUSS'D	D IH0 S K AH1 SH D
+DISCUSSED  D IH0 S K AH1 S T
+DISCUSSES  D IH0 S K AH1 S AH0 Z
+DISCUSSES  D IH0 S K AH1 S IH0 Z
+DISCUSSIN	D IH0 S K AH1 S IH0 N
+DISCUSSING  D IH0 S K AH1 S IH0 NG
+DISCUSSION  D IH0 S K AH1 SH AH0 N
+DISCUSSIONS  D IH0 S K AH1 SH AH0 N Z
+DISCUST	D IH1 S K AH0 S T
+DISDAIN  D IH0 S D EY1 N
+DISDAIN'D	D IH0 S D EY1 N D
+DISDAINE	D IH0 S D EY1 N
+DISDAINED  D IH0 S D EY1 N D
+DISDAINFUL  D IH0 S D EY1 N F AH0 L
+DISDAINFULLY	D IH0 S D EY1 N F AH0 L IY0
+DISDAINING  D IH0 S D EY1 N IH0 NG
+DISDAINS  D IH0 S D EY1 N Z
+DISE  D AY1 S
+DISE  D AY1 Z
+DISEAS'D	D IH0 Z IY1 Z D
+DISEASE  D IH0 Z IY1 Z
+DISEASED  D IH0 Z IY1 Z D
+DISEASES  D IH0 Z IY1 Z AH0 Z
+DISEASES  D IH0 Z IY1 Z IH0 Z
+DISEMBARK  D IH0 S EH0 M B AA1 R K
+DISEMBARKATION  D IH0 S EH2 M B AA0 R K EY1 SH AH0 N
+DISEMBARKED	D IH0 S EH0 M B AA1 R K T
+DISEMBARKING	D IH0 S EH0 M B AA1 R K IH0 NG
+DISEMBARKS	D IH0 S EH0 M B AA1 R K S
+DISEMBARRASS	D IH0 S IH0 B EH1 R AH0 S
+DISEMBARRASSED	D IH0 S IH0 B EH1 R AH0 S T
+DISEMBARRASSING	D IH0 S IH0 B EH1 R AH0 S IH0 NG
+DISEMBODIED  D IH0 S IH0 B AA1 D IY0 D
+DISEMBODIMENT	D IH0 S IH0 B AA1 D IY0 M AH0 N T
+DISEMBOGUES	D IH0 S IH0 B AA1 G Z
+DISEMBOWEL	D IH0 S IH0 B AW1 AH0 L
+DISEMBOWELED	D IH0 S IH0 B AW1 AH0 L D
+DISEMBOWELING	D IH0 S IH0 B AW1 AH0 L IH0 NG
+DISEMBOWELLED	D IH0 S IH0 B AA1 W EH0 L D
+DISEMBOWELLING	D IH0 S IH0 B AA1 W EH1 L IH0 NG
+DISENCHANT	D IH0 S IH0 N CH AE1 N T
+DISENCHANTED  D IH0 S IH0 N CH AE1 N IH0 D
+DISENCHANTED  D IH0 S IH0 N CH AE1 N T IH0 D
+DISENCHANTING	D IH0 S IH0 N CH AE1 N T IH0 NG
+DISENCHANTMENT  D IH0 S IH0 N CH AE1 N T M AH0 N T
+DISENCHANTMENTS	D IH0 S IH0 N CH AE1 N T M AH0 N T S
+DISENCUMBER	D IH0 S IH0 N K AH1 M B ER0
+DISENCUMBERED	D IH0 S IH0 N K AH1 M B ER0 D
+DISENCUMBERING	D IH0 S IH0 N K AH1 M B ER0 IH0 NG
+DISENDOWMENT	D IH0 S IH0 N D AW1 M AH0 N T
+DISENFRANCHISED  D IH0 S IH0 N F R AE1 N CH AY2 Z D
+DISENGAGE  D IH0 S IH0 N G EY1 JH
+DISENGAGED  D IH0 S IH0 N G EY1 JH D
+DISENGAGEMENT  D IH0 S IH0 N G EY1 JH M AH0 N T
+DISENGAGES	D IH0 S IH0 N G EY1 JH IH0 Z
+DISENGAGING  D IH0 S IH0 N G EY1 JH IH0 NG
+DISENT	D IH0 S IH0 N T
+DISENTANGLE  D IH2 S AH0 N T AE1 NG G AH0 L
+DISENTANGLED	D IH0 S IH0 N T AE1 NG G AH0 L D
+DISENTANGLEMENT	D IH0 S IH0 N T AE1 NG G AH0 L M AH0 N T
+DISENTANGLERS	D IH0 S IH0 N T AE1 NG G AH0 L ER0 Z
+DISENTANGLES	D IH0 S IH0 N T AE1 NG G AH0 L Z
+DISENTANGLING	D IH0 S IH0 N T AE1 NG G AH0 L IH0 NG
+DISENTHRALLED	D IH0 S IH0 N TH R AO1 L D
+DISENTHRALMENT	D IH0 S IH0 N TH R AO1 L M AH0 N T
+DISENTOMBED	D IH0 S IH0 N T UW1 M D
+DISESTABLISH	D IH0 S IH0 S T AE1 B L IH0 SH
+DISESTABLISHED	D IH0 S IH0 S T AE1 B L IH0 SH T
+DISESTABLISHING	D IH0 S IH0 S T AE1 B L IH0 SH IH0 NG
+DISESTABLISHMENT  D IH0 S IH0 S T AE1 B L IH0 SH M AH0 N T
+DISESTEEM	D IH0 S IH0 S T IY1 M
+DISESTEEMED	D IH0 S IH0 S T IY1 M D
+DISFAVOR  D IH0 S F EY1 V ER0
+DISFAVOUR	D IH0 S F EY1 V ER0
+DISFIGURATION	D IH0 S F IH1 G Y ER0 EY1 SH AH0 N
+DISFIGURE  D IH0 S F IH1 G Y ER0
+DISFIGURED  D IH0 S F IH1 G Y ER0 D
+DISFIGUREMENT  D IH0 S F IH1 G Y ER0 M EH0 N T
+DISFIGUREMENTS	D IH0 S F IH1 G Y ER0 M AH0 N T S
+DISFIGURES	D IH0 S F IH1 G Y ER0 Z
+DISFIGURING  D IH0 S F IH1 G Y ER0 IH0 NG
+DISFRANCHISE	D IH0 S F R AE2 N CH AY0 Z
+DISFRANCHISED	D IH0 S F R AE1 N CH AY2 Z D
+DISFRANCHISEMENT	D IH0 S F R AE1 N CH AY2 Z M AH0 N T
+DISFRANCHISING	D IH0 S F R AE2 N CH AY0 Z IH0 NG
+DISGORGE  D IH0 S G AO1 R JH
+DISGORGED  D IH0 S G AO1 R JH D
+DISGORGES	D IH0 S G AO1 R JH IH0 Z
+DISGORGING	D IH0 S G AO1 R JH IH0 NG
+DISGRAC'D	D IH0 S G R EY1 S D
+DISGRACE  D IH0 S G R EY1 S
+DISGRACED  D IH0 S G R EY1 S T
+DISGRACEFUL  D IH0 S G R EY1 S F AH0 L
+DISGRACEFULLY	D IH0 S G R EY1 S F AH0 L IY0
+DISGRACES	D IH0 S G R EY1 S IH0 Z
+DISGRACIN	D IH0 S G R EY1 S IH0 N
+DISGRACING	D IH0 S G R EY1 S IH0 NG
+DISGRAZIA	D IH0 S G R AA1 Z IY0 AH0
+DISGRUNTLED  D IH0 S G R AH1 N AH0 L D
+DISGRUNTLED  D IH0 S G R AH1 N T AH0 L D
+DISGUIS'D	D IH0 S G AY1 Z D
+DISGUISE  D IH0 S G AY1 Z
+DISGUISED  D IH0 S G AY1 Z D
+DISGUISEMENT	D IH0 S G AY1 Z M AH0 N T
+DISGUISES  D IH0 S G AY1 Z IH0 Z
+DISGUISING  D IH0 S G AY1 Z IH0 NG
+DISGUST  D IH0 S G AH1 S T
+DISGUSTED  D IH0 S G AH1 S T AH0 D
+DISGUSTED  D IH0 S G AH1 S T IH0 D
+DISGUSTEDLY	D IH0 S G AH1 S T IH0 D L IY0
+DISGUSTFUL	D IH0 S G AH1 S T F AH0 L
+DISGUSTIN	D IH0 S G AH1 S T IH0 N
+DISGUSTING  D IH0 S G AH1 S T IH0 NG
+DISGUSTINGLY	D IH0 S G AH1 S T IH0 NG L IY0
+DISGUSTS  D IH0 S G AH1 S T S
+DISH  D IH1 SH
+DISHABILLE	D IH0 SH AH0 B IH2 L
+DISHARMONIES	D IH0 S HH AA1 R M AH0 N IY0 Z
+DISHARMONY  D IH0 S HH AA1 R M AH0 N IY0
+DISHART	D IH0 S HH AA2 R T
+DISHART'S	D IH0 S HH AA1 R T S
+DISHCLOTH	D IH1 SH K L AO2 TH
+DISHCLOTHS	D IH1 SH K L AO2 TH S
+DISHCLOUT	D IH1 SH K L AW1 T
+DISHEARTEN	D IH0 S HH AA1 R T AH0 N
+DISHEARTENED  D IH0 S HH AA1 R T AH0 N D
+DISHEARTENING  D IH0 S HH AA1 R T AH0 N IH0 NG
+DISHEARTENING  D IH2 S HH AA1 R T N IH0 NG
+DISHEARTENINGLY	D IH0 S HH AA1 R T AH0 N IH0 NG L IY0
+DISHEARTENMENT	D IH0 S HH AA1 R T AH0 N M AH0 N T
+DISHEARTENS	D IH0 S HH AA1 R T AH0 N Z
+DISHED  D IH1 SH T
+DISHER  D IH1 SH ER0
+DISHERITED	D IH0 S HH EH1 R AH0 T IH0 D
+DISHES  D IH1 SH AH0 Z
+DISHES  D IH1 SH IH0 Z
+DISHEVEL'D	D IH0 SH EH1 V AH0 L D
+DISHEVELED  D IH0 SH EH1 V AH0 L D
+DISHEVELLED	D IH0 SH EH1 V AH0 L D
+DISHEVELMENT	D IH0 SH EH1 V AH0 L M AH0 N T
+DISHFUL	D IH1 SH F AH0 L
+DISHIN	D IH1 SH IH0 N
+DISHING  D IH1 SH IH0 NG
+DISHON  D IH1 S AH0 N
+DISHONEST  D IH0 S AA1 N AH0 S T
+DISHONESTIES	D IH0 S AA1 N AH0 S T IY0 Z
+DISHONESTLY  D IH0 S AO1 N AH0 S T L IY0
+DISHONESTY  D IH0 S AA1 N AH0 S T IY0
+DISHONOR  D IH0 S AA1 N ER0
+DISHONORABLE  D IH0 S AA1 N ER0 AH0 B AH0 L
+DISHONORABLY	D IH0 S AA1 N ER0 AH0 B L IY0
+DISHONORED  D IH0 S AA1 N ER0 D
+DISHONORING	D IH0 S AA1 N ER0 IH0 NG
+DISHONORS	D IH0 S AA1 N ER0 Z
+DISHONOUR	D IH0 S AA1 N ER0
+DISHONOUR'D	D IH0 S AA1 N AW0 R D
+DISHONOURABLE	D IH0 S AA1 N UH1 R AH0 B AH0 L
+DISHONOURABLY	D IH0 S AA1 N ER0 AH0 B L IY0
+DISHONOURED	D IH0 S AA1 N AW1 ER0 D
+DISHONOURING	D IH0 S AA1 N ER1 IH0 NG
+DISHONOURS	D IH0 S AA1 N AO1 R Z
+DISHPAN	D IH0 S P AH0 N
+DISHPANS	D IH0 S P AE2 N Z
+DISHRAG	D IH0 S R AE1 G
+DISHWASHER  D IH1 SH W AA2 SH ER0
+DISHWASHING  D IH1 SH W AA2 SH IH0 NG
+DISHWATER	D IH1 SH W AA2 T ER0
+DISHYER	D IH0 S HH IY0 ER0
+DISILLUSION  D IH2 S IH0 L UW1 ZH AH0 N
+DISILLUSIONED  D IH2 S IH0 L UW1 ZH AH0 N D
+DISILLUSIONING  D IH2 S IH0 L UW1 ZH AH0 N IH0 NG
+DISILLUSIONISED	D IH2 S IH0 L UW1 ZH AH0 N AH0 S T
+DISILLUSIONIZE	D IH2 S IH0 L UW1 ZH AH0 N AY2 Z
+DISILLUSIONIZED	D IH2 S IH0 L UW1 ZH AH0 N AY2 Z D
+DISILLUSIONIZING	D IH2 S IH0 L UW1 ZH AH0 N AY2 Z IH0 NG
+DISILLUSIONMENT  D IH2 S IH0 L UW1 ZH AH0 N M AH0 N T
+DISILLUSIONMENTS	D IH2 S IH0 L UW1 ZH AH0 N M AH0 N T S
+DISILLUSIONS	D IH2 S IH0 L UW1 ZH AH0 N Z
+DISINCLINATION  D IH0 S IH0 N K L AH0 N EY1 SH AH0 N
+DISINCLINE  D IH2 S IH0 N K L AY1 N
+DISINCLINED  D IH2 S IH0 N K L AY1 N D
+DISINFECT  D IH0 S IH0 N F EH1 K T
+DISINFECTANT  D IH0 S IH0 N F EH1 K T AH0 N T
+DISINFECTANTS  D IH0 S IH0 N F EH1 K T AH0 N T S
+DISINFECTED	D IH2 S IH0 N F EH1 K T IH0 D
+DISINFECTING	D IH2 S IH0 N F EH1 K T IH0 NG
+DISINFECTION  D IH0 S IH0 N F EH1 K SH AH0 N
+DISINGENUITY	D IH0 S IH1 N JH AH0 N UW1 AH0 T IY0
+DISINGENUOUS  D IH0 S IH0 N JH EH1 N Y UW0 AH0 S
+DISINGENUOUSLY	D IH2 S IH0 N JH EH1 N Y UW0 AH0 S L IY0
+DISINGENUOUSNESS  D IH2 S IH0 N JH EH1 N Y UW0 AH0 S N AH0 S
+DISINHERIT	D IH2 S IH0 N HH EH1 R AH0 T
+DISINHERITANCE	D IH2 S IH0 N HH EH1 R AH0 T AH0 N S
+DISINHERITED	D IH2 S IH0 N HH EH1 R AH0 T IH0 D
+DISINHERITING	D IH2 S IH0 N HH EH1 R AH0 T IH0 NG
+DISINHERITS	D IH2 S IH0 N HH EH1 R AH0 T S
+DISINTEGRATE  D IH0 S IH1 N T AH0 G R EY2 T
+DISINTEGRATED  D IH0 S IH1 N T AH0 G R EY2 T IH0 D
+DISINTEGRATES  D IH0 S IH1 N T AH0 G R EY2 T S
+DISINTEGRATING  D IH0 S IH1 N T AH0 G R EY2 T IH0 NG
+DISINTEGRATION  D IH0 S IH2 N AH0 G R EY1 SH AH0 N
+DISINTEGRATION  D IH0 S IH2 N T AH0 G R EY1 SH AH0 N
+DISINTEGRATIONS	D IH0 S IH1 N T AH0 G R EY1 SH AH0 N Z
+DISINTEGRATOR	D IH0 S IH1 N T AH0 G R EY2 T ER0
+DISINTEGRATORS	D IH0 S IH1 N T AH0 G R EY2 T ER0 Z
+DISINTER	D IH0 S IH0 N T ER0
+DISINTEREST  D IH0 S IH1 N T ER0 AH0 S T
+DISINTERESTED  D IH0 S IH1 N R IH0 S T IH0 D
+DISINTERESTED  D IH0 S IH1 N T R IH0 S T IH0 D
+DISINTERESTEDLY	D IH0 S IH1 N T R IH0 S T IH0 D L IY0
+DISINTERESTEDNESS	D IH0 S IH1 N T R IH0 S T IH0 D N AH0 S
+DISINTERMENT	D IH0 S IH0 N T ER0 M AH0 N T
+DISINTERRED	D IH0 S IH0 N T ER1 D
+DISINTERRING	D IH0 S IH0 N T ER1 IH0 NG
+DISJECTA	D IH0 S JH IH0 K T AH0
+DISJOIN	D IH0 S JH OY1 N
+DISJOIN'D	D IH0 S JH OY1 N D
+DISJOINED	D IH0 S JH OY1 N D
+DISJOINT  D IH0 S JH OY1 N T
+DISJOINTED  D IH0 S JH OY1 N T IH0 D
+DISJOINTEDLY	D IH0 S JH OY1 N T IH0 D L IY0
+DISJOINTING	D IH0 S JH OY1 N T IH0 NG
+DISJUNCTION	D IH0 S JH AH1 NG K SH AH0 N
+DISJUNCTIVE	D IH0 S JH AH2 NG K T IH0 V
+DISJUNCTIVELY	D IH0 S JH AH2 NG K T IH0 V L IY0
+DISK  D IH1 S K
+DISKED	D IH1 S K T
+DISKIVER	D IH1 S K AY1 V ER0
+DISKIVERED	D IH1 S K AY1 V ER0 D
+DISKO	D IH1 S K OW0
+DISKO'S	D IH1 S K OW0 Z
+DISKOS	D IH1 S K OW0 Z
+DISKS  D IH1 S K S
+DISLIKE  D IH0 S L AY1 K
+DISLIKED  D IH0 S L AY1 K T
+DISLIKES  D IH0 S L AY1 K S
+DISLIKING  D IH0 S L AY1 K IH0 NG
+DISLIKINGS	D IH0 S L AY1 K IH0 NG Z
+DISLOCATE  D IH1 S L OW0 K EY0 T
+DISLOCATED  D IH1 S L OW0 K EY0 T IH0 D
+DISLOCATES	D IH1 S L OW0 K EY0 T S
+DISLOCATING	D IH1 S L OW0 K EY0 T IH0 NG
+DISLOCATION  D IH0 S L OW1 K EY1 SH AH0 N
+DISLOCATIONS  D IH0 S L OW1 K EY1 SH AH0 N Z
+DISLODGE  D IH0 S L AA1 JH
+DISLODGED  D IH0 S L AA1 JH D
+DISLODGES	D IH0 S L AA1 JH IH0 Z
+DISLODGING  D IH0 S L AA1 JH IH0 NG
+DISLODGMENT	D IH0 S L AA1 JH M AH0 N T
+DISLOYAL  D IH0 S L OY1 AH0 L
+DISLOYALLY	D IH0 S L OY1 AH0 L IY0
+DISLOYALTY  D IH0 S L OY1 AH0 L T IY0
+DISMAL  D IH1 Z M AH0 L
+DISMALEST	D IH0 S M EY1 L AH0 S T
+DISMALLEST	D IH0 S M AE1 L IH0 S T
+DISMALLY  D IH1 Z M AH0 L IY0
+DISMALNESS	D IH1 Z M AH0 L N EH0 S
+DISMALS	D IH1 Z M AH0 L Z
+DISMANTLE  D IH0 S M AE1 N AH0 L
+DISMANTLE  D IH0 S M AE1 N T AH0 L
+DISMANTLED  D IH0 S M AE1 N AH0 L D
+DISMANTLED  D IH0 S M AE1 N T AH0 L D
+DISMANTLING  D IH0 S M AE1 N AH0 L IH0 NG
+DISMANTLING  D IH0 S M AE1 N T AH0 L IH0 NG
+DISMANTLING  D IH2 S M AE1 N T L IH0 NG
+DISMAS	D IH1 Z M AH0 Z
+DISMASTED	D IH0 S M EY1 S T IH0 D
+DISMAY  D IH0 S M EY1
+DISMAY'D	D IH0 S M EY1 D
+DISMAYED  D IH0 S M EY1 D
+DISMAYING  D IH0 S M EY1 IH0 NG
+DISMAYINGLY	D IH0 S M EY1 IH0 NG L IY0
+DISMAYS  D IH0 S M EY1 Z
+DISMEMBER  D IH0 S M EH1 M B ER0
+DISMEMBERED  D IH0 S M EH1 M B ER0 D
+DISMEMBERING  D IH0 S M EH1 M B ER0 IH0 NG
+DISMEMBERMENT  D IH0 S M EH1 M B ER0 M AH0 N T
+DISMISS  D IH0 S M IH1 S
+DISMISS'D	D IH0 S M IH1 SH D
+DISMISSAL  D IH0 S M IH1 S AH0 L
+DISMISSALS  D IH0 S M IH1 S AH0 L Z
+DISMISSED  D IH0 S M IH1 S T
+DISMISSES  D IH0 S M IH1 S IH0 Z
+DISMISSING  D IH0 S M IH1 S IH0 NG
+DISMISSION	D IH0 S M IH1 SH AH0 N
+DISMIST	D IH0 S M AH0 S T
+DISMOUNT	D IH0 S M AW2 N T
+DISMOUNTED	D IH0 S M AW2 N T IH0 D
+DISMOUNTING	D IH0 S M AW1 N T IH0 NG
+DISMOUNTS	D IH0 S M AW2 N T S
+DISMUKE  D IH1 S M Y UW0 K
+DISNA	D IH0 S N AH0
+DISNEY  D IH1 Z N IY0
+DISNEY'S  D IH1 Z N IY0 Z
+DISOBEDIENCE  D IH2 S AH0 B IY1 D IY0 AH0 N S
+DISOBEDIENCE  D IH2 S OW0 B IY1 D IY0 AH0 N S
+DISOBEDIENT  D IH2 S AH0 B IY1 D IY0 AH0 N T
+DISOBEDIENT  D IH2 S OW0 B IY1 D IY0 AH0 N T
+DISOBEDIENTLY	D IH2 S OW0 B IY1 D IY0 AH0 N T L IY0
+DISOBEY  D IH2 S AH0 B EY1
+DISOBEYED  D IH2 S OW0 B EY1 D
+DISOBEYING  D IH2 S OW0 B EY1 IH0 NG
+DISOBEYS	D IH2 S OW0 B EY1 Z
+DISOBLIGE	D IH2 S AH0 B L AY1 JH
+DISOBLIGED	D IH2 S AH0 B L AY1 JH D
+DISOBLIGING	D IH2 S AH0 B L AY1 JH IH0 NG
+DISOCCUPATION	D IH2 S AA2 K Y AH0 P EY1 SH AH0 N
+DISOIT	D IH0 S OY1 T
+DISORDER  D IH0 S AO1 R D ER0
+DISORDER'D	D IH0 S AO1 R D ER0 D
+DISORDERED  D IH0 S AO1 R D ER0 D
+DISORDERING	D IH0 S AO1 R D ER0 IH0 NG
+DISORDERLIES	D IH0 S AO1 R D ER0 L IY0 Z
+DISORDERLINESS	D IH0 S AO1 R D ER0 L IY0 N AH0 S
+DISORDERLY  D IH0 S AO1 R D ER0 L IY0
+DISORDERS  D IH0 S AO1 R D ER0 Z
+DISORGANISATION	D IH0 S AO1 R G AH0 N IH0 Z EY1 SH AH0 N
+DISORGANISE	D IH0 S AO1 R G AH0 N AH0 S
+DISORGANISED	D IH0 S AO1 R G AH0 N IH0 Z D
+DISORGANISING	D IH0 S AO1 R G AH0 N IH1 Z IH0 NG
+DISORGANIZATION  D IH0 S AO2 R G AH0 N AH0 Z EY1 SH AH0 N
+DISORGANIZE  D IH0 S AO1 R G AH0 N AY2 Z
+DISORGANIZED  D IH0 S AO1 R G AH0 N AY2 Z D
+DISORGANIZERS	D IH0 S AO1 R G AH0 N AY2 Z ER0 Z
+DISORGANIZES	D IH0 S AO1 R G AH0 N AY2 Z IH0 Z
+DISORGANIZING	D IH0 S AO1 R G AH0 N AY2 Z IH0 NG
+DISORIENTATION  D IH0 S AO2 R IY0 AH0 N T EY1 SH AH0 N
+DISORIENTED  D IH0 S AO1 R IY0 EH2 N T IH0 D
+DISOWN  D IH0 S OW1 N
+DISOWNED  D IH0 S OW1 N D
+DISOWNING	D IH0 S OW1 N IH0 NG
+DISOWNS	D IH0 S OW1 N Z
+DISP	D IH1 S P
+DISPAIR  D IH0 S P EH1 R
+DISPARAGE  D IH0 S P EH1 R IH0 JH
+DISPARAGED  D IH0 S P EH1 R IH0 JH D
+DISPARAGEMENT	D IH0 S P EH1 R IH0 JH M AH0 N T
+DISPARAGEMENTS	D IH0 S P EH1 R IH0 JH M AH0 N T S
+DISPARAGES  D IH0 S P EH1 R IH0 JH IH0 Z
+DISPARAGING  D IH0 S P EH1 R IH0 JH IH0 NG
+DISPARAGINGLY  D IH0 S P EH1 R IH0 JH IH0 NG L IY0
+DISPARATE  D IH0 S P EH1 R IH0 T
+DISPARATE  D IH1 S P ER0 IH0 T
+DISPARITIES  D IH0 S P EH1 R AH0 T IY0 Z
+DISPARITY  D IH0 S P EH1 R AH0 T IY0
+DISPART	D IH0 S P AA2 R T
+DISPARTED	D IH0 S P AA1 R T IH0 D
+DISPARTING	D IH0 S P AA2 R T IH0 NG
+DISPASSION	D IH0 S P AE1 SH AH0 N
+DISPASSIONATE  D IH0 S P AE1 SH AH0 N AH0 T
+DISPASSIONATELY  D IH0 S P AE1 SH AH0 N AH0 T L IY0
+DISPASSIONATENESS	D IH0 S P AE1 SH AH0 N AH0 T N AH0 S
+DISPATCH  D IH0 S P AE1 CH
+DISPATCH'D	D IH0 S P AE1 CH IH0 D
+DISPATCHED  D IH0 S P AE1 CH T
+DISPATCHER  D IH0 S P AE1 CH ER0
+DISPATCHER  D IH1 S P AE2 CH ER0
+DISPATCHER'S  D IH0 S P AE1 CH ER0 Z
+DISPATCHER'S  D IH1 S P AE2 CH ER0 Z
+DISPATCHES  D IH0 S P AE1 CH IH0 Z
+DISPATCHING  D IH0 S P AE1 CH IH0 NG
+DISPEACE	D IH2 S P IY2 S
+DISPEL  D IH0 S P EH1 L
+DISPELL'D	D IH0 S P EH1 L D
+DISPELLED  D IH0 S P EH1 L D
+DISPELLER	D IH0 S P EH1 L ER0
+DISPELLING  D IH0 S P EH1 L IH0 NG
+DISPELS  D IH0 S P EH1 L Z
+DISPENCE	D IH0 S P EH1 N S
+DISPENSABLE  D IH0 S P EH1 N S AH0 B AH0 L
+DISPENSARIES	D IH1 S P EH0 N S EH2 R IY0 Z
+DISPENSARY  D IH0 S P EH1 N S ER0 IY0
+DISPENSARY  D IH1 S P EH0 N S EH2 R IY0
+DISPENSATION  D IH2 S P AH0 N S EY1 SH AH0 N
+DISPENSATIONAL	D IH2 S P AH0 N S EY1 SH AH0 N AH0 L
+DISPENSATIONS	D IH2 S P AH0 N S EY1 SH AH0 N Z
+DISPENSATOR	D IH2 S P AH0 N S EY2 T ER0
+DISPENSATORY	D IH0 S P EH1 N S AH0 T AO2 R IY0
+DISPENSE  D IH0 S P EH1 N S
+DISPENSED  D IH0 S P EH1 N S T
+DISPENSER  D IH0 S P EH1 N S ER0
+DISPENSERS  D IH0 S P EH1 N S ER0 Z
+DISPENSES  D IH0 S P EH1 N S IH0 Z
+DISPENSING  D IH0 S P EH1 N S IH0 NG
+DISPEOPLED	D IH0 S P IY2 P AH0 L D
+DISPERS'D	D IH0 S P ER1 S AH0 D
+DISPERSAL  D IH0 S P ER1 S AH0 L
+DISPERSE  D IH0 S P ER1 S
+DISPERSED  D IH0 S P ER1 S T
+DISPERSEDLY	D IH0 S P ER1 S AH0 D L IY0
+DISPERSES	D IH0 S P ER1 S AH0 Z
+DISPERSING  D IH0 S P ER1 S IH0 NG
+DISPERSION  D IH0 S P ER1 ZH AH0 N
+DISPERSIONS	D IH0 S P ER1 ZH AH0 N Z
+DISPERSIVE  D IH0 S P ER1 S IH0 V
+DISPIRIT	D IH0 S P IH1 R IH0 T
+DISPIRITED  D IH0 S P IH1 R AH0 T IH0 D
+DISPIRITEDLY	D IH0 S P IH1 R IH0 T AH0 D L IY0
+DISPIRITING  D IH0 S P IH1 R IH0 T IH0 NG
+DISPLACE  D IH0 S P L EY1 S
+DISPLACED  D IH0 S P L EY1 S T
+DISPLACEMENT  D IH0 S P L EY1 S M AH0 N T
+DISPLACEMENTS  D IH0 S P L EY1 S M AH0 N T S
+DISPLACER	D IH0 S P L EY1 S ER0
+DISPLACES  D IH0 S P L EY1 S IH0 Z
+DISPLACING  D IH0 S P L EY1 S IH0 NG
+DISPLAY  D IH0 S P L EY1
+DISPLAY'D	D IH0 S P L EY1 D
+DISPLAYED  D IH0 S P L EY1 D
+DISPLAYIN	D IH0 S P L EY1 IH0 N
+DISPLAYING  D IH0 S P L EY1 IH0 NG
+DISPLAYS  D IH0 S P L EY1 Z
+DISPLEAS'D	D IH0 S P L IY1 Z D
+DISPLEASE  D IH0 S P L IY1 Z
+DISPLEASED  D IH0 S P L IY1 Z D
+DISPLEASES	D IH0 S P L IY1 Z IH0 Z
+DISPLEASING	D IH0 S P L IY1 Z IH0 NG
+DISPLEASURE  D IH0 S P L EH1 ZH ER0
+DISPLEASURES	D IH0 S P L EH1 ZH ER0 Z
+DISPOGED	D IH2 S P AH0 JH D
+DISPORT	D IH0 S P AO2 R T
+DISPORTED	D IH0 S P AO1 R T AH0 D
+DISPORTING	D IH0 S P AO1 R T IH0 NG
+DISPORTS	D IH0 S P AO2 R T S
+DISPOS'D	D IH2 S P AH0 D
+DISPOSABLE  D IH0 S P OW1 Z AH0 B AH0 L
+DISPOSAL  D IH0 S P OW1 Z AH0 L
+DISPOSE  D IH0 S P OW1 Z
+DISPOSED  D IH0 S P OW1 Z D
+DISPOSEDLY	D IH0 S P OW1 Z AH0 D L IY0
+DISPOSER  D IH0 S P OW1 Z ER0
+DISPOSES  D IH0 S P OW1 Z IH0 Z
+DISPOSETH	D IH0 S P OW1 Z AH0 TH
+DISPOSICION	D IH0 S P AH0 Z IH1 SH AH0 N
+DISPOSING  D IH0 S P OW1 Z IH0 NG
+DISPOSITION  D IH2 S P AH0 Z IH1 SH AH0 N
+DISPOSITIONED	D IH0 S P AH0 Z IH1 SH AH0 N D
+DISPOSITIONS  D IH2 S P AH0 Z IH1 SH AH0 N Z
+DISPOSSESS  D IH2 S P AH0 Z EH1 S
+DISPOSSESSED  D IH2 S P AH0 Z EH1 S T
+DISPOSSESSING	D IH2 S P AH0 Z EH1 S IH0 NG
+DISPOSSESSION	D IH2 S P AH0 Z EH1 SH AH0 N
+DISPOSSEST	D IH2 S P AH0 Z EH1
+DISPRAISE	D IH0 S P R EY1 Z
+DISPRAISED	D IH0 S P R EY1 Z D
+DISPREAD	D IH0 S P R EH1 D
+DISPROOF	D IH0 S P R UW2 F
+DISPROPORTION	D IH2 S P R AH0 P AO1 R SH AH0 N
+DISPROPORTIONATE  D IH2 S P R AH0 P AO1 R SH AH0 N IH0 T
+DISPROPORTIONATELY  D IH2 S P R AH0 P AO1 R SH AH0 N AH0 T L IY0
+DISPROPORTIONED	D IH2 S P R AH0 P AO1 R SH AH0 N D
+DISPROPORTIONS	D IH2 S P R AH0 P AO1 R SH AH0 N Z
+DISPROVE  D IH0 S P R UW1 V
+DISPROVED  D IH0 S P R UW1 V D
+DISPROVEN  D IH0 S P R UW1 V IH0 N
+DISPROVES  D IH0 S P R UW1 V Z
+DISPROVING	D IH0 S P R UW1 V IH0 NG
+DISPUTA	D IH0 S P Y UW1 T AH0
+DISPUTABLE  D IH0 S P Y UW1 T AH0 B AH0 L
+DISPUTANDUM	D IH0 S P Y UW1 T AH0 N D AH0 M
+DISPUTANT	D IH0 S P Y UW1 T AH0 N T
+DISPUTANTS	D IH0 S P Y UW1 T AH0 N T S
+DISPUTATION  D IH0 S P Y UW1 T EY1 SH AH0 N
+DISPUTATIONS	D IH0 S P Y UW1 T EY1 SH AH0 N Z
+DISPUTATIOUS	D IH0 S P Y UW1 T EY1 SH AH0 S
+DISPUTATIVE	D IH0 S P Y UW1 T EY2 T IH0 V
+DISPUTE  D IH0 S P Y UW1 T
+DISPUTED  D IH0 S P Y UW1 T AH0 D
+DISPUTED  D IH0 S P Y UW1 T IH0 D
+DISPUTER	D IH0 S P Y UW1 T ER0
+DISPUTES  D IH0 S P Y UW1 T S
+DISPUTIN	D IH0 S P Y UW1 T AH0 N
+DISPUTING  D IH0 S P Y UW1 T IH0 NG
+DISPUTINGS	D IH0 S P Y UW1 T IH0 NG Z
+DISQUALIFICATION  D IH0 S K W AA2 L AH0 F AH0 K EY1 SH AH0 N
+DISQUALIFICATIONS	D IH0 S K W AA2 L AH0 F AH0 K EY1 SH AH0 N Z
+DISQUALIFIED  D IH0 S K W AA1 L AH0 F AY2 D
+DISQUALIFIES  D IH0 S K W AA1 L AH0 F AY2 Z
+DISQUALIFY  D IH0 S K W AA1 L AH0 F AY2
+DISQUALIFYING  D IH0 S K W AA1 L AH0 F AY2 IH0 NG
+DISQUIET  D IH0 S K W AY1 AH0 T
+DISQUIETED	D IH0 S K W AY1 AH0 T AH0 D
+DISQUIETETH	D IH0 S K W AY1 AH0 T AH0 TH
+DISQUIETING  D IH0 S K W AY1 AH0 T IH0 NG
+DISQUIETINGLY	D IH0 S K W AY1 AH0 T IH0 NG L IY0
+DISQUIETS	D IH0 S K W AY1 AH0 T S
+DISQUIETUDE	D IH0 S K W AY1 AH0 T UW2 D
+DISQUIETUDES	D IH0 S K W AY1 AH0 T UW2 D Z
+DISQUISITION	D IH0 S K W AH0 Z IH1 SH AH0 N
+DISQUISITIONS	D IH0 S K W AH0 Z IH1 SH AH0 N Z
+DISRAELI  D IH0 Z R EY1 L IY0
+DISRAELI'S	D IH0 Z R EY1 L IY0 Z
+DISRATED	D IH0 S R EY2 T IH0 D
+DISREGARD  D IH2 S R IH0 G AA1 R D
+DISREGARDED  D IH2 S R IH0 G AA1 R D IH0 D
+DISREGARDFUL	D IH2 S R IH0 G AA1 R D F AH0 L
+DISREGARDING  D IH2 S R IH0 G AA1 R D IH0 NG
+DISREGARDS  D IH2 S R IH0 G AA1 R D Z
+DISRELISH	D IH0 S R EH1 L IH0 SH
+DISRELISHED	D IH0 S R EH1 L IH0 SH T
+DISREMEMBER	D IH0 S R IY0 M EH1 M B ER0
+DISREMEMBERED	D IH0 S R IY0 M EH1 M B ER0 D
+DISREMEMBERS	D IH0 S R IY0 M EH1 M B ER0 Z
+DISREPAIR  D IH2 S R IH0 P EH1 R
+DISREPUTABILITY	D IH2 S R IH0 P Y UW1 T AH0 B IH1 L IH0 T IY0
+DISREPUTABLE  D IH0 S R EH1 P Y AH0 T AH0 B AH0 L
+DISREPUTABLES	D IH2 S R IH0 P Y UW1 T AH0 B AH0 L Z
+DISREPUTABLY	D IH2 S R IH0 P Y UW1 T AH0 B L IY0
+DISREPUTATION	D IH0 S R EH1 P Y AH0 T EY1 SH AH0 N
+DISREPUTE  D IH2 S R IH0 P Y UW1 T
+DISRESPECT  D IH2 S R IH0 S P EH1 K T
+DISRESPECTABLE	D IH2 S R IH0 S P EH1 K T AH0 B AH0 L
+DISRESPECTFUL  D IH2 S R IH0 S P EH1 K T F AH0 L
+DISRESPECTFULLY	D IH2 S R IH0 S P EH1 K T F AH0 L IY0
+DISROBE	D IH0 S R OW2 B
+DISROBED	D IH0 S R OW1 B D
+DISROBING	D IH0 S R AA2 B IH0 NG
+DISRUPT  D IH0 S R AH1 P T
+DISRUPTED  D IH0 S R AH1 P T IH0 D
+DISRUPTING  D IH0 S R AH1 P T IH0 NG
+DISRUPTION  D IH0 S R AH1 P SH AH0 N
+DISRUPTIONS  D IH0 S R AH1 P SH AH0 N Z
+DISRUPTIVE  D IH0 S R AH1 P T IH0 V
+DISRUPTOR	D IH0 S R AH1 P T ER0
+DISRUPTS  D IH0 S R AH1 P T S
+DISRUPTURE	D IH0 S R AH1 P CH ER0
+DISS  D IH1 S
+DISSA	D IH1 S AH0
+DISSATISFACTION  D IH2 S AE0 T IH0 S F AE1 K SH AH0 N
+DISSATISFACTIONS	D IH0 S AE1 T AH0 S F AE1 K SH AH0 N Z
+DISSATISFIED  D IH0 S AE1 T AH0 S F AY2 D
+DISSATISFIES	D IH0 S AE1 T AH0 S F AY2 Z
+DISSATISFY  D IH0 S AE1 T AH0 S F AY2
+DISSATISFYING	D IH0 S AE1 T AH0 S F AY2 IH0 NG
+DISSCOURN	D IH2 S K UH1 R N
+DISSE	D IH0 S
+DISSECT  D AY0 S EH1 K T
+DISSECTED  D AY0 S EH1 K T AH0 D
+DISSECTING  D AY0 S EH1 K T IH0 NG
+DISSECTION  D AY0 S EH1 K SH AH0 N
+DISSECTION  D AY1 S EH0 K SH AH0 N
+DISSECTIONS  D AY0 S EH1 K SH AH0 N Z
+DISSECTIONS  D AY1 S EH0 K SH AH0 N Z
+DISSECTOR	D AY0 S EH1 K T ER0
+DISSECTORS	D AY0 S EH1 K T ER0 Z
+DISSECTS  D AY0 S EH1 K S
+DISSECTS  D AY0 S EH1 K T S
+DISSEISIN	D IH0 S EY1 S AH0 N
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+DISSEMBLANCE	D IH0 S EH1 M B L AH0 N S
+DISSEMBLE  D IH0 S EH1 M B AH0 L
+DISSEMBLED	D IH0 S EH1 M B AH0 L D
+DISSEMBLER	D IH0 S EH1 M B L ER0
+DISSEMBLERS	D IH0 S EH1 M B L ER0 Z
+DISSEMBLES	D IH0 S EH1 M B AH0 L Z
+DISSEMBLING  D IH0 S EH1 M B L IH0 NG
+DISSEMINATE  D IH0 S EH1 M AH0 N EY2 T
+DISSEMINATED  D IH0 S EH1 M AH0 N EY2 T AH0 D
+DISSEMINATES  D IH0 S EH1 M AH0 N EY2 T S
+DISSEMINATING  D IH0 S EH1 M AH0 N EY2 T IH0 NG
+DISSEMINATION  D IH0 S EH2 M AH0 N EY1 SH AH0 N
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+DISSENTING  D IH0 S EH1 N IH0 NG
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+DISSIMULATION	D IH2 S IH2 M Y AH0 L EY1 SH AH0 N
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+DISSOCIATE  D IH0 S OW1 S IY0 EY0 T
+DISSOCIATED	D IH0 S OW1 S IY0 EY2 T AH0 D
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+DISTASTED	D IH0 S T EY1 S T IH0 D
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+GALEOTTI	G EY1 L IY0 AA1 T IY0
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+HARL  HH AA1 R L
+HARLAEM	HH AA1 R L EY1 M
+HARLAN  HH AA1 R L AH0 N
+HARLAN'S	HH AA1 R L AH0 N Z
+HARLAND  HH AA1 R L AH0 N D
+HARLAND'S	HH AA1 R L AH0 N D Z
+HARLAW	HH AA1 R L AO2
+HARLAY	HH AA1 R L EY0
+HARLE  HH AA1 R AH0 L
+HARLECH	HH AA1 R AH0 L CH
+HARLED	HH AA1 R L D
+HARLEIAN	HH AA1 R L IY2 AH0 N
+HARLEIGH	HH AA1 R L AH0
+HARLEM  HH AA1 R L AH0 M
+HARLEQUIN  HH AA1 R L AH0 K W AH0 N
+HARLEQUIN'S	HH AA1 R L AH0 K IH0 N Z
+HARLEQUINADE	HH AA1 R L AH0 K IH0 N EY1 D
+HARLEQUINS	HH AA1 R L AH0 K IH0 N Z
+HARLESDEN	HH AA1 R L Z D AH0 N
+HARLESTON	HH AA1 R L S T AH0 N
+HARLESTON'S	HH AA1 R L S T AH0 N Z
+HARLETH	HH AA1 R L AH0 TH
+HARLEY  HH AA1 R L IY0
+HARLEY'S  HH AA1 R L IY0 Z
+HARLEYS	HH AA1 R L IY0 Z
+HARLINGEN  HH AA1 R L IH0 NG AH0 N
+HARLINGTON	HH AA1 R L IH0 NG T AH0 N
+HARLOT  HH AA1 R L AH0 T
+HARLOT'S	HH AA1 R L AH0 T S
+HARLOTRY	HH AA1 R L AH0 T R IY0
+HARLOTS	HH AA1 R L AH0 T S
+HARLOW  HH AA1 R L OW0
+HARLOW'S	HH AA1 R L OW2 Z
+HARLOWE	HH AA1 R L OW2
+HARLOWE'S	HH AA1 R L OW2 Z
+HARLOWES	HH AA1 R L OW2 Z
+HARM  HH AA1 R M
+HARM'D	HH AA1 R M D
+HARM'S  HH AA1 R M Z
+HARMAC	HH AA2 R M AH0 K
+HARMACHIS	HH AA2 R M AH0 K IH1 Z
+HARMAKHIS	HH AA2 R M AH0 K IH0 S
+HARMAN  HH AA1 R M AH0 N
+HARMAN'S	HH AA1 R M AH0 N Z
+HARMANS	HH AA1 R M AH0 N Z
+HARMAR	HH AA0 R M AA1 R
+HARMAS	HH AA1 R M AH0 Z
+HARMATTAN  HH AA2 R M AH0 T AE1 N
+HARME	HH AA1 R M
+HARMED  HH AA1 R M D
+HARMER  HH AA1 R M ER0
+HARMER'S	HH AA1 R M ER0 Z
+HARMERS	HH AA1 R M ER0 Z
+HARMES  HH AA1 R M Z
+HARMFUL  HH AA1 R M F AH0 L
+HARMFULLY  HH AA1 R M F AH0 L IY0
+HARMFULNESS  HH AA1 R M F AH0 L N AH0 S
+HARMHABI	HH AA1 R M HH AE2 B IY0
+HARMING  HH AA1 R M IH0 NG
+HARMLESS  HH AA1 R M L AH0 S
+HARMLESSLY  HH AA1 R M L AH0 S L IY0
+HARMLESSNESS	HH AA1 R M L AH0 S N AH0 S
+HARMODIUS	HH AA0 R M OW1 D IY0 AH0 S
+HARMON  HH AA1 R M AH0 N
+HARMON'S  HH AA1 R M AH0 N Z
+HARMONIA  HH AA0 R M OW1 N IY0 AH0
+HARMONIC  HH AA0 R M AA1 N IH0 K
+HARMONICA  HH AA0 R M AA1 N IH0 K AH0
+HARMONICAL	HH AA0 R M AA1 N IH0 K AH0 L
+HARMONICALLY	HH AA0 R M AA1 N IH0 K L IY0
+HARMONICAS	HH AA0 R M AA1 N IH0 K AH0 Z
+HARMONICON	HH AA0 R M AA1 N IH0 K AA2 N
+HARMONICS  HH AA0 R M AA1 N IH0 K S
+HARMONIE  HH AA1 R M AH0 N IY0
+HARMONIES  HH AA1 R M AH0 N IY0 Z
+HARMONIOUS  HH AA0 R M OW1 N IY0 AH0 S
+HARMONIOUSLY  HH AA0 R M OW1 N IY0 AH0 S L IY0
+HARMONISE	HH AA1 R M AH0 N IH0 Z
+HARMONISED	HH AA1 R M AH0 N IH0 Z D
+HARMONISES	HH AA1 R M AH0 N IH0 Z IH0 Z
+HARMONISING	HH AA1 R M AH0 N IH0 Z IH0 NG
+HARMONIST	HH AA1 R M AH0 N IH0 S T
+HARMONISTS	HH AA1 R M AH0 N IH0 S T S
+HARMONIUM  HH AA0 R M OW1 N IY0 AH0 M
+HARMONIUMS	HH AA0 R M OW1 N IY0 AH0 M Z
+HARMONIZATION  HH AA2 R M AH0 N IH0 Z EY1 SH AH0 N
+HARMONIZE  HH AA1 R M AH0 N AY2 Z
+HARMONIZED  HH AA1 R M AH0 N AY2 Z D
+HARMONIZES  HH AA1 R M AH0 N AY2 Z IH0 Z
+HARMONIZING  HH AA1 R M AH0 N AY2 Z IH0 NG
+HARMONS	HH AA1 R M AH0 N Z
+HARMONY  HH AA1 R M AH0 N IY0
+HARMONY'S	HH AA1 R M AH0 N IY0 Z
+HARMOST	HH AA1 R M OW2 S T
+HARMOUTH	HH AA1 R M AH0 TH
+HARMS  HH AA1 R M Z
+HARMSWORTH	HH AA1 R M S W ER0 TH
+HARMY	HH AA1 R M IY0
+HARN  HH AA1 R N
+HARNACH	HH AA1 R N AH0 K
+HARNACK  HH AA1 R N AH0 K
+HARNED  HH AA1 R N D
+HARNESS  HH AA1 R N AH0 S
+HARNESS  HH AA1 R N IH0 S
+HARNESS'D	HH AA1 R N AH0 S D
+HARNESSED  HH AA1 R N AH0 S T
+HARNESSES  HH AA1 R N AH0 S AH0 Z
+HARNESSES  HH AA1 R N AH0 S IH0 Z
+HARNESSING  HH AA1 R N AH0 S IH0 NG
+HARNEY  HH AA1 R N IY0
+HARNEY'S	HH AA1 R N IY0 Z
+HARNHAM	HH AA1 R N AH0 M
+HARNISH  HH AA1 R N IH0 SH
+HARNISS	HH AA1 R N IH0 S
+HARNOSH	HH AA1 R N AH0 SH
+HARNS	HH AA1 R N Z
+HARNSOME	HH AA1 R N S AH0 M
+HARNT	HH AA1 R N T
+HARO  HH AA1 R OW0
+HAROL	HH EH1 R AH0 L
+HAROLD  HH EH1 R AH0 L D
+HAROLD'S  HH EH1 R AH0 L D Z
+HAROLDS	HH EH1 R AH0 L D Z
+HAROOD	HH ER0 UW1 D
+HAROON	HH ER0 UW1 N
+HAROUN	HH AA1 R UW1 N
+HAROUN'S	HH ER0 AW1 N Z
+HARP  HH AA1 R P
+HARP'S	HH AA1 R P S
+HARPAGAS	HH AA1 R P AA1 G AH0 Z
+HARPAGON	HH AA1 R P AH0 G AA2 N
+HARPAGUS	HH AA1 R P AH0 G AH0 S
+HARPALUS	HH AA1 R P AH0 L AH0 S
+HARPAR	HH AA1 R P AA2 R
+HARPE  HH AA1 R P
+HARPE'S	HH AA1 R P S
+HARPED  HH AA1 R P T
+HARPEN	HH AA1 R P AH0 N
+HARPENDEN	HH AA0 R P EH1 N D AH0 N
+HARPENDING	HH AA0 R P EH1 N D IH0 NG
+HARPER  HH AA1 R P ER0
+HARPER'S  HH AA1 R P ER0 Z
+HARPERS  HH AA1 R P ER0 Z
+HARPETH	HH AA1 R P IH0 TH
+HARPIES	HH AA1 R P IY0 Z
+HARPIGNIES	HH AA1 R P IH0 G N IY0 Z
+HARPIN  HH AA0 R P AE1 N
+HARPING  HH AA1 R P IH0 NG
+HARPINGS	HH AA1 R P IH0 NG Z
+HARPIST  HH AA1 R P IH0 S T
+HARPISTS  HH AA1 R P IH0 S T S
+HARPIT	HH AA1 R P IH0 T
+HARPOCRATES	HH AA1 R P AH0 K R EY2 T S
+HARPOON  HH AA0 R P UW1 N
+HARPOONED	HH AA0 R P UW1 N D
+HARPOONEER	HH AA0 R P UW1 N IH1 R
+HARPOONEERS	HH AA0 R P UW1 N IH1 R Z
+HARPOONER	HH AA0 R P UW1 N ER0
+HARPOONERS	HH AA0 R P UW1 N ER0 Z
+HARPOONING	HH AA0 R P UW1 N IH0 NG
+HARPOONS  HH AA0 R P UW1 N Z
+HARPS  HH AA1 R P S
+HARPSICHORD  HH AA1 R P S AH0 K AO2 R D
+HARPSICHORDS	HH AA1 R P S AH0 K AO2 R D Z
+HARPSTENAH	HH AA1 R P S T EY1 N AH0
+HARPSTRING	HH AA1 R P S T R IH2 NG
+HARPSTRINGS	HH AA1 R P S T R IH1 NG Z
+HARPSWELL	HH AA1 R P S W EH2 L
+HARPUR	HH AA1 R P ER0
+HARPY	HH AA1 R P IY0
+HARQUEBUS  HH AA1 R K W AH0 B AH0 S
+HARQUEBUSS	HH AA1 R K W AH0 B AH0 S
+HARRACLES	HH AE1 R AH0 K AH0 L Z
+HARRADEN	HH AE1 R AH0 D EH2 N
+HARRAN	HH AE1 R AH0 N
+HARRAN'S	HH AE1 R AH0 N Z
+HARRAP	HH AE1 R AH0 P
+HARRAS	HH AE1 R AH0 Z
+HARRASS	HH EH1 R AH0 S
+HARRASSED	HH EH1 R AH0 S T
+HARRASSING	HH EH1 R AH0 S IH0 NG
+HARRD	HH AE1 R D
+HARREL  HH AE1 R AH0 L
+HARREL'S	HH AE1 R AH0 L Z
+HARRELS	HH AE1 R AH0 L Z
+HARRER  HH AA1 R ER0
+HARRICANE	HH AE1 R AH0 K EY2 N
+HARRICUTT	HH AE1 R AH0 K AH0 T
+HARRIDAN	HH EH1 R IH0 D AH0 N
+HARRIDANS	HH EH1 R IH0 D AH0 N Z
+HARRIE	HH EH1 R IY0
+HARRIE'S	HH EH1 R IY0 Z
+HARRIED  HH EH1 R IY0 D
+HARRIER  HH EH1 R IY0 ER0
+HARRIERS	HH EH1 R IY0 ER0 Z
+HARRIES  HH EH1 R IY0 Z
+HARRIET  HH EH1 R IY0 AH0 T
+HARRIET'S	HH EH1 R IY0 AH0 T S
+HARRIETT	HH AE1 R IY0 EH0 T
+HARRIETT'S	HH AE1 R IY0 EH0 T S
+HARRIETTE  HH AE1 R IY0 EH0 T
+HARRIGAN  HH EH1 R IH0 G AH0 N
+HARRIGAN'S	HH AE1 R IH0 G AH0 N Z
+HARRIGANS	HH AE1 R IH0 G AH0 N Z
+HARRIGATE	HH AE1 R AH0 G EY2 T
+HARRIHAN	HH EH1 R IH0 HH AE0 N
+HARRIK	HH AE1 R IH0 K
+HARRIMAN  HH EH1 R IH0 M AH0 N
+HARRIMAN'S	HH EH1 R IH0 M AH0 N Z
+HARRINGAY	HH EH1 R IH0 NG EY2
+HARRINGFORD	HH EH1 R IH0 NG F ER0 D
+HARRINGTON  HH EH1 R IH0 NG T AH0 N
+HARRINGTON'S	HH EH1 R IH0 NG T AH0 N Z
+HARRINGTONS	HH EH1 R IH0 NG T AH0 N Z
+HARRIOT	HH AE1 R IY0 AH0 T
+HARRIOT'S	HH AE1 R IY0 AH0 T S
+HARRIOTT  HH AE1 R IY0 AH0 T
+HARRIS  HH EH1 R IH0 S
+HARRIS'S  HH EH1 R IH0 S IH0 Z
+HARRISBURG  HH AE1 R IH0 S B ER0 G
+HARRISBURG  HH EH1 R IH0 S B ER0 G
+HARRISES  HH AE1 R IH0 S IH0 Z
+HARRISMITH	HH EH1 R IH0 S M IH2 TH
+HARRISON  HH EH1 R IH0 S AH0 N
+HARRISON'S  HH EH1 R IH0 S AH0 N Z
+HARRISONBURG	HH EH1 R IH0 S AH0 N B ER0 G
+HARRISONS	HH EH1 R IH0 S AH0 N Z
+HARRISSE	HH AE1 R IH0 S
+HARRISSON	HH AE1 R IH0 S AH0 N
+HARRITY  HH AE1 R IH0 T IY0
+HARRIWELL	HH EH1 R IH0 W EH0 L
+HARROD  HH EH1 R AH0 D
+HARROD'S  HH EH1 R AH0 D Z
+HARRODS  HH EH1 R AH0 D Z
+HARRODSBURG	HH EH1 R AH0 D Z B ER0 G
+HARRODSBURGH	HH EH1 R AH0 D Z B ER0 G
+HARROGATE	HH EH1 R AH0 G EY2 T
+HARROP  HH EH1 R AH0 P
+HARROR	HH AE1 R ER0
+HARROVIAN	HH AE1 R OW0 V IY1 AH0 N
+HARROVIANS	HH AE1 R OW1 V IY0 AH0 N Z
+HARROW  HH AE1 R OW0
+HARROW'S	HH AE1 R OW0 Z
+HARROWBY	HH AE1 R OW0 B IY0
+HARROWBY'S	HH AE1 R OW0 B IY0 Z
+HARROWDEN	HH AE1 R OW0 D AH0 N
+HARROWED	HH AE1 R OW0 D
+HARROWELL'S	HH AE1 R OW0 W EH2 L Z
+HARROWFIELD	HH AE1 R OW0 F IY2 L D
+HARROWFORK	HH AE1 R OW0 F AO2 R K
+HARROWGATE	HH AE1 R OW0 G EY2 T
+HARROWIN	HH AE1 R OW0 IH0 N
+HARROWING  HH EH1 R OW0 IH0 NG
+HARROWINGLY	HH AE1 R OW0 IH0 NG L IY0
+HARROWS	HH AE1 R OW0 Z
+HARRUM	HH AE1 R AH0 M
+HARRY  HH EH1 R IY0
+HARRY'S  HH EH1 R IY0 Z
+HARRYING	HH EH1 R IY0 IH0 NG
+HARRYMAN  HH AE1 R IY0 M AH0 N
+HARRYS	HH AE1 R IY0 Z
+HARSANYI	HH AA1 R S AA1 N Y IY0
+HARSE	HH AA1 R S
+HARSES	HH AA1 R S IH0 Z
+HARSH  HH AA1 R SH
+HARSHA  HH AA1 R SH AH0
+HARSHAW  HH AA1 R SH AO2
+HARSHENED	HH AA1 R SH AH0 N D
+HARSHER  HH AA1 R SH ER0
+HARSHEST  HH AA1 R SH AH0 S T
+HARSHLY  HH AA1 R SH L IY0
+HARSHNESS  HH AA1 R SH N AH0 S
+HARSHNESSES	HH AA1 R SH N AH0 S IH0 Z
+HARSON	HH AA1 R S AH0 N
+HARSTON  HH AA1 R S T AH0 N
+HART  HH AA1 R T
+HART'S  HH AA1 R T S
+HARTE  HH AA1 R T
+HARTE'S	HH AA1 R T EY2 Z
+HARTEBEEST	HH AA1 R T AH0 B IY1 IH0 S T
+HARTEBEESTE	HH AA1 R T AH0 B IY1 S T
+HARTEBEESTS	HH AA1 R T AH0 B AH0 S T S
+HARTENSTEIN  HH AA1 R T AH0 N S T AY2 N
+HARTENSTEIN  HH AA1 R T AH0 N S T IY2 N
+HARTER  HH AA1 R T ER0
+HARTES	HH AA1 R T IY0 Z
+HARTFIELD  HH AA1 R T F IY2 L D
+HARTFORD  HH AA1 R T F ER0 D
+HARTFORD'S  HH AA1 R T F ER0 D Z
+HARTHBOROUGH	HH AA1 R TH B ER2 OW0
+HARTHE	HH AA1 R T
+HARTHILD	HH AA1 R T AY2 L D
+HARTHOVER	HH AA1 R T OW0 V ER0
+HARTIGAN  HH AA1 R T IH0 G AH0 N
+HARTIGAN'S	HH AA1 R T IH0 G AH0 N Z
+HARTILL	HH AA1 R T IH0 L
+HARTILL'S	HH AA1 R T IH0 L Z
+HARTILY	HH AA1 R T AH0 L IY0
+HARTING  HH AA1 R T IH0 NG
+HARTINGTON	HH AA1 R T IH0 NG T AH0 N
+HARTINGTON'S	HH AA1 R T IH0 NG T AH0 N Z
+HARTIST	HH AA1 R T IH0 S T
+HARTLAND  HH AA1 R T L AH0 N D
+HARTLEDON	HH AA1 R T AH0 L D AH0 N
+HARTLEDON'S	HH AA1 R T AH0 L D AH0 N Z
+HARTLEPOD	HH AA1 R T L AH0 P AA2 D
+HARTLEPOOL	HH AA1 R T L AH0 P UW2 L
+HARTLETOP	HH AA1 R T AH0 L T AA2 P
+HARTLEY  HH AA1 R T L IY0
+HARTLEY'S	HH AA1 R T L IY0 Z
+HARTLEYS	HH AA1 R T L IY0 Z
+HARTLY	HH AA1 R T L IY0
+HARTMAN  HH AA1 R T M AH0 N
+HARTMAN'S	HH AA1 R T M AH0 N Z
+HARTMANN  HH AA1 R T M AH0 N
+HARTMANN'S	HH AA1 R T M AH0 N Z
+HARTNESS  HH AA1 R T N AH0 S
+HARTNETT  HH AA1 R T N IH0 T
+HARTNOLL	HH AA1 R T N OW2 L
+HARTOG  HH AA1 R T AH0 G
+HARTON  HH AA1 R T AH0 N
+HARTOP	HH AA1 R T AO0 P
+HARTOPP	HH AA1 R T AH0 P
+HARTOPP'S	HH AA1 R T AH0 P S
+HARTRATH	HH AA1 R T R AH0 TH
+HARTREY	HH AA1 R T R IY0
+HARTRICH	HH AA1 R T R IH0 K
+HARTRIDGE	HH AA1 R T R IH0 JH
+HARTRIGHT	HH AA1 R T R AY2 T
+HARTRIGHT'S	HH AA1 R T R AY2 T S
+HARTROTT	HH AA1 R T R AA1 T
+HARTS  HH AA1 R T S
+HARTSCHWERT	HH AA1 R CH W ER0 T
+HARTSED	HH AA1 R T S T
+HARTSEL	HH AA1 R T S AH0 L
+HARTSEL'S	HH AA1 R T S AH0 L Z
+HARTSHORN  HH AA1 R T S HH AO2 R N
+HARTSHORNE  HH AA1 R CH ER0 N
+HARTSON  HH AA1 R T S AH0 N
+HARTSOOK  HH AA1 R T S UH0 K
+HARTSTEIN  HH AA1 R T S T AY2 N
+HARTSTEIN  HH AA1 R T S T IY2 N
+HARTSTONGUE	HH AA1 R T S T AA1 NG G
+HARTSUFF	HH AA1 R CH AH0 F
+HARTSVILLE  HH AA1 R T S V IH0 L
+HARTVIG	HH AA1 R T V IH0 G
+HARTVIK	HH AA1 R T V IH0 K
+HARTWELL  HH AA1 R T W EH2 L
+HARTWELL'S	HH AA1 R T W EH2 L Z
+HARTWICH	HH AA1 R T W IH0 CH
+HARTWICH'S	HH AA1 R T W IH0 CH IH0 Z
+HARTWIG  HH AA1 R T W IH0 K
+HARTWIG'S	HH AA1 R T W IH0 G Z
+HARTY  HH AA1 R T IY0
+HARTZ  HH AA1 R T S
+HARTZENBOSCH	HH AA1 R T S AH0 N B AO2 SH
+HARTZENBUSCH	HH AA1 R T S AH0 N B UH0 SH
+HARTZMANN	HH AA1 R T S M AH0 N
+HARU	HH AA1 R UW0
+HARU'S	HH AA1 R UW0 Z
+HARUHIKU	HH AA0 R UW0 HH IY1 K UW2
+HARUM	HH EH1 R AH0 M
+HARUN	HH ER0 UW1 N
+HARUSPEX	HH AA0 R UW1 S P EH2 K S
+HARUSPICES	HH AA0 R UW1 S P AH0 S IH0 Z
+HARUT	HH AA0 R UW1 T
+HARV  HH AA1 R V
+HARVARD  HH AA1 R V ER0 D
+HARVARD'S  HH AA1 R V ER0 D Z
+HARVE  HH AA1 R V
+HARVEST  HH AA1 R V AH0 S T
+HARVEST'S	HH AA1 R V AH0 S T S
+HARVESTED  HH AA1 R V AH0 S T AH0 D
+HARVESTED  HH AA1 R V AH0 S T IH0 D
+HARVESTER  HH AA1 R V AH0 S T ER0
+HARVESTER'S	HH AA1 R V AH0 S T ER0 Z
+HARVESTERS  HH AA1 R V AH0 S T ER0 Z
+HARVESTIN	HH AA1 R V AH0 S T IH0 N
+HARVESTING  HH AA1 R V AH0 S T IH0 NG
+HARVESTMAN	HH AA1 R V AH0 S T M AH0 N
+HARVESTMEN	HH AA1 R V EH1 S M AH0 N
+HARVESTON	HH AA1 R V AH0 S T AH0 N
+HARVESTS  HH AA1 R V AH0 S T S
+HARVEY  HH AA1 R V IY0
+HARVEY'S  HH AA1 R V IY0 Z
+HARVEYS	HH AA1 R V EY1 Z
+HARVIE  HH AA1 R V IY0
+HARVILLE  HH AA1 R V IH2 L
+HARVILLE'S	HH AA1 R V IH2 L Z
+HARVILLES	HH AA1 R V IH2 L Z
+HARVINGTON	HH AA1 R V IH0 NG T AH0 N
+HARVISS	HH AA1 R V IH0 S
+HARVY	HH AA1 R V IY0
+HARWELL  HH AA1 R W EH2 L
+HARWICH	HH AA1 R W IH0 CH
+HARWOOD  HH AA1 R W UH2 D
+HARWOOD'S	HH AA1 R W UH2 D Z
+HARZ	HH ER0 Z
+HARZA	HH AA1 R Z AH0
+HAS  HH AE1 Z
+HAS  HH AH0 Z
+HAS'T	HH AE1 S AH0 T
+HASAN  HH EY1 Z AH0 N
+HASARD	HH AE1 S ER0 D
+HASAYAMPA	HH AE1 S AA0 Y AA1 M P AH0
+HASBROOK  HH AE1 S B R UH0 K
+HASBROOK'S	HH AE1 Z B R UH0 K S
+HASBROUCK  HH AE1 Z B R UH2 K
+HASBROUCK'S	HH AE1 Z B R UH2 K S
+HASCHICH	HH AE1 SH IH0 K
+HASCHISCH	HH AE1 SH IH0 SH
+HASCHISH	HH AE1 SH IH0 SH
+HASDRUBAL	HH AE1 S D R UW0 B AE1 L
+HASDRUBAL'S	HH AE1 S D R AH2 B AH0 L Z
+HASE  HH EY1 Z
+HASEBROEK	HH AE1 S IH0 B R OW0 K
+HASELFORD	HH AE1 S IH0 L F ER0 D
+HASELTINE  HH AE1 S IH0 L T IY0 N
+HASELTON  HH AH0 S EH1 L T AH0 N
+HASENCLEVER	HH AE1 S IH0 N K L IY1 V ER0
+HASH  HH AE1 SH
+HASHA	HH AE1 SH AH0
+HASHED  HH AE1 SH T
+HASHEESH	HH AH0 SH IY1 SH
+HASHEM  HH AE1 SH IH0 M
+HASHES  HH AE1 SH AH0 Z
+HASHI  HH AE1 SH IY2
+HASHING  HH AE1 SH IH0 NG
+HASHISH  HH AE1 SH IH0 SH
+HASHISH  HH AH0 SH IY1 SH
+HASHISHIN	HH AH0 SH IY1 SH IH0 N
+HASHMI  HH AA1 SH M IY0
+HASHMI  HH AE1 SH M IY0
+HASHNU	HH AE1 SH N UW0
+HASHOLOM	HH AE1 SH AH0 L AH0 M
+HASIDIM  HH AH0 S IH1 D IH0 M
+HASIDIM  HH AH0 S IY1 D IH0 M
+HASIDISM	HH AH0 S IY1 D IH0 Z AH0 M
+HASISADRA	HH EY1 S IH0 S AA1 D R AH0
+HASJELTI	HH AE1 S JH EH1 L T IY0
+HASK	HH AE1 S K
+HASKALAH	HH AE1 S K AA1 L AH0
+HASKELL  HH AE1 S K AH0 L
+HASKELL'S	HH AE1 S K AH0 L Z
+HASKERS	HH AE1 S K ER0 Z
+HASKET	HH AE1 S K AH0 T
+HASKETT  HH AE1 S K IH0 T
+HASKETT'S	HH AE1 S K IH0 T S
+HASKINS  HH AE1 S K IH0 N Z
+HASLAM  HH AE1 S L AH0 M
+HASLAM'S	HH AE1 S L AH0 M Z
+HASLAR	HH AE1 S L ER0
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+HELGE	HH EH1 L JH
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+HELGOLAND	HH EH1 L G OW1 L AH0 N D
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+HELIACAL	HH IY1 L IY0 AE2 K AH0 L
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+HELICON  HH EH1 L IH0 K AA2 N
+HELICON'S	HH EH1 L IH0 K AA2 N Z
+HELICONIAN	HH EH1 L IH0 K OW1 N IY0 AH0 N
+HELICONIAS	HH EH1 L IH0 K AA2 N IY0 AH0 Z
+HELICOPTER  HH EH1 L IH0 K AA2 P T ER0
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+HELIDOR	HH IY1 L IY0 D AO1 R
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+HELIODORA	HH IY1 L IY0 AH0 D AO1 R AH0
+HELIODORE	HH IY1 L IY0 AH0 D AO2 R
+HELIODORUS	HH IY1 L IY0 AH0 D AO1 R AH0 S
+HELIOGABALUS	HH IY2 L IY0 AA1 G AH0 B AH0 L AH0 S
+HELIOGRAPH	HH IY1 L IY0 AH0 G R AE2 F
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+HELIOGRAPHIC	HH IY1 L IY0 AH0 G R AE1 F IH0 K
+HELIOGRAPHING	HH IY2 L IY0 AA1 G R AH0 F IH0 NG
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+HELLBEAM	HH EH1 L B IY2 M
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+HELLENISTIC  HH EH2 L AH0 N IH1 S T IH0 K
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+HELLENIZED  HH EH1 L AH0 N AY2 Z D
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+HELLINGSLEY	HH EH1 L IH0 NG Z L IY0
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+HELLISH  HH EH1 L IH0 SH
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+HELMONT  HH EH1 L M AA2 N T
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+HELMSLEY  HH EH1 L M Z L IY0
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+HELMSMAN  HH EH1 L M Z M AE2 N
+HELMSMAN'S	HH EH1 L M Z M AE2 N Z
+HELMSMEN	HH EH1 L M Z M IH0 N
+HELMSTONE	HH EH1 L M S T OW2 N
+HELMSTONE'S	HH EH1 L M S T OW2 N Z
+HELMSTONES	HH EH1 L M S T OW2 N Z
+HELMSWOMAN	HH EH1 L M Z W UH2 M AH0 N
+HELMSWORTH	HH EH1 L M Z W ER2 TH
+HELMUND	HH EH1 L M AH0 N D
+HELMUT  HH EH1 L M AH0 T
+HELMUTH  HH EH1 L M UW2 TH
+HELMY	HH IH0 L M IY0
+HELOISE	HH EH1 L OW0 IY0 Z
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+HELON	HH EH1 L AH0 N
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+HELOUAN	HH EH1 L AH0 W AA1 N
+HELOUIN	HH EH1 L W IY1 N
+HELP  HH EH1 L P
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+HELPER  HH EH1 L P ER0
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+HELPFUL  HH EH1 L P F AH0 L
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+HELPMAKAAR	HH EH1 L P M AH0 K AA1 R
+HELPMAN	HH EH1 L P M AH0 N
+HELPMATE	HH EH1 L P M EY2 T
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+HELPMEET	HH EH1 L P M IY1 T
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+HELSING	HH EH1 L S IH0 NG
+HELSING'S	HH EH1 L S IH0 NG Z
+HELSINGBORG	HH EH1 L S IH0 NG B AO0 R G
+HELSINGFORS	HH EH1 L S IH0 NG F ER0 Z
+HELSINGHAM	HH EH1 L S IH0 NG HH AE2 M
+HELST	HH EH1 L S T
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+HELUVA	HH EH1 L UW1 V AH0
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+HELVELLYN	HH EH1 L V EH1 L IH0 N
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+HELVETIA	HH EH1 L V AH0 T Y AH0
+HELVETIAN	HH EH1 L V IY1 SH AH0 N
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+HELVETIC	HH EH1 L V AH0 T IH2 K
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+HELVIDIUS	HH EH1 L V IH0 D IY0 AH0 S
+HELVOETSLUYS	HH EH1 L V OW1 S L AY1 Z
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+HELY	HH EH1 L IY0
+HELZEPHRON	HH EH1 L Z EH1 F R AA0 N
+HELZEPHRON'S	HH EH1 L Z EH1 F R AA0 N Z
+HEM  HH EH1 M
+HEM'D	HH EH1 M D
+HEMAN  HH IY1 M AH0 N
+HEMAN'S	HH EH1 M AH0 N Z
+HEMANGINI	HH EH1 M AA0 NG G IY1 N IY0
+HEMANS	HH EH1 M AH0 N Z
+HEMANS'S	HH EH1 M AH0 N Z IH0 Z
+HEMANTA	HH AH0 M AA1 N T AH0
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+HEMBURG	HH EH1 M B ER0 G
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+HEMERLINGUE	HH EH1 M ER0 L IH0 NG
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+HEMI	HH EH1 M IY0
+HEMICYCLE	HH EH2 M AH0 S AY2 K AH0 L
+HEMING  HH EH1 M IH0 NG
+HEMINGE	HH EH1 M IH0 NG
+HEMINGWAY  HH EH1 M IH0 NG W EY2
+HEMINGWAY'S  HH EH1 M IH0 NG W EY2 Z
+HEMINGWAYS	HH EH1 M IH0 NG W EY2 Z
+HEMINWAY	HH EH1 M IH0 N W EY2
+HEMIPLEGIA  HH EH2 M AH0 P L IY1 JH IY0 AH0
+HEMIPLEGIA  HH EH2 M AH0 P L IY1 JH Y AH0
+HEMIPO	HH EH2 M AH0 P OW0
+HEMIPTERA	HH EH2 M AH0 P T EH1 R AH0
+HEMISPHERE  HH EH1 M IH0 S F IH2 R
+HEMISPHERES	HH EH2 M AH0 S F IH1 R Z
+HEMISPHERIC  HH EH2 M AH0 S F IH1 R IH0 K
+HEMISPHERICAL	HH EH2 M AH0 S F EH1 R IH0 K AH0 L
+HEMLOCK  HH EH1 M L AA2 K
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+HEMM'D	HH EH1 M D
+HEMMED  HH EH1 M D
+HEMMELINE	HH EH1 M IH0 L AY2 N
+HEMMENSHAW	HH EH1 M IH0 N SH AO2
+HEMMING  HH EH1 M IH0 NG
+HEMMING'S	HH EH1 M IH0 NG Z
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+HEMMINGWAY	HH EH1 M IH0 NG W EY2
+HEMMINGWELL	HH EH1 M IH0 NG W EH2 L
+HEMMY	HH EH1 M IY0
+HEMOGLOBIN  HH IY2 M AH0 G L OW1 B AH0 N
+HEMORRHAGE  HH EH1 M ER0 IH0 JH
+HEMORRHAGE  HH EH1 M R AH0 JH
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+HEMP  HH EH1 M P
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+HEMPSTEAD  HH EH1 M P S T EH0 D
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+HEMSTITCH	HH EH1 M S T IH0 CH
+HEMSTITCHED	HH EH1 M S T IH1 CH T
+HEMSTITCHING	HH EH1 M S T IH1 CH IH0 NG
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+HEN  HH EH1 N
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+HENCE  HH EH1 N S
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+HENCHARD	HH EH1 N CH ER0 D
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+HENDERSON  HH EH1 N D ER0 S AH0 N
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+HENDERSONVILLE	HH EH1 N D ER0 S AH0 N V IH2 L
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+HENDRIKSEN  HH EH1 N D R IH0 K S AH0 N
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+HENDRY  HH EH1 N D R IY0
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+HENEAGE	HH EH1 N IY0 AH0 JH
+HENERY  HH EH1 N ER0 IY0
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+HENEY  HH EH1 N IY0
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+HENG  HH EH1 NG
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+HENGLAND	HH EH1 NG G L AH0 N D
+HENGLER'S	HH EH1 NG L ER0 Z
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+HENGLY	HH EH1 NG L IY0
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+HENGSTENBERG	HH EH1 NG G S T AH0 N B ER0 G
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+HENLOPEN	HH EH1 N L OW1 P AH0 N
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+HENNEBON	HH EH1 N IH0 B AO0 N
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+IMPEDIMENT  IH0 M P EH1 D AH0 M AH0 N T
+IMPEDIMENTA	IH0 M P EH1 D AH0 M EH1 N T AH0
+IMPEDIMENTS  IH0 M P EH1 D AH0 M AH0 N T S
+IMPEDING  IH0 M P IY1 D IH0 NG
+IMPEL  IH0 M P EH1 L
+IMPELL'D	IH0 M P EH1 L D
+IMPELLED  IH0 M P EH1 L D
+IMPELLING	IH0 M P EH1 L IH0 NG
+IMPELS	IH0 M P EH1 L Z
+IMPEND  IH0 M P EH1 N D
+IMPENDED	IH0 M P EH1 N D AH0 D
+IMPENDENT	IH0 M P EH1 N D AH0 N T
+IMPENDING  IH0 M P EH1 N D IH0 NG
+IMPENDS	IH0 M P EH1 N D Z
+IMPENETRABILITY	IH0 M P EH1 N AH0 T R AH0 B IH1 L IH0 T IY0
+IMPENETRABLE  IH0 M P EH1 N AH0 T R AH0 B AH0 L
+IMPENETRABLY	IH0 M P EH1 N AH0 T R AH0 B L IY0
+IMPENITENCE	IH0 M P EH1 N IH0 T IH0 N S
+IMPENITENT	IH0 M P EH1 N IH0 T IH0 N T
+IMPENITENTLY	IH0 M P EH1 N IH0 T IH0 N T L IY0
+IMPERA	IH0 M P EH1 R AH0
+IMPERANCE	IH2 M P ER0 AH0 N S
+IMPERATIVE  IH0 M P EH1 R AH0 T IH0 V
+IMPERATIVELY	IH0 M P EH1 R AH0 T IH0 V L IY0
+IMPERATIVENESS	IH0 M P EH1 R AH0 T IH0 V N AH0 S
+IMPERATIVES  IH0 M P EH1 R AH0 T IH0 V Z
+IMPERATOR	IH0 M P EH1 R AH0 T ER0
+IMPERATOR'S	IH0 M P EH1 R AH0 T ER0 Z
+IMPERATRIX	IH2 M P ER0 AE1 T R IH0 K S
+IMPERCEPTIBLE  IH2 M P ER0 S EH1 P T IH0 B AH0 L
+IMPERCEPTIBLY  IH2 M P ER0 S EH1 P T IH0 B L IY0
+IMPERDENCE	IH0 M P ER1 D AH0 N S
+IMPERDENT	IH0 M P ER1 D AH0 N T
+IMPERENCE	IH0 M P IH1 R AH0 N S
+IMPERENT	IH0 M P IH1 R AH0 N T
+IMPERFECT  IH0 M P ER1 F IH0 K T
+IMPERFECTION  IH2 M P ER0 F EH1 K SH AH0 N
+IMPERFECTIONS  IH2 M P ER0 F EH1 K SH AH0 N Z
+IMPERFECTLY  IH0 M P ER1 F IH0 K T L IY0
+IMPERFECTNESS	IH0 M P ER1 F AH0 K T N AH0 S
+IMPERI	IH0 M P EH1 R IY0
+IMPERIA  IH0 M P IY1 R IY0 AH0
+IMPERIAL  IH0 M P IH1 R IY0 AH0 L
+IMPERIALE  IH0 M P ER0 IY0 AA1 L IY0
+IMPERIALIS	IH0 M P IH1 R IY0 AH0 L IH0 S
+IMPERIALISM  IH0 M P IH1 R IY0 AH0 L IH2 Z AH0 M
+IMPERIALIST  IH0 M P IH1 R IY0 AH0 L IH0 S T
+IMPERIALISTIC  IH0 M P IY2 R IY0 AH0 L IH1 S T IH0 K
+IMPERIALISTS  IH0 M P IH1 R IY0 AH0 L IH0 S T S
+IMPERIALLY	IH0 M P IH1 R IY0 AH0 L IY0
+IMPERIALS	IH0 M P IH1 R IY0 AH0 L Z
+IMPERIEUSE	IH0 M P EH1 R IY0 UW2 Z
+IMPERII	IH0 M P IH1 R IY0 IY2
+IMPERIL  IH0 M P EH1 R AH0 L
+IMPERILED  IH0 M P EH1 R AH0 L D
+IMPERILING  IH0 M P EH1 R AH0 L IH0 NG
+IMPERILLED	IH0 M P EH1 R AH0 L D
+IMPERILLING	IH0 M P EH1 R AH0 L IH0 NG
+IMPERILS  IH0 M P EH1 R AH0 L Z
+IMPERIO	IH0 M P EH1 R IY0 OW0
+IMPERIOUS  IH0 M P IH1 R IY0 AH0 S
+IMPERIOUSLY	IH0 M P IH1 R IY0 AH0 S L IY0
+IMPERIOUSNESS	IH0 M P IH1 R IY0 AH0 S N AH0 S
+IMPERISHABLE	IH0 M P EH1 R IH0 SH AH0 B AH0 L
+IMPERISHABLY	IH0 M P EH1 R IH0 SH AH0 B L IY0
+IMPERIUM	IH0 M P IH1 R IY0 AH0 M
+IMPERMANENCE	IH0 M P ER1 M AH0 N AH0 N S
+IMPERMANENCY	IH0 M P ER1 M AH0 N AH0 N S IY0
+IMPERMANENT	IH0 M P ER1 M AH0 N AH0 N T
+IMPERMEABLE	IH0 M P ER1 M IY0 AH0 B AH0 L
+IMPERMISSIBLE  IH2 M P ER0 M IH1 S IH0 B AH0 L
+IMPEROR	IH2 M P ER0 ER0
+IMPERSONAL  IH0 M P ER1 S AH0 N AH0 L
+IMPERSONALITY  IH0 M P ER2 S AH0 N AE1 L AH0 T IY0
+IMPERSONALLY	IH0 M P ER1 S N AH0 L IY0
+IMPERSONATE  IH0 M P ER1 S AH0 N EY2 T
+IMPERSONATED  IH0 M P ER1 S AH0 N EY2 T IH0 D
+IMPERSONATES  IH0 M P ER1 S AH0 N EY2 T S
+IMPERSONATING  IH0 M P ER1 S AH0 N EY2 T IH0 NG
+IMPERSONATION  IH2 M P ER0 S AH0 N EY1 SH AH0 N
+IMPERSONATIONS	IH0 M P ER1 S AH0 N EY1 SH AH0 N Z
+IMPERSONATOR  IH0 M P ER1 S AH0 N EY0 T ER0
+IMPERSONATORS  IH0 M P ER1 S AH0 N EY2 T ER0 Z
+IMPERTINENCE	IH0 M P ER1 T AH0 N AH0 N S
+IMPERTINENCES	IH0 M P ER1 T AH0 N AH0 N S AH0 Z
+IMPERTINENCIES	IH0 M P ER1 T AH0 N AH0 N S IY0 Z
+IMPERTINENT  IH0 M P ER1 T AH0 N AH0 N T
+IMPERTINENTLY	IH0 M P ER1 T IH0 N IH0 N T L IY0
+IMPERTINENTS	IH0 M P ER1 T IH0 N IH0 N T S
+IMPERTURBABILITY	IH0 M P ER2 T ER0 B AH0 B IH1 L IH0 T IY0
+IMPERTURBABLE	IH0 M P ER2 T ER0 B EY1 B AH0 L
+IMPERTURBABLY	IH0 M P ER2 T ER0 B AH0 B L IY0
+IMPERTURBED	IH2 M P ER0 T ER1 B D
+IMPERVIOUS  IH0 M P ER1 V IY0 AH0 S
+IMPERVIOUSNESS	IH0 M P ER1 V IY0 AH0 S N AH0 S
+IMPETT	IH1 M P AH0 T
+IMPETUOSITIES	IH0 M P EH1 CH UW0 AA1 S IH0 T IY0 Z
+IMPETUOSITY	IH0 M P EH1 CH UW0 AA1 S IH0 T IY0
+IMPETUOUS  IH0 M P EH1 CH W AH0 S
+IMPETUOUSLY	IH0 M P EH1 CH W AH0 S L IY0
+IMPETUOUSNESS	IH0 M P EH1 CH W AH0 S N AH0 S
+IMPETUS  IH1 M P AH0 T AH0 S
+IMPEY	IH0 M P IY0
+IMPHM	IH0 M P M
+IMPI	IH0 M P IY0
+IMPIDENCE	IH0 M P AH0 D AH0 N S
+IMPIDENT	IH0 M P IH0 D AH0 N T
+IMPIETIES	IH0 M P AY1 AH0 T IY0 Z
+IMPIETY	IH0 M P AY1 AH0 T IY0
+IMPINGE  IH0 M P IH1 N JH
+IMPINGED  IH0 M P IH1 N JH D
+IMPINGEMENT	IH0 M P IH1 N JH M AH0 N T
+IMPINGES  IH0 M P IH1 N JH IH0 Z
+IMPINGING  IH0 M P IH1 N JH IH0 NG
+IMPINGTON	IH1 M P IH0 NG T AH0 N
+IMPIOUS  IH0 M P AY1 AH0 S
+IMPIOUSLY	IH0 M P AY1 AH0 S L IY0
+IMPIS	IH0 M P IH0 S
+IMPISH  IH1 M P IH0 SH
+IMPISHLY	IH0 M P IH0 SH L IY0
+IMPISHNESS	IH0 M P IH1 SH N AH0 S
+IMPLACABILITY	IH0 M P L AE1 K AH0 B IH1 L IH0 T IY0
+IMPLACABLE  IH0 M P L AE1 K AH0 B AH0 L
+IMPLACABLENESS	IH0 M P L AE1 K AH0 B AH2 L N AH0 S
+IMPLACABLY	IH0 M P L AE1 K AH0 B L IY0
+IMPLANT  IH0 M P L AE1 N T
+IMPLANT  IH1 M P L AE2 N T
+IMPLANTATION  IH0 M P L AE0 N T EY1 SH AH0 N
+IMPLANTED  IH0 M P L AE1 N T IH0 D
+IMPLANTING  IH0 M P L AE1 N T IH0 NG
+IMPLANTS  IH0 M P L AE1 N T S
+IMPLANTS  IH1 M P L AE2 N T S
+IMPLEMENT  IH1 M P L AH0 M AH0 N T
+IMPLEMENTATION  IH2 M P L AH0 M EH0 N T EY1 SH AH0 N
+IMPLEMENTED  IH1 M P L AH0 M EH2 N AH0 D
+IMPLEMENTED  IH1 M P L AH0 M EH2 N T AH0 D
+IMPLEMENTING  IH1 M P L AH0 M EH2 N IH0 NG
+IMPLEMENTING  IH1 M P L AH0 M EH2 N T IH0 NG
+IMPLEMENTS  IH1 M P L AH0 M AH0 N T S
+IMPLEX	IH1 M P L EH2 K S
+IMPLICATE  IH1 M P L IH0 K EY2 T
+IMPLICATED  IH1 M P L IH0 K EY2 T IH0 D
+IMPLICATES  IH1 M P L IH0 K EY2 T S
+IMPLICATING  IH1 M P L IH0 K EY2 T IH0 NG
+IMPLICATION  IH2 M P L AH0 K EY1 SH AH0 N
+IMPLICATIONS  IH2 M P L AH0 K EY1 SH AH0 N Z
+IMPLICIT  IH0 M P L IH1 S AH0 T
+IMPLICITLY  IH0 M P L IH1 S AH0 T L IY0
+IMPLICITY	IH0 M P L IH1 S AH0 T IY0
+IMPLIED  IH0 M P L AY1 D
+IMPLIEDLY	IH0 M P L AY1 D L IY0
+IMPLIES  IH0 M P L AY1 Z
+IMPLOR'D	IH0 M P L AO1 R D
+IMPLORA	IH0 M P L AO1 R AH0
+IMPLORATION	IH0 M P L AO0 R EY1 SH AH0 N
+IMPLORE  IH0 M P L AO1 R
+IMPLORED  IH0 M P L AO1 R D
+IMPLORES  IH0 M P L AO1 R Z
+IMPLORING  IH0 M P L AO1 R IH0 NG
+IMPLORINGLY	IH0 M P L AO1 R IH0 NG L IY0
+IMPLORINGS	IH0 M P L AO1 R IH0 NG Z
+IMPLOY	IH0 M P L OY1
+IMPLOYED	IH0 M P L OY1 D
+IMPLOYMENT	IH0 M P L OY1 M AH0 N T
+IMPLUVIUM	IH0 M P L UW1 V IY0 AH0 M
+IMPLY  IH0 M P L AY1
+IMPLYING  IH0 M P L AY1 IH0 NG
+IMPOLICY	IH0 M P AA1 L AH0 S IY0
+IMPOLITE  IH0 M P AH0 L AY2 T
+IMPOLITELY	IH0 M P AH0 L AY1 T L IY0
+IMPOLITENESS	IH0 M P AH0 L AY1 T N AH0 S
+IMPOLITIC  IH0 M P AO1 L IH1 T IH2 K
+IMPONDERABLE  IH0 M P AA1 N D ER0 AH0 B AH0 L
+IMPONDERABLES  IH0 M P AA1 N D ER0 AH0 B AH0 L Z
+IMPORT  IH0 M P AO1 R T
+IMPORT  IH1 M P AO0 R T
+IMPORTA	IH0 M P AO1 R T AH0
+IMPORTANCE  IH0 M P AO1 R T AH0 N S
+IMPORTANCES	IH0 M P AO1 R T AH0 N S IH0 Z
+IMPORTANT  IH0 M P AO1 R T AH0 N T
+IMPORTANTLY  IH0 M P AO1 R T AH0 N T L IY0
+IMPORTANTS	IH0 M P AO1 R T AH0 N T S
+IMPORTATION  IH2 M P AO0 R T EY1 SH AH0 N
+IMPORTATIONS	IH1 M P AO0 R T EY1 SH AH0 N Z
+IMPORTED  IH0 M P AO1 R T IH0 D
+IMPORTER  IH0 M P AO1 R T ER0
+IMPORTERS  IH0 M P AO1 R T ER0 Z
+IMPORTETH	IH1 M P AO0 R T EH1 TH
+IMPORTING  IH0 M P AO1 R T IH0 NG
+IMPORTS  IH0 M P AO1 R T S
+IMPORTS  IH1 M P AO0 R T S
+IMPORTUNATE	IH0 M P AO1 R CH AH0 N AH0 T
+IMPORTUNATELY	IH0 M P AO1 R CH AH0 N AH0 T L IY0
+IMPORTUNE	IH0 M P AO1 R CH AH0 N
+IMPORTUNED	IH0 M P ER0 T UW1 N D
+IMPORTUNES	IH0 M P AO1 R CH AH0 N Z
+IMPORTUNING	IH0 M P ER0 T UW1 N IH0 NG
+IMPORTUNITIES	IH0 M P ER0 T UW1 N AH0 T IY0 Z
+IMPORTUNITY	IH0 M P ER0 T UW1 N AH0 T IY0
+IMPOS'D	IH0 M P AO2 D
+IMPOSE  IH0 M P OW1 Z
+IMPOSED  IH0 M P OW1 Z D
+IMPOSER	IH0 M P OW1 Z ER0
+IMPOSERS	IH0 M P OW1 Z ER0 Z
+IMPOSES  IH0 M P OW1 Z AH0 Z
+IMPOSES  IH0 M P OW1 Z IH0 Z
+IMPOSIN	IH0 M P AA1 Z AH0 N
+IMPOSING  IH0 M P OW1 Z IH0 NG
+IMPOSINGLY	IH0 M P OW1 Z IH0 NG L IY0
+IMPOSITION  IH2 M P AH0 Z IH1 SH AH0 N
+IMPOSITIONS	IH0 M P AH0 Z IH1 SH AH0 N Z
+IMPOSSIBILE	IH0 M P AA1 S AH0 B IH1 L IY0
+IMPOSSIBILITIES	IH0 M P AA1 S AH0 B IH1 L AH0 T IY0 Z
+IMPOSSIBILITY  IH0 M P AO2 S IH0 B IH1 L IH0 T IY0
+IMPOSSIBLE  IH0 M P AA1 S AH0 B AH0 L
+IMPOSSIBLES	IH0 M P AA1 S AH0 B AH0 L Z
+IMPOSSIBLY  IH0 M P AA1 S AH0 B L IY0
+IMPOST	IH0 M P AO1 S T
+IMPOSTER	IH0 M P AO1 S T ER0
+IMPOSTERS	IH0 M P AO1 S T ER0 Z
+IMPOSTHUME	IH0 M P AA1 S CH UH0 M
+IMPOSTHUMES	IH0 M P AA1 S CH UH0 M Z
+IMPOSTOR  IH0 M P AO1 S T ER0
+IMPOSTOR'S	IH0 M P AO1 S T ER0 Z
+IMPOSTORS  IH0 M P AO1 S T ER0 Z
+IMPOSTS	IH0 M P AO1 S T S
+IMPOSTURE	IH0 M P AA1 S CH ER0
+IMPOSTURES	IH0 M P AA1 S CH ER0 Z
+IMPOT	IH1 M P AH0 T
+IMPOTENCE  IH1 M P AH0 T AH0 N S
+IMPOTENCY	IH1 M P AH0 T AH0 N S IY0
+IMPOTENT  IH1 M P AH0 T AH0 N T
+IMPOTENTLY	IH1 M P AH0 T AH0 N T L IY0
+IMPOUND  IH0 M P AW1 N D
+IMPOUNDED  IH0 M P AW1 N D IH0 D
+IMPOUNDING  IH0 M P AW1 N D IH0 NG
+IMPOVERISH  IH0 M P AA1 V R IH0 SH
+IMPOVERISHED  IH0 M P AA1 V R IH0 SH T
+IMPOVERISHES  IH0 M P AA1 V R IH0 SH AH0 Z
+IMPOVERISHING  IH0 M P AA1 V R IH0 SH IH0 NG
+IMPOVERISHMENT  IH0 M P AA1 V R IH0 SH M AH0 N T
+IMPOWERED	IH0 M P AW1 ER0 D
+IMPOWERING	IH0 M P AW1 ER0 IH0 NG
+IMPRACTICABILITY	IH0 M P R AE1 K T AH0 K AH0 B IH1 L IH0 T IY0
+IMPRACTICABLE  IH0 M P R AE1 K T IH0 K AH0 B AH0 L
+IMPRACTICAL  IH0 M P R AE1 K T AH0 K AH0 L
+IMPRACTICAL  IH0 M P R AE1 K T IH0 K AH0 L
+IMPRACTICALITY	IH0 M P R AE1 K T IH0 K AE1 L AH0 T IY0
+IMPRECATE	IH1 M P R AH0 K EY2 T
+IMPRECATED	IH1 M P R AH0 K EY2 T AH0 D
+IMPRECATES	IH1 M P R AH0 K EY2 T S
+IMPRECATING	IH1 M P R AH0 K EY2 T IH0 NG
+IMPRECATION	IH1 M P R AH0 K EY2 SH AH0 N
+IMPRECATIONS	IH1 M P R AH0 K EY2 SH AH0 N Z
+IMPRECATORY	IH0 M P R EH2 K AH0 T AO2 R IY0
+IMPREGNABILITY	IH0 M P R EH1 G N AH0 B IH1 L IH0 T IY0
+IMPREGNABLE  IH0 M P R EH1 G N AH0 B AH0 L
+IMPREGNABLY	IH0 M P R EH1 G N AH0 B L IY0
+IMPREGNATE  IH0 M P R EH1 G N EY2 T
+IMPREGNATED  IH0 M P R EH1 G N EY2 T AH0 D
+IMPREGNATES  IH0 M P R EH1 G N EY2 T S
+IMPREGNATING  IH0 M P R EH1 G N EY2 T IH0 NG
+IMPREGNATION  IH0 M P R EH1 G N EY1 SH AH0 N
+IMPRESARIO  IH2 M P R IH0 S AA1 R IY0 OW2
+IMPRESARIOS	IH2 M P R IH0 S AA1 R IY0 OW2 Z
+IMPRESCRIPTIBLE	IH1 M P R AH0 S K R IH1 P T AH0 B AH0 L
+IMPRESS  IH0 M P R EH1 S
+IMPRESS  IH1 M P R EH2 S
+IMPRESS'D	IH0 M P R EH1 SH D
+IMPRESSARIO	IH0 M P R EH0 S AA1 R IY0 OW2
+IMPRESSED  IH0 M P R EH1 S T
+IMPRESSES  IH0 M P R EH1 S IH0 Z
+IMPRESSIBILITY	IH0 M P R EH1 S AH0 B IH1 L AH0 T IY0
+IMPRESSIBLE	IH0 M P R EH1 S AH0 B AH0 L
+IMPRESSING  IH0 M P R EH1 S IH0 NG
+IMPRESSION  IH0 M P R EH1 SH AH0 N
+IMPRESSIONABILITY	IH0 M P R EH1 SH AH0 N AH0 B IH1 L IH0 T IY0
+IMPRESSIONABLE  IH0 M P R EH1 SH AH0 N AH0 B AH0 L
+IMPRESSIONABLENESS	IH0 M P R EH1 SH AH0 N AH0 B AH2 L N AH0 S
+IMPRESSIONISM  IH0 M P R EH1 SH AH0 N IH2 Z AH0 M
+IMPRESSIONIST  IH0 M P R EH1 SH AH0 N AH0 S T
+IMPRESSIONIST  IH0 M P R EH1 SH AH0 N IH0 S T
+IMPRESSIONISTIC  IH0 M P R EH2 SH AH0 N IH1 S T IH0 K
+IMPRESSIONISTS  IH0 M P R EH1 SH AH0 N IH0 S T S
+IMPRESSIONS  IH0 M P R EH1 SH AH0 N Z
+IMPRESSIVE  IH0 M P R EH1 S IH0 V
+IMPRESSIVELY  IH0 M P R EH1 S IH0 V L IY0
+IMPRESSIVENESS	IH0 M P R EH1 S IH0 V N AH0 S
+IMPRESSMENT  IH0 M P R EH1 S M AH0 N T
+IMPRESSMENTS	IH0 M P R EH1 S M AH0 N T S
+IMPRESSOR	IH0 M P R EH1 S ER0
+IMPREST	IH0 M P R EH1 S T
+IMPRIMATUR  IH2 M P R IH0 M AA1 T ER0
+IMPRIMIS  IH0 M P R IY1 M IH0 S
+IMPRINT  IH0 M P R IH1 N T
+IMPRINT  IH1 M P R IH0 N T
+IMPRINTED  IH0 M P R IH1 N IH0 D
+IMPRINTED  IH0 M P R IH1 N T IH0 D
+IMPRINTING  IH0 M P R IH1 N IH0 NG
+IMPRINTING  IH0 M P R IH1 N T IH0 NG
+IMPRINTS  IH0 M P R IH1 N T S
+IMPRISON  IH0 M P R IH1 Z AH0 N
+IMPRISON'D	IH0 M P R IH1 Z AH0 N D
+IMPRISONED  IH0 M P R IH1 Z AH0 N D
+IMPRISONING  IH0 M P R IH1 Z AH0 N IH0 NG
+IMPRISONMENT  IH0 M P R IH1 Z AH0 N M AH0 N T
+IMPRISONMENTS	IH0 M P R IH1 Z AH0 N M AH0 N T S
+IMPRISONS  IH0 M P R IH1 Z AH0 N Z
+IMPROBABILITIES	IH0 M P R AA1 B AH0 B IH1 L AH0 T IY0 Z
+IMPROBABILITY	IH0 M P R AA1 B AH0 B IH1 L IH0 T IY0
+IMPROBABLE  IH0 M P R AA1 B AH0 B AH0 L
+IMPROBABLY  IH0 M P R AA1 B AH0 B L IY0
+IMPROBITY	IH0 M P R OW1 B AH0 T IY0
+IMPROBUS	IH0 M P R OW1 B IH0 S
+IMPROMPTU  IH0 M P R AA1 M P T UW0
+IMPROMPTUS	IH0 M P R AA1 M P T AH0 S
+IMPROPER  IH0 M P R AA1 P ER0
+IMPROPERLY  IH0 M P R AA1 P ER0 L IY0
+IMPROPRIETIES  IH2 M P R AH0 P R AY1 AH0 T IY0 Z
+IMPROPRIETY  IH2 M P R AH0 P R AY1 AH0 T IY0
+IMPROV'D	IH2 M P R AA0 V D
+IMPROVABLE	IH0 M P R UW1 V AH0 B AH0 L
+IMPROVE  IH0 M P R UW1 V
+IMPROVEABLE	IH0 M P R UW1 V AH0 B AH0 L
+IMPROVED  IH0 M P R UW1 V D
+IMPROVEMENT  IH0 M P R UW1 V M AH0 N T
+IMPROVEMENTS  IH0 M P R UW1 V M AH0 N T S
+IMPROVER	IH0 M P R UW1 V ER0
+IMPROVERS	IH0 M P R UW1 V ER0 Z
+IMPROVES  IH0 M P R UW1 V Z
+IMPROVIDENCE	IH0 M P R AA1 V AH0 D AH0 N S
+IMPROVIDENT	IH0 M P R AA1 V AH0 D AH0 N T
+IMPROVIDENTLY	IH0 M P R AA1 V AH0 D AH0 N T L IY0
+IMPROVIN	IH2 M P R AA0 V IH0 N
+IMPROVING  IH0 M P R UW1 V IH0 NG
+IMPROVISATION  IH2 M P R AA0 V IH0 Z EY1 SH AH0 N
+IMPROVISATIONS  IH2 M P R AA0 V IH0 Z EY1 SH AH0 N Z
+IMPROVISATOR	IH2 M P R AA0 V IH0 Z EY1 T ER0
+IMPROVISATORE	IH2 M P R AA0 V IH0 Z AA0 T AO1 R IY0
+IMPROVISATORI	IH2 M P R AA0 V IH0 Z AH0 T AO1 R IY0
+IMPROVISATRICE	IH2 M P R AA0 V IH0 Z EY1 T R IH0 S
+IMPROVISE  IH1 M P R AH0 V AY2 Z
+IMPROVISE  IH2 M P R AH0 V AY1 Z
+IMPROVISED  IH1 M P R AH0 V AY2 Z D
+IMPROVISER	IH1 M P R AH0 V AY1 Z ER0
+IMPROVISERS	IH1 M P R AH0 V AY2 Z ER0 Z
+IMPROVISES  IH1 M P R AH0 V AY2 Z AH0 Z
+IMPROVISES  IH2 M P R AH0 V AY1 Z AH0 Z
+IMPROVISING  IH1 M P R AH0 V AY2 Z IH0 NG
+IMPROVISO	IH1 M P R AH0 V AY1 Z OW0
+IMPROVVISATORE	IH0 M P R UW1 V IY0 S AA0 T AO1 R IY0
+IMPRUDENCE  IH0 M P R UW1 D AH0 N S
+IMPRUDENCES	IH0 M P R UW1 D AH0 N S IH0 Z
+IMPRUDENT  IH0 M P R UW1 D AH0 N T
+IMPRUDENTLY  IH0 M P R UW1 D AH0 N T L IY0
+IMPS	IH1 M P S
+IMPTY	IH0 M P T IY0
+IMPUDENCE	IH0 M P Y UW1 D AH0 N S
+IMPUDENT	IH0 M P Y UW1 D AH0 N T
+IMPUDENTLY	IH0 M P Y UW1 D AH0 N T L IY0
+IMPUGN  IH0 M P Y UW1 N
+IMPUGNED  IH0 M P Y UW1 N D
+IMPUGNING  IH0 M P Y UW1 N IH0 NG
+IMPUGNS  IH0 M P Y UW1 N Z
+IMPULSE  IH0 M P AH1 L S
+IMPULSE  IH1 M P AH0 L S
+IMPULSES  IH0 M P AH1 L S IH0 Z
+IMPULSES  IH1 M P AH0 L S IH0 Z
+IMPULSION	IH0 M P AH1 L SH AH0 N
+IMPULSIONS	IH0 M P AH1 L SH AH0 N Z
+IMPULSIVE  IH0 M P AH1 L S IH0 V
+IMPULSIVELY  IH0 M P AH1 L S IH0 V L IY0
+IMPULSIVENESS	IH0 M P AH1 L S IH0 V N AH0 S
+IMPUNE	IH0 M P Y UW1 N
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+INAUSPICIOUS  IH0 N AW2 S P IH1 SH IH0 S
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+INCALCULABLE  IH0 N K AE1 L K Y AH0 L AH0 B AH0 L
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+INCONCEIVABLE  IH2 N K AH0 N S IY1 V AH0 B AH0 L
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+INCONCLUSIVELY  IH0 NG K AA1 N K L UW0 S IH0 V L IY0
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+INCONGRUOUSNESS	IH0 NG K AO1 NG R UW0 AH0 S N AH0 S
+INCONNU	IH0 N K AH0 N UW0
+INCONNUE	IH0 N K AH0 N UW0
+INCONSCIENT	IH0 N K AA1 N SH AH0 N T
+INCONSECUTIVE	IH2 N K AH0 N S EH1 K Y AH0 T IH0 V
+INCONSECUTIVENESS	IH2 N K AH0 N S EH1 K Y AH0 T IH0 V N AH0 S
+INCONSEQUENCE	IH0 NG K AA1 N S AH0 K W AH0 N S
+INCONSEQUENCES	IH0 NG K AA1 N S AH0 K W AH0 N S IH0 Z
+INCONSEQUENT	IH0 NG K AA1 N S AH0 K W AH0 N T
+INCONSEQUENTIAL  IH0 NG K AA2 N S AH0 K W EH1 N CH AH0 L
+INCONSEQUENTIALITIES	IH0 NG K AA1 N S AH0 K W EH2 N SH IY0 AE1 L AH0 T IY0 Z
+INCONSEQUENTIALITY	IH0 NG K AA1 N S AH0 K W EH2 N SH IY0 AE1 L AH0 T IY0
+INCONSEQUENTIALLY	IH0 NG K AA1 N S AH0 K W EH1 N CH AH0 L IY0
+INCONSEQUENTLY	IH0 NG K AA1 N S AH0 K W AH0 N T L IY0
+INCONSIDERABLE	IH2 N K AH0 N S IH1 D ER0 AH0 B AH0 L
+INCONSIDERABLY	IH2 N K AH0 N S IH1 D ER0 AH0 B L IY0
+INCONSIDERATE	IH2 N K AH0 N S IH1 D ER0 AH0 T
+INCONSIDERATELY	IH2 N K AH0 N S IH1 D ER0 AH0 T L IY0
+INCONSIDERATENESS	IH2 N K AH0 N S IH1 D ER0 AH0 T N AH0 S
+INCONSIDERATION	IH2 N K AH0 N S IH2 D ER0 EY1 SH AH0 N
+INCONSISTENCE	IH2 N K AH0 N S IH1 S T AH0 N S
+INCONSISTENCIES  IH0 NG K AA1 N S IH0 S T EH2 N S IY0 Z
+INCONSISTENCY  IH2 N K AH0 N S IH1 S T AH0 N S IY0
+INCONSISTENT  IH2 N K AH0 N S IH1 S T AH0 N T
+INCONSISTENTLY	IH2 N K AH0 N S IH1 S T AH0 N T L IY0
+INCONSOLABLE	IH2 N K AH0 N S OW1 L AH0 B AH0 L
+INCONSOLABLY	IH2 N K AH0 N S OW1 L AH0 B L IY0
+INCONSPICUOUS  IH0 NG K AA1 N S P IH0 K W AH0 S
+INCONSPICUOUSLY	IH2 N K AH0 N S P IH1 K Y UW0 AH0 S L IY0
+INCONSPICUOUSNESS	IH2 N K AH0 N S P IH1 K Y UW0 AH0 S N AH0 S
+INCONSTANCY  IH0 N K AA1 N S T AH0 N S IY0
+INCONSTANT	IH0 NG K AA1 N S T AH0 N T
+INCONTESTABLE	IH2 N K AH0 N T EH1 S T AH0 B AH0 L
+INCONTESTABLY	IH2 N K AH0 N T EH1 S T AH0 B L IY0
+INCONTESTIBLE	IH2 N K AH0 N T EH1 S T AH0 B AH0 L
+INCONTESTIBLY	IH2 N K AH0 N T EH1 S T AH0 B L IY0
+INCONTINENCE  IH0 N K AA1 N T AH0 N AH0 N S
+INCONTINENCY	IH0 N K AA1 N T AH0 N AH0 N S IY0
+INCONTINENT  IH0 N K AA1 N T AH0 N AH0 N T
+INCONTINENTLY	IH0 N K AA1 N T AH0 N AH0 N T L IY0
+INCONTROLLABLE	IH2 N K AH0 N T R OW1 L AH0 B AH0 L
+INCONTROVERTIBLE  IH0 NG K AA2 N T R OW0 V ER1 T IH0 B AH0 L
+INCONTROVERTIBLY	IH0 N K AA1 N T R OW0 V ER2 T AH0 B L IY0
+INCONVENIENCE  IH2 N K AH0 N V IY1 N Y AH0 N S
+INCONVENIENCED  IH2 N K AH0 N V IY1 N Y AH0 N S T
+INCONVENIENCES  IH2 N K AH0 N V IY1 N Y AH0 N S IH0 Z
+INCONVENIENCIES	IH2 N K AH0 N V IY1 N Y AH0 N S IY0 Z
+INCONVENIENCING	IH2 N K AH0 N V IY1 N Y AH0 N S IH0 NG
+INCONVENIENCY	IH2 N K AH0 N V IY1 N Y AH0 N S IY0
+INCONVENIENT  IH2 N K AH0 N V IY1 N Y AH0 N T
+INCONVENIENTLY	IH2 N K AH0 N V IY1 N Y AH0 N T L IY0
+INCONVERTIBLE	IH2 N K AH0 N V ER1 T AH0 B AH0 L
+INCORPORATE  IH0 N K AO1 R P ER0 EY2 T
+INCORPORATED  IH0 N K AO1 R P ER0 EY2 T AH0 D
+INCORPORATED  IH0 N K AO1 R P ER0 EY2 T IH0 D
+INCORPORATES  IH0 N K AO1 R P ER0 EY2 T S
+INCORPORATING  IH0 N K AO1 R P ER0 EY2 T IH0 NG
+INCORPORATION  IH0 N K AO2 R P ER0 EY1 SH AH0 N
+INCORPORATIONS  IH0 N K AO2 R P ER0 EY1 SH AH0 N Z
+INCORPORATORS	IH0 N K AO1 R P ER0 EY2 T ER0 Z
+INCORPOREAL	IH0 N K AO1 R P R IY0 AH0 L
+INCORRECT  IH0 N K ER0 EH1 K T
+INCORRECTLY  IH0 N K ER0 EH1 K T L IY0
+INCORRECTNESS	IH0 N K ER0 EH1 K N AH0 S
+INCORRIGIBILITY	IH0 NG K AO1 R AH0 JH AH0 B IH1 L AH0 T IY0
+INCORRIGIBLE  IH0 N K AA1 R AH0 JH AH0 B AH0 L
+INCORRIGIBLES	IH0 NG K AO1 R AH0 JH AH0 B AH0 L Z
+INCORRIGIBLY	IH0 NG K AO1 R AH0 JH AH0 B L IY0
+INCORRUPT	IH0 N K ER0 AH1 P T
+INCORRUPTIBILITY	IH0 N K ER0 AH1 P T AH0 B IH1 L AH0 T IY0
+INCORRUPTIBLE	IH0 N K ER0 AH1 P T AH0 B AH0 L
+INCORRUPTIBLY	IH0 N K ER0 AH1 P T IH0 B L IY0
+INCORRUPTION	IH0 N K ER0 AH1 P SH AH0 N
+INCOS	IH1 NG K OW0 Z
+INCOURAGEMENT	IH0 N K ER1 IH0 JH M AH0 N T
+INCREAS'D	IH0 N K R IY2 S AH0 D
+INCREASE  IH0 N K R IY1 S
+INCREASE  IH1 N K R IY2 S
+INCREASED  IH0 N K R IY1 S T
+INCREASED  IH1 N K R IY2 S T
+INCREASES  IH0 N K R IY1 S AH0 Z
+INCREASES  IH0 N K R IY1 S IH0 Z
+INCREASES  IH1 N K R IY0 S AH0 Z
+INCREASETH	IH0 N K R IY2 S IH0 TH
+INCREASIN	IH0 N K R IY2 S IH0 N
+INCREASING  IH0 N K R IY1 S IH0 NG
+INCREASINGLY  IH0 N K R IY1 S IH0 NG G L IY0
+INCREASINGLY  IH0 N K R IY1 S IH0 NG L IY0
+INCREATE	IH1 N K R IY0 EY1 T
+INCREDIBILITIES	IH0 N K R EH1 D AH0 B IH1 L AH0 T IY0 Z
+INCREDIBILITY	IH0 N K R EH1 D AH0 B IH1 L AH0 T IY0
+INCREDIBLE  IH0 N K R EH1 D AH0 B AH0 L
+INCREDIBLY  IH0 N K R EH1 D AH0 B L IY0
+INCREDULITIES	IH2 N K R AH0 D UW1 L IH0 T IY0 Z
+INCREDULITY  IH2 N K R AH0 D UW1 L IH0 T IY0
+INCREDULOUS  IH0 N K R EH1 JH AH0 L AH0 S
+INCREDULOUSLY	IH0 N K R EH1 JH AH0 L AH0 S L IY0
+INCREDULOUSNESS	IH0 N K R EH1 JH AH0 L AH0 S N AH0 S
+INCREMENT  IH1 N K R AH0 M AH0 N T
+INCREMENTS  IH1 NG K R AH0 M AH0 N T S
+INCRIMINATE  IH0 N K R IH1 M AH0 N EY2 T
+INCRIMINATED	IH0 N K R IH1 M AH0 N EY2 T AH0 D
+INCRIMINATES	IH0 N K R IH1 M AH0 N EY2 T S
+INCRIMINATING  IH0 N K R IH1 M AH0 N EY2 T IH0 NG
+INCRIMINATION  IH0 N K R IH2 M AH0 N EY1 SH AH0 N
+INCRIMINATORY	IH0 N K R IH1 M AH0 N AH0 T AO2 R IY0
+INCROYABLE	IH0 N K R OY1 AH0 B AH0 L
+INCROYABLES	IH0 N K R OY1 AH0 B AH0 L Z
+INCRUSTATION  IH2 N K R AH0 S T EY1 SH AH0 N
+INCRUSTATIONS	IH2 N K R AH0 S T EY1 SH AH0 N Z
+INCRUSTED	IH0 N K R AH1 S T IH0 D
+INCUBACY	IH1 N K Y AH0 B AH0 S IY0
+INCUBATE  IH1 N K Y AH0 B EY2 T
+INCUBATED	IH1 N K Y AH0 B EY2 T IH0 D
+INCUBATING  IH1 N K Y AH0 B EY2 T IH0 NG
+INCUBATION  IH2 NG K Y UW0 B EY1 SH AH0 N
+INCUBATOR  IH1 NG K Y AH0 B EY2 T ER0
+INCUBATORS  IH1 NG K Y UW0 B EY2 T ER0 Z
+INCUBI	IH1 N K Y AH0 B AY2
+INCUBU	IH1 N K Y AH0 B UW0
+INCUBUS	IH1 N K Y AH0 B IH0 S
+INCULCATE  IH1 NG K AH0 L K EY2 T
+INCULCATED  IH1 NG K AH0 L K EY2 T AH0 D
+INCULCATES  IH1 NG K AH0 L K EY2 T S
+INCULCATING	IH1 NG K AH0 L K EY2 T IH0 NG
+INCULCATION	IH1 NG K AH0 L K EY2 SH AH0 N
+INCULCATIONS	IH1 NG K AH0 L K EY2 SH AH0 N Z
+INCULPATE	IH0 N K AH1 L P EY0 T
+INCULPATED	IH0 N K AH1 L P EY0 T AH0 D
+INCULPATING	IH0 N K AH1 L P EY0 T IH0 NG
+INCULPATION	IH0 N K AH1 L P EY0 SH AH0 N
+INCUMBENCY  IH0 N K AH1 M B AH0 N S IY0
+INCUMBENT  IH0 N K AH1 M B AH0 N T
+INCUMBENT'S  IH0 N K AH1 M B AH0 N T S
+INCUMBENTS  IH0 N K AH1 M B AH0 N T S
+INCUMBER	IH0 N K AH1 M B ER0
+INCUMBERED	IH0 N K AH1 M B ER0 D
+INCUMBRANCE	IH0 N K AH1 M B R AH0 N S
+INCUMBRANCES	IH0 N K AH1 M B R AH0 N S IH0 Z
+INCUNABULA	IH1 NG K AH0 N AA0 B UW1 L AH0
+INCUR  IH0 N K ER1
+INCURABLE  IH0 N K Y UH1 R AH0 B AH0 L
+INCURABLES	IH0 N K Y UH1 R AH0 B AH0 L Z
+INCURABLY	IH1 N K Y ER0 AH0 B L IY0
+INCURIOSITY	IH0 N K Y UH2 R IY0 AA1 S AH0 T IY0
+INCURIOUS	IH0 N K Y UH1 R IY0 AH0 S
+INCURIOUSLY	IH0 N K Y UH1 R IY0 AH0 S L IY0
+INCURR'D	IH0 N K ER1 D
+INCURRED  IH0 N K ER1 D
+INCURRING  IH0 N K ER1 IH0 NG
+INCURS  IH0 N K ER1 Z
+INCURSION  IH0 N K ER1 ZH AH0 N
+INCURSIONS  IH0 N K ER1 ZH AH0 N Z
+INCURVE	IH0 N K ER1 V
+INCURVED	IH0 N K ER1 V D
+INCURVING	IH0 N K ER1 V IH0 NG
+IND	IH0 N D
+INDABA  IH0 N D AA1 B AH0
+INDABEZIMBI	IH0 N D AH0 B IH0 Z IH1 M B IY0
+INDADE	IH0 N D EY1 D
+INDARA	IH0 N D AA1 R AH0
+INDE	IH0 N D
+INDEANS	IH0 N D IY1 AH0 N Z
+INDEAVOUR	IH0 N D EH1 V ER0
+INDEAVOURED	IH0 N D EH1 V ER0 D
+INDEBTED  IH0 N D EH1 T AH0 D
+INDEBTED  IH0 N D EH1 T IH0 D
+INDEBTEDNESS  IH0 N D EH1 T IH0 D N IH0 S
+INDECENCIES	IH0 N D IY1 S AH0 N S IY0 Z
+INDECENCY  IH0 N D IY1 S AH0 N S IY0
+INDECENT  IH0 N D IY1 S AH0 N T
+INDECENTLY	IH0 N D IY1 S AH0 N T L IY0
+INDECIPHERABLE	IH2 N D IH0 S AY1 F ER0 AH0 B AH0 L
+INDECISION  IH0 N D IH0 S IH1 ZH AH0 N
+INDECISIONS	IH2 N D IH0 S IH1 ZH AH0 N Z
+INDECISIVE  IH2 N D IH0 S AY1 S IH0 V
+INDECISIVELY	IH2 N D IH0 S AY1 S IH0 V L IY0
+INDECISIVENESS  IH0 N D EH1 S IH0 S IH0 V N AH0 S
+INDECOROUS	IH0 N D EH1 K ER0 AH0 S
+INDECOROUSLY	IH0 N D EH1 K ER0 AH0 S L IY0
+INDECORUM	IH2 N D IH0 K AO1 R AH0 M
+INDECORUMS	IH2 N D IH0 K AO1 R AH0 M Z
+INDEED  IH0 N D IY1 D
+INDEEDE	IH0 N D IY1 D
+INDEEDY	IH0 N D IY1 D IY0
+INDEET	IH0 N D IY1 T
+INDEFATIGABILITY	IH2 N D IH0 F AE1 T IH0 G AH0 B IH1 L IH0 T IY0
+INDEFATIGABLE  IH2 N D IH0 F AE1 T IH0 G AH0 B AH0 L
+INDEFATIGABLY	IH2 N D IH0 F AE1 T IH0 G AH0 B L IY0
+INDEFEASIBLE	IH2 N D IH0 F IY1 Z AH0 B AH0 L
+INDEFEASIBLY	IH2 N D IH0 F IY1 Z AH0 B L IY0
+INDEFECTIBLE	IH2 N D IH0 F EH1 K T IH0 B AH0 L
+INDEFENSIBLE  IH0 N D IH0 F EH1 N S AH0 B AH0 L
+INDEFENSIBLY	IH2 N D IH0 F EH1 N S AH0 B L IY0
+INDEFER	IH2 N D IH0 F ER0
+INDEFINABLE  IH0 N D IH0 F AY1 N AH0 B AH0 L
+INDEFINABLY	IH2 N D IH0 F AY1 N AH0 B L IY0
+INDEFINITE  IH0 N D EH1 F AH0 N AH0 T
+INDEFINITELY  IH0 N D EH1 F AH0 N AH0 T L IY0
+INDEFINITENESS	IH0 N D EH1 F AH0 N AH0 T N AH0 S
+INDEFINITUM	IH0 N D EH1 F AH0 N AH0 T AH0 M
+INDELIBLE  IH0 N D EH1 L IH0 B AH0 L
+INDELIBLY  IH0 N D EH1 L AH0 B L IY0
+INDELICACIES	IH0 N D EH1 L IH0 K AH0 S IY0 Z
+INDELICACY	IH0 N D EH1 L IH0 K AH0 S IY0
+INDELICATE  IH0 N D EH1 L IH0 K AH0 T
+INDELICATELY	IH0 N D EH1 L AH0 K AH0 T L IY0
+INDEMNIFICATION  IH0 N D EH2 M N AH0 F IH0 K EY1 SH AH0 N
+INDEMNIFIED  IH0 N D EH1 M N AH0 F AY2 D
+INDEMNIFIES	IH0 N D EH1 M N AH0 F AY2 Z
+INDEMNIFY  IH0 N D EH1 M N AH0 F AY2
+INDEMNIFYING  IH0 N D EH2 M N IH0 F AY1 IH0 NG
+INDEMNITIES  IH0 N D EH1 M N IH0 T IY0 Z
+INDEMNITY  IH0 N D EH1 M N AH0 T IY0
+INDEMNITY  IH0 N D EH1 M N IH0 T IY0
+INDENT  IH0 N D EH1 N T
+INDENTATION  IH0 N D EH2 N T EY1 SH AH0 N
+INDENTATIONS	IH0 N D EH2 N T EY1 SH AH0 N Z
+INDENTED	IH0 N D EH2 N T IH0 D
+INDENTING	IH0 N D EH1 N T IH0 NG
+INDENTS	IH2 N D AH0 N T S
+INDENTURE  IH0 N D EH1 N CH ER0
+INDENTURED  IH0 N D EH1 N CH ER0 D
+INDENTURES  IH0 N D EH1 N CH ER0 Z
+INDEPENDANCE	IH2 N D IH0 P EH1 N D AH0 N S
+INDEPENDANT	IH2 N D IH0 P EH1 N D AH0 N T
+INDEPENDENCE  IH2 N D IH0 P EH1 N D AH0 N S
+INDEPENDENCIA	IH2 N D IH0 P EH1 N D AH0 N S IY0 AH0
+INDEPENDENCY	IH2 N D IH0 P EH1 N D AH0 N S IY0
+INDEPENDENT  IH2 N D IH0 P EH1 N D AH0 N T
+INDEPENDENTLY  IH2 N D IH0 P EH1 N D AH0 N T L IY0
+INDEPENDENTS  IH2 N D IH0 P EH1 N D AH0 N T S
+INDES	IH1 N D EH0 S
+INDESCRIBABLE  IH2 N D IH0 S K R AY1 B AH0 B AH0 L
+INDESCRIBABLES	IH2 N D IH0 S K R AY1 B AH0 B AH0 L Z
+INDESCRIBABLY	IH2 N D IH0 S K R AY1 B AH0 B L IY0
+INDESTRUCTIBILITY  IH2 N D AH0 S T R AH2 K T IH0 B IH1 L IH0 T IY0
+INDESTRUCTIBLE  IH2 N D AH0 S T R AH1 K T IH0 B AH0 L
+INDESTRUCTIBLY	IH2 N D IH0 S T R AH1 K T IH0 B L IY0
+INDETERMINABLE	IH2 N D IH0 T ER1 M AH0 N AH0 B AH0 L
+INDETERMINATE  IH2 N D IH0 T ER1 M IH0 N IH0 T
+INDETERMINATELY	IH2 N D IH0 T ER1 M AH0 N AH0 T L IY0
+INDETERMINATENESS	IH2 N D IH0 T ER1 M AH0 N AH0 T N AH0 S
+INDETERMINATION	IH2 N D IH0 T ER2 M AH0 N EY1 SH AH0 N
+INDEX  IH1 N D EH0 K S
+INDEXED  IH1 N D EH0 K S T
+INDEXES  IH1 N D EH0 K S IH0 Z
+INDEXING  IH1 N D EH0 K S IH0 NG
+INDEXOGRAPH	IH1 N D EH0 K S AH0 G R AE2 F
+INDHLOVU	IH0 N D L OW1 V UW1
+INDI	IH1 N D IY0
+INDIA  IH1 N D IY0 AH0
+INDIA'S  IH1 N D IY0 AH0 Z
+INDIAMAN	IH0 N D AY1 AH0 M AH0 N
+INDIAMAN'S	IH0 N D AY1 AH0 M AH0 N Z
+INDIAMEN	IH1 N D IY0 EY1 M AH0 N
+INDIAN  IH1 N D IY0 AH0 N
+INDIAN'S  IH1 N D IY0 AH0 N Z
+INDIANA  IH2 N D IY0 AE1 N AH0
+INDIANA'S  IH2 N D IY0 AE1 N AH0 Z
+INDIANAPOLIS  IH2 N D IY0 AH0 N AE1 P AH0 L IH0 S
+INDIANIAN  IH2 N D IY0 AE1 N IY0 AH0 N
+INDIANIANS  IH2 N D IY0 AE1 N IY0 AH0 N Z
+INDIANIZED	IH1 N D IY0 AH0 N AY2 Z D
+INDIANNA	IH2 N D IY0 AE1 N AH0
+INDIANOLA  IH1 N D IY0 AH0 N OW1 L AH0
+INDIANS  IH1 N D IY0 AH0 N Z
+INDIANTOWN	IH1 N D IY0 AH0 N T AW2 N
+INDIANY	IH1 N D IY0 AH0 N IY0
+INDIARUBBER	IH0 N D IY0 ER0 AH1 B ER0
+INDIAS	IH1 N D IY0 AH0 Z
+INDICA  IH1 D AH0 K AH0
+INDICATE  IH1 N D AH0 K EY2 T
+INDICATED  IH1 N D AH0 K EY2 T AH0 D
+INDICATED  IH1 N D AH0 K EY2 T IH0 D
+INDICATES  IH1 N D IH0 K EY2 T S
+INDICATIN	IH0 N D IH1 K AH0 T IH0 N
+INDICATING  IH1 N D AH0 K EY2 T IH0 NG
+INDICATION  IH2 N D AH0 K EY1 SH AH0 N
+INDICATIONS  IH2 N D AH0 K EY1 SH AH0 N Z
+INDICATIVE  IH0 N D IH1 K AH0 T IH0 V
+INDICATIVELY	IH0 N D IH1 K AH0 T IH0 V L IY0
+INDICATOR  IH1 N D AH0 K EY2 T ER0
+INDICATORS  IH1 N D AH0 K EY2 T ER0 Z
+INDICATORY	IH0 N D IH1 K AH0 T AO2 R IY0
+INDICES  IH1 N D AH0 S IH0 Z
+INDICES  IH1 N D IH0 S IY2 Z
+INDICIA  IH0 N D IH1 S Y AH0
+INDICIUM	IH0 N D IH1 S Y AH0 M
+INDICO	IH1 N D AH0 K OW0
+INDICT  IH0 N D AY1 T
+INDICTABLE	IH0 N D IH1 K T AH0 B AH0 L
+INDICTED  IH0 N D AY1 T AH0 D
+INDICTED  IH0 N D AY1 T IH0 D
+INDICTING  IH0 N D AY1 T IH0 NG
+INDICTMENT  IH0 N D AY1 T M AH0 N T
+INDICTMENTS  IH0 N D AY1 T M AH0 N T S
+INDICUS	IH1 N D IH0 K AH0 S
+INDIENNE	IH1 N D IY0 EH2 N
+INDIES  IH1 N D IY0 Z
+INDIFFERENCE  IH0 N D IH1 F ER0 AH0 N S
+INDIFFERENCE  IH0 N D IH1 F R AH0 N S
+INDIFFERENCES	IH0 N D IH1 F R AH0 N S IH0 Z
+INDIFFERENCY	IH0 N D IH1 F R AH0 N S IY0
+INDIFFERENT  IH0 N D IH1 F ER0 AH0 N T
+INDIFFERENT  IH0 N D IH1 F R AH0 N T
+INDIFFERENTISM	IH0 N D IH1 F R AH0 N T IH0 Z AH0 M
+INDIFFERENTIST	IH0 N D IH1 F R AH0 N T IH0 S T
+INDIFFERENTLY	IH0 N D IH1 F R AH0 N T L IY0
+INDIFFERENTS	IH0 N D IH1 F R AH0 N T S
+INDIGENCE	IH0 N D IH1 JH AH0 N S
+INDIGENE	IH1 N D IH0 JH IY2 N
+INDIGENES	IH0 N D AY0 JH IY1 N Z
+INDIGENOUS  IH0 N D IH1 JH AH0 N AH0 S
+INDIGENT  IH1 N D IH0 JH AH0 N T
+INDIGESTED	IH0 N D AY0 JH EH1 S T AH0 D
+INDIGESTIBLE	IH0 N D AY0 JH EH1 S T AH0 B AH0 L
+INDIGESTION  IH2 N D AY0 JH EH1 S CH AH0 N
+INDIGESTIONS	IH0 N D AY0 JH EH1 S CH AH0 N Z
+INDIGNANT  IH0 N D IH1 G N AH0 N T
+INDIGNANTLY  IH0 N D IH1 G N AH0 N T L IY0
+INDIGNATIO	IH1 N D IH0 G N EY1 SH IY0 OW0
+INDIGNATION  IH2 N D IH0 G N EY1 SH AH0 N
+INDIGNATIONS	IH1 N D IH0 G N EY1 SH AH0 N Z
+INDIGNE	IH0 N D IH1 G N IY0
+INDIGNITIES  IH0 N D IH1 G N AH0 T IY0 Z
+INDIGNITY  IH0 N D IH1 G N AH0 T IY0
+INDIGO  IH1 N D AH0 G OW2
+INDIGO  IH1 N D IH0 G OW2
+INDIGOES	IH1 N D AH0 G OW2 Z
+INDIO	IH1 N D IY0 OW0
+INDIOS	IH1 N D IY0 OW1 S
+INDIRECT  IH0 N D ER0 EH1 K T
+INDIRECTION	IH0 N D ER0 EH1 K SH AH0 N
+INDIRECTIONS	IH0 N D ER0 EH1 K SH AH0 N Z
+INDIRECTLY  IH0 N D ER0 EH1 K L IY0
+INDIRECTLY  IH0 N D ER0 EH1 K T L IY0
+INDIRECTNESS	IH0 N D ER0 EH1 K N AH0 S
+INDISCERNIBLE	IH1 N D IH0 S ER1 N AH0 B AH0 L
+INDISCIPLINE	IH0 N D IH1 S AH0 P L AH0 N
+INDISCOVERABLE	IH2 N D IH0 S K AH1 V ER0 AH0 B AH0 L
+INDISCREET  IH0 N D IH0 S K R IY1 T
+INDISCREETLY	IH2 N D IH0 S K R IY1 T L IY0
+INDISCRETION  IH2 N D IH0 S K R EH1 SH AH0 N
+INDISCRETIONS  IH2 N D IH0 S K R EH1 SH AH0 N Z
+INDISCRIMINATE  IH0 N D IH0 S K R IH1 M AH0 N AH0 T
+INDISCRIMINATELY  IH2 N D IH0 S K R IH1 M AH0 N AH0 T L IY0
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+INDULGENCES  IH0 N D AH1 L JH AH0 N S IH0 Z
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+INTENSIFYING  IH0 N T EH1 N S AH0 F AY2 IH0 NG
+INTENSION	IH0 N T EH1 N SH AH0 N
+INTENSITIES	IH0 N T EH1 N S AH0 T IY0 Z
+INTENSITY  IH0 N T EH1 N S AH0 T IY0
+INTENSITY  IH0 N T EH1 N S IH0 T IY0
+INTENSIVE  IH0 N T EH1 N S IH0 V
+INTENSIVELY  IH0 N T EH1 N S IH0 V L IY0
+INTENT  IH0 N T EH1 N T
+INTENTION  IH0 N T EH1 N CH AH0 N
+INTENTIONAL  IH0 N T EH1 N SH AH0 N AH0 L
+INTENTIONALLY  IH0 N T EH1 N SH AH0 N AH0 L IY0
+INTENTIONED  IH0 N T EH1 N CH AH0 N D
+INTENTIONS  IH0 N T EH1 N CH AH0 N Z
+INTENTLY  IH0 N T EH1 N T L IY0
+INTENTNESS	IH0 N T EH1 N T N AH0 S
+INTENTS  IH0 N T EH1 N T S
+INTER  IH0 N T ER1
+INTERACT  IH2 N ER0 AE1 K T
+INTERACT  IH2 N T ER0 AE1 K T
+INTERACTING  IH2 N ER0 AE1 K T IH0 NG
+INTERACTING  IH2 N T ER0 AE1 K T IH0 NG
+INTERACTION  IH2 N ER0 AE1 K SH AH0 N
+INTERACTION  IH2 N T ER0 AE1 K SH AH0 N
+INTERACTIONS  IH2 N ER0 AE1 K SH AH0 N Z
+INTERACTIONS  IH2 N T ER0 AE1 K SH AH0 N Z
+INTERBLENDED	IH2 N T ER0 B L EH1 N D IH0 D
+INTERBREED	IH2 N T ER0 B R IY1 D
+INTERBREEDING	IH2 N T ER0 B R IY1 D IH0 NG
+INTERCALARY	IH2 N T ER0 K EY1 L ER0 IY0
+INTERCALATED	IH2 N T ER0 K AH0 L EY2 T IH0 D
+INTERCALATION	IH2 N T ER0 K AH0 L EY1 SH AH0 N
+INTERCEDE  IH2 N T ER0 S IY1 D
+INTERCEDED  IH2 N T ER0 S IY1 D IH0 D
+INTERCEDES	IH2 N T ER0 S IY1 D Z
+INTERCEDING  IH2 N T ER0 S IY1 D IH0 NG
+INTERCEPT  IH2 N ER0 S EH1 P T
+INTERCEPT  IH2 N T ER0 S EH1 P T
+INTERCEPTED  IH2 N ER0 S EH1 P T AH0 D
+INTERCEPTED  IH2 N ER0 S EH1 P T IH0 D
+INTERCEPTED  IH2 N T ER0 S EH1 P T AH0 D
+INTERCEPTED  IH2 N T ER0 S EH1 P T IH0 D
+INTERCEPTING  IH2 N ER0 S EH1 P T IH0 NG
+INTERCEPTING  IH2 N T ER0 S EH1 P T IH0 NG
+INTERCEPTION  IH2 N ER0 S EH1 P SH AH0 N
+INTERCEPTION  IH2 N T ER0 S EH1 P SH AH0 N
+INTERCEPTOR  IH2 N ER0 S EH1 P T ER0
+INTERCEPTOR  IH2 N T ER0 S EH1 P T ER0
+INTERCEPTORS  IH2 N ER0 S EH1 P T ER0 Z
+INTERCEPTORS  IH2 N T ER0 S EH1 P T ER0 Z
+INTERCEPTS  IH2 N ER0 S EH1 P T S
+INTERCEPTS  IH2 N T ER0 S EH1 P T S
+INTERCESSION  IH2 N ER0 S EH1 SH AH0 N
+INTERCESSION  IH2 N T ER0 S EH1 SH AH0 N
+INTERCESSIONS	IH2 N ER0 S EH1 SH AH0 N Z
+INTERCESSOR	IH2 N ER0 S EH1 S ER0
+INTERCESSORS	IH2 N ER0 S EH1 S ER0 Z
+INTERCESSORY	IH2 N ER0 S EH1 S ER0 IY0
+INTERCHANGE  IH2 N ER0 CH EY1 N JH
+INTERCHANGE  IH2 N T ER0 CH EY1 N JH
+INTERCHANGEABLE  IH2 N ER0 CH EY1 N JH AH0 B AH0 L
+INTERCHANGEABLE  IH2 N T ER0 CH EY1 N JH AH0 B AH0 L
+INTERCHANGEABLY  IH2 N ER0 CH EY1 N JH AH0 B L IY0
+INTERCHANGEABLY  IH2 N T ER0 CH EY1 N JH AH0 B L IY0
+INTERCHANGED	IH2 N ER0 CH EY1 N JH D
+INTERCHANGES  IH1 N ER0 CH EY2 N JH IH0 Z
+INTERCHANGES  IH1 N T ER0 CH EY2 N JH IH0 Z
+INTERCHANGING	IH2 N ER0 CH EY1 N JH IH0 NG
+INTERCOLLEGIATE  IH2 N T ER0 K AH0 L IY1 JH AH0 T
+INTERCOLONIAL	IH2 N T ER0 K AH0 L OW1 N IY0 AH0 L
+INTERCOM  IH1 N T ER0 K AA2 M
+INTERCOMMUNED	IH2 N T ER0 K AH0 M Y UW1 N D
+INTERCOMMUNICATING	IH2 N T ER0 K AH0 M Y UW1 N AH0 K EY2 T IH0 NG
+INTERCOMMUNICATION	IH2 N T ER0 K AH0 M Y UW2 N AH0 K EY1 SH AH0 N
+INTERCOMMUNING	IH2 N T ER0 K AH0 M Y UW1 N IH0 NG
+INTERCOMMUNION	IH2 N T ER0 K AH0 M Y UW1 N Y AH0 N
+INTERCONNECTED  IH2 N T ER0 K AH0 N EH1 K T IH0 D
+INTERCONNECTION  IH2 N T ER0 K AH0 N EH1 K SH AH0 N
+INTERCONTINENTAL  IH2 N T ER0 K AA2 N T AH0 N EH1 N T AH0 L
+INTERCOSTAL	IH2 N T ER0 K AO1 S T AH0 L
+INTERCOURSE  IH1 N ER0 K AO2 R S
+INTERCOURSE  IH1 N T ER0 K AO2 R S
+INTERCOURSES	IH2 N T ER0 K AO2 R S IH0 Z
+INTERCROSSING	IH2 N T ER0 K R AO1 S IH0 NG
+INTERDENOMINATIONAL	IH2 N T ER0 D IH0 N AO2 M AH0 N EY1 SH AH0 N AH0 L
+INTERDEPENDENCE  IH2 N T ER0 D AH0 P EH1 N D AH0 N S
+INTERDEPENDENT  IH2 N T ER0 D IH0 P EH1 N D AH0 N T
+INTERDICT  IH1 N T ER0 D IH2 K T
+INTERDICTED  IH1 N T ER0 D IH2 K T IH0 D
+INTERDICTING  IH2 N T ER0 D IH1 K T IH0 NG
+INTERDICTION  IH2 N T ER0 D IH1 K SH AH0 N
+INTERDICTIONS	IH2 N T ER0 D IH1 K SH AH0 N Z
+INTERDICTS	IH2 N T ER0 D IH2 K T S
+INTERDUCE	IH2 N T ER0 D UW1 S
+INTERDUCED	IH2 N T ER0 D UW1 S T
+INTERDUM	IH2 N T ER0 D AH0 M
+INTEREA	IH2 N ER0 IY1 AH0
+INTERESSANT	IH2 N T ER0 EH1 S AH0 N T
+INTEREST  IH1 N T ER0 AH0 S T
+INTEREST  IH1 N T ER0 IH0 S T
+INTEREST  IH1 N T R AH0 S T
+INTEREST  IH1 N T R IH0 S T
+INTEREST'S	IH1 N T R AH0 S T S
+INTERESTED  IH1 N T ER0 AH0 S T AH0 D
+INTERESTED  IH1 N T ER0 IH0 S T IH0 D
+INTERESTED  IH1 N T R AH0 S T AH0 D
+INTERESTED  IH1 N T R IH0 S T IH0 D
+INTERESTEDLY	IH1 N T R IH0 S T IH0 D L IY0
+INTERESTEDNESS	IH1 N T R IH0 S T IH0 D N AH0 S
+INTERESTIN	IH1 N T R IH0 S T IH0 N
+INTERESTING  IH1 N T ER0 AH0 S T IH0 NG
+INTERESTING  IH1 N T ER0 IH0 S T IH0 NG
+INTERESTING  IH1 N T R AH0 S T IH0 NG
+INTERESTING  IH1 N T R IH0 S T IH0 NG
+INTERESTINGLY  IH1 N T ER0 EH2 S T IH0 NG L IY0
+INTERESTINGNESS	IH1 N T R AH0 S T IH0 NG N IH0 S
+INTERESTS  IH1 N T ER0 AH0 S T S
+INTERESTS  IH1 N T ER0 IH0 S T S
+INTERESTS  IH1 N T R AH0 S T S
+INTERESTS  IH1 N T R IH0 S T S
+INTERET	IH2 N T ER0 IH0 T
+INTERETS	IH2 N T ER0 IH0 T S
+INTERFERE  IH2 N ER0 F IH1 R
+INTERFERE  IH2 N T ER0 F IH1 R
+INTERFERED  IH2 N ER0 F IH1 R D
+INTERFERED  IH2 N T ER0 F IH1 R D
+INTERFERENCE  IH2 N ER0 F IH1 R AH0 N S
+INTERFERENCE  IH2 N T ER0 F IH1 R AH0 N S
+INTERFERENCES	IH2 N T ER0 F IH1 R AH0 N S IH0 Z
+INTERFERER	IH2 N T ER0 F IH1 R ER0
+INTERFERERS	IH2 N T ER0 F IH1 R ER0 Z
+INTERFERES  IH2 N ER0 F IH1 R Z
+INTERFERES  IH2 N T ER0 F IH1 R Z
+INTERFERIN	IH2 N T ER0 F IH1 R IH0 N
+INTERFERING  IH2 N ER0 F IH1 R IH0 NG
+INTERFERING  IH2 N T ER0 F IH1 R IH0 NG
+INTERFUSE	IH2 N T ER0 F Y UW1 Z
+INTERFUSED	IH2 N T ER0 F Y UW1 Z D
+INTERFUSION	IH2 N T ER0 F Y UW1 ZH AH0 N
+INTERGALACTIC	IH2 N T ER0 G AH0 L AE1 K T IH0 K
+INTERGLACIAL	IH2 N T ER0 G L EY1 SH AH0 L
+INTERIEUR	IH0 N T IH1 R IY0 ER0
+INTERIEURE	IH0 N T EH1 R IY0 UW2 R
+INTERIM  IH1 N ER0 AH0 M
+INTERIM  IH1 N T ER0 AH0 M
+INTERIM  IH1 N T R AH0 M
+INTERIMS	IH1 N T R AH0 M Z
+INTERIOR  IH0 N T IH1 R IY0 ER0
+INTERIORLY	IH0 N T IH1 R IY0 ER0 L IY0
+INTERIORS  IH0 N T IH1 R IY0 ER0 Z
+INTERJACENT	IH2 N T ER0 JH EY1 S AH0 N T
+INTERJECT  IH2 N T ER0 JH EH1 K T
+INTERJECTED  IH2 N T ER0 JH EH1 K T IH0 D
+INTERJECTING	IH2 N T ER0 JH EH1 K T IH0 NG
+INTERJECTION  IH2 N T ER0 JH EH1 K SH AH0 N
+INTERJECTIONAL	IH2 N T ER0 JH EH1 K SH AH0 N AH0 L
+INTERJECTIONS  IH2 N T ER0 JH EH1 K SH AH0 N Z
+INTERJECTORY	IH2 N T ER0 JH EH1 K T ER0 IY0
+INTERJECTS  IH2 N T ER0 JH EH1 K T S
+INTERLACE  IH2 N T ER0 L EY1 S
+INTERLACED  IH2 N T ER0 L EY1 S T
+INTERLACEMENT	IH2 N T ER0 L EY1 S M AH0 N T
+INTERLACHEN	IH2 N T ER0 L AE1 K AH0 N
+INTERLACING	IH2 N T ER0 L EY1 S IH0 NG
+INTERLACINGS	IH2 N T ER0 L EY1 S IH0 NG Z
+INTERLAKEN	IH2 N T ER0 L EY1 K AH0 N
+INTERLARD	IH2 N T ER0 L ER0 D
+INTERLARDED	IH2 N T ER0 L AA1 R D IH0 D
+INTERLARDING	IH2 N T ER0 L AA1 R D IH0 NG
+INTERLARDS	IH2 N T ER0 L ER0 D Z
+INTERLEAVED  IH2 N T ER0 L IY1 V D
+INTERLINEAR	IH2 N T ER0 L IH1 N IY0 ER0
+INTERLINEATION	IH2 N T ER0 L IH2 N IY0 EY1 SH AH0 N
+INTERLINEATIONS	IH2 N T ER0 L IH2 N IY0 EY1 SH AH0 N Z
+INTERLINED	IH2 N T ER0 L AY2 N D
+INTERLINING	IH2 N T ER0 L AY2 N IH0 NG
+INTERLINKED  IH2 N T ER0 L IH1 NG K T
+INTERLINKING	IH2 N T ER0 L IH1 NG K IH0 NG
+INTERLOCK  IH2 N T ER0 L AA1 K
+INTERLOCKED  IH2 N T ER0 L AA1 K D
+INTERLOCKING  IH1 N T ER0 L AA2 K IH0 NG
+INTERLOCUTOR  IH2 N T ER0 L AA1 K Y AH0 T ER0
+INTERLOCUTOR'S	IH2 N T ER0 L AA1 K Y AH0 T ER0 Z
+INTERLOCUTORS  IH2 N T ER0 L AA1 K Y AH0 T ER0 Z
+INTERLOCUTORY	IH2 N T ER0 L AA1 K Y AH0 T ER0 IY0
+INTERLOCUTRESS	IH2 N T ER0 L AA1 K Y AH0 T R IH0 S
+INTERLOPER  IH1 N T ER0 L OW2 P ER0
+INTERLOPER'S	IH2 N T ER0 L OW2 P ER0 Z
+INTERLOPERS  IH1 N T ER0 L OW2 P ER0 Z
+INTERLOPING	IH2 N T ER0 L AH0 P IH0 NG
+INTERLUDE  IH1 N T ER0 L UW2 D
+INTERLUDES  IH1 N T ER0 L UW2 D Z
+INTERMARRIAGE  IH2 N T ER0 M EH1 R IH0 JH
+INTERMARRIAGES	IH2 N T ER0 M EH1 R IH0 JH IH0 Z
+INTERMARRIED  IH2 N T ER0 M AE1 R IY0 D
+INTERMARRY  IH2 N T ER0 M AE1 R IY0
+INTERMARRYING	IH2 N T ER0 M EH1 R IY0 IH0 NG
+INTERMEDDLE	IH2 N T ER0 M EH1 D AH0 L
+INTERMEDDLED	IH2 N T ER0 M EH1 D AH0 L D
+INTERMEDDLER	IH2 N T ER0 M EH1 D L ER0
+INTERMEDDLERS	IH2 N T ER0 M EH1 D AH0 L ER0 Z
+INTERMEDDLETH	IH2 N ER0 M IY1 D AH0 L EH2 TH
+INTERMEDDLING	IH2 N T ER0 M EH1 D AH0 L IH0 NG
+INTERMEDIARIES  IH2 N ER0 M IY1 D IY0 EH2 R IY0 Z
+INTERMEDIARIES  IH2 N T ER0 M IY1 D IY0 EH2 R IY0 Z
+INTERMEDIARY  IH2 N ER0 M IY1 D IY0 EH0 R IY0
+INTERMEDIARY  IH2 N T ER0 M IY1 D IY0 EH0 R IY0
+INTERMEDIATE  IH2 N ER0 M IY1 D IY0 IH0 T
+INTERMEDIATE  IH2 N T ER0 M IY1 D IY0 IH0 T
+INTERMEDIATELY	IH2 N ER0 M IY1 D IY0 AH0 T L IY0
+INTERMEDIATES  IH2 N ER0 M IY1 D IY0 AH0 T S
+INTERMEDIATES  IH2 N ER0 M IY1 D IY0 EY0 T S
+INTERMEDIATES  IH2 N T ER0 M IY1 D IY0 AH0 T S
+INTERMEDIATES  IH2 N T ER0 M IY1 D IY0 EY0 T S
+INTERMEDIATION	IH2 N ER0 M IY1 D IY0 EY1 SH AH0 N
+INTERMENT	IH2 N T ER0 M AH0 N T
+INTERMENTS	IH2 N T ER0 M AH0 N T S
+INTERMEZZO	IH2 N T ER0 M EH2 Z OW0
+INTERMINABLE  IH0 N T ER1 M AH0 N AH0 B AH0 L
+INTERMINABLY  IH1 N T ER0 M IH0 N AE2 B L IY0
+INTERMINABLY  IH2 N T ER1 M IH0 N AH0 B L IY0
+INTERMINGLE  IH2 N T ER0 M IH1 NG G AH0 L
+INTERMINGLED  IH2 N T ER0 M IH1 NG G AH0 L D
+INTERMINGLES	IH2 N T ER0 M IH1 NG G AH0 L Z
+INTERMINGLING  IH2 N T ER0 M IH1 NG G AH0 L IH0 NG
+INTERMINGLING  IH2 N T ER0 M IH1 NG G L IH0 NG
+INTERMISSION  IH2 N T ER0 M IH1 SH AH0 N
+INTERMISSIONS  IH2 N T ER0 M IH1 SH AH0 N Z
+INTERMIT	IH2 N T ER0 M IH1 T
+INTERMITION	IH2 N T ER0 M IH1 SH AH0 N
+INTERMITS	IH0 N T ER1 M AH0 T S
+INTERMITTED	IH2 N T ER0 M IH1 T IH0 D
+INTERMITTENCE	IH2 N T ER0 M IH1 T AH0 N S
+INTERMITTENT  IH2 N T ER0 M IH1 T AH0 N T
+INTERMITTENTLY  IH2 N T ER0 M IH1 T AH0 N T L IY0
+INTERMITTING	IH2 N T ER0 M IH1 T IH0 NG
+INTERMIX  IH2 N T ER0 M IH1 K S
+INTERMIXED  IH2 N T ER0 M IH1 K S T
+INTERMIXING  IH1 N T ER0 M IH1 K S IH0 NG
+INTERMIXT	IH2 N T ER0 M IH1 K S T
+INTERMIXTURE	IH2 N T ER0 M IH1 K S CH ER0
+INTERMIXTURES	IH2 N T ER0 M IH1 K S CH ER0 Z
+INTERMOUNTAIN  IH0 N T ER0 M AW1 N T IH0 N
+INTERN  IH1 N T ER0 N
+INTERNAL  IH0 N T ER1 N AH0 L
+INTERNALLY  IH0 N T ER1 N AH0 L IY0
+INTERNALS	IH0 N T ER1 N AH0 L Z
+INTERNATIONAL  IH2 N ER0 N AE1 SH AH0 N AH0 L
+INTERNATIONAL  IH2 N T ER0 N AE1 SH AH0 N AH0 L
+INTERNATIONALE  IH0 N ER0 N AE2 SH AH0 N AA1 L IY0
+INTERNATIONALE  IH0 N T ER0 N AE2 SH AH0 N AA1 L IY0
+INTERNATIONALISM  IH0 N ER0 N AE1 SH AH0 N AH0 L IH2 Z AH0 M
+INTERNATIONALISM  IH0 N T ER0 N AE1 SH AH0 N AH0 L IH2 Z AH0 M
+INTERNATIONALIST  IH0 N ER0 N AE1 SH AH0 N AH0 L IH0 S T
+INTERNATIONALIST  IH0 N T ER0 N AE1 SH AH0 N AH0 L IH0 S T
+INTERNATIONALISTS  IH2 N ER0 N AE1 SH AH0 N AH0 L IH0 S
+INTERNATIONALISTS  IH2 N ER0 N AE1 SH AH0 N AH0 L IH0 S T S
+INTERNATIONALISTS  IH2 N T ER0 N AE1 SH AH0 N AH0 L IH0 S
+INTERNATIONALISTS  IH2 N T ER0 N AE1 SH AH0 N AH0 L IH0 S T S
+INTERNATIONALLY  IH2 N ER0 N AE1 SH AH0 N AH0 L IY0
+INTERNATIONALLY  IH2 N ER0 N AE1 SH N AH0 L IY0
+INTERNATIONALLY  IH2 N T ER0 N AE1 SH AH0 N AH0 L IY0
+INTERNATIONALLY  IH2 N T ER0 N AE1 SH N AH0 L IY0
+INTERNATIONALS  IH2 N T ER0 N AE1 SH AH0 N AH0 L Z
+INTERNE	IH0 N T ER1 N
+INTERNECINE  IH0 N T ER1 N AH0 S IY2 N
+INTERNED  IH1 N T ER2 N D
+INTERNES	IH0 N T ER1 N Z
+INTERNET  IH1 N T ER0 N EH2 T
+INTERNING	IH0 N T ER1 N IH0 NG
+INTERNMENT  IH0 N T ER1 N M AH0 N T
+INTERNS  IH1 N T ER0 N Z
+INTEROCEANIC	IH2 N ER0 OW2 SH IY2 AE1 N IH0 K
+INTERPELLATION	IH0 N T ER1 P AH0 L EY1 SH AH0 N
+INTERPELLATIONS	IH0 N T ER1 P AH0 L EY1 SH AH0 N Z
+INTERPENETRATE	IH0 N T ER1 P EH1 N AH0 T R EY2 T
+INTERPENETRATED	IH0 N T ER1 P EH1 N AH0 T R EY2 T IH0 D
+INTERPENETRATES	IH0 N T ER1 P EH1 N AH0 T R EY2 T S
+INTERPENETRATING	IH0 N T ER1 P EH1 N AH0 T R EY2 T IH0 NG
+INTERPENETRATION	IH0 N T ER1 P EH1 N AH0 T R EY1 SH AH0 N
+INTERPETER	IH0 N T ER1 P AH0 T ER0
+INTERPETERS	IH0 N T ER1 P AH0 T ER0 Z
+INTERPHONE	IH2 N ER0 F OW1 N
+INTERPHONES	IH2 N ER0 F OW2 N Z
+INTERPLANET	IH2 N T ER0 P L AE1 N AH0 T
+INTERPLANETARIAN	IH2 N T ER0 P L AE2 N AH0 T EH1 R IY0 AH0 N
+INTERPLANETARY  IH2 N T ER0 P L AE1 N AH0 T EH2 R IY0
+INTERPLAY  IH1 N T ER0 P L EY2
+INTERPOLATE  IH2 T ER1 P AH0 L EY2 T
+INTERPOLATED  IH2 T ER1 P AH0 L EY2 T IH0 D
+INTERPOLATES	IH0 N T ER1 P AH0 L EY2 T S
+INTERPOLATING	IH0 N T ER1 P AH0 L EY2 T IH0 NG
+INTERPOLATION	IH0 N T ER1 P AH0 L EY1 SH AH0 N
+INTERPOLATIONS	IH0 N T ER1 P AH0 L EY1 SH AH0 N Z
+INTERPOS'D	IH0 N T ER1 P AH0 D
+INTERPOSE  IH2 N T ER0 P OW1 Z
+INTERPOSED	IH2 N T ER0 P OW1 Z D
+INTERPOSES	IH2 N T ER0 P OW1 Z IH0 Z
+INTERPOSING	IH2 N T ER0 P OW1 Z IH0 NG
+INTERPOSITION	IH0 N T ER1 P AH0 Z IH1 SH AH0 N
+INTERPOSITIONS	IH0 N T ER1 P AH0 Z IH1 SH AH0 N Z
+INTERPRET  IH0 N T ER1 P R AH0 T
+INTERPRETATION  IH0 N T ER2 P R IH0 T EY1 SH AH0 N
+INTERPRETATIONS  IH0 N T ER2 P R IH0 T EY1 SH AH0 N Z
+INTERPRETATIVE	IH0 N T ER1 P R AH0 T EY1 T IH0 V
+INTERPRETE	IH0 N T ER1 P R AH0 T
+INTERPRETED  IH0 N T ER1 P R AH0 T AH0 D
+INTERPRETER  IH0 N T ER1 P R AH0 T ER0
+INTERPRETER'S	IH0 N T ER1 P R AH0 T ER0 Z
+INTERPRETERS  IH0 N T ER1 P R AH0 T ER0 Z
+INTERPRETING  IH1 N T ER0 P R EH2 T IH0 NG
+INTERPRETIVE  IH0 N T ER1 P R AH0 T IH0 V
+INTERPRETS  IH0 N T ER1 P R AH0 T S
+INTERPROVINCIAL  IH2 N T ER0 P R OW0 V IH1 N SH AH0 L
+INTERR'D	IH2 N T ER0 D
+INTERRED  IH2 N T ER1 D
+INTERREGNUM  IH2 N T ER0 R EH1 G N AH0 M
+INTERRELATED  IH2 N T ER0 R IH0 L EY1 T IH0 D
+INTERRELATED  IH2 N T ER0 R IY0 L EY1 T IH0 D
+INTERRELATION	IH2 N T ER0 R IY0 L EY1 SH AH0 N
+INTERRELATIONS	IH2 N T ER0 R IY0 L EY1 SH AH0 N Z
+INTERRELATIONSHIP  IH1 N T ER0 R IY0 L EY1 SH AH0 N SH IH0 P
+INTERREX	IH2 N ER0 R EH0 K S
+INTERRING	IH0 N T ER1 IH0 NG
+INTERROGATE  IH0 N T EH1 R AH0 G EY2 T
+INTERROGATED  IH0 N T EH1 R AH0 G EY2 T IH0 D
+INTERROGATES	IH0 N T EH2 R AH0 G EY2 T S
+INTERROGATING  IH0 N T EH1 R AH0 G EY2 T IH0 NG
+INTERROGATINGLY	IH0 N T EH2 R AH0 G EY2 T IH0 NG L IY0
+INTERROGATION  IH0 N T EH2 R AH0 G EY1 SH AH0 N
+INTERROGATIONS  IH0 N T EH2 R AH0 G EY1 SH AH0 N Z
+INTERROGATIVE	IH0 N T EH0 R AA1 G AH0 T IH0 V
+INTERROGATIVELY	IH0 N T EH0 R AA1 G AH0 T IH0 V L IY0
+INTERROGATIVES	IH0 N T EH0 R AA1 G AH0 T IH0 V Z
+INTERROGATOR  IH2 N T EH1 R AH0 G EY2 T ER0
+INTERROGATOR'S	IH0 N T EH2 R AH0 G EY2 T ER0 Z
+INTERROGATORIES  IH0 N T EH0 R AA1 G AH0 T AO2 R IY0 Z
+INTERROGATORS  IH2 N T EH1 R AH0 G EY2 T ER0 Z
+INTERROGATORY  IH0 N T EH0 R AA1 G AH0 T AO2 R IY0
+INTERRUP	IH2 N ER0 AH1 P
+INTERRUPT  IH2 N ER0 AH1 P T
+INTERRUPT  IH2 N T ER0 AH1 P T
+INTERRUPTED  IH2 N ER0 AH1 P T IH0 D
+INTERRUPTED  IH2 N T ER0 AH1 P T IH0 D
+INTERRUPTEDLY	IH2 N ER0 AH1 P T IH0 D L IY0
+INTERRUPTER	IH2 N ER0 AH1 P T ER0
+INTERRUPTERS	IH2 N ER0 AH1 P T ER0 Z
+INTERRUPTIN	IH2 N ER0 AH1 P T IH0 N
+INTERRUPTING  IH2 N ER0 AH1 P T IH0 NG
+INTERRUPTING  IH2 N T ER0 AH1 P T IH0 NG
+INTERRUPTION  IH2 N ER0 AH1 P SH AH0 N
+INTERRUPTION  IH2 N T ER0 AH1 P SH AH0 N
+INTERRUPTIONS  IH2 N ER0 AH1 P SH AH0 N Z
+INTERRUPTIONS  IH2 N T ER0 AH1 P SH AH0 N Z
+INTERRUPTS  IH2 N ER0 AH1 P T S
+INTERRUPTS  IH2 N T ER0 AH1 P T S
+INTERSCHOLASTIC  IH2 N T ER0 S K AH0 L AE1 S T IH0 K
+INTERSECT  IH2 N ER0 S EH1 K T
+INTERSECT  IH2 N T ER0 S EH1 K T
+INTERSECTED  IH2 N ER0 S EH1 K T IH0 D
+INTERSECTED  IH2 N T ER0 S EH1 K T IH0 D
+INTERSECTING  IH2 N ER0 S EH1 K T IH0 NG
+INTERSECTING  IH2 N T ER0 S EH1 K T IH0 NG
+INTERSECTION  IH2 N ER0 S EH1 K SH AH0 N
+INTERSECTION  IH2 N T ER0 S EH1 K SH AH0 N
+INTERSECTIONS  IH2 N ER0 S EH1 K SH AH0 N Z
+INTERSECTIONS  IH2 N T ER0 S EH1 K SH AH0 N Z
+INTERSECTS  IH2 N ER0 S EH1 K S
+INTERSECTS  IH2 N ER0 S EH1 K T S
+INTERSECTS  IH2 N T ER0 S EH1 K S
+INTERSECTS  IH2 N T ER0 S EH1 K T S
+INTERSPACE	IH2 N T ER0 S P EY2 S
+INTERSPACED	IH2 N T ER0 S P EY2 S T
+INTERSPACES	IH2 N T ER0 S P EY2 S IH0 Z
+INTERSPERSE  IH2 N T ER0 S P ER1 S
+INTERSPERSED  IH2 N T ER0 S P ER1 S T
+INTERSPERSES  IH2 N T ER0 S P ER1 S AH0 Z
+INTERSPERSING	IH2 N T ER0 S P ER1 S IH0 NG
+INTERSTATE  IH2 N ER0 S T EY1 T
+INTERSTATE  IH2 N T ER0 S T EY1 T
+INTERSTELLAR  IH2 N T ER0 S T EH1 L ER0
+INTERSTICE	IH2 N T ER0 S T IH0 S
+INTERSTICES	IH2 N T ER0 S T IH0 S IH0 Z
+INTERSTITIA	IH2 N T ER0 S T IH1 SH AH0
+INTERSTITIAL	IH2 N T ER0 S T IH1 SH AH0 L
+INTERTANGLED	IH2 N T ER0 T AE2 NG G AH0 L D
+INTERTEXTURE	IH2 N ER0 T EH1 K S CH ER0
+INTERTRIBAL  IH2 N T ER0 T R AY1 B AH0 L
+INTERTROPICAL	IH2 N T ER0 T R AA1 P IH0 K AH0 L
+INTERTWINE  IH0 N T ER0 T W AY1 N
+INTERTWINED  IH0 N T ER0 T W AY1 N D
+INTERTWINING  IH1 N T ER0 T W AY2 N IH0 NG
+INTERTWISTED	IH2 N T ER0 T W IH1 S T IH0 D
+INTERTWISTING	IH2 N T ER0 T W IH1 S T IH0 NG
+INTERURBAN  IH2 N T ER0 ER1 B AH0 N
+INTERVAL  IH1 N ER0 V AH0 L
+INTERVAL  IH1 N T ER0 V AH0 L
+INTERVALE	IH2 N T ER0 V EY2 L
+INTERVALES	IH2 N ER0 V AH0 L Z
+INTERVALLO	IH2 N T ER0 V AA1 L OW0
+INTERVALS  IH1 N ER0 V AH0 L Z
+INTERVALS  IH1 N T ER0 V AH0 L Z
+INTERVENE  IH2 N ER0 V IY1 N
+INTERVENE  IH2 N T ER0 V IY1 N
+INTERVENED  IH2 N ER0 V IY1 N D
+INTERVENED  IH2 N T ER0 V IY1 N D
+INTERVENER	IH2 N ER0 V IY1 N ER0
+INTERVENES  IH2 N ER0 V IY1 N Z
+INTERVENES  IH2 N T ER0 V IY1 N Z
+INTERVENING  IH2 N ER0 V IY1 N IH0 NG
+INTERVENING  IH2 N T ER0 V IY1 N IH0 NG
+INTERVENTION  IH2 N ER0 V EH1 N CH AH0 N
+INTERVENTION  IH2 N T ER0 V EH1 N CH AH0 N
+INTERVENTIONS  IH2 N ER0 V EH1 N CH AH0 N Z
+INTERVENTIONS  IH2 N T ER0 V EH1 N CH AH0 N Z
+INTERVIEW  IH1 N ER0 V Y UW2
+INTERVIEW  IH1 N T ER0 V Y UW2
+INTERVIEWED  IH1 N ER0 V Y UW2 D
+INTERVIEWED  IH1 N T ER0 V Y UW2 D
+INTERVIEWER  IH1 N ER0 V Y UW2 ER0
+INTERVIEWER  IH1 N T ER0 V Y UW2 ER0
+INTERVIEWER'S	IH2 N ER0 V Y UW2 ER0 Z
+INTERVIEWERS  IH1 N ER0 V Y UW2 ER0 Z
+INTERVIEWERS  IH1 N T ER0 V Y UW2 ER0 Z
+INTERVIEWING  IH1 N ER0 V Y UW2 IH0 NG
+INTERVIEWING  IH1 N T ER0 V Y UW2 IH0 NG
+INTERVIEWS  IH1 N ER0 V Y UW2 Z
+INTERVIEWS  IH1 N T ER0 V Y UW2 Z
+INTERVOLVED	IH2 N T ER0 V AA1 L V D
+INTERWEAVE  IH2 N T ER0 W IY1 V
+INTERWEAVES	IH2 N T ER0 W IY1 V Z
+INTERWEAVING  IH2 N T ER0 W IY1 V IH0 NG
+INTERWORLD	IH2 N T ER0 W ER2 L D
+INTERWOVE	IH2 N T ER0 W OW1 V
+INTERWOVEN  IH2 N T ER0 W OW1 V AH0 N
+INTESTACY	IH0 N T EH1 S T AH0 S IY0
+INTESTATE	IH0 N T EH1 S T EY2 T
+INTESTINAL  IH0 N T EH1 S T AH0 N AH0 L
+INTESTINE  IH0 N T EH1 S T AH0 N
+INTESTINES  IH0 N T EH1 S T AH0 N Z
+INTEW	IH2 N T UW1
+INTHEREST	IH0 N TH ER0 AH0 S T
+INTHRALLED	IH1 N TH R AO1 L D
+INTIALO	IH1 N T IY0 AE1 L OW0
+INTICE	IH1 N T AH0 S
+INTIL	IH1 N T AH0 L
+INTIL'T	IH1 N T AH0 L T
+INTILL	IH0 N T IH1 L
+INTIMACIES	IH1 N T AH0 M AH0 S IY0 Z
+INTIMACY  IH1 N T AH0 M AH0 S IY0
+INTIMATE  IH1 N AH0 M AH0 T
+INTIMATE  IH1 N T AH0 M AH0 T
+INTIMATE  IH1 N T AH0 M EY2 T
+INTIMATED  IH1 N T AH0 M EY2 T IH0 D
+INTIMATELY  IH1 N T AH0 M AH0 T L IY0
+INTIMATES  IH1 N T AH0 M AH0 T S
+INTIMATING	IH1 N T AH0 M EY2 T IH0 NG
+INTIMATION  IH2 N T AH0 M EY1 SH AH0 N
+INTIMATIONS  IH2 N T AH0 M EY1 SH AH0 N Z
+INTIME	IH0 N T AY2 M
+INTIMES	IH0 N T AY2 M Z
+INTIMIDATE  IH0 N T IH1 M IH0 D EY2 T
+INTIMIDATED  IH0 N T IH1 M IH0 D EY2 T IH0 D
+INTIMIDATES  IH0 N T IH1 M IH0 D EY2 T S
+INTIMIDATING  IH0 N T IH1 M IH0 D EY2 T IH0 NG
+INTIMIDATION  IH0 N T IH2 M IH0 D EY1 SH AH0 N
+INTIMIDATIONS	IH0 N T IH1 M IH0 D EY1 SH AH0 N Z
+INTIND	IH1 N T IH0 N D
+INTINDED	IH1 N T AH0 N D IH0 D
+INTINTION	IH1 N T AH0 N SH AH0 N
+INTIRE	IH2 N T AY2 R
+INTIRELY	IH0 N T AY1 ER0 L IY0
+INTITLED	IH0 N T AY1 T AH0 L D
+INTITULED	IH1 N T AH0 T Y UW2 L D
+INTO  IH0 N T AH0
+INTO  IH0 N T UW1
+INTO  IH1 N T UW0
+INTOIRELY	IH0 N T W AA1 R L IY0
+INTOLERABLE  IH0 N T AA1 L ER0 AH0 B AH0 L
+INTOLERABLY  IH0 N T AA1 L ER0 AH0 B L IY0
+INTOLERANCE  IH0 N T AA1 L ER0 AH0 N S
+INTOLERANT  IH0 N T AA1 L ER0 AH0 N T
+INTOLERANTLY	IH0 N T AA1 L ER0 AH0 N T L IY0
+INTOMBI	IH1 N T AH0 M B IY0
+INTONATED	IH1 N T AH0 N EY2 T IH0 D
+INTONATION  IH0 N T AH0 N EY1 SH AH0 N
+INTONATIONS  IH0 N T AH0 N EY1 SH AH0 N Z
+INTONE  IH0 N T OW1 N
+INTONED  IH0 N T OW1 N D
+INTONES  IH0 N T OW1 N Z
+INTONING  IH0 N T OW1 N IH0 NG
+INTORNO	IH0 N T AO1 R N OW0
+INTOSH	IH0 N T AA2 SH
+INTOSH'S	IH0 N T AA2 SH IH0 Z
+INTOURIST  IH0 N T UH1 R IH0 S T
+INTOXICANT	IH0 N T AA1 K S IH0 K AH0 N T
+INTOXICANTS	IH0 N T AA1 K S IH0 K AH0 N T S
+INTOXICATE  IH0 N T AA1 K S AH0 K EY2 T
+INTOXICATED  IH0 N T AA1 K S AH0 K EY2 T AH0 D
+INTOXICATED  IH0 N T AA1 K S IH0 K EY2 T IH0 D
+INTOXICATES  IH0 N T AA1 K S AH0 K EY2 T S
+INTOXICATING  IH0 N T AA1 K S IH0 K EY2 T IH0 NG
+INTOXICATINGLY	IH0 N T AA1 K S AH0 K EY2 T IH0 NG L IY0
+INTOXICATION  IH0 N T AA2 K S AH0 K EY1 SH AH0 N
+INTOXICATIONS	IH0 N T AA1 K S AH0 K EY1 SH AH0 N Z
+INTRA  IH1 N T R AH0
+INTRACTABILITY	IH0 N T R AE1 K T AH0 B IH1 L IH0 T IY0
+INTRACTABLE  IH0 N T R AE1 K T AH0 B AH0 L
+INTRAMURAL  IH2 N T R AH0 M Y UH1 R AH0 L
+INTRANSIGEANT	IH0 N T R AE1 N S AH0 JH AH0 N T
+INTRANSIGENT  IH0 N T R AE1 N S AH0 JH AH0 N T
+INTRANSIGENT  IH0 N T R AE1 N Z AH0 JH AH0 N T
+INTRANSITIVE	IH0 N T R AE1 N Z IH0 T IH0 V
+INTRAVENOUS  IH2 N T R AH0 V IY1 N AH0 S
+INTRAVISCERAL	IH2 N T R AH0 V IH1 S ER0 AH0 L
+INTREAT	IH0 N T R IY1 T
+INTREATED	IH0 N T R IY1 T IH0 D
+INTREATIES	IH0 N T R IY1 T IY0 Z
+INTREATING	IH0 N T R IY1 T IH0 NG
+INTREATY	IH0 N T R IY1 T IY0
+INTRENCH	IH0 N T R EH1 N CH
+INTRENCHED	IH0 N T R EH1 N CH T
+INTRENCHING	IH0 N T R EH1 N CH IH0 NG
+INTRENCHMENT	IH0 N T R EH1 N CH M AH0 N T
+INTRENCHMENTS	IH0 N T R EH1 N CH M AH0 N T S
+INTREPID  IH0 N T R EH1 P AH0 D
+INTREPID'S	IH0 N T R EH1 P AH0 D Z
+INTREPIDITY	IH0 N T R EH1 P AH0 D AH0 T IY0
+INTREPIDLY  IH0 N T R EH1 P AH0 D L IY0
+INTRICACIES  IH1 N T R AH0 K AH0 S IY0 Z
+INTRICACY  IH1 N T R AH0 K AH0 S IY0
+INTRICATE  IH1 N T R AH0 K AH0 T
+INTRICATELY  IH1 N T R AH0 K AH0 T L IY0
+INTRIGANT	IH0 N T R IY1 G AH0 N T
+INTRIGANTE	IH2 N T R IY0 G AA1 N T IY0
+INTRIGUANT	IH0 N T R IY1 G AH0 N T
+INTRIGUANTE	IH0 N T R IY1 G AA1 N T IY0
+INTRIGUE  IH0 N T R IY1 G
+INTRIGUE  IH1 N T R IY0 G
+INTRIGUED  IH0 N T R IY1 G D
+INTRIGUED  IH1 N T R IY0 G D
+INTRIGUER	IH0 N T R IY1 G ER0
+INTRIGUERS	IH0 N T R IY1 G ER0 Z
+INTRIGUES  IH0 N T R IY1 G Z
+INTRIGUES  IH1 N T R IY0 G Z
+INTRIGUING  IH0 N T R IY1 G IH0 NG
+INTRINSIC  IH0 N T R IH1 N S IH0 K
+INTRINSICALLY  IH0 N T R IH1 N S IH0 K AH0 L IY0
+INTRINSICALLY  IH0 N T R IH1 N S IH0 K L IY0
+INTRINSICK	IH0 N T R IH1 N S IH0 K
+INTRO  IH1 N T R OW0
+INTROD	IH2 N T R AH0 D
+INTRODUC'D	IH2 N T R AH0 D UW1 S D
+INTRODUCE  IH2 N T R AH0 D UW1 S
+INTRODUCE  IH2 N T R OW0 D UW1 S
+INTRODUCED  IH2 N T R AH0 D UW1 S T
+INTRODUCED  IH2 N T R OW0 D UW1 S T
+INTRODUCER	IH2 N T R AH0 D UW1 S ER0
+INTRODUCERS	IH2 N T R AH0 D UW1 S ER0 Z
+INTRODUCES  IH2 N T R AH0 D UW1 S IH0 Z
+INTRODUCES  IH2 N T R OW0 D UW1 S IH0 Z
+INTRODUCETH	IH2 N T R AH0 D UW1 S IH0 TH
+INTRODUCIN	IH2 N T R AH0 D UW1 S IH0 N
+INTRODUCING  IH2 N T R AH0 D UW1 S IH0 NG
+INTRODUCING  IH2 N T R OW0 D UW1 S IH0 NG
+INTRODUCTION  IH2 N T R AH0 D AH1 K SH AH0 N
+INTRODUCTION  IH2 N T R OW0 D AH1 K SH AH0 N
+INTRODUCTIONS  IH2 N T R AH0 D AH1 K SH AH0 N Z
+INTRODUCTIONS  IH2 N T R OW0 D AH1 K SH AH0 N Z
+INTRODUCTORY  IH2 N T R AH0 D AH1 K T ER0 IY0
+INTRODUCTORY  IH2 N T R OW0 D AH1 K T ER0 IY0
+INTROIT	IH0 N T R OY1 T
+INTROMISSION	IH2 N T R OW0 M IH1 SH AH0 N
+INTROMISSIONS	IH2 N T R OW0 M IH1 SH AH0 N Z
+INTROSPECTION  IH2 N T R AH0 S P EH1 K SH AH0 N
+INTROSPECTION  IH2 N T R OW0 S P EH1 K SH AH0 N
+INTROSPECTIONS	IH2 N T R AH0 S P EH1 K SH AH0 N Z
+INTROSPECTIVE  IH2 N T R AH0 S P EH1 K T IH0 V
+INTROSPECTIVE  IH2 N T R OW0 S P EH1 K T IH0 V
+INTROSPECTIVELY	IH2 N T R AH0 S P EH1 K T IH0 V L IY0
+INTROVERSION	IH2 N T R AH0 V ER1 ZH AH0 N
+INTROVERT  IH1 N T R OW0 V ER2 T
+INTROVERTED  IH1 N T R OW0 V ER2 T IH0 D
+INTRUDE  IH0 N T R UW1 D
+INTRUDED  IH0 N T R UW1 D AH0 D
+INTRUDER  IH0 N T R UW1 D ER0
+INTRUDER'S	IH0 N T R UW1 D ER0 Z
+INTRUDERS  IH0 N T R UW1 D ER0 Z
+INTRUDES  IH0 N T R UW1 D Z
+INTRUDIN	IH0 N T R UW1 D IH0 N
+INTRUDING  IH0 N T R UW1 D IH0 NG
+INTRUSION  IH0 N T R UW1 ZH AH0 N
+INTRUSIONS  IH0 N T R UW1 ZH AH0 N Z
+INTRUSIVE  IH0 N T R UW1 S IH0 V
+INTRUSIVELY	IH0 N T R UW1 S IH0 V L IY0
+INTRUSIVENESS  IH0 N T R UW1 S IH0 V N EH0 S
+INTRUST	IH0 N T R AH2 S T
+INTRUSTED	IH0 N T R AH1 S T IH0 D
+INTRUSTING	IH0 N T R AH1 S T IH0 NG
+INTRUSTS	IH0 N T R AH1 S T S
+INTSIZA	IH1 N T S IY1 Z AH0
+INTU	IH2 N T UW0
+INTUH	IH2 N T UW0
+INTUITE	IH0 N T UW1 AH0 T
+INTUITED	IH0 N T UW1 AH0 T IH0 D
+INTUITION  IH2 N T UW0 IH1 SH AH0 N
+INTUITIONAL	IH2 N T UW0 IH1 SH AH0 N AH0 L
+INTUITIONS	IH2 N T UW0 IH1 SH AH0 N Z
+INTUITIVE  IH0 N T UW1 AH0 T IH0 V
+INTUITIVELY  IH0 N T UW1 IH0 T IH0 V L IY0
+INTUITIVENESS	IH0 N T UW1 AH0 T IH0 V N AH0 S
+INTUS	IH1 N T AH0 S
+INTWINED	IH0 N T W AY1 N D
+INTYRE	IH0 N T AY2 R
+INTYRE'S	IH0 N T AY2 R Z
+INTYRES	IH0 N T AY2 R Z
+INUA	IH0 N Y UW1 AH0
+INUENDO	IH0 N Y UW0 EH1 N D OW0
+INUENDOES	IH0 N Y UW0 EH1 N D OW0 Z
+INUENDOS	IH0 N Y UW0 EH1 N D OW0 Z
+INUIT  IH1 N UW0 T
+INUITS	IH0 N UW1 IH0 T S
+INUNDATE  IH1 N AH0 N D EY2 T
+INUNDATED  IH1 N AH0 N D EY2 T IH0 D
+INUNDATES	IH1 N AH0 N D EY2 T S
+INUNDATING  IH0 N AH1 N D EY2 T IH0 NG
+INUNDATING  IH1 N AH0 N D EY2 T IH0 NG
+INUNDATION  IH2 N AH0 N D EY1 SH AH0 N
+INUNDATIONS  IH2 N AH0 N D EY1 SH AH0 N Z
+INUR'D	IH0 N Y UH1 R D
+INURE  IH0 N Y UH1 R
+INURED  IH0 N Y UH1 R D
+INURES	IH0 N Y UH1 R Z
+INURING	IH0 N Y UH1 R IH0 NG
+INURNED	IH0 N Y UH1 R N D
+INUTILE	IH0 N Y UW2 T AH0 L
+INUTILITY	IH0 N Y UW0 T IH1 L AH0 T IY0
+INVADE  IH0 N V EY1 D
+INVADED  IH0 N V EY1 D AH0 D
+INVADED  IH0 N V EY1 D IH0 D
+INVADER  IH0 N V EY1 D ER0
+INVADER'S	IH0 N V EY1 D ER0 Z
+INVADERS  IH0 N V EY1 D ER0 Z
+INVADES  IH0 N V EY1 D Z
+INVADING  IH0 N V EY1 D IH0 NG
+INVALID  IH0 N V AE1 L AH0 D
+INVALID  IH1 N V AH0 L AH0 D
+INVALID  IH1 N V AH0 L IH0 D
+INVALID'S	IH0 N V AE1 L IH0 D Z
+INVALIDATE  IH0 N V AE1 L IH0 D EY2 T
+INVALIDATED  IH0 N V AE1 L AH0 D EY2 T AH0 D
+INVALIDATED  IH0 N V AE1 L IH0 D EY2 T IH0 D
+INVALIDATES	IH0 N V AE1 L AH0 D EY0 T S
+INVALIDATING  IH0 N V AE1 L AH0 D EY2 T IH0 NG
+INVALIDATION  IH0 N V AE2 L AH0 D EY1 SH AH0 N
+INVALIDED	IH0 N V AE1 L AH0 D IH0 D
+INVALIDES	IH0 N V AE1 L AH0 D Z
+INVALIDISH	IH0 N V AE1 L IH0 D IH0 SH
+INVALIDISM	IH0 N V AE1 L AH0 D IH2 Z AH0 M
+INVALIDITY	IH1 N V AH0 L IH1 D AH0 T IY0
+INVALIDS  IH1 N V AH0 L AH0 D Z
+INVALUABLE  IH0 N V AE1 L Y AH0 B AH0 L
+INVAR	IH0 N V ER0
+INVAR'S	IH0 N V EH1 R Z
+INVARIABILITY	IH0 N V EH1 R IY0 AH0 B IH1 L IH0 T IY0
+INVARIABLE  IH0 N V EH1 R IY0 AH0 B AH0 L
+INVARIABLENESS	IH0 N V EH1 R IY0 AH0 B AH0 L N AH0 S
+INVARIABLY  IH0 N V EH1 R IY0 AH0 B L IY0
+INVASION  IH0 N V EY1 ZH AH0 N
+INVASIONS  IH0 N V EY1 ZH AH0 N Z
+INVASIVE  IH0 N V EY1 S IH0 V
+INVECTIVE  IH0 N V EH1 K T IH0 V
+INVECTIVES	IH0 N V EH1 K T IH0 V Z
+INVEIGH	IH0 N V EY1
+INVEIGHED	IH0 N V EY1 D
+INVEIGHING	IH0 N V EY1 IH0 NG
+INVEIGHS	IH0 N V EY1 Z
+INVEIGLE	IH0 N V IY1 G AH0 L
+INVEIGLED	IH0 N V IY1 G AH0 L D
+INVEIGLES	IH0 N V IY1 G AH0 L Z
+INVEIGLING	IH0 N V IY1 G AH0 L IH0 NG
+INVENIT	IH0 N V EH1 N IH0 T
+INVENT  IH0 N V EH1 N T
+INVENTED  IH0 N V EH1 N T AH0 D
+INVENTED  IH0 N V EH1 N T IH0 D
+INVENTER	IH0 N V EH1 N T ER0
+INVENTIN	IH0 N V EH1 N T IH0 N
+INVENTING  IH0 N V EH1 N T IH0 NG
+INVENTION  IH0 N V EH1 N SH AH0 N
+INVENTIONS  IH0 N V EH1 N SH AH0 N Z
+INVENTIVE  IH0 N V EH1 N T IH0 V
+INVENTIVENESS  IH0 N V EH1 N T IH0 V N AH0 S
+INVENTOR  IH0 N V EH1 N T ER0
+INVENTOR'S	IH2 N V AH0 N T AO2 R Z
+INVENTORIED  IH1 N V AH0 N T AO2 R IY0 D
+INVENTORIES  IH2 N V AH0 N T AO1 R IY0 Z
+INVENTORS  IH0 N V EH1 N T ER0 Z
+INVENTORY  IH2 N V AH0 N T AO1 R IY0
+INVENTRESS	IH0 N V EH1 N T R AH0 S
+INVENTS  IH0 N V EH1 N T S
+INVENTUS	IH0 N V EH1 N T AH0 S
+INVERARAY	IH0 N V ER1 AH0 R EY0
+INVERARITY	IH0 N V EH1 R EH1 R IH0 T IY0
+INVERARY	IH1 N V ER0 EH2 R IY0
+INVERASHIEL	IH0 N V ER1 AH0 SH IY2 L
+INVERAWE	IH0 N V EH1 R AO1
+INVERELL	IH0 N V EH0 R EY1 L
+INVERESK	IH1 N V ER0 EH1 S K
+INVEREY	IH0 N V EH0 R EY1
+INVERFORTH	IH0 N V ER1 F ER0 TH
+INVERLEITH	IH0 N V ER1 L IY1 TH
+INVERLOCHY	IH1 N V ER0 L OW2 CH IY0
+INVERMALLUCH	IH1 N V ER0 M AE1 L AH0 K
+INVERNESS  IH1 N V ER0 N EH2 S
+INVERSE  IH0 N V ER1 S
+INVERSELY  IH0 N V ER1 S L IY0
+INVERSION  IH0 N V ER1 ZH AH0 N
+INVERSIONS	IH0 N V ER1 ZH AH0 N Z
+INVERSNAID	IH0 N V ER1 S N EY1 D
+INVERT  IH0 N V ER1 T
+INVERTEBRATA	IH0 N V ER1 T AH0 B R EY2 T AH0
+INVERTEBRATE  IH2 N V ER1 T AH0 B R AH0 T
+INVERTEBRATE  IH2 N V ER1 T AH0 B R EY2 T
+INVERTEBRATES  IH0 N V ER1 T AH0 B R AH0 T S
+INVERTEBRATES  IH2 N V ER1 T AH0 B R EY2 T S
+INVERTED  IH0 N V ER1 T IH0 D
+INVERTING	IH0 N V ER1 T IH0 NG
+INVERTS	IH0 N V ER1 T S
+INVEST  IH0 N V EH1 S T
+INVESTED  IH0 N V EH1 S T AH0 D
+INVESTED  IH0 N V EH1 S T IH0 D
+INVESTIGATE  IH0 N V EH1 S T AH0 G EY2 T
+INVESTIGATED  IH0 N V EH1 S T AH0 G EY2 T AH0 D
+INVESTIGATED  IH0 N V EH1 S T AH0 G EY2 T IH0 D
+INVESTIGATES  IH0 N V EH1 S T AH0 G EY2 T S
+INVESTIGATIN	IH0 N V EH1 S T AH0 G EY2 T IH0 N
+INVESTIGATING  IH0 N V EH1 S T AH0 G EY2 T IH0 NG
+INVESTIGATION  IH0 N V EH2 S T AH0 G EY1 SH AH0 N
+INVESTIGATIONS  IH0 N V EH2 S T AH0 G EY1 SH AH0 N Z
+INVESTIGATIVE  IH0 N V EH1 S T AH0 G EY2 T IH0 V
+INVESTIGATOR  IH0 N V EH1 S T AH0 G EY2 T ER0
+INVESTIGATOR'S  IH0 N V EH1 S T AH0 G EY2 T ER0 Z
+INVESTIGATORS  IH0 N V EH1 S T AH0 G EY2 T ER0 Z
+INVESTIGATORY  IH0 N V EH1 S T AH0 G AH0 T AO2 R IY0
+INVESTIN	IH0 N V EH1 S T AH0 N
+INVESTING  IH0 N V EH1 S T IH0 NG
+INVESTITURE  IH0 N V EH1 S T AH0 CH ER0
+INVESTITURES	IH0 N V EH1 S T IH0 CH ER0 Z
+INVESTMENT  IH0 N V EH1 S M AH0 N T
+INVESTMENT  IH0 N V EH1 S T M AH0 N T
+INVESTMENTS  IH0 N V EH1 S M AH0 N T S
+INVESTMENTS  IH0 N V EH1 S T M AH0 N T S
+INVESTOR  IH0 N V EH1 S T ER0
+INVESTORS  IH0 N V EH1 S T ER0 Z
+INVESTS  IH0 N V EH1 S T S
+INVETERACY	IH0 N V EH1 T ER0 AH0 S IY0
+INVETERATE  IH0 N V EH1 T ER0 AH0 T
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+OVERBALANCE	OW1 V ER0 B AE2 L AH0 N S
+OVERBALANCED	OW1 V ER0 B AE2 L AH0 N S T
+OVERBALANCES	OW1 V ER0 B AE2 L AH0 N S IH0 Z
+OVERBALANCING	OW1 V ER0 B AE2 L AH0 N S IH0 NG
+OVERBEAR	OW1 V ER0 B EH2 R
+OVERBEARIN	OW1 V ER0 B EH1 R IH0 N
+OVERBEARING  OW1 V ER0 B EH1 R IH0 NG
+OVERBEARS	OW1 V ER0 B EH2 R Z
+OVERBECK  OW1 V ER0 B EH2 K
+OVERBERG	OW1 V ER0 B ER0 G
+OVERBID	OW1 V ER0 B IH2 D
+OVERBLOWN  OW2 V ER0 B L OW1 N
+OVERBOARD  OW1 V ER0 B AO2 R D
+OVERBOLD	OW1 V ER0 B OW2 L D
+OVERBORE	OW1 V ER0 B AO1 R IY0
+OVERBORNE	OW1 V ER0 B ER0 N
+OVERBRIGHT	OW1 V ER0 B R AY2 T
+OVERBRIMMING	OW1 V ER0 B R IH1 M IH0 NG
+OVERBROOK	OW1 V ER0 B R UH2 K
+OVERBUILT  OW1 V ER0 B IH1 L T
+OVERBURDEN  OW1 V ER0 B ER1 D AH0 N
+OVERBURDENED  OW1 V ER0 B ER1 D AH0 N D
+OVERBURDENING	OW1 V ER0 B ER1 D AH0 N IH0 NG
+OVERBURTHENED	OW1 V ER0 B ER1 TH AH0 N D
+OVERBURY	OW1 V ER0 B EH2 R IY0
+OVERBURY'S	OW1 V ER0 B EH2 R IY0 Z
+OVERCAME  OW1 V ER0 K EY1 M
+OVERCAREFUL	OW1 V ER0 K EH1 R F AH0 L
+OVERCAST  OW1 V ER0 K AE2 S T
+OVERCASTING	OW1 V ER0 K AE2 S T IH0 NG
+OVERCAUTIOUS	OW1 V ER0 K AO1 SH AH0 S
+OVERCHARGE  OW1 V ER0 CH AA2 R JH
+OVERCHARGED  OW1 V ER0 CH AA1 R JH D
+OVERCHARGES  OW1 V ER0 CH AA2 R JH IH0 Z
+OVERCHARGING  OW2 V ER0 CH AA1 R JH IH0 NG
+OVERCLEAN	OW1 V ER0 K L EY1 N
+OVERCLOUD	OW1 V ER0 K L AW1 D
+OVERCLOUDED	OW1 V ER0 K L AW1 D IH0 D
+OVERCLOUDING	OW1 V ER0 K L AW1 D IH0 NG
+OVERCOAT  OW1 V ER0 K OW2 T
+OVERCOATED	OW1 V ER0 K OW2 T IH0 D
+OVERCOATS  OW1 V ER0 K OW2 T S
+OVERCOMBE	OW1 V ER0 K AH0 M
+OVERCOME  OW1 V ER0 K AH2 M
+OVERCOMER	OW1 V ER0 K AH2 M ER0
+OVERCOMES  OW1 V ER0 K AH2 M Z
+OVERCOMETH	OW1 V ER0 K AH2 M TH
+OVERCOMING  OW1 V ER0 K AH2 M IH0 NG
+OVERCONFIDENCE  OW2 V ER0 K AA1 N F IH0 D AH0 N S
+OVERCONFIDENT  OW2 V ER0 K AA1 N F IH0 D AH0 N T
+OVERCROWD  OW2 V ER0 K R AW1 D
+OVERCROWDED  OW1 V ER0 K R AW2 D IH0 D
+OVERCROWDING  OW1 V ER0 K R AW2 D IH0 NG
+OVERCROWED	OW1 V ER0 K R AW1 D
+OVERDENE	OW1 V ER0 D IY0 N
+OVERDEVELOPED	OW1 V ER0 D IH0 V EH1 L AH0 P T
+OVERDID  OW1 V ER0 D IH2 D
+OVERDO  OW1 V ER0 D UW1
+OVERDOES  OW2 V ER0 D AH1 Z
+OVERDOING  OW1 V ER0 D UW1 IH0 NG
+OVERDONE  OW1 V ER0 D AH1 N
+OVERDOSE  OW1 V ER0 D OW2 S
+OVERDOSED  OW1 V ER0 D OW2 S T
+OVERDRAFT  OW1 V ER0 D R AE2 F T
+OVERDRAFTS  OW1 V ER0 D R AE2 F T S
+OVERDRAW  OW1 V ER0 D R AO2
+OVERDRAWING	OW1 V ER0 D R AO1 IH0 NG
+OVERDRAWN  OW1 V ER0 D R AO1 N
+OVERDRESS  OW1 V ER0 D R EH2 S
+OVERDRESSED	OW1 V ER0 D R EH2 S T
+OVERDRESSING	OW1 V ER0 D R EH2 S IH0 NG
+OVERDREW  OW1 V ER0 D R UW2
+OVERDRIVE  OW1 V ER0 D R AY2 V
+OVERDRIVEN	OW1 V ER0 D R IH1 V AH0 N
+OVERDUE  OW1 V ER0 D UW1
+OVEREAT  OW1 V ER0 IY2 T
+OVEREATEN	OW1 V ER0 IY1 T AH0 N
+OVEREATING  OW1 V ER0 IY1 T IH0 NG
+OVEREMPHASIS	OW2 V ER0 EH1 M F AH0 S AH0 S
+OVEREMPHASIZED  OW1 V ER0 EH1 M F AH0 S AY2 Z D
+OVEREND	OW2 V ER0 EH1 N D
+OVERESTIMATE  OW2 V ER0 EH1 S T AH0 M EY2 T
+OVERESTIMATED  OW2 V ER0 EH1 S T AH0 M EY2 T IH0 D
+OVERESTIMATES  OW2 V ER0 EH1 S T AH0 M EY2 T S
+OVERESTIMATING  OW2 V ER0 EH1 S T AH0 M EY2 T IH0 NG
+OVEREXCITED  OW2 V ER0 EH0 K S AY1 T IH0 D
+OVEREXERTION	OW2 V ER0 IH0 G Z ER1 SH AH0 N
+OVERFALLS	OW1 V ER0 F AO2 L Z
+OVERFATIGUE	OW1 V ER0 F AH0 T IY1 G
+OVERFATIGUED	OW1 V ER0 F AH0 T IY1 G D
+OVERFED  OW2 V ER0 F EH1 D
+OVERFEED  OW2 V ER0 F IY1 D
+OVERFEEDING	OW1 V ER0 F IY1 D IH0 NG
+OVERFILL  OW1 V ER0 F IH2 L
+OVERFILLED	OW1 V ER0 F IH2 L D
+OVERFLOW  OW1 V ER0 F L OW2
+OVERFLOW  OW2 V ER0 F L OW1
+OVERFLOWED  OW2 V ER0 F L OW1 D
+OVERFLOWING  OW1 V ER0 F L OW2 IH0 NG
+OVERFLOWINGS	OW1 V ER0 F L OW2 IH0 NG Z
+OVERFLOWN	OW1 V ER0 F L OW1 N
+OVERFLOWS  OW1 V ER0 F L OW2 Z
+OVERFOND	OW1 V ER0 F AA1 N D
+OVERFULL	OW1 V ER0 F AH0 L
+OVERGENEROUS  OW1 V ER0 JH EH2 N ER0 AH0 S
+OVERGONE	OW1 V ER0 G AO1 N
+OVERGREW	OW1 V ER0 G R UW1
+OVERGROUND	OW1 V ER0 G R AW2 N D
+OVERGROW	OW1 V ER0 G R OW2
+OVERGROWING  OW2 V ER0 G R OW1 W IH0 NG
+OVERGROWN  OW2 V ER0 G R OW1 N
+OVERGROWTH  OW1 V ER0 G R OW0 TH
+OVERHADDON	OW1 V ER0 HH AE1 D AH0 N
+OVERHAND	OW1 V ER0 HH AE2 N D
+OVERHANG  OW1 V ER0 HH AE2 NG
+OVERHANGING  OW1 V ER0 HH AE2 NG IH0 NG
+OVERHANGS  OW1 V ER0 HH AE2 NG Z
+OVERHASTE	OW1 V ER0 HH EY2 S T
+OVERHASTY	OW1 V ER0 HH AE2 S T IY0
+OVERHAUL  OW1 V ER0 HH AO2 L
+OVERHAULED  OW1 V ER0 HH AO2 L D
+OVERHAULIN	OW1 V ER0 HH AO2 L IH0 N
+OVERHAULING  OW1 V ER0 HH AO2 L IH0 NG
+OVERHAULS  OW1 V ER0 HH AO2 L Z
+OVERHEAD  OW1 V ER0 HH EH1 D
+OVERHEAR  OW1 V ER0 HH IY1 R
+OVERHEARD  OW1 V ER0 HH ER1 D
+OVERHEARING  OW2 V ER0 HH IY1 R IH0 NG
+OVERHEARS  OW1 V ER0 HH IY1 R Z
+OVERHEAT  OW1 V ER0 HH IY2 T
+OVERHEATED  OW1 V ER0 HH IY2 T IH0 D
+OVERHEATING  OW1 V ER0 HH IY2 T IH0 NG
+OVERHET	OW1 V ER0 HH EH1 T
+OVERHILL	OW1 V ER0 HH IH2 L
+OVERHOLT  OW1 V ER0 HH OW0 L T
+OVERHUNG  OW0 V ER0 HH AH1 NG
+OVERIJSSEL	OW1 V ER0 IY1 S AH0 L
+OVERINDULGED	OW1 V ER0 IH0 N D AH1 L JH D
+OVERINDULGENCE	OW1 V ER0 IH0 N D AH1 L JH AH0 N S
+OVERISH	OW1 V ER0 IH0 SH
+OVERJOYED  OW2 V ER0 JH OY1 D
+OVERKIND	OW1 V ER0 K IH2 N D
+OVERKIRK	OW1 V ER0 K ER0 K
+OVERLADEN	OW1 V ER0 L EY1 D AH0 N
+OVERLAID  OW1 V ER0 L EY2 D
+OVERLAIN  OW1 V ER0 L EY2 N
+OVERLAND  OW1 V ER0 L AE2 N D
+OVERLAND  OW1 V ER0 L AH0 N D
+OVERLAND'S	OW1 V ER0 L AE2 N D Z
+OVERLANDERS	OW1 V ER0 L AE2 N D ER0 Z
+OVERLAP  OW1 V ER0 L AE2 P
+OVERLAPPED  OW1 V ER0 L AE2 P T
+OVERLAPPING  OW1 V ER0 L AE2 P IH0 NG
+OVERLAPS  OW1 V ER0 L AE2 P S
+OVERLARGE	OW1 V ER0 L AA1 R JH
+OVERLAY  OW1 V ER0 L EY2
+OVERLAYING	OW1 V ER0 L EY2 IH0 NG
+OVERLAYS  OW1 V ER0 L EY2 Z
+OVERLEAF	OW1 V ER0 L IY2 F
+OVERLEANING	OW1 V ER0 L IY2 N IH0 NG
+OVERLEAP	OW1 V ER0 L IY1 P
+OVERLEAPED	OW1 V ER0 L IY1 P T
+OVERLEAPING	OW1 V ER0 L IY1 P IH0 NG
+OVERLEAPS	OW1 V ER0 L IY1 P S
+OVERLEAPT	OW1 V ER0 L IY1 P T
+OVERLIE	OW1 V ER0 L IY0
+OVERLIES	OW1 V ER0 L IY0 Z
+OVERLIVE	OW1 V ER0 L AY2 V
+OVERLIVED	OW1 V ER0 L AY2 V D
+OVERLOAD  OW1 V ER0 L OW2 D
+OVERLOADED  OW1 V ER0 L OW2 D IH0 D
+OVERLOADING  OW1 V ER0 L OW2 D IH0 NG
+OVERLOADS  OW1 V ER0 L OW2 D Z
+OVERLONG	OW1 V ER0 L AO2 NG
+OVERLOOK  OW1 V ER0 L UH2 K
+OVERLOOK'D	OW1 V ER0 L UH2 K D
+OVERLOOKED  OW1 V ER0 L UH2 K T
+OVERLOOKER	OW1 V ER0 L UH2 K ER0
+OVERLOOKERS	OW1 V ER0 L UH2 K ER0 Z
+OVERLOOKIN	OW1 V ER0 L UH2 K IH0 N
+OVERLOOKING  OW1 V ER0 L UH2 K IH0 NG
+OVERLOOKS  OW1 V ER0 L UH2 K S
+OVERLORD  OW1 V ER0 L AO2 R D
+OVERLORDS  OW1 V ER0 L AO2 R D Z
+OVERLORDSHIP	OW1 V ER0 L AO2 R D SH IH0 P
+OVERLY  OW1 V ER0 L IY0
+OVERLYING  OW2 V ER0 L AY1 IH0 NG
+OVERMAN  OW1 V ER0 M AH0 N
+OVERMAN'S	OW1 V ER0 M AH0 N Z
+OVERMANTEL	OW1 V ER0 M AE1 N T EH2 L
+OVERMASTER	OW1 V ER0 M AE2 S T ER0
+OVERMASTERED	OW1 V ER0 M AE2 S T ER0 D
+OVERMASTERING	OW1 V ER0 M AE2 S T ER0 IH0 NG
+OVERMASTERS	OW1 V ER0 M AE2 S T ER0 Z
+OVERMATCH  OW2 V ER0 M AE1 CH
+OVERMATCHED  OW1 V ER0 M AE1 CH T
+OVERMORE	OW1 V ER0 M AO2 R
+OVERMORE'S	OW1 V ER0 M AO2 R Z
+OVERMUCH	OW1 V ER0 M AH1 CH
+OVERNIGHT  OW1 V ER0 N AY1 T
+OVERPAID  OW1 V ER0 P EY1 D
+OVERPASS  OW1 V ER0 P AE2 S
+OVERPASSED	OW1 V ER0 P AE2 S T
+OVERPASSING	OW1 V ER0 P AE2 S IH0 NG
+OVERPAST	OW1 V ER0 P AE2 S T
+OVERPAY  OW1 V ER0 P EY2
+OVERPAYING  OW1 V ER0 P EY2 IH0 NG
+OVERPERSUADED	OW1 V ER0 P ER0 S W EY1 D IH0 D
+OVERPLAY  OW1 V ER0 P L EY1
+OVERPLAYED  OW1 V ER0 P L EY1 D
+OVERPLAYING  OW1 V ER0 P L EY1 IH0 NG
+OVERPLEASED	OW1 V ER0 P L IY1 Z D
+OVERPLUS	OW1 V ER0 P L AH0 S
+OVERPOPULATED  OW2 V ER0 P AA1 P Y AH0 L EY0 T IH0 D
+OVERPOPULATION  OW2 V ER0 P AA2 P Y AH0 L EY1 SH AH0 N
+OVERPOWER  OW2 V ER0 P AW1 ER0
+OVERPOWERED  OW2 V ER0 P AW1 ER0 D
+OVERPOWERING  OW1 V ER0 P AW1 R IH0 NG
+OVERPOWERINGLY	OW1 V ER0 P AW1 R IH0 NG L IY0
+OVERPOWERS  OW2 V ER0 P AW1 ER0 Z
+OVERPRAISE	OW1 V ER0 P R EY1 Z
+OVERPRAISED	OW1 V ER0 P R EY1 Z D
+OVERPRESSED	OW1 V ER0 P R EH2 S T
+OVERPRODUCTION  OW1 V ER0 P R AH0 D AH1 K SH AH0 N
+OVERPROUD	OW1 V ER0 P R AW1 D
+OVERRAN  OW1 V ER0 R AE1 N
+OVERRATE  OW2 V ER0 R EY1 T
+OVERRATED  OW2 V ER0 R EY1 T IH0 D
+OVERRATES  OW2 V ER0 R EY1 T S
+OVERRATING  OW2 V ER0 R EY1 T IH0 NG
+OVERREACH  OW1 V ER0 R IY2 CH
+OVERREACHED  OW1 V ER0 R IY2 CH T
+OVERREACHES  OW1 V ER0 R IY2 CH IH0 Z
+OVERREACHING  OW1 V ER0 R IY2 CH IH0 NG
+OVERRIDDEN  OW1 V ER0 R IH1 D AH0 N
+OVERRIDE  OW1 V ER0 R AY2 D
+OVERRIDES  OW1 V ER0 R AY2 D Z
+OVERRIDING  OW1 V ER0 R AY2 D IH0 NG
+OVERRIPE  OW1 V ER0 R AY1 P
+OVERRODE  OW1 V ER0 R OW1 D
+OVERRULE  OW1 V ER0 R UW2 L
+OVERRULED  OW2 V ER0 R UW1 L D
+OVERRULES  OW1 V ER0 R UW2 L Z
+OVERRULING  OW1 V ER0 R UW2 L IH0 NG
+OVERRUN  OW1 V ER0 R AH2 N
+OVERRUNNING  OW1 V ER0 R AH2 N IH0 NG
+OVERRUNS  OW1 V ER0 R AH2 N Z
+OVERS  OW1 V ER0 Z
+OVERSAW  OW1 V ER0 S AO2
+OVERSCORED	OW1 V ER0 S K AO1 R D
+OVERSCRUPULOUS	OW1 V ER0 S K R UW1 P Y AH0 L AH0 S
+OVERSEA  OW2 V ER0 S IY1
+OVERSEAS  OW1 V ER0 S IY1 Z
+OVERSEE  OW1 V ER0 S IY2
+OVERSEEING  OW1 V ER0 S IY2 IH0 NG
+OVERSEEN  OW1 V ER0 S IY2 N
+OVERSEER  OW1 V ER0 S IY1 ER0
+OVERSEER'S	OW1 V ER0 S IY1 ER0 Z
+OVERSEERS  OW2 V ER0 S IY1 ER0 Z
+OVERSEERSHIP	OW1 V ER0 S IY1 ER0 SH IH2 P
+OVERSEES  OW1 V ER0 S IY2 Z
+OVERSENSITIVE  OW2 V ER0 S EH1 N S AH0 T IH0 V
+OVERSET	OW1 V ER0 S EH2 T
+OVERSETS	OW1 V ER0 S EH2 T S
+OVERSETTING	OW1 V ER0 S EH2 T IH0 NG
+OVERSEXED	OW1 V ER0 S EH2 K S T
+OVERSHADED	OW1 V ER0 SH EY1 D IH0 D
+OVERSHADOW  OW1 V ER0 SH AE1 D OW0
+OVERSHADOWED  OW2 V ER0 SH AE1 D OW0 D
+OVERSHADOWING  OW1 V ER0 SH AE1 D OW0 IH0 NG
+OVERSHADOWS  OW1 V ER0 SH AE1 D OW0 Z
+OVERSHIRT	OW1 V ER0 SH ER2 T
+OVERSHOE	OW1 V ER0 SH UW2
+OVERSHOES	OW1 V ER0 SH UW2 Z
+OVERSHOOT  OW1 V ER0 SH UW2 T
+OVERSHOOTING  OW1 V ER0 SH UW2 T IH0 NG
+OVERSHOOTS	OW1 V ER0 SH UW2 T S
+OVERSHOT  OW1 V ER0 SH AA2 T
+OVERSHUTE	OW1 V ER0 SH UW1 T
+OVERSIDE	OW1 V ER0 S AY2 D
+OVERSIGHT  OW1 V ER0 S AY2 T
+OVERSIGHTS	OW1 V ER0 S AY2 T S
+OVERSIZE  OW2 V ER0 S AY1 Z
+OVERSIZED  OW1 V ER0 S AY2 Z D
+OVERSKIRT	OW1 V ER0 S K ER2 T
+OVERSLAUGH	OW1 V ER0 S L AO2
+OVERSLEEP  OW2 V ER0 S L IY1 P
+OVERSLEEPING	OW1 V ER0 S L IY1 P IH0 NG
+OVERSLEPT  OW2 V ER0 S L EH1 P T
+OVERSOUL	OW1 V ER0 S OW1 L
+OVERSPENT  OW1 V ER0 S P EH1 N T
+OVERSPREAD	OW1 V ER0 S P R EH1 D
+OVERSPREADING	OW1 V ER0 S P R EH1 D IH0 NG
+OVERSPREADS	OW1 V ER0 S P R EH1 D Z
+OVERSTATE  OW1 V ER0 S T EY2 T
+OVERSTATED  OW1 V ER0 S T EY2 T IH0 D
+OVERSTATEMENT  OW1 V ER0 S T EY2 T M AH0 N T
+OVERSTATING  OW1 V ER0 S T EY2 T IH0 NG
+OVERSTAY  OW2 V ER0 S T EY1
+OVERSTAYED  OW2 V ER0 S T EY1 D
+OVERSTAYING	OW1 V ER0 S T EY1 IH0 NG
+OVERSTEP  OW1 V ER0 S T EH2 P
+OVERSTEPPED  OW1 V ER0 S T EH2 P T
+OVERSTEPPING  OW1 V ER0 S T EH2 P IH0 NG
+OVERSTEPS	OW1 V ER0 S T EH2 P S
+OVERSTIMULATED	OW1 V ER0 S T IH1 M Y AH0 L EY2 T IH0 D
+OVERSTIMULATION	OW1 V ER0 S T IH1 M Y AH0 L EY1 SH AH0 N
+OVERSTOCK  OW1 V ER0 S T AA1 K
+OVERSTOCKED  OW1 V ER0 S T AA1 K T
+OVERSTOCKING	OW1 V ER0 S T AA1 K IH0 NG
+OVERSTONE	OW1 V ER0 S T OW2 N
+OVERSTOW	OW1 V ER0 S T OW2
+OVERSTRAIN	OW1 V ER0 S T R EY1 N
+OVERSTRAINED	OW1 V ER0 S T R EY1 N D
+OVERSTRAINING	OW1 V ER0 S T R EY1 N IH0 NG
+OVERSTRAND	OW1 V ER0 S T R AH0 N D
+OVERSTREET  OW1 V ER0 S T R IY2 T
+OVERSTRETCHED	OW1 V ER0 S T R EH1 CH T
+OVERSTRONG	OW1 V ER0 S T R AO1 NG
+OVERSTRUNG	OW1 V ER0 S T R AH2 NG
+OVERSTUDY	OW1 V ER0 S T AH2 D IY0
+OVERSTUFFED  OW1 V ER0 S T AH2 F T
+OVERSUPPLY  OW2 V ER0 S AH0 P L AY1
+OVERSWEPT	OW1 V ER0 S W EH2 P T
+OVERT  OW0 V ER1 T
+OVERT  OW1 V ER0 T
+OVERTAKE  OW1 V ER0 T EY2 K
+OVERTAKEN  OW1 V ER0 T EY2 K AH0 N
+OVERTAKES  OW1 V ER0 T EY2 K S
+OVERTAKETH	OW1 V ER0 T EY2 K AH0 TH
+OVERTAKING  OW1 V ER0 T EY2 K IH0 NG
+OVERTASK	OW1 V ER0 T AE2 S K
+OVERTASKED	OW1 V ER0 T AE1 S K T
+OVERTASKING	OW1 V ER0 T AE2 S K IH0 NG
+OVERTAX  OW1 V ER0 T AE2 K S
+OVERTAXED  OW2 V ER0 T AE1 K S T
+OVERTAXING	OW1 V ER0 T AE1 K S IH0 NG
+OVERTHREW  OW2 V ER0 TH R UW1
+OVERTHROW  OW1 V ER0 TH R OW2
+OVERTHROWETH	OW1 V ER0 TH R OW2 EH1 TH
+OVERTHROWING  OW1 V ER0 TH R OW2 IH0 NG
+OVERTHROWN  OW2 V ER0 TH R OW1 N
+OVERTHROWS  OW1 V ER0 TH R OW2 Z
+OVERTHRUST	OW1 V ER0 TH R AH1 S T
+OVERTHWART	OW1 V ER0 TH W AO1 R T
+OVERTIME  OW1 V ER0 T AY2 M
+OVERTIRE	OW1 V ER0 T AY2 R
+OVERTIRED	OW1 V ER0 T AY1 ER0 D
+OVERTLY  OW0 V ER1 T L IY0
+OVERTON  OW1 V ER0 T AH0 N
+OVERTON'S	OW1 V ER0 T AH0 N Z
+OVERTONE  OW1 V ER0 T OW2 N
+OVERTONES  OW1 V ER0 T OW2 N Z
+OVERTOOK  OW2 V ER0 T UH1 K
+OVERTOP	OW1 V ER0 T AA2 P
+OVERTOP'S	OW1 V ER0 T AA2 P S
+OVERTOPPED	OW1 V ER0 T AA1 P T
+OVERTOPPING	OW1 V ER0 T AA1 P IH0 NG
+OVERTOPS	OW1 V ER0 T AA2 P S
+OVERTOWERING	OW1 V ER0 T AW1 ER0 IH0 NG
+OVERTRAINED	OW1 V ER0 T R EY1 N D
+OVERTURE  OW1 V ER0 CH ER0
+OVERTURES  OW1 V ER0 CH UH2 R Z
+OVERTURN  OW1 V ER0 T ER2 N
+OVERTURN'D	OW1 V ER0 T ER2 N D
+OVERTURNED  OW1 V ER0 T ER2 N D
+OVERTURNING  OW1 V ER0 T ER2 N IH0 NG
+OVERTURNINGS	OW1 V ER0 T ER2 N IH0 NG Z
+OVERTURNS  OW1 V ER0 T ER2 N Z
+OVERVALUE  OW1 V ER0 V AE2 L Y UW0
+OVERVALUED  OW1 V ER0 V AE2 L Y UW0 D
+OVERVALUING	OW1 V ER0 V AE2 L Y UW0 IH0 NG
+OVERWEARIED	OW2 V ER0 W IH1 R IY0 D
+OVERWEARY	OW2 V ER0 W EH2 R IY0
+OVERWEENING	OW2 V ER0 W IY1 N IH0 NG
+OVERWEENINGLY	OW2 V ER0 W IY1 N IH0 NG L IY0
+OVERWEG	OW2 V ER0 W EH2 G
+OVERWEIGH	OW2 V ER0 W EY2
+OVERWEIGHED	OW2 V ER0 W EY1 D
+OVERWEIGHT  OW2 V ER0 W EY1 T
+OVERWEIGHTED  OW2 V ER0 W EY1 T IH0 D
+OVERWELL	OW2 V ER0 W EH2 L
+OVERWHELM  OW2 V ER0 HH W EH1 L M
+OVERWHELM  OW2 V ER0 W EH1 L M
+OVERWHELM'D	OW2 V ER0 W EH1 L M D
+OVERWHELMED  OW2 V ER0 HH W EH1 L M D
+OVERWHELMED  OW2 V ER0 W EH1 L M D
+OVERWHELMING  OW2 V ER0 HH W EH1 L M IH0 NG
+OVERWHELMING  OW2 V ER0 W EH1 L M IH0 NG
+OVERWHELMINGLY  OW2 V ER0 HH W EH1 L M IH0 NG L IY0
+OVERWHELMINGLY  OW2 V ER0 W EH1 L M IH0 NG L IY0
+OVERWHELMS  OW2 V ER0 HH W EH1 L M Z
+OVERWHELMS  OW2 V ER0 W EH1 L M Z
+OVERWISE	OW2 V ER0 W AY2 Z
+OVERWORK  OW2 V ER0 W ER1 K
+OVERWORKED  OW2 V ER0 W ER1 K T
+OVERWORKING  OW2 V ER0 W ER1 K IH0 NG
+OVERWORKS  OW2 V ER0 W ER1 K S
+OVERWORN	OW2 V ER0 W ER0 N
+OVERWROUGHT  OW2 V ER0 R AO1 T
+OVERYSSEL	OW1 V ER0 IH1 S AH0 L
+OVERZEALOUS  OW2 V ER0 Z EH1 L AH0 S
+OVES	OW1 V Z
+OVID	OW1 V IH0 D
+OVID'S	OW0 V IH1 D Z
+OVIDE	OW0 V AY1 D
+OVIDIAN	OW0 V IH1 D IY0 AH0 N
+OVIDIO	OW0 V IY1 D IY0 OW0
+OVIDIUS	OW0 V IH1 D IY0 AH0 S
+OVIDUCT	OW0 V IH1 D AH0 K T
+OVIDUCTS	OW0 V IH1 D AH0 K T S
+OVIEDO  OW0 V IY0 EY1 D OW0
+OVINE	OW0 V IY1 N IY0
+OVINGTON	OW1 V IH0 NG T AH0 N
+OVINGTON'S	OW1 V IH0 NG T AH0 N Z
+OVIPAROUS  OW0 V IH1 P ER0 AH0 S
+OVIPOSITOR	OW0 V IH1 P AA1 Z IH0 T ER0
+OVIS	OW1 V IH0 S
+OVO	OW1 V OW0
+OVOID  OW1 V OY2 D
+OVOIDS	OW1 V OY2 D Z
+OVREBO	OW1 V R IY1 B OW0
+OVULATION  OW2 V Y AH0 L EY1 SH AH0 N
+OVULE  OW1 V Y UW0 L
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+RESTALRIG	R IY0 S T AA1 L R IH0 G
+RESTANTE	R EH0 S T AA0 N T
+RESTART  R IY0 S T AA1 R T
+RESTARTED  R IY0 S T AA1 R T IH0 D
+RESTARTING  R IY0 S T AA1 R T IH0 NG
+RESTATE  R IY0 S T EY1 T
+RESTATED  R IY0 S T EY1 T IH0 D
+RESTATEMENT  R IY0 S T EY1 T M AH0 N T
+RESTATING  R IY0 S T EY1 T IH0 NG
+RESTAUD	R EH1 S T D
+RESTAUD'S	R EH1 S T D Z
+RESTAURANT  R EH1 S T ER0 AA2 N T
+RESTAURANT  R EH1 S T R AA2 N T
+RESTAURANT'S  R EH1 S T ER0 AA2 N T S
+RESTAURANT'S  R EH1 S T R AA2 N T S
+RESTAURANTS  R EH1 S T ER0 AA2 N T S
+RESTAURANTS  R EH1 S T R AA2 N T S
+RESTAURATEUR  R EH2 S T ER0 AH0 T ER1
+RESTAURATEUR  R EH2 S T R AH0 T ER1
+RESTAURATEUR'S	R EH1 S T R AH0 T ER1 Z
+RESTAURATEURS  R EH2 S T ER0 AH0 T ER1 Z
+RESTAURATEURS  R EH2 S T R AH0 T ER1 Z
+RESTAURATION	R EH1 S T ER0 EY1 SH AH0 N
+RESTE	R EH1 S T
+RESTED  R EH1 S T AH0 D
+RESTED  R EH1 S T IH0 D
+RESTER  R EH1 S T ER0
+RESTES	R EH1 S T IY0 Z
+RESTETH	R IH0 S T IH0 TH
+RESTEZ	R IY0 S T EH0 Z
+RESTFUL  R EH1 S T F AH0 L
+RESTFULLY	R EH1 S T F AH0 L IY0
+RESTFULNESS	R EH1 S T F AH0 L N AH0 S
+RESTIFF	R EH1 S T AH0 F
+RESTIGOUCHE	R EH1 S T AH0 G UW1 SH
+RESTIN	R EH1 S T IH0 N
+RESTING  R EH1 S T IH0 NG
+RESTITUTION  R EH2 S T IH0 T UW1 SH AH0 N
+RESTITUTIONS	R EH1 S T AH0 T UW1 SH AH0 N Z
+RESTIVE  R EH1 S T IH0 V
+RESTIVELY	R EH1 S T IH0 V L IY0
+RESTIVENESS  R EH1 S T IH0 V N AH0 S
+RESTLESS  R EH1 S T L AH0 S
+RESTLESSLY  R EH1 S T L AH0 S L IY0
+RESTLESSNESS  R EH1 S T L AH0 S N AH0 S
+RESTOCK  R IY0 S T AA1 K
+RESTOCKED  R IY0 S T AA1 K T
+RESTOCKING  R IY0 S T AA1 K IH0 NG
+RESTON  R EH1 S T AH0 N
+RESTOR'D	R EH1 S T ER0 D
+RESTORATION  R EH2 S T ER0 EY1 SH AH0 N
+RESTORATIONS  R EH2 S T ER0 EY1 SH AH0 N Z
+RESTORATIVE  R AH0 S T AO1 R AH0 T IH0 V
+RESTORATIVES	R EH2 S T ER0 AH0 T IH0 V Z
+RESTORE  R IH0 S T AO1 R
+RESTORED  R IH0 S T AO1 R D
+RESTORER  R IH0 S T AO1 R ER0
+RESTORERS	R IH0 S T AO1 R ER0 Z
+RESTORES  R IH0 S T AO1 R Z
+RESTORETH	R IH0 S T AO1 R TH
+RESTORING  R IH0 S T AO1 R IH0 NG
+RESTRAIN  R IY0 S T R EY1 N
+RESTRAIN'D	R IY0 S T R EY1 N D
+RESTRAINED  R IY0 S T R EY1 N D
+RESTRAINEDLY	R IY0 S T R EY1 N D L IY0
+RESTRAINER	R IY0 S T R EY1 N ER0
+RESTRAINING  R IY0 S T R EY1 N IH0 NG
+RESTRAININGLY	R IY0 S T R EY1 N IH0 NG L IY0
+RESTRAINS  R IY0 S T R EY1 N Z
+RESTRAINT  R IH0 S T R EY1 N T
+RESTRAINT  R IY0 S T R EY1 N T
+RESTRAINTS  R IH0 S T R EY1 N T S
+RESTRAINTS  R IY0 S T R EY1 N T S
+RESTRICT  R IY0 S T R IH1 K T
+RESTRICTED  R IY0 S T R IH1 K T AH0 D
+RESTRICTED  R IY0 S T R IH1 K T IH0 D
+RESTRICTING  R IY0 S T R IH1 K T IH0 NG
+RESTRICTION  R IY0 S T R IH1 K SH AH0 N
+RESTRICTIONS  R IY0 S T R IH1 K SH AH0 N Z
+RESTRICTIVE  R IY0 S T R IH1 K T IH0 V
+RESTRICTS  R IY0 S T R IH1 K T S
+RESTRONGUET	R IY0 S T R AO1 NG W EH2 T
+RESTRONGUET'S	R IY0 S T R AO1 NG W EH2 T S
+RESTRUNG	R IY0 S T R AH1 NG
+RESTS  R EH1 S T S
+RESTU	R IY0 S T UW0
+RESTY	R EH1 S T IY0
+RESULT  R IH0 Z AH1 L T
+RESULT  R IY0 Z AH1 L T
+RESULT'S	R IY0 Z AH1 L T S
+RESULTANT  R IY0 Z AH1 L T AH0 N T
+RESULTANTS	R IY0 Z AH1 L T AH0 N T S
+RESULTED  R IH0 Z AH1 L T IH0 D
+RESULTED  R IY0 Z AH1 L T AH0 D
+RESULTED  R IY0 Z AH1 L T IH0 D
+RESULTING  R IH0 Z AH1 L T IH0 NG
+RESULTING  R IY0 Z AH1 L T IH0 NG
+RESULTLESS	R IY0 Z AH1 L T L AH0 S
+RESULTS  R IH0 Z AH1 L T S
+RESULTS  R IY0 Z AH1 L T S
+RESUM'D	R IY0 Z AH1 M D
+RESUME  R EH1 Z AH0 M EY2
+RESUME  R IH0 Z UW1 M
+RESUME  R IY0 Z UW1 M
+RESUMED  R IH0 Z UW1 M D
+RESUMED  R IY0 Z UW1 M D
+RESUMES  R EH1 Z AH0 M EY2 Z
+RESUMES  R IH0 Z UW1 M Z
+RESUMES  R IY0 Z UW1 M Z
+RESUMING  R IH0 Z UW1 M IH0 NG
+RESUMING  R IY0 Z UW1 M IH0 NG
+RESUMPTION  R IH0 Z AH1 M P SH AH0 N
+RESUMPTION  R IH0 Z AH1 M SH AH0 N
+RESUMPTION  R IY0 Z AH1 M P SH AH0 N
+RESUMPTION  R IY0 Z AH1 M SH AH0 N
+RESURGAM	R IY0 S ER1 G AH0 M
+RESURGED	R IY0 S ER1 JH D
+RESURGENCE  R IY0 S ER1 JH AH0 N S
+RESURGENT  R IH0 S ER1 JH AH0 N T
+RESURGENT  R IY0 S ER1 JH AH0 N T
+RESURGING  R IY0 S ER1 JH IH0 NG
+RESURRECT  R EH2 Z ER0 EH1 K T
+RESURRECTED  R EH2 Z ER0 EH1 K T IH0 D
+RESURRECTING  R EH2 Z ER0 EH1 K T IH0 NG
+RESURRECTION  R EH2 Z ER0 EH1 K SH AH0 N
+RESURRECTIONIST	R EH2 Z ER0 EH1 K SH AH0 N IH0 S T
+RESURRECTIONISTS	R EH2 Z ER0 EH1 K SH AH0 N IH0 S T S
+RESURRECTIONS	R EH2 Z ER0 EH1 K SH AH0 N Z
+RESUSCITATE  R IH0 S AH1 S IH0 T EY2 T
+RESUSCITATE  R IY0 S AH1 S IH0 T EY2 T
+RESUSCITATED  R IH0 S AH1 S IH0 T EY2 T IH0 D
+RESUSCITATES	R IH0 S AH1 S IH0 T EY2 T S
+RESUSCITATING  R IH0 S AH1 S IH0 T EY2 T IH0 NG
+RESUSCITATION  R IH0 S AH2 S IH0 T EY1 SH AH0 N
+RESZKE	R IY0 SH K IY0
+RET  R EH1 T
+RETAIL  R IY1 T EY2 L
+RETAILED  R IY1 T EY2 L D
+RETAILER  R IY1 T EY2 L ER0
+RETAILERS  R IY1 T EY2 L ER0 Z
+RETAILING  R IY1 T EY2 L IH0 NG
+RETAILS  R IY1 T EY2 L Z
+RETAIN  R IH0 T EY1 N
+RETAIN  R IY0 T EY1 N
+RETAIN'D	R IY1 T EY2 N D
+RETAINED  R IH0 T EY1 N D
+RETAINED  R IY0 T EY1 N D
+RETAINER  R IH0 T EY1 N ER0
+RETAINER  R IY0 T EY1 N ER0
+RETAINER'S	R IH0 T EY1 N ER0 Z
+RETAINERS  R IH0 T EY1 N ER0 Z
+RETAINETH	R IH0 T EY1 N TH
+RETAINING  R IH0 T EY1 N IH0 NG
+RETAINING  R IY0 T EY1 N IH0 NG
+RETAINS  R IH0 T EY1 N Z
+RETAINS  R IY0 T EY1 N Z
+RETAKE  R IY0 T EY1 K
+RETAKE  R IY1 T EY1 K
+RETAKEN  R IY0 T EY1 K AH0 N
+RETAKES	R IY0 T EY1 K S
+RETAKING  R IY0 T EY1 K IH0 NG
+RETALIATE  R IH0 T AE1 L IY0 EY2 T
+RETALIATE  R IY0 T AE1 L IY0 EY2 T
+RETALIATED  R IH0 T AE1 L IY0 EY2 T IH0 D
+RETALIATED  R IY0 T AE1 L IY0 EY2 T IH0 D
+RETALIATES	R IH0 T AE1 L IY0 EY2 T S
+RETALIATING  R IH0 T AE1 L IY0 EY2 T IH0 NG
+RETALIATION  R IY0 T AE2 L IY0 EY1 SH AH0 N
+RETALIATIONS	R IY0 T AE2 L IY0 EY1 SH AH0 N Z
+RETALIATORY  R IY0 T AE1 L Y AH0 T AO2 R IY0
+RETAMOSO	R EH1 T AH0 M OW1 S OW0
+RETARD  R IH0 T AA1 R D
+RETARD  R IY0 T AA1 R D
+RETARDATION  R IY0 T AA0 R D EY1 SH AH0 N
+RETARDED  R IH0 T AA1 R D IH0 D
+RETARDED  R IY0 T AA1 R D AH0 D
+RETARDED  R IY0 T AA1 R D IH0 D
+RETARDER	R IY0 T AA1 R D ER0
+RETARDING  R IH0 T AA1 R D IH0 NG
+RETARDING  R IY0 T AA1 R D IH0 NG
+RETARDS  R IH0 T AA1 R D Z
+RETARDS  R IY0 T AA1 R D Z
+RETAUX	R IY1 T AO1 K S
+RETCH	R EH1 CH
+RETCH'S	R EH1 CH IH0 Z
+RETCHED	R EH1 CH T
+RETCHING	R EH1 CH IH0 NG
+RETCHINGS	R EH1 CH IH0 NG Z
+RETCLIFFE	R EH1 T K L IH0 F
+RETE	R IY0 T
+RETEAU	R IY2 T OW1
+RETELL  R IY0 T EH1 L
+RETELLING  R IY0 T EH1 L IH0 NG
+RETENTION  R IY0 T EH1 N SH AH0 N
+RETENTIVE	R IY0 T EH1 N T IH0 V
+RETENTIVENESS	R IY0 T EH1 N T IH0 V N AH0 S
+RETENUE	R IY0 T EH1 N Y UW0
+RETFORD	R EH1 T F ER0 D
+RETHEL	R EH1 TH AH0 L
+RETHER	R EH1 DH ER0
+RETHERTON	R IH0 TH ER1 T AH0 N
+RETIARIUS	R IY0 T IY0 EH1 R IY0 AH0 S
+RETICENCE  R EH1 T IH0 S AH0 N S
+RETICENCES	R EH1 T IH0 S AH0 N S IH0 Z
+RETICENT  R EH1 T IH0 S AH0 N T
+RETICENTLY	R EH1 T IH0 S AH0 N T L IY0
+RETICULATED	R IH0 T IH1 K Y AH0 L EY2 T IH0 D
+RETICULATION	R IH0 T IH1 K Y AH0 L EY1 SH AH0 N
+RETICULATIONS	R IH0 T IH1 K Y AH0 L EY1 SH AH0 N Z
+RETICULE	R EH1 T AH0 K Y UW2 L
+RETICULES	R EH1 T AH0 K Y UW2 L Z
+RETIE	R EH1 T IY0
+RETIED	R IH0 T AY1 D
+RETIEF	R IH0 T AY1 F
+RETIEF'S	R IH0 T AY1 F S
+RETINA  R EH1 T AH0 N AH0
+RETINAL  R EH1 T AH0 N AH0 L
+RETINAS	R IH0 T AY1 N AH0 Z
+RETINUE  R EH1 T AH0 N UW2
+RETINUES	R EH1 T AH0 N UW2 Z
+RETIR'D	R IY0 T AY1 ER0 D
+RETIRACY	R IH0 T AY1 R AH0 S IY0
+RETIRE  R IH0 T AY1 R
+RETIRE  R IY0 T AY1 R
+RETIRE  R IY2 T AY1 ER0
+RETIRED  R IH0 T AY1 R D
+RETIRED  R IY0 T AY1 ER0 D
+RETIRED  R IY0 T AY1 R D
+RETIREMENT  R IH0 T AY1 ER0 M AH0 N T
+RETIREMENT  R IY0 T AY1 ER0 M AH0 N T
+RETIREMENTS  R IH0 T AY1 R M AH0 N T S
+RETIREMENTS  R IY0 T AY1 R M AH0 N T S
+RETIRER	R IY0 T AY1 ER0 R
+RETIRES  R IH0 T AY1 R Z
+RETIRES  R IY0 T AY1 ER0 Z
+RETIRES  R IY0 T AY1 R Z
+RETIRIN	R IH0 T AY1 R IH0 N
+RETIRING  R IH0 T AY1 R IH0 NG
+RETIRING  R IY0 T AY1 ER0 IH0 NG
+RETIRING  R IY0 T AY1 R IH0 NG
+RETIRO	R IH0 T AY1 R OW0
+RETOC	R IY0 T AO1 K
+RETOC'S	R IY0 T AO1 K S
+RETOLD	R EH1 T OW0 L D
+RETOOK  R IY0 T UH1 K
+RETORT  R IY1 T AO2 R T
+RETORTED  R IY0 T AO1 R T IH0 D
+RETORTII	R IY1 T AO2 R T IY1 IY0
+RETORTING	R IY0 T AO1 R T IH0 NG
+RETORTS  R IH0 T AO1 R T S
+RETORTS  R IY0 T AO1 R T S
+RETOUCH	R IY0 T AH1 CH
+RETOUCHED	R IY0 T AH1 CH T
+RETOUCHER	R IY0 T AH1 CH ER0
+RETOUCHING  R IY0 T AH1 CH IH0 NG
+RETOUR	R IY0 T UH1 R
+RETOURNE	R IY0 T UH1 R N
+RETOURNER	R IY0 T ER1 N ER0
+RETRACE  R IY0 T R EY1 S
+RETRACED  R IY0 T R EY1 S T
+RETRACES	R IY0 T R EY1 S IH0 Z
+RETRACING  R IY0 T R EY1 S IH0 NG
+RETRACT  R IY0 T R AE1 K T
+RETRACTATION	R IY0 T R AE1 K T EY1 SH AH0 N
+RETRACTATIONS	R IY0 T R AE1 K T EY1 SH AH0 N Z
+RETRACTED  R IY0 T R AE1 K T AH0 D
+RETRACTILE	R IY0 T R AE1 K T AH0 L
+RETRACTING  R IY0 T R AE1 K T IH0 NG
+RETRACTION  R IY0 T R AE1 K SH AH0 N
+RETRACTIONS	R IY0 T R AE1 K SH AH0 N Z
+RETRACTS  R IY0 T R AE1 K T S
+RETRAITE	R IY0 T R EY1 T
+RETRANSLATED	R IY2 T R AE0 N Z L EY1 T AH0 D
+RETRANSLATION	R IY2 T R AE0 N Z L EY1 SH AH0 N
+RETREAD  R IY0 T R EH1 D
+RETREAT  R IY0 T R IY1 T
+RETREATED  R IY0 T R IY1 T AH0 D
+RETREATED  R IY0 T R IY1 T IH0 D
+RETREATIN	R IY0 T R IY1 T AH0 N
+RETREATING  R IY0 T R IY1 T IH0 NG
+RETREATS  R IY0 T R IY1 T S
+RETRENCH  R IY0 T R EH1 N CH
+RETRENCHED  R IY0 T R EH1 N CH T
+RETRENCHING  R IY0 T R EH1 N CH IH0 NG
+RETRENCHMENT  R IY0 T R EH1 N CH M AH0 N T
+RETRENCHMENTS  R IY0 T R EH1 N CH M AH0 N T S
+RETRIAL  R IY0 T R AY1 AH0 L
+RETRIBUTION  R EH2 T R AH0 B Y UW1 SH AH0 N
+RETRIBUTIONS	R EH2 T R AH0 B Y UW1 SH AH0 N Z
+RETRIBUTIVE	R IY0 T R IH1 B Y AH0 T IH0 V
+RETRIBUTORY	R IY0 T R IH1 B Y AH0 T AO2 R IY0
+RETRIEVAL  R IH0 T R IY1 V AH0 L
+RETRIEVAL  R IY0 T R IY1 V AH0 L
+RETRIEVE  R IH0 T R IY1 V
+RETRIEVE  R IY0 T R IY1 V
+RETRIEVED  R IY0 T R IY1 V D
+RETRIEVER  R IY0 T R IY1 V ER0
+RETRIEVER'S	R IY0 T R IY1 V ER0 Z
+RETRIEVERS  R IY0 T R IY1 V ER0 Z
+RETRIEVES  R IH0 T R IY1 V Z
+RETRIEVES  R IY0 T R IY1 V Z
+RETRIEVING  R IY0 T R IY1 V IH0 NG
+RETRIMMED	R IY0 T R IH1 M D
+RETRIMMING	R IY0 T R IH1 M IH0 NG
+RETRO  R EH1 T R OW0
+RETROACTIVE  R EH2 T R OW0 AE1 K T IH0 V
+RETROCESSION  R EH2 T R OW0 S EH1 SH AH0 N
+RETROD	R EH2 T R AH0 D
+RETROGRADATION	R EH2 T R AH0 G R EY0 D EY1 SH AH0 N
+RETROGRADE  R EH1 T R AH0 G R EY2 D
+RETROGRADED	R EH2 T R AH0 G R EY2 D IH0 D
+RETROGRADES	R EH2 T R AH0 G R EY2 D Z
+RETROGRADING	R EH2 T R AH0 G R EY2 D IH0 NG
+RETROGRESSION	R EH2 T R AH0 G R EH1 SH AH0 N
+RETROGRESSIONS	R EH2 T R AH0 G R EH1 SH AH0 N Z
+RETROGRESSIVE	R EH2 T R AH0 G R EH1 S IH0 V
+RETRORSUM	R IY0 T R AO0 R S AH0 M
+RETROSPECT  R EH1 T R AH0 S P EH2 K T
+RETROSPECTION	R EH2 T R AH0 S P EH1 K SH AH0 N
+RETROSPECTIONS	R EH2 T R AH0 S P EH1 K SH AH0 N Z
+RETROSPECTIVE  R EH2 T R AH0 S P EH1 K T IH0 V
+RETROSPECTIVELY  R EH2 T R OW0 S P EH1 K T IH0 V L IY0
+RETROSPECTS	R EH2 T R AH0 S P EH1 K S
+RETROUSSE	R IY0 T R AW1 S
+RETROUVE	R IY0 T R UW2 V
+RETTA	R EH1 T AH0
+RETTEL	R EH1 T AH0 L
+RETTIMO	R EH1 T IH0 M OW2
+RETTO	R EH1 T OW0
+RETTY	R EH1 T IY0
+RETURN  R IH0 T ER1 N
+RETURN  R IY0 T ER1 N
+RETURN'D	R IH0 T ER0 N D
+RETURNABLE  R IY0 T ER1 N AH0 B AH0 L
+RETURND	R IY0 T ER1 N T
+RETURNE	R IH0 T ER0 N
+RETURNED  R IH0 T ER1 N D
+RETURNED  R IY0 T ER1 N D
+RETURNERS	R IY0 T ER1 N ER0 Z
+RETURNEST	R IY0 T ER1 N IH0 S T
+RETURNETH	R IY0 T ER1 N AH0 TH
+RETURNIN	R IY0 T ER1 N IH0 N
+RETURNING  R IH0 T ER1 N IH0 NG
+RETURNING  R IY0 T ER1 N IH0 NG
+RETURNINGS	R IY0 T ER1 N IH0 NG Z
+RETURNLESS	R IY0 T ER1 N L AH0 S
+RETURNS  R IH0 T ER1 N Z
+RETURNS  R IY0 T ER1 N Z
+RETY	R IH0 T IY0
+RETY'S	R IY0 T IY0 Z
+RETYING	R IY0 T IY0 IH0 NG
+RETYS	R IY0 T IY0 Z
+RETZ  R EH1 T S
+RETZ'S	R EH1 T S IH0 Z
+RETZOW	R EH1 T Z OW0
+REUBEN  R UW1 B AH0 N
+REUBEN'S	R UW1 B AH0 N Z
+REUBENS	R UW1 B AH0 N Z
+REUBIN	R UW1 B IH0 N
+REUBY	R UW1 B IY0
+REUCH'S	R OY1 K S
+REUCHLIN	R OY1 K L IH0 N
+REUEL	R UW1 AH0 L
+REUILLY	R IY0 UW1 L IY0
+REULAH	R UW1 L AH0
+REUMONT	R UW1 M AA2 N T
+REUNION  R IY0 UW1 N Y AH0 N
+REUNIONS  R IY0 UW1 N Y AH0 N Z
+REUNIS	R IY0 UW1 N AH0 S
+REUNITE  R IY2 UW0 N AY1 T
+REUNITED  R IY2 UW0 N AY1 T IH0 D
+REUNITES  R IY2 UW0 N AY1 T S
+REUNITING  R IY2 UW0 N AY1 T IH0 NG
+REUS	R IY0 UW1 Z
+REUSS  R UW1 S
+REUTER  R OY1 T ER0
+REUTER'S  R OY1 T ER0 Z
+REUTHER  R OY1 DH ER0
+REUTHER'S	R OY1 DH ER0 Z
+REUTLINGEN	R OY1 T L IH0 NG AH0 N
+REUTON	R OY1 T AH0 N
+REUTTER  R OY1 T ER0
+REV  R EH1 V
+REV'RENCE	R EH2 V R AH0 N S
+REV'REND	R EH2 V R EH0 N D
+REVA  R EY1 V AH0
+REVAILLOUD	R EH1 V EY2 L UW1 D
+REVAL	R EH1 V AH0 L
+REVALUATION  R IY0 V AE1 L Y UW0 EY1 SH AH0 N
+REVAMP  R IY0 V AE1 M P
+REVAMPED  R IY0 V AE1 M P T
+REVANCHE	R EH0 V AA1 N K IY0
+REVARD  R IH0 V AA1 R D
+REVARNISHED	R IH0 V AA1 R N IH0 SH T
+REVE	R IH0 V
+REVEAL  R IH0 V IY1 L
+REVEAL  R IY0 V IY1 L
+REVEAL'D	R IY0 V IY1 L D
+REVEALED  R IH0 V IY1 L D
+REVEALED  R IY0 V IY1 L D
+REVEALER	R IY0 V IY1 L ER0
+REVEALERS	R IY0 V IY1 L ER0 Z
+REVEALETH	R IY0 V IY1 L EH2 TH
+REVEALING  R IH0 V IY1 L IH0 NG
+REVEALING  R IY0 V IY1 L IH0 NG
+REVEALINGLY	R IY0 V IY1 L IH0 NG L IY0
+REVEALINGS	R IY0 V IY1 L IH0 NG Z
+REVEALMENT	R IY0 V IY1 L M AH0 N T
+REVEALS  R IH0 V IY1 L Z
+REVEALS  R IY0 V IY1 L Z
+REVECCE	R IH0 V EH1 CH IY0
+REVEIL	R EY0 V EY1 L
+REVEILLE	R EH1 V AY0 L
+REVEILLEE	R EH1 V AY0 L IY0
+REVEILLERE	R EH1 V AY0 L ER0
+REVEILLON	R EY0 V EY1 L AH0 N
+REVEL  R EH1 V AH0 L
+REVEL'S	R EH1 V AH0 L Z
+REVELATION  R EH2 V AH0 L EY1 SH AH0 N
+REVELATIONS  R EH2 V AH0 L EY1 SH AH0 N Z
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+SHIJIRO	SH AY1 JH IH1 R OW0
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+SHIMONOSEKI	SH IY1 M OW0 N OW1 S EY1 K IY0
+SHIN  SH IH1 N
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+SHINAGAWA	SH IH1 N AA0 G AA1 W AH0
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+SHINEVONBOODLE	SH IH1 N IH0 V AA1 N B UW1 D AH0 L
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+SHING  SH IH1 NG
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+SHINTOISM	SH IH1 N T OW0 IH2 Z AH0 M
+SHINTOIST	SH IH0 N T OY1 S T
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+SHINTOWN	SH IH0 N T AW2 N
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+SHIP  SH IH1 P
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+SHIPMAN  SH IH1 P M AH0 N
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+SHIPMASTER	SH IH1 P M AE2 S T ER0
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+SHIRO	SH IH1 R OW0
+SHIRO'S	SH IH1 R OW0 Z
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+SHIRRA	SH ER1 AH0
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+SHIRTS  SH ER1 T S
+SHIRTSLEEVE  SH ER1 T S L IY2 V
+SHIRTSLEEVES	SH ER1 T S L IY2 V Z
+SHIRTWAIST	SH ER1 T W EY1 S T
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+SHISHAK	SH IY0 SH AH0 K
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+SHORTCAKES	SH AO1 R T K EY2 K S
+SHORTCOMING  SH AO1 R T K AH2 M IH0 NG
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+SHORTED  SH AO1 R T IH0 D
+SHORTEN  SH AO1 R T AH0 N
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+SHORTENED  SH AO1 R T AH0 N D
+SHORTENIN	SH AO1 R T AH0 N IH0 N
+SHORTENING  SH AO1 R T AH0 N IH0 NG
+SHORTENING  SH AO1 R T N IH0 NG
+SHORTENS  SH AO1 R T AH0 N Z
+SHORTER  SH AO1 R T ER0
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+SHORTEST  SH AO1 R T IH0 S T
+SHORTHAND  SH AO1 R T HH AE2 N D
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+SHORTHORN	SH AO1 R T HH ER0 N
+SHORTHORNED	SH AO1 R T HH AO0 R N D
+SHORTHORNS	SH AO1 R T HH AO2 R N Z
+SHORTHOUSE	SH AO1 R T HH AW2 S
+SHORTIE	SH AO1 R T IY0
+SHORTISH	SH AO1 R T IH0 SH
+SHORTLANDS	SH AO1 R T L AH0 N D Z
+SHORTLIVED  SH AO1 R T L AY1 V D
+SHORTLIVED  SH AO1 R T L IH1 V D
+SHORTLY  SH AO1 R T L IY0
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+SHORTPOINTZ	SH AO1 R T P OY1 N T S
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+SHORTSTOP  SH AO1 R T S T AA2 P
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+SHORTY  SH AO1 R T IY0
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+SHOXFORD	SH AA1 K S F ER0 D
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+SHRINK  SH R IH1 NG K
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+STRACHINSKY	S T R AE1 CH IH1 N S K IY0
+STRACHINSKY'S	S T R AE1 CH IH1 N S K IY0 Z
+STRACTED	S T R AE1 K T IH0 D
+STRAD	S T R AE2 D
+STRADA  S T R AA1 D AH0
+STRADDLE  S T R AE1 D AH0 L
+STRADDLED  S T R AE1 D AH0 L D
+STRADDLES  S T R AE1 D AH0 L Z
+STRADDLING  S T R AE1 D L IH0 NG
+STRADELLA	S T R AE1 D EH1 L AH0
+STRADELLA'S	S T R AE1 D EH1 L AH0 Z
+STRADIOTES	S T R EY1 D IY0 OW2 T S
+STRADIOTS	S T R AE1 D IY0 AH0 T S
+STRADIVARI	S T R AE2 D IH0 V EH1 R IY0
+STRADIVARIUS  S T R AE2 D IH0 V EH1 R IY0 AH0 S
+STRADLING  S T R AE1 D L IH0 NG
+STRAE	S T R EY1
+STRAET	S T R EH0 T
+STRAFE  S T R EY1 F
+STRAFED	S T R EY1 F T
+STRAFFORD	S T R AE1 F ER0 D
+STRAFFORD'S	S T R AE1 F ER0 D Z
+STRAFING  S T R EY1 F IH0 NG
+STRAGGALLS	S T R AE1 G AO2 L Z
+STRAGGLE  S T R AE1 G AH0 L
+STRAGGLED  S T R AE1 G AH0 L D
+STRAGGLER  S T R AE1 G L ER0
+STRAGGLERS  S T R AE1 G L ER0 Z
+STRAGGLES	S T R AE1 G AH0 L Z
+STRAGGLING	S T R AE1 G AH0 L IH0 NG
+STRAGGLY	S T R AE1 G AH0 L IY0
+STRAHAN  S T R AE1 HH AH0 N
+STRAHAN'S	S T R AE1 HH AH0 N Z
+STRAHL  S T R AA1 L
+STRAIGHT  S T R EY1 T
+STRAIGHT'S	S T R EY1 T S
+STRAIGHTAWAY  S T R EY1 T AH0 W EY2
+STRAIGHTEN  S T R EY1 T AH0 N
+STRAIGHTENED  S T R EY1 T AH0 N D
+STRAIGHTENERS	S T R EY1 T AH0 N ER0 Z
+STRAIGHTENIN	S T R EY1 T N IH0 N
+STRAIGHTENING  S T R EY1 T AH0 N IH0 NG
+STRAIGHTENING  S T R EY1 T N IH0 NG
+STRAIGHTENS  S T R EY1 T AH0 N Z
+STRAIGHTER  S T R EY1 T ER0
+STRAIGHTEST	S T R EY1 T AH0 S T
+STRAIGHTFORWARD  S T R EY1 T F AO1 R W ER0 D
+STRAIGHTFORWARDLY  S T R EY2 T F AO1 R W ER0 D L IY0
+STRAIGHTFORWARDNESS	S T R EY1 T F AO1 R W ER0 D N AH0 S
+STRAIGHTLY	S T R EY1 T L IY0
+STRAIGHTNESS	S T R EY1 T N AH0 S
+STRAIGHTS  S T R EY1 T S
+STRAIGHTWAY	S T R EY1 T W EY2
+STRAIGHTWAYS	S T R EY1 T W EY2 Z
+STRAIK	S T R EY1 K
+STRAIN  S T R EY1 N
+STRAIN'D	S T R EY1 N D
+STRAIN'S	S T R EY1 N Z
+STRAINED  S T R EY1 N D
+STRAINER	S T R EY1 N ER0
+STRAINERS	S T R EY1 N ER0 Z
+STRAININ	S T R EY1 N IH0 N
+STRAINING  S T R EY1 N IH0 NG
+STRAININGLY	S T R EY1 N IH0 NG L IY0
+STRAININGS	S T R EY1 N IH0 NG Z
+STRAINS  S T R EY1 N Z
+STRAIT  S T R EY1 T
+STRAITEN	S T R EY1 T AH0 N
+STRAITENED	S T R EY1 T AH0 N D
+STRAITENING	S T R EY1 T N IH0 NG
+STRAITER	S T R EY1 T ER0
+STRAITEST	S T R EY1 T AH0 S T
+STRAITHWAITE	S T R EY1 TH W EY2 T
+STRAITJACKET  S T R EY1 T JH AE2 K AH0 T
+STRAITLACED	S T R EY1 T L AH0 S T
+STRAITLY	S T R EY1 T L IY0
+STRAITNESS	S T R EY1 T N AH0 S
+STRAITS  S T R EY1 T S
+STRAKE	S T R EY1 K
+STRAKED	S T R EY1 K T
+STRAKENCZ	S T R AA1 K IH0 N CH
+STRAKER  S T R EY1 K ER0
+STRAKES	S T R EY1 K S
+STRAKOSCH	S T R AA1 K AO1 SH
+STRALEY  S T R AE1 L IY0
+STRALSUND	S T R AE1 L S AH0 N D
+STRAMASH	S T R AE1 M AE2 SH
+STRAME	S T R EY1 M
+STRAMMFEST	S T R AE1 M F EH2 S T
+STRAMONIUM	S T R AH0 M OW1 N IY0 AH0 M
+STRAN	S T R AE1 N
+STRAND  S T R AE1 N D
+STRANDED  S T R AE1 N D AH0 D
+STRANDED  S T R AE1 N D IH0 D
+STRANDING  S T R AE1 N D IH0 NG
+STRANDS  S T R AE1 N D Z
+STRANDVIK	S T R AE1 N D V IH0 K
+STRANG  S T R AE1 NG
+STRANG'S	S T R AE1 NG Z
+STRANGE  S T R EY1 N JH
+STRANGE'S	S T R EY1 N JH IH0 Z
+STRANGELY  S T R EY1 N JH L IY0
+STRANGENESS  S T R EY1 N JH N AH0 S
+STRANGENESSES	S T R EY1 N JH N AH0 S IH0 Z
+STRANGER  S T R EY1 N JH ER0
+STRANGER'S  S T R EY1 N JH ER0 Z
+STRANGERS  S T R EY1 N JH ER0 Z
+STRANGERSHIP	S T R EY1 N JH ER0 SH IH2 P
+STRANGES	S T R EY1 N JH IH0 Z
+STRANGEST  S T R EY1 N JH IH0 S T
+STRANGEWAYS	S T R EY1 N JH W EY2 Z
+STRANGFORD	S T R AE1 NG F ER0 D
+STRANGLE  S T R AE1 NG G AH0 L
+STRANGLED  S T R AE1 NG G AH0 L D
+STRANGLEHOLD  S T R AE1 NG G AH0 L HH OW2 L D
+STRANGLER  S T R AE1 NG G L ER0
+STRANGLER'S	S T R AE1 NG G L ER0 Z
+STRANGLERS	S T R AE1 NG G L ER0 Z
+STRANGLES	S T R AE1 NG G AH0 L Z
+STRANGLING  S T R AE1 NG G AH0 L IH0 NG
+STRANGLING  S T R AE1 NG G L IH0 NG
+STRANGMAN	S T R AE1 NG M AH0 N
+STRANGULATED	S T R AE2 NG G Y AH0 L EY2 T IH0 D
+STRANGULATION  S T R AE2 NG G Y AH0 L EY1 SH AH0 N
+STRANGWAY	S T R AE1 NG W EY2
+STRANGWAYS	S T R AE1 NG W EY2 Z
+STRANGWISE	S T R AE1 NG W AY2 Z
+STRANGWISE'S	S T R AE1 NG W AY2 Z IH0 Z
+STRANGWYN	S T R AE1 NG W IH0 N
+STRANN	S T R AE1 N
+STRANNGER	S T R AE1 N JH ER0
+STRANOR	S T R AE1 N ER0
+STRANRAER	S T R AE1 N R EY1 R
+STRANSOM	S T R AE1 N S AH0 M
+STRAP  S T R AE1 P
+STRAP'S	S T R AE1 P S
+STRAPPADO	S T R AE1 P AA1 D OW0
+STRAPPED  S T R AE1 P T
+STRAPPER	S T R AE1 P ER0
+STRAPPERS	S T R AE1 P ER0 Z
+STRAPPIN	S T R AE1 P IH0 N
+STRAPPING  S T R AE1 P IH0 NG
+STRAPPINGS	S T R AE1 P IH0 NG Z
+STRAPS  S T R AE1 P S
+STRASBOURG  S T R AE1 S B AO2 R G
+STRASBURG  S T R AE1 S B ER0 G
+STRASBURGH	S T R AE1 S B ER0 G
+STRASOLDA	S T R AE1 S OW1 L D AH0
+STRASS	S T R AE1 S
+STRASSBURG  S T R AE1 S B ER0 G
+STRASSE	S T R AE1 S
+STRATA  S T R AE1 T AH0
+STRATAGEM  S T R AE1 T AH0 JH AH0 M
+STRATAGEMS  S T R AE1 T AH0 JH AH0 M Z
+STRATAS  S T R AE1 T AH0 Z
+STRATE  S T R EY1 T
+STRATEGEM  S T R AE1 T IH0 JH EH0 M
+STRATEGETIC	S T R AE1 T IH0 JH EH1 T IH0 K
+STRATEGI	S T R AE1 T IH0 JH IY0
+STRATEGIAN	S T R AE1 T IH0 JH IY0 AH0 N
+STRATEGIANS	S T R AH0 T IY1 JH AH0 N Z
+STRATEGIC  S T R AH0 T IY1 JH IH0 K
+STRATEGICAL  S T R AH0 T IY1 JH IH0 K AH0 L
+STRATEGICALLY  S T R AH0 T IY1 JH IH0 K L IY0
+STRATEGIES  S T R AE1 T AH0 JH IY0 Z
+STRATEGIST  S T R AE1 T IH0 JH IH0 S T
+STRATEGISTS  S T R AE1 T IH0 JH IH0 S T S
+STRATEGUS	S T R AE1 T IH0 JH IH0 S
+STRATEGY  S T R AE1 T AH0 JH IY0
+STRATEMEYER	S T R AE1 T IH0 M AY0 ER0
+STRATES	S T R EY1 T S
+STRATFIELD	S T R AE1 T F IY2 L D
+STRATFORD  S T R AE1 T F ER0 D
+STRATFORD'S	S T R AE1 T F ER0 D Z
+STRATFORDTOWN	S T R AE1 T F AO2 R D T AW2 N
+STRATH	S T R AE1 TH
+STRATHAIRLIE	S T R AE1 T HH EH1 R L IY0
+STRATHAIVRON	S T R AE1 TH EY1 V R AH0 N
+STRATHBOGIE	S T R AE1 TH B UH1 G IY0
+STRATHBOGY	S T R AE1 TH B OW2 JH IY0
+STRATHCLYDE	S T R AE1 TH K L AY1 D
+STRATHCONA	S T R AE1 TH K OW1 N AH0
+STRATHCONA'S	S T R AE1 TH K AH0 N AH0 Z
+STRATHDENE	S T R AE1 TH D IY0 N
+STRATHEARN	S T R AE1 TH ER0 N
+STRATHEDEN	S T R AE1 TH IY1 D AH0 N
+STRATHFIELDSAYE	S T R AE1 TH F IY2 L D Z EY0
+STRATHGOVAN	S T R AE1 TH G OW1 V AH0 N
+STRATHMOIRA	S T R AE1 TH M OY1 R AH0
+STRATHMORE	S T R AE1 TH M AO2 R
+STRATHMUIR	S T R AE1 TH M Y UH1 R
+STRATHORAN	S T R AE1 TH AO0 R AE1 N
+STRATHPEFFER	S T R AE1 TH P EH1 F ER0
+STRATHS	S T R AE1 TH S
+STRATHSAY	S T R AE1 TH S EY0
+STRATHSAY'S	S T R AE1 TH S EY2 Z
+STRATHSEY	S T R AE1 TH S IY0
+STRATHSPEY	S T R AE1 TH S P IY0
+STRATHSPEYS	S T R AE1 TH S P EY1 Z
+STRATHYRE	S T R AE1 TH AY1 R
+STRATIFICATION	S T R AE1 T AH0 F AH0 K EY1 SH AH0 N
+STRATIFICATIONS	S T R AE1 T AH0 F AH0 K EY1 SH AH0 N Z
+STRATIFIED  S T R AE1 T AH0 F AY2 D
+STRATIMOJEFF	S T R AE1 T AH0 M AH0 JH EH1 F
+STRATO	S T R AE1 T OW0
+STRATON	S T R AE1 T AH0 N
+STRATONICE	S T R AE1 T AH0 N IH0 S
+STRATOSPHERE  S T R AE1 T AH0 S F IH2 R
+STRATTON  S T R AE1 T AH0 N
+STRATTON'S	S T R AE1 T AH0 N Z
+STRATUM  S T R AE1 T AH0 M
+STRATUS  S T R AE1 T AH0 S
+STRAUCHT	S T R AW1 K T
+STRAUDENHEIM	S T R AW1 D IH0 N HH AY0 M
+STRAUGHT	S T R AO1 T
+STRAUMEY	S T R AW1 M IY0
+STRAUNGE	S T R AW1 N JH
+STRAUNGER	S T R AW1 NG ER0
+STRAUS  S T R AW1 S
+STRAUSS  S T R AW1 S
+STRAUSS'S  S T R AW1 S IH0 Z
+STRAVINSKY  S T R AH0 V IH1 N S K IY0
+STRAW  S T R AO1
+STRAW'S	S T R AO1 Z
+STRAWBERRIES  S T R AO1 B EH2 R IY0 Z
+STRAWBERRY  S T R AO1 B EH2 R IY0
+STRAWED	S T R AO1 D
+STRAWINSKY	S T R AA1 V IH1 N S K IY0
+STRAWN  S T R AO1 N
+STRAWN'S	S T R AO1 N Z
+STRAWS  S T R AO1 Z
+STRAWSTACK	S T R AO1 S T AE2 K
+STRAWY	S T R AO1 IY0
+STRAWYERS	S T R AO1 Y ER0 Z
+STRAY  S T R EY1
+STRAY'D	S T R EY1 D
+STRAYED  S T R EY1 D
+STRAYER  S T R EY1 ER0
+STRAYHORN  S T R EY1 HH ER0 N
+STRAYIN	S T R EY1 IH0 N
+STRAYING  S T R EY1 IH0 NG
+STRAYINGS	S T R EY1 IH0 NG Z
+STRAYS  S T R EY1 Z
+STREAK  S T R IY1 K
+STREAK'D	S T R IY1 K D
+STREAKED  S T R IY1 K T
+STREAKIN	S T R IY1 K IH0 N
+STREAKING  S T R IY1 K IH0 NG
+STREAKINGS	S T R IY1 K IH0 NG Z
+STREAKS  S T R IY1 K S
+STREAKY	S T R IY1 K IY0
+STREAM  S T R IY1 M
+STREAM'D	S T R IY2 M D
+STREAM'S	S T R IY2 M Z
+STREAMED  S T R IY1 M D
+STREAMER  S T R IY1 M ER0
+STREAMERS  S T R IY1 M ER0 Z
+STREAMETH	S T R IY2 M IH0 TH
+STREAMIN	S T R IY2 M IH0 N
+STREAMING  S T R IY1 M IH0 NG
+STREAMLET	S T R IY1 M L AH0 T
+STREAMLET'S	S T R IY1 M L AH0 T S
+STREAMLETS	S T R IY1 M L AH0 T S
+STREAMLINE  S T R IY1 M L AY2 N
+STREAMLINED  S T R IY1 M L AY2 N D
+STREAMS  S T R IY1 M Z
+STREAMSIDE	S T R IY2 M S AY2 D
+STREAMY	S T R IY1 M IY0
+STREATFEILD	S T R IY1 T F IY1 L D
+STREATFIELD	S T R IY1 T F IY2 L D
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+STREBER	S T R IY1 B ER0
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+STREESES	S T R IY1 Z IH0 Z
+STREET  S T R IY1 T
+STREET'S  S T R IY1 T S
+STREETCAR  S T R IY1 T K AA2 R
+STREETCARS	S T R IY1 T K AA2 R Z
+STREETE	S T R IY1 T
+STREETED	S T R IY1 T IH0 D
+STREETER  S T R IY1 T ER0
+STREETER'S	S T R IY1 T ER0 Z
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+STREETWALKER	S T R IY1 T W AO2 K ER0
+STREFF  S T R EH1 F
+STREFFORD	S T R EH1 F ER0 D
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+STREFFY	S T R EH1 F IY0
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+STREIGHT  S T R EY1 T
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+STREIGHTS	S T R AY1 T S
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+STRELITSKI	S T R EH1 L IH1 T S K IY0
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+STRELSAU	S T R EH1 L S AO0
+STRELTSI	S T R EH1 L T S IY0
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+STRENGTH  S T R EH1 NG K TH
+STRENGTH  S T R EH1 NG TH
+STRENGTH'S	S T R EH1 NG TH S
+STRENGTHEN  S T R EH1 NG TH AH0 N
+STRENGTHEN'D	S T R EH1 NG TH AH0 N D
+STRENGTHENED  S T R EH1 NG TH AH0 N D
+STRENGTHENER	S T R EH1 NG TH AH0 N ER0
+STRENGTHENETH	S T R EH1 NG TH AH0 N EH1 TH
+STRENGTHENING  S T R EH1 NG TH AH0 N IH0 NG
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+STRENGTHLESS	S T R EH1 NG TH L AH0 S
+STRENGTHS  S T R EH1 NG K TH S
+STRENGTHS  S T R EH1 NG TH S
+STRENUOSITY	S T R EH1 N Y UW0 S AH0 T IY0
+STRENUOUS  S T R EH1 N Y UW0 AH0 S
+STRENUOUSLY  S T R EH1 N Y UW0 AH0 S L IY0
+STRENUOUSNESS	S T R EH1 N Y UW0 AH0 S N AH0 S
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+STRESS  S T R EH1 S
+STRESSED  S T R EH1 S T
+STRESSES  S T R EH1 S AH0 Z
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+STRESSFUL  S T R EH1 S F AH0 L
+STRESSING  S T R EH1 S IH0 NG
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+STRETCHEST	S T R EH1 CH AH0 S T
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+STRETCHIN	S T R EH1 CH IH0 N
+STRETCHING  S T R EH1 CH IH0 NG
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+STRETT	S T R EH1 T
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+STRETTO  S T R EH1 T OW2
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+STREVA	S T R EH1 V AH0
+STREW  S T R UW1
+STREW'D	S T R UW1 D
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+STREWING	S T R UW1 IH0 NG
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+STRIATA	S T R IY0 AA1 T AH0
+STRIATED	S T R IY0 EY2 T IH0 D
+STRIATIONS	S T R IY0 EY1 SH AH0 N Z
+STRIBBLE	S T R IH1 B AH0 L
+STRIBLING  S T ER1 AH0 B AH0 L IH0 NG
+STRIBLING  S T R IH1 B L IH0 NG
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+STRICKLANDS	S T R IH1 K L AH0 N D Z
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+STRICT  S T R IH1 K T
+STRICTER  S T R IH1 K T ER0
+STRICTEST  S T R IH1 K T AH0 S T
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+STRIDULATION	S T R IH1 JH AH0 L EY1 SH AH0 N
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+STRIDULOUS	S T R IH1 JH AH0 L AH0 S
+STRIEM	S T R IY1 M
+STRIFE  S T R AY1 F
+STRIFE'S	S T R AY1 F S
+STRIFES  S T R AY1 F S
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+STRIKE'S  S T R AY1 K S
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+STRIKEN	S T R AY1 K AH0 N
+STRIKER  S T R AY1 K ER0
+STRIKER'S  S T R AY1 K ER0 Z
+STRIKERS  S T R AY1 K ER0 Z
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+STRIKETH	S T R AY1 K TH
+STRIKIN	S T R AY1 K IH0 N
+STRIKING  S T R AY1 K IH0 NG
+STRIKINGLY  S T R AY1 K IH0 NG L IY0
+STRINDBERG	S T R AY1 N D B ER0 G
+STRINDBERG'S	S T R AY1 N D B ER0 G Z
+STRING  S T R IH1 NG
+STRING'S	S T R IH1 NG Z
+STRINGED  S T R IH1 NG D
+STRINGENCY  S T R IH1 N JH AH0 N S IY0
+STRINGENT  S T R IH1 N JH AH0 N T
+STRINGENTLY  S T R IH1 N JH AH0 N T L IY0
+STRINGER  S T R IH1 NG ER0
+STRINGER'S	S T R IH1 NG ER0 Z
+STRINGERS  S T R IH1 NG ER0 Z
+STRINGFELLOW  S T R IH1 NG F EH2 L OW0
+STRINGHAM  S T R IH1 NG HH AE2 M
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+TRALEIGH	T R EY1 L IY0
+TRAM  T R AE1 M
+TRAMCAR	T R AE1 M K AA2 R
+TRAMCARS	T R AE1 M K AA2 R Z
+TRAMLINE	T R AE1 M L AY2 N
+TRAMLINES	T R AE1 M L AY2 N Z
+TRAMMEL  T R AE1 M AH0 L
+TRAMMELED	T R AE1 M AH0 L D
+TRAMMELLED	T R AE1 M AH0 L D
+TRAMMELLING	T R AE1 M AH0 L IH0 NG
+TRAMMELS	T R AE1 M AH0 L Z
+TRAMONTANA  T R AA0 M OW0 N T AE1 N AH0
+TRAMONTANE	T R AA0 M OW1 N T EY2 N
+TRAMP  T R AE1 M P
+TRAMP'S	T R AE1 M P S
+TRAMPAS	T R AE1 M P AH0 Z
+TRAMPED  T R AE1 M P T
+TRAMPER	T R AE1 M P ER0
+TRAMPERS	T R AE1 M P ER0 Z
+TRAMPIN	T R AE1 M P IH0 N
+TRAMPING  T R AE1 M P IH0 NG
+TRAMPINGS	T R AE1 M P IH0 NG Z
+TRAMPISH	T R AE1 M P IH0 SH
+TRAMPLE  T R AE1 M P AH0 L
+TRAMPLEASURE	T R AE1 M P L EH1 ZH ER0
+TRAMPLED  T R AE1 M P AH0 L D
+TRAMPLER	T R AE1 M P L ER0
+TRAMPLES  T R AE1 M P AH0 L Z
+TRAMPLIN	T R AE1 M P L IH0 N
+TRAMPLING  T R AE1 M P L IH0 NG
+TRAMPLINGS	T R AE1 M P L IH0 NG Z
+TRAMPS  T R AE1 M P Z
+TRAMS  T R AE1 M Z
+TRAMTRIST	T R AE1 M T R IH0 S T
+TRAMU	T R AA0 M UW1
+TRAMWAY	T R AE1 M W EY2
+TRAMWAYS	T R AE1 M W EY2 Z
+TRAMWORTH	T R AE1 M W ER0 TH
+TRANA	T R AE1 N AH0
+TRANCE  T R AE1 N S
+TRANCED	T R AE1 N S T
+TRANCELIKE	T R AE1 N S L AY2 K
+TRANCES	T R AE1 N S IH0 Z
+TRANCHANT	T R AE1 N CH AH0 N T
+TRANCHE  T R AE1 N CH
+TRANCHEES	T R AE1 N CH IY2 Z
+TRANDAFIR	T R AE1 N D AH0 F ER0
+TRANEST	T R AE2 N IH0 S T
+TRANGOIL	T R AE1 NG G OY2 L
+TRANI  T R AA1 N IY0
+TRANK	T R AE1 NG K
+TRANKWILLITATIN	T R AE1 NG K W IH2 L AH0 T EY1 T IH0 N
+TRANNEL	T R AE1 N AH0 L
+TRANQUIL  T R AE1 NG K W AH0 L
+TRANQUIL  T R AE1 NG K W IH0 L
+TRANQUILITY  T R AE0 NG K W IH1 L IH0 T IY0
+TRANQUILIZE  T R AE1 NG K W AH0 L AY2 Z
+TRANQUILIZED	T R AE1 NG K W AH0 L AY2 Z D
+TRANQUILIZER  T R AE1 NG K W AH0 L AY2 Z ER0
+TRANQUILIZING  T R AE1 NG K W AH0 L AY2 Z IH0 NG
+TRANQUILLA	T R AE1 NG K W IH1 L AH0
+TRANQUILLE	T R AE1 NG K W IH0 L
+TRANQUILLISE	T R AE1 NG K W IY0 L IY1 S
+TRANQUILLISED	T R AE1 NG K W IH1 L IH0 S T
+TRANQUILLISING	T R AE1 NG K W AH0 L L AY2 Z IH0 NG
+TRANQUILLITIES	T R AE1 NG K W IH1 L AH0 T IY0 Z
+TRANQUILLITY	T R AE1 NG K W IH1 L AH0 T IY0
+TRANQUILLIZE	T R AE1 NG K W AH0 L L AY2 Z
+TRANQUILLIZED	T R AE1 NG K W AH0 L L AY2 Z D
+TRANQUILLIZING	T R AE1 NG K W AH0 L L AY2 Z IH0 NG
+TRANQUILLO	T R AE1 NG K W IH1 L OW0
+TRANQUILLUS	T R AE1 NG K W IH1 L AH0 S
+TRANQUILLY	T R AE1 NG K W AH0 L IY0
+TRANS  T R AE1 N Z
+TRANSACT  T R AE0 N Z AE1 K T
+TRANSACTED  T R AE0 N S AE1 K T IH0 D
+TRANSACTED  T R AE0 N Z AE1 K T IH0 D
+TRANSACTING	T R AE0 N Z AE1 K T IH0 NG
+TRANSACTION  T R AE0 N Z AE1 K SH AH0 N
+TRANSACTIONS  T R AE0 N Z AE1 K SH AH0 N Z
+TRANSACTS	T R AE0 N Z AE1 K T S
+TRANSALPINE	T R AE0 N Z AE1 L P AY1 N
+TRANSATLANTIC  T R AE2 N Z AH0 T L AE1 N IH0 K
+TRANSATLANTIC  T R AE2 N Z AH0 T L AE1 N T IH0 K
+TRANSATLANTIQUE	T R AE2 N Z AH0 T L AE1 N T AH0 K EY2
+TRANSBAIKAL	T R AE1 N Z B AA2 IY1 K AH0 L
+TRANSBAIKALIA	T R AE1 N Z B AA2 IH0 K AA1 L Y AH0
+TRANSCAUCASIA	T R AE2 N Z K AO0 K EY1 ZH AH0
+TRANSCEIVER	T R AE2 N S IY1 V ER0
+TRANSCEND  T R AE0 N S EH1 N D
+TRANSCENDANT	T R AE0 N S EH1 N D AH0 N T
+TRANSCENDED  T R AE0 N S EH1 N D IH0 D
+TRANSCENDENCE  T R AE0 N S EH1 N D AH0 N S
+TRANSCENDENCY	T R AE0 N S EH1 N D AH0 N S IY0
+TRANSCENDENT  T R AE0 N S EH1 N D AH0 N T
+TRANSCENDENTAL  T R AE2 N S AH0 N D EH1 N AH0 L
+TRANSCENDENTAL  T R AE2 N S AH0 N D EH1 N T AH0 L
+TRANSCENDENTALISM	T R AE2 N S AH0 N D EH1 N AH0 L IH2 Z AH0 M
+TRANSCENDENTALIST	T R AE2 N S AH0 N D EH1 N AH0 L IH0 S T
+TRANSCENDENTALISTS	T R AE2 N S AH0 N D EH1 N AH0 L IH0 S T S
+TRANSCENDENTALLY	T R AE2 N S AH0 N D EH1 N AH0 L IY0
+TRANSCENDENTLY	T R AE0 N S EH1 N D AH0 N T L IY0
+TRANSCENDETH	T R AE0 N S EH1 N D IH0 TH
+TRANSCENDING  T R AE0 N S EH1 N D IH0 NG
+TRANSCENDS  T R AE0 N S EH1 N D Z
+TRANSCONTINENTAL  T R AE2 N Z K AA2 N IH0 N EH1 N AH0 L
+TRANSCONTINENTAL  T R AE2 N Z K AA2 N IH0 N EH1 N T AH0 L
+TRANSCONTINENTAL  T R AE2 N Z K AA2 N T IH0 N EH1 N AH0 L
+TRANSCONTINENTAL  T R AE2 N Z K AA2 N T IH0 N EH1 N T AH0 L
+TRANSCRIBE  T R AE0 N S K R AY1 B
+TRANSCRIBED  T R AE0 N S K R AY1 B D
+TRANSCRIBER  T R AE0 N S K R AY1 B ER0
+TRANSCRIBER'S	T R AE0 N S K R AY1 B ER0 Z
+TRANSCRIBERS  T R AE0 N S K R AY1 B ER0 Z
+TRANSCRIBES  T R AE0 N S K R AY1 B Z
+TRANSCRIBING  T R AE0 N S K R AY1 B IH0 NG
+TRANSCRIPT  T R AE1 N S K R IH2 P T
+TRANSCRIPTION  T R AE2 N S K R IH1 P SH AH0 N
+TRANSCRIPTIONS  T R AE2 N S K R IH1 P SH AH0 N Z
+TRANSCRIPTS  T R AE1 N S K R IH2 P T S
+TRANSDUCERS  T R AE0 N S D UW1 S ER0 Z
+TRANSEPT	T R AE0 N S EH1 P T
+TRANSEPTS	T R AE1 N S EH2 P T S
+TRANSFER  T R AE0 N S F ER1
+TRANSFER  T R AE1 N S F ER0
+TRANSFERABLE  T R AE0 N S F ER1 AH0 B AH0 L
+TRANSFERENCE  T R AE0 N S F ER1 AH0 N S
+TRANSFERMENT	T R AE0 N S F ER1 M AH0 N T
+TRANSFERR'D	T R AE0 N S F ER1 D
+TRANSFERRED  T R AE0 N S F ER1 D
+TRANSFERRED  T R AE1 N S F ER0 D
+TRANSFERRENCE	T R AE0 N S F ER1 AH0 N S
+TRANSFERRING  T R AE0 N S F ER1 IH0 NG
+TRANSFERS  T R AE0 N S F ER1 Z
+TRANSFERS  T R AE1 N S F ER0 Z
+TRANSFIGURATION	T R AE0 N S F IH1 G Y ER0 EY1 SH AH0 N
+TRANSFIGURATIONS	T R AE0 N S F IH1 G Y ER0 EY1 SH AH0 N Z
+TRANSFIGURE	T R AE0 N S F IH1 G Y ER0
+TRANSFIGURED	T R AE0 N S F IH1 G Y ER0 D
+TRANSFIGURES	T R AE0 N S F IH1 G Y ER0 Z
+TRANSFIGURING	T R AE0 N S F IH1 G Y ER0 IH0 NG
+TRANSFIX  T R AE0 N S F IH1 K S
+TRANSFIX'D	T R AE0 N S F IH1 K S D
+TRANSFIXED  T R AE0 N S F IH1 K S T
+TRANSFIXES	T R AE0 N S F IH1 K S IH0 Z
+TRANSFIXING	T R AE0 N S F IH1 K S IH0 NG
+TRANSFORM  T R AE0 N S F AO1 R M
+TRANSFORM  T R AE1 N S F AO0 R M
+TRANSFORM'D	T R AE0 N S F AO1 R M D
+TRANSFORMATION  T R AE2 N S F ER0 M EY1 SH AH0 N
+TRANSFORMATIONS  T R AE2 N S F ER0 M EY1 SH AH0 N Z
+TRANSFORMED  T R AE0 N S F AO1 R M D
+TRANSFORMER  T R AE0 N S F AO1 R M ER0
+TRANSFORMERS  T R AE0 N S F AO1 R M ER0 Z
+TRANSFORMING  T R AE0 N S F AO1 R M IH0 NG
+TRANSFORMISM	T R AE0 N S F AO1 R M IH0 Z AH0 M
+TRANSFORMS  T R AE0 N S F AO1 R M Z
+TRANSFUSE  T R AE0 N S F Y UW1 Z
+TRANSFUSED  T R AE0 N S F Y UW1 Z D
+TRANSFUSING	T R AE0 N S F Y UW1 Z IH0 NG
+TRANSFUSION  T R AE0 N S F Y UW1 ZH AH0 N
+TRANSGRESS  T R AE0 N Z G R EH1 S
+TRANSGRESSED  T R AE0 N Z G R EH1 S T
+TRANSGRESSES  T R AE0 N Z G R EH1 S IH0 S
+TRANSGRESSING  T R AE0 N Z G R EH1 S IH0 NG
+TRANSGRESSION  T R AE0 N Z G R EH1 SH AH0 N
+TRANSGRESSIONS  T R AE0 N Z G R EH1 SH AH0 N Z
+TRANSGRESSOR  T R AE0 N Z G R EH1 S ER0
+TRANSGRESSORS	T R AE0 N Z G R EH1 S ER0 Z
+TRANSHIP	T R AE1 N SH IH2 P
+TRANSHIPMENT	T R AE1 N SH IH1 P M AH0 N T
+TRANSHIPPED	T R AE1 N ZH IH1 P T
+TRANSHIPPING	T R AE1 N ZH IH1 P IH0 NG
+TRANSIENCE  T R AE1 N Z IY0 AH0 N S
+TRANSIENCY	T R AE1 N Z IY0 AH0 N S IY0
+TRANSIENT  T R AE1 N ZH AH0 N T
+TRANSIENTLY	T R AE1 N ZH AH0 N T L IY0
+TRANSIENTS  T R AE1 N Z IY0 AH0 N T S
+TRANSISTOR  T R AE0 N Z IH1 S T ER0
+TRANSISTORS  T R AE0 N Z IH1 S T ER0 Z
+TRANSIT  T R AE1 N Z IH0 T
+TRANSITA	T R AE0 N S IY1 T AH0
+TRANSITION  T R AE0 N Z IH1 SH AH0 N
+TRANSITIONAL  T R AE0 N S IH1 SH AH0 N AH0 L
+TRANSITIONAL  T R AE0 N Z IH1 SH AH0 N AH0 L
+TRANSITIONS  T R AE0 N Z IH1 SH AH0 N Z
+TRANSITIVE	T R AE1 N Z IH0 T IH0 V
+TRANSITORILY	T R AE1 N Z AH0 T AO2 R AH0 L IY0
+TRANSITORINESS	T R AE1 N Z AH0 T AO2 R IY0 N AH0 S
+TRANSITORY  T R AE1 N Z AH0 T AO2 R IY0
+TRANSITS  T R AE1 N Z IH0 T S
+TRANSITU	T R AE1 N Z IH0 T UW0
+TRANSKEI  T R AE1 N Z K EY2
+TRANSL	T R AE0 N S L
+TRANSLATABLE	T R AE0 N Z L EY1 T AH0 B AH0 L
+TRANSLATE  T R AE0 N S L EY1 T
+TRANSLATE  T R AE0 N Z L EY1 T
+TRANSLATED  T R AE0 N S L EY1 T IH0 D
+TRANSLATED  T R AE0 N Z L EY1 T AH0 D
+TRANSLATES  T R AE0 N Z L EY1 T S
+TRANSLATES  T R AE1 N S L EY2 T S
+TRANSLATING  T R AE0 N Z L EY1 T IH0 NG
+TRANSLATING  T R AE1 N S L EY2 T IH0 NG
+TRANSLATION  T R AE0 N S L EY1 SH AH0 N
+TRANSLATION  T R AE0 N Z L EY1 SH AH0 N
+TRANSLATIONS  T R AE0 N S L EY1 SH AH0 N Z
+TRANSLATIONS  T R AE0 N Z L EY1 SH AH0 N Z
+TRANSLATOR  T R AE0 N S L EY1 T ER0
+TRANSLATOR  T R AE0 N Z L EY1 T ER0
+TRANSLATOR'S	T R AE0 N Z L EY1 T ER0 Z
+TRANSLATORS  T R AE0 N S L EY1 T ER0 Z
+TRANSLATORS  T R AE0 N Z L EY1 T ER0 Z
+TRANSLITERATED	T R AE0 N S L IH1 T ER0 EY2 T IH0 D
+TRANSLITERATION	T R AE0 N S L IH2 T ER0 EY1 SH AH0 N
+TRANSLUCENCE	T R AE0 N S L UW1 S AH0 N S
+TRANSLUCENCY	T R AE0 N S L UW1 S AH0 N S IY0
+TRANSLUCENT  T R AE0 N S L UW1 S AH0 N T
+TRANSLUCENTLY	T R AE0 N S L UW1 S AH0 N T L IY0
+TRANSLUCID	T R AE0 N S L UW1 S AH0 D
+TRANSLUNAR	T R AE0 N S L UW1 N ER0
+TRANSMARINE	T R AE0 N Z M ER0 IY1 N
+TRANSMIGRATE	T R AE0 N Z M AH0 G R EY2 T
+TRANSMIGRATED	T R AE0 N Z M AH0 G R EY2 T IH0 D
+TRANSMIGRATES	T R AE0 N Z M AH0 G R EY2 T S
+TRANSMIGRATION	T R AE0 N Z M AH0 G R EY1 SH AH0 N
+TRANSMIGRATIONS	T R AE0 N Z M AH0 G R EY1 SH AH0 N Z
+TRANSMISSIBLE	T R AE0 N S M IH1 S AH0 B AH0 L
+TRANSMISSION  T R AE0 N S M IH1 SH AH0 N
+TRANSMISSION  T R AE0 N Z M IH1 SH AH0 N
+TRANSMISSIONS  T R AE0 N Z M IH1 SH AH0 N Z
+TRANSMIT  T R AE0 N Z M IH1 T
+TRANSMITS  T R AE0 N Z M IH1 T S
+TRANSMITTABLE  T R AE0 N Z M IH1 T AH0 B AH0 L
+TRANSMITTAL  T R AE0 N S M IH1 T AH0 L
+TRANSMITTED  T R AE0 N S M IH1 T IH0 D
+TRANSMITTED  T R AE0 N Z M IH1 T AH0 D
+TRANSMITTER  T R AE0 N S M IH1 T ER0
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+TRANSMITTING  T R AE0 N S M IH1 T IH0 NG
+TRANSMOGRIFICATION	T R AE0 N Z M OW0 G R IH0 F IH0 K EY1 SH AH0 N
+TRANSMOGRIFIED	T R AE0 N Z M OW0 G R AH0 F AY2 D
+TRANSMUTABLE	T R AE0 N S M Y UW1 T AH0 B AH0 L
+TRANSMUTATION	T R AE0 N S M Y UW0 T EY1 SH AH0 N
+TRANSMUTATIONS	T R AE0 N S M Y UW0 T EY1 SH AH0 N Z
+TRANSMUTE	T R AE0 N S M Y UW2 T
+TRANSMUTED	T R AE0 N S M Y UW2 T IH0 D
+TRANSMUTES	T R AE0 N S M Y UW2 T S
+TRANSMUTING	T R AE0 N S M Y UW2 T IH0 NG
+TRANSNATIONAL  T R AE0 N S N AE1 SH AH0 N AH0 L
+TRANSOCEANIC  T R AE2 N Z OW0 SH IY0 AE1 N IH0 K
+TRANSOM  T R AE1 N S AH0 M
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+TRANSPARENCE	T R AE0 N S P EH1 R AH0 N S
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+TRANSPARENCY  T R AE0 N S P EH1 R AH0 N S IY0
+TRANSPARENT  T R AE0 N S P EH1 R AH0 N T
+TRANSPARENTLY  T R AE0 N S P EH1 R AH0 N T L IY0
+TRANSPIERCE	T R AE0 N S P IH1 R S
+TRANSPIERCED	T R AE0 N S P IH1 R S T
+TRANSPIRATION	T R AE0 N S P ER0 EY1 SH AH0 N
+TRANSPIRE  T R AE0 N S P AY1 ER0
+TRANSPIRED  T R AE0 N S P AY1 ER0 D
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+TRANSPIRING  T R AE0 N S P AY1 ER0 IH0 NG
+TRANSPLANT  T R AE0 N S P L AE1 N T
+TRANSPLANTATION  T R AE2 N Z P L AE0 N T EY1 SH AH0 N
+TRANSPLANTATIONS	T R AE2 N Z P L AE0 N T EY1 SH AH0 N Z
+TRANSPLANTED  T R AE0 N S P L AE1 N T IH0 D
+TRANSPLANTING  T R AE0 N S P L AE1 N T IH0 NG
+TRANSPLANTS  T R AE0 N S P L AE1 N T S
+TRANSPONTINE	T R AE0 N S P AA1 N T IY2 N
+TRANSPORT  T R AE0 N S P AO1 R T
+TRANSPORT  T R AE1 N S P AO0 R T
+TRANSPORT'S	T R AE0 N S P AO1 R T S
+TRANSPORTABLE  T R AE0 N S P AO1 R T AH0 B AH0 L
+TRANSPORTATION  T R AE2 N S P ER0 T EY1 SH AH0 N
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+TRANSPORTED  T R AE0 N S P AO1 R T AH0 D
+TRANSPORTER  T R AE0 N S P AO1 R T ER0
+TRANSPORTING  T R AE0 N S P AO1 R T IH0 NG
+TRANSPORTS  T R AE0 N S P AO1 R T S
+TRANSPORTS  T R AE1 N S P AO0 R T S
+TRANSPOSE  T R AE0 N S P OW1 Z
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+TRANSPOSES	T R AE0 N S P OW1 Z IH0 Z
+TRANSPOSING	T R AE0 N S P OW1 Z IH0 NG
+TRANSPOSITION	T R AE0 N S P AH0 Z IH1 SH AH0 N
+TRANSPOSITIONS	T R AE0 N S P AH0 Z IH1 SH AH0 N Z
+TRANSSHIP	T R AE0 N SH IH2 P
+TRANSSHIPMENT	T R AE0 N SH IH1 P M AH0 N T
+TRANSSHIPPED	T R AE0 N SH IH1 P T
+TRANSUBSTANTIATION	T R AE0 N S AH0 B S T AE2 N SH IY0 EY1 SH AH0 N
+TRANSVAAL  T R AE0 N Z V AA1 L
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+TRANSVALUATION	T R AE0 N Z V AE2 L Y UW0 EY1 SH AH0 N
+TRANSVERSAL	T R AE0 N Z V ER1 S AH0 L
+TRANSVERSE  T R AE0 N Z V ER1 S
+TRANSVERSELY	T R AE0 N Z V ER1 S L IY0
+TRANSVERSLY	T R AE0 N Z V ER1 S L IY0
+TRANSYLVANIA  T R AE2 N S IH0 L V EY1 N Y AH0
+TRANSYLVANIA  T R AE2 N Z IY0 L V EY1 N IY0 AH0
+TRANSYLVANIAN	T R AE2 N S IH0 L V EY1 N IY0 AH0 N
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+TRANT  T R AE1 N T
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+TRANTRIDGE	T R AE1 N T R IH0 JH
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+TRAPANI  T R AA0 P AA1 N IY0
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+TRAPDOOR	T R AE1 P D AO2 R
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+TRAPEZE  T R AH0 P IY1 Z
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+TRASIMENE	T R AE1 S IH0 M IY2 N
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+TRATE	T R EY2 T
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+TRAVAIL  T R AH0 V EY1 L
+TRAVAILE	T R AH0 V EY1 L
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+UNCELEBRATED	AH2 N S EH2 L AH0 B R EY2 T AH0 D
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+UNCENSORED  AH0 N S EH1 N S ER0 D
+UNCENSURED	AH0 N S EH1 N SH ER0 D
+UNCEREMONIOUS  AH2 N S EH2 R AH0 M OW1 N IY0 AH0 S
+UNCEREMONIOUSLY  AH2 N S EH2 R AH0 M OW1 N IY0 AH0 S L IY0
+UNCEREMONIOUSNESS	AH2 N S EH2 R AH0 M OW1 N IY0 AH0 S N AH0 S
+UNCERTAIN  AH0 N S ER1 T AH0 N
+UNCERTAINLY  AH0 N S ER1 T AH0 N L IY0
+UNCERTAINTIES  AH0 N S ER1 T AH0 N T IY0 Z
+UNCERTAINTY  AH0 N S ER1 T AH0 N T IY0
+UNCERTIFICATED	AH0 N S ER1 T AH0 F AH0 K EY2 T IH0 D
+UNCERTIFIED	AH0 N S ER1 T AH0 F AY2 D
+UNCHAIN	AH2 N K AY0 N
+UNCHAINED	AH0 N CH EY1 N D
+UNCHAINING	AH0 N CH EY1 N IH0 NG
+UNCHALLENGEABLE	AH0 N CH AE1 L AH0 N JH AH0 B AH0 L
+UNCHALLENGED  AH0 N CH AE1 L IH0 N JH D
+UNCHANCY	AH0 N CH AE1 N S IY0
+UNCHANG'D	AH0 N CH AA1 NG D
+UNCHANGEABLE	AH0 N CH EY1 N JH AH0 B AH0 L
+UNCHANGEABLENESS	AH0 N CH EY1 N JH AH0 B AH0 L N AH0 S
+UNCHANGEABLY	AH0 N CH EY1 N JH AH0 B L IY0
+UNCHANGED  AH0 N CH EY1 N JH D
+UNCHANGING  AH0 N CH EY1 N JH IH0 NG
+UNCHANGINGLY	AH0 N CH EY1 N JH IH0 NG L IY0
+UNCHAPERONED	AH0 N CH AE1 P ER0 OW2 N D
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+UNCHARACTERISTICALLY  AH2 N K EH2 R IH0 K T ER0 IH1 S T IH0 K L IY0
+UNCHARGED	AH0 N CH AA1 R JH D
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+UNCHARTED  AH0 N CH AA1 R T IH0 D
+UNCHARTERED  AH0 N CH AA1 R T ER0 D
+UNCHASTE	AH0 N CH EY1 S T
+UNCHASTENED	AH0 N CH EY1 S AH0 N D
+UNCHASTISED	AH0 N CH AE0 S T AY1 Z D
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+UNCHECKERED	AH0 N CH EH1 K ER0 D
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+UNCHEERED	AH2 N K IH1 R D
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+UNCHEERING	AH2 N K IH1 R IH0 NG
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+UNCHILLED	AH0 N CH IH1 L D
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+UNCIVIL  AH0 N S IH1 V AH0 L
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+UNCLEANED	AH0 N K L IY1 N D
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+UNCLEANSED	AH0 N K L EH1 N Z D
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+UNCLENCH	AH0 N K L EH1 N CH
+UNCLENCHED	AH0 N K L EH1 N CH T
+UNCLENCHING	AH0 N K L EH1 N CH IH0 NG
+UNCLERICAL	AH0 N K L EH1 R IH0 K AH0 L
+UNCLES  AH1 NG K AH0 L Z
+UNCLIMBABLE	AH0 N K L IH1 M B AH0 B AH0 L
+UNCLINCHED	AH0 N K L IH1 N CH T
+UNCLIPPED	AH0 N K L IH1 P T
+UNCLOAKED	AH0 N K L OW1 K T
+UNCLOGGED	AH0 N K L AO1 G D
+UNCLOSE	AH0 N K L OW1 Z
+UNCLOSED	AH0 N K L OW1 Z D
+UNCLOSES	AH0 N K L OW1 Z IH0 Z
+UNCLOSING	AH0 N K L OW1 Z IH0 NG
+UNCLOTHED	AH0 N K L AO2 TH T
+UNCLOUD	AH0 N K L AW1 D
+UNCLOUDED	AH0 N K L AW1 D IH0 D
+UNCLOYING	AH0 N K L OY1 IH0 NG
+UNCO	AH0 N K OW0
+UNCOCKED	AH0 N K AA1 K T
+UNCOCKING	AH0 N K AA1 K IH0 NG
+UNCOFFINED	AH0 N K AA1 F AY1 N D
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+UNCOILED  AH2 N K OY1 L D
+UNCOILING	AH2 N K OY1 L IH0 NG
+UNCOILS	AH2 N K OY1 L Z
+UNCOINED	AH2 N K OY1 N D
+UNCOLLECTED  AH0 N K AH0 L EH1 K T IH0 D
+UNCOLORED	AH0 N K AH1 L ER0 D
+UNCOLOURED	AH0 N K AH1 L ER0 D
+UNCOMBED	AH2 N K AH0 M D
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+UNCOMELINESS	AH0 N K AH1 M L IY0 N AH0 S
+UNCOMELY	AH0 N K AH1 M L IY0
+UNCOMFORTABLE  AH0 N K AH1 M F ER0 T AH0 B AH0 L
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+UNCOMFORTABLY  AH0 N K AH1 M F T AH0 B L IY0
+UNCOMFORTED	AH0 N K AH1 M F ER0 T IH0 D
+UNCOMFORTING	AH0 N K AH1 M F ER0 T IH0 NG
+UNCOMFY	AH0 N K AH1 M F IY0
+UNCOMMANDED	AH2 N K AH0 M AE1 N D IH0 D
+UNCOMMENTED	AH0 N K AA1 M EH0 N T IH0 D
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+UNCOMMITTED  AH2 N K AH0 M IH1 T IH0 D
+UNCOMMON  AH0 N K AA1 M AH0 N
+UNCOMMONLY  AH2 N K AA1 M AH0 N L IY0
+UNCOMMONNESS	AH0 N K AA1 M AH0 N N AH0 S
+UNCOMMUNICATED	AH2 N K AH0 M Y UW1 N AH0 K EY2 T IH0 D
+UNCOMMUNICATIVE	AH2 N K AH0 M Y UW1 N AH0 K EY2 T IH0 V
+UNCOMMUNICATIVENESS	AH2 N K AH0 M Y UW1 N AH0 K EY2 T IH0 V N AH0 S
+UNCOMPAHGRE	AH0 N K AH1 M P AH0 HH G ER0
+UNCOMPANIONABLE	AH2 N K AH0 M P AE1 N Y AH0 N AH0 B AH0 L
+UNCOMPANIONED	AH2 N K AH0 M P AE1 N Y AH0 N D
+UNCOMPELLED	AH2 N K AH0 M P EH1 L D
+UNCOMPENSATED  AH0 N K AA1 M P AH0 N S EY2 T IH0 D
+UNCOMPLAINING	AH2 N K AH0 M P L EY1 N IH0 NG
+UNCOMPLAININGLY	AH2 N K AH0 M P L EY1 N IH0 NG L IY0
+UNCOMPLETED  AH2 N K AH0 M P L IY1 T IH0 D
+UNCOMPLEX	AH0 N K AA1 M P L EH0 K S
+UNCOMPLICATED  AH0 N K AA1 M P L AH0 K EY2 T IH0 D
+UNCOMPLIMENTARY	AH0 N K AA1 M P L AH0 M EH1 N ER0 IY0
+UNCOMPOUNDED	AH2 N K AH0 M P AW1 N D IH0 D
+UNCOMPREHENDED	AH2 N K AA2 M P R IH0 HH EH1 N D IH0 D
+UNCOMPREHENDING	AH2 N K AA2 M P R IH0 HH EH1 N D IH0 NG
+UNCOMPREHENDINGLY	AH2 N K AA2 M P R IH0 HH EH1 N D IH0 NG L IY0
+UNCOMPROMISED	AH0 N K AA1 M P R AH0 M AY2 Z D
+UNCOMPROMISING  AH0 N K AA1 M P R AH0 M AY0 Z IH0 NG
+UNCOMPROMISINGLY	AH0 N K AA1 M P R AH0 M AY2 Z IH0 NG L IY0
+UNCONCEALABLE	AH2 N K AH0 N S IY1 L AH0 B AH0 L
+UNCONCEALED  AH2 N K AH0 N S IY1 L D
+UNCONCEIVABLE	AH2 N K AH0 N S IY1 V AH0 B AH0 L
+UNCONCEIVED	AH2 N K AH0 N S IY1 V D
+UNCONCERN  AH2 N K AH0 N S ER1 N
+UNCONCERN'D	AH2 N K AH0 N S ER1 N D
+UNCONCERNED  AH2 N K AH0 N S ER1 N D
+UNCONCERNEDLY	AH2 N K AH0 N S ER1 N AH0 D L IY0
+UNCONCILIATORY	AH2 N K AH0 N S IH1 L IY2 AH0 T AO2 R IY0
+UNCONDEMNED	AH2 N K AH0 N D EH1 M D
+UNCONDITIONAL  AH2 N K AH0 N D IH1 SH AH0 N AH0 L
+UNCONDITIONALLY  AH2 N K AH0 N D IH1 SH AH0 N AH0 L IY0
+UNCONDITIONALLY  AH2 N K AH0 N D IH1 SH N AH0 L IY0
+UNCONDITIONED	AH2 N K AH0 N D IH1 SH AH0 N D
+UNCONFESSED	AH2 N K AH0 N F EH1 S T
+UNCONFIN'D	AH2 N K AH0 N F AY1 N D
+UNCONFINED  AH2 N K AH0 N F AY1 N D
+UNCONFIRMED  AH2 N K AH0 N F ER1 M D
+UNCONFUSED	AH2 N K AH0 N F Y UW1 Z D
+UNCONGENIAL	AH2 N K AH0 N JH IY1 N Y AH0 L
+UNCONGENIALITY	AH2 N K AH0 N JH IY2 N IY0 AE1 L AH0 T IY0
+UNCONJECTURED	AH2 N K AH0 N JH EH1 K CH ER0 D
+UNCONNECTED  AH2 N K AH0 N EH1 K T IH0 D
+UNCONNECTEDLY	AH2 N K AH0 N EH1 K T IH0 D L IY0
+UNCONQUER'D	AH0 N K AA1 NG K ER0 D
+UNCONQUERABLE	AH0 N K AA1 NG K ER0 AH0 B AH0 L
+UNCONQUERABLY	AH0 N K AA1 NG K ER0 AH0 B L IY0
+UNCONQUERED	AH0 N K AA1 NG K ER0 D
+UNCONSCIENTIOUS	AH2 N K AA1 N CH IY0 EH1 N CH AH0 S
+UNCONSCIONABLE  AH0 N K AA1 N SH AH0 N AH0 B AH0 L
+UNCONSCIONABLY	AH2 N K AA1 N SH AH0 N AH0 B L IY0
+UNCONSCIOUS  AH2 N K AA1 N SH AH0 S
+UNCONSCIOUSLY  AH2 N K AA1 N SH AH0 S L IY0
+UNCONSCIOUSNESS  AH2 N K AA1 N SH AH0 S N IH0 S
+UNCONSECRATED	AH2 N K AA2 N S AH0 K R EY1 T IH0 D
+UNCONSIDERED	AH2 N K AH0 N S IH1 D ER0 D
+UNCONSOLED	AH2 N K AH0 N S OW1 L D
+UNCONSTANT	AH2 N K AA2 N S T AH0 N T
+UNCONSTITUTIONAL  AH2 N K AA2 N S T AH0 T UW1 SH AH0 N AH0 L
+UNCONSTITUTIONALITY	AH2 N K AA2 N S T IH0 T UW1 SH AH0 N AE1 L IH0 T IY0
+UNCONSTITUTIONALLY  AH2 N K AA2 N S T AH0 T UW1 SH AH0 N AH0 L IY0
+UNCONSTITUTIONALLY  AH2 N K AA2 N S T AH0 T UW1 SH N AH0 L IY0
+UNCONSTRAINED  AH2 N K AH0 N S T R EY1 N D
+UNCONSTRAINEDLY	AH2 N K AH0 N S T R EY1 N D L IY0
+UNCONSTRAINT	AH2 N K AH0 N S T R EY1 N T
+UNCONSULTED	AH2 N K AH0 N S AH1 L T IH0 D
+UNCONSUMED	AH2 N K AH0 N S UW1 M D
+UNCONSUMING	AH2 N K AH0 N S UW1 M IH0 NG
+UNCONTAMINATED  AH2 N K AH0 N T AE1 M AH0 N EY2 T AH0 D
+UNCONTENT	AH2 N K AH0 N T EH1 N T
+UNCONTESTED  AH2 N K AH0 N T EH1 S T IH0 D
+UNCONTRADICTED  AH2 N K AO0 N T R AH0 D IH1 K T IH0 D
+UNCONTROLABLE	AH2 N K AH0 N T R OW2 L AH0 B AH0 L
+UNCONTROLL'D	AH2 N K AH0 N T R OW1 L D
+UNCONTROLLABLE  AH2 N K AH0 N T R OW1 L AH0 B AH0 L
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+UNCONTROULABLE	AH2 N K AH0 N T R UW1 L AH0 B AH0 L
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+UNCONTROVERTED	AH2 N K AA1 N T R OW0 V ER2 T IH0 D
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+UNPRESUMING	AH2 N P R IH0 Z UW1 M IH0 NG
+UNPRETENDING	AH2 N P R IY0 T EH1 N D IH0 NG
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+UNPRINCELY	AH0 N P R IH1 N S L IY0
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+UNPROFANED	AH1 P R OW0 F EY1 N D
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+UNPROFESSIONALLY	AH2 N P R AH0 F EH1 SH AH0 N AH0 L IY0
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+UNPROGRESSIVE	AH2 N P R AH0 G R EH1 S IH0 V
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+UNREASONINGLY	AH0 N R IY1 Z AH0 N IH0 NG L IY0
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+UNRECIPROCATED	AH0 N R IH0 S IH1 P R AH0 K EY2 T IH0 D
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+UNRECKONABLE	AH0 N R EH1 K AH0 N AH0 B AH0 L
+UNRECKONED	AH0 N R EH1 K AH0 N D
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+UNRECLAIMED	AH2 N R IY0 K L EY1 M D
+UNRECOGNISABLE	AH0 N R EH1 K AH0 G N AY2 Z AH0 B AH0 L
+UNRECOGNISED	AH0 N R EH1 K AH0 G N AY1 Z D
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+UNRECOMPENSED	AH0 N R EH1 K AH0 M P EH1 N S T
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+UNREFRESHING	AH2 N R IY0 F R EH1 SH IH0 NG
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+UNREGRETTED	AH2 N R IH0 G R EH1 T IH0 D
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+UNRELATED  AH2 N R IY0 L EY1 T IH0 D
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+UNRELAXING	AH2 N R IY0 L AE1 K S IH0 NG
+UNRELEASED  AH0 N R IH0 L IY1 S T
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+UNRELIABLE  AH2 N R IH0 L AY1 AH0 B AH0 L
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+VALUABLE  V AE1 L Y AH0 B AH0 L
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+VALUABLES  V AE1 L Y AH0 B AH0 L Z
+VALUABLES  V AE1 L Y UW0 B AH0 L Z
+VALUABLY	V AE1 L Y AH0 B L IY0
+VALUATION  V AE0 L Y UW0 EY1 SH AH0 N
+VALUATIONS  V AE0 L Y UW0 EY1 SH AH0 N Z
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+VALUELESS  V AE1 L Y UW0 L AH0 S
+VALUER	V AE1 L Y UW2 ER0
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+VALUES  V AE1 L Y UW0 Z
+VALUEST	V AH0 L UW1 IH0 S T
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+VALVE  V AE1 L V
+VALVED	V AE1 L V D
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+ZORILLA	Z AO0 R IH1 L AH0
+ZORKA	Z AO1 R K AH0
+ZORKI	Z AO1 R K IY0
+ZORN  Z AO1 R N
+ZORNDORF	Z AO1 R N D AO0 R F
+ZORO	Z AO1 R OW0
+ZOROASTER	Z AO0 R OW1 S T ER0
+ZOROASTER'S	Z AO0 R OW1 S T ER0 Z
+ZOROASTRIAN	Z AO1 R OW0 AE1 S T R IY0 AH0 N
+ZOROASTRIANISM	Z AO1 R OW0 AE1 S T R IY0 AH0 N IH2 Z AH0 M
+ZOROASTRIANS	Z AO1 R OW0 AE1 S T R IY0 AH0 N Z
+ZOROBABEL	Z AO1 R AH0 B AE1 B AH0 L
+ZORRA	Z AO1 R AH0
+ZORRILLO	Z AO1 R IH1 L OW0
+ZORRILTA	Z AO1 R AH0 L T AH0
+ZORRY	Z AO1 R IY0
+ZORT	Z AO1 R T
+ZORTAN	Z AO1 R T AH0 N
+ZORZI  Z AO1 R Z IY0
+ZOSCO	Z AA1 S K OW0
+ZOSE	Z OW1 Z
+ZOSEPHINE	Z OW1 Z AH0 F IY1 N IY0
+ZOSIA	Z OW1 ZH AH0
+ZOSIM	Z OW0 S IY1 M
+ZOSIMUS	Z AA1 S IH0 M IH0 S
+ZOSKA	Z OW1 S K AH0
+ZOSSEN	Z AO1 S AH0 N
+ZOSSIMA	Z AO1 S IY1 M AH0
+ZOSSIMA'S	Z AO1 S IY1 M AH0 Z
+ZOSSIMOV	Z AO1 S IH0 M AA0 V
+ZOSTER  Z AA1 S T ER0
+ZOSTROV	Z AA1 S T R AA0 V
+ZOTIQUE	Z OW1 T IY1 K
+ZOTOV	Z OW1 T AA2 F
+ZOU	Z UW0
+ZOUAVE	Z W AA1 V
+ZOUAVES	Z AW1 EY2 V Z
+ZOUCH	Z UW1 HH
+ZOUCH'S	Z AW1 CH IH0 Z
+ZOUCHE	Z UW1 SH
+ZOUG	Z AW1 G
+ZOUGA	Z UW1 G AH0
+ZOULVISIA	Z AW1 L V IH2 ZH AH0
+ZOUNDS  Z AW1 N D Z
+ZOUROFF	Z UH1 R AO0 F
+ZOUROFF'S	Z UH1 R AO0 F S
+ZOUTPANSBERG	Z AW2 T P AE2 N Z B ER0 G
+ZOYA	Z OY1 AH0
+ZOYA'S	Z OY1 AH0 Z
+ZOYLAND	Z OY1 L AH0 N D
+ZOZA	Z OW1 Z AH0
+ZOZO	Z OW1 Z OW0
+ZRINYI	Z R IY1 N Y IY0
+ZSABAKOFF	ZH AA1 B AH0 K AO0 F
+ZSCHOKKE	ZH AA1 K
+ZU	Z UW0
+ZU'L	Z UW1 AH0 L
+ZU'N	Z UW1 AH0 N
+ZUAGIRS	Z UW2 AH0 JH IH1 R Z
+ZUANSTROOM	Z UW0 AA1 N S T R UW2 M
+ZUBA  Z UW1 B AH0
+ZUBEHR	Z UW1 B IH0 R
+ZUBIN  Z UW1 B IH0 N
+ZUBOV	Z UW1 B AO0 V
+ZUBU	Z UW1 B UW0
+ZUBY	Z UW1 B IY0
+ZUCCARI	Z UW0 K AA1 R IY0
+ZUCCHERO	Z UW0 K EH1 R OW0
+ZUCCHI	Z UW1 K IY0
+ZUCHIN	Z UW0 K IH0 N
+ZUDAR	Z UW1 D ER0
+ZUDI	Z UW1 D IY0
+ZUECCA	Z UH1 K AH0
+ZUELA	Z UW1 L AH0
+ZUELFIKAR	Z UW1 L F IH0 K AA1 R
+ZUEMBUEL	Z UH1 M B Y UW1 L
+ZUERICH	Z UH1 R IH0 K
+ZUES	Z UW1 Z
+ZUG  Z AH1 G
+ZUGVITZ	Z AH1 G V IH0 T S
+ZUIDER	Z UW0 IH0 D ER0
+ZUILIKA	Z UW0 IH0 L IH0 K AH0
+ZUINGLIUS	Z UW0 IH0 NG G L IY0 IH0 S
+ZUKER  Z UW1 K ER0
+ZUKOVO	Z UW1 K AH0 V OW0
+ZUKUNFT	Z UW1 K AH0 N F T
+ZULA	Z UW1 L AH0
+ZULA'S	Z UW1 L AH0 Z
+ZULANNAH	Z UW2 L AE1 N AH0
+ZULEIKA	Z UW1 L AY1 K AH0
+ZULEIKA'S	Z UW1 L AY1 K AH0 Z
+ZULEMA	Z UW1 L IH0 M AH0
+ZULESTEIN	Z UW1 L IH0 S T IY0 N
+ZULEYKA	Z UW1 L IH1 K AH0
+ZULFIKAR  Z AH2 L F IH0 K AA1 R
+ZULFKAR	Z AH2 L F K AA2 R
+ZULIESKI	Z UW1 L IY0 EH1 S K IY0
+ZULKA	Z UH1 L K AH0
+ZULMA  Z UH1 L M AH0
+ZULMA'S	Z UH1 L M AH0 Z
+ZULOAGA	Z UW1 L OW0 AA1 G AH0
+ZULU  Z UW1 L UW2
+ZULU'S	Z UW1 L UW0 Z
+ZULULAND	Z UW1 L UW0 L AE0 N D
+ZULUS  Z UW1 L UW0 Z
+ZUM	Z AH1 M
+ZUMA	Z UW1 M AH0
+ZUMALACARREGUI	Z UW0 M AA1 L AH0 K AA2 R EY1 G W IY0
+ZUMMAT	Z UW1 M AE0 T
+ZUMMAUN	Z UW1 M AW0 N
+ZUNGA	Z AH1 N G AH0
+ZUNI  Z UW1 N IY2
+ZUNIGA  Z UW0 N IY1 G AH0
+ZUNIS	Z UW0 N IY1 S
+ZUNSER	Z AH1 N S ER0
+ZUPPA	Z AH1 P AH0
+ZUR	Z ER0
+ZURA	Z UH1 R AH0
+ZURA'S	Z UH1 R AH0 Z
+ZURB	Z ER1 B
+ZURBARAN	Z ER1 B ER0 AH0 N
+ZURDOKI	Z ER0 D OW1 K IY0
+ZURE	Z UH1 R
+ZUREDA	Z ER0 EY1 D AH0
+ZURICH  Z UH1 R IH0 K
+ZURICHAN	Z UH1 R IH0 K AH0 N
+ZURICHERS	Z UH1 R IH0 K ER0 Z
+ZURIE	Z UH1 R IY0
+ZURIEL	Z UH1 R IY0 AH0 L
+ZURLINE	Z ER1 L AY0 N
+ZUROAGA	Z UH1 R OW0 AA1 G AH0
+ZURVAN	Z ER1 V AH0 N
+ZUSANETTE	Z UW1 S AA0 N EH1 T
+ZUSSMANN	Z AH1 S M AH0 N
+ZUSSMANN'S	Z AH1 S M AH0 N Z
+ZUT	Z AH0 T
+ZUTEN	Z UW1 T AH0 N
+ZUTPHEN	Z AH1 T F AH0 N
+ZUYDER	Z AY1 D ER0
+ZUYLEN	Z UW1 L AH0 N
+ZVANTZEV	Z V AA1 N T Z AH0 V
+ZVERKOV	Z V ER0 K AO2 V
+ZWAAN	Z W AA2 N
+ZWANZIGER	Z W AA1 N Z IH0 G ER0
+ZWART  Z W AO1 R T
+ZWEETE	Z W IY1 T
+ZWEI	Z W AY1
+ZWEIBRUCKEN	Z W AY1 B R AH1 K AH0 N
+ZWEIG  Z W AY1 G
+ZWEISIMMEN	Z W AY1 Z IY0 M AH0 N
+ZWEMER	Z W IY1 M ER0
+ZWENGLER	Z W IH1 NG AH0 L ER0
+ZWENKEN	Z W EH1 NG K AH0 N
+ZWENO	Z W IY1 N OW0
+ZWICKAU	Z W IH1 K AW0
+ZWIDE	Z W AY2 D
+ZWIEBACK	Z W IY1 B AE0 K
+ZWILK	Z W IH1 L K
+ZWILLIEVIC	Z W IH1 L Y AH0 V IH0 K
+ZWINGER	Z W IH1 NG ER0
+ZWINGLI	Z W IH1 NG G L IY0
+ZWINGLI'S	Z W IH1 NG G L IY0 Z
+ZWINGLIAN	Z W IH1 NG G L IY0 AH0 N
+ZWIRINA	Z W IH0 R IY1 N AH0
+ZWISCHEN	Z W IH1 SH AH0 N
+ZWOLLE	Z W AA1 L
+ZY	Z IY0
+ZYARULLA	Z Y AA0 R UW1 L AH0
+ZYCH  Z AY1 CH
+ZYGFRIED	Z AY1 G F R IY2 D
+ZYGFRIED'S	Z AY1 G F R IY2 D Z
+ZYGOMATIC	Z AY1 G OW0 M AE1 T IH0 K
+ZYL	Z IH1 L
+ZYL'S	Z IH1 L Z
+ZYMOTIC	Z AY1 M AA1 T IH0 K
+ZYNDRAM	Z IH1 N D R AE2 M
+ZYNOOL	Z IH1 N UW1 L
+ZYNOOL'S	Z IH1 N UW1 L Z
+ZYOBOR	Z AY1 OW0 B AO0 R
+ZZ	Z
+ZZZ	Z Z
+ZZZZ	Z AH0 Z
diff --git a/text/lexicon/pinyin-lexicon-r.txt b/text/lexicon/pinyin-lexicon-r.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3ccd50dd74df47c8e36e250661feabe734195cc9
--- /dev/null
+++ b/text/lexicon/pinyin-lexicon-r.txt
@@ -0,0 +1,4120 @@
+a1 a1
+a2 a2
+a3 a3
+a4 a4
+a5 a5
+ai1 ai1
+ai2 ai2
+ai3 ai3
+ai4 ai4
+ai5 ai5
+an1 an1
+an2 an2
+an3 an3
+an4 an4
+an5 an5
+ang1 ang1
+ang2 ang2
+ang3 ang3
+ang4 ang4
+ang5 ang5
+ao1 ao1
+ao2 ao2
+ao3 ao3
+ao4 ao4
+ao5 ao5
+ba1 b a1
+ba2 b a2
+ba3 b a3
+ba4 b a4
+ba5 b a5
+bai1 b ai1
+bai2 b ai2
+bai3 b ai3
+bai4 b ai4
+bai5 b ai5
+ban1 b an1
+ban2 b an2
+ban3 b an3
+ban4 b an4
+ban5 b an5
+bang1 b ang1
+bang2 b ang2
+bang3 b ang3
+bang4 b ang4
+bang5 b ang5
+bao1 b ao1
+bao2 b ao2
+bao3 b ao3
+bao4 b ao4
+bao5 b ao5
+bei1 b ei1
+bei2 b ei2
+bei3 b ei3
+bei4 b ei4
+bei5 b ei5
+ben1 b en1
+ben2 b en2
+ben3 b en3
+ben4 b en4
+ben5 b en5
+beng1 b eng1
+beng2 b eng2
+beng3 b eng3
+beng4 b eng4
+beng5 b eng5
+bi1 b i1
+bi2 b i2
+bi3 b i3
+bi4 b i4
+bi5 b i5
+bian1 b ian1
+bian2 b ian2
+bian3 b ian3
+bian4 b ian4
+bian5 b ian5
+biao1 b iao1
+biao2 b iao2
+biao3 b iao3
+biao4 b iao4
+biao5 b iao5
+bie1 b ie1
+bie2 b ie2
+bie3 b ie3
+bie4 b ie4
+bie5 b ie5
+bin1 b in1
+bin2 b in2
+bin3 b in3
+bin4 b in4
+bin5 b in5
+bing1 b ing1
+bing2 b ing2
+bing3 b ing3
+bing4 b ing4
+bing5 b ing5
+bo1 b o1
+bo2 b o2
+bo3 b o3
+bo4 b o4
+bo5 b o5
+bu1 b u1
+bu2 b u2
+bu3 b u3
+bu4 b u4
+bu5 b u5
+ca1 c a1
+ca2 c a2
+ca3 c a3
+ca4 c a4
+ca5 c a5
+cai1 c ai1
+cai2 c ai2
+cai3 c ai3
+cai4 c ai4
+cai5 c ai5
+can1 c an1
+can2 c an2
+can3 c an3
+can4 c an4
+can5 c an5
+cang1 c ang1
+cang2 c ang2
+cang3 c ang3
+cang4 c ang4
+cang5 c ang5
+cao1 c ao1
+cao2 c ao2
+cao3 c ao3
+cao4 c ao4
+cao5 c ao5
+ce1 c e1
+ce2 c e2
+ce3 c e3
+ce4 c e4
+ce5 c e5
+cen1 c en1
+cen2 c en2
+cen3 c en3
+cen4 c en4
+cen5 c en5
+ceng1 c eng1
+ceng2 c eng2
+ceng3 c eng3
+ceng4 c eng4
+ceng5 c eng5
+cha1 ch a1
+cha2 ch a2
+cha3 ch a3
+cha4 ch a4
+cha5 ch a5
+chai1 ch ai1
+chai2 ch ai2
+chai3 ch ai3
+chai4 ch ai4
+chai5 ch ai5
+chan1 ch an1
+chan2 ch an2
+chan3 ch an3
+chan4 ch an4
+chan5 ch an5
+chang1 ch ang1
+chang2 ch ang2
+chang3 ch ang3
+chang4 ch ang4
+chang5 ch ang5
+chao1 ch ao1
+chao2 ch ao2
+chao3 ch ao3
+chao4 ch ao4
+chao5 ch ao5
+che1 ch e1
+che2 ch e2
+che3 ch e3
+che4 ch e4
+che5 ch e5
+chen1 ch en1
+chen2 ch en2
+chen3 ch en3
+chen4 ch en4
+chen5 ch en5
+cheng1 ch eng1
+cheng2 ch eng2
+cheng3 ch eng3
+cheng4 ch eng4
+cheng5 ch eng5
+chi1 ch iii1
+chi2 ch iii2
+chi3 ch iii3
+chi4 ch iii4
+chi5 ch iii5
+chong1 ch ong1
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+chong3 ch ong3
+chong4 ch ong4
+chong5 ch ong5
+chou1 ch ou1
+chou2 ch ou2
+chou3 ch ou3
+chou4 ch ou4
+chou5 ch ou5
+chu1 ch u1
+chu2 ch u2
+chu3 ch u3
+chu4 ch u4
+chu5 ch u5
+chuai1 ch uai1
+chuai2 ch uai2
+chuai3 ch uai3
+chuai4 ch uai4
+chuai5 ch uai5
+chuan1 ch uan1
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+chuan3 ch uan3
+chuan4 ch uan4
+chuan5 ch uan5
+chuang1 ch uang1
+chuang2 ch uang2
+chuang3 ch uang3
+chuang4 ch uang4
+chuang5 ch uang5
+chui1 ch uei1
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+chui3 ch uei3
+chui4 ch uei4
+chui5 ch uei5
+chun1 ch uen1
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+chun3 ch uen3
+chun4 ch uen4
+chun5 ch uen5
+chuo1 ch uo1
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+chuo3 ch uo3
+chuo4 ch uo4
+chuo5 ch uo5
+ci1 c ii1
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+ci3 c ii3
+ci4 c ii4
+ci5 c ii5
+cong1 c ong1
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+cong3 c ong3
+cong4 c ong4
+cong5 c ong5
+cou1 c ou1
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+cou3 c ou3
+cou4 c ou4
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+cu1 c u1
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+cui1 c uei1
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+cun1 c uen1
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+cuo1 c uo1
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+cuo3 c uo3
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+da1 d a1
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+de1 d e1
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+di1 d i1
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+dia1 d ia1
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+dian1 d ian1
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+diao1 d iao1
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+die1 d ie1
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+diu1 d iou1
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+dong1 d ong1
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+dou1 d ou1
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+fa1 f a1
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+ha1 h a1
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+yangr1 y iang1 rr
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+yaor1 y iao1 rr
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+yer1 y ie1 rr
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+yir1 y i1 rr
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+yinr1 y in1 rr
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+yinr5 y in5 rr
+yingr1 y ing1 rr
+yingr2 y ing2 rr
+yingr3 y ing3 rr
+yingr4 y ing4 rr
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+yor1 y iou1 rr
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+yor3 y iou3 rr
+yor4 y iou4 rr
+yor5 y iou5 rr
+yongr1 y iong1 rr
+yongr2 y iong2 rr
+yongr3 y iong3 rr
+yongr4 y iong4 rr
+yongr5 y iong5 rr
+your1 y iou1 rr
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+your4 y iou4 rr
+your5 y iou5 rr
+yur1 y v1 rr
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+yuanr1 y van1 rr
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+yuanr3 y van3 rr
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+yuanr5 y van5 rr
+yuer1 y ve1 rr
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+yuer4 y ve4 rr
+yuer5 y ve5 rr
+yunr1 y vn1 rr
+yunr2 y vn2 rr
+yunr3 y vn3 rr
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+zar1 z a1 rr
+zar2 z a2 rr
+zar3 z a3 rr
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+zair1 z ai1 rr
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+zanr4 z an4 rr
+zanr5 z an5 rr
+zangr1 z ang1 rr
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+zangr3 z ang3 rr
+zangr4 z ang4 rr
+zangr5 z ang5 rr
+zaor1 z ao1 rr
+zaor2 z ao2 rr
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+zaor4 z ao4 rr
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+zer1 z e1 rr
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+zer3 z e3 rr
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+zeir1 z ei1 rr
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+zeir3 z ei3 rr
+zeir4 z ei4 rr
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+zenr1 z en1 rr
+zenr2 z en2 rr
+zenr3 z en3 rr
+zenr4 z en4 rr
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+zengr1 z eng1 rr
+zengr2 z eng2 rr
+zengr3 z eng3 rr
+zengr4 z eng4 rr
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+zhar1 zh a1 rr
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+zhair1 zh ai1 rr
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+zhair4 zh ai4 rr
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+zhanr1 zh an1 rr
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+zhangr1 zh ang1 rr
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+zhengr1 zh eng1 rr
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+zhengr3 zh eng3 rr
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+zhir1 zh iii1 rr
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+zhour1 zh ou1 rr
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+zhuar1 zh ua1 rr
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+zhuair1 zh uai1 rr
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+zhuair3 zh uai3 rr
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+zhuanr3 zh uan3 rr
+zhuanr4 zh uan4 rr
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+zhuangr1 zh uang1 rr
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+zhuangr3 zh uang3 rr
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+zhuangr5 zh uang5 rr
+zhuir1 zh uei1 rr
+zhuir2 zh uei2 rr
+zhuir3 zh uei3 rr
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+zhunr1 zh uen1 rr
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+zhunr3 zh uen3 rr
+zhunr4 zh uen4 rr
+zhunr5 zh uen5 rr
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+zhuor2 zh uo2 rr
+zhuor3 zh uo3 rr
+zhuor4 zh uo4 rr
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+zir1 z ii1 rr
+zir2 z ii2 rr
+zir3 z ii3 rr
+zir4 z ii4 rr
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+zour2 z ou2 rr
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+zur3 z u3 rr
+zur4 z u4 rr
+zur5 z u5 rr
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+zuanr2 z uan2 rr
+zuanr3 z uan3 rr
+zuanr4 z uan4 rr
+zuanr5 z uan5 rr
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+zuir2 z uei2 rr
+zuir3 z uei3 rr
+zuir4 z uei4 rr
+zuir5 z uei5 rr
+zunr1 z uen1 rr
+zunr2 z uen2 rr
+zunr3 z uen3 rr
+zunr4 z uen4 rr
+zunr5 z uen5 rr
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+zuor2 z uo2 rr
+zuor3 z uo3 rr
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diff --git a/text/numbers.py b/text/numbers.py
new file mode 100644
index 0000000000000000000000000000000000000000..660a2964b10a273c0c0b7e8998c8111d7e4aa3be
--- /dev/null
+++ b/text/numbers.py
@@ -0,0 +1,77 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+""" This code is modified from https://github.com/keithito/tacotron """
+
+import inflect
+import re
+
+_inflect = inflect.engine()
+_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])")
+_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)")
+_pounds_re = re.compile(r"£([0-9\,]*[0-9]+)")
+_dollars_re = re.compile(r"\$([0-9\.\,]*[0-9]+)")
+_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)")
+_number_re = re.compile(r"[0-9]+")
+
+
+def _remove_commas(m):
+    return m.group(1).replace(",", "")
+
+
+def _expand_decimal_point(m):
+    return m.group(1).replace(".", " point ")
+
+
+def _expand_dollars(m):
+    match = m.group(1)
+    parts = match.split(".")
+    if len(parts) > 2:
+        return match + " dollars"  # Unexpected format
+    dollars = int(parts[0]) if parts[0] else 0
+    cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
+    if dollars and cents:
+        dollar_unit = "dollar" if dollars == 1 else "dollars"
+        cent_unit = "cent" if cents == 1 else "cents"
+        return "%s %s, %s %s" % (dollars, dollar_unit, cents, cent_unit)
+    elif dollars:
+        dollar_unit = "dollar" if dollars == 1 else "dollars"
+        return "%s %s" % (dollars, dollar_unit)
+    elif cents:
+        cent_unit = "cent" if cents == 1 else "cents"
+        return "%s %s" % (cents, cent_unit)
+    else:
+        return "zero dollars"
+
+
+def _expand_ordinal(m):
+    return _inflect.number_to_words(m.group(0))
+
+
+def _expand_number(m):
+    num = int(m.group(0))
+    if num > 1000 and num < 3000:
+        if num == 2000:
+            return "two thousand"
+        elif num > 2000 and num < 2010:
+            return "two thousand " + _inflect.number_to_words(num % 100)
+        elif num % 100 == 0:
+            return _inflect.number_to_words(num // 100) + " hundred"
+        else:
+            return _inflect.number_to_words(
+                num, andword="", zero="oh", group=2
+            ).replace(", ", " ")
+    else:
+        return _inflect.number_to_words(num, andword="")
+
+
+def normalize_numbers(text):
+    text = re.sub(_comma_number_re, _remove_commas, text)
+    text = re.sub(_pounds_re, r"\1 pounds", text)
+    text = re.sub(_dollars_re, _expand_dollars, text)
+    text = re.sub(_decimal_number_re, _expand_decimal_point, text)
+    text = re.sub(_ordinal_re, _expand_ordinal, text)
+    text = re.sub(_number_re, _expand_number, text)
+    return text
diff --git a/text/pinyin.py b/text/pinyin.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb4b41b5c3990d0c9026beb288a60c1edc86528e
--- /dev/null
+++ b/text/pinyin.py
@@ -0,0 +1,218 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+initials = [
+    "b",
+    "c",
+    "ch",
+    "d",
+    "f",
+    "g",
+    "h",
+    "j",
+    "k",
+    "l",
+    "m",
+    "n",
+    "p",
+    "q",
+    "r",
+    "s",
+    "sh",
+    "t",
+    "w",
+    "x",
+    "y",
+    "z",
+    "zh",
+]
+finals = [
+    "a1",
+    "a2",
+    "a3",
+    "a4",
+    "a5",
+    "ai1",
+    "ai2",
+    "ai3",
+    "ai4",
+    "ai5",
+    "an1",
+    "an2",
+    "an3",
+    "an4",
+    "an5",
+    "ang1",
+    "ang2",
+    "ang3",
+    "ang4",
+    "ang5",
+    "ao1",
+    "ao2",
+    "ao3",
+    "ao4",
+    "ao5",
+    "e1",
+    "e2",
+    "e3",
+    "e4",
+    "e5",
+    "ei1",
+    "ei2",
+    "ei3",
+    "ei4",
+    "ei5",
+    "en1",
+    "en2",
+    "en3",
+    "en4",
+    "en5",
+    "eng1",
+    "eng2",
+    "eng3",
+    "eng4",
+    "eng5",
+    "er1",
+    "er2",
+    "er3",
+    "er4",
+    "er5",
+    "i1",
+    "i2",
+    "i3",
+    "i4",
+    "i5",
+    "ia1",
+    "ia2",
+    "ia3",
+    "ia4",
+    "ia5",
+    "ian1",
+    "ian2",
+    "ian3",
+    "ian4",
+    "ian5",
+    "iang1",
+    "iang2",
+    "iang3",
+    "iang4",
+    "iang5",
+    "iao1",
+    "iao2",
+    "iao3",
+    "iao4",
+    "iao5",
+    "ie1",
+    "ie2",
+    "ie3",
+    "ie4",
+    "ie5",
+    "ii1",
+    "ii2",
+    "ii3",
+    "ii4",
+    "ii5",
+    "iii1",
+    "iii2",
+    "iii3",
+    "iii4",
+    "iii5",
+    "in1",
+    "in2",
+    "in3",
+    "in4",
+    "in5",
+    "ing1",
+    "ing2",
+    "ing3",
+    "ing4",
+    "ing5",
+    "iong1",
+    "iong2",
+    "iong3",
+    "iong4",
+    "iong5",
+    "iou1",
+    "iou2",
+    "iou3",
+    "iou4",
+    "iou5",
+    "o1",
+    "o2",
+    "o3",
+    "o4",
+    "o5",
+    "ong1",
+    "ong2",
+    "ong3",
+    "ong4",
+    "ong5",
+    "ou1",
+    "ou2",
+    "ou3",
+    "ou4",
+    "ou5",
+    "u1",
+    "u2",
+    "u3",
+    "u4",
+    "u5",
+    "ua1",
+    "ua2",
+    "ua3",
+    "ua4",
+    "ua5",
+    "uai1",
+    "uai2",
+    "uai3",
+    "uai4",
+    "uai5",
+    "uan1",
+    "uan2",
+    "uan3",
+    "uan4",
+    "uan5",
+    "uang1",
+    "uang2",
+    "uang3",
+    "uang4",
+    "uang5",
+    "uei1",
+    "uei2",
+    "uei3",
+    "uei4",
+    "uei5",
+    "uen1",
+    "uen2",
+    "uen3",
+    "uen4",
+    "uen5",
+    "uo1",
+    "uo2",
+    "uo3",
+    "uo4",
+    "uo5",
+    "v1",
+    "v2",
+    "v3",
+    "v4",
+    "v5",
+    "van1",
+    "van2",
+    "van3",
+    "van4",
+    "van5",
+    "ve1",
+    "ve2",
+    "ve3",
+    "ve4",
+    "ve5",
+    "vn1",
+    "vn2",
+    "vn3",
+    "vn4",
+    "vn5",
+]
+valid_symbols = initials + finals + ["rr"]
diff --git a/text/symbol_table.py b/text/symbol_table.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d061d7b6ebc548ed41a2d883570f6ba0772790f
--- /dev/null
+++ b/text/symbol_table.py
@@ -0,0 +1,292 @@
+# Copyright      2020  Mobvoi Inc.        (authors: Fangjun Kuang)
+#
+# See ../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass
+from dataclasses import field
+from typing import Dict
+from typing import Generic
+from typing import List
+from typing import Optional
+from typing import TypeVar
+from typing import Union
+
+Symbol = TypeVar("Symbol")
+
+# SymbolTable is copied from
+# https://github.com/k2-fsa/k2/blob/master/k2/python/k2/symbol_table.py
+
+"""
+    SymbolTable: map symbol to id
+"""
+
+
+@dataclass(repr=False)
+class SymbolTable(Generic[Symbol]):
+    """SymbolTable that maps symbol IDs, found on the FSA arcs to
+    actual objects. These objects can be arbitrary Python objects
+    that can serve as keys in a dictionary (i.e. they need to be
+    hashable and immutable).
+
+    The SymbolTable can only be read to/written from disk if the
+    symbols are strings.
+    """
+
+    _id2sym: Dict[int, Symbol] = field(default_factory=dict)
+    """Map an integer to a symbol.
+    """
+
+    _sym2id: Dict[Symbol, int] = field(default_factory=dict)
+    """Map a symbol to an integer.
+    """
+
+    _next_available_id: int = 1
+    """A helper internal field that helps adding new symbols
+    to the table efficiently.
+    """
+
+    eps: Symbol = "<eps>"
+    """Null symbol, always mapped to index 0.
+    """
+
+    def __post_init__(self):
+        assert all(self._sym2id[sym] == idx for idx, sym in self._id2sym.items())
+        assert all(self._id2sym[idx] == sym for sym, idx in self._sym2id.items())
+        assert 0 not in self._id2sym or self._id2sym[0] == self.eps
+
+        self._next_available_id = max(self._id2sym, default=0) + 1
+        self._id2sym.setdefault(0, self.eps)
+        self._sym2id.setdefault(self.eps, 0)
+
+    @staticmethod
+    def from_str(s: str) -> "SymbolTable":
+        """Build a symbol table from a string.
+
+        The string consists of lines. Every line has two fields separated
+        by space(s), tab(s) or both. The first field is the symbol and the
+        second the integer id of the symbol.
+
+        Args:
+          s:
+            The input string with the format described above.
+        Returns:
+          An instance of :class:`SymbolTable`.
+        """
+        id2sym: Dict[int, str] = dict()
+        sym2id: Dict[str, int] = dict()
+
+        for line in s.split("\n"):
+            fields = line.split()
+            if len(fields) == 0:
+                continue  # skip empty lines
+            assert (
+                len(fields) == 2
+            ), f"Expect a line with 2 fields. Given: {len(fields)}"
+            sym, idx = fields[0], int(fields[1])
+            assert sym not in sym2id, f"Duplicated symbol {sym}"
+            assert idx not in id2sym, f"Duplicated id {idx}"
+            id2sym[idx] = sym
+            sym2id[sym] = idx
+
+        eps = id2sym.get(0, "<eps>")
+
+        return SymbolTable(_id2sym=id2sym, _sym2id=sym2id, eps=eps)
+
+    @staticmethod
+    def from_file(filename: str) -> "SymbolTable":
+        """Build a symbol table from file.
+
+        Every line in the symbol table file has two fields separated by
+        space(s), tab(s) or both. The following is an example file:
+
+        .. code-block::
+
+            <eps> 0
+            a 1
+            b 2
+            c 3
+
+        Args:
+          filename:
+            Name of the symbol table file. Its format is documented above.
+
+        Returns:
+          An instance of :class:`SymbolTable`.
+
+        """
+        with open(filename, "r", encoding="utf-8") as f:
+            return SymbolTable.from_str(f.read().strip())
+
+    def to_str(self) -> str:
+        """
+        Returns:
+          Return a string representation of this object. You can pass
+          it to the method ``from_str`` to recreate an identical object.
+        """
+        s = ""
+        for idx, symbol in sorted(self._id2sym.items()):
+            s += f"{symbol} {idx}\n"
+        return s
+
+    def to_file(self, filename: str):
+        """Serialize the SymbolTable to a file.
+
+        Every line in the symbol table file has two fields separated by
+        space(s), tab(s) or both. The following is an example file:
+
+        .. code-block::
+
+            <eps> 0
+            a 1
+            b 2
+            c 3
+
+        Args:
+          filename:
+            Name of the symbol table file. Its format is documented above.
+        """
+        with open(filename, "w") as f:
+            for idx, symbol in sorted(self._id2sym.items()):
+                print(symbol, idx, file=f)
+
+    def add(self, symbol: Symbol, index: Optional[int] = None) -> int:
+        """Add a new symbol to the SymbolTable.
+
+        Args:
+            symbol:
+                The symbol to be added.
+            index:
+                Optional int id to which the symbol should be assigned.
+                If it is not available, a ValueError will be raised.
+
+        Returns:
+            The int id to which the symbol has been assigned.
+        """
+        # Already in the table? Return its ID.
+        if symbol in self._sym2id:
+            return self._sym2id[symbol]
+        # Specific ID not provided - use next available.
+        if index is None:
+            index = self._next_available_id
+        # Specific ID provided but not available.
+        if index in self._id2sym:
+            raise ValueError(
+                f"Cannot assign id '{index}' to '{symbol}' - "
+                f"already occupied by {self._id2sym[index]}"
+            )
+        self._sym2id[symbol] = index
+        self._id2sym[index] = symbol
+
+        # Update next available ID if needed
+        if self._next_available_id <= index:
+            self._next_available_id = index + 1
+
+        return index
+
+    def get(self, k: Union[int, Symbol]) -> Union[Symbol, int]:
+        """Get a symbol for an id or get an id for a symbol
+
+        Args:
+          k:
+            If it is an id, it tries to find the symbol corresponding
+            to the id; if it is a symbol, it tries to find the id
+            corresponding to the symbol.
+
+        Returns:
+          An id or a symbol depending on the given `k`.
+        """
+        if isinstance(k, int):
+            return self._id2sym[k]
+        else:
+            return self._sym2id[k]
+
+    def merge(self, other: "SymbolTable") -> "SymbolTable":
+        """Create a union of two SymbolTables.
+        Raises an AssertionError if the same IDs are occupied by
+        different symbols.
+
+        Args:
+            other:
+                A symbol table to merge with ``self``.
+
+        Returns:
+            A new symbol table.
+        """
+        self._check_compatible(other)
+        return SymbolTable(
+            _id2sym={**self._id2sym, **other._id2sym},
+            _sym2id={**self._sym2id, **other._sym2id},
+            eps=self.eps,
+        )
+
+    def _check_compatible(self, other: "SymbolTable") -> None:
+        # Epsilon compatibility
+        assert self.eps == other.eps, (
+            f"Mismatched epsilon symbol: " f"{self.eps} != {other.eps}"
+        )
+        # IDs compatibility
+        common_ids = set(self._id2sym).intersection(other._id2sym)
+        for idx in common_ids:
+            assert self[idx] == other[idx], (
+                f"ID conflict for id: {idx}, "
+                f'self[idx] = "{self[idx]}", '
+                f'other[idx] = "{other[idx]}"'
+            )
+        # Symbols compatibility
+        common_symbols = set(self._sym2id).intersection(other._sym2id)
+        for sym in common_symbols:
+            assert self[sym] == other[sym], (
+                f"ID conflict for id: {sym}, "
+                f'self[sym] = "{self[sym]}", '
+                f'other[sym] = "{other[sym]}"'
+            )
+
+    def __getitem__(self, item: Union[int, Symbol]) -> Union[Symbol, int]:
+        return self.get(item)
+
+    def __contains__(self, item: Union[int, Symbol]) -> bool:
+        if isinstance(item, int):
+            return item in self._id2sym
+        else:
+            return item in self._sym2id
+
+    def __len__(self) -> int:
+        return len(self._id2sym)
+
+    def __eq__(self, other: "SymbolTable") -> bool:
+        if len(self) != len(other):
+            return False
+
+        for s in self.symbols:
+            if self[s] != other[s]:
+                return False
+
+        return True
+
+    @property
+    def ids(self) -> List[int]:
+        """Returns a list of integer IDs corresponding to the symbols."""
+        ans = list(self._id2sym.keys())
+        ans.sort()
+        return ans
+
+    @property
+    def symbols(self) -> List[Symbol]:
+        """Returns a list of symbols (e.g., strings) corresponding to
+        the integer IDs.
+        """
+        ans = list(self._sym2id.keys())
+        ans.sort()
+        return ans
diff --git a/text/symbols.py b/text/symbols.py
new file mode 100644
index 0000000000000000000000000000000000000000..a75cebd57c090c0827284a3e8885e17b42831ed2
--- /dev/null
+++ b/text/symbols.py
@@ -0,0 +1,34 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+""" This code is modified from https://github.com/keithito/tacotron """
+
+"""
+Defines the set of symbols used in text input to the model.
+
+The default is a set of ASCII characters that works well for English or text that has been run through Unidecode. For other data, you can modify _characters. See TRAINING_DATA.md for details. """
+
+from text import cmudict, pinyin
+
+_pad = "_"
+_punctuation = "!'(),.:;? "
+_special = "-"
+_letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
+_silences = ["@sp", "@spn", "@sil"]
+
+# Prepend "@" to ARPAbet symbols to ensure uniqueness (some are the same as uppercase letters):
+_arpabet = ["@" + s for s in cmudict.valid_symbols]
+_pinyin = ["@" + s for s in pinyin.valid_symbols]
+
+# Export all symbols:
+symbols = (
+    [_pad]
+    + list(_special)
+    + list(_punctuation)
+    + list(_letters)
+    + _arpabet
+    + _silences
+    # + _pinyin # for chinese
+)
diff --git a/text/text_token_collation.py b/text/text_token_collation.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c19e308ad6a79b31155b90c88a0cd0d9481555a
--- /dev/null
+++ b/text/text_token_collation.py
@@ -0,0 +1,123 @@
+# Copyright (c) 2023 Amphion.
+
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from pathlib import Path
+from typing import List, Tuple
+import os
+import numpy as np
+import torch
+from text.symbol_table import SymbolTable
+from text import text_to_sequence
+
+
+"""
+    TextToken: map text to id
+"""
+
+
+# TextTokenCollator is modified from
+# https://github.com/lifeiteng/vall-e/blob/9c69096d603ce13174fb5cb025f185e2e9b36ac7/valle/data/collation.py
+class TextTokenCollator:
+    def __init__(
+        self,
+        text_tokens: List[str],
+        add_eos: bool = True,
+        add_bos: bool = True,
+        pad_symbol: str = "<pad>",
+        bos_symbol: str = "<bos>",
+        eos_symbol: str = "<eos>",
+    ):
+        self.pad_symbol = pad_symbol
+        self.add_eos = add_eos
+        self.add_bos = add_bos
+        self.bos_symbol = bos_symbol
+        self.eos_symbol = eos_symbol
+
+        unique_tokens = [pad_symbol]
+        if add_bos:
+            unique_tokens.append(bos_symbol)
+        if add_eos:
+            unique_tokens.append(eos_symbol)
+        unique_tokens.extend(sorted(text_tokens))
+
+        self.token2idx = {token: idx for idx, token in enumerate(unique_tokens)}
+        self.idx2token = unique_tokens
+
+    def index(self, tokens_list: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:
+        seqs, seq_lens = [], []
+        for tokens in tokens_list:
+            assert all([True if s in self.token2idx else False for s in tokens]) is True
+            seq = (
+                ([self.bos_symbol] if self.add_bos else [])
+                + list(tokens)
+                + ([self.eos_symbol] if self.add_eos else [])
+            )
+            seqs.append(seq)
+            seq_lens.append(len(seq))
+
+        max_len = max(seq_lens)
+        for k, (seq, seq_len) in enumerate(zip(seqs, seq_lens)):
+            seq.extend([self.pad_symbol] * (max_len - seq_len))
+
+        tokens = torch.from_numpy(
+            np.array(
+                [[self.token2idx[token] for token in seq] for seq in seqs],
+                dtype=np.int64,
+            )
+        )
+        tokens_lens = torch.IntTensor(seq_lens)
+
+        return tokens, tokens_lens
+
+    def __call__(self, text):
+        tokens_seq = [p for p in text]
+        seq = (
+            ([self.bos_symbol] if self.add_bos else [])
+            + tokens_seq
+            + ([self.eos_symbol] if self.add_eos else [])
+        )
+
+        token_ids = [self.token2idx[token] for token in seq]
+        token_lens = len(tokens_seq) + self.add_eos + self.add_bos
+
+        return token_ids, token_lens
+
+
+def get_text_token_collater(text_tokens_file: str) -> TextTokenCollator:
+    text_tokens_path = Path(text_tokens_file)
+    unique_tokens = SymbolTable.from_file(text_tokens_path)
+    collater = TextTokenCollator(unique_tokens.symbols, add_bos=True, add_eos=True)
+    token2idx = collater.token2idx
+    return collater, token2idx
+
+
+class phoneIDCollation:
+    def __init__(self, cfg, dataset=None, symbols_dict_file=None) -> None:
+        if cfg.preprocess.phone_extractor != "lexicon":
+            ### get text token collator
+            if symbols_dict_file is None:
+                assert dataset is not None
+                symbols_dict_file = os.path.join(
+                    cfg.preprocess.processed_dir, dataset, cfg.preprocess.symbols_dict
+                )
+            self.text_token_colloator, token2idx = get_text_token_collater(
+                symbols_dict_file
+            )
+            # # unique_tokens = SymbolTable.from_file(symbols_dict_path)
+            # # text_tokenizer = TextToken(unique_tokens.symbols, add_bos=True, add_eos=True)
+
+            # # update phone symbols dict file with pad_symbol or optional tokens (add_bos and add_eos) in TextTokenCollator
+            # phone_symbol_dict = SymbolTable()
+            # for s in sorted(list(set(token2idx.keys()))):
+            #     phone_symbol_dict.add(s)
+            # phone_symbol_dict.to_file(symbols_dict_file)
+
+    def get_phone_id_sequence(self, cfg, phones_seq):
+        if cfg.preprocess.phone_extractor == "lexicon":
+            phones_seq = " ".join(phones_seq)
+            sequence = text_to_sequence(phones_seq, cfg.preprocess.text_cleaners)
+        else:
+            sequence, seq_len = self.text_token_colloator(phones_seq)
+        return sequence
diff --git a/utils/HyperParams/__init__.py b/utils/HyperParams/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..706e31b1c499f11548d08d38e1c3091aeb2dadaa
--- /dev/null
+++ b/utils/HyperParams/__init__.py
@@ -0,0 +1,6 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .hps import HyperParams
diff --git a/utils/HyperParams/hps.py b/utils/HyperParams/hps.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc6f474c4e28d092ea8ba2cdcf7233322c5ff281
--- /dev/null
+++ b/utils/HyperParams/hps.py
@@ -0,0 +1,43 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+class HyperParams:
+    """The class to store hyperparameters. The key is case-insensitive.
+
+    Args:
+        *args: a list of dict or HyperParams.
+        **kwargs: a list of key-value pairs.
+    """
+
+    def __init__(self, **kwargs):
+        for k, v in kwargs.items():
+            if type(v) == dict:
+                v = HyperParams(**v)
+            self[k] = v
+
+    def keys(self):
+        return self.__dict__.keys()
+
+    def items(self):
+        return self.__dict__.items()
+
+    def values(self):
+        return self.__dict__.values()
+
+    def __len__(self):
+        return len(self.__dict__)
+
+    def __getitem__(self, key):
+        return getattr(self, key)
+
+    def __setitem__(self, key, value):
+        return setattr(self, key, value)
+
+    def __contains__(self, key):
+        return key in self.__dict__
+
+    def __repr__(self):
+        return self.__dict__.__repr__()
diff --git a/utils/__init__.py b/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/utils/audio.py b/utils/audio.py
new file mode 100644
index 0000000000000000000000000000000000000000..374d50915cafa106a3035e77b99adc96e8484f0b
--- /dev/null
+++ b/utils/audio.py
@@ -0,0 +1,74 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import numpy as np
+from numpy import linalg as LA
+import librosa
+import soundfile as sf
+import librosa.filters
+
+
+def load_audio_torch(wave_file, fs):
+    """Load audio data into torch tensor
+
+    Args:
+        wave_file (str): path to wave file
+        fs (int): sample rate
+
+    Returns:
+        audio (tensor): audio data in tensor
+        fs (int): sample rate
+    """
+
+    audio, sample_rate = librosa.load(wave_file, sr=fs, mono=True)
+    # audio: (T,)
+    assert len(audio) > 2
+
+    # Check the audio type (for soundfile loading backbone) - float, 8bit or 16bit
+    if np.issubdtype(audio.dtype, np.integer):
+        max_mag = -np.iinfo(audio.dtype).min
+    else:
+        max_mag = max(np.amax(audio), -np.amin(audio))
+        max_mag = (
+            (2**31) + 1
+            if max_mag > (2**15)
+            else ((2**15) + 1 if max_mag > 1.01 else 1.0)
+        )
+
+    # Normalize the audio
+    audio = torch.FloatTensor(audio.astype(np.float32)) / max_mag
+
+    if (torch.isnan(audio) | torch.isinf(audio)).any():
+        return [], sample_rate or fs or 48000
+
+    # Resample the audio to our target samplerate
+    if fs is not None and fs != sample_rate:
+        audio = torch.from_numpy(
+            librosa.core.resample(audio.numpy(), orig_sr=sample_rate, target_sr=fs)
+        )
+        sample_rate = fs
+
+    return audio, fs
+
+
+def _stft(y, cfg):
+    return librosa.stft(
+        y=y, n_fft=cfg.n_fft, hop_length=cfg.hop_size, win_length=cfg.win_size
+    )
+
+
+def energy(wav, cfg):
+    D = _stft(wav, cfg)
+    magnitudes = np.abs(D).T  # [F, T]
+    return LA.norm(magnitudes, axis=1)
+
+
+def get_energy_from_tacotron(audio, _stft):
+    audio = torch.clip(torch.FloatTensor(audio).unsqueeze(0), -1, 1)
+    audio = torch.autograd.Variable(audio, requires_grad=False)
+    mel, energy = _stft.mel_spectrogram(audio)
+    energy = torch.squeeze(energy, 0).numpy().astype(np.float32)
+    return mel, energy
diff --git a/utils/audio_slicer.py b/utils/audio_slicer.py
new file mode 100644
index 0000000000000000000000000000000000000000..28474596b42c8f8215b878a80967112960d0c9e0
--- /dev/null
+++ b/utils/audio_slicer.py
@@ -0,0 +1,476 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import json
+import numpy as np
+from tqdm import tqdm
+import torch
+import torchaudio
+
+from utils.io import save_audio
+from utils.audio import load_audio_torch
+
+
+# This function is obtained from librosa.
+def get_rms(
+    y,
+    *,
+    frame_length=2048,
+    hop_length=512,
+    pad_mode="constant",
+):
+    padding = (int(frame_length // 2), int(frame_length // 2))
+    y = np.pad(y, padding, mode=pad_mode)
+
+    axis = -1
+    # put our new within-frame axis at the end for now
+    out_strides = y.strides + tuple([y.strides[axis]])
+    # Reduce the shape on the framing axis
+    x_shape_trimmed = list(y.shape)
+    x_shape_trimmed[axis] -= frame_length - 1
+    out_shape = tuple(x_shape_trimmed) + tuple([frame_length])
+    xw = np.lib.stride_tricks.as_strided(y, shape=out_shape, strides=out_strides)
+    if axis < 0:
+        target_axis = axis - 1
+    else:
+        target_axis = axis + 1
+    xw = np.moveaxis(xw, -1, target_axis)
+    # Downsample along the target axis
+    slices = [slice(None)] * xw.ndim
+    slices[axis] = slice(0, None, hop_length)
+    x = xw[tuple(slices)]
+
+    # Calculate power
+    power = np.mean(np.abs(x) ** 2, axis=-2, keepdims=True)
+
+    return np.sqrt(power)
+
+
+class Slicer:
+    """
+    Copy from: https://github.com/openvpi/audio-slicer/blob/main/slicer2.py
+    """
+
+    def __init__(
+        self,
+        sr: int,
+        threshold: float = -40.0,
+        min_length: int = 5000,
+        min_interval: int = 300,
+        hop_size: int = 10,
+        max_sil_kept: int = 5000,
+    ):
+        if not min_length >= min_interval >= hop_size:
+            raise ValueError(
+                "The following condition must be satisfied: min_length >= min_interval >= hop_size"
+            )
+        if not max_sil_kept >= hop_size:
+            raise ValueError(
+                "The following condition must be satisfied: max_sil_kept >= hop_size"
+            )
+        min_interval = sr * min_interval / 1000
+        self.threshold = 10 ** (threshold / 20.0)
+        self.hop_size = round(sr * hop_size / 1000)
+        self.win_size = min(round(min_interval), 4 * self.hop_size)
+        self.min_length = round(sr * min_length / 1000 / self.hop_size)
+        self.min_interval = round(min_interval / self.hop_size)
+        self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size)
+
+    def _apply_slice(self, waveform, begin, end):
+        begin = begin * self.hop_size
+        if len(waveform.shape) > 1:
+            end = min(waveform.shape[1], end * self.hop_size)
+            return waveform[:, begin:end], begin, end
+        else:
+            end = min(waveform.shape[0], end * self.hop_size)
+            return waveform[begin:end], begin, end
+
+    # @timeit
+    def slice(self, waveform, return_chunks_positions=False):
+        if len(waveform.shape) > 1:
+            # (#channle, wave_len) -> (wave_len)
+            samples = waveform.mean(axis=0)
+        else:
+            samples = waveform
+        if samples.shape[0] <= self.min_length:
+            return [waveform]
+        rms_list = get_rms(
+            y=samples, frame_length=self.win_size, hop_length=self.hop_size
+        ).squeeze(0)
+        sil_tags = []
+        silence_start = None
+        clip_start = 0
+        for i, rms in enumerate(rms_list):
+            # Keep looping while frame is silent.
+            if rms < self.threshold:
+                # Record start of silent frames.
+                if silence_start is None:
+                    silence_start = i
+                continue
+            # Keep looping while frame is not silent and silence start has not been recorded.
+            if silence_start is None:
+                continue
+            # Clear recorded silence start if interval is not enough or clip is too short
+            is_leading_silence = silence_start == 0 and i > self.max_sil_kept
+            need_slice_middle = (
+                i - silence_start >= self.min_interval
+                and i - clip_start >= self.min_length
+            )
+            if not is_leading_silence and not need_slice_middle:
+                silence_start = None
+                continue
+            # Need slicing. Record the range of silent frames to be removed.
+            if i - silence_start <= self.max_sil_kept:
+                pos = rms_list[silence_start : i + 1].argmin() + silence_start
+                if silence_start == 0:
+                    sil_tags.append((0, pos))
+                else:
+                    sil_tags.append((pos, pos))
+                clip_start = pos
+            elif i - silence_start <= self.max_sil_kept * 2:
+                pos = rms_list[
+                    i - self.max_sil_kept : silence_start + self.max_sil_kept + 1
+                ].argmin()
+                pos += i - self.max_sil_kept
+                pos_l = (
+                    rms_list[
+                        silence_start : silence_start + self.max_sil_kept + 1
+                    ].argmin()
+                    + silence_start
+                )
+                pos_r = (
+                    rms_list[i - self.max_sil_kept : i + 1].argmin()
+                    + i
+                    - self.max_sil_kept
+                )
+                if silence_start == 0:
+                    sil_tags.append((0, pos_r))
+                    clip_start = pos_r
+                else:
+                    sil_tags.append((min(pos_l, pos), max(pos_r, pos)))
+                    clip_start = max(pos_r, pos)
+            else:
+                pos_l = (
+                    rms_list[
+                        silence_start : silence_start + self.max_sil_kept + 1
+                    ].argmin()
+                    + silence_start
+                )
+                pos_r = (
+                    rms_list[i - self.max_sil_kept : i + 1].argmin()
+                    + i
+                    - self.max_sil_kept
+                )
+                if silence_start == 0:
+                    sil_tags.append((0, pos_r))
+                else:
+                    sil_tags.append((pos_l, pos_r))
+                clip_start = pos_r
+            silence_start = None
+        # Deal with trailing silence.
+        total_frames = rms_list.shape[0]
+        if (
+            silence_start is not None
+            and total_frames - silence_start >= self.min_interval
+        ):
+            silence_end = min(total_frames, silence_start + self.max_sil_kept)
+            pos = rms_list[silence_start : silence_end + 1].argmin() + silence_start
+            sil_tags.append((pos, total_frames + 1))
+        # Apply and return slices.
+        if len(sil_tags) == 0:
+            return [waveform]
+        else:
+            chunks = []
+            chunks_pos_of_waveform = []
+
+            if sil_tags[0][0] > 0:
+                chunk, begin, end = self._apply_slice(waveform, 0, sil_tags[0][0])
+                chunks.append(chunk)
+                chunks_pos_of_waveform.append((begin, end))
+
+            for i in range(len(sil_tags) - 1):
+                chunk, begin, end = self._apply_slice(
+                    waveform, sil_tags[i][1], sil_tags[i + 1][0]
+                )
+                chunks.append(chunk)
+                chunks_pos_of_waveform.append((begin, end))
+
+            if sil_tags[-1][1] < total_frames:
+                chunk, begin, end = self._apply_slice(
+                    waveform, sil_tags[-1][1], total_frames
+                )
+                chunks.append(chunk)
+                chunks_pos_of_waveform.append((begin, end))
+
+            return (
+                chunks
+                if not return_chunks_positions
+                else (
+                    chunks,
+                    chunks_pos_of_waveform,
+                )
+            )
+
+
+def split_utterances_from_audio(
+    wav_file,
+    output_dir,
+    max_duration_of_utterance=10.0,
+    min_interval=300,
+    db_threshold=-40,
+):
+    """
+    Split a long audio into utterances accoring to the silence (VAD).
+
+    max_duration_of_utterance (second):
+        The maximum duration of every utterance (seconds)
+    min_interval (millisecond):
+        The smaller min_interval is, the more sliced audio clips this script is likely to generate.
+    """
+    print("File:", wav_file.split("/")[-1])
+    waveform, fs = torchaudio.load(wav_file)
+
+    slicer = Slicer(sr=fs, min_interval=min_interval, threshold=db_threshold)
+    chunks, positions = slicer.slice(waveform, return_chunks_positions=True)
+
+    durations = [(end - begin) / fs for begin, end in positions]
+    print(
+        "Slicer's min silence part is {}ms, min and max duration of sliced utterances is {}s and {}s".format(
+            min_interval, min(durations), max(durations)
+        )
+    )
+
+    res_chunks, res_positions = [], []
+    for i, chunk in enumerate(chunks):
+        if len(chunk.shape) == 1:
+            chunk = chunk[None, :]
+
+        begin, end = positions[i]
+        assert end - begin == chunk.shape[-1]
+
+        max_wav_len = max_duration_of_utterance * fs
+        if chunk.shape[-1] <= max_wav_len:
+            res_chunks.append(chunk)
+            res_positions.append(positions[i])
+        else:
+            # TODO: to reserve overlapping and conduct fade-in, fade-out
+
+            # Get segments number
+            number = 2
+            while chunk.shape[-1] // number >= max_wav_len:
+                number += 1
+            seg_len = chunk.shape[-1] // number
+
+            # Split
+            for num in range(number):
+                s = seg_len * num
+                t = min(s + seg_len, chunk.shape[-1])
+
+                seg_begin = begin + s
+                seg_end = begin + t
+
+                res_chunks.append(chunk[:, s:t])
+                res_positions.append((seg_begin, seg_end))
+
+    # Save utterances
+    os.makedirs(output_dir, exist_ok=True)
+    res = {"fs": int(fs)}
+    for i, chunk in enumerate(res_chunks):
+        filename = "{:04d}.wav".format(i)
+        res[filename] = [int(p) for p in res_positions[i]]
+        save_audio(os.path.join(output_dir, filename), chunk, fs)
+
+    # Save positions
+    with open(os.path.join(output_dir, "positions.json"), "w") as f:
+        json.dump(res, f, indent=4, ensure_ascii=False)
+    return res
+
+
+def is_silence(
+    wavform,
+    fs,
+    threshold=-40.0,
+    min_interval=300,
+    hop_size=10,
+    min_length=5000,
+):
+    """
+    Detect whether the given wavform is a silence
+
+    wavform: (T, )
+    """
+    threshold = 10 ** (threshold / 20.0)
+
+    hop_size = round(fs * hop_size / 1000)
+    win_size = min(round(min_interval), 4 * hop_size)
+    min_length = round(fs * min_length / 1000 / hop_size)
+
+    if wavform.shape[0] <= min_length:
+        return True
+
+    # (#Frame,)
+    rms_array = get_rms(y=wavform, frame_length=win_size, hop_length=hop_size).squeeze(
+        0
+    )
+    return (rms_array < threshold).all()
+
+
+def split_audio(
+    wav_file, target_sr, output_dir, max_duration_of_segment=10.0, overlap_duration=1.0
+):
+    """
+    Split a long audio into segments.
+
+    target_sr:
+        The target sampling rate to save the segments.
+    max_duration_of_utterance (second):
+        The maximum duration of every utterance (second)
+    overlap_duraion:
+        Each segment has "overlap duration" (second) overlap with its previous and next segment
+    """
+    # (#channel, T) -> (T,)
+    waveform, fs = torchaudio.load(wav_file)
+    waveform = torchaudio.functional.resample(
+        waveform, orig_freq=fs, new_freq=target_sr
+    )
+    waveform = torch.mean(waveform, dim=0)
+
+    # waveform, _ = load_audio_torch(wav_file, target_sr)
+    assert len(waveform.shape) == 1
+
+    assert overlap_duration < max_duration_of_segment
+    length = int(max_duration_of_segment * target_sr)
+    stride = int((max_duration_of_segment - overlap_duration) * target_sr)
+    chunks = []
+    for i in range(0, len(waveform), stride):
+        # (length,)
+        chunks.append(waveform[i : i + length])
+        if i + length >= len(waveform):
+            break
+
+    # Save segments
+    os.makedirs(output_dir, exist_ok=True)
+    results = []
+    for i, chunk in enumerate(chunks):
+        uid = "{:04d}".format(i)
+        filename = os.path.join(output_dir, "{}.wav".format(uid))
+        results.append(
+            {"Uid": uid, "Path": filename, "Duration": len(chunk) / target_sr}
+        )
+        save_audio(
+            filename,
+            chunk,
+            target_sr,
+            turn_up=not is_silence(chunk, target_sr),
+            add_silence=False,
+        )
+
+    return results
+
+
+def merge_segments_torchaudio(wav_files, fs, output_path, overlap_duration=1.0):
+    """Merge the given wav_files (may have overlaps) into a long audio
+
+    fs:
+        The sampling rate of the wav files.
+    output_path:
+        The output path to save the merged audio.
+    overlap_duration (float, optional):
+        Each segment has "overlap duration" (second) overlap with its previous and next segment. Defaults to 1.0.
+    """
+
+    waveforms = []
+    for file in wav_files:
+        # (T,)
+        waveform, _ = load_audio_torch(file, fs)
+        waveforms.append(waveform)
+
+    if len(waveforms) == 1:
+        save_audio(output_path, waveforms[0], fs, add_silence=False, turn_up=False)
+        return
+
+    overlap_len = int(overlap_duration * fs)
+    fade_out = torchaudio.transforms.Fade(fade_out_len=overlap_len)
+    fade_in = torchaudio.transforms.Fade(fade_in_len=overlap_len)
+    fade_in_and_out = torchaudio.transforms.Fade(fade_out_len=overlap_len)
+
+    segments_lens = [len(wav) for wav in waveforms]
+    merged_waveform_len = sum(segments_lens) - overlap_len * (len(waveforms) - 1)
+    merged_waveform = torch.zeros(merged_waveform_len)
+
+    start = 0
+    for index, wav in enumerate(
+        tqdm(waveforms, desc="Merge for {}".format(output_path))
+    ):
+        wav_len = len(wav)
+
+        if index == 0:
+            wav = fade_out(wav)
+        elif index == len(waveforms) - 1:
+            wav = fade_in(wav)
+        else:
+            wav = fade_in_and_out(wav)
+
+        merged_waveform[start : start + wav_len] = wav
+        start += wav_len - overlap_len
+
+    save_audio(output_path, merged_waveform, fs, add_silence=False, turn_up=True)
+
+
+def merge_segments_encodec(wav_files, fs, output_path, overlap_duration=1.0):
+    """Merge the given wav_files (may have overlaps) into a long audio
+
+    fs:
+        The sampling rate of the wav files.
+    output_path:
+        The output path to save the merged audio.
+    overlap_duration (float, optional):
+        Each segment has "overlap duration" (second) overlap with its previous and next segment. Defaults to 1.0.
+    """
+
+    waveforms = []
+    for file in wav_files:
+        # (T,)
+        waveform, _ = load_audio_torch(file, fs)
+        waveforms.append(waveform)
+
+    if len(waveforms) == 1:
+        save_audio(output_path, waveforms[0], fs, add_silence=False, turn_up=False)
+        return
+
+    device = waveforms[0].device
+    dtype = waveforms[0].dtype
+    shape = waveforms[0].shape[:-1]
+
+    overlap_len = int(overlap_duration * fs)
+    segments_lens = [len(wav) for wav in waveforms]
+    merged_waveform_len = sum(segments_lens) - overlap_len * (len(waveforms) - 1)
+
+    sum_weight = torch.zeros(merged_waveform_len, device=device, dtype=dtype)
+    out = torch.zeros(*shape, merged_waveform_len, device=device, dtype=dtype)
+    offset = 0
+
+    for frame in waveforms:
+        frame_length = frame.size(-1)
+        t = torch.linspace(0, 1, frame_length + 2, device=device, dtype=torch.float32)[
+            1:-1
+        ]
+        weight = 0.5 - (t - 0.5).abs()
+        weighted_frame = frame * weight
+
+        cur = out[..., offset : offset + frame_length]
+        cur += weighted_frame[..., : cur.size(-1)]
+        out[..., offset : offset + frame_length] = cur
+
+        cur = sum_weight[offset : offset + frame_length]
+        cur += weight[..., : cur.size(-1)]
+        sum_weight[offset : offset + frame_length] = cur
+
+        offset += frame_length - overlap_len
+
+    assert sum_weight.min() > 0
+    merged_waveform = out / sum_weight
+    save_audio(output_path, merged_waveform, fs, add_silence=False, turn_up=True)
diff --git a/utils/data_utils.py b/utils/data_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..7976d050f01990c8a98a37d48be67bc68695a6c3
--- /dev/null
+++ b/utils/data_utils.py
@@ -0,0 +1,575 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import json
+import os
+
+import numpy as np
+from scipy.interpolate import interp1d
+from tqdm import tqdm
+from sklearn.preprocessing import StandardScaler
+
+
+def load_content_feature_path(meta_data, processed_dir, feat_dir):
+    utt2feat_path = {}
+    for utt_info in meta_data:
+        utt = utt_info["Dataset"] + "_" + utt_info["Uid"]
+        feat_path = os.path.join(
+            processed_dir, utt_info["Dataset"], feat_dir, f'{utt_info["Uid"]}.npy'
+        )
+        utt2feat_path[utt] = feat_path
+
+    return utt2feat_path
+
+
+def load_source_content_feature_path(meta_data, feat_dir):
+    utt2feat_path = {}
+    for utt in meta_data:
+        feat_path = os.path.join(feat_dir, f"{utt}.npy")
+        utt2feat_path[utt] = feat_path
+
+    return utt2feat_path
+
+
+def get_spk_map(spk2id_path, utt2spk_path):
+    utt2spk = {}
+    with open(spk2id_path, "r") as spk2id_file:
+        spk2id = json.load(spk2id_file)
+    with open(utt2spk_path, encoding="utf-8") as f:
+        for line in f.readlines():
+            utt, spk = line.strip().split("\t")
+            utt2spk[utt] = spk
+    return spk2id, utt2spk
+
+
+def get_target_f0_median(f0_dir):
+    total_f0 = []
+    for utt in os.listdir(f0_dir):
+        if not utt.endswith(".npy"):
+            continue
+        f0_feat_path = os.path.join(f0_dir, utt)
+        f0 = np.load(f0_feat_path)
+        total_f0 += f0.tolist()
+
+    total_f0 = np.array(total_f0)
+    voiced_position = np.where(total_f0 != 0)
+    return np.median(total_f0[voiced_position])
+
+
+def get_conversion_f0_factor(source_f0, target_median, source_median=None):
+    """Align the median between source f0 and target f0
+
+    Note: Here we use multiplication, whose factor is target_median/source_median
+
+    Reference: Frequency and pitch interval
+    http://blog.ccyg.studio/article/be12c2ee-d47c-4098-9782-ca76da3035e4/
+    """
+    if source_median is None:
+        voiced_position = np.where(source_f0 != 0)
+        source_median = np.median(source_f0[voiced_position])
+    factor = target_median / source_median
+    return source_median, factor
+
+
+def transpose_key(frame_pitch, trans_key):
+    # Transpose by user's argument
+    print("Transpose key = {} ...\n".format(trans_key))
+
+    transed_pitch = frame_pitch * 2 ** (trans_key / 12)
+    return transed_pitch
+
+
+def pitch_shift_to_target(frame_pitch, target_pitch_median, source_pitch_median=None):
+    # Loading F0 Base (median) and shift
+    source_pitch_median, factor = get_conversion_f0_factor(
+        frame_pitch, target_pitch_median, source_pitch_median
+    )
+    print(
+        "Auto transposing: source f0 median = {:.1f}, target f0 median = {:.1f}, factor = {:.2f}".format(
+            source_pitch_median, target_pitch_median, factor
+        )
+    )
+    transed_pitch = frame_pitch * factor
+    return transed_pitch
+
+
+def load_frame_pitch(
+    meta_data,
+    processed_dir,
+    pitch_dir,
+    use_log_scale=False,
+    return_norm=False,
+    interoperate=False,
+    utt2spk=None,
+):
+    utt2pitch = {}
+    utt2uv = {}
+    if utt2spk is None:
+        pitch_scaler = StandardScaler()
+        for utt_info in meta_data:
+            utt = utt_info["Dataset"] + "_" + utt_info["Uid"]
+            pitch_path = os.path.join(
+                processed_dir, utt_info["Dataset"], pitch_dir, f'{utt_info["Uid"]}.npy'
+            )
+            pitch = np.load(pitch_path)
+            assert len(pitch) > 0
+            uv = pitch != 0
+            utt2uv[utt] = uv
+            if use_log_scale:
+                nonzero_idxes = np.where(pitch != 0)[0]
+                pitch[nonzero_idxes] = np.log(pitch[nonzero_idxes])
+            utt2pitch[utt] = pitch
+            pitch_scaler.partial_fit(pitch.reshape(-1, 1))
+
+        mean, std = pitch_scaler.mean_[0], pitch_scaler.scale_[0]
+        if return_norm:
+            for utt_info in meta_data:
+                utt = utt_info["Dataset"] + "_" + utt_info["Uid"]
+                pitch = utt2pitch[utt]
+                normalized_pitch = (pitch - mean) / std
+                utt2pitch[utt] = normalized_pitch
+        pitch_statistic = {"mean": mean, "std": std}
+    else:
+        spk2utt = {}
+        pitch_statistic = []
+        for utt_info in meta_data:
+            utt = utt_info["Dataset"] + "_" + utt_info["Uid"]
+            if not utt2spk[utt] in spk2utt:
+                spk2utt[utt2spk[utt]] = []
+            spk2utt[utt2spk[utt]].append(utt)
+
+        for spk in spk2utt:
+            pitch_scaler = StandardScaler()
+            for utt in spk2utt[spk]:
+                dataset = utt.split("_")[0]
+                uid = "_".join(utt.split("_")[1:])
+                pitch_path = os.path.join(
+                    processed_dir, dataset, pitch_dir, f"{uid}.npy"
+                )
+                pitch = np.load(pitch_path)
+                assert len(pitch) > 0
+                uv = pitch != 0
+                utt2uv[utt] = uv
+                if use_log_scale:
+                    nonzero_idxes = np.where(pitch != 0)[0]
+                    pitch[nonzero_idxes] = np.log(pitch[nonzero_idxes])
+                utt2pitch[utt] = pitch
+                pitch_scaler.partial_fit(pitch.reshape(-1, 1))
+
+            mean, std = pitch_scaler.mean_[0], pitch_scaler.scale_[0]
+            if return_norm:
+                for utt in spk2utt[spk]:
+                    pitch = utt2pitch[utt]
+                    normalized_pitch = (pitch - mean) / std
+                    utt2pitch[utt] = normalized_pitch
+            pitch_statistic.append({"spk": spk, "mean": mean, "std": std})
+
+    return utt2pitch, utt2uv, pitch_statistic
+
+
+# discard
+def load_phone_pitch(
+    meta_data,
+    processed_dir,
+    pitch_dir,
+    utt2dur,
+    use_log_scale=False,
+    return_norm=False,
+    interoperate=True,
+    utt2spk=None,
+):
+    print("Load Phone Pitch")
+    utt2pitch = {}
+    utt2uv = {}
+    if utt2spk is None:
+        pitch_scaler = StandardScaler()
+        for utt_info in tqdm(meta_data):
+            utt = utt_info["Dataset"] + "_" + utt_info["Uid"]
+            pitch_path = os.path.join(
+                processed_dir, utt_info["Dataset"], pitch_dir, f'{utt_info["Uid"]}.npy'
+            )
+            frame_pitch = np.load(pitch_path)
+            assert len(frame_pitch) > 0
+            uv = frame_pitch != 0
+            utt2uv[utt] = uv
+            phone_pitch = phone_average_pitch(frame_pitch, utt2dur[utt], interoperate)
+            if use_log_scale:
+                nonzero_idxes = np.where(phone_pitch != 0)[0]
+                phone_pitch[nonzero_idxes] = np.log(phone_pitch[nonzero_idxes])
+            utt2pitch[utt] = phone_pitch
+            pitch_scaler.partial_fit(remove_outlier(phone_pitch).reshape(-1, 1))
+
+        mean, std = pitch_scaler.mean_[0], pitch_scaler.scale_[0]
+        max_value = np.finfo(np.float64).min
+        min_value = np.finfo(np.float64).max
+        if return_norm:
+            for utt_info in meta_data:
+                utt = utt_info["Dataset"] + "_" + utt_info["Uid"]
+                pitch = utt2pitch[utt]
+                normalized_pitch = (pitch - mean) / std
+                max_value = max(max_value, max(normalized_pitch))
+                min_value = min(min_value, min(normalized_pitch))
+                utt2pitch[utt] = normalized_pitch
+                phone_normalized_pitch_path = os.path.join(
+                    processed_dir,
+                    utt_info["Dataset"],
+                    "phone_level_" + pitch_dir,
+                    f'{utt_info["Uid"]}.npy',
+                )
+        pitch_statistic = {
+            "mean": mean,
+            "std": std,
+            "min_value": min_value,
+            "max_value": max_value,
+        }
+    else:
+        spk2utt = {}
+        pitch_statistic = []
+        for utt_info in tqdm(meta_data):
+            utt = utt_info["Dataset"] + "_" + utt_info["Uid"]
+            if not utt2spk[utt] in spk2utt:
+                spk2utt[utt2spk[utt]] = []
+            spk2utt[utt2spk[utt]].append(utt)
+
+        for spk in spk2utt:
+            pitch_scaler = StandardScaler()
+            for utt in spk2utt[spk]:
+                dataset = utt.split("_")[0]
+                uid = "_".join(utt.split("_")[1:])
+                pitch_path = os.path.join(
+                    processed_dir, dataset, pitch_dir, f"{uid}.npy"
+                )
+                frame_pitch = np.load(pitch_path)
+                assert len(frame_pitch) > 0
+                uv = frame_pitch != 0
+                utt2uv[utt] = uv
+                phone_pitch = phone_average_pitch(
+                    frame_pitch, utt2dur[utt], interoperate
+                )
+                if use_log_scale:
+                    nonzero_idxes = np.where(phone_pitch != 0)[0]
+                    phone_pitch[nonzero_idxes] = np.log(phone_pitch[nonzero_idxes])
+                utt2pitch[utt] = phone_pitch
+                pitch_scaler.partial_fit(remove_outlier(phone_pitch).reshape(-1, 1))
+
+            mean, std = pitch_scaler.mean_[0], pitch_scaler.scale_[0]
+            max_value = np.finfo(np.float64).min
+            min_value = np.finfo(np.float64).max
+
+            if return_norm:
+                for utt in spk2utt[spk]:
+                    pitch = utt2pitch[utt]
+                    normalized_pitch = (pitch - mean) / std
+                    max_value = max(max_value, max(normalized_pitch))
+                    min_value = min(min_value, min(normalized_pitch))
+                    utt2pitch[utt] = normalized_pitch
+            pitch_statistic.append(
+                {
+                    "spk": spk,
+                    "mean": mean,
+                    "std": std,
+                    "min_value": min_value,
+                    "max_value": max_value,
+                }
+            )
+
+    return utt2pitch, utt2uv, pitch_statistic
+
+
+def phone_average_pitch(pitch, dur, interoperate=False):
+    pos = 0
+
+    if interoperate:
+        nonzero_ids = np.where(pitch != 0)[0]
+        interp_fn = interp1d(
+            nonzero_ids,
+            pitch[nonzero_ids],
+            fill_value=(pitch[nonzero_ids[0]], pitch[nonzero_ids[-1]]),
+            bounds_error=False,
+        )
+        pitch = interp_fn(np.arange(0, len(pitch)))
+    phone_pitch = np.zeros(len(dur))
+
+    for i, d in enumerate(dur):
+        d = int(d)
+        if d > 0 and pos < len(pitch):
+            phone_pitch[i] = np.mean(pitch[pos : pos + d])
+        else:
+            phone_pitch[i] = 0
+        pos += d
+    return phone_pitch
+
+
+def load_energy(
+    meta_data,
+    processed_dir,
+    energy_dir,
+    use_log_scale=False,
+    return_norm=False,
+    utt2spk=None,
+):
+    utt2energy = {}
+    if utt2spk is None:
+        for utt_info in meta_data:
+            utt = utt_info["Dataset"] + "_" + utt_info["Uid"]
+            energy_path = os.path.join(
+                processed_dir, utt_info["Dataset"], energy_dir, f'{utt_info["Uid"]}.npy'
+            )
+            if not os.path.exists(energy_path):
+                continue
+            energy = np.load(energy_path)
+            assert len(energy) > 0
+
+            if use_log_scale:
+                nonzero_idxes = np.where(energy != 0)[0]
+                energy[nonzero_idxes] = np.log(energy[nonzero_idxes])
+            utt2energy[utt] = energy
+
+        if return_norm:
+            with open(
+                os.path.join(
+                    processed_dir, utt_info["Dataset"], energy_dir, "statistics.json"
+                )
+            ) as f:
+                stats = json.load(f)
+                mean, std = (
+                    stats[utt_info["Dataset"] + "_" + utt_info["Singer"]][
+                        "voiced_positions"
+                    ]["mean"],
+                    stats["LJSpeech_LJSpeech"]["voiced_positions"]["std"],
+                )
+            for utt in utt2energy.keys():
+                energy = utt2energy[utt]
+                normalized_energy = (energy - mean) / std
+                utt2energy[utt] = normalized_energy
+
+        energy_statistic = {"mean": mean, "std": std}
+    else:
+        spk2utt = {}
+        energy_statistic = []
+        for utt_info in meta_data:
+            utt = utt_info["Dataset"] + "_" + utt_info["Uid"]
+            if not utt2spk[utt] in spk2utt:
+                spk2utt[utt2spk[utt]] = []
+            spk2utt[utt2spk[utt]].append(utt)
+
+        for spk in spk2utt:
+            energy_scaler = StandardScaler()
+            for utt in spk2utt[spk]:
+                dataset = utt.split("_")[0]
+                uid = "_".join(utt.split("_")[1:])
+                energy_path = os.path.join(
+                    processed_dir, dataset, energy_dir, f"{uid}.npy"
+                )
+                if not os.path.exists(energy_path):
+                    continue
+                frame_energy = np.load(energy_path)
+                assert len(frame_energy) > 0
+
+                if use_log_scale:
+                    nonzero_idxes = np.where(frame_energy != 0)[0]
+                    frame_energy[nonzero_idxes] = np.log(frame_energy[nonzero_idxes])
+                utt2energy[utt] = frame_energy
+                energy_scaler.partial_fit(frame_energy.reshape(-1, 1))
+
+            mean, std = energy_scaler.mean_[0], energy_scaler.scale_[0]
+            if return_norm:
+                for utt in spk2utt[spk]:
+                    energy = utt2energy[utt]
+                    normalized_energy = (energy - mean) / std
+                    utt2energy[utt] = normalized_energy
+            energy_statistic.append({"spk": spk, "mean": mean, "std": std})
+
+    return utt2energy, energy_statistic
+
+
+def load_frame_energy(
+    meta_data,
+    processed_dir,
+    energy_dir,
+    use_log_scale=False,
+    return_norm=False,
+    interoperate=False,
+    utt2spk=None,
+):
+    utt2energy = {}
+    if utt2spk is None:
+        energy_scaler = StandardScaler()
+        for utt_info in meta_data:
+            utt = utt_info["Dataset"] + "_" + utt_info["Uid"]
+            energy_path = os.path.join(
+                processed_dir, utt_info["Dataset"], energy_dir, f'{utt_info["Uid"]}.npy'
+            )
+            frame_energy = np.load(energy_path)
+            assert len(frame_energy) > 0
+
+            if use_log_scale:
+                nonzero_idxes = np.where(frame_energy != 0)[0]
+                frame_energy[nonzero_idxes] = np.log(frame_energy[nonzero_idxes])
+            utt2energy[utt] = frame_energy
+            energy_scaler.partial_fit(frame_energy.reshape(-1, 1))
+
+        mean, std = energy_scaler.mean_[0], energy_scaler.scale_[0]
+        if return_norm:
+            for utt_info in meta_data:
+                utt = utt_info["Dataset"] + "_" + utt_info["Uid"]
+                energy = utt2energy[utt]
+                normalized_energy = (energy - mean) / std
+                utt2energy[utt] = normalized_energy
+        energy_statistic = {"mean": mean, "std": std}
+
+    else:
+        spk2utt = {}
+        energy_statistic = []
+        for utt_info in meta_data:
+            utt = utt_info["Dataset"] + "_" + utt_info["Uid"]
+            if not utt2spk[utt] in spk2utt:
+                spk2utt[utt2spk[utt]] = []
+            spk2utt[utt2spk[utt]].append(utt)
+
+        for spk in spk2utt:
+            energy_scaler = StandardScaler()
+            for utt in spk2utt[spk]:
+                dataset = utt.split("_")[0]
+                uid = "_".join(utt.split("_")[1:])
+                energy_path = os.path.join(
+                    processed_dir, dataset, energy_dir, f"{uid}.npy"
+                )
+                frame_energy = np.load(energy_path)
+                assert len(frame_energy) > 0
+
+                if use_log_scale:
+                    nonzero_idxes = np.where(frame_energy != 0)[0]
+                    frame_energy[nonzero_idxes] = np.log(frame_energy[nonzero_idxes])
+                utt2energy[utt] = frame_energy
+                energy_scaler.partial_fit(frame_energy.reshape(-1, 1))
+
+            mean, std = energy_scaler.mean_[0], energy_scaler.scale_[0]
+            if return_norm:
+                for utt in spk2utt[spk]:
+                    energy = utt2energy[utt]
+                    normalized_energy = (energy - mean) / std
+                    utt2energy[utt] = normalized_energy
+            energy_statistic.append({"spk": spk, "mean": mean, "std": std})
+
+    return utt2energy, energy_statistic
+
+
+def align_length(feature, target_len, pad_value=0.0):
+    feature_len = feature.shape[-1]
+    dim = len(feature.shape)
+    # align 1-D data
+    if dim == 2:
+        if target_len > feature_len:
+            feature = np.pad(
+                feature,
+                ((0, 0), (0, target_len - feature_len)),
+                constant_values=pad_value,
+            )
+        else:
+            feature = feature[:, :target_len]
+    # align 2-D data
+    elif dim == 1:
+        if target_len > feature_len:
+            feature = np.pad(
+                feature, (0, target_len - feature_len), constant_values=pad_value
+            )
+        else:
+            feature = feature[:target_len]
+    else:
+        raise NotImplementedError
+    return feature
+
+
+def align_whisper_feauture_length(
+    feature, target_len, fast_mapping=True, source_hop=320, target_hop=256
+):
+    factor = np.gcd(source_hop, target_hop)
+    source_hop //= factor
+    target_hop //= factor
+    # print(
+    #     "Mapping source's {} frames => target's {} frames".format(
+    #         target_hop, source_hop
+    #     )
+    # )
+
+    max_source_len = 1500
+    target_len = min(target_len, max_source_len * source_hop // target_hop)
+
+    width = feature.shape[-1]
+
+    if fast_mapping:
+        source_len = target_len * target_hop // source_hop + 1
+        feature = feature[:source_len]
+
+    else:
+        source_len = max_source_len
+
+    # const ~= target_len * target_hop
+    const = source_len * source_hop // target_hop * target_hop
+
+    # (source_len * source_hop, dim)
+    up_sampling_feats = np.repeat(feature, source_hop, axis=0)
+    # (const, dim) -> (const/target_hop, target_hop, dim) -> (const/target_hop, dim)
+    down_sampling_feats = np.average(
+        up_sampling_feats[:const].reshape(-1, target_hop, width), axis=1
+    )
+    assert len(down_sampling_feats) >= target_len
+
+    # (target_len, dim)
+    feat = down_sampling_feats[:target_len]
+
+    return feat
+
+
+def align_content_feature_length(feature, target_len, source_hop=320, target_hop=256):
+    factor = np.gcd(source_hop, target_hop)
+    source_hop //= factor
+    target_hop //= factor
+    # print(
+    #     "Mapping source's {} frames => target's {} frames".format(
+    #         target_hop, source_hop
+    #     )
+    # )
+
+    # (source_len, 256)
+    source_len, width = feature.shape
+
+    # const ~= target_len * target_hop
+    const = source_len * source_hop // target_hop * target_hop
+
+    # (source_len * source_hop, dim)
+    up_sampling_feats = np.repeat(feature, source_hop, axis=0)
+    # (const, dim) -> (const/target_hop, target_hop, dim) -> (const/target_hop, dim)
+    down_sampling_feats = np.average(
+        up_sampling_feats[:const].reshape(-1, target_hop, width), axis=1
+    )
+
+    err = abs(target_len - len(down_sampling_feats))
+    if err > 4:  ## why 4 not 3?
+        print("target_len:", target_len)
+        print("raw feature:", feature.shape)
+        print("up_sampling:", up_sampling_feats.shape)
+        print("down_sampling_feats:", down_sampling_feats.shape)
+        exit()
+    if len(down_sampling_feats) < target_len:
+        # (1, dim) -> (err, dim)
+        end = down_sampling_feats[-1][None, :].repeat(err, axis=0)
+        down_sampling_feats = np.concatenate([down_sampling_feats, end], axis=0)
+
+    # (target_len, dim)
+    feat = down_sampling_feats[:target_len]
+
+    return feat
+
+
+def remove_outlier(values):
+    values = np.array(values)
+    p25 = np.percentile(values, 25)
+    p75 = np.percentile(values, 75)
+    lower = p25 - 1.5 * (p75 - p25)
+    upper = p75 + 1.5 * (p75 - p25)
+    normal_indices = np.logical_and(values > lower, values < upper)
+    return values[normal_indices]
diff --git a/utils/distribution.py b/utils/distribution.py
new file mode 100644
index 0000000000000000000000000000000000000000..de3000e99194f7e848712d8b4cb77c988f098fd2
--- /dev/null
+++ b/utils/distribution.py
@@ -0,0 +1,270 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from torch.distributions import Normal
+
+
+def log_sum_exp(x):
+    """numerically stable log_sum_exp implementation that prevents overflow"""
+    # TF ordering
+    axis = len(x.size()) - 1
+    m, _ = torch.max(x, dim=axis)
+    m2, _ = torch.max(x, dim=axis, keepdim=True)
+    return m + torch.log(torch.sum(torch.exp(x - m2), dim=axis))
+
+
+def discretized_mix_logistic_loss(
+    y_hat, y, num_classes=256, log_scale_min=-7.0, reduce=True
+):
+    """Discretized mixture of logistic distributions loss
+
+    Note that it is assumed that input is scaled to [-1, 1].
+
+    Args:
+        y_hat (Tensor): Predicted output (B x C x T)
+        y (Tensor): Target (B x T x 1).
+        num_classes (int): Number of classes
+        log_scale_min (float): Log scale minimum value
+        reduce (bool): If True, the losses are averaged or summed for each
+          minibatch.
+
+    Returns
+        Tensor: loss
+    """
+    assert y_hat.dim() == 3
+    assert y_hat.size(1) % 3 == 0
+    nr_mix = y_hat.size(1) // 3
+
+    # (B x T x C)
+    y_hat = y_hat.transpose(1, 2)
+
+    # unpack parameters. (B, T, num_mixtures) x 3
+    logit_probs = y_hat[:, :, :nr_mix]
+    means = y_hat[:, :, nr_mix : 2 * nr_mix]
+    log_scales = torch.clamp(y_hat[:, :, 2 * nr_mix : 3 * nr_mix], min=log_scale_min)
+
+    # B x T x 1 -> B x T x num_mixtures
+    y = y.expand_as(means)
+
+    centered_y = y - means
+    inv_stdv = torch.exp(-log_scales)
+    plus_in = inv_stdv * (centered_y + 1.0 / (num_classes - 1))
+    cdf_plus = torch.sigmoid(plus_in)
+    min_in = inv_stdv * (centered_y - 1.0 / (num_classes - 1))
+    cdf_min = torch.sigmoid(min_in)
+
+    # log probability for edge case of 0 (before scaling)
+    # equivalent: torch.log(torch.sigmoid(plus_in))
+    log_cdf_plus = plus_in - F.softplus(plus_in)
+
+    # log probability for edge case of 255 (before scaling)
+    # equivalent: (1 - torch.sigmoid(min_in)).log()
+    log_one_minus_cdf_min = -F.softplus(min_in)
+
+    # probability for all other cases
+    cdf_delta = cdf_plus - cdf_min
+
+    mid_in = inv_stdv * centered_y
+    # log probability in the center of the bin, to be used in extreme cases
+    # (not actually used in our code)
+    log_pdf_mid = mid_in - log_scales - 2.0 * F.softplus(mid_in)
+
+    # tf equivalent
+    """
+    log_probs = tf.where(x < -0.999, log_cdf_plus,
+                         tf.where(x > 0.999, log_one_minus_cdf_min,
+                                  tf.where(cdf_delta > 1e-5,
+                                           tf.log(tf.maximum(cdf_delta, 1e-12)),
+                                           log_pdf_mid - np.log(127.5))))
+    """
+    # TODO: cdf_delta <= 1e-5 actually can happen. How can we choose the value
+    # for num_classes=65536 case? 1e-7? not sure..
+    inner_inner_cond = (cdf_delta > 1e-5).float()
+
+    inner_inner_out = inner_inner_cond * torch.log(
+        torch.clamp(cdf_delta, min=1e-12)
+    ) + (1.0 - inner_inner_cond) * (log_pdf_mid - np.log((num_classes - 1) / 2))
+    inner_cond = (y > 0.999).float()
+    inner_out = (
+        inner_cond * log_one_minus_cdf_min + (1.0 - inner_cond) * inner_inner_out
+    )
+    cond = (y < -0.999).float()
+    log_probs = cond * log_cdf_plus + (1.0 - cond) * inner_out
+
+    log_probs = log_probs + F.log_softmax(logit_probs, -1)
+
+    if reduce:
+        return -torch.sum(log_sum_exp(log_probs))
+    else:
+        return -log_sum_exp(log_probs).unsqueeze(-1)
+
+
+def to_one_hot(tensor, n, fill_with=1.0):
+    # we perform one hot encore with respect to the last axis
+    one_hot = torch.FloatTensor(tensor.size() + (n,)).zero_()
+    if tensor.is_cuda:
+        one_hot = one_hot.cuda()
+    one_hot.scatter_(len(tensor.size()), tensor.unsqueeze(-1), fill_with)
+    return one_hot
+
+
+def sample_from_discretized_mix_logistic(y, log_scale_min=-7.0, clamp_log_scale=False):
+    """
+    Sample from discretized mixture of logistic distributions
+
+    Args:
+        y (Tensor): B x C x T
+        log_scale_min (float): Log scale minimum value
+
+    Returns:
+        Tensor: sample in range of [-1, 1].
+    """
+    assert y.size(1) % 3 == 0
+    nr_mix = y.size(1) // 3
+
+    # B x T x C
+    y = y.transpose(1, 2)
+    logit_probs = y[:, :, :nr_mix]
+
+    # sample mixture indicator from softmax
+    temp = logit_probs.data.new(logit_probs.size()).uniform_(1e-5, 1.0 - 1e-5)
+    temp = logit_probs.data - torch.log(-torch.log(temp))
+    _, argmax = temp.max(dim=-1)
+
+    # (B, T) -> (B, T, nr_mix)
+    one_hot = to_one_hot(argmax, nr_mix)
+    # select logistic parameters
+    means = torch.sum(y[:, :, nr_mix : 2 * nr_mix] * one_hot, dim=-1)
+    log_scales = torch.sum(y[:, :, 2 * nr_mix : 3 * nr_mix] * one_hot, dim=-1)
+    if clamp_log_scale:
+        log_scales = torch.clamp(log_scales, min=log_scale_min)
+    # sample from logistic & clip to interval
+    # we don't actually round to the nearest 8bit value when sampling
+    u = means.data.new(means.size()).uniform_(1e-5, 1.0 - 1e-5)
+    x = means + torch.exp(log_scales) * (torch.log(u) - torch.log(1.0 - u))
+
+    x = torch.clamp(torch.clamp(x, min=-1.0), max=1.0)
+
+    return x
+
+
+# we can easily define discretized version of the gaussian loss, however,
+# use continuous version as same as the https://clarinet-demo.github.io/
+def mix_gaussian_loss(y_hat, y, log_scale_min=-7.0, reduce=True):
+    """Mixture of continuous gaussian distributions loss
+
+    Note that it is assumed that input is scaled to [-1, 1].
+
+    Args:
+        y_hat (Tensor): Predicted output (B x C x T)
+        y (Tensor): Target (B x T x 1).
+        log_scale_min (float): Log scale minimum value
+        reduce (bool): If True, the losses are averaged or summed for each
+          minibatch.
+    Returns
+        Tensor: loss
+    """
+    assert y_hat.dim() == 3
+    C = y_hat.size(1)
+    if C == 2:
+        nr_mix = 1
+    else:
+        assert y_hat.size(1) % 3 == 0
+        nr_mix = y_hat.size(1) // 3
+
+    # (B x T x C)
+    y_hat = y_hat.transpose(1, 2)
+
+    # unpack parameters.
+    if C == 2:
+        # special case for C == 2, just for compatibility
+        logit_probs = None
+        means = y_hat[:, :, 0:1]
+        log_scales = torch.clamp(y_hat[:, :, 1:2], min=log_scale_min)
+    else:
+        #  (B, T, num_mixtures) x 3
+        logit_probs = y_hat[:, :, :nr_mix]
+        means = y_hat[:, :, nr_mix : 2 * nr_mix]
+        log_scales = torch.clamp(
+            y_hat[:, :, 2 * nr_mix : 3 * nr_mix], min=log_scale_min
+        )
+
+    # B x T x 1 -> B x T x num_mixtures
+    y = y.expand_as(means)
+
+    centered_y = y - means
+    dist = Normal(loc=0.0, scale=torch.exp(log_scales))
+    # do we need to add a trick to avoid log(0)?
+    log_probs = dist.log_prob(centered_y)
+
+    if nr_mix > 1:
+        log_probs = log_probs + F.log_softmax(logit_probs, -1)
+
+    if reduce:
+        if nr_mix == 1:
+            return -torch.sum(log_probs)
+        else:
+            return -torch.sum(log_sum_exp(log_probs))
+    else:
+        if nr_mix == 1:
+            return -log_probs
+        else:
+            return -log_sum_exp(log_probs).unsqueeze(-1)
+
+
+def sample_from_mix_gaussian(y, log_scale_min=-7.0):
+    """
+    Sample from (discretized) mixture of gaussian distributions
+    Args:
+        y (Tensor): B x C x T
+        log_scale_min (float): Log scale minimum value
+    Returns:
+        Tensor: sample in range of [-1, 1].
+    """
+    C = y.size(1)
+    if C == 2:
+        nr_mix = 1
+    else:
+        assert y.size(1) % 3 == 0
+        nr_mix = y.size(1) // 3
+
+    # B x T x C
+    y = y.transpose(1, 2)
+
+    if C == 2:
+        logit_probs = None
+    else:
+        logit_probs = y[:, :, :nr_mix]
+
+    if nr_mix > 1:
+        # sample mixture indicator from softmax
+        temp = logit_probs.data.new(logit_probs.size()).uniform_(1e-5, 1.0 - 1e-5)
+        temp = logit_probs.data - torch.log(-torch.log(temp))
+        _, argmax = temp.max(dim=-1)
+
+        # (B, T) -> (B, T, nr_mix)
+        one_hot = to_one_hot(argmax, nr_mix)
+
+        # Select means and log scales
+        means = torch.sum(y[:, :, nr_mix : 2 * nr_mix] * one_hot, dim=-1)
+        log_scales = torch.sum(y[:, :, 2 * nr_mix : 3 * nr_mix] * one_hot, dim=-1)
+    else:
+        if C == 2:
+            means, log_scales = y[:, :, 0], y[:, :, 1]
+        elif C == 3:
+            means, log_scales = y[:, :, 1], y[:, :, 2]
+        else:
+            assert False, "shouldn't happen"
+
+    scales = torch.exp(log_scales)
+    dist = Normal(loc=means, scale=scales)
+    x = dist.sample()
+
+    x = torch.clamp(x, min=-1.0, max=1.0)
+    return x
diff --git a/utils/dsp.py b/utils/dsp.py
new file mode 100644
index 0000000000000000000000000000000000000000..18f9466f6b12e5539ce221f86030f5114ccdb503
--- /dev/null
+++ b/utils/dsp.py
@@ -0,0 +1,97 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+import torch
+
+# ZERO = 1e-12
+
+
+def gaussian_normalize_mel_channel(mel, mu, sigma):
+    """
+    Shift to Standorm Normal Distribution
+
+    Args:
+        mel: (n_mels, frame_len)
+        mu: (n_mels,), mean value
+        sigma: (n_mels,), sd value
+    Return:
+        Tensor like mel
+    """
+    mu = np.expand_dims(mu, -1)
+    sigma = np.expand_dims(sigma, -1)
+    return (mel - mu) / sigma
+
+
+def de_gaussian_normalize_mel_channel(mel, mu, sigma):
+    """
+
+    Args:
+        mel: (n_mels, frame_len)
+        mu: (n_mels,), mean value
+        sigma: (n_mels,), sd value
+    Return:
+        Tensor like mel
+    """
+    mu = np.expand_dims(mu, -1)
+    sigma = np.expand_dims(sigma, -1)
+    return sigma * mel + mu
+
+
+def decompress(audio_compressed, bits):
+    mu = 2**bits - 1
+    audio = np.sign(audio_compressed) / mu * ((1 + mu) ** np.abs(audio_compressed) - 1)
+    return audio
+
+
+def compress(audio, bits):
+    mu = 2**bits - 1
+    audio_compressed = np.sign(audio) * np.log(1 + mu * np.abs(audio)) / np.log(mu + 1)
+    return audio_compressed
+
+
+def label_to_audio(quant, bits):
+    classes = 2**bits
+    audio = 2 * quant / (classes - 1.0) - 1.0
+    return audio
+
+
+def audio_to_label(audio, bits):
+    """Normalized audio data tensor to digit array
+
+    Args:
+        audio (tensor): audio data
+        bits (int): data bits
+
+    Returns:
+        array<int>: digit array of audio data
+    """
+    classes = 2**bits
+    # initialize an increasing array with values from -1 to 1
+    bins = np.linspace(-1, 1, classes)
+    # change value in audio tensor to digits
+    quant = np.digitize(audio, bins) - 1
+    return quant
+
+
+def label_to_onehot(x, bits):
+    """Converts a class vector (integers) to binary class matrix.
+    Args:
+        x: class vector to be converted into a matrix
+            (integers from 0 to num_classes).
+        num_classes: total number of classes.
+    Returns:
+        A binary matrix representation of the input. The classes axis
+        is placed last.
+    """
+    classes = 2**bits
+
+    result = torch.zeros((x.shape[0], classes), dtype=torch.float32)
+    for i in range(x.shape[0]):
+        result[i, x[i]] = 1
+
+    output_shape = x.shape + (classes,)
+    output = torch.reshape(result, output_shape)
+    return output
diff --git a/utils/duration.py b/utils/duration.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9544b40b88c68b4e1df33ab1c81a6196a43111e
--- /dev/null
+++ b/utils/duration.py
@@ -0,0 +1,86 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+import os
+import tgt
+
+
+def get_alignment(tier, cfg):
+    sample_rate = cfg["sample_rate"]
+    hop_size = cfg["hop_size"]
+
+    sil_phones = ["sil", "sp", "spn"]
+
+    phones = []
+    durations = []
+    start_time = 0
+    end_time = 0
+    end_idx = 0
+
+    for t in tier._objects:
+        s, e, p = t.start_time, t.end_time, t.text
+
+        # Trim leading silences
+        if phones == []:
+            if p in sil_phones:
+                continue
+            else:
+                start_time = s
+
+        if p not in sil_phones:
+            # For ordinary phones
+            phones.append(p)
+            end_time = e
+            end_idx = len(phones)
+        else:
+            # For silent phones
+            phones.append(p)
+
+        durations.append(
+            int(
+                np.round(e * sample_rate / hop_size)
+                - np.round(s * sample_rate / hop_size)
+            )
+        )
+
+    # Trim tailing silences
+    phones = phones[:end_idx]
+    durations = durations[:end_idx]
+
+    return phones, durations, start_time, end_time
+
+
+def get_duration(utt, wav, cfg):
+    speaker = utt["Singer"]
+    basename = utt["Uid"]
+    dataset = utt["Dataset"]
+    sample_rate = cfg["sample_rate"]
+
+    # print(cfg.processed_dir, dataset, speaker, basename)
+    wav_path = os.path.join(
+        cfg.processed_dir, dataset, "raw_data", speaker, "{}.wav".format(basename)
+    )
+    text_path = os.path.join(
+        cfg.processed_dir, dataset, "raw_data", speaker, "{}.lab".format(basename)
+    )
+    tg_path = os.path.join(
+        cfg.processed_dir, dataset, "TextGrid", speaker, "{}.TextGrid".format(basename)
+    )
+
+    # Read raw text
+    with open(text_path, "r") as f:
+        raw_text = f.readline().strip("\n")
+
+    # Get alignments
+    textgrid = tgt.io.read_textgrid(tg_path)
+    phone, duration, start, end = get_alignment(
+        textgrid.get_tier_by_name("phones"), cfg
+    )
+    text = "{" + " ".join(phone) + "}"
+    if start >= end:
+        return None
+
+    return duration, text, int(sample_rate * start), int(sample_rate * end)
diff --git a/utils/f0.py b/utils/f0.py
new file mode 100644
index 0000000000000000000000000000000000000000..fcd95396a6b08d8ffa1a310c5bee6f0d8b556796
--- /dev/null
+++ b/utils/f0.py
@@ -0,0 +1,299 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import librosa
+import numpy as np
+import torch
+import parselmouth
+import torchcrepe
+import pyworld as pw
+
+
+def get_bin_index(f0, m, M, n_bins, use_log_scale):
+    """
+    WARNING: to abandon!
+
+    Args:
+        raw_f0: tensor whose shpae is (N, frame_len)
+    Returns:
+        index: tensor whose shape is same to f0
+    """
+    raw_f0 = f0.clone()
+    raw_m, raw_M = m, M
+
+    if use_log_scale:
+        f0[torch.where(f0 == 0)] = 1
+        f0 = torch.log(f0)
+        m, M = float(np.log(m)), float(np.log(M))
+
+    # Set normal index in [1, n_bins - 1]
+    width = (M + 1e-7 - m) / (n_bins - 1)
+    index = (f0 - m) // width + 1
+    # Set unvoiced frames as 0, Therefore, the vocabulary is [0, n_bins- 1], whose size is n_bins
+    index[torch.where(f0 == 0)] = 0
+
+    # TODO: Boundary check (special: to judge whether 0 for unvoiced)
+    if torch.any(raw_f0 > raw_M):
+        print("F0 Warning: too high f0: {}".format(raw_f0[torch.where(raw_f0 > raw_M)]))
+        index[torch.where(raw_f0 > raw_M)] = n_bins - 1
+    if torch.any(raw_f0 < raw_m):
+        print("F0 Warning: too low f0: {}".format(raw_f0[torch.where(f0 < m)]))
+        index[torch.where(f0 < m)] = 0
+
+    return torch.as_tensor(index, dtype=torch.long, device=f0.device)
+
+
+def f0_to_coarse(f0, pitch_bin, pitch_min, pitch_max):
+    ## TODO: Figure out the detail of this function
+
+    f0_mel_min = 1127 * np.log(1 + pitch_min / 700)
+    f0_mel_max = 1127 * np.log(1 + pitch_max / 700)
+
+    is_torch = isinstance(f0, torch.Tensor)
+    f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700)
+    f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (pitch_bin - 2) / (
+        f0_mel_max - f0_mel_min
+    ) + 1
+
+    f0_mel[f0_mel <= 1] = 1
+    f0_mel[f0_mel > pitch_bin - 1] = pitch_bin - 1
+    f0_coarse = (f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(np.int32)
+    assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (
+        f0_coarse.max(),
+        f0_coarse.min(),
+    )
+    return f0_coarse
+
+
+def interpolate(f0):
+    """Interpolate the unvoiced part. Thus the f0 can be passed to a subtractive synthesizer.
+    Args:
+        f0: A numpy array of shape (seq_len,)
+    Returns:
+        f0: Interpolated f0 of shape (seq_len,)
+        uv: Unvoiced part of shape (seq_len,)
+    """
+    uv = f0 == 0
+    if len(f0[~uv]) > 0:
+        # interpolate the unvoiced f0
+        f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv])
+        uv = uv.astype("float")
+        uv = np.min(np.array([uv[:-2], uv[1:-1], uv[2:]]), axis=0)
+        uv = np.pad(uv, (1, 1))
+    return f0, uv
+
+
+def get_log_f0(f0):
+    f0[np.where(f0 == 0)] = 1
+    log_f0 = np.log(f0)
+    return log_f0
+
+
+# ========== Methods ==========
+
+
+def get_f0_features_using_pyin(audio, cfg):
+    """Using pyin to extract the f0 feature.
+    Args:
+        audio
+        fs
+        win_length
+        hop_length
+        f0_min
+        f0_max
+    Returns:
+        f0: numpy array of shape (frame_len,)
+    """
+    f0, voiced_flag, voiced_probs = librosa.pyin(
+        y=audio,
+        fmin=cfg.f0_min,
+        fmax=cfg.f0_max,
+        sr=cfg.sample_rate,
+        win_length=cfg.win_size,
+        hop_length=cfg.hop_size,
+    )
+    # Set nan to 0
+    f0[voiced_flag == False] = 0
+    return f0
+
+
+def get_f0_features_using_parselmouth(audio, cfg, speed=1):
+    """Using parselmouth to extract the f0 feature.
+    Args:
+        audio
+        mel_len
+        hop_length
+        fs
+        f0_min
+        f0_max
+        speed(default=1)
+    Returns:
+        f0: numpy array of shape (frame_len,)
+        pitch_coarse: numpy array of shape (frame_len,)
+    """
+    hop_size = int(np.round(cfg.hop_size * speed))
+
+    # Calculate the time step for pitch extraction
+    time_step = hop_size / cfg.sample_rate * 1000
+
+    f0 = (
+        parselmouth.Sound(audio, cfg.sample_rate)
+        .to_pitch_ac(
+            time_step=time_step / 1000,
+            voicing_threshold=0.6,
+            pitch_floor=cfg.f0_min,
+            pitch_ceiling=cfg.f0_max,
+        )
+        .selected_array["frequency"]
+    )
+
+    # Pad the pitch to the mel_len
+    # pad_size = (int(len(audio) // hop_size) - len(f0) + 1) // 2
+    # f0 = np.pad(f0, [[pad_size, mel_len - len(f0) - pad_size]], mode="constant")
+
+    # Get the coarse part
+    pitch_coarse = f0_to_coarse(f0, cfg.pitch_bin, cfg.f0_min, cfg.f0_max)
+    return f0, pitch_coarse
+
+
+def get_f0_features_using_dio(audio, cfg):
+    """Using dio to extract the f0 feature.
+    Args:
+        audio
+        mel_len
+        fs
+        hop_length
+        f0_min
+        f0_max
+    Returns:
+        f0: numpy array of shape (frame_len,)
+    """
+    # Get the raw f0
+    _f0, t = pw.dio(
+        audio.astype("double"),
+        cfg.sample_rate,
+        f0_floor=cfg.f0_min,
+        f0_ceil=cfg.f0_max,
+        channels_in_octave=2,
+        frame_period=(1000 * cfg.hop_size / cfg.sample_rate),
+    )
+    # Get the f0
+    f0 = pw.stonemask(audio.astype("double"), _f0, t, cfg.sample_rate)
+    return f0
+
+
+def get_f0_features_using_harvest(audio, mel_len, fs, hop_length, f0_min, f0_max):
+    """Using harvest to extract the f0 feature.
+    Args:
+        audio
+        mel_len
+        fs
+        hop_length
+        f0_min
+        f0_max
+    Returns:
+        f0: numpy array of shape (frame_len,)
+    """
+    f0, _ = pw.harvest(
+        audio.astype("double"),
+        fs,
+        f0_floor=f0_min,
+        f0_ceil=f0_max,
+        frame_period=(1000 * hop_length / fs),
+    )
+    f0 = f0.astype("float")[:mel_len]
+    return f0
+
+
+def get_f0_features_using_crepe(
+    audio, mel_len, fs, hop_length, hop_length_new, f0_min, f0_max, threshold=0.3
+):
+    """Using torchcrepe to extract the f0 feature.
+    Args:
+        audio
+        mel_len
+        fs
+        hop_length
+        hop_length_new
+        f0_min
+        f0_max
+        threshold(default=0.3)
+    Returns:
+        f0: numpy array of shape (frame_len,)
+    """
+    # Currently, crepe only supports 16khz audio
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    audio_16k = librosa.resample(audio, orig_sr=fs, target_sr=16000)
+    audio_16k_torch = torch.FloatTensor(audio_16k).unsqueeze(0).to(device)
+
+    # Get the raw pitch
+    f0, pd = torchcrepe.predict(
+        audio_16k_torch,
+        16000,
+        hop_length_new,
+        f0_min,
+        f0_max,
+        pad=True,
+        model="full",
+        batch_size=1024,
+        device=device,
+        return_periodicity=True,
+    )
+
+    # Filter, de-silence, set up threshold for unvoiced part
+    pd = torchcrepe.filter.median(pd, 3)
+    pd = torchcrepe.threshold.Silence(-60.0)(pd, audio_16k_torch, 16000, hop_length_new)
+    f0 = torchcrepe.threshold.At(threshold)(f0, pd)
+    f0 = torchcrepe.filter.mean(f0, 3)
+
+    # Convert unvoiced part to 0hz
+    f0 = torch.where(torch.isnan(f0), torch.full_like(f0, 0), f0)
+
+    # Interpolate f0
+    nzindex = torch.nonzero(f0[0]).squeeze()
+    f0 = torch.index_select(f0[0], dim=0, index=nzindex).cpu().numpy()
+    time_org = 0.005 * nzindex.cpu().numpy()
+    time_frame = np.arange(mel_len) * hop_length / fs
+    f0 = np.interp(time_frame, time_org, f0, left=f0[0], right=f0[-1])
+    return f0
+
+
+def get_f0(audio, cfg):
+    if cfg.pitch_extractor == "dio":
+        f0 = get_f0_features_using_dio(audio, cfg)
+    elif cfg.pitch_extractor == "pyin":
+        f0 = get_f0_features_using_pyin(audio, cfg)
+    elif cfg.pitch_extractor == "parselmouth":
+        f0, _ = get_f0_features_using_parselmouth(audio, cfg)
+    # elif cfg.data.f0_extractor == 'cwt': # todo
+
+    return f0
+
+
+def get_cents(f0_hz):
+    """
+    F_{cent} = 1200 * log2 (F/440)
+
+    Reference:
+        APSIPA'17, Perceptual Evaluation of Singing Quality
+    """
+    voiced_f0 = f0_hz[f0_hz != 0]
+    return 1200 * np.log2(voiced_f0 / 440)
+
+
+def get_pitch_derivatives(f0_hz):
+    """
+    f0_hz: (,T)
+    """
+    f0_cent = get_cents(f0_hz)
+    return f0_cent[1:] - f0_cent[:-1]
+
+
+def get_pitch_sub_median(f0_hz):
+    """
+    f0_hz: (,T)
+    """
+    f0_cent = get_cents(f0_hz)
+    return f0_cent - np.median(f0_cent)
diff --git a/utils/hparam.py b/utils/hparam.py
new file mode 100644
index 0000000000000000000000000000000000000000..c5dd35c6a3158b0aaf8d936dba139a030a48bc62
--- /dev/null
+++ b/utils/hparam.py
@@ -0,0 +1,659 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+# This code is modified from https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/training/python/training/hparam.py  pylint: disable=line-too-long
+"""Hyperparameter values."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import json
+import numbers
+import re
+import six
+
+# Define the regular expression for parsing a single clause of the input
+# (delimited by commas).  A legal clause looks like:
+#   <variable name>[<index>]? = <rhs>
+# where <rhs> is either a single token or [] enclosed list of tokens.
+# For example:  "var[1] = a" or "x = [1,2,3]"
+PARAM_RE = re.compile(
+    r"""
+  (?P<name>[a-zA-Z][\w\.]*)      # variable name: "var" or "x"
+  (\[\s*(?P<index>\d+)\s*\])?  # (optional) index: "1" or None
+  \s*=\s*
+  ((?P<val>[^,\[]*)            # single value: "a" or None
+   |
+   \[(?P<vals>[^\]]*)\])       # list of values: None or "1,2,3"
+  ($|,\s*)""",
+    re.VERBOSE,
+)
+
+
+def _parse_fail(name, var_type, value, values):
+    """Helper function for raising a value error for bad assignment."""
+    raise ValueError(
+        "Could not parse hparam '%s' of type '%s' with value '%s' in %s"
+        % (name, var_type.__name__, value, values)
+    )
+
+
+def _reuse_fail(name, values):
+    """Helper function for raising a value error for reuse of name."""
+    raise ValueError("Multiple assignments to variable '%s' in %s" % (name, values))
+
+
+def _process_scalar_value(name, parse_fn, var_type, m_dict, values, results_dictionary):
+    """Update results_dictionary with a scalar value.
+
+    Used to update the results_dictionary to be returned by parse_values when
+    encountering a clause with a scalar RHS (e.g.  "s=5" or "arr[0]=5".)
+
+    Mutates results_dictionary.
+
+    Args:
+      name: Name of variable in assignment ("s" or "arr").
+      parse_fn: Function for parsing the actual value.
+      var_type: Type of named variable.
+      m_dict: Dictionary constructed from regex parsing.
+        m_dict['val']: RHS value (scalar)
+        m_dict['index']: List index value (or None)
+      values: Full expression being parsed
+      results_dictionary: The dictionary being updated for return by the parsing
+        function.
+
+    Raises:
+      ValueError: If the name has already been used.
+    """
+    try:
+        parsed_value = parse_fn(m_dict["val"])
+    except ValueError:
+        _parse_fail(name, var_type, m_dict["val"], values)
+
+    # If no index is provided
+    if not m_dict["index"]:
+        if name in results_dictionary:
+            _reuse_fail(name, values)
+        results_dictionary[name] = parsed_value
+    else:
+        if name in results_dictionary:
+            # The name has already been used as a scalar, then it
+            # will be in this dictionary and map to a non-dictionary.
+            if not isinstance(results_dictionary.get(name), dict):
+                _reuse_fail(name, values)
+        else:
+            results_dictionary[name] = {}
+
+        index = int(m_dict["index"])
+        # Make sure the index position hasn't already been assigned a value.
+        if index in results_dictionary[name]:
+            _reuse_fail("{}[{}]".format(name, index), values)
+        results_dictionary[name][index] = parsed_value
+
+
+def _process_list_value(name, parse_fn, var_type, m_dict, values, results_dictionary):
+    """Update results_dictionary from a list of values.
+
+    Used to update results_dictionary to be returned by parse_values when
+    encountering a clause with a list RHS (e.g.  "arr=[1,2,3]".)
+
+    Mutates results_dictionary.
+
+    Args:
+      name: Name of variable in assignment ("arr").
+      parse_fn: Function for parsing individual values.
+      var_type: Type of named variable.
+      m_dict: Dictionary constructed from regex parsing.
+        m_dict['val']: RHS value (scalar)
+      values: Full expression being parsed
+      results_dictionary: The dictionary being updated for return by the parsing
+        function.
+
+    Raises:
+      ValueError: If the name has an index or the values cannot be parsed.
+    """
+    if m_dict["index"] is not None:
+        raise ValueError("Assignment of a list to a list index.")
+    elements = filter(None, re.split("[ ,]", m_dict["vals"]))
+    # Make sure the name hasn't already been assigned a value
+    if name in results_dictionary:
+        raise _reuse_fail(name, values)
+    try:
+        results_dictionary[name] = [parse_fn(e) for e in elements]
+    except ValueError:
+        _parse_fail(name, var_type, m_dict["vals"], values)
+
+
+def _cast_to_type_if_compatible(name, param_type, value):
+    """Cast hparam to the provided type, if compatible.
+
+    Args:
+      name: Name of the hparam to be cast.
+      param_type: The type of the hparam.
+      value: The value to be cast, if compatible.
+
+    Returns:
+      The result of casting `value` to `param_type`.
+
+    Raises:
+      ValueError: If the type of `value` is not compatible with param_type.
+        * If `param_type` is a string type, but `value` is not.
+        * If `param_type` is a boolean, but `value` is not, or vice versa.
+        * If `param_type` is an integer type, but `value` is not.
+        * If `param_type` is a float type, but `value` is not a numeric type.
+    """
+    fail_msg = "Could not cast hparam '%s' of type '%s' from value %r" % (
+        name,
+        param_type,
+        value,
+    )
+
+    # Some callers use None, for which we can't do any casting/checking. :(
+    if issubclass(param_type, type(None)):
+        return value
+
+    # Avoid converting a non-string type to a string.
+    if issubclass(param_type, (six.string_types, six.binary_type)) and not isinstance(
+        value, (six.string_types, six.binary_type)
+    ):
+        raise ValueError(fail_msg)
+
+    # Avoid converting a number or string type to a boolean or vice versa.
+    if issubclass(param_type, bool) != isinstance(value, bool):
+        raise ValueError(fail_msg)
+
+    # Avoid converting float to an integer (the reverse is fine).
+    if issubclass(param_type, numbers.Integral) and not isinstance(
+        value, numbers.Integral
+    ):
+        raise ValueError(fail_msg)
+
+    # Avoid converting a non-numeric type to a numeric type.
+    if issubclass(param_type, numbers.Number) and not isinstance(value, numbers.Number):
+        raise ValueError(fail_msg)
+
+    return param_type(value)
+
+
+def parse_values(values, type_map, ignore_unknown=False):
+    """Parses hyperparameter values from a string into a python map.
+
+    `values` is a string containing comma-separated `name=value` pairs.
+    For each pair, the value of the hyperparameter named `name` is set to
+    `value`.
+
+    If a hyperparameter name appears multiple times in `values`, a ValueError
+    is raised (e.g. 'a=1,a=2', 'a[1]=1,a[1]=2').
+
+    If a hyperparameter name in both an index assignment and scalar assignment,
+    a ValueError is raised.  (e.g. 'a=[1,2,3],a[0] = 1').
+
+    The hyperparameter name may contain '.' symbols, which will result in an
+    attribute name that is only accessible through the getattr and setattr
+    functions.  (And must be first explicit added through add_hparam.)
+
+    WARNING: Use of '.' in your variable names is allowed, but is not well
+    supported and not recommended.
+
+    The `value` in `name=value` must follows the syntax according to the
+    type of the parameter:
+
+    *  Scalar integer: A Python-parsable integer point value.  E.g.: 1,
+       100, -12.
+    *  Scalar float: A Python-parsable floating point value.  E.g.: 1.0,
+       -.54e89.
+    *  Boolean: Either true or false.
+    *  Scalar string: A non-empty sequence of characters, excluding comma,
+       spaces, and square brackets.  E.g.: foo, bar_1.
+    *  List: A comma separated list of scalar values of the parameter type
+       enclosed in square brackets.  E.g.: [1,2,3], [1.0,1e-12], [high,low].
+
+    When index assignment is used, the corresponding type_map key should be the
+    list name.  E.g. for "arr[1]=0" the type_map must have the key "arr" (not
+    "arr[1]").
+
+    Args:
+      values: String.  Comma separated list of `name=value` pairs where
+        'value' must follow the syntax described above.
+      type_map: A dictionary mapping hyperparameter names to types.  Note every
+        parameter name in values must be a key in type_map.  The values must
+        conform to the types indicated, where a value V is said to conform to a
+        type T if either V has type T, or V is a list of elements of type T.
+        Hence, for a multidimensional parameter 'x' taking float values,
+        'x=[0.1,0.2]' will parse successfully if type_map['x'] = float.
+      ignore_unknown: Bool. Whether values that are missing a type in type_map
+        should be ignored. If set to True, a ValueError will not be raised for
+        unknown hyperparameter type.
+
+    Returns:
+      A python map mapping each name to either:
+      * A scalar value.
+      * A list of scalar values.
+      * A dictionary mapping index numbers to scalar values.
+      (e.g. "x=5,L=[1,2],arr[1]=3" results in {'x':5,'L':[1,2],'arr':{1:3}}")
+
+    Raises:
+      ValueError: If there is a problem with input.
+      * If `values` cannot be parsed.
+      * If a list is assigned to a list index (e.g. 'a[1] = [1,2,3]').
+      * If the same rvalue is assigned two different values (e.g. 'a=1,a=2',
+        'a[1]=1,a[1]=2', or 'a=1,a=[1]')
+    """
+    results_dictionary = {}
+    pos = 0
+    while pos < len(values):
+        m = PARAM_RE.match(values, pos)
+        if not m:
+            raise ValueError("Malformed hyperparameter value: %s" % values[pos:])
+        # Check that there is a comma between parameters and move past it.
+        pos = m.end()
+        # Parse the values.
+        m_dict = m.groupdict()
+        name = m_dict["name"]
+        if name not in type_map:
+            if ignore_unknown:
+                continue
+            raise ValueError("Unknown hyperparameter type for %s" % name)
+        type_ = type_map[name]
+
+        # Set up correct parsing function (depending on whether type_ is a bool)
+        if type_ == bool:
+
+            def parse_bool(value):
+                if value in ["true", "True"]:
+                    return True
+                elif value in ["false", "False"]:
+                    return False
+                else:
+                    try:
+                        return bool(int(value))
+                    except ValueError:
+                        _parse_fail(name, type_, value, values)
+
+            parse = parse_bool
+        else:
+            parse = type_
+
+        # If a singe value is provided
+        if m_dict["val"] is not None:
+            _process_scalar_value(
+                name, parse, type_, m_dict, values, results_dictionary
+            )
+
+        # If the assigned value is a list:
+        elif m_dict["vals"] is not None:
+            _process_list_value(name, parse, type_, m_dict, values, results_dictionary)
+
+        else:  # Not assigned a list or value
+            _parse_fail(name, type_, "", values)
+
+    return results_dictionary
+
+
+class HParams(object):
+    """Class to hold a set of hyperparameters as name-value pairs.
+
+    A `HParams` object holds hyperparameters used to build and train a model,
+    such as the number of hidden units in a neural net layer or the learning rate
+    to use when training.
+
+    You first create a `HParams` object by specifying the names and values of the
+    hyperparameters.
+
+    To make them easily accessible the parameter names are added as direct
+    attributes of the class.  A typical usage is as follows:
+
+    ```python
+    # Create a HParams object specifying names and values of the model
+    # hyperparameters:
+    hparams = HParams(learning_rate=0.1, num_hidden_units=100)
+
+    # The hyperparameter are available as attributes of the HParams object:
+    hparams.learning_rate ==> 0.1
+    hparams.num_hidden_units ==> 100
+    ```
+
+    Hyperparameters have type, which is inferred from the type of their value
+    passed at construction type.   The currently supported types are: integer,
+    float, boolean, string, and list of integer, float, boolean, or string.
+
+    You can override hyperparameter values by calling the
+    [`parse()`](#HParams.parse) method, passing a string of comma separated
+    `name=value` pairs.  This is intended to make it possible to override
+    any hyperparameter values from a single command-line flag to which
+    the user passes 'hyper-param=value' pairs.  It avoids having to define
+    one flag for each hyperparameter.
+
+    The syntax expected for each value depends on the type of the parameter.
+    See `parse()` for a description of the syntax.
+
+    Example:
+
+    ```python
+    # Define a command line flag to pass name=value pairs.
+    # For example using argparse:
+    import argparse
+    parser = argparse.ArgumentParser(description='Train my model.')
+    parser.add_argument('--hparams', type=str,
+                        help='Comma separated list of "name=value" pairs.')
+    args = parser.parse_args()
+    ...
+    def my_program():
+      # Create a HParams object specifying the names and values of the
+      # model hyperparameters:
+      hparams = tf.HParams(learning_rate=0.1, num_hidden_units=100,
+                           activations=['relu', 'tanh'])
+
+      # Override hyperparameters values by parsing the command line
+      hparams.parse(args.hparams)
+
+      # If the user passed `--hparams=learning_rate=0.3` on the command line
+      # then 'hparams' has the following attributes:
+      hparams.learning_rate ==> 0.3
+      hparams.num_hidden_units ==> 100
+      hparams.activations ==> ['relu', 'tanh']
+
+      # If the hyperparameters are in json format use parse_json:
+      hparams.parse_json('{"learning_rate": 0.3, "activations": "relu"}')
+    ```
+    """
+
+    _HAS_DYNAMIC_ATTRIBUTES = True  # Required for pytype checks.
+
+    def __init__(self, model_structure=None, **kwargs):
+        """Create an instance of `HParams` from keyword arguments.
+
+        The keyword arguments specify name-values pairs for the hyperparameters.
+        The parameter types are inferred from the type of the values passed.
+
+        The parameter names are added as attributes of `HParams` object, so they
+        can be accessed directly with the dot notation `hparams._name_`.
+
+        Example:
+
+        ```python
+        # Define 3 hyperparameters: 'learning_rate' is a float parameter,
+        # 'num_hidden_units' an integer parameter, and 'activation' a string
+        # parameter.
+        hparams = tf.HParams(
+            learning_rate=0.1, num_hidden_units=100, activation='relu')
+
+        hparams.activation ==> 'relu'
+        ```
+
+        Note that a few names are reserved and cannot be used as hyperparameter
+        names.  If you use one of the reserved name the constructor raises a
+        `ValueError`.
+
+        Args:
+          model_structure: An instance of ModelStructure, defining the feature
+            crosses to be used in the Trial.
+          **kwargs: Key-value pairs where the key is the hyperparameter name and
+            the value is the value for the parameter.
+
+        Raises:
+          ValueError: If both `hparam_def` and initialization values are provided,
+            or if one of the arguments is invalid.
+
+        """
+        # Register the hyperparameters and their type in _hparam_types.
+        # This simplifies the implementation of parse().
+        # _hparam_types maps the parameter name to a tuple (type, bool).
+        # The type value is the type of the parameter for scalar hyperparameters,
+        # or the type of the list elements for multidimensional hyperparameters.
+        # The bool value is True if the value is a list, False otherwise.
+        self._hparam_types = {}
+        self._model_structure = model_structure
+        for name, value in six.iteritems(kwargs):
+            self.add_hparam(name, value)
+
+    def add_hparam(self, name, value):
+        """Adds {name, value} pair to hyperparameters.
+
+        Args:
+          name: Name of the hyperparameter.
+          value: Value of the hyperparameter. Can be one of the following types:
+            int, float, string, int list, float list, or string list.
+
+        Raises:
+          ValueError: if one of the arguments is invalid.
+        """
+        # Keys in kwargs are unique, but 'name' could the name of a pre-existing
+        # attribute of this object.  In that case we refuse to use it as a
+        # hyperparameter name.
+        if getattr(self, name, None) is not None:
+            raise ValueError("Hyperparameter name is reserved: %s" % name)
+        if isinstance(value, (list, tuple)):
+            if not value:
+                raise ValueError(
+                    "Multi-valued hyperparameters cannot be empty: %s" % name
+                )
+            self._hparam_types[name] = (type(value[0]), True)
+        else:
+            self._hparam_types[name] = (type(value), False)
+        setattr(self, name, value)
+
+    def set_hparam(self, name, value):
+        """Set the value of an existing hyperparameter.
+
+        This function verifies that the type of the value matches the type of the
+        existing hyperparameter.
+
+        Args:
+          name: Name of the hyperparameter.
+          value: New value of the hyperparameter.
+
+        Raises:
+          KeyError: If the hyperparameter doesn't exist.
+          ValueError: If there is a type mismatch.
+        """
+        param_type, is_list = self._hparam_types[name]
+        if isinstance(value, list):
+            if not is_list:
+                raise ValueError(
+                    "Must not pass a list for single-valued parameter: %s" % name
+                )
+            setattr(
+                self,
+                name,
+                [_cast_to_type_if_compatible(name, param_type, v) for v in value],
+            )
+        else:
+            if is_list:
+                raise ValueError(
+                    "Must pass a list for multi-valued parameter: %s." % name
+                )
+            setattr(self, name, _cast_to_type_if_compatible(name, param_type, value))
+
+    def del_hparam(self, name):
+        """Removes the hyperparameter with key 'name'.
+
+        Does nothing if it isn't present.
+
+        Args:
+          name: Name of the hyperparameter.
+        """
+        if hasattr(self, name):
+            delattr(self, name)
+            del self._hparam_types[name]
+
+    def parse(self, values):
+        """Override existing hyperparameter values, parsing new values from a string.
+
+        See parse_values for more detail on the allowed format for values.
+
+        Args:
+          values: String.  Comma separated list of `name=value` pairs where 'value'
+            must follow the syntax described above.
+
+        Returns:
+          The `HParams` instance.
+
+        Raises:
+          ValueError: If `values` cannot be parsed or a hyperparameter in `values`
+          doesn't exist.
+        """
+        type_map = {}
+        for name, t in self._hparam_types.items():
+            param_type, _ = t
+            type_map[name] = param_type
+
+        values_map = parse_values(values, type_map)
+        return self.override_from_dict(values_map)
+
+    def override_from_dict(self, values_dict):
+        """Override existing hyperparameter values, parsing new values from a dictionary.
+
+        Args:
+          values_dict: Dictionary of name:value pairs.
+
+        Returns:
+          The `HParams` instance.
+
+        Raises:
+          KeyError: If a hyperparameter in `values_dict` doesn't exist.
+          ValueError: If `values_dict` cannot be parsed.
+        """
+        for name, value in values_dict.items():
+            self.set_hparam(name, value)
+        return self
+
+    def set_model_structure(self, model_structure):
+        self._model_structure = model_structure
+
+    def get_model_structure(self):
+        return self._model_structure
+
+    def to_json(self, indent=None, separators=None, sort_keys=False):
+        """Serializes the hyperparameters into JSON.
+
+        Args:
+          indent: If a non-negative integer, JSON array elements and object members
+            will be pretty-printed with that indent level. An indent level of 0, or
+            negative, will only insert newlines. `None` (the default) selects the
+            most compact representation.
+          separators: Optional `(item_separator, key_separator)` tuple. Default is
+            `(', ', ': ')`.
+          sort_keys: If `True`, the output dictionaries will be sorted by key.
+
+        Returns:
+          A JSON string.
+        """
+
+        def remove_callables(x):
+            """Omit callable elements from input with arbitrary nesting."""
+            if isinstance(x, dict):
+                return {
+                    k: remove_callables(v)
+                    for k, v in six.iteritems(x)
+                    if not callable(v)
+                }
+            elif isinstance(x, list):
+                return [remove_callables(i) for i in x if not callable(i)]
+            return x
+
+        return json.dumps(
+            remove_callables(self.values()),
+            indent=indent,
+            separators=separators,
+            sort_keys=sort_keys,
+        )
+
+    def parse_json(self, values_json):
+        """Override existing hyperparameter values, parsing new values from a json object.
+
+        Args:
+          values_json: String containing a json object of name:value pairs.
+
+        Returns:
+          The `HParams` instance.
+
+        Raises:
+          KeyError: If a hyperparameter in `values_json` doesn't exist.
+          ValueError: If `values_json` cannot be parsed.
+        """
+        values_map = json.loads(values_json)
+        return self.override_from_dict(values_map)
+
+    def values(self):
+        """Return the hyperparameter values as a Python dictionary.
+
+        Returns:
+          A dictionary with hyperparameter names as keys.  The values are the
+          hyperparameter values.
+        """
+        return {n: getattr(self, n) for n in self._hparam_types.keys()}
+
+    def get(self, key, default=None):
+        """Returns the value of `key` if it exists, else `default`."""
+        if key in self._hparam_types:
+            # Ensure that default is compatible with the parameter type.
+            if default is not None:
+                param_type, is_param_list = self._hparam_types[key]
+                type_str = "list<%s>" % param_type if is_param_list else str(param_type)
+                fail_msg = (
+                    "Hparam '%s' of type '%s' is incompatible with "
+                    "default=%s" % (key, type_str, default)
+                )
+
+                is_default_list = isinstance(default, list)
+                if is_param_list != is_default_list:
+                    raise ValueError(fail_msg)
+
+                try:
+                    if is_default_list:
+                        for value in default:
+                            _cast_to_type_if_compatible(key, param_type, value)
+                    else:
+                        _cast_to_type_if_compatible(key, param_type, default)
+                except ValueError as e:
+                    raise ValueError("%s. %s" % (fail_msg, e))
+
+            return getattr(self, key)
+
+        return default
+
+    def __contains__(self, key):
+        return key in self._hparam_types
+
+    def __str__(self):
+        return str(sorted(self.values().items()))
+
+    def __repr__(self):
+        return "%s(%s)" % (type(self).__name__, self.__str__())
+
+    @staticmethod
+    def _get_kind_name(param_type, is_list):
+        """Returns the field name given parameter type and is_list.
+
+        Args:
+          param_type: Data type of the hparam.
+          is_list: Whether this is a list.
+
+        Returns:
+          A string representation of the field name.
+
+        Raises:
+          ValueError: If parameter type is not recognized.
+        """
+        if issubclass(param_type, bool):
+            # This check must happen before issubclass(param_type, six.integer_types),
+            # since Python considers bool to be a subclass of int.
+            typename = "bool"
+        elif issubclass(param_type, six.integer_types):
+            # Setting 'int' and 'long' types to be 'int64' to ensure the type is
+            # compatible with both Python2 and Python3.
+            typename = "int64"
+        elif issubclass(param_type, (six.string_types, six.binary_type)):
+            # Setting 'string' and 'bytes' types to be 'bytes' to ensure the type is
+            # compatible with both Python2 and Python3.
+            typename = "bytes"
+        elif issubclass(param_type, float):
+            typename = "float"
+        else:
+            raise ValueError("Unsupported parameter type: %s" % str(param_type))
+
+        suffix = "list" if is_list else "value"
+        return "_".join([typename, suffix])
diff --git a/utils/hubert.py b/utils/hubert.py
new file mode 100644
index 0000000000000000000000000000000000000000..84b509fb9fde8485cfb504a675e5d3b7d27622ff
--- /dev/null
+++ b/utils/hubert.py
@@ -0,0 +1,155 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+# This code is modified from https://github.com/svc-develop-team/so-vits-svc/blob/4.0/preprocess_hubert_f0.py
+
+import os
+import librosa
+import torch
+import numpy as np
+from fairseq import checkpoint_utils
+from tqdm import tqdm
+import torch
+
+
+def load_hubert_model(hps):
+    # Load model
+    ckpt_path = hps.hubert_file
+    print("Load Hubert Model...")
+
+    models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
+        [ckpt_path],
+        suffix="",
+    )
+    model = models[0]
+    model.eval()
+
+    if torch.cuda.is_available():
+        model = model.cuda()
+
+    return model
+
+
+def get_hubert_content(hmodel, wav_16k_tensor):
+    feats = wav_16k_tensor
+    if feats.dim() == 2:  # double channels
+        feats = feats.mean(-1)
+    assert feats.dim() == 1, feats.dim()
+    feats = feats.view(1, -1)
+    padding_mask = torch.BoolTensor(feats.shape).fill_(False)
+    inputs = {
+        "source": feats.to(wav_16k_tensor.device),
+        "padding_mask": padding_mask.to(wav_16k_tensor.device),
+        "output_layer": 9,  # layer 9
+    }
+    with torch.no_grad():
+        logits = hmodel.extract_features(**inputs)
+        feats = hmodel.final_proj(logits[0]).squeeze(0)
+
+    return feats
+
+
+def content_vector_encoder(model, audio_path, default_sampling_rate=16000):
+    """
+    # content vector default sr: 16000
+    """
+
+    wav16k, sr = librosa.load(audio_path, sr=default_sampling_rate)
+    device = next(model.parameters()).device
+    wav16k = torch.from_numpy(wav16k).to(device)
+
+    # (1, 256, frame_len)
+    content_feature = get_hubert_content(model, wav_16k_tensor=wav16k)
+
+    return content_feature.cpu().detach().numpy()
+
+
+def repeat_expand_2d(content, target_len):
+    """
+    content : [hubert_dim(256), src_len]
+    target: [hubert_dim(256), target_len]
+    """
+    src_len = content.shape[-1]
+    target = torch.zeros([content.shape[0], target_len], dtype=torch.float).to(
+        content.device
+    )
+    temp = torch.arange(src_len + 1) * target_len / src_len
+    current_pos = 0
+    for i in range(target_len):
+        if i < temp[current_pos + 1]:
+            target[:, i] = content[:, current_pos]
+        else:
+            current_pos += 1
+            target[:, i] = content[:, current_pos]
+
+    return target
+
+
+def get_mapped_features(raw_content_features, mapping_features):
+    """
+    Content Vector: frameshift = 20ms, hop_size = 480 in 24k
+
+    Now it's only used for mapping to bigvgan's mels (sr = 24k, hop_size = 256, frameshift ~= 10.7 ms)
+    """
+    source_hop = 480
+    target_hop = 256
+
+    factor = np.gcd(source_hop, target_hop)
+    source_hop //= factor
+    target_hop //= factor
+    print(
+        "Mapping source's {} frames => target's {} frames".format(
+            target_hop, source_hop
+        )
+    )
+
+    results = []
+    for index, mapping_feat in enumerate(tqdm(mapping_features)):
+        # mappping_feat: (mels_frame_len, n_mels)
+        target_len = len(mapping_feat)
+
+        # (source_len, 256)
+        raw_feats = raw_content_features[index][0].cpu().numpy().T
+        source_len, width = raw_feats.shape
+
+        # const ~= target_len * target_hop
+        const = source_len * source_hop // target_hop * target_hop
+
+        # (source_len * source_hop, dim)
+        up_sampling_feats = np.repeat(raw_feats, source_hop, axis=0)
+        # (const, dim) -> (const/target_hop, target_hop, dim) -> (const/target_hop, dim)
+        down_sampling_feats = np.average(
+            up_sampling_feats[:const].reshape(-1, target_hop, width), axis=1
+        )
+
+        err = abs(target_len - len(down_sampling_feats))
+        if err > 3:
+            print("index:", index)
+            print("mels:", mapping_feat.shape)
+            print("raw content vector:", raw_feats.shape)
+            print("up_sampling:", up_sampling_feats.shape)
+            print("down_sampling_feats:", down_sampling_feats.shape)
+            exit()
+        if len(down_sampling_feats) < target_len:
+            # (1, dim) -> (err, dim)
+            end = down_sampling_feats[-1][None, :].repeat(err, axis=0)
+            down_sampling_feats = np.concatenate([down_sampling_feats, end], axis=0)
+
+        # (target_len, dim)
+        feats = down_sampling_feats[:target_len]
+        results.append(feats)
+
+    return results
+
+
+def extract_hubert_features_of_dataset(datasets, model, out_dir):
+    for utt in tqdm(datasets):
+        uid = utt["Uid"]
+        audio_path = utt["Path"]
+
+        content_vector_feature = content_vector_encoder(model, audio_path)  # (T, 256)
+
+        save_path = os.path.join(out_dir, uid + ".npy")
+        np.save(save_path, content_vector_feature)
diff --git a/utils/io.py b/utils/io.py
new file mode 100644
index 0000000000000000000000000000000000000000..a93e75c64bb99eae801af95267658cb3bbbb6c75
--- /dev/null
+++ b/utils/io.py
@@ -0,0 +1,182 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import numpy as np
+import torch
+import torchaudio
+
+
+def save_feature(process_dir, feature_dir, item, feature, overrides=True):
+    """Save features to path
+
+    Args:
+        process_dir (str): directory to store features
+        feature_dir (_type_): directory to store one type of features (mel, energy, ...)
+        item (str): uid
+        feature (tensor): feature tensor
+        overrides (bool, optional): whether to override existing files. Defaults to True.
+    """
+    process_dir = os.path.join(process_dir, feature_dir)
+    os.makedirs(process_dir, exist_ok=True)
+    out_path = os.path.join(process_dir, item + ".npy")
+
+    if os.path.exists(out_path):
+        if overrides:
+            np.save(out_path, feature)
+    else:
+        np.save(out_path, feature)
+
+
+def save_txt(process_dir, feature_dir, item, feature, overrides=True):
+    process_dir = os.path.join(process_dir, feature_dir)
+    os.makedirs(process_dir, exist_ok=True)
+    out_path = os.path.join(process_dir, item + ".txt")
+
+    if os.path.exists(out_path):
+        if overrides:
+            f = open(out_path, "w")
+            f.writelines(feature)
+            f.close()
+    else:
+        f = open(out_path, "w")
+        f.writelines(feature)
+        f.close()
+
+
+def save_audio(path, waveform, fs, add_silence=False, turn_up=False, volume_peak=0.9):
+    """Save audio to path with processing  (turn up volume, add silence)
+    Args:
+        path (str): path to save audio
+        waveform (numpy array): waveform to save
+        fs (int): sampling rate
+        add_silence (bool, optional): whether to add silence to beginning and end. Defaults to False.
+        turn_up (bool, optional): whether to turn up volume. Defaults to False.
+        volume_peak (float, optional): volume peak. Defaults to 0.9.
+    """
+    if turn_up:
+        # continue to turn up to volume_peak
+        ratio = volume_peak / max(waveform.max(), abs(waveform.min()))
+        waveform = waveform * ratio
+
+    if add_silence:
+        silence_len = fs // 20
+        silence = np.zeros((silence_len,), dtype=waveform.dtype)
+        result = np.concatenate([silence, waveform, silence])
+        waveform = result
+
+    waveform = torch.as_tensor(waveform, dtype=torch.float32, device="cpu")
+    if len(waveform.size()) == 1:
+        waveform = waveform[None, :]
+    elif waveform.size(0) != 1:
+        # Stereo to mono
+        waveform = torch.mean(waveform, dim=0, keepdim=True)
+    torchaudio.save(path, waveform, fs, encoding="PCM_S", bits_per_sample=16)
+
+
+def save_torch_audio(process_dir, feature_dir, item, wav_torch, fs, overrides=True):
+    """Save torch audio to path without processing
+    Args:
+        process_dir (str): directory to store features
+        feature_dir (_type_): directory to store one type of features (mel, energy, ...)
+        item (str): uid
+        wav_torch (tensor): feature tensor
+        fs (int): sampling rate
+        overrides (bool, optional): whether to override existing files. Defaults to True.
+    """
+    if wav_torch.shape != 2:
+        wav_torch = wav_torch.unsqueeze(0)
+
+    process_dir = os.path.join(process_dir, feature_dir)
+    os.makedirs(process_dir, exist_ok=True)
+    out_path = os.path.join(process_dir, item + ".wav")
+
+    torchaudio.save(out_path, wav_torch, fs)
+
+
+async def async_load_audio(path, sample_rate: int = 24000):
+    r"""
+    Args:
+        path: The source loading path.
+        sample_rate: The target sample rate, will automatically resample if necessary.
+
+    Returns:
+        waveform: The waveform object. Should be [1 x sequence_len].
+    """
+
+    async def use_torchaudio_load(path):
+        return torchaudio.load(path)
+
+    waveform, sr = await use_torchaudio_load(path)
+    waveform = torch.mean(waveform, dim=0, keepdim=True)
+
+    if sr != sample_rate:
+        waveform = torchaudio.functional.resample(waveform, sr, sample_rate)
+
+    if torch.any(torch.isnan(waveform) or torch.isinf(waveform)):
+        raise ValueError("NaN or Inf found in waveform.")
+    return waveform
+
+
+async def async_save_audio(
+    path,
+    waveform,
+    sample_rate: int = 24000,
+    add_silence: bool = False,
+    volume_peak: float = 0.9,
+):
+    r"""
+    Args:
+        path: The target saving path.
+        waveform: The waveform object. Should be [n_channel x sequence_len].
+        sample_rate: Sample rate.
+        add_silence: If ``true``, concat 0.05s silence to beginning and end.
+        volume_peak: Turn up volume for larger number, vice versa.
+    """
+
+    async def use_torchaudio_save(path, waveform, sample_rate):
+        torchaudio.save(
+            path, waveform, sample_rate, encoding="PCM_S", bits_per_sample=16
+        )
+
+    waveform = torch.as_tensor(waveform, device="cpu", dtype=torch.float32)
+    shape = waveform.size()[:-1]
+
+    ratio = abs(volume_peak) / max(waveform.max(), abs(waveform.min()))
+    waveform = waveform * ratio
+
+    if add_silence:
+        silence_len = sample_rate // 20
+        silence = torch.zeros((*shape, silence_len), dtype=waveform.type())
+        waveform = torch.concatenate((silence, waveform, silence), dim=-1)
+
+    if waveform.dim() == 1:
+        waveform = waveform[None]
+
+    await use_torchaudio_save(path, waveform, sample_rate)
+
+
+def load_mel_extrema(cfg, dataset_name, split):
+    dataset_dir = os.path.join(
+        cfg.OUTPUT_PATH,
+        "preprocess/{}_version".format(cfg.data.process_version),
+        dataset_name,
+    )
+
+    min_file = os.path.join(
+        dataset_dir,
+        "mel_min_max",
+        split.split("_")[-1],
+        "mel_min.npy",
+    )
+    max_file = os.path.join(
+        dataset_dir,
+        "mel_min_max",
+        split.split("_")[-1],
+        "mel_max.npy",
+    )
+    mel_min = np.load(min_file)
+    mel_max = np.load(max_file)
+    return mel_min, mel_max
diff --git a/utils/io_optim.py b/utils/io_optim.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9afaa061412f7ffd98ea4f7ef6bdb7660efb73c
--- /dev/null
+++ b/utils/io_optim.py
@@ -0,0 +1,123 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torchaudio
+import json
+import os
+import numpy as np
+import librosa
+import whisper
+from torch.nn.utils.rnn import pad_sequence
+
+
+class TorchaudioDataset(torch.utils.data.Dataset):
+    def __init__(self, cfg, dataset, sr, accelerator=None, metadata=None):
+        """
+        Args:
+            cfg: config
+            dataset: dataset name
+
+        """
+        assert isinstance(dataset, str)
+
+        self.sr = sr
+        self.cfg = cfg
+
+        if metadata is None:
+            self.train_metadata_path = os.path.join(
+                cfg.preprocess.processed_dir, dataset, cfg.preprocess.train_file
+            )
+            self.valid_metadata_path = os.path.join(
+                cfg.preprocess.processed_dir, dataset, cfg.preprocess.valid_file
+            )
+            self.metadata = self.get_metadata()
+        else:
+            self.metadata = metadata
+
+        if accelerator is not None:
+            self.device = accelerator.device
+        elif torch.cuda.is_available():
+            self.device = torch.device("cuda")
+        else:
+            self.device = torch.device("cpu")
+
+    def get_metadata(self):
+        metadata = []
+        with open(self.train_metadata_path, "r", encoding="utf-8") as t:
+            metadata.extend(json.load(t))
+        with open(self.valid_metadata_path, "r", encoding="utf-8") as v:
+            metadata.extend(json.load(v))
+        return metadata
+
+    def __len__(self):
+        return len(self.metadata)
+
+    def __getitem__(self, index):
+        utt_info = self.metadata[index]
+        wav_path = utt_info["Path"]
+
+        wav, sr = torchaudio.load(wav_path)
+
+        # resample
+        if sr != self.sr:
+            wav = torchaudio.functional.resample(wav, sr, self.sr)
+        # downmixing
+        if wav.shape[0] > 1:
+            wav = torch.mean(wav, dim=0, keepdim=True)
+        assert wav.shape[0] == 1
+        wav = wav.squeeze(0)
+        # record the length of wav without padding
+        length = wav.shape[0]
+        # wav: (T)
+        return utt_info, wav, length
+
+
+class LibrosaDataset(TorchaudioDataset):
+    def __init__(self, cfg, dataset, sr, accelerator=None, metadata=None):
+        super().__init__(cfg, dataset, sr, accelerator, metadata)
+
+    def __getitem__(self, index):
+        utt_info = self.metadata[index]
+        wav_path = utt_info["Path"]
+
+        wav, _ = librosa.load(wav_path, sr=self.sr)
+        # wav: (T)
+        wav = torch.from_numpy(wav)
+
+        # record the length of wav without padding
+        length = wav.shape[0]
+        return utt_info, wav, length
+
+
+class FFmpegDataset(TorchaudioDataset):
+    def __init__(self, cfg, dataset, sr, accelerator=None, metadata=None):
+        super().__init__(cfg, dataset, sr, accelerator, metadata)
+
+    def __getitem__(self, index):
+        utt_info = self.metadata[index]
+        wav_path = utt_info["Path"]
+
+        # wav: (T,)
+        wav = whisper.load_audio(wav_path, sr=16000)  # sr = 16000
+        # convert to torch tensor
+        wav = torch.from_numpy(wav)
+        # record the length of wav without padding
+        length = wav.shape[0]
+
+        return utt_info, wav, length
+
+
+def collate_batch(batch_list):
+    """
+    Args:
+        batch_list: list of (metadata, wav, length)
+    """
+    metadata = [item[0] for item in batch_list]
+    # wavs: (B, T)
+    wavs = pad_sequence([item[1] for item in batch_list], batch_first=True)
+    lens = [item[2] for item in batch_list]
+
+    return metadata, wavs, lens
diff --git a/utils/mel.py b/utils/mel.py
new file mode 100644
index 0000000000000000000000000000000000000000..d32d38226dfe6c0162527b3136a392b9168c7f06
--- /dev/null
+++ b/utils/mel.py
@@ -0,0 +1,283 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+from librosa.filters import mel as librosa_mel_fn
+
+
+def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
+    return torch.log(torch.clamp(x, min=clip_val) * C)
+
+
+def spectral_normalize_torch(magnitudes):
+    output = dynamic_range_compression_torch(magnitudes)
+    return output
+
+
+def extract_linear_features(y, cfg, center=False):
+    if torch.min(y) < -1.0:
+        print("min value is ", torch.min(y))
+    if torch.max(y) > 1.0:
+        print("max value is ", torch.max(y))
+
+    global hann_window
+    hann_window[str(y.device)] = torch.hann_window(cfg.win_size).to(y.device)
+
+    y = torch.nn.functional.pad(
+        y.unsqueeze(1),
+        (int((cfg.n_fft - cfg.hop_size) / 2), int((cfg.n_fft - cfg.hop_size) / 2)),
+        mode="reflect",
+    )
+    y = y.squeeze(1)
+
+    # complex tensor as default, then use view_as_real for future pytorch compatibility
+    spec = torch.stft(
+        y,
+        cfg.n_fft,
+        hop_length=cfg.hop_size,
+        win_length=cfg.win_size,
+        window=hann_window[str(y.device)],
+        center=center,
+        pad_mode="reflect",
+        normalized=False,
+        onesided=True,
+        return_complex=True,
+    )
+    spec = torch.view_as_real(spec)
+    spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
+    spec = torch.squeeze(spec, 0)
+    return spec
+
+
+def mel_spectrogram_torch(y, cfg, center=False):
+    if torch.min(y) < -1.0:
+        print("min value is ", torch.min(y))
+    if torch.max(y) > 1.0:
+        print("max value is ", torch.max(y))
+
+    global mel_basis, hann_window
+    if cfg.fmax not in mel_basis:
+        mel = librosa_mel_fn(
+            sr=cfg.sample_rate,
+            n_fft=cfg.n_fft,
+            n_mels=cfg.n_mel,
+            fmin=cfg.fmin,
+            fmax=cfg.fmax,
+        )
+        mel_basis[str(cfg.fmax) + "_" + str(y.device)] = (
+            torch.from_numpy(mel).float().to(y.device)
+        )
+        hann_window[str(y.device)] = torch.hann_window(cfg.win_size).to(y.device)
+
+    y = torch.nn.functional.pad(
+        y.unsqueeze(1),
+        (int((cfg.n_fft - cfg.hop_size) / 2), int((cfg.n_fft - cfg.hop_size) / 2)),
+        mode="reflect",
+    )
+    y = y.squeeze(1)
+
+    spec = torch.stft(
+        y,
+        cfg.n_fft,
+        hop_length=cfg.hop_size,
+        win_length=cfg.win_size,
+        window=hann_window[str(y.device)],
+        center=center,
+        pad_mode="reflect",
+        normalized=False,
+        onesided=True,
+        return_complex=True,
+    )
+
+    spec = torch.view_as_real(spec)
+    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
+
+    spec = torch.matmul(mel_basis[str(cfg.fmax) + "_" + str(y.device)], spec)
+    spec = spectral_normalize_torch(spec)
+
+    return spec
+
+
+mel_basis = {}
+hann_window = {}
+
+
+def extract_mel_features(
+    y,
+    cfg,
+    center=False
+    # n_fft, n_mel, sampling_rate, hop_size, win_size, fmin, fmax, center=False
+):
+    """Extract mel features
+
+    Args:
+        y (tensor): audio data in tensor
+        cfg (dict): configuration in cfg.preprocess
+        center (bool, optional): In STFT, whether t-th frame is centered at time t*hop_length. Defaults to False.
+
+    Returns:
+        tensor: a tensor containing the mel feature calculated based on STFT result
+    """
+    if torch.min(y) < -1.0:
+        print("min value is ", torch.min(y))
+    if torch.max(y) > 1.0:
+        print("max value is ", torch.max(y))
+
+    global mel_basis, hann_window
+    if cfg.fmax not in mel_basis:
+        mel = librosa_mel_fn(
+            sr=cfg.sample_rate,
+            n_fft=cfg.n_fft,
+            n_mels=cfg.n_mel,
+            fmin=cfg.fmin,
+            fmax=cfg.fmax,
+        )
+        mel_basis[str(cfg.fmax) + "_" + str(y.device)] = (
+            torch.from_numpy(mel).float().to(y.device)
+        )
+        hann_window[str(y.device)] = torch.hann_window(cfg.win_size).to(y.device)
+
+    y = torch.nn.functional.pad(
+        y.unsqueeze(1),
+        (int((cfg.n_fft - cfg.hop_size) / 2), int((cfg.n_fft - cfg.hop_size) / 2)),
+        mode="reflect",
+    )
+    y = y.squeeze(1)
+
+    # complex tensor as default, then use view_as_real for future pytorch compatibility
+    spec = torch.stft(
+        y,
+        cfg.n_fft,
+        hop_length=cfg.hop_size,
+        win_length=cfg.win_size,
+        window=hann_window[str(y.device)],
+        center=center,
+        pad_mode="reflect",
+        normalized=False,
+        onesided=True,
+        return_complex=True,
+    )
+    spec = torch.view_as_real(spec)
+    spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
+
+    spec = torch.matmul(mel_basis[str(cfg.fmax) + "_" + str(y.device)], spec)
+    spec = spectral_normalize_torch(spec)
+
+    return spec.squeeze(0)
+
+
+def extract_mel_features_tts(
+    y,
+    cfg,
+    center=False,
+    taco=False,
+    _stft=None,
+):
+    """Extract mel features
+
+    Args:
+        y (tensor): audio data in tensor
+        cfg (dict): configuration in cfg.preprocess
+        center (bool, optional): In STFT, whether t-th frame is centered at time t*hop_length. Defaults to False.
+        taco: use tacotron mel
+
+    Returns:
+        tensor: a tensor containing the mel feature calculated based on STFT result
+    """
+    if not taco:
+        if torch.min(y) < -1.0:
+            print("min value is ", torch.min(y))
+        if torch.max(y) > 1.0:
+            print("max value is ", torch.max(y))
+
+        global mel_basis, hann_window
+        if cfg.fmax not in mel_basis:
+            mel = librosa_mel_fn(
+                sr=cfg.sample_rate,
+                n_fft=cfg.n_fft,
+                n_mels=cfg.n_mel,
+                fmin=cfg.fmin,
+                fmax=cfg.fmax,
+            )
+            mel_basis[str(cfg.fmax) + "_" + str(y.device)] = (
+                torch.from_numpy(mel).float().to(y.device)
+            )
+            hann_window[str(y.device)] = torch.hann_window(cfg.win_size).to(y.device)
+
+        y = torch.nn.functional.pad(
+            y.unsqueeze(1),
+            (int((cfg.n_fft - cfg.hop_size) / 2), int((cfg.n_fft - cfg.hop_size) / 2)),
+            mode="reflect",
+        )
+        y = y.squeeze(1)
+
+        # complex tensor as default, then use view_as_real for future pytorch compatibility
+        spec = torch.stft(
+            y,
+            cfg.n_fft,
+            hop_length=cfg.hop_size,
+            win_length=cfg.win_size,
+            window=hann_window[str(y.device)],
+            center=center,
+            pad_mode="reflect",
+            normalized=False,
+            onesided=True,
+            return_complex=True,
+        )
+        spec = torch.view_as_real(spec)
+        spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
+
+        spec = torch.matmul(mel_basis[str(cfg.fmax) + "_" + str(y.device)], spec)
+        spec = spectral_normalize_torch(spec)
+        spec = spec.squeeze(0)
+    else:
+        audio = torch.clip(y, -1, 1)
+        audio = torch.autograd.Variable(audio, requires_grad=False)
+        spec, energy = _stft.mel_spectrogram(audio)
+        spec = torch.squeeze(spec, 0)
+
+    spec = torch.matmul(mel_basis[str(cfg.fmax) + "_" + str(y.device)], spec)
+    spec = spectral_normalize_torch(spec)
+
+    return spec.squeeze(0)
+
+
+def amplitude_phase_spectrum(y, cfg):
+    hann_window = torch.hann_window(cfg.win_size).to(y.device)
+
+    y = torch.nn.functional.pad(
+        y.unsqueeze(1),
+        (int((cfg.n_fft - cfg.hop_size) / 2), int((cfg.n_fft - cfg.hop_size) / 2)),
+        mode="reflect",
+    )
+    y = y.squeeze(1)
+
+    stft_spec = torch.stft(
+        y,
+        cfg.n_fft,
+        hop_length=cfg.hop_size,
+        win_length=cfg.win_size,
+        window=hann_window,
+        center=False,
+        return_complex=True,
+    )
+
+    stft_spec = torch.view_as_real(stft_spec)
+    if stft_spec.size()[0] == 1:
+        stft_spec = stft_spec.squeeze(0)
+
+    if len(list(stft_spec.size())) == 4:
+        rea = stft_spec[:, :, :, 0]  # [batch_size, n_fft//2+1, frames]
+        imag = stft_spec[:, :, :, 1]  # [batch_size, n_fft//2+1, frames]
+    else:
+        rea = stft_spec[:, :, 0]  # [n_fft//2+1, frames]
+        imag = stft_spec[:, :, 1]  # [n_fft//2+1, frames]
+
+    log_amplitude = torch.log(
+        torch.abs(torch.sqrt(torch.pow(rea, 2) + torch.pow(imag, 2))) + 1e-5
+    )  # [n_fft//2+1, frames]
+    phase = torch.atan2(imag, rea)  # [n_fft//2+1, frames]
+
+    return log_amplitude, phase, rea, imag
diff --git a/utils/mert.py b/utils/mert.py
new file mode 100644
index 0000000000000000000000000000000000000000..4181429feb36f5013bafafff9505b0b9571485b4
--- /dev/null
+++ b/utils/mert.py
@@ -0,0 +1,139 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+# This code is modified from https://huggingface.co/m-a-p/MERT-v1-330M
+
+import torch
+from tqdm import tqdm
+import numpy as np
+
+from transformers import Wav2Vec2FeatureExtractor
+from transformers import AutoModel
+import torchaudio
+import torchaudio.transforms as T
+from sklearn.preprocessing import StandardScaler
+
+
+def mert_encoder(model, processor, audio_path, hps):
+    """
+    # mert default sr: 24000
+    """
+    with torch.no_grad():
+        resample_rate = processor.sampling_rate
+        device = next(model.parameters()).device
+
+        input_audio, sampling_rate = torchaudio.load(audio_path)
+        input_audio = input_audio.squeeze()
+
+        if sampling_rate != resample_rate:
+            resampler = T.Resample(sampling_rate, resample_rate)
+            input_audio = resampler(input_audio)
+
+        inputs = processor(
+            input_audio, sampling_rate=resample_rate, return_tensors="pt"
+        ).to(
+            device
+        )  # {input_values: tensor, attention_mask: tensor}
+
+        outputs = model(**inputs, output_hidden_states=True)  # list: len is 25
+
+    # [25 layer, Time steps, 1024 feature_dim]
+    # all_layer_hidden_states = torch.stack(outputs.hidden_states).squeeze()
+    # mert_features.append(all_layer_hidden_states)
+
+    feature = outputs.hidden_states[
+        hps.mert_feature_layer
+    ].squeeze()  # [1, frame len, 1024] ->  [frame len, 1024]
+
+    return feature.cpu().detach().numpy()
+
+
+def mert_features_normalization(raw_mert_features):
+    normalized_mert_features = list()
+
+    mert_features = np.array(raw_mert_features)
+    scaler = StandardScaler().fit(mert_features)
+    for raw_mert_feature in raw_mert_feature:
+        normalized_mert_feature = scaler.transform(raw_mert_feature)
+        normalized_mert_features.append(normalized_mert_feature)
+    return normalized_mert_features
+
+
+def get_mapped_mert_features(raw_mert_features, mapping_features, fast_mapping=True):
+    source_hop = 320
+    target_hop = 256
+
+    factor = np.gcd(source_hop, target_hop)
+    source_hop //= factor
+    target_hop //= factor
+    print(
+        "Mapping source's {} frames => target's {} frames".format(
+            target_hop, source_hop
+        )
+    )
+
+    mert_features = []
+    for index, mapping_feat in enumerate(tqdm(mapping_features)):
+        # mapping_feat: (mels_frame_len, n_mels)
+        target_len = mapping_feat.shape[0]
+
+        # (frame_len, 1024)
+        raw_feats = raw_mert_features[index].cpu().numpy()
+        source_len, width = raw_feats.shape
+
+        # const ~= target_len * target_hop
+        const = source_len * source_hop // target_hop * target_hop
+
+        # (source_len * source_hop, dim)
+        up_sampling_feats = np.repeat(raw_feats, source_hop, axis=0)
+        # (const, dim) -> (const/target_hop, target_hop, dim) -> (const/target_hop, dim)
+        down_sampling_feats = np.average(
+            up_sampling_feats[:const].reshape(-1, target_hop, width), axis=1
+        )
+
+        err = abs(target_len - len(down_sampling_feats))
+        if err > 3:
+            print("index:", index)
+            print("mels:", mapping_feat.shape)
+            print("raw mert vector:", raw_feats.shape)
+            print("up_sampling:", up_sampling_feats.shape)
+            print("const:", const)
+            print("down_sampling_feats:", down_sampling_feats.shape)
+            exit()
+        if len(down_sampling_feats) < target_len:
+            # (1, dim) -> (err, dim)
+            end = down_sampling_feats[-1][None, :].repeat(err, axis=0)
+            down_sampling_feats = np.concatenate([down_sampling_feats, end], axis=0)
+
+        # (target_len, dim)
+        feats = down_sampling_feats[:target_len]
+        mert_features.append(feats)
+
+    return mert_features
+
+
+def load_mert_model(hps):
+    print("Loading MERT Model: ", hps.mert_model)
+
+    # Load model
+    model_name = hps.mert_model
+    model = AutoModel.from_pretrained(model_name, trust_remote_code=True)
+
+    if torch.cuda.is_available():
+        model = model.cuda()
+
+    # model = model.eval()
+
+    preprocessor = Wav2Vec2FeatureExtractor.from_pretrained(
+        model_name, trust_remote_code=True
+    )
+    return model, preprocessor
+
+
+# loading the corresponding preprocessor config
+# def load_preprocessor (model_name="m-a-p/MERT-v1-330M"):
+#     print('load_preprocessor...')
+#     preprocessor = Wav2Vec2FeatureExtractor.from_pretrained(model_name,trust_remote_code=True)
+#     return preprocessor
diff --git a/utils/model_summary.py b/utils/model_summary.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec72b0d17869dc0886a9eb665efedb70bb307bbf
--- /dev/null
+++ b/utils/model_summary.py
@@ -0,0 +1,74 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import humanfriendly
+import numpy as np
+import torch
+
+
+def get_human_readable_count(number: int) -> str:
+    """Return human_readable_count
+
+    Originated from:
+    https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pytorch_lightning/core/memory.py
+
+    Abbreviates an integer number with K, M, B, T for thousands, millions,
+    billions and trillions, respectively.
+    Examples:
+        >>> get_human_readable_count(123)
+        '123  '
+        >>> get_human_readable_count(1234)  # (one thousand)
+        '1 K'
+        >>> get_human_readable_count(2e6)   # (two million)
+        '2 M'
+        >>> get_human_readable_count(3e9)   # (three billion)
+        '3 B'
+        >>> get_human_readable_count(4e12)  # (four trillion)
+        '4 T'
+        >>> get_human_readable_count(5e15)  # (more than trillion)
+        '5,000 T'
+    Args:
+        number: a positive integer number
+    Return:
+        A string formatted according to the pattern described above.
+    """
+    assert number >= 0
+    labels = [" ", "K", "M", "B", "T"]
+    num_digits = int(np.floor(np.log10(number)) + 1 if number > 0 else 1)
+    num_groups = int(np.ceil(num_digits / 3))
+    num_groups = min(num_groups, len(labels))
+    shift = -3 * (num_groups - 1)
+    number = number * (10**shift)
+    index = num_groups - 1
+    return f"{number:.2f} {labels[index]}"
+
+
+def to_bytes(dtype) -> int:
+    return int(str(dtype)[-2:]) // 8
+
+
+def model_summary(model: torch.nn.Module) -> str:
+    message = "Model structure:\n"
+    message += str(model)
+    tot_params = sum(p.numel() for p in model.parameters())
+    num_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+    percent_trainable = "{:.1f}".format(num_params * 100.0 / tot_params)
+    tot_params = get_human_readable_count(tot_params)
+    num_params = get_human_readable_count(num_params)
+    message += "\n\nModel summary:\n"
+    message += f"    Class Name: {model.__class__.__name__}\n"
+    message += f"    Total Number of model parameters: {tot_params}\n"
+    message += (
+        f"    Number of trainable parameters: {num_params} ({percent_trainable}%)\n"
+    )
+    num_bytes = humanfriendly.format_size(
+        sum(
+            p.numel() * to_bytes(p.dtype) for p in model.parameters() if p.requires_grad
+        )
+    )
+    message += f"    Size: {num_bytes}\n"
+    dtype = next(iter(model.parameters())).dtype
+    message += f"    Type: {dtype}"
+    return message
diff --git a/utils/prompt_preparer.py b/utils/prompt_preparer.py
new file mode 100644
index 0000000000000000000000000000000000000000..945a5e241c0fbb38ec4021e280dfa2907c9c6abf
--- /dev/null
+++ b/utils/prompt_preparer.py
@@ -0,0 +1,68 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+
+
+class PromptPreparer:
+    def prepare_prompts(self, y, y_lens, codes, nar_stage, y_prompts_codes):
+        if self.prefix_mode == 0:
+            y_emb, prefix_len = self._handle_prefix_mode_0(y, codes, nar_stage)
+        elif self.prefix_mode == 1:
+            y_emb, prefix_len = self._handle_prefix_mode_1(y, y_lens, codes, nar_stage)
+        elif self.prefix_mode in [2, 4]:
+            y_emb, prefix_len = self._handle_prefix_mode_2_4(
+                y, y_lens, codes, nar_stage, y_prompts_codes
+            )
+        else:
+            raise ValueError("Invalid prefix mode")
+
+        return y_emb, prefix_len
+
+    def _handle_prefix_mode_0(self, y, codes, nar_stage):
+        prefix_len = 0
+        y_emb = self.nar_audio_embeddings[0](y)
+        for j in range(1, nar_stage):
+            y_emb = y_emb + self.nar_audio_embeddings[j](codes[..., j])
+        return y_emb, 0
+
+    def _handle_prefix_mode_1(self, y, y_lens, codes, nar_stage):
+        int_low = (0.25 * y_lens.min()).type(torch.int64).item()
+        prefix_len = torch.randint(int_low, int_low * 2, size=()).item()
+        prefix_len = min(prefix_len, 225)
+
+        y_prompts = self.nar_audio_embeddings[0](y[:, :prefix_len])
+        y_emb = self.nar_audio_embeddings[0](y[:, prefix_len:])
+        for j in range(1, self.num_quantizers):
+            y_prompts += self.nar_audio_embeddings[j](codes[:, :prefix_len, j])
+            if j < nar_stage:
+                y_emb += self.nar_audio_embeddings[j](codes[:, prefix_len:, j])
+        y_emb = torch.concat([y_prompts, y_emb], axis=1)
+        return y_emb, prefix_len
+
+    def _handle_prefix_mode_2_4(self, y, y_lens, codes, nar_stage, y_prompts_codes):
+        if self.prefix_mode == 2:
+            prefix_len = min(225, int(0.25 * y_lens.min().item()))
+
+            y_prompts_codes = []
+            for b in range(codes.shape[0]):
+                start = self.rng.randint(0, y_lens[b].item() - prefix_len)
+                y_prompts_codes.append(
+                    torch.clone(codes[b, start : start + prefix_len])
+                )
+                codes[b, start : start + prefix_len, nar_stage] = self.audio_token_num
+            y_prompts_codes = torch.stack(y_prompts_codes, dim=0)
+        else:
+            prefix_len = y_prompts_codes.shape[1]
+
+        y_prompts = self.nar_audio_embeddings[0](y_prompts_codes[..., 0])
+        y_emb = self.nar_audio_embeddings[0](y)
+        for j in range(1, self.num_quantizers):
+            y_prompts += self.nar_audio_embeddings[j](y_prompts_codes[..., j])
+            if j < nar_stage:
+                y_emb += self.nar_audio_embeddings[j](codes[..., j])
+        y_emb = torch.concat([y_prompts, y_emb], axis=1)
+
+        return y_emb, prefix_len
diff --git a/utils/ssim.py b/utils/ssim.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0b95007b2c225f3cae869f0653a733d1d92043a
--- /dev/null
+++ b/utils/ssim.py
@@ -0,0 +1,80 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+# This code is modified from https://github.com/Po-Hsun-Su/pytorch-ssim
+
+import torch
+import torch.nn.functional as F
+from torch.autograd import Variable
+from math import exp
+
+
+def gaussian(window_size, sigma):
+    gauss = torch.Tensor(
+        [
+            exp(-((x - window_size // 2) ** 2) / float(2 * sigma**2))
+            for x in range(window_size)
+        ]
+    )
+    return gauss / gauss.sum()
+
+
+def create_window(window_size, channel):
+    _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
+    _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
+    window = Variable(
+        _2D_window.expand(channel, 1, window_size, window_size).contiguous()
+    )
+    return window
+
+
+def _ssim(img1, img2, window, window_size, channel, size_average=True):
+    mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
+    mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)
+
+    mu1_sq = mu1.pow(2)
+    mu2_sq = mu2.pow(2)
+    mu1_mu2 = mu1 * mu2
+
+    sigma1_sq = (
+        F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq
+    )
+    sigma2_sq = (
+        F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq
+    )
+    sigma12 = (
+        F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel)
+        - mu1_mu2
+    )
+
+    C1 = 0.01**2
+    C2 = 0.03**2
+
+    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / (
+        (mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)
+    )
+
+    if size_average:
+        return ssim_map.mean()
+    else:
+        return ssim_map.mean(1)
+
+
+class SSIM(torch.nn.Module):
+    def __init__(self, window_size=11, size_average=True):
+        super(SSIM, self).__init__()
+        self.window_size = window_size
+        self.size_average = size_average
+        self.channel = 1
+        self.window = create_window(window_size, self.channel)
+
+    def forward(self, fake, real, bias=6.0):
+        fake = fake[:, None, :, :] + bias  # [B, 1, T, n_mels]
+        real = real[:, None, :, :] + bias  # [B, 1, T, n_mels]
+        self.window = self.window.to(dtype=fake.dtype, device=fake.device)
+        loss = 1 - _ssim(
+            fake, real, self.window, self.window_size, self.channel, self.size_average
+        )
+        return loss
diff --git a/utils/stft.py b/utils/stft.py
new file mode 100644
index 0000000000000000000000000000000000000000..bcec4c84ace0cc40d65361316222b090428cc391
--- /dev/null
+++ b/utils/stft.py
@@ -0,0 +1,278 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn.functional as F
+import numpy as np
+from scipy.signal import get_window
+from librosa.util import pad_center, tiny
+from librosa.filters import mel as librosa_mel_fn
+
+import torch
+import numpy as np
+import librosa.util as librosa_util
+from scipy.signal import get_window
+
+
+def window_sumsquare(
+    window,
+    n_frames,
+    hop_length,
+    win_length,
+    n_fft,
+    dtype=np.float32,
+    norm=None,
+):
+    """
+    # from librosa 0.6
+    Compute the sum-square envelope of a window function at a given hop length.
+
+    This is used to estimate modulation effects induced by windowing
+    observations in short-time fourier transforms.
+
+    Parameters
+    ----------
+    window : string, tuple, number, callable, or list-like
+        Window specification, as in `get_window`
+
+    n_frames : int > 0
+        The number of analysis frames
+
+    hop_length : int > 0
+        The number of samples to advance between frames
+
+    win_length : [optional]
+        The length of the window function.  By default, this matches `n_fft`.
+
+    n_fft : int > 0
+        The length of each analysis frame.
+
+    dtype : np.dtype
+        The data type of the output
+
+    Returns
+    -------
+    wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))`
+        The sum-squared envelope of the window function
+    """
+    if win_length is None:
+        win_length = n_fft
+
+    n = n_fft + hop_length * (n_frames - 1)
+    x = np.zeros(n, dtype=dtype)
+
+    # Compute the squared window at the desired length
+    win_sq = get_window(window, win_length, fftbins=True)
+    win_sq = librosa_util.normalize(win_sq, norm=norm) ** 2
+    win_sq = librosa_util.pad_center(win_sq, n_fft)
+
+    # Fill the envelope
+    for i in range(n_frames):
+        sample = i * hop_length
+        x[sample : min(n, sample + n_fft)] += win_sq[: max(0, min(n_fft, n - sample))]
+    return x
+
+
+def griffin_lim(magnitudes, stft_fn, n_iters=30):
+    """
+    PARAMS
+    ------
+    magnitudes: spectrogram magnitudes
+    stft_fn: STFT class with transform (STFT) and inverse (ISTFT) methods
+    """
+
+    angles = np.angle(np.exp(2j * np.pi * np.random.rand(*magnitudes.size())))
+    angles = angles.astype(np.float32)
+    angles = torch.autograd.Variable(torch.from_numpy(angles))
+    signal = stft_fn.inverse(magnitudes, angles).squeeze(1)
+
+    for i in range(n_iters):
+        _, angles = stft_fn.transform(signal)
+        signal = stft_fn.inverse(magnitudes, angles).squeeze(1)
+    return signal
+
+
+def dynamic_range_compression(x, C=1, clip_val=1e-5):
+    """
+    PARAMS
+    ------
+    C: compression factor
+    """
+    return torch.log(torch.clamp(x, min=clip_val) * C)
+
+
+def dynamic_range_decompression(x, C=1):
+    """
+    PARAMS
+    ------
+    C: compression factor used to compress
+    """
+    return torch.exp(x) / C
+
+
+class STFT(torch.nn.Module):
+    """adapted from Prem Seetharaman's https://github.com/pseeth/pytorch-stft"""
+
+    def __init__(self, filter_length, hop_length, win_length, window="hann"):
+        super(STFT, self).__init__()
+        self.filter_length = filter_length
+        self.hop_length = hop_length
+        self.win_length = win_length
+        self.window = window
+        self.forward_transform = None
+        scale = self.filter_length / self.hop_length
+        fourier_basis = np.fft.fft(np.eye(self.filter_length))
+
+        cutoff = int((self.filter_length / 2 + 1))
+        fourier_basis = np.vstack(
+            [np.real(fourier_basis[:cutoff, :]), np.imag(fourier_basis[:cutoff, :])]
+        )
+
+        forward_basis = torch.FloatTensor(fourier_basis[:, None, :])
+        inverse_basis = torch.FloatTensor(
+            np.linalg.pinv(scale * fourier_basis).T[:, None, :]
+        )
+
+        if window is not None:
+            assert filter_length >= win_length
+            # get window and zero center pad it to filter_length
+            fft_window = get_window(window, win_length, fftbins=True)
+            fft_window = pad_center(fft_window, filter_length)
+            fft_window = torch.from_numpy(fft_window).float()
+
+            # window the bases
+            forward_basis *= fft_window
+            inverse_basis *= fft_window
+
+        self.register_buffer("forward_basis", forward_basis.float())
+        self.register_buffer("inverse_basis", inverse_basis.float())
+
+    def transform(self, input_data):
+        num_batches = input_data.size(0)
+        num_samples = input_data.size(1)
+
+        self.num_samples = num_samples
+
+        # similar to librosa, reflect-pad the input
+        input_data = input_data.view(num_batches, 1, num_samples)
+        input_data = F.pad(
+            input_data.unsqueeze(1),
+            (int(self.filter_length / 2), int(self.filter_length / 2), 0, 0),
+            mode="reflect",
+        )
+        input_data = input_data.squeeze(1)
+
+        forward_transform = F.conv1d(
+            input_data.cuda(),
+            torch.autograd.Variable(self.forward_basis, requires_grad=False).cuda(),
+            stride=self.hop_length,
+            padding=0,
+        ).cpu()
+
+        cutoff = int((self.filter_length / 2) + 1)
+        real_part = forward_transform[:, :cutoff, :]
+        imag_part = forward_transform[:, cutoff:, :]
+
+        magnitude = torch.sqrt(real_part**2 + imag_part**2)
+        phase = torch.autograd.Variable(torch.atan2(imag_part.data, real_part.data))
+
+        return magnitude, phase
+
+    def inverse(self, magnitude, phase):
+        recombine_magnitude_phase = torch.cat(
+            [magnitude * torch.cos(phase), magnitude * torch.sin(phase)], dim=1
+        )
+
+        inverse_transform = F.conv_transpose1d(
+            recombine_magnitude_phase,
+            torch.autograd.Variable(self.inverse_basis, requires_grad=False),
+            stride=self.hop_length,
+            padding=0,
+        )
+
+        if self.window is not None:
+            window_sum = window_sumsquare(
+                self.window,
+                magnitude.size(-1),
+                hop_length=self.hop_length,
+                win_length=self.win_length,
+                n_fft=self.filter_length,
+                dtype=np.float32,
+            )
+            # remove modulation effects
+            approx_nonzero_indices = torch.from_numpy(
+                np.where(window_sum > tiny(window_sum))[0]
+            )
+            window_sum = torch.autograd.Variable(
+                torch.from_numpy(window_sum), requires_grad=False
+            )
+            window_sum = window_sum.cuda() if magnitude.is_cuda else window_sum
+            inverse_transform[:, :, approx_nonzero_indices] /= window_sum[
+                approx_nonzero_indices
+            ]
+
+            # scale by hop ratio
+            inverse_transform *= float(self.filter_length) / self.hop_length
+
+        inverse_transform = inverse_transform[:, :, int(self.filter_length / 2) :]
+        inverse_transform = inverse_transform[:, :, : -int(self.filter_length / 2) :]
+
+        return inverse_transform
+
+    def forward(self, input_data):
+        self.magnitude, self.phase = self.transform(input_data)
+        reconstruction = self.inverse(self.magnitude, self.phase)
+        return reconstruction
+
+
+class TacotronSTFT(torch.nn.Module):
+    def __init__(
+        self,
+        filter_length,
+        hop_length,
+        win_length,
+        n_mel_channels,
+        sampling_rate,
+        mel_fmin,
+        mel_fmax,
+    ):
+        super(TacotronSTFT, self).__init__()
+        self.n_mel_channels = n_mel_channels
+        self.sampling_rate = sampling_rate
+        self.stft_fn = STFT(filter_length, hop_length, win_length)
+        mel_basis = librosa_mel_fn(
+            sampling_rate, filter_length, n_mel_channels, mel_fmin, mel_fmax
+        )
+        mel_basis = torch.from_numpy(mel_basis).float()
+        self.register_buffer("mel_basis", mel_basis)
+
+    def spectral_normalize(self, magnitudes):
+        output = dynamic_range_compression(magnitudes)
+        return output
+
+    def spectral_de_normalize(self, magnitudes):
+        output = dynamic_range_decompression(magnitudes)
+        return output
+
+    def mel_spectrogram(self, y):
+        """Computes mel-spectrograms from a batch of waves
+        PARAMS
+        ------
+        y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1]
+
+        RETURNS
+        -------
+        mel_output: torch.FloatTensor of shape (B, n_mel_channels, T)
+        """
+        assert torch.min(y.data) >= -1
+        assert torch.max(y.data) <= 1
+
+        magnitudes, phases = self.stft_fn.transform(y)
+        magnitudes = magnitudes.data
+        mel_output = torch.matmul(self.mel_basis, magnitudes)
+        mel_output = self.spectral_normalize(mel_output)
+        energy = torch.norm(magnitudes, dim=1)
+
+        return mel_output, energy
diff --git a/utils/symbol_table.py b/utils/symbol_table.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0e736fe1b77e518f1074ddd25a857ba96954d1c
--- /dev/null
+++ b/utils/symbol_table.py
@@ -0,0 +1,317 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+# This code is modified from
+# https://github.com/lifeiteng/vall-e/blob/9c69096d603ce13174fb5cb025f185e2e9b36ac7/valle/utils/symbol_table.py
+
+from dataclasses import dataclass
+from dataclasses import field
+from typing import Dict
+from typing import Generic
+from typing import List
+from typing import Optional
+from typing import TypeVar
+from typing import Union
+
+Symbol = TypeVar("Symbol")
+
+
+@dataclass(repr=False)
+class SymbolTable(Generic[Symbol]):
+    """SymbolTable that maps symbol IDs, found on the FSA arcs to
+    actual objects. These objects can be arbitrary Python objects
+    that can serve as keys in a dictionary (i.e. they need to be
+    hashable and immutable).
+
+    The SymbolTable can only be read to/written from disk if the
+    symbols are strings.
+    """
+
+    _id2sym: Dict[int, Symbol] = field(default_factory=dict)
+    """Map an integer to a symbol.
+    """
+
+    _sym2id: Dict[Symbol, int] = field(default_factory=dict)
+    """Map a symbol to an integer.
+    """
+
+    _next_available_id: int = 1
+    """A helper internal field that helps adding new symbols
+    to the table efficiently.
+    """
+
+    eps: Symbol = "<eps>"
+    """Null symbol, always mapped to index 0.
+    """
+
+    def __post_init__(self):
+        assert all(self._sym2id[sym] == idx for idx, sym in self._id2sym.items())
+        assert all(self._id2sym[idx] == sym for sym, idx in self._sym2id.items())
+        assert 0 not in self._id2sym or self._id2sym[0] == self.eps
+
+        self._next_available_id = max(self._id2sym, default=0) + 1
+        self._id2sym.setdefault(0, self.eps)
+        self._sym2id.setdefault(self.eps, 0)
+
+    @staticmethod
+    def from_str(s: str) -> "SymbolTable":
+        """Build a symbol table from a string.
+
+        The string consists of lines. Every line has two fields separated
+        by space(s), tab(s) or both. The first field is the symbol and the
+        second the integer id of the symbol.
+
+        Args:
+          s:
+            The input string with the format described above.
+        Returns:
+          An instance of :class:`SymbolTable`.
+        """
+        id2sym: Dict[int, str] = dict()
+        sym2id: Dict[str, int] = dict()
+
+        for line in s.split("\n"):
+            fields = line.split()
+            if len(fields) == 0:
+                continue  # skip empty lines
+            assert (
+                len(fields) == 2
+            ), f"Expect a line with 2 fields. Given: {len(fields)}"
+            sym, idx = fields[0], int(fields[1])
+            assert sym not in sym2id, f"Duplicated symbol {sym}"
+            assert idx not in id2sym, f"Duplicated id {idx}"
+            id2sym[idx] = sym
+            sym2id[sym] = idx
+
+        eps = id2sym.get(0, "<eps>")
+
+        return SymbolTable(_id2sym=id2sym, _sym2id=sym2id, eps=eps)
+
+    @staticmethod
+    def from_file(filename: str) -> "SymbolTable":
+        """Build a symbol table from file.
+
+        Every line in the symbol table file has two fields separated by
+        space(s), tab(s) or both. The following is an example file:
+
+        .. code-block::
+
+            <eps> 0
+            a 1
+            b 2
+            c 3
+
+        Args:
+          filename:
+            Name of the symbol table file. Its format is documented above.
+
+        Returns:
+          An instance of :class:`SymbolTable`.
+
+        """
+        with open(filename, "r", encoding="utf-8") as f:
+            return SymbolTable.from_str(f.read().strip())
+
+    def to_str(self) -> str:
+        """
+        Returns:
+          Return a string representation of this object. You can pass
+          it to the method ``from_str`` to recreate an identical object.
+        """
+        s = ""
+        for idx, symbol in sorted(self._id2sym.items()):
+            s += f"{symbol} {idx}\n"
+        return s
+
+    def to_file(self, filename: str):
+        """Serialize the SymbolTable to a file.
+
+        Every line in the symbol table file has two fields separated by
+        space(s), tab(s) or both. The following is an example file:
+
+        .. code-block::
+
+            <eps> 0
+            a 1
+            b 2
+            c 3
+
+        Args:
+          filename:
+            Name of the symbol table file. Its format is documented above.
+        """
+        with open(filename, "w") as f:
+            for idx, symbol in sorted(self._id2sym.items()):
+                print(symbol, idx, file=f)
+
+    def add(self, symbol: Symbol, index: Optional[int] = None) -> int:
+        """Add a new symbol to the SymbolTable.
+
+        Args:
+            symbol:
+                The symbol to be added.
+            index:
+                Optional int id to which the symbol should be assigned.
+                If it is not available, a ValueError will be raised.
+
+        Returns:
+            The int id to which the symbol has been assigned.
+        """
+        # Already in the table? Return its ID.
+        if symbol in self._sym2id:
+            return self._sym2id[symbol]
+        # Specific ID not provided - use next available.
+        if index is None:
+            index = self._next_available_id
+        # Specific ID provided but not available.
+        if index in self._id2sym:
+            raise ValueError(
+                f"Cannot assign id '{index}' to '{symbol}' - "
+                f"already occupied by {self._id2sym[index]}"
+            )
+        self._sym2id[symbol] = index
+        self._id2sym[index] = symbol
+
+        # Update next available ID if needed
+        if self._next_available_id <= index:
+            self._next_available_id = index + 1
+
+        return index
+
+    def get(self, k: Union[int, Symbol]) -> Union[Symbol, int]:
+        """Get a symbol for an id or get an id for a symbol
+
+        Args:
+          k:
+            If it is an id, it tries to find the symbol corresponding
+            to the id; if it is a symbol, it tries to find the id
+            corresponding to the symbol.
+
+        Returns:
+          An id or a symbol depending on the given `k`.
+        """
+        if isinstance(k, int):
+            return self._id2sym[k]
+        else:
+            return self._sym2id[k]
+
+    def merge(self, other: "SymbolTable") -> "SymbolTable":
+        """Create a union of two SymbolTables.
+        Raises an AssertionError if the same IDs are occupied by
+        different symbols.
+
+        Args:
+            other:
+                A symbol table to merge with ``self``.
+
+        Returns:
+            A new symbol table.
+        """
+        self._check_compatible(other)
+        return SymbolTable(
+            _id2sym={**self._id2sym, **other._id2sym},
+            _sym2id={**self._sym2id, **other._sym2id},
+            eps=self.eps,
+        )
+
+    def _check_compatible(self, other: "SymbolTable") -> None:
+        # Epsilon compatibility
+        assert self.eps == other.eps, (
+            f"Mismatched epsilon symbol: " f"{self.eps} != {other.eps}"
+        )
+        # IDs compatibility
+        common_ids = set(self._id2sym).intersection(other._id2sym)
+        for idx in common_ids:
+            assert self[idx] == other[idx], (
+                f"ID conflict for id: {idx}, "
+                f'self[idx] = "{self[idx]}", '
+                f'other[idx] = "{other[idx]}"'
+            )
+        # Symbols compatibility
+        common_symbols = set(self._sym2id).intersection(other._sym2id)
+        for sym in common_symbols:
+            assert self[sym] == other[sym], (
+                f"ID conflict for id: {sym}, "
+                f'self[sym] = "{self[sym]}", '
+                f'other[sym] = "{other[sym]}"'
+            )
+
+    def __getitem__(self, item: Union[int, Symbol]) -> Union[Symbol, int]:
+        return self.get(item)
+
+    def __contains__(self, item: Union[int, Symbol]) -> bool:
+        if isinstance(item, int):
+            return item in self._id2sym
+        else:
+            return item in self._sym2id
+
+    def __len__(self) -> int:
+        return len(self._id2sym)
+
+    def __eq__(self, other: "SymbolTable") -> bool:
+        if len(self) != len(other):
+            return False
+
+        for s in self.symbols:
+            if self[s] != other[s]:
+                return False
+
+        return True
+
+    @property
+    def ids(self) -> List[int]:
+        """Returns a list of integer IDs corresponding to the symbols."""
+        ans = list(self._id2sym.keys())
+        ans.sort()
+        return ans
+
+    @property
+    def symbols(self) -> List[Symbol]:
+        """Returns a list of symbols (e.g., strings) corresponding to
+        the integer IDs.
+        """
+        ans = list(self._sym2id.keys())
+        ans.sort()
+        return ans
+
+
+class TextToken:
+    def __init__(
+        self,
+        text_tokens: List[str],
+        add_eos: bool = True,
+        add_bos: bool = True,
+        pad_symbol: str = "<pad>",
+        bos_symbol: str = "<bos>",
+        eos_symbol: str = "<eos>",
+    ):
+        self.pad_symbol = pad_symbol
+        self.add_eos = add_eos
+        self.add_bos = add_bos
+        self.bos_symbol = bos_symbol
+        self.eos_symbol = eos_symbol
+
+        unique_tokens = [pad_symbol]
+        if add_bos:
+            unique_tokens.append(bos_symbol)
+        if add_eos:
+            unique_tokens.append(eos_symbol)
+        unique_tokens.extend(sorted(text_tokens))
+
+        self.token2idx = {token: idx for idx, token in enumerate(unique_tokens)}
+        self.idx2token = unique_tokens
+
+    def get_token_id_seq(self, text):
+        tokens_seq = [p for p in text]
+        seq = (
+            ([self.bos_symbol] if self.add_bos else [])
+            + tokens_seq
+            + ([self.eos_symbol] if self.add_eos else [])
+        )
+
+        token_ids = [self.token2idx[token] for token in seq]
+        token_lens = len(tokens_seq) + self.add_eos + self.add_bos
+
+        return token_ids, token_lens
diff --git a/utils/tokenizer.py b/utils/tokenizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..7eeef58686dc174203f87473836b23be0f2898a2
--- /dev/null
+++ b/utils/tokenizer.py
@@ -0,0 +1,150 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+# This code is modified from
+# https://github.com/lifeiteng/vall-e/blob/9c69096d603ce13174fb5cb025f185e2e9b36ac7/valle/data/tokenizer.py
+
+import re
+from typing import Any, Dict, List, Optional, Pattern, Union
+
+import torch
+import torchaudio
+from encodec import EncodecModel
+from encodec.utils import convert_audio
+
+
+class AudioTokenizer:
+    """EnCodec audio tokenizer for encoding and decoding audio.
+
+    Attributes:
+        device: The device on which the codec model is loaded.
+        codec: The pretrained EnCodec model.
+        sample_rate: Sample rate of the model.
+        channels: Number of audio channels in the model.
+    """
+
+    def __init__(self, device: Any = None) -> None:
+        model = EncodecModel.encodec_model_24khz()
+        model.set_target_bandwidth(6.0)
+        remove_encodec_weight_norm(model)
+
+        if not device:
+            device = torch.device("cpu")
+            if torch.cuda.is_available():
+                device = torch.device("cuda:0")
+
+        self._device = device
+
+        self.codec = model.to(device)
+        self.sample_rate = model.sample_rate
+        self.channels = model.channels
+
+    @property
+    def device(self):
+        return self._device
+
+    def encode(self, wav: torch.Tensor) -> torch.Tensor:
+        """Encode the audio waveform.
+
+        Args:
+            wav: A tensor representing the audio waveform.
+
+        Returns:
+            A tensor representing the encoded audio.
+        """
+        return self.codec.encode(wav.to(self.device))
+
+    def decode(self, frames: torch.Tensor) -> torch.Tensor:
+        """Decode the encoded audio frames.
+
+        Args:
+            frames: A tensor representing the encoded audio frames.
+
+        Returns:
+            A tensor representing the decoded audio waveform.
+        """
+        return self.codec.decode(frames)
+
+
+def tokenize_audio(tokenizer: AudioTokenizer, audio_path: str):
+    """
+    Tokenize the audio waveform using the given AudioTokenizer.
+
+    Args:
+        tokenizer: An instance of AudioTokenizer.
+        audio_path: Path to the audio file.
+
+    Returns:
+        A tensor of encoded frames from the audio.
+
+    Raises:
+        FileNotFoundError: If the audio file is not found.
+        RuntimeError: If there's an error processing the audio data.
+    """
+    # try:
+    # Load and preprocess the audio waveform
+    wav, sr = torchaudio.load(audio_path)
+    wav = convert_audio(wav, sr, tokenizer.sample_rate, tokenizer.channels)
+    wav = wav.unsqueeze(0)
+
+    # Extract discrete codes from EnCodec
+    with torch.no_grad():
+        encoded_frames = tokenizer.encode(wav)
+    return encoded_frames
+
+    # except FileNotFoundError:
+    #     raise FileNotFoundError(f"Audio file not found at {audio_path}")
+    # except Exception as e:
+    #     raise RuntimeError(f"Error processing audio data: {e}")
+
+
+def remove_encodec_weight_norm(model):
+    from encodec.modules import SConv1d
+    from encodec.modules.seanet import SConvTranspose1d, SEANetResnetBlock
+    from torch.nn.utils import remove_weight_norm
+
+    encoder = model.encoder.model
+    for key in encoder._modules:
+        if isinstance(encoder._modules[key], SEANetResnetBlock):
+            remove_weight_norm(encoder._modules[key].shortcut.conv.conv)
+            block_modules = encoder._modules[key].block._modules
+            for skey in block_modules:
+                if isinstance(block_modules[skey], SConv1d):
+                    remove_weight_norm(block_modules[skey].conv.conv)
+        elif isinstance(encoder._modules[key], SConv1d):
+            remove_weight_norm(encoder._modules[key].conv.conv)
+
+    decoder = model.decoder.model
+    for key in decoder._modules:
+        if isinstance(decoder._modules[key], SEANetResnetBlock):
+            remove_weight_norm(decoder._modules[key].shortcut.conv.conv)
+            block_modules = decoder._modules[key].block._modules
+            for skey in block_modules:
+                if isinstance(block_modules[skey], SConv1d):
+                    remove_weight_norm(block_modules[skey].conv.conv)
+        elif isinstance(decoder._modules[key], SConvTranspose1d):
+            remove_weight_norm(decoder._modules[key].convtr.convtr)
+        elif isinstance(decoder._modules[key], SConv1d):
+            remove_weight_norm(decoder._modules[key].conv.conv)
+
+
+def extract_encodec_token(wav_path):
+    model = EncodecModel.encodec_model_24khz()
+    model.set_target_bandwidth(6.0)
+
+    wav, sr = torchaudio.load(wav_path)
+    wav = convert_audio(wav, sr, model.sample_rate, model.channels)
+    wav = wav.unsqueeze(0)
+    if torch.cuda.is_available():
+        model = model.cuda()
+        wav = wav.cuda()
+    with torch.no_grad():
+        encoded_frames = model.encode(wav)
+        codes_ = torch.cat(
+            [encoded[0] for encoded in encoded_frames], dim=-1
+        )  # [B, n_q, T]
+        codes = codes_.cpu().numpy()[0, :, :].T  # [T, 8]
+
+        return codes
diff --git a/utils/topk_sampling.py b/utils/topk_sampling.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d1e41f1d1f62c22fee95f264f27c9e97cd9c8a2
--- /dev/null
+++ b/utils/topk_sampling.py
@@ -0,0 +1,89 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+import torch
+import torch.nn.functional as F
+
+
+# This function is modified from https://github.com/microsoft/unilm/blob/master/xtune/src/transformers/modeling_utils.py
+def top_k_top_p_filtering(
+    logits, top_k=0, top_p=1.0, filter_value=-float("Inf"), min_tokens_to_keep=1
+):
+    """
+    Filter a distribution of logits using top-k and/or nucleus (top-p) filtering.
+
+    Args:
+        logits (torch.Tensor): Logits distribution with shape (batch size, vocabulary size).
+        top_k (int, optional): Keep only top k tokens with highest probability (top-k filtering).
+                               Set to 0 to disable. Defaults to 0.
+        top_p (float, optional): Keep the top tokens with a cumulative probability >= top_p (nucleus filtering).
+                                 Must be between 0 and 1, inclusive. Defaults to 1.0.
+        filter_value (float, optional): The value to assign to filtered logits. Defaults to -float('Inf').
+        min_tokens_to_keep (int, optional): Ensure that at least this number of tokens are kept per batch example.
+                                            Defaults to 1.
+
+    Returns:
+        torch.Tensor: The filtered logits.
+    """
+    """
+        Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
+        Make sure we keep at least min_tokens_to_keep per batch example in the output
+        From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
+    """
+    if top_k > 0:
+        # Apply top-k filtering
+        top_k = min(max(top_k, min_tokens_to_keep), logits.size(-1))
+        indices_to_remove = logits < torch.topk(logits, top_k).values[..., -1, None]
+        logits[indices_to_remove] = filter_value
+
+    if top_p < 1.0:
+        # Apply top-p filtering
+        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+        cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
+
+        # Create a mask to remove tokens with cumulative probability above the top_p threshold
+        sorted_indices_to_remove = cumulative_probs > top_p
+        if min_tokens_to_keep > 1:
+            sorted_indices_to_remove[..., :min_tokens_to_keep] = 0
+        sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
+        sorted_indices_to_remove[..., 0] = 0
+
+        # Scatter sorted tensors back to original indexing
+        indices_to_remove = sorted_indices.scatter(
+            1, sorted_indices, sorted_indices_to_remove
+        )
+        logits[indices_to_remove] = filter_value
+
+    return logits
+
+
+def topk_sampling(logits, top_k=50, top_p=1.0, temperature=1.0):
+    """
+    Perform top-k and top-p sampling on logits.
+
+    Args:
+        logits (torch.Tensor): The logits to sample from.
+        top_k (int, optional): The number of highest probability tokens to keep for top-k filtering.
+                               Must be a positive integer. Defaults to 50.
+        top_p (float, optional): The cumulative probability threshold for nucleus sampling.
+                                 Must be between 0 and 1. Defaults to 1.0.
+        temperature (float, optional): The scaling factor to adjust the logits distribution.
+                                       Must be strictly positive. Defaults to 1.0.
+
+    Returns:
+        torch.Tensor: The sampled token.
+    """
+
+    # Adjust logits using temperature
+    if temperature != 1.0:
+        logits = logits / temperature
+
+    # Top-p/top-k filtering
+    logits = top_k_top_p_filtering(logits, top_k=top_k, top_p=top_p)
+
+    # Sample from the filtered distribution
+    token = torch.multinomial(F.softmax(logits, dim=-1), num_samples=1)
+    return token
diff --git a/utils/trainer_utils.py b/utils/trainer_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5d9ad794864aa3ee0c49a86e9be293f69442886
--- /dev/null
+++ b/utils/trainer_utils.py
@@ -0,0 +1,16 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+
+
+def check_nan(logger, loss, y_pred, y_gt):
+    if torch.any(torch.isnan(loss)):
+        logger.info("out has nan: ", torch.any(torch.isnan(y_pred)))
+        logger.info("y_gt has nan: ", torch.any(torch.isnan(y_gt)))
+        logger.info("out: ", y_pred)
+        logger.info("y_gt: ", y_gt)
+        logger.info("loss = {:.4f}\n".format(loss.item()))
+        exit()
diff --git a/utils/util.py b/utils/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7eaf1aae38378498fd6f74da1dae19859752474
--- /dev/null
+++ b/utils/util.py
@@ -0,0 +1,687 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+import collections
+import glob
+import os
+import random
+import time
+import argparse
+from collections import OrderedDict
+
+import json5
+import numpy as np
+import glob
+from torch.nn import functional as F
+
+
+try:
+    from ruamel.yaml import YAML as yaml
+except:
+    from ruamel_yaml import YAML as yaml
+
+import torch
+
+from utils.hparam import HParams
+import logging
+from logging import handlers
+
+
+def str2bool(v):
+    """Used in argparse.ArgumentParser.add_argument to indicate
+    that a type is a bool type and user can enter
+
+        - yes, true, t, y, 1, to represent True
+        - no, false, f, n, 0, to represent False
+
+    See https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse  # noqa
+    """
+    if isinstance(v, bool):
+        return v
+    if v.lower() in ("yes", "true", "t", "y", "1"):
+        return True
+    elif v.lower() in ("no", "false", "f", "n", "0"):
+        return False
+    else:
+        raise argparse.ArgumentTypeError("Boolean value expected.")
+
+
+def find_checkpoint_of_mapper(mapper_ckpt_dir):
+    mapper_ckpts = glob.glob(os.path.join(mapper_ckpt_dir, "ckpts/*.pt"))
+
+    # Select the max steps
+    mapper_ckpts.sort()
+    mapper_weights_file = mapper_ckpts[-1]
+    return mapper_weights_file
+
+
+def pad_f0_to_tensors(f0s, batched=None):
+    # Initialize
+    tensors = []
+
+    if batched == None:
+        # Get the max frame for padding
+        size = -1
+        for f0 in f0s:
+            size = max(size, f0.shape[-1])
+
+        tensor = torch.zeros(len(f0s), size)
+
+        for i, f0 in enumerate(f0s):
+            tensor[i, : f0.shape[-1]] = f0[:]
+
+        tensors.append(tensor)
+    else:
+        start = 0
+        while start + batched - 1 < len(f0s):
+            end = start + batched - 1
+
+            # Get the max frame for padding
+            size = -1
+            for i in range(start, end + 1):
+                size = max(size, f0s[i].shape[-1])
+
+            tensor = torch.zeros(batched, size)
+
+            for i in range(start, end + 1):
+                tensor[i - start, : f0s[i].shape[-1]] = f0s[i][:]
+
+            tensors.append(tensor)
+
+            start = start + batched
+
+        if start != len(f0s):
+            end = len(f0s)
+
+            # Get the max frame for padding
+            size = -1
+            for i in range(start, end):
+                size = max(size, f0s[i].shape[-1])
+
+            tensor = torch.zeros(len(f0s) - start, size)
+
+            for i in range(start, end):
+                tensor[i - start, : f0s[i].shape[-1]] = f0s[i][:]
+
+            tensors.append(tensor)
+
+    return tensors
+
+
+def pad_mels_to_tensors(mels, batched=None):
+    """
+    Args:
+        mels: A list of mel-specs
+    Returns:
+        tensors: A list of tensors containing the batched mel-specs
+        mel_frames: A list of tensors containing the frames of the original mel-specs
+    """
+    # Initialize
+    tensors = []
+    mel_frames = []
+
+    # Split mel-specs into batches to avoid cuda memory exceed
+    if batched == None:
+        # Get the max frame for padding
+        size = -1
+        for mel in mels:
+            size = max(size, mel.shape[-1])
+
+        tensor = torch.zeros(len(mels), mels[0].shape[0], size)
+        mel_frame = torch.zeros(len(mels), dtype=torch.int32)
+
+        for i, mel in enumerate(mels):
+            tensor[i, :, : mel.shape[-1]] = mel[:]
+            mel_frame[i] = mel.shape[-1]
+
+        tensors.append(tensor)
+        mel_frames.append(mel_frame)
+    else:
+        start = 0
+        while start + batched - 1 < len(mels):
+            end = start + batched - 1
+
+            # Get the max frame for padding
+            size = -1
+            for i in range(start, end + 1):
+                size = max(size, mels[i].shape[-1])
+
+            tensor = torch.zeros(batched, mels[0].shape[0], size)
+            mel_frame = torch.zeros(batched, dtype=torch.int32)
+
+            for i in range(start, end + 1):
+                tensor[i - start, :, : mels[i].shape[-1]] = mels[i][:]
+                mel_frame[i - start] = mels[i].shape[-1]
+
+            tensors.append(tensor)
+            mel_frames.append(mel_frame)
+
+            start = start + batched
+
+        if start != len(mels):
+            end = len(mels)
+
+            # Get the max frame for padding
+            size = -1
+            for i in range(start, end):
+                size = max(size, mels[i].shape[-1])
+
+            tensor = torch.zeros(len(mels) - start, mels[0].shape[0], size)
+            mel_frame = torch.zeros(len(mels) - start, dtype=torch.int32)
+
+            for i in range(start, end):
+                tensor[i - start, :, : mels[i].shape[-1]] = mels[i][:]
+                mel_frame[i - start] = mels[i].shape[-1]
+
+            tensors.append(tensor)
+            mel_frames.append(mel_frame)
+
+    return tensors, mel_frames
+
+
+def load_model_config(args):
+    """Load model configurations (in args.json under checkpoint directory)
+
+    Args:
+        args (ArgumentParser): arguments to run bins/preprocess.py
+
+    Returns:
+        dict: dictionary that stores model configurations
+    """
+    if args.checkpoint_dir is None:
+        assert args.checkpoint_file is not None
+        checkpoint_dir = os.path.split(args.checkpoint_file)[0]
+    else:
+        checkpoint_dir = args.checkpoint_dir
+    config_path = os.path.join(checkpoint_dir, "args.json")
+    print("config_path: ", config_path)
+
+    config = load_config(config_path)
+    return config
+
+
+def remove_and_create(dir):
+    if os.path.exists(dir):
+        os.system("rm -r {}".format(dir))
+    os.makedirs(dir, exist_ok=True)
+
+
+def has_existed(path, warning=False):
+    if not warning:
+        return os.path.exists(path)
+
+    if os.path.exists(path):
+        answer = input(
+            "The path {} has existed. \nInput 'y' (or hit Enter) to skip it, and input 'n' to re-write it [y/n]\n".format(
+                path
+            )
+        )
+        if not answer == "n":
+            return True
+
+    return False
+
+
+def remove_older_ckpt(saved_model_name, checkpoint_dir, max_to_keep=5):
+    if os.path.exists(os.path.join(checkpoint_dir, "checkpoint")):
+        with open(os.path.join(checkpoint_dir, "checkpoint"), "r") as f:
+            ckpts = [x.strip() for x in f.readlines()]
+    else:
+        ckpts = []
+    ckpts.append(saved_model_name)
+    for item in ckpts[:-max_to_keep]:
+        if os.path.exists(os.path.join(checkpoint_dir, item)):
+            os.remove(os.path.join(checkpoint_dir, item))
+    with open(os.path.join(checkpoint_dir, "checkpoint"), "w") as f:
+        for item in ckpts[-max_to_keep:]:
+            f.write("{}\n".format(item))
+
+
+def set_all_random_seed(seed: int):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.random.manual_seed(seed)
+
+
+def save_checkpoint(
+    args,
+    generator,
+    g_optimizer,
+    step,
+    discriminator=None,
+    d_optimizer=None,
+    max_to_keep=5,
+):
+    saved_model_name = "model.ckpt-{}.pt".format(step)
+    checkpoint_path = os.path.join(args.checkpoint_dir, saved_model_name)
+
+    if discriminator and d_optimizer:
+        torch.save(
+            {
+                "generator": generator.state_dict(),
+                "discriminator": discriminator.state_dict(),
+                "g_optimizer": g_optimizer.state_dict(),
+                "d_optimizer": d_optimizer.state_dict(),
+                "global_step": step,
+            },
+            checkpoint_path,
+        )
+    else:
+        torch.save(
+            {
+                "generator": generator.state_dict(),
+                "g_optimizer": g_optimizer.state_dict(),
+                "global_step": step,
+            },
+            checkpoint_path,
+        )
+
+    print("Saved checkpoint: {}".format(checkpoint_path))
+
+    if os.path.exists(os.path.join(args.checkpoint_dir, "checkpoint")):
+        with open(os.path.join(args.checkpoint_dir, "checkpoint"), "r") as f:
+            ckpts = [x.strip() for x in f.readlines()]
+    else:
+        ckpts = []
+    ckpts.append(saved_model_name)
+    for item in ckpts[:-max_to_keep]:
+        if os.path.exists(os.path.join(args.checkpoint_dir, item)):
+            os.remove(os.path.join(args.checkpoint_dir, item))
+    with open(os.path.join(args.checkpoint_dir, "checkpoint"), "w") as f:
+        for item in ckpts[-max_to_keep:]:
+            f.write("{}\n".format(item))
+
+
+def attempt_to_restore(
+    generator, g_optimizer, checkpoint_dir, discriminator=None, d_optimizer=None
+):
+    checkpoint_list = os.path.join(checkpoint_dir, "checkpoint")
+    if os.path.exists(checkpoint_list):
+        checkpoint_filename = open(checkpoint_list).readlines()[-1].strip()
+        checkpoint_path = os.path.join(checkpoint_dir, "{}".format(checkpoint_filename))
+        print("Restore from {}".format(checkpoint_path))
+        checkpoint = torch.load(checkpoint_path, map_location="cpu")
+        if generator:
+            if not list(generator.state_dict().keys())[0].startswith("module."):
+                raw_dict = checkpoint["generator"]
+                clean_dict = OrderedDict()
+                for k, v in raw_dict.items():
+                    if k.startswith("module."):
+                        clean_dict[k[7:]] = v
+                    else:
+                        clean_dict[k] = v
+                generator.load_state_dict(clean_dict)
+            else:
+                generator.load_state_dict(checkpoint["generator"])
+        if g_optimizer:
+            g_optimizer.load_state_dict(checkpoint["g_optimizer"])
+        global_step = 100000
+        if discriminator and "discriminator" in checkpoint.keys():
+            discriminator.load_state_dict(checkpoint["discriminator"])
+            global_step = checkpoint["global_step"]
+            print("restore discriminator")
+        if d_optimizer and "d_optimizer" in checkpoint.keys():
+            d_optimizer.load_state_dict(checkpoint["d_optimizer"])
+            print("restore d_optimizer...")
+    else:
+        global_step = 0
+    return global_step
+
+
+class ExponentialMovingAverage(object):
+    def __init__(self, decay):
+        self.decay = decay
+        self.shadow = {}
+
+    def register(self, name, val):
+        self.shadow[name] = val.clone()
+
+    def update(self, name, x):
+        assert name in self.shadow
+        update_delta = self.shadow[name] - x
+        self.shadow[name] -= (1.0 - self.decay) * update_delta
+
+
+def apply_moving_average(model, ema):
+    for name, param in model.named_parameters():
+        if name in ema.shadow:
+            ema.update(name, param.data)
+
+
+def register_model_to_ema(model, ema):
+    for name, param in model.named_parameters():
+        if param.requires_grad:
+            ema.register(name, param.data)
+
+
+class YParams(HParams):
+    def __init__(self, yaml_file):
+        if not os.path.exists(yaml_file):
+            raise IOError("yaml file: {} is not existed".format(yaml_file))
+        super().__init__()
+        self.d = collections.OrderedDict()
+        with open(yaml_file) as fp:
+            for _, v in yaml().load(fp).items():
+                for k1, v1 in v.items():
+                    try:
+                        if self.get(k1):
+                            self.set_hparam(k1, v1)
+                        else:
+                            self.add_hparam(k1, v1)
+                        self.d[k1] = v1
+                    except Exception:
+                        import traceback
+
+                        print(traceback.format_exc())
+
+    # @property
+    def get_elements(self):
+        return self.d.items()
+
+
+def override_config(base_config, new_config):
+    """Update new configurations in the original dict with the new dict
+
+    Args:
+        base_config (dict): original dict to be overridden
+        new_config (dict): dict with new configurations
+
+    Returns:
+        dict: updated configuration dict
+    """
+    for k, v in new_config.items():
+        if type(v) == dict:
+            if k not in base_config.keys():
+                base_config[k] = {}
+            base_config[k] = override_config(base_config[k], v)
+        else:
+            base_config[k] = v
+    return base_config
+
+
+def get_lowercase_keys_config(cfg):
+    """Change all keys in cfg to lower case
+
+    Args:
+        cfg (dict): dictionary that stores configurations
+
+    Returns:
+        dict: dictionary that stores configurations
+    """
+    updated_cfg = dict()
+    for k, v in cfg.items():
+        if type(v) == dict:
+            v = get_lowercase_keys_config(v)
+        updated_cfg[k.lower()] = v
+    return updated_cfg
+
+
+def _load_config(config_fn, lowercase=False):
+    """Load configurations into a dictionary
+
+    Args:
+        config_fn (str): path to configuration file
+        lowercase (bool, optional): whether changing keys to lower case. Defaults to False.
+
+    Returns:
+        dict: dictionary that stores configurations
+    """
+    with open(config_fn, "r") as f:
+        data = f.read()
+    config_ = json5.loads(data)
+    if "base_config" in config_:
+        # load configurations from new path
+        p_config_path = os.path.join(os.getenv("WORK_DIR"), config_["base_config"])
+        p_config_ = _load_config(p_config_path)
+        config_ = override_config(p_config_, config_)
+    if lowercase:
+        # change keys in config_ to lower case
+        config_ = get_lowercase_keys_config(config_)
+    return config_
+
+
+def load_config(config_fn, lowercase=False):
+    """Load configurations into a dictionary
+
+    Args:
+        config_fn (str): path to configuration file
+        lowercase (bool, optional): _description_. Defaults to False.
+
+    Returns:
+        JsonHParams: an object that stores configurations
+    """
+    config_ = _load_config(config_fn, lowercase=lowercase)
+    # create an JsonHParams object with configuration dict
+    cfg = JsonHParams(**config_)
+    return cfg
+
+
+def save_config(save_path, cfg):
+    """Save configurations into a json file
+
+    Args:
+        save_path (str): path to save configurations
+        cfg (dict): dictionary that stores configurations
+    """
+    with open(save_path, "w") as f:
+        json5.dump(
+            cfg, f, ensure_ascii=False, indent=4, quote_keys=True, sort_keys=True
+        )
+
+
+class JsonHParams:
+    def __init__(self, **kwargs):
+        for k, v in kwargs.items():
+            if type(v) == dict:
+                v = JsonHParams(**v)
+            self[k] = v
+
+    def keys(self):
+        return self.__dict__.keys()
+
+    def items(self):
+        return self.__dict__.items()
+
+    def values(self):
+        return self.__dict__.values()
+
+    def __len__(self):
+        return len(self.__dict__)
+
+    def __getitem__(self, key):
+        return getattr(self, key)
+
+    def __setitem__(self, key, value):
+        return setattr(self, key, value)
+
+    def __contains__(self, key):
+        return key in self.__dict__
+
+    def __repr__(self):
+        return self.__dict__.__repr__()
+
+
+class ValueWindow:
+    def __init__(self, window_size=100):
+        self._window_size = window_size
+        self._values = []
+
+    def append(self, x):
+        self._values = self._values[-(self._window_size - 1) :] + [x]
+
+    @property
+    def sum(self):
+        return sum(self._values)
+
+    @property
+    def count(self):
+        return len(self._values)
+
+    @property
+    def average(self):
+        return self.sum / max(1, self.count)
+
+    def reset(self):
+        self._values = []
+
+
+class Logger(object):
+    def __init__(
+        self,
+        filename,
+        level="info",
+        when="D",
+        backCount=10,
+        fmt="%(asctime)s : %(message)s",
+    ):
+        self.level_relations = {
+            "debug": logging.DEBUG,
+            "info": logging.INFO,
+            "warning": logging.WARNING,
+            "error": logging.ERROR,
+            "crit": logging.CRITICAL,
+        }
+        if level == "debug":
+            fmt = "%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s"
+        self.logger = logging.getLogger(filename)
+        format_str = logging.Formatter(fmt)
+        self.logger.setLevel(self.level_relations.get(level))
+        sh = logging.StreamHandler()
+        sh.setFormatter(format_str)
+        th = handlers.TimedRotatingFileHandler(
+            filename=filename, when=when, backupCount=backCount, encoding="utf-8"
+        )
+        th.setFormatter(format_str)
+        self.logger.addHandler(sh)
+        self.logger.addHandler(th)
+        self.logger.info(
+            "==========================New Starting Here=============================="
+        )
+
+
+def init_weights(m, mean=0.0, std=0.01):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        m.weight.data.normal_(mean, std)
+
+
+def get_padding(kernel_size, dilation=1):
+    return int((kernel_size * dilation - dilation) / 2)
+
+
+def slice_segments(x, ids_str, segment_size=4):
+    ret = torch.zeros_like(x[:, :, :segment_size])
+    for i in range(x.size(0)):
+        idx_str = ids_str[i]
+        idx_end = idx_str + segment_size
+        ret[i] = x[i, :, idx_str:idx_end]
+    return ret
+
+
+def rand_slice_segments(x, x_lengths=None, segment_size=4):
+    b, d, t = x.size()
+    if x_lengths is None:
+        x_lengths = t
+    ids_str_max = x_lengths - segment_size + 1
+    ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
+    ret = slice_segments(x, ids_str, segment_size)
+    return ret, ids_str
+
+
+def subsequent_mask(length):
+    mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
+    return mask
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+    n_channels_int = n_channels[0]
+    in_act = input_a + input_b
+    t_act = torch.tanh(in_act[:, :n_channels_int, :])
+    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+    acts = t_act * s_act
+    return acts
+
+
+def convert_pad_shape(pad_shape):
+    l = pad_shape[::-1]
+    pad_shape = [item for sublist in l for item in sublist]
+    return pad_shape
+
+
+def sequence_mask(length, max_length=None):
+    if max_length is None:
+        max_length = length.max()
+    x = torch.arange(max_length, dtype=length.dtype, device=length.device)
+    return x.unsqueeze(0) < length.unsqueeze(1)
+
+
+def generate_path(duration, mask):
+    """
+    duration: [b, 1, t_x]
+    mask: [b, 1, t_y, t_x]
+    """
+    device = duration.device
+
+    b, _, t_y, t_x = mask.shape
+    cum_duration = torch.cumsum(duration, -1)
+
+    cum_duration_flat = cum_duration.view(b * t_x)
+    path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
+    path = path.view(b, t_x, t_y)
+    path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
+    path = path.unsqueeze(1).transpose(2, 3) * mask
+    return path
+
+
+def clip_grad_value_(parameters, clip_value, norm_type=2):
+    if isinstance(parameters, torch.Tensor):
+        parameters = [parameters]
+    parameters = list(filter(lambda p: p.grad is not None, parameters))
+    norm_type = float(norm_type)
+    if clip_value is not None:
+        clip_value = float(clip_value)
+
+    total_norm = 0
+    for p in parameters:
+        param_norm = p.grad.data.norm(norm_type)
+        total_norm += param_norm.item() ** norm_type
+        if clip_value is not None:
+            p.grad.data.clamp_(min=-clip_value, max=clip_value)
+    total_norm = total_norm ** (1.0 / norm_type)
+    return total_norm
+
+
+def get_current_time():
+    pass
+
+
+def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor:
+    """
+    Args:
+      lengths:
+        A 1-D tensor containing sentence lengths.
+      max_len:
+        The length of masks.
+    Returns:
+      Return a 2-D bool tensor, where masked positions
+      are filled with `True` and non-masked positions are
+      filled with `False`.
+
+    >>> lengths = torch.tensor([1, 3, 2, 5])
+    >>> make_pad_mask(lengths)
+    tensor([[False,  True,  True,  True,  True],
+            [False, False, False,  True,  True],
+            [False, False,  True,  True,  True],
+            [False, False, False, False, False]])
+    """
+    assert lengths.ndim == 1, lengths.ndim
+    max_len = max(max_len, lengths.max())
+    n = lengths.size(0)
+    seq_range = torch.arange(0, max_len, device=lengths.device)
+    expaned_lengths = seq_range.unsqueeze(0).expand(n, max_len)
+
+    return expaned_lengths >= lengths.unsqueeze(-1)
diff --git a/utils/whisper.py b/utils/whisper.py
new file mode 100644
index 0000000000000000000000000000000000000000..16462c7e84f2ce71d4fd5e57832d705fd07b95ca
--- /dev/null
+++ b/utils/whisper.py
@@ -0,0 +1,165 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import os
+import pickle
+from tqdm import tqdm
+import numpy as np
+
+from modules import whisper_extractor as whisper
+
+
+def whisper_encoder_batch(model, audio_paths):
+    batch = len(audio_paths)
+    batch_mel = torch.zeros((batch, 80, 3000), dtype=torch.float32, device=model.device)
+
+    for i, audio_path in enumerate(audio_paths):
+        # (48000,)
+        audio = whisper.load_audio(str(audio_path))
+        audio = whisper.pad_or_trim(audio)
+
+        # (80, 3000)
+        mel = whisper.log_mel_spectrogram(audio).to(model.device)
+        batch_mel[i] = mel
+
+    with torch.no_grad():
+        # (batch, 1500, 1024)
+        features = model.embed_audio(batch_mel)
+
+    return features.cpu().detach().numpy()
+
+
+def whisper_encoder(model, audio_path):
+    audio = whisper.load_audio(str(audio_path))
+    audio = whisper.pad_or_trim(audio)
+
+    # (80, 3000)
+    mel = whisper.log_mel_spectrogram(audio).to(model.device).unsqueeze(0)
+
+    with torch.no_grad():
+        # (1, 1500, 1024) -> # (1500, 1024)
+        features = model.embed_audio(mel).squeeze(0)
+
+    return features.cpu().detach().numpy()
+
+
+def get_mapped_whisper_features(
+    raw_whisper_features, mapping_features, fast_mapping=True
+):
+    """
+    Whisper: frameshift = 20ms (30s audio -> 1500 frames), hop_size = 480 in 24k
+    # Ref: https://github.com/openai/whisper/blob/7858aa9c08d98f75575035ecd6481f462d66ca27/whisper/model.py#L136
+
+    Now it's only used for mapping to bigvgan's mels (sr = 24k, hop_size = 256, frameshift ~= 10.7 ms)
+    """
+    source_hop = 480
+    target_hop = 256
+
+    factor = np.gcd(source_hop, target_hop)
+    source_hop //= factor
+    target_hop //= factor
+    print(
+        "Mapping source's {} frames => target's {} frames".format(
+            target_hop, source_hop
+        )
+    )
+
+    max_source_len = 1500
+    whisper_features = []
+    for index, mapping_feat in enumerate(tqdm(mapping_features)):
+        # mapping_feat: (mels_frame_len, n_mels)
+        target_len = mapping_feat.shape[0]
+        # The max target_len is 2812
+        target_len = min(target_len, max_source_len * source_hop // target_hop)
+
+        # (1500, dim)
+        raw_feats = raw_whisper_features[index]
+        width = raw_feats.shape[-1]
+
+        if fast_mapping:
+            source_len = target_len * target_hop // source_hop + 1
+            raw_feats = raw_feats[:source_len]
+        else:
+            source_len = max_source_len
+
+        # const ~= target_len * target_hop
+        const = source_len * source_hop // target_hop * target_hop
+
+        # (source_len * source_hop, dim)
+        up_sampling_feats = np.repeat(raw_feats, source_hop, axis=0)
+        # (const, dim) -> (const/target_hop, target_hop, dim) -> (const/target_hop, dim)
+        down_sampling_feats = np.average(
+            up_sampling_feats[:const].reshape(-1, target_hop, width), axis=1
+        )
+        assert len(down_sampling_feats) >= target_len
+
+        # (target_len, dim)
+        feats = down_sampling_feats[:target_len]
+        whisper_features.append(feats)
+
+    return whisper_features
+
+
+def load_whisper_model(hps):
+    print("Loading Whisper Model: ", hps.whisper_model)
+    model = whisper.load_model(hps.whisper_model)
+    if torch.cuda.is_available():
+        model = model.cuda()
+
+    model = model.eval()
+    return model
+
+
+def load_target_acoustic_features(
+    output_path, dataset, acoustic_features_name, acoustic_features_fs, dataset_type
+):
+    mapping_dir = os.path.join(
+        output_path,
+        dataset,
+        "{}/{}".format(acoustic_features_name, acoustic_features_fs),
+    )
+    with open(os.path.join(mapping_dir, "{}.pkl".format(dataset_type)), "rb") as f:
+        mapping_features = pickle.load(f)
+
+    # Mels: (n_mels, frame_len) -> (frame_len, n_mels)
+    if acoustic_features_name == "mels":
+        print("Transposing mel features...")
+        mapping_features = [feat.T for feat in mapping_features]
+
+    print(
+        "Mapping to the acoustic features {}, #sz = {}, feats[0] is {}".format(
+            acoustic_features_name, len(mapping_features), mapping_features[0].shape
+        )
+    )
+    return mapping_features
+
+
+def extract_whisper_features_of_dataset(
+    datasets,
+    model,
+    batch_size,
+    out_dir,
+):
+    audio_paths = [utt["Path"] for utt in datasets]
+    if len(audio_paths) < batch_size:
+        batch_size = len(audio_paths)
+
+    start, end = 0, 0
+    while end < len(audio_paths):
+        # Raw features: (batch_size, 1500, dim)
+        start = end
+        end = start + batch_size
+        tmp_raw_whisper_features = whisper_encoder_batch(model, audio_paths[start:end])
+
+        # Mapping to acoustic features' lengths
+        for index, utt in enumerate(tqdm(datasets[start:end])):
+            uid = utt["Uid"]
+            raw_whisper_feature = tmp_raw_whisper_features[index]
+
+            save_path = os.path.join(out_dir, uid + ".npy")
+            np.save(save_path, raw_whisper_feature)
+
+        print("{}/{} Done...".format(end, len(audio_paths)))
diff --git a/utils/world.py b/utils/world.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce5f61bd9b571607fd83da6b22283757e67201da
--- /dev/null
+++ b/utils/world.py
@@ -0,0 +1,92 @@
+# Copyright (c) 2023 Amphion.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+# 1. Extract WORLD features including F0, AP, SP
+# 2. Transform between SP and MCEP
+import torchaudio
+import pyworld as pw
+import numpy as np
+import torch
+import diffsptk
+import os
+from tqdm import tqdm
+import pickle
+import torchaudio
+
+
+def get_mcep_params(fs):
+    """Hyperparameters of transformation between SP and MCEP
+
+    Reference:
+        https://github.com/CSTR-Edinburgh/merlin/blob/master/misc/scripts/vocoder/world_v2/copy_synthesis.sh
+
+    """
+    if fs in [44100, 48000]:
+        fft_size = 2048
+        alpha = 0.77
+    if fs in [16000]:
+        fft_size = 1024
+        alpha = 0.58
+    return fft_size, alpha
+
+
+def extract_world_features(waveform, frameshift=10):
+    # waveform: (1, seq)
+    # x: (seq,)
+    x = np.array(waveform, dtype=np.double)
+
+    _f0, t = pw.dio(x, fs, frame_period=frameshift)  # raw pitch extractor
+    f0 = pw.stonemask(x, _f0, t, fs)  # pitch refinement
+    sp = pw.cheaptrick(x, f0, t, fs)  # extract smoothed spectrogram
+    ap = pw.d4c(x, f0, t, fs)  # extract aperiodicity
+
+    return f0, sp, ap, fs
+
+
+def sp2mcep(x, mcsize, fs):
+    fft_size, alpha = get_mcep_params(fs)
+    x = torch.as_tensor(x, dtype=torch.float)
+
+    tmp = diffsptk.ScalarOperation("SquareRoot")(x)
+    tmp = diffsptk.ScalarOperation("Multiplication", 32768.0)(tmp)
+    mgc = diffsptk.MelCepstralAnalysis(
+        cep_order=mcsize - 1, fft_length=fft_size, alpha=alpha, n_iter=1
+    )(tmp)
+    return mgc.numpy()
+
+
+def mcep2sp(x, mcsize, fs):
+    fft_size, alpha = get_mcep_params(fs)
+    x = torch.as_tensor(x, dtype=torch.float)
+
+    tmp = diffsptk.MelGeneralizedCepstrumToSpectrum(
+        alpha=alpha,
+        cep_order=mcsize - 1,
+        fft_length=fft_size,
+    )(x)
+    tmp = diffsptk.ScalarOperation("Division", 32768.0)(tmp)
+    sp = diffsptk.ScalarOperation("Power", 2)(tmp)
+    return sp.double().numpy()
+
+
+def f0_statistics(f0_features, path):
+    print("\nF0 statistics...")
+
+    total_f0 = []
+    for f0 in tqdm(f0_features):
+        total_f0 += [f for f in f0 if f != 0]
+
+    mean = sum(total_f0) / len(total_f0)
+    print("Min = {}, Max = {}, Mean = {}".format(min(total_f0), max(total_f0), mean))
+
+    with open(path, "wb") as f:
+        pickle.dump([mean, total_f0], f)
+
+
+def world_synthesis(f0, sp, ap, fs, frameshift):
+    y = pw.synthesize(
+        f0, sp, ap, fs, frame_period=frameshift
+    )  # synthesize an utterance using the parameters
+    return y