diff --git a/.dockerignore b/.dockerignore
new file mode 100644
index 0000000000000000000000000000000000000000..bf1b3a0378eee07e7e2d9a8480820a14bd5093da
--- /dev/null
+++ b/.dockerignore
@@ -0,0 +1,18 @@
+# Dockerfile.deploy用の.dockerignore
+# 日本語のJP-Extraのエディター稼働のみに必要なファイルを指定する
+
+*
+
+!/style_bert_vits2/
+
+!/bert/deberta-v2-large-japanese-char-wwm/
+!/configs/
+!/dict_data/default.csv
+!/model_assets/
+!/static/
+
+!/config.py
+!/default_config.yml
+!/initialize.py
+!/requirements.txt
+!/server_editor.py
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..56e45a48a9347abee03acc8e604df779a1f991bc
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,33 @@
+.vscode/
+
+__pycache__/
+venv/
+.ipynb_checkpoints/
+
+/*.yml
+!/default_config.yml
+# /bert/*/*.bin
+/bert/*/*.h5
+/bert/*/*.model
+/bert/*/*.safetensors
+/bert/*/*.msgpack
+
+/pretrained/*.safetensors
+/pretrained/*.pth
+
+/pretrained_jp_extra/*.safetensors
+/pretrained_jp_extra/*.pth
+
+/slm/*/*.bin
+
+/scripts/test/
+*.zip
+*.csv
+*.bak
+/mos_results/
+
+safetensors.ipynb
+*.wav
+
+# pyopenjtalk's dictionary
+*.dic
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..368a79d101904bb1502107a502b6443a69b6e06a
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,23 @@
+# Hugging face spaces (CPU) でエディタ (server_editor.py) のデプロイ用
+
+# See https://huggingface.co/docs/hub/spaces-sdks-docker-first-demo
+
+FROM python:3.10
+
+RUN useradd -m -u 1000 user
+
+USER user
+
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+RUN pip install --no-cache-dir --upgrade pip
+
+COPY --chown=user . $HOME/app
+
+RUN pip install --no-cache-dir -r $HOME/app/requirements.txt
+
+# 必要に応じて制限を変更してください
+CMD ["python", "server_editor.py", "--line_length", "50", "--line_count", "3", "--skip_static_files", "--skip_default_models", "--port", "7860"]
diff --git a/README.md b/README.md
index 3c9457b95e69b54b31a6cca7b07ab871a717b9c4..bde722a86dd464dc32c7ec7b5c26b50bb54b970d 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,8 @@
 ---
-title: DolceTTS
-emoji: 📈
+title: Chino TTS
+emoji: ☕🐰🎤
 colorFrom: gray
-colorTo: indigo
+colorTo: yellow
 sdk: docker
 pinned: false
 ---
diff --git a/bert/deberta-v2-large-japanese-char-wwm/.gitattributes b/bert/deberta-v2-large-japanese-char-wwm/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..c7d9f3332a950355d5a77d85000f05e6f45435ea
--- /dev/null
+++ b/bert/deberta-v2-large-japanese-char-wwm/.gitattributes
@@ -0,0 +1,34 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
diff --git a/bert/deberta-v2-large-japanese-char-wwm/README.md b/bert/deberta-v2-large-japanese-char-wwm/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..b7317110ce981c229ebd7b3b975545de5f35fa30
--- /dev/null
+++ b/bert/deberta-v2-large-japanese-char-wwm/README.md
@@ -0,0 +1,89 @@
+---
+language: ja
+license: cc-by-sa-4.0
+library_name: transformers
+tags:
+  - deberta
+  - deberta-v2
+  - fill-mask
+  - character
+  - wwm
+datasets:
+  - wikipedia
+  - cc100
+  - oscar
+metrics:
+  - accuracy
+mask_token: "[MASK]"
+widget:
+    - text: "京都大学で自然言語処理を[MASK][MASK]する。"
+---
+
+# Model Card for Japanese character-level DeBERTa V2 large
+
+## Model description
+
+This is a Japanese DeBERTa V2 large model pre-trained on Japanese Wikipedia, the Japanese portion of CC-100, and the Japanese portion of OSCAR.
+This model is trained with character-level tokenization and whole word masking.
+
+## How to use
+
+You can use this model for masked language modeling as follows:
+
+```python
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+tokenizer = AutoTokenizer.from_pretrained('ku-nlp/deberta-v2-large-japanese-char-wwm')
+model = AutoModelForMaskedLM.from_pretrained('ku-nlp/deberta-v2-large-japanese-char-wwm')
+
+sentence = '京都大学で自然言語処理を[MASK][MASK]する。'
+encoding = tokenizer(sentence, return_tensors='pt')
+...
+```
+
+You can also fine-tune this model on downstream tasks.
+
+## Tokenization
+
+There is no need to tokenize texts in advance, and you can give raw texts to the tokenizer.
+The texts are tokenized into character-level tokens by [sentencepiece](https://github.com/google/sentencepiece).
+
+## Training data
+
+We used the following corpora for pre-training:
+
+- Japanese Wikipedia (as of 20221020, 3.2GB, 27M sentences, 1.3M documents)
+- Japanese portion of CC-100 (85GB, 619M sentences, 66M documents)
+- Japanese portion of OSCAR (54GB, 326M sentences, 25M documents)
+
+Note that we filtered out documents annotated with "header", "footer", or "noisy" tags in OSCAR.
+Also note that Japanese Wikipedia was duplicated 10 times to make the total size of the corpus comparable to that of CC-100 and OSCAR. As a result, the total size of the training data is 171GB.
+
+## Training procedure
+
+We first segmented texts in the corpora into words using [Juman++ 2.0.0-rc3](https://github.com/ku-nlp/jumanpp/releases/tag/v2.0.0-rc3) for whole word masking.
+Then, we built a sentencepiece model with 22,012 tokens including all characters that appear in the training corpus.
+
+We tokenized raw corpora into character-level subwords using the sentencepiece model and trained the Japanese DeBERTa model using [transformers](https://github.com/huggingface/transformers) library.
+The training took 26 days using 16 NVIDIA A100-SXM4-40GB GPUs.
+
+The following hyperparameters were used during pre-training:
+
+- learning_rate: 1e-4
+- per_device_train_batch_size: 26
+- distributed_type: multi-GPU
+- num_devices: 16
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 3,328
+- max_seq_length: 512
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
+- lr_scheduler_type: linear schedule with warmup (lr = 0 at 300k steps)
+- training_steps: 260,000
+- warmup_steps: 10,000
+
+The accuracy of the trained model on the masked language modeling task was 0.795.
+The evaluation set consists of 5,000 randomly sampled documents from each of the training corpora.
+
+## Acknowledgments
+
+This work was supported by Joint Usage/Research Center for Interdisciplinary Large-scale Information Infrastructures (JHPCN) through General Collaboration Project no. jh221004, "Developing a Platform for Constructing and Sharing of Large-Scale Japanese Language Models".
+For training models, we used the mdx: a platform for the data-driven future.
diff --git a/bert/deberta-v2-large-japanese-char-wwm/config.json b/bert/deberta-v2-large-japanese-char-wwm/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..9fb6b0ac2ec49b6556e58b5ed9492eb33166714d
--- /dev/null
+++ b/bert/deberta-v2-large-japanese-char-wwm/config.json
@@ -0,0 +1,37 @@
+{
+  "architectures": [
+    "DebertaV2ForMaskedLM"
+  ],
+  "attention_head_size": 64,
+  "attention_probs_dropout_prob": 0.1,
+  "conv_act": "gelu",
+  "conv_kernel_size": 3,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-07,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "norm_rel_ebd": "layer_norm",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 1024,
+  "pos_att_type": [
+    "p2c",
+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "relative_attention": true,
+  "share_att_key": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.25.1",
+  "type_vocab_size": 0,
+  "vocab_size": 22012
+}
diff --git a/bert/deberta-v2-large-japanese-char-wwm/pytorch_model.bin b/bert/deberta-v2-large-japanese-char-wwm/pytorch_model.bin
new file mode 100644
index 0000000000000000000000000000000000000000..bfdf594911d2558901734173c459aef5f760ae37
--- /dev/null
+++ b/bert/deberta-v2-large-japanese-char-wwm/pytorch_model.bin
@@ -0,0 +1,3 @@
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+oid sha256:bf0dab8ad87bd7c22e85ec71e04f2240804fda6d33196157d6b5923af6ea1201
+size 1318456639
diff --git a/bert/deberta-v2-large-japanese-char-wwm/special_tokens_map.json b/bert/deberta-v2-large-japanese-char-wwm/special_tokens_map.json
new file mode 100644
index 0000000000000000000000000000000000000000..a8b3208c2884c4efb86e49300fdd3dc877220cdf
--- /dev/null
+++ b/bert/deberta-v2-large-japanese-char-wwm/special_tokens_map.json
@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}
diff --git a/bert/deberta-v2-large-japanese-char-wwm/tokenizer_config.json b/bert/deberta-v2-large-japanese-char-wwm/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..8ab2175580e45760875557201e5543019ca3039b
--- /dev/null
+++ b/bert/deberta-v2-large-japanese-char-wwm/tokenizer_config.json
@@ -0,0 +1,19 @@
+{
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "do_subword_tokenize": true,
+  "do_word_tokenize": true,
+  "jumanpp_kwargs": null,
+  "mask_token": "[MASK]",
+  "mecab_kwargs": null,
+  "model_max_length": 1000000000000000019884624838656,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "special_tokens_map_file": null,
+  "subword_tokenizer_type": "character",
+  "sudachi_kwargs": null,
+  "tokenizer_class": "BertJapaneseTokenizer",
+  "unk_token": "[UNK]",
+  "word_tokenizer_type": "basic"
+}
diff --git a/bert/deberta-v2-large-japanese-char-wwm/vocab.txt b/bert/deberta-v2-large-japanese-char-wwm/vocab.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ef3652a1877f4c898e6fcb3e605c432c7bcc56b1
--- /dev/null
+++ b/bert/deberta-v2-large-japanese-char-wwm/vocab.txt
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+[PAD]
+[CLS]
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+]
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+𡫡
+𡰥
+𡳳
+𡶫
+𡸫
+𡸳
+𡸴
+𡽫
+𢀛
+𢄓
+𢆫
+𢈴
+𢈻
+𢎭
+𢭐
+𢮦
+𢴎
+𢷄
+𢾺
+𣂑
+𣅶
+𣈜
+𣏾
+𣒱
+𣘻
+𣚴
+𣛦
+𣛭
+𣜌
+𣦵
+𣳔
+𣳫
+𣴎
+𣴴
+𣶬
+𣾼
+𤃝
+𤄃
+𤅶
+𤆬
+𤇅
+𤉇
+𤊫
+𤊹
+𤌤
+𤐜
+𤑫
+𤘽
+𤚥
+𤞼
+𤠝
+𤧶
+𤪠
+𤭖
+𤯩
+𤰃
+𤱂
+𥇥
+𥔲
+𥢓
+𥬊
+𥳑
+𥳽
+𥸮
+𥸯
+𥹠
+𥹷
+𥻘
+𥻹
+𥻻
+𦣝
+𦤾
+𦩀
+𦯶
+𦱳
+𧈢
+𧌇
+𧏡
+𧒽
+𧖱
+𧘔
+𧘕
+𧙖
+𧟨
+𧮾
+𧳜
+𧵑
+𧸘
+𧸝
+𧾷
+𨀈
+𨈭
+𨑻
+𨕭
+𨗉
+𨜒
+𨤔
+𨦈
+𨨞
+𨬊
+𨭻
+𨯁
+𨱽
+𩖃
+𩙻
+𩙿
+𩝐
+𩠼
+𩡤
+𩢴
+𩣡
+𩥇
+𩧥
+𩩓
+𩩫
+𩲃
+𩲈
+𩳐
+𩵄
+𩵋
+𩶗
+𩷑
+𩸶
+𩹉
+𩻩
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+𪅀
+𪏰
+𪏽
+𪕰
+𪗱
+𪘚
+𪚢
+𪻆
+𫇠
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+𫝂
+𫝼
+𫞔
+𫡷
+𫵾
+𬐐
+𰑨
+𰢱
+𱏑
+𱖥
+𱗺
+𱗻
+𱬒
+𲄜
+𲄠
+𲄤
+𲄬
+𲇡
+񂪑
+񣘠
+񤭥
+񻋃
+񻋆
+򂢃
+򖃖
+򤬥
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+󾫵
+󾬍
+󾭙
+􀀀
+􂷂
+􏰁
diff --git a/config.py b/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..a05cbd16e91bd209a09b17f46079c7507b63f7a6
--- /dev/null
+++ b/config.py
@@ -0,0 +1,307 @@
+"""
+@Desc: 全局配置文件读取
+"""
+
+import shutil
+from pathlib import Path
+from typing import Any
+
+import torch
+import yaml
+
+from style_bert_vits2.logging import logger
+
+
+class PathConfig:
+    def __init__(self, dataset_root: str, assets_root: str):
+        self.dataset_root = Path(dataset_root)
+        self.assets_root = Path(assets_root)
+
+
+# If not cuda available, set possible devices to cpu
+cuda_available = torch.cuda.is_available()
+
+
+class Resample_config:
+    """重采样配置"""
+
+    def __init__(self, in_dir: str, out_dir: str, sampling_rate: int = 44100):
+        self.sampling_rate = sampling_rate  # 目标采样率
+        self.in_dir = Path(in_dir)  # 待处理音频目录路径
+        self.out_dir = Path(out_dir)  # 重采样输出路径
+
+    @classmethod
+    def from_dict(cls, dataset_path: Path, data: dict[str, Any]):
+        """从字典中生成实例"""
+
+        # 不检查路径是否有效，此逻辑在resample.py中处理
+        data["in_dir"] = dataset_path / data["in_dir"]
+        data["out_dir"] = dataset_path / data["out_dir"]
+
+        return cls(**data)
+
+
+class Preprocess_text_config:
+    """数据预处理配置"""
+
+    def __init__(
+        self,
+        transcription_path: str,
+        cleaned_path: str,
+        train_path: str,
+        val_path: str,
+        config_path: str,
+        val_per_lang: int = 5,
+        max_val_total: int = 10000,
+        clean: bool = True,
+    ):
+        self.transcription_path = Path(transcription_path)
+        self.train_path = Path(train_path)
+        if cleaned_path == "" or cleaned_path is None:
+            self.cleaned_path = self.transcription_path.with_name(
+                self.transcription_path.name + ".cleaned"
+            )
+        else:
+            self.cleaned_path = Path(cleaned_path)
+        self.val_path = Path(val_path)
+        self.config_path = Path(config_path)
+        self.val_per_lang = val_per_lang
+        self.max_val_total = max_val_total
+        self.clean = clean
+
+    @classmethod
+    def from_dict(cls, dataset_path: Path, data: dict[str, Any]):
+        """从字典中生成实例"""
+
+        data["transcription_path"] = dataset_path / data["transcription_path"]
+        if data["cleaned_path"] == "" or data["cleaned_path"] is None:
+            data["cleaned_path"] = ""
+        else:
+            data["cleaned_path"] = dataset_path / data["cleaned_path"]
+        data["train_path"] = dataset_path / data["train_path"]
+        data["val_path"] = dataset_path / data["val_path"]
+        data["config_path"] = dataset_path / data["config_path"]
+
+        return cls(**data)
+
+
+class Bert_gen_config:
+    """bert_gen 配置"""
+
+    def __init__(
+        self,
+        config_path: str,
+        num_processes: int = 1,
+        device: str = "cuda",
+        use_multi_device: bool = False,
+    ):
+        self.config_path = Path(config_path)
+        self.num_processes = num_processes
+        if not cuda_available:
+            device = "cpu"
+        self.device = device
+        self.use_multi_device = use_multi_device
+
+    @classmethod
+    def from_dict(cls, dataset_path: Path, data: dict[str, Any]):
+        data["config_path"] = dataset_path / data["config_path"]
+
+        return cls(**data)
+
+
+class Style_gen_config:
+    """style_gen 配置"""
+
+    def __init__(
+        self,
+        config_path: str,
+        num_processes: int = 4,
+        device: str = "cuda",
+    ):
+        self.config_path = Path(config_path)
+        self.num_processes = num_processes
+        if not cuda_available:
+            device = "cpu"
+        self.device = device
+
+    @classmethod
+    def from_dict(cls, dataset_path: Path, data: dict[str, Any]):
+        data["config_path"] = dataset_path / data["config_path"]
+
+        return cls(**data)
+
+
+class Train_ms_config:
+    """训练配置"""
+
+    def __init__(
+        self,
+        config_path: str,
+        env: dict[str, Any],
+        # base: Dict[str, any],
+        model_dir: str,
+        num_workers: int,
+        spec_cache: bool,
+        keep_ckpts: int,
+    ):
+        self.env = env  # 需要加载的环境变量
+        # self.base = base  # 底模配置
+        self.model_dir = Path(
+            model_dir
+        )  # 训练模型存储目录，该路径为相对于dataset_path的路径，而非项目根目录
+        self.config_path = Path(config_path)  # 配置文件路径
+        self.num_workers = num_workers  # worker数量
+        self.spec_cache = spec_cache  # 是否启用spec缓存
+        self.keep_ckpts = keep_ckpts  # ckpt数量
+
+    @classmethod
+    def from_dict(cls, dataset_path: Path, data: dict[str, Any]):
+        # data["model"] = os.path.join(dataset_path, data["model"])
+        data["config_path"] = dataset_path / data["config_path"]
+
+        return cls(**data)
+
+
+class Webui_config:
+    """webui 配置 (for webui.py, not supported now)"""
+
+    def __init__(
+        self,
+        device: str,
+        model: str,
+        config_path: str,
+        language_identification_library: str,
+        port: int = 7860,
+        share: bool = False,
+        debug: bool = False,
+    ):
+        if not cuda_available:
+            device = "cpu"
+        self.device = device
+        self.model = Path(model)
+        self.config_path = Path(config_path)
+        self.port: int = port
+        self.share: bool = share
+        self.debug: bool = debug
+        self.language_identification_library: str = language_identification_library
+
+    @classmethod
+    def from_dict(cls, dataset_path: Path, data: dict[str, Any]):
+        data["config_path"] = dataset_path / data["config_path"]
+        data["model"] = dataset_path / data["model"]
+        return cls(**data)
+
+
+class Server_config:
+    def __init__(
+        self,
+        port: int = 5000,
+        device: str = "cuda",
+        limit: int = 100,
+        language: str = "JP",
+        origins: list[str] = ["*"],
+    ):
+        self.port: int = port
+        if not cuda_available:
+            device = "cpu"
+        self.device: str = device
+        self.language: str = language
+        self.limit: int = limit
+        self.origins: list[str] = origins
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]):
+        return cls(**data)
+
+
+class Translate_config:
+    """翻译api配置"""
+
+    def __init__(self, app_key: str, secret_key: str):
+        self.app_key = app_key
+        self.secret_key = secret_key
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]):
+        return cls(**data)
+
+
+class Config:
+    def __init__(self, config_path: str, path_config: PathConfig):
+        if not Path(config_path).exists():
+            shutil.copy(src="default_config.yml", dst=config_path)
+            logger.info(
+                f"A configuration file {config_path} has been generated based on the default configuration file default_config.yml."
+            )
+            logger.info(
+                "Please do not modify default_config.yml. Instead, modify config.yml."
+            )
+            # sys.exit(0)
+        with open(config_path, encoding="utf-8") as file:
+            yaml_config: dict[str, Any] = yaml.safe_load(file.read())
+            model_name: str = yaml_config["model_name"]
+            self.model_name: str = model_name
+            if "dataset_path" in yaml_config:
+                dataset_path = Path(yaml_config["dataset_path"])
+            else:
+                dataset_path = path_config.dataset_root / model_name
+            self.dataset_path = dataset_path
+            self.dataset_root = path_config.dataset_root
+            self.assets_root = path_config.assets_root
+            self.out_dir = self.assets_root / model_name
+            self.resample_config: Resample_config = Resample_config.from_dict(
+                dataset_path, yaml_config["resample"]
+            )
+            self.preprocess_text_config: Preprocess_text_config = (
+                Preprocess_text_config.from_dict(
+                    dataset_path, yaml_config["preprocess_text"]
+                )
+            )
+            self.bert_gen_config: Bert_gen_config = Bert_gen_config.from_dict(
+                dataset_path, yaml_config["bert_gen"]
+            )
+            self.style_gen_config: Style_gen_config = Style_gen_config.from_dict(
+                dataset_path, yaml_config["style_gen"]
+            )
+            self.train_ms_config: Train_ms_config = Train_ms_config.from_dict(
+                dataset_path, yaml_config["train_ms"]
+            )
+            self.webui_config: Webui_config = Webui_config.from_dict(
+                dataset_path, yaml_config["webui"]
+            )
+            self.server_config: Server_config = Server_config.from_dict(
+                yaml_config["server"]
+            )
+            # self.translate_config: Translate_config = Translate_config.from_dict(
+            #     yaml_config["translate"]
+            # )
+
+
+# Load and initialize the configuration
+
+
+def get_path_config() -> PathConfig:
+    path_config_path = Path("configs/paths.yml")
+    if not path_config_path.exists():
+        shutil.copy(src="configs/default_paths.yml", dst=path_config_path)
+        logger.info(
+            f"A configuration file {path_config_path} has been generated based on the default configuration file default_paths.yml."
+        )
+        logger.info(
+            "Please do not modify configs/default_paths.yml. Instead, modify configs/paths.yml."
+        )
+    with open(path_config_path, encoding="utf-8") as file:
+        path_config_dict: dict[str, str] = yaml.safe_load(file.read())
+    return PathConfig(**path_config_dict)
+
+
+def get_config() -> Config:
+    path_config = get_path_config()
+    try:
+        config = Config("config.yml", path_config)
+    except (TypeError, KeyError):
+        logger.warning("Old config.yml found. Replace it with default_config.yml.")
+        shutil.copy(src="default_config.yml", dst="config.yml")
+        config = Config("config.yml", path_config)
+
+    return config
diff --git a/configs/config.json b/configs/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..75629e9ea99064dc237cc991508966b6057eb0bf
--- /dev/null
+++ b/configs/config.json
@@ -0,0 +1,73 @@
+{
+  "model_name": "Dummy",
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 1000,
+    "seed": 42,
+    "epochs": 1000,
+    "learning_rate": 0.0002,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 2,
+    "bf16_run": false,
+    "lr_decay": 0.99995,
+    "segment_size": 16384,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0,
+    "skip_optimizer": false,
+    "freeze_ZH_bert": false,
+    "freeze_JP_bert": false,
+    "freeze_EN_bert": false,
+    "freeze_style": false,
+    "freeze_encoder": false
+  },
+  "data": {
+    "use_jp_extra": false,
+    "training_files": "Data/Dummy/train.list",
+    "validation_files": "Data/Dummy/val.list",
+    "max_wav_value": 32768.0,
+    "sampling_rate": 44100,
+    "filter_length": 2048,
+    "hop_length": 512,
+    "win_length": 2048,
+    "n_mel_channels": 128,
+    "mel_fmin": 0.0,
+    "mel_fmax": null,
+    "add_blank": true,
+    "n_speakers": 1,
+    "cleaned_text": true,
+    "num_styles": 1,
+    "style2id": {
+      "Neutral": 0
+    }
+  },
+  "model": {
+    "use_spk_conditioned_encoder": true,
+    "use_noise_scaled_mas": true,
+    "use_mel_posterior_encoder": false,
+    "use_duration_discriminator": true,
+    "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, 2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16, 16, 8, 2, 2],
+    "n_layers_q": 3,
+    "use_spectral_norm": false,
+    "gin_channels": 256
+  },
+  "version": "2.5.0"
+}
diff --git a/configs/config_jp_extra.json b/configs/config_jp_extra.json
new file mode 100644
index 0000000000000000000000000000000000000000..d7e135ad0cd8507346dec5d4487b3d396eff8dc5
--- /dev/null
+++ b/configs/config_jp_extra.json
@@ -0,0 +1,80 @@
+{
+  "model_name": "Dummy",
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 1000,
+    "seed": 42,
+    "epochs": 1000,
+    "learning_rate": 0.0001,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 2,
+    "bf16_run": false,
+    "fp16_run": false,
+    "lr_decay": 0.99996,
+    "segment_size": 16384,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0,
+    "c_commit": 100,
+    "skip_optimizer": false,
+    "freeze_ZH_bert": false,
+    "freeze_JP_bert": false,
+    "freeze_EN_bert": false,
+    "freeze_emo": false,
+    "freeze_style": false,
+    "freeze_decoder": false
+  },
+  "data": {
+    "use_jp_extra": true,
+    "training_files": "Data/Dummy/train.list",
+    "validation_files": "Data/Dummy/val.list",
+    "max_wav_value": 32768.0,
+    "sampling_rate": 44100,
+    "filter_length": 2048,
+    "hop_length": 512,
+    "win_length": 2048,
+    "n_mel_channels": 128,
+    "mel_fmin": 0.0,
+    "mel_fmax": null,
+    "add_blank": true,
+    "n_speakers": 512,
+    "cleaned_text": true
+  },
+  "model": {
+    "use_spk_conditioned_encoder": true,
+    "use_noise_scaled_mas": true,
+    "use_mel_posterior_encoder": false,
+    "use_duration_discriminator": false,
+    "use_wavlm_discriminator": true,
+    "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, 2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16, 16, 8, 2, 2],
+    "n_layers_q": 3,
+    "use_spectral_norm": false,
+    "gin_channels": 512,
+    "slm": {
+      "model": "./slm/wavlm-base-plus",
+      "sr": 16000,
+      "hidden": 768,
+      "nlayers": 13,
+      "initial_channel": 64
+    }
+  },
+  "version": "2.5.0-JP-Extra"
+}
diff --git a/configs/default_paths.yml b/configs/default_paths.yml
new file mode 100644
index 0000000000000000000000000000000000000000..d743748f7f52d5c41f1487b53872143c2f010222
--- /dev/null
+++ b/configs/default_paths.yml
@@ -0,0 +1,8 @@
+# Root directory of the training dataset.
+# The training dataset of {model_name} should be placed in {dataset_root}/{model_name}.
+dataset_root: Data
+
+# Root directory of the model assets (for inference).
+# In training, the model assets will be saved to {assets_root}/{model_name},
+# and in inference, we load all the models from {assets_root}.
+assets_root: model_assets
diff --git a/configs/paths.yml b/configs/paths.yml
new file mode 100644
index 0000000000000000000000000000000000000000..d743748f7f52d5c41f1487b53872143c2f010222
--- /dev/null
+++ b/configs/paths.yml
@@ -0,0 +1,8 @@
+# Root directory of the training dataset.
+# The training dataset of {model_name} should be placed in {dataset_root}/{model_name}.
+dataset_root: Data
+
+# Root directory of the model assets (for inference).
+# In training, the model assets will be saved to {assets_root}/{model_name},
+# and in inference, we load all the models from {assets_root}.
+assets_root: model_assets
diff --git a/default_config.yml b/default_config.yml
new file mode 100644
index 0000000000000000000000000000000000000000..8ee8bb89623c960794cad8863a70ba71c0dbed28
--- /dev/null
+++ b/default_config.yml
@@ -0,0 +1,70 @@
+model_name: "model_name"
+
+# If you want to use a specific dataset path, uncomment the following line.
+# Otherwise, the dataset path is `{dataset_root}/{model_name}`.
+
+# dataset_path: "your/dataset/path"
+
+resample:
+  sampling_rate: 44100
+  in_dir: "raw"
+  out_dir: "wavs"
+
+preprocess_text:
+  transcription_path: "esd.list"
+  cleaned_path: ""
+  train_path: "train.list"
+  val_path: "val.list"
+  config_path: "config.json"
+  val_per_lang: 0
+  max_val_total: 12
+  clean: true
+
+bert_gen:
+  config_path: "config.json"
+  num_processes: 1
+  device: "cuda"
+  use_multi_device: false
+
+style_gen:
+  config_path: "config.json"
+  num_processes: 4
+  device: "cuda"
+
+train_ms:
+  env:
+    MASTER_ADDR: "localhost"
+    MASTER_PORT: 10086
+    WORLD_SIZE: 1
+    LOCAL_RANK: 0
+    RANK: 0
+  model_dir: "models"  # The directory to save the model (for training), relative to `{dataset_root}/{model_name}`.
+  config_path: "config.json"
+  num_workers: 16
+  spec_cache: True
+  keep_ckpts: 1 # Set this to 0 to keep all checkpoints
+
+webui:  # For `webui.py`, which is not supported yet in Style-Bert-VITS2.
+  # 推理设备
+  device: "cuda"
+  # 模型路径
+  model: "models/G_8000.pth"
+  # 配置文件路径
+  config_path: "config.json"
+  # 端口号
+  port: 7860
+  # 是否公开部署，对外网开放
+  share: false
+  # 是否开启debug模式
+  debug: false
+  # 语种识别库，可选langid, fastlid
+  language_identification_library: "langid"
+
+# server_fastapi's config
+server:
+  port: 5000
+  device: "cuda"
+  language: "JP"
+  limit: 100
+  origins:
+    - "*"
diff --git a/initialize.py b/initialize.py
new file mode 100644
index 0000000000000000000000000000000000000000..267465e38d35f1da4ab4838ea7626aeef175fa1c
--- /dev/null
+++ b/initialize.py
@@ -0,0 +1,147 @@
+import argparse
+import json
+import shutil
+from pathlib import Path
+
+import yaml
+from huggingface_hub import hf_hub_download
+
+from style_bert_vits2.logging import logger
+
+
+def download_bert_models():
+    with open("bert/bert_models.json", encoding="utf-8") as fp:
+        models = json.load(fp)
+    for k, v in models.items():
+        local_path = Path("bert").joinpath(k)
+        for file in v["files"]:
+            if not Path(local_path).joinpath(file).exists():
+                logger.info(f"Downloading {k} {file}")
+                hf_hub_download(v["repo_id"], file, local_dir=local_path)
+
+
+def download_slm_model():
+    local_path = Path("slm/wavlm-base-plus/")
+    file = "pytorch_model.bin"
+    if not Path(local_path).joinpath(file).exists():
+        logger.info(f"Downloading wavlm-base-plus {file}")
+        hf_hub_download("microsoft/wavlm-base-plus", file, local_dir=local_path)
+
+
+def download_pretrained_models():
+    files = ["G_0.safetensors", "D_0.safetensors", "DUR_0.safetensors"]
+    local_path = Path("pretrained")
+    for file in files:
+        if not Path(local_path).joinpath(file).exists():
+            logger.info(f"Downloading pretrained {file}")
+            hf_hub_download(
+                "litagin/Style-Bert-VITS2-1.0-base", file, local_dir=local_path
+            )
+
+
+def download_jp_extra_pretrained_models():
+    files = ["G_0.safetensors", "D_0.safetensors", "WD_0.safetensors"]
+    local_path = Path("pretrained_jp_extra")
+    for file in files:
+        if not Path(local_path).joinpath(file).exists():
+            logger.info(f"Downloading JP-Extra pretrained {file}")
+            hf_hub_download(
+                "litagin/Style-Bert-VITS2-2.0-base-JP-Extra", file, local_dir=local_path
+            )
+
+
+def download_default_models():
+    files = [
+        "jvnv-F1-jp/config.json",
+        "jvnv-F1-jp/jvnv-F1-jp_e160_s14000.safetensors",
+        "jvnv-F1-jp/style_vectors.npy",
+        "jvnv-F2-jp/config.json",
+        "jvnv-F2-jp/jvnv-F2_e166_s20000.safetensors",
+        "jvnv-F2-jp/style_vectors.npy",
+        "jvnv-M1-jp/config.json",
+        "jvnv-M1-jp/jvnv-M1-jp_e158_s14000.safetensors",
+        "jvnv-M1-jp/style_vectors.npy",
+        "jvnv-M2-jp/config.json",
+        "jvnv-M2-jp/jvnv-M2-jp_e159_s17000.safetensors",
+        "jvnv-M2-jp/style_vectors.npy",
+    ]
+    for file in files:
+        if not Path(f"model_assets/{file}").exists():
+            logger.info(f"Downloading {file}")
+            hf_hub_download(
+                "litagin/style_bert_vits2_jvnv",
+                file,
+                local_dir="model_assets",
+            )
+    additional_files = {
+        "litagin/sbv2_koharune_ami": [
+            "koharune-ami/config.json",
+            "koharune-ami/style_vectors.npy",
+            "koharune-ami/koharune-ami.safetensors",
+        ],
+        "litagin/sbv2_amitaro": [
+            "amitaro/config.json",
+            "amitaro/style_vectors.npy",
+            "amitaro/amitaro.safetensors",
+        ],
+    }
+    for repo_id, files in additional_files.items():
+        for file in files:
+            if not Path(f"model_assets/{file}").exists():
+                logger.info(f"Downloading {file}")
+                hf_hub_download(
+                    repo_id,
+                    file,
+                    local_dir="model_assets",
+                )
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--skip_default_models", action="store_true")
+    parser.add_argument("--only_infer", action="store_true")
+    parser.add_argument(
+        "--dataset_root",
+        type=str,
+        help="Dataset root path (default: Data)",
+        default=None,
+    )
+    parser.add_argument(
+        "--assets_root",
+        type=str,
+        help="Assets root path (default: model_assets)",
+        default=None,
+    )
+    args = parser.parse_args()
+
+    download_bert_models()
+
+    if not args.skip_default_models:
+        download_default_models()
+    if not args.only_infer:
+        download_slm_model()
+        download_pretrained_models()
+        download_jp_extra_pretrained_models()
+
+    # If configs/paths.yml not exists, create it
+    default_paths_yml = Path("configs/default_paths.yml")
+    paths_yml = Path("configs/paths.yml")
+    if not paths_yml.exists():
+        shutil.copy(default_paths_yml, paths_yml)
+
+    if args.dataset_root is None and args.assets_root is None:
+        return
+
+    # Change default paths if necessary
+    with open(paths_yml, encoding="utf-8") as f:
+        yml_data = yaml.safe_load(f)
+    if args.assets_root is not None:
+        yml_data["assets_root"] = args.assets_root
+    if args.dataset_root is not None:
+        yml_data["dataset_root"] = args.dataset_root
+    with open(paths_yml, "w", encoding="utf-8") as f:
+        yaml.dump(yml_data, f, allow_unicode=True)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/model_assets/.gitignore b/model_assets/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..87512bec25cd2bac15b4c1df086dc04217e9c2bc
--- /dev/null
+++ b/model_assets/.gitignore
@@ -0,0 +1,2 @@
+# *
+!.gitignore
\ No newline at end of file
diff --git "a/model_assets/\343\202\277\343\203\253\343\203\210/config.json" "b/model_assets/\343\202\277\343\203\253\343\203\210/config.json"
new file mode 100644
index 0000000000000000000000000000000000000000..4fd6a606632ef6a83216a8dd8f478e7738c4cc92
--- /dev/null
+++ "b/model_assets/\343\202\277\343\203\253\343\203\210/config.json"
@@ -0,0 +1,118 @@
+{
+  "model_name": "tart",
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 1000,
+    "seed": 42,
+    "epochs": 100,
+    "learning_rate": 0.0001,
+    "betas": [
+      0.8,
+      0.99
+    ],
+    "eps": 1e-09,
+    "batch_size": 4,
+    "bf16_run": false,
+    "fp16_run": false,
+    "lr_decay": 0.99996,
+    "segment_size": 16384,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0,
+    "c_commit": 100,
+    "skip_optimizer": false,
+    "freeze_ZH_bert": false,
+    "freeze_JP_bert": false,
+    "freeze_EN_bert": false,
+    "freeze_emo": false,
+    "freeze_style": false,
+    "freeze_decoder": false
+  },
+  "data": {
+    "use_jp_extra": true,
+    "training_files": "/content/drive/MyDrive/Style-Bert-VITS2/Data/tart/train.list",
+    "validation_files": "/content/drive/MyDrive/Style-Bert-VITS2/Data/tart/val.list",
+    "max_wav_value": 32768.0,
+    "sampling_rate": 44100,
+    "filter_length": 2048,
+    "hop_length": 512,
+    "win_length": 2048,
+    "n_mel_channels": 128,
+    "mel_fmin": 0.0,
+    "mel_fmax": null,
+    "add_blank": true,
+    "n_speakers": 1,
+    "cleaned_text": true,
+    "spk2id": {
+      "tart": 0
+    },
+    "num_styles": 1,
+    "style2id": {
+      "Neutral": 0
+    }
+  },
+  "model": {
+    "use_spk_conditioned_encoder": true,
+    "use_noise_scaled_mas": true,
+    "use_mel_posterior_encoder": false,
+    "use_duration_discriminator": false,
+    "use_wavlm_discriminator": true,
+    "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,
+      2
+    ],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [
+      16,
+      16,
+      8,
+      2,
+      2
+    ],
+    "n_layers_q": 3,
+    "use_spectral_norm": false,
+    "gin_channels": 512,
+    "slm": {
+      "model": "./slm/wavlm-base-plus",
+      "sr": 16000,
+      "hidden": 768,
+      "nlayers": 13,
+      "initial_channel": 64
+    }
+  },
+  "version": "2.4.1-JP-Extra"
+}
\ No newline at end of file
diff --git "a/model_assets/\343\202\277\343\203\253\343\203\210/style_vectors.npy" "b/model_assets/\343\202\277\343\203\253\343\203\210/style_vectors.npy"
new file mode 100644
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--- /dev/null
+++ "b/model_assets/\343\202\277\343\203\253\343\203\210/style_vectors.npy"
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a99fa9de1781563ffd546451f26b4907acbb8c475a8a4fa4d71e6476e2b0d398
+size 1152
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new file mode 100644
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+++ "b/model_assets/\343\202\277\343\203\253\343\203\210/tart_e100_s2100.safetensors"
@@ -0,0 +1,3 @@
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+size 251150980
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new file mode 100644
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git "a/model_assets/\343\202\277\343\203\253\343\203\210/tart_e96_s2000.safetensors" "b/model_assets/\343\202\277\343\203\253\343\203\210/tart_e96_s2000.safetensors"
new file mode 100644
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--- /dev/null
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 251150980
diff --git "a/model_assets/\343\203\206\343\202\243\343\203\251\343\203\237\343\202\271/config.json" "b/model_assets/\343\203\206\343\202\243\343\203\251\343\203\237\343\202\271/config.json"
new file mode 100644
index 0000000000000000000000000000000000000000..bb68a57ab65581b8e3c418e5c380fd980cfcf15a
--- /dev/null
+++ "b/model_assets/\343\203\206\343\202\243\343\203\251\343\203\237\343\202\271/config.json"
@@ -0,0 +1,122 @@
+{
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 1000,
+    "seed": 42,
+    "epochs": 100,
+    "learning_rate": 0.0001,
+    "betas": [
+      0.8,
+      0.99
+    ],
+    "eps": 1e-09,
+    "batch_size": 4,
+    "bf16_run": false,
+    "fp16_run": false,
+    "lr_decay": 0.99996,
+    "segment_size": 16384,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0,
+    "c_commit": 100,
+    "skip_optimizer": false,
+    "freeze_ZH_bert": false,
+    "freeze_JP_bert": false,
+    "freeze_EN_bert": false,
+    "freeze_emo": false,
+    "freeze_style": false,
+    "freeze_decoder": false
+  },
+  "data": {
+    "use_jp_extra": true,
+    "training_files": "/content/drive/MyDrive/Style-Bert-VITS2/Data/tiramisu/train.list",
+    "validation_files": "/content/drive/MyDrive/Style-Bert-VITS2/Data/tiramisu/val.list",
+    "max_wav_value": 32768.0,
+    "sampling_rate": 44100,
+    "filter_length": 2048,
+    "hop_length": 512,
+    "win_length": 2048,
+    "n_mel_channels": 128,
+    "mel_fmin": 0.0,
+    "mel_fmax": null,
+    "add_blank": true,
+    "n_speakers": 1,
+    "cleaned_text": true,
+    "spk2id": {
+      "tiramisu": 0
+    },
+    "num_styles": 5,
+    "style2id": {
+      "Neutral": 0,
+      "問いかけ": 1,
+      "困惑": 2,
+      "明るい": 3,
+      "落着き": 4
+    }
+  },
+  "model": {
+    "use_spk_conditioned_encoder": true,
+    "use_noise_scaled_mas": true,
+    "use_mel_posterior_encoder": false,
+    "use_duration_discriminator": false,
+    "use_wavlm_discriminator": true,
+    "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,
+      2
+    ],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [
+      16,
+      16,
+      8,
+      2,
+      2
+    ],
+    "n_layers_q": 3,
+    "use_spectral_norm": false,
+    "gin_channels": 512,
+    "slm": {
+      "model": "./slm/wavlm-base-plus",
+      "sr": 16000,
+      "hidden": 768,
+      "nlayers": 13,
+      "initial_channel": 64
+    }
+  },
+  "version": "2.3.1-JP-Extra",
+  "model_name": "tiramisu"
+}
\ No newline at end of file
diff --git "a/model_assets/\343\203\206\343\202\243\343\203\251\343\203\237\343\202\271/style_vectors.npy" "b/model_assets/\343\203\206\343\202\243\343\203\251\343\203\237\343\202\271/style_vectors.npy"
new file mode 100644
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+++ "b/model_assets/\343\203\206\343\202\243\343\203\251\343\203\237\343\202\271/style_vectors.npy"
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c3b2daf20680066ed54888e8d52d8653d053b26d8d8b311976f47611ebed5a18
+size 5248
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@@ -0,0 +1,3 @@
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+oid sha256:9071a122bed4d48e0bb5f1b217d1c8fed596481f8de457a614204ad081b84f5d
+size 251150980
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diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a96d285df6293fdaa0c97a9a33bddca0a50bf91e
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,25 @@
+# cmudict
+# cn2an
+# faster-whisper==0.10.1
+fastapi
+# g2p_en
+# GPUtil
+# gradio
+# jieba
+# librosa==0.9.2
+loguru
+num2words
+# protobuf==4.25
+# psutil
+# punctuators
+pyannote.audio>=3.1.0
+# pyloudnorm
+pyopenjtalk-dict
+# pypinyin
+pyworld-prebuilt
+# stable_ts
+# tensorboard
+torch
+transformers
+# umap-learn
+numpy==1.26.4
\ No newline at end of file
diff --git a/server_editor.py b/server_editor.py
new file mode 100644
index 0000000000000000000000000000000000000000..8caea47e503f8aa0a4b6afac4a3fac70686bd53c
--- /dev/null
+++ b/server_editor.py
@@ -0,0 +1,450 @@
+"""
+Style-Bert-VITS2-Editor用のサーバー。
+次のリポジトリ
+https://github.com/litagin02/Style-Bert-VITS2-Editor
+をビルドしてできあがったファイルをWebフォルダに入れて実行する。
+
+TODO: リファクタリングやドキュメンテーションやAPI整理、辞書周りの改善などが必要。
+"""
+
+import argparse
+import io
+import shutil
+import sys
+import webbrowser
+import zipfile
+from datetime import datetime
+from io import BytesIO
+from pathlib import Path
+from typing import Optional
+
+import numpy as np
+import requests
+import torch
+import uvicorn
+from fastapi import APIRouter, FastAPI, HTTPException, status
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse, Response
+from fastapi.staticfiles import StaticFiles
+from pydantic import BaseModel
+from scipy.io import wavfile
+
+from config import get_path_config
+from initialize import download_default_models
+from style_bert_vits2.constants import (
+    DEFAULT_ASSIST_TEXT_WEIGHT,
+    DEFAULT_NOISE,
+    DEFAULT_NOISEW,
+    DEFAULT_SDP_RATIO,
+    DEFAULT_STYLE,
+    DEFAULT_STYLE_WEIGHT,
+    VERSION,
+    Languages,
+)
+from style_bert_vits2.logging import logger
+from style_bert_vits2.nlp import bert_models
+from style_bert_vits2.nlp.japanese import pyopenjtalk_worker as pyopenjtalk
+from style_bert_vits2.nlp.japanese.g2p_utils import g2kata_tone, kata_tone2phone_tone
+from style_bert_vits2.nlp.japanese.normalizer import normalize_text
+from style_bert_vits2.nlp.japanese.user_dict import (
+    apply_word,
+    delete_word,
+    read_dict,
+    rewrite_word,
+    update_dict,
+)
+from style_bert_vits2.tts_model import TTSModelHolder, TTSModelInfo
+
+
+# ---フロントエンド部分に関する処理---
+
+# エディターのビルドファイルを配置するディレクトリ
+STATIC_DIR = Path("static")
+# エディターの最新のビルドファイルのダウンロード日時を記録するファイル
+LAST_DOWNLOAD_FILE = STATIC_DIR / "last_download.txt"
+
+
+def download_static_files(user, repo, asset_name):
+    """Style-Bert-VITS2エディターの最新のビルドzipをダウンロードして展開する。"""
+
+    logger.info("Checking for new release...")
+    latest_release = get_latest_release(user, repo)
+    if latest_release is None:
+        logger.warning(
+            "Failed to fetch the latest release. Proceeding without static files."
+        )
+        return
+
+    if not new_release_available(latest_release):
+        logger.info("No new release available. Proceeding with existing static files.")
+        return
+
+    logger.info("New release available. Downloading static files...")
+    asset_url = get_asset_url(latest_release, asset_name)
+    if asset_url:
+        if STATIC_DIR.exists():
+            shutil.rmtree(STATIC_DIR)
+        STATIC_DIR.mkdir(parents=True, exist_ok=True)
+        download_and_extract(asset_url, STATIC_DIR)
+        save_last_download(latest_release)
+    else:
+        logger.warning("Asset not found. Proceeding without static files.")
+
+
+def get_latest_release(user, repo):
+    url = f"https://api.github.com/repos/{user}/{repo}/releases/latest"
+    try:
+        response = requests.get(url)
+        response.raise_for_status()
+        return response.json()
+    except requests.RequestException:
+        return None
+
+
+def get_asset_url(release, asset_name):
+    for asset in release["assets"]:
+        if asset["name"] == asset_name:
+            return asset["browser_download_url"]
+    return None
+
+
+def download_and_extract(url, extract_to: Path):
+    response = requests.get(url)
+    response.raise_for_status()
+    with zipfile.ZipFile(io.BytesIO(response.content)) as zip_ref:
+        zip_ref.extractall(extract_to)
+
+    # 展開先が1つのディレクトリだけの場合、その中身を直下に移動する
+    extracted_dirs = list(extract_to.iterdir())
+    if len(extracted_dirs) == 1 and extracted_dirs[0].is_dir():
+        for file in extracted_dirs[0].iterdir():
+            file.rename(extract_to / file.name)
+        extracted_dirs[0].rmdir()
+
+    # index.htmlが存在するかチェック
+    if not (extract_to / "index.html").exists():
+        logger.warning("index.html not found in the extracted files.")
+
+
+def new_release_available(latest_release):
+    if LAST_DOWNLOAD_FILE.exists():
+        with open(LAST_DOWNLOAD_FILE) as file:
+            last_download_str = file.read().strip()
+            # 'Z'を除去して日時オブジェクトに変換
+            last_download_str = last_download_str.replace("Z", "+00:00")
+            last_download = datetime.fromisoformat(last_download_str)
+        return (
+            datetime.fromisoformat(
+                latest_release["published_at"].replace("Z", "+00:00")
+            )
+            > last_download
+        )
+    return True
+
+
+def save_last_download(latest_release):
+    with open(LAST_DOWNLOAD_FILE, "w") as file:
+        file.write(latest_release["published_at"])
+
+
+# ---フロントエンド部分に関する処理ここまで---
+# 以降はAPIの設定
+
+# pyopenjtalk_worker を起動
+## pyopenjtalk_worker は TCP ソケットサーバーのため、ここで起動する
+pyopenjtalk.initialize_worker()
+
+# pyopenjtalk の辞書を更新
+update_dict()
+
+# 事前に BERT モデル/トークナイザーをロードしておく
+## ここでロードしなくても必要になった際に自動ロードされるが、時間がかかるため事前にロードしておいた方が体験が良い
+## server_editor.py は日本語にしか対応していないため、日本語の BERT モデル/トークナイザーのみロードする
+bert_models.load_model(Languages.JP)
+bert_models.load_tokenizer(Languages.JP)
+
+
+class AudioResponse(Response):
+    media_type = "audio/wav"
+
+
+origins = [
+    "http://localhost:3000",
+    "http://localhost:8000",
+    "http://127.0.0.1:3000",
+    "http://127.0.0.1:8000",
+]
+
+path_config = get_path_config()
+parser = argparse.ArgumentParser()
+parser.add_argument("--model_dir", type=str, default=path_config.assets_root)
+parser.add_argument("--device", type=str, default="cuda")
+parser.add_argument("--port", type=int, default=8000)
+parser.add_argument("--inbrowser", action="store_true")
+parser.add_argument("--line_length", type=int, default=None)
+parser.add_argument("--line_count", type=int, default=None)
+parser.add_argument("--skip_default_models", action="store_true")
+parser.add_argument("--skip_static_files", action="store_true")
+args = parser.parse_args()
+device = args.device
+if device == "cuda" and not torch.cuda.is_available():
+    device = "cpu"
+model_dir = Path(args.model_dir)
+port = int(args.port)
+if not args.skip_default_models:
+    download_default_models()
+skip_static_files = bool(args.skip_static_files)
+
+model_holder = TTSModelHolder(model_dir, device)
+if len(model_holder.model_names) == 0:
+    logger.error(f"Models not found in {model_dir}.")
+    sys.exit(1)
+
+
+app = FastAPI()
+
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+router = APIRouter()
+
+
+@router.get("/version")
+def version() -> str:
+    return VERSION
+
+
+class MoraTone(BaseModel):
+    mora: str
+    tone: int
+
+
+class TextRequest(BaseModel):
+    text: str
+
+
+@router.post("/g2p")
+async def read_item(item: TextRequest):
+    try:
+        # 最初に正規化しないと整合性がとれない
+        text = normalize_text(item.text)
+        kata_tone_list = g2kata_tone(text)
+    except Exception as e:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Failed to convert {item.text} to katakana and tone, {e}",
+        )
+    return [MoraTone(mora=kata, tone=tone) for kata, tone in kata_tone_list]
+
+
+@router.post("/normalize")
+async def normalize(item: TextRequest):
+    return normalize_text(item.text)
+
+
+@router.get("/models_info", response_model=list[TTSModelInfo])
+def models_info():
+    return model_holder.models_info
+
+
+class SynthesisRequest(BaseModel):
+    model: str
+    modelFile: str
+    text: str
+    moraToneList: list[MoraTone]
+    style: str = DEFAULT_STYLE
+    styleWeight: float = DEFAULT_STYLE_WEIGHT
+    assistText: str = ""
+    assistTextWeight: float = DEFAULT_ASSIST_TEXT_WEIGHT
+    speed: float = 1.0
+    noise: float = DEFAULT_NOISE
+    noisew: float = DEFAULT_NOISEW
+    sdpRatio: float = DEFAULT_SDP_RATIO
+    language: Languages = Languages.JP
+    silenceAfter: float = 0.5
+    pitchScale: float = 1.0
+    intonationScale: float = 1.0
+    speaker: Optional[str] = None
+
+
+@router.post("/synthesis", response_class=AudioResponse)
+def synthesis(request: SynthesisRequest):
+    if args.line_length is not None and len(request.text) > args.line_length:
+        raise HTTPException(
+            status_code=400,
+            detail=f"1行の文字数は{args.line_length}文字以下にしてください。",
+        )
+    try:
+        model = model_holder.get_model(
+            model_name=request.model, model_path_str=request.modelFile
+        )
+    except Exception as e:
+        logger.error(e)
+        raise HTTPException(
+            status_code=500,
+            detail=f"Failed to load model {request.model} from {request.modelFile}, {e}",
+        )
+    text = request.text
+    kata_tone_list = [
+        (mora_tone.mora, mora_tone.tone) for mora_tone in request.moraToneList
+    ]
+    phone_tone = kata_tone2phone_tone(kata_tone_list)
+    tone = [t for _, t in phone_tone]
+    try:
+        sid = 0 if request.speaker is None else model.spk2id[request.speaker]
+    except KeyError:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Speaker {request.speaker} not found in {model.spk2id}",
+        )
+    sr, audio = model.infer(
+        text=text,
+        language=request.language,
+        sdp_ratio=request.sdpRatio,
+        noise=request.noise,
+        noise_w=request.noisew,
+        length=1 / request.speed,
+        given_tone=tone,
+        style=request.style,
+        style_weight=request.styleWeight,
+        assist_text=request.assistText,
+        assist_text_weight=request.assistTextWeight,
+        use_assist_text=bool(request.assistText),
+        line_split=False,
+        pitch_scale=request.pitchScale,
+        intonation_scale=request.intonationScale,
+        speaker_id=sid,
+    )
+
+    with BytesIO() as wavContent:
+        wavfile.write(wavContent, sr, audio)
+        return Response(content=wavContent.getvalue(), media_type="audio/wav")
+
+
+class MultiSynthesisRequest(BaseModel):
+    lines: list[SynthesisRequest]
+
+
+@router.post("/multi_synthesis", response_class=AudioResponse)
+def multi_synthesis(request: MultiSynthesisRequest):
+    lines = request.lines
+    if args.line_count is not None and len(lines) > args.line_count:
+        raise HTTPException(
+            status_code=400,
+            detail=f"行数は{args.line_count}行以下にしてください。",
+        )
+    audios = []
+    sr = None
+    for i, req in enumerate(lines):
+        if args.line_length is not None and len(req.text) > args.line_length:
+            raise HTTPException(
+                status_code=400,
+                detail=f"1行の文字数は{args.line_length}文字以下にしてください。",
+            )
+        try:
+            model = model_holder.get_model(
+                model_name=req.model, model_path_str=req.modelFile
+            )
+        except Exception as e:
+            logger.error(e)
+            raise HTTPException(
+                status_code=500,
+                detail=f"Failed to load model {req.model} from {req.modelFile}, {e}",
+            )
+        text = req.text
+        kata_tone_list = [
+            (mora_tone.mora, mora_tone.tone) for mora_tone in req.moraToneList
+        ]
+        phone_tone = kata_tone2phone_tone(kata_tone_list)
+        tone = [t for _, t in phone_tone]
+        sr, audio = model.infer(
+            text=text,
+            language=req.language,
+            sdp_ratio=req.sdpRatio,
+            noise=req.noise,
+            noise_w=req.noisew,
+            length=1 / req.speed,
+            given_tone=tone,
+            style=req.style,
+            style_weight=req.styleWeight,
+            assist_text=req.assistText,
+            assist_text_weight=req.assistTextWeight,
+            use_assist_text=bool(req.assistText),
+            line_split=False,
+            pitch_scale=req.pitchScale,
+            intonation_scale=req.intonationScale,
+        )
+        audios.append(audio)
+        if i < len(lines) - 1:
+            silence = int(sr * req.silenceAfter)
+            audios.append(np.zeros(silence, dtype=np.int16))
+    audio = np.concatenate(audios)
+
+    with BytesIO() as wavContent:
+        wavfile.write(wavContent, sr, audio)
+        return Response(content=wavContent.getvalue(), media_type="audio/wav")
+
+
+class UserDictWordRequest(BaseModel):
+    surface: str
+    pronunciation: str
+    accent_type: int  # アクセント核位置（存在しない場合は0、1文字目は1）
+    priority: int = 5
+
+
+@router.get("/user_dict")
+def get_user_dict():
+    return read_dict()
+
+
+@router.post("/user_dict_word")
+def add_user_dict_word(request: UserDictWordRequest):
+    uuid = apply_word(
+        surface=request.surface,
+        pronunciation=request.pronunciation,
+        accent_type=request.accent_type,
+        priority=request.priority,
+    )
+    update_dict()
+
+    return JSONResponse(
+        status_code=status.HTTP_201_CREATED,
+        content={"uuid": uuid},
+    )
+
+
+@router.put("/user_dict_word/{uuid}")
+def update_user_dict_word(uuid: str, request: UserDictWordRequest):
+    rewrite_word(
+        word_uuid=uuid,
+        surface=request.surface,
+        pronunciation=request.pronunciation,
+        accent_type=request.accent_type,
+        priority=request.priority,
+    )
+    update_dict()
+    return JSONResponse(status_code=status.HTTP_200_OK, content={"uuid": uuid})
+
+
+@router.delete("/user_dict_word/{uuid}")
+def delete_user_dict_word(uuid: str):
+    delete_word(uuid)
+    update_dict()
+    return JSONResponse(status_code=status.HTTP_200_OK, content={"uuid": uuid})
+
+
+app.include_router(router, prefix="/api")
+
+if __name__ == "__main__":
+    if not skip_static_files:
+        download_static_files("litagin02", "Style-Bert-VITS2-Editor", "out.zip")
+    app.mount("/", StaticFiles(directory=STATIC_DIR, html=True), name="static")
+    if args.inbrowser:
+        webbrowser.open(f"http://localhost:{port}")
+    uvicorn.run(app, host="0.0.0.0", port=port)
diff --git a/static/404.html b/static/404.html
new file mode 100644
index 0000000000000000000000000000000000000000..923048411ec5f5b82c7a6030e13c27328ca87472
--- /dev/null
+++ b/static/404.html
@@ -0,0 +1 @@
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diff --git a/static/_next/static/chunks/app/_not-found/page-88e5ec60d72eb81e.js b/static/_next/static/chunks/app/_not-found/page-88e5ec60d72eb81e.js
new file mode 100644
index 0000000000000000000000000000000000000000..9a14bf61c2471fc691f3cd5201c6c50a859e221d
--- /dev/null
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+ * @license React
+ * react-jsx-runtime.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */var r=t(7294),l=Symbol.for("react.element"),a=Symbol.for("react.fragment"),u=Object.prototype.hasOwnProperty,o=r.__SECRET_INTERNALS_DO_NOT_USE_OR_YOU_WILL_BE_FIRED.ReactCurrentOwner,i={key:!0,ref:!0,__self:!0,__source:!0};function s(e,n,t){var r,a={},s=null,c=null;for(r in void 0!==t&&(s=""+t),void 0!==n.key&&(s=""+n.key),void 0!==n.ref&&(c=n.ref),n)u.call(n,r)&&!i.hasOwnProperty(r)&&(a[r]=n[r]);if(e&&e.defaultProps)for(r in n=e.defaultProps)void 0===a[r]&&(a[r]=n[r]);return{$$typeof:l,type:e,key:s,ref:c,props:a,_owner:o.current}}n.Fragment=a,n.jsx=s,n.jsxs=s},2408:function(e,n){/**
+ * @license React
+ * react.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
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diff --git a/static/_next/static/chunks/webpack-ef267f3d2baed886.js b/static/_next/static/chunks/webpack-ef267f3d2baed886.js
new file mode 100644
index 0000000000000000000000000000000000000000..6b2681a6b7ad68e8c7f12520859e223e93bc3365
--- /dev/null
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print{.mui-1k371a6{position:absolute!important;}}</style></head><body class="__className_aaf875"><div class="MuiContainer-root MuiContainer-maxWidthXl mui-1ekb41w"><header class="MuiPaper-root MuiPaper-elevation MuiPaper-elevation4 MuiAppBar-root MuiAppBar-colorPrimary MuiAppBar-positionStatic mui-1x7skt0"><div class="MuiToolbar-root MuiToolbar-gutters MuiToolbar-regular mui-i6s8oy"><button class="MuiButtonBase-root MuiButton-root MuiButton-text MuiButton-textInherit MuiButton-sizeMedium MuiButton-textSizeMedium MuiButton-colorInherit MuiButton-root MuiButton-text MuiButton-textInherit MuiButton-sizeMedium MuiButton-textSizeMedium MuiButton-colorInherit mui-1sejpc" tabindex="0" type="button"><span class="MuiButton-startIcon MuiButton-iconSizeMedium mui-6xugel"><svg class="MuiSvgIcon-root MuiSvgIcon-fontSizeMedium mui-vubbuv" focusable="false" aria-hidden="true" viewBox="0 0 24 24" data-testid="MenuIcon"><path d="M3 18h18v-2H3zm0-5h18v-2H3zm0-7v2h18V6z"></path></svg></span>メニュー</button><h6 class="MuiTypography-root MuiTypography-h6 mui-7ie9cz">ごちうさ TTS</h6><h6 class="MuiTypography-root MuiTypography-subtitle1 mui-1iwnixi">SBV2 ver: <!-- -->, editor ver: <!-- -->0.4.1</h6></div></header><div class="MuiBox-root mui-c225ge"><div class="MuiBox-root mui-mjpszt"><div class="MuiPaper-root MuiPaper-elevation MuiPaper-rounded MuiPaper-elevation2 mui-iemphu"><div class="MuiGrid2-root MuiGrid2-container MuiGrid2-direction-xs-row MuiGrid2-spacing-xs-1 mui-i4bptn"><div class="MuiGrid2-root MuiGrid2-direction-xs-row MuiGrid2-grid-xs-auto mui-jq8sgo"><svg class="MuiSvgIcon-root MuiSvgIcon-fontSizeSmall mui-o43ana" focusable="false" aria-hidden="true" viewBox="0 0 24 24" data-testid="ChevronRightIcon"><path d="M10 6 8.59 7.41 13.17 12l-4.58 4.59L10 18l6-6z"></path></svg></div><div class="MuiGrid2-root MuiGrid2-direction-xs-row MuiGrid2-grid-xs-true mui-1hoyqw5"><div class="MuiFormControl-root MuiFormControl-fullWidth MuiTextField-root mui-feqhe6"><label class="MuiFormLabel-root MuiInputLabel-root MuiInputLabel-formControl MuiInputLabel-animated MuiInputLabel-shrink MuiInputLabel-sizeMedium MuiInputLabel-outlined MuiFormLabel-colorPrimary Mui-focused MuiInputLabel-root MuiInputLabel-formControl MuiInputLabel-animated MuiInputLabel-shrink MuiInputLabel-sizeMedium MuiInputLabel-outlined mui-18fk72y" data-shrink="true" for=":Rclhj5afja:" id=":Rclhj5afja:-label">テキスト1</label><div class="MuiInputBase-root MuiOutlinedInput-root MuiInputBase-colorPrimary MuiInputBase-fullWidth Mui-focused MuiInputBase-formControl mui-10xw3rz"><input aria-invalid="false" id=":Rclhj5afja:" type="text" class="MuiInputBase-input MuiOutlinedInput-input mui-1x5jdmq" value=""/><fieldset aria-hidden="true" class="MuiOutlinedInput-notchedOutline mui-igs3ac"><legend class="mui-14lo706"><span>テキスト1</span></legend></fieldset></div></div></div><div class="MuiGrid2-root MuiGrid2-direction-xs-row MuiGrid2-grid-xs-auto mui-jq8sgo"><button class="MuiButtonBase-root Mui-disabled MuiIconButton-root Mui-disabled MuiIconButton-sizeMedium delete-button mui-1yxmbwk" tabindex="-1" type="button" disabled="" title="この行を削除する"><svg class="MuiSvgIcon-root MuiSvgIcon-fontSizeMedium mui-vubbuv" focusable="false" aria-hidden="true" viewBox="0 0 24 24" data-testid="DeleteIcon"><path d="M6 19c0 1.1.9 2 2 2h8c1.1 0 2-.9 2-2V7H6zM19 4h-3.5l-1-1h-5l-1 1H5v2h14z"></path></svg></button></div><div class="MuiGrid2-root MuiGrid2-direction-xs-row MuiGrid2-grid-xs-auto mui-jq8sgo"><button class="MuiButtonBase-root MuiIconButton-root MuiIconButton-sizeMedium add-line-button mui-1yxmbwk" tabindex="0" type="button" title="行を追加する"><svg class="MuiSvgIcon-root MuiSvgIcon-fontSizeMedium mui-vubbuv" focusable="false" aria-hidden="true" viewBox="0 0 24 24" data-testid="AddCircleRoundedIcon"><path d="M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2m4 11h-3v3c0 .55-.45 1-1 1s-1-.45-1-1v-3H8c-.55 0-1-.45-1-1s.45-1 1-1h3V8c0-.55.45-1 1-1s1 .45 1 1v3h3c.55 0 1 .45 1 1s-.45 1-1 1"></path></svg></button></div></div></div><div class="MuiPaper-root MuiPaper-elevation MuiPaper-rounded MuiPaper-elevation1 mui-1u3gb8q"><div class="MuiStack-root mui-1w00n8f"><div class="MuiStack-root mui-1wdoqzi"><p class="MuiTypography-root MuiTypography-body1 mui-ayb5df">ア</p><div role="group" class="MuiToggleButtonGroup-root MuiToggleButtonGroup-vertical mui-12faktu"><button class="MuiButtonBase-root MuiToggleButtonGroup-grouped MuiToggleButtonGroup-groupedVertical MuiToggleButton-root MuiToggleButton-sizeMedium MuiToggleButton-primary MuiToggleButtonGroup-grouped MuiToggleButtonGroup-groupedVertical MuiToggleButtonGroup-firstButton mui-asv8te" tabindex="0" type="button" value="1" aria-pressed="false">高</button><button class="MuiButtonBase-root MuiToggleButtonGroup-grouped MuiToggleButtonGroup-groupedVertical MuiToggleButton-root Mui-selected MuiToggleButton-sizeMedium MuiToggleButton-primary MuiToggleButtonGroup-grouped MuiToggleButtonGroup-groupedVertical MuiToggleButtonGroup-lastButton mui-asv8te" tabindex="0" type="button" value="0" aria-pressed="true">低</button></div></div></div></div><div class="MuiBox-root mui-ah541z"><button class="MuiButtonBase-root MuiButton-root MuiButton-contained MuiButton-containedPrimary MuiButton-sizeMedium MuiButton-containedSizeMedium MuiButton-root MuiButton-contained MuiButton-containedPrimary MuiButton-sizeMedium MuiButton-containedSizeMedium mui-1s9ct60" tabindex="0" type="button">音声合成</button></div><div class="MuiBox-root mui-ah541z"><button class="MuiButtonBase-root MuiButton-root MuiButton-outlined MuiButton-outlinedPrimary MuiButton-sizeMedium MuiButton-outlinedSizeMedium MuiButton-root MuiButton-outlined MuiButton-outlinedPrimary MuiButton-sizeMedium MuiButton-outlinedSizeMedium mui-1jjdp2u" tabindex="0" type="button">全てのテキストを音声合成</button></div></div><div class="MuiBox-root mui-186hbip"><div class="MuiPaper-root MuiPaper-elevation MuiPaper-rounded MuiPaper-elevation1 mui-c1c6ll"><div class="MuiBox-root mui-1z6l7v"><p class="MuiTypography-root MuiTypography-body1 mui-ayb5df">テキスト<!-- -->1<!-- -->の設定</p><button class="MuiButtonBase-root MuiIconButton-root MuiIconButton-sizeMedium mui-1yxmbwk" tabindex="0" type="button" aria-label="デフォルト設定に戻す"><svg class="MuiSvgIcon-root MuiSvgIcon-fontSizeMedium mui-vubbuv" focusable="false" aria-hidden="true" viewBox="0 0 24 24" data-testid="RefreshIcon"><path d="M17.65 6.35C16.2 4.9 14.21 4 12 4c-4.42 0-7.99 3.58-7.99 8s3.57 8 7.99 8c3.73 0 6.84-2.55 7.73-6h-2.08c-.82 2.33-3.04 4-5.65 4-3.31 0-6-2.69-6-6s2.69-6 6-6c1.66 0 3.14.69 4.22 1.78L13 11h7V4z"></path></svg></button></div><div class="MuiFormControl-root MuiFormControl-fullWidth mui-i46dfq"><label class="MuiFormLabel-root MuiInputLabel-root MuiInputLabel-formControl MuiInputLabel-animated MuiInputLabel-sizeMedium MuiInputLabel-standard MuiFormLabel-colorPrimary MuiInputLabel-root MuiInputLabel-formControl MuiInputLabel-animated MuiInputLabel-sizeMedium MuiInputLabel-standard mui-htaey7" data-shrink="false">モデル</label><div class="MuiInputBase-root MuiInput-root MuiInput-underline MuiInputBase-colorPrimary MuiInputBase-formControl  mui-1qy93j4"><div tabindex="0" role="combobox" aria-controls=":R15akal5afja:" aria-expanded="false" aria-haspopup="listbox" class="MuiSelect-select MuiSelect-standard MuiInputBase-input MuiInput-input mui-1cccqvr"><span class="notranslate">​</span></div><input aria-invalid="false" aria-hidden="true" tabindex="-1" class="MuiSelect-nativeInput mui-1k3x8v3" value=""/><svg class="MuiSvgIcon-root MuiSvgIcon-fontSizeMedium MuiSelect-icon MuiSelect-iconStandard mui-1utq5rl" focusable="false" aria-hidden="true" viewBox="0 0 24 24" data-testid="ArrowDropDownIcon"><path d="M7 10l5 5 5-5z"></path></svg></div></div><div class="MuiFormControl-root MuiFormControl-fullWidth mui-i46dfq"><label class="MuiFormLabel-root MuiInputLabel-root MuiInputLabel-formControl MuiInputLabel-animated MuiInputLabel-sizeMedium MuiInputLabel-standard MuiFormLabel-colorPrimary MuiInputLabel-root MuiInputLabel-formControl MuiInputLabel-animated MuiInputLabel-sizeMedium MuiInputLabel-standard mui-htaey7" data-shrink="false">モデルファイル</label><div class="MuiInputBase-root MuiInput-root MuiInput-underline MuiInputBase-colorPrimary MuiInputBase-formControl  mui-1qy93j4"><div tabindex="0" role="combobox" aria-controls=":R15akel5afja:" aria-expanded="false" aria-haspopup="listbox" class="MuiSelect-select MuiSelect-standard MuiInputBase-input MuiInput-input mui-1cccqvr"><span class="notranslate">​</span></div><input aria-invalid="false" aria-hidden="true" tabindex="-1" class="MuiSelect-nativeInput mui-1k3x8v3" value=""/><svg class="MuiSvgIcon-root MuiSvgIcon-fontSizeMedium MuiSelect-icon MuiSelect-iconStandard mui-1utq5rl" focusable="false" aria-hidden="true" viewBox="0 0 24 24" data-testid="ArrowDropDownIcon"><path d="M7 10l5 5 5-5z"></path></svg></div></div><div class="MuiFormControl-root MuiFormControl-fullWidth mui-i46dfq"><label class="MuiFormLabel-root MuiInputLabel-root MuiInputLabel-formControl MuiInputLabel-animated MuiInputLabel-sizeMedium MuiInputLabel-standard MuiFormLabel-colorPrimary MuiInputLabel-root MuiInputLabel-formControl MuiInputLabel-animated MuiInputLabel-sizeMedium MuiInputLabel-standard mui-htaey7" data-shrink="false">話者</label><div class="MuiInputBase-root MuiInput-root MuiInput-underline MuiInputBase-colorPrimary MuiInputBase-formControl  mui-1qy93j4"><div tabindex="0" role="combobox" aria-controls=":R15akil5afja:" aria-expanded="false" aria-haspopup="listbox" class="MuiSelect-select MuiSelect-standard MuiInputBase-input MuiInput-input mui-1cccqvr"><span class="notranslate">​</span></div><input aria-invalid="false" aria-hidden="true" tabindex="-1" class="MuiSelect-nativeInput mui-1k3x8v3" value=""/><svg class="MuiSvgIcon-root MuiSvgIcon-fontSizeMedium MuiSelect-icon MuiSelect-iconStandard mui-1utq5rl" focusable="false" aria-hidden="true" viewBox="0 0 24 24" data-testid="ArrowDropDownIcon"><path d="M7 10l5 5 5-5z"></path></svg></div></div><div class="MuiFormControl-root MuiFormControl-fullWidth mui-12vna06"><label class="MuiFormLabel-root MuiInputLabel-root MuiInputLabel-formControl MuiInputLabel-animated MuiInputLabel-sizeMedium MuiInputLabel-standard MuiFormLabel-colorPrimary MuiInputLabel-root MuiInputLabel-formControl MuiInputLabel-animated MuiInputLabel-sizeMedium MuiInputLabel-standard mui-htaey7" data-shrink="false">スタイル</label><div class="MuiInputBase-root MuiInput-root MuiInput-underline MuiInputBase-colorPrimary MuiInputBase-formControl  mui-1qy93j4"><div tabindex="0" role="combobox" aria-controls=":R15akml5afja:" aria-expanded="false" aria-haspopup="listbox" class="MuiSelect-select MuiSelect-standard MuiInputBase-input MuiInput-input mui-1cccqvr"><span class="notranslate">​</span></div><input aria-invalid="false" aria-hidden="true" tabindex="-1" class="MuiSelect-nativeInput mui-1k3x8v3" value=""/><svg class="MuiSvgIcon-root MuiSvgIcon-fontSizeMedium MuiSelect-icon MuiSelect-iconStandard mui-1utq5rl" focusable="false" aria-hidden="true" viewBox="0 0 24 24" data-testid="ArrowDropDownIcon"><path d="M7 10l5 5 5-5z"></path></svg></div></div><div class="MuiBox-root mui-0"><div class="MuiGrid2-root MuiGrid2-container MuiGrid2-direction-xs-row MuiGrid2-spacing-xs-2 mui-10t8xal"><div class="MuiGrid2-root MuiGrid2-direction-xs-row MuiGrid2-grid-xs-9 mui-1229sep"><p class="MuiTypography-root MuiTypography-body1 MuiTypography-gutterBottom mui-15c2r3j" id="input-slider">スタイルの強さ上限設定</p><span class="MuiSlider-root MuiSlider-colorPrimary MuiSlider-sizeMedium mui-exkjcx"><span class="MuiSlider-rail mui-b04pc9"></span><span style="left:0%;width:47.36842105263158%" class="MuiSlider-track mui-1t2bqnt"></span><span data-index="0" class="MuiSlider-thumb MuiSlider-thumbSizeMedium MuiSlider-thumbColorPrimary MuiSlider-thumb MuiSlider-thumbSizeMedium MuiSlider-thumbColorPrimary mui-1s3fy7y" style="left:47.36842105263158%"><input data-index="0" aria-valuenow="10" aria-labelledby="input-slider" aria-orientation="horizontal" aria-valuemax="20" aria-valuemin="1" type="range" min="1" max="20" step="0.1" style="border:0;clip:rect(0 0 0 0);height:100%;margin:-1px;overflow:hidden;padding:0;position:absolute;white-space:nowrap;width:100%;direction:ltr" value="10"/></span></span></div><div class="MuiGrid2-root MuiGrid2-direction-xs-row MuiGrid2-grid-xs-3 mui-1oypdbk"><div class="MuiInputBase-root MuiInput-root MuiInput-underline MuiInputBase-colorPrimary mui-9e9415"><input type="number" step="0.1" min="1" max="20" class="MuiInputBase-input MuiInput-input mui-mnn31" value="10"/></div></div></div></div><div class="MuiBox-root mui-0"><div class="MuiGrid2-root MuiGrid2-container MuiGrid2-direction-xs-row MuiGrid2-spacing-xs-2 mui-10t8xal"><div class="MuiGrid2-root MuiGrid2-direction-xs-row MuiGrid2-grid-xs-9 mui-1229sep"><p class="MuiTypography-root MuiTypography-body1 MuiTypography-gutterBottom mui-15c2r3j" id="input-slider">スタイルの強さ（崩壊したら下げて）</p><span class="MuiSlider-root MuiSlider-colorPrimary MuiSlider-sizeMedium mui-exkjcx"><span class="MuiSlider-rail mui-b04pc9"></span><span style="left:0%;width:10%" class="MuiSlider-track mui-1t2bqnt"></span><span data-index="0" class="MuiSlider-thumb MuiSlider-thumbSizeMedium MuiSlider-thumbColorPrimary MuiSlider-thumb MuiSlider-thumbSizeMedium MuiSlider-thumbColorPrimary mui-1s3fy7y" style="left:10%"><input data-index="0" aria-valuenow="1" aria-labelledby="input-slider" aria-orientation="horizontal" aria-valuemax="10" aria-valuemin="0" type="range" min="0" max="10" step="0.1" style="border:0;clip:rect(0 0 0 0);height:100%;margin:-1px;overflow:hidden;padding:0;position:absolute;white-space:nowrap;width:100%;direction:ltr" value="1"/></span></span></div><div class="MuiGrid2-root MuiGrid2-direction-xs-row MuiGrid2-grid-xs-3 mui-1oypdbk"><div class="MuiInputBase-root MuiInput-root MuiInput-underline MuiInputBase-colorPrimary mui-9e9415"><input type="number" step="0.1" min="0" max="10" class="MuiInputBase-input MuiInput-input mui-mnn31" value="1"/></div></div></div></div><div class="MuiBox-root mui-0"><div class="MuiGrid2-root MuiGrid2-container MuiGrid2-direction-xs-row MuiGrid2-spacing-xs-2 mui-10t8xal"><div class="MuiGrid2-root MuiGrid2-direction-xs-row MuiGrid2-grid-xs-9 mui-1229sep"><p class="MuiTypography-root MuiTypography-body1 MuiTypography-gutterBottom mui-15c2r3j" id="input-slider">話速</p><span class="MuiSlider-root MuiSlider-colorPrimary MuiSlider-sizeMedium mui-exkjcx"><span class="MuiSlider-rail mui-b04pc9"></span><span style="left:0%;width:33.333333333333336%" class="MuiSlider-track mui-1t2bqnt"></span><span data-index="0" class="MuiSlider-thumb MuiSlider-thumbSizeMedium MuiSlider-thumbColorPrimary MuiSlider-thumb MuiSlider-thumbSizeMedium MuiSlider-thumbColorPrimary mui-1s3fy7y" style="left:33.333333333333336%"><input data-index="0" aria-valuenow="1" aria-labelledby="input-slider" aria-orientation="horizontal" aria-valuemax="2" aria-valuemin="0.5" type="range" min="0.5" max="2" step="0.05" style="border:0;clip:rect(0 0 0 0);height:100%;margin:-1px;overflow:hidden;padding:0;position:absolute;white-space:nowrap;width:100%;direction:ltr" value="1"/></span></span></div><div class="MuiGrid2-root MuiGrid2-direction-xs-row MuiGrid2-grid-xs-3 mui-1oypdbk"><div class="MuiInputBase-root MuiInput-root MuiInput-underline MuiInputBase-colorPrimary mui-9e9415"><input type="number" step="0.05" min="0.5" max="2" class="MuiInputBase-input MuiInput-input mui-mnn31" value="1"/></div></div></div></div><div class="MuiBox-root mui-0"><div class="MuiGrid2-root MuiGrid2-container MuiGrid2-direction-xs-row MuiGrid2-spacing-xs-2 mui-10t8xal"><div class="MuiGrid2-root MuiGrid2-direction-xs-row MuiGrid2-grid-xs-9 mui-1229sep"><p class="MuiTypography-root MuiTypography-body1 MuiTypography-gutterBottom mui-15c2r3j" id="input-slider">テンポの緩急</p><span class="MuiSlider-root MuiSlider-colorPrimary MuiSlider-sizeMedium mui-exkjcx"><span class="MuiSlider-rail mui-b04pc9"></span><span style="left:0%;width:20%" class="MuiSlider-track mui-1t2bqnt"></span><span data-index="0" class="MuiSlider-thumb MuiSlider-thumbSizeMedium MuiSlider-thumbColorPrimary MuiSlider-thumb MuiSlider-thumbSizeMedium MuiSlider-thumbColorPrimary mui-1s3fy7y" style="left:20%"><input data-index="0" aria-valuenow="0.2" aria-labelledby="input-slider" aria-orientation="horizontal" aria-valuemax="1" aria-valuemin="0" type="range" min="0" max="1" step="0.05" style="border:0;clip:rect(0 0 0 0);height:100%;margin:-1px;overflow:hidden;padding:0;position:absolute;white-space:nowrap;width:100%;direction:ltr" value="0.2"/></span></span></div><div class="MuiGrid2-root MuiGrid2-direction-xs-row MuiGrid2-grid-xs-3 mui-1oypdbk"><div class="MuiInputBase-root MuiInput-root MuiInput-underline MuiInputBase-colorPrimary mui-9e9415"><input type="number" step="0.05" min="0" max="1" class="MuiInputBase-input MuiInput-input mui-mnn31" value="0.2"/></div></div></div></div><div class="MuiBox-root mui-0"><div class="MuiGrid2-root MuiGrid2-container MuiGrid2-direction-xs-row MuiGrid2-spacing-xs-2 mui-10t8xal"><div class="MuiGrid2-root MuiGrid2-direction-xs-row MuiGrid2-grid-xs-9 mui-1229sep"><p class="MuiTypography-root MuiTypography-body1 MuiTypography-gutterBottom mui-15c2r3j" id="input-slider">Noise</p><span class="MuiSlider-root MuiSlider-colorPrimary MuiSlider-sizeMedium mui-exkjcx"><span class="MuiSlider-rail mui-b04pc9"></span><span style="left:0%;width:60%" class="MuiSlider-track mui-1t2bqnt"></span><span data-index="0" class="MuiSlider-thumb MuiSlider-thumbSizeMedium MuiSlider-thumbColorPrimary MuiSlider-thumb MuiSlider-thumbSizeMedium MuiSlider-thumbColorPrimary mui-1s3fy7y" style="left:60%"><input data-index="0" aria-valuenow="0.6" aria-labelledby="input-slider" aria-orientation="horizontal" aria-valuemax="1" aria-valuemin="0" type="range" min="0" max="1" step="0.05" style="border:0;clip:rect(0 0 0 0);height:100%;margin:-1px;overflow:hidden;padding:0;position:absolute;white-space:nowrap;width:100%;direction:ltr" value="0.6"/></span></span></div><div class="MuiGrid2-root MuiGrid2-direction-xs-row MuiGrid2-grid-xs-3 mui-1oypdbk"><div class="MuiInputBase-root MuiInput-root MuiInput-underline MuiInputBase-colorPrimary mui-9e9415"><input type="number" step="0.05" min="0" max="1" class="MuiInputBase-input MuiInput-input mui-mnn31" value="0.6"/></div></div></div></div><div class="MuiBox-root mui-0"><div class="MuiGrid2-root MuiGrid2-container MuiGrid2-direction-xs-row MuiGrid2-spacing-xs-2 mui-10t8xal"><div class="MuiGrid2-root MuiGrid2-direction-xs-row MuiGrid2-grid-xs-9 mui-1229sep"><p class="MuiTypography-root MuiTypography-body1 MuiTypography-gutterBottom mui-15c2r3j" id="input-slider">NoiseW</p><span class="MuiSlider-root MuiSlider-colorPrimary MuiSlider-sizeMedium mui-exkjcx"><span class="MuiSlider-rail mui-b04pc9"></span><span style="left:0%;width:80%" class="MuiSlider-track mui-1t2bqnt"></span><span data-index="0" class="MuiSlider-thumb MuiSlider-thumbSizeMedium MuiSlider-thumbColorPrimary MuiSlider-thumb MuiSlider-thumbSizeMedium MuiSlider-thumbColorPrimary mui-1s3fy7y" style="left:80%"><input data-index="0" aria-valuenow="0.8" aria-labelledby="input-slider" aria-orientation="horizontal" aria-valuemax="1" aria-valuemin="0" type="range" min="0" max="1" step="0.05" style="border:0;clip:rect(0 0 0 0);height:100%;margin:-1px;overflow:hidden;padding:0;position:absolute;white-space:nowrap;width:100%;direction:ltr" value="0.8"/></span></span></div><div class="MuiGrid2-root MuiGrid2-direction-xs-row MuiGrid2-grid-xs-3 mui-1oypdbk"><div class="MuiInputBase-root MuiInput-root MuiInput-underline MuiInputBase-colorPrimary mui-9e9415"><input type="number" step="0.05" min="0" max="1" class="MuiInputBase-input MuiInput-input mui-mnn31" value="0.8"/></div></div></div></div><div class="MuiBox-root mui-0"><div class="MuiGrid2-root MuiGrid2-container MuiGrid2-direction-xs-row MuiGrid2-spacing-xs-2 mui-10t8xal"><div class="MuiGrid2-root MuiGrid2-direction-xs-row MuiGrid2-grid-xs-9 mui-1229sep"><p class="MuiTypography-root MuiTypography-body1 MuiTypography-gutterBottom mui-15c2r3j" id="input-slider">音高(1以外では音質劣化)</p><span class="MuiSlider-root MuiSlider-colorPrimary MuiSlider-sizeMedium mui-exkjcx"><span class="MuiSlider-rail mui-b04pc9"></span><span style="left:0%;width:50%" class="MuiSlider-track mui-1t2bqnt"></span><span data-index="0" class="MuiSlider-thumb MuiSlider-thumbSizeMedium MuiSlider-thumbColorPrimary MuiSlider-thumb MuiSlider-thumbSizeMedium MuiSlider-thumbColorPrimary mui-1s3fy7y" style="left:50%"><input data-index="0" aria-valuenow="1" aria-labelledby="input-slider" aria-orientation="horizontal" aria-valuemax="1.3" aria-valuemin="0.7" type="range" min="0.7" max="1.3" step="0.05" style="border:0;clip:rect(0 0 0 0);height:100%;margin:-1px;overflow:hidden;padding:0;position:absolute;white-space:nowrap;width:100%;direction:ltr" value="1"/></span></span></div><div class="MuiGrid2-root MuiGrid2-direction-xs-row MuiGrid2-grid-xs-3 mui-1oypdbk"><div class="MuiInputBase-root MuiInput-root MuiInput-underline MuiInputBase-colorPrimary mui-9e9415"><input type="number" step="0.05" min="0.7" max="1.3" class="MuiInputBase-input MuiInput-input mui-mnn31" value="1"/></div></div></div></div><div class="MuiBox-root mui-0"><div class="MuiGrid2-root MuiGrid2-container MuiGrid2-direction-xs-row MuiGrid2-spacing-xs-2 mui-10t8xal"><div class="MuiGrid2-root MuiGrid2-direction-xs-row MuiGrid2-grid-xs-9 mui-1229sep"><p class="MuiTypography-root MuiTypography-body1 MuiTypography-gutterBottom mui-15c2r3j" id="input-slider">抑揚(1以外では音質劣化)</p><span class="MuiSlider-root MuiSlider-colorPrimary MuiSlider-sizeMedium mui-exkjcx"><span class="MuiSlider-rail mui-b04pc9"></span><span style="left:0%;width:50%" class="MuiSlider-track mui-1t2bqnt"></span><span data-index="0" class="MuiSlider-thumb MuiSlider-thumbSizeMedium MuiSlider-thumbColorPrimary MuiSlider-thumb MuiSlider-thumbSizeMedium MuiSlider-thumbColorPrimary mui-1s3fy7y" style="left:50%"><input data-index="0" aria-valuenow="1" aria-labelledby="input-slider" aria-orientation="horizontal" aria-valuemax="1.3" aria-valuemin="0.7" type="range" min="0.7" max="1.3" step="0.05" style="border:0;clip:rect(0 0 0 0);height:100%;margin:-1px;overflow:hidden;padding:0;position:absolute;white-space:nowrap;width:100%;direction:ltr" value="1"/></span></span></div><div class="MuiGrid2-root MuiGrid2-direction-xs-row MuiGrid2-grid-xs-3 mui-1oypdbk"><div class="MuiInputBase-root MuiInput-root MuiInput-underline MuiInputBase-colorPrimary mui-9e9415"><input type="number" step="0.05" min="0.7" max="1.3" class="MuiInputBase-input MuiInput-input mui-mnn31" value="1"/></div></div></div></div><div class="MuiBox-root mui-0"><div class="MuiGrid2-root MuiGrid2-container MuiGrid2-direction-xs-row MuiGrid2-spacing-xs-2 mui-10t8xal"><div class="MuiGrid2-root MuiGrid2-direction-xs-row MuiGrid2-grid-xs-9 mui-1229sep"><p class="MuiTypography-root MuiTypography-body1 MuiTypography-gutterBottom mui-15c2r3j" id="input-slider">次のテキストとの間の無音</p><span class="MuiSlider-root MuiSlider-colorPrimary MuiSlider-sizeMedium mui-exkjcx"><span class="MuiSlider-rail mui-b04pc9"></span><span style="left:0%;width:33.333333333333336%" class="MuiSlider-track mui-1t2bqnt"></span><span data-index="0" class="MuiSlider-thumb MuiSlider-thumbSizeMedium MuiSlider-thumbColorPrimary MuiSlider-thumb MuiSlider-thumbSizeMedium MuiSlider-thumbColorPrimary mui-1s3fy7y" style="left:33.333333333333336%"><input data-index="0" aria-valuenow="0.5" aria-labelledby="input-slider" aria-orientation="horizontal" aria-valuemax="1.5" aria-valuemin="0" type="range" min="0" max="1.5" step="0.05" style="border:0;clip:rect(0 0 0 0);height:100%;margin:-1px;overflow:hidden;padding:0;position:absolute;white-space:nowrap;width:100%;direction:ltr" value="0.5"/></span></span></div><div class="MuiGrid2-root MuiGrid2-direction-xs-row MuiGrid2-grid-xs-3 mui-1oypdbk"><div class="MuiInputBase-root MuiInput-root MuiInput-underline MuiInputBase-colorPrimary mui-9e9415"><input type="number" step="0.05" min="0" max="1.5" class="MuiInputBase-input MuiInput-input mui-mnn31" value="0.5"/></div></div></div></div></div></div></div><div aria-hidden="true" class="MuiBackdrop-root mui-xuaqpw" style="opacity:0;visibility:hidden"><span class="MuiCircularProgress-root MuiCircularProgress-indeterminate MuiCircularProgress-colorInherit mui-8yzpf7" style="width:40px;height:40px" role="progressbar"><svg class="MuiCircularProgress-svg mui-13o7eu2" viewBox="22 22 44 44"><circle class="MuiCircularProgress-circle MuiCircularProgress-circleIndeterminate mui-14891ef" cx="44" cy="44" r="20.2" fill="none" 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diff --git a/static/last_download.txt b/static/last_download.txt
new file mode 100644
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diff --git a/style_bert_vits2/.editorconfig b/style_bert_vits2/.editorconfig
new file mode 100644
index 0000000000000000000000000000000000000000..bbc8a70b6090343e8fc54649ccc18d15e3df8b1f
--- /dev/null
+++ b/style_bert_vits2/.editorconfig
@@ -0,0 +1,15 @@
+root = true
+
+[*]
+charset = utf-8
+end_of_line = lf
+insert_final_newline = true
+indent_size = 4
+indent_style = space
+trim_trailing_whitespace = true
+
+[*.md]
+trim_trailing_whitespace = false
+
+[*.yml]
+indent_size = 2
diff --git a/style_bert_vits2/__init__.py b/style_bert_vits2/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/style_bert_vits2/constants.py b/style_bert_vits2/constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..eba3f1dad10e5cd4e60f6d62fe48f14e7fb1c9b6
--- /dev/null
+++ b/style_bert_vits2/constants.py
@@ -0,0 +1,48 @@
+from pathlib import Path
+
+from style_bert_vits2.utils.strenum import StrEnum
+
+
+# Style-Bert-VITS2 のバージョン
+VERSION = "2.5.0"
+
+# Style-Bert-VITS2 のベースディレクトリ
+BASE_DIR = Path(__file__).parent.parent
+
+
+# 利用可能な言語
+## JP-Extra モデル利用時は JP 以外の言語の音声合成はできない
+class Languages(StrEnum):
+    JP = "JP"
+    EN = "EN"
+    ZH = "ZH"
+
+
+# 言語ごとのデフォルトの BERT トークナイザーのパス
+DEFAULT_BERT_TOKENIZER_PATHS = {
+    Languages.JP: BASE_DIR / "bert" / "deberta-v2-large-japanese-char-wwm",
+    Languages.EN: BASE_DIR / "bert" / "deberta-v3-large",
+    Languages.ZH: BASE_DIR / "bert" / "chinese-roberta-wwm-ext-large",
+}
+
+# デフォルトのユーザー辞書ディレクトリ
+## style_bert_vits2.nlp.japanese.user_dict モジュールのデフォルト値として利用される
+## ライブラリとしての利用などで外部のユーザー辞書を指定したい場合は、user_dict 以下の各関数の実行時、引数に辞書データファイルのパスを指定する
+DEFAULT_USER_DICT_DIR = BASE_DIR / "dict_data"
+
+# デフォルトの推論パラメータ
+DEFAULT_STYLE = "Neutral"
+DEFAULT_STYLE_WEIGHT = 1.0
+DEFAULT_SDP_RATIO = 0.2
+DEFAULT_NOISE = 0.6
+DEFAULT_NOISEW = 0.8
+DEFAULT_LENGTH = 1.0
+DEFAULT_LINE_SPLIT = True
+DEFAULT_SPLIT_INTERVAL = 0.5
+DEFAULT_ASSIST_TEXT_WEIGHT = 0.7
+DEFAULT_ASSIST_TEXT_WEIGHT = 1.0
+
+# Gradio のテーマ
+## Built-in theme: "default", "base", "monochrome", "soft", "glass"
+## See https://huggingface.co/spaces/gradio/theme-gallery for more themes
+GRADIO_THEME = "NoCrypt/miku"
diff --git a/style_bert_vits2/logging.py b/style_bert_vits2/logging.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5c216a0053dfbc2f9a3d5d26511e8885384c25f
--- /dev/null
+++ b/style_bert_vits2/logging.py
@@ -0,0 +1,15 @@
+from loguru import logger
+
+from style_bert_vits2.utils.stdout_wrapper import SAFE_STDOUT
+
+
+# Remove all default handlers
+logger.remove()
+
+# Add a new handler
+logger.add(
+    SAFE_STDOUT,
+    format="<g>{time:MM-DD HH:mm:ss}</g> |<lvl>{level:^8}</lvl>| {file}:{line} | {message}",
+    backtrace=True,
+    diagnose=True,
+)
diff --git a/style_bert_vits2/models/__init__.py b/style_bert_vits2/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/style_bert_vits2/models/attentions.py b/style_bert_vits2/models/attentions.py
new file mode 100644
index 0000000000000000000000000000000000000000..b851b5329541272f288ec9d7487c3fbc11865f40
--- /dev/null
+++ b/style_bert_vits2/models/attentions.py
@@ -0,0 +1,491 @@
+import math
+from typing import Any, Optional
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from style_bert_vits2.models import commons
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, channels: int, eps: float = 1e-5) -> None:
+        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: torch.Tensor) -> torch.Tensor:
+        x = x.transpose(1, -1)
+        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+        return x.transpose(1, -1)
+
+
+@torch.jit.script  # type: ignore
+def fused_add_tanh_sigmoid_multiply(
+    input_a: torch.Tensor, input_b: torch.Tensor, n_channels: list[int]
+) -> torch.Tensor:
+    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
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        hidden_channels: int,
+        filter_channels: int,
+        n_heads: int,
+        n_layers: int,
+        kernel_size: int = 1,
+        p_dropout: float = 0.0,
+        window_size: int = 4,
+        isflow: bool = True,
+        **kwargs: Any,
+    ) -> None:
+        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
+        # if isflow:
+        #  cond_layer = torch.nn.Conv1d(256, 2*hidden_channels*n_layers, 1)
+        #  self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1)
+        #  self.cond_layer = weight_norm(cond_layer, name='weight')
+        #  self.gin_channels = 256
+        self.cond_layer_idx = self.n_layers
+        if "gin_channels" in kwargs:
+            self.gin_channels = kwargs["gin_channels"]
+            if self.gin_channels != 0:
+                self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels)
+                # vits2 says 3rd block, so idx is 2 by default
+                self.cond_layer_idx = (
+                    kwargs["cond_layer_idx"] if "cond_layer_idx" in kwargs else 2
+                )
+                # logger.debug(self.gin_channels, self.cond_layer_idx)
+                assert (
+                    self.cond_layer_idx < self.n_layers
+                ), "cond_layer_idx should be less than n_layers"
+        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: torch.Tensor, x_mask: torch.Tensor, g: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+        x = x * x_mask
+        for i in range(self.n_layers):
+            if i == self.cond_layer_idx and g is not None:
+                g = self.spk_emb_linear(g.transpose(1, 2))
+                assert g is not None
+                g = g.transpose(1, 2)
+                x = x + g
+                x = x * x_mask
+            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: int,
+        filter_channels: int,
+        n_heads: int,
+        n_layers: int,
+        kernel_size: int = 1,
+        p_dropout: float = 0.0,
+        proximal_bias: bool = False,
+        proximal_init: bool = True,
+        **kwargs: Any,
+    ) -> None:
+        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: torch.Tensor,
+        x_mask: torch.Tensor,
+        h: torch.Tensor,
+        h_mask: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        x: decoder input
+        h: encoder output
+        """
+        self_attn_mask = commons.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: int,
+        out_channels: int,
+        n_heads: int,
+        p_dropout: float = 0.0,
+        window_size: Optional[int] = None,
+        heads_share: bool = True,
+        block_length: Optional[int] = None,
+        proximal_bias: bool = False,
+        proximal_init: bool = False,
+    ) -> None:
+        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)
+                assert self.conv_k.bias is not None
+                assert self.conv_q.bias is not None
+                self.conv_k.bias.copy_(self.conv_q.bias)
+
+    def forward(
+        self, x: torch.Tensor, c: torch.Tensor, attn_mask: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        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: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        mask: Optional[torch.Tensor] = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # 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: torch.Tensor, y: torch.Tensor
+    ) -> torch.Tensor:
+        """
+        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: torch.Tensor, y: torch.Tensor
+    ) -> torch.Tensor:
+        """
+        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: torch.Tensor, length: int
+    ) -> torch.Tensor:
+        assert self.window_size is not None
+        2 * self.window_size + 1  # type: ignore
+        # 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,
+                commons.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: torch.Tensor) -> torch.Tensor:
+        """
+        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, commons.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, commons.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: torch.Tensor) -> torch.Tensor:
+        """
+        x: [b, h, l, l]
+        ret: [b, h, l, 2*l-1]
+        """
+        batch, heads, length, _ = x.size()
+        # pad along column
+        x = F.pad(
+            x, commons.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, commons.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: int) -> torch.Tensor:
+        """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: int,
+        out_channels: int,
+        filter_channels: int,
+        kernel_size: int,
+        p_dropout: float = 0.0,
+        activation: Optional[str] = None,
+        causal: bool = False,
+    ) -> None:
+        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: torch.Tensor, x_mask: torch.Tensor) -> torch.Tensor:
+        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: torch.Tensor) -> torch.Tensor:
+        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, commons.convert_pad_shape(padding))
+        return x
+
+    def _same_padding(self, x: torch.Tensor) -> torch.Tensor:
+        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, commons.convert_pad_shape(padding))
+        return x
diff --git a/style_bert_vits2/models/commons.py b/style_bert_vits2/models/commons.py
new file mode 100644
index 0000000000000000000000000000000000000000..38f548cd7f533b902c4eec830c2f686fe245572e
--- /dev/null
+++ b/style_bert_vits2/models/commons.py
@@ -0,0 +1,223 @@
+"""
+以下に記述されている関数のコメントはリファクタリング時に GPT-4 に生成させたもので、
+コードと完全に一致している保証はない。あくまで参考程度とすること。
+"""
+
+from typing import Any, Optional, Union
+
+import torch
+from torch.nn import functional as F
+
+
+def init_weights(m: torch.nn.Module, mean: float = 0.0, std: float = 0.01) -> None:
+    """
+    モジュールの重みを初期化する
+
+    Args:
+        m (torch.nn.Module): 重みを初期化する対象のモジュール
+        mean (float): 正規分布の平均
+        std (float): 正規分布の標準偏差
+    """
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        m.weight.data.normal_(mean, std)
+
+
+def get_padding(kernel_size: int, dilation: int = 1) -> int:
+    """
+    カーネルサイズと膨張率からパディングの大きさを計算する
+
+    Args:
+        kernel_size (int): カーネルのサイズ
+        dilation (int): 膨張率
+
+    Returns:
+        int: 計算されたパディングの大きさ
+    """
+    return int((kernel_size * dilation - dilation) / 2)
+
+
+def convert_pad_shape(pad_shape: list[list[Any]]) -> list[Any]:
+    """
+    パディングの形状を変換する
+
+    Args:
+        pad_shape (list[list[Any]]): 変換前のパディングの形状
+
+    Returns:
+        list[Any]: 変換後のパディングの形状
+    """
+    layer = pad_shape[::-1]
+    new_pad_shape = [item for sublist in layer for item in sublist]
+    return new_pad_shape
+
+
+def intersperse(lst: list[Any], item: Any) -> list[Any]:
+    """
+    リストの要素の間に特定のアイテムを挿入する
+
+    Args:
+        lst (list[Any]): 元のリスト
+        item (Any): 挿入するアイテム
+
+    Returns:
+        list[Any]: 新しいリスト
+    """
+    result = [item] * (len(lst) * 2 + 1)
+    result[1::2] = lst
+    return result
+
+
+def slice_segments(
+    x: torch.Tensor, ids_str: torch.Tensor, segment_size: int = 4
+) -> torch.Tensor:
+    """
+    テンソルからセグメントをスライスする
+
+    Args:
+        x (torch.Tensor): 入力テンソル
+        ids_str (torch.Tensor): スライスを開始するインデックス
+        segment_size (int, optional): スライスのサイズ (デフォルト: 4)
+
+    Returns:
+        torch.Tensor: スライスされたセグメント
+    """
+    gather_indices = ids_str.view(x.size(0), 1, 1).repeat(
+        1, x.size(1), 1
+    ) + torch.arange(segment_size, device=x.device)
+    return torch.gather(x, 2, gather_indices)
+
+
+def rand_slice_segments(
+    x: torch.Tensor, x_lengths: Optional[torch.Tensor] = None, segment_size: int = 4
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """
+    ランダムなセグメントをスライスする
+
+    Args:
+        x (torch.Tensor): 入力テンソル
+        x_lengths (Optional[torch.Tensor], optional): 各バッチの長さ (デフォルト: None)
+        segment_size (int, optional): スライスのサイズ (デフォルト: 4)
+
+    Returns:
+        tuple[torch.Tensor, torch.Tensor]: スライスされたセグメントと開始インデックス
+    """
+    b, d, t = x.size()
+    if x_lengths is None:
+        x_lengths = t  # type: ignore
+    ids_str_max = torch.clamp(x_lengths - segment_size + 1, min=0)  # type: ignore
+    ids_str = (torch.rand([b], 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: int) -> torch.Tensor:
+    """
+    後続のマスクを生成する
+
+    Args:
+        length (int): マスクのサイズ
+
+    Returns:
+        torch.Tensor: 生成されたマスク
+    """
+    mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
+    return mask
+
+
+@torch.jit.script  # type: ignore
+def fused_add_tanh_sigmoid_multiply(
+    input_a: torch.Tensor, input_b: torch.Tensor, n_channels: torch.Tensor
+) -> torch.Tensor:
+    """
+    加算、tanh、sigmoid の活性化関数を組み合わせた演算を行う
+
+    Args:
+        input_a (torch.Tensor): 入力テンソル A
+        input_b (torch.Tensor): 入力テンソル B
+        n_channels (torch.Tensor): チャネル数
+
+    Returns:
+        torch.Tensor: 演算結果
+    """
+    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 sequence_mask(
+    length: torch.Tensor, max_length: Optional[int] = None
+) -> torch.Tensor:
+    """
+    シーケンスマスクを生成する
+
+    Args:
+        length (torch.Tensor): 各シーケンスの長さ
+        max_length (Optional[int]): 最大のシーケンス長さ。指定されていない場合は length の最大値を使用
+
+    Returns:
+        torch.Tensor: 生成されたシーケンスマスク
+    """
+    if max_length is None:
+        max_length = length.max()  # type: ignore
+    x = torch.arange(max_length, dtype=length.dtype, device=length.device)  # type: ignore
+    return x.unsqueeze(0) < length.unsqueeze(1)
+
+
+def generate_path(duration: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
+    """
+    パスを生成する
+
+    Args:
+        duration (torch.Tensor): 各時間ステップの持続時間
+        mask (torch.Tensor): マスクテンソル
+
+    Returns:
+        torch.Tensor: 生成されたパス
+    """
+    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: Union[torch.Tensor, list[torch.Tensor]],
+    clip_value: Optional[float],
+    norm_type: float = 2.0,
+) -> float:
+    """
+    勾配の値をクリップする
+
+    Args:
+        parameters (Union[torch.Tensor, list[torch.Tensor]]): クリップするパラメータ
+        clip_value (Optional[float]): クリップする値。None の場合はクリップしない
+        norm_type (float): ノルムの種類
+
+    Returns:
+        float: 総ノルム
+    """
+    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 = 0.0
+    for p in parameters:
+        assert p.grad is not None
+        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
diff --git a/style_bert_vits2/models/hyper_parameters.py b/style_bert_vits2/models/hyper_parameters.py
new file mode 100644
index 0000000000000000000000000000000000000000..827ce6dea5b69e3dee9e3f1378bece0289790489
--- /dev/null
+++ b/style_bert_vits2/models/hyper_parameters.py
@@ -0,0 +1,129 @@
+"""
+Style-Bert-VITS2 モデルのハイパーパラメータを表す Pydantic モデル。
+デフォルト値は configs/config_jp_extra.json 内の定義と概ね同一で、
+万が一ロードした config.json に存在しないキーがあった際のフェイルセーフとして適用される。
+"""
+
+from pathlib import Path
+from typing import Optional, Union
+
+from pydantic import BaseModel, ConfigDict
+
+
+class HyperParametersTrain(BaseModel):
+    log_interval: int = 200
+    eval_interval: int = 1000
+    seed: int = 42
+    epochs: int = 1000
+    learning_rate: float = 0.0001
+    betas: tuple[float, float] = (0.8, 0.99)
+    eps: float = 1e-9
+    batch_size: int = 2
+    bf16_run: bool = False
+    fp16_run: bool = False
+    lr_decay: float = 0.99996
+    segment_size: int = 16384
+    init_lr_ratio: int = 1
+    warmup_epochs: int = 0
+    c_mel: int = 45
+    c_kl: float = 1.0
+    c_commit: int = 100
+    skip_optimizer: bool = False
+    freeze_ZH_bert: bool = False
+    freeze_JP_bert: bool = False
+    freeze_EN_bert: bool = False
+    freeze_emo: bool = False
+    freeze_style: bool = False
+    freeze_decoder: bool = False
+
+
+class HyperParametersData(BaseModel):
+    use_jp_extra: bool = True
+    training_files: str = "Data/Dummy/train.list"
+    validation_files: str = "Data/Dummy/val.list"
+    max_wav_value: float = 32768.0
+    sampling_rate: int = 44100
+    filter_length: int = 2048
+    hop_length: int = 512
+    win_length: int = 2048
+    n_mel_channels: int = 128
+    mel_fmin: float = 0.0
+    mel_fmax: Optional[float] = None
+    add_blank: bool = True
+    n_speakers: int = 1
+    cleaned_text: bool = True
+    spk2id: dict[str, int] = {
+        "Dummy": 0,
+    }
+    num_styles: int = 1
+    style2id: dict[str, int] = {
+        "Neutral": 0,
+    }
+
+
+class HyperParametersModelSLM(BaseModel):
+    model: str = "./slm/wavlm-base-plus"
+    sr: int = 16000
+    hidden: int = 768
+    nlayers: int = 13
+    initial_channel: int = 64
+
+
+class HyperParametersModel(BaseModel):
+    use_spk_conditioned_encoder: bool = True
+    use_noise_scaled_mas: bool = True
+    use_mel_posterior_encoder: bool = False
+    use_duration_discriminator: bool = False
+    use_wavlm_discriminator: bool = True
+    inter_channels: int = 192
+    hidden_channels: int = 192
+    filter_channels: int = 768
+    n_heads: int = 2
+    n_layers: int = 6
+    kernel_size: int = 3
+    p_dropout: float = 0.1
+    resblock: str = "1"
+    resblock_kernel_sizes: list[int] = [3, 7, 11]
+    resblock_dilation_sizes: list[list[int]] = [
+        [1, 3, 5],
+        [1, 3, 5],
+        [1, 3, 5],
+    ]
+    upsample_rates: list[int] = [8, 8, 2, 2, 2]
+    upsample_initial_channel: int = 512
+    upsample_kernel_sizes: list[int] = [16, 16, 8, 2, 2]
+    n_layers_q: int = 3
+    use_spectral_norm: bool = False
+    gin_channels: int = 512
+    slm: HyperParametersModelSLM = HyperParametersModelSLM()
+
+
+class HyperParameters(BaseModel):
+    model_name: str = "Dummy"
+    version: str = "2.0-JP-Extra"
+    train: HyperParametersTrain = HyperParametersTrain()
+    data: HyperParametersData = HyperParametersData()
+    model: HyperParametersModel = HyperParametersModel()
+
+    # 以下は学習時にのみ動的に設定されるパラメータ (通常 config.json には存在しない)
+    model_dir: Optional[str] = None
+    speedup: bool = False
+    repo_id: Optional[str] = None
+
+    # model_ 以下を Pydantic の保護対象から除外する
+    model_config = ConfigDict(protected_namespaces=())
+
+    @staticmethod
+    def load_from_json(json_path: Union[str, Path]) -> "HyperParameters":
+        """
+        与えられた JSON ファイルからハイパーパラメータを読み込む。
+
+        Args:
+            json_path (Union[str, Path]): JSON ファイルのパス
+
+        Returns:
+            HyperParameters: ハイパーパラメータ
+        """
+
+        with open(json_path, encoding="utf-8") as f:
+            return HyperParameters.model_validate_json(f.read())
diff --git a/style_bert_vits2/models/infer.py b/style_bert_vits2/models/infer.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9a486975564ac61ef1491f5cbe57f3bca0ed085
--- /dev/null
+++ b/style_bert_vits2/models/infer.py
@@ -0,0 +1,308 @@
+from typing import Any, Optional, Union, cast
+
+import torch
+from numpy.typing import NDArray
+
+from style_bert_vits2.constants import Languages
+from style_bert_vits2.logging import logger
+from style_bert_vits2.models import commons, utils
+from style_bert_vits2.models.hyper_parameters import HyperParameters
+from style_bert_vits2.models.models import SynthesizerTrn
+from style_bert_vits2.models.models_jp_extra import (
+    SynthesizerTrn as SynthesizerTrnJPExtra,
+)
+from style_bert_vits2.nlp import (
+    clean_text,
+    cleaned_text_to_sequence,
+    extract_bert_feature,
+)
+from style_bert_vits2.nlp.symbols import SYMBOLS
+
+
+def get_net_g(model_path: str, version: str, device: str, hps: HyperParameters):
+    if version.endswith("JP-Extra"):
+        logger.info("Using JP-Extra model")
+        net_g = SynthesizerTrnJPExtra(
+            n_vocab=len(SYMBOLS),
+            spec_channels=hps.data.filter_length // 2 + 1,
+            segment_size=hps.train.segment_size // hps.data.hop_length,
+            n_speakers=hps.data.n_speakers,
+            # hps.model 以下のすべての値を引数に渡す
+            use_spk_conditioned_encoder=hps.model.use_spk_conditioned_encoder,
+            use_noise_scaled_mas=hps.model.use_noise_scaled_mas,
+            use_mel_posterior_encoder=hps.model.use_mel_posterior_encoder,
+            use_duration_discriminator=hps.model.use_duration_discriminator,
+            use_wavlm_discriminator=hps.model.use_wavlm_discriminator,
+            inter_channels=hps.model.inter_channels,
+            hidden_channels=hps.model.hidden_channels,
+            filter_channels=hps.model.filter_channels,
+            n_heads=hps.model.n_heads,
+            n_layers=hps.model.n_layers,
+            kernel_size=hps.model.kernel_size,
+            p_dropout=hps.model.p_dropout,
+            resblock=hps.model.resblock,
+            resblock_kernel_sizes=hps.model.resblock_kernel_sizes,
+            resblock_dilation_sizes=hps.model.resblock_dilation_sizes,
+            upsample_rates=hps.model.upsample_rates,
+            upsample_initial_channel=hps.model.upsample_initial_channel,
+            upsample_kernel_sizes=hps.model.upsample_kernel_sizes,
+            n_layers_q=hps.model.n_layers_q,
+            use_spectral_norm=hps.model.use_spectral_norm,
+            gin_channels=hps.model.gin_channels,
+            slm=hps.model.slm,
+        ).to(device)
+    else:
+        logger.info("Using normal model")
+        net_g = SynthesizerTrn(
+            n_vocab=len(SYMBOLS),
+            spec_channels=hps.data.filter_length // 2 + 1,
+            segment_size=hps.train.segment_size // hps.data.hop_length,
+            n_speakers=hps.data.n_speakers,
+            # hps.model 以下のすべての値を引数に渡す
+            use_spk_conditioned_encoder=hps.model.use_spk_conditioned_encoder,
+            use_noise_scaled_mas=hps.model.use_noise_scaled_mas,
+            use_mel_posterior_encoder=hps.model.use_mel_posterior_encoder,
+            use_duration_discriminator=hps.model.use_duration_discriminator,
+            use_wavlm_discriminator=hps.model.use_wavlm_discriminator,
+            inter_channels=hps.model.inter_channels,
+            hidden_channels=hps.model.hidden_channels,
+            filter_channels=hps.model.filter_channels,
+            n_heads=hps.model.n_heads,
+            n_layers=hps.model.n_layers,
+            kernel_size=hps.model.kernel_size,
+            p_dropout=hps.model.p_dropout,
+            resblock=hps.model.resblock,
+            resblock_kernel_sizes=hps.model.resblock_kernel_sizes,
+            resblock_dilation_sizes=hps.model.resblock_dilation_sizes,
+            upsample_rates=hps.model.upsample_rates,
+            upsample_initial_channel=hps.model.upsample_initial_channel,
+            upsample_kernel_sizes=hps.model.upsample_kernel_sizes,
+            n_layers_q=hps.model.n_layers_q,
+            use_spectral_norm=hps.model.use_spectral_norm,
+            gin_channels=hps.model.gin_channels,
+            slm=hps.model.slm,
+        ).to(device)
+    net_g.state_dict()
+    _ = net_g.eval()
+    if model_path.endswith(".pth") or model_path.endswith(".pt"):
+        _ = utils.checkpoints.load_checkpoint(
+            model_path, net_g, None, skip_optimizer=True
+        )
+    elif model_path.endswith(".safetensors"):
+        _ = utils.safetensors.load_safetensors(model_path, net_g, True)
+    else:
+        raise ValueError(f"Unknown model format: {model_path}")
+    return net_g
+
+
+def get_text(
+    text: str,
+    language_str: Languages,
+    hps: HyperParameters,
+    device: str,
+    assist_text: Optional[str] = None,
+    assist_text_weight: float = 0.7,
+    given_phone: Optional[list[str]] = None,
+    given_tone: Optional[list[int]] = None,
+):
+    use_jp_extra = hps.version.endswith("JP-Extra")
+    # 推論時のみ呼び出されるので、raise_yomi_error は False に設定
+    norm_text, phone, tone, word2ph = clean_text(
+        text,
+        language_str,
+        use_jp_extra=use_jp_extra,
+        raise_yomi_error=False,
+    )
+    # phone と tone の両方が与えられた場合はそれを使う
+    if given_phone is not None and given_tone is not None:
+        # 指定された phone と指定された tone 両方の長さが一致していなければならない
+        if len(given_phone) != len(given_tone):
+            raise InvalidPhoneError(
+                f"Length of given_phone ({len(given_phone)}) != length of given_tone ({len(given_tone)})"
+            )
+        # 与えられた音素数と pyopenjtalk で生成した読みの音素数が一致しない
+        if len(given_phone) != sum(word2ph):
+            # 日本語の場合、len(given_phone) と sum(word2ph) が一致するように word2ph を適切に調整する
+            # 他の言語は word2ph の調整方法が思いつかないのでエラー
+            if language_str == Languages.JP:
+                from style_bert_vits2.nlp.japanese.g2p import adjust_word2ph
+
+                word2ph = adjust_word2ph(word2ph, phone, given_phone)
+                # 上記処理により word2ph の合計が given_phone の長さと一致するはず
+                # それでも一致しない場合、大半は読み上げテキストと given_phone が著しく乖離していて調整し切れなかったことを意味する
+                if len(given_phone) != sum(word2ph):
+                    raise InvalidPhoneError(
+                        f"Length of given_phone ({len(given_phone)}) != sum of word2ph ({sum(word2ph)})"
+                    )
+            else:
+                raise InvalidPhoneError(
+                    f"Length of given_phone ({len(given_phone)}) != sum of word2ph ({sum(word2ph)})"
+                )
+        phone = given_phone
+        # 生成あるいは指定された phone と指定された tone 両方の長さが一致していなければならない
+        if len(phone) != len(given_tone):
+            raise InvalidToneError(
+                f"Length of phone ({len(phone)}) != length of given_tone ({len(given_tone)})"
+            )
+        tone = given_tone
+    # tone だけが与えられた場合は clean_text() で生成した phone と合わせて使う
+    elif given_tone is not None:
+        # 生成した phone と指定された tone 両方の長さが一致していなければならない
+        if len(phone) != len(given_tone):
+            raise InvalidToneError(
+                f"Length of phone ({len(phone)}) != length of given_tone ({len(given_tone)})"
+            )
+        tone = given_tone
+    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
+
+    if hps.data.add_blank:
+        phone = commons.intersperse(phone, 0)
+        tone = commons.intersperse(tone, 0)
+        language = commons.intersperse(language, 0)
+        for i in range(len(word2ph)):
+            word2ph[i] = word2ph[i] * 2
+        word2ph[0] += 1
+    bert_ori = extract_bert_feature(
+        norm_text,
+        word2ph,
+        language_str,
+        device,
+        assist_text,
+        assist_text_weight,
+    )
+    del word2ph
+    assert bert_ori.shape[-1] == len(phone), phone
+
+    if language_str == Languages.ZH:
+        bert = bert_ori
+        ja_bert = torch.zeros(1024, len(phone))
+        en_bert = torch.zeros(1024, len(phone))
+    elif language_str == Languages.JP:
+        bert = torch.zeros(1024, len(phone))
+        ja_bert = bert_ori
+        en_bert = torch.zeros(1024, len(phone))
+    elif language_str == Languages.EN:
+        bert = torch.zeros(1024, len(phone))
+        ja_bert = torch.zeros(1024, len(phone))
+        en_bert = bert_ori
+    else:
+        raise ValueError("language_str should be ZH, JP or EN")
+
+    assert bert.shape[-1] == len(
+        phone
+    ), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
+
+    phone = torch.LongTensor(phone)
+    tone = torch.LongTensor(tone)
+    language = torch.LongTensor(language)
+    return bert, ja_bert, en_bert, phone, tone, language
+
+
+def infer(
+    text: str,
+    style_vec: NDArray[Any],
+    sdp_ratio: float,
+    noise_scale: float,
+    noise_scale_w: float,
+    length_scale: float,
+    sid: int,  # In the original Bert-VITS2, its speaker_name: str, but here it's id
+    language: Languages,
+    hps: HyperParameters,
+    net_g: Union[SynthesizerTrn, SynthesizerTrnJPExtra],
+    device: str,
+    skip_start: bool = False,
+    skip_end: bool = False,
+    assist_text: Optional[str] = None,
+    assist_text_weight: float = 0.7,
+    given_phone: Optional[list[str]] = None,
+    given_tone: Optional[list[int]] = None,
+):
+    is_jp_extra = hps.version.endswith("JP-Extra")
+    bert, ja_bert, en_bert, phones, tones, lang_ids = get_text(
+        text,
+        language,
+        hps,
+        device,
+        assist_text=assist_text,
+        assist_text_weight=assist_text_weight,
+        given_phone=given_phone,
+        given_tone=given_tone,
+    )
+    if skip_start:
+        phones = phones[3:]
+        tones = tones[3:]
+        lang_ids = lang_ids[3:]
+        bert = bert[:, 3:]
+        ja_bert = ja_bert[:, 3:]
+        en_bert = en_bert[:, 3:]
+    if skip_end:
+        phones = phones[:-2]
+        tones = tones[:-2]
+        lang_ids = lang_ids[:-2]
+        bert = bert[:, :-2]
+        ja_bert = ja_bert[:, :-2]
+        en_bert = en_bert[:, :-2]
+    with torch.no_grad():
+        x_tst = phones.to(device).unsqueeze(0)
+        tones = tones.to(device).unsqueeze(0)
+        lang_ids = lang_ids.to(device).unsqueeze(0)
+        bert = bert.to(device).unsqueeze(0)
+        ja_bert = ja_bert.to(device).unsqueeze(0)
+        en_bert = en_bert.to(device).unsqueeze(0)
+        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
+        style_vec_tensor = torch.from_numpy(style_vec).to(device).unsqueeze(0)
+        del phones
+        sid_tensor = torch.LongTensor([sid]).to(device)
+        if is_jp_extra:
+            output = cast(SynthesizerTrnJPExtra, net_g).infer(
+                x_tst,
+                x_tst_lengths,
+                sid_tensor,
+                tones,
+                lang_ids,
+                ja_bert,
+                style_vec=style_vec_tensor,
+                sdp_ratio=sdp_ratio,
+                noise_scale=noise_scale,
+                noise_scale_w=noise_scale_w,
+                length_scale=length_scale,
+            )
+        else:
+            output = cast(SynthesizerTrn, net_g).infer(
+                x_tst,
+                x_tst_lengths,
+                sid_tensor,
+                tones,
+                lang_ids,
+                bert,
+                ja_bert,
+                en_bert,
+                style_vec=style_vec_tensor,
+                sdp_ratio=sdp_ratio,
+                noise_scale=noise_scale,
+                noise_scale_w=noise_scale_w,
+                length_scale=length_scale,
+            )
+        audio = output[0][0, 0].data.cpu().float().numpy()
+        del (
+            x_tst,
+            tones,
+            lang_ids,
+            bert,
+            x_tst_lengths,
+            sid_tensor,
+            ja_bert,
+            en_bert,
+            style_vec,
+        )  # , emo
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        return audio
+
+
+class InvalidPhoneError(ValueError):
+    pass
+
+
+class InvalidToneError(ValueError):
+    pass
diff --git a/style_bert_vits2/models/models.py b/style_bert_vits2/models/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..eaff2fadfc168b97c1a59cdecf34a535ed9cd4b0
--- /dev/null
+++ b/style_bert_vits2/models/models.py
@@ -0,0 +1,1102 @@
+import math
+from typing import Any, Optional
+
+import torch
+from torch import nn
+from torch.nn import Conv1d, Conv2d, ConvTranspose1d
+from torch.nn import functional as F
+from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
+
+from style_bert_vits2.models import attentions, commons, modules, monotonic_alignment
+from style_bert_vits2.nlp.symbols import NUM_LANGUAGES, NUM_TONES, SYMBOLS
+
+
+class DurationDiscriminator(nn.Module):  # vits2
+    def __init__(
+        self,
+        in_channels: int,
+        filter_channels: int,
+        kernel_size: int,
+        p_dropout: float,
+        gin_channels: int = 0,
+    ) -> None:
+        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 = modules.LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_2 = modules.LayerNorm(filter_channels)
+        self.dur_proj = nn.Conv1d(1, filter_channels, 1)
+
+        self.pre_out_conv_1 = nn.Conv1d(
+            2 * filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.pre_out_norm_1 = modules.LayerNorm(filter_channels)
+        self.pre_out_conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.pre_out_norm_2 = modules.LayerNorm(filter_channels)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+        self.output_layer = nn.Sequential(nn.Linear(filter_channels, 1), nn.Sigmoid())
+
+    def forward_probability(
+        self,
+        x: torch.Tensor,
+        x_mask: torch.Tensor,
+        dur: torch.Tensor,
+        g: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        dur = self.dur_proj(dur)
+        x = torch.cat([x, dur], dim=1)
+        x = self.pre_out_conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.pre_out_norm_1(x)
+        x = self.drop(x)
+        x = self.pre_out_conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.pre_out_norm_2(x)
+        x = self.drop(x)
+        x = x * x_mask
+        x = x.transpose(1, 2)
+        output_prob = self.output_layer(x)
+        return output_prob
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_mask: torch.Tensor,
+        dur_r: torch.Tensor,
+        dur_hat: torch.Tensor,
+        g: Optional[torch.Tensor] = None,
+    ) -> list[torch.Tensor]:
+        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)
+
+        output_probs = []
+        for dur in [dur_r, dur_hat]:
+            output_prob = self.forward_probability(x, x_mask, dur, g)
+            output_probs.append(output_prob)
+
+        return output_probs
+
+
+class TransformerCouplingBlock(nn.Module):
+    def __init__(
+        self,
+        channels: int,
+        hidden_channels: int,
+        filter_channels: int,
+        n_heads: int,
+        n_layers: int,
+        kernel_size: int,
+        p_dropout: float,
+        n_flows: int = 4,
+        gin_channels: int = 0,
+        share_parameter: bool = False,
+    ) -> None:
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+
+        self.wn = (
+            # attentions.FFT(
+            #     hidden_channels,
+            #     filter_channels,
+            #     n_heads,
+            #     n_layers,
+            #     kernel_size,
+            #     p_dropout,
+            #     isflow=True,
+            #     gin_channels=self.gin_channels,
+            # )
+            None
+            if share_parameter
+            else None
+        )
+
+        for i in range(n_flows):
+            self.flows.append(
+                modules.TransformerCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    n_layers,
+                    n_heads,
+                    p_dropout,
+                    filter_channels,
+                    mean_only=True,
+                    wn_sharing_parameter=self.wn,
+                    gin_channels=self.gin_channels,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_mask: torch.Tensor,
+        g: Optional[torch.Tensor] = None,
+        reverse: bool = False,
+    ) -> torch.Tensor:
+        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 StochasticDurationPredictor(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        filter_channels: int,
+        kernel_size: int,
+        p_dropout: float,
+        n_flows: int = 4,
+        gin_channels: int = 0,
+    ) -> None:
+        super().__init__()
+        filter_channels = in_channels  # it needs to be removed from future version.
+        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 = modules.Log()
+        self.flows = nn.ModuleList()
+        self.flows.append(modules.ElementwiseAffine(2))
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
+            )
+            self.flows.append(modules.Flip())
+
+        self.post_pre = nn.Conv1d(1, filter_channels, 1)
+        self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.post_convs = modules.DDSConv(
+            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
+        )
+        self.post_flows = nn.ModuleList()
+        self.post_flows.append(modules.ElementwiseAffine(2))
+        for i in range(4):
+            self.post_flows.append(
+                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
+            )
+            self.post_flows.append(modules.Flip())
+
+        self.pre = nn.Conv1d(in_channels, filter_channels, 1)
+        self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.convs = modules.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: torch.Tensor,
+        x_mask: torch.Tensor,
+        w: Optional[torch.Tensor] = None,
+        g: Optional[torch.Tensor] = None,
+        reverse: bool = False,
+        noise_scale: float = 1.0,
+    ) -> torch.Tensor:
+        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  # [b]
+        else:
+            flows = list(reversed(self.flows))
+            flows = flows[:-2] + [flows[-1]]  # remove a useless vflow
+            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
+
+
+class DurationPredictor(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        filter_channels: int,
+        kernel_size: int,
+        p_dropout: float,
+        gin_channels: int = 0,
+    ) -> None:
+        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 = modules.LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_2 = modules.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: torch.Tensor, x_mask: torch.Tensor, g: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        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
+
+
+class TextEncoder(nn.Module):
+    def __init__(
+        self,
+        n_vocab: int,
+        out_channels: int,
+        hidden_channels: int,
+        filter_channels: int,
+        n_heads: int,
+        n_layers: int,
+        kernel_size: int,
+        p_dropout: float,
+        n_speakers: int,
+        gin_channels: int = 0,
+    ) -> None:
+        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.gin_channels = gin_channels
+        self.emb = nn.Embedding(len(SYMBOLS), hidden_channels)
+        nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
+        self.tone_emb = nn.Embedding(NUM_TONES, hidden_channels)
+        nn.init.normal_(self.tone_emb.weight, 0.0, hidden_channels**-0.5)
+        self.language_emb = nn.Embedding(NUM_LANGUAGES, hidden_channels)
+        nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels**-0.5)
+        self.bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+        self.ja_bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+        self.en_bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+        self.style_proj = nn.Linear(256, hidden_channels)
+
+        self.encoder = attentions.Encoder(
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            gin_channels=self.gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_lengths: torch.Tensor,
+        tone: torch.Tensor,
+        language: torch.Tensor,
+        bert: torch.Tensor,
+        ja_bert: torch.Tensor,
+        en_bert: torch.Tensor,
+        style_vec: torch.Tensor,
+        sid: torch.Tensor,
+        g: Optional[torch.Tensor] = None,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        bert_emb = self.bert_proj(bert).transpose(1, 2)
+        ja_bert_emb = self.ja_bert_proj(ja_bert).transpose(1, 2)
+        en_bert_emb = self.en_bert_proj(en_bert).transpose(1, 2)
+        style_emb = self.style_proj(style_vec.unsqueeze(1))
+
+        x = (
+            self.emb(x)
+            + self.tone_emb(tone)
+            + self.language_emb(language)
+            + bert_emb
+            + ja_bert_emb
+            + en_bert_emb
+            + style_emb
+        ) * math.sqrt(
+            self.hidden_channels
+        )  # [b, t, h]
+        x = torch.transpose(x, 1, -1)  # [b, h, t]
+        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
+            x.dtype
+        )
+
+        x = self.encoder(x * x_mask, x_mask, g=g)
+        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: int,
+        hidden_channels: int,
+        kernel_size: int,
+        dilation_rate: int,
+        n_layers: int,
+        n_flows: int = 4,
+        gin_channels: int = 0,
+    ) -> None:
+        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(
+                modules.ResidualCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    dilation_rate,
+                    n_layers,
+                    gin_channels=gin_channels,
+                    mean_only=True,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_mask: torch.Tensor,
+        g: Optional[torch.Tensor] = None,
+        reverse: bool = False,
+    ) -> torch.Tensor:
+        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: int,
+        out_channels: int,
+        hidden_channels: int,
+        kernel_size: int,
+        dilation_rate: int,
+        n_layers: int,
+        gin_channels: int = 0,
+    ) -> None:
+        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 = modules.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: torch.Tensor,
+        x_lengths: torch.Tensor,
+        g: Optional[torch.Tensor] = None,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        x_mask = torch.unsqueeze(commons.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 Generator(torch.nn.Module):
+    def __init__(
+        self,
+        initial_channel: int,
+        resblock_str: str,
+        resblock_kernel_sizes: list[int],
+        resblock_dilation_sizes: list[list[int]],
+        upsample_rates: list[int],
+        upsample_initial_channel: int,
+        upsample_kernel_sizes: list[int],
+        gin_channels: int = 0,
+    ) -> None:
+        super(Generator, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = Conv1d(
+            initial_channel, upsample_initial_channel, 7, 1, padding=3
+        )
+        resblock = modules.ResBlock1 if resblock_str == "1" else modules.ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(
+                weight_norm(
+                    ConvTranspose1d(
+                        upsample_initial_channel // (2**i),
+                        upsample_initial_channel // (2 ** (i + 1)),
+                        k,
+                        u,
+                        padding=(k - u) // 2,
+                    )
+                )
+            )
+
+        self.resblocks = nn.ModuleList()
+        ch = None
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel // (2 ** (i + 1))
+            for j, (k, d) in enumerate(
+                zip(resblock_kernel_sizes, resblock_dilation_sizes)
+            ):
+                self.resblocks.append(resblock(ch, k, d))  # type: ignore
+
+        assert ch is not None
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(commons.init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+    def forward(
+        self, x: torch.Tensor, g: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        x = self.conv_pre(x)
+        if g is not None:
+            x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            assert xs is not None
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self) -> None:
+        print("Removing weight norm...")
+        for layer in self.ups:
+            remove_weight_norm(layer)
+        for layer in self.resblocks:
+            layer.remove_weight_norm()
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(
+        self,
+        period: int,
+        kernel_size: int = 5,
+        stride: int = 3,
+        use_spectral_norm: bool = False,
+    ) -> None:
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(
+                    Conv2d(
+                        1,
+                        32,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(commons.get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        32,
+                        128,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(commons.get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        128,
+                        512,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(commons.get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        512,
+                        1024,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(commons.get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        1024,
+                        1024,
+                        (kernel_size, 1),
+                        1,
+                        padding=(commons.get_padding(kernel_size, 1), 0),
+                    )
+                ),
+            ]
+        )
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, list[torch.Tensor]]:
+        fmap = []
+
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0:  # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for layer in self.convs:
+            x = layer(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm: bool = False) -> None:
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
+                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
+                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
+                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
+                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
+                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
+            ]
+        )
+        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, list[torch.Tensor]]:
+        fmap = []
+
+        for layer in self.convs:
+            x = layer(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, use_spectral_norm: bool = False) -> None:
+        super(MultiPeriodDiscriminator, self).__init__()
+        periods = [2, 3, 5, 7, 11]
+
+        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+        discs = discs + [
+            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
+        ]
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(
+        self,
+        y: torch.Tensor,
+        y_hat: torch.Tensor,
+    ) -> tuple[
+        list[torch.Tensor], list[torch.Tensor], list[torch.Tensor], list[torch.Tensor]
+    ]:
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            y_d_gs.append(y_d_g)
+            fmap_rs.append(fmap_r)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+class ReferenceEncoder(nn.Module):
+    """
+    inputs --- [N, Ty/r, n_mels*r]  mels
+    outputs --- [N, ref_enc_gru_size]
+    """
+
+    def __init__(self, spec_channels: int, gin_channels: int = 0) -> None:
+        super().__init__()
+        self.spec_channels = spec_channels
+        ref_enc_filters = [32, 32, 64, 64, 128, 128]
+        K = len(ref_enc_filters)
+        filters = [1] + ref_enc_filters
+        convs = [
+            weight_norm(
+                nn.Conv2d(
+                    in_channels=filters[i],
+                    out_channels=filters[i + 1],
+                    kernel_size=(3, 3),
+                    stride=(2, 2),
+                    padding=(1, 1),
+                )
+            )
+            for i in range(K)
+        ]
+        self.convs = nn.ModuleList(convs)
+        # self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)])
+
+        out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K)
+        self.gru = nn.GRU(
+            input_size=ref_enc_filters[-1] * out_channels,
+            hidden_size=256 // 2,
+            batch_first=True,
+        )
+        self.proj = nn.Linear(128, gin_channels)
+
+    def forward(
+        self, inputs: torch.Tensor, mask: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        N = inputs.size(0)
+        out = inputs.view(N, 1, -1, self.spec_channels)  # [N, 1, Ty, n_freqs]
+        for conv in self.convs:
+            out = conv(out)
+            # out = wn(out)
+            out = F.relu(out)  # [N, 128, Ty//2^K, n_mels//2^K]
+
+        out = out.transpose(1, 2)  # [N, Ty//2^K, 128, n_mels//2^K]
+        T = out.size(1)
+        N = out.size(0)
+        out = out.contiguous().view(N, T, -1)  # [N, Ty//2^K, 128*n_mels//2^K]
+
+        self.gru.flatten_parameters()
+        memory, out = self.gru(out)  # out --- [1, N, 128]
+
+        return self.proj(out.squeeze(0))
+
+    def calculate_channels(
+        self, L: int, kernel_size: int, stride: int, pad: int, n_convs: int
+    ) -> int:
+        for i in range(n_convs):
+            L = (L - kernel_size + 2 * pad) // stride + 1
+        return L
+
+
+class SynthesizerTrn(nn.Module):
+    """
+    Synthesizer for Training
+    """
+
+    def __init__(
+        self,
+        n_vocab: int,
+        spec_channels: int,
+        segment_size: int,
+        inter_channels: int,
+        hidden_channels: int,
+        filter_channels: int,
+        n_heads: int,
+        n_layers: int,
+        kernel_size: int,
+        p_dropout: float,
+        resblock: str,
+        resblock_kernel_sizes: list[int],
+        resblock_dilation_sizes: list[list[int]],
+        upsample_rates: list[int],
+        upsample_initial_channel: int,
+        upsample_kernel_sizes: list[int],
+        n_speakers: int = 256,
+        gin_channels: int = 256,
+        use_sdp: bool = True,
+        n_flow_layer: int = 4,
+        n_layers_trans_flow: int = 4,
+        flow_share_parameter: bool = False,
+        use_transformer_flow: bool = True,
+        **kwargs: Any,
+    ) -> None:
+        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.n_layers_trans_flow = n_layers_trans_flow
+        self.use_spk_conditioned_encoder = kwargs.get(
+            "use_spk_conditioned_encoder", True
+        )
+        self.use_sdp = use_sdp
+        self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False)
+        self.mas_noise_scale_initial = kwargs.get("mas_noise_scale_initial", 0.01)
+        self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6)
+        self.current_mas_noise_scale = self.mas_noise_scale_initial
+        if self.use_spk_conditioned_encoder and gin_channels > 0:
+            self.enc_gin_channels = gin_channels
+        self.enc_p = TextEncoder(
+            n_vocab,
+            inter_channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            self.n_speakers,
+            gin_channels=self.enc_gin_channels,
+        )
+        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,
+        )
+        if use_transformer_flow:
+            self.flow = TransformerCouplingBlock(
+                inter_channels,
+                hidden_channels,
+                filter_channels,
+                n_heads,
+                n_layers_trans_flow,
+                5,
+                p_dropout,
+                n_flow_layer,
+                gin_channels=gin_channels,
+                share_parameter=flow_share_parameter,
+            )
+        else:
+            self.flow = ResidualCouplingBlock(
+                inter_channels,
+                hidden_channels,
+                5,
+                1,
+                n_flow_layer,
+                gin_channels=gin_channels,
+            )
+        self.sdp = StochasticDurationPredictor(
+            hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels
+        )
+        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)
+        else:
+            self.ref_enc = ReferenceEncoder(spec_channels, gin_channels)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_lengths: torch.Tensor,
+        y: torch.Tensor,
+        y_lengths: torch.Tensor,
+        sid: torch.Tensor,
+        tone: torch.Tensor,
+        language: torch.Tensor,
+        bert: torch.Tensor,
+        ja_bert: torch.Tensor,
+        en_bert: torch.Tensor,
+        style_vec: torch.Tensor,
+    ) -> tuple[
+        torch.Tensor,
+        torch.Tensor,
+        torch.Tensor,
+        torch.Tensor,
+        torch.Tensor,
+        torch.Tensor,
+        tuple[torch.Tensor, ...],
+        tuple[torch.Tensor, ...],
+    ]:
+        if self.n_speakers > 0:
+            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
+        else:
+            g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1)
+        x, m_p, logs_p, x_mask = self.enc_p(
+            x, x_lengths, tone, language, bert, ja_bert, en_bert, style_vec, sid, g=g
+        )
+        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
+            if self.use_noise_scaled_mas:
+                epsilon = (
+                    torch.std(neg_cent)
+                    * torch.randn_like(neg_cent)
+                    * self.current_mas_noise_scale
+                )
+                neg_cent = neg_cent + epsilon
+
+            attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+            attn = (
+                monotonic_alignment.maximum_path(neg_cent, attn_mask.squeeze(1))
+                .unsqueeze(1)
+                .detach()
+            )
+
+        w = attn.sum(2)
+
+        l_length_sdp = self.sdp(x, x_mask, w, g=g)
+        l_length_sdp = l_length_sdp / torch.sum(x_mask)
+
+        logw_ = torch.log(w + 1e-6) * x_mask
+        logw = self.dp(x, x_mask, g=g)
+        # logw_sdp = self.sdp(x, x_mask, g=g, reverse=True, noise_scale=1.0)
+        l_length_dp = torch.sum((logw - logw_) ** 2, [1, 2]) / torch.sum(
+            x_mask
+        )  # for averaging
+        # l_length_sdp += torch.sum((logw_sdp - logw_) ** 2, [1, 2]) / torch.sum(x_mask)
+
+        l_length = l_length_dp + l_length_sdp
+
+        # 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 = commons.rand_slice_segments(
+            z, y_lengths, self.segment_size
+        )
+        o = self.dec(z_slice, g=g)
+        return (
+            o,
+            l_length,
+            attn,
+            ids_slice,
+            x_mask,
+            y_mask,
+            (z, z_p, m_p, logs_p, m_q, logs_q),
+            (x, logw, logw_),
+        )
+
+    def infer(
+        self,
+        x: torch.Tensor,
+        x_lengths: torch.Tensor,
+        sid: torch.Tensor,
+        tone: torch.Tensor,
+        language: torch.Tensor,
+        bert: torch.Tensor,
+        ja_bert: torch.Tensor,
+        en_bert: torch.Tensor,
+        style_vec: torch.Tensor,
+        noise_scale: float = 0.667,
+        length_scale: float = 1.0,
+        noise_scale_w: float = 0.8,
+        max_len: Optional[int] = None,
+        sdp_ratio: float = 0.0,
+        y: Optional[torch.Tensor] = None,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, tuple[torch.Tensor, ...]]:
+        # x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert)
+        # g = self.gst(y)
+        if self.n_speakers > 0:
+            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
+        else:
+            assert y is not None
+            g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1)
+        x, m_p, logs_p, x_mask = self.enc_p(
+            x, x_lengths, tone, language, bert, ja_bert, en_bert, style_vec, sid, g=g
+        )
+        logw = self.sdp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) * (
+            sdp_ratio
+        ) + self.dp(x, x_mask, g=g) * (1 - sdp_ratio)
+        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(commons.sequence_mask(y_lengths, None), 1).to(
+            x_mask.dtype
+        )
+        attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+        attn = commons.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)
+        return o, attn, y_mask, (z, z_p, m_p, logs_p)
diff --git a/style_bert_vits2/models/models_jp_extra.py b/style_bert_vits2/models/models_jp_extra.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8df7d9fa7860956ba8c58249f4cf7aac1de2f35
--- /dev/null
+++ b/style_bert_vits2/models/models_jp_extra.py
@@ -0,0 +1,1157 @@
+import math
+from typing import Any, Optional
+
+import torch
+from torch import nn
+from torch.nn import Conv1d, Conv2d, ConvTranspose1d
+from torch.nn import functional as F
+from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
+
+from style_bert_vits2.models import attentions, commons, modules, monotonic_alignment
+from style_bert_vits2.nlp.symbols import NUM_LANGUAGES, NUM_TONES, SYMBOLS
+
+
+class DurationDiscriminator(nn.Module):  # vits2
+    def __init__(
+        self,
+        in_channels: int,
+        filter_channels: int,
+        kernel_size: int,
+        p_dropout: float,
+        gin_channels: int = 0,
+    ) -> None:
+        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 = modules.LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_2 = modules.LayerNorm(filter_channels)
+        self.dur_proj = nn.Conv1d(1, filter_channels, 1)
+
+        self.LSTM = nn.LSTM(
+            2 * filter_channels, filter_channels, batch_first=True, bidirectional=True
+        )
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+        self.output_layer = nn.Sequential(
+            nn.Linear(2 * filter_channels, 1), nn.Sigmoid()
+        )
+
+    def forward_probability(self, x: torch.Tensor, dur: torch.Tensor) -> torch.Tensor:
+        dur = self.dur_proj(dur)
+        x = torch.cat([x, dur], dim=1)
+        x = x.transpose(1, 2)
+        x, _ = self.LSTM(x)
+        output_prob = self.output_layer(x)
+        return output_prob
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_mask: torch.Tensor,
+        dur_r: torch.Tensor,
+        dur_hat: torch.Tensor,
+        g: Optional[torch.Tensor] = None,
+    ) -> list[torch.Tensor]:
+        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)
+
+        output_probs = []
+        for dur in [dur_r, dur_hat]:
+            output_prob = self.forward_probability(x, dur)
+            output_probs.append(output_prob)
+
+        return output_probs
+
+
+class TransformerCouplingBlock(nn.Module):
+    def __init__(
+        self,
+        channels: int,
+        hidden_channels: int,
+        filter_channels: int,
+        n_heads: int,
+        n_layers: int,
+        kernel_size: int,
+        p_dropout: float,
+        n_flows: int = 4,
+        gin_channels: int = 0,
+        share_parameter: bool = False,
+    ) -> None:
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+
+        self.wn = (
+            # attentions.FFT(
+            #     hidden_channels,
+            #     filter_channels,
+            #     n_heads,
+            #     n_layers,
+            #     kernel_size,
+            #     p_dropout,
+            #     isflow=True,
+            #     gin_channels=self.gin_channels,
+            # )
+            None
+            if share_parameter
+            else None
+        )
+
+        for i in range(n_flows):
+            self.flows.append(
+                modules.TransformerCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    n_layers,
+                    n_heads,
+                    p_dropout,
+                    filter_channels,
+                    mean_only=True,
+                    wn_sharing_parameter=self.wn,
+                    gin_channels=self.gin_channels,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_mask: torch.Tensor,
+        g: Optional[torch.Tensor] = None,
+        reverse: bool = False,
+    ) -> torch.Tensor:
+        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 StochasticDurationPredictor(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        filter_channels: int,
+        kernel_size: int,
+        p_dropout: float,
+        n_flows: int = 4,
+        gin_channels: int = 0,
+    ) -> None:
+        super().__init__()
+        filter_channels = in_channels  # it needs to be removed from future version.
+        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 = modules.Log()
+        self.flows = nn.ModuleList()
+        self.flows.append(modules.ElementwiseAffine(2))
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
+            )
+            self.flows.append(modules.Flip())
+
+        self.post_pre = nn.Conv1d(1, filter_channels, 1)
+        self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.post_convs = modules.DDSConv(
+            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
+        )
+        self.post_flows = nn.ModuleList()
+        self.post_flows.append(modules.ElementwiseAffine(2))
+        for i in range(4):
+            self.post_flows.append(
+                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
+            )
+            self.post_flows.append(modules.Flip())
+
+        self.pre = nn.Conv1d(in_channels, filter_channels, 1)
+        self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.convs = modules.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: torch.Tensor,
+        x_mask: torch.Tensor,
+        w: Optional[torch.Tensor] = None,
+        g: Optional[torch.Tensor] = None,
+        reverse: bool = False,
+        noise_scale: float = 1.0,
+    ) -> torch.Tensor:
+        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  # [b]
+        else:
+            flows = list(reversed(self.flows))
+            flows = flows[:-2] + [flows[-1]]  # remove a useless vflow
+            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
+
+
+class DurationPredictor(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        filter_channels: int,
+        kernel_size: int,
+        p_dropout: float,
+        gin_channels: int = 0,
+    ) -> None:
+        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 = modules.LayerNorm(filter_channels)
+        self.conv_2 = nn.Conv1d(
+            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
+        )
+        self.norm_2 = modules.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: torch.Tensor, x_mask: torch.Tensor, g: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        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
+
+
+class Bottleneck(nn.Sequential):
+    def __init__(self, in_dim: int, hidden_dim: int) -> None:
+        c_fc1 = nn.Linear(in_dim, hidden_dim, bias=False)
+        c_fc2 = nn.Linear(in_dim, hidden_dim, bias=False)
+        super().__init__(c_fc1, c_fc2)
+
+
+class Block(nn.Module):
+    def __init__(self, in_dim: int, hidden_dim: int) -> None:
+        super().__init__()
+        self.norm = nn.LayerNorm(in_dim)
+        self.mlp = MLP(in_dim, hidden_dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x + self.mlp(self.norm(x))
+        return x
+
+
+class MLP(nn.Module):
+    def __init__(self, in_dim: int, hidden_dim: int) -> None:
+        super().__init__()
+        self.c_fc1 = nn.Linear(in_dim, hidden_dim, bias=False)
+        self.c_fc2 = nn.Linear(in_dim, hidden_dim, bias=False)
+        self.c_proj = nn.Linear(hidden_dim, in_dim, bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = F.silu(self.c_fc1(x)) * self.c_fc2(x)
+        x = self.c_proj(x)
+        return x
+
+
+class TextEncoder(nn.Module):
+    def __init__(
+        self,
+        n_vocab: int,
+        out_channels: int,
+        hidden_channels: int,
+        filter_channels: int,
+        n_heads: int,
+        n_layers: int,
+        kernel_size: int,
+        p_dropout: float,
+        gin_channels: int = 0,
+    ) -> None:
+        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.gin_channels = gin_channels
+        self.emb = nn.Embedding(len(SYMBOLS), hidden_channels)
+        nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
+        self.tone_emb = nn.Embedding(NUM_TONES, hidden_channels)
+        nn.init.normal_(self.tone_emb.weight, 0.0, hidden_channels**-0.5)
+        self.language_emb = nn.Embedding(NUM_LANGUAGES, hidden_channels)
+        nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels**-0.5)
+        self.bert_proj = nn.Conv1d(1024, hidden_channels, 1)
+
+        # Remove emo_vq since it's not working well.
+        self.style_proj = nn.Linear(256, hidden_channels)
+
+        self.encoder = attentions.Encoder(
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            gin_channels=self.gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_lengths: torch.Tensor,
+        tone: torch.Tensor,
+        language: torch.Tensor,
+        bert: torch.Tensor,
+        style_vec: torch.Tensor,
+        g: Optional[torch.Tensor] = None,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        bert_emb = self.bert_proj(bert).transpose(1, 2)
+        style_emb = self.style_proj(style_vec.unsqueeze(1))
+        x = (
+            self.emb(x)
+            + self.tone_emb(tone)
+            + self.language_emb(language)
+            + bert_emb
+            + style_emb
+        ) * math.sqrt(
+            self.hidden_channels
+        )  # [b, t, h]
+        x = torch.transpose(x, 1, -1)  # [b, h, t]
+        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
+            x.dtype
+        )
+
+        x = self.encoder(x * x_mask, x_mask, g=g)
+        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: int,
+        hidden_channels: int,
+        kernel_size: int,
+        dilation_rate: int,
+        n_layers: int,
+        n_flows: int = 4,
+        gin_channels: int = 0,
+    ) -> None:
+        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(
+                modules.ResidualCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    dilation_rate,
+                    n_layers,
+                    gin_channels=gin_channels,
+                    mean_only=True,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_mask: torch.Tensor,
+        g: Optional[torch.Tensor] = None,
+        reverse: bool = False,
+    ) -> torch.Tensor:
+        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: int,
+        out_channels: int,
+        hidden_channels: int,
+        kernel_size: int,
+        dilation_rate: int,
+        n_layers: int,
+        gin_channels: int = 0,
+    ) -> None:
+        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 = modules.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: torch.Tensor,
+        x_lengths: torch.Tensor,
+        g: Optional[torch.Tensor] = None,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        x_mask = torch.unsqueeze(commons.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 Generator(torch.nn.Module):
+    def __init__(
+        self,
+        initial_channel: int,
+        resblock_str: str,
+        resblock_kernel_sizes: list[int],
+        resblock_dilation_sizes: list[list[int]],
+        upsample_rates: list[int],
+        upsample_initial_channel: int,
+        upsample_kernel_sizes: list[int],
+        gin_channels: int = 0,
+    ) -> None:
+        super(Generator, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = Conv1d(
+            initial_channel, upsample_initial_channel, 7, 1, padding=3
+        )
+        resblock = modules.ResBlock1 if resblock_str == "1" else modules.ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(
+                weight_norm(
+                    ConvTranspose1d(
+                        upsample_initial_channel // (2**i),
+                        upsample_initial_channel // (2 ** (i + 1)),
+                        k,
+                        u,
+                        padding=(k - u) // 2,
+                    )
+                )
+            )
+
+        self.resblocks = nn.ModuleList()
+        ch = None
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel // (2 ** (i + 1))
+            for j, (k, d) in enumerate(
+                zip(resblock_kernel_sizes, resblock_dilation_sizes)
+            ):
+                self.resblocks.append(resblock(ch, k, d))  # type: ignore
+
+        assert ch is not None
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(commons.init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+    def forward(
+        self, x: torch.Tensor, g: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        x = self.conv_pre(x)
+        if g is not None:
+            x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            assert xs is not None
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self) -> None:
+        print("Removing weight norm...")
+        for layer in self.ups:
+            remove_weight_norm(layer)
+        for layer in self.resblocks:
+            layer.remove_weight_norm()
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(
+        self,
+        period: int,
+        kernel_size: int = 5,
+        stride: int = 3,
+        use_spectral_norm: bool = False,
+    ) -> None:
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(
+                    Conv2d(
+                        1,
+                        32,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(commons.get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        32,
+                        128,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(commons.get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        128,
+                        512,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(commons.get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        512,
+                        1024,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(commons.get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        1024,
+                        1024,
+                        (kernel_size, 1),
+                        1,
+                        padding=(commons.get_padding(kernel_size, 1), 0),
+                    )
+                ),
+            ]
+        )
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, list[torch.Tensor]]:
+        fmap = []
+
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0:  # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for layer in self.convs:
+            x = layer(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm: bool = False) -> None:
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm is False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
+                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
+                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
+                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
+                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
+                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
+            ]
+        )
+        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, list[torch.Tensor]]:
+        fmap = []
+
+        for layer in self.convs:
+            x = layer(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, use_spectral_norm: bool = False) -> None:
+        super(MultiPeriodDiscriminator, self).__init__()
+        periods = [2, 3, 5, 7, 11]
+
+        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+        discs = discs + [
+            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
+        ]
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(
+        self,
+        y: torch.Tensor,
+        y_hat: torch.Tensor,
+    ) -> tuple[
+        list[torch.Tensor], list[torch.Tensor], list[torch.Tensor], list[torch.Tensor]
+    ]:
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            y_d_gs.append(y_d_g)
+            fmap_rs.append(fmap_r)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+class WavLMDiscriminator(nn.Module):
+    """docstring for Discriminator."""
+
+    def __init__(
+        self,
+        slm_hidden: int = 768,
+        slm_layers: int = 13,
+        initial_channel: int = 64,
+        use_spectral_norm: bool = False,
+    ) -> None:
+        super(WavLMDiscriminator, self).__init__()
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.pre = norm_f(
+            Conv1d(slm_hidden * slm_layers, initial_channel, 1, 1, padding=0)
+        )
+
+        self.convs = nn.ModuleList(
+            [
+                norm_f(
+                    nn.Conv1d(
+                        initial_channel, initial_channel * 2, kernel_size=5, padding=2
+                    )
+                ),
+                norm_f(
+                    nn.Conv1d(
+                        initial_channel * 2,
+                        initial_channel * 4,
+                        kernel_size=5,
+                        padding=2,
+                    )
+                ),
+                norm_f(
+                    nn.Conv1d(initial_channel * 4, initial_channel * 4, 5, 1, padding=2)
+                ),
+            ]
+        )
+
+        self.conv_post = norm_f(Conv1d(initial_channel * 4, 1, 3, 1, padding=1))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.pre(x)
+
+        fmap = []
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x
+
+
+class ReferenceEncoder(nn.Module):
+    """
+    inputs --- [N, Ty/r, n_mels*r]  mels
+    outputs --- [N, ref_enc_gru_size]
+    """
+
+    def __init__(self, spec_channels: int, gin_channels: int = 0) -> None:
+        super().__init__()
+        self.spec_channels = spec_channels
+        ref_enc_filters = [32, 32, 64, 64, 128, 128]
+        K = len(ref_enc_filters)
+        filters = [1] + ref_enc_filters
+        convs = [
+            weight_norm(
+                nn.Conv2d(
+                    in_channels=filters[i],
+                    out_channels=filters[i + 1],
+                    kernel_size=(3, 3),
+                    stride=(2, 2),
+                    padding=(1, 1),
+                )
+            )
+            for i in range(K)
+        ]
+        self.convs = nn.ModuleList(convs)
+        # self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)])
+
+        out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K)
+        self.gru = nn.GRU(
+            input_size=ref_enc_filters[-1] * out_channels,
+            hidden_size=256 // 2,
+            batch_first=True,
+        )
+        self.proj = nn.Linear(128, gin_channels)
+
+    def forward(
+        self, inputs: torch.Tensor, mask: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        N = inputs.size(0)
+        out = inputs.view(N, 1, -1, self.spec_channels)  # [N, 1, Ty, n_freqs]
+        for conv in self.convs:
+            out = conv(out)
+            # out = wn(out)
+            out = F.relu(out)  # [N, 128, Ty//2^K, n_mels//2^K]
+
+        out = out.transpose(1, 2)  # [N, Ty//2^K, 128, n_mels//2^K]
+        T = out.size(1)
+        N = out.size(0)
+        out = out.contiguous().view(N, T, -1)  # [N, Ty//2^K, 128*n_mels//2^K]
+
+        self.gru.flatten_parameters()
+        memory, out = self.gru(out)  # out --- [1, N, 128]
+
+        return self.proj(out.squeeze(0))
+
+    def calculate_channels(
+        self, L: int, kernel_size: int, stride: int, pad: int, n_convs: int
+    ) -> int:
+        for i in range(n_convs):
+            L = (L - kernel_size + 2 * pad) // stride + 1
+        return L
+
+
+class SynthesizerTrn(nn.Module):
+    """
+    Synthesizer for Training
+    """
+
+    def __init__(
+        self,
+        n_vocab: int,
+        spec_channels: int,
+        segment_size: int,
+        inter_channels: int,
+        hidden_channels: int,
+        filter_channels: int,
+        n_heads: int,
+        n_layers: int,
+        kernel_size: int,
+        p_dropout: float,
+        resblock: str,
+        resblock_kernel_sizes: list[int],
+        resblock_dilation_sizes: list[list[int]],
+        upsample_rates: list[int],
+        upsample_initial_channel: int,
+        upsample_kernel_sizes: list[int],
+        n_speakers: int = 256,
+        gin_channels: int = 256,
+        use_sdp: bool = True,
+        n_flow_layer: int = 4,
+        n_layers_trans_flow: int = 6,
+        flow_share_parameter: bool = False,
+        use_transformer_flow: bool = True,
+        **kwargs: Any,
+    ) -> None:
+        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.n_layers_trans_flow = n_layers_trans_flow
+        self.use_spk_conditioned_encoder = kwargs.get(
+            "use_spk_conditioned_encoder", True
+        )
+        self.use_sdp = use_sdp
+        self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False)
+        self.mas_noise_scale_initial = kwargs.get("mas_noise_scale_initial", 0.01)
+        self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6)
+        self.current_mas_noise_scale = self.mas_noise_scale_initial
+        if self.use_spk_conditioned_encoder and gin_channels > 0:
+            self.enc_gin_channels = gin_channels
+        self.enc_p = TextEncoder(
+            n_vocab,
+            inter_channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            gin_channels=self.enc_gin_channels,
+        )
+        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,
+        )
+        if use_transformer_flow:
+            self.flow = TransformerCouplingBlock(
+                inter_channels,
+                hidden_channels,
+                filter_channels,
+                n_heads,
+                n_layers_trans_flow,
+                5,
+                p_dropout,
+                n_flow_layer,
+                gin_channels=gin_channels,
+                share_parameter=flow_share_parameter,
+            )
+        else:
+            self.flow = ResidualCouplingBlock(
+                inter_channels,
+                hidden_channels,
+                5,
+                1,
+                n_flow_layer,
+                gin_channels=gin_channels,
+            )
+        self.sdp = StochasticDurationPredictor(
+            hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels
+        )
+        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)
+        else:
+            self.ref_enc = ReferenceEncoder(spec_channels, gin_channels)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_lengths: torch.Tensor,
+        y: torch.Tensor,
+        y_lengths: torch.Tensor,
+        sid: torch.Tensor,
+        tone: torch.Tensor,
+        language: torch.Tensor,
+        bert: torch.Tensor,
+        style_vec: torch.Tensor,
+    ) -> tuple[
+        torch.Tensor,
+        torch.Tensor,
+        torch.Tensor,
+        torch.Tensor,
+        torch.Tensor,
+        torch.Tensor,
+        torch.Tensor,
+        tuple[torch.Tensor, ...],
+        tuple[torch.Tensor, ...],
+    ]:
+        if self.n_speakers > 0:
+            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
+        else:
+            g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1)
+        x, m_p, logs_p, x_mask = self.enc_p(
+            x, x_lengths, tone, language, bert, style_vec, g=g
+        )
+        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
+            if self.use_noise_scaled_mas:
+                epsilon = (
+                    torch.std(neg_cent)
+                    * torch.randn_like(neg_cent)
+                    * self.current_mas_noise_scale
+                )
+                neg_cent = neg_cent + epsilon
+
+            attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+            attn = (
+                monotonic_alignment.maximum_path(neg_cent, attn_mask.squeeze(1))
+                .unsqueeze(1)
+                .detach()
+            )
+
+        w = attn.sum(2)
+
+        l_length_sdp = self.sdp(x, x_mask, w, g=g)
+        l_length_sdp = l_length_sdp / torch.sum(x_mask)
+
+        logw_ = torch.log(w + 1e-6) * x_mask
+        logw = self.dp(x, x_mask, g=g)
+        # logw_sdp = self.sdp(x, x_mask, g=g, reverse=True, noise_scale=1.0)
+        l_length_dp = torch.sum((logw - logw_) ** 2, [1, 2]) / torch.sum(
+            x_mask
+        )  # for averaging
+        # l_length_sdp += torch.sum((logw_sdp - logw_) ** 2, [1, 2]) / torch.sum(x_mask)
+
+        l_length = l_length_dp + l_length_sdp
+
+        # 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 = commons.rand_slice_segments(
+            z, y_lengths, self.segment_size
+        )
+        o = self.dec(z_slice, g=g)
+        return (
+            o,
+            l_length,
+            attn,
+            ids_slice,
+            x_mask,
+            y_mask,
+            (z, z_p, m_p, logs_p, m_q, logs_q),  # type: ignore
+            (x, logw, logw_),  # , logw_sdp),
+            g,
+        )
+
+    def infer(
+        self,
+        x: torch.Tensor,
+        x_lengths: torch.Tensor,
+        sid: torch.Tensor,
+        tone: torch.Tensor,
+        language: torch.Tensor,
+        bert: torch.Tensor,
+        style_vec: torch.Tensor,
+        noise_scale: float = 0.667,
+        length_scale: float = 1.0,
+        noise_scale_w: float = 0.8,
+        max_len: Optional[int] = None,
+        sdp_ratio: float = 0.0,
+        y: Optional[torch.Tensor] = None,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, tuple[torch.Tensor, ...]]:
+        # x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert)
+        # g = self.gst(y)
+        if self.n_speakers > 0:
+            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
+        else:
+            assert y is not None
+            g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1)
+        x, m_p, logs_p, x_mask = self.enc_p(
+            x, x_lengths, tone, language, bert, style_vec, g=g
+        )
+        logw = self.sdp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) * (
+            sdp_ratio
+        ) + self.dp(x, x_mask, g=g) * (1 - sdp_ratio)
+        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(commons.sequence_mask(y_lengths, None), 1).to(
+            x_mask.dtype
+        )
+        attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+        attn = commons.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)
+        return o, attn, y_mask, (z, z_p, m_p, logs_p)
diff --git a/style_bert_vits2/models/modules.py b/style_bert_vits2/models/modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebc52730ca5e689164c9636a09c7291c20deb35d
--- /dev/null
+++ b/style_bert_vits2/models/modules.py
@@ -0,0 +1,642 @@
+import math
+from typing import Any, Optional, Union
+
+import torch
+from torch import nn
+from torch.nn import Conv1d
+from torch.nn import functional as F
+from torch.nn.utils import remove_weight_norm, weight_norm
+
+from style_bert_vits2.models import commons
+from style_bert_vits2.models.attentions import Encoder
+from style_bert_vits2.models.transforms import piecewise_rational_quadratic_transform
+
+
+LRELU_SLOPE = 0.1
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, channels: int, eps: float = 1e-5) -> None:
+        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: torch.Tensor) -> torch.Tensor:
+        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: int,
+        hidden_channels: int,
+        out_channels: int,
+        kernel_size: int,
+        n_layers: int,
+        p_dropout: float,
+    ) -> None:
+        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_()
+        assert self.proj.bias is not None
+        self.proj.bias.data.zero_()
+
+    def forward(self, x: torch.Tensor, x_mask: torch.Tensor) -> torch.Tensor:
+        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
+
+
+class DDSConv(nn.Module):
+    """
+    Dialted and Depth-Separable Convolution
+    """
+
+    def __init__(
+        self, channels: int, kernel_size: int, n_layers: int, p_dropout: float = 0.0
+    ) -> None:
+        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: torch.Tensor, x_mask: torch.Tensor, g: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        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: int,
+        kernel_size: int,
+        dilation_rate: int,
+        n_layers: int,
+        gin_channels: int = 0,
+        p_dropout: float = 0,
+    ) -> None:
+        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: torch.Tensor,
+        x_mask: torch.Tensor,
+        g: Optional[torch.Tensor] = None,
+        **kwargs: Any,
+    ) -> torch.Tensor:
+        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 = commons.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) -> None:
+        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: int,
+        kernel_size: int = 3,
+        dilation: tuple[int, int, int] = (1, 3, 5),
+    ) -> None:
+        super(ResBlock1, self).__init__()
+        self.convs1 = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[0],
+                        padding=commons.get_padding(kernel_size, dilation[0]),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[1],
+                        padding=commons.get_padding(kernel_size, dilation[1]),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[2],
+                        padding=commons.get_padding(kernel_size, dilation[2]),
+                    )
+                ),
+            ]
+        )
+        self.convs1.apply(commons.init_weights)
+
+        self.convs2 = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        padding=commons.get_padding(kernel_size, 1),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        padding=commons.get_padding(kernel_size, 1),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        padding=commons.get_padding(kernel_size, 1),
+                    )
+                ),
+            ]
+        )
+        self.convs2.apply(commons.init_weights)
+
+    def forward(
+        self, x: torch.Tensor, x_mask: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        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) -> None:
+        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: int, kernel_size: int = 3, dilation: tuple[int, int] = (1, 3)
+    ) -> None:
+        super(ResBlock2, self).__init__()
+        self.convs = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[0],
+                        padding=commons.get_padding(kernel_size, dilation[0]),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[1],
+                        padding=commons.get_padding(kernel_size, dilation[1]),
+                    )
+                ),
+            ]
+        )
+        self.convs.apply(commons.init_weights)
+
+    def forward(
+        self, x: torch.Tensor, x_mask: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        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) -> None:
+        for l in self.convs:
+            remove_weight_norm(l)
+
+
+class Log(nn.Module):
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_mask: torch.Tensor,
+        reverse: bool = False,
+        **kwargs: Any,
+    ) -> Union[tuple[torch.Tensor, torch.Tensor], torch.Tensor]:
+        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: torch.Tensor,
+        *args: Any,
+        reverse: bool = False,
+        **kwargs: Any,
+    ) -> Union[tuple[torch.Tensor, torch.Tensor], torch.Tensor]:
+        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: int) -> None:
+        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: torch.Tensor,
+        x_mask: torch.Tensor,
+        reverse: bool = False,
+        **kwargs: Any,
+    ) -> Union[tuple[torch.Tensor, torch.Tensor], torch.Tensor]:
+        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: int,
+        hidden_channels: int,
+        kernel_size: int,
+        dilation_rate: int,
+        n_layers: int,
+        p_dropout: float = 0,
+        gin_channels: int = 0,
+        mean_only: bool = False,
+    ) -> None:
+        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_()
+        assert self.post.bias is not None
+        self.post.bias.data.zero_()
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_mask: torch.Tensor,
+        g: Optional[torch.Tensor] = None,
+        reverse: bool = False,
+    ) -> Union[tuple[torch.Tensor, torch.Tensor], torch.Tensor]:
+        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: int,
+        filter_channels: int,
+        kernel_size: int,
+        n_layers: int,
+        num_bins: int = 10,
+        tail_bound: float = 5.0,
+    ) -> None:
+        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_()
+        assert self.proj.bias is not None
+        self.proj.bias.data.zero_()
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_mask: torch.Tensor,
+        g: Optional[torch.Tensor] = None,
+        reverse: bool = False,
+    ) -> Union[tuple[torch.Tensor, torch.Tensor], torch.Tensor]:
+        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
+
+
+class TransformerCouplingLayer(nn.Module):
+    def __init__(
+        self,
+        channels: int,
+        hidden_channels: int,
+        kernel_size: int,
+        n_layers: int,
+        n_heads: int,
+        p_dropout: float = 0,
+        filter_channels: int = 0,
+        mean_only: bool = False,
+        wn_sharing_parameter: Optional[nn.Module] = None,
+        gin_channels: int = 0,
+    ) -> None:
+        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.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 = (
+            Encoder(
+                hidden_channels,
+                filter_channels,
+                n_heads,
+                n_layers,
+                kernel_size,
+                p_dropout,
+                isflow=True,
+                gin_channels=gin_channels,
+            )
+            if wn_sharing_parameter is None
+            else wn_sharing_parameter
+        )
+        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
+        self.post.weight.data.zero_()
+        assert self.post.bias is not None
+        self.post.bias.data.zero_()
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_mask: torch.Tensor,
+        g: Optional[torch.Tensor] = None,
+        reverse: bool = False,
+    ) -> Union[tuple[torch.Tensor, torch.Tensor], torch.Tensor]:
+        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
diff --git a/style_bert_vits2/models/monotonic_alignment.py b/style_bert_vits2/models/monotonic_alignment.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ec9d247f5a06cfcdef808267e455e883aa4b0e9
--- /dev/null
+++ b/style_bert_vits2/models/monotonic_alignment.py
@@ -0,0 +1,89 @@
+"""
+以下に記述されている関数のコメントはリファクタリング時に GPT-4 に生成させたもので、
+コードと完全に一致している保証はない。あくまで参考程度とすること。
+"""
+
+from typing import Any
+
+import numba
+import torch
+from numpy import float32, int32, zeros
+
+
+def maximum_path(neg_cent: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
+    """
+    与えられた負の中心とマスクを使用して最大パスを計算する
+
+    Args:
+        neg_cent (torch.Tensor): 負の中心を表すテンソル
+        mask (torch.Tensor): マスクを表すテンソル
+
+    Returns:
+        Tensor: 計算された最大パスを表すテンソル
+    """
+
+    device = neg_cent.device
+    dtype = neg_cent.dtype
+    neg_cent = neg_cent.data.cpu().numpy().astype(float32)
+    path = zeros(neg_cent.shape, dtype=int32)
+
+    t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(int32)
+    t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(int32)
+    __maximum_path_jit(path, neg_cent, t_t_max, t_s_max)
+
+    return torch.from_numpy(path).to(device=device, dtype=dtype)
+
+
+@numba.jit(
+    numba.void(
+        numba.int32[:, :, ::1],
+        numba.float32[:, :, ::1],
+        numba.int32[::1],
+        numba.int32[::1],
+    ),
+    nopython=True,
+    nogil=True,
+)  # type: ignore
+def __maximum_path_jit(paths: Any, values: Any, t_ys: Any, t_xs: Any) -> None:
+    """
+    与えられたパス、値、およびターゲットの y と x 座標を使用して JIT で最大パスを計算する
+
+    Args:
+        paths: 計算されたパスを格納するための整数型の 3 次元配列
+        values: 値を格納するための浮動小数点型の 3 次元配列
+        t_ys: ターゲットの y 座標を格納するための整数型の 1 次元配列
+        t_xs: ターゲットの x 座標を格納するための整数型の 1 次元配列
+    """
+
+    b = paths.shape[0]
+    max_neg_val = -1e9
+    for i in range(int(b)):
+        path = paths[i]
+        value = values[i]
+        t_y = t_ys[i]
+        t_x = t_xs[i]
+
+        v_prev = v_cur = 0.0
+        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 = 0.0
+                    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
diff --git a/style_bert_vits2/models/transforms.py b/style_bert_vits2/models/transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6f4420f9af44597f40505236e259ed98efea5d2
--- /dev/null
+++ b/style_bert_vits2/models/transforms.py
@@ -0,0 +1,215 @@
+from typing import Optional
+
+import numpy as np
+import torch
+from torch.nn import functional as F
+
+
+DEFAULT_MIN_BIN_WIDTH = 1e-3
+DEFAULT_MIN_BIN_HEIGHT = 1e-3
+DEFAULT_MIN_DERIVATIVE = 1e-3
+
+
+def piecewise_rational_quadratic_transform(
+    inputs: torch.Tensor,
+    unnormalized_widths: torch.Tensor,
+    unnormalized_heights: torch.Tensor,
+    unnormalized_derivatives: torch.Tensor,
+    inverse: bool = False,
+    tails: Optional[str] = None,
+    tail_bound: float = 1.0,
+    min_bin_width: float = DEFAULT_MIN_BIN_WIDTH,
+    min_bin_height: float = DEFAULT_MIN_BIN_HEIGHT,
+    min_derivative: float = DEFAULT_MIN_DERIVATIVE,
+) -> tuple[torch.Tensor, torch.Tensor]:
+
+    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,  # type: ignore
+    )
+    return outputs, logabsdet
+
+
+def searchsorted(
+    bin_locations: torch.Tensor, inputs: torch.Tensor, eps: float = 1e-6
+) -> torch.Tensor:
+    bin_locations[..., -1] += eps
+    return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1
+
+
+def unconstrained_rational_quadratic_spline(
+    inputs: torch.Tensor,
+    unnormalized_widths: torch.Tensor,
+    unnormalized_heights: torch.Tensor,
+    unnormalized_derivatives: torch.Tensor,
+    inverse: bool = False,
+    tails: str = "linear",
+    tail_bound: float = 1.0,
+    min_bin_width: float = DEFAULT_MIN_BIN_WIDTH,
+    min_bin_height: float = DEFAULT_MIN_BIN_HEIGHT,
+    min_derivative: float = DEFAULT_MIN_DERIVATIVE,
+) -> tuple[torch.Tensor, torch.Tensor]:
+
+    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(f"{tails} tails are not implemented.")
+
+    (
+        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: torch.Tensor,
+    unnormalized_widths: torch.Tensor,
+    unnormalized_heights: torch.Tensor,
+    unnormalized_derivatives: torch.Tensor,
+    inverse: bool = False,
+    left: float = 0.0,
+    right: float = 1.0,
+    bottom: float = 0.0,
+    top: float = 1.0,
+    min_bin_width: float = DEFAULT_MIN_BIN_WIDTH,
+    min_bin_height: float = DEFAULT_MIN_BIN_HEIGHT,
+    min_derivative: float = DEFAULT_MIN_DERIVATIVE,
+) -> tuple[torch.Tensor, torch.Tensor]:
+
+    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/style_bert_vits2/models/utils/__init__.py b/style_bert_vits2/models/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b91c74aeead2f410a29f94c86f9b1861f4b32d2f
--- /dev/null
+++ b/style_bert_vits2/models/utils/__init__.py
@@ -0,0 +1,264 @@
+import glob
+import logging
+import os
+import re
+import subprocess
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Optional, Union
+
+import numpy as np
+import torch
+from numpy.typing import NDArray
+
+from style_bert_vits2.logging import logger
+from style_bert_vits2.models.utils import checkpoints  # type: ignore
+from style_bert_vits2.models.utils import safetensors  # type: ignore
+
+
+if TYPE_CHECKING:
+    # tensorboard はライブラリとしてインストールされている場合は依存関係に含まれないため、型チェック時のみインポートする
+    from torch.utils.tensorboard import SummaryWriter
+
+
+__is_matplotlib_imported = False
+
+
+def summarize(
+    writer: "SummaryWriter",
+    global_step: int,
+    scalars: dict[str, float] = {},
+    histograms: dict[str, Any] = {},
+    images: dict[str, Any] = {},
+    audios: dict[str, Any] = {},
+    audio_sampling_rate: int = 22050,
+) -> None:
+    """
+    指定されたデータを TensorBoard にまとめて追加する
+
+    Args:
+        writer (SummaryWriter): TensorBoard への書き込みを行うオブジェクト
+        global_step (int): グローバルステップ数
+        scalars (dict[str, float]): スカラー値の辞書
+        histograms (dict[str, Any]): ヒストグラムの辞書
+        images (dict[str, Any]): 画像データの辞書
+        audios (dict[str, Any]): 音声データの辞書
+        audio_sampling_rate (int): 音声データのサンプリングレート
+    """
+    for k, v in scalars.items():
+        writer.add_scalar(k, v, global_step)
+    for k, v in histograms.items():
+        writer.add_histogram(k, v, global_step)
+    for k, v in images.items():
+        writer.add_image(k, v, global_step, dataformats="HWC")
+    for k, v in audios.items():
+        writer.add_audio(k, v, global_step, audio_sampling_rate)
+
+
+def is_resuming(dir_path: Union[str, Path]) -> bool:
+    """
+    指定されたディレクトリパスに再開可能なモデルが存在するかどうかを返す
+
+    Args:
+        dir_path: チェックするディレクトリのパス
+
+    Returns:
+        bool: 再開可能なモデルが存在するかどうか
+    """
+    # JP-ExtraバージョンではDURがなくWDがあったり変わるため、Gのみで判断する
+    g_list = glob.glob(os.path.join(dir_path, "G_*.pth"))
+    # d_list = glob.glob(os.path.join(dir_path, "D_*.pth"))
+    # dur_list = glob.glob(os.path.join(dir_path, "DUR_*.pth"))
+    return len(g_list) > 0
+
+
+def plot_spectrogram_to_numpy(spectrogram: NDArray[Any]) -> NDArray[Any]:
+    """
+    指定されたスペクトログラムを画像データに変換する
+
+    Args:
+        spectrogram (NDArray[Any]): スペクトログラム
+
+    Returns:
+        NDArray[Any]: 画像データ
+    """
+
+    global __is_matplotlib_imported
+    if not __is_matplotlib_imported:
+        import matplotlib
+
+        matplotlib.use("Agg")
+        __is_matplotlib_imported = True
+        mpl_logger = logging.getLogger("matplotlib")
+        mpl_logger.setLevel(logging.WARNING)
+    import matplotlib.pylab as plt
+    import numpy as np
+
+    fig, ax = plt.subplots(figsize=(10, 2))
+    im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none")
+    plt.colorbar(im, ax=ax)
+    plt.xlabel("Frames")
+    plt.ylabel("Channels")
+    plt.tight_layout()
+
+    fig.canvas.draw()
+    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")  # type: ignore
+    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+    plt.close()
+    return data
+
+
+def plot_alignment_to_numpy(
+    alignment: NDArray[Any], info: Optional[str] = None
+) -> NDArray[Any]:
+    """
+    指定されたアライメントを画像データに変換する
+
+    Args:
+        alignment (NDArray[Any]): アライメント
+        info (Optional[str]): 画像に追加する情報
+
+    Returns:
+        NDArray[Any]: 画像データ
+    """
+
+    global __is_matplotlib_imported
+    if not __is_matplotlib_imported:
+        import matplotlib
+
+        matplotlib.use("Agg")
+        __is_matplotlib_imported = True
+        mpl_logger = logging.getLogger("matplotlib")
+        mpl_logger.setLevel(logging.WARNING)
+    import matplotlib.pylab as plt
+
+    fig, ax = plt.subplots(figsize=(6, 4))
+    im = ax.imshow(
+        alignment.transpose(), aspect="auto", origin="lower", interpolation="none"
+    )
+    fig.colorbar(im, ax=ax)
+    xlabel = "Decoder timestep"
+    if info is not None:
+        xlabel += "\n\n" + info
+    plt.xlabel(xlabel)
+    plt.ylabel("Encoder timestep")
+    plt.tight_layout()
+
+    fig.canvas.draw()
+    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")  # type: ignore
+    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+    plt.close()
+    return data
+
+
+def load_wav_to_torch(full_path: Union[str, Path]) -> tuple[torch.FloatTensor, int]:
+    """
+    指定された音声ファイルを読み込み、PyTorch のテンソルに変換して返す
+
+    Args:
+        full_path (Union[str, Path]): 音声ファイルのパス
+
+    Returns:
+        tuple[torch.FloatTensor, int]: 音声データのテンソルとサンプリングレート
+    """
+
+    # この関数は学習時以外使われないため、ライブラリとしての style_bert_vits2 が
+    # 重たい scipy に依存しないように遅延 import する
+    try:
+        from scipy.io.wavfile import read
+    except ImportError:
+        raise ImportError("scipy is required to load wav file")
+
+    sampling_rate, data = read(full_path)
+    return torch.FloatTensor(data.astype(np.float32)), sampling_rate
+
+
+def load_filepaths_and_text(
+    filename: Union[str, Path], split: str = "|"
+) -> list[list[str]]:
+    """
+    指定されたファイルからファイルパスとテキストを読み込む
+
+    Args:
+        filename (Union[str, Path]): ファイルのパス
+        split (str): ファイルの区切り文字 (デフォルト: "|")
+
+    Returns:
+        list[list[str]]: ファイルパスとテキストのリスト
+    """
+
+    with open(filename, encoding="utf-8") as f:
+        filepaths_and_text = [line.strip().split(split) for line in f]
+    return filepaths_and_text
+
+
+def get_logger(
+    model_dir_path: Union[str, Path], filename: str = "train.log"
+) -> logging.Logger:
+    """
+    ロガーを取得する
+
+    Args:
+        model_dir_path (Union[str, Path]): ログを保存するディレクトリのパス
+        filename (str): ログファイルの名前 (デフォルト: "train.log")
+
+    Returns:
+        logging.Logger: ロガー
+    """
+
+    global logger
+    logger = logging.getLogger(os.path.basename(model_dir_path))
+    logger.setLevel(logging.DEBUG)
+
+    formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
+    if not os.path.exists(model_dir_path):
+        os.makedirs(model_dir_path)
+    h = logging.FileHandler(os.path.join(model_dir_path, filename))
+    h.setLevel(logging.DEBUG)
+    h.setFormatter(formatter)
+    logger.addHandler(h)
+    return logger
+
+
+def get_steps(model_path: Union[str, Path]) -> Optional[int]:
+    """
+    モデルのパスからイテレーション回数を取得する
+
+    Args:
+        model_path (Union[str, Path]): モデルのパス
+
+    Returns:
+        Optional[int]: イテレーション回数
+    """
+
+    matches = re.findall(r"\d+", model_path)  # type: ignore
+    return matches[-1] if matches else None
+
+
+def check_git_hash(model_dir_path: Union[str, Path]) -> None:
+    """
+    モデルのディレクトリに .git ディレクトリが存在する場合、ハッシュ値を比較する
+
+    Args:
+        model_dir_path (Union[str, Path]): モデルのディレクトリのパス
+    """
+
+    source_dir = os.path.dirname(os.path.realpath(__file__))
+    if not os.path.exists(os.path.join(source_dir, ".git")):
+        logger.warning(
+            f"{source_dir} is not a git repository, therefore hash value comparison will be ignored."
+        )
+        return
+
+    cur_hash = subprocess.getoutput("git rev-parse HEAD")
+
+    path = os.path.join(model_dir_path, "githash")
+    if os.path.exists(path):
+        with open(path, encoding="utf-8") as f:
+            saved_hash = f.read()
+        if saved_hash != cur_hash:
+            logger.warning(
+                f"git hash values are different. {saved_hash[:8]}(saved) != {cur_hash[:8]}(current)"
+            )
+    else:
+        with open(path, "w", encoding="utf-8") as f:
+            f.write(cur_hash)
diff --git a/style_bert_vits2/models/utils/checkpoints.py b/style_bert_vits2/models/utils/checkpoints.py
new file mode 100644
index 0000000000000000000000000000000000000000..c601dda8a1df774b77a728c9a786ec17458201dc
--- /dev/null
+++ b/style_bert_vits2/models/utils/checkpoints.py
@@ -0,0 +1,202 @@
+import glob
+import os
+import re
+from pathlib import Path
+from typing import Any, Optional, Union
+
+import torch
+
+from style_bert_vits2.logging import logger
+
+
+def load_checkpoint(
+    checkpoint_path: Union[str, Path],
+    model: torch.nn.Module,
+    optimizer: Optional[torch.optim.Optimizer] = None,
+    skip_optimizer: bool = False,
+    for_infer: bool = False,
+) -> tuple[torch.nn.Module, Optional[torch.optim.Optimizer], float, int]:
+    """
+    指定されたパスからチェックポイントを読み込み、モデルとオプティマイザーを更新する。
+
+    Args:
+        checkpoint_path (Union[str, Path]): チェックポイントファイルのパス
+        model (torch.nn.Module): 更新するモデル
+        optimizer (Optional[torch.optim.Optimizer]): 更新するオプティマイザー。None の場合は更新しない
+        skip_optimizer (bool): オプティマイザーの更新をスキップするかどうかのフラグ
+        for_infer (bool): 推論用に読み込むかどうかのフラグ
+
+    Returns:
+        tuple[torch.nn.Module, Optional[torch.optim.Optimizer], float, int]: 更新されたモデルとオプティマイザー、学習率、イテレーション回数
+    """
+
+    assert os.path.isfile(checkpoint_path)
+    checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
+    iteration = checkpoint_dict["iteration"]
+    learning_rate = checkpoint_dict["learning_rate"]
+    logger.info(
+        f"Loading model and optimizer at iteration {iteration} from {checkpoint_path}"
+    )
+    if (
+        optimizer is not None
+        and not skip_optimizer
+        and checkpoint_dict["optimizer"] is not None
+    ):
+        optimizer.load_state_dict(checkpoint_dict["optimizer"])
+    elif optimizer is None and not skip_optimizer:
+        # else:      Disable this line if Infer and resume checkpoint,then enable the line upper
+        new_opt_dict = optimizer.state_dict()  # type: ignore
+        new_opt_dict_params = new_opt_dict["param_groups"][0]["params"]
+        new_opt_dict["param_groups"] = checkpoint_dict["optimizer"]["param_groups"]
+        new_opt_dict["param_groups"][0]["params"] = new_opt_dict_params
+        optimizer.load_state_dict(new_opt_dict)  # type: ignore
+
+    saved_state_dict = checkpoint_dict["model"]
+    if hasattr(model, "module"):
+        state_dict = model.module.state_dict()
+    else:
+        state_dict = model.state_dict()
+
+    new_state_dict = {}
+    for k, v in state_dict.items():
+        try:
+            # assert "emb_g" not in k
+            new_state_dict[k] = saved_state_dict[k]
+            assert saved_state_dict[k].shape == v.shape, (
+                saved_state_dict[k].shape,
+                v.shape,
+            )
+        except:
+            # For upgrading from the old version
+            if "ja_bert_proj" in k:
+                v = torch.zeros_like(v)
+                logger.warning(
+                    f"Seems you are using the old version of the model, the {k} is automatically set to zero for backward compatibility"
+                )
+            elif "enc_q" in k and for_infer:
+                continue
+            else:
+                logger.error(f"{k} is not in the checkpoint {checkpoint_path}")
+
+            new_state_dict[k] = v
+
+    if hasattr(model, "module"):
+        model.module.load_state_dict(new_state_dict, strict=False)
+    else:
+        model.load_state_dict(new_state_dict, strict=False)
+
+    logger.info(f"Loaded '{checkpoint_path}' (iteration {iteration})")
+
+    return model, optimizer, learning_rate, iteration
+
+
+def save_checkpoint(
+    model: torch.nn.Module,
+    optimizer: Union[torch.optim.Optimizer, torch.optim.AdamW],
+    learning_rate: float,
+    iteration: int,
+    checkpoint_path: Union[str, Path],
+) -> None:
+    """
+    モデルとオプティマイザーの状態を指定されたパスに保存する。
+
+    Args:
+        model (torch.nn.Module): 保存するモデル
+        optimizer (Union[torch.optim.Optimizer, torch.optim.AdamW]): 保存するオプティマイザー
+        learning_rate (float): 学習率
+        iteration (int): イテレーション回数
+        checkpoint_path (Union[str, Path]): 保存先のパス
+    """
+    logger.info(
+        f"Saving model and optimizer state at iteration {iteration} to {checkpoint_path}"
+    )
+    if hasattr(model, "module"):
+        state_dict = model.module.state_dict()
+    else:
+        state_dict = model.state_dict()
+    torch.save(
+        {
+            "model": state_dict,
+            "iteration": iteration,
+            "optimizer": optimizer.state_dict(),
+            "learning_rate": learning_rate,
+        },
+        checkpoint_path,
+    )
+
+
+def clean_checkpoints(
+    model_dir_path: Union[str, Path] = "logs/44k/",
+    n_ckpts_to_keep: int = 2,
+    sort_by_time: bool = True,
+) -> None:
+    """
+    指定されたディレクトリから古いチェックポイントを削除して空き容量を確保する
+
+    Args:
+        model_dir_path (Union[str, Path]): モデルが保存されているディレクトリのパス
+        n_ckpts_to_keep (int): 保持するチェックポイントの数（G_0.pth と D_0.pth を除く）
+        sort_by_time (bool): True の場合、時間順に削除。False の場合、名前順に削除
+    """
+
+    ckpts_files = [
+        f
+        for f in os.listdir(model_dir_path)
+        if os.path.isfile(os.path.join(model_dir_path, f))
+    ]
+
+    def name_key(_f: str) -> int:
+        return int(re.compile("._(\\d+)\\.pth").match(_f).group(1))  # type: ignore
+
+    def time_key(_f: str) -> float:
+        return os.path.getmtime(os.path.join(model_dir_path, _f))
+
+    sort_key = time_key if sort_by_time else name_key
+
+    def x_sorted(_x: str) -> list[str]:
+        return sorted(
+            [f for f in ckpts_files if f.startswith(_x) and not f.endswith("_0.pth")],
+            key=sort_key,
+        )
+
+    to_del = [
+        os.path.join(model_dir_path, fn)
+        for fn in (
+            x_sorted("G_")[:-n_ckpts_to_keep]
+            + x_sorted("D_")[:-n_ckpts_to_keep]
+            + x_sorted("WD_")[:-n_ckpts_to_keep]
+            + x_sorted("DUR_")[:-n_ckpts_to_keep]
+        )
+    ]
+
+    def del_info(fn: str) -> None:
+        return logger.info(f"Free up space by deleting ckpt {fn}")
+
+    def del_routine(x: str) -> list[Any]:
+        return [os.remove(x), del_info(x)]
+
+    [del_routine(fn) for fn in to_del]
+
+
+def get_latest_checkpoint_path(
+    model_dir_path: Union[str, Path], regex: str = "G_*.pth"
+) -> str:
+    """
+    指定されたディレクトリから最新のチェックポイントのパスを取得する
+
+    Args:
+        model_dir_path (Union[str, Path]): モデルが保存されているディレクトリのパス
+        regex (str): チェックポイントのファイル名の正規表現
+
+    Returns:
+        str: 最新のチェックポイントのパス
+    """
+
+    f_list = glob.glob(os.path.join(str(model_dir_path), regex))
+    f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
+    try:
+        x = f_list[-1]
+    except IndexError:
+        raise ValueError(f"No checkpoint found in {model_dir_path} with regex {regex}")
+
+    return x
diff --git a/style_bert_vits2/models/utils/safetensors.py b/style_bert_vits2/models/utils/safetensors.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b4ef3fbd7c7528df03252fa2840b573cfcbad06
--- /dev/null
+++ b/style_bert_vits2/models/utils/safetensors.py
@@ -0,0 +1,91 @@
+from pathlib import Path
+from typing import Any, Optional, Union
+
+import torch
+from safetensors import safe_open
+from safetensors.torch import save_file
+
+from style_bert_vits2.logging import logger
+
+
+def load_safetensors(
+    checkpoint_path: Union[str, Path],
+    model: torch.nn.Module,
+    for_infer: bool = False,
+) -> tuple[torch.nn.Module, Optional[int]]:
+    """
+    指定されたパスから safetensors モデルを読み込み、モデルとイテレーションを返す。
+
+    Args:
+        checkpoint_path (Union[str, Path]): モデルのチェックポイントファイルのパス
+        model (torch.nn.Module): 読み込む対象のモデル
+        for_infer (bool): 推論用に読み込むかどうかのフラグ
+
+    Returns:
+        tuple[torch.nn.Module, Optional[int]]: 読み込まれたモデルとイテレーション回数（存在する場合）
+    """
+
+    tensors: dict[str, Any] = {}
+    iteration: Optional[int] = None
+    with safe_open(str(checkpoint_path), framework="pt", device="cpu") as f:  # type: ignore
+        for key in f.keys():
+            if key == "iteration":
+                iteration = f.get_tensor(key).item()
+            tensors[key] = f.get_tensor(key)
+    if hasattr(model, "module"):
+        result = model.module.load_state_dict(tensors, strict=False)
+    else:
+        result = model.load_state_dict(tensors, strict=False)
+    for key in result.missing_keys:
+        if key.startswith("enc_q") and for_infer:
+            continue
+        logger.warning(f"Missing key: {key}")
+    for key in result.unexpected_keys:
+        if key == "iteration":
+            continue
+        logger.warning(f"Unexpected key: {key}")
+    if iteration is None:
+        logger.info(f"Loaded '{checkpoint_path}'")
+    else:
+        logger.info(f"Loaded '{checkpoint_path}' (iteration {iteration})")
+
+    return model, iteration
+
+
+def save_safetensors(
+    model: torch.nn.Module,
+    iteration: int,
+    checkpoint_path: Union[str, Path],
+    is_half: bool = False,
+    for_infer: bool = False,
+) -> None:
+    """
+    モデルを safetensors 形式で保存する。
+
+    Args:
+        model (torch.nn.Module): 保存するモデル
+        iteration (int): イテレーション回数
+        checkpoint_path (Union[str, Path]): 保存先のパス
+        is_half (bool): モデルを半精度で保存するかどうかのフラグ
+        for_infer (bool): 推論用に保存するかどうかのフラグ
+    """
+
+    if hasattr(model, "module"):
+        state_dict = model.module.state_dict()
+    else:
+        state_dict = model.state_dict()
+    keys = []
+    for k in state_dict:
+        if "enc_q" in k and for_infer:
+            continue
+        keys.append(k)
+
+    new_dict = (
+        {k: state_dict[k].half() for k in keys}
+        if is_half
+        else {k: state_dict[k] for k in keys}
+    )
+    new_dict["iteration"] = torch.LongTensor([iteration])
+    logger.info(f"Saved safetensors to {checkpoint_path}")
+
+    save_file(new_dict, checkpoint_path)
diff --git a/style_bert_vits2/nlp/__init__.py b/style_bert_vits2/nlp/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0c908e42dd340de508c2c7b6a5b7458bd50ad52
--- /dev/null
+++ b/style_bert_vits2/nlp/__init__.py
@@ -0,0 +1,120 @@
+from typing import TYPE_CHECKING, Optional
+
+from style_bert_vits2.constants import Languages
+from style_bert_vits2.nlp.symbols import (
+    LANGUAGE_ID_MAP,
+    LANGUAGE_TONE_START_MAP,
+    SYMBOLS,
+)
+
+
+# __init__.py は配下のモジュールをインポートした時点で実行される
+# PyTorch のインポートは重いので、型チェック時以外はインポートしない
+if TYPE_CHECKING:
+    import torch
+
+
+__symbol_to_id = {s: i for i, s in enumerate(SYMBOLS)}
+
+
+def extract_bert_feature(
+    text: str,
+    word2ph: list[int],
+    language: Languages,
+    device: str,
+    assist_text: Optional[str] = None,
+    assist_text_weight: float = 0.7,
+) -> "torch.Tensor":
+    """
+    テキストから BERT の特徴量を抽出する
+
+    Args:
+        text (str): テキスト
+        word2ph (list[int]): 元のテキストの各文字に音素が何個割り当てられるかを表すリスト
+        language (Languages): テキストの言語
+        device (str): 推論に利用するデバイス
+        assist_text (Optional[str], optional): 補助テキスト (デフォルト: None)
+        assist_text_weight (float, optional): 補助テキストの重み (デフォルト: 0.7)
+
+    Returns:
+        torch.Tensor: BERT の特徴量
+    """
+
+    if language == Languages.JP:
+        from style_bert_vits2.nlp.japanese.bert_feature import extract_bert_feature
+    elif language == Languages.EN:
+        from style_bert_vits2.nlp.english.bert_feature import extract_bert_feature
+    elif language == Languages.ZH:
+        from style_bert_vits2.nlp.chinese.bert_feature import extract_bert_feature
+    else:
+        raise ValueError(f"Language {language} not supported")
+
+    return extract_bert_feature(text, word2ph, device, assist_text, assist_text_weight)
+
+
+def clean_text(
+    text: str,
+    language: Languages,
+    use_jp_extra: bool = True,
+    raise_yomi_error: bool = False,
+) -> tuple[str, list[str], list[int], list[int]]:
+    """
+    テキストをクリーニングし、音素に変換する
+
+    Args:
+        text (str): クリーニングするテキスト
+        language (Languages): テキストの言語
+        use_jp_extra (bool, optional): テキストが日本語の場合に JP-Extra モデルを利用するかどうか。Defaults to True.
+        raise_yomi_error (bool, optional): False の場合、読めない文字が消えたような扱いとして処理される。Defaults to False.
+
+    Returns:
+        tuple[str, list[str], list[int], list[int]]: クリーニングされたテキストと、音素・アクセント・元のテキストの各文字に音素が何個割り当てられるかのリスト
+    """
+
+    # Changed to import inside if condition to avoid unnecessary import
+    if language == Languages.JP:
+        from style_bert_vits2.nlp.japanese.g2p import g2p
+        from style_bert_vits2.nlp.japanese.normalizer import normalize_text
+
+        norm_text = normalize_text(text)
+        phones, tones, word2ph = g2p(norm_text, use_jp_extra, raise_yomi_error)
+    elif language == Languages.EN:
+        from style_bert_vits2.nlp.english.g2p import g2p
+        from style_bert_vits2.nlp.english.normalizer import normalize_text
+
+        norm_text = normalize_text(text)
+        phones, tones, word2ph = g2p(norm_text)
+    elif language == Languages.ZH:
+        from style_bert_vits2.nlp.chinese.g2p import g2p
+        from style_bert_vits2.nlp.chinese.normalizer import normalize_text
+
+        norm_text = normalize_text(text)
+        phones, tones, word2ph = g2p(norm_text)
+    else:
+        raise ValueError(f"Language {language} not supported")
+
+    return norm_text, phones, tones, word2ph
+
+
+def cleaned_text_to_sequence(
+    cleaned_phones: list[str], tones: list[int], language: Languages
+) -> tuple[list[int], list[int], list[int]]:
+    """
+    音素リスト・アクセントリスト・言語を、テキスト内の対応する ID に変換する
+
+    Args:
+        cleaned_phones (list[str]): clean_text() でクリーニングされた音素のリスト
+        tones (list[int]): 各音素のアクセント
+        language (Languages): テキストの言語
+
+    Returns:
+        tuple[list[int], list[int], list[int]]: List of integers corresponding to the symbols in the text
+    """
+
+    phones = [__symbol_to_id[symbol] for symbol in cleaned_phones]
+    tone_start = LANGUAGE_TONE_START_MAP[language]
+    tones = [i + tone_start for i in tones]
+    lang_id = LANGUAGE_ID_MAP[language]
+    lang_ids = [lang_id for i in phones]
+
+    return phones, tones, lang_ids
diff --git a/style_bert_vits2/nlp/bert_models.py b/style_bert_vits2/nlp/bert_models.py
new file mode 100644
index 0000000000000000000000000000000000000000..1166846f5e74b649e4bd275cf9936601d2078b50
--- /dev/null
+++ b/style_bert_vits2/nlp/bert_models.py
@@ -0,0 +1,210 @@
+"""
+Style-Bert-VITS2 の学習・推論に必要な各言語ごとの BERT モデルをロード/取得するためのモジュール。
+
+オリジナルの Bert-VITS2 では各言語ごとの BERT モデルが初回インポート時にハードコードされたパスから「暗黙的に」ロードされているが、
+場合によっては多重にロードされて非効率なほか、BERT モデルのロード元のパスがハードコードされているためライブラリ化ができない。
+
+そこで、ライブラリの利用前に、音声合成に利用する言語の BERT モデルだけを「明示的に」ロードできるようにした。
+一度 load_model/tokenizer() で当該言語の BERT モデルがロードされていれば、ライブラリ内部のどこからでもロード済みのモデル/トークナイザーを取得できる。
+"""
+
+import gc
+from typing import Optional, Union, cast
+
+import torch
+from transformers import (
+    AutoModelForMaskedLM,
+    AutoTokenizer,
+    DebertaV2Model,
+    DebertaV2Tokenizer,
+    PreTrainedModel,
+    PreTrainedTokenizer,
+    PreTrainedTokenizerFast,
+)
+
+from style_bert_vits2.constants import DEFAULT_BERT_TOKENIZER_PATHS, Languages
+from style_bert_vits2.logging import logger
+
+
+# 各言語ごとのロード済みの BERT モデルを格納する辞書
+__loaded_models: dict[Languages, Union[PreTrainedModel, DebertaV2Model]] = {}
+
+# 各言語ごとのロード済みの BERT トークナイザーを格納する辞書
+__loaded_tokenizers: dict[
+    Languages, Union[PreTrainedTokenizer, PreTrainedTokenizerFast, DebertaV2Tokenizer]
+] = {}
+
+
+def load_model(
+    language: Languages,
+    pretrained_model_name_or_path: Optional[str] = None,
+    cache_dir: Optional[str] = None,
+    revision: str = "main",
+) -> Union[PreTrainedModel, DebertaV2Model]:
+    """
+    指定された言語の BERT モデルをロードし、ロード済みの BERT モデルを返す。
+    一度ロードされていれば、ロード済みの BERT モデルを即座に返す。
+    ライブラリ利用時は常に必ず pretrain_model_name_or_path (Hugging Face のリポジトリ名 or ローカルのファイルパス) を指定する必要がある。
+    ロードにはそれなりに時間がかかるため、ライブラリ利用前に明示的に pretrained_model_name_or_path を指定してロードしておくべき。
+    cache_dir と revision は pretrain_model_name_or_path がリポジトリ名の場合のみ有効。
+
+    Style-Bert-VITS2 では、BERT モデルに下記の 3 つが利用されている。
+    これ以外の BERT モデルを指定した場合は正常に動作しない可能性が高い。
+    - 日本語: ku-nlp/deberta-v2-large-japanese-char-wwm
+    - 英語: microsoft/deberta-v3-large
+    - 中国語: hfl/chinese-roberta-wwm-ext-large
+
+    Args:
+        language (Languages): ロードする学習済みモデルの対象言語
+        pretrained_model_name_or_path (Optional[str]): ロードする学習済みモデルの名前またはパス。指定しない場合はデフォルトのパスが利用される (デフォルト: None)
+        cache_dir (Optional[str]): モデルのキャッシュディレクトリ。指定しない場合はデフォルトのキャッシュディレクトリが利用される (デフォルト: None)
+        revision (str): モデルの Hugging Face 上の Git リビジョン。指定しない場合は最新の main ブランチの内容が利用される (デフォルト: None)
+
+    Returns:
+        Union[PreTrainedModel, DebertaV2Model]: ロード済みの BERT モデル
+    """
+
+    # すでにロード済みの場合はそのまま返す
+    if language in __loaded_models:
+        return __loaded_models[language]
+
+    # pretrained_model_name_or_path が指定されていない場合はデフォルトのパスを利用
+    if pretrained_model_name_or_path is None:
+        assert DEFAULT_BERT_TOKENIZER_PATHS[
+            language
+        ].exists(), f"The default {language} BERT model does not exist on the file system. Please specify the path to the pre-trained model."
+        pretrained_model_name_or_path = str(DEFAULT_BERT_TOKENIZER_PATHS[language])
+
+    # BERT モデルをロードし、辞書に格納して返す
+    ## 英語のみ DebertaV2Model でロードする必要がある
+    if language == Languages.EN:
+        model = cast(
+            DebertaV2Model,
+            DebertaV2Model.from_pretrained(
+                pretrained_model_name_or_path, cache_dir=cache_dir, revision=revision
+            ),
+        )
+    else:
+        model = AutoModelForMaskedLM.from_pretrained(
+            pretrained_model_name_or_path, cache_dir=cache_dir, revision=revision
+        )
+    __loaded_models[language] = model
+    logger.info(
+        f"Loaded the {language} BERT model from {pretrained_model_name_or_path}"
+    )
+
+    return model
+
+
+def load_tokenizer(
+    language: Languages,
+    pretrained_model_name_or_path: Optional[str] = None,
+    cache_dir: Optional[str] = None,
+    revision: str = "main",
+) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast, DebertaV2Tokenizer]:
+    """
+    指定された言語の BERT モデルをロードし、ロード済みの BERT トークナイザーを返す。
+    一度ロードされていれば、ロード済みの BERT トークナイザーを即座に返す。
+    ライブラリ利用時は常に必ず pretrain_model_name_or_path (Hugging Face のリポジトリ名 or ローカルのファイルパス) を指定する必要がある。
+    ロードにはそれなりに時間がかかるため、ライブラリ利用前に明示的に pretrained_model_name_or_path を指定してロードしておくべき。
+    cache_dir と revision は pretrain_model_name_or_path がリポジトリ名の場合のみ有効。
+
+    Style-Bert-VITS2 では、BERT モデルに下記の 3 つが利用されている。
+    これ以外の BERT モデルを指定した場合は正常に動作しない可能性が高い。
+    - 日本語: ku-nlp/deberta-v2-large-japanese-char-wwm
+    - 英語: microsoft/deberta-v3-large
+    - 中国語: hfl/chinese-roberta-wwm-ext-large
+
+    Args:
+        language (Languages): ロードする学習済みモデルの対象言語
+        pretrained_model_name_or_path (Optional[str]): ロードする学習済みモデルの名前またはパス。指定しない場合はデフォルトのパスが利用される (デフォルト: None)
+        cache_dir (Optional[str]): モデルのキャッシュディレクトリ。指定しない場合はデフォルトのキャッシュディレクトリが利用される (デフォルト: None)
+        revision (str): モデルの Hugging Face 上の Git リビジョン。指定しない場合は最新の main ブランチの内容が利用される (デフォルト: None)
+
+    Returns:
+        Union[PreTrainedTokenizer, PreTrainedTokenizerFast, DebertaV2Tokenizer]: ロード済みの BERT トークナイザー
+    """
+
+    # すでにロード済みの場合はそのまま返す
+    if language in __loaded_tokenizers:
+        return __loaded_tokenizers[language]
+
+    # pretrained_model_name_or_path が指定されていない場合はデフォルトのパスを利用
+    if pretrained_model_name_or_path is None:
+        assert DEFAULT_BERT_TOKENIZER_PATHS[
+            language
+        ].exists(), f"The default {language} BERT tokenizer does not exist on the file system. Please specify the path to the pre-trained model."
+        pretrained_model_name_or_path = str(DEFAULT_BERT_TOKENIZER_PATHS[language])
+
+    # BERT トークナイザーをロードし、辞書に格納して返す
+    ## 英語のみ DebertaV2Tokenizer でロードする必要がある
+    if language == Languages.EN:
+        tokenizer = DebertaV2Tokenizer.from_pretrained(
+            pretrained_model_name_or_path,
+            cache_dir=cache_dir,
+            revision=revision,
+        )
+    else:
+        tokenizer = AutoTokenizer.from_pretrained(
+            pretrained_model_name_or_path,
+            cache_dir=cache_dir,
+            revision=revision,
+        )
+    __loaded_tokenizers[language] = tokenizer
+    logger.info(
+        f"Loaded the {language} BERT tokenizer from {pretrained_model_name_or_path}"
+    )
+
+    return tokenizer
+
+
+def unload_model(language: Languages) -> None:
+    """
+    指定された言語の BERT モデルをアンロードする。
+
+    Args:
+        language (Languages): アンロードする BERT モデルの言語
+    """
+
+    if language in __loaded_models:
+        del __loaded_models[language]
+        gc.collect()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        logger.info(f"Unloaded the {language} BERT model")
+
+
+def unload_tokenizer(language: Languages) -> None:
+    """
+    指定された言語の BERT トークナイザーをアンロードする。
+
+    Args:
+        language (Languages): アンロードする BERT トークナイザーの言語
+    """
+
+    if language in __loaded_tokenizers:
+        del __loaded_tokenizers[language]
+        gc.collect()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        logger.info(f"Unloaded the {language} BERT tokenizer")
+
+
+def unload_all_models() -> None:
+    """
+    すべての BERT モデルをアンロードする。
+    """
+
+    for language in list(__loaded_models.keys()):
+        unload_model(language)
+    logger.info("Unloaded all BERT models")
+
+
+def unload_all_tokenizers() -> None:
+    """
+    すべての BERT トークナイザーをアンロードする。
+    """
+
+    for language in list(__loaded_tokenizers.keys()):
+        unload_tokenizer(language)
+    logger.info("Unloaded all BERT tokenizers")
diff --git a/style_bert_vits2/nlp/chinese/__init__.py b/style_bert_vits2/nlp/chinese/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/style_bert_vits2/nlp/chinese/bert_feature.py b/style_bert_vits2/nlp/chinese/bert_feature.py
new file mode 100644
index 0000000000000000000000000000000000000000..adc07887d69e56c047439237cc4dc2c6fac78545
--- /dev/null
+++ b/style_bert_vits2/nlp/chinese/bert_feature.py
@@ -0,0 +1,125 @@
+from typing import Optional
+
+import torch
+
+from style_bert_vits2.constants import Languages
+from style_bert_vits2.nlp import bert_models
+
+
+def extract_bert_feature(
+    text: str,
+    word2ph: list[int],
+    device: str,
+    assist_text: Optional[str] = None,
+    assist_text_weight: float = 0.7,
+) -> torch.Tensor:
+    """
+    中国語のテキストから BERT の特徴量を抽出する
+
+    Args:
+        text (str): 中国語のテキスト
+        word2ph (list[int]): 元のテキストの各文字に音素が何個割り当てられるかを表すリスト
+        device (str): 推論に利用するデバイス
+        assist_text (Optional[str], optional): 補助テキスト (デフォルト: None)
+        assist_text_weight (float, optional): 補助テキストの重み (デフォルト: 0.7)
+
+    Returns:
+        torch.Tensor: BERT の特徴量
+    """
+
+    if device == "cuda" and not torch.cuda.is_available():
+        device = "cpu"
+    model = bert_models.load_model(Languages.ZH).to(device)  # type: ignore
+
+    style_res_mean = None
+    with torch.no_grad():
+        tokenizer = bert_models.load_tokenizer(Languages.ZH)
+        inputs = tokenizer(text, return_tensors="pt")
+        for i in inputs:
+            inputs[i] = inputs[i].to(device)  # type: ignore
+        res = model(**inputs, output_hidden_states=True)
+        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
+        if assist_text:
+            style_inputs = tokenizer(assist_text, return_tensors="pt")
+            for i in style_inputs:
+                style_inputs[i] = style_inputs[i].to(device)  # type: ignore
+            style_res = model(**style_inputs, output_hidden_states=True)
+            style_res = torch.cat(style_res["hidden_states"][-3:-2], -1)[0].cpu()
+            style_res_mean = style_res.mean(0)
+
+    assert len(word2ph) == len(text) + 2
+    word2phone = word2ph
+    phone_level_feature = []
+    for i in range(len(word2phone)):
+        if assist_text:
+            assert style_res_mean is not None
+            repeat_feature = (
+                res[i].repeat(word2phone[i], 1) * (1 - assist_text_weight)
+                + style_res_mean.repeat(word2phone[i], 1) * assist_text_weight
+            )
+        else:
+            repeat_feature = res[i].repeat(word2phone[i], 1)
+        phone_level_feature.append(repeat_feature)
+
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+
+    return phone_level_feature.T
+
+
+if __name__ == "__main__":
+    word_level_feature = torch.rand(38, 1024)  # 12个词,每个词1024维特征
+    word2phone = [
+        1,
+        2,
+        1,
+        2,
+        2,
+        1,
+        2,
+        2,
+        1,
+        2,
+        2,
+        1,
+        2,
+        2,
+        2,
+        2,
+        2,
+        1,
+        1,
+        2,
+        2,
+        1,
+        2,
+        2,
+        2,
+        2,
+        1,
+        2,
+        2,
+        2,
+        2,
+        2,
+        1,
+        2,
+        2,
+        2,
+        2,
+        1,
+    ]
+
+    # 计算总帧数
+    total_frames = sum(word2phone)
+    print(word_level_feature.shape)
+    print(word2phone)
+    phone_level_feature = []
+    for i in range(len(word2phone)):
+        print(word_level_feature[i].shape)
+
+        # 对每个词重复word2phone[i]次
+        repeat_feature = word_level_feature[i].repeat(word2phone[i], 1)
+        phone_level_feature.append(repeat_feature)
+
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+    print(phone_level_feature.shape)  # torch.Size([36, 1024])
diff --git a/style_bert_vits2/nlp/chinese/g2p.py b/style_bert_vits2/nlp/chinese/g2p.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f0894f9526dec74977aa99896e9a4a47ee0340e
--- /dev/null
+++ b/style_bert_vits2/nlp/chinese/g2p.py
@@ -0,0 +1,139 @@
+import re
+from pathlib import Path
+
+import jieba.posseg as psg
+from pypinyin import Style, lazy_pinyin
+
+from style_bert_vits2.nlp.chinese.tone_sandhi import ToneSandhi
+from style_bert_vits2.nlp.symbols import PUNCTUATIONS
+
+
+with open(Path(__file__).parent / "opencpop-strict.txt", encoding="utf-8") as f:
+    __PINYIN_TO_SYMBOL_MAP = {
+        line.split("\t")[0]: line.strip().split("\t")[1] for line in f.readlines()
+    }
+
+
+def g2p(text: str) -> tuple[list[str], list[int], list[int]]:
+    pattern = r"(?<=[{0}])\s*".format("".join(PUNCTUATIONS))
+    sentences = [i for i in re.split(pattern, text) if i.strip() != ""]
+    phones, tones, word2ph = __g2p(sentences)
+    assert sum(word2ph) == len(phones)
+    assert len(word2ph) == len(text)  # Sometimes it will crash,you can add a try-catch.
+    phones = ["_"] + phones + ["_"]
+    tones = [0] + tones + [0]
+    word2ph = [1] + word2ph + [1]
+    return phones, tones, word2ph
+
+
+def __g2p(segments: list[str]) -> tuple[list[str], list[int], list[int]]:
+    phones_list = []
+    tones_list = []
+    word2ph = []
+    tone_modifier = ToneSandhi()
+    for seg in segments:
+        # Replace all English words in the sentence
+        seg = re.sub("[a-zA-Z]+", "", seg)
+        seg_cut = psg.lcut(seg)
+        initials = []
+        finals = []
+        seg_cut = tone_modifier.pre_merge_for_modify(seg_cut)  # type: ignore
+        for word, pos in seg_cut:
+            if pos == "eng":
+                continue
+            sub_initials, sub_finals = __get_initials_finals(word)
+            sub_finals = tone_modifier.modified_tone(word, pos, sub_finals)
+            initials.append(sub_initials)
+            finals.append(sub_finals)
+
+            # assert len(sub_initials) == len(sub_finals) == len(word)
+        initials = sum(initials, [])
+        finals = sum(finals, [])
+        #
+        for c, v in zip(initials, finals):
+            raw_pinyin = c + v
+            # NOTE: post process for pypinyin outputs
+            # we discriminate i, ii and iii
+            if c == v:
+                assert c in PUNCTUATIONS
+                phone = [c]
+                tone = "0"
+                word2ph.append(1)
+            else:
+                v_without_tone = v[:-1]
+                tone = v[-1]
+
+                pinyin = c + v_without_tone
+                assert tone in "12345"
+
+                if c:
+                    # 多音节
+                    v_rep_map = {
+                        "uei": "ui",
+                        "iou": "iu",
+                        "uen": "un",
+                    }
+                    if v_without_tone in v_rep_map:
+                        pinyin = c + v_rep_map[v_without_tone]
+                else:
+                    # 单音节
+                    pinyin_rep_map = {
+                        "ing": "ying",
+                        "i": "yi",
+                        "in": "yin",
+                        "u": "wu",
+                    }
+                    if pinyin in pinyin_rep_map:
+                        pinyin = pinyin_rep_map[pinyin]
+                    else:
+                        single_rep_map = {
+                            "v": "yu",
+                            "e": "e",
+                            "i": "y",
+                            "u": "w",
+                        }
+                        if pinyin[0] in single_rep_map:
+                            pinyin = single_rep_map[pinyin[0]] + pinyin[1:]
+
+                assert pinyin in __PINYIN_TO_SYMBOL_MAP, (
+                    pinyin,
+                    seg,
+                    raw_pinyin,
+                )
+                phone = __PINYIN_TO_SYMBOL_MAP[pinyin].split(" ")
+                word2ph.append(len(phone))
+
+            phones_list += phone
+            tones_list += [int(tone)] * len(phone)
+    return phones_list, tones_list, word2ph
+
+
+def __get_initials_finals(word: str) -> tuple[list[str], list[str]]:
+    initials = []
+    finals = []
+    orig_initials = lazy_pinyin(word, neutral_tone_with_five=True, style=Style.INITIALS)
+    orig_finals = lazy_pinyin(
+        word, neutral_tone_with_five=True, style=Style.FINALS_TONE3
+    )
+    for c, v in zip(orig_initials, orig_finals):
+        initials.append(c)
+        finals.append(v)
+    return initials, finals
+
+
+if __name__ == "__main__":
+    from style_bert_vits2.nlp.chinese.bert_feature import extract_bert_feature
+    from style_bert_vits2.nlp.chinese.normalizer import normalize_text
+
+    text = "啊！但是《原神》是由,米哈游自主，  [研发]的一款全.新开放世界.冒险游戏"
+    text = normalize_text(text)
+    print(text)
+    phones, tones, word2ph = g2p(text)
+    bert = extract_bert_feature(text, word2ph, "cuda")
+
+    print(phones, tones, word2ph, bert.shape)
+
+
+# 示例用法
+# text = "这是一个示例文本：,你好！这是一个测试...."
+# print(g2p_paddle(text))  # 输出: 这是一个示例文本你好这是一个测试
diff --git a/style_bert_vits2/nlp/chinese/normalizer.py b/style_bert_vits2/nlp/chinese/normalizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..c076408dccc569058de0da499f0bab403cadf0a4
--- /dev/null
+++ b/style_bert_vits2/nlp/chinese/normalizer.py
@@ -0,0 +1,62 @@
+import re
+
+import cn2an
+
+from style_bert_vits2.nlp.symbols import PUNCTUATIONS
+
+
+__REPLACE_MAP = {
+    "：": ",",
+    "；": ",",
+    "，": ",",
+    "。": ".",
+    "！": "!",
+    "？": "?",
+    "\n": ".",
+    "·": ",",
+    "、": ",",
+    "...": "…",
+    "$": ".",
+    "“": "'",
+    "”": "'",
+    '"': "'",
+    "‘": "'",
+    "’": "'",
+    "（": "'",
+    "）": "'",
+    "(": "'",
+    ")": "'",
+    "《": "'",
+    "》": "'",
+    "【": "'",
+    "】": "'",
+    "[": "'",
+    "]": "'",
+    "—": "-",
+    "～": "-",
+    "~": "-",
+    "「": "'",
+    "」": "'",
+}
+
+
+def normalize_text(text: str) -> str:
+    numbers = re.findall(r"\d+(?:\.?\d+)?", text)
+    for number in numbers:
+        text = text.replace(number, cn2an.an2cn(number), 1)
+    text = replace_punctuation(text)
+    return text
+
+
+def replace_punctuation(text: str) -> str:
+
+    text = text.replace("嗯", "恩").replace("呣", "母")
+    pattern = re.compile("|".join(re.escape(p) for p in __REPLACE_MAP))
+
+    replaced_text = pattern.sub(lambda x: __REPLACE_MAP[x.group()], text)
+
+    replaced_text = re.sub(
+        r"[^\u4e00-\u9fa5" + "".join(PUNCTUATIONS) + r"]+", "", replaced_text
+    )
+
+    return replaced_text
diff --git a/style_bert_vits2/nlp/chinese/opencpop-strict.txt b/style_bert_vits2/nlp/chinese/opencpop-strict.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1bede89a429e34b1a58781ee570fc313b80c0aee
--- /dev/null
+++ b/style_bert_vits2/nlp/chinese/opencpop-strict.txt
@@ -0,0 +1,429 @@
+a	AA a
+ai	AA ai
+an	AA an
+ang	AA ang
+ao	AA ao
+ba	b a
+bai	b ai
+ban	b an
+bang	b ang
+bao	b ao
+bei	b ei
+ben	b en
+beng	b eng
+bi	b i
+bian	b ian
+biao	b iao
+bie	b ie
+bin	b in
+bing	b ing
+bo	b o
+bu	b u
+ca	c a
+cai	c ai
+can	c an
+cang	c ang
+cao	c ao
+ce	c e
+cei	c ei
+cen	c en
+ceng	c eng
+cha	ch a
+chai	ch ai
+chan	ch an
+chang	ch ang
+chao	ch ao
+che	ch e
+chen	ch en
+cheng	ch eng
+chi	ch ir
+chong	ch ong
+chou	ch ou
+chu	ch u
+chua	ch ua
+chuai	ch uai
+chuan	ch uan
+chuang	ch uang
+chui	ch ui
+chun	ch un
+chuo	ch uo
+ci	c i0
+cong	c ong
+cou	c ou
+cu	c u
+cuan	c uan
+cui	c ui
+cun	c un
+cuo	c uo
+da	d a
+dai	d ai
+dan	d an
+dang	d ang
+dao	d ao
+de	d e
+dei	d ei
+den	d en
+deng	d eng
+di	d i
+dia	d ia
+dian	d ian
+diao	d iao
+die	d ie
+ding	d ing
+diu	d iu
+dong	d ong
+dou	d ou
+du	d u
+duan	d uan
+dui	d ui
+dun	d un
+duo	d uo
+e	EE e
+ei	EE ei
+en	EE en
+eng	EE eng
+er	EE er
+fa	f a
+fan	f an
+fang	f ang
+fei	f ei
+fen	f en
+feng	f eng
+fo	f o
+fou	f ou
+fu	f u
+ga	g a
+gai	g ai
+gan	g an
+gang	g ang
+gao	g ao
+ge	g e
+gei	g ei
+gen	g en
+geng	g eng
+gong	g ong
+gou	g ou
+gu	g u
+gua	g ua
+guai	g uai
+guan	g uan
+guang	g uang
+gui	g ui
+gun	g un
+guo	g uo
+ha	h a
+hai	h ai
+han	h an
+hang	h ang
+hao	h ao
+he	h e
+hei	h ei
+hen	h en
+heng	h eng
+hong	h ong
+hou	h ou
+hu	h u
+hua	h ua
+huai	h uai
+huan	h uan
+huang	h uang
+hui	h ui
+hun	h un
+huo	h uo
+ji	j i
+jia	j ia
+jian	j ian
+jiang	j iang
+jiao	j iao
+jie	j ie
+jin	j in
+jing	j ing
+jiong	j iong
+jiu	j iu
+ju	j v
+jv	j v
+juan	j van
+jvan	j van
+jue	j ve
+jve	j ve
+jun	j vn
+jvn	j vn
+ka	k a
+kai	k ai
+kan	k an
+kang	k ang
+kao	k ao
+ke	k e
+kei	k ei
+ken	k en
+keng	k eng
+kong	k ong
+kou	k ou
+ku	k u
+kua	k ua
+kuai	k uai
+kuan	k uan
+kuang	k uang
+kui	k ui
+kun	k un
+kuo	k uo
+la	l a
+lai	l ai
+lan	l an
+lang	l ang
+lao	l ao
+le	l e
+lei	l ei
+leng	l eng
+li	l i
+lia	l ia
+lian	l ian
+liang	l iang
+liao	l iao
+lie	l ie
+lin	l in
+ling	l ing
+liu	l iu
+lo	l o
+long	l ong
+lou	l ou
+lu	l u
+luan	l uan
+lun	l un
+luo	l uo
+lv	l v
+lve	l ve
+ma	m a
+mai	m ai
+man	m an
+mang	m ang
+mao	m ao
+me	m e
+mei	m ei
+men	m en
+meng	m eng
+mi	m i
+mian	m ian
+miao	m iao
+mie	m ie
+min	m in
+ming	m ing
+miu	m iu
+mo	m o
+mou	m ou
+mu	m u
+na	n a
+nai	n ai
+nan	n an
+nang	n ang
+nao	n ao
+ne	n e
+nei	n ei
+nen	n en
+neng	n eng
+ni	n i
+nian	n ian
+niang	n iang
+niao	n iao
+nie	n ie
+nin	n in
+ning	n ing
+niu	n iu
+nong	n ong
+nou	n ou
+nu	n u
+nuan	n uan
+nun	n un
+nuo	n uo
+nv	n v
+nve	n ve
+o	OO o
+ou	OO ou
+pa	p a
+pai	p ai
+pan	p an
+pang	p ang
+pao	p ao
+pei	p ei
+pen	p en
+peng	p eng
+pi	p i
+pian	p ian
+piao	p iao
+pie	p ie
+pin	p in
+ping	p ing
+po	p o
+pou	p ou
+pu	p u
+qi	q i
+qia	q ia
+qian	q ian
+qiang	q iang
+qiao	q iao
+qie	q ie
+qin	q in
+qing	q ing
+qiong	q iong
+qiu	q iu
+qu	q v
+qv	q v
+quan	q van
+qvan	q van
+que	q ve
+qve	q ve
+qun	q vn
+qvn	q vn
+ran	r an
+rang	r ang
+rao	r ao
+re	r e
+ren	r en
+reng	r eng
+ri	r ir
+rong	r ong
+rou	r ou
+ru	r u
+rua	r ua
+ruan	r uan
+rui	r ui
+run	r un
+ruo	r uo
+sa	s a
+sai	s ai
+san	s an
+sang	s ang
+sao	s ao
+se	s e
+sen	s en
+seng	s eng
+sha	sh a
+shai	sh ai
+shan	sh an
+shang	sh ang
+shao	sh ao
+she	sh e
+shei	sh ei
+shen	sh en
+sheng	sh eng
+shi	sh ir
+shou	sh ou
+shu	sh u
+shua	sh ua
+shuai	sh uai
+shuan	sh uan
+shuang	sh uang
+shui	sh ui
+shun	sh un
+shuo	sh uo
+si	s i0
+song	s ong
+sou	s ou
+su	s u
+suan	s uan
+sui	s ui
+sun	s un
+suo	s uo
+ta	t a
+tai	t ai
+tan	t an
+tang	t ang
+tao	t ao
+te	t e
+tei	t ei
+teng	t eng
+ti	t i
+tian	t ian
+tiao	t iao
+tie	t ie
+ting	t ing
+tong	t ong
+tou	t ou
+tu	t u
+tuan	t uan
+tui	t ui
+tun	t un
+tuo	t uo
+wa	w a
+wai	w ai
+wan	w an
+wang	w ang
+wei	w ei
+wen	w en
+weng	w eng
+wo	w o
+wu	w u
+xi	x i
+xia	x ia
+xian	x ian
+xiang	x iang
+xiao	x iao
+xie	x ie
+xin	x in
+xing	x ing
+xiong	x iong
+xiu	x iu
+xu	x v
+xv	x v
+xuan	x van
+xvan	x van
+xue	x ve
+xve	x ve
+xun	x vn
+xvn	x vn
+ya	y a
+yan	y En
+yang	y ang
+yao	y ao
+ye	y E
+yi	y i
+yin	y in
+ying	y ing
+yo	y o
+yong	y ong
+you	y ou
+yu	y v
+yv	y v
+yuan	y van
+yvan	y van
+yue	y ve
+yve	y ve
+yun	y vn
+yvn	y vn
+za	z a
+zai	z ai
+zan	z an
+zang	z ang
+zao	z ao
+ze	z e
+zei	z ei
+zen	z en
+zeng	z eng
+zha	zh a
+zhai	zh ai
+zhan	zh an
+zhang	zh ang
+zhao	zh ao
+zhe	zh e
+zhei	zh ei
+zhen	zh en
+zheng	zh eng
+zhi	zh ir
+zhong	zh ong
+zhou	zh ou
+zhu	zh u
+zhua	zh ua
+zhuai	zh uai
+zhuan	zh uan
+zhuang	zh uang
+zhui	zh ui
+zhun	zh un
+zhuo	zh uo
+zi	z i0
+zong	z ong
+zou	z ou
+zu	z u
+zuan	z uan
+zui	z ui
+zun	z un
+zuo	z uo
diff --git a/style_bert_vits2/nlp/chinese/tone_sandhi.py b/style_bert_vits2/nlp/chinese/tone_sandhi.py
new file mode 100644
index 0000000000000000000000000000000000000000..4945ab952d724bfc43321ef98a29f7cc3ccb6758
--- /dev/null
+++ b/style_bert_vits2/nlp/chinese/tone_sandhi.py
@@ -0,0 +1,771 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+import jieba
+from pypinyin import Style, lazy_pinyin
+
+
+class ToneSandhi:
+    def __init__(self):
+        self.must_neural_tone_words = {
+            "麻烦",
+            "麻利",
+            "鸳鸯",
+            "高粱",
+            "骨头",
+            "骆驼",
+            "马虎",
+            "首饰",
+            "馒头",
+            "馄饨",
+            "风筝",
+            "难为",
+            "队伍",
+            "阔气",
+            "闺女",
+            "门道",
+            "锄头",
+            "铺盖",
+            "铃铛",
+            "铁匠",
+            "钥匙",
+            "里脊",
+            "里头",
+            "部分",
+            "那么",
+            "道士",
+            "造化",
+            "迷糊",
+            "连累",
+            "这么",
+            "这个",
+            "运气",
+            "过去",
+            "软和",
+            "转悠",
+            "踏实",
+            "跳蚤",
+            "跟头",
+            "趔趄",
+            "财主",
+            "豆腐",
+            "讲究",
+            "记性",
+            "记号",
+            "认识",
+            "规矩",
+            "见识",
+            "裁缝",
+            "补丁",
+            "衣裳",
+            "衣服",
+            "衙门",
+            "街坊",
+            "行李",
+            "行当",
+            "蛤蟆",
+            "蘑菇",
+            "薄荷",
+            "葫芦",
+            "葡萄",
+            "萝卜",
+            "荸荠",
+            "苗条",
+            "苗头",
+            "苍蝇",
+            "芝麻",
+            "舒服",
+            "舒坦",
+            "舌头",
+            "自在",
+            "膏药",
+            "脾气",
+            "脑袋",
+            "脊梁",
+            "能耐",
+            "胳膊",
+            "胭脂",
+            "胡萝",
+            "胡琴",
+            "胡同",
+            "聪明",
+            "耽误",
+            "耽搁",
+            "耷拉",
+            "耳朵",
+            "老爷",
+            "老实",
+            "老婆",
+            "老头",
+            "老太",
+            "翻腾",
+            "罗嗦",
+            "罐头",
+            "编辑",
+            "结实",
+            "红火",
+            "累赘",
+            "糨糊",
+            "糊涂",
+            "精神",
+            "粮食",
+            "簸箕",
+            "篱笆",
+            "算计",
+            "算盘",
+            "答应",
+            "笤帚",
+            "笑语",
+            "笑话",
+            "窟窿",
+            "窝囊",
+            "窗户",
+            "稳当",
+            "稀罕",
+            "称呼",
+            "秧歌",
+            "秀气",
+            "秀才",
+            "福气",
+            "祖宗",
+            "砚台",
+            "码头",
+            "石榴",
+            "石头",
+            "石匠",
+            "知识",
+            "眼睛",
+            "眯缝",
+            "眨巴",
+            "眉毛",
+            "相声",
+            "盘算",
+            "白净",
+            "痢疾",
+            "痛快",
+            "疟疾",
+            "疙瘩",
+            "疏忽",
+            "畜生",
+            "生意",
+            "甘蔗",
+            "琵琶",
+            "琢磨",
+            "琉璃",
+            "玻璃",
+            "玫瑰",
+            "玄乎",
+            "狐狸",
+            "状元",
+            "特务",
+            "牲口",
+            "牙碜",
+            "牌楼",
+            "爽快",
+            "爱人",
+            "热闹",
+            "烧饼",
+            "烟筒",
+            "烂糊",
+            "点心",
+            "炊帚",
+            "灯笼",
+            "火候",
+            "漂亮",
+            "滑溜",
+            "溜达",
+            "温和",
+            "清楚",
+            "消息",
+            "浪头",
+            "活泼",
+            "比方",
+            "正经",
+            "欺负",
+            "模糊",
+            "槟榔",
+            "棺材",
+            "棒槌",
+            "棉花",
+            "核桃",
+            "栅栏",
+            "柴火",
+            "架势",
+            "枕头",
+            "枇杷",
+            "机灵",
+            "本事",
+            "木头",
+            "木匠",
+            "朋友",
+            "月饼",
+            "月亮",
+            "暖和",
+            "明白",
+            "时候",
+            "新鲜",
+            "故事",
+            "收拾",
+            "收成",
+            "提防",
+            "挖苦",
+            "挑剔",
+            "指甲",
+            "指头",
+            "拾掇",
+            "拳头",
+            "拨弄",
+            "招牌",
+            "招呼",
+            "抬举",
+            "护士",
+            "折腾",
+            "扫帚",
+            "打量",
+            "打算",
+            "打点",
+            "打扮",
+            "打听",
+            "打发",
+            "扎实",
+            "扁担",
+            "戒指",
+            "懒得",
+            "意识",
+            "意思",
+            "情形",
+            "悟性",
+            "怪物",
+            "思量",
+            "怎么",
+            "念头",
+            "念叨",
+            "快活",
+            "忙活",
+            "志气",
+            "心思",
+            "得罪",
+            "张罗",
+            "弟兄",
+            "开通",
+            "应酬",
+            "庄稼",
+            "干事",
+            "帮手",
+            "帐篷",
+            "希罕",
+            "师父",
+            "师傅",
+            "巴结",
+            "巴掌",
+            "差事",
+            "工夫",
+            "岁数",
+            "屁股",
+            "尾巴",
+            "少爷",
+            "小气",
+            "小伙",
+            "将就",
+            "对头",
+            "对付",
+            "寡妇",
+            "家伙",
+            "客气",
+            "实在",
+            "官司",
+            "学问",
+            "学生",
+            "字号",
+            "嫁妆",
+            "媳妇",
+            "媒人",
+            "婆家",
+            "娘家",
+            "委屈",
+            "姑娘",
+            "姐夫",
+            "妯娌",
+            "妥当",
+            "妖精",
+            "奴才",
+            "女婿",
+            "头发",
+            "太阳",
+            "大爷",
+            "大方",
+            "大意",
+            "大夫",
+            "多少",
+            "多么",
+            "外甥",
+            "壮实",
+            "地道",
+            "地方",
+            "在乎",
+            "困难",
+            "嘴巴",
+            "嘱咐",
+            "嘟囔",
+            "嘀咕",
+            "喜欢",
+            "喇嘛",
+            "喇叭",
+            "商量",
+            "唾沫",
+            "哑巴",
+            "哈欠",
+            "哆嗦",
+            "咳嗽",
+            "和尚",
+            "告诉",
+            "告示",
+            "含糊",
+            "吓唬",
+            "后头",
+            "名字",
+            "名堂",
+            "合同",
+            "吆喝",
+            "叫唤",
+            "口袋",
+            "厚道",
+            "厉害",
+            "千斤",
+            "包袱",
+            "包涵",
+            "匀称",
+            "勤快",
+            "动静",
+            "动弹",
+            "功夫",
+            "力气",
+            "前头",
+            "刺猬",
+            "刺激",
+            "别扭",
+            "利落",
+            "利索",
+            "利害",
+            "分析",
+            "出息",
+            "凑合",
+            "凉快",
+            "冷战",
+            "冤枉",
+            "冒失",
+            "养活",
+            "关系",
+            "先生",
+            "兄弟",
+            "便宜",
+            "使唤",
+            "佩服",
+            "作坊",
+            "体面",
+            "位置",
+            "似的",
+            "伙计",
+            "休息",
+            "什么",
+            "人家",
+            "亲戚",
+            "亲家",
+            "交情",
+            "云彩",
+            "事情",
+            "买卖",
+            "主意",
+            "丫头",
+            "丧气",
+            "两口",
+            "东西",
+            "东家",
+            "世故",
+            "不由",
+            "不在",
+            "下水",
+            "下巴",
+            "上头",
+            "上司",
+            "丈夫",
+            "丈人",
+            "一辈",
+            "那个",
+            "菩萨",
+            "父亲",
+            "母亲",
+            "咕噜",
+            "邋遢",
+            "费用",
+            "冤家",
+            "甜头",
+            "介绍",
+            "荒唐",
+            "大人",
+            "泥鳅",
+            "幸福",
+            "熟悉",
+            "计划",
+            "扑腾",
+            "蜡烛",
+            "姥爷",
+            "照顾",
+            "喉咙",
+            "吉他",
+            "弄堂",
+            "蚂蚱",
+            "凤凰",
+            "拖沓",
+            "寒碜",
+            "糟蹋",
+            "倒腾",
+            "报复",
+            "逻辑",
+            "盘缠",
+            "喽啰",
+            "牢骚",
+            "咖喱",
+            "扫把",
+            "惦记",
+        }
+        self.must_not_neural_tone_words = {
+            "男子",
+            "女子",
+            "分子",
+            "原子",
+            "量子",
+            "莲子",
+            "石子",
+            "瓜子",
+            "电子",
+            "人人",
+            "虎虎",
+        }
+        self.punc = "：，；。？！“”‘’':,;.?!"
+
+    # the meaning of jieba pos tag: https://blog.csdn.net/weixin_44174352/article/details/113731041
+    # e.g.
+    # word: "家里"
+    # pos: "s"
+    # finals: ['ia1', 'i3']
+    def _neural_sandhi(self, word: str, pos: str, finals: list[str]) -> list[str]:
+        # reduplication words for n. and v. e.g. 奶奶, 试试, 旺旺
+        for j, item in enumerate(word):
+            if (
+                j - 1 >= 0
+                and item == word[j - 1]
+                and pos[0] in {"n", "v", "a"}
+                and word not in self.must_not_neural_tone_words
+            ):
+                finals[j] = finals[j][:-1] + "5"
+        ge_idx = word.find("个")
+        if (
+            len(word) >= 1
+            and word[-1] in "吧呢啊呐噻嘛吖嗨呐哦哒额滴哩哟喽啰耶喔诶"
+            or len(word) >= 1
+            and word[-1] in "的地得"
+            or (
+                (
+                    len(word) > 1
+                    and word[-1] in "们子"
+                    and pos in {"r", "n"}
+                    and word not in self.must_not_neural_tone_words
+                )
+                or len(word) > 1
+                and word[-1] in "上下里"
+                and pos in {"s", "l", "f"}
+            )
+            or len(word) > 1
+            and word[-1] in "来去"
+            and word[-2] in "上下进出回过起开"
+        ):
+            finals[-1] = finals[-1][:-1] + "5"
+        # 个做量词
+        elif (
+            ge_idx >= 1
+            and (
+                word[ge_idx - 1].isnumeric()
+                or word[ge_idx - 1] in "几有两半多各整每做是"
+            )
+        ) or word == "个":
+            finals[ge_idx] = finals[ge_idx][:-1] + "5"
+        elif (
+            word in self.must_neural_tone_words
+            or word[-2:] in self.must_neural_tone_words
+        ):
+            finals[-1] = finals[-1][:-1] + "5"
+
+        word_list = self._split_word(word)
+        finals_list = [finals[: len(word_list[0])], finals[len(word_list[0]) :]]
+        for i, word in enumerate(word_list):
+            # conventional neural in Chinese
+            if (
+                word in self.must_neural_tone_words
+                or word[-2:] in self.must_neural_tone_words
+            ):
+                finals_list[i][-1] = finals_list[i][-1][:-1] + "5"
+        finals = sum(finals_list, [])
+        return finals
+
+    def _bu_sandhi(self, word: str, finals: list[str]) -> list[str]:
+        # e.g. 看不懂
+        if len(word) == 3 and word[1] == "不":
+            finals[1] = finals[1][:-1] + "5"
+        else:
+            for i, char in enumerate(word):
+                # "不" before tone4 should be bu2, e.g. 不怕
+                if char == "不" and i + 1 < len(word) and finals[i + 1][-1] == "4":
+                    finals[i] = finals[i][:-1] + "2"
+        return finals
+
+    def _yi_sandhi(self, word: str, finals: list[str]) -> list[str]:
+        # "一" in number sequences, e.g. 一零零, 二一零
+        if word.find("一") != -1 and all(
+            [item.isnumeric() for item in word if item != "一"]
+        ):
+            return finals
+        # "一" between reduplication words should be yi5, e.g. 看一看
+        elif len(word) == 3 and word[1] == "一" and word[0] == word[-1]:
+            finals[1] = finals[1][:-1] + "5"
+        # when "一" is ordinal word, it should be yi1
+        elif word.startswith("第一"):
+            finals[1] = finals[1][:-1] + "1"
+        else:
+            for i, char in enumerate(word):
+                if char == "一" and i + 1 < len(word):
+                    # "一" before tone4 should be yi2, e.g. 一段
+                    if finals[i + 1][-1] == "4":
+                        finals[i] = finals[i][:-1] + "2"
+                    # "一" before non-tone4 should be yi4, e.g. 一天
+                    elif word[i + 1] not in self.punc:
+                        finals[i] = finals[i][:-1] + "4"
+        return finals
+
+    def _split_word(self, word: str) -> list[str]:
+        word_list = jieba.cut_for_search(word)
+        word_list = sorted(word_list, key=lambda i: len(i), reverse=False)  # type: ignore
+        first_subword = word_list[0]
+        first_begin_idx = word.find(first_subword)
+        if first_begin_idx == 0:
+            second_subword = word[len(first_subword) :]
+            new_word_list = [first_subword, second_subword]
+        else:
+            second_subword = word[: -len(first_subword)]
+            new_word_list = [second_subword, first_subword]
+        return new_word_list
+
+    def _three_sandhi(self, word: str, finals: list[str]) -> list[str]:
+        if len(word) == 2 and self._all_tone_three(finals):
+            finals[0] = finals[0][:-1] + "2"
+        elif len(word) == 3:
+            word_list = self._split_word(word)
+            if self._all_tone_three(finals):
+                #  disyllabic + monosyllabic, e.g. 蒙古/包
+                if len(word_list[0]) == 2:
+                    finals[0] = finals[0][:-1] + "2"
+                    finals[1] = finals[1][:-1] + "2"
+                #  monosyllabic + disyllabic, e.g. 纸/老虎
+                elif len(word_list[0]) == 1:
+                    finals[1] = finals[1][:-1] + "2"
+            else:
+                finals_list = [finals[: len(word_list[0])], finals[len(word_list[0]) :]]
+                if len(finals_list) == 2:
+                    for i, sub in enumerate(finals_list):
+                        # e.g. 所有/人
+                        if self._all_tone_three(sub) and len(sub) == 2:
+                            finals_list[i][0] = finals_list[i][0][:-1] + "2"
+                        # e.g. 好/喜欢
+                        elif (
+                            i == 1
+                            and not self._all_tone_three(sub)
+                            and finals_list[i][0][-1] == "3"
+                            and finals_list[0][-1][-1] == "3"
+                        ):
+                            finals_list[0][-1] = finals_list[0][-1][:-1] + "2"
+                        finals = sum(finals_list, [])
+        # split idiom into two words who's length is 2
+        elif len(word) == 4:
+            finals_list = [finals[:2], finals[2:]]
+            finals = []
+            for sub in finals_list:
+                if self._all_tone_three(sub):
+                    sub[0] = sub[0][:-1] + "2"
+                finals += sub
+
+        return finals
+
+    def _all_tone_three(self, finals: list[str]) -> bool:
+        return all(x[-1] == "3" for x in finals)
+
+    # merge "不" and the word behind it
+    # if don't merge, "不" sometimes appears alone according to jieba, which may occur sandhi error
+    def _merge_bu(self, seg: list[tuple[str, str]]) -> list[tuple[str, str]]:
+        new_seg = []
+        last_word = ""
+        for word, pos in seg:
+            if last_word == "不":
+                word = last_word + word
+            if word != "不":
+                new_seg.append((word, pos))
+            last_word = word[:]
+        if last_word == "不":
+            new_seg.append((last_word, "d"))
+            last_word = ""
+        return new_seg
+
+    # function 1: merge "一" and reduplication words in it's left and right, e.g. "听","一","听" ->"听一听"
+    # function 2: merge single  "一" and the word behind it
+    # if don't merge, "一" sometimes appears alone according to jieba, which may occur sandhi error
+    # e.g.
+    # input seg: [('听', 'v'), ('一', 'm'), ('听', 'v')]
+    # output seg: [['听一听', 'v']]
+    def _merge_yi(self, seg: list[tuple[str, str]]) -> list[tuple[str, str]]:
+        new_seg = [] * len(seg)
+        # function 1
+        i = 0
+        while i < len(seg):
+            word, pos = seg[i]
+            if (
+                i - 1 >= 0
+                and word == "一"
+                and i + 1 < len(seg)
+                and seg[i - 1][0] == seg[i + 1][0]
+                and seg[i - 1][1] == "v"
+            ):
+                new_seg[i - 1][0] = new_seg[i - 1][0] + "一" + new_seg[i - 1][0]
+                i += 2
+            else:
+                if (
+                    i - 2 >= 0
+                    and seg[i - 1][0] == "一"
+                    and seg[i - 2][0] == word
+                    and pos == "v"
+                ):
+                    continue
+                else:
+                    new_seg.append([word, pos])
+                i += 1
+        seg = [i for i in new_seg if len(i) > 0]
+        new_seg = []
+        # function 2
+        for i, (word, pos) in enumerate(seg):
+            if new_seg and new_seg[-1][0] == "一":
+                new_seg[-1][0] = new_seg[-1][0] + word
+            else:
+                new_seg.append([word, pos])
+        return new_seg
+
+    # the first and the second words are all_tone_three
+    def _merge_continuous_three_tones(
+        self, seg: list[tuple[str, str]]
+    ) -> list[tuple[str, str]]:
+        new_seg = []
+        sub_finals_list = [
+            lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
+            for (word, pos) in seg
+        ]
+        assert len(sub_finals_list) == len(seg)
+        merge_last = [False] * len(seg)
+        for i, (word, pos) in enumerate(seg):
+            if (
+                i - 1 >= 0
+                and self._all_tone_three(sub_finals_list[i - 1])
+                and self._all_tone_three(sub_finals_list[i])
+                and not merge_last[i - 1]
+            ):
+                # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
+                if (
+                    not self._is_reduplication(seg[i - 1][0])
+                    and len(seg[i - 1][0]) + len(seg[i][0]) <= 3
+                ):
+                    new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
+                    merge_last[i] = True
+                else:
+                    new_seg.append([word, pos])
+            else:
+                new_seg.append([word, pos])
+
+        return new_seg
+
+    def _is_reduplication(self, word: str) -> bool:
+        return len(word) == 2 and word[0] == word[1]
+
+    # the last char of first word and the first char of second word is tone_three
+    def _merge_continuous_three_tones_2(
+        self, seg: list[tuple[str, str]]
+    ) -> list[tuple[str, str]]:
+        new_seg = []
+        sub_finals_list = [
+            lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
+            for (word, pos) in seg
+        ]
+        assert len(sub_finals_list) == len(seg)
+        merge_last = [False] * len(seg)
+        for i, (word, pos) in enumerate(seg):
+            if (
+                i - 1 >= 0
+                and sub_finals_list[i - 1][-1][-1] == "3"
+                and sub_finals_list[i][0][-1] == "3"
+                and not merge_last[i - 1]
+            ):
+                # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
+                if (
+                    not self._is_reduplication(seg[i - 1][0])
+                    and len(seg[i - 1][0]) + len(seg[i][0]) <= 3
+                ):
+                    new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
+                    merge_last[i] = True
+                else:
+                    new_seg.append([word, pos])
+            else:
+                new_seg.append([word, pos])
+        return new_seg
+
+    def _merge_er(self, seg: list[tuple[str, str]]) -> list[tuple[str, str]]:
+        new_seg = []
+        for i, (word, pos) in enumerate(seg):
+            if i - 1 >= 0 and word == "儿" and seg[i - 1][0] != "#":
+                new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
+            else:
+                new_seg.append([word, pos])
+        return new_seg
+
+    def _merge_reduplication(self, seg: list[tuple[str, str]]) -> list[tuple[str, str]]:
+        new_seg = []
+        for i, (word, pos) in enumerate(seg):
+            if new_seg and word == new_seg[-1][0]:
+                new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
+            else:
+                new_seg.append([word, pos])
+        return new_seg
+
+    def pre_merge_for_modify(self, seg: list[tuple[str, str]]) -> list[tuple[str, str]]:
+        seg = self._merge_bu(seg)
+        try:
+            seg = self._merge_yi(seg)
+        except:
+            print("_merge_yi failed")
+        seg = self._merge_reduplication(seg)
+        seg = self._merge_continuous_three_tones(seg)
+        seg = self._merge_continuous_three_tones_2(seg)
+        seg = self._merge_er(seg)
+        return seg
+
+    def modified_tone(self, word: str, pos: str, finals: list[str]) -> list[str]:
+        finals = self._bu_sandhi(word, finals)
+        finals = self._yi_sandhi(word, finals)
+        finals = self._neural_sandhi(word, pos, finals)
+        finals = self._three_sandhi(word, finals)
+        return finals
diff --git a/style_bert_vits2/nlp/english/__init__.py b/style_bert_vits2/nlp/english/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/style_bert_vits2/nlp/english/bert_feature.py b/style_bert_vits2/nlp/english/bert_feature.py
new file mode 100644
index 0000000000000000000000000000000000000000..7692c9e4c6ab0aa7400e3da38ac90f5d77280d46
--- /dev/null
+++ b/style_bert_vits2/nlp/english/bert_feature.py
@@ -0,0 +1,66 @@
+from typing import Optional
+
+import torch
+
+from style_bert_vits2.constants import Languages
+from style_bert_vits2.nlp import bert_models
+
+
+def extract_bert_feature(
+    text: str,
+    word2ph: list[int],
+    device: str,
+    assist_text: Optional[str] = None,
+    assist_text_weight: float = 0.7,
+) -> torch.Tensor:
+    """
+    英語のテキストから BERT の特徴量を抽出する
+
+    Args:
+        text (str): 英語のテキスト
+        word2ph (list[int]): 元のテキストの各文字に音素が何個割り当てられるかを表すリスト
+        device (str): 推論に利用するデバイス
+        assist_text (Optional[str], optional): 補助テキスト (デフォルト: None)
+        assist_text_weight (float, optional): 補助テキストの重み (デフォルト: 0.7)
+
+    Returns:
+        torch.Tensor: BERT の特徴量
+    """
+
+    if device == "cuda" and not torch.cuda.is_available():
+        device = "cpu"
+    model = bert_models.load_model(Languages.EN).to(device)  # type: ignore
+
+    style_res_mean = None
+    with torch.no_grad():
+        tokenizer = bert_models.load_tokenizer(Languages.EN)
+        inputs = tokenizer(text, return_tensors="pt")
+        for i in inputs:
+            inputs[i] = inputs[i].to(device)  # type: ignore
+        res = model(**inputs, output_hidden_states=True)
+        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
+        if assist_text:
+            style_inputs = tokenizer(assist_text, return_tensors="pt")
+            for i in style_inputs:
+                style_inputs[i] = style_inputs[i].to(device)  # type: ignore
+            style_res = model(**style_inputs, output_hidden_states=True)
+            style_res = torch.cat(style_res["hidden_states"][-3:-2], -1)[0].cpu()
+            style_res_mean = style_res.mean(0)
+
+    assert len(word2ph) == res.shape[0], (text, res.shape[0], len(word2ph))
+    word2phone = word2ph
+    phone_level_feature = []
+    for i in range(len(word2phone)):
+        if assist_text:
+            assert style_res_mean is not None
+            repeat_feature = (
+                res[i].repeat(word2phone[i], 1) * (1 - assist_text_weight)
+                + style_res_mean.repeat(word2phone[i], 1) * assist_text_weight
+            )
+        else:
+            repeat_feature = res[i].repeat(word2phone[i], 1)
+        phone_level_feature.append(repeat_feature)
+
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+
+    return phone_level_feature.T
diff --git a/style_bert_vits2/nlp/english/cmudict.py b/style_bert_vits2/nlp/english/cmudict.py
new file mode 100644
index 0000000000000000000000000000000000000000..fd17405f500ab387b6b8a4b40c31687bc281b13b
--- /dev/null
+++ b/style_bert_vits2/nlp/english/cmudict.py
@@ -0,0 +1,46 @@
+import pickle
+from pathlib import Path
+
+
+CMU_DICT_PATH = Path(__file__).parent / "cmudict.rep"
+CACHE_PATH = Path(__file__).parent / "cmudict_cache.pickle"
+
+
+def get_dict() -> dict[str, list[list[str]]]:
+    if CACHE_PATH.exists():
+        with open(CACHE_PATH, "rb") as pickle_file:
+            g2p_dict = pickle.load(pickle_file)
+    else:
+        g2p_dict = read_dict()
+        cache_dict(g2p_dict, CACHE_PATH)
+
+    return g2p_dict
+
+
+def read_dict() -> dict[str, list[list[str]]]:
+    g2p_dict = {}
+    start_line = 49
+    with open(CMU_DICT_PATH, encoding="utf-8") as f:
+        line = f.readline()
+        line_index = 1
+        while line:
+            if line_index >= start_line:
+                line = line.strip()
+                word_split = line.split("  ")
+                word = word_split[0]
+
+                syllable_split = word_split[1].split(" - ")
+                g2p_dict[word] = []
+                for syllable in syllable_split:
+                    phone_split = syllable.split(" ")
+                    g2p_dict[word].append(phone_split)
+
+            line_index = line_index + 1
+            line = f.readline()
+
+    return g2p_dict
+
+
+def cache_dict(g2p_dict: dict[str, list[list[str]]], file_path: Path) -> None:
+    with open(file_path, "wb") as pickle_file:
+        pickle.dump(g2p_dict, pickle_file)
diff --git a/style_bert_vits2/nlp/english/cmudict.rep b/style_bert_vits2/nlp/english/cmudict.rep
new file mode 100644
index 0000000000000000000000000000000000000000..9bc61ebd759c72458da28bb74e039298068fcc0b
--- /dev/null
+++ b/style_bert_vits2/nlp/english/cmudict.rep
@@ -0,0 +1,129530 @@
+## Date:  August 8, 1998
+##
+## The Carnegie Mellon Pronouncing Dictionary [cmudict.0.6] is Copyright 1998
+## by Carnegie Mellon University. Use of this dictionary, for any research or
+## commercial purpose, is completely unrestricted.  If you make use of or
+## redistribute this material, we would appreciate acknowlegement of its
+## origin.
+##
+## cmudict.0.6 is the fifth release of cmudict, first released as cmudict.0.1
+## in September of 1993.  There was no generally available public release
+## of version 0.5.
+##
+## See the README in this directory before you use this dictionary.
+##
+## Thanks to Bill Huggins at BBN; Bill Fisher at NIST; Alex Hauptman,
+## Alex Rudnicky, Jack Mostow, Roni Rosenfeld, Richard Stern,
+## Matthew Siegler, Kevin Lenzo, Maxine Eskenazi, Mosur Ravishankar,
+## Eric Thayer, Kristie Seymore, and Raj Reddy at CMU; Lin Chase at
+## LIMSI; Doug Paul at MIT Lincoln Labs; Ben Serridge at MIT SLS; Murray
+## Spiegel at Bellcore; Tony Robinson at Cambridge UK; David Bowness of
+## CAE Electronics Ltd. and CRIM; Stephen Hocking; Jerry Quinn at BNR
+## Canada, and Marshal Midden for bringing to our attention problems and
+## inadequacies with the first releases. Most special thanks to Bob Weide
+## for all his work on prior versions of the dictionary.
+##
+## We welcome input from users and will continue to acknowledge such input
+## in subsequent releases. If I failed to acknowledge your input in this
+## release, please remind me and I will update these comments. If I failed to
+## fix things that you brought to my attention, please remind me and have
+## patience. If I actually fixed things that you brought to my attention and
+## you appreciate it, I wouldn't mind a pat on the back.
+##
+## This version differs from previous releases of cmudict most significantly
+## in the addition of new words from the common ARPA tasks for 1996 and 1997.
+##
+## There are undoubtedly still errors and inconsistencies in this dictionary
+## so keep your eyes open for problems and mail them to me.
+##
+## We hope this dictionary is an improvement over cmudict.0.4.
+##
+## email: cmudict@cs.cmu.edu
+## web:   http://www.speech.cs.cmu.edu/cgi-bin/cmudict
+## ftp:   ftp://ftp.cs.cmu.edu/project/speech/dict/
+##
+## Thank you for your continued interest in the CMU Pronouncing
+## Dictionary.  Further addictions and improvements are planned
+## for forthcoming releases.
+##
+!EXCLAMATION-POINT  EH2 K - S K L AH0 - M EY1 - SH AH0 N - P OY2 N T
+"CLOSE-QUOTE  K L OW1 Z - K W OW1 T
+"DOUBLE-QUOTE  D AH1 - B AH0 L - K W OW1 T
+"END-OF-QUOTE  EH1 N - D AH0 V - K W OW1 T
+"END-QUOTE  EH1 N D - K W OW1 T
+"IN-QUOTES  IH1 N - K W OW1 T S
+"QUOTE  K W OW1 T
+"UNQUOTE  AH1 N - K W OW1 T
+#SHARP-SIGN  SH AA1 R P - S AY1 N
+%PERCENT  P ER0 - S EH1 N T
+&AMPERSAND  AE1 M - P ER0 - S AE2 N D
+'CAUSE  K AH0 Z
+'COURSE  K AO1 R S
+'EM  AH0 M
+'END-INNER-QUOTE  EH1 N - D IH1 - N ER0 - K W OW1 T
+'END-QUOTE  EH1 N D - K W OW1 T
+'INNER-QUOTE  IH1 - N ER0 - K W OW1 T
+'M  AH0 M
+'N  AH0 N
+'QUOTE  K W OW1 T
+'S  EH1 S
+'SINGLE-QUOTE  S IH1 NG - G AH0 L - K W OW1 T
+'TIL  T IH1 L
+'TIS  T IH1 Z
+'TWAS  T W AH1 Z
+(BEGIN-PARENS  B IH0 - G IH1 N - P ER0 - EH1 N Z
+(IN-PARENTHESES  IH1 N - P ER0 - EH1 N - TH AH0 - S IY2 Z
+(LEFT-PAREN  L EH1 F T - P ER0 - EH1 N
+(OPEN-PARENTHESES  OW1 - P AH0 N - P ER0 - EH1 N - TH AH0 - S IY2 Z
+(PAREN  P ER0 - EH1 N
+(PARENS  P ER0 - EH1 N Z
+(PARENTHESES  P ER0 - EH1 N - TH AH0 - S IY2 Z
+)CLOSE-PAREN  K L OW1 Z - P ER0 - EH1 N
+)CLOSE-PARENTHESES  K L OW1 Z - P ER0 - EH1 N - TH AH0 - S IY2 Z
+)END-PAREN  EH1 N D - P ER0 - EH1 N
+)END-PARENS  EH1 N D - P ER0 - EH1 N Z
+)END-PARENTHESES  EH1 N D - P ER0 - EH1 N - TH AH0 - S IY2 Z
+)END-THE-PAREN  EH1 N D - DH AH0 - P ER0 - EH1 N
+)PAREN  P ER0 - EH1 N
+)PARENS  P ER0 - EH1 N Z
+)RIGHT-PAREN  R AY1 T - P ER0 - EH1 N
+)RIGHT-PAREN(2)  R AY1 T - P EH1 - R AH0 N
+)UN-PARENTHESES  AH1 N - P ER0 - EH1 N - TH AH0 - S IY1 Z
+,COMMA  K AA1 - M AH0
+-DASH  D AE1 SH
+-HYPHEN  HH AY1 - F AH0 N
+...ELLIPSIS  IH0 - L IH1 P - S IH0 S
+.DECIMAL  D EH1 - S AH0 - M AH0 L
+.DOT  D AA1 T
+.FULL-STOP  F UH1 L - S T AA1 P
+.PERIOD  P IH1 - R IY0 - AH0 D
+.POINT  P OY1 N T
+/SLASH  S L AE1 SH
+0MALEFACTORS  M AE1 - L AH0 - F AE2 K - T ER0 Z
+:COLON  K OW1 - L AH0 N
+;SEMI-COLON  S EH1 - M IY0 - K OW1 - L AH0 N
+;SEMI-COLON(2)  S EH1 - M IH0 - K OW2 - L AH0 N
+?QUESTION-MARK  K W EH1 S - CH AH0 N - M AA1 R K
+A  AH0
+A'S  EY1 Z
+A(2)  EY1
+A.  EY1
+A.'S  EY1 Z
+A.S  EY1 Z
+A42128  EY1 - F AO1 R - T UW1 - W AH1 N - T UW1 - EY1 T
+AAA  T R IH2 - P AH0 - L EY1
+AABERG  AA1 - B ER0 G
+AACHEN  AA1 - K AH0 N
+AAKER  AA1 - K ER0
+AALSETH  AA1 L - S EH0 TH
+AAMODT  AA1 - M AH0 T
+AANCOR  AA1 N - K AO2 R
+AARDEMA  AA0 R - D EH1 - M AH0
+AARDVARK  AA1 R D - V AA2 R K
+AARON  EH1 - R AH0 N
+AARON'S  EH1 - R AH0 N Z
+AARONS  EH1 - R AH0 N Z
+AARONSON  EH1 - R AH0 N - S AH0 N
+AARONSON'S  EH1 - R AH0 N - S AH0 N Z
+AARONSON'S(2)  AA1 - R AH0 N - S AH0 N Z
+AARONSON(2)  AA1 - R AH0 N - S AH0 N
+AARTI  AA1 R - T IY2
+AASE  AA1 S
+AASEN  AA1 - S AH0 N
+AB  AE1 B
+AB(2)  EY1 - B IY1
+ABABA  AH0 - B AA1 - B AH0
+ABABA(2)  AA1 - B AH0 - B AH0
+ABACHA  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
+ABADAKA  AH0 - B AE1 - D AH0 - K AH0
+ABADI  AH0 - B AE1 - D IY0
+ABADIE  AH0 - B AE1 - D IY0
+ABAIR  AH0 - B EH1 R
+ABALKIN  AH0 - B AA1 L - K IH0 N
+ABALONE  AE2 - B AH0 - L OW1 - N IY0
+ABALOS  AA0 - B AA1 - L OW0 Z
+ABANDON  AH0 - B AE1 N - D AH0 N
+ABANDONED  AH0 - B AE1 N - D AH0 N D
+ABANDONING  AH0 - B AE1 N - D AH0 - N IH0 NG
+ABANDONMENT  AH0 - B AE1 N - D AH0 N - M AH0 N T
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+ACTUALIZE  AE1 K - CH AH0 W - AH0 - L AY2 Z
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+ACTUALLY(2)  AE1 K SH - L IY0
+ACTUALLY(3)  AE1 K CH - L IY0
+ACTUALLY(4)  AE1 K - SH AH0 - L IY0
+ACTUARIAL  AE2 K - CH AH0 W - EH1 - R IY0 - AH0 L
+ACTUARIES  AE1 K - CH AH0 W - EH2 - R IY0 Z
+ACTUARY  AE1 K - CH AH0 W - EH2 - R IY0
+ACTUATE  AE1 K - CH UW0 W - EY2 T
+ACTUATOR  AE1 K - T Y UW0 - EY2 - T ER0
+ACTUATOR(2)  AE1 K - CH UW0 - EY2 - T ER0
+ACTUATORS  AE1 K - T Y UW0 - EY2 - T ER0 Z
+ACTUATORS(2)  AE1 K - CH UW0 - EY2 - T ER0 Z
+ACTUS  AE1 K - T AH0 S
+ACUFF  AH0 - K AH1 F
+ACUITY  AH0 - K Y UW1 - AH0 - T IY0
+ACUMEN  AH0 - K Y UW1 - M AH0 N
+ACUNA  AA0 - K UW1 - N AH0
+ACUPUNCTURE  AE1 - K Y UW0 - P AH2 NG K - CH ER0
+ACURA  AE1 - K Y ER0 - AH0
+ACURA'S  AE1 - K Y ER0 - AH0 Z
+ACURAS  AE1 - K Y ER0 - AH0 Z
+ACUSON  AE2 - K Y UW1 - S AH0 N
+ACUSTAR  AE1 - K Y UW0 - S T AA2 R
+ACUSYST  AE1 - K Y UW0 - S IH0 S T
+ACUTE  AH0 - K Y UW1 T
+ACUTELY  AH0 - K Y UW1 T - L IY0
+ACUTENESS  AH0 - K Y UW1 T - N AH0 S
+ACYCLOVIR  AH0 - S IH1 - K L OW0 - V IH2 R
+AD  AE1 D
+AD'S  AE1 D Z
+AD-HOC  AE1 D - HH AA1 K
+AD-LIB  AE1 D - L IH1 B
+ADA  EY1 - D AH0
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+ADABELLE  AE1 - D AH0 - B AH0 L
+ADACHI  AA0 - D AA1 - K IY0
+ADAGE  AE1 - D AH0 JH
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+ADAGIO  AH0 - D AA1 - ZH IY0 - OW2
+ADAH  AE1 - D AA0
+ADAIR  AH0 - D EH1 R
+ADAIRE  AA0 - D EH1 R
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+ADALAH  AA0 - D AA1 - L AH0
+ADALIA  AA0 - D AA1 - L IY0 - AH0
+ADAM  AE1 - D AH0 M
+ADAM'S  AE1 - D AH0 M Z
+ADAMANT  AE1 - D AH0 - M AH0 N T
+ADAMANTLY  AE1 - D AH0 - M AH0 N T - L IY0
+ADAMCIK  AA1 - D AH0 M - CH IH0 K
+ADAMCZAK  AA1 - D AH0 M - CH AE0 K
+ADAMCZYK  AA1 - D AH0 M - CH IH0 K
+ADAME  AA0 - D AA1 - M IY0
+ADAMEC  AH0 - D AA1 - M IH0 K
+ADAMEK  AH0 - D AA1 - M EH0 K
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+ADAMI  AA0 - D AA1 - M IY0
+ADAMIK  AH0 - D AA1 - M IH0 K
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+ADAMKUS  AE1 - D AH0 M - K AH0 S
+ADAMO  AA0 - D AA1 - M OW0
+ADAMOWICZ  AH0 - D AA1 - M AH0 - V IH0 CH
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+ADAPT  AH0 - D AE1 P T
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+ADAPTABLE  AH0 - D AE1 P - T AH0 - B AH0 L
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+ADAPTATION  AE2 - D AH0 P - T EY1 - SH AH0 N
+ADAPTATIONS  AE2 - D AE0 P - T EY1 - SH AH0 N Z
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+ADAPTEC  AH0 - D AE1 P - T EH2 K
+ADAPTED  AH0 - D AE1 P - T AH0 D
+ADAPTED(2)  AH0 - D AE1 P - T IH0 D
+ADAPTER  AH0 - D AE1 P - T ER0
+ADAPTERS  AH0 - D AE1 P - T ER0 Z
+ADAPTING  AH0 - D AE1 P - T IH0 NG
+ADAPTIVE  AH0 - D AE1 P - T IH0 V
+ADAPTOR  AH0 - D AE1 P - T ER0
+ADAPTS  AH0 - D AE1 P T S
+ADAR  AH0 - D AA1 R
+ADARAND  AE1 - D AH0 - R AE2 N D
+ADAY  AH0 - D EY1
+ADAZA  AH0 - D AA1 - Z AH0
+ADCOCK  AH0 D - K AA1 K
+ADCOX  AH0 D - K AA1 K S
+ADD  AE1 D
+ADDAIR  AH0 - D EH1 R
+ADDAMS  AE1 - D AH0 M Z
+ADDED  AE1 - D AH0 D
+ADDED(2)  AE1 - D IH0 D
+ADDENDUM  AH0 - D EH1 - D AH0 M
+ADDENDUMS  AH0 - D EH1 - D AH0 M Z
+ADDEO  AA1 - D IY0 - OW0
+ADDER  AE1 - D ER0
+ADDERLEY  AH0 - D ER1 - L IY0
+ADDICKS  AE1 - D IH0 K S
+ADDICT  AH0 - D IH1 K T
+ADDICT(2)  AE1 - D IH2 K T
+ADDICTED  AH0 - D IH1 K - T AH0 D
+ADDICTED(2)  AH0 - D IH1 K - T IH0 D
+ADDICTING  AH0 - D IH1 K - T IH0 NG
+ADDICTION  AH0 - D IH1 K - SH AH0 N
+ADDICTIONS  AH0 - D IH1 K - SH AH0 N Z
+ADDICTIVE  AH0 - D IH1 K - T IH0 V
+ADDICTS  AH0 - D IH1 K T S
+ADDICTS(2)  AE1 - D IH2 K T S
+ADDIDAS  AH0 - D IY1 - D AH0 S
+ADDIDAS'  AH0 - D IY1 - D AH0 S
+ADDIDAS'S  AH0 - D IY1 - D AH0 - S IH0 Z
+ADDIDASES  AH0 - D IY1 - D AH0 - S IH0 Z
+ADDIE  AE1 - D IY0
+ADDING  AE1 - D IH0 NG
+ADDINGTON  AE1 - D IH0 NG - T AH0 N
+ADDIS  AA1 - D IH0 S
+ADDIS-ABABA  AA1 - D IH0 S - AH0 - B AA1 - B AH0
+ADDIS-ABABA(2)  AA1 - D IY0 - S AH0 - B AA1 - B AH0
+ADDISON  AE1 - D AH0 - S AH0 N
+ADDISON'S  AE1 - D IH0 - S AH0 N Z
+ADDISON(2)  AE1 - D IH0 - S AH0 N
+ADDITION  AH0 - D IH1 - SH AH0 N
+ADDITIONAL  AH0 - D IH1 - SH AH0 - N AH0 L
+ADDITIONAL(2)  AH0 - D IH1 SH - N AH0 L
+ADDITIONALLY  AH0 - D IH1 - SH AH0 N - AH0 - L IY0
+ADDITIONALLY(2)  AH0 - D IH1 SH - N AH0 - L IY0
+ADDITIONS  AH0 - D IH1 - SH AH0 N Z
+ADDITIVE  AE1 - D AH0 - T IH0 V
+ADDITIVE(2)  AE1 - D IH0 - T IH0 V
+ADDITIVES  AE1 - D AH0 - T IH0 V Z
+ADDITIVES(2)  AE1 - D IH0 - T IH0 V Z
+ADDLE  AE1 - D AH0 L
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+ADDRESS  AE1 - D R EH2 S
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+ADDRESSABLE  AH0 - D R EH1 - S AH0 - B AH0 L
+ADDRESSED  AH0 - D R EH1 S T
+ADDRESSEE  AE2 - D R EH0 - S IY1
+ADDRESSES  AE1 - D R EH1 - S IH0 Z
+ADDRESSES(2)  AH0 - D R EH1 - S IH0 Z
+ADDRESSING  AH0 - D R EH1 - S IH0 NG
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+ADDUCI  AA0 - D UW1 - CH IY0
+ADDWEST  AE2 D - W EH1 S T
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+ADEE  AH0 - D IY1
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+ADHERENCE  AH0 D - HH IH1 - R AH0 N S
+ADHERENT  AH0 D - HH IH1 - R AH0 N T
+ADHERENTS  AE0 D - HH IH1 - R AH0 N T S
+ADHERES  AH0 D - HH IH1 R Z
+ADHERING  AH0 D - HH IH1 - R IH0 NG
+ADHESIVE  AE0 D - HH IY1 - S IH0 V
+ADHESIVE(2)  AH0 D - HH IY1 - S IH0 V
+ADHESIVES  AE0 D - HH IY1 - S IH0 V Z
+ADHESIVES(2)  AH0 D - HH IY1 - S IH0 V Z
+ADIA  AA1 - D IY0 - AH0
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+ADIEU  AH0 - D UW1
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+ADINE  AA0 - D IY1 - N IY0
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+ADIOS  AA2 - D IY0 - OW1 S
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+ADMISSIBLE  AH0 D - M IH1 - S AH0 - B AH0 L
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+ANYPLACE  EH1 - N IY0 - P L EY2 S
+ANYTHING  EH1 - N IY0 - TH IH2 NG
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+AOUN  AW1 - AH0 N
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+APOLLINE  AE1 - P AH0 - L AY2 N
+APOLLINIAN  AE2 - P AH0 - L IH1 - N IY0 - AH0 N
+APOLLO  AH0 - P AA1 - L OW0
+APOLLO'S  AH0 - P AA1 - L OW0 Z
+APOLLONIAN  AE2 - P AH0 - L OW1 - N IY0 - AH0 N
+APOLOGETIC  AH0 - P AA2 - L AH0 - JH EH1 - T IH0 K
+APOLOGETICALLY  AH0 - P AA2 - L AH0 - JH EH1 - T IH0 K - L IY0
+APOLOGIES  AH0 - P AA1 - L AH0 - JH IY0 Z
+APOLOGIST  AH0 - P AA1 - L AH0 - JH AH0 S T
+APOLOGISTS  AH0 - P AA1 - L AH0 - JH AH0 S T S
+APOLOGISTS(2)  AH0 - P AA1 - L AH0 - JH AH0 S S
+APOLOGISTS(3)  AH0 - P AA1 - L AH0 - JH AH0 S
+APOLOGIZE  AH0 - P AA1 - L AH0 - JH AY2 Z
+APOLOGIZED  AH0 - P AA1 - L AH0 - JH AY2 Z D
+APOLOGIZES  AH0 - P AA1 - L AH0 - JH AY2 - Z IH0 Z
+APOLOGIZING  AH0 - P AA1 - L AH0 - JH AY2 - Z IH0 NG
+APOLOGY  AH0 - P AA1 - L AH0 - JH IY0
+APONTE  AH0 - P AA1 N - T IY0
+APOPA  AH0 - P OW1 - P AH0
+APOPKA  AH0 - P AO1 P - K AH0
+APOPLECTIC  AE2 - P AH0 - P L EH1 K - T IH0 K
+APOPLEXY  AE1 - P AH0 - P L EH2 K - S IY0
+APOSTLE  AH0 - P AA1 - S AH0 L
+APOSTLES  AH0 - P AA1 - S AH0 L Z
+APOSTOL  AE1 - P AH0 - S T AH0 L
+APOSTOLOPOUL  AH0 - P AA2 - S T OW0 - L OW0 - P UW1 L
+APOSTROPHE  AH0 - P AA1 S - T R AH0 - F IY0
+APOTHECARY  AH0 - P AA1 - TH AH0 - K EH2 - R IY0
+APOTHEOSIS  AH0 - P AA2 - TH IY0 - OW1 - S AH0 S
+APP  AE1 P
+APPALACHIA  AE2 - P AH0 - L AE1 - CH IY0 - AH0
+APPALACHIAN  AE2 - P AH0 - L EY1 - CH AH0 N
+APPALACHIAN(2)  AE2 - P AH0 - L EY1 - SH AH0 N
+APPALACHIAN(3)  AE2 - P AH0 - L AE1 - CH AH0 N
+APPALACHIANS  AE2 - P AH0 - L EY1 - CH AH0 N Z
+APPALACHIANS(2)  AE2 - P AH0 - L EY1 - SH AH0 N Z
+APPALACHIANS(3)  AE1 - P AH0 - L AE1 - CH AH0 N Z
+APPALL  AH0 - P AO1 L
+APPALLED  AH0 - P AO1 L D
+APPALLING  AH0 - P AO1 - L IH0 NG
+APPALLINGLY  AH0 - P AO1 - L IH0 NG - L IY0
+APPALLS  AH0 - P AO1 L Z
+APPARATCHIK  AE2 - P ER0 - AE1 - CH IH0 K
+APPARATCHIKS  AE2 - P ER0 - AE1 - CH IH0 K S
+APPARATUS  AE2 - P ER0 - AE1 - T AH0 S
+APPARATUSES  AE2 - P ER0 - AE1 - T AH0 - S AH0 Z
+APPAREL  AH0 - P AE1 - R AH0 L
+APPAREL(2)  AH0 - P EH1 - R AH0 L
+APPARENT  AH0 - P EH1 - R AH0 N T
+APPARENTLY  AH0 - P EH1 - R AH0 N T - L IY0
+APPARITION  AE2 - P ER0 - IH1 - SH AH0 N
+APPARITIONS  AE2 - P ER0 - IH1 - SH AH0 N Z
+APPEAL  AH0 - P IY1 L
+APPEALABLE  AH0 - P IY1 - L AH0 - B AH0 L
+APPEALED  AH0 - P IY1 L D
+APPEALING  AH0 - P IY1 - L IH0 NG
+APPEALINGLY  AH0 - P IY1 - L IH0 NG - L IY0
+APPEALS  AH0 - P IY1 L Z
+APPEAR  AH0 - P IH1 R
+APPEARANCE  AH0 - P IH1 - R AH0 N S
+APPEARANCES  AH0 - P IH1 - R AH0 N - S AH0 Z
+APPEARED  AH0 - P IH1 R D
+APPEARING  AH0 - P IH1 - R IH0 NG
+APPEARS  AH0 - P IH1 R Z
+APPEASE  AH0 - P IY1 Z
+APPEASED  AH0 - P IY1 Z D
+APPEASEMENT  AH0 - P IY1 Z - M AH0 N T
+APPEASING  AH0 - P IY1 - Z IH0 NG
+APPEL  AE1 - P AH0 L
+APPELBAUM  AE1 - P AH0 L - B AW2 M
+APPELHANS  AE1 - P IH0 L - HH AH0 N Z
+APPELL  AE1 - P AH0 L
+APPELLATE  AH0 - P EH1 - L IH0 T
+APPELLATE(2)  AH0 - P EH1 - L EY2 T
+APPELLATION  AE2 - P AH0 - L EY1 - SH AH0 N
+APPELLATIONS  AE2 - P AH0 - L EY1 - SH AH0 N Z
+APPELMAN  AE1 - P AH0 L - M AH0 N
+APPELT  AE1 - P IH0 L T
+APPEND  AH0 - P EH1 N D
+APPENDAGE  AH0 - P EH1 N - D IH0 JH
+APPENDAGES  AH0 - P EH1 N - D IH0 - JH IH0 Z
+APPENDECTOMIES  AE2 - P AH0 N - D EH1 K - T AH0 - M IY0 Z
+APPENDECTOMY  AE2 - P IH0 N - D EH1 K - T AH0 - M IY0
+APPENDED  AH0 - P EH1 N - D IH0 D
+APPENDIX  AH0 - P EH1 N - D IH0 K S
+APPENDIXES  AH0 - P EH1 N - D IH0 K - S IH0 Z
+APPENZELLER  AE1 - P IH0 N - Z AH0 L - ER0
+APPERSON  AE1 - P ER0 - S AH0 N
+APPERT  AE1 - P ER0 T
+APPETITE  AE1 - P AH0 - T AY2 T
+APPETITES  AE1 - P AH0 - T AY2 T S
+APPETITO  AE2 - P AH0 - T IY1 - T OW2
+APPETIZER  AE1 - P AH0 - T AY2 - Z ER0
+APPETIZERS  AE1 - P AH0 - T AY2 - Z ER0 Z
+APPETIZING  AE1 - P IH0 - T AY2 - Z IH0 NG
+APPIA  AE1 - P IY0 - AH0
+APPIAN  AE1 - P IY0 - AH0 N
+APPLAUD  AH0 - P L AO1 D
+APPLAUDED  AH0 - P L AO1 - D AH0 D
+APPLAUDED(2)  AH0 - P L AO1 - D IH0 D
+APPLAUDING  AH0 - P L AO1 - D IH0 NG
+APPLAUDS  AH0 - P L AO1 D Z
+APPLAUSE  AH0 - P L AO1 Z
+APPLE  AE1 - P AH0 L
+APPLE'S  AE1 - P AH0 L Z
+APPLEBAUM  AE1 - P AH0 L - B AW2 M
+APPLEBEE  AE1 - P AH0 L - B IY2
+APPLEBEE'S  AE1 - P AH0 L - B IY2 Z
+APPLEBERRY  AE1 - P AH0 L - B EH2 - R IY0
+APPLEBY  AE1 - P IH0 L - B IY0
+APPLEGARTH  AE1 - P L IH0 - G AA0 R TH
+APPLEGATE  AE1 - P AH0 L - G EY2 T
+APPLEMAN  AE1 - P AH0 L - M AH0 N
+APPLEQUIST  AE1 - P AH0 L - K W IH0 S T
+APPLES  AE1 - P AH0 L Z
+APPLESAUCE  AE1 - P AH0 L - S AO2 S
+APPLESEED  AE1 - P AH0 L - S IY2 D
+APPLESHARE  AE1 - P AH0 L - SH EH2 R
+APPLETON  AE1 - P AH0 L - T AH0 N
+APPLEWHITE  AE1 - P AH0 L - W AY2 T
+APPLEYARD  AE1 - P AH0 L - Y AA2 R D
+APPLIANCE  AH0 - P L AY1 - AH0 N S
+APPLIANCES  AH0 - P L AY1 - AH0 N - S AH0 Z
+APPLIANCES(2)  AH0 - P L AY1 - AH0 N - S IH0 Z
+APPLICABILITY  AE2 - P L AH0 - K AH0 - B IH1 - L AH0 - T IY0
+APPLICABLE  AE1 - P L AH0 - K AH0 - B AH0 L
+APPLICANT  AE1 - P L IH0 - K AH0 N T
+APPLICANT'S  AE1 - P L IH0 - K AH0 N T S
+APPLICANTS  AE1 - P L IH0 - K AH0 N T S
+APPLICANTS'  AE1 - P L AH0 - K AH0 N T S
+APPLICATION  AE2 - P L AH0 - K EY1 - SH AH0 N
+APPLICATIONS  AE2 - P L AH0 - K EY1 - SH AH0 N Z
+APPLICATOR  AE1 - P L AH0 - K EY2 - T ER0
+APPLICATORS  AE1 - P L AH0 - K EY2 - T ER0 Z
+APPLIED  AH0 - P L AY1 D
+APPLIES  AH0 - P L AY1 Z
+APPLIN  AE1 - P L IH0 N
+APPLING  AE1 - P AH0 L - IH0 NG
+APPLING(2)  AE1 - P L IH0 NG
+APPLY  AH0 - P L AY1
+APPLYING  AH0 - P L AY1 - IH0 NG
+APPOINT  AH0 - P OY1 N T
+APPOINTED  AH0 - P OY1 N - T AH0 D
+APPOINTED(2)  AH0 - P OY1 N - T IH0 D
+APPOINTEE  AH0 - P OY0 N - T IY1
+APPOINTEE(2)  AH0 - P OY1 N - T IY0
+APPOINTEES  AH0 - P OY0 N - T IY1 Z
+APPOINTEES(2)  AH0 - P OY1 N - T IY0 Z
+APPOINTING  AH0 - P OY1 N - T IH0 NG
+APPOINTIVE  AH0 - P OY1 N - T IH0 V
+APPOINTMENT  AH0 - P OY1 N T - M AH0 N T
+APPOINTMENTS  AH0 - P OY1 N T - M AH0 N T S
+APPOINTS  AH0 - P OY1 N T S
+APPORTION  AH0 - P AO1 R - SH AH0 N
+APPORTIONED  AH0 - P AO1 R - SH AH0 N D
+APPORTIONING  AH0 - P AO1 R - SH AH0 N - IH0 NG
+APPORTIONMENT  AH0 - P AO1 R - SH AH0 N - M AH0 N T
+APPRAISAL  AH0 - P R EY1 - Z AH0 L
+APPRAISALS  AH0 - P R EY1 - Z AH0 L Z
+APPRAISE  AH0 - P R EY1 Z
+APPRAISED  AH0 - P R EY1 Z D
+APPRAISER  AH0 - P R EY1 - Z ER0
+APPRAISERS  AH0 - P R EY1 - Z ER0 Z
+APPRAISES  AH0 - P R EY1 - Z IH0 Z
+APPRAISING  AH0 - P R EY1 - Z IH0 NG
+APPRECIABLE  AH0 - P R IY1 - SH AH0 - B AH0 L
+APPRECIABLY  AH0 - P R IY1 - SH AH0 - B L IY0
+APPRECIATE  AH0 - P R IY1 - SH IY0 - EY2 T
+APPRECIATED  AH0 - P R IY1 - SH IY0 - EY2 - T IH0 D
+APPRECIATES  AH0 - P R IY1 - SH IY0 - EY2 T S
+APPRECIATING  AH0 - P R IY1 - SH IY0 - EY2 - T IH0 NG
+APPRECIATION  AH0 - P R IY2 - SH IY0 - EY1 - SH AH0 N
+APPRECIATIVE  AH0 - P R IY1 - SH IY0 - EY2 - T IH0 V
+APPRECIATIVE(2)  AH0 - P R IY1 - SH Y AH0 - T IH0 V
+APPRECIATIVELY  AH0 - P R IY1 - SH IY0 - EY2 - T IH0 V - L IY0
+APPRECIATIVELY(2)  AH0 - P R IY1 - SH Y AH0 - T IH0 V - L IY0
+APPREHEND  AE2 - P R IH0 - HH EH1 N D
+APPREHENDED  AE2 - P R IH0 - HH EH1 N - D AH0 D
+APPREHENDING  AE2 - P R IH0 - HH EH1 N - D IH0 NG
+APPREHENSION  AE2 - P R IH0 - HH EH1 N - SH AH0 N
+APPREHENSIONS  AE2 - P R IH0 - HH EH1 N - SH AH0 N Z
+APPREHENSIVE  AE2 - P R IH0 - HH EH1 N - S IH0 V
+APPREHENSIVE(2)  AE2 - P R IY0 - HH EH1 N - S IH0 V
+APPRENTICE  AH0 - P R EH1 N - T AH0 S
+APPRENTICE(2)  AH0 - P R EH1 N - T IH0 S
+APPRENTICED  AH0 - P R EH1 N - T AH0 S T
+APPRENTICES  AH0 - P R EH1 N - T AH0 - S IH0 Z
+APPRENTICESHIP  AH0 - P R EH1 N - T AH0 S - SH IH2 P
+APPRENTICESHIPS  AH0 - P R EH1 N - T AH0 S - SH IH2 P S
+APPRISE  AH0 - P R AY1 Z
+APPRISED  AH0 - P R AY1 Z D
+APPROACH  AH0 - P R OW1 CH
+APPROACHABLE  AH0 - P R OW1 - CH AH0 - B AH0 L
+APPROACHED  AH0 - P R OW1 CH T
+APPROACHES  AH0 - P R OW1 - CH AH0 Z
+APPROACHES(2)  AH0 - P R OW1 - CH IH0 Z
+APPROACHING  AH0 - P R OW1 - CH IH0 NG
+APPROBATION  AE2 - P R AH0 - B EY1 - SH AH0 N
+APPROPRIATE  AH0 - P R OW1 - P R IY0 - AH0 T
+APPROPRIATE(2)  AH0 - P R OW1 - P R IY0 - EY2 T
+APPROPRIATED  AH0 - P R OW1 - P R IY0 - EY2 - T IH0 D
+APPROPRIATELY  AH0 - P R OW1 - P R IY0 - IH0 T - L IY0
+APPROPRIATENESS  AH0 - P R OW1 - P R IY0 - AH0 T - N AH0 S
+APPROPRIATES  AH0 - P R OW1 - P R IY0 - EY2 T S
+APPROPRIATING  AH0 - P R OW1 - P R IY0 - EY2 - T IH0 NG
+APPROPRIATION  AH0 - P R OW2 - P R IY0 - EY1 - SH AH0 N
+APPROPRIATIONS  AH0 - P R OW2 - P R IY0 - EY1 - SH AH0 N Z
+APPROPRIATOR  AH0 - P R OW1 - P R IY0 - EY2 - T ER0
+APPROPRIATORS  AH0 - P R OW1 - P R IY0 - EY2 - T ER0 Z
+APPROVABLE  AH0 - P R UW1 - V AH0 - B AH0 L
+APPROVAL  AH0 - P R UW1 - V AH0 L
+APPROVALS  AH0 - P R UW1 - V AH0 L Z
+APPROVE  AH0 - P R UW1 V
+APPROVED  AH0 - P R UW1 V D
+APPROVES  AH0 - P R UW1 V Z
+APPROVING  AH0 - P R UW1 - V IH0 NG
+APPROVINGLY  AH0 - P R UW1 - V IH0 NG - L IY0
+APPROXIMATE  AH0 - P R AA1 K - S AH0 - M AH0 T
+APPROXIMATE(2)  AH0 - P R AA1 K - S AH0 - M EY2 T
+APPROXIMATED  AH0 - P R AA1 K - S AH0 - M EY2 - T AH0 D
+APPROXIMATELY  AH0 - P R AA1 K - S AH0 - M AH0 T - L IY0
+APPROXIMATES  AH0 - P R AA1 K - S AH0 - M AH0 T S
+APPROXIMATES(2)  AH0 - P R AA1 K - S AH0 - M EY2 T S
+APPROXIMATING  AH0 - P R AA1 K - S IH0 - M EY2 - T IH0 NG
+APPROXIMATION  AH0 - P R AA2 K - S AH0 - M EY1 - SH AH0 N
+APPROXIMATIONS  AH0 - P R AA2 K - S AH0 - M EY1 - SH AH0 N Z
+APPS  AE1 P S
+APRA  EY1 - P R AH0
+APREA  AA1 - P R IY0 - AH0
+APRICOT  EY1 - P R AH0 - K AA2 T
+APRICOTS  AE1 - P R AH0 - K AA2 T S
+APRIL  EY1 - P R AH0 L
+APRIL'S  EY1 - P R AH0 L Z
+APRILE  AA1 - P R AH0 L
+APRODIVA  AE2 - P R AO0 - D IY1 - V AH0
+APRON  EY1 - P R AH0 N
+APRONS  EY1 - P R AH0 N Z
+APROPOS  AE2 - P R AH0 - P OW1
+APSELL  AE1 P - S AH0 L
+APSEY  AE1 P - S IY0
+APSO  AE1 P - S OW0
+APSTAR  AE1 P - S T AA2 R
+APT  AE1 P T
+APTER  AE1 P - T ER0
+APTITUDE  AE1 P - T AH0 - T UW2 D
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+APTLY  AE1 P T - L IY0
+APUZZO  AA0 - P UW1 - Z OW0
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+AQUA  AE1 - K W AH0
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+AQUACULTURE  AE1 - K W AH0 - K AH2 L - CH ER0
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+AQUARIUS  AH0 - K W EH1 - R IY0 - AH0 S
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+AQUITAINE  AE2 - K W IH0 - T EY1 N
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+ARABIAN  ER0 - EY1 - B IY0 - AH0 N
+ARABIANS  ER0 - EY1 - B IY0 - AH0 N Z
+ARABIC  AE1 - R AH0 - B IH0 K
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+ARABICA  ER0 - AE1 - B IH0 - K AH0
+ARABICHON  AH0 - R AE1 - B IH0 - SH AO2 N
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+ARABIE  ER0 - AE1 - B IY0
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+BALK  B AO1 K
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+BALLCLUB  B AO1 L - K L AH0 B
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+BALLISTIC  B AH0 - L IH1 - S T IH0 K
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+BALLPARK  B AO1 L - P AA2 R K
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+BALLPLAYER  B AO1 L - P L EY2 - ER0
+BALLPLAYERS  B AO0 L - P L EY1 - ER0 Z
+BALLPOINT  B AO1 L - P OY0 N T
+BALLPOINTS  B AO1 L - P OY0 N T S
+BALLROOM  B AO1 L - R UW2 M
+BALLROOMS  B AO1 L - R UW2 M Z
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+BALLWEG  B AE1 L - W IH0 G
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+BALLY(2)  B EY1 - L IY0
+BALLYHOO  B AE1 - L IY0 - HH UW1
+BALLYHOOED  B AE1 - L IY0 - HH UW1 D
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+BANCORP(2)  B AE1 N - K AO0 R P
+BANCORPORATION  B AE1 N - K AO2 R - P ER0 - EY0 - SH AH0 N
+BANCROFT  B AE1 NG - K R AO0 F T
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+BANCSERVE  B AE1 N K - S ER0 V
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+BANDAGES  B AE1 N - D AH0 - JH AH0 Z
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+BANDERAS  B AE0 N - D ER1 - AH0 S
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+BANDICOOTS  B AE1 N - D IH0 - K UW2 T S
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+BANDIED  B AE1 N - D IY0 D
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+BANDLEADER  B AE1 N D - L IY0 - D ER0
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+BANET  B AE1 - N IH0 T
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+BANFF  B AE1 N F
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+BANGHART  B AE1 NG - HH AA2 R T
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+BANGKOK  B AE0 NG - K AA1 K
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+BANGLADESH  B AE1 NG - L AH0 - D EH2 SH
+BANGLADESH'S  B AE1 NG - L AH0 - D EH2 - SH IH0 Z
+BANGLADESHI  B AE1 NG - L AH0 - D EH2 - SH IY0
+BANGLADESHI'S  B AE1 NG - L AH0 - D EH2 - SH IY0 Z
+BANGLADESHIS  B AE1 NG - L AH0 - D EH2 - SH IY0 Z
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+BANICK  B AE1 - N IH0 K
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+BANISHMENT  B AE1 - N IH0 SH - M AH0 N T
+BANISTER  B AE1 - N IH0 - S T ER0
+BANJA  B AA1 - N Y AH0
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+BANK  B AE1 NG K
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+BANKAMERICA'S  B AE2 NG - K AH0 - M EH1 - R IH0 - K AH0 Z
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+BANKROLLING  B AE1 NG K - R OW2 - L IH0 NG
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+BANKRUPCTY  B AE1 NG - K R AH0 P T - S IY0
+BANKRUPT  B AE1 NG - K R AH0 P T
+BANKRUPTCIES  B AE1 NG - K R AH0 P T - S IY0 Z
+BANKRUPTCY  B AE1 NG - K R AH0 P - S IY0
+BANKRUPTCY'S  B AE1 NG - K R AH0 P - S IY0 Z
+BANKRUPTCY(2)  B AE1 NG - K R AH0 P T - S IY0
+BANKRUPTED  B AE1 NG - K R AH0 P - T IH0 D
+BANKRUPTING  B AE1 NG - K R AH2 P - T IH0 NG
+BANKS  B AE1 NG K S
+BANKS'  B AE1 NG K S
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+BANKSHARE  B AE1 NG K - SH EH2 R
+BANKSHARES  B AE1 NG K - SH EH2 R Z
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+BANKSTON  B AE1 NG K - S T AH0 N
+BANKVEREIN  B AE1 NG K - V ER0 - AY2 N
+BANKVERMONT  B AE1 NG K - V ER0 - M AA1 N T
+BANKWORCESTER  B AE1 NG K - W ER1 - CH EH2 - S T ER0
+BANKWORCESTER(2)  B AE1 NG - K W UW1 - S T ER0
+BANN  B AE1 N
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+BANNED  B AE1 N D
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+BANNERMAN  B AE1 - N ER0 - M AH0 N
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+BANNICK  B AE1 - N IH0 K
+BANNING  B AE1 - N IH0 NG
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+BANNISTER  B AE1 - N AH0 - S T ER0
+BANNISTER(2)  B AE1 - N IH0 - S T ER0
+BANNON  B AE1 - N AH0 N
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+BANOUN  B AH0 - N UW1 N
+BANPAIS  B AE2 N - P EY1
+BANPONCE  B AE1 N - P AA0 N S
+BANQUE  B AE1 NG K
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+BANQUETS  B AE1 NG - K W AH0 T S
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+BANS  B AE1 N Z
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+BANSHEE  B AE0 N - SH IY1
+BANSHEE(2)  B AE1 N - SH IY0
+BANTA  B AE1 N - T AH0
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+CHAGRINED  SH AH0 - G R IH1 N D
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+CHAIDEZ  CH AA0 - IY1 - D EH0 Z
+CHAIKEN  CH EY1 - K AH0 N
+CHAIKIN  CH EY1 - K IH0 N
+CHAIM  HH AY1 - IH0 M
+CHAIN  CH EY1 N
+CHAIN'S  CH EY1 N Z
+CHAINED  CH EY1 N D
+CHAINING  CH EY1 - N IH0 NG
+CHAINS  CH EY1 N Z
+CHAINS'  CH EY1 N Z
+CHAINSAW  CH EY1 N - S AO2
+CHAINSAWS  CH EY1 N - S AO2 Z
+CHAIR  CH EH1 R
+CHAIRED  CH EH1 R D
+CHAIRES  SH EH1 R Z
+CHAIREZ  CH AA0 - IH1 - R EH0 Z
+CHAIRING  CH EH1 - R IH0 NG
+CHAIRMAN  CH EH1 R - M AH0 N
+CHAIRMAN'S  CH EH1 R - M AH0 N Z
+CHAIRMANSHIP  CH EH1 R - M AH0 N - SH IH2 P
+CHAIRMANSHIPS  CH EH1 R - M AH0 N - SH IH2 P S
+CHAIRMEN  CH EH1 R - M IH0 N
+CHAIRPEOPLE  CH EH1 R - P IY2 - P AH0 L
+CHAIRPERSON  CH EH1 R - P ER2 - S AH0 N
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+CHAIRWOMAN  CH EH1 R - W UH2 - M AH0 N
+CHAIRWOMEN  CH EH1 R - W IH2 - M EH0 N
+CHAISE  SH EY1 Z
+CHAISSON  CH EY1 - S AH0 N
+CHAIT  CH EY1 T
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+CHALABI  CH AH0 - L AA1 - B IY0
+CHALASANI  CH AE2 - L AH0 - S AE1 - N IY0
+CHALET  SH AE1 - L EY2
+CHALET(2)  SH AH0 - L EY1
+CHALETS  SH AH0 - L EY1 Z
+CHALETS(2)  SH AE1 - L EY2 Z
+CHALFANT  CH AE1 - F AH0 N T
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+CHALFIN  CH AE1 L - F IH0 N
+CHALIFOUX  SH AE1 - L IH0 - F UW0
+CHALK  CH AA1 K
+CHALK(2)  CH AO1 K
+CHALKED  CH AO1 K T
+CHALKER  CH AO1 - K ER0
+CHALKING  CH AO1 - K IH0 NG
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+CHALLENGED  CH AE1 - L AH0 JH D
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+CHALLENGERS  CH AE1 - L AH0 N - JH ER0 Z
+CHALLENGERY  CH AE1 - L AH0 N - JH ER0 - IY0
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+CHAMBERED  CH EY1 M - B ER0 D
+CHAMBERLAIN  CH EY1 M - B ER0 - L AH0 N
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+CHAMBLEE  CH AE1 M - B L IY0
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+CHAMPLAIN  SH AE0 M - P L EY1 N
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+CHANGE  CH EY1 N JH
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+CHARLESWORTH(2)  CH AA1 - R AH0 L Z - W ER0 TH
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+CHARPENTIER  SH AA2 R - P AH0 N - T IH1 R
+CHARPIE  CH AA1 R - P IY0
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+CHARREN  CH EH1 - R AH0 N
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+CHARRING  CH AA1 - R IH0 NG
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+CHARTIER  CH AA1 R - T IY0 - ER0
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+CHARTIST  CH AA1 R - T IH0 S T
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+CHASKA  CH AE1 S - K AH0
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+CHATICHAI  CH AE1 - T IH0 - CH AY2
+CHATICHAI'S  CH AE1 - T IH2 - K AY0 Z
+CHATICHAI'S(2)  CH AE1 - T IH0 - CH AY2 Z
+CHATIHACHI  CH AE2 - T IH0 - HH AA1 - CH IY0
+CHATIHACHI'S  CH AE2 - T IH0 - HH AA1 - CH IY0 Z
+CHATMAN  CH AE1 T - M AH0 N
+CHATMON  CH AE1 T - M AH0 N
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+CHATSWOOD  CH AE1 T S - W UH2 D
+CHATSWORTH  CH AE1 T - S W ER2 TH
+CHATTAHOOCHEE  CH AE2 - T AH0 - HH UW1 - CH IY0
+CHATTAHOOCHEE'S  CH AE2 - T AH0 - HH UW1 - CH IY0 Z
+CHATTANOOGA  CH AE2 - T AH0 - N UW1 - G AH0
+CHATTANOOGA'S  CH AE2 - T AH0 - N UW1 - G AH0 Z
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+CHATTED  CH AE1 - T AH0 D
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+CHATTEL  CH AE1 - T AH0 L
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+CHATTERING  CH AE1 - T ER0 - IH0 NG
+CHATTERJEE  CH AH0 - T ER1 - JH IY0
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+CHATTY  CH AE1 - T IY0
+CHATWAL  CH AE1 T - W AO2 L
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+CHAUCER  CH AO1 - S ER0
+CHAUCER'S  CH AO1 - S ER0 Z
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+CHAUDOIN  SH OW0 - D OY1 N
+CHAUFFEUR  SH OW0 - F ER1
+CHAUFFEUR'S  SH OW0 - F ER1 Z
+CHAUFFEUR'S(2)  SH OW1 - F ER0 Z
+CHAUFFEUR(2)  SH OW1 - F ER0
+CHAUFFEURED  SH OW0 - F ER1 D
+CHAUFFEURED(2)  SH OW1 - F ER0 D
+CHAUFFEURS  SH OW0 - F ER1 Z
+CHAUFFEURS(2)  SH OW1 - F ER0 Z
+CHAUMET  CH AO1 - M IH0 T
+CHAUNCE  CH AO1 N S
+CHAUNCEY  CH AO1 N - S IY0
+CHAUNCY  CH AO1 N - S IY0
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+CLAIBORNE'S(2)  K L EY1 - B ER0 N Z
+CLAIM  K L EY1 M
+CLAIMANT  K L EY1 - M AH0 N T
+CLAIMANTS  K L EY1 - M AH0 N T S
+CLAIMANTS'  K L EY1 - M AH0 N T S
+CLAIMED  K L EY1 M D
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+CLAIR  K L EH1 R
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+CLAIRMONT  K L EH1 R - M AH0 N T
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+CLAIRVOYANCE  K L EH0 R - V OY1 - AH0 N S
+CLAIRVOYANT  K L EH0 R - V OY1 - AH0 N T
+CLAM  K L AE1 M
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+CLANDESTINE  K L AE0 N - D EH1 - S T IH0 N
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+CLARIDGE  K L EH1 - R IH0 JH
+CLARIDGES  K L EH1 - R IH0 - JH IH0 Z
+CLARIFICATION  K L EH2 - R AH0 - F AH0 - K EY1 - SH AH0 N
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+CLARIFIED  K L EH1 - R AH0 - F AY2 D
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+CLARITA  K L AA0 - R IY1 - T AH0
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+CLARITY  K L EH1 - R AH0 - T IY0
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+CLARK  K L AA1 R K
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+CLARKE  K L AA1 R K
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+CLASSIFIED  K L AE1 - S AH0 - F AY2 D
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+CLEANTHA  K L IY1 N - TH AH0
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+CLEAR  K L IH1 R
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+CLEARCUT  K L IH1 R - K AH2 T
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+CLEVETRUST  K L IY1 V - T R AH1 S T
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+CLEVITE  K L AH0 - V AY1 T
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+CLIETT  K L IY1 T
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+CLIFFORD  K L IH1 - F ER0 D
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+CLIFFS  K L IH1 F S
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+CLINCHING  K L IH1 N - CH IH0 NG
+CLINE  K L AY1 N
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+CLING  K L IH1 NG
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+COLONIZERS  K AA1 - L AH0 - N AY2 - Z ER0 Z
+COLONNA  K OW0 - L OW1 - N AH0
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+COLOPY  K AH0 - L OW1 - P IY0
+COLOR  K AH1 - L ER0
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+COLORADAN  K AA2 - L ER0 - AA1 - D AH0 N
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+COLORADO  K AA2 - L ER0 - AA1 - D OW0
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+COLORADO(2)  K AA2 - L ER0 - AE1 - D OW0
+COLORATION  K AH2 - L ER0 - EY1 - SH AH0 N
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+COLORED  K AH1 - L ER0 D
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+COLORFUL  K AH1 - L ER0 - F AH0 L
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+COLORISTIC  K AH2 - L ER0 - IH1 - S T IH0 K
+COLORIZATION  K AH2 - L ER0 - AH0 - Z EY1 - SH AH0 N
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+COLORIZED  K AH1 - L ER0 - AY2 Z D
+COLORIZING  K AH1 - L ER0 - AY2 - Z IH0 NG
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+COLORS  K AH1 - L ER0 Z
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+COLOSIMO  K OW0 - L OW0 - S IY1 - M OW0
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+COLUCCIO  K OW0 - L UW1 - CH IY0 - OW0
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+COLUMBIAN  K OW2 - L AH1 M - B IY0 - AH0 N
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+COLUMBINE  K AA1 - L AH0 M - B AY2 N
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+COMARCO  K OW0 - M AA1 R - K OW0
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+COMBATING  K AH0 M - B AE1 - T IH0 NG
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+COMBATIVENESS  K AH0 M - B AE1 - T IH0 V - N AH0 S
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+COMBINABILITY  K AH2 M - B IH0 N - AH0 - B IH1 - L AH0 - T IY0
+COMBINABLE  K AH0 M - B AY1 - N AH0 - B AH0 L
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+COMBING  K OW1 - M IH0 NG
+COMBINING  K AH0 M - B AY1 - N IH0 NG
+COMBO  K AA1 M - B OW2
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+COMEAU  K AH0 - M OW1
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+COMEDIAN'S  K AH0 - M IY1 - D IY0 - AH0 N Z
+COMEDIANS  K AH0 - M IY1 - D IY0 - AH0 N Z
+COMEDIC  K AH0 - M IY1 - D IH0 K
+COMEDIENNE  K AH0 - M IY2 - D IY0 - EH1 N
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+COMELLA  K OW0 - M EH1 - L AH0
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+COMFORTED  K AH1 M - F ER0 - T IH0 D
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+COMFORTERS  K AH1 M - F ER0 - T ER0 Z
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+COMFORTS  K AH1 M - F ER0 T S
+COMFREY  K AH1 M - F R IY0
+COMFY  K AH1 M - F IY0
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+COMICAL  K AA1 - M IH0 - K AH0 L
+COMICALLY  K AA1 - M IH0 - K AH0 - L IY0
+COMICALLY(2)  K AA1 - M IH0 K - L IY0
+COMICOPIA  K AA2 - M IH0 - K OW1 - P IY0 - AH0
+COMICS  K AA1 - M IH0 K S
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+COMINCO  K OW0 - M IH1 NG - K OW0
+COMING  K AH1 - M IH0 NG
+COMINGS  K AH1 - M IH0 NG Z
+COMINO  K AH0 - M IY1 - N OW0
+COMINS  K OW1 - M IH0 N Z
+COMINSKY  K AH0 - M IH1 N - S K IY0
+COMISKEY  K OW1 - M IH0 S - K IY1
+COMITATUS  K AO0 - M AH0 - T EY1 - T AH0 S
+COMITO  K OW0 - M IY1 - T OW0
+COMITY  K OW1 - M IH0 - T IY0
+COMLEY  K AA1 M - L IY0
+COMLY  K AA1 M - L IY0
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+COMMACK  K AA1 - M AH0 K
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+COMMANDANT  K AA2 - M AH0 N - D AA1 N T
+COMMANDED  K AH0 - M AE1 N - D AH0 D
+COMMANDED(2)  K AH0 - M AE1 N - D IH0 D
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+COMMANDING  K AH0 - M AE1 N - D IH0 NG
+COMMANDMENT  K AH0 - M AE1 N D - M AH0 N T
+COMMANDMENTS  K AH0 - M AE1 N D - M AH0 N T S
+COMMANDO  K AH0 - M AE1 N - D OW2
+COMMANDOS  K AH0 - M AE1 N - D OW2 Z
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+COMMEMORATE  K AH0 - M EH1 - M ER0 - EY2 T
+COMMEMORATED  K AH0 - M EH1 - M ER0 - EY2 - T IH0 D
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+COMMEMORATION  K AH0 - M EH2 - M ER0 - EY1 - SH AH0 N
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+COMMENSURATELY  K AH0 - M EH1 N - S ER0 - AH0 T - L IY0
+COMMENSURATELY  K AH0 - M EH1 N - SH ER0 - AH0 T - L IY0
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+COMMENTATOR  K AA1 - M AH0 N - T EY2 - T ER0
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+COMMERCIAL  K AH0 - M ER1 - SH AH0 L
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+COMMERCIALIZE  K AH0 - M ER1 - SH AH0 - L AY2 Z
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+COMMERCIALIZING  K AH0 - M ER1 - SH AH0 - L AY2 - Z IH0 NG
+COMMERCIALLY  K AH0 - M ER1 - SH AH0 - L IY0
+COMMERCIALS  K AH0 - M ER1 - SH AH0 L Z
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+COMMERICAL  K AH0 - M ER1 - SH AH0 L
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+COMMIE  K AA1 - M IY0
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+COMMINGLED  K AA0 - M IH1 NG - G AH0 L D
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+COMMISSAR  K AA1 - M AH0 - S AA2 R
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+COMMISSION  K AH0 - M IH1 - SH AH0 N
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+COMMISSIONERS  K AH0 - M IH1 - SH AH0 N - ER0 Z
+COMMISSIONING  K AH0 - M IH1 - SH AH0 N - IH0 NG
+COMMISSIONS  K AH0 - M IH1 - SH AH0 N Z
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+COMMIT  K AH0 - M IH1 T
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+COMMITMENT  K AH0 - M IH1 T - M AH0 N T
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+COMMITS  K AH0 - M IH1 T S
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+COMMITTED  K AH0 - M IH1 - T AH0 D
+COMMITTEE  K AH0 - M IH1 - T IY0
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+COMMITTEEMAN  K AH0 - M IH1 - T IY0 - M AH0 N
+COMMITTEES  K AH0 - M IH1 - T IY0 Z
+COMMITTEES'  K AH0 - M IH1 - T IY0 Z
+COMMITTING  K AH0 - M IH1 - T IH0 NG
+COMMODE  K AH0 - M OW1 D
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+COMMODITY  K AH0 - M AA1 - D AH0 - T IY0
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+COMMODORE  K AA1 - M AH0 - D AO2 R
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+COMMONALITY  K AA2 - M AH0 - N AE1 - L AH0 - T IY0
+COMMONER  K AA1 - M AH0 - N ER0
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+COMMONS  K AA1 - M AH0 N Z
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+COMMONSENSICAL  K AA2 - M AH0 N - S EH1 N - S IH0 - K AH0 L
+COMMONWEALTH  K AA1 - M AH0 N - W EH2 L TH
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+COMMOTION  K AH0 - M OW1 - SH AH0 N
+COMMUNAL  K AH0 - M Y UW1 - N AH0 L
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+COMMUNES  K AA1 - M Y UW0 N Z
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+COMMUNICABLE  K AH0 - M Y UW1 - N AH0 - K AH0 - B AH0 L
+COMMUNICATE  K AH0 - M Y UW1 - N AH0 - K EY2 T
+COMMUNICATED  K AH0 - M Y UW1 - N AH0 - K EY2 - T IH0 D
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+COMMUNICATING  K AH0 - M Y UW1 - N AH0 - K EY2 - T IH0 NG
+COMMUNICATION  K AH0 - M Y UW2 - N AH0 - K EY1 - SH AH0 N
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+COMMUNION  K AH0 - M Y UW1 - N Y AH0 N
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+COMMUNIQUE(2)  K AH0 - M Y UW2 - N AH0 - K EY1
+COMMUNIQUES  K AH0 - M Y UW2 - N IH0 - K EY1 Z
+COMMUNISM  K AA1 - M Y AH0 - N IH2 - Z AH0 M
+COMMUNISM'S  K AA1 - M Y AH0 - N IH2 - Z AH0 M Z
+COMMUNIST  K AA1 - M Y AH0 - N AH0 S T
+COMMUNIST'S  K AA1 - M Y AH0 - N AH0 S T S
+COMMUNISTS  K AA1 - M Y AH0 - N AH0 S T S
+COMMUNISTS'  K AA1 - M Y UW0 - N IH0 S T S
+COMMUNISTS(2)  K AA1 - M Y AH0 - N AH0 S S
+COMMUNISTS(3)  K AA1 - M Y AH0 - N AH0 S
+COMMUNITIES  K AH0 - M Y UW1 - N AH0 - T IY0 Z
+COMMUNITIES(2)  K AH0 - M Y UW1 - N IH0 - T IY0 Z
+COMMUNITY  K AH0 - M Y UW1 - N AH0 - T IY0
+COMMUNITY'S  K AH0 - M Y UW1 - N IH0 - T IY0 Z
+COMMUNITY(2)  K AH0 - M Y UW1 - N IH0 - T IY0
+COMMUNITYWIDE  K AH0 - M Y UW1 - N IH0 - T IY0 - W AY2 D
+COMMUNIZATION  K AA2 - M Y AH0 - N AH0 - Z EY1 - SH AH0 N
+COMMUNIZE  K AA1 - M Y AH0 - N AY2 Z
+COMMUTE  K AH0 - M Y UW1 T
+COMMUTED  K AH0 - M Y UW1 - T IH0 D
+COMMUTER  K AH0 - M Y UW1 - T ER0
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+COMO  K OW1 - M OW0
+COMP  K AA1 M P
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+COMPACTOR  K AH0 M - P AE1 K - T ER0
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+COMPACTS(2)  K AA1 M - P AE0 K T S
+COMPAGNIE  K AH1 M - P AH0 - N IY0
+COMPANHIA  K AH0 M - P AE1 N - HH IY0 - AH0
+COMPANIA  K AH0 M - P EY1 - N IY0 - AH0
+COMPANIES  K AH1 M - P AH0 - N IY0 Z
+COMPANIES'  K AH1 M - P AH0 - N IY0 Z
+COMPANIES'S  K AH1 M - P AH0 - N IY0 - Z IH0 Z
+COMPANION  K AH0 M - P AE1 - N Y AH0 N
+COMPANIONS  K AH0 M - P AE1 - N Y AH0 N Z
+COMPANIONSHIP  K AH0 M - P AE1 - N Y AH0 N - SH IH2 P
+COMPANIONWAY  K AH0 M - P AE1 - N Y AH0 N - W EY2
+COMPANY  K AH1 M - P AH0 - N IY0
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+COMPANYWIDE  K AH1 M - P AH0 - N IY0 - W AY2 D
+COMPAQ  K AA1 M - P AE2 K
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+COMPARABILITY  K AA2 M - P ER0 - AH0 - B IH1 - L IH0 - T IY0
+COMPARABLE  K AA1 M - P ER0 - AH0 - B AH0 L
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+COMPARABLY  K AA1 M - P ER0 - AH0 - B L IY0
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+COMPARATOR  K AH0 M - P ER1 - AH0 - T ER0
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+COMPARTMENT  K AH0 M - P AA1 R T - M AH0 N T
+COMPARTMENTAL  K AH0 M - P AA2 R T - M EH1 N - T AH0 L
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+COMPASSIONATE  K AH0 M - P AE1 - SH AH0 - N AH0 T
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+COMPATIBLE  K AH0 M - P AE1 - T AH0 - B AH0 L
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+COMPENSABLE  K AH0 M - P EH1 N - S AH0 - B AH0 L
+COMPENSATE  K AA1 M - P AH0 N - S EY2 T
+COMPENSATED  K AA1 M - P AH0 N - S EY2 - T AH0 D
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+COMPETENT  K AA1 M - P AH0 - T IH0 N T
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+COMPETITION  K AA2 M - P AH0 - T IH1 - SH AH0 N
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+COMPETITOR(2)  K AH0 M - P EH1 - T IH0 - T ER0
+COMPETITORS  K AH0 M - P EH1 - T AH0 - T ER0 Z
+COMPETITORS'  K AH0 M - P EH1 - T IH0 - T ER0 Z
+COMPETITORS(2)  K AH0 M - P EH1 - T IH0 - T ER0 Z
+COMPHER  K AA1 M - F ER0
+COMPILATION  K AA2 M - P AH0 - L EY1 - SH AH0 N
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+COMPILER  K AH0 M - P AY1 - L ER0
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+COMPILING  K AH0 M - P AY1 - L IH0 NG
+COMPLACENCY  K AH0 M - P L EY1 - S AH0 N - S IY0
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+COMPLACENTLY  K AH0 M - P L EY1 - S AH0 N T - L IY0
+COMPLAIN  K AH0 M - P L EY1 N
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+COMPLAINS  K AH0 M - P L EY1 N Z
+COMPLAINT  K AH0 M - P L EY1 N T
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+COMPLEMENT  K AA1 M - P L AH0 - M AH0 N T
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+COMPLEMENTED  K AA1 M - P L AH0 - M EH2 N - T IH0 D
+COMPLEMENTING  K AA1 M - P L AH0 - M EH2 N - T IH0 NG
+COMPLEMENTS  K AA1 M - P L AH0 - M AH0 N T S
+COMPLETE  K AH0 M - P L IY1 T
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+COMPLICIT  K AH0 M - P L IH1 - S AH0 T
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+COMPOSERS  K AH0 M - P OW1 - Z ER0 Z
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+COMPOSITE  K AH0 M - P AA1 - Z AH0 T
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+COMPOSITE(2)  K AA0 M - P AA1 - Z AH0 T
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+CONCENTRIC  K AH0 N - S EH1 N - T R IH0 K
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+CONCERTI  K AH0 N - CH EH1 R - T IY0
+CONCERTINA  K AA0 N - S ER0 - T IY1 - N AH0
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+CONCHITA  K AH0 N - CH IY1 - T AH0
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+CONCLUSION  K AH0 N - K L UW1 - ZH AH0 N
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+CONCORD  K AA1 N - K AO2 R D
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+CONDESCENSION  K AA2 N - D AH0 - S EH1 N - SH AH0 N
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+CONDREN  K AA1 N - D ER0 - AH0 N
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+CONDUITS  K AA1 N - D UW0 - AH0 T S
+CONDUITS(2)  K AA1 N - D W AH0 T S
+CONE  K OW1 N
+CONE'S  K OW1 N Z
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+CONEHEAD  K OW1 N - HH EH0 D
+CONEHEADS  K OW1 N - HH EH0 D Z
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+CONEY  K OW1 - N IY0
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+CONFAIR  K AA1 N - F EH2 R
+CONFECT  K AH0 N - F EH1 K T
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+CONFECTIONARY  K AH0 N - F EH1 K - SH AH0 N - EH2 - R IY0
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+CONFECTIONERS  K AH0 N - F EH1 K - SH AH0 N - ER0 Z
+CONFECTIONERY  K AH0 N - F EH1 K - SH AH0 N - EH2 - R IY0
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+CONFEDERACY  K AH0 N - F EH1 - D ER0 - AH0 - S IY0
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+CONFEDERACY(2)  K AH0 N - F EH1 - D R AH0 - S IY0
+CONFEDERATE  K AH0 N - F EH1 - D ER0 - AH0 T
+CONFEDERATE(2)  K AH0 N - F EH1 - D ER0 - EY2 T
+CONFEDERATION  K AH0 N - F EH2 - D ER0 - EY1 - SH AH0 N
+CONFER  K AH0 N - F ER1
+CONFEREE  K AA2 N - F ER0 - IY1
+CONFEREES  K AA2 N - F ER0 - IY1 Z
+CONFERENCE  K AA1 N - F ER0 - AH0 N S
+CONFERENCE'S  K AA1 N - F ER0 - AH0 N - S IH0 Z
+CONFERENCE'S(2)  K AA1 N - F R AH0 N - S IH0 Z
+CONFERENCE(2)  K AA1 N - F R AH0 N S
+CONFERENCES  K AA1 N - F ER0 - AH0 N - S AH0 Z
+CONFERENCES(2)  K AA1 N - F R AH0 N - S AH0 Z
+CONFERENCING  K AA1 N - F R AH0 N - S IH0 NG
+CONFERRED  K AH0 N - F ER1 D
+CONFERRING  K AH0 N - F ER1 - IH0 NG
+CONFERS  K AH0 N - F ER1 Z
+CONFESS  K AH0 N - F EH1 S
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+CONFESSES  K AH0 N - F EH1 - S IH0 Z
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+CONFESSION  K AH0 N - F EH1 - SH AH0 N
+CONFESSIONAL  K AH0 N - F EH1 - SH AH0 - N AH0 L
+CONFESSIONALS  K AH0 N - F EH1 - SH AH0 - N AH0 L Z
+CONFESSIONS  K AH0 N - F EH1 - SH AH0 N Z
+CONFETTI  K AH0 N - F EH1 - T IY0
+CONFIDANT  K AA1 N - F AH0 - D AA2 N T
+CONFIDANTE  K AA1 N - F AH0 - D AE2 N T
+CONFIDANTS  K AA1 N - F AH0 - D AE2 N T S
+CONFIDE  K AH0 N - F AY1 D
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+CONFIDENCE  K AA1 N - F AH0 - D AH0 N S
+CONFIDENCE'S  K AA1 N - F AH0 - D AH0 N - S AH0 Z
+CONFIDENCES  K AA1 N - F AH0 - D AH0 N - S IH0 Z
+CONFIDENT  K AA1 N - F AH0 - D AH0 N T
+CONFIDENTIAL  K AA2 N - F AH0 - D EH1 N - SH AH0 L
+CONFIDENTIAL(2)  K AA2 N - F AH0 - D EH1 N - CH AH0 L
+CONFIDENTIALITY  K AA2 N - F AH0 - D EH2 N - SH IY0 - AE1 - L AH0 - T IY0
+CONFIDENTIALITY(2)  K AA2 N - F AH0 - D EH2 N - CH IY0 - AE1 - L AH0 - T IY0
+CONFIDENTIALLY  K AA2 N - F AH0 - D EH1 N - SH AH0 - L IY0
+CONFIDENTIALLY(2)  K AA2 N - F AH0 - D EH1 N - CH AH0 - L IY0
+CONFIDENTLY  K AA1 N - F AH0 - D AH0 N T - L IY0
+CONFIDES  K AH0 N - F AY1 D Z
+CONFIDING  K AH0 N - F AY1 - D IH0 NG
+CONFIGURATION  K AH0 N - F IH2 - G Y ER0 - EY1 - SH AH0 N
+CONFIGURATIONS  K AH0 N - F IH2 - G Y ER0 - EY1 - SH AH0 N Z
+CONFIGURE  K AH0 N - F IH1 - G Y ER0
+CONFIGURED  K AH0 N - F IH1 - G Y ER0 D
+CONFIGURING  K AH0 N - F IH1 - G Y ER0 - IH0 NG
+CONFINDUSTRIA  K AA2 N - F IH0 N - D AH1 S - T R IY0 - AH0
+CONFINE  K AH0 N - F AY1 N
+CONFINED  K AH0 N - F AY1 N D
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+CONFINES  K AA1 N - F AY2 N Z
+CONFINES(2)  K AH0 N - F AY1 N Z
+CONFINING  K AH0 N - F AY1 - N IH0 NG
+CONFIRM  K AH0 N - F ER1 M
+CONFIRMABLE  K AH0 N - F ER1 - M AH0 - B AH0 L
+CONFIRMATION  K AA2 N - F ER0 - M EY1 - SH AH0 N
+CONFIRMATIONS  K AA2 N - F ER0 - M EY1 - SH AH0 N Z
+CONFIRMATORY  K AH0 N - F ER1 - M AH0 - T AO2 - R IY0
+CONFIRMED  K AH0 N - F ER1 M D
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+CONFIRMS  K AH0 N - F ER1 M Z
+CONFISCATE  K AA1 N - F AH0 - S K EY2 T
+CONFISCATED  K AA1 N - F AH0 - S K EY2 - T AH0 D
+CONFISCATING  K AA1 N - F AH0 - S K EY2 - T IH0 NG
+CONFISCATION  K AA2 N - F AH0 - S K EY1 - SH AH0 N
+CONFISCATORY  K AH0 N - F IH1 S - K AH0 - T AO2 - R IY0
+CONFITERIAS  K AA2 N - F IH0 - T IH1 - R IY0 - AH0 Z
+CONFLAGRATION  K AA2 N - F L AH0 - G R EY1 - SH AH0 N
+CONFLATE  K AH0 N - F L EY1 T
+CONFLATES  K AH0 N - F L EY1 T S
+CONFLICT  K AA1 N - F L IH0 K T
+CONFLICT(2)  K AH0 N - F L IH1 K T
+CONFLICTED  K AH0 N - F L IH1 K - T IH0 D
+CONFLICTING  K AH0 N - F L IH1 K - T IH0 NG
+CONFLICTS  K AH0 N - F L IH1 K T S
+CONFLICTS(2)  K AA1 N - F L IH0 K T S
+CONFLICTS(3)  K AH0 N - F L IH1 K S
+CONFLICTS(4)  K AA1 N - F L IH0 K S
+CONFLUENCE  K AA1 N - F L UW0 - AH0 N S
+CONFLUENT  K AA0 N - F L UW1 - AH0 N T
+CONFORM  K AH0 N - F AO1 R M
+CONFORMANCE  K AH0 N - F AO1 R - M AH0 N S
+CONFORMATIONAL  K AA2 N - F ER0 - M EY1 - SH AH0 - N AH0 L
+CONFORMED  K AH0 N - F AO1 R M D
+CONFORMING  K AH0 N - F AO1 R - M IH0 NG
+CONFORMIST  K AH0 N - F AO1 R - M IH0 S T
+CONFORMISTS  K AH0 N - F AO1 R - M AH0 S T S
+CONFORMISTS(2)  K AH0 N - F AO1 R - M AH0 S S
+CONFORMISTS(3)  K AH0 N - F AO1 R - M AH0 S
+CONFORMITY  K AH0 N - F AO1 R - M AH0 - T IY0
+CONFORMS  K AH0 N - F AO1 R M Z
+CONFORTI  K AA0 N - F AO1 R - T IY0
+CONFOUND  K AA0 N - F AW1 N D
+CONFOUND(2)  K AA1 N - F AW2 N D
+CONFOUND(3)  K AH0 N - F AW1 N D
+CONFOUNDED  K AH0 N - F AW1 N - D IH0 D
+CONFOUNDING  K AH0 N - F AW1 N - D IH0 NG
+CONFOUNDS  K AH0 N - F AW1 N D Z
+CONFRONT  K AH0 N - F R AH1 N T
+CONFRONTATION  K AA2 N - F R AH0 N - T EY1 - SH AH0 N
+CONFRONTATIONAL  K AA2 N - F R AH0 N - T EY1 - SH AH0 - N AH0 L
+CONFRONTATIONS  K AA2 N - F R AH0 N - T EY1 - SH AH0 N Z
+CONFRONTED  K AH0 N - F R AH1 N - T AH0 D
+CONFRONTED(2)  K AH0 N - F R AH1 N - T IH0 D
+CONFRONTING  K AH0 N - F R AH1 N - T IH0 NG
+CONFRONTS  K AH0 N - F R AH1 N T S
+CONFUCIAN  K AH0 N - F Y UW1 - SH AH0 N
+CONFUCIANISM  K AH0 N - F Y UW1 - SH AH0 - N IH2 - Z AH0 M
+CONFUCIUS  K AH0 N - F Y UW1 - SH AH0 S
+CONFUSE  K AH0 N - F Y UW1 Z
+CONFUSED  K AH0 N - F Y UW1 Z D
+CONFUSES  K AH0 N - F Y UW1 - Z IH0 Z
+CONFUSING  K AH0 N - F Y UW1 - Z IH0 NG
+CONFUSINGLY  K AH0 N - F Y UW1 - Z IH0 NG - L IY0
+CONFUSION  K AH0 N - F Y UW1 - ZH AH0 N
+CONFUSIONS  K AH0 N - F Y UW1 - ZH AH0 N Z
+CONG  K AO1 NG
+CONGA  K AO1 NG - G AH0
+CONGDON  K AA1 NG - D AH0 N
+CONGEAL  K AH0 N - JH IY1 L
+CONGEALED  K AH0 N - JH IY1 L D
+CONGENIAL  K AH0 N - JH IY1 - N Y AH0 L
+CONGENIALITY  K AH0 N - JH IY2 - N IY0 - AE1 - L AH0 - T IY0
+CONGENITAL  K AH0 N - JH EH1 - N AH0 - T AH0 L
+CONGER  K AO1 NG - ER0
+CONGEST  K AH0 N - JH EH1 S T
+CONGESTED  K AH0 N - JH EH1 - S T AH0 D
+CONGESTED(2)  K AH0 N - JH EH1 - S T IH0 D
+CONGESTION  K AH0 N - JH EH1 S - CH AH0 N
+CONGESTIVE  K AH0 N - JH EH1 - S T IH0 V
+CONGLETON  K AA1 NG - G AH0 L - T AA0 N
+CONGLOMERATE  K AH0 N - G L AA1 - M ER0 - AH0 T
+CONGLOMERATE'S  K AH0 N - G L AA1 - M ER0 - AH0 T S
+CONGLOMERATES  K AH0 N - G L AA1 - M ER0 - AH0 T S
+CONGLOMERATION  K AH0 N - G L AA2 - M ER0 - EY1 - SH AH0 N
+CONGO  K AA1 NG - G OW0
+CONGRATULATE  K AH0 N - G R AE1 - CH AH0 - L EY2 T
+CONGRATULATED  K AH0 N - G R AE1 - CH AH0 - L EY2 - T IH0 D
+CONGRATULATING  K AH0 N - G R AE1 - CH AH0 - L EY2 - T IH0 NG
+CONGRATULATION  K AH0 N - G R AE2 - CH AH0 - L EY1 - SH AH0 N
+CONGRATULATIONS  K AH0 N - G R AE2 - CH AH0 - L EY1 - SH AH0 N Z
+CONGRATULATORY  K AH0 N - G R AE1 - CH AH0 - L AH0 - T AO2 - R IY0
+CONGREGATE  K AA1 NG - G R AH0 - G EY2 T
+CONGREGATED  K AA1 NG - G R IH0 - G EY2 - T IH0 D
+CONGREGATION  K AA2 NG - G R AH0 - G EY1 - SH AH0 N
+CONGREGATION'S  K AA2 NG - G R AH0 - G EY1 - SH AH0 N Z
+CONGREGATIONAL  K AA2 NG - G R AH0 - G EY1 - SH AH0 - N AH0 L
+CONGREGATIONS  K AA2 NG - G R AH0 - G EY1 - SH AH0 N Z
+CONGRESS  K AA1 NG - G R AH0 S
+CONGRESS'  K AA1 N - G R AH0 - S IH0 Z
+CONGRESS'(2)  K AA1 NG - G R AH0 S
+CONGRESS'S  K AA1 NG - G R AH0 - S IH0 Z
+CONGRESSES  K AA1 NG - G R AH0 - S IH0 Z
+CONGRESSIONAL  K AH0 N - G R EH1 - SH AH0 - N AH0 L
+CONGRESSIONALLY  K AH0 N - G R EH1 - SH AH0 N - AH0 - L IY0
+CONGRESSIONALLY(2)  K AH0 N - G R EH1 SH - N AH0 - L IY0
+CONGRESSMAN  K AA1 NG - G R AH0 S - M AH0 N
+CONGRESSMAN'S  K AA1 NG - G R AH0 S - M AH0 N Z
+CONGRESSMEN  K AA1 NG - G R AH0 S - M IH0 N
+CONGRESSPEOPLE  K AA1 NG - G R AH0 - S P IY2 - P AH0 L
+CONGRESSPERSON  K AA1 NG - G R AH0 - S P ER2 - S AH0 N
+CONGRESSPERSONS  K AA1 NG - G R AH0 - S P ER2 - S AH0 N Z
+CONGRESSWOMAN  K AA1 NG - G R AH0 S - W UH2 - M AH0 N
+CONGRESSWOMAN'S  K AA1 NG - G R AH0 S - W UH2 - M AH0 N Z
+CONGRESSWOMEN  K AA1 NG - G R AH0 S - W IH2 - M IH0 N
+CONGROVE  K AA1 NG - G R AH0 V
+CONGRUENCE  K AO1 N - G R UW0 - AH0 N S
+CONGRUITY  K AH0 N - G R UW1 - AH0 - T IY0
+CONIC  K AA1 - N IH0 K
+CONIC(2)  K OW1 - N IH0 K
+CONICAL  K AA1 - N IH0 - K AH0 L
+CONICAL(2)  K OW1 - N IH0 - K AH0 L
+CONICS  K AA1 - N IH0 K S
+CONICS(2)  K OW1 - N IH0 K S
+CONIFER  K AA1 - N AH0 - F ER0
+CONIFER'S  K AA1 - N AH0 - F ER0 Z
+CONIFEROUS  K AH0 - N IH1 - F ER0 - AH0 S
+CONIFERS  K AA1 - N AH0 - F ER0 Z
+CONIGLIARO  K AH0 - N IH2 G - L IY0 - AA1 - R OW0
+CONIGLIO  K AH0 - N IH1 G - L IY0 - OW0
+CONISTON  K AA1 - N AH0 - S T AH0 N
+CONJECTURE  K AH0 N - JH EH1 K - CH ER0
+CONJECTURE(2)  K AH0 N - JH EH1 K - SH ER0
+CONJECTURES  K AH0 N - JH EH1 K - CH ER0 Z
+CONJECTURES(2)  K AH0 N - JH EH1 K - SH ER0 Z
+CONJOIN  K AA2 N - JH OY1 N
+CONJOINED  K AA2 N - JH OY1 N D
+CONJUGAL  K AA1 N - JH AH0 - G AH0 L
+CONJUGATE  K AA2 N - JH AH0 - G EY1 T
+CONJUGATE(2)  K AA1 N - JH AH0 - G EY2 T
+CONJUGATED  K AA2 N - JH AH0 - G EY1 - T IH0 D
+CONJUGATED(2)  K AA1 N - JH AH0 - G EY2 - T IH0 D
+CONJUGATES  K AA2 N - JH AH0 - G EY1 T S
+CONJUGATES(2)  K AA1 N - JH AH0 - G EY2 T S
+CONJUGATION  K AA2 N - JH AH0 - G EY1 - SH AH0 N
+CONJUGATIONS  K AA2 N - JH AH0 - G EY1 - SH AH0 N Z
+CONJUL  K AA1 N - JH AH0 L
+CONJUNCTION  K AH0 N - JH AH1 NG K - SH AH0 N
+CONJUNCTIONS  K AH0 N - JH AH1 NG K - SH AH0 N Z
+CONJUNCTIVA  K AA2 N - JH AH0 NG K - T AY1 - V AH0
+CONJURE  K AA1 N - JH ER0
+CONJURED  K AA1 N - JH ER0 D
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+CONJURING  K AA1 N - JH ER0 - IH0 NG
+CONJUROR  K AA1 N - JH ER0 - ER0
+CONK  K AA1 NG K
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+CONKLIN  K AA1 NG - K L IH0 N
+CONKLING  K AA1 NG - K L IH0 NG
+CONKRIGHT  K AA1 NG K - R AY2 T
+CONLAN  K AA1 N - L AH0 N
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+CONLIN  K AA1 N - L IH0 N
+CONLON  K AA1 N - L AH0 N
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+CONLY  K AA1 N - L IY0
+CONN  K AA1 N
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+CONNALLY  K AA1 - N AH0 - L IY0
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+CONNAUGHT  K AA1 - N AO0 T
+CONNAUGHT'S  K AA1 - N AO0 T S
+CONNAUGHTON  K AA1 - N AO0 - T AA0 N
+CONNAWAY  K AA1 N - AH0 - W EY2
+CONNECT  K AH0 - N EH1 K T
+CONNECTED  K AH0 - N EH1 K - T AH0 D
+CONNECTED(2)  K AH0 - N EH1 K - T IH0 D
+CONNECTER  K AH0 - N EH1 K - T ER0
+CONNECTICUT  K AH0 - N EH1 - T AH0 - K AH0 T
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+CONNECTING  K AH0 - N EH1 K - T IH0 NG
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+CONNECTIONS  K AH0 - N EH1 K - SH AH0 N Z
+CONNECTIVE  K AH0 - N EH1 K - T IH0 V
+CONNECTIVITY  K AH0 - N EH0 K - T IH1 - V IH0 - T IY0
+CONNECTOR  K AH0 - N EH1 K - T ER0
+CONNECTORS  K AH0 - N EH1 K - T ER0 Z
+CONNECTS  K AH0 - N EH1 K T S
+CONNED  K AA1 N D
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+CONNELL  K AA1 - N AH0 L
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+CONNELLY  K AA1 - N AH0 - L IY0
+CONNELLY'S  K AA1 - N AH0 - L IY0 Z
+CONNELY  K AA1 N - L IY0
+CONNER  K AA1 - N ER0
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+CONNERLY  K AA1 - N ER0 - L IY0
+CONNERS  K AA1 - N ER0 Z
+CONNERY  K AA1 - N ER0 - IY0
+CONNERY'S  K AA1 - N ER0 - IY0 Z
+CONNETT  K AA1 - N IH0 T
+CONNICK  K AA1 - N IH0 K
+CONNIE  K AO1 - N IY0
+CONNIE'S  K AO1 - N IY0 Z
+CONNIFF  K AA1 - N IH0 F
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+CONNOISSEUR  K AA2 - N AH0 - S ER1
+CONNOISSEURS  K AA2 - N AH0 - S ER1 Z
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+CONNOLLY  K AO1 - N AH0 - L IY0
+CONNON  K AA1 - N AH0 N
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+CONNOTATION  K AA2 - N AH0 - T EY1 - SH AH0 N
+CONNOTATIONAL  K AA2 - N AH0 - T EY1 - SH AH0 - N AH0 L
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+CONNOTE  K AH0 - N OW1 T
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+CONNY  K AA1 - N IY0
+CONOCO  K AA1 - N AH0 - K OW0
+CONOCO'S  K AA1 - N AH0 - K OW0 Z
+CONOCO(2)  K AH0 - N AA1 - K OW0
+CONOLLY  K AA1 - N OW0 - L IY0
+CONOLY  K AA1 - N OW0 - L IY0
+CONOVER  K AA1 - N AH0 - V ER0
+CONQUER  K AA1 NG - K ER0
+CONQUERED  K AA1 NG - K ER0 D
+CONQUERING  K AA1 NG - K ER0 - IH0 NG
+CONQUEROR  K AA1 NG - K ER0 - ER0
+CONQUEROR'S  K AA1 NG - K ER0 - ER0 Z
+CONQUERORS  K AA1 NG - K ER0 - ER0 Z
+CONQUERS  K AA1 NG - K ER0 Z
+CONQUEST  K AA1 NG - K W EH0 S T
+CONQUEST'S  K AA1 NG - K W EH0 S T S
+CONQUESTS  K AA1 N - K W EH2 S T S
+CONQUESTS(2)  K AA1 N - K W EH2 S S
+CONQUESTS(3)  K AA1 N - K W EH2 S
+CONRAC  K AA1 N - R AE0 K
+CONRAD  K AA1 N - R AE0 D
+CONRAD'S  K AA1 N - R AE0 D Z
+CONRADES  K AH0 N - R EY1 D Z
+CONRADI  K AA0 N - R AA1 - D IY0
+CONRADINE  K AA1 N - R AH0 - D AY2 N
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+CONRAIL  K AA1 N - R EY2 L
+CONRAIL'S  K AA1 N - R EY2 L Z
+CONRAN  K AA1 N - R AH0 N
+CONRATH  K AA1 N - R AH0 TH
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+CONROW  K AA1 N - R OW0
+CONROY  K AO1 N - R OY0
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+CONSCIENCE  K AA1 N - SH AH0 N S
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+CONSCIENTIOUS  K AA2 N - SH IY0 - EH1 N - SH AH0 S
+CONSCIENTIOUSLY  K AA2 N - CH IY0 - EH1 N - CH AH0 S - L IY0
+CONSCIOUS  K AA1 N - SH AH0 S
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+CONSCIOUSNESS  K AA1 N - SH AH0 S - N AH0 S
+CONSCRIPT  K AA1 N - S K R IH2 P T
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+CONSCRIPTED  K AH0 N - S K R IH1 P - T IH0 D
+CONSCRIPTION  K AH0 N - S K R IH1 P - SH AH0 N
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+CONSECO  K AA0 N - S EY1 - K OW0
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+CONSECO(2)  K AH0 N - S EY1 - K OW0
+CONSECRATE  K AA1 N - S AH0 - K R EY2 T
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+CONSECRATION  K AA2 N - S AH0 - K R EY1 - SH AH0 N
+CONSECRATIONS  K AA2 N - S AH0 - K R EY1 - SH AH0 N Z
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+CONSECUTIVELY  K AH0 N - S EH1 - K Y AH0 - T IH0 V - L IY0
+CONSENSUAL  K AH0 N - S EH1 N - S UW0 - AH0 L
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+CONSENSUS  K AH0 N - S EH1 N - S AH0 S
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+CONSENTINO  K AA0 N - S EH0 N - T IY1 - N OW0
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+CONSEQUENCES  K AA1 N - S AH0 - K W EH2 N - S AH0 Z
+CONSEQUENT  K AA1 N - S AH0 - K W AH0 N T
+CONSEQUENTIAL  K AA2 N - S AH0 - K W EH1 N - CH AH0 L
+CONSEQUENTLY  K AA1 N - S AH0 - K W AH0 N T - L IY0
+CONSEQUENTLY(2)  K AA1 N - S AH0 - K W EH2 N T - L IY0
+CONSER  K AA1 N - S ER0
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+CONSERVANCY'S  K AH0 N - S ER1 - V AH0 N - S IY0 Z
+CONSERVATION  K AA2 N - S ER0 - V EY1 - SH AH0 N
+CONSERVATIONIST  K AA2 N - S ER0 - V EY1 - SH AH0 N - AH0 S T
+CONSERVATIONISTS  K AA2 N - S ER0 - V EY1 - SH AH0 N - AH0 S T S
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+CONSERVATISM  K AH0 N - S ER1 - V AH0 - T IH2 - Z AH0 M
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+CONSERVATIVES  K AH0 N - S ER1 - V AH0 - T IH0 V Z
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+CONSERVATORY  K AH0 N - S ER1 - V AH0 - T AO0 - R IY0
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+CONSIDERATE  K AH0 N - S IH1 - D ER0 - AH0 T
+CONSIDERATION  K AH0 N - S IH2 - D ER0 - EY1 - SH AH0 N
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+CONSIDERED  K AH0 N - S IH1 - D ER0 D
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+CONSISTENCY  K AH0 N - S IH1 - S T AH0 N - S IY0
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+CONSOLIDATION  K AH0 N - S AA2 - L AH0 - D EY1 - SH AH0 N
+CONSOLIDATIONS  K AH0 N - S AA2 - L IH0 - D EY1 - SH AH0 N Z
+CONSOLIDATOR  K AH0 N - S AA1 - L IH0 - D EY2 - T ER0
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+CONSONANTAL  K AA2 N - S AH0 - N AA1 N - T AH0 L
+CONSONANTAL(2)  K AA2 N - S AH0 - N AA1 - N AH0 L
+CONSONANTS  K AA1 N - S AH0 - N AH0 N T S
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+CONSORTIUM(2)  K AH0 N - S AO1 R - SH Y AH0 M
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+CONSPIRED  K AH0 N - S P AY1 - ER0 D
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+CONSTANCE  K AA1 N - S T AH0 N S
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+CONSTANTA  K AA0 N - S T AA1 N - T AH0
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+CONSTANTINA  K AA0 N - S T AA0 N - T IY1 - N AH0
+CONSTANTINE  K AA1 N - S T AH0 N - T IY2 N
+CONSTANTINE(2)  K AA1 N - S T AH0 N - T AY2 N
+CONSTANTINO  K AA2 N - S T AH0 N - T IY1 - N OW0
+CONSTANTINOPLE  K AA2 N - S T AE0 N - T AH0 - N OW1 - P AH0 L
+CONSTANTINOS  K AA2 N - S T AH0 N - T IY1 - N OW0 S
+CONSTANTLY  K AA1 N - S T AH0 N T - L IY0
+CONSTANTS  K AA1 N - S T AH0 N T S
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+CONSTELLATIONS  K AA2 N - S T AH0 - L EY1 - SH AH0 N Z
+CONSTERNATION  K AA2 N - S T ER0 - N EY1 - SH AH0 N
+CONSTIPATE  K AA1 N - S T AH0 - P EY2 T
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+CONSTIPATION  K AA2 N - S T AH0 - P EY1 - SH AH0 N
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+CONSTITUENCY  K AH0 N - S T IH1 - CH UW0 - AH0 N - S IY0
+CONSTITUENT  K AH0 N - S T IH1 - CH UW0 - AH0 N T
+CONSTITUENTS  K AH0 N - S T IH1 - CH UW0 - AH0 N T S
+CONSTITUENTS'  K AH0 N - S T IH1 - CH UW0 - AH0 N T S
+CONSTITUTE  K AA1 N - S T AH0 - T UW2 T
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+CONSTITUTES  K AA1 N - S T AH0 - T UW2 T S
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+CONSTITUTION  K AA2 N - S T AH0 - T UW1 - SH AH0 N
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+CONSTITUTIONAL  K AA2 N - S T AH0 - T UW1 - SH AH0 - N AH0 L
+CONSTITUTIONALITY  K AA2 N - S T IH0 - T UW2 - SH AH0 - N AE1 - L IH0 - T IY0
+CONSTITUTIONALLY  K AA2 N - S T AH0 - T UW1 - SH AH0 N - AH0 L - IY0
+CONSTITUTIONIST  K AA2 N - S T AH0 - T UW1 - SH AH0 - N IH0 S T
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+CONSTITUTIONISTS(3)  K AA2 N - S T AH0 - T UW1 - SH AH0 N - IH0 S
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+CONSTRAIN  K AH0 N - S T R EY1 N
+CONSTRAINED  K AH0 N - S T R EY1 N D
+CONSTRAINING  K AH0 N - S T R EY1 - N IH0 NG
+CONSTRAINS  K AH0 N - S T R EY1 N Z
+CONSTRAINT  K AH0 N - S T R EY1 N T
+CONSTRAINTS  K AH0 N - S T R EY1 N T S
+CONSTRICT  K AH0 N - S T R IH1 K T
+CONSTRICTED  K AH0 N - S T R IH1 K - T AH0 D
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+CONSTRICTION  K AH0 N - S T R IH1 K - SH AH0 N
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+CONSTRICTOR  K AH0 N - S T R IH1 K - T ER0
+CONSTRICTORS  K AH0 N - S T R IH1 K - T ER0 Z
+CONSTRUCCIONES  K AH0 N - S T R UW1 - CH IY0 - OW2 - N EY0 Z
+CONSTRUCT  K AH0 N - S T R AH1 K T
+CONSTRUCT(2)  K AA1 N - S T R AH0 K T
+CONSTRUCTED  K AH0 N - S T R AH1 K - T AH0 D
+CONSTRUCTED(2)  K AH0 N - S T R AH1 K - T IH0 D
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+CONSTRUCTION  K AH0 N - S T R AH1 K - SH AH0 N
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+CONSTRUCTIVE  K AH0 N - S T R AH1 K - T IH0 V
+CONSTRUCTIVELY  K AH0 N - S T R AH1 K - T IH0 V - L IY0
+CONSTRUCTOR  K AH0 N - S T R AH1 K - T ER0
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+CONSTRUCTS  K AH0 N - S T R AH1 K T S
+CONSTRUCTS(2)  K AA1 N - S T R AH0 K T S
+CONSTRUE  K AH0 N - S T R UW1
+CONSTRUED  K AH0 N - S T R UW1 D
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+CONSULTANTS  K AH0 N - S AH1 L - T AH0 N T S
+CONSULTANTS'  K AH0 N - S AH1 L - T AH2 N T S
+CONSULTATION  K AA2 N - S AH0 L - T EY1 - SH AH0 N
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+CONSULTATIVE  K AH0 N - S AH1 L - T AH0 - T IH0 V
+CONSULTED  K AH0 N - S AH1 L - T AH0 D
+CONSULTED(2)  K AH0 N - S AH1 L - T IH0 D
+CONSULTING  K AH0 N - S AH1 L - T IH0 NG
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+CONSUMED  K AH0 N - S UW1 M D
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+CONSUMERIST  K AH0 N - S UW1 - M ER0 - IH0 S T
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+CONSUMMATION  K AA2 N - S AH0 - M EY1 - SH AH0 N
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+CONTAINERBOARD  K AH0 N - T EY1 - N ER0 - B AO2 R D
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+CONTAINERIZED  K AH0 N - T EY1 - N ER0 - AY2 Z D
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+CONTAINING  K AH0 N - T EY1 - N IH0 NG
+CONTAINMENT  K AH0 N - T EY1 N - M AH0 N T
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+CONTAMINANTS  K AH0 N - T AE1 - M AH0 - N AH0 N T S
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+CONTAMINATION  K AH0 N - T AE2 - M AH0 - N EY1 - SH AH0 N
+CONTANT  K AA1 N - T AH0 N T
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+CONTINGENT  K AH0 N - T IH1 N - JH AH0 N T
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+CONTRADICTORY  K AA2 N - T R AH0 - D IH1 K - T ER0 - IY0
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+CRAGUN  K R AE1 - G AH0 N
+CRAIB  K R EY1 B
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+CRAIL  K R EY1 L
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+CRAKER  K R EY1 - K ER0
+CRALL  K R AO1 L
+CRAM  K R AE1 M
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+CRAMER  K R EY1 - M ER0
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+CRANCE  K R AE1 N S
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+CRANE  K R EY1 N
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+CRAW  K R AO1
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+CRAWFORD  K R AO1 - F ER0 D
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+CREAMIER  K R IY1 - M IY0 - ER0
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+CREATED  K R IY0 - EY1 - T AH0 D
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+CREATES  K R IY0 - EY1 T S
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+CREATING  K R IY0 - EY1 - T IH0 NG
+CREATION  K R IY0 - EY1 - SH AH0 N
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+CREATIONS  K R IY0 - EY1 - SH AH0 N Z
+CREATIVE  K R IY0 - EY1 - T IH0 V
+CREATIVELY  K R IY0 - EY1 - T IH0 V - L IY0
+CREATIVENESS  K R IY0 - EY1 - T IH0 V - N AH0 S
+CREATIVITY  K R IY2 - EY0 - T IH1 - V AH0 - T IY0
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+CREATOR  K R IY0 - EY1 - T ER0
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+CREOSOTE  K R IY1 - AH0 - S OW2 T
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+CREUTZFELDT  K R UH1 T S - F EH0 L D
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+CREVELING  K R EH1 - V AH0 L - IH0 NG
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+CRIMINALIST'S  K R IH1 - M AH0 - N AH0 - L IH0 S T S
+CRIMINALISTICS  K R IH2 - M AH0 - N AH0 - L IH1 - S T IH0 K S
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+CRIMINALISTS(2)  K R IH1 - M AH0 - N AH0 - L IH0 S S
+CRIMINALISTS(3)  K R IH1 - M AH0 - N AH0 - L IH0 S
+CRIMINALITY  K R IH2 - M AH0 - N AE1 - L IH0 - T IY0
+CRIMINALIZATION  K R IH2 - M AH0 - N AH0 - L AH0 - Z EY1 - SH AH0 N
+CRIMINALIZE  K R IH1 - M AH0 - N AH0 - L AY2 Z
+CRIMINALIZED  K R IH1 - M AH0 - N AH0 - L AY2 Z D
+CRIMINALIZING  K R IH1 - M AH0 - N AH0 - L AY2 - Z IH0 NG
+CRIMINALLY  K R IH1 - M AH0 - N AH0 - L IY0
+CRIMINALS  K R IH1 - M AH0 - N AH0 L Z
+CRIMINALS'  K R IH1 - M AH0 - N AH0 L Z
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+CRIMINOLOGISTS  K R IH2 - M IH0 - N AA1 - L AH0 - JH IH0 S T S
+CRIMINOLOGISTS(2)  K R IH2 - M IH0 - N AA1 - L AH0 - JH IH0 S S
+CRIMINOLOGISTS(3)  K R IH2 - M IH0 - N AA1 - L AH0 - JH IH0 S
+CRIMINOLOGY  K R IH2 - M IH0 - N AA1 - L AH0 - JH IY0
+CRIMM  K R IH1 M
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+CRIPPLING  K R IH1 - P AH0 L - IH0 NG
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+CRIPPS  K R IH1 P S
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+CRISANTI  K R IH0 - S AE1 N - T IY0
+CRISCI  K R IH1 - S IY0
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+CRISE  K R AY1 Z
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+CRISHMAN  K R IH1 SH - M AH0 N
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+CRISIS  K R AY1 - S AH0 S
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+CRISLIP  K R IH1 S - L IH0 P
+CRISMAN  K R IH1 S - M AH0 N
+CRISMON  K R IH1 Z - M AH0 N
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+CRISP  K R IH1 S P
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+CRISPIN  K R IH1 - S P IH0 N
+CRISPINA  K R IY0 - S P IY1 - N AH0
+CRISPINO  K R IY0 - S P IY1 - N OW0
+CRISPLY  K R IH1 S P - L IY0
+CRISPNESS  K R IH1 S P - N AH0 S
+CRISPO  K R IY1 - S P OW0
+CRISPY  K R IH1 - S P IY0
+CRISS  K R IH1 S
+CRISS-CROSS  K R IH1 S - K R AO1 S
+CRISS-CROSSED  K R IH1 S - K R AO1 S T
+CRISSCROSS  K R IH1 S - K R AO2 S
+CRISSCROSSED  K R IH1 S - K R AO2 S T
+CRISSCROSSING  K R IH1 S - K R AO2 - S IH0 NG
+CRISSEY  K R IH1 - S IY0
+CRISSINGER  K R IH1 - S IH0 N - JH ER0
+CRISSLOW  K R IH1 S - L OW0
+CRISSMAN  K R IH1 S - M AH0 N
+CRIST  K R IH1 S T
+CRISTABEL  K R IH1 - S T AH0 - B EH0 L
+CRISTAL  K R IH1 - S T AH0 L
+CRISTIANI  K R IH2 - S T IY0 - AA1 - N IY0
+CRISTIANI'S  K R IH2 - S T IY0 - AA1 - N IY0 Z
+CRISTIANI'S(2)  K R IH2 - S CH IY0 - AA1 - N IY0 Z
+CRISTIANI(2)  K R IH2 - S CH IY0 - AA1 - N IY0
+CRISTIANO  K R IY0 - S T IY0 - AA1 - N OW0
+CRISTINA  K R IH0 - S T IY1 - N AH0
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+CRISTOBAL  K R IH1 - S T AH0 - B AH0 L
+CRISTOBEL  K R IH1 - S T AH0 - B EH0 L
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+CRISWELL  K R IH1 - S W EH0 L
+CRITCHER  K R IH1 - CH ER0
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+CRITERION  K R AY0 - T IH1 - R IY0 - AH0 N
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+CRITICAL  K R IH1 - T IH0 - K AH0 L
+CRITICALITY  K R IH2 - T AH0 - K AE1 - L AH0 - T IY0
+CRITICALLY  K R IH1 - T IH0 - K AH0 - L IY0
+CRITICALLY(2)  K R IH1 - T IH0 K - L IY0
+CRITICISM  K R IH1 - T IH0 - S IH2 - Z AH0 M
+CRITICISMS  K R IH1 - T IH0 - S IH2 - Z AH0 M Z
+CRITICIZE  K R IH1 - T IH0 - S AY2 Z
+CRITICIZED  K R IH1 - T AH0 - S AY2 Z D
+CRITICIZES  K R IH1 - T IH0 - S AY2 - Z IH0 Z
+CRITICIZING  K R IH1 - T IH0 - S AY2 - Z IH0 NG
+CRITICS  K R IH1 - T IH0 K S
+CRITICS'  K R IH1 - T IH0 K S
+CRITIQUE  K R AH0 - T IY1 K
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+CRITIQUING  K R IH0 - T IY1 - K IH0 NG
+CRITON  K R AY1 - T AH0 N
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+CRITTENDEN  K R IH0 - T EH1 N - D AH0 N
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+CRITTER  K R IH1 - T ER0
+CRITTERS  K R IH1 - T ER0 Z
+CRITZ  K R IH1 T S
+CRITZER  K R IH1 T - Z ER0
+CRIVARO  K R IH0 - V AE1 - R OW0
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+CRIVELLO  K R IH0 - V EH1 - L OW0
+CRNKOVICH  S ER1 - N AH0 - V IH2 CH
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+CROATIAN  K R OW0 - EY1 - SH AH0 N
+CROATIAN'S  K R OW0 - EY1 - SH AH0 N Z
+CROATIANS  K R OW0 - EY1 - SH AH0 N Z
+CROATIANS'  K R OW0 - EY1 - SH AH0 N Z
+CROATS  K R OW1 - AA2 T S
+CROC  K R AA1 K
+CROCCO  K R AA1 - K OW0
+CROCE  K R OW1 - CH IY0
+CROCHET  K R OW0 - SH EY1
+CROCHETED  K R OW0 - SH EY1 D
+CROCHETIERE  K R OW2 - SH AH0 - T IH1 R
+CROCHETING  K R OW0 - SH EY1 - IH0 NG
+CROCK  K R AA1 K
+CROCKER  K R AA1 - K ER0
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+CROCKERY  K R AA1 - K ER0 - IY0
+CROCKETT  K R AA1 - K IH0 T
+CROCKFORD  K R AA1 K - F ER0 D
+CROCODILE  K R AA1 - K AH0 - D AY2 L
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+CROCUS  K R OW1 - K AH0 S
+CROCUSES  K R OW1 - K Y UW0 - S IH0 Z
+CROFF  K R AO1 F
+CROFFORD  K R AA1 - F ER0 D
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+CROISSANT  K W AA2 - S AA1 N T
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+CROISSANTS(2)  K R AH0 - S AA1 N T S
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+CRON  K R AA1 N
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+CRONIN  K R OW1 - N IH0 N
+CRONK  K R AA1 NG K
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+CROOK  K R UH1 K
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+CROOM  K R UW1 M
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+CROP  K R AA1 P
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+CROSSBOW  K R AO1 S - B OW2
+CROSSCURRENT  K R AO1 S - K ER2 - AH0 N T
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+CROSSE  K R AA1 S
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+CROSSROAD  K R AO1 S - R OW2 D
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+CROSSTALK  K R AO1 S T - AO1 K
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+CRUD  K R AH1 D
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+CRYOGENIC  K R AY1 - AH0 - JH EH2 - N IH0 K
+CRYOGENICS  K R AY1 - AH0 - JH EH2 - N IH0 K S
+CRYOLITE  K R AY1 - AH0 - L AY2 T
+CRYPT  K R IH1 P T
+CRYPTIC  K R IH1 P - T IH0 K
+CRYPTO  K R IH1 P - T OW0
+CRYPTOCLEARANCE  K R IH2 P - T OW0 - L IH1 - R AH0 N S
+CRYPTOSPORIDIUM  K R IH2 P - T OW0 - S P AO0 - R IH1 - D IY0 - AH0 M
+CRYPTS  K R IH1 P T S
+CRYSLER  K R IH1 - S AH0 L - ER0
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+CRYSTAL  K R IH1 - S T AH0 L
+CRYSTAL'S  K R IH1 - S T AH0 L Z
+CRYSTALLINE  K R IH1 - S T AH0 - L AY2 N
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+CRYSTALLIZED  K R IH1 - S T AH0 - L AY2 Z D
+CRYSTALLIZING  K R IH1 - S T AH0 - L AY2 - Z IH0 NG
+CRYSTALLOGRAPHER  K R IH2 - S T AH0 - L AA1 - G R AH0 - F ER0
+CRYSTALLOGRAPHY  K R IH2 - S T AH0 - L AA1 - G R AH0 - F IY0
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+DEBUTED(2)  D EY1 - B Y UW0 D
+DEBUTING  D EY0 - B Y UW1 - IH0 NG
+DEBUTS  D EY1 - B Y UW0 Z
+DEC  D EH1 K
+DEC'S  D EH1 K S
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+DECADE'S  D EH1 - K EY0 D Z
+DECADE(2)  D EH1 - K EY0 D
+DECADENCE  D EH1 - K AH0 - D AH0 N S
+DECADENT  D EH1 - K AH0 - D AH0 N T
+DECADES  D EH0 - K EY1 D Z
+DECADES(2)  D EH1 - K EY0 D Z
+DECAF  D IY1 - K AE0 F
+DECAFFEINATE  D IY0 - K AE1 - F AH0 - N EY2 T
+DECAFFEINATED  D IY0 - K AE1 - F AH0 - N EY2 - T IH0 D
+DECAFFEINATING  D IY0 - K AE1 - F AH0 - N EY2 - T IH0 NG
+DECAFFEINATION  D IY0 - K AE2 - F AH0 - N EY1 - SH AH0 N
+DECAIRE  D IY1 - K EH0 R
+DECALS  D IY1 - K AE2 L Z
+DECAMILLO  D EH2 - K AH0 - M IH1 - L OW0
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+DECAMP  D AH0 - K AE1 M P
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+DECANTING  D AH0 - K AE1 N - T IH0 NG
+DECAPITATE  D IY0 - K AE1 - P AH0 - T EY2 T
+DECAPITATED  D IY0 - K AE1 - P AH0 - T EY2 - T IH0 D
+DECAPITATION  D IH0 - K AE2 - P IH0 - T EY1 - SH AH0 N
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+DECAPRIO  D IH0 - K AA1 - P R IY0 - OW0
+DECAPUA  D IH0 - K AA0 - P UW1 - AH0
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+DECARO  D IH0 - K AA1 - R OW0
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+DECASTRO  D IH0 - K AE1 - S T R OW0
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+DECAYED  D AH0 - K EY1 D
+DECAYING  D AH0 - K EY1 - IH0 NG
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+DECAYS  D AH0 - K EY1 Z
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+DECECCO  D IH0 - CH EH1 - K OW0
+DECEDENT  D EH0 - S IY1 - D AH0 N T
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+DECEIT  D AH0 - S IY1 T
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+DECEITFUL  D AH0 - S IY1 T - F AH0 L
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+DECEIVED  D IH0 - S IY1 V D
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+DECELERATED  D IH0 - S EH1 - L ER0 - EY2 - T IH0 D
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+DECELERATION  D IH0 - S EH2 - L ER0 - EY1 - SH AH0 N
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+DECEPTION  D IH0 - S EH1 P - SH AH0 N
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+DECERTIFICATION  D IY0 - S ER2 - T AH0 - F AH0 - K EY1 - SH AH0 N
+DECERTIFIED  D IY0 - S ER1 - T AH0 - F AY2 D
+DECERTIFY  D IY0 - S ER1 - T AH0 - F AY2
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+DECESARIS  D IH0 - S EH1 - S ER0 - IH0 S
+DECH  D EH1 K
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+DECHRISTOPHER  D EH1 - K R IH0 - S T AA0 - F ER0
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+DECIMATED  D EH1 - S AH0 - M EY2 - T IH0 D
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+DECIMATION  D EH1 - S AH0 - M EY2 - SH AH0 N
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+DECLARANT  D IH0 - K L EH1 - R AH0 N T
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+DEFENSIBLE  D IH0 - F EH1 N - S AH0 - B AH0 L
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+DEFER  D IH0 - F ER1
+DEFERENCE  D EH1 - F ER0 - AH0 N S
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+DEFERENTIAL  D EH2 - F ER0 - EH1 N - CH AH0 L
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+DEFERMENT  D IH0 - F ER1 - M AH0 N T
+DEFERMENTS  D IH0 - F ER1 - M AH0 N T S
+DEFERRAL  D IH0 - F ER1 - AH0 L
+DEFERRALS  D IH0 - F ER1 - AH0 L Z
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+DEFIANT  D IH0 - F AY1 - AH0 N T
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+DEFIBAUGH  D EH1 - F IH0 - B AO2
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+DEFICIENT  D IH0 - F IH1 - SH AH0 N T
+DEFICIT  D EH1 - F AH0 - S AH0 T
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+DEFICITS  D EH1 - F IH0 - S IH0 T S
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+DEFINED  D IH0 - F AY1 N D
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+DEFINITION  D EH2 - F AH0 - N IH1 - SH AH0 N
+DEFINITIONS  D EH2 - F AH0 - N IH1 - SH AH0 N Z
+DEFINITIVE  D IH0 - F IH1 - N IH0 - T IH0 V
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+DEFLATOR  D IH0 - F L EY1 - T ER0
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+DEFOLIANT  D IH0 - F OW1 - L IY0 - AH0 N T
+DEFOLIANTS  D IH0 - F OW1 - L IY0 - AH0 N T S
+DEFOOR  D EH1 - F UH0 R
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+DEFORESTATION  D IH0 - F AO2 - R IH0 - S T EY1 - SH AH0 N
+DEFORGE  D EH1 - F ER0 G
+DEFORM  D IY2 - F AO1 R M
+DEFORMATION  D IY2 - F AO0 R - M EY1 - SH AH0 N
+DEFORMED  D IH0 - F AO1 R M D
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+DEFORREST  D EY0 - F AO1 - R IH0 S T
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+DEFRAIN  D IH0 - F R EY1 N
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+DEFRANCESCO  D IH0 - F R AA0 N - CH EH1 - S K OW0
+DEFRANCISCO  D IH0 - F R AA0 N - CH IY1 - S K OW0
+DEFRANCO  D IH0 - F R AA1 N - K OW0
+DEFRANK  D EH1 - F R AH0 NG K
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+DEFRAUD  D IH0 - F R AO1 D
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+DEFREESE  D EH1 - F R IY0 S
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+DEFROST  D IH0 - F R AO1 S T
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+DEFUSE  D IH0 - F Y UW1 Z
+DEFUSED  D IH0 - F Y UW1 Z D
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+DEFUSING  D IH0 - F Y UW1 - Z IH0 NG
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+DEFYING  D IH0 - F AY1 - IH0 NG
+DEGAETANO  D IH0 - G AA0 - EH0 - T AA1 - N OW0
+DEGAN  D IY1 - G AH0 N
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+DEGAULLE  D AH0 - G AA1 L
+DEGAULLE'S  D AH0 - G AA1 L Z
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+DEGENER  D EH1 - G IY0 - N ER0
+DEGENERACY  D IH0 - JH EH1 - N ER0 - AH0 - S IY0
+DEGENERATE  D IH0 - JH EH1 - N ER0 - AH0 T
+DEGENERATE(2)  D IH0 - JH EH1 - N ER0 - EY2 T
+DEGENERATED  D IH0 - JH EH1 - N ER0 - EY2 - T IH0 D
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+DEGENERES  D IH0 - JH IH0 - N EH1 - R EH0 S
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+DEGENHARDT  D EH1 - G IH0 N - HH AA0 R T
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+DEGRADATION  D EH2 - G R AH0 - D EY1 - SH AH0 N
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+DEGRADE  D IH0 - G R EY1 D
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+DEMODULATE  D IY2 - M AA2 - JH AH0 - L EY1 T
+DEMODULATION  D IY2 - M AA2 - JH AH0 - L EY1 - SH AH0 N
+DEMOGRAPHER  D IH0 - M AA1 - G R AH0 - F ER0
+DEMOGRAPHERS  D IH0 - M AA1 - G R AH0 - F ER0 Z
+DEMOGRAPHIC  D EH2 - M AH0 - G R AE1 - F IH0 K
+DEMOGRAPHICALLY  D EH2 - M AH0 - G R AE1 - F IH0 K - L IY0
+DEMOGRAPHICS  D EH2 - M AH0 - G R AE1 - F IH0 K S
+DEMOGRAPHY  D IH0 - M AA1 - G R AH0 - F IY0
+DEMOLISH  D IH0 - M AA1 - L IH0 SH
+DEMOLISHED  D IH0 - M AA1 - L IH0 SH T
+DEMOLISHING  D IH0 - M AA1 - L IH0 - SH IH0 NG
+DEMOLITION  D EH2 - M AH0 - L IH1 - SH AH0 N
+DEMON  D IY1 - M AH0 N
+DEMOND  D AH0 - M AA1 N D
+DEMONIC  D IH0 - M AA1 - N IH0 K
+DEMONIZATION  D IY2 - M AH0 - N AH0 - Z EY1 - SH AH0 N
+DEMONIZE  D IY1 - M AH0 - N AY2 Z
+DEMONIZED  D IY1 - M AH0 - N AY2 Z D
+DEMONIZER  D IY1 - M AH0 - N AY2 - Z ER0
+DEMONIZEZ  D IY1 - M AH0 - N AY2 - Z IH0 Z
+DEMONIZING  D IY1 - M AH0 - N AY2 - Z IH0 NG
+DEMONS  D IY1 - M AH0 N Z
+DEMONSTRABLE  D EH1 - M AH0 N - S T R AH0 - B AH0 L
+DEMONSTRABLY  D IH0 - M AA1 N - S T R AH0 - B L IY0
+DEMONSTRATE  D EH1 - M AH0 N - S T R EY2 T
+DEMONSTRATED  D EH1 - M AH0 N - S T R EY2 - T AH0 D
+DEMONSTRATED(2)  D EH1 - M AH0 N - S T R EY2 - T IH0 D
+DEMONSTRATES  D EH1 - M AH0 N - S T R EY2 T S
+DEMONSTRATING  D EH1 - M AH0 N - S T R EY2 - T IH0 NG
+DEMONSTRATION  D EH2 - M AH0 N - S T R EY1 - SH AH0 N
+DEMONSTRATIONS  D EH2 - M AH0 N - S T R EY1 - SH AH0 N Z
+DEMONSTRATIVE  D IH0 - M AA1 N - S T R AH0 - T IH0 V
+DEMONSTRATOR  D EH1 - M AH0 N - S T R EY2 - T ER0
+DEMONSTRATORS  D EH1 - M AH0 N - S T R EY2 - T ER0 Z
+DEMONT  D EH1 - M AH0 N T
+DEMONTE  D AH0 - M AA1 N - T IY0
+DEMOPOULOS  D AH0 - M AA1 - P AH0 - L IH0 S
+DEMORALIZATION  D IH0 - M AO2 - R AH0 - L IH0 - Z EY1 - SH AH0 N
+DEMORALIZE  D IH0 - M AO1 - R AH0 - L AY2 Z
+DEMORALIZED  D IH0 - M AO1 - R AH0 - L AY2 Z D
+DEMORALIZING  D IH0 - M AO1 - R AH0 - L AY2 - Z IH0 NG
+DEMORE  D EH1 - M AO0 R
+DEMOREST  D EY0 - M AO1 - R IH0 S T
+DEMORY  D IH0 - M ER1 - IY0
+DEMORY'S  D IH0 - M ER1 - IY0 Z
+DEMOS  D EH1 - M OW2 Z
+DEMOSS  D AH0 - M AA1 S
+DEMOTE  D IH0 - M OW1 T
+DEMOTED  D IH0 - M OW1 - T IH0 D
+DEMOTION  D IH0 - M OW1 - SH AH0 N
+DEMOTIONS  D IH0 - M OW1 - SH AH0 N Z
+DEMOTT  D AH0 - M AA1 T
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+DEMOV  D EH1 - M AA0 V
+DEMPEWOLF  D EH1 M - P Y UW0 - UH0 L F
+DEMPS  D EH1 M P S
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+DEMPSTER  D EH1 M P - S T ER0
+DEMSKI  D EH1 M S - K IY0
+DEMSKY  D EH1 M S - K IY0
+DEMUR  D IH0 - M ER1
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+DEMURRED  D IH0 - M ER1 D
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+DEMURS  D IH0 - M ER1 Z
+DEMUS  D IY1 - M AH0 S
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+DEMYAN  D EH1 - M Y AH0 N
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+DEN  D EH1 N
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+DENATIONALIZATION  D IY2 - N AE2 - SH AH0 N - AH0 - L IH0 - Z EY1 - SH AH0 N
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+DENATIONALIZE  D IH0 - N AE1 - SH AH0 N - AH0 - L AY2 Z
+DENATIONALIZED  D IH0 - N AE1 - SH AH0 N - AH0 - L AY2 Z D
+DENATIONALIZING  D IH0 - N AE1 - SH AH0 N - AH0 - L AY2 - Z IH0 NG
+DENATURE  D IH0 - N EY1 - CH ER0
+DENATURED  D IH0 - N EY1 - CH ER0 D
+DENAULT  D IH0 - N OW1
+DENBO  D IY1 N - B OW0
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+DENBOW  D EH1 N - B OW0
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+DENDINGER  D IY1 N - D IH0 - NG ER0
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+DENDROCHRONOLOGY  D EH2 N - D R OW2 - K R AH0 - N AA1 - L AH0 - JH IY0
+DENDY  D EH1 N - D IY0
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+DENEEN  D IH0 - N IY1 N
+DENEKE  D EH1 - N IH0 K
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+DENES  D IY1 N Z
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+DENEVE  D EH1 - N IH0 V
+DENG  D EH1 NG
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+DENGEL  D EH1 NG - G AH0 L
+DENGLER  D IH1 - NG AH0 - L ER0
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+DENGUE  D EH1 N G
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+DENHART  D EH1 N - HH AA2 R T
+DENHARTOG  D EH1 N - HH AA0 R - T AH0 G
+DENHERDER  D EH1 N - HH ER2 - D ER0
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+DENIABILITY  D IH0 - N AY2 - AH0 - B IH1 - L IH0 - T IY0
+DENIAL  D IH0 - N AY1 - AH0 L
+DENIALS  D IH0 - N AY1 - AH0 L Z
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+DENIED  D IH0 - N AY1 D
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+DENIO  D IY1 - N IY0 - OW0
+DENIRO  D IH0 - N IH1 - R OW0
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+DENISTON  D EH1 - N IH0 - S T AA0 N
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+DENIZEN  D EH1 - N AH0 - Z AH0 N
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+DENK  D EH1 NG K
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+DENOUNCE  D IH0 - N AW1 N S
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+DENOYER  D EH1 - N OY0 - ER0
+DENS  D EH1 N Z
+DENSE  D EH1 N S
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+DENSER  D EH1 N - S ER0
+DENSEST  D EH1 N - S AH0 S T
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+DESPER  D EH1 - S P ER0
+DESPERADO  D EH2 - S P ER0 - AA1 - D OW0
+DESPERADOES  D EH2 - S P ER0 - AA1 - D OW0 Z
+DESPERATE  D EH1 - S P R IH0 T
+DESPERATE(2)  D EH1 - S P ER0 - IH0 T
+DESPERATELY  D EH1 - S P ER0 - AH0 T - L IY0
+DESPERATELY(2)  D EH1 - S P R AH0 T - L IY0
+DESPERATION  D EH2 - S P ER0 - EY1 - SH AH0 N
+DESPERATION(2)  D EH2 - S P ER0 - EY1 - SH IH0 N
+DESPICABLE  D IH0 - S P IH1 - K AH0 - B AH0 L
+DESPINA'S  D EH1 - S P IY0 - N AH0 Z
+DESPISE  D IH0 - S P AY1 Z
+DESPISED  D IH0 - S P AY1 Z D
+DESPISES  D IH0 - S P AY1 - Z IH0 Z
+DESPITE  D IH0 - S P AY1 T
+DESPONDENCY  D IH0 - S P AA1 N - D AH0 N - S IY0
+DESPONDENT  D IH0 - S P AA1 N - D AH0 N T
+DESPOSITO  D IH0 - S P OW0 - S IY1 - T OW0
+DESPOT  D EH1 - S P AH0 T
+DESPOTIC  D IH0 - S P AA1 - T IH0 K
+DESPOTISM  D EH1 - S P AH0 - T IH2 - Z AH0 M
+DESPRES  D EH1 - S P ER0 Z
+DESROCHERS  D EY0 - R OW1 - SH ER0 Z
+DESROCHES  D EY0 - R OW1 - SH IH0 Z
+DESROSIER  D EY0 - R OW1 - SH IY0 - ER0
+DESROSIERS  D EY0 - R OW1 - SH IY0 - ER0 Z
+DESROSIERS(2)  D EY2 - R OW0 - ZH IH1 R Z
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+DESSAUER(2)  D EH1 - S AW2 R
+DESSELLE  D IH0 - S EH1 L
+DESSENT  D IH0 - S EH1 N T
+DESSERT  D IH0 - Z ER1 T
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+DEST  D EH1 S T
+DESTABILIZATION  D IY0 - S T EY2 - B AH0 - L AH0 - Z EY1 - SH AH0 N
+DESTABILIZE  D IH0 - S T EY1 - B AH0 - L AY2 Z
+DESTABILIZED  D IH0 - S T EY1 - B AH0 - L AY2 Z D
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+DESTEC  D EH1 - S T EH2 K
+DESTEFANIS  D IH0 - S T IH0 - F AA1 - N IH0 S
+DESTEFANO  D IH0 - S T EH0 - F AA1 - N OW0
+DESTIN  D EH1 - S T IH0 N
+DESTINATION  D EH2 - S T AH0 - N EY1 - SH AH0 N
+DESTINATION(2)  D EH2 - S T IH0 - N EY1 - SH AH0 N
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+DESTINED  D EH1 - S T IH0 N D
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+DESTINY  D EH1 - S T AH0 - N IY0
+DESTITUTE  D EH1 - S T AH0 - T UW2 T
+DESTITUTION  D EH1 - S T AH0 - T UW2 - SH AH0 N
+DESTROY  D IH0 - S T R OY1
+DESTROYED  D IH0 - S T R OY1 D
+DESTROYER  D IH0 - S T R OY1 - ER0
+DESTROYERS  D IH0 - S T R OY1 - ER0 Z
+DESTROYING  D IH0 - S T R OY1 - IH0 NG
+DESTROYS  D IH0 - S T R OY1 Z
+DESTRUCT  D IH0 - S T R AH1 K T
+DESTRUCTED  D IH0 - S T R AH1 K - T IH0 D
+DESTRUCTING  D IH0 - S T R AH1 K - T IH0 NG
+DESTRUCTION  D IH0 S - T R AH1 K - SH AH0 N
+DESTRUCTIVE  D IH0 - S T R AH1 K - T IH0 V
+DESTRUCTIVENESS  D IH0 - S T R AH1 K - T IH0 V - N IH0 S
+DESTRUCTS  D IH0 - S T R AH1 K T S
+DESULTORY  D EH1 - S AH0 L - T AO2 - R IY0
+DETACH  D IH0 - T AE1 CH
+DETACH(2)  D IY0 - T AE1 CH
+DETACHABLE  D IH0 - T AE1 - CH AH0 - B AH0 L
+DETACHABLE(2)  D IY0 - T AE1 - CH AH0 - B AH0 L
+DETACHED  D IH0 - T AE1 CH T
+DETACHED(2)  D IY0 - T AE1 CH T
+DETACHES  D IH0 - T AE1 - CH IH0 Z
+DETACHES(2)  D IY0 - T AE1 - CH AH0 Z
+DETACHMENT  D IH0 - T AE1 CH - M AH0 N T
+DETACHMENT(2)  D IY0 - T AE1 CH - M AH0 N T
+DETAIL  D IH0 - T EY1 L
+DETAIL(2)  D IY1 - T EY0 L
+DETAILED  D IH0 - T EY1 L D
+DETAILEE  D IH0 - T EY2 - L IY1
+DETAILER  D IY1 - T EY0 - L ER0
+DETAILING  D IH0 - T EY1 - L IH0 NG
+DETAILS  D IH0 - T EY1 L Z
+DETAILS(2)  D IY1 - T EY0 L Z
+DETAIN  D IH0 - T EY1 N
+DETAINED  D IH0 - T EY1 N D
+DETAINEE  D IY2 - T EY0 - N IY1
+DETAINEES  D IH0 - T EY2 - N IY1 Z
+DETAINING  D IH0 - T EY1 - N IH0 NG
+DETAMORE  D IH0 - T AA1 - M AO0 R
+DETAR  D IH0 - T AA1 R
+DETAR(2)  D IY0 - T AA0 R
+DETECT  D IH0 - T EH1 K T
+DETECTABLE  D IH0 - T EH1 K - T AH0 - B AH0 L
+DETECTED  D IH0 - T EH1 K - T AH0 D
+DETECTED(2)  D IH0 - T EH1 K - T IH0 D
+DETECTING  D IH0 - T EH1 K - T IH0 NG
+DETECTION  D IH0 - T EH1 K - SH AH0 N
+DETECTIVE  D IH0 - T EH1 K - T IH0 V
+DETECTIVE'S  D IH0 - T EH1 K - T IH0 V Z
+DETECTIVES  D IH0 - T EH1 K - T IH0 V Z
+DETECTOR  D IH0 - T EH1 K - T ER0
+DETECTORS  D IH0 - T EH1 K - T ER0 Z
+DETECTS  D IH0 - T EH1 K T S
+DETEMPLE  D EH1 - T IH0 M - P AH0 L
+DETENTE  D EY0 - T AA1 N T
+DETENTION  D IH0 - T EH1 N - SH AH0 N
+DETENTIONS  D IH0 - T EH1 N - SH AH0 N Z
+DETER  D IH0 - T ER1
+DETERDING  D EH1 - T ER0 - D IH0 NG
+DETERGENT  D IH0 - T ER1 - JH AH0 N T
+DETERGENTS  D IH0 - T ER1 - JH AH0 N T S
+DETERIORATE  D IH0 - T IH1 - R IY0 - ER0 - EY2 T
+DETERIORATED  D IH0 - T IH1 - R IY0 - ER0 - EY2 - T IH0 D
+DETERIORATES  D IH0 - T IH1 - R IY0 - ER0 - EY2 T S
+DETERIORATING  D IH0 - T IH1 - R IY0 - ER0 - EY2 - T IH0 NG
+DETERIORATION  D IH0 - T IH1 - R IY0 - ER0 - EY2 - SH AH0 N
+DETERMAN  D IY1 - T ER0 - M AH0 N
+DETERMENT  D IH0 - T ER1 - M AH0 N T
+DETERMINABLE  D IH0 - T ER1 - M AH0 - N AH0 - B AH0 L
+DETERMINANT  D IH0 - T ER1 - M AH0 - N AH0 N T
+DETERMINANTS  D IH0 - T ER1 - M AH0 - N AH0 N T S
+DETERMINATE  D IH0 - T ER1 - M AH0 - N EY2 T
+DETERMINATION  D IH0 - T ER2 - M AH0 - N EY1 - SH AH0 N
+DETERMINATION'S  D IH0 - T ER2 - M IH0 - N EY1 - SH AH0 N Z
+DETERMINATIONS  D IH0 - T ER2 - M IH0 - N EY1 - SH AH0 N Z
+DETERMINATIVE  D IH0 - T ER1 - M IH0 - N AH0 - T IH2 V
+DETERMINE  D AH0 - T ER1 - M AH0 N
+DETERMINE(2)  D IH0 - T ER1 - M AH0 N
+DETERMINED  D IH0 - T ER1 - M AH0 N D
+DETERMINEDLY  D AH0 - T ER1 - M AH0 - N AH0 D - L IY0
+DETERMINEDLY(2)  D AH0 - T ER1 - M AH0 N D - L IY0
+DETERMINES  D AH0 - T ER1 - M AH0 N Z
+DETERMINES(2)  D IH0 - T ER1 - M AH0 N Z
+DETERMINING  D IH0 - T ER1 - M AH0 - N IH0 NG
+DETERMINISM  D IH0 - T ER1 - M AH0 - N IH2 - Z AH0 M
+DETERMINIST  D IH0 - T ER1 - M AH0 - N AH0 S T
+DETERMINISTIC  D IH0 - T ER2 - M AH0 - N IH1 - S T IH0 K
+DETERRED  D IH0 - T ER1 D
+DETERRENCE  D IH0 - T ER1 - AH0 N S
+DETERRENT  D IH0 - T ER1 - R AH0 N T
+DETERRENTS  D IH0 - T ER1 - AH0 N T S
+DETERRING  D IH0 - T ER1 - IH0 NG
+DETERS  D IH0 - T ER1 Z
+DETERT  D EH1 - T ER0 T
+DETEST  D IH0 - T EH1 S T
+DETEST(2)  D IY0 - T EH1 S T
+DETESTED  D IH0 - T EH1 - S T IH0 D
+DETESTED(2)  D IY0 - T EH1 - S T IH0 D
+DETHERAGE  D EH1 - DH ER0 - IH0 JH
+DETHLEFS  D EH1 TH - L IH0 F S
+DETHLEFSEN  D EH1 TH - L IH0 F - S AH0 N
+DETHLOFF  D EH1 TH - L AO0 F
+DETHOMAS  D IH0 - TH OW1 - M AH0 Z
+DETHOMAS(2)  D IH0 - T AA1 - M AH0 S
+DETHRONE  D IH0 - TH R OW1 N
+DETHRONE(2)  D IY0 - TH R OW1 N
+DETHRONED  D IH0 - TH R OW1 N D
+DETHRONED(2)  D IY0 - TH R OW1 N D
+DETIENNE  D EH1 - T IY0 - EH0 N
+DETJEN  D EH1 T - JH AH0 N
+DETLEFSEN  D EH1 T - L IH0 F - S AH0 N
+DETLOFF  D EH1 T - L AO0 F
+DETMER  D EH1 T - M ER0
+DETONATE  D EH1 - T AH0 - N EY2 T
+DETONATED  D EH1 - T AH0 - N EY2 - T AH0 D
+DETONATING  D EH1 - T AH0 - N EY2 - T IH0 NG
+DETONATION  D EH2 - T AH0 - N EY1 - SH AH0 N
+DETONATIONS  D EH2 - T AH0 - N EY1 - SH AH0 N Z
+DETONATOR  D EH1 - T AH0 - N EY2 - T ER0
+DETONATORS  D EH1 - T AH0 - N EY2 - T ER0 Z
+DETORE  D IH0 - T AO1 - R IY0
+DETOUR  D IH0 - T UH1 R
+DETOUR(2)  D IY1 - T UH0 R
+DETOURED  D IY1 - T UH0 R D
+DETOURS  D IH0 - T UH1 R Z
+DETOURS(2)  D IY1 - T UH0 R Z
+DETOX  D IY1 - T AA2 K S
+DETOXICATION  D IH0 - T AA2 K - S AH0 - K EY1 - SH AH0 N
+DETOXIFICATION  D IH0 - T AA2 K - S IH0 - F IH0 - K EY1 - SH AH0 N
+DETOXIFY  D IH0 - T AA1 K - S AH0 - F AY2
+DETRACT  D IH0 - T R AE1 K T
+DETRACTED  D IH0 - T R AE1 K - T IH0 D
+DETRACTING  D IH0 - T R AE1 K - T IH0 NG
+DETRACTOR(2)  D IY0 - T R AE1 K - T ER0
+DETRACTORS  D IH0 - T R AE1 K - T ER0 Z
+DETRACTORS(2)  D IY0 - T R AE1 K - T ER0 Z
+DETRACTS  D IH0 - T R AE1 K T S
+DETRICH  D EH1 - T R IH0 K
+DETRICK  D EH1 - T R IH0 K
+DETRIMENT  D EH1 - T R AH0 - M AH0 N T
+DETRIMENTAL  D EH2 - T R AH0 - M EH1 N - T AH0 L
+DETRIMENTAL(2)  D EH2 - T R AH0 - M EH1 - N AH0 L
+DETRITUS  D IH0 - T R AY1 - T AH0 S
+DETRITUS(2)  D EH1 - T R AH0 - T AH0 S
+DETRO  D IY1 - T R OW0
+DETROIT  D IH0 - T R OY1 T
+DETROIT'S  D AH0 - T R OY1 T S
+DETROIT'S(2)  D IH0 - T R OY1 T S
+DETROIT(2)  D IY1 - T R OY2 T
+DETROITERS  D AH0 - T R OY1 - T ER0 Z
+DETROITERS(2)  D IY1 - T R OY2 - T ER0 Z
+DETTER  D EH1 - T ER0
+DETTINGER  D EH1 - T IH0 N - JH ER0
+DETTLING  D EH1 T - L IH0 NG
+DETTLOFF  D EH1 T - L AO0 F
+DETTMAN  D EH1 T - M AH0 N
+DETTMANN  D EH1 T - M AH0 N
+DETTMER  D EH1 T - M ER0
+DETTORE  D IH0 - T AO1 - R IY0
+DETTY  D EH1 - T IY0
+DETURK  D EH1 - T ER0 K
+DETWEILER  D EH1 T - W AY2 - L ER0
+DETWILER  D EH1 T - W AY2 - L ER0
+DETZEL  D EH1 T - Z AH0 L
+DEUBEL  D OY1 - B AH0 L
+DEUBLER  D OY1 - B AH0 L - ER0
+DEUBLER(2)  D OY1 - B L ER0
+DEUCE  D UW1 S
+DEUEL  D UW1 - EH0 L
+DEUKMEJIAN  D UW0 K - M EY1 - JH IY0 - AH0 N
+DEUKMEJIAN'S  D UW0 K - M EY1 - JH IY0 - AH0 N Z
+DEUPREE  D UW0 - P R IY1
+DEUSCHLE  D OY1 - SH AH0 L
+DEUSER  D OY1 - S ER0
+DEUSS  D UW1 S
+DEUTCH  D OY1 CH
+DEUTERIUM  D UW0 - T IY1 - R IY0 - AH0 M
+DEUTERONS  D UW1 - T ER0 - AA2 N Z
+DEUTSCH  D OY1 CH
+DEUTSCHE  D OY1 CH
+DEUTSCHEMARK  D OY1 CH - M AA2 R K
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+DEUTSCHEMARKS  D OY1 CH - M AA2 R K S
+DEUTSCHER  D OY1 - CH ER0
+DEUTSCHLAND  D OY1 CH - L AE0 N D
+DEUTSCHMAN  D OY1 CH - M AH0 N
+DEUTZ  D OY1 T S
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+DEVALLE(2)  D IH0 - V AE1 - L IY0
+DEVALUATE  D IH0 - V AE1 L - Y UW0 - EY2 T
+DEVALUATED  D IH0 - V AE1 L - Y UW0 - EY2 - T IH0 D
+DEVALUATION  D IH0 - V AE2 L - Y UW0 - EY1 - SH AH0 N
+DEVALUATION(2)  D IY2 - V AE0 L - Y UW0 - EY1 - SH AH0 N
+DEVALUATIONS  D IY2 - V AE0 L - Y UW0 - EY1 - SH AH0 N Z
+DEVALUE  D IH0 - V AE1 L - Y UW2
+DEVALUED  D IH0 - V AE1 L - Y UW2 D
+DEVALUED(2)  D IY0 - V AE1 L - Y UW2 D
+DEVALUING  D IH0 - V AE1 L - Y UW0 - IH0 NG
+DEVAN  D EH1 - V AH0 N
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+DEVASTATED  D EH1 - V AH0 - S T EY2 - T AH0 D
+DEVASTATED(2)  D EH1 - V AH0 - S T EY2 - T IH0 D
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+DEVASTATINGLY  D EH1 - V AH0 - S T EY2 - T IH0 NG - L IY0
+DEVASTATION  D EH2 - V AH0 - S T EY1 - SH AH0 N
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+DEVELCON  D AH0 - V EH1 L - K AH0 N
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+DEVELOPED  D IH0 - V EH1 - L AH0 P T
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+DEVELOPERS  D IH0 - V EH1 - L AH0 - P ER0 Z
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+DEVELOPING  D IH0 - V EH1 - L AH0 - P IH0 NG
+DEVELOPMENT  D IH0 - V EH1 - L AH0 P - M AH0 N T
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+DEVELOPMENTAL  D IH0 - V EH2 - L AH0 P - M EH1 N - T AH0 L
+DEVELOPMENTAL(2)  D IH0 - V EH2 - L AH0 P - M EH1 - N AH0 L
+DEVELOPMENTALLY  D IH0 - V EH2 - L AH0 P - M EH1 N - AH0 - L IY0
+DEVELOPMENTALLY(2)  D IH0 - V EH2 - L AH0 P - M EH1 N - T AH0 - L IY0
+DEVELOPMENTS  D IH0 - V EH1 - L AH0 P - M AH0 N T S
+DEVELOPS  D IH0 - V EH1 - L AH0 P S
+DEVENDORF  D EH1 - V IH0 N - D AO0 R F
+DEVENEY  D EH1 - V IH0 - N IY0
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+DEVENNY  D EH1 - V IH0 - N IY0
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+DEVENS  D IY1 - V AH0 N Z
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+DEVIANCE  D IY1 - V IY0 - AH0 N S
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+DEVIANCY(2)  D IY1 - V Y EH0 N - S IY0
+DEVIANT  D IY1 - V IY0 - AH0 N T
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+DEVIATION  D IY2 - V IY0 - EY1 - SH AH0 N
+DEVIATIONS  D IY0 - V IY0 - EY1 - SH AH0 N Z
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+DEVILISH  D EH1 V - L IH0 SH
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+DEVILLE  D AH0 - V IH1 L
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+DIFF  D IH1 F
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+DIFFERENCE  D IH1 - F ER0 - AH0 N S
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+DIFFERENT  D IH1 - F ER0 - AH0 N T
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+DIFFERENTIAL  D IH2 - F ER0 - EH1 N - CH AH0 L
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+DIFFERENTIALS  D IH2 - F ER0 - EH1 N - CH AH0 L Z
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+DIFFERENTIATE  D IH2 - F ER0 - EH1 N - SH IY0 - EY2 T
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+DIFFERENTIATED  D IH2 - F ER0 - EH1 N - CH IY0 - EY2 - T IH0 D
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+DIFFERENTIATING  D IH2 - F ER0 - EH1 N - CH IY0 - EY2 - T IH0 NG
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+DIFFERENTIATION  D IH0 - F ER0 - EH2 N - SH IY0 - EY1 - SH AH0 N
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+DIFRANCO  D IH0 - F R AA1 N - K OW0
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+DIGEST  D AY0 - JH EH1 S T
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+DIGESTED  D AY1 - JH EH2 - S T IH0 D
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+DIGESTIBLE  D AY0 - JH EH1 - S T AH0 - B AH0 L
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+DIGESTION  D AY0 - JH EH1 S - CH AH0 N
+DIGESTIVE  D AY0 - JH EH1 - S T IH0 V
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+DIGGA  D IH1 - G AH0
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+DIGGES  D IH1 G Z
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+DIGIACOMO  D IY1 - JH AH0 - K OW0 - M OW0
+DIGICON  D IH1 - JH IH0 - K AA2 N
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+DIGIT  D IH1 - JH AH0 T
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+DIGITAL  D IH1 - JH AH0 - T AH0 L
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+DIGITAL(2)  D IH1 - JH IH0 - T AH0 L
+DIGITALIS  D IH2 - JH AH0 - T AE1 - L AH0 S
+DIGITALLY  D IH1 - JH AH0 - T AH0 - L IY0
+DIGITECH  D IH1 - JH AH0 - T EH2 K
+DIGITIZE  D IH1 - JH AH0 - T AY2 Z
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+DIGITIZING  D IH1 - JH AH0 - T AY2 - Z IH0 NG
+DIGITS  D IH1 - JH AH0 T S
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+DIGIULIO  D IH0 - JH UW1 - L IY0 - OW0
+DIGIUSEPPE  D IY2 - JH UW0 - S EH1 - P IY0
+DIGMAN  D IH1 G - M AH0 N
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+DILAPIDATE  D AH0 - L AE1 - P AH0 - D EY2 T
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+DILIGENT  D IH1 - L IH0 - JH AH0 N T
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+DIMENSIONED  D AH0 - M EH1 N - CH AH0 N D
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+DINNING  D IH1 - N IH0 NG
+DINO  D IY1 - N OW0
+DINOSAUR  D AY1 - N AH0 - S AO2 R
+DINOSAURS  D AY1 - N AH0 - S AO2 R Z
+DINOSEB  D AY1 - N OW0 - S EH2 B
+DINOTO  D IH0 - N OW1 - T OW0
+DINOTOPIA  D AY2 - N AH0 - T OW1 - P IY0 - AH0
+DINOVO  D IH0 - N OW1 - V OW0
+DINSA  D IH1 N - S AH0
+DINSDALE  D IH1 N Z - D EY2 L
+DINSE  D IH1 N S
+DINSMORE  D IY1 N S - M AO0 R
+DINT  D IH1 N T
+DINUNZIO  D IH0 - N AH1 N - Z IY0 - OW0
+DINWIDDIE  D IH1 N - W IH0 - D IY0
+DIOCESAN  D AY0 - AA1 - S AH0 - S AH0 N
+DIOCESE  D AY1 - AH0 - S IY2 Z
+DIOCESE(2)  D AY1 - AH0 - S AH0 S
+DIOCESES  D AY1 - AH0 - S IY2 Z
+DIOCESES(2)  D AY1 - AH0 - S IY2 - Z AH0 Z
+DIODATI  D IY0 - OW0 - D AA1 - T IY0
+DIODATO  D IY0 - OW0 - D AA1 - T OW0
+DIODE  D AY1 - OW2 D
+DIODES  D AY1 - OW2 D Z
+DIOGUARDI  D IY0 - OW0 - G AA1 R - D IY0
+DION  D AY1 - AH0 N
+DION(2)  D IY1 - AO2 N
+DIONA  D IY0 - OW1 - N AH0
+DIONE  D IY1 - AA0 N
+DIONISIO  D AY2 - AH0 - N IH1 - S IY0 - OW0
+DIONNE  D IY1 - AA0 N
+DIONYSIUS  D AY2 - AH0 - N IH1 - S IY0 - AH0 S
+DIOR  D IY2 - AO1 R
+DIORIO  D IY0 - AO1 - R IY0 - OW0
+DIORITE  D AY1 - ER0 - AY2 T
+DIOS  D IY1 - OW0 S
+DIOS'  D IY1 - OW0 S
+DIOS'S  D IY1 - OW0 - S IH0 Z
+DIOXIDE  D AY0 - AA1 K - S AY2 D
+DIOXIDES  D AY0 - AA1 K - S AY0 D Z
+DIOXIN  D AY2 - AA1 K - S IH0 N
+DIOXINS  D AY2 - AA1 K - S IH0 N Z
+DIP  D IH1 P
+DIPALMA  D IH0 - P AA1 L - M AH0
+DIPAOLA  D IH0 - P AA0 - OW1 - L AH0
+DIPAOLO  D IH0 - P AA0 - OW1 - L OW0
+DIPASQUALE  D IH0 - P AA0 S - K W AA1 - L IY0
+DIPASQUALE(2)  D IY0 - P AA0 S - K W AA1 - L IY0
+DIPERNA  D IH0 - P EH1 R - N AH0
+DIPHTHERIA  D IH0 F - TH IH1 - R IY0 - AH0
+DIPIAZZA  D IH0 - P IY0 - AA1 T - S AH0
+DIPIAZZA(2)  D IY0 - P IY0 - AA1 T - S AH0
+DIPIERO  D IH0 - P IH1 - R OW0
+DIPIERRO  D IH0 - P IH1 - R OW0
+DIPIETRO  D IH0 - P IY1 - T R OW0
+DIPINTO  D IH0 - P IH1 N - T OW0
+DIPIRRO  D IH0 - P IH1 - R OW0
+DIPLOMA  D IH0 - P L OW1 - M AH0
+DIPLOMACY  D IH0 - P L OW1 - M AH0 - S IY0
+DIPLOMAS  D IH0 - P L OW1 - M AH0 Z
+DIPLOMAT  D IH1 - P L AH0 - M AE2 T
+DIPLOMAT'S  D IH1 - P L AH0 - M AE2 T S
+DIPLOMATIC  D IH2 - P L AH0 - M AE1 - T IH0 K
+DIPLOMATICALLY  D IH2 - P L AH0 - M AE1 - T IH0 K - L IY0
+DIPLOMATS  D IH1 - P L AH0 - M AE2 T S
+DIPLOMATS'  D IH1 - P L AH0 - M AE2 T S
+DIPOLE  D AY1 - P OW2 L
+DIPPED  D IH1 P T
+DIPPEL  D IH1 - P AH0 L
+DIPPER  D IH1 - P ER0
+DIPPERS  D IH1 - P ER0 Z
+DIPPING  D IH1 - P IH0 NG
+DIPPLE  D IH1 - P AH0 L
+DIPPOLD  D IH1 - P OW2 L D
+DIPPOLITO  D IH0 - P OW0 - L IY1 - T OW0
+DIPPY  D IH1 - P IY0
+DIPRIMA  D IH0 - P R IY1 - M AH0
+DIPS  D IH1 P S
+DIPSTICK  D IH1 P - S T IH2 K
+DIRCKS  D ER1 K S
+DIRE  D AY1 R
+DIRE(2)  D AY1 - ER0
+DIRECT  D ER0 - EH1 K T
+DIRECT(2)  D AY0 - R EH1 K T
+DIRECT(3)  D IY0 - R EH1 K T
+DIRECT(4)  D IH0 - R EH1 K T
+DIRECTED  D ER0 - EH1 K - T AH0 D
+DIRECTED(2)  D ER0 - EH1 K - T IH0 D
+DIRECTED(3)  D AY0 - R EH1 K - T IH0 D
+DIRECTED(4)  D IY0 - R EH1 K - T IH0 D
+DIRECTED(5)  D IH0 - R EH1 K - T IH0 D
+DIRECTING  D ER0 - EH1 K - T IH0 NG
+DIRECTING(2)  D IY0 - R EH1 K - T IH0 NG
+DIRECTING(3)  D AY0 - R EH1 K - T IH0 NG
+DIRECTING(4)  D IH0 - R EH1 K - T IH0 NG
+DIRECTION  D ER0 - EH1 K - SH AH0 N
+DIRECTION(2)  D IY0 - R EH1 K - SH IH0 N
+DIRECTION(3)  D AY0 - R EH1 K - SH IH0 N
+DIRECTION(4)  D IH0 - R EH1 K - SH IH0 N
+DIRECTIONAL  D ER0 - EH1 K - SH AH0 - N AH0 L
+DIRECTIONAL(2)  D IY0 - R EH1 K - SH IH0 - N AH0 L
+DIRECTIONAL(3)  D AY0 - R EH1 K - SH IH0 - N AH0 L
+DIRECTIONAL(4)  D IH0 - R EH1 K - SH IH0 - N AH0 L
+DIRECTIONLESS  D ER0 - EH1 K - SH AH0 N - L AH0 S
+DIRECTIONLESS(2)  D IY0 - R EH1 K - SH IH0 N - L AH0 S
+DIRECTIONLESS(3)  D AY0 - R EH1 K - SH IH0 N - L AH0 S
+DIRECTIONLESS(4)  D IH0 - R EH1 K - SH IH0 N - L AH0 S
+DIRECTIONS  D ER0 - EH1 K - SH AH0 N Z
+DIRECTIONS(2)  D IY0 - R EH1 K - SH IH0 N Z
+DIRECTIONS(3)  D AY0 - R EH1 K - SH IH0 N Z
+DIRECTIONS(4)  D IH0 - R EH1 K - SH IH0 N Z
+DIRECTIVE  D ER0 - EH1 K - T IH0 V
+DIRECTIVE(2)  D IY0 - R EH1 K - T IH0 V
+DIRECTIVE(3)  D AY0 - R EH1 K - T IH0 V
+DIRECTIVE(4)  D IH0 - R EH1 K - T IH0 V
+DIRECTIVES  D AY0 - R EH1 K - T IH0 V Z
+DIRECTIVES(2)  D IY0 - R EH1 K - T IH0 V Z
+DIRECTIVES(3)  D ER0 - EH1 K - T IH0 V Z
+DIRECTIVES(4)  D IH0 - R EH1 K - T IH0 V Z
+DIRECTLY  D ER0 - EH1 K T - L IY0
+DIRECTLY(2)  D IY0 - R EH1 K - L IY0
+DIRECTLY(3)  D AY0 - R EH1 K - L IY0
+DIRECTLY(4)  D IH0 - R EH1 K - L IY0
+DIRECTNESS  D ER0 - EH1 K T - N AH0 S
+DIRECTNESS(2)  D IY0 - R EH1 K - N AH0 S
+DIRECTNESS(3)  D AY0 - R EH1 K - N AH0 S
+DIRECTNESS(4)  D IH0 - R EH1 K - N AH0 S
+DIRECTOR  D ER0 - EH1 K - T ER0
+DIRECTOR'S  D AY0 - R EH1 K - T ER0 Z
+DIRECTOR'S(2)  D ER0 - EH1 K - T ER0 Z
+DIRECTOR'S(3)  D IY0 - R EH1 K - T ER0 Z
+DIRECTOR'S(4)  D IH0 - R EH1 K - T ER0 Z
+DIRECTOR(2)  D AY0 - R EH1 K - T ER0
+DIRECTOR(3)  D IY0 - R EH1 K - T ER0
+DIRECTOR(4)  D IH0 - R EH1 K - T ER0
+DIRECTORAL  D ER0 - EH1 K - T ER0 - AH0 L
+DIRECTORATE  D ER0 - EH1 K - T ER0 - AH0 T
+DIRECTORATE(2)  D AY0 - R EH1 K - T ER0 - AH0 T
+DIRECTORATE(3)  D IY0 - R EH1 K - T ER0 - AH0 T
+DIRECTORATE(4)  D IH0 - R EH1 K - T ER0 - AH0 T
+DIRECTORIAL  D ER0 - EH0 K - T AO1 - R IY0 - AH0 L
+DIRECTORIAL(2)  D AY0 - R EH0 K - T AO1 - R IY0 - AH0 L
+DIRECTORIAL(3)  D IY0 - R EH0 K - T AO1 - R IY0 - AH0 L
+DIRECTORIAL(4)  D IH0 - R EH0 K - T AO1 - R IY0 - AH0 L
+DIRECTORIES  D AY0 - R EH1 K - T ER0 - IY0 Z
+DIRECTORIES(2)  D ER0 - EH1 K - T ER0 - IY0 Z
+DIRECTORIES(3)  D IY0 - R EH1 K - T ER0 - IY0 Z
+DIRECTORIES(4)  D IH0 - R EH1 K - T ER0 - IY0 Z
+DIRECTORS  D ER0 - EH1 K - T ER0 Z
+DIRECTORS'  D IH0 - R EH1 K - T ER0 Z
+DIRECTORS'(2)  D ER0 - EH1 K - T ER0 Z
+DIRECTORS'(3)  D IY0 - R EH1 K - T ER0 Z
+DIRECTORS(2)  D AY0 - R EH1 K - T ER0 Z
+DIRECTORS(3)  D IY0 - R EH1 K - T ER0 Z
+DIRECTORS(4)  D IH0 - R EH1 K - T ER0 Z
+DIRECTORSHIP  D ER0 - EH1 K - T ER0 - SH IH2 P
+DIRECTORSHIP(2)  D AY0 - R EH1 K - T ER0 - SH IH2 P
+DIRECTORSHIP(3)  D IY0 - R EH1 K - T ER0 - SH IH2 P
+DIRECTORSHIP(4)  D IH0 - R EH1 K - T ER0 - SH IH2 P
+DIRECTORSHIPS  D ER0 - EH1 K - T ER0 - SH IH2 P S
+DIRECTORSHIPS(2)  D AY0 - R EH1 K - T ER0 - SH IH2 P S
+DIRECTORSHIPS(3)  D IY0 - R EH1 K - T ER0 - SH IH2 P S
+DIRECTORSHIPS(4)  D IH0 - R EH1 K - T ER0 - SH IH2 P S
+DIRECTORY  D ER0 - EH1 K - T ER0 - IY0
+DIRECTORY(2)  D AY0 - R EH1 K - T ER0 - IY0
+DIRECTORY(3)  D IY0 - R EH1 K - T ER0 - IY0
+DIRECTORY(4)  D IH0 - R EH1 K - T ER0 - IY0
+DIRECTS  D ER0 - EH1 K T S
+DIRECTS(2)  D AY0 - R EH1 K T S
+DIRECTS(3)  D IY0 - R EH1 K T S
+DIRECTS(4)  D IH0 - R EH1 K T S
+DIRECTV  D ER0 - EH1 K - T IY1 - V IY1
+DIRECTV(2)  D AY0 - R EH1 K - T IY1 - V IY1
+DIRECTV(3)  D IY0 - R EH1 K - T IY1 - V IY1
+DIRECTV(4)  D IH0 - R EH1 K - T IY1 - V IY1
+DIRENZO  D IH0 - R EH1 N - Z OW0
+DIREST  D AY1 - R AH0 S T
+DIRGE  D ER1 JH
+DIRHAMS  D ER1 - AH0 M Z
+DIRICKSON  D AO1 - R IH0 K - S AH0 N
+DIRIENZO  D IH0 - R IY1 N - Z OW0
+DIRK  D ER1 K
+DIRKES  D ER1 K S
+DIRKS  D ER1 K S
+DIRKSE  D ER1 K S
+DIRKSEN  D ER1 K - S AH0 N
+DIRLAM  D ER0 - L AE1 M
+DIROCCO  D IH0 - R AA1 - K OW0
+DIROSA  D IH0 - R OW1 - S AH0
+DIRR  D ER1
+DIRT  D ER1 T
+DIRT'S  D ER1 T S
+DIRTIER  D ER1 - T IY0 - ER0
+DIRTIEST  D ER1 - T IY0 - AH0 S T
+DIRTY  D ER1 - T IY0
+DIRUSSO  D IH0 - R UW1 - S OW0
+DIS  D IH1 S
+DISA  D IH1 - S AH0
+DISABATINO  D IH0 S - AA0 - B AA0 - T IY1 - N OW0
+DISABATO  D IH0 S - AA0 - B AA1 - T OW0
+DISABILITIES  D IH0 S - AH0 - B IH1 - L AH0 - T IY0 Z
+DISABILITIES(2)  D IH0 S - AH0 - B IH1 - L IH0 - T IY0 Z
+DISABILITY  D IH2 S - AH0 - B IH1 - L IH0 - T IY0
+DISABILITY(2)  D IH0 S - AH0 - B IH1 - L IH0 - T IY0 Z
+DISABLE  D IH0 S - EY1 - B AH0 L
+DISABLED  D IH0 S - EY1 - B AH0 L D
+DISABLES  D IH0 S - EY1 - B AH0 L Z
+DISABLING  D IH0 S - EY1 - B AH0 L - IH0 NG
+DISABLING(2)  D IH2 S - EY1 - B L IH0 NG
+DISABUSE  D IH0 S - AH0 - B Y UW1 S
+DISABUSE(2)  D IH0 S - AH0 - B Y UW1 Z
+DISABUSED  D IH0 S - AH0 - B Y UW1 Z D
+DISABUSES  D IH0 S - AH0 - B Y UW1 - S IH0 Z
+DISADVANTAGE  D IH2 S - AH0 D - V AE1 N - T IH0 JH
+DISADVANTAGE(2)  D IH2 S - AH0 D - V AE1 - N IH0 JH
+DISADVANTAGED  D IH0 S - AH0 D - V AE1 N - T IH0 JH D
+DISADVANTAGED(2)  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
+DISADVANTAGES  D IH2 S - AH0 D - V AE1 N - T IH0 - JH IH0 Z
+DISADVANTAGES(2)  D IH2 S - AH0 D - V AE1 - N IH0 JH Z
+DISAFFECT  D IH2 S - AH0 - F EH1 K T
+DISAFFECTED  D IH2 S - AH0 - F EH1 K - T IH0 D
+DISAFFECTION  D IH2 S - AH0 - F EH1 K - SH AH0 N
+DISAGREE  D IH0 S - AH0 - G R IY1
+DISAGREEABLE  D IH2 S - AH0 - G R IY1 - AH0 - B AH0 L
+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
+DISALLOW  D IH2 S - AH0 - L AW1
+DISALLOWANCE  D IH2 S - AH0 - L AW1 - AH0 N S
+DISALLOWANCES  D IH2 S - AH0 - L AW1 - AH0 N - S IH0 Z
+DISALLOWED  D IH2 S - AH0 - L AW1 D
+DISALLOWING  D IH0 S - AH0 - L AW1 - IH0 NG
+DISALVO  D IH0 - S AA1 L - V OW0
+DISANO  D IH0 S - AA1 - N OW0
+DISANTI  D IH0 - S AE1 N - T IY0
+DISANTIS  D IH0 - S AA1 N - T IH0 S
+DISANTO  D IH0 - S AE1 N - T OW0
+DISAPPEAR  D IH2 S - AH0 - P IH1 R
+DISAPPEAR(2)  D IH2 - S AH0 - P IY1 R
+DISAPPEARANCE  D IH2 S - AH0 - P IH1 - R AH0 N S
+DISAPPEARANCE(2)  D IH2 S - AH0 - P IY1 - R AH0 N S
+DISAPPEARANCES  D IH2 S - AH0 - P IH1 - R AH0 N - S IH0 Z
+DISAPPEARANCES(2)  D IH2 S - AH0 - P IY1 - R AH0 N - S IH0 Z
+DISAPPEARED  D IH2 S - AH0 - P IH1 R D
+DISAPPEARED(2)  D IH2 S - AH0 - P IY1 R D
+DISAPPEARING  D IH2 S - AH0 - P IH1 - R IH0 NG
+DISAPPEARING(2)  D IH2 S - AH0 - P IY1 - R IH0 NG
+DISAPPEARS  D IH2 S - AH0 - P IH1 R Z
+DISAPPEARS(2)  D IH2 S - AH0 - P IY1 R Z
+DISAPPOINT  D IH2 S - AH0 - P OY1 N T
+DISAPPOINTED  D IH2 S - AH0 - P OY1 N - T IH0 D
+DISAPPOINTED(2)  D IH2 S - AH0 - P OY1 - N IH0 D
+DISAPPOINTING  D IH2 S - AH0 - P OY1 N - T IH0 NG
+DISAPPOINTING(2)  D IH2 S - AH0 - P OY1 - N IH0 NG
+DISAPPOINTINGLY  D IH0 S - AH0 - P OY1 N - T IH0 NG - L IY0
+DISAPPOINTINGLY(2)  D IH0 S - AH0 - P OY1 - N IH0 NG - L IY0
+DISAPPOINTMENT  D IH2 S - AH0 - P OY1 N T - M AH0 N T
+DISAPPOINTMENTS  D IH0 S - AH0 - P OY1 N T - M AH0 N T S
+DISAPPOINTS  D IH2 S - AH0 - P OY1 N T S
+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
+DISARM  D IH0 S - AA1 R M
+DISARMAMENT  D IH0 S - AA1 R - M AH0 - M AH0 N T
+DISARMED  D IH0 S - AA1 R M D
+DISARMING  D IH0 S - AA1 R - M IH0 NG
+DISARMINGLY  D IH0 S - AA1 R - M IH0 NG - L IY0
+DISARRAY  D IH2 S - ER0 - EY1
+DISARRAY(2)  D IH2 S - AH0 - R EY1
+DISASSEMBLE  D IH2 S - AH0 - S EH1 M - B AH0 L
+DISASSEMBLED  D IH2 S - AH0 - S EH1 M - B AH0 L D
+DISASSOCIATE  D IH2 S - AH0 - S OW1 - SH IY0 - EY0 T
+DISASSOCIATE(2)  D IH2 S - AH0 - S OW1 - S IY0 - EY0 T
+DISASSOCIATED  D IH2 S - AH0 - S OW1 - SH IY0 - EY0 - T AH0 D
+DISASSOCIATED(2)  D IH2 S - AH0 - S OW1 - S IY0 - EY0 - T AH0 D
+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
+DISAVOWED  D IH2 S - AH0 - V AW1 D
+DISAVOWING  D IH0 S - AH0 - V AW1 - IH0 NG
+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
+DISBAR  D IH2 S - B AA1 R
+DISBARMENT  D IH2 S - B AA1 R - M AH0 N T
+DISBARRED  D IH0 S - B AA1 R D
+DISBELIEF  D IH2 S - B IH0 - L IY1 F
+DISBELIEVE  D IH0 S - B AH0 - L IY1 V
+DISBELIEVING  D IH0 S - B AH0 - L IY1 - V IH0 NG
+DISBRO  D IH1 S - B R OW0
+DISBROW  D IH1 S - B R AW0
+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
+DISC  D IH1 S K
+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
+DISCENZA  D IH0 S - CH EH1 N - Z AH0
+DISCERN  D IH0 - S ER1 N
+DISCERNED  D IH0 - S ER1 N D
+DISCERNIBLE  D IH0 - S ER1 - N AH0 - B AH0 L
+DISCERNING  D IH0 - S ER1 - N IH0 NG
+DISCERNMENT  D IH0 - S ER1 N - M AH0 N T
+DISCH  D IH1 SH
+DISCHARGE  D IH0 S - CH AA1 R JH
+DISCHARGE(2)  D IH1 S - CH AA2 R JH
+DISCHARGED  D IH0 S - CH AA1 R JH D
+DISCHARGED(2)  D IH1 S - CH AA2 R JH D
+DISCHARGES  D IH0 S - CH AA1 R - JH AH0 Z
+DISCHARGES(2)  D IH1 S - CH AA2 R - JH AH0 Z
+DISCHARGING  D IH0 S - CH AA1 R - JH IH0 NG
+DISCHARGING(2)  D IH1 S - CH AA2 R - JH IH0 NG
+DISCHER  D IH1 - SH ER0
+DISCHINGER  D IH1 - SH IH0 N - JH ER0
+DISCIPLE  D IH0 - S AY1 - P AH0 L
+DISCIPLES  D IH0 - S AY1 - P AH0 L Z
+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
+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
+DISCLAND  D IH1 S K - L AE2 N 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
+DISCOGRAPHY  D IH0 - S K AO1 - G R AH0 - F IY0
+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
+DISCOLORS  D IH0 - S K AH1 - L ER0 Z
+DISCOMFORT  D IH0 S - K AH1 M - F ER0 T
+DISCONCERT  D IH2 S - K AH0 N - S ER1 T
+DISCONCERTING  D IH2 S - K AH0 N - S ER1 - T IH0 NG
+DISCONNECT  D IH0 S - K AH0 - N EH1 K T
+DISCONNECTED  D IH2 S - K AH0 - N EH1 K - T IH0 D
+DISCONNECTING  D IH2 S - K AH0 - N EH1 K - T IH0 NG
+DISCONNECTION  D IH2 S - K AH0 - N EH1 K - 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
+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
+DISCONTINUATION  D IH2 S - K AH0 N - T IH2 - N Y UW0 - EY1 - SH AH0 N
+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
+DISCORD  D IH1 - S K AO0 R D
+DISCORDANT  D IH0 - S K AO1 R - D AH0 N T
+DISCOS  D IH1 - S K OW0 Z
+DISCOTHEQUE  D IH1 - S K OW0 - T EH2 K
+DISCOUNT  D IH0 S - K AW1 N T
+DISCOUNT(2)  D IH1 S - K AW0 N T
+DISCOUNTABLE  D IH1 S - K AW2 N - T AH0 - B AH0 L
+DISCOUNTED  D IH1 S - K AW2 N - T IH0 D
+DISCOUNTED(2)  D IH1 S - K AW2 - N IH0 D
+DISCOUNTER  D IH1 S - K AW2 N - T ER0
+DISCOUNTERS  D IH0 S - K AW1 N - T ER0 Z
+DISCOUNTERS(2)  D IH1 S - K AW2 - N ER0 R Z
+DISCOUNTING  D IH1 S - K AW2 N - T IH0 NG
+DISCOUNTING(2)  D IH1 S - K AW2 - N IH0 NG
+DISCOUNTS  D IH0 S - K AW1 N T S
+DISCOUNTS(2)  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(2)  D IH0 - S K ER1 - IH0 JH D
+DISCOURAGEMENT  D IH0 - S K ER1 - IH0 JH - M AH0 N T
+DISCOURAGES  D IH0 - S K ER1 - IH0 - JH IH0 Z
+DISCOURAGING  D IH0 - S K ER1 - AH0 - JH IH0 NG
+DISCOURAGING(2)  D IH0 - S K ER1 - IH0 - JH IH0 NG
+DISCOURSE  D IH1 S - K AO0 R S
+DISCOURSES  D IH0 S - K AO1 R - S IH0 Z
+DISCOURSES(2)  D IH1 S - K AO0 R - S IH0 Z
+DISCOVER  D IH0 - S K AH1 - V ER0
+DISCOVERABLE  D IH0 - S K AH1 - V ER0 - AH0 - B AH0 L
+DISCOVERABLE(2)  D IH0 - S K AH1 - V R AH0 - B AH0 L
+DISCOVERED  D IH0 - S K AH1 - V ER0 D
+DISCOVERIES  D IH0 - S K AH1 - V ER0 - IY0 Z
+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'S  D IH0 - S K AH1 - V ER0 - IY0 Z
+DISCOVERY'S(2)  D IH0 - S K AH1 - V R IY0 Z
+DISCOVERY(2)  D IH0 - S K AH1 - V R IY0
+DISCOVERY(2)  D IH2 - S K AH1 - V R IY0
+DISCREDIT  D IH0 S - K R EH1 - D AH0 T
+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
+DISCREETLY  D IH0 - S K R IY1 T - L IY0
+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
+DISCRETE  D IH0 - S K R IY1 T
+DISCRETION  D IH0 - S K R EH1 - SH AH0 N
+DISCRETIONARY  D IH0 - S K R EH1 - SH AH0 N - EH2 - R IY0
+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(2)  D IH0 - S K R IH1 - M AH0 - N EY2 - T IH0 D
+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
+DISCRIMINATION  D IH0 - S K R IH2 - M AH0 - N EY1 - SH AH0 N
+DISCRIMINATORY  D IH0 - S K R IH1 - M AH0 - N AH0 - T AO2 - R IY0
+DISCS  D IH1 S K S
+DISCURSIVE  D IH0 - S K ER1 - S IH0 V
+DISCUS  D IH1 - S K AH0 S
+DISCUSS  D IH0 - S K AH1 S
+DISCUSSED  D IH0 - S K AH1 S T
+DISCUSSES  D IH0 - S K AH1 - S AH0 Z
+DISCUSSES(2)  D IH0 - S K AH1 - S IH0 Z
+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
+DISDAIN  D IH0 S - D EY1 N
+DISDAINED  D IH0 S - D EY1 N D
+DISDAINFUL  D IH0 S - D EY1 N - F AH0 L
+DISDAINING  D IH0 S - D EY1 - N IH0 NG
+DISDAINS  D IH0 S - D EY1 N Z
+DISE  D AY1 S
+DISE(2)  D AY1 Z
+DISEASE  D IH0 - Z IY1 Z
+DISEASE'S  D IH0 - Z IY1 - Z IH0 Z
+DISEASED  D IH0 - Z IY1 Z D
+DISEASES  D IH0 - Z IY1 - Z AH0 Z
+DISEASES(2)  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
+DISEMBODIED  D IH0 S - IH0 - B AA1 - D IY0 D
+DISEMBODY  D IH0 S - IH0 - B AA1 - D IY0
+DISENCHANTED  D IH0 S - IH0 N - CH AE1 N - T IH0 D
+DISENCHANTED(2)  D IH0 S - IH0 N - CH AE1 - N IH0 D
+DISENCHANTMENT  D IH0 S - IH0 N - CH AE1 N T - M AH0 N T
+DISENFRANCHISE  D IH0 S - IH0 N - F R AE1 N - CH AY2 Z
+DISENFRANCHISED  D IH0 S - IH0 N - F R AE1 N - CH AY2 Z D
+DISENFRANCHISEMENT  D IH0 S - IH0 N - F R AE1 N - CH AY2 Z - M AH0 N T
+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
+DISENGAGING  D IH0 S - IH0 N - G EY1 - JH IH0 NG
+DISENTANGLE  D IH2 S - AH0 N - T AE1 NG - G AH0 L
+DISEQUILIBRIUM  D IH0 - S IY2 - K W AH0 - L IH1 - B R IY0 - AH0 M
+DISESTABLISHMENT  D IH0 - S IH0 - S T AE1 - B L IH0 SH - M AH0 N T
+DISFAVOR  D IH0 S - F EY1 - V ER0
+DISFAVORING  D IH0 S - F EY1 - V ER0 - IH0 NG
+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
+DISFIGURING  D IH0 S - F IH1 - G Y ER0 - IH0 NG
+DISGORGE  D IH0 S - G AO1 R JH
+DISGORGED  D IH0 S - G AO1 R JH D
+DISGORGEMENT  D IH0 S - G AO1 R JH - M AH0 N T
+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
+DISGRUNTLED  D IH0 S - G R AH1 N - T AH0 L D
+DISGRUNTLED(2)  D IH0 S - G R AH1 - N AH0 L D
+DISGRUNTLEMENT  D IH0 S - G R AH1 N - T AH0 L - M AH0 N T
+DISGRUNTLING  D IH0 S - G R AH1 N - T AH0 L - IH0 NG
+DISGRUNTLING(2)  D IH0 S - G R AH1 N T - L IH0 NG
+DISGUISE  D IH0 S - G AY1 Z
+DISGUISED  D IH0 S - G AY1 Z D
+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(2)  D IH0 S - G AH1 - S T IH0 D
+DISGUSTING  D IH0 S - G AH1 - S T IH0 NG
+DISGUSTS  D IH0 S - G AH1 S T S
+DISGUSTS(2)  D IH0 S - G AH1 S S
+DISGUSTS(3)  D IH0 S - G AH1 S
+DISH  D IH1 SH
+DISHARMONY  D IH0 S - HH AA1 R - M AH0 - N IY0
+DISHAROON  D IH0 - SH ER0 - UW1 N
+DISHAW  D IH1 - SH AO2
+DISHEARTENED  D IH0 S - HH AA1 R - T AH0 N D
+DISHEARTENING  D IH0 S - HH AA1 R - T AH0 N - IH0 NG
+DISHEARTENING(2)  D IH2 S - HH AA1 R T - N IH0 NG
+DISHED  D IH1 SH T
+DISHER  D IH1 - SH ER0
+DISHES  D IH1 - SH AH0 Z
+DISHES(2)  D IH1 - SH IH0 Z
+DISHEVEL  D IH0 - SH EH1 - V AH0 L
+DISHEVELED  D IH0 - SH EH1 - V AH0 L D
+DISHING  D IH1 - SH IH0 NG
+DISHMAN  D IH1 SH - M AH0 N
+DISHNER  D IH1 SH - N ER0
+DISHON  D IH1 - S AH0 N
+DISHONEST  D IH0 S - AA1 - N AH0 S T
+DISHONESTLY  D IH0 S - AO1 - N AH0 S T - L IY0
+DISHONESTY  D IH0 S - AA1 - N AH0 - S T IY0
+DISHONG  D IH1 - S AO0 NG
+DISHONOR  D IH0 S - AA1 - N ER0
+DISHONORABLE  D IH0 S - AA1 - N ER0 - AH0 - B AH0 L
+DISHONORED  D IH0 S - AA1 - N ER0 D
+DISHWASHER  D IH1 SH - W AA2 - SH ER0
+DISHWASHERS  D IH1 SH - W AA2 - SH ER0 Z
+DISHWASHING  D IH1 SH - W AA2 - SH IH0 NG
+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
+DISILLUSIONMENT  D IH2 S - IH0 - L UW1 - ZH AH0 N - M AH0 N T
+DISILVESTRO  D IH0 - S IY0 L - V EH1 - S T R OW0
+DISIMONE  D IH0 - S IY0 - M OW1 - N IY0
+DISINCENTIVE  D IH2 S - IH0 N - S EH1 N - T IH0 V
+DISINCENTIVES  D IH2 S - IH0 N - S EH1 N - T IH0 V 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
+DISINFECTION  D IH0 S - IH0 N - F EH1 K - SH AH0 N
+DISINFLATE  D IH2 S - IH0 N - F L EY1 T
+DISINFLATION  D IH2 S - IH0 N - F L EY1 - SH AH0 N
+DISINFLATIONARY  D IH2 S - IH0 N - F L EY1 - SH AH0 N - EH2 - R IY0
+DISINFORM  D IH0 S - IH0 N - F AO1 R M
+DISINFORMATION  D IH0 - Z IH2 N - F ER0 - M EY1 - SH AH0 N
+DISINGENUOUS  D IH0 S - IH0 N - JH EH1 - N Y UW0 - AH0 S
+DISINGENUOUSNESS  D IH2 S - IH0 N - JH EH1 - N Y UW0 - AH0 S - N AH0 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 - T AH0 - G R EY1 - SH AH0 N
+DISINTEGRATION(2)  D IH0 S - IH2 - N AH0 - G R EY1 - SH AH0 N
+DISINTEREST  D IH0 S - IH1 N - T ER0 - AH0 S T
+DISINTERESTED  D IH0 S - IH1 N - T R IH0 - S T IH0 D
+DISINTERESTED(2)  D IH0 S - IH1 N - R IH0 - S T IH0 D
+DISINVESTMENT  D IH0 S - IH0 N - V EH1 S T - M AH0 N T
+DISINVESTMENT(2)  D IH0 S - IH0 N - V EH1 S - M AH0 N T
+DISINVITE  D IH0 - S IH0 N - V AY1 T
+DISINVITED  D IH2 - S IH2 N - V AY1 - T IH0 D
+DISJOINT  D IH0 S - JH OY1 N T
+DISJOINTED  D IH0 S - JH OY1 N - T IH0 D
+DISK  D IH1 S K
+DISKETTE  D IH0 - S K EH1 T
+DISKETTES  D IH0 - S K EH1 T S
+DISKIN  D IH1 - S K IH0 N
+DISKLESS  D IH1 S K - L AH0 S
+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
+DISLOCATE  D IH1 S - L OW0 - K EY0 T
+DISLOCATED  D IH1 S - L OW0 - K EY0 - T IH0 D
+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
+DISLODGING  D IH0 S - L AA1 - JH IH0 NG
+DISLOYAL  D IH0 S - L OY1 - AH0 L
+DISLOYALTY  D IH0 S - L OY1 - AH0 L - T IY0
+DISMAL  D IH1 Z - M AH0 L
+DISMALLY  D IH1 Z - M AH0 - L IY0
+DISMANTLE  D IH0 S - M AE1 N - T AH0 L
+DISMANTLE(2)  D IH0 S - M AE1 - N AH0 L
+DISMANTLED  D IH0 S - M AE1 N - T AH0 L D
+DISMANTLED(2)  D IH0 S - M AE1 - N AH0 L D
+DISMANTLEMENT  D IH0 S - M AE1 N - T AH0 L - M AH0 N T
+DISMANTLES  D IH0 S - M AE1 N - T AH0 L Z
+DISMANTLING  D IH0 S - M AE1 N - T AH0 L - IH0 NG
+DISMANTLING(2)  D IH0 S - M AE1 - N AH0 L - IH0 NG
+DISMANTLING(3)  D IH2 S - M AE1 N T - L IH0 NG
+DISMAY  D IH0 S - M EY1
+DISMAYED  D IH0 S - M EY1 D
+DISMAYING  D IH0 S - M EY1 - IH0 NG
+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
+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
+DISMISSIVE  D IH0 S - M IH1 - S IH0 V
+DISMORE  D IH1 S - M AO0 R
+DISMUKE  D IH1 S - M Y UW0 K
+DISMUKES  D IH1 S - M Y UW0 K S
+DISMUTASE  D IH2 S - M Y UW0 - T EY1 Z
+DISNEY  D IH1 Z - N IY0
+DISNEY'S  D IH1 Z - N IY0 Z
+DISNEYLAND  D IH1 Z - N IY0 - L AE2 N D
+DISNEYWORLD  D IH1 Z - N IY0 - W ER0 L D
+DISOBEDIENCE  D IH2 S - AH0 - B IY1 - D IY0 - AH0 N S
+DISOBEDIENCE(2)  D IH2 S - OW0 - B IY1 - D IY0 - AH0 N S
+DISOBEDIENT  D IH2 S - AH0 - B IY1 - D IY0 - AH0 N T
+DISOBEDIENT(2)  D IH2 S - OW0 - B IY1 - D IY0 - AH0 N T
+DISOBEY  D IH2 S - AH0 - B EY1
+DISOBEYED  D IH2 S - OW0 - B EY1 D
+DISOBEYING  D IH2 S - OW0 - B EY1 - IH0 NG
+DISON  D IH1 - S AH0 N
+DISORDER  D IH0 S - AO1 R - D ER0
+DISORDERED  D IH0 S - AO1 R - D ER0 D
+DISORDERLY  D IH0 S - AO1 R - D ER0 - L IY0
+DISORDERS  D IH0 S - AO1 R - D ER0 Z
+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
+DISORIENT  D IH0 S - AO1 - R IY0 - EH2 N T
+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
+DISORIENTING  D IH0 S - AO1 - R IY0 - EH2 N - T IH0 NG
+DISOWN  D IH0 S - OW1 N
+DISOWNED  D IH0 S - OW1 N D
+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
+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 IH1 - S P ER0 - IH0 T
+DISPARATE(2)  D IH0 - S P EH1 - R IH0 T
+DISPARITIES  D IH0 - S P EH1 - R AH0 - T IY0 Z
+DISPARITY  D IH0 - S P EH1 - R AH0 - T IY0
+DISPASSIONATE  D IH0 - S P AE1 - SH AH0 N - AH0 T
+DISPASSIONATELY  D IH0 - S P AE1 - SH AH0 N - AH0 T - L IY0
+DISPATCH  D IH0 - S P AE1 CH
+DISPATCHED  D IH0 - S P AE1 CH T
+DISPATCHER  D IH0 - S P AE1 - CH ER0
+DISPATCHER'S  D IH0 - S P AE1 - CH ER0 Z
+DISPATCHER'S(2)  D IH1 - S P AE2 - CH ER0 Z
+DISPATCHER(2)  D IH1 - S P AE2 - CH ER0
+DISPATCHERS  D IH0 - S P AE1 - CH ER0 Z
+DISPATCHES  D IH0 - S P AE1 - CH IH0 Z
+DISPATCHING  D IH0 - S P AE1 - CH IH0 NG
+DISPEL  D IH0 - S P EH1 L
+DISPELL  D IH0 - S P EH1 L
+DISPELLED  D IH0 - S P EH1 L D
+DISPELLING  D IH0 - S P EH1 - L IH0 NG
+DISPELS  D IH0 - S P EH1 L Z
+DISPENSABLE  D IH0 - S P EH1 N - S AH0 - B AH0 L
+DISPENSARY  D IH0 - S P EH1 N - S ER0 - IY0
+DISPENSARY(2)  D IH1 - S P EH0 N - S EH2 - R IY0
+DISPENSATION  D IH2 - S P AH0 N - S EY1 - SH AH0 N
+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
+DISPENZA  D IH0 - S P EH1 N - Z AH0
+DISPERSAL  D IH0 - S P ER1 - S AH0 L
+DISPERSANT  D IH2 - S P ER1 - S AH0 N T
+DISPERSANTS  D IH2 - S P ER1 - S AH0 N T S
+DISPERSE  D IH0 - S P ER1 S
+DISPERSED  D IH0 - S P ER1 S T
+DISPERSING  D IH0 - S P ER1 - S IH0 NG
+DISPERSION  D IH0 - S P ER1 - ZH AH0 N
+DISPERSIVE  D IH0 - S P ER1 - S IH0 V
+DISPIRITED  D IH0 - S P IH1 - R AH0 - T IH0 D
+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
+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
+DISPLAYED  D IH0 - S P L EY1 D
+DISPLAYING  D IH0 - S P L EY1 - IH0 NG
+DISPLAYS  D IH0 - S P L EY1 Z
+DISPLAYWRITE  D IH0 - S P L EY1 - R AY2 T
+DISPLEASE  D IH0 S - P L IY1 Z
+DISPLEASED  D IH0 S - P L IY1 Z D
+DISPLEASURE  D IH0 S - P L EH1 - ZH ER0
+DISPOSABLE  D IH0 - S P OW1 - Z AH0 - B AH0 L
+DISPOSABLES  D IH0 - S P OW1 - Z AH0 - B AH0 L Z
+DISPOSAL  D IH0 - S P OW1 - Z AH0 L
+DISPOSALS  D IH0 - S P OW1 - Z AH0 L Z
+DISPOSE  D IH0 - S P OW1 Z
+DISPOSED  D IH0 - S P OW1 Z D
+DISPOSER  D IH0 - S P OW1 - Z ER0
+DISPOSES  D IH0 - S P OW1 - Z IH0 Z
+DISPOSING  D IH0 - S P OW1 - Z IH0 NG
+DISPOSITION  D IH2 - S P AH0 - Z IH1 - SH AH0 N
+DISPOSITIONS  D IH2 - S P AH0 - Z IH1 - SH AH0 N Z
+DISPOSITIVE  D IH2 - S P AA1 - Z AH0 - T IH0 V
+DISPOSSESS  D IH2 S - P AH0 - Z EH1 S
+DISPOSSESSED  D IH2 S - P AH0 - Z EH1 S T
+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
+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
+DISPUTATION  D IH0 - S P Y UW1 - T EY1 - SH AH0 N
+DISPUTE  D IH0 - S P Y UW1 T
+DISPUTED  D IH0 - S P Y UW1 - T AH0 D
+DISPUTED(2)  D IH0 - S P Y UW1 - T IH0 D
+DISPUTES  D IH0 - S P Y UW1 T S
+DISPUTING  D IH0 - S P Y UW1 - T IH0 NG
+DISQUALIFICATION  D IH0 S - K W AA2 - L AH0 - F AH0 - K EY1 - SH AH0 N
+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
+DISQUE  D IH1 S K
+DISQUIET  D IH0 S - K W AY1 - AH0 T
+DISQUIETING  D IH0 S - K W AY1 - AH0 - T IH0 NG
+DISRAELI  D IH0 Z - R EY1 - L IY0
+DISREGARD  D IH2 S - R IH0 - G AA1 R D
+DISREGARDED  D IH2 S - R IH0 - G AA1 R - D IH0 D
+DISREGARDING  D IH2 S - R IH0 - G AA1 R - D IH0 NG
+DISREGARDS  D IH2 S - R IH0 - G AA1 R D Z
+DISREPAIR  D IH2 S - R IH0 - P EH1 R
+DISREPUTABLE  D IH0 S - R EH1 - P Y AH0 - T AH0 - B AH0 L
+DISREPUTE  D IH2 S - R IH0 - P Y UW1 T
+DISRESPECT  D IH2 S - R IH0 - S P EH1 K T
+DISRESPECTED  D IH2 S - R IH0 - S P EH1 K - T IH0 D
+DISRESPECTFUL  D IH2 S - R IH0 - S P EH1 K T - F AH0 L
+DISRESPECTING  D IH2 S - R IH0 - S P EH1 K - T IH0 NG
+DISRESPECTS  D IH2 S - R IH0 - S P EH1 K T S
+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
+DISRUPTS  D IH0 S - R AH1 P T S
+DISS  D IH1 S
+DISSATISFACTION  D IH2 S - AE0 - T IH0 S - F AE1 K - SH AH0 N
+DISSATISFIED  D IH0 S - AE1 - T AH0 S - F AY2 D
+DISSATISFY  D IH0 S - AE1 - T AH0 S - F AY2
+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(2)  D AY1 - S EH0 K - SH AH0 N
+DISSECTIONS  D AY0 - S EH1 K - SH AH0 N Z
+DISSECTIONS(2)  D AY1 - S EH0 K - SH AH0 N Z
+DISSECTS  D AY0 - S EH1 K T S
+DISSECTS(2)  D AY0 - S EH1 K S
+DISSEMBLE  D IH0 - S EH1 M - B AH0 L
+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
+DISSEMINATING  D IH0 - S EH1 - M AH0 - N EY2 - T IH0 NG
+DISSEMINATION  D IH0 - S EH2 - M AH0 - N EY1 - SH AH0 N
+DISSENSION  D IH0 - S EH1 N - SH AH0 N
+DISSENT  D IH0 - S EH1 N T
+DISSENTED  D IH0 - S EH1 N - T IH0 D
+DISSENTED(2)  D IH0 - S EH1 - N IH0 D
+DISSENTER  D IH0 - S EH1 N - T ER0
+DISSENTERS  D IH0 - S EH1 N - T ER0 Z
+DISSENTERS(2)  D IH0 - S EH1 - N ER0 Z
+DISSENTING  D IH0 - S EH1 N - T IH0 NG
+DISSENTING(2)  D IH0 - S EH1 - N IH0 NG
+DISSENTS  D IH0 - S EH1 N T S
+DISSERTATION  D IH2 - S ER0 - T EY1 - SH AH0 N
+DISSERVICE  D IH0 S - S ER1 - V AH0 S
+DISSERVICE(2)  D IH0 - S ER1 - V AH0 S
+DISSIDENCE  D IH1 - S AH0 - D IH0 N S
+DISSIDENT  D IH1 - S AH0 - D IH0 N T
+DISSIDENTS  D IH1 - S AH0 - D AH0 N T S
+DISSIDENTS'  D IH1 - S AH0 - D AH0 N T S
+DISSIMILAR  D IH0 - S S IH1 - M AH0 - L ER0
+DISSIMILAR(2)  D IH0 - S IH1 - M AH0 - L ER0
+DISSIMILARITY  D IH0 - S S IH2 - M AH0 - L AE1 - R AH0 - T IY0
+DISSIMILARITY(2)  D IH2 - S IH2 - M AH0 - L AE1 - R AH0 - T IY0
+DISSINGER  D IH1 S - IH0 N - JH ER0
+DISSIPATE  D IH1 - S AH0 - P EY2 T
+DISSIPATED  D IH1 - S AH0 - P EY2 - T IH0 D
+DISSIPATES  D IH1 - S AH0 - P EY2 T S
+DISSIPATING  D IH1 - S AH0 - P EY2 - T IH0 NG
+DISSIPATION  D IH2 - S IH0 - P EY1 - SH AH0 N
+DISSIPATIVE  D IH1 - S AH0 - P EY2 - T IH0 V
+DISSOCIATE  D IH0 - S OW1 - S IY0 - EY0 T
+DISSOCIATION  D IH0 - S OW2 - S IY0 - EY1 - SH AH0 N
+DISSOLUTION  D IH2 - S AH0 - L UW1 - SH AH0 N
+DISSOLVE  D IH0 - Z AA1 L V
+DISSOLVED  D IH0 - Z AA1 L V D
+DISSOLVER  D IH0 - Z AA1 L - V ER0
+DISSOLVERS  D IH0 - Z AA1 L - V ER0 Z
+DISSOLVES  D IH0 - Z AA1 L V Z
+DISSOLVING  D IH0 - Z AO1 L - V IH0 NG
+DISSONANCE  D IH1 - S AH0 - N AH0 N S
+DISSONANT  D IH1 - S AH0 - N AH0 N T
+DISSUADE  D IH0 - S W EY1 D
+DISSUADED  D IH0 - S W EY1 - D IH0 D
+DISSYMMETRIC  D IH2 - S IH0 - M EH1 - T R IH0 K
+DISSYMMETRY  D IH0 - S IH1 - M AH0 - T R IY0
+DISTAD  D IH1 - S T AH0 D
+DISTAL  D IH1 - S T AH0 L
+DISTANCE  D IH1 - S T AH0 N S
+DISTANCED  D IH1 - S T AH0 N S T
+DISTANCES  D IH1 - S T AH0 N - S AH0 Z
+DISTANCES(2)  D IH1 - S T AH0 N - S IH0 Z
+DISTANCING  D IH1 - S T AH0 N - S IH0 NG
+DISTANT  D IH1 - S T AH0 N T
+DISTASI  D IH0 - S T AA1 - S IY0
+DISTASIO  D IH0 - S T AA1 - S IY0 - OW0
+DISTASTE  D IH0 S - T EY1 S T
+DISTASTEFUL  D IH0 S - T EY1 S T - F AH0 L
+DISTEFANO  D IH0 - S T EH0 - F AA1 - N OW0
+DISTEL  D IH1 - S T AH0 L
+DISTEMPER  D IH0 - S T EH1 M - P ER0
+DISTEND  D IH0 - S T EH1 N D
+DISTENDED  D IH0 - S T EH1 N - D IH0 D
+DISTIL  D IH0 - S T IH1 L
+DISTILL  D IH0 - S T IH1 L
+DISTILLATE  D IH1 - S T AH0 - L EY2 T
+DISTILLATE(2)  D IH1 - S T AH0 - L AH0 T
+DISTILLATES  D IH1 - S T AH0 - L EY2 T S
+DISTILLATION  D IH2 - S T AH0 - L EY1 - SH AH0 N
+DISTILLED  D IH0 - S T IH1 L D
+DISTILLER  D IH0 - S T IH1 - L ER0
+DISTILLER'S  D IH0 - S T IH1 - L ER0 Z
+DISTILLERIES  D IH0 - S T IH1 - L ER0 - IY0 Z
+DISTILLERS  D IH0 - S T IH1 - L ER0 Z
+DISTILLERS'  D IH0 - S T IH1 - L ER0 Z
+DISTILLERS'S  D IH0 - S T IH1 - L ER0 - Z IH0 Z
+DISTILLERY  D IH0 - S T IH1 - L ER0 - IY0
+DISTILLING  D IH0 - S T IH1 - L IH0 NG
+DISTILLS  D IH0 - S T IH1 L Z
+DISTINCT  D IH0 - S T IH1 NG K T
+DISTINCTION  D IH0 - S T IH1 NG K - SH AH0 N
+DISTINCTIONS  D IH0 - S T IH1 NG K - SH AH0 N Z
+DISTINCTIVE  D IH0 - S T IH1 NG K - T IH0 V
+DISTINCTIVELY  D IH0 - S T IH1 NG K - T IH0 V - L IY0
+DISTINCTIVENESS  D IH0 - S T IH1 NG K - T IH0 V - N AH0 S
+DISTINCTLY  D IH0 - S T IH1 NG K T - L IY0
+DISTINGUISH  D IH0 - S T IH1 NG - G W IH0 SH
+DISTINGUISHABLE  D IH0 - S T IH1 NG - G W IH0 - SH AH0 - B AH0 L
+DISTINGUISHED  D IH0 - S T IH1 NG - G W IH0 SH T
+DISTINGUISHES  D IH0 - S T IH1 NG - G W IH0 - SH IH0 Z
+DISTINGUISHING  D IH0 - S T IH1 NG - G W IH0 - SH IH0 NG
+DISTLER  D IH1 S T - L ER0
+DISTORT  D IH0 - S T AO1 R T
+DISTORTED  D IH0 - S T AO1 R - T AH0 D
+DISTORTED(2)  D IH0 - S T AO1 R - T IH0 D
+DISTORTING  D IH0 - S T AO1 R - T IH0 NG
+DISTORTION  D IH0 - S T AO1 R - SH AH0 N
+DISTORTIONS  D IH0 - S T AO1 R - SH AH0 N Z
+DISTORTS  D IH0 - S T AO1 R T S
+DISTRACT  D IH0 - S T R AE1 K T
+DISTRACTED  D IH0 - S T R AE1 K - T AH0 D
+DISTRACTED(2)  D IH0 - S T R AE1 K - T IH0 D
+DISTRACTING  D IH0 - S T R AE1 K - T IH0 NG
+DISTRACTION  D IH0 S - T R AE1 K - SH AH0 N
+DISTRACTIONS  D IH0 S - T R AE1 K - SH AH0 N Z
+DISTRACTS  D IH0 - S T R AE1 K T S
+DISTRAUGHT  D IH0 - S T R AO1 T
+DISTRESS  D IH0 - S T R EH1 S
+DISTRESSED  D IH0 - S T R EH1 S T
+DISTRESSES  D IH0 - S T R EH1 - S IH0 Z
+DISTRESSING  D IH0 - S T R EH1 - S IH0 NG
+DISTRESSINGLY  D IH0 - S T R EH1 - S IH0 NG - L IY0
+DISTRIBUTE  D IH0 - S T R IH1 - B Y UW0 T
+DISTRIBUTED  D IH0 S - T R IH1 - B Y AH0 - T AH0 D
+DISTRIBUTES  D IH0 - S T R IH1 - B Y UW0 T S
+DISTRIBUTING  D IH0 - S T R IH1 - B Y UW0 - T IH0 NG
+DISTRIBUTION  D IH2 S - T R AH0 - B Y UW1 - SH AH0 N
+DISTRIBUTIONS  D IH2 S - T R AH0 - B Y UW1 - SH AH0 N Z
+DISTRIBUTIVE  D IH0 - S T R IH1 - B Y UW0 - T IH0 V
+DISTRIBUTOR  D IH0 S - T R IH1 - B Y AH0 - T ER0
+DISTRIBUTOR'S  D IH0 - S T R IH1 - B Y UW0 - T ER0 Z
+DISTRIBUTORS  D IH0 S - T R IH1 - B Y AH0 - T ER0 Z
+DISTRIBUTORS'  D IH0 S - T R IH1 - B Y AH0 - T ER0 Z
+DISTRIBUTORSHIP  D IH0 - S T R IH1 - B Y UW0 - T ER0 - SH IH2 P
+DISTRIBUTORSHIPS  D IH0 - S T R IH1 - B Y UW0 - T ER0 - SH IH2 P S
+DISTRICT  D IH1 - S T R IH0 K T
+DISTRICT'S  D IH1 - S T R IH0 K T S
+DISTRICTING  D IH1 - S T R IH0 K - T IH0 N G
+DISTRICTS  D IH1 - S T R IH0 K T S
+DISTRIGAS  D IH0 S - T R IY1 - G AH0 S
+DISTRON  D IH1 - S T R AA2 N
+DISTRUST  D IH0 S - T R AH1 S T
+DISTRUSTED  D IH0 S - T R AH1 - S T AH0 D
+DISTRUSTED(2)  D IH0 S - T R AH1 - S T IH0 D
+DISTRUSTFUL  D IH0 S - T R AH1 S T - F AH0 L
+DISTRUSTS  D IH0 S - T R AH1 S T S
+DISTRUSTS(2)  D IH0 - S T R AH1 S S
+DISTRUSTS(3)  D IH0 - S T R AH1 S
+DISTURB  D IH0 - S T ER1 B
+DISTURBANCE  D IH0 - S T ER1 - B AH0 N S
+DISTURBANCES  D IH0 - S T ER1 - B AH0 N - S AH0 Z
+DISTURBANCES(2)  D IH0 - S T ER1 - B AH0 N - S IH0 Z
+DISTURBED  D IH0 - S T ER1 B D
+DISTURBING  D IH0 - S T ER1 - B IH0 NG
+DISTURBINGLY  D IH0 - S T ER1 - B IH0 NG - L IY0
+DISTURBS  D IH0 - S T ER1 B Z
+DISUNION  D IH0 S - Y UW1 - N Y AH0 N
+DISUNITY  D IH0 S - Y UW1 - N AH0 - T IY0
+DISUSE  D IH0 S - Y UW1 S
+DITCH  D IH1 CH
+DITCHED  D IH1 CH T
+DITCHES  D IH1 - CH AH0 Z
+DITCHES(2)  D IH1 - CH IH0 Z
+DITCHING  D IH1 - CH IH0 NG
+DITH  D IH1 TH
+DITHER  D IH1 - DH ER0
+DITHERING  D IH1 - DH ER0 - IH0 NG
+DITHERS  D IH1 - DH ER0 Z
+DITHYRAMB  D IH1 - TH ER0 - AE2 M
+DITKA  D IH1 T - K AH0
+DITKA'S  D IH1 T - K AH0 Z
+DITLOW  D IH1 T - L OW0
+DITMARS  D IH1 T - M ER0 Z
+DITMER  D IH1 T - M ER0
+DITMORE  D IH1 T - M AO0 R
+DITOMASSO  D IH0 - T OW0 - M AA1 - S OW0
+DITOMMASO  D IH0 - T OW0 - M AA1 - S OW0
+DITSY  D IH1 T - S IY0
+DITTBERNER  D IH1 T - B ER0 - N ER0
+DITTEMORE  D IH0 - T EH1 - M AO0 R
+DITTER  D IH1 - T ER0
+DITTIES  D IH1 - T IY0 Z
+DITTMAN  D IH1 T - M AH0 N
+DITTMANN  D IH1 T - M AH0 N
+DITTMAR  D IH1 T - M ER0
+DITTMER  D IH1 T - M ER0
+DITTO  D IH1 - T OW0
+DITTON  D IH1 - T AH0 N
+DITTRICH  D IH1 - T R IH0 K
+DITTUS  D IH1 - T AH0 S
+DITTY  D IH1 - T IY0
+DITULLIO  D IH0 - T AH1 - L IY0 - OW0
+DITZEL  D IH1 T - Z AH0 L
+DITZLER  D IH1 T - S L ER0
+DIURETIC  D AY2 - UW0 - R EH1 - T IH0 K
+DIURETICS  D AY2 - UW0 - R EH1 - T IH0 K S
+DIURNAL  D AY0 - ER1 - N AH0 L
+DIURNALLY  D AY0 - ER1 - N AH0 - L IY0
+DIVA  D IY1 - V AH0
+DIVAD  D IH1 - V AE0 D
+DIVALENT  D AY0 - V EY1 - L AH0 N T
+DIVALL  D IH1 - V AH0 L
+DIVAN  D IH0 - V AE1 N
+DIVAS  D IY1 - V AH0 Z
+DIVE  D AY1 V
+DIVED  D AY1 V D
+DIVELBISS  D IH0 - V EH1 L - B IH0 S
+DIVELEY  D IH1 - V IH0 - L IY0
+DIVELY  D AY1 V - L IY0
+DIVEN  D AY1 - V AH0 N
+DIVENS  D AY1 - V AH0 N Z
+DIVER  D AY1 - V ER0
+DIVER'S  D AY1 - V ER0 Z
+DIVERGE  D IH0 - V ER1 JH
+DIVERGED  D AY0 - V ER1 JH D
+DIVERGENCE  D AY0 - V ER1 - JH AH0 N S
+DIVERGENCE(2)  D IH0 - V ER1 - JH AH0 N S
+DIVERGENCES  D AY0 - V ER1 - JH AH0 N - S IH0 Z
+DIVERGENT  D AY0 - V ER1 - JH AH0 N T
+DIVERGENT(2)  D IH0 - V ER1 - JH AH0 N T
+DIVERGES  D AY0 - V ER1 - JH IH0 Z
+DIVERGING  D AY0 - V ER1 - JH IH0 NG
+DIVERS  D AY1 - V ER0 Z
+DIVERSE  D AY0 - V ER1 S
+DIVERSE(2)  D IH0 - V ER1 S
+DIVERSICARE  D IH1 - V ER0 - S IH0 - K EH2 R
+DIVERSIFICATION  D AY0 - V ER2 - S AH0 - F AH0 - K EY1 - SH AH0 N
+DIVERSIFICATION(2)  D IH0 - V ER2 - S AH0 - F AH0 - K EY1 - SH AH0 N
+DIVERSIFICATIONS  D AY0 - V ER2 - S AH0 - F AH0 - K EY1 - SH AH0 N Z
+DIVERSIFICATIONS(2)  D IH0 - V ER2 - S AH0 - F AH0 - K EY1 - SH AH0 N Z
+DIVERSIFIED  D AY0 - V ER1 - S AH0 - F AY2 D
+DIVERSIFIED(2)  D IH0 - V ER1 - S AH0 - F AY2 D
+DIVERSIFY  D AY0 - V ER1 - S AH0 - F AY2
+DIVERSIFY(2)  D IH0 - V ER1 - S AH0 - F AY2
+DIVERSIFYING  D AY0 - V ER1 - S AH0 - F AY2 - IH0 NG
+DIVERSIFYING(2)  D IH0 - V ER1 - S AH0 - F AY2 - IH0 NG
+DIVERSION  D AY0 - V ER1 - ZH AH0 N
+DIVERSION(2)  D IH0 - V ER1 - ZH AH0 N
+DIVERSIONARY  D AY0 - V ER1 - ZH AH0 N - EH2 - R IY0
+DIVERSIONARY(2)  D IH0 - V ER1 - ZH AH0 N - EH2 - R IY0
+DIVERSIONS  D IH0 - V ER1 - ZH AH0 N Z
+DIVERSIONS(2)  D AY0 - V ER1 - ZH AH0 N Z
+DIVERSITY  D IH0 - V ER1 - S AH0 - T IY0
+DIVERSITY(2)  D IH0 - V ER1 - S IH0 - T IY0
+DIVERSITY(3)  D AY0 - V ER1 - S AH0 - T IY0
+DIVERSITY(4)  D AY0 - V ER1 - S IH0 - T IY0
+DIVERT  D AY0 - V ER1 T
+DIVERT(2)  D IH0 - V ER1 T
+DIVERTED  D AY0 - V ER1 - T IH0 D
+DIVERTED(2)  D IH0 - V ER1 - T IH0 D
+DIVERTICULA  D AY2 - V ER0 - T IH1 - K Y AH0 - L AH0
+DIVERTICULUM  D AY2 - V ER0 - T IH1 - K Y AH0 - L AH0 M
+DIVERTIMENTO  D IH0 - V ER2 - T AH0 - M EH1 N - T OW2
+DIVERTING  D AY0 - V ER1 - T IH0 NG
+DIVERTING(2)  D IH0 - V ER1 - T IH0 NG
+DIVERTS  D AY0 - V ER1 T S
+DIVERTS(2)  D IH0 - V ER1 T S
+DIVES  D AY1 V Z
+DIVEST  D AY0 - V EH1 S T
+DIVEST(2)  D IH0 - V EH1 S T
+DIVESTED  D AY0 - V EH1 - S T IH0 D
+DIVESTING  D AY0 - V EH1 - S T IH0 NG
+DIVESTITURE  D IH0 - V EH1 - S T IH0 - CH ER0
+DIVESTITURE(2)  D AY0 - V EH1 - S T IH0 - CH ER0
+DIVESTITURES  D IH0 - V EH1 - S T IH0 - CH ER0 Z
+DIVESTITURES(2)  D AY0 - V EH1 - S T IH0 - CH ER0 Z
+DIVESTMENT  D AY0 - V EH1 S T - M AH0 N T
+DIVESTMENT(2)  D AY0 - V EH1 S - M AH0 N T
+DIVESTMENT(3)  D IH0 - V EH1 S - M AH0 N T
+DIVESTMENTS  D AY0 - V EH1 S T - M AH0 N T S
+DIVESTMENTS(2)  D AY0 - V EH1 S - M AH0 N T S
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+DOUBTFIRE  D AW1 T - F AY1 - ER0
+DOUBTFIRE'S  D AW1 T - F AY1 - ER0 Z
+DOUBTFIRES  D AW1 T - F AY1 - ER0 Z
+DOUBTFUL  D AW1 T - F AH0 L
+DOUBTING  D AW1 - T IH0 NG
+DOUBTLESS  D AW1 T - L AH0 S
+DOUBTS  D AW1 T S
+DOUCET  D UW0 - S EH1 T
+DOUCETTE  D UW1 - S EH1 T
+DOUD  D AW1 D
+DOUDNA  D AW1 D - N AH0
+DOUDS  D AW1 D Z
+DOUG  D AH1 G
+DOUG'S  D AH1 G Z
+DOUGAL  D UW1 - G AH0 L
+DOUGALL  D AW1 - G AH0 L
+DOUGALL(2)  D UW1 - G AH0 L
+DOUGAN  D AW1 - G AH0 N
+DOUGAN(2)  D UW1 - G AH0 N
+DOUGANS  D AW1 - G AH0 N
+DOUGANS(2)  D UW1 - G AH0 N
+DOUGENS  D AW1 - G AH0 N Z
+DOUGENS(2)  D UW1 - G AH0 N Z
+DOUGH  D OW1
+DOUGHBOY  D OW1 - B OY2
+DOUGHER  D OW1 - ER0
+DOUGHERTY  D OW1 - ER0 - T IY0
+DOUGHERTY(2)  D AA1 - G ER0 - T IY0
+DOUGHMAN  D OW1 - M AH0 N
+DOUGHNUT  D OW1 - N AH2 T
+DOUGHNUTS  D OW1 - N AH2 T S
+DOUGHTEN  D AO1 - T AH0 N
+DOUGHTIE  D AO1 - T IY0
+DOUGHTY  D AO1 - T IY0
+DOUGHY  D OW1 - IY0
+DOUGIE  D UW1 - G IY0
+DOUGIE'S  D UW1 - G IY0 Z
+DOUGL  D UW1 - G AH0 L
+DOUGLAS  D AH1 G - L AH0 S
+DOUGLAS'  D AH1 G - L AH0 S
+DOUGLAS'S  D AH1 G - L AH0 - S IH0 Z
+DOUGLASS  D AH1 G - L AH0 - S IH0 Z
+DOUKAS  D AW1 - K AH0 Z
+DOULTON  D OW1 L - T AH0 N
+DOUMA  D OW1 - M AH0
+DOUPE  D UW1 P
+DOUR  D AW1 - ER0
+DOUR(2)  D AW1 R
+DOUSE  D AW1 S
+DOUSED  D AW1 S T
+DOUSING  D AW1 - S IH0 NG
+DOUTHAT  D AW1 - TH AH0 T
+DOUTHETT  D AW1 - TH IH0 T
+DOUTHIT  D UW0 - TH IH1 T
+DOUTHITT  D UW0 - TH IH1 T
+DOUTT  D AW1 T
+DOUTY  D AW1 - T IY0
+DOUVILLE  D UW0 - V IH1 L
+DOUWE  D UW1
+DOV  D AA1 V
+DOVE  D AH1 V
+DOVE(2)  D OW1 V
+DOVEL  D OW0 - V EH1 L
+DOVER  D OW1 - V ER0
+DOVER'S  D OW1 - V ER0 Z
+DOVERSPIKE  D AH0 - V ER1 - S P IH0 K
+DOVES  D AH1 V Z
+DOVETAIL  D AH1 V - T EY2 L
+DOVETAILED  D AH1 V - T EY2 L D
+DOVETAILS  D AH1 V - T EY2 L Z
+DOVEY  D AH0 - V EY1
+DOVIDIO  D OW0 - V IY1 - D IY0 - OW0
+DOVISH  D OW1 - V IH0 SH
+DOVISH(2)  D AH1 - V IH0 SH
+DOW  D AW1
+DOW'S  D AW1 Z
+DOWAGER  D AW1 - AH0 - JH ER0
+DOWD  D AW1 D
+DOWDA  D AW1 - D AH0
+DOWDALL  D AW1 - D AH0 L
+DOWDELL  D AW1 - D AH0 L
+DOWDEN  D AW1 - D AH0 N
+DOWDING  D AW1 - D IH0 NG
+DOWDLE  D AW1 - D AH0 L
+DOWDS  D AW1 D Z
+DOWDY  D AW1 - D IY0
+DOWE  D AW1
+DOWEL  D AW1 - AH0 L
+DOWELL  D AA1 - W EH0 L
+DOWELS  D AW1 - AH0 L Z
+DOWEN  D AW1 - AH0 N
+DOWER  D AW1 R
+DOWERS  D AW1 R Z
+DOWIE  D AW1 - IY0
+DOWIS  D AW1 - IH0 S
+DOWLAND  D AW1 - L AH0 N D
+DOWLEN  D AW1 - L AH0 N
+DOWLER  D AW1 - L ER0
+DOWLESS  D AW1 - L AH0 S
+DOWLING  D AW1 - L IH0 NG
+DOWN  D AW1 N
+DOWN'S  D AW1 N Z
+DOWNARD  D AW1 - N ER0 D
+DOWNBEAT  D AW0 N - B IY1 T
+DOWNCAST  D AW1 N - K AE2 S T
+DOWNDRAFT  D AW1 N - D R AE2 F T
+DOWNE  D AW1 N
+DOWNED  D AW1 N D
+DOWNEN  D AW1 - N AH0 N
+DOWNER  D AW1 - N ER0
+DOWNERS  D AW1 - N ER0 Z
+DOWNES  D AW1 N Z
+DOWNES'S  D AW1 N - Z IH0 Z
+DOWNEY  D AW1 - N IY0
+DOWNEY'S  D AW1 - N IY0 Z
+DOWNFALL  D AW1 N - F AO2 L
+DOWNGRADE  D AW1 N - G R EY1 D
+DOWNGRADED  D AW1 N - G R EY1 - D AH0 D
+DOWNGRADES  D AW1 N - G R EY1 D Z
+DOWNGRADING  D AW1 N - G R EY1 - D IH0 NG
+DOWNGRADINGS  D AW1 N - G R EY2 - D IH0 NG Z
+DOWNHAM  D AW1 N - HH AH0 M
+DOWNHILL  D AW1 N - HH IH1 L
+DOWNIE  D AW1 - N IY0
+DOWNING  D AW1 - N IH0 NG
+DOWNINGTOWN  D AW1 - N IH0 NG - T AW2 N
+DOWNLOAD  D AW1 N - L OW2 D
+DOWNLOADED  D AW1 N - L OW2 - D IH0 D
+DOWNLOADING  D AW1 N - L OW2 - D IH0 NG
+DOWNPAYMENT  D AW2 N - P EY1 - M AH0 N T
+DOWNPLAY  D AW1 N - P L EY2
+DOWNPLAYED  D AW1 N - P L EY2 D
+DOWNPLAYING  D AW1 N - P L EY2 - IH0 NG
+DOWNPLAYS  D AW1 N - P L EY2 Z
+DOWNPOUR  D AW1 N - P AO0 R
+DOWNPOURS  D AW1 N - P AO2 R Z
+DOWNRANGE  D AW1 N - R EY1 N JH
+DOWNRIGHT  D AW1 N - R AY2 T
+DOWNRIVER  D AW2 N - R IH1 - V ER0
+DOWNS  D AW1 N Z
+DOWNSCALE  D AW1 N - S K EY2 L
+DOWNSIDE  D AW1 N - S AY2 D
+DOWNSIDES  D AW1 N - S AY2 D Z
+DOWNSIZE  D AW1 N - S AY2 Z
+DOWNSIZED  D AW1 N - S AY2 Z D
+DOWNSIZING  D AW1 N - S AY2 - Z IH0 NG
+DOWNSIZINGS  D AW1 N - S AY2 - Z IH0 NG Z
+DOWNSTAGE  D AW1 N - S T EY2 JH
+DOWNSTAIRS  D AW1 N - S T EH1 R Z
+DOWNSTATE  D AW1 N - S T EY2 T
+DOWNSTREAM  D AW1 N - S T R IY1 M
+DOWNSWING  D AW1 N - S W IH2 NG
+DOWNTIME  D AW1 N - T AY2 M
+DOWNTOWN  D AW1 N - T AW1 N
+DOWNTOWNS  D AW1 N - T AW1 N Z
+DOWNTREND  D AW1 N - T R EH2 N D
+DOWNTRODDEN  D AW2 N - T R AA1 - D AH0 N
+DOWNTURN  D AW1 N - T ER2 N
+DOWNTURNS  D AW1 N - T ER2 N Z
+DOWNUM  D AW1 - N AH0 M
+DOWNWARD  D AW1 N - W ER0 D
+DOWNWARDLY  D AW1 N - W ER0 D - L IY0
+DOWNWARDS  D AW1 N - W ER0 D Z
+DOWNWIND  D AW0 N - W IH1 N D
+DOWNY  D AW1 - N IY0
+DOWRY  D AW1 - R IY0
+DOWSE  D AW1 S
+DOWSING  D AW1 - S IH0 NG
+DOWSMAN  D AW1 Z - M AH0 N
+DOWTY  D AW1 - T IY0
+DOXEY  D AA1 K - S IY0
+DOXIE  D AA1 K - S IY0
+DOXOLOGIES  D AA0 K - S AA1 - L AH0 - JH IY0 Z
+DOXOLOGY  D AA0 K - S AA1 - L AH0 - JH IY0
+DOXTATER  D AA1 K - S T EY0 - T ER0
+DOXY  D AA1 K - S IY0
+DOYAL  D OY0 - AA1 L
+DOYEL  D OY1 - AH0 L
+DOYEN  D OY1 - IH0 N
+DOYLE  D OY1 L
+DOYLE'S  D OY1 L Z
+DOYON  D OY1 - AH0 N
+DOZE  D OW1 Z
+DOZED  D OW1 Z D
+DOZEN  D AH1 - Z AH0 N
+DOZENS  D AH1 - Z AH0 N Z
+DOZES  D OW1 - Z IH0 Z
+DOZIER  D OW1 - Z IY0 - ER0
+DOZING  D OW1 - Z IH0 NG
+DQALPHA  D IY1 - K Y UW1 - AE1 L - F AH0
+DR  D R AY1 V
+DR(2)  D AA1 K - T ER0
+DR.  D R AY1 V
+DR.(2)  D AA1 K - T ER0
+DRAB  D R AE1 B
+DRABBED  D R AE1 B D
+DRABBLE  D R AE1 - B AH0 L
+DRABEK  D R AE1 - B IH0 K
+DRABENSTOTT  D R AH0 - B EH1 N - S T AH0 T
+DRABIK  D R AA1 - B IH0 K
+DRABINSKY  D R AH0 - B IH1 N - S K IY0
+DRABKIN  D R AE1 B - K IH2 N
+DRABS  D R AE1 B Z
+DRACE  D R EY1 S
+DRACH  D R AE1 CH
+DRACHENBERG  D R AE1 - K AH0 N - B ER0 G
+DRACHMA  D R AE1 K - M AH0
+DRACHMAS  D R AA1 K - M AH0 Z
+DRACKETT  D R AE1 - K IH0 T
+DRACO  D R EY1 - K OW0
+DRACO(2)  D R AE1 - K OW0
+DRACONIAN  D R EY0 - K OW1 - N IY0 - AH0 N
+DRACONIAN(2)  D R AH0 - K OW1 - N IY0 - AH0 N
+DRACULA  D R AE1 - K Y UW0 - L AH0
+DRAEGER  D R EH1 - G ER0
+DRAFFEN  D R AE1 - F AH0 N
+DRAFT  D R AE1 F T
+DRAFT'S  D R AE1 F T S
+DRAFTED  D R AE1 F - T IH0 D
+DRAFTEE  D R AE1 F - T IY1
+DRAFTEES  D R AE1 F - T IY1 Z
+DRAFTER  D R AE1 F - T ER0
+DRAFTERS  D R AE1 F - T ER0 Z
+DRAFTING  D R AE1 F - T IH0 NG
+DRAFTS  D R AE1 F T S
+DRAFTSMAN  D R AE1 F T S - M AH0 N
+DRAFTSMANSHIP  D R AE1 F T S - M AH0 N - SH IH2 P
+DRAFTSMEN  D R AE1 F T S - M AH0 N
+DRAFTY  D R AE1 F - T IY0
+DRAG  D R AE1 G
+DRAGAN  D R AA1 - G AH0 N
+DRAGE  D R EY1 JH
+DRAGER  D R EY1 - G ER0
+DRAGGED  D R AE1 G D
+DRAGGING  D R AE1 - G IH0 NG
+DRAGGY  D R AE1 - G IY0
+DRAGLINE  D R AE1 - G L AY2 N
+DRAGNET  D R AE1 G - N EH2 T
+DRAGNETS  D R AE1 G - N EH2 T S
+DRAGO  D R AA1 - G OW0
+DRAGON  D R AE1 - G AH0 N
+DRAGON'S  D R AE1 - G AH0 N Z
+DRAGONAIR  D R AE1 - G AH0 - N EH2 R
+DRAGONE  D R AH0 - G OW1 N
+DRAGONHEAD  D R AE1 - G AH0 N - HH EH2 D
+DRAGONHEART  D R AE1 - G AH0 N - HH AA2 R T
+DRAGONS  D R AE1 - G AH0 N Z
+DRAGOO  D R AA1 - G UW0
+DRAGOVICH  D R AE1 - G AH0 - V IH0 CH
+DRAGS  D R AE1 G Z
+DRAHEIM  D R AE1 - HH AY0 M
+DRAHOS  D R EY1 - HH OW0 Z
+DRAHUSCHAK  D R AE1 - HH AH0 - SH AE0 K
+DRAHUSCHAK(2)  D R AH0 - HH UW1 - SH AH0 K
+DRAIN  D R EY1 N
+DRAINAGE  D R EY1 - N AH0 JH
+DRAINAGE(2)  D R EY1 - N IH0 JH
+DRAINE  D R EY1 N
+DRAINED  D R EY1 N D
+DRAINER  D R EY1 - N ER0
+DRAINERS  D R EY1 - N ER0 Z
+DRAINING  D R EY1 - N IH0 NG
+DRAINS  D R EY1 N Z
+DRAKE  D R EY1 K
+DRAKEFORD  D R AE1 K - F AO0 R D
+DRAKES  D R EY1 K S
+DRAKOS  D R EY1 - K OW0 Z
+DRALLE  D R EY1 L
+DRAM  D R AE1 M
+DRAM(2)  D IY1 - R AE2 M
+DRAMA  D R AA1 - M AH0
+DRAMAMINE  D R AE1 - M AH0 - M IY2 N
+DRAMAS  D R AA1 - M AH0 Z
+DRAMATIC  D R AH0 - M AE1 - T IH0 K
+DRAMATICALLY  D R AH0 - M AE1 - T IH0 K - L IY0
+DRAMATICALLY(2)  D R AH0 - M AE1 - T IH0 - K AH0 - L IY0
+DRAMATIST  D R AA1 - M AH0 - T IH0 S T
+DRAMATIZATION  D R AE2 - M AH0 - T AH0 - Z EY1 - SH AH0 N
+DRAMATIZATIONS  D R AE2 - M AH0 - T AH0 - Z EY1 - SH AH0 N Z
+DRAMATIZE  D R AA1 - M AH0 - T AY2 Z
+DRAMATIZE(2)  D R AE1 - M AH0 - T AY2 Z
+DRAMATIZED  D R AE1 - M AH0 - T AY2 Z D
+DRAMATIZES  D R AE1 - M AH0 - T AY2 - Z IH0 Z
+DRAMATIZING  D R AE1 - M AH0 - T AY2 - Z IH0 NG
+DRAMS  D R AE1 M Z
+DRAMS(2)  D IY1 - R AE2 M Z
+DRANE  D R EY1 N
+DRANEY  D R EY1 - N IY0
+DRANG  D R AE1 NG
+DRANK  D R AE1 NG K
+DRANSFIELD  D R AE1 N S - F IY2 L D
+DRAPE  D R EY1 P
+DRAPEAU  D R AH0 - P OW1
+DRAPED  D R EY1 P T
+DRAPER  D R EY1 - P ER0
+DRAPERIES  D R EY1 - P ER0 - IY0 Z
+DRAPERY  D R EY1 - P ER0 - IY0
+DRAPES  D R EY1 P S
+DRAPING  D R EY1 - P IH0 NG
+DRAPKIN  D R AE1 P - K IH0 N
+DRASNER  D R AE1 S - N ER0
+DRASTIC  D R AE1 - S T IH0 K
+DRASTICALLY  D R AE1 - S T IH0 K - L IY0
+DRAUGHN  D R AO1 N
+DRAUGHON  D R AO1 - AH0 N
+DRAUGHT  D R AE1 F T
+DRAUGHTS  D R AE1 F T S
+DRAUS  D R AO1 Z
+DRAVECKY  D R AH0 - V EH1 - K IY0
+DRAVES  D R EY1 V Z
+DRAVIS  D R AE1 - V IH0 S
+DRAVO  D R AE1 - V OW0
+DRAVO'S  D R AE1 - V OW0 Z
+DRAW  D R AO1
+DRAWBACK  D R AO1 - B AE2 K
+DRAWBACKS  D R AO1 - B AE2 K S
+DRAWBAUGH  D R AO1 - B AO2
+DRAWBRIDGE  D R AO1 - B R IH2 JH
+DRAWDOWN  D R AO1 - D AW2 N
+DRAWDOWNS  D R AO1 - D AW2 N Z
+DRAWDY  D R AO1 - D IY0
+DRAWER  D R AO1 R
+DRAWERS  D R AO1 R Z
+DRAWING  D R AO1 - IH0 NG
+DRAWINGS  D R AO1 - IH0 NG Z
+DRAWL  D R AO1 L
+DRAWLED  D R AO1 L D
+DRAWLS  D R AO1 L Z
+DRAWN  D R AO1 N
+DRAWS  D R AO1 Z
+DRAY  D R EY1
+DRAYER  D R EY1 - ER0
+DRAYTON  D R EY1 - T AH0 N
+DRAYTON'S  D R EY1 - T AH0 N Z
+DREAD  D R EH1 D
+DREADED  D R EH1 - D IH0 D
+DREADFUL  D R EH1 D - F AH0 L
+DREADFULLY  D R EH1 D - F AH0 - L IY0
+DREADING  D R EH1 - D IH0 NG
+DREADNOUGHT  D R EH1 D - N AO2 T
+DREADS  D R EH1 D Z
+DREAM  D R IY1 M
+DREAMED  D R IY1 M D
+DREAMER  D R IY1 - M ER0
+DREAMERS  D R IY1 - M ER0 Z
+DREAMING  D R IY1 - M IH0 NG
+DREAMLAND  D R IY1 M - L AE2 N D
+DREAMLIKE  D R IY1 M - L AY2 K
+DREAMS  D R IY1 M Z
+DREAMT  D R EH1 M T
+DREAMWORKS  D R IY1 M - W ER2 K S
+DREAMWORKS'  D R IY1 M - W ER2 K S
+DREAMWORLD  D R IY1 M - W ER2 L D
+DREAMY  D R IY1 - M IY0
+DREARINESS  D R IY1 - R IY0 - N AH0 S
+DREARY  D R IH1 - R IY0
+DREBSKY  D R EH1 B S - K IY0
+DRECHSEL  D R EH1 K - S AH0 L
+DRECHSLER  D R EH1 K - S AH0 - L ER0
+DRECHSLER(2)  D R EH1 K S - L ER0
+DRED  D R EH1 D
+DREDD  D R EH1 D
+DREDGE  D R EH1 JH
+DREDGED  D R EH1 JH D
+DREDGES  D R EH1 - JH AH0 Z
+DREDGES(2)  D R EH1 - JH IH0 Z
+DREDGING  D R EH1 - JH IH0 NG
+DREES  D R IY1 Z
+DREESE  D R IY1 Z
+DREESSEN  D R IY1 - S AH0 N
+DREGER  D R EH1 - G ER0
+DREGS  D R EH1 G Z
+DREHER  D R EH1 R
+DREIBELBIS  D R AY1 - B IH0 L - B IH0 S
+DREIER  D R AY1 - ER0
+DREIGHTON  D R AY1 - T IH0 N
+DREILING  D R AY1 - L IH0 NG
+DREIS  D R IY1 Z
+DREISBACH  D R AY1 S - B AA2 K
+DREMAN  D R IY1 - M AH0 N
+DRENCH  D R EH1 N CH
+DRENCHED  D R EH1 N CH T
+DRENCHING  D R EH1 N - CH IH0 NG
+DRENNAN  D R EH1 - N AH0 N
+DRENNEN  D R EH1 - N AH0 N
+DRENNING  D R EH1 - N IH0 NG
+DRENNON  D R EH1 - N AH0 N
+DRENTH  D R EH1 N TH
+DREPUNG  D R EY1 - P AH2 NG
+DREPUNG(2)  D R EY2 - P AO1 NG
+DRESBACH  D R EH1 S - B AA2 K
+DRESCH  D R EH1 SH
+DRESCHER  D R EH1 - SH ER0
+DRESDEN  D R EH1 Z - D IH0 N
+DRESDNER  D R EH1 Z D - N ER0
+DRESDNER'S  D R EH1 Z D - N ER0 Z
+DRESEN  D R IY1 - Z AH0 N
+DRESHER  D R EH1 - SH ER0
+DRESNER  D R EH1 Z - N ER0
+DRESS  D R EH1 S
+DRESSAGE  D R EH0 - S AA1 ZH
+DRESSED  D R EH1 S T
+DRESSEL  D R EH1 - S AH0 L
+DRESSEN  D R EH1 - S AH0 N
+DRESSER  D R EH1 - S ER0
+DRESSER'S  D R EH1 - S ER0 Z
+DRESSERS  D R EH1 - S ER0 Z
+DRESSES  D R EH1 - S AH0 Z
+DRESSES(2)  D R EH1 - S IH0 Z
+DRESSIER  D R EH1 - S IY0 - ER0
+DRESSING  D R EH1 - S IH0 NG
+DRESSINGS  D R EH1 - S IH0 NG Z
+DRESSLER  D R EH1 S - L ER0
+DRESSMAKER  D R EH1 S - M EY2 - K ER0
+DRESSMAKERS  D R EH1 S - M EY2 - K ER0 Z
+DRESSMAKING  D R EH1 S - M EY2 - K IH0 NG
+DRESSY  D R EH1 - S IY0
+DREW  D R UW1
+DREWERY  D R UW1 - ER0 - IY0
+DREWES  D R UW1 Z
+DREWETT  D R UW1 - IH0 T
+DREWRY  D R UW1 - R IY0
+DREWS  D R UW1 Z
+DREXEL  D R EH1 K - S AH0 L
+DREXEL'S  D R EH1 K - S AH0 L Z
+DREXLER  D R EH1 K S - L ER0
+DREY  D R EY1
+DREYER  D R EY1 - ER0
+DREYFUS  D R AY1 - F AH0 S
+DREYFUS'S  D R EY1 - F AH0 - S IH0 Z
+DREYFUS(2)  D R EY1 - F AH0 S
+DREYFUSS  D R EY1 - F AH0 S
+DRIBBED  D R IH1 B D
+DRIBBLE  D R IH1 - B AH0 L
+DRIBBLED  D R IH1 - B AH0 L D
+DRIBBLING  D R IH1 - B AH0 L - IH0 NG
+DRIBBLING(2)  D R IH1 - B L IH0 NG
+DRIBS  D R IH1 B Z
+DRIED  D R AY1 D
+DRIEHAUS  D R IY1 - HH AW2 S
+DRIER  D R AY1 - ER0
+DRIES  D R AY1 Z
+DRIESSEN  D R IY1 - S AH0 N
+DRIEST  D R AY1 - AH0 S T
+DRIEVER  D R IY1 - V ER0
+DRIFT  D R IH1 F T
+DRIFTED  D R IH1 F - T AH0 D
+DRIFTED(2)  D R IH1 F - T IH0 D
+DRIFTER  D R IH1 F - T ER0
+DRIFTERS  D R IH1 F - T ER0 Z
+DRIFTING  D R IH1 F - T IH0 NG
+DRIFTNET  D R IH1 F T - N EH2 T
+DRIFTS  D R IH1 F T S
+DRIFTWOOD  D R IH1 F T - W UH2 D
+DRIGGERS  D R IH1 - G ER0 Z
+DRIGGS  D R IH1 G Z
+DRILL  D R IH1 L
+DRILLBIT  D R IH1 L - B IH2 T
+DRILLED  D R IH1 L D
+DRILLER  D R IH1 - L ER0
+DRILLERS  D R IH1 - L ER0 Z
+DRILLING  D R IH1 - L IH0 NG
+DRILLING'S  D R IH1 - L IH0 NG Z
+DRILLS  D R IH1 L Z
+DRINA  D IY1 - N AH0
+DRINA'S  D IY1 - N AH0 Z
+DRINAS  D IY1 - N AH0 Z
+DRING  D R IH1 NG
+DRINK  D R IH1 NG K
+DRINKABLE  D R IH1 N - K AH0 - B AH0 L
+DRINKARD  D R IH1 NG - K ER0 D
+DRINKER  D R IH1 NG - K ER0
+DRINKER'S  D R IH1 NG - K ER0 Z
+DRINKERS  D R IH1 NG - K ER0 Z
+DRINKING  D R IH1 NG - K IH0 NG
+DRINKS  D R IH1 NG K S
+DRINKWATER  D R IH1 NG - K W AO2 - T ER0
+DRINKWINE  D R IH1 NG - K W AY2 N
+DRINNON  D R IH1 - N AH0 N
+DRIP  D R IH1 P
+DRIPPED  D R IH1 P T
+DRIPPING  D R IH1 - P IH0 NG
+DRIPPS  D R IH1 P S
+DRIPS  D R IH1 P S
+DRISCOLL  D R IH1 S - K AH0 L
+DRISKELL  D R IH1 S - K AH0 L
+DRISKILL  D R IH1 - S K IH0 L
+DRIVABLE  D R AY1 - V AH0 - B AH0 L
+DRIVE  D R AY1 V
+DRIVE'S  D R AY1 V Z
+DRIVEL  D R IH1 - V AH0 L
+DRIVEN  D R IH1 - V AH0 N
+DRIVER  D R AY1 - V ER0
+DRIVER'S  D R AY1 - V ER0 Z
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+DRIVERS  D R AY1 - V ER0 Z
+DRIVERS'  D R AY1 - V ER0 Z
+DRIVES  D R AY1 V Z
+DRIVEWAY  D R AY1 V - W EY2
+DRIVEWAYS  D R AY1 V - W EY2 Z
+DRIVING  D R AY1 - V IH0 NG
+DRIZZLE  D R IH1 - Z AH0 L
+DRIZZLING  D R IH1 - Z AH0 L - IH0 NG
+DRIZZLING(2)  D R IH1 Z - L IH0 NG
+DRIZZLY  D R IH1 Z - L IY0
+DROBKOV  D R AO1 B - K AO0 V
+DROBKOV(2)  D R AO1 B - K AO0 F
+DROBNY  D R AA1 B - N IY0
+DRODDY  D R AA1 - D IY0
+DROEGE  D R OW1 JH
+DROESSLER  D R OW1 - S AH0 - L ER0
+DROESSLER(2)  D R OW1 S - L ER0
+DROGE  D R OW1 JH
+DROGOUL  D R OW0 - G UW1 L
+DROGOUL'S  D R OW0 - G UW1 L Z
+DROGUE  D R OW1 G
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+DROLET  D R OW1 - L IH0 T
+DROLL  D R OW1 L
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+DROMEDARY  D R AA1 - M AH0 - D EH2 - R IY0
+DROMER  D R OW1 - M ER0
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+DROMGOOLE  D R AA1 M - G UW2 L
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+DROMI  D R AA1 - M IY0
+DROMOMANIA  D R OW2 - M OW0 - M EY1 - N IY0 - AH0
+DROMOMANIA(2)  D R OW2 - M OW0 - M EY1 - N Y AH0
+DROMON  D R OW1 - M AH0 N
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+DRONED  D R OW1 N D
+DRONES  D R OW1 N Z
+DRONET  D R OW1 - N IH0 T
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+DROOL  D R UW1 L
+DROOLING  D R UW1 - L IH0 NG
+DROOP  D R UW1 P
+DROOPED  D R UW1 P T
+DROOPING  D R UW1 - P IH0 NG
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+DROP  D R AA1 P
+DROP(2)  D R AO1 P
+DROPKIN  D R AA1 P - K IH0 N
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+DROPLETS  D R AA1 - P L AH0 T S
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+DROPOUT  D R AA1 P - AW2 T
+DROPOUTS  D R AA1 P - AW2 T S
+DROPPED  D R AA1 P T
+DROPPER  D R AA1 - P ER0
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+DROPPING  D R AA1 - P IH0 NG
+DROPPINGS  D R AA1 - P IH0 NG Z
+DROPS  D R AA1 P S
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+DROSER  D R OW1 - Z ER0
+DROSS  D R AO1 S
+DROST  D R AA1 S T
+DROSTE  D R OW1 S T
+DROTAR  D R OW1 - T ER0
+DROUGHT  D R AW1 T
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+DROUGHTS  D R AW1 T S
+DROUILLARD  D R W IY0 - L AA1 R D
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+DROVES  D R OW1 V Z
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+DROZ  D R AA1 Z
+DROZD  D R AA1 Z D
+DROZDA  D R AA1 Z - D AH0
+DROZDOWSKI  D R AH0 Z - D AO1 F S - K IY0
+DRU  D R UW1
+DRUB  D R AH1 B
+DRUBBED  D R AH1 B D
+DRUBBING  D R AH1 - B IH0 NG
+DRUCE  D R UW1 S
+DRUCIE  D R AH1 - K IY0
+DRUCK  D R AH1 K
+DRUCKENMILLER  D R AH1 - K IH0 N - M IH2 - L ER0
+DRUCKER  D R AH1 - K ER0
+DRUCKMAN  D R AH1 K - M AH0 N
+DRUDGE  D R AH1 JH
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+DRUELLA  D R UW2 - EH1 - L AH0
+DRUG  D R AH1 G
+DRUG'S  D R AH1 G Z
+DRUGAN  D R UW1 - G AH0 N
+DRUGGED  D R AH1 G D
+DRUGGING  D R AH1 - G IH0 NG
+DRUGGIST  D R AH1 - G IH0 S T
+DRUGGIST'S  D R AH1 - G AH0 S T S
+DRUGGIST'S(2)  D R AH1 - G IH0 S T S
+DRUGGISTS  D R AH1 - G AH0 S T S
+DRUGGISTS(2)  D R AH1 - G IH0 S T S
+DRUGGISTS(3)  D R AH1 - G IH0 S S
+DRUGGISTS(4)  D R AH1 - G IH0 S
+DRUGMAKER  D R AH1 G - M EY2 - K ER0
+DRUGMAKERS  D R AH1 G - M EY2 - K ER0 Z
+DRUGS  D R AH1 G Z
+DRUGS'  D R AH1 G Z
+DRUGSTORE  D R AH1 G - S T AO2 R
+DRUGSTORES  D R AH1 G - S T AO2 R Z
+DRUID  D R UW1 - IH0 D
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+DRUM  D R AH1 M
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+DRUMHELLER  D R AH1 M - HH EH2 - L ER0
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+DRUMM  D R AH1 M
+DRUMMED  D R AH1 M D
+DRUMMER  D R AH1 - M ER0
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+DRUMMING  D R AH1 - M IH0 NG
+DRUMMOND  D R AH1 - M AH0 N D
+DRUMMONDS  D R AH1 - M AH0 N D Z
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+DRUNK  D R AH1 NG K
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+DRUNKS  D R AH1 NG K S
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+DRUSE  D R UW1 Z
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+DRY  D R AY1
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+DRZEWIECKI  JH UW0 - IY1 T S - K IY0
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+DUMAN  D UW1 - M AH0 N
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+DUMB  D AH1 M
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+DUMBER  D AH1 - M ER0
+DUMBEST  D AH1 - M AH0 S T
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+DUMMER  D AH1 - M ER0
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+DUMMY  D AH1 - M IY0
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+DUMPED  D AH1 M P T
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+DUMPING  D AH1 M - P IH0 NG
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+DUMPLINGS  D AH1 M - P L IH0 NG Z
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+DUMPSTERS  D AH1 M P - S T ER0 Z
+DUMPTRUCK  D AH1 M P - T R AH2 K
+DUMPTRUCKS  D AH1 M P - T R AH2 K S
+DUMPTY  D AH1 M P - T IY0
+DUMPY  D AH1 M - P IY0
+DUN  D AH1 N
+DUN'S  D AH1 N Z
+DUNA  D UW1 - N AH0
+DUNAGAN  D UW0 - N AA1 - G AA0 N
+DUNAHOO  D UW0 - N AA1 - HH UW0
+DUNAJ  D UW1 - N AH0 JH
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+DUNAWAY  D AH1 N - AH0 - W EY2
+DUNAY  D AH1 - N EY0
+DUNBAR  D AH1 N - B AA0 R
+DUNBLANE  D AH1 N - B L EY2 N
+DUNC  D AH1 NG K
+DUNCAN  D AH1 NG - K AH0 N
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+DUNDON  D AH1 N - D AH0 N
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+DUNE  D UW1 N
+DUNEDIN  D UW1 - N AH0 - D IH0 N
+DUNEGAN  D AH1 - N IH0 - G AE0 N
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+DUNFEE  D AH1 N - F IY2
+DUNFORD  D AH1 N - F ER0 D
+DUNG  D AH1 NG
+DUNGAN  D AH1 NG - G AH0 N
+DUNGEON  D AH1 N - JH AH0 N
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+DUNHAM  D AH1 - N AH0 M
+DUNHILL  D AH1 N - HH IH2 L
+DUNIGAN  D AH1 - N IH0 - G AE0 N
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+DUNIVAN  D AH1 - N IH0 - V AE0 N
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+DUNK  D AH1 NG K
+DUNKED  D AH1 NG K T
+DUNKEL  D AH1 NG - K AH0 L
+DUNKELBERG  D AH1 NG - K AH0 L - B ER0 G
+DUNKELBERGER  D AH1 NG - K AH0 L - B ER0 - G ER0
+DUNKER  D AH1 NG - K ER0
+DUNKERLEY  D AH1 NG - K ER0 - L IY0
+DUNKIN  D AH1 NG - K IH0 N
+DUNKIN'  D AH1 NG - K IH0 N
+DUNKIRK  D AH1 N - K ER0 K
+DUNKLE  D AH1 NG - K AH0 L
+DUNKLEBERGER  D AH1 NG - K AH0 L - B ER0 - G ER0
+DUNKLEE  D AH1 NG - K L IY2
+DUNKLEY  D AH1 NG - K L IY0
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+DUNLAP  D AH1 N - L AE0 P
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+DUNLAVY  D AH1 N - L AH0 - V IY0
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+DUNLEAVY  D UW1 N - L AH0 - V IY0
+DUNLEVY  D UW1 N - L IH0 - V IY0
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+DUNN  D AH1 N
+DUNNAGAN  D AH1 - N AH0 - G AE0 N
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+DUNNAVANT  D AH1 - N AH0 - V AH0 N T
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+DUNNE  D AH1 N
+DUNNED  D AH1 N D
+DUNNELL  D AH1 - N AH0 L
+DUNNETT  D AH1 - N IH0 T
+DUNNIGAN  D AH1 - N IH0 - G AH0 N
+DUNNING  D AH1 - N IH0 NG
+DUNNINGTON  D AH1 - N IH0 NG - T AH0 N
+DUNPHY  D AH1 N - F IY0
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+DUNSHEE  D AH1 N - SH IY0
+DUNSMOOR  D AH1 N Z - M UH2 R
+DUNSMORE  D AH1 N - S M AO0 R
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+DUNST  D AH1 N S T
+DUNSTAN  D AH1 N - S T AH0 N
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+DUNSTON  D AH1 N - S T AH0 N
+DUNSWORTH  D AH1 N - Z W ER2 TH
+DUNTON  D AH1 N - T AH0 N
+DUNWOODY  D AH1 N - W UH2 - D IY0
+DUNWORTH  D AH1 N - W ER2 TH
+DUO  D UW1 - OW0
+DUODENAL  D UW1 - AH0 - D IY1 - N AH0 L
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+DUONG  D UW1 - OW0 NG
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+DUPED  D UW1 P T
+DUPEE  D UW1 - P IY1
+DUPER  D UW1 - P ER0
+DUPES  D UW1 P S
+DUPIN  D AH0 - P AE1 N
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+DUPLECHAIN  D UW1 - P L IH0 - SH EY0 N
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+DUPLICATING  D UW1 - P L IH0 - K EY2 - T IH0 NG
+DUPLICATION  D Y UW2 - P L AH0 - K EY1 - SH AH0 N
+DUPLICATIONS  D UW2 - P L IH0 - K EY1 - SH AH0 N Z
+DUPLICATIVE  D UW0 - P L IH1 - K AH0 - T IH0 V
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+DUPONT  D UW1 - P AA0 N T
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+DUPREY  D AH1 - P R IY0
+DUPRIEST  D AH1 - P ER0 - IY0 - IH0 S T
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+DUPUIS  D AH1 - P UW0 - IH0 Z
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+DUQUE  D UW1 K
+DUQUESNE  D UW0 - K EY1 N
+DUQUESNE'S  D UW0 - K EY1 N Z
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+DURA  D UH1 - R AH0
+DURABILITY  D ER0 - AH0 - B IH1 - L IH0 - T IY0
+DURABLE  D UH1 - R AH0 - B AH0 L
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+DURANT  D UH1 - R AH0 N T
+DURANT'S  D ER0 - AE1 N T S
+DURANTE  D UH0 - R AA1 N - T IY0
+DURATION  D UH1 - R EY1 - SH AH0 N
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+DURAZO  D UH0 - R AA1 - Z OW0
+DURBAN  D ER1 - B AH0 N
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+DURCHHOLZ  D ER1 - CH OW2 L T S
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+DURELL  D Y UW1 - R AH0 L
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+DURFEY  D ER1 - F IY0
+DURFLINGER  D ER1 - F AH0 L - IH0 - NG ER0
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+DURGAN  D ER1 - G AH0 N
+DURGIN  D ER1 - JH IH0 N
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+DURHAM(2)  D UH1 R - HH AE1 M
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+DURI  D UH1 - R IY0
+DURICK  D Y UW1 - R IH0 K
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+DURING  D UH1 - R IH0 NG
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+DURIO  D UH1 - R IY0 - OW0
+DURIRON  D UW0 - R IH1 - R AH0 N
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+DURKHEIM  D ER1 K - HH AY2 M
+DURKHEIM'S  D ER1 K - HH AY2 M Z
+DURKIN  D ER1 - K IH0 N
+DURLAND  D ER1 - L AH0 N D
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+DURLING  D ER1 - L IH0 NG
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+DURNELL  D ER1 - N AH0 L
+DURNEY  D ER1 - N IY0
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+DUROCHER  D Y UW1 - R AH0 - K ER0
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+DURRAH  D AO1 - R AH0
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+DUSCH  D AH1 SH
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+DUSENBERY  D AH0 - S EH1 N - B ER0 - IY0
+DUSENBURY  D UW1 - S AH0 N - B EH0 - R IY0
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+DUSHANE  D UW2 - SH EY1 N
+DUSHYANTH  D UW2 - SH IY0 - AA1 N TH
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+EDITIONS  IH0 - D IH1 - SH AH0 N Z
+EDITOR  EH1 - D AH0 - T ER0
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+EDITORIAL  EH2 - D AH0 - T AO1 - R IY0 - AH0 L
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+EMBRACE  EH0 M - B R EY1 S
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+EMBRACEABLE  IH0 M - B R EY1 - S AH0 - B AH0 L
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+EMBROIDERERS  EH0 M - B R OY1 - D ER0 - ER0 Z
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+EMBROIDERING  EH0 M - B R OY1 - D ER0 - IH0 NG
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+EMCH  EH1 M CH
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+EMEL  EH1 - M AH0 L
+EMELDA  EH0 - M EH1 L - D AH0
+EMELIE  EH1 - M AH0 - L IY0
+EMELINA  EH2 - M EH0 - L IY1 - N AH0
+EMELINE  EH1 - M IH0 - L AY2 N
+EMELITA  EH0 - M EH0 - L IY1 - T AH0
+EMELYNE  EH1 - M IH0 - L AY0 N
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+EMERA  EH0 - M EH1 - R AH0
+EMERALD  EH1 M - R AH0 L D
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+EMERALDS  EH1 M - R AH0 L D Z
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+EMERANT  EY0 - M EH1 - R AH0 N T
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+EMERGE  IH0 - M ER1 JH
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+EMERGED  IH0 - M ER1 JH D
+EMERGED(2)  IY1 - M ER0 JH D
+EMERGENCE  IH0 - M ER1 - JH AH0 N S
+EMERGENCE(2)  IY1 - M ER0 - JH AH0 N S
+EMERGENCIES  IH0 - M ER1 - JH AH0 N - S IY0 Z
+EMERGENCIES(2)  IY1 - M ER0 - JH AH0 N - S IY0 Z
+EMERGENCY  IH0 - M ER1 - JH AH0 N - S IY0
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+EMERGENT  IH0 - M ER1 - JH AH0 N T
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+EMERGES  IH0 - M ER1 - JH IH0 Z
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+EMERGING  IH0 - M ER1 - JH IH0 NG
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+EMERICH  EH1 - M ER0 - IH0 K
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+EMERINE  EH1 - M ER0 - IY2 N
+EMERITUS  IH0 - M EH1 - R AH0 - T AH0 S
+EMERSE  IH0 - M ER1 S
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+EMERSON  EH1 - M ER0 - S AH0 N
+EMERSON'S  EH1 - M ER0 - S AH0 N Z
+EMERT  EH1 - M ER0 T
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+EMERY  EH1 - M ER0 - IY0
+EMERY'S  EH1 - M ER0 - IY0 Z
+EMERYVILLE  EH1 - M ER0 - IY0 - V IH2 L
+EMETIC  IH0 - M EH1 - T IH0 K
+EMFINGER  EH1 M - F IH0 - NG ER0
+EMGE  EH1 M JH
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+EMICK  EH1 - M IH0 K
+EMIG  EH1 - M IH0 G
+EMIGH  EH1 - M AY0
+EMIGRANT  EH1 - M AH0 - G R AH0 N T
+EMIGRANTS  EH1 - M AH0 - G R AH0 N T S
+EMIGRATE  EH1 - M AH0 - G R EY2 T
+EMIGRATED  EH1 - M AH0 - G R EY2 - T IH0 D
+EMIGRATES  EH1 - M AH0 - G R EY2 T S
+EMIGRATING  EH1 - M AH0 - G R EY2 - T IH0 NG
+EMIGRATION  EH2 - M AH0 - G R EY1 - SH AH0 N
+EMIGRATIONS  EH2 - M AH0 - G R EY1 - SH AH0 N Z
+EMIGRE  EH1 - M AH0 - G R EY2
+EMIGRES  EH1 - M AH0 - G R EY2 Z
+EMIL  EH0 - M IY1 L
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+EMILIA  AH0 - M IY1 - L IY0 - AH0
+EMILIE  EH1 - M AH0 - L IY0
+EMILIO  AH0 - M IY1 - L IY0 - OW0
+EMILO  EH0 - M IY1 - L OW0
+EMILO'S  EH0 - M IY1 - L OW0 Z
+EMILY  EH1 - M IH0 - L IY0
+EMILY'S  EH1 - M IH0 - L IY0 Z
+EMINA  EH0 - M IY1 - N AH0
+EMINASE  EH2 - M IH0 - N AA1 - S IY0
+EMINENCE  EH1 - M AH0 - N AH0 N S
+EMINENCES  EH1 - M AH0 - N AH0 N - S IH0 Z
+EMINENT  EH1 - M AH0 - N AH0 N T
+EMINENTLY  EH1 - M AH0 - N AH0 N T - L IY0
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+EMIRATE  EH1 - M ER0 - AH0 T
+EMIRATE(2)  EH1 - M ER0 - EY2 T
+EMIRATES  EH1 - M ER0 - AH0 T S
+EMIRATES'  EH1 - M ER0 - AH0 T S
+EMIRATES'(2)  EH1 - M ER0 - EY2 T S
+EMIRATES(2)  EH1 - M ER0 - EY2 T S
+EMISON  EH1 - M IH0 - S AH0 N
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+EMISSARY  EH1 - M AH0 - S EH2 - R IY0
+EMISSION  IH0 - M IH1 - SH AH0 N
+EMISSIONS  IH0 - M IH1 - SH AH0 N Z
+EMIT  IH0 - M IH1 T
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+EMITTING  IH0 - M IH1 - T IH0 NG
+EMLER  EH1 M - L ER0
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+EMLING  EH1 M - L IH0 NG
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+EMLYNNE  IH0 M - L AY1 N
+EMMA  EH1 - M AH0
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+EMMANUEL  IH0 - M AE1 - N Y UW0 - AH0 L
+EMMAUS  EH1 - M AW0 S
+EMME  EH1 M
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+EMMELINE  EH1 - M AH0 - L AY2 N
+EMMENDORFER  EH1 - M IH0 N - D AO0 R - F ER0
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+EMMERT  EH1 - M ER0 T
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+EMMET  EH1 - M IH0 T
+EMMETT  EH1 - M IH0 T
+EMMI  EH1 - M IY0
+EMMICK  EH1 - M IH0 K
+EMMIE  EH1 - M IY0
+EMMINGER  EH1 - M IH0 - NG ER0
+EMMIS  EH1 - M IH0 S
+EMMIT  EH1 - M IH0 T
+EMMITT  EH1 - M IH0 T
+EMMONS  EH1 - M AH0 N Z
+EMMOTT  EH1 - M AH0 T
+EMMY  EH1 - M IY0
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+EMOGENE  EH1 - M AH0 - G IY0 N
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+EMORY'S  EH1 - M ER0 - IY0 Z
+EMOTION  IH0 - M OW1 - SH AH0 N
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+EMOTIONALLY(2)  IY1 - M OW0 SH - N AH0 - L IY0
+EMOTIONS  IH0 - M OW1 - SH AH0 N Z
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+EMPANEL  IH0 M - P AE1 - N AH0 L
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+EMPATHIZE  EH1 M - P AH0 - TH AY2 Z
+EMPATHY  EH1 M - P AH0 - TH IY0
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+EMPIRICISM  EH0 M - P IH1 - R AH0 - S IH2 - Z AH0 M
+EMPIRICIST  IH0 M - P IH1 - R AH0 - S AH0 S T
+EMPLACEMENT  IH0 M - P L EY1 S - M AH0 N T
+EMPLACEMENTS  IH0 M - P L EY1 S - M AH0 N T S
+EMPLOY  EH0 M - P L OY1
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+EMPLOYABLE  EH0 M - P L OY1 - AH0 - B AH0 L
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+EMPLOYER  EH0 M - P L OY1 - ER0
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+EMPLOYER'S(2)  IH0 M - P L OY1 - ER0 Z
+EMPLOYER(2)  IH0 M - P L OY1 - ER0
+EMPLOYERS  EH0 M - P L OY1 - ER0 Z
+EMPLOYERS'  EH0 M - P L OY1 - ER0 Z
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+EMPLOYERS(2)  IH0 M - P L OY1 - ER0 Z
+EMPLOYING  EH0 M - P L OY1 - IH0 NG
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+EMPLOYMENT  EH0 M - P L OY1 - M AH0 N T
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+EMPORIUM  EH2 M - P AO1 - R IY0 - AH0 M
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+EMPOWERED  IH0 M - P AW1 - ER0 D
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+EMPTINESS  EH1 M P - T IY0 - N AH0 S
+EMPTINESS(2)  EH1 M - T IY0 - N AH0 S
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+EMPTYING  EH1 M P - T IY0 - IH0 NG
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+EN  EH1 N
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+ENCHANT  EH0 N - CH AE1 N T
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+ENCOR  EH1 N - K AO2 R
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+ENTOMOLOGY  EH2 N - T AH0 - M AA1 - L AH0 - JH IY0
+ENTOURAGE  AA2 N - T UH0 - R AA1 ZH
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+ENTRAIL  EH1 N - T R AH0 L
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+ENTRANCES  EH1 N - T R AH0 N - S AH0 Z
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+ENTRAP  IH0 N - T R AE1 P
+ENTRAPMENT  IH0 N - T R AE1 P - M AH0 N T
+ENTRAPPED  IH0 N - T R AE1 P T
+ENTRE  AA1 N - T R EY0
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+ENTREATY  EH0 N - T R IY1 - T IY0
+ENTREE  AA1 N - T R EY2
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+ENTREKIN  EH1 N - T R IH0 - K IH0 N
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+ENTREPRENEUR  AA2 N - T R AH0 - P R AH0 - N ER1
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+ENTREPRENEUR(2)  AA2 N - T R AH0 - P R AH0 - N UH1 R
+ENTREPRENEURIAL  AA2 N - T R AH0 - P R AH0 - N ER1 - IY0 - AH0 L
+ENTREPRENEURIALISM  EH2 N - T R AH0 - P R AH0 - N UW2 - R IY1 - AH0 - L IH2 - Z AH0 M
+ENTREPRENEURS  AA2 N - T R AH0 - P R AH0 - N ER1 Z
+ENTREPRENEURSHIP  AA2 N - T R AH0 - P R AH0 - N ER1 - SH IH0 P
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+ENTRIKIN  EH1 N - T R IH0 - K IH0 N
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+ENTSMINGER  EH1 N T - S AH0 - M IH0 - NG ER0
+ENTWINE  EH0 N - T W AY1 N
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+ENUMERATE  IH0 - N UW1 - M ER0 - EY2 T
+ENUMERATED  IH0 - N UW1 - M ER0 - EY2 - T IH0 D
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+ENVELOP  IH0 N - V EH1 - L AH0 P
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+ENVIRONMENT  IH0 N - V AY1 - R AH0 N - M AH0 N T
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+ENVIRONMENTAL  IH0 N - V AY2 - R AH0 N - M EH1 N - T AH0 L
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+ENVIRONMENTALISM  EH0 N - V AY1 - R AH0 N - M EH2 N - T AH0 - L IH2 - Z AH0 M
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+ENVIRONMENTALIST  IH0 N - V AY2 - R AH0 N - M EH1 N - T AH0 - L IH0 S T
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+ENVIRONMENTALISTS(2)  EH0 N - V AY1 - R AH0 N - M EH2 N - T AH0 - L IH0 S S
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+ENVIRONMENTALISTS(4)  EH0 N - V AY1 - R AH0 N - M EH2 - N AH0 - L IH0 S S
+ENVIRONMENTALISTS(5)  EH0 N - V AY1 - R AH0 N - M EH2 N - T AH0 - L IH0 S
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+EPISCOPO  IH0 - P IH0 S - K OW1 - P OW0
+EPISODE  EH1 - P AH0 - S OW2 D
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+EPITOMIZED  IH0 - P IH1 - T AH0 - M AY2 Z D
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+EPOCH  EH1 - P AH0 K
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+EPOCHS  EH1 - P AH0 K S
+EPOCHS(2)  IY1 - P AH0 K S
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+EPPEL  EH1 - P AH0 L
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+EPPERS  EH1 - P ER0 Z
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+EPPINGER  EH1 - P IH0 - NG ER0
+EPPLE  EH1 - P AH0 L
+EPPLER  EH1 P - L ER0
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+EPPOLITO  EH0 - P OW0 - L IY1 - T OW0
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+EPROM  EH1 - P R AH0 M
+EPROMS  EH1 - P R AH0 M Z
+EPSCO  EH1 P - S K OW0
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+EPSOM  EH1 P - S AH0 M
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+EPSTEIN(2)  EH1 P - S T AY2 N
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+EQUABLE  EH1 - K W AH0 - B AH0 L
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+EQUALED  IY1 - K W AH0 L D
+EQUALING  IY1 - K W AH0 L - IH0 NG
+EQUALITY  IH0 - K W AA1 - L AH0 - T IY0
+EQUALIZATION  IY2 - K W AH0 - L IH0 - Z EY1 - SH AH0 N
+EQUALIZE  IY1 - K W AH0 - L AY2 Z
+EQUALIZED  IY1 - K W AH0 - L AY2 Z D
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+EQUILIBRIUM  IY2 - K W AH0 - L IH1 - B R IY0 - AH0 M
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+EQUIP  IH0 - K W IH1 P
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+EQUIPPED  IH0 - K W IH1 P T
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+EQUITABLE  EH1 - K W AH0 - T AH0 - B AH0 L
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+EQUITABLY  EH1 - K W IH0 - T AH0 - B L IY0
+EQUITAS  EH1 - K W AH0 - T AH0 S
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+EQUITEC  EH1 - K W AH0 - T EH2 K
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+EQUITY  EH1 - K W AH0 - T IY0
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+EQUIVALENCE  IH0 - K W IH1 - V AH0 - L AH0 N S
+EQUIVALENCY  IH0 - K W IH1 - V AH0 - L AH0 N - S IY0
+EQUIVALENT  IH0 - K W IH1 - V AH0 - L AH0 N T
+EQUIVALENTS  IH0 - K W IH1 - V AH0 - L AH0 N T S
+EQUIVOCAL  IH0 - K W IH1 - V AH0 - K AH0 L
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+EQUIVOCATION  IH0 - K W IH0 - V AH0 - K EY1 - SH AH0 N
+ER  ER0
+ERA  EH1 - R AH0
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+ERA(2)  IH1 - R AH0
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+ERANTHE  EH1 - R AH0 N TH
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+ERASES  IH0 - R EY1 - S IH0 Z
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+ERASMUS  IH0 - R AE1 Z - M AH0 S
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+ERBES  ER1 B Z
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+ERDAHL  ER1 - D AA0 L
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+ERDMANN  ER1 D - M AH0 N
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+ERECTED  IH0 - R EH1 K - T AH0 D
+ERECTED(2)  IH0 - R EH1 K - T IH0 D
+ERECTING  IH0 - R EH1 K - T IH0 NG
+ERECTION  IH0 - R EH1 K - SH AH0 N
+ERECTIONS  IH0 - R EH1 K - SH AH0 N Z
+ERECTOR  IH0 - R EH1 K - T ER0
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+ERENSEL  EH1 - R AH0 N - S EH0 L
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+ERGO  ER1 - G OW0
+ERGONOMIC  ER2 - G AH0 - N AA1 - M IH0 K
+ERGONOMICS  ER2 - G AH0 - N AA1 - M IH0 K S
+ERGOT  ER1 - G AH0 T
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+ERGOTISM  ER1 - G AH0 - T IH2 - Z AH0 M
+ERGOTS  ER1 - G AH0 T S
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+ERIC  EH1 - R IH0 K
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+ERICA  EH1 - R IH0 - K AH0
+ERICH  EH1 - R IH0 K
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+ERICSON  EH1 - R IH0 K - S AH0 N
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+ERIE  IH1 - R IY0
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+ERIKSSON  EH1 - R IH0 K - S AH0 N
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+ERITREAN  EH2 - R IH0 - T R IY1 - AH0 N
+ERITREAN(2)  EH2 - R IH0 - T R EY1 - AH0 N
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+ERITREANS(2)  EH2 - R IH0 - T R EY1 - AH0 N Z
+ERK  ER1 K
+ERKER  ER1 - K ER0
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+ERLACH  ER1 - L AA2 K
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+ERLANGER  EH1 R - L AE0 - NG ER0
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+ERLENE  ER1 - L IY0 N
+ERLER  ER1 - L ER0
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+ERLICK(2)  EH1 R - L IH0 K
+ERLINE  ER1 - L AY0 N
+ERLING  ER1 - L IH0 NG
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+ERMA  ER1 - M AH0
+ERMA'S  ER1 - M AH0 Z
+ERMAN  ER1 - M AH0 N
+ERMER  ER1 - M ER0
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+ERMINE  ER1 - M AH0 N
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+ERMINIE  ER1 - M IH0 - N IY0
+ERMIS  ER1 - M IH0 S
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+ERNESTO  ER0 - N EH1 - S T OW0
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+ERRA  EH1 - R AH0
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+ERRED  EH1 R D
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+EXCELAN  EH2 K - S EH1 - L AH0 N
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+EXCEPTION  IH0 K - S EH1 P - SH AH0 N
+EXCEPTIONAL  IH0 K - S EH1 P - SH AH0 - N AH0 L
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+EXCEPTIONS  IH0 K - S EH1 P - SH AH0 N Z
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+EXCHANGEABLE  IH0 K S - CH EY1 N - JH AH0 - B AH0 L
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+EXCLUSIVES  IH0 K - S K L UW1 - S IH0 V Z
+EXCLUSIVITY  EH2 K - S K L UW2 - S IH1 - V AH0 - T IY0
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+EXECUTIONERS  EH2 K - S AH0 - K Y UW1 - SH AH0 N - ER0 Z
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+EXPERIMENTAL(5)  IH0 K - S P EH2 - R IH0 - M EH1 - N AH0 L
+EXPERIMENTAL(6)  IH0 K - S P ER0 - M EH1 - N AH0 L
+EXPERIMENTALIST  IH0 K - S P EH2 - R AH0 - M EH1 N - T AH0 - L IH0 S T
+EXPERIMENTALIST(2)  IH0 K - S P EH2 - R AH0 - M EH1 - N AH0 - L IH0 S T
+EXPERIMENTALLY  IH0 K - S P EH0 - R AH0 - M EH1 N - T AH0 - L IY0
+EXPERIMENTALLY(2)  IH0 K - S P EH0 - R AH0 - M EH1 - N AH0 - L IY0
+EXPERIMENTATION  IH0 K - S P EH2 - R AH0 - M AH0 N - T EY1 - SH AH0 N
+EXPERIMENTED  IH0 K - S P EH1 - R AH0 - M AH0 N - T AH0 D
+EXPERIMENTER  IH0 K - S P EH1 - R AH0 - M EH2 N - T ER0
+EXPERIMENTERS  IH0 K - S P EH1 - R AH0 - M EH2 N - T ER0 Z
+EXPERIMENTING  EH0 K - S P EH1 - R AH0 - M EH2 N - T IH0 NG
+EXPERIMENTING(2)  EH0 K - S P EH1 - R AH0 - M EH0 - N IH0 NG
+EXPERIMENTS  IH0 K - S P EH1 - R AH0 - M AH0 N T S
+EXPERT  EH1 K - S P ER0 T
+EXPERT'S  EH1 K - S P ER0 T S
+EXPERTISE  EH2 K - S P ER0 - T IY1 Z
+EXPERTLY  EH1 K - S P ER0 T - L IY0
+EXPERTS  EH1 K - S P ER0 T S
+EXPERTS'  EH1 K - S P ER0 T S
+EXPIATE  EH1 K - S P IY0 - EY2 T
+EXPIRATION  EH2 K - S P ER0 - EY1 - SH AH0 N
+EXPIRATIONS  EH2 K - S P ER0 - EY1 - SH AH0 N Z
+EXPIRATORY  IH0 K - S P AY1 - R AH0 - T AO2 - R IY0
+EXPIRE  IH0 K - S P AY1 R
+EXPIRED  IH0 K - S P AY1 R D
+EXPIRES  IH0 K - S P AY1 - ER0 Z
+EXPIRING  IH0 K - S P AY1 - R IH0 NG
+EXPIRY  EH2 K - S P AY1 - R IY0
+EXPLAIN  IH0 K - S P L EY1 N
+EXPLAINABLE  IH0 K - S P L EY1 - N AH0 - B AH0 L
+EXPLAINED  IH0 K - S P L EY1 N D
+EXPLAINING  IH0 K - S P L EY1 - N IH0 NG
+EXPLAINS  IH0 K - S P L EY1 N Z
+EXPLANATION  EH2 K - S P L AH0 - N EY1 - SH AH0 N
+EXPLANATIONS  EH2 K - S P L AH0 - N EY1 - SH AH0 N Z
+EXPLANATORY  IH0 K - S P L AE1 - N AH0 - T AO2 - R IY0
+EXPLETIVE  EH1 K - S P L AH0 - T IH0 V
+EXPLETIVES  EH1 K - S P L AH0 - T IH0 V Z
+EXPLICATION  EH2 K - S P L AH0 - K EY1 - SH AH0 N
+EXPLICIT  IH0 K - S P L IH1 - S AH0 T
+EXPLICITLY  IH0 K - S P L IH1 - S AH0 T - L IY0
+EXPLODE  IH0 K - S P L OW1 D
+EXPLODED  IH0 K - S P L OW1 - D AH0 D
+EXPLODED(2)  IH0 K - S P L OW1 - D IH0 D
+EXPLODES  IH0 K - S P L OW1 D Z
+EXPLODING  IH0 K - S P L OW1 - D IH0 NG
+EXPLOIT  EH1 K - S P L OY2 T
+EXPLOIT(2)  EH2 K - S P L OY1 T
+EXPLOITATION  EH2 K - S P L OY2 - T EY1 - SH AH0 N
+EXPLOITATIVE  EH2 K - S P L OY1 - T AH0 - T IH0 V
+EXPLOITED  EH1 K - S P L OY2 - T AH0 D
+EXPLOITED(2)  IH0 K - S P L OY1 - T AH0 D
+EXPLOITING  EH1 K - S P L OY2 - T IH0 NG
+EXPLOITING(2)  IH0 K - S P L OY1 - T IH0 NG
+EXPLOITIVE  IH0 K - S P L OY1 - T IH0 V
+EXPLOITIVE(2)  EH0 K - S P L OY1 - T IH0 V
+EXPLOITS  EH1 K - S P L OY2 T S
+EXPLORATION  EH2 K - S P L ER0 - EY1 - SH AH0 N
+EXPLORATION'S  EH2 K - S P L ER0 - EY1 - SH AH0 N Z
+EXPLORATION'S(2)  EH1 K - S P L AO0 - R EY1 - SH AH0 N Z
+EXPLORATION(2)  EH2 K - S P L AO0 - R EY1 - SH AH0 N
+EXPLORATIONS  EH2 K - S P L ER0 - EY1 - SH AH0 N Z
+EXPLORATIONS(2)  EH1 K - S P L AO0 - R EY1 - SH AH0 N Z
+EXPLORATORY  IH0 K - S P L AO1 - R AH0 - T AO2 - R IY0
+EXPLORE  IH0 K - S P L AO1 R
+EXPLORED  IH0 K - S P L AO1 R D
+EXPLORER  IH0 K - S P L AO1 - R ER0
+EXPLORERS  IH0 K - S P L AO1 - R ER0 Z
+EXPLORES  IH0 K - S P L AO1 R Z
+EXPLORING  IH0 K - S P L AO1 - R IH0 NG
+EXPLOSION  IH0 K - S P L OW1 - ZH AH0 N
+EXPLOSIONS  IH0 K - S P L OW1 - ZH AH0 N Z
+EXPLOSIVE  IH0 K - S P L OW1 - S IH0 V
+EXPLOSIVELY  EH2 K - S P L OW1 - S IH0 V - L IY0
+EXPLOSIVES  IH0 K - S P L OW1 - S IH0 V Z
+EXPLOSIVOS  EH2 K - S P L AH0 - S IY1 - V OW0 S
+EXPO  EH1 K - S P OW0
+EXPONENT  EH1 K - S P OW2 - N AH0 N T
+EXPONENTIAL  EH2 K - S P OW0 - N EH1 N - CH AH0 L
+EXPONENTIAL(2)  EH2 K - S P OW0 - N EH1 N - SH AH0 L
+EXPONENTIALLY  EH2 K - S P OW0 - N EH1 N - SH AH0 - L IY0
+EXPONENTIALLY(2)  EH2 K - S P OW0 - N EH1 N - CH AH0 - L IY0
+EXPONENTS  IH0 K - S P OW1 - N AH0 N T S
+EXPORT  EH1 K - S P AO0 R T
+EXPORTABLE  EH0 K - S P AO1 R - T AH0 - B AH0 L
+EXPORTED  IH0 K - S P AO1 R - T AH0 D
+EXPORTER  IH0 K - S P AO1 R - T ER0
+EXPORTERS  IH0 K - S P AO1 R - T ER0 Z
+EXPORTERS'  EH2 K - S P AO1 R - T ER0 Z
+EXPORTING  IH0 K - S P AO1 R - T IH0 NG
+EXPORTS  EH1 K - S P AO0 R T S
+EXPOS  EH1 K - S P OW0 Z
+EXPOSE  IH0 K - S P OW1 Z
+EXPOSED  IH0 K - S P OW1 Z D
+EXPOSES  IH0 K - S P OW1 - Z IH0 Z
+EXPOSING  IH0 K - S P OW1 - Z IH0 NG
+EXPOSITION  EH2 K - S P AH0 - Z IH1 - SH AH0 N
+EXPOSITIONS  EH2 K - S P AH0 - Z IH1 - SH AH0 N Z
+EXPOSITO  EH0 K - S P AH0 - S AY1 - T OW0
+EXPOSITO(2)  EH0 K - S P AH0 - Z IY1 - T OW0
+EXPOSURE  IH0 K - S P OW1 - ZH ER0
+EXPOSURES  IH0 K - S P OW1 - ZH ER0 Z
+EXPOUND  IH0 K - S P AW1 N D
+EXPOUNDED  IH0 K - S P AW1 N - D AH0 D
+EXPOUNDING  IH0 K - S P AW1 N - D IH0 NG
+EXPOUNDS  IH0 K - S P AW1 N D Z
+EXPRESS  IH0 K - S P R EH1 S
+EXPRESS'  IH0 K - S P R EH1 S
+EXPRESS'S  IH0 K - S P R EH1 - S IH0 Z
+EXPRESSED  IH0 K - S P R EH1 S T
+EXPRESSES  IH0 K - S P R EH1 - S AH0 Z
+EXPRESSES(2)  IH0 K - S P R EH1 - S IH0 Z
+EXPRESSING  IH0 K - S P R EH1 - S IH0 NG
+EXPRESSION  IH0 K - S P R EH1 - SH AH0 N
+EXPRESSIONISM  IH0 K - S P R EH1 - SH AH0 N - IH2 - Z AH0 M
+EXPRESSIONIST  IH0 K - S P R EH1 - SH AH0 N - AH0 S T
+EXPRESSIONISTIC  IH0 K - S P R EH2 - SH AH0 - N IH1 - S T IH0 K
+EXPRESSIONLESS  IH0 K - S P R EH2 - SH AH0 N - L IH0 S
+EXPRESSIONS  IH0 K - S P R EH1 - SH AH0 N Z
+EXPRESSIVE  IH0 K - S P R EH1 - S IH0 V
+EXPRESSIVITY  EH2 K - S P R AH0 - S IH1 - V IH0 - T IY0
+EXPRESSLY  EH0 K - S P R EH1 S - L IY0
+EXPRESSO  IH0 K - S P EH1 - S OW0
+EXPRESSO(2)  EH0 K - S P EH1 - S OW0
+EXPRESSWAY  IH0 K - S P R EH1 S - W EY2
+EXPROPRIATE  EH0 K S - P R OW1 - P R IY0 - EY2 T
+EXPROPRIATED  EH0 K S - P R OW1 - P R IY0 - EY2 - T IH0 D
+EXPROPRIATION  EH2 K S - P R OW2 - P R IY0 - EY1 - SH AH0 N
+EXPROPRIATIONS  EH2 K S - P R OW2 - P R IY0 - EY1 - SH AH0 N Z
+EXPULSION  IH0 K - S P AH1 L - SH AH0 N
+EXPULSIONS  IH0 K - S P AH1 L - SH AH0 N Z
+EXPUNGE  IH0 K - S P AH1 N JH
+EXPUNGED  IH0 K - S P AH1 N JH D
+EXQUISITE  EH1 K - S K W AH0 - Z AH0 T
+EXQUISITELY  EH2 K - S K W IH1 - Z IH0 T - L IY0
+EXTANT  EH1 K - S T AH0 N T
+EXTEL  EH1 K - S T EH2 L
+EXTEND  IH0 K - S T EH1 N D
+EXTENDABLE  EH2 K - S T EH1 N - D AH0 - B AH0 L
+EXTENDED  IH0 K - S T EH1 N - D AH0 D
+EXTENDED(2)  IH0 K - S T EH1 N - D IH0 D
+EXTENDER  EH1 K - S T EH2 N - D ER0
+EXTENDERS  EH1 K - S T EH2 N - D ER0 Z
+EXTENDIBLE  EH2 K - S T EH1 N - D IH0 - B AH0 L
+EXTENDING  IH0 K - S T EH1 N - D IH0 NG
+EXTENDS  IH0 K - S T EH1 N D Z
+EXTENSION  IH0 K - S T EH1 N - SH AH0 N
+EXTENSIONS  IH0 K - S T EH1 N - SH AH0 N Z
+EXTENSIVE  IH0 K - S T EH1 N - S IH0 V
+EXTENSIVELY  IH0 K - S T EH1 N - S IH0 V - L IY0
+EXTENT  IH0 K - S T EH1 N T
+EXTENUATE  IH0 K - S T EH1 - N Y UW0 - EY2 T
+EXTENUATING  IH0 K - S T EH1 - N Y UW0 - EY2 - T IH0 NG
+EXTERIOR  IH0 K - S T IH1 - R IY0 - ER0
+EXTERIORS  EH0 K - S T IH1 - R IY0 - ER0 Z
+EXTERMINATE  IH0 K - S T ER1 - M AH0 - N EY2 T
+EXTERMINATED  IH0 K - S T ER1 - M AH0 - N EY2 - T IH0 D
+EXTERMINATES  IH0 K - S T ER1 - M AH0 - N EY2 T S
+EXTERMINATING  IH0 K - S T ER1 - M AH0 - N EY2 - T IH0 NG
+EXTERMINATION  IH0 K - S T ER2 - M AH0 - N EY1 - SH AH0 N
+EXTERMINATOR  IH0 K - S T ER1 - M AH0 - N EY2 - T ER0
+EXTERMINATORS  IH0 K - S T ER1 - M AH0 - N EY2 - T ER0 Z
+EXTERNAL  IH0 K - S T ER1 - N AH0 L
+EXTERNALLY  IH0 K - S T ER1 - N AH0 - L IY0
+EXTINCT  IH0 K - S T IH1 NG K T
+EXTINCTION  IH0 K - S T IH1 NG K - SH AH0 N
+EXTINCTION(2)  IH0 K - S T IH1 NG - SH AH0 N
+EXTINGUISH  IH0 K - S T IH1 NG - G W IH0 SH
+EXTINGUISHED  IH0 K - S T IH1 NG - G W IH0 SH T
+EXTINGUISHER  IH0 K - S T IH1 NG - G W IH0 - SH ER0
+EXTINGUISHERS  IH0 K - S T IH1 NG - G W IH0 - SH ER0 Z
+EXTINGUISHING  IH0 K - S T IH1 NG - G W IH0 - SH IH0 NG
+EXTINGUISHMENT  IH0 K - S T IH1 NG - G W IH0 SH - M AH0 N T
+EXTIRPATE  EH1 K - S T ER0 - P EY2 T
+EXTOL  IH0 K - S T OW1 L
+EXTOLLED  IH0 K - S T OW1 L D
+EXTOLLING  IH0 K - S T OW1 - L IH0 NG
+EXTOLS  IH0 K - S T OW1 L Z
+EXTON  EH1 K - S T AH0 N
+EXTORT  IH0 K - S T AO1 R T
+EXTORTED  IH0 K - S T AO1 R - T IH0 D
+EXTORTING  IH0 K - S T AO1 R - T IH0 NG
+EXTORTION  IH0 K - S T AO1 R - SH AH0 N
+EXTORTIONATE  IH0 K - S T AO1 R - SH AH0 N - AH0 T
+EXTORTIONATE(2)  IH0 K - S T AO1 R - SH AH0 N - EY2 T
+EXTRA  EH1 K - S T R AH0
+EXTRACELLULARLY  EH1 K - S T R AH0 - S EH2 - L Y AH0 - L ER0 - L IY0
+EXTRACT  IH0 K - S T R AE1 K T
+EXTRACT(2)  EH1 K - S T R AE2 K T
+EXTRACTED  IH0 K - S T R AE1 K - T AH0 D
+EXTRACTED(2)  IH0 K - S T R AE1 K - T IH0 D
+EXTRACTING  IH0 K - S T R AE1 K - T IH0 NG
+EXTRACTION  IH0 K - S T R AE1 K - SH AH0 N
+EXTRACTIONS  IH0 K - S T R AE1 K - SH AH0 N Z
+EXTRACTS  IH0 K - S T R AE1 K T S
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+EXTRACURRICULAR  EH2 K - S T R AH0 - K ER0 - IH1 - K Y AH0 - L ER0
+EXTRADITE  EH1 K - S T R AH0 - D AY2 T
+EXTRADITED  EH1 K - S T R AH0 - D AY2 - T IH0 D
+EXTRADITING  EH1 K - S T R AH0 - D AY2 - T IH0 NG
+EXTRADITION  EH2 K - S T R AH0 - D IH1 - SH AH0 N
+EXTRAGALACTIC  EH2 K - S T R AH0 - G AH0 - L AE1 K - T IH0 K
+EXTRALEGAL  EH2 K - S T R AH0 - L IY1 - G AH0 L
+EXTRAMARITAL  EH2 K - S T R AH0 - M EH1 - R AH0 - T AH0 L
+EXTRANEOUS  EH0 K - S T R EY1 - N IY0 - AH0 S
+EXTRAORDINAIRE  EH2 K - S T R AH0 - AO1 R - D IH0 - N EH2 R
+EXTRAORDINARILY  IH0 K - S T R AO2 R - D AH0 - N EH1 - R AH0 - L IY0
+EXTRAORDINARY  IH0 K - S T R AO1 R - D AH0 N - EH2 - R IY0
+EXTRAORDINARY(2)  EH2 K - S T R AH0 - AO1 R - D AH0 N - EH2 - R IY0
+EXTRAPOLATE  IH0 K - S T R AE1 - P AH0 - L EY2 T
+EXTRAPOLATED  IH0 K - S T R AE1 - P AH0 - L EY2 - T IH0 D
+EXTRAPOLATING  IH0 K - S T R AE1 - P AH0 - L EY2 - T IH0 NG
+EXTRAPOLATION  IH0 K - S T R AE2 - P AH0 - L EY1 - SH AH0 N
+EXTRAS  EH1 K - S T R AH0 Z
+EXTRASENSORY  EH2 K - S T R AH0 - S EH1 N - S ER0 - IY0
+EXTRATERRESTRIAL  EH2 K - S T R AH0 - T ER0 - EH1 S - T R IY0 - AH0 L
+EXTRATERRESTRIALS  EH2 K - S T R AH0 - T ER2 - EH1 S - T R IY0 - AH0 L Z
+EXTRATERRITORIAL  EH2 K - S T R AH0 - T EH2 - R IH0 - T AO1 - R IY0 - AH0 L
+EXTRATERRITORIALITY  EH2 K - S T R AH0 - T EH2 - R AH0 - T AO2 - R IY0 - AE1 - L AH0 - T IY0
+EXTRAVAGANCE  IH0 K - S T R AE1 - V AH0 - G AH0 N S
+EXTRAVAGANT  IH0 K - S T R AE1 - V AH0 - G AH0 N T
+EXTRAVAGANTLY  EH2 K - S T R AE1 - V AH0 - G AH0 N T - L IY0
+EXTRAVAGANZA  IH0 K - S T R AE2 - V AH0 - G AE1 N - Z AH0
+EXTRAVAGANZAS  IH0 K - S T R AE2 - V AH0 - G AE1 N - Z AH0 Z
+EXTREME  IH0 K - S T R IY1 M
+EXTREMELY  IH0 K - S T R IY1 M - L IY0
+EXTREMES  IH0 K - S T R IY1 M Z
+EXTREMISM  EH2 K - S T R EH1 - M IH0 - Z AH0 M
+EXTREMISM(2)  EH2 K - S T R IY1 - M IH2 - Z AH0 M
+EXTREMIST  IH0 K - S T R IY1 - M IH0 S T
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+EXTREMISTS(2)  IH0 K - S T R IY1 - M IH0 S T S
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+EXTRICATED  EH1 K - S T R IH0 - K EY2 - T IH0 D
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+EXTROVERTED  EH1 K - S T R AH0 - V ER2 - T IH0 D
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+EXTRUSION  IH0 K S - T R UW1 - ZH AH0 N
+EXUBERANCE  IH0 G - Z UW1 - B ER0 - AH0 N S
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+EYDE  EY1 D
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+EYE  AY1
+EYE'S  AY1 Z
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+EYEBROW  AY1 - B R AW2
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+FABRICATION  F AE2 - B R IH0 - K EY1 - SH AH0 N
+FABRICATIONS  F AE2 - B R IH0 - K EY1 - SH AH0 N Z
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+FIRSTHAND  F ER0 S T - HH AE1 N D
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+FIRSTHAND(3)  F ER1 S T - HH AE0 N D
+FIRSTHAND(4)  F ER1 S - HH AE0 N D
+FIRSTIER  F ER1 S T - Y ER0
+FIRSTIER(2)  F ER1 - S T IY0 - ER0
+FIRSTLY  F ER1 S T - L IY0
+FIRSTS  F ER1 S T S
+FIRSTS(2)  F ER1 S S
+FIRSTS(3)  F ER1 S
+FIRSTSOUTH  F ER1 S T - S AW2 TH
+FIRTH  F ER1 TH
+FIRZITE  F ER1 - Z AY2 T
+FIS  F IH1 S
+FISC  F IH1 S K
+FISCAL  F IH1 S - K AH0 L
+FISCALLY  F IH1 S - K AH0 - L IY0
+FISCH  F IH1 SH
+FISCHBACH  F IH1 SH - B AA2 K
+FISCHBACH'S  F IH1 SH - B AA2 K S
+FISCHBEIN  F IH1 SH - B AY2 N
+FISCHEL  F IH1 - SH AH0 L
+FISCHER  F IH1 - SH ER0
+FISCHER'S  F IH1 - SH ER0 Z
+FISCHETTI  F IY0 - S K EH1 - T IY0
+FISCHL  F IH1 S - K AH0 L
+FISCHLER  F IH1 - SH AH0 - L ER0
+FISCHLER(2)  F IH1 SH - L ER0
+FISCHMAN  F IH1 SH - M AH0 N
+FISCUS  F IH1 S - K AH0 S
+FISER  F AY1 - Z ER0
+FISERV  F IH1 - S ER0 V
+FISETTE  F IH0 - S EH1 T
+FISH  F IH1 SH
+FISH'S  F IH1 - SH IH0 Z
+FISHBACH  F IH1 SH - B AH2 K
+FISHBACK  F IH1 SH - B AE2 K
+FISHBAINE  F IH1 SH - B EY2 N
+FISHBAUGH  F IH1 SH - B AO2
+FISHBECK  F IH1 SH - B EH2 K
+FISHBEIN  F IH1 SH - B AY2 N
+FISHBOWL  F IH1 SH - B OW2 L
+FISHBURN  F IH1 SH - B ER2 N
+FISHBURNE  F IH1 SH - B ER0 N
+FISHEATER  F IH1 - SH IY2 - T ER0
+FISHEATERS  F IH1 - SH IY2 - T ER0 Z
+FISHED  F IH1 SH T
+FISHEL  F IH1 - SH AH0 L
+FISHELL  F IH1 - SH AH0 L
+FISHER  F IH1 - SH ER0
+FISHER'S  F IH1 - SH ER0 Z
+FISHERIES  F IH1 - SH ER0 - IY0 Z
+FISHERMAN  F IH1 - SH ER0 - M AE2 N
+FISHERMAN'S  F IH1 - SH ER0 - M AH0 N Z
+FISHERMAN(2)  F IH1 - SH ER0 - M AH0 N
+FISHERMEN  F IH1 - SH ER0 - M IH0 N
+FISHERS  F IH1 - SH ER0 Z
+FISHERY  F IH1 - SH ER0 - IY0
+FISHES  F IH1 - SH AH0 Z
+FISHES(2)  F IH1 - SH IH0 Z
+FISHING  F IH1 - SH IH0 NG
+FISHKILL  F IH1 SH - K IH2 L
+FISHKIN  F IH1 SH - K IH0 N
+FISHLOW  F IH1 SH - L OW2
+FISHMAN  F IH1 SH - M AE2 N
+FISHMAN(2)  F IH1 SH - M AH0 N
+FISHMONGER  F IH1 SH - M AA2 NG - G ER0
+FISHY  F IH1 - SH IY0
+FISK  F IH1 S K
+FISK'S  F IH1 S K S
+FISKE  F IH1 S K
+FISKE'S  F IH1 S K S
+FISLER  F IH1 - S AH0 - L ER0
+FISLER(2)  F IH1 S - L ER0
+FISONS  F AY1 - Z AH0 N Z
+FISS  F IH1 S
+FISSEL  F IH1 - S AH0 L
+FISSELL  F IH1 - S AH0 L
+FISSILE  F IH1 - S AH0 L
+FISSION  F IH1 - SH AH0 N
+FISSIONABLE  F IH1 - SH AH0 N - AH0 - B AH0 L
+FISSURE  F IH1 - SH ER0
+FISSURED  F IH1 - SH ER0 D
+FISSURES  F IH1 - SH ER0 Z
+FIST  F IH1 S T
+FISTED  F IH1 - S T IH0 D
+FISTER  F IH1 - S T ER0
+FISTFUL  F IH1 S T - F AH0 L
+FISTICUFF  F IH1 - S T IH0 - K AH2 F
+FISTICUFFS  F IH1 - S T IH0 - K AH2 F S
+FISTS  F IH1 S T S
+FIT  F IH1 T
+FITAK  F IH1 - T AE2 K
+FITCH  F IH1 CH
+FITCHBURG  F IH1 CH - B ER0 G
+FITCHETT  F IH1 - CH IH0 T
+FITE  F AY1 T
+FITES  F AY1 T S
+FITFUL  F IH1 T - F AH0 L
+FITFULLY  F IH1 T - F AH0 - L IY0
+FITHE  F IH1 TH
+FITHIAN  F IH1 - TH IY0 - AH0 N
+FITNESS  F IH1 T - N AH0 S
+FITS  F IH1 T S
+FITSWATER  F IH1 T - S W AO2 - T ER0
+FITT  F IH1 T
+FITTED  F IH1 - T AH0 D
+FITTED(2)  F IH1 - T IH0 D
+FITTER  F IH1 - T ER0
+FITTERER  F IH1 - T ER0 - ER0
+FITTEST  F IH1 - T AH0 S T
+FITTING  F IH1 - T IH0 NG
+FITTINGLY  F IH1 - T IH0 NG - L IY0
+FITTINGS  F IH1 - T IH0 NG Z
+FITTIPALDI  F IH0 - T IH0 - P AA1 L - D IY0
+FITTON  F IH1 - T AH0 N
+FITTRO  F IH1 - T R OW0
+FITTS  F IH1 T S
+FITZ  F IH1 T S
+FITZCO  F IH1 T - S K OW0
+FITZER  F IH1 T - S ER0
+FITZGERALD  F IH0 T - S JH EH1 - R AH0 L D
+FITZGERALD'S  F IH0 T - S JH EH1 - R AH0 L D Z
+FITZGIBBON  F IH2 T S - JH IH1 - B AH0 N
+FITZGIBBONS  F IH2 T S - JH IH1 - B AH0 N Z
+FITZHARRIS  F IH0 T S - HH AE1 - R IH0 S
+FITZHENRY  F IH0 T S - HH EH1 - N ER0 - IY0
+FITZHENRY(2)  F IH0 T S - HH EH1 N - R IY0
+FITZHUGH  F IH0 T S - HH Y UW1
+FITZMAURICE  F IH0 T S - M AO1 - R IH0 S
+FITZMORRIS  F IH0 T S - M AO1 - R IH0 S
+FITZNER  F IH1 T S - N ER0
+FITZPATRICK  F IH2 T - S P AE1 - T R IH0 K
+FITZROY  F IH1 T - S R OY2
+FITZSIMMONS  F IH0 T - S IH1 - M AH0 N Z
+FITZSIMONS  F IH0 T - S IH1 - M AH0 N Z
+FITZWATER  F IH1 T - S W AO2 - T ER0
+FITZWATER'S  F IH1 T S - W AO2 - T ER0 Z
+FITZWILLIAM  F IH0 T - S W IH1 - L Y AH0 M
+FIUMARA  F IY2 - UW0 - M AA1 - R AH0
+FIVE  F AY1 V
+FIVE'S  F AY1 V Z
+FIVEASH  F AY1 - V AE2 SH
+FIVECOAT  F AY1 V - K OW2 T
+FIVEFOLD  F AY1 V - F OW2 L D
+FIVES  F AY1 V Z
+FIX  F IH1 K S
+FIXABLE  F IH1 K - S AH0 - B AH0 L
+FIXATE  F IH1 K - S EY2 T
+FIXATED  F IH1 K - S EY2 - T IH0 D
+FIXATION  F IH0 K - S EY1 - SH AH0 N
+FIXATIVE  F IH1 K - S AH0 - T IH0 V
+FIXATIVES  F IH1 K - S AH0 - T IH0 V Z
+FIXED  F IH1 K S T
+FIXER  F IH1 K - S ER0
+FIXES  F IH1 K - S IH0 Z
+FIXING  F IH1 K - S IH0 NG
+FIXINGS  F IH1 K - S IH0 NG Z
+FIXLER  F IH1 K S - L ER0
+FIXTURE  F IH1 K S - CH ER0
+FIXTURES  F IH1 K S - CH ER0 Z
+FIZER  F AY1 - Z ER0
+FIZZ  F IH1 Z
+FIZZLE  F IH1 - Z AH0 L
+FIZZLED  F IH1 - Z AH0 L D
+FIZZLES  F IH1 - Z AH0 L Z
+FIZZLING  F IH1 - Z AH0 L - IH0 NG
+FIZZLING(2)  F IH1 Z - L IH0 NG
+FJELD  F Y EH1 L D
+FJELSTAD  F Y EH1 L - S T AH0 D
+FJORD  F Y AO1 R D
+FJORDS  F Y AO1 R D Z
+FLAB  F L AE1 B
+FLABBERGAST  F L AE1 - B ER0 - G AE2 S T
+FLABBERGASTED  F L AE1 - B ER0 - G AE2 - S T IH0 D
+FLABBY  F L AE1 - B IY0
+FLACCID  F L AE1 K - S IH0 D
+FLACCID(2)  F L AE1 - K IH0 D
+FLACH  F L AE1 CH
+FLACK  F L AE1 K
+FLAD  F L AE1 D
+FLAG  F L AE1 G
+FLAGELLA  F L AH0 - JH EH1 - L AH0
+FLAGELLATE  F L AE1 - JH AH0 - L EY2 T
+FLAGELLATED  F L AE1 - JH AH0 - L EY2 - T AH0 D
+FLAGELLUM  F L AH0 - JH EH1 - L AH0 M
+FLAGEOLET  F L AE2 - JH AH0 - L EH1 T
+FLAGG  F L AE1 G
+FLAGGED  F L AE1 G D
+FLAGGING  F L AE1 - G IH0 NG
+FLAGLER  F L AE1 G - L ER0
+FLAGPOLE  F L AE1 G - P OW2 L
+FLAGPOLES  F L AE1 G - P OW2 L Z
+FLAGRANT  F L EY1 - G R AH0 N T
+FLAGRANTLY  F L EY1 - G R AH0 N T - L IY0
+FLAGS  F L AE1 G Z
+FLAGSHIP  F L AE1 G - SH IH2 P
+FLAGSHIPS  F L AE1 G - SH IH2 P S
+FLAGSTAFF  F L AE1 G - S T AE2 F
+FLAGSTAR  F L AE1 G - S T AA2 R
+FLAGSTONE  F L AE1 G - S T OW2 N
+FLAHARTY  F L EH1 R - T IY0
+FLAHERTY  F L EH1 R - T IY0
+FLAHIVE  F L AE1 - HH IH0 V
+FLAIG  F L EY1 G
+FLAIL  F L EY1 L
+FLAILING  F L EY1 - L IH0 NG
+FLAIM  F L EY1 M
+FLAIR  F L EH1 R
+FLAK  F L AE1 K
+FLAKE  F L EY1 K
+FLAKES  F L EY1 K S
+FLAKING  F L EY1 - K IH0 NG
+FLAKY  F L EY1 - K IY0
+FLAM  F L AE1 M
+FLAMBOYANCE  F L AE0 M - B OY1 - AH0 N S
+FLAMBOYANT  F L AE0 M - B OY1 - AH0 N T
+FLAMBOYANTLY  F L AE0 M - B OY1 - AH0 N T - L IY0
+FLAME  F L EY1 M
+FLAMED  F L EY1 M D
+FLAMEMASTER  F L EY1 - M AE2 - S T ER0
+FLAMENCO  F L AH0 - M EH1 NG - K OW2
+FLAMER  F L EY1 - M ER0
+FLAMES  F L EY1 M Z
+FLAMING  F L EY1 - M IH0 NG
+FLAMINGO  F L AH0 - M IH1 NG - G OW0
+FLAMINGOS  F L AH0 - M IH1 NG - G OW0 Z
+FLAMINIAN  F L AH0 - M IH1 - N IY0 - AH0 N
+FLAMM  F L AE1 M
+FLAMMABILITY  F L AE2 - M AH0 - B IH1 - L IH0 - T IY0
+FLAMMABLE  F L AE1 - M AH0 - B AH0 L
+FLAMMANG  F L AE1 - M AH0 NG
+FLAMMER  F L AE1 - M ER0
+FLAMMIA  F L AE1 - M IY0 - AH0
+FLAMSON  F L AE1 M - S AH0 N
+FLAN  F L AE1 N
+FLANAGAN  F L AE1 - N AH0 - G AH0 N
+FLANAGIN  F L AE1 - N AH0 - G IH0 N
+FLANARY  F L AE1 - N ER0 - IY0
+FLANDERS  F L AE1 N - D ER0 Z
+FLANERY  F L EY1 - N ER0 - IY0
+FLANGE  F L AE1 N JH
+FLANGES  F L AE1 N - JH AH0 Z
+FLANIGAN  F L AE1 - N IH0 - G AH0 N
+FLANIGAN'S  F L AE1 - N IH0 - G AH0 N Z
+FLANK  F L AE1 NG K
+FLANKED  F L AE1 NG K T
+FLANKING  F L AE1 NG - K IH0 NG
+FLANKS  F L AE1 NG K S
+FLANN  F L AE1 N
+FLANNA  F L AE1 - N AH0
+FLANNAGAN  F L AE1 - N AH0 - G AH0 N
+FLANNEL  F L AE1 - N AH0 L
+FLANNELED  F L AE1 - N AH0 L D
+FLANNELS  F L AE1 - N AH0 L Z
+FLANNERY  F L AE1 - N ER0 - IY0
+FLANNIGAN  F L AE1 - N IH0 - G AH0 N
+FLANSBURG  F L AE1 N S - B ER0 G
+FLAP  F L AE1 P
+FLAPLIKE  F L AE1 P - L AY2 K
+FLAPPED  F L AE1 P T
+FLAPPER  F L AE1 - P ER0
+FLAPPERS  F L AE1 - P ER0 Z
+FLAPPING  F L AE1 - P IH0 NG
+FLAPS  F L AE1 P S
+FLARE  F L EH1 R
+FLARED  F L EH1 R D
+FLARES  F L EH1 R Z
+FLARING  F L EH1 - R IH0 NG
+FLASCH  F L AE1 SH
+FLASH  F L AE1 SH
+FLASH'S  F L AE1 - SH IH0 Z
+FLASHBACK  F L AE1 SH - B AE2 K
+FLASHBACKS  F L AE1 SH - B AE2 K S
+FLASHBULB  F L AE1 SH - B AH0 L B
+FLASHDANCE  F L AE1 SH - D AE2 N S
+FLASHED  F L AE1 SH T
+FLASHER  F L AE1 - SH ER0
+FLASHER'S  F L AE1 - SH ER0 Z
+FLASHERS  F L AE1 - SH ER0 Z
+FLASHES  F L AE1 - SH IH0 Z
+FLASHIER  F L AE1 - SH IY0 - ER0
+FLASHING  F L AE1 - SH IH0 NG
+FLASHLIGHT  F L AE1 SH - L AY2 T
+FLASHLIGHTS  F L AE1 SH - L AY2 T S
+FLASHPOINT  F L AE1 SH - P OY2 N T
+FLASHPOINTS  F L AE1 SH - P OY2 N T S
+FLASHY  F L AE1 - SH IY0
+FLASK  F L AE1 S K
+FLASKS  F L AE1 S K S
+FLAT  F L AE1 T
+FLATAU  F L AE1 - T AW0
+FLATBED  F L AE1 T - B EH2 D
+FLATBOAT  F L AE1 T - B OW2 T
+FLATBUSH  F L AE1 T - B UH2 SH
+FLATEN  F L AE1 - T AH0 N
+FLATER  F L EY1 - T ER0
+FLATFISH  F L AE1 T - F IH2 SH
+FLATFISHES  F L AE1 T - F IH2 - SH IH0 Z
+FLATH  F L AE1 TH
+FLATHEAD  F L AE1 T - HH EH2 D
+FLATHEADS  F L AE1 T - HH EH2 D Z
+FLATHERS  F L AE1 - DH ER0 Z
+FLATLAND  F L AE1 T - L AE2 N D
+FLATLANDS  F L AE1 T - L AE0 N D Z
+FLATLEY  F L AE1 T - L IY0
+FLATLY  F L AE1 T - L IY0
+FLATNESS  F L AE1 T - N AH0 S
+FLATS  F L AE1 T S
+FLATT  F L AE1 T
+FLATTEN  F L AE1 - T AH0 N
+FLATTENED  F L AE1 - T AH0 N D
+FLATTENING  F L AE1 - T AH0 - N IH0 NG
+FLATTENING(2)  F L AE1 T - N IH0 NG
+FLATTER  F L AE1 - T ER0
+FLATTERED  F L AE1 - T ER0 D
+FLATTERING  F L AE1 - T ER0 - IH0 NG
+FLATTERY  F L AE1 - T ER0 - IY0
+FLATTISH  F L AE1 - T IH0 SH
+FLATULENT  F L AE1 - CH AH0 - L AH0 N T
+FLATWARE  F L AE1 T - W EH2 R
+FLATWORM  F L AE1 T - W ER0 M
+FLAUBERT  F L AW1 - B ER0 T
+FLAUGH  F L AO1
+FLAUGHER  F L AO1 - ER0
+FLAUM  F L AO1 M
+FLAUNT  F L AO1 N T
+FLAUNTED  F L AO1 N - T IH0 D
+FLAUNTING  F L AO1 N - T IH0 NG
+FLAUNTS  F L AO1 N T S
+FLAVELL  F L AE1 - V AH0 L
+FLAVIA  F L AE1 - V IY0 - AH0
+FLAVIER  F L EY1 - V Y ER0
+FLAVIER(2)  F L AE1 - V Y ER0
+FLAVIN  F L EY1 - V IH0 N
+FLAVIO  F L AA1 - V IY0 - OW0
+FLAVIUS  F L EY1 - V IY0 - IH0 S
+FLAVOR  F L EY1 - V ER0
+FLAVORED  F L EY1 - V ER0 D
+FLAVORFUL  F L EY1 - V ER0 - F AH0 L
+FLAVORING  F L EY1 - V ER0 - IH0 NG
+FLAVORINGS  F L EY1 - V ER0 - IH0 NG Z
+FLAVORIST  F L EY1 - V ER0 - IH0 S T
+FLAVORISTS  F L EY1 - V ER0 - IH0 S T S
+FLAVORISTS(2)  F L EY1 - V ER0 - IH0 S S
+FLAVORISTS(3)  F L EY1 - V ER0 - IH0 S
+FLAVORS  F L EY1 - V ER0 Z
+FLAW  F L AO1
+FLAWED  F L AO1 D
+FLAWLESS  F L AO1 - L AH0 S
+FLAWLESSLY  F L AO1 - L AH0 S - L IY0
+FLAWN  F L AO1 N
+FLAWS  F L AO1 Z
+FLAX  F L AE1 K S
+FLAXMAN  F L AE1 K S - M AH0 N
+FLAXSEED  F L AE1 K - S IY2 D
+FLAY  F L EY1
+FLAYED  F L EY1 D
+FLEA  F L IY1
+FLEAGLE  F L IY1 - G AH0 L
+FLEAS  F L IY1 Z
+FLECK  F L EH1 K
+FLECKENSTEIN  F L EH1 - K AH0 N - S T AY2 N
+FLECKENSTEIN(2)  F L EH1 - K AH0 N - S T IY2 N
+FLECKS  F L EH1 K S
+FLED  F L EH1 D
+FLEDERMAUS  F L EH1 - D ER0 - M AW0 S
+FLEDGE  F L EH1 JH
+FLEDGED  F L EH1 JH D
+FLEDGING  F L EH1 - JH IH0 NG
+FLEDGLING  F L EH1 JH - L IH0 NG
+FLEE  F L IY1
+FLEECE  F L IY1 S
+FLEECED  F L IY1 S T
+FLEECY  F L IY1 - S IY0
+FLEEGER  F L IY1 - G ER0
+FLEEING  F L IY1 - IH0 NG
+FLEEK  F L IY1 K
+FLEEMAN  F L IY1 - M AH0 N
+FLEENER  F L IY1 - N ER0
+FLEENOR  F L IY1 - N ER0
+FLEER  F L IH1 R
+FLEES  F L IY1 Z
+FLEET  F L IY1 T
+FLEET'S  F L IY1 T S
+FLEETING  F L IY1 - T IH0 NG
+FLEETINGLY  F L IY1 - T IH0 NG - L IY0
+FLEETS  F L IY1 T S
+FLEETWOOD  F L IY1 T - W UH2 D
+FLEGAL  F L IY1 - G AH0 L
+FLEGEL  F L EH1 - G AH0 L
+FLEHARTY  F L EH1 - HH AA0 R - T IY0
+FLEIG  F L IY1 G
+FLEISCH  F L AY1 SH
+FLEISCHAUER  F L AY1 - SH AW0 - ER0
+FLEISCHER  F L AY1 - SH ER0
+FLEISCHHACKER  F L AY1 SH - HH AH0 - K ER0
+FLEISCHMAN  F L AY1 SH - M AH0 N
+FLEISCHMANN  F L AY1 SH - M AH0 N
+FLEISHER  F L AY1 - SH ER0
+FLEISHMAN  F L AY1 SH - M AH0 N
+FLEISS  F L AY1 SH
+FLEISS'  F L AY1 SH
+FLEISS'(2)  F L AY1 S
+FLEISS'S  F L AY1 - SH IH0 Z
+FLEISS'S(2)  F L AY1 - S IH0 Z
+FLEISS(2)  F L AY1 S
+FLEISSNER  F L AY1 S - N ER0
+FLEITAS  F L AY1 - T AH0 S
+FLEMING  F L EH1 - M IH0 NG
+FLEMING'S  F L EH1 - M IH0 NG Z
+FLEMINGS  F L EH1 - M IH0 NG Z
+FLEMINGTON  F L EH1 - M IH0 NG - T AH0 N
+FLEMISH  F L EH1 - M IH0 SH
+FLEMISTER  F L EH1 - M IH0 - S T ER0
+FLEMMER  F L EH1 - M ER0
+FLEMMING  F L EH1 - M IH0 NG
+FLEMONS  F L EH1 - M AH0 N Z
+FLENER  F L IY1 - N ER0
+FLENNER  F L EH1 - N ER0
+FLENNIKEN  F L EH1 - N IH0 - K AH0 N
+FLESCH  F L EH1 SH
+FLESH  F L EH1 SH
+FLESHED  F L EH1 SH T
+FLESHER  F L EH1 - SH ER0
+FLESHMAN  F L EH1 SH - M AH0 N
+FLESHY  F L EH1 - SH IY0
+FLESNER  F L EH1 S - N ER0
+FLESSNER  F L EH1 S - N ER0
+FLETA  F L IY1 - T AH0
+FLETCHALL  F L EH1 - CH AH0 L
+FLETCHER  F L EH1 - CH ER0
+FLETT  F L EH1 T
+FLEUR  F L ER1
+FLEURETTE  F L ER0 - EH1 T
+FLEURI  F L ER1 - R IY1
+FLEURI(2)  F L UH1 - R IY1
+FLEURY  F L UH1 - R IY0
+FLEW  F L UW1
+FLEWELLEN  F L UW2 - EH1 - L AH0 N
+FLEWELLING  F L UW2 - EH1 - L IH0 NG
+FLEX  F L EH1 K S
+FLEXED  F L EH1 K S T
+FLEXER  F L EH1 K - S ER0
+FLEXES  F L EH1 K - S IH0 Z
+FLEXI  F L EH1 K - S IY0
+FLEXIBILITY  F L EH2 K - S AH0 - B IH1 - L AH0 - T IY0
+FLEXIBLE  F L EH1 K - S AH0 - B AH0 L
+FLEXIBLY  F L EH1 K - S AH0 - B L IY0
+FLEXING  F L EH1 K - S IH0 NG
+FLEXION  F L EH1 K - SH AH0 N
+FLEXNOR  F L EH1 K S - N AO0 R
+FLEXTIME  F L EH1 K - S T AY2 M
+FLEXTRONIC  F L EH2 K - S T R AA1 - N IH0 K
+FLEXTRONICS  F L EH2 K - S T R AA1 - N IH0 K S
+FLICEK  F L IH1 - CH EH0 K
+FLICK  F L IH1 K
+FLICKER  F L IH1 - K ER0
+FLICKERED  F L IH1 - K ER0 D
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+FUNDRAISER'S  F AH1 N - D R EY2 - Z ER0 Z
+FUNDRAISERS  F AH1 N - D R EY2 - Z ER0 Z
+FUNDRAISING  F AH1 N - D R EY2 - S IH0 NG
+FUNDS  F AH1 N D Z
+FUNDS'  F AH1 N D Z
+FUNERAL  F Y UW1 - N ER0 - AH0 L
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+FUNERARY  F Y UW1 - N ER0 - EH2 - R IY0
+FUNES  F Y UW1 N Z
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+FUNGI  F AH1 N - JH AY0
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+FUNNELS  F AH1 - N AH0 L Z
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+FUNNIEST  F AH1 - N IY0 - AH0 S T
+FUNNINESS  F AH1 - N IY0 - N AH0 S
+FUNNY  F AH1 - N IY0
+FUNS  F AH1 N Z
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+FURIA  F Y UH1 - R IY0 - AH0
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+FURIOUS  F Y UH1 - R IY0 - AH0 S
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+FURNARI  F UH0 R - N AA1 - R IY0
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+FURNISS  F ER0 - N IH1 S
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+FURNO  F UH1 R - N OW0
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+FURRER  F ER1 - ER0
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+FURRIERS  F ER1 - IY0 - ER0 Z
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+FURROWED  F ER1 - OW0 D
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+FURTADO  F UH0 R - T AA1 - D OW0
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+FURTH  F ER1 TH
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+FUSE  F Y UW1 Z
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+FUSELIER  F Y UW1 S - L IY0 - ER0
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+FUSING  F Y UW1 - Z IH0 NG
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+FUTURES'  F Y UW1 - CH ER0 Z
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+FUTURIST  F Y UW1 - CH ER0 - IH0 S T
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+FUZES  F Y UW1 - Z IH0 Z
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+G  JH IY1
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+GA  G AA1
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+GAAL  G AA1 L
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+GABALDON  G AA0 - B AA0 L - D AO1 N
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+GABRIELLI  G AA0 - B R IY0 - EH1 - L IY0
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+GAGLIARDI(2)  G AE0 G - L IY0 - AA1 R - D IY0
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+GAGNON  G AE1 - N Y AH0 N
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+GAINING  G EY1 - N IH0 NG
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+GAITSKILL  G EY1 T - S K IH1 L
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+GALATI  G AA0 - L AA1 - T IY0
+GALAVIZ  G AE1 - L AH0 - V IH0 Z
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+GALICIA  G AH0 - L IH1 - SH IY0 - AH0
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+GALILEO(2)  G AE2 - L AH0 - L EY1 - OW0
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+GALIOTO  G AA0 - L IY0 - OW1 - T OW0
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+GALLAUDET  G AE1 - L AH0 - D EH0 T
+GALLAWAY  G AO1 L - AH0 - W EY2
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+IMMIGRANTS  IH1 - M AH0 - G R AH0 N T S
+IMMIGRANTS'  IH1 - M IH0 - G R AH0 N T S
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+IMMUNOTHERAPY  IH2 - M Y UW0 - N OW0 - TH EH1 - R AH0 - P IY0
+IMMUTABLE  IH0 - M Y UW1 - T AH0 - B AH0 L
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+IMOGENE  IH1 - M AH0 - JH IY2 N
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+IMP  IH1 M P
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+IMPREGNATION  IH0 M - P R EH1 G - N EY1 - SH AH0 N
+IMPRESARIO  IH2 M - P R IH0 - S AA1 - R IY0 - OW2
+IMPRESS  IH0 M - P R EH1 S
+IMPRESS(2)  IH1 M - P R EH2 S
+IMPRESSED  IH0 M - P R EH1 S T
+IMPRESSES  IH0 M - P R EH1 - S IH0 Z
+IMPRESSING  IH0 M - P R EH1 - S IH0 NG
+IMPRESSION  IH0 M - P R EH1 - SH AH0 N
+IMPRESSIONABLE  IH0 M - P R EH1 - SH AH0 N - AH0 - B AH0 L
+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(2)  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
+IMPRESSIONISTS(2)  IH0 M - P R EH1 - SH AH0 N - IH0 S S
+IMPRESSIONISTS(3)  IH0 M - P R EH1 - SH AH0 N - IH0 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
+IMPRESSMENT  IH0 M - P R EH1 S - M AH0 N 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(2)  IH1 M - P R IH0 N T
+IMPRINTED  IH0 M - P R IH1 N - T IH0 D
+IMPRINTED(2)  IH0 M - P R IH1 - N IH0 D
+IMPRINTING  IH0 M - P R IH1 N - T IH0 NG
+IMPRINTING(2)  IH0 M - P R IH1 - N IH0 NG
+IMPRINTS  IH0 M - P R IH1 N T S
+IMPRISON  IH0 M - P R IH1 - Z AH0 N
+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
+IMPROBABLE  IH0 M - P R AA1 - B AH0 - B AH0 L
+IMPROBABLY  IH0 M - P R AA1 - B AH0 - B L IY0
+IMPROMPTU  IH0 M - P R AA1 M P - T UW0
+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  IH1 M - P R AA2 V
+IMPROVE  IH0 M - P R UW1 V
+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
+IMPROVES  IH0 M - P R UW1 V Z
+IMPROVING  IH0 M - P R UW1 - V IH0 NG
+IMPROVISATION  IH2 M - P R AA0 - V IH0 - Z EY1 - SH AH0 N
+IMPROVISATIONAL  IH2 M - P R AA0 - V IH0 - Z EY1 - SH AH0 - N AH0 L
+IMPROVISATIONS  IH2 M - P R AA0 - V IH0 - Z EY1 - SH AH0 N Z
+IMPROVISE  IH1 M - P R AH0 - V AY2 Z
+IMPROVISE(2)  IH2 M - P R AH0 - V AY1 Z
+IMPROVISED  IH1 M - P R AH0 - V AY2 Z D
+IMPROVISING  IH1 M - P R AH0 - V AY2 - Z IH0 NG
+IMPRUDENCE  IH0 M - P R UW1 - D AH0 N S
+IMPRUDENT  IH0 M - P R UW1 - D AH0 N T
+IMPRUDENTLY  IH0 M - P R UW1 - D AH0 N T - L IY0
+IMPSON  IH1 M P - S AH0 N
+IMPUGN  IH0 M - P Y UW1 N
+IMPUGNED  IH0 M - P Y UW1 N D
+IMPUGNING  IH0 M - P Y UW1 - N IH0 NG
+IMPULSE  IH1 M - P AH0 L S
+IMPULSE(2)  IH0 M - P AH1 L S
+IMPULSES  IH1 M - P AH0 L - S IH0 Z
+IMPULSES(2)  IH0 M - P AH1 L - S IH0 Z
+IMPULSIVE  IH0 M - P AH1 L - S IH0 V
+IMPULSIVELY  IH0 M - P AH1 L - S IH0 V - L IY0
+IMPUNITY  IH0 M - P Y UW1 - N IH0 - T IY0
+IMPURE  IH0 M - P Y UH1 R
+IMPURITIES  IH0 M - P Y UH1 - R AH0 - T IY0 Z
+IMPURITY  IH0 M - P Y UH1 - R AH0 - T IY0
+IMPUTATION  IH2 M - P Y AH0 - T EY1 - SH AH0 N
+IMPUTE  IH0 M - P Y UW1 T
+IMPUTED  IH0 M - P Y UW1 - T IH0 D
+IMRE  IH1 - M R AH0
+IMREG  IH2 M - R EH1 G
+IMREG'S  IH2 M - R EH1 G Z
+IMRIE  IH1 - M ER0 - IY0
+IMUS  AY1 - M AH0 S
+IN  IH0 N
+IN(2)  IH1 N
+IN.  IH1 N
+IN.(2)  IH1 N CH
+INA  IY1 - N AH0
+INABILITY  IH2 N - AH0 - B IH1 - L IH0 - T IY0
+INABINET  IH0 N - AH0 - B IH1 - N IH0 T
+INACCESSIBILITY  IH2 N - AH0 K - S EH2 - S AH0 - B IH1 - L AH0 - T IY0
+INACCESSIBLE  IH2 N - AH0 K - S EH1 - S AH0 - B AH0 L
+INACCURACIES  IH0 N - AE1 - K Y ER0 - AE2 - S IY0 Z
+INACCURACY  IH0 N - AE1 - K Y ER0 - AH0 - S IY0
+INACCURATE  IH0 N - AE1 - K Y ER0 - AH0 T
+INACCURATELY  IH0 N - AE1 - K Y ER0 - AH0 T - L IY0
+INACOM  IH1 N - AH0 - K AA2 M
+INACOM(2)  AY1 - N AH0 - K AA2 M
+INACOMP  AY1 - N AH0 - K AA2 M P
+INACOMP(2)  IH1 N - AH0 - K AA2 M P
+INACTION  IH0 N - AE1 K - SH AH0 N
+INACTIVATE  IH0 N - AE1 K - T IH0 - V EY2 T
+INACTIVATED  IH0 N - AE1 K - T IH0 - V EY2 - T IH0 D
+INACTIVATION  IH0 N - AE2 K - T IH0 - V EY1 - SH AH0 N
+INACTIVE  IH0 N - AE1 K - T IH0 V
+INACTIVITY  IH0 N - AE0 K - T IH1 - V IH0 - T IY0
+INADEQUACIES  IH0 N - AE1 - D AH0 - K W AH0 - S IY0 Z
+INADEQUACY  IH0 N - AE1 - D IH0 - K W AH0 - S IY0
+INADEQUATE  IH0 N - AE1 - D AH0 - K W AH0 T
+INADEQUATE(2)  IH0 N - AE1 - D AH0 - K W EY2 T
+INADEQUATELY  IH0 N - AE1 - D AH0 - K W AH0 T - L IY0
+INADMISSIBLE  IH0 N - AH0 D - M IH1 - S AH0 - B AH0 L
+INADMISSIBLE(2)  IH0 N - AE0 D - M IH1 - S AH0 - B AH0 L
+INADVERTENCE  IH2 N - AH0 D - V ER1 - T AH0 N S
+INADVERTENCE(2)  IH2 N - AE0 D - V ER1 - T AH0 N S
+INADVERTENT  IH2 N - AH0 D - V ER1 - T AH0 N T
+INADVERTENT(2)  IH2 N - AE0 D - V ER1 - T AH0 N T
+INADVERTENTLY  IH2 N - AH0 D - V ER1 - T AH0 N T - L IY0
+INADVERTENTLY(2)  IH2 N - AE0 D - V ER1 - T AH0 N T - L IY0
+INADVISABLE  IH0 N - AH0 D - V AY1 - Z AH0 - B AH0 L
+INADVISABLE(2)  IH0 N - AE0 D - V AY1 - Z AH0 - B AH0 L
+INAEZ  IH2 N - AE1 Z
+INAEZ(2)  IH2 - N EY1 Z
+INALIENABLE  IH0 N - EY1 - L Y AH0 - N AH0 - B AH0 L
+INAMURA  IH2 - N AH0 - M UH1 - R AH0
+INANE  IH0 N - EY1 N
+INANIMATE  IH0 N - AE1 - N AH0 - M AH0 T
+INAPPLICABLE  IH0 N - AE1 - P L IH0 - K AH0 - B AH0 L
+INAPPROPRIATE  IH2 N - AH0 - P R OW1 - P R IY0 - IH0 T
+INAPPROPRIATELY  IH0 N - AH0 - P R AA1 - P R IY0 - AH0 T - L IY0
+INARTICULATE  IH0 N - AA0 R - T IH1 - K Y AH0 - L AH0 T
+INASMUCH  IH0 N - AE1 S - M AH0 K
+INATTENTION  IH0 N - AH0 - T EH1 N - CH AH0 N
+INATTENTIVE  IH2 N - AH0 - T EH1 N - T IH0 V
+INAUDIBLE  IH0 N - AO1 - D AH0 - B AH0 L
+INAUGURAL  IH0 - N AO1 - G ER0 - AH0 L
+INAUGURAL(2)  IH0 - N AO1 - G Y ER0 - AH0 L
+INAUGURATE  IH0 - N AO1 - G Y ER0 - IH0 T
+INAUGURATE(2)  IH0 - N AO1 - G Y ER0 - EY0 T
+INAUGURATED  IH0 - N AO1 - G ER0 - EY2 - T IH0 D
+INAUGURATED(2)  IH0 - N AO1 - G Y ER0 - EY2 - T IH0 D
+INAUGURATES  IH0 - N AO1 - G Y ER0 - IH0 T S
+INAUGURATING  IH0 - N AO1 - G Y ER0 - EY2 - T IH0 NG
+INAUGURATION  IH0 - N AO2 - G Y ER0 - EY1 - SH AH0 N
+INAUGURATIONS  IH0 - N AO2 - G Y ER0 - EY1 - SH AH0 N Z
+INAUSPICIOUS  IH0 N - AW2 - S P IH1 - SH IH0 S
+INBIO  IH0 N - B AY1 - OW0
+INBOARD  IH1 N - B AO2 R D
+INBODEN  IH1 N - B OW0 - D AH0 N
+INBODY  IH1 N - B AA0 - D IY0
+INBORN  IH1 N - B AO2 R N
+INBOUND  IH0 N - B AW1 N D
+INBOUND(2)  IH1 N - B AW0 N D
+INBRED  IH1 N - B R EH2 D
+INBREED  IH1 N - B R IY2 D
+INBREEDING  IH2 N - B R IY1 - D IH0 NG
+INC  IH1 NG K
+INC.  IH1 NG K
+INC.'S  IH1 NG K S
+INC.(2)  IH0 NG - K AO1 R - P AO0 - R EY0 - T AH0 D
+INCA  IH1 NG - K AH0
+INCALCULABLE  IH0 N - K AE1 L - K Y AH0 - L AH0 - B AH0 L
+INCANDESCENT  IH2 N - K AH0 N - D EH1 - S AH0 N T
+INCANT  IH0 N - K AE1 N T
+INCANTATION  IH0 N - K AE1 N - T EY1 - SH AH0 N
+INCANTATORY  IH0 N - K AE1 N - T AH0 - T AO2 - R IY0
+INCAPABLE  IH0 N - K EY1 - P AH0 - B AH0 L
+INCAPACITATE  IH0 N - K AH0 - P AE1 - S IH0 - T EY2 T
+INCAPACITATED  IH0 N - K AH0 - P AE1 - S IH0 - T EY2 - T IH0 D
+INCAPACITATING  IH0 N - K AH0 - P AE1 - S IH0 - T EY2 - T IH0 NG
+INCAPACITATION  IH0 N - K AH0 - P AE2 - S IH0 - T EY1 - SH AH0 N
+INCAPACITY  IH0 N - K AH0 - P AE1 - S AH0 - T IY0
+INCARCERATE  IH0 N - K AA1 R - S ER0 - EY2 T
+INCARCERATED  IH0 N - K AA1 R - S ER0 - EY2 - T IH0 D
+INCARCERATING  IH0 N - K AA1 R - S ER0 - EY2 - T IH0 NG
+INCARCERATION  IH0 N - K AA2 R - S ER0 - EY1 - SH AH0 N
+INCARNATE  IH0 N - K AA1 R - N AH0 T
+INCARNATE(2)  IH0 N - K AA1 R - N EY2 T
+INCARNATION  IH0 N - K AA1 R - N EY1 - SH AH0 N
+INCARNATIONS  IH0 N - K AA0 R - N EY1 - SH AH0 N Z
+INCAS  IH1 NG - K AH0 Z
+INCASE  IH0 N - K EY1 S
+INCATA  IH0 NG - K AA1 - T AH0
+INCATA'S  IH0 NG - K AA1 - T AH0 Z
+INCE  IH1 N S
+INCENDIARY  IH0 N - S EH1 N - D IY0 - EH0 - R IY0
+INCENSE  IH0 N - S EH1 N S
+INCENSE(2)  IH1 N - S EH2 N S
+INCENSED  IH1 N - S EH2 N S T
+INCENTIVE  IH0 N - S EH1 N - T IH0 V
+INCENTIVE(2)  IH0 N - S EH1 - N IH0 V
+INCENTIVES  IH0 N - S EH1 N - T IH0 V Z
+INCENTIVES(2)  IH0 N - S IH1 - N IH0 V Z
+INCEPTION  IH0 N - S EH1 P - SH AH0 N
+INCESSANT  IH0 N - S EH1 - S AH0 N T
+INCESSANTLY  IH0 N - S EH1 - S AH0 N T - L IY0
+INCEST  IH1 N - S EH2 S T
+INCESTUOUS  IH0 N - S EH1 - S CH W AH0 S
+INCH  IH1 N CH
+INCHCAPE  IH1 N - CH K EY2 P
+INCHED  IH1 N CH T
+INCHES  IH1 N - CH AH0 Z
+INCHES'  IH1 N - CH AH0 Z
+INCHES'(2)  IH1 N - CH IH0 Z
+INCHES(2)  IH1 N - CH IH0 Z
+INCHING  IH1 N - CH IH0 NG
+INCHON  IH1 N - CH AH0 N
+INCHON(2)  IH1 N - CH AA0 N
+INCIDENCE  IH1 N - S AH0 - D AH0 N S
+INCIDENCE(2)  IH1 N - S IH0 - D AH0 N S
+INCIDENCES  IH1 N - S AH0 - D AH0 N - S IH0 Z
+INCIDENT  IH1 N - S AH0 - D AH0 N T
+INCIDENTAL  IH2 N - S IH0 - D EH1 N - T AH0 L
+INCIDENTALLY  IH2 N - S IH0 - D EH1 N - T AH0 - L IY0
+INCIDENTALLY(2)  IH2 N - S IH0 - D EH1 N T - L IY0
+INCIDENTALS  IH2 N - S IH0 - D EH1 N - T AH0 L Z
+INCIDENTS  IH1 N - S AH0 - D AH0 N T S
+INCINERATE  IH0 N - S IH1 - N ER0 - EY2 T
+INCINERATED  IH0 N - S IH1 - N ER0 - EY2 - T IH0 D
+INCINERATING  IH0 N - S IH1 - N ER0 - EY2 - T IH0 NG
+INCINERATION  IH0 N - S IH1 - N ER0 - EY2 - SH AH0 N
+INCINERATOR  IH0 N - S IH1 - N ER0 - EY2 - T ER0
+INCINERATORS  IH0 N - S IH1 - N ER0 - EY2 - T ER0 Z
+INCIPIENT  IH0 N - S IH1 - P IY0 - AH0 N T
+INCIRLIK  IH0 N - S ER1 - L IH0 K
+INCISE  IH0 N - S AY1 Z
+INCISED  IH0 N - S AY1 Z D
+INCISION  IH0 N - S IH1 - ZH AH0 N
+INCISIONS  IH0 N - S IH1 - ZH AH0 N Z
+INCISIVE  IH0 N - S AY1 - S IH0 V
+INCISOR  IH0 N - S AY1 - Z ER0
+INCISORS  IH0 N - S AY1 - Z ER0 Z
+INCITE  IH0 N - S AY1 T
+INCITED  IH0 N - S AY1 - T IH0 D
+INCITEMENT  IH0 N - S AY1 T - M AH0 N T
+INCITEMENTS  IH0 N - S AY1 T - M AH0 N T S
+INCITES  IH0 N - S AY1 T S
+INCITING  IH0 N - S AY1 - T IH0 NG
+INCIVILITY  IH0 N - S IH0 - V IH1 - L IH0 - T IY0
+INCLEMENT  IH0 N - K L EH1 - M AH0 N T
+INCLEMENT(2)  IH1 N - K L IH0 - M AH0 N T
+INCLINATION  IH2 N - K L AH0 - N EY1 - SH AH0 N
+INCLINATIONS  IH2 N - K L AH0 - N EY1 - SH AH0 N Z
+INCLINE  IH0 N - K L AY1 N
+INCLINE(2)  IH1 N - K L AY0 N
+INCLINED  IH0 N - K L AY1 N D
+INCLINES  IH0 N - K L AY1 N Z
+INCLINES(2)  IH1 N - K L AY0 N Z
+INCLOSURE  IH0 N - K L OW1 - ZH ER0
+INCLUDE  IH0 N - K L UW1 D
+INCLUDED  IH0 N - K L UW1 - D AH0 D
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+INCLUDES  IH0 N - K L UW1 D Z
+INCLUDING  IH0 N - K L UW1 - D IH0 NG
+INCLUSION  IH0 N - K L UW1 - ZH AH0 N
+INCLUSIONS  IH0 N - K L UW1 - ZH AH0 N Z
+INCLUSIVE  IH0 N - K L UW1 - S IH0 V
+INCLUSIVENESS  IH0 N - K L UW1 - S IH0 V - N IH0 S
+INCLUSIVENESS(2)  IH0 N - K L UW1 - S IH0 V - N EH0 S
+INCO  IH2 N - K OW1
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+INCOGNITO  IH0 N - K AO0 G - N IY1 - T OW0
+INCOHERENCE  IH0 N - K OW0 - HH IH1 - R AH0 N S
+INCOHERENT  IH0 N - K OW0 - HH IH1 - R AH0 N T
+INCOHERENTLY  IH0 N - K OW0 - HH IH1 - R AH0 N T - L IY0
+INCOM  IH1 NG - K AA0 M
+INCOME  IH1 N - K AH2 M
+INCOMES  IH1 N - K AH2 M Z
+INCOMING  IH1 N - K AH2 - M IH0 NG
+INCOMMUNICADO  IH2 N - K AH0 - M Y UW2 - N AH0 - K AA1 - D OW0
+INCOMPARABLE  IH0 N - K AA1 M - P ER0 - AH0 - B AH0 L
+INCOMPARABLY  IH0 N - K AA1 M - P ER0 - AH0 - B L IY0
+INCOMPATIBILITY  IH0 N - K AA2 M - P AH0 - T IH0 - B IH1 - L IH0 - T IY0
+INCOMPATIBLE  IH0 N - K AH0 M - P AE1 - T AH0 - B AH0 L
+INCOMPETENCE  IH0 N - K AA1 M - P AH0 - T AH0 N S
+INCOMPETENCY  IH0 N - K AA1 M - P AH0 - T AH0 N - S IY0
+INCOMPETENT  IH0 N - K AA1 M - P AH0 - T AH0 N T
+INCOMPETENTLY  IH0 N - K AA1 M - P AH0 - T AH0 N T - L IY0
+INCOMPETENTS  IH0 NG - K AA1 M - P AH0 - T AH0 N T S
+INCOMPLETE  IH0 N - K AH0 M - P L IY1 T
+INCOMPREHENSIBLE  IH0 NG - K AA2 M - P R AH0 - HH EH1 N - S IH0 - B AH0 L
+INCOMPRESSIBLE  IH0 N - K AH0 M - P R EH1 - S AH0 - B AH0 L
+INCONCEIVABLE  IH2 N - K AH0 N - S IY1 - V AH0 - B AH0 L
+INCONCLUSIVE  IH0 N - K AH0 N - K L UW1 - S IH0 V
+INCONCLUSIVELY  IH0 NG - K AA1 N - K L UW0 - S IH0 V - L IY0
+INCONGRUITY  IH2 NG - K AO0 NG - R UW1 - IH0 - T IY0
+INCONGRUOUS  IH0 NG - K AO1 NG - R UW0 - AH0 S
+INCONGRUOUSLY  IH0 NG - K AO1 NG - R UW0 - AH0 S - L IY0
+INCONSEQUENTIAL  IH0 NG - K AA2 N - S AH0 - K W EH1 N - CH AH0 L
+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
+INCONSPICUOUS  IH0 NG - K AA1 N - S P IH0 - K W AH0 S
+INCONSTANCY  IH0 N - K AA1 N - S T AH0 N - S IY0
+INCONTINENCE  IH0 N - K AA1 N - T AH0 - N AH0 N S
+INCONTINENT  IH0 N - K AA1 N - T AH0 - N AH0 N T
+INCONTROVERTIBLE  IH0 NG - K AA2 N - T R OW0 - V ER1 - T IH0 - B AH0 L
+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
+INCONVENIENT  IH2 N - K AH0 N - V IY1 - N Y AH0 N T
+INCOORDINATION  IH0 N - K OW1 - AO1 R - D AH0 N - EY1 - SH AH0 N
+INCORPORATE  IH0 N - K AO1 R - P ER0 - EY2 T
+INCORPORATED  IH0 N - K AO1 R - P ER0 - EY2 - T AH0 D
+INCORPORATED'S  IH0 N - K AO1 R - P ER0 - EY2 - T IH0 D Z
+INCORPORATED(2)  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
+INCORPORATION'S  IH0 N - K AO2 R - P ER0 - EY1 - SH AH0 N Z
+INCORPORATIONS  IH0 N - K AO2 R - P ER0 - EY1 - SH AH0 N Z
+INCORRECT  IH0 N - K ER0 - EH1 K T
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+INCORRIGIBLE  IH0 N - K AA1 - R AH0 - JH AH0 - B AH0 L
+INCORVAIA  IH0 N - K AO0 R - V AA1 - Y AH0
+INCREASE  IH0 N - K R IY1 S
+INCREASE(2)  IH1 N - K R IY2 S
+INCREASED  IH0 N - K R IY1 S T
+INCREASED(2)  IH1 N - K R IY2 S T
+INCREASES  IH0 N - K R IY1 - S AH0 Z
+INCREASES(2)  IH0 N - K R IY1 - S IH0 Z
+INCREASES(3)  IH1 N - K R IY0 - S AH0 Z
+INCREASING  IH0 N - K R IY1 - S IH0 NG
+INCREASINGLY  IH0 N - K R IY1 - S IH0 NG - L IY0
+INCREASINGLY(2)  IH0 N - K R IY1 - S IH0 NG - G L IY0
+INCREDIBLE  IH0 N - K R EH1 - D AH0 - B AH0 L
+INCREDIBLY  IH0 N - K R EH1 - D AH0 - B L IY0
+INCREDULITY  IH2 N - K R AH0 - D UW1 - L IH0 - T IY0
+INCREDULOUS  IH0 N - K R EH1 - JH AH0 - L AH0 S
+INCREMENT  IH1 N - K R AH0 - M AH0 N T
+INCREMENTAL  IH2 N - K R AH0 - M EH1 N - T AH0 L
+INCREMENTAL(2)  IH2 N - K R AH0 - M EH1 - N AH0 L
+INCREMENTALISM  IH2 N - K R AH0 - M EH1 N - T AH0 - L IH0 Z M
+INCREMENTALISM(2)  IH2 N - K R AH0 - M EH1 - N AH0 - L IH0 Z M
+INCREMENTALLY  IH0 N - K R AH0 - M EH1 N - T AH0 - L IY0
+INCREMENTALLY(2)  IH0 N - K R AH0 - M EH1 - N AH0 - L IY0
+INCREMENTS  IH1 NG - K R AH0 - M AH0 N T S
+INCRIMINATE  IH0 N - K R IH1 - M AH0 - N EY2 T
+INCRIMINATING  IH0 N - K R IH1 - M AH0 - N EY2 - T IH0 NG
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+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
+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
+INCUR  IH0 N - K ER1
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+INDATA  IH0 N - D AA1 - T AH0
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+INDECENT  IH0 N - D IY1 - S AH0 N T
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+INDENTURES  IH0 N - D EH1 N - CH ER0 Z
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+INDEPENDENT  IH2 N - D IH0 - P EH1 N - D AH0 N T
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+INDEXATION  IH1 N - D EH2 K - S EY1 - SH AH0 N
+INDEXED  IH1 N - D EH0 K S T
+INDEXER  IH1 N - D EH2 K - S ER0
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+INDEXES  IH1 N - D EH0 K - S IH0 Z
+INDEXING  IH1 N - D EH0 K - S IH0 NG
+INDIA  IH1 N - D IY0 - AH0
+INDIA'S  IH1 N - D IY0 - AH0 Z
+INDIAN  IH1 N - D IY0 - AH0 N
+INDIAN'S  IH1 N - D IY0 - AH0 N Z
+INDIANA  IH2 N - D IY0 - AE1 - N AH0
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+INDIANAPOLIS  IH2 N - D IY0 - AH0 N - AE1 - P AH0 - L IH0 S
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+INDIANOLA  IH1 N - D IY0 - AH0 - N OW1 - L AH0
+INDIANS  IH1 N - D IY0 - AH0 N Z
+INDIANS'  IH1 N - D IY0 - AH0 N Z
+INDIC  IH1 N - D IH0 K
+INDICATE  IH1 N - D AH0 - K EY2 T
+INDICATED  IH1 N - D AH0 - K EY2 - T AH0 D
+INDICATED(2)  IH1 N - D AH0 - K EY2 - T IH0 D
+INDICATES  IH1 N - D IH0 - K EY2 T S
+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
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+INDICES  IH1 N - D IH0 - S IY2 Z
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+INDICIA  IH0 N - D IH1 S - Y AH0
+INDICT  IH0 N - D AY1 T
+INDICTED  IH0 N - D AY1 - T AH0 D
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+INDICTING  IH0 N - D AY1 - T IH0 NG
+INDICTMENT  IH0 N - D AY1 T - M AH0 N T
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+INDIES  IH1 N - D IY0 Z
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+INDIFFERENT  IH0 N - D IH1 - F R AH0 N T
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+INDIGENT  IH1 N - D IH0 - JH AH0 N T
+INDIGENTS  IH1 N - D IH0 - JH AH0 N T S
+INDIGEST  IH0 N - D AY0 - JH EH1 S T
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+INDIGNANT  IH0 N - D IH1 G - N AH0 N T
+INDIGNANTLY  IH0 N - D IH1 G - N AH0 N T - L IY0
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+INDISCRETION  IH2 N - D IH0 - S K R EH1 - SH AH0 N
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+INDITED  IH0 N - D AY1 - T IH0 D
+INDIUM  IH1 N - D IY0 - AH0 M
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+INDIVIDUALISTIC  IH2 N - D IH0 - V IH2 - JH UW0 - AH0 - L IH1 - S T IH0 K
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+INDIVIDUALITY  IH0 N - D IH2 - V IH0 - JH UW0 - AE1 - L IH0 - T IY0
+INDIVIDUALIZE  IH2 N - D IH0 - V IH1 - JH UW0 - AH0 - L AY0 Z
+INDIVIDUALIZE(2)  IH2 N - D IH0 - V IH1 - JH AH0 - L AY0 Z
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+INDIVIDUALLY  IH2 N - D IH0 - V IH1 - JH UW0 - AH0 - L IY0
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+INDIVISIBLE  IH2 N - D IH0 - V IH1 - S IH0 - B AH0 L
+INDO  IH1 N - D OW0
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+INDOLENT  IH1 N - D AH0 - L AH0 N T
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+INDONESIA  IH2 N - D OW0 - N IY1 - ZH AH0
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+INDUCT  IH0 N - D AH1 K T
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+MESSAGING  M EH1 - S IH0 - JH IH0 NG
+MESSAMORE  M EH0 - S AA1 - M AO0 R
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+MESSED  M EH1 S T
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+MESSENGER  M EH1 - S AH0 N - JH ER0
+MESSENGER(2)  M EH1 - S IH0 N - JH ER0
+MESSENGERS  M EH1 - S AH0 N - JH ER0 Z
+MESSER  M EH1 - S ER0
+MESSERLI  M EH1 - S ER0 - L IY0
+MESSERLY  M EH1 - S ER0 - L IY0
+MESSERSCHMIDT  M EH1 - S ER0 SH - M IH2 T
+MESSERSCHMITT  M EH1 - S ER0 SH - M IH2 T
+MESSERSMITH  M EH1 - S ER0 - S M IH2 TH
+MESSES  M EH1 - S IH0 Z
+MESSIAEN  M EH1 - S IY0 - EY2 N
+MESSIAEN'S  M EH1 - S IY0 - EY2 N Z
+MESSIAH  M AH0 - S AY1 - AH0
+MESSIAHS  M AH0 - S AY1 - AH0 Z
+MESSIANIC  M EH2 - S IY0 - AE1 - N IH0 K
+MESSICK  M EH1 - S IH0 K
+MESSIER  M EH1 - S IY0 - ER0
+MESSIMER  M EH1 - S IH0 - M ER0
+MESSINA  M IH0 - S IY1 - N AH0
+MESSINEO  M EH2 - S IH1 - N IY0 - OW0
+MESSING  M EH1 - S IH0 NG
+MESSINGER  M EH1 - S IH0 - NG ER0
+MESSLER  M EH1 S - L ER0
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+MESSMORE  M EH1 S - M AO0 R
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+MESSRS  M EH1 - S ER0 Z
+MESSRS.  M EH1 - S ER0 Z
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+MESSY  M EH1 - S IY0
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+MESTA  M EH1 - S T AH0
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+METACARPAL  M EH2 - T AH0 - K AA1 R - P AH0 L
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+METAIRIE  M AH0 - T EH1 - R IY0
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+METALLGESELLSCHAFT  M EH2 - T AH0 L - G EH1 - S AH0 L - SH AE2 F T
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+METALLIC  M AH0 - T AE1 - L IH0 K
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+METALLICA  M AH0 - T AE1 - L IH0 - K AH0
+METALLICA'S  M AH0 - T AE1 - L IH0 - K AH0 Z
+METALLO  M EH0 - T AA1 - L OW0
+METALLURGICAL  M EH2 - T AH0 - L ER1 - JH IH0 - K AH0 L
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+METHODISM  M EH1 - TH AH0 - D IH2 - Z AH0 M
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+METHODOLOGICAL  M EH2 - TH AH0 - D AH0 - L AA1 - JH IH0 - K AH0 L
+METHODOLOGIES  M EH2 - TH OW0 - D AA1 - L AH0 - JH IY0 Z
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+MICROCHIP  M AY1 - K R OW2 - CH IH1 P
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+MICROELECTRONIC  M AY2 - K R OW0 - IH0 - L EH0 K - T R AA1 - N IH0 K
+MICROELECTRONICS  M AY2 - K R OW0 - IH0 - L EH0 K - T R AA1 - N IH0 K S
+MICROELETTRONICA  M AY2 - K R OW0 - IH0 - L EH0 - T R AA1 - N IH0 - K AH0
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+MICROGRAM  M AY1 - K R OW0 - G R AE2 M
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+MICROGRAPHIC  M AY2 - K R OW0 - G R AE1 - F IH0 K
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+MICROLITER  M AY1 - K R OW0 - L IY0 - T ER0
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+MICROMANAGE  M AY2 - K R OW0 - M AE1 - N IH0 JH
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+MICROMETER  M AY0 - K R AA1 - M AH0 - T ER0
+MICRON  M AY1 - K R AA2 N
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+MICRONIC  M AY2 - K R AO1 - N IH2 K
+MICRONICS  M AY2 - K R AO1 - N IH2 K S
+MICRONS  M AY1 - K R AA2 N Z
+MICROORGANISM  M AY2 - K R OW0 - AO1 R - G AH0 - N IH2 - Z AH0 M
+MICROORGANISMS  M AY2 - K R OW0 - AO1 R - G AH0 - N IH2 - Z AH0 M Z
+MICROPALEONTOLOGY  M AY2 - K R OW0 - P EY2 - L IY0 - AH0 N - T AA1 - L AH0 - JH IY0
+MICROPHONE  M AY1 - K R AH0 - F OW2 N
+MICROPHONES  M AY1 - K R OW0 - F OW2 N Z
+MICROPOLIS  M AY2 - K R AO1 - P AH0 - L IH0 S
+MICROPRO  M AY1 - K R OW0 - P R OW2
+MICROPROCESSOR  M AY2 - K R OW0 P - R AA1 - S EH0 - S ER0
+MICROPROCESSORS  M AY2 - K R OW0 P - R AA1 - S EH0 - S ER0 Z
+MICROPROSE  M AY1 - K R OW0 - P R OW2 Z
+MICROS  M AY1 - K R OW0 Z
+MICROSCOPE  M AY1 - K R AH0 S - K OW2 P
+MICROSCOPES  M AY1 - K R AH0 S - K OW2 P S
+MICROSCOPIC  M AY2 - K R AH0 - S K AA1 - P IH0 K
+MICROSCOPICALLY  M AY2 - K R AH0 - S K AA1 - P IH0 K - L IY0
+MICROSCOPY  M AY0 - K R AH1 S - K AH0 - P IY0
+MICROSEMI  M AY2 - K R OW0 - S EH1 - M IY0
+MICROSOFT  M AY1 - K R OW2 - S AO1 F T
+MICROSOFT'S  M AY1 - K R OW2 - S AO1 F T S
+MICROSOFT'S(2)  M AY1 - K R OW2 - S AO1 F S
+MICROSYSTEMS  M AY1 - K R OW2 - S IH1 - S T AH0 M Z
+MICROSYSTEMS'  M AY1 - K R OW0 - S IH2 - S T AH0 M Z
+MICROTEL  M AY1 - K R OW0 - T EH2 L
+MICROTUBULES  M AY1 - K R OW2 - T UW1 - B Y UW0 L Z
+MICROVAX  M AY1 - K R OW0 - V AE2 K S
+MICROWARE  M AY1 - K R OW2 - W EH1 R
+MICROWAVABLE  M AY2 - K R OW0 - W EY1 - V AH0 - B AH0 L
+MICROWAVE  M AY1 - K R AH0 - W EY2 V
+MICROWAVED  M AY1 - K R OW0 - W EY2 V D
+MICROWAVES  M AY1 - K R OW0 - W EY2 V Z
+MICROX  M IH1 - K R AH0 K S
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+MIDCOURSE  M IH1 D - K AO1 R S
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+MIDDENDORF  M IH1 - D AH0 N - D AO2 R F
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+MIDDLEKAUFF  M IH1 - D AH0 L - K OW0 F
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+MIDFLIGHT  M IH1 D - F L AY2 T
+MIDGE  M IH1 JH
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+MIDGET  M IH1 - JH AH0 T
+MIDGETMAN  M IH1 - JH AH0 T - M AE2 N
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+MIDWINTER  M IH1 D - W IH1 N - T ER0
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+MINOAN  M AH0 - N OW1 - AH0 N
+MINOGUE  M IY1 - N AO0 G
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+MINOLI  M IH0 - N OW1 - L IY0
+MINOLTA  M IH0 - N AA1 L - T AH2
+MINOLTA(2)  M IH0 - N OW1 L - T AH0
+MINOR  M AY1 - N ER0
+MINORCA  M AH0 - N AO1 R - K AH0
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+MINORED  M AY1 - N ER0 D
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+MINORITE  M IH1 - N ER0 - AY2 T
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+MINORITY  M AY0 - N AO1 - R AH0 - T IY0
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+MINORS  M AY1 - N ER0 Z
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+MINOT  M IH1 - N AH0 T
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+MINTA  M IH1 N - T AH0
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+MINUET  M IH2 - N Y AH0 W - EH1 T
+MINUS  M AY1 - N AH0 S
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+MINUTEMEN  M IH1 - N AH0 T - M EH2 N
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+MINUTIAE  M IH0 - N UW1 - SH IY0 - AH0
+MINYARD  M IH1 N - Y AA0 R D
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+MIRABEL  M IH0 - R AA0 - B EH1 L
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+MIRABELLE  M AO1 - R AH0 - B AH0 L
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+MIRACID  M IH2 - R AE1 - S AH0 D
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+MIRACLE  M IH1 - R AH0 - K AH0 L
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+MIRELLA  M IH0 - R EH1 - L AH0
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+MIRKIN  M ER1 - K IH0 N
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+MIRO  M IH1 - R OW0
+MIRO'S  M IH1 - R OW0 Z
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+MIRON  M IH0 - R AO1 N
+MIRONENKO  M IH2 - R OW0 - N EH1 NG - K OW0
+MIROSLAV  M IH1 - R AH0 - S L AA0 V
+MIRRA  M IH1 - R AH0
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+MIRRORED  M IH1 - R ER0 D
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+MIRSAD  M IH1 R - S AE2 D
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+MIRTH  M ER1 TH
+MIRTI  M ER1 - T IY0
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+MIRTLE  M ER1 - T AH0 L
+MIRTO  M IH1 R - T OW0
+MIRV  M ER1 V
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+MIS  M IH1 S
+MISA  M IY1 - Z AH0
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+MISADVENTURES  M IH0 S - AH0 D - V EH1 N - CH ER0 Z
+MISALLOCATE  M IH0 S - AE1 - L AH0 - K EY2 T
+MISALLOCATED  M IH0 S - AE1 - L AH0 - K EY2 - T IH0 D
+MISALLOCATION  M IH0 S - AE2 - L AH0 - K EY1 - SH AH0 N
+MISANTHROPE  M IH1 - S AH0 N - TH R OW2 P
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+MISAPPLIED  M IH2 S - AH0 - P L AY1 D
+MISAPPLY  M IH0 S - AH0 - P L AY1
+MISAPPLYING  M IH0 S - AH0 - P L AY1 - IH0 NG
+MISAPPREHENSION  M IH0 S - AE2 - P R IY0 - HH EH1 N - SH AH0 N
+MISAPPROPRIATE  M IH0 S - AH0 - P R OW1 - P R IY0 - EY2 T
+MISAPPROPRIATED  M IH2 S - AH0 - P R OW1 - P R IY0 - EY2 - T IH0 D
+MISAPPROPRIATING  M IH2 S - AH0 - P R OW1 - P R IY0 - EY2 - T IH0 NG
+MISAPPROPRIATION  M IH2 S - AH0 - P R OW2 - P R IY0 - EY1 - SH AH0 N
+MISATER  M IH0 S - EY1 - T ER0
+MISAWA  M IH0 - S AA1 - W AH0
+MISBEGOTTEN  M IH2 S - B AH0 - G AA1 - T AH0 N
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+MISBEHAVING  M IH2 S - B AH0 - HH EY1 - V IH0 NG
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+MISCALCULATE  M IH0 S - K AE1 L - K Y AH0 - L EY2 T
+MISCALCULATED  M IH0 S - K AE1 L - K Y AH0 - L EY2 - T IH0 D
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+MISCARRIAGE  M IH0 S - K EH1 - R AH0 JH
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+MISCAST  M IH0 S - K AE1 S T
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+MISCAYUNA  M IH2 S - K AY0 - Y UW1 - N AH0
+MISCAYUNA'S  M IH2 S - K AY0 - Y UW1 - N AH0 Z
+MISCELLANEOUS  M IH2 - S AH0 - L EY1 - N IY0 - AH0 S
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+MISCH  M IH1 SH
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+MISCHARACTERIZE  M IH0 S - K AE1 - R AH0 K - T ER0 - AY2 Z
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+MISCHARGED  M IH0 S - CH AA1 R JH D
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+MISCHARGING  M IH0 S - CH AA1 R - JH IH0 NG
+MISCHEL  M IH1 - SH AH0 L
+MISCHER  M IH1 - SH ER0
+MISCHIEF  M IH1 S - CH AH0 F
+MISCHIEVOUS  M IH1 S - CH AH0 - V AH0 S
+MISCHKE  M IH1 SH K
+MISCHLER  M IH1 - SH AH0 L - ER0
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+MISCIBILITY  M IH2 S - IH0 - B IH1 - L IH0 - T IY0
+MISCIBLE  M IH1 - S AH0 - B AH0 L
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+MISCOMMUNICATION  M IH0 S - K AH0 - M Y UW2 - N AH0 - K EY1 - SH AH0 N
+MISCONCEIVE  M IH0 S - K AH0 N - S IY1 V
+MISCONCEIVED  M IH0 S - K AH0 N - S IY1 V D
+MISCONCEPTION  M IH0 S - K AH0 N - S EH1 P - SH AH0 N
+MISCONCEPTIONS  M IH0 S - K AH0 N - S EH1 P - SH AH0 N Z
+MISCONDUCT  M IH0 S - K AA1 N - D AH0 K T
+MISCONSTRUE  M IH0 S - K AH0 N - S T R UW1
+MISCONSTRUED  M IH2 S - K AH0 N - S T R UW1 D
+MISCOUNT  M IH1 S - K AW1 N T
+MISCREANT  M IH1 S - K R IY0 - AH0 N T
+MISCREANTS  M IH1 S - K R IY0 - AH0 N T S
+MISCREATION  M IH0 S - K R IY0 - EY1 - SH AH0 N
+MISCREATIONS  M IH2 S - K R IY0 - EY1 - SH AH0 N Z
+MISCUE  M IH1 S - K Y UW2
+MISCUES  M IH1 S - K Y UW2 Z
+MISDEED  M IH1 S - D IY1 D
+MISDEEDS  M IH0 S - D IY1 D Z
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+MISDEMEANORS  M IH2 S - D AH0 - M IY1 - N ER0 Z
+MISDIAGNOSE  M IH0 S - D AY2 - IH0 G - N OW1 Z
+MISDIAGNOSED  M IH0 S - D AY2 - IH0 G - N OW1 Z D
+MISDIAGNOSES  M IH0 S - D AY2 - IH0 G - N OW1 - S IY0 Z
+MISDIAGNOSES(2)  M IH0 S - D AY2 - IH0 G - N OW1 - S AH0 Z
+MISDIAGNOSIS  M IH0 S - D AY2 - IH0 G - N OW1 - S AH0 S
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+MISER  M AY1 - Z ER0
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+MISERABLES(2)  M IH1 Z - R AH0 - B AH0 L Z
+MISERABLES(3)  M IH2 - Z ER0 - AA1 B
+MISERABLY  M IH1 - Z ER0 - AH0 - B L IY0
+MISERABLY(2)  M IH1 Z - R AH0 - B L IY0
+MISERATION  M IH0 - Z ER0 - EY1 - SH AH0 N
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+MISFIT  M IH1 S - F IH2 T
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+MISGIVE  M IH0 S - G IH1 V
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+MISGIVINGS  M IH0 S - G IH1 - V IH0 NG Z
+MISGOVERNMENT  M IH0 S - G AH1 - V ER0 N - M AH0 N T
+MISGUIDE  M IH0 S - G AY1 D
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+MISHANDLED  M IH0 S - HH AE1 N - D AH0 L D
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+MISHAWAUM  M IH1 - SH AH0 W - AA2 M
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+MISIAK  M IH1 - S IY0 - AE0 K
+MISIASZEK  M IH0 - S IY0 - AA1 - SH EH0 K
+MISIDENTIFICATION  M IH2 - S AY0 - D EH2 N - T IH0 - F IH0 - K EY1 - SH AH0 N
+MISIDENTIFICATION(2)  M IH2 - S AY0 - D EH2 - N IH0 - F IH0 - K EY1 - SH AH0 N
+MISIDENTIFIED  M IH0 - S AY0 - D EH1 N - T IH0 - F AY2 D
+MISIDENTIFIED(2)  M IH0 - S AY0 - D EH1 - N IH0 - F AY2 D
+MISIDENTIFY  M IH0 - S AY0 - D EH1 N - T AH0 - F AY2
+MISIDENTIFY(2)  M IH0 - S AY0 - D EH1 - N AH0 - F AY2
+MISIEWICZ  M IH1 S - AH0 - V IH0 CH
+MISIMPRESSION  M IH0 S - IH0 M - P R EH1 - SH AH0 N
+MISINFORM  M IH0 S - IH2 N - F AO1 R M
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+MISINFORMED  M IH0 S - IH0 N - F AO1 R M D
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+MISINTERPRET  M IH0 S - IH0 N - T ER1 - P R AH0 T
+MISINTERPRETATION  M IH0 S - IH0 N - T ER2 - P R AH0 - T EY1 - SH AH0 N
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+MISINTERPRETING  M IH0 S - IH0 N - T ER1 - P R AH0 - T IH0 NG
+MISJUDGE  M IH0 S - JH AH1 JH
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+MISJUDGMENT  M IH0 S - JH AH1 JH - M AH0 N T
+MISJUDGMENTS  M IH0 S - JH AH1 JH - M AH0 N T S
+MISKA  M IH1 S - K AH0
+MISKE  M IH1 S K
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+MISKITO  M IH0 S - K IY1 - T OW0
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+MISLABELED  M IH0 S - L EY1 - B AH0 L D
+MISLABELING  M IH0 S - L EY1 - B AH0 L - IH0 NG
+MISLABELING(2)  M IH0 S - L EY1 - B L IH0 NG
+MISLAID  M IH0 S - L EY1 D
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+MISMANAGING  M IH0 S - M AE1 - N IH0 - JH IH0 NG
+MISMATCH  M IH0 S - M AE1 CH
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+MISNER  M IH1 Z - N ER0
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+MISOGYNE  M IH1 - Z AH0 - JH IH0 N
+MISOGYNIST  M IH1 - Z AH0 - JH IH0 - N IH0 S T
+MISOGYNY  M IH1 - Z AH0 - JH IH0 - N IY0
+MISPERCEIVE  M IH0 - S P ER0 - S IY1 V
+MISPERCEIVES  M IH0 - S P ER0 - S IY1 V Z
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+MISPLACE  M IH0 S - P L EY1 S
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+MISPRONOUNCE  M IH0 S - P R AH0 - N AW1 N S
+MISPRONOUNCED  M IH0 S - P R AH0 - N AW1 N S T
+MISQUOTE  M IH0 S - K W OW1 T
+MISQUOTED  M IH0 S - K W OW1 - T IH0 D
+MISREAD  M IH0 S - R IY1 D
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+MISREMEMBERED  M IH0 S - R IY0 - M EH1 M - B ER0 D
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+MISREPORTED  M IH0 S - R IH0 - P AO1 R - T IH0 D
+MISREPRESENT  M IH0 S - R EH2 - P R AH0 - Z EH1 N T
+MISREPRESENTATION  M IH0 S - R EH2 - P R IH0 - Z EH0 N - T EY1 - SH AH0 N
+MISREPRESENTATIONS  M IH0 S - R EH2 - P R IH0 - Z EH0 N - T EY1 - SH AH0 N Z
+MISREPRESENTED  M IH2 S - R EH0 - P R IH0 - Z EH1 N - T IH0 D
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+MISREPRESENTING  M IH0 S - R EH2 - P R AH0 - Z EH1 N - T IH0 NG
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+NONBITING  N AA0 N - B AY1 - T IH0 NG
+NONBUILDING  N AA1 N - B IH1 L - D IH0 NG
+NONBUSINESS  N AA1 N - B IH1 Z - N AH0 S
+NONCALLABLE  N AA0 N - K AO1 - L AH0 - B AH0 L
+NONCASH  N AA1 N - K AE1 SH
+NONCHALANCE  N AA1 N - SH AH0 - L AA1 N S
+NONCHALANT  N AA2 N - SH AH0 - L AA1 N T
+NONCHALANTLY  N AA1 N - SH AH0 - L AA1 N T - L IY0
+NONCOLOR  N AA0 N - K AH1 - L ER0
+NONCOMBATANT  N AA2 N - K AH0 M - B AE1 - T AH0 N T
+NONCOMBATANTS  N AA2 N - K AH0 M - B AE1 - T AH0 N T S
+NONCOMMERCIAL  N AA1 N - K AH0 - M ER1 - SH AH0 L
+NONCOMMITTAL  N AA1 N - K AH0 - M IH1 - T AH0 L
+NONCOMMUNIST  N AA1 N - K AA1 - M Y UW0 - N IH0 S T
+NONCOMPETE  N AA0 N - K AH0 M - P IY1 T
+NONCOMPETITIVE  N AA2 N - K AH0 M - P EH1 - T AH0 - T IH0 V
+NONCOMPLIANCE  N AA2 N - K AH0 M - P L AY1 - AH0 N S
+NONCONFORMIST  N AA2 N - K AH0 N - F AO1 R - M IH0 S T
+NONCONFORMISTS  N AA2 N - K AH0 N - F AO1 R - M AH0 S T S
+NONCONFORMISTS(2)  N AA2 N - K AH0 N - F AO1 R - M AH0 S S
+NONCONFORMISTS(3)  N AA2 N - K AH0 N - F AO1 R - M AH0 S
+NONCONFORMITY  N AA2 N - K AH0 N - F AO1 R - M AH0 - T IY0
+NONCONTRACT  N AA0 N - K AA1 N - T R AE2 K T
+NONCONTROVERSIAL  N AA0 N - K AA2 N - T R AH0 - V ER1 - SH AH0 L
+NONCONVERTIBLE  N AA0 N - K AH0 N - V ER1 - T AH0 - B AH0 L
+NONCORE  N AA1 N - K AO1 R
+NONCORPORATE  N AA0 N - K AO1 R - P R AH0 T
+NONCRIMINAL  N AA0 N - K R IH1 - M IH0 - N AH0 L
+NONCUMULATIVE  N AA0 N - K Y UW1 - M Y AH0 - L AH0 - T IH0 V
+NONDEDUCTIBLE  N AA0 N - D IH0 - D AH1 K - T IH0 - B AH0 L
+NONDEFENSE  N AA0 N - D IH0 - F EH1 N S
+NONDESCRIPT  N AA1 N - D IH0 - S K R IH1 P T
+NONDIRECT  N AA1 N - D ER0 - EH1 K T
+NONDISCRIMINATE  N AA0 N - D IH2 - S K R IH1 - M AH0 - N AH0 T
+NONDISCRIMINATION  N AA0 N - D IH2 - S K R IH0 - M IH0 - N EY1 - SH AH0 N
+NONDISCRIMINATORY  N AA1 N - D IH0 - S K R IH1 - M AH0 - N AH0 - T AO2 - R IY0
+NONDOLLAR  N AA1 N - D AA1 - L ER0
+NONDURABLE  N AA0 N - D UH1 - R AH0 - B AH0 L
+NONDURABLES  N AA0 N - D UH1 - R AH0 - B AH0 L Z
+NONE  N AH1 N
+NONECONOMIC  N AA2 N - EH2 - K AH0 - N AA1 - M IH0 K
+NONELECTRICAL  N AA0 N - IH0 - L EH1 K - T R IH0 - K AH0 L
+NONEMERGENCY  N AA2 - N IH0 - M ER1 - JH AH0 N - S IY0
+NONENTITY  N AA0 N - EH1 N - T AH0 - T IY0
+NONESSENTIAL  N AA2 N - IH0 - S EH1 N - CH AH0 L
+NONESUCH  N AH1 N - S AH1 CH
+NONETHELESS  N AH2 N - DH AH0 - L EH1 S
+NONEVENT  N AA1 N - IH0 - V EH1 N T
+NONEXCLUSIVE  N AA2 N - IH0 K - S K L UW1 - S IH0 V
+NONEXECUTIVE  N AA1 N - IH0 G - Z EH1 - K Y AH0 - T IH0 V
+NONEXISTENT  N AA2 N - AH0 G - Z IH1 - S T AH0 N T
+NONFARM  N AA1 N - F AA1 R M
+NONFAT  N AA1 N - F AE1 T
+NONFATAL  N AA1 N - F EY1 - T AH0 L
+NONFERROUS  N AA0 N - F EH1 - R AH0 S
+NONFICTION  N AA0 N - F IH1 K - SH AH0 N
+NONFINANCIAL  N AA0 N - F AH0 - N AE1 N - SH AH0 L
+NONFINANCIAL(2)  N AA0 N - F AY0 - N AE1 N - SH AH0 L
+NONFOOD  N AA1 N - F UW1 D
+NONGOVERNMENT  N AA0 N - G AH1 - V ER0 N - M AH0 N T
+NONGOVERNMENTAL  N AA0 N - G AH2 - V ER0 N - M EH1 N - T AH0 L
+NONGREEK  N AA2 N - G R IY1 K
+NONHUMAN  N AA0 N - HH Y UW1 - M AH0 N
+NONIE  N AA1 - N IY0
+NONINFLATIONARY  N AA2 N - IH0 N - F L EY1 - SH AH0 N - EH2 - R IY0
+NONINTEREST  N AA0 N - IH1 N - T R AH0 S T
+NONINTERFERENCE  N AA2 N - IH2 N - T ER0 - F IH1 - R AH0 N S
+NONINTERVENTION  N AA2 N - IH2 N - T ER0 - V EH1 N - CH AH0 N
+NONJET  N AA1 N - JH EH1 T
+NONJETS  N AA1 N - JH EH1 T S
+NONLETHAL  N AA0 N - L IY1 - TH AH0 L
+NONLINEAR  N AA0 N - L IH1 - N IY2 - ER0
+NONLITURGICAL  N AA0 N - L AH0 - T ER1 - JH IH0 - K AH0 L
+NONMANAGEMENT  N AA0 N - M AE1 - N IH0 JH - M AH0 N T
+NONMANUFACTURING  N AA2 N - M AE2 - N Y AH0 - F AE1 K - CH ER0 - IH0 NG
+NONMEMBER  N AA0 N - M EH1 M - B ER0
+NONMEMBERS  N AA0 N - M EH1 M - B ER0 Z
+NONMILITARY  N AA0 N - M IH1 - L AH0 - T EH2 - R IY0
+NONNATIVE  N AA1 - N EY1 - T IH0 V
+NONNEMACHER  N AA1 - N IH0 - M AH0 - K ER0
+NONNUCLEAR  N AA1 N - UW1 - K L IY2 - ER0
+NONOPERATING  N AA1 N - AO1 - P ER0 - EY2 - T IH0 NG
+NONPACIFIST  N AA2 N - P AE1 - S IH0 - F IH0 S T
+NONPACIFISTS  N AA2 N - P AE1 - S IH0 - F IH0 S T S
+NONPACIFISTS(2)  N AA2 N - P AE1 - S IH0 - F IH0 S S
+NONPACIFISTS(3)  N AA2 N - P AE1 - S IH0 - F IH0 S
+NONPARTISAN  N AA0 N - P AA1 R - T AH0 - Z AH0 N
+NONPAYING  N AA0 N - P EY1 - IH0 NG
+NONPAYMENT  N AA0 N - P EY1 - M AH0 N T
+NONPERFORMING  N AA0 N - P ER0 - F AO1 R - M IH0 NG
+NONPLANAR  N AA0 N - P L EY1 - N ER0
+NONPLUSS  N AA0 N - P L AH1 S
+NONPLUSSED  N AA0 N - P L AH1 S T
+NONPOISONOUS  N AA0 N - P OY1 - Z AH0 - N AH0 S
+NONPOLITICAL  N AA2 N - P AH0 - L IH1 - T IH0 - K AH0 L
+NONPRESCRIPTION  N AA2 N - P R AH0 - S K R IH1 P - SH AH0 N
+NONPRODUCTIVE  N AA2 N - P R AH0 - D AH1 K - T IH0 V
+NONPROFESSIONAL  N AA2 N - P R AH0 - F EH1 - SH AH0 - N AH0 L
+NONPROFESSIONALS  N AA0 N - P R AH0 - F EH1 - SH AH0 - N AH0 L Z
+NONPROFIT  N AA0 N - P R AA1 - F AH0 T
+NONPROFITS  N AA1 N - P R AA1 - F IH0 T S
+NONPROLIFERATION  N AA0 N - P R AH0 - L IH2 - F ER0 - EY1 - SH AH0 N
+NONPUBLIC  N AA0 N - P AH1 - B L IH0 K
+NONQUALIFIED  N AA0 N - K W AA1 - L AH0 - F AY2 D
+NONRACIAL  N AA0 N - R EY1 - SH AH0 L
+NONRECURRING  N AA0 N - R IH0 - K ER1 - IH0 NG
+NONREFUNDABLE  N AA0 N - R IH0 - F AH1 N - D AH0 - B AH0 L
+NONREGULATED  N AA0 N - R EH1 - G Y AH0 - L EY2 - T IH0 D
+NONRELIGIOUS  N AA2 N - R IH0 - L IH1 - JH AH0 S
+NONRENEWABLE  N AA0 N - R IY0 - N UW1 - AH0 - B AH0 L
+NONRESIDENT  N AA0 N - R EH1 - Z AH0 - D AH0 N T
+NONRESIDENTIAL  N AA2 N - R EH2 - Z AH0 - D EH1 N - SH AH0 L
+NONRESIDENTIAL(2)  N AA2 N - R EH2 - Z AH0 - D EH1 N - CH AH0 L
+NONRESIDENTS  N AA0 N - R EH1 - Z AH0 - D AH0 N T S
+NONRESPONSIVE  N AA0 N - R IH0 - S P AA1 N - S IH0 V
+NONROMAN  N AA0 N - R OW1 - M AH0 N
+NONRULING  N AA0 N - R UW1 - L IH0 NG
+NONSECTARIAN  N AA2 N - S EH0 K - T EH1 - R IY0 - AH0 N
+NONSENSE  N AA1 N - S EH0 N S
+NONSENSICAL  N AA0 N - S EH1 N - S IH0 - K AH0 L
+NONSMOKER  N AA0 N - S M OW1 - K ER0
+NONSMOKERS  N AA0 N - S M OW1 - K ER0 Z
+NONSMOKERS'  N AA1 N - S M OW1 - K ER0 Z
+NONSMOKING  N AA1 N - S M OW1 - K IH0 NG
+NONSPECIFIC  N AA2 N - S P AH0 - S IH1 - F IH0 K
+NONSPORTING  N AA0 N - S P AO1 R - T IH0 NG
+NONSTANDARD  N AA0 N - S T AE1 N - D ER0 D
+NONSTARTER  N AA0 N - S T AA1 R - T ER0
+NONSTICK  N AA0 N - S T IH1 K
+NONSTOP  N AA2 N - S T AA1 P
+NONSTOPS  N AA2 N - S T AA1 P S
+NONSTRATEGIC  N AA0 N - S T R AH0 - T IY1 - JH IH0 K
+NONSURGICAL  N AA0 N - S ER1 - JH IH0 - K AH0 L
+NONTAXABLE  N AA0 N - T AE1 K - S AH0 - B AH0 L
+NONTECHNICAL  N AA0 N - T EH1 K - N IH0 - K AH0 L
+NONTOXIC  N AA0 N - T AA1 K - S IH0 K
+NONTRADITIONAL  N AA2 N - T R AH0 - D IH1 - SH AH0 - N AH0 L
+NONTRANSFERABLE  N AA2 N - T R AE2 N S - F ER1 - AH0 - B AH0 L
+NONTURBO  N AO0 N - T ER1 - B OW0
+NONTURBOS  N AO0 N - T ER1 - B OW0 Z
+NONUNION  N AA0 N - Y UW1 - N Y AH0 N
+NONUNIONIZED  N AA0 N - Y UW1 - N Y AH0 - N AY2 Z D
+NONUTILITY  N AA2 N - Y UW0 - T IH1 - L IH0 - T IY0
+NONVERBAL  N AA0 N - V ER1 - B AH0 L
+NONVIOLENCE  N AA0 N - V AY1 - AH0 - L AH0 N S
+NONVIOLENT  N AA0 N - V AY1 - AH0 - L AH0 N T
+NONVOLATILE  N AA0 N - V AA1 - L AH0 - T AH0 L
+NONVOTING  N AA0 N - V OW1 - T IH0 NG
+NONWHITE  N AA1 N - W AY1 T
+NONWHITE(2)  N AA1 N HH - W AY1 T
+NONWHITES  N AA1 N - W AY1 T S
+NONWHITES(2)  N AA1 N HH - W AY1 T S
+NONWOVEN  N AA1 N - W OW1 - V IH0 N
+NONWOVENS  N AA1 N - W OW1 - V AH0 N Z
+NOODLE  N UW1 - D AH0 L
+NOODLES  N UW1 - D AH0 L Z
+NOOK  N UH1 K
+NOOKS  N UH1 K S
+NOON  N UW1 N
+NOONAN  N UW1 - N AH0 N
+NOONE  N UW1 N
+NOONEY  N UW1 - N IY0
+NOONS  N UW1 N Z
+NOONTIME  N UW1 N - T AY2 M
+NOORDA  N UH1 R - D AH0
+NOOSE  N UW1 S
+NOPE  N OW1 P
+NOR  N AO1 R
+NORA  N AO1 - R AH0
+NORA'S  N AO1 - R AH0 Z
+NORAH  N AO1 - R AH0
+NORAMCO  N AO0 - R AE1 M - K OW0
+NORANDA  N AO0 - R AE1 N - D AH0
+NORANDA'S  N AO0 - R AE1 N - D AH0 Z
+NORBECK  N AO1 R - B EH0 K
+NORBERG  N AO1 R - B ER0 G
+NORBERT  N AO1 R - B ER0 T
+NORBERTA  N AO0 R - B EH1 R - T AH0
+NORBERTO  N AO0 R - B ER1 - T OW2
+NORBURY  N AO1 R - B EH0 - R IY0
+NORBY  N AO1 R - B IY0
+NORCEN  N AO1 R - S AH0 N
+NORCEN'S  N AO1 R - S AH0 N Z
+NORCIA  N AO1 R - CH AH0
+NORCO  N AO1 R - K OW0
+NORCROSS  N AO1 R - K R AO0 S
+NORD  N AO1 R D
+NORDAHL  N AO1 R - D AA0 L
+NORDAN  N AO1 R - D AH0 N
+NORDBANKEN  N AO1 R D - B AE2 NG - K AH0 N
+NORDBERG  N AO1 R D - B ER0 G
+NORDBY  N AO1 R D - B IY0
+NORDEEN  N ER0 - D IY1 N
+NORDELL  N AO1 R - D AH0 L
+NORDEN  N AO1 R - D AH0 N
+NORDER  N AO1 R - D ER0
+NORDGREN  N AO1 R D - G R EH0 N
+NORDIC  N AO1 R - D IH0 K
+NORDICA  N AO1 R - D IH0 - K AH0
+NORDICTRACK  N AO1 R - D IH0 K - T R AE2 K
+NORDIN  N AO1 R - D IH0 N
+NORDINE  N AO0 R - D IY1 - N IY0
+NORDIO  N AO1 R - D IY0 - OW0
+NORDISK  N AO1 R - D IH2 S K
+NORDLING  N AO1 R - D AH0 L - IH0 NG
+NORDLING(2)  N AO1 R D - L IH0 NG
+NORDLUND  N AO1 R D - L AH0 N D
+NORDMAN  N AO1 R D - M AH0 N
+NORDMANN  N AO1 R D - M AH0 N
+NORDMEYER  N AO1 R D - M AY0 - ER0
+NORDQUIST  N AO1 R D - K W IH0 S T
+NORDSON  N AO1 R D - S AH0 N
+NORDSTROM  N AO1 R D - S T R AH0 M
+NORDSTROM'S  N AO1 R D - S T R AH0 M Z
+NORDSTROMS  N AO1 R D - S T R AH0 M Z
+NORDYKE  N AO1 R - D AY2 K
+NORECO  N AO0 - R EH1 - K OW0
+NORED  N AO1 R D
+NOREEN  N ER0 - IY1 N
+NOREIGA  N AO0 - R EY1 - G AH0
+NORELCO  N ER0 - EH1 L - K OW0
+NORELL  N AO1 - R AH0 L
+NOREM  N AO1 - R IH0 M
+NOREN  N AO1 - R AH0 N
+NORENBERG  N AO1 - R AH0 N - B ER0 G
+NORENCO  N AO1 - R AH0 N - K OW0
+NORENKO  N AH0 - R EH1 N - K OW0
+NOREX  N AO1 - R AH0 K S
+NORFLEET  N AO1 R F - L IY2 T
+NORFOLK  N AO1 R - F AH0 K
+NORFOLK'S  N AO1 R - F AH0 K S
+NORGAARD  N AO1 R - G AA0 R D
+NORGARD  N AO1 R - G ER0 D
+NORGE  N AO1 R JH
+NORGREN  N AO1 R - G R AH0 N
+NORICK  N AO1 - R IH0 K
+NORIEGA  N AO2 - R IY0 - EY1 - G AH0
+NORIEGA'S  N AO2 - R IY0 - EY1 - G AH0 Z
+NORIEGAS  N AO2 - R IY0 - EY1 - G AH0 S
+NORILSK  N AO2 - R IH1 L S K
+NORIMATSU  N AO2 - R IY0 - M AA1 T - S UW0
+NORINCHUKIN  N AO2 - R IH0 N - CH UW1 - K IH0 N
+NORINKO  N AO2 - R IH1 NG - K OW0
+NORK  N AO1 R K
+NORKUS  N AO1 R - K IH0 S
+NORLAND  N AO1 R - L AH0 N D
+NORLANDER  N AO1 R - L AH0 N - D ER0
+NORLIN  N AO1 R - L IH0 N
+NORLING  N AO1 R - L IH0 NG
+NORM  N AO1 R M
+NORMA  N AO1 R - M AH0
+NORMAL  N AO1 R - M AH0 L
+NORMALCY  N AO1 R - M AH0 L - S IY0
+NORMALITY  N AO2 R - M AE1 - L AH0 - T IY0
+NORMALIZATION  N AO2 R - M AH0 - L IH0 - Z EY1 - SH AH0 N
+NORMALIZE  N AO1 R - M AH0 - L AY2 Z
+NORMALIZED  N AO1 R - M AH0 - L AY2 Z D
+NORMALIZING  N AO1 R - M AH0 - L AY2 - Z IH0 NG
+NORMALLY  N AO1 R - M AH0 - L IY0
+NORMALLY(2)  N AO1 R M - L IY0
+NORMAN  N AO1 R - M AH0 N
+NORMAN'S  N AO1 R - M AH0 N Z
+NORMAND  N AO1 R - M AH0 N D
+NORMANDIE  N AO1 R - M AH0 N - D IY0
+NORMANDIN  N AO1 R - M AH0 N - D IH0 N
+NORMANDY  N AO1 R - M AH0 N - D IY0
+NORMATIVE  N AO1 R - M AH0 - T IH0 V
+NORMENT  N AO1 R - M AH0 N T
+NORMICK  N AO1 R - M IH0 K
+NORMIE  N AO1 R - M IY0
+NORMILE  N AO1 R - M AY0 L
+NORMING  N AO1 R - M IH0 NG
+NORMINGTON  N AO1 R - M IH0 NG - T AH0 N
+NORMOYLE  N AO1 R - M OY0 L
+NORMS  N AO1 R M Z
+NORMY  N AO1 R - M IY0
+NORNA  N AO1 R - N AH0
+NORODOM  N AO1 - R OW0 - D AH2 M
+NORODOM(2)  N AO1 - R AH0 - D AH2 M
+NORPLANT  N AO1 R - P L AE2 N T
+NORPLANT'S  N AO1 R - P L AE2 N T S
+NORQUIST  N AO1 R - K W IH0 S T
+NORRED  N AO1 R D
+NORRELL  N AO1 - R AH0 L
+NORRIE  N AO1 - R IY0
+NORRINGTON  N AO1 - R IH0 NG - T AH0 N
+NORRIS  N AO1 - R IH0 S
+NORRIS'S  N AO1 - R AH0 - S IH0 Z
+NORROD  N AO1 - R AH0 D
+NORRY  N AO1 - R IY0
+NORSE  N AO1 R S
+NORSEMEN  N AO1 R S - M IH0 N
+NORSK  N AO1 R S K
+NORSKE  N AO1 R S - K IY0
+NORSTAN  N AO1 R - S T AH0 N
+NORSTAR  N AO1 R - S T AA2 R
+NORSTROM  N AO1 R S - T R AH0 M
+NORSWORTHY  N AO1 R S - W ER0 - DH IY0
+NORTE  N AO1 R T
+NORTEK  N AO1 R - T EH2 K
+NORTEL  N AO1 R - T EH0 L
+NORTH  N AO1 R TH
+NORTH'S  N AO1 R TH S
+NORTHAM  N AO1 R - TH AH0 M
+NORTHAMPTON  N AO2 R - TH AE1 M P - T AH0 N
+NORTHBOUND  N AO1 R TH - B AW0 N D
+NORTHBROOK  N AO1 R TH - B R UH2 K
+NORTHCLIFF  N AO1 R TH - K L IH0 F
+NORTHCOTT  N AO1 R TH - K AH0 T
+NORTHCRAFT  N AO1 R TH - K R AE2 F T
+NORTHCUTT  N AO1 R TH - K AH0 T
+NORTHEAST  N AO2 R TH - IY1 S T
+NORTHEAST'S  N AO2 R TH - IY1 S T S
+NORTHEASTERLY  N AO2 R TH - IY1 - S T ER0 - L IY0
+NORTHEASTERN  N AO2 R TH - IY1 - S T ER0 N
+NORTHEASTERNER  N AO0 R TH - IY1 - S T ER0 - N ER0
+NORTHEASTERNERS  N AO0 R TH - IY1 S - T ER0 - N ER0 Z
+NORTHEASTWARD  N AO2 R TH - IY1 S T - W ER0 D
+NORTHER  N AO1 R - DH ER0
+NORTHERLY  N AO1 R - DH ER0 - L IY0
+NORTHERN  N AO1 R - DH ER0 N
+NORTHERN'S  N AO1 R - DH ER0 N Z
+NORTHERNER  N AO1 R - DH ER0 - N ER0
+NORTHERNERS  N AO1 R - DH ER0 - N ER0 Z
+NORTHERNMOST  N AO1 R - DH ER0 N - M OW2 S T
+NORTHEY  N AO1 R - DH IY0
+NORTHFIELD  N AO1 R TH - F IY2 L D
+NORTHGATE  N AO1 R TH - G EY2 T
+NORTHINGTON  N AO1 R - TH IH0 NG - T AH0 N
+NORTHLAND  N AO1 R TH - L AE2 N D
+NORTHRIDGE  N AO1 R - TH R IH2 JH
+NORTHROP  N AO1 R - TH R AH0 P
+NORTHROP'S  N AO1 R - TH R AH0 P S
+NORTHRUP  N AO1 R - TH R AH0 P
+NORTHRUP'S  N AO1 R - TH R AH0 P S
+NORTHSTAR  N AO1 R TH - S T AA2 R
+NORTHUP  N AO1 R TH - AH2 P
+NORTHVALE  N AO1 R TH - V EY2 L
+NORTHVIEW  N AO1 R TH - V Y UW2
+NORTHWARD  N AO1 R TH - W ER0 D
+NORTHWAY  N AO1 R TH - W EY2
+NORTHWEST  N AO2 R TH - W EH1 S T
+NORTHWEST'S  N AO2 R TH - W EH1 S T S
+NORTHWESTERLY  N AO2 R TH - W EH1 - S T ER0 - L IY0
+NORTHWESTERN  N AO2 R TH - W EH1 - S T ER0 N
+NORTHWESTERN'S  N AO2 R TH - W EH1 - S T ER0 N Z
+NORTHWOOD  N AO1 R TH - W UH2 D
+NORTON  N AO1 R - T AH0 N
+NORTON'S  N AO1 R - T AH0 N Z
+NORVEL  N AO1 R - V AH0 L
+NORVELL  N AO1 R - V AH0 L
+NORVIL  N AO1 R - V AH0 L
+NORVILLE  N AO1 R - V IH0 L
+NORVIN  N AO1 R - V IH0 N
+NORWALK  N AO1 R - W AO2 K
+NORWARD  N AO1 R - W ER0 D
+NORWAY  N AO1 R - W EY2
+NORWAY'S  N AO1 R - W EY2 Z
+NORWEB  N AO1 R - W EH2 B
+NORWEGIAN  N AO2 R - W IY1 - JH AH0 N
+NORWEGIANS  N AO0 R - W IY1 - JH AH0 N Z
+NORWELL  N AO1 R - W EH0 L
+NORWEST  N AO2 R - W EH1 S T
+NORWEST'S  N AO2 R - W EH1 S T S
+NORWICH  N AO1 R - W IH0 CH
+NORWIN  N AO1 R - W IH0 N
+NORWITZ  N AO1 R - W IH0 T S
+NORWOOD  N AO1 R - W UH0 D
+NORWYN  N AO1 R - W IH0 N
+NOSAIR  N OW1 - Z EY0 R
+NOSAIR'S  N OW1 - Z EY0 R Z
+NOSAIR'S(2)  N OW1 - S EY0 R Z
+NOSAIR(2)  N OW1 - S EY0 R
+NOSAL  N OW1 - Z AH0 L
+NOSE  N OW1 Z
+NOSEBLEED  N OW1 Z - B L IY2 D
+NOSED  N OW1 Z D
+NOSEDIVE  N OW1 Z - D AY2 V
+NOSEDIVED  N OW1 Z - D AY2 V D
+NOSEK  N OW1 - S EH0 K
+NOSES  N OW1 - Z IH0 Z
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+PARADISO  P ER0 - AH0 - D IY1 - S OW0
+PARADOR  P EH1 - R AH0 - D AO2 R
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+PARADOXICAL  P EH2 - R AH0 - D AA1 K - S IH0 - K AH0 L
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+PARADOXICALLY(2)  P EH2 - R AH0 - D AA1 K - S AH0 - K AH0 - L IY0
+PARADY  P ER0 - AE1 - D IY0
+PARADYNE  P EH1 - R AH0 - D AY2 N
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+PARAFFIN  P EH1 - R AH0 - F AH0 N
+PARAG  P ER0 - AA1 G
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+PARAGRAPH  P AE1 - R AH0 - G R AE2 F
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+PARAGRAPHS  P AE1 - R AH0 - G R AE2 F S
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+PARALLAX  P EH1 - R AH0 - L AE2 K S
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+PARALLELED  P EH1 - R AH0 - L EH2 L D
+PARALLELING  P EH1 - R AH0 - L EH2 - L IH0 NG
+PARALLELISM  P EH1 - R AH0 - L EH2 - L IH2 - Z AH0 M
+PARALLELS  P EH1 - R AH0 - L EH2 L Z
+PARALYMPIC  P EH2 - R AH0 - L IH1 M - P IH0 K
+PARALYSIS  P ER0 - AE1 - L AH0 - S AH0 S
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+PARALYTIC  P EH2 - R AH0 - L IH1 - T IH0 K
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+PARALYZED  P EH1 - R AH0 - L AY2 Z D
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+PARAMETER  P ER0 - AE1 - M AH0 - T ER0
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+PARAMETRIC  P EH2 - R AH0 - M EH1 - T R IH0 K
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+PARAMO  P ER0 - AE1 - M OW0
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+PARANOIAC  P EH2 - R AH0 - N OY1 - AE2 K
+PARANOID  P EH1 - R AH0 - N OY2 D
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+PARASYMPATHETIC  P EH2 - R AH0 - S IH2 M - P AH0 - TH EH1 - T IH0 K
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+PRECEDED(3)  P R IY0 - S IY1 - D IH0 D
+PRECEDENCE  P R EH1 - S AH0 - D AH0 N S
+PRECEDENT  P R EH1 - S IH0 - D AH0 N T
+PRECEDENTS  P R EH1 - S AH0 - D AH0 N T S
+PRECEDENTS(2)  P R EH1 - S AH0 - D EH2 N T S
+PRECEDES  P R IH0 - S IY1 D Z
+PRECEDING  P R IY0 - S IY1 - D IH0 NG
+PRECEEDING  P R IH0 - S IY1 - D IH0 NG
+PRECEPT  P R IY1 - S EH2 P T
+PRECEPTS  P R IY1 - S EH2 P T S
+PRECESSION  P R IY0 - S EH1 - SH AH0 N
+PRECHT  P R EH1 K T
+PRECHTER  P R EH1 K - T ER0
+PRECHTL  P R EH1 K - T AH0 L
+PRECIADO  P R EH0 - CH AA1 - D OW0
+PRECINCT  P R IY1 - S IH2 NG K T
+PRECINCT(2)  P R IY1 - S IH2 NG K
+PRECINCTS  P R IY1 - S IH2 NG K T S
+PRECINCTS(2)  P R IY1 - S IH2 NG K S
+PRECIOUS  P R EH1 - SH AH0 S
+PRECIPICE  P R EH1 - S AH0 - P AH0 S
+PRECIPITATE  P R IH0 - S IH1 - P IH0 - T EY2 T
+PRECIPITATED  P R IH0 - S IH1 - P IH0 - T EY2 - T IH0 D
+PRECIPITATING  P R IH0 - S IH1 - P AH0 - T EY2 - T IH0 NG
+PRECIPITATION  P R IH0 - S IH2 - P IH0 - T EY1 - SH AH0 N
+PRECIPITOUS  P R IH0 - S IH1 - P IH0 - T AH0 S
+PRECIPITOUSLY  P R IY2 - S IH1 - P IH0 - T AH0 S - L IY0
+PRECIS  P R EY1 - S IY2
+PRECISE  P R IH0 - S AY1 S
+PRECISE(2)  P R IY0 - S AY1 S
+PRECISELY  P R IH0 - S AY1 S - L IY0
+PRECISELY(2)  P R IY0 - S AY1 S - L IY0
+PRECISION  P R IY0 - S IH1 - ZH AH0 N
+PRECLINICAL  P R IY0 K - L IH1 - N IH0 - K AH0 L
+PRECLUDE  P R IH0 - K L UW1 D
+PRECLUDE(2)  P R IY0 - K L UW1 D
+PRECLUDED  P R IH0 - K L UW1 - D IH0 D
+PRECLUDED(2)  P R IY0 - K L UW1 - D IH0 D
+PRECLUDES  P R IH0 - K L UW1 D Z
+PRECLUDES(2)  P R IY0 - K L UW1 D Z
+PRECLUDING  P R IH0 - K L UW1 - D IH0 NG
+PRECLUSION  P R IH0 - K L UW1 - ZH AH0 N
+PRECLUSION(2)  P R IY0 - K L UW1 - ZH AH0 N
+PRECOCIOUS  P R IH0 - K OW1 - SH AH0 S
+PRECOCIOUS(2)  P R IY0 - K OW1 - SH AH0 S
+PRECONCEIVE  P R IY2 - K AH0 N - S IY1 V
+PRECONCEIVED  P R IY2 - K AH0 N - S IY1 V D
+PRECONCEPTION  P R IY0 - K AH0 N - S EH1 P - SH AH0 N
+PRECONCEPTIONS  P R IY0 - K AH0 N - S EH1 P - SH AH0 N Z
+PRECONDITION  P R IY2 - K AH0 N - D IH1 - SH AH0 N
+PRECONDITIONS  P R IY2 - K AH0 N - D IH1 - SH AH0 N Z
+PRECOOK  P R IY1 - K UH1 K
+PRECOOKED  P R IY0 - K UH1 K T
+PRECOURT  P R IH0 - K AO1 R T
+PRECRASH  P R IY0 - K R AE1 SH
+PRECURSOR  P R IY0 - K ER1 - S ER0
+PRECURSORS  P R IY0 - K ER1 - S ER0 Z
+PREDACEOUS  P R IY0 - D EY1 - SH AH0 S
+PREDATE  P R IY0 - D EY1 T
+PREDATE(2)  P R IY1 - D EY1 T
+PREDATED  P R IY0 - D EY1 - T IH0 D
+PREDATED(2)  P R IY1 - D EY1 - T IH0 D
+PREDATES  P R IY1 - D EY1 T S
+PREDATOR  P R EH1 - D AH0 - T ER0
+PREDATORS  P R EH1 - D AH0 - T ER0 Z
+PREDATORY  P R EH1 - D AH0 - T AO2 - R IY0
+PREDAWN  P R IY0 - D AO1 N
+PREDDY  P R EH1 - D IY0
+PREDECESSOR  P R EH1 - D AH0 - S EH2 - S ER0
+PREDECESSOR'S  P R EH1 - D AH0 - S EH2 - S ER0 Z
+PREDECESSORS  P R EH1 - D AH0 - S EH2 - S ER0 Z
+PREDECESSORS'  P R EH2 - D AH0 - S EH1 - S ER0 Z
+PREDESTINATION  P R IY2 - D EH2 - S T AH0 - N EY1 - SH AH0 N
+PREDESTINE  P R IY2 - D EH1 - S T AH0 N
+PREDESTINED  P R IY2 - D EH1 - S T AH0 N D
+PREDETERMINE  P R IY2 - D IH0 - T ER1 - M AH0 N
+PREDETERMINE(2)  P R IY2 - D IY0 - T ER1 - M AH0 N
+PREDETERMINED  P R IY2 - D IY0 - T ER1 - M IH0 N D
+PREDICAMENT  P R IH0 - D IH1 - K AH0 - M AH0 N T
+PREDICAMENT(2)  P R IY0 - D IH1 - K AH0 - M AH0 N T
+PREDICAMENTS  P R IH0 - D IH1 - K AH0 - M AH0 N T S
+PREDICATE  P R EH1 - D AH0 - K EY2 T
+PREDICATE(2)  P R EH1 - D IH0 - K AH0 T
+PREDICATED  P R EH1 - D AH0 - K EY2 - T IH0 D
+PREDICATES  P R EH1 - D AH0 - K EY2 T S
+PREDICATES(2)  P R EH1 - D IH0 - K AH0 T S
+PREDICT  P R IH0 - D IH1 K T
+PREDICT(2)  P R IY0 - D IH1 K T
+PREDICTABILITY  P R IH0 - D IH2 K - T AH0 - B IH1 - L IH0 - T IY0
+PREDICTABLE  P R IH0 - D IH1 K - T AH0 - B AH0 L
+PREDICTABLE(2)  P R IY0 - D IH1 K - T AH0 - B AH0 L
+PREDICTABLY  P R IH0 - D IH1 K - T AH0 - B L IY0
+PREDICTED  P R IH0 - D IH1 K - T IH0 D
+PREDICTED(2)  P R IY0 - D IH1 K - T AH0 D
+PREDICTED(3)  P R IY0 - D IH1 K - T IH0 D
+PREDICTING  P R IH0 - D IH1 K - T IH0 NG
+PREDICTING(2)  P R IY0 - D IH1 K - T IH0 NG
+PREDICTION  P R IY0 - D IH1 K - SH AH0 N
+PREDICTIONS  P R IY0 - D IH1 K - SH AH0 N Z
+PREDICTIVE  P R IH0 - D IH1 K - T IH0 V
+PREDICTIVE(2)  P R IY0 - D IH1 K - T IH0 V
+PREDICTOR  P R IH0 - D IH1 K - T ER0
+PREDICTORS  P R IH0 - D IH1 K - T ER0 Z
+PREDICTORS(2)  P R IY0 - D IH1 K - T ER0 Z
+PREDICTS  P R IH0 - D IH1 K T S
+PREDICTS(2)  P R IY0 - D IH1 K T S
+PREDICTS(3)  P R IH0 - D IH1 K S
+PREDICTS(4)  P R IY0 - D IH1 K S
+PREDILECTION  P R EH2 - D AH0 - L EH1 K - SH AH0 N
+PREDILECTIONS  P R EH2 - D AH0 - L EH1 K - SH AH0 N Z
+PREDISPOSE  P R IY2 - D IH0 - S P OW1 Z
+PREDISPOSED  P R IY2 - D IH0 - S P OW1 Z D
+PREDISPOSING  P R IY2 - D IH0 - S P OW1 - Z IH0 NG
+PREDISPOSITION  P R IY2 - D IH0 S - P AH0 - Z IH1 - SH AH0 N
+PREDISPOSITIONS  P R IY2 - D IH0 S - P AH0 - Z IH1 - SH AH0 N Z
+PREDMORE  P R EH1 D - M AO0 R
+PREDOMINANCE  P R IH0 - D AA1 - M AH0 - N AH0 N S
+PREDOMINANT  P R IH0 - D AA1 - M AH0 - N AH0 N T
+PREDOMINANTLY  P R IH0 - D AA1 - M AH0 - N AH0 N T - L IY0
+PREDOMINATE  P R IH0 - D AA1 - M AH0 - N EY2 T
+PREDOMINATE(2)  P R IH0 - D AA1 - M AH0 - N AH0 T
+PREDOMINATED  P R IH0 - D AA1 - M AH0 - N EY2 - T IH0 D
+PREDOMINATES  P R IH0 - D AA1 - M AH0 - N EY2 T S
+PREDOMINATING  P R IH0 - D AA1 - M AH0 - N EY2 - T IH0 NG
+PREE  P R IY1
+PREECE  P R IY1 S
+PREELECTION  P R IY0 - IH0 - L EH1 K - SH AH0 N
+PREEMINENCE  P R IY0 - EH1 - M AH0 - N AH0 N S
+PREEMINENT  P R IY0 - EH1 - M AH0 - N AH0 N T
+PREEMPT  P R IY1 - EH2 M P T
+PREEMPTED  P R IY0 - EH1 M P - T IH0 D
+PREEMPTING  P R IY0 - EH1 M P - T IH0 NG
+PREEMPTION  P R IY2 - EH1 M P - SH AH0 N
+PREEMPTIVE  P R IY0 - EH1 M P - T IH0 V
+PREEMPTIVELY  P R IY0 - EH1 M P - T IH0 V - L IY0
+PREEN  P R IY1 N
+PREENED  P R IY1 N D
+PREENING  P R IY1 - N IH0 NG
+PREEXIST  P R IY1 - IH0 G - Z IH1 S T
+PREEXISTED  P R IY1 - IH0 G - Z IH1 - S T IH0 D
+PREEXISTING  P R IY1 - IH0 G - Z IH1 - S T IH0 NG
+PREEXISTS  P R IY1 - IH0 G - Z IH1 S T S
+PREEXISTS(2)  P R IY1 - IH0 G - Z IH1 S S
+PREEXISTS(3)  P R IY1 - IH0 G - Z IH1 S
+PREFAB  P R IY1 - F AE1 B
+PREFABRICATE  P R IY0 - F AE1 - B R IH0 - K EY2 T
+PREFABRICATED  P R IY0 - F AE1 - B R IH0 - K EY2 - T IH0 D
+PREFABRICATION  P R IY2 - F AE2 - B R AH0 - K EY1 - SH AH0 N
+PREFABS  P R IY1 - F AE1 B Z
+PREFACE  P R EH1 - F AH0 S
+PREFACED  P R EH1 - F AH0 S T
+PREFECT  P R IY1 - F EH2 K T
+PREFECTURAL  P R IY0 - F EH1 K - CH ER0 - AH0 L
+PREFECTURE  P R IY1 - F EH2 K - CH ER0
+PREFER  P R AH0 - F ER1
+PREFER(2)  P R IH0 - F ER1
+PREFER(3)  P R IY0 - F ER1
+PREFERABLE  P R EH1 - F ER0 - AH0 - B AH0 L
+PREFERABLE(2)  P R EH1 - F R AH0 - B AH0 L
+PREFERABLY  P R EH1 - F ER0 - AH0 - B L IY0
+PREFERABLY(2)  P R EH1 - F R AH0 - B L IY0
+PREFERED  P R IH0 - F ER1 D
+PREFERENCE  P R EH1 - F ER0 - AH0 N S
+PREFERENCE(2)  P R EH1 - F R AH0 N S
+PREFERENCED  P R EH1 - F ER0 - AH0 N S T
+PREFERENCED(2)  P R EH1 - F R AH0 N S T
+PREFERENCES  P R EH1 - F ER0 - AH0 N - S IH0 Z
+PREFERENTIAL  P R EH2 - F ER0 - EH1 N - CH AH0 L
+PREFERENTIAL(2)  P R EH2 - F ER0 - EH1 N - SH AH0 L
+PREFERENTIALLY  P R EH2 - F ER0 - EH1 N - CH AH0 - L IY0
+PREFERENTIALLY(2)  P R EH2 - F ER0 - EH1 N - SH AH0 - L IY0
+PREFERRED  P R AH0 - F ER1 D
+PREFERRED(2)  P R IH0 - F ER1 D
+PREFERRED(3)  P R IY0 - F ER1 D
+PREFERREDS  P R IY0 - F ER1 - AH0 D Z
+PREFERRING  P R IH0 - F ER1 - IH0 NG
+PREFERS  P R AH0 - F ER1 Z
+PREFERS(2)  P R IH0 - F ER1 Z
+PREFERS(3)  P R IY0 - F ER1 Z
+PREFIX  P R IY1 - F IH0 K S
+PREFRONTAL  P R IY0 - F R AH1 N - T AH0 L
+PREGLER  P R EH1 G - L ER0
+PREGNANCIES  P R EH1 G - N AH0 N - S IY0 Z
+PREGNANCY  P R EH1 G - N AH0 N - S IY0
+PREGNANT  P R EH1 G - N AH0 N T
+PREGO  P R EY1 - G OW0
+PREHEIM  P R EH1 - HH AY0 M
+PREHISTORIC  P R IY2 - HH IH0 - S T AO1 - R IH0 K
+PREHN  P R EH1 N
+PREHOLIDAY  P R IY2 - HH AO1 - L IH0 - D EY0
+PREIGNITION  P R IY2 - AH0 G - N IH1 - SH AH0 N
+PREIS  P R IY1 Z
+PREISER  P R AY1 - S ER0
+PREISIG  P R AY1 - Z IH0 G
+PREISLER  P R AY1 - S AH0 - L ER0
+PREISLER(2)  P R AY1 S - L ER0
+PREISS  P R AY1 S
+PREJEAN  P R IY2 - JH IY1 N
+PREJUDGE  P R IY0 - JH AH1 JH
+PREJUDGED  P R IY0 - JH AH1 JH D
+PREJUDGMENT  P R IY0 - JH AH1 JH - M AH0 N T
+PREJUDICE  P R EH1 - JH AH0 - D IH0 S
+PREJUDICED  P R EH1 - JH AH0 - D AH0 S T
+PREJUDICES  P R EH1 - JH AH0 - D IH0 - S IH0 Z
+PREJUDICIAL  P R EH2 - JH AH0 - D IH1 - SH AH0 L
+PREJUDICING  P R EH1 - JH AH0 - D IH0 - S IH0 NG
+PREKINDERGARTEN  P R IY0 - K IH1 N - D ER0 - G AA2 - D AH0 N
+PRELATE  P R EH1 - L IH0 T
+PRELATE(2)  P R IY1 - L EY2 T
+PRELATES  P R EH1 - L IH0 T S
+PRELIM  P R IH0 - L IH1 M
+PRELIM(2)  P R IY0 - L IH1 M
+PRELIMINARIES  P R IH0 - L IH1 - M AH0 - N EH2 - R IY0 Z
+PRELIMINARIES(2)  P R IY0 - L IH1 - M AH0 - N EH2 - R IY0 Z
+PRELIMINARILY  P R IH0 - L IH2 - M AH0 - N EH1 - R IH0 - L IY0
+PRELIMINARY  P R IH0 - L IH1 - M AH0 - N EH2 - R IY0
+PRELIMINARY(2)  P R IY0 - L IH1 - M AH0 - N EH2 - R IY0
+PRELIMS  P R IY1 - L IH2 M Z
+PRELL  P R EH1 L
+PRELLWITZ  P R EH1 L - W IH0 T S
+PRELUDE  P R EY1 - L UW2 D
+PRELUDES  P R EY1 - L UW2 D Z
+PREM  P R EH1 M
+PREMADASA  P R IY2 - M AH0 - D AA1 - S AH0
+PREMARIN  P R EH1 - M ER0 - IH0 N
+PREMARITAL  P R IY0 - M EH1 - R AH0 - T AH0 L
+PREMARK  P R IY0 - M AA1 R K
+PREMARKET  P R IY1 - M AA1 R - K AH0 T
+PREMATURE  P R IY2 - M AH0 - CH UH1 R
+PREMATURELY  P R IY2 - M AH0 - CH UH1 R - L IY0
+PREMEDITATE  P R IY0 - M EH1 - D AH0 - T EY2 T
+PREMEDITATED  P R IY0 - M EH1 - D AH0 - T EY2 - T IH0 D
+PREMEDITATION  P R IY0 - M EH2 - D AH0 - T EY1 - SH AH0 N
+PREMIER  P R EH0 - M IH1 R
+PREMIER'S  P R EH0 - M IH1 R Z
+PREMIER'S(2)  P R IY0 - M IH1 R Z
+PREMIER(2)  P R IY0 - M IH1 R
+PREMIERE  P R EH0 - M IH1 R
+PREMIERED  P R EH0 - M IH1 R D
+PREMIERES  P R EH0 - M IH1 R Z
+PREMIERING  P R EH0 - M IH1 - R IH0 NG
+PREMIERS  P R EH0 - M IH1 R Z
+PREMIERSHIP  P R EH0 - M IH1 R - SH IH2 P
+PREMISE  P R EH1 - M IH0 S
+PREMISED  P R EH1 - M AH0 S T
+PREMISES  P R EH1 - M AH0 - S AH0 Z
+PREMIUM  P R IY1 - M IY0 - AH0 M
+PREMIUMS  P R IY1 - M IY0 - AH0 M Z
+PREMO  P R EH1 - M OW0
+PREMODERN  P R IY0 - M AO1 - D ER0 N
+PREMONITION  P R EH0 - M AH0 - N IH1 - SH AH0 N
+PREMONITORY  P R AH0 - M AH1 - N AH0 - T ER0 - IY0
+PRENATAL  P R IY0 - N EY1 - T AH0 L
+PRENDERGAST  P R EH1 N - D ER0 - G AE2 S T
+PRENGER  P R EH1 N - JH ER0
+PRENN  P R EH1 N
+PRENSA  P R EH1 N - S AH0
+PRENTICE  P R EH1 N - T IH0 S
+PRENTISS  P R EH1 N - T IH0 S
+PRENTNIEKS  P R EH1 N T - N IY0 - EH2 K S
+PRENUPTIAL  P R IY0 - N AH1 P - SH AH0 L
+PREOCCUPATION  P R IY0 - AA2 - K Y AH0 - P EY1 - SH AH0 N
+PREOCCUPATIONS  P R IY0 - AA2 - K Y AH0 - P EY1 - SH AH0 N Z
+PREOCCUPIED  P R IY0 - AA1 - K Y AH0 - P AY2 D
+PREOCCUPIES  P R IY0 - AA1 - K Y AH0 - P AY2 Z
+PREOCCUPY  P R IY0 - AA1 - K Y AH0 - P AY2
+PREORDAIN  P R IY2 - AO0 R - D EY1 N
+PREORDAINED  P R IY2 - AO0 R - D EY1 N D
+PREP  P R EH1 P
+PREPACKAGE  P R IY0 - P AE1 - K AH0 JH
+PREPACKAGED  P R IY0 - P AE1 - K AH0 JH D
+PREPAID  P R IY0 - P EY1 D
+PREPARATION  P R EH2 - P ER0 - EY1 - SH AH0 N
+PREPARATIONS  P R EH2 - P ER0 - EY1 - SH AH0 N Z
+PREPARATORY  P R IH0 - P EH1 - R AH0 - T AO2 - R IY0
+PREPARATORY(2)  P R EH1 - P R AH0 - T AO2 - R IY0
+PREPARE  P R IY0 - P EH1 R
+PREPARED  P R IY0 - P EH1 R D
+PREPAREDNESS  P R IY0 - P EH1 - R AH0 D - N AH0 S
+PREPARER  P R IY0 - P EH1 - R ER0
+PREPARERS  P R IY0 - P EH1 - R ER0 Z
+PREPARES  P R IY0 - P EH1 R Z
+PREPARES(2)  P ER0 - P EH1 R Z
+PREPARING  P R IY0 - P EH1 - R IH0 NG
+PREPARING(2)  P ER0 - P EH1 - R IH0 NG
+PREPAY  P R IY0 - P EY1
+PREPAYING  P R IY0 - P EY1 - IH0 NG
+PREPAYMENT  P R IY0 - P EY1 - M AH0 N T
+PREPAYMENTS  P R IY0 - P EY1 - M AH0 N T S
+PREPLANNING  P R IY0 - P L AE1 - N IH0 NG
+PREPONDERANCE  P R IY0 - P AA1 N - D R AH0 N S
+PREPOSTEROUS  P R IH0 - P AA1 - S T ER0 - AH0 S
+PREPOSTEROUS(2)  P R IH0 - P AA1 S - T R AH0 S
+PREPPED  P R EH1 P T
+PREPPIE  P R EH1 - P IY0
+PREPPING  P R EH1 - P IH0 NG
+PREPPY  P R EH1 - P IY0
+PRERECORD  P R IY2 - R IY0 - K AO1 R D
+PRERECORD(2)  P R IY2 - R IH0 - K AO1 R D
+PRERECORDED  P R IY2 - R IY0 - K AO1 R - D IH0 D
+PREREQUISITE  P R IY0 - R EH1 - K W AH0 - Z AH0 T
+PREREQUISITES  P R IY0 - R EH1 - K W AH0 - Z AH0 T S
+PREROGATIVE  P R IH0 - R AA1 - G AH0 - T IH0 V
+PREROGATIVE(2)  P ER1 - AA1 - G AH0 - T IH0 V
+PREROGATIVES  P R IH0 - R AA1 - G AH0 - T IH0 V Z
+PREROGATIVES(2)  P ER1 - AA1 - G AH0 - T IH0 V Z
+PRES  P R EH1 Z
+PRESAGE  P R EH1 - S IH0 JH
+PRESAGED  P R EH1 - S IH0 JH D
+PRESAGES  P R EH1 - S IH0 - JH IH0 Z
+PRESAGING  P R EH1 - S IH0 - JH IH0 NG
+PRESALE  P R IY1 - S EY2 L
+PRESARIO  P R IH0 - S AA1 - R IY0 - OW2
+PRESBY  P R EH1 S - B IY0
+PRESBY(2)  P R EH1 Z - B IY0
+PRESBYTERIAN  P R EH2 S - B IH0 - T IH1 - R IY0 - AH0 N
+PRESBYTERIAN(2)  P R EH2 Z - B IH0 - T IH1 - R IY0 - AH0 N
+PRESBYTERIANS  P R EH2 S - B IH0 - T IH1 - R IY0 - AH0 N Z
+PRESBYTERIANS(2)  P R EH2 Z - B IH0 - T IH1 - R IY0 - AH0 N Z
+PRESCHEL  P R EH1 - SH AH0 L
+PRESCHER  P R EH1 - SH ER0
+PRESCHOOL  P R IY0 - S K UW1 L
+PRESCHOOL(2)  P R IY1 - S K UW2 L
+PRESCHOOLER  P R IY1 - S K UW2 - L ER0
+PRESCHOOLERS  P R IY1 - S K UW2 - L ER0 Z
+PRESCHOOLS  P R IY0 - S K UW1 L Z
+PRESCHOOLS(2)  P R IY1 - S K UW2 L Z
+PRESCIENCE  P R IY1 - SH IY0 - AH0 N S
+PRESCIENT  P R EH1 - S IY0 - AH0 N T
+PRESCOT  P R EH1 - S K AH0 T
+PRESCOTT  P R EH1 - S K AA0 T
+PRESCRIBE  P R AH0 - S K R AY1 B
+PRESCRIBE(2)  P R IY0 - S K R AY1 B
+PRESCRIBED  P R IY0 - S K R AY1 B D
+PRESCRIBER  P R IY0 - S K R AY1 - B ER0
+PRESCRIBERS  P R IY0 - S K R AY1 - B ER0 Z
+PRESCRIBES  P R IY0 - S K R AY1 B Z
+PRESCRIBING  P R IY0 - S K R AY1 - B IH0 NG
+PRESCRIPTION  P R AH0 - S K R IH1 P - SH AH0 N
+PRESCRIPTIONS  P R AH0 - S K R IH1 P - SH AH0 N Z
+PRESENCE  P R EH1 - Z AH0 N S
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+PROHIBITION  P R OW2 - AH0 - B IH1 - SH AH0 N
+PROHIBITION'S  P R OW2 - AH0 - B IH1 - SH AH0 N Z
+PROHIBITIONS  P R OW2 - AH0 - B IH1 - SH AH0 N Z
+PROHIBITIVE  P R OW0 - HH IH1 - B AH0 - T IH0 V
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+PROJECT  P R AA1 - JH EH0 K T
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+PROMPT  P R AA1 M P T
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+PROOF  P R UW1 F
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+PROPAGANDISTIC  P R AA2 - P AH0 - G AH0 N - D IH1 - S T IH0 K
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+QUAKER  K W EY1 - K ER0
+QUAKER'S  K W EY1 - K ER0 Z
+QUAKERS  K W EY1 - K ER0 Z
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+QUAL  K W AA1 L
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+QUALCOMM'S  K W AA1 L - K AA2 M Z
+QUALE  K W EY1 L
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+QUALIFICATION  K W AA2 - L AH0 - F AH0 - K EY1 - SH AH0 N
+QUALIFICATIONS  K W AA2 - L AH0 - F AH0 - K EY1 - SH AH0 N Z
+QUALIFIED  K W AA1 - L AH0 - F AY2 D
+QUALIFIER  K W AA1 - L AH0 - F AY2 - ER0
+QUALIFIERS  K W AA1 - L AH0 - F AY2 - ER0 Z
+QUALIFIES  K W AA1 - L AH0 - F AY2 Z
+QUALIFY  K W AA1 - L AH0 - F AY2
+QUALIFYING  K W AA1 - L AH0 - F AY2 - IH0 NG
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+QUALITATIVELY  K W AA2 - L AH0 - T EY1 - T IH0 V - L IY0
+QUALITIES  K W AA1 - L AH0 - T IY0 Z
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+QUALLS  K W AA1 L Z
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+QUAMME  K W AE1 M
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+QUANDARY  K W AA1 N - D ER0 - IY0
+QUANDT  K W AO1 N D T
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+QUANG  K W AO1 NG
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+QUANTIFIABLE  K W AA2 N - T IH0 - F AY1 - AH0 - B EH0 L
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+QUANTIFICATION  K W AA2 N - T IH0 - F AH0 - K EY1 - SH AH0 N
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+QUANTIFY  K W AA1 N - T IH0 - F AY2
+QUANTIFY(2)  K W AA1 - N IH0 - F AY2
+QUANTIFYING  K W AA1 N - T IH0 - F AY2 - IH0 NG
+QUANTIFYING(2)  K W AA1 - N IH0 - F AY2 - IH0 NG
+QUANTITATIVE  K W AA1 N - T IH0 - T EY2 - T IH0 V
+QUANTITATIVE(2)  K W AA1 - N IH0 - T EY2 - T IH0 V
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+QUANTITIES  K W AA1 N - T AH0 - T IY0 Z
+QUANTITIES(2)  K W AA1 - N AH0 - T IY0 Z
+QUANTITY  K W AA1 N - T AH0 - T IY0
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+QUANTUM  K W AA1 N - T AH0 M
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+QUANTUM'S(2)  K W AA1 - N AH0 M Z
+QUANTUM(2)  K W AA1 - N AH0 M
+QUAQUIL  K W AE1 K - W IH0 L
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+QUARANTINES(2)  K W AO1 - R AH0 N - T AY2 N Z
+QUARANTINING  K W AO1 - R AH0 N - T IY2 - N IH0 NG
+QUARANTINING(2)  K W AO1 - R AH0 N - T AY2 - N IH0 NG
+QUARK  K W AA1 R K
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+QUARRY  K W AO1 - R IY0
+QUARRYING  K W AO1 - R IY0 - IH0 NG
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+QUARTER(2)  K AO1 R - T ER0
+QUARTERBACK  K W AO1 R - T ER0 - B AE2 K
+QUARTERBACK(2)  K AO1 R - T ER0 - B AE2 K
+QUARTERBACKING  K W AO1 R - T ER0 - B AE2 - K IH0 NG
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+QUARTERBACKS  K W AO1 R - T ER0 - B AE2 K S
+QUARTERBACKS(2)  K AO1 R - T ER0 - B AE2 K S
+QUARTERDECK  K W AO1 R - T ER0 - D EH2 K
+QUARTERDECK(2)  K AO1 R - T ER0 - D EH2 K
+QUARTERLY  K W AO1 R - T ER0 - L IY0
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+QUARTERMAN  K W AO1 R - T ER0 - M AH0 N
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+QUARTERMASTER  K W AO1 R - T ER0 - M AE2 - S T ER0
+QUARTERMASTER(2)  K AO1 R - T ER0 - M AE2 - S T ER0
+QUARTERS  K W AO1 R - T ER0 Z
+QUARTERS'  K W AA1 R - T ER0 Z
+QUARTERS'S  K AO1 R - T ER0 Z
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+QUARTET  K W AO0 R - T EH1 T
+QUARTET'S  K W AO0 R - T EH1 T S
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+QUARTS  K W AO1 R T S
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+QUASHING  K W AO1 - SH IH0 NG
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+QUASSIA  K W AA1 - SH IY0 - AH0
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+QUATTLEBAUM  K W AO1 - T AH0 L - B AW0 M
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+QUAYE  K EY1
+QUAYE(2)  K W EY1
+QUAYLE  K W EY1 L
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+QUAZULU  K W AA2 - Z UW1 - L UW0
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+QUBILAH  K UW1 - B IH0 - L AH0
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+QUEASY  K W IY1 - Z IY0
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+QUEBEC  K W AH0 - B EH1 K
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+QUEBECKERS  K W EH1 - B EH2 - K ER0 Z
+QUEBECKERS(2)  K W AH0 - B EH1 - K ER0 Z
+QUEBECOIS  K W EH1 - B AH0 S - W AA2
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+QUEBECOISE  K EH1 - B EH0 - K W AA2
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+QUEER  K W IH1 R
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+QUESTIONING  K W EH1 S - CH AH0 - N IH0 NG
+QUESTIONINGS  K W EH1 S - CH AH0 - N IH0 NG Z
+QUESTIONNAIRE  K W EH2 S - CH AH0 - N EH1 R
+QUESTIONNAIRES  K W EH2 S - CH AH0 - N EH1 R Z
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+QUESTIONS'  K W EH1 S - CH AH0 N Z
+QUESTRAN  K W EH1 S - T R AE2 N
+QUESTROM  K W EH1 S - T R AH0 M
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+QUIBBLING  K W IH1 - B AH0 L - IH0 NG
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+REASSURINGLY  R IY0 - AH0 - SH ER1 - IH0 NG - L IY0
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+REATTA  R IY2 - AA1 - T AH0
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+REATTACHED  R IY1 - AH0 - T AE1 CH T
+REAUME  R UW1 M
+REAUTHORIZATION  R IY0 - AO2 - TH ER0 - IH0 - Z EY1 - SH AH0 N
+REAUTHORIZE  R IY0 - AO1 - TH ER0 - AY2 Z
+REAUTHORIZED  R IY0 - AO1 - TH ER0 - AY2 Z D
+REAUTHORIZING  R IY0 - AO1 - TH ER0 - AY2 - Z IH0 NG
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+REAVE  R IY1 V
+REAVER  R IY1 - V ER0
+REAVER'S  R IY1 - V ER0 Z
+REAVES  R IY1 V Z
+REAVIS  R IY1 - V IH0 S
+REAWAKEN  R IY2 - AH0 - W EY1 - K AH0 N
+REAWAKENED  R IY2 - AH0 - W EY1 - K AH0 N D
+REAWAKENING  R IY2 - AH0 - W EY1 - K AH0 - N IH0 NG
+REAY  R EY1
+REBA  R IY1 - B AH0
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+REBALANCING  R IY0 - B AE1 - L AH0 N - S IH0 NG
+REBAR  R IY1 - B AA2 R
+REBATE  R IY1 - B EY2 T
+REBATED  R IY1 - B EY2 - T IH0 D
+REBATES  R IY1 - B EY2 T S
+REBBE  R IY1 - B AH0
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+REBECCA  R AH0 - B EH1 - K AH0
+REBECCA'S  R AH0 - B EH1 - K AH0 Z
+REBECK  R IY1 - B EH0 K
+REBEKA  R IH0 - B IY1 - K AH0
+REBEKKA  R IH0 - B EH1 - K AH0
+REBEL  R EH1 - B AH0 L
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+REBEL(2)  R IH0 - B EH1 L
+REBELLED  R IH0 - B EH1 L D
+REBELLING  R IH0 - B EH1 - L IH0 NG
+REBELLION  R IH0 - B EH1 L - Y AH0 N
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+REBELLIONS  R IH0 - B EH1 L - Y AH0 N Z
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+REBELLIOUS(2)  R AH0 - B EH1 L - Y AH0 S
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+REBELO  R EH0 - B EH1 - L OW0
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+REBELS'  R EH1 - B AH0 L Z
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+REBHOLZ  R EH1 B - HH OW0 L Z
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+REBO  R IY1 - B OW0
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+REBOUNDING  R IY0 - B AW1 N - D IH0 NG
+REBOUNDS  R IY1 - B AW2 N D Z
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+REBSTOCK  R EH1 B - S T AA2 K
+REBUCK  R EH1 - B AH0 K
+REBUFF  R IH0 - B AH1 F
+REBUFF(2)  R IY0 - B AH1 F
+REBUFFED  R IH0 - B AH1 F T
+REBUFFED(2)  R IY0 - B AH1 F T
+REBUFFING  R IY0 - B AH1 - F IH0 NG
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+REBUILDER  R IY0 - B IH1 L - D ER0
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+REBUKED  R IH0 - B Y UW1 K T
+REBUKED(2)  R IY0 - B Y UW1 K T
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+REBUKING  R IY0 - B Y UW1 - K IH0 NG
+REBUS  R IY1 - B AH0 S
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+REBUTTAL  R IH0 - B AH1 - T AH0 L
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+REBUTTED  R IH0 - B AH1 - T IH0 D
+REBUTTING  R IH0 - B AH1 - T IH0 NG
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+RECALCITRANT  R IH0 - K AE1 L - S IH0 - T R AH0 N T
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+RECALCULATED  R IY0 - K AE1 L - K Y AH0 - L EY2 - T IH0 D
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+RECALL(2)  R IH0 - K AO1 L
+RECALLED  R IH0 - K AO1 L D
+RECALLING  R IH0 - K AO1 - L IH0 NG
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+RECANT  R IY0 - K AE1 N T
+RECANTATION  R EH2 - K AH0 N - T EY1 - SH AH0 N
+RECANTATION(2)  R IY0 - K AE0 N - T EY1 - SH AH0 N
+RECANTED  R IY0 - K AE1 N - T IH0 D
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+RECAP(2)  R IH0 - K AE1 P
+RECAPITALIZATION  R IY2 - K AE2 - P IH0 - T AH0 - L IH0 - Z EY1 - SH AH0 N
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+RECAPITULATE  R IY2 - K AH0 - P IH1 - CH AH0 - L EY2 T
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+RECAPPED  R IY0 - K AE1 P T
+RECAPPING  R IY0 - K AE1 - P IH0 NG
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+RECASTING  R IY0 - K AE1 - S T IH0 NG
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+RECEDED  R AH0 - S IY1 - D AH0 D
+RECEDED(2)  R IH0 - S IY1 - D IH0 D
+RECEDED(3)  R IY0 - S IY1 - D IH0 D
+RECEDES  R IY0 - S IY1 D Z
+RECEDING  R IH0 - S IY1 - D IH0 NG
+RECEDING(2)  R IY0 - S IY1 - D IH0 NG
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+RECEIPT(2)  R IY0 - S IY1 T
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+RECEIPTS(2)  R IY0 - S IY1 T S
+RECEIVABLE  R IH0 - S IY1 - V AH0 - B AH0 L
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+RECEIVE(3)  R IY0 - S IY1 V
+RECEIVED  R AH0 - S IY1 V D
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+RECEIVED(3)  R IY0 - S IY1 V D
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+RECEIVERS  R AH0 - S IY1 - V ER0 Z
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+RECENTLY(2)  R IY1 - S AH0 N - L IY0
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+RECEPTIONS  R IH0 - S EH1 P - SH AH0 N Z
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+RECEPTIVITY  R IY1 - S EH2 P - T IH1 - V IH0 - T IY0
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+RECEPTORS  R AH0 - S EH1 P - T ER0 Z
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+RECERTIFIED  R IY0 - S ER1 - T IH0 - F AY0 D
+RECERTIFY  R IY0 - S ER1 - T AH0 - F AY0
+RECERTIFYING  R IY0 - S ER1 - T AH0 - F AY0 - IH0 NG
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+RECESSIONARY  R IY0 - S EH1 - SH AH0 N - EH2 - R IY0
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+RECHARGEABLE  R IY0 - CH AA1 R - JH AH0 - B AH0 L
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+RECISSION  R AH0 - S IH1 - SH AH0 N
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+RECKONED  R EH1 - K AH0 N D
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+RECLAIMING  R IY0 - K L EY1 - M IH0 NG
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+RECLASSIFIED  R IY0 - K L AE1 - S AH0 - F AY2 D
+RECLASSIFY  R IY0 - K L AE1 - S IH0 - F AY2
+RECLASSIFYING  R IY0 - K L AE1 - S IH0 - F AY2 - IH0 NG
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+RECLINING  R IH0 - K L AY1 - N IH0 NG
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+RECOGNIZABLE  R EH2 - K AH0 G - N AY1 - Z AH0 - B AH0 L
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+RECOLLECTED  R EH2 - K AH0 - L EH1 K - T IH0 D
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+RECOLLECTING  R EH2 - K AH0 - L EH1 K - T IH0 NG
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+RECOLLECTION  R EH2 - K AH0 - L EH1 K - SH AH0 N
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+RECOMBINANT  R IH0 - K AA1 M - B IH0 - N AH0 N T
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+RECOMMEND  R EH2 - K AH0 - M EH1 N D
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+RECOMMENDED  R EH2 - K AH0 - M EH1 N - D AH0 D
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+RECOMMIT  R IH0 - K AA1 - M IH0 T
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+RECOMMITED  R IH0 - K AA1 - M IH2 - T IH0 D
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+RECOMPENSE  R EH1 - K AH0 M - P EH2 N S
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+RECONFIGURE  R IY2 - K AH0 N - F IH1 - G Y ER0
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+RECONQUERED  R IY0 - K AO1 NG - K ER0 D
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+RECONSIDERATION  R IY0 - K AH0 N - S IH2 - D ER0 - EY1 - SH AH0 N
+RECONSIDERED  R IY2 - K AH0 N - S IH1 - D ER0 D
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+RECORDS(3)  R IH0 - K AO1 R D Z
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+RECOUNT  R IH0 - K AW1 N T
+RECOUNT(2)  R IY2 - K AW1 N T
+RECOUNTED  R IH0 - K AW1 N - T IH0 D
+RECOUNTED(2)  R IY2 - K AW1 N - T IH0 D
+RECOUNTING  R IH0 - K AW1 N - T IH0 NG
+RECOUNTING(2)  R IY2 - K AW1 N - T IH0 NG
+RECOUNTS  R IY2 - K AW1 N T S
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+RECOUP  R IH0 - K UW1 P
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+RECOUPING  R IH0 - K UW1 - P IH0 NG
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+RECOVERABLE  R IH0 - K AH1 - V ER0 - AH0 - B AH0 L
+RECOVERED  R AH0 - K AH1 - V ER0 D
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+RECRUITED  R IH0 - K R UW1 - T IH0 D
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+RELAYING  R IY1 - L EY2 - IH0 NG
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+RELEARNING  R IY0 - L EH1 R - N IH0 NG
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+RELEND  R IY0 - L EH1 N D
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+RELIANCE(2)  R IY0 - L AY1 - AH0 N S
+RELIANT  R IH0 - L AY1 - AH0 N T
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+RELIC  R EH1 - L IH0 K
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+RELIGIONIST  R IY0 - L IH1 - JH AH0 - N IH0 S T
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+REMARKED  R IH0 - M AA1 R K T
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+RENSING  R EH1 N - S IH0 NG
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+RENTERS  R EH1 N - T ER0 Z
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+RENTING  R EH1 N - T IH0 NG
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+RENTZ  R EH1 N T S
+RENUNCIATE  R IH0 - N AH1 N - S IY0 - EY2 T
+RENUNCIATION  R IH0 - N AH2 N - S IY0 - EY1 - SH AH0 N
+RENUZIT  R IY0 - N UW1 - Z IH0 T
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+REOFFEND  R IY0 - AO0 - F EH1 N D
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+REOFFERED  R IY0 - AO1 - F ER0 D
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+REOPENED  R IY0 - OW1 - P AH0 N D
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+REORDER  R IY0 - AO1 R - D ER0
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+REORGANIZATION  R IY2 - AO0 R - G AH0 - N AH0 - Z EY1 - SH AH0 N
+REORGANIZATIONS  R IY0 - AO2 R - G AH0 - N IH0 - Z EY1 - SH AH0 N Z
+REORGANIZE  R IY0 - AO1 R - G AH0 - N AY2 Z
+REORGANIZED  R IY0 - AO1 R - G AH0 - N AY2 Z D
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+REPACK  R IY0 - P AE1 K
+REPACKAGE  R IY2 - P AE1 - K IH0 JH
+REPACKAGED  R IY1 - P AE0 - K IH0 JH D
+REPACKAGES  R IY2 - P AE1 - K IH0 - JH IH0 Z
+REPACKAGING  R IY1 - P AE0 - K IH0 - JH IH0 NG
+REPACKED  R IY0 - P AE1 K T
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+REPAINTED  R IY0 - P EY1 N - T AH0 D
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+REPAIRMEN  R IH0 - P EH1 R - M EH2 N
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+REPAYABLE  R IY0 - P EY1 - AH0 - B AH0 L
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+REPAYS  R IY2 - P EY1 Z
+REPEAL  R IH0 - P IY1 L
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+REPEALED  R IH0 - P IY1 L D
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+REPEATABLE  R IH0 - P IY1 - T AH0 - B AH0 L
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+REPEATED(3)  R IY0 - P IY1 - T IH0 D
+REPEATEDLY  R IH0 - P IY1 - T IH0 D - L IY0
+REPEATER  R IH0 - P IY1 - T ER0
+REPEATERS  R IH0 - P IY1 - T ER0 Z
+REPEATING  R IH0 - P IY1 - T IH0 NG
+REPEATING(2)  R IY0 - P IY1 - T IH0 NG
+REPEATS  R IH0 - P IY1 T S
+REPEATS(2)  R IY0 - P IY1 T S
+REPEL  R IH0 - P EH1 L
+REPELLED  R AH0 - P EH1 L D
+REPELLED(2)  R IH0 - P EH1 L D
+REPELLENT  R IH0 - P EH1 - L AH0 N T
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+REPELS  R IH0 - P EH1 L Z
+REPENT  R IH0 - P EH1 N T
+REPENTANCE  R IH0 - P EH1 N - T AH0 N S
+REPENTANT  R IH0 - P EH1 N - T AH0 N T
+REPENTED  R IH0 - P EH1 N - T IH0 D
+REPERCUSSION  R IY2 - P ER0 - K AH1 - SH AH0 N
+REPERCUSSION(2)  R IY2 - P R AH0 - K AH1 - SH AH0 N
+REPERCUSSIONS  R IY2 - P ER0 - K AH1 - SH AH0 N Z
+REPERCUSSIONS(2)  R IY2 - P R AH0 - K AH1 - SH AH0 N Z
+REPERFUSION  R EH2 - P ER0 - F Y UW1 - ZH AH0 N
+REPERTOIRE  R EH1 - P ER0 - T W AA2 R
+REPERTORY  R EH1 - P ER0 - T AO2 - R IY0
+REPETITION  R EH2 - P AH0 - T IH1 - SH AH0 N
+REPETITIONS  R EH2 - P AH0 - T IH1 - SH AH0 N Z
+REPETITIOUS  R EH2 - P AH0 - T IH1 - SH AH0 S
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+REPETTI  R EH0 - P EH1 - T IY0
+REPETTO  R EH0 - P EH1 - T OW0
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+REPINSKI  R IH0 - P IH1 N - S K IY0
+REPKA  R EH1 P - K AH0
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+REPLACE(2)  ER0 - P L EY1 S
+REPLACEABLE  R IY2 - P L EY1 - S AH0 - B AH0 L
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+REPLAYING  R IY0 - P L EY1 - IH0 NG
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+REPLENISHED  R IY0 - P L EH1 - N IH0 SH T
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+REPLENISHMENT  R IH0 - P L EH1 - N IH0 SH - M AH0 N T
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+REPLICATE  R EH1 - P L AH0 - K EY2 T
+REPLICATED  R EH1 - P L IH0 - K EY2 - T IH0 D
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+REPORT(2)  R IH0 - P AO1 R T
+REPORTABLE  R IH0 - P AO1 R - T AH0 - B AH0 L
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+REPORTER  R IH0 - P AO1 R - T ER0
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+REPORTERS  R IH0 - P AO1 R - T ER0 Z
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+REPORTORIAL  R EH2 - P ER0 - T AO1 - R IY0 - AH0 L
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+REPOSE  R IY0 - P OW1 Z
+REPOSITION  R IY2 - P AH0 - Z IH1 - SH AH0 N
+REPOSITIONED  R IY2 - P AH0 - Z IH1 - SH AH0 N D
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+REPOSITORY  R IY0 - P AA1 - Z AH0 - T AO2 - R IY0
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+REPRESENTATION  R EH2 - P R AH0 - Z EH0 N - T EY1 - SH AH0 N
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+REPRESENTATIVES'  R EH2 - P R AH0 - S EH1 N - T AH0 - T IH0 V Z
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+REPRINT  R IY0 - P R IH1 N T
+REPRINTED  R IY0 - P R IH1 N - T IH0 D
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+REPRISE  R IH0 - P R AY1 Z
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+REPROCESSING  R IY0 - P R AO1 - S EH0 - S IH0 NG
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+REPRODUCED  R IY2 - P R AH0 - D UW1 S T
+REPRODUCES  R IY2 - P R AH0 - D UW1 - S IH0 Z
+REPRODUCING  R IY2 - P R AH0 - D UW1 - S IH0 NG
+REPRODUCTION  R IY2 - P R AH0 - D AH1 K - SH AH0 N
+REPRODUCTIONS  R IY2 - P R AH0 - D AH1 K - SH AH0 N Z
+REPRODUCTIVE  R IY2 - P R AH0 - D AH1 K - T IH0 V
+REPROGRAM  R IY0 - P R OW1 - G R AE2 M
+REPROGRAMMED  R IY0 - P R OW1 - G R AE2 M D
+REPROGRAMMING  R IY0 - P R OW1 - G R AE2 - M IH0 NG
+REPROGRAMS  R IY0 - P R OW1 - G R AE2 M Z
+REPROGRAPH  R EH1 - P R OW0 - G R AE2 F
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+REPROGRAPHICS  R EH2 - P R OW0 - G R AE1 - F IH0 K S
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+REPUBLIC  R IY0 - P AH1 - B L AH0 K
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+REPUBLIC(2)  R IY0 - P AH1 - B L IH0 K
+REPUBLICAN  R IH0 - P AH1 - B L IH0 - K AH0 N
+REPUBLICAN'S  R IY0 - P AH1 - B L IH0 - K AH0 N Z
+REPUBLICAN(2)  R IY0 - P AH1 - B L AH0 - K AH0 N
+REPUBLICAN(3)  R IY0 - P AH1 - B L IH0 - K AH0 N
+REPUBLICANISM  R IH0 - P AH1 - B L IH0 - K AH0 - N IH2 - Z AH0 M
+REPUBLICANS  R IH0 - P AH1 - B L IH0 - K AH0 N Z
+REPUBLICANS'  R IH0 - P AH1 - B L IH0 - K AH0 N Z
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+REPUBLICANS(2)  R IY0 - P AH1 - B L AH0 - K AH0 N Z
+REPUBLICANS(3)  R IY0 - P AH1 - B L IH0 - K AH0 N Z
+REPUBLICBANK  R IY0 - P AH1 - B L IH0 K - B AE2 NG K
+REPUBLICBANK'S  R IY0 - P AH1 - B L IH0 K - B AE2 NG K S
+REPUBLICS  R IY0 - P AH1 - B L IH0 K S
+REPUDIATE  R IY0 - P Y UW1 - D IY0 - EY2 T
+REPUDIATED  R IY0 - P Y UW1 - D IY0 - EY2 - T AH0 D
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+REPUDIATING  R IY0 - P Y UW1 - D IY0 - EY2 - T IH0 NG
+REPUDIATION  R IH0 - P Y UW2 - D IY0 - EY1 - SH AH0 N
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+REPUGNANT(2)  R IY0 - P AH1 G - N AH0 N T
+REPULSE  R IY0 - P AH1 L S
+REPULSED  R IY0 - P AH1 L S T
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+REPURCHASE  R IY0 - P ER1 - CH AH0 S
+REPURCHASED  R IY0 - P ER1 - CH AH0 S T
+REPURCHASES  R IY0 - P ER1 - CH AH0 - S IH0 Z
+REPURCHASING  R IY0 - P ER1 - CH AH0 - S IH0 NG
+REPUTABLE  R EH1 - P Y AH0 - T AH0 - B AH0 L
+REPUTATION  R EH2 - P Y AH0 - T EY1 - SH AH0 N
+REPUTATIONS  R EH2 - P Y AH0 - T EY1 - SH AH0 N Z
+REPUTE  R IY0 - P Y UW1 T
+REPUTED  R IH0 - P Y UW1 - T IH0 D
+REPUTED(2)  R IY0 - P Y UW1 - T AH0 D
+REPUTED(3)  R IY0 - P Y UW1 - T IH0 D
+REPUTEDLY  R IH0 - P Y UW1 - T IH0 D - L IY0
+REQUA  R EY1 - K W AH0
+REQUALIFY  R IY2 - K W AA1 - L AH0 - F AY2
+REQUEST  R IH0 - K W EH1 S T
+REQUEST(2)  R IY0 - K W EH1 S T
+REQUESTED  R IH0 - K W EH1 - S T IH0 D
+REQUESTED(2)  R IY0 - K W EH1 - S T AH0 D
+REQUESTED(3)  R IY0 - K W EH1 - S T IH0 D
+REQUESTER  R IH0 - K W EH1 - S T ER0
+REQUESTING  R IH0 - K W EH1 - S T IH0 NG
+REQUESTING(2)  R IY0 - K W EH1 - S T IH0 NG
+REQUESTS  R IH0 - K W EH1 S T S
+REQUESTS(2)  R IY0 - K W EH1 S T S
+REQUESTS(3)  R IH0 - K W EH1 S S
+REQUESTS(4)  R IY0 - K W EH1 S S
+REQUESTS(5)  R IH0 - K W EH1 S
+REQUESTS(6)  R IY0 - K W EH1 S
+REQUIEM  R EH1 - K W IY0 - AH0 M
+REQUIRE  R IY2 - K W AY1 - ER0
+REQUIRE(2)  R IY0 - K W AY1 R
+REQUIRE(3)  R IH0 - K W AY1 - ER0
+REQUIRED  R IY0 - K W AY1 - ER0 D
+REQUIRED(2)  R IY0 - K W AY1 R D
+REQUIREMENT  R IH0 - K W AY1 R - M AH0 N T
+REQUIREMENTS  R IH0 - K W AY1 R - M AH0 N T S
+REQUIRES  R IY0 - K W AY1 - ER0 Z
+REQUIRES(2)  R IY0 - K W AY1 R Z
+REQUIRING  R IY0 - K W AY1 - ER0 - IH0 NG
+REQUIRING(2)  R IY0 - K W AY1 - R IH0 NG
+REQUISITE  R EH1 - K W AH0 - Z AH0 T
+REQUISITES  R EH1 - K W AH0 - Z AH0 T S
+REQUISITION  R EH2 - K W AH0 - Z IH1 - SH AH0 N
+REQUISITIONED  R EH2 - K W AH0 - Z IH1 - SH AH0 N D
+REREAD  R IY1 - R IY1 D
+REREADING  R IY1 - R IY1 - D IH0 NG
+REREGULATE  R IY0 - R EH1 - G Y AH0 - L EY2 T
+REREGULATION  R IY0 - R EH0 - G Y AH0 - L EY1 - SH AH0 N
+REROUTE  R IY0 - R UW1 T
+REROUTE(2)  R IY0 R - AW1 T
+REROUTED  R IY0 - R UW1 - T IH0 D
+REROUTED(2)  R IY0 - R AW1 - T IH0 D
+REROUTING  R IY0 - R UW1 - T IH0 NG
+REROUTING(2)  R IY0 - R AW1 - T IH0 NG
+RERUN  R IY1 - R AH1 N
+RERUNNING  R IY1 - R AH1 - N IH0 NG
+RERUNS  R IY1 - R AH1 N Z
+RES  R EY1 Z
+RESALE  R IY1 - S EY2 L
+RESALES  R IY1 - S EY2 L Z
+RESCH  R EH1 SH
+RESCHEDULE  R IY0 - S K EH1 - JH UW0 L
+RESCHEDULED  R IY0 - S K EH1 - JH UW0 L D
+RESCHEDULING  R IY0 - S K EH1 - JH UW0 - L IH0 NG
+RESCHEDULINGS  R IY0 SH - K EH1 - JH UW0 - L IH0 NG Z
+RESCHKE  R EH1 SH K
+RESCIGNO  R EH0 - S CH IY1 G - N OW0
+RESCIND  R IH0 - S IH1 N D
+RESCIND(2)  R IY0 - S IH1 N D
+RESCINDED  R IH0 - S IH1 N - D IH0 D
+RESCINDED(2)  R IY0 - S IH1 N - D AH0 D
+RESCINDED(3)  R IY0 - S IH1 N - D IH0 D
+RESCINDING  R IH0 - S IH1 N - D IH0 NG
+RESCISSION  R IH0 - S IH1 - ZH AH0 N
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+RESCUE  R EH1 - S K Y UW0
+RESCUED  R EH1 - S K Y UW0 D
+RESCUER  R EH1 - S K Y UW2 - ER0
+RESCUERS  R EH1 - S K Y UW2 - ER0 Z
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+RESCUING  R EH1 - S K Y UW0 - IH0 NG
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+RESEACHERS  R IY0 - S ER1 - CH ER0 Z
+RESEAL  R IY0 - S IY1 L
+RESEALABLE  R IY0 - S IY1 - L AH0 - B AH0 L
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+RESEALS  R IY0 - S IY1 L Z
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+RESEARCH'S  R IY0 - S ER1 - CH IH0 Z
+RESEARCH(2)  R IY1 - S ER0 CH
+RESEARCHED  R IY0 - S ER1 CH T
+RESEARCHER  R IY1 - S ER0 - CH ER0
+RESEARCHERS  R IY1 - S ER0 - CH ER0 Z
+RESEARCHERS'  R IY1 - S ER0 - CH ER0 Z
+RESEARCHES  R IY0 - S ER1 - CH IH0 Z
+RESEARCHING  R IY0 - S ER1 - CH IH0 NG
+RESEDA  R EH0 - S EY1 - D AH0
+RESEED  R IY0 - S IY1 D
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+RESELLER  R IY0 - S EH1 - L ER0
+RESELLERS  R IY0 - S EH1 - L ER0 Z
+RESELLING  R IY0 - S EH1 - L IH0 NG
+RESELLS  R IY0 - S EH1 L Z
+RESEMBLANCE  R IH0 - Z EH1 M - B L AH0 N S
+RESEMBLANCE(2)  R IY0 - Z EH1 M - B L AH0 N S
+RESEMBLANCES  R IY0 - Z EH1 M - B L AH0 N - S AH0 Z
+RESEMBLE  R IH0 - Z EH1 M - B AH0 L
+RESEMBLE(2)  R IY0 - Z EH1 M - B AH0 L
+RESEMBLED  R IH0 - Z EH1 M - B AH0 L D
+RESEMBLED(2)  R IY0 - Z EH1 M - B AH0 L D
+RESEMBLES  R IH0 - Z EH1 M - B AH0 L Z
+RESEMBLES(2)  R IY0 - Z EH1 M - B AH0 L Z
+RESEMBLING  R IH0 - Z EH1 M - B AH0 L - IH0 NG
+RESEMBLING(2)  R IY0 - Z EH1 M - B AH0 L - IH0 NG
+RESEMBLING(3)  R IY0 - Z EH1 M - B L IH0 NG
+RESENDE  R IH0 - S EH1 N - D EY0
+RESENDES  R EH1 - S IH0 N D Z
+RESENDEZ  R EY0 - S EY1 N - D EH0 Z
+RESENDIZ  R IH0 - S EH1 N - D IH0 Z
+RESENT  R IH0 - Z EH1 N T
+RESENT(2)  R IY0 - Z EH1 N T
+RESENTED  R IY0 - Z EH1 N - T AH0 D
+RESENTED(2)  R IY0 - Z EH1 - N AH0 D
+RESENTFUL  R IH0 - Z EH1 N T - F AH0 L
+RESENTING  R IH0 - Z EH1 N - T IH0 NG
+RESENTING(2)  R IY0 - Z EH1 N - T IH0 NG
+RESENTING(3)  R IH0 - Z EH1 - N IH0 NG
+RESENTING(4)  R IY0 - Z EH1 - N IH0 NG
+RESENTMENT  R IH0 - Z EH1 N T - M AH0 N T
+RESENTMENT(2)  R IH0 - Z EH1 N - M AH0 N T
+RESENTMENTS  R IH0 - Z EH1 N T - M AH0 N T S
+RESENTMENTS(2)  R IH0 - Z EH1 N - M AH0 N T S
+RESENTS  R IH0 - Z EH1 N T S
+RESER  R IY1 - Z ER0
+RESERVATION  R EH2 - Z ER0 - V EY1 - SH AH0 N
+RESERVATIONIST  R EH2 - Z ER0 - V EY1 - SH AH0 N - IH0 S T
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+RESERVATIONISTS(2)  R EH2 - Z ER0 - V EY1 - SH AH0 N - IH0 S S
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+RESERVATIONS  R EH2 - Z ER0 - V EY1 - SH AH0 N Z
+RESERVE  R IH0 - Z ER1 V
+RESERVE'S  R IH0 - Z ER1 V Z
+RESERVE'S(2)  R IY0 - Z ER1 V Z
+RESERVE(2)  R IY0 - Z ER1 V
+RESERVED  R IH0 - Z ER1 V D
+RESERVED(2)  R IY0 - Z ER1 V D
+RESERVEESE  R EH2 - Z ER0 - V IY1 S
+RESERVES  R IH0 - Z ER1 V Z
+RESERVES(2)  R IY0 - Z ER1 V Z
+RESERVING  R IH0 - Z ER1 - V IH0 NG
+RESERVING(2)  R IY0 - Z ER1 - V IH0 NG
+RESERVIST  R IH0 - Z ER1 - V IH0 S T
+RESERVISTS  R IH0 - Z ER1 - V IH0 S T S
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+RESERVISTS(3)  R IH0 - Z ER1 - V IH0 S
+RESERVOIR  R EH1 - Z AH0 - V W AA2 R
+RESERVOIR(2)  R EH1 - Z ER0 - V W AA2 R
+RESERVOIRS  R EH1 - Z ER0 - V W AA2 R Z
+RESET  R IY0 - S EH1 T
+RESET(2)  R IY1 - S EH0 T
+RESETAR  R EH1 - S IH0 - T ER0
+RESETING  R IY0 - S EH1 - T IH0 NG
+RESETING(2)  R IY1 - S EH0 - T IH0 NG
+RESETS  R IY0 - S EH1 T S
+RESETS(2)  R IY1 - S EH0 T S
+RESETTABLE  R IY0 - S EH1 - T AH0 - B AH0 L
+RESETTLE  R IY0 - S EH1 - T AH0 L
+RESETTLED  R IY0 - S EH1 - T AH0 L D
+RESETTLEMENT  R IY0 - S EH1 - T AH0 L - M AH0 N T
+RESH  R EH1 SH
+RESHAPE  R IY0 - SH EY1 P
+RESHAPED  R IY0 - SH EY1 P T
+RESHAPING  R IY0 - SH EY1 - P IH0 NG
+RESHOT  R IY0 - SH AO1 T
+RESHUFFLE  R IY0 - SH AH1 - F AH0 L
+RESHUFFLED  R IY0 - SH AH1 - F AH0 L D
+RESHUFFLING  R IY0 - SH AH1 - F AH0 L - IH0 NG
+RESHUFFLING(2)  R IY0 - SH AH1 - F L IH0 NG
+RESIDE  R IH0 - Z AY1 D
+RESIDE(2)  R IY0 - Z AY1 D
+RESIDED  R IH0 - Z AY1 - D IH0 D
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+RESIDENCIES  R EH1 - Z IH0 - D EH2 N - S IY0 Z
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+RESIDENTIAL  R EH2 - Z IH0 - D EH1 N - CH AH0 L
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+RESIDES  R IH0 - Z AY1 D Z
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+RESIDING  R IH0 - Z AY1 - D IH0 NG
+RESIDING(2)  R IY0 - Z AY1 - D IH0 NG
+RESIDUAL  R IH0 - Z IH1 - JH UW0 - AH0 L
+RESIDUALS  R IH0 - Z IH1 - JH UW0 - AH0 L Z
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+RESIDUES  R EH1 - Z AH0 - D UW2 Z
+RESIGN  R IH0 - Z AY1 N
+RESIGN(2)  R IY0 - Z AY1 N
+RESIGN(3)  R IY0 - S AY1 N
+RESIGNATION  R EH2 - Z AH0 G - N EY1 - SH AH0 N
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+RESIGNATIONS  R EH2 - Z IH0 G - N EY1 - SH AH0 N Z
+RESIGNED  R IH0 - Z AY1 N D
+RESIGNED(2)  R IY0 - Z AY1 N D
+RESIGNED(3)  R IY0 - S AY1 N D
+RESIGNEE  R EH2 - Z IH0 G - N IY1
+RESIGNEES  R EH2 - Z IH0 G - N IY1 Z
+RESIGNING  R IH0 - Z AY1 - N IH0 NG
+RESIGNING(2)  R IY0 - Z AY1 - N IH0 NG
+RESIGNING(3)  R IY0 - S AY1 - N IH0 NG
+RESIGNS  R IH0 - Z AY1 N Z
+RESIGNS(2)  R IY0 - Z AY1 N Z
+RESIGNS(3)  R IY0 - S AY1 N Z
+RESILIENCE  R IH0 - Z IH1 - L IY0 - AH0 N S
+RESILIENCE(2)  R IH0 - Z IH1 - L Y AH0 N S
+RESILIENCY  R IH0 - Z IH1 - L Y AH0 N - S IY0
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+RESISTANCE  R IH0 - Z IH1 - S T AH0 N S
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+RESISTANT  R IH0 - Z IH1 - S T AH0 N T
+RESISTANT(2)  R IY0 - Z IH1 - S T AH0 N T
+RESISTED  R IH0 - Z IH1 - S T IH0 D
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+RESISTING(2)  R IY0 - Z IH1 - S T IH0 NG
+RESISTIVENESS  R IH2 - Z IH1 - S T IH2 V - N AH0 S
+RESISTORS  R IH0 - Z IH1 - S T ER0 Z
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+RESISTS(3)  R IH0 - Z IH1 S S
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+RESISTS(5)  R IH0 - Z IH1 S
+RESISTS(6)  R IY0 - Z IH1 S
+RESKE  R EH1 S K
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+RESNICK  R EH1 Z - N IH0 K
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+RESNIK  R EH1 S - N IH0 K
+RESO  R IH1 - Z OW0
+RESO'S  R IY1 - S OW0 Z
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+RESO(2)  R IY1 - Z OW0
+RESO(3)  R IH1 - S OW0
+RESO(4)  R IY1 - S OW0
+RESOLD  R IY0 - S OW1 L D
+RESOLUTE  R EH1 - Z AH0 - L UW2 T
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+RESOLUTION  R EH2 - Z AH0 - L UW1 - SH AH0 N
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+RESOLUTIONS  R EH2 - Z AH0 - L UW1 - SH AH0 N Z
+RESOLVE  R IY0 - Z AA1 L V
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+RESONATING  R EH1 - Z AH0 - N EY2 - T IH0 NG
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+RESOUND  R IY2 - S AW1 N D
+RESOUND(2)  R IY2 - Z AW1 N D
+RESOUNDING  R IY0 - S AW1 N - D IH0 NG
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+RESOURCEFUL  R IY0 - S AO1 R S - F AH0 L
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+RESPECTABILITY  R IY0 - S P EH2 K - T AH0 - B IH1 - L IH0 - T IY0
+RESPECTABLE  R IH0 - S P EH1 K - T AH0 - B AH0 L
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+RESPECTABLY  R IY0 - S P EH1 K - T AH0 - B L IY0
+RESPECTED  R IH0 - S P EH1 K - T IH0 D
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+RESPECTFUL  R IH0 - S P EH1 K T - F AH0 L
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+RESPECTING  R IY0 - S P EH1 K - T IH0 NG
+RESPECTIVE  R IH0 - S P EH1 K - T IH0 V
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+RESPECTIVELY  R IH0 - S P EH1 K - T IH0 V - L IY0
+RESPECTS  R IH0 - S P EH1 K T S
+RESPECTS(2)  R IY0 - S P EH1 K T S
+RESPECTS(3)  R AH0 - S P EH1 K S
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+RESPESS  R EY1 S - P IH0 S
+RESPIRATION  R EH2 - S P ER0 - EY1 - SH AH0 N
+RESPIRATOR  R EH1 - S P ER0 - EY2 - T ER0
+RESPIRATORS  R EH1 - S P ER0 - EY2 - T ER0 Z
+RESPIRATORY  R EH1 - S P ER0 - AH0 - T AO2 - R IY0
+RESPIRONIC  R EH2 - S P ER0 - AA1 - N IH0 K
+RESPIRONICS  R EH2 - S ER0 - AA1 - N IH0 K S
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+RESPLENDENT  R IY0 - S P L EH1 N - D AH0 N T
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+RESPONDED  R IH0 - S P AA1 N - D IH0 D
+RESPONDED(2)  R IY0 - S P AA1 N - D AH0 D
+RESPONDED(3)  R IY0 - S P AA1 N - D IH0 D
+RESPONDENT  R IH0 - S P AA1 N - D AH0 N T
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+RESPONDER  R IH0 - S P AA1 N - D ER0
+RESPONDERS  R IH0 - S P AA1 N - D ER0 Z
+RESPONDING  R IH0 - S P AA1 N - D IH0 NG
+RESPONDING(2)  R IY0 - S P AA1 N - D IH0 NG
+RESPONDS  R IH0 - S P AA1 N D Z
+RESPONDS(2)  R IY0 - S P AA1 N D Z
+RESPONSE  R IH0 - S P AA1 N S
+RESPONSE(2)  R IY0 - S P AA1 N S
+RESPONSES  R IH0 - S P AA1 N - S IH0 Z
+RESPONSES(2)  R IY0 - S P AA1 N - S AH0 Z
+RESPONSES(3)  R IY0 - S P AA1 N - S IH0 Z
+RESPONSIBILITIES  R IY0 - S P AA2 N - S AH0 - B IH1 - L AH0 - T IY0 Z
+RESPONSIBILITY  R IY0 - S P AA2 N - S AH0 - B IH1 - L AH0 - T IY0
+RESPONSIBLE  R IY0 - S P AA1 N - S AH0 - B AH0 L
+RESPONSIBLY  R IH0 - S P AA1 N - S AH0 - B L IY0
+RESPONSIVE  R IH0 - S P AA1 N - S IH0 V
+RESPONSIVENESS  R IH0 - S P AA1 N - S IH0 V - N AH0 S
+RESPRESS  R EH1 - S P R IH0 S
+RESS  R EH1 S
+RESSA  R EH1 - S AH0
+RESSA'S  R EH1 - S AH0 Z
+RESSEGUIE  R EH1 - S IH0 - G W IY0
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+RESSLER  R EH1 S - L ER0
+REST  R EH1 S T
+RESTAGE  R IY0 - S T EY1 JH
+RESTAGED  R IY0 - S T EY1 JH D
+RESTAINO  R EH0 - S T AA0 - IY1 - N OW0
+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
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+RESTATING  R IY0 - S T EY1 - T IH0 NG
+RESTAURANT  R EH1 - S T ER0 - AA2 N T
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+RESTAURANT'S(2)  R EH1 - S T R AA2 N T S
+RESTAURANT(2)  R EH1 - S T R AA2 N T
+RESTAURANTS  R EH1 - S T ER0 - AA2 N T S
+RESTAURANTS'  R EH1 - S T ER0 - AA2 N T S
+RESTAURANTS'(2)  R EH1 - S T R AA2 N T S
+RESTAURANTS(2)  R EH1 - S T R AA2 N T S
+RESTAURATEUR  R EH2 - S T ER0 - AH0 - T ER1
+RESTAURATEUR(2)  R EH2 - S T R AH0 - T ER1
+RESTAURATEURS  R EH2 - S T ER0 - AH0 - T ER1 Z
+RESTAURATEURS(2)  R EH2 - S T R AH0 - T ER1 Z
+RESTED  R EH1 - S T AH0 D
+RESTED(2)  R EH1 - S T IH0 D
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+RESTFUL  R EH1 S T - F AH0 L
+RESTING  R EH1 - S T IH0 NG
+RESTITUTE  R EH1 - S T IH0 - T UW2 T
+RESTITUTION  R EH2 - S T IH0 - T UW1 - SH AH0 N
+RESTIVE  R EH1 - S T IH0 V
+RESTIVENESS  R EH1 - S T IH0 V - N AH0 S
+RESTIVO  R EH0 - S T IY1 - V OW0
+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
+RESTO  R EH1 - S T OW0
+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
+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
+RESTORE  R IH0 - S T AO1 R
+RESTORED  R IH0 - S T AO1 R D
+RESTORER  R IH0 - S T AO1 - R ER0
+RESTORES  R IH0 - S T AO1 R Z
+RESTORING  R IH0 - S T AO1 - R IH0 NG
+RESTRAIN  R IY0 - S T R EY1 N
+RESTRAINED  R IY0 - S T R EY1 N D
+RESTRAINING  R IY0 - S T R EY1 - N IH0 NG
+RESTRAINS  R IY0 - S T R EY1 N Z
+RESTRAINT  R IH0 - S T R EY1 N T
+RESTRAINT(2)  R IY0 - S T R EY1 N T
+RESTRAINTS  R IH0 - S T R EY1 N T S
+RESTRAINTS(2)  R IY0 - S T R EY1 N T S
+RESTREPO  R EH0 - S T R EH1 - P OW0
+RESTRICT  R IY0 - S T R IH1 K T
+RESTRICTED  R IY0 - S T R IH1 K - T AH0 D
+RESTRICTED(2)  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
+RESTRICTIVENESS  R AH0 - S T R IH1 K - T IH0 V - N AH0 S
+RESTRICTS  R IY0 - S T R IH1 K T S
+RESTROOM  R EH1 S T - R UW2 M
+RESTROOMS  R EH1 S T - R UW2 M Z
+RESTRUCTURE  R IY0 - S T R AH1 K - CH ER0
+RESTRUCTURED  R IY0 - S T R AH1 K - CH ER0 D
+RESTRUCTURES  R IY0 - S T R AH1 K - CH ER0 Z
+RESTRUCTURING  R IY0 - S T R AH1 K - CH ER0 - IH0 NG
+RESTRUCTURINGS  R IY0 - S T R AH1 K - CH ER0 - IH0 NG Z
+RESTS  R EH1 S T S
+RESTYLE  R IY0 - S T AY1 L
+RESTYLED  R IY0 - S T AY1 L D
+RESUBMIT  R IY2 - S AH0 B - M IH1 T
+RESUBMITTED  R IY2 - S AH0 B - M IH1 - T IH0 D
+RESUBMITTING  R IY2 - S AH0 B - M IH1 - T IH0 NG
+RESULT  R IH0 - Z AH1 L T
+RESULT(2)  R IY0 - Z AH1 L T
+RESULTANT  R IY0 - Z AH1 L - T AH0 N T
+RESULTED  R IH0 - Z AH1 L - T IH0 D
+RESULTED(2)  R IY0 - Z AH1 L - T AH0 D
+RESULTED(3)  R IY0 - Z AH1 L - T IH0 D
+RESULTING  R IH0 - Z AH1 L - T IH0 NG
+RESULTING(2)  R IY0 - Z AH1 L - T IH0 NG
+RESULTS  R IH0 - Z AH1 L T S
+RESULTS(2)  R IY0 - Z AH1 L T S
+RESUME  R IH0 - Z UW1 M
+RESUME(2)  R IY0 - Z UW1 M
+RESUME(3)  R EH1 - Z AH0 - M EY2
+RESUMED  R IH0 - Z UW1 M D
+RESUMED(2)  R IY0 - Z UW1 M D
+RESUMES  R IH0 - Z UW1 M Z
+RESUMES(2)  R IY0 - Z UW1 M Z
+RESUMES(3)  R EH1 - Z AH0 - M EY2 Z
+RESUMING  R IH0 - Z UW1 - M IH0 NG
+RESUMING(2)  R IY0 - Z UW1 - M IH0 NG
+RESUMPTION  R IH0 - Z AH1 M P - SH AH0 N
+RESUMPTION(2)  R IY0 - Z AH1 M P - SH AH0 N
+RESUMPTION(3)  R IH0 - Z AH1 M - SH AH0 N
+RESUMPTION(4)  R IY0 - Z AH1 M - SH AH0 N
+RESUPPLY  R IY0 - S AH0 - P L AY1
+RESURFACE  R IY0 - S ER1 - F AH0 S
+RESURFACED  R IY0 - S ER1 - F AH0 S T
+RESURFACING  R IY0 - S ER1 - F AH0 - S IH0 NG
+RESURGENCE  R IY0 - S ER1 - JH AH0 N S
+RESURGENCY  R IH0 - S ER1 - JH AH0 N - S IY0
+RESURGENCY(2)  R IY0 - S ER1 - JH AH0 N - S IY0
+RESURGENT  R IH0 - S ER1 - JH AH0 N T
+RESURGENT(2)  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
+RESUSCITATE  R IH0 - S AH1 - S IH0 - T EY2 T
+RESUSCITATE(2)  R IY0 - S AH1 - S IH0 - T EY2 T
+RESUSCITATED  R IH0 - S AH1 - S IH0 - T EY2 - T IH0 D
+RESUSCITATING  R IH0 - S AH1 - S IH0 - T EY2 - T IH0 NG
+RESUSCITATION  R IH0 - S AH2 - S IH0 - T EY1 - SH AH0 N
+RET  R EH1 T
+RETA  R EH1 - T AH0
+RETABLOS  R IY0 - T AE1 - B L OW0 S
+RETAIL  R IY1 - T EY2 L
+RETAILED  R IY1 - T EY2 L D
+RETAILER  R IY1 - T EY2 - L ER0
+RETAILER'S  R IY1 - T EY2 - L ER0 Z
+RETAILERS  R IY1 - T EY2 - L ER0 Z
+RETAILERS'  R IY1 - T EY2 - L ER0 Z
+RETAILING  R IY1 - T EY2 - L IH0 NG
+RETAILING'S  R IY1 - T EY2 - L IH0 NG Z
+RETAILS  R IY1 - T EY2 L Z
+RETAIN  R IH0 - T EY1 N
+RETAIN(2)  R IY0 - T EY1 N
+RETAINED  R IH0 - T EY1 N D
+RETAINED(2)  R IY0 - T EY1 N D
+RETAINER  R IH0 - T EY1 - N ER0
+RETAINER(2)  R IY0 - T EY1 - N ER0
+RETAINERS  R IH0 - T EY1 - N ER0 Z
+RETAINING  R IH0 - T EY1 - N IH0 NG
+RETAINING(2)  R IY0 - T EY1 - N IH0 NG
+RETAINS  R IH0 - T EY1 N Z
+RETAINS(2)  R IY0 - T EY1 N Z
+RETAKE  R IY1 - T EY1 K
+RETAKE(2)  R IY0 - T EY1 K
+RETAKEN  R IY0 - T EY1 - K AH0 N
+RETAKING  R IY0 - T EY1 - K IH0 NG
+RETALIATE  R IH0 - T AE1 - L IY0 - EY2 T
+RETALIATE(2)  R IY0 - T AE1 - L IY0 - EY2 T
+RETALIATED  R IH0 - T AE1 - L IY0 - EY2 - T IH0 D
+RETALIATED(2)  R IY0 - T AE1 - L IY0 - EY2 - T IH0 D
+RETALIATING  R IH0 - T AE1 - L IY0 - EY2 - T IH0 NG
+RETALIATION  R IY0 - T AE2 - L IY0 - EY1 - SH AH0 N
+RETALIATORY  R IY0 - T AE1 - L Y AH0 - T AO2 - R IY0
+RETANA  R EH0 - T AE1 - N AH0
+RETARD  R IH0 - T AA1 R D
+RETARD(2)  R IY0 - T AA1 R D
+RETARDANT  R IY0 - T AA1 R - D AH0 N T
+RETARDATION  R IY0 - T AA0 R - D EY1 - SH AH0 N
+RETARDED  R IH0 - T AA1 R - D IH0 D
+RETARDED(2)  R IY0 - T AA1 R - D AH0 D
+RETARDED(3)  R IY0 - T AA1 R - D IH0 D
+RETARDING  R IH0 - T AA1 R - D IH0 NG
+RETARDING(2)  R IY0 - T AA1 R - D IH0 NG
+RETARDS  R IH0 - T AA1 R D Z
+RETARDS(2)  R IY0 - T AA1 R D Z
+RETELL  R IY0 - T EH1 L
+RETELLING  R IY0 - T EH1 - L IH0 NG
+RETEMEYER  R EH1 - T AH0 - M AY2 R
+RETENTION  R IY0 - T EH1 N - SH AH0 N
+RETEST  R IY1 - T EH1 S T
+RETESTED  R IY0 - T EH1 - S T IH0 D
+RETESTING  R IY0 - T EH1 - S T IH0 NG
+RETESTS  R IY1 - T EH1 S T S
+RETESTS(2)  R IY1 - T EH1 S S
+RETESTS(3)  R IY1 - T EH1 S
+RETHERFORD  R IH0 - TH ER1 - F ER0 D
+RETHINK  R IY0 - TH IH1 NG K
+RETHINKING  R IY0 - TH IH1 NG - K IH0 NG
+RETHOUGHT  R IY0 - TH AO1 T
+RETICENCE  R EH1 - T IH0 - S AH0 N S
+RETICENT  R EH1 - T IH0 - S AH0 N T
+RETIN  R EH1 - T IH0 N
+RETINA  R EH1 - T AH0 - N AH0
+RETINAL  R EH1 - T AH0 - N AH0 L
+RETINOBLASTOMA  R EH2 - T IH0 - N OW2 - B L AE2 - S T OW1 - M AH0
+RETINOID  R EH1 - T IH0 - N OY0 D
+RETINOIDS  R EH1 - T IH0 - N OY0 D Z
+RETINUE  R EH1 - T AH0 - N UW2
+RETINYL  R EH1 - T IH0 - N AH0 L
+RETIRE  R IH0 - T AY1 R
+RETIRE(2)  R IY0 - T AY1 R
+RETIRE(3)  R IY2 - T AY1 - ER0
+RETIRED  R IH0 - T AY1 R D
+RETIRED(2)  R IY0 - T AY1 - ER0 D
+RETIRED(3)  R IY0 - T AY1 R D
+RETIREE  R IY0 - T AY1 - R IY1
+RETIREE'S  R IH0 - T AY2 - R IY1 Z
+RETIREES  R IY0 - T AY1 - R IY1 Z
+RETIREES'  R IH0 - T AY2 - R IY1 Z
+RETIREMENT  R IY0 - T AY1 - ER0 - M AH0 N T
+RETIREMENT(2)  R IH0 - T AY1 - ER0 - M AH0 N T
+RETIREMENTS  R IH0 - T AY1 R - M AH0 N T S
+RETIREMENTS(2)  R IY0 - T AY1 R - M AH0 N T S
+RETIRES  R IH0 - T AY1 R Z
+RETIRES(2)  R IY0 - T AY1 - ER0 Z
+RETIRES(3)  R IY0 - T AY1 R Z
+RETIRING  R IH0 - T AY1 - R IH0 NG
+RETIRING(2)  R IY0 - T AY1 - ER0 - IH0 NG
+RETIRING(3)  R IY0 - T AY1 - R IH0 NG
+RETLIN  R EH1 T - L IH0 N
+RETO  R IY1 - T UW1
+RETOOK  R IY0 - T UH1 K
+RETOOL  R IY0 - T UW1 L
+RETOOLED  R IY0 - T UW1 L D
+RETOOLING  R IY0 - T UW1 - L IH0 NG
+RETORT  R IY1 - T AO2 R T
+RETORTED  R IY0 - T AO1 R - T IH0 D
+RETORTS  R IH0 - T AO1 R T S
+RETORTS(2)  R IY0 - T AO1 R T S
+RETOUCHING  R IY0 - T AH1 - CH IH0 NG
+RETRACE  R IY0 - T R EY1 S
+RETRACED  R IY0 - T R EY1 S T
+RETRACEMENT  R IY0 - T R EY1 S - M AH0 N T
+RETRACING  R IY0 - T R EY1 - S IH0 NG
+RETRACT  R IY0 - T R AE1 K T
+RETRACTABLE  R IY0 - T R AE1 K - T AH0 - B AH0 L
+RETRACTED  R IY0 - T R AE1 K - T AH0 D
+RETRACTING  R IY0 - T R AE1 K - T IH0 NG
+RETRACTION  R IY0 - T R AE1 K - SH AH0 N
+RETRACTS  R IY0 - T R AE1 K T S
+RETRAIN  R IY0 - T R EY1 N
+RETRAINED  R IY0 - T R EY1 N D
+RETRAINING  R IY0 - T R EY1 - N IH0 NG
+RETRANSMISSION  R IY2 - T R AE0 N Z - M IH1 - SH AH0 N
+RETREAD  R IY0 - T R EH1 D
+RETREADING  R IY0 - T R EH1 - D IH0 NG
+RETREADS  R IY0 - T R EH1 D Z
+RETREAT  R IY0 - T R IY1 T
+RETREATED  R IY0 - T R IY1 - T AH0 D
+RETREATED(2)  R IY0 - T R IY1 - T IH0 D
+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
+RETRIED  R IY0 - T R AY1 D
+RETRIEVAL  R IH0 - T R IY1 - V AH0 L
+RETRIEVAL(2)  R IY0 - T R IY1 - V AH0 L
+RETRIEVE  R IH0 - T R IY1 V
+RETRIEVE(2)  R IY0 - T R IY1 V
+RETRIEVED  R IY0 - T R IY1 V D
+RETRIEVER  R IY0 - T R IY1 - V ER0
+RETRIEVERS  R IY0 - T R IY1 - V ER0 Z
+RETRIEVES  R IH0 - T R IY1 V Z
+RETRIEVES(2)  R IY0 - T R IY1 V Z
+RETRIEVING  R IY0 - T R IY1 - V IH0 NG
+RETRO  R EH1 - T R OW0
+RETROACTIVE  R EH2 - T R OW0 - AE1 K - T IH0 V
+RETROACTIVELY  R EH2 - T R OW0 - AE1 K - T IH0 V - L IY0
+RETROACTIVITY  R EH2 - T R OW0 - AE0 K - T IH1 - V IH0 - T IY0
+RETROFIT  R EH1 - T R OW0 - F IH2 T
+RETROFITS  R EH1 - T R OW0 - F IH2 T S
+RETROFITTED  R EH1 - T R OW2 - F IH2 - T IH0 D
+RETROFITTER  R EH1 - T R OW2 - F IH2 - T ER0
+RETROFITTING  R EH1 - T R OW0 - F IH2 - T IH0 NG
+RETROGRADE  R EH1 - T R AH0 - G R EY2 D
+RETROSPECT  R EH1 - T R AH0 - S P EH2 K T
+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
+RETROVIR  R EH1 - T R OW0 - V IH2 R
+RETROVIRUS  R EH2 - T R OW0 - V AY1 - R AH0 S
+RETROVIRUSES  R EH2 - T R OW0 - V AY1 - R AH0 - S IH0 Z
+RETRY  R IY0 - T R AY1
+RETRYING  R IY0 - T R AY1 - IH0 NG
+RETTBERG  R EH1 T - B ER0 G
+RETTER  R EH1 - T ER0
+RETTEW  R EH1 - CH UW0
+RETTIG  R EH1 - T IH0 G
+RETTINGER  R EH1 - T IH0 N - JH ER0
+RETTINGER(2)  R EH1 - T IH0 - NG ER0
+RETTKE  R EH1 T - K IY0
+RETTON  R EH1 - T AH0 N
+RETURN  R IH0 - T ER1 N
+RETURN(2)  R IY0 - T ER1 N
+RETURNABLE  R IY0 - T ER1 - N AH0 - B AH0 L
+RETURNED  R IH0 - T ER1 N D
+RETURNED(2)  R IY0 - T ER1 N D
+RETURNEE  R IH0 - T ER0 - N IY1
+RETURNEES  R IH0 - T ER0 - N IY1 Z
+RETURNING  R IH0 - T ER1 - N IH0 NG
+RETURNING(2)  R IY0 - T ER1 - N IH0 NG
+RETURNS  R IH0 - T ER1 N Z
+RETURNS'  R AH0 - T ER1 N Z
+RETURNS'(2)  R IY0 - T ER1 N Z
+RETURNS(2)  R IY0 - T ER1 N Z
+RETZ  R EH1 T S
+RETZER  R EH1 T - Z ER0
+RETZLAFF  R EH1 T Z - L AH0 F
+RETZLOFF  R EH1 T Z - L AO0 F
+REUBEN  R UW1 - B AH0 N
+REUBER  R OY1 - B ER0
+REUL  R UW1 L
+REULAND  R OY1 - L AH0 N D
+REULE  R UW1 L
+REUM  R IY1 - AH0 M
+REUNIFICATION  R IY0 - UW2 - N AH0 - F AH0 - K EY1 - SH AH0 N
+REUNIFIED  R IY0 - UW1 - N AH0 - F AY2 D
+REUNIFY  R IY0 - UW1 - N AH0 - F AY2
+REUNION  R IY0 - UW1 - N Y AH0 N
+REUNIONS  R IY0 - UW1 - N Y AH0 N Z
+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
+REUSABLE  R IY0 - UW1 - Z AH0 - B AH0 L
+REUSCH  R OY1 SH
+REUSE  R IY0 - Y UW1 S
+REUSE(2)  R IY0 - Y UW1 Z
+REUSED  R IY0 - UW1 Z D
+REUSING  R IY0 - Y UW1 - Z IH0 NG
+REUSS  R UW1 S
+REUSSER  R OY1 - S ER0
+REUST  R UW1 S T
+REUTER  R OY1 - T ER0
+REUTER'S  R OY1 - T ER0 Z
+REUTERS  R OY1 - T ER0 Z
+REUTERS'  R OY1 - T ER0 Z
+REUTERS'S  R OY1 - T ER0 - Z IH0 Z
+REUTERS'S(2)  R OY1 - T ER0 Z
+REUTGERS  R OY1 T - G ER0 Z
+REUTHER  R OY1 - DH ER0
+REUTTER  R OY1 - T ER0
+REUTZEL  R OY1 T - Z AH0 L
+REV  R EH1 V
+REVA  R EY1 - V AH0
+REVAK  R EH1 - V AH0 K
+REVALUATION  R IY0 - V AE1 L - Y UW0 - EY1 - SH AH0 N
+REVALUATIONS  R IY0 - IH0 - V AE2 L - Y UW0 - EY1 - SH AH0 N Z
+REVALUE  R IY0 - V AE1 L - Y UW2
+REVALUED  R IY0 - V AE1 L - Y UW0 D
+REVALUING  R IY0 - V AE1 L - Y UW0 - IH0 NG
+REVAMP  R IY0 - V AE1 M P
+REVAMPED  R IY0 - V AE1 M P T
+REVAMPING  R IY0 - V AE1 M - P IH0 NG
+REVAMPS  R IY0 - V AE1 M P S
+REVARD  R IH0 - V AA1 R D
+REVCO  R EH1 V - K OW0
+REVCO'S  R EH1 V - K OW0 Z
+REVEAL  R IH0 - V IY1 L
+REVEAL(2)  R IY0 - V IY1 L
+REVEALED  R IH0 - V IY1 L D
+REVEALED(2)  R IY0 - V IY1 L D
+REVEALING  R IH0 - V IY1 - L IH0 NG
+REVEALING(2)  R IY0 - V IY1 - L IH0 NG
+REVEALS  R IH0 - V IY1 L Z
+REVEALS(2)  R IY0 - V IY1 L Z
+REVEL  R EH1 - V AH0 L
+REVELATION  R EH2 - V AH0 - L EY1 - SH AH0 N
+REVELATIONS  R EH2 - V AH0 - L EY1 - SH AH0 N Z
+REVELATORY  R IH0 - V EH1 - L AH0 - T AO2 - R IY0
+REVELED  R EH1 - V AH0 L D
+REVELER  R EH1 - V AH0 - L ER0
+REVELERS  R EH1 - V AH0 - L ER0 Z
+REVELES  R EY0 - V EY1 - L EH0 S
+REVELING  R EH1 - V AH0 L - IH0 NG
+REVELL  R EY0 - V EY1 L
+REVELLE  R IH0 - V EH1 L
+REVELLO  R EH0 - V EH1 - L OW0
+REVELRY  R EH1 - V AH0 L - R IY0
+REVELS  R EH1 - V AH0 L Z
+REVENGE  R IY0 - V EH1 N JH
+REVENUE  R EH1 - V AH0 - N UW2
+REVENUE(2)  R EH1 - V AH0 - N Y UW2
+REVENUES  R EH1 - V AH0 - N UW2 Z
+REVENUES(2)  R EH1 - V AH0 - N Y UW2 Z
+REVERB  R IY0 - V ER1 B
+REVERBED  R IY0 - V ER1 B D
+REVERBERATE  R IH0 - V ER1 - B ER0 - AH0 T
+REVERBERATE(2)  R IH0 - V ER1 - B ER0 - EY0 T
+REVERBERATED  R IH0 - V ER1 - B ER0 - EY2 - T IH0 D
+REVERBERATED(2)  R IY0 - V ER1 - B ER0 - EY2 - T AH0 D
+REVERBERATED(3)  R IY0 - V ER1 - B ER0 - EY2 - T IH0 D
+REVERBERATES  R IH0 - V ER1 - B ER0 - EY2 T S
+REVERBERATING  R IH0 - V ER1 - B ER0 - EY2 - T IH0 NG
+REVERBERATING(2)  R IY0 - V ER1 - B ER0 - EY2 - T IH0 NG
+REVERBERATION  R IY0 - V ER2 - B ER0 - EY1 - SH AH0 N
+REVERBERATIONS  R IH0 - V ER2 - B ER0 - EY1 - SH AH0 N Z
+REVERCOMB  R EH1 - V ER0 - K AH0 M
+REVERE  R IH0 - V IH1 R
+REVERED  R IH0 - V IH1 R D
+REVERENCE  R EH1 - V ER0 - AH0 N S
+REVEREND  R EH1 - V ER0 - AH0 N D
+REVEREND(2)  R EH1 - V R AH0 N D
+REVERENT  R EH1 - V ER0 - AH0 N T
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+SEISMOLOGIST  S AY2 Z - M AA1 - L AH0 - JH AH0 S T
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+SEISMOLOGISTS(2)  S AY2 Z - M AA1 - L AH0 - JH AH0 S S
+SEISMOLOGISTS(3)  S AY2 Z - M AA1 - L AH0 - JH AH0 S
+SEISMOLOGY  S AY2 Z - M AA1 - L AH0 - JH IY0
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+SEITZINGER  S AY1 T - Z IH0 - NG ER0
+SEIVERT  S AY1 - V ER0 T
+SEIWERT  S AY1 - W ER0 T
+SEIX  S IY1 K S
+SEIXAS  S IY1 K - S AH0 S
+SEIYAKU  S EY2 - Y AA1 - K UW2
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+SEIZE  S IY1 Z
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+SEKISUI  S EY2 - K IH0 - S UW1 - IY0
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+SELANDER  S EH1 - L AH0 N - D ER0
+SELAS  S EH1 - L AH0 S
+SELASSIE  S AH0 - L AE1 - S IY0
+SELBE  S EH1 L B
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+SELDIN  S EH1 L - D IH0 N
+SELDOM  S EH1 L - D AH0 M
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+SELECTED  S AH0 - L EH1 K - T AH0 D
+SELECTED(2)  S AH0 - L EH1 K - T IH0 D
+SELECTING  S AH0 - L EH1 K - T IH0 NG
+SELECTION  S AH0 - L EH1 K - SH AH0 N
+SELECTIONS  S AH0 - L EH1 K - SH AH0 N Z
+SELECTIVE  S AH0 - L EH1 K - T IH0 V
+SELECTIVELY  S AH0 - L EH1 K - T IH0 V - L IY0
+SELECTIVITY  S IH0 - L EH2 K - T IH1 - V AH0 - T IY0
+SELECTNET  S AH0 - L EH1 K T - N EH2 T
+SELECTS  S AH0 - L EH1 K T S
+SELENA  S AH0 - L IY1 - N AH0
+SELENA'S  S AH0 - L IY1 - N AH0 Z
+SELENE  S AH0 - L IY1 N
+SELENITE  S EH1 - L IH0 - N AY2 T
+SELENIUM  S AH0 - L IY1 - N IY0 - AH0 M
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+SELES'  S EH1 - L EH0 S
+SELEY  S IY1 - L IY0
+SELF  S EH1 L F
+SELF'S  S EH1 L F S
+SELF-AGGRANDIZEMENT  S EH1 L - F AE1 - G R AH0 N - D AY2 Z - M AH0 N T
+SELF-AGGRANDIZING  S EH1 L - F AH0 - G R AE1 N - D AY2 - Z IH0 NG
+SELF-CONFIDENCE  S EH1 L F - K AA1 N - F AH0 - D AH0 N S
+SELF-CONFIDENT  S EH1 L F - K AA1 N - F AH0 - D AH0 N T
+SELF-CONGRATULATION  S EH1 L F - K AH0 N - G R AE2 - CH AH0 - L EY1 - SH AH0 N
+SELF-CONSISTENT  S EH2 L F - K AH0 N - S IH1 - S T AH0 N T
+SELF-CONTAINED  S EH1 L F - K AH0 N - T EY1 N D
+SELF-CONTROL  S EH1 L F - K AH0 N - T R OW1 L
+SELF-DECEIVING  S EH2 L F - D IY0 - S IY1 - V IH0 NG
+SELF-DECEPTION  S EH1 L F - D AH0 - S EH1 P - SH AH0 N
+SELF-DELIVERANCE  S EH1 L F - D IH0 - L IH1 - V ER0 - AH0 N S
+SELF-DEPRECATING  S EH1 L F - D EH1 - P R AH0 - K EY2 - T IH0 NG
+SELF-DETERMINATION  S EH1 L F - D IH0 - T ER2 - M AH0 - N EY1 - SH AH0 N
+SELF-DORMANT  S EH1 L F - D AO1 R - M AH0 N T
+SELF-ENERGIZING  S EH1 L - F EH1 - N ER0 - JH AY2 - Z IH0 NG
+SELF-ENRICHMENT  S EH2 L - F AH0 N - R IH1 CH - M AH0 N T
+SELF-FERTILIZING  S EH1 L F - F ER1 - T AH0 - L AY2 - Z IH0 NG
+SELF-FRUITFUL  S EH1 L F - F R UW1 T - F AH0 L
+SELF-GOVERN  S EH1 L F - G AH1 - V ER0 N
+SELF-GOVERNING  S EH1 L F - G AH1 - V ER0 - N IH0 NG
+SELF-GOVERNMENT  S EH1 L F - G AH1 - V ER0 N - M AH0 N T
+SELF-HELP  S EH1 L F - HH EH1 L P
+SELF-IMPROVEMENT  S EH1 L - F IH0 M - P R UW1 V - M AH0 N T
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+SELF-PERPETUATION  S EH1 L F - P ER0 - P EH2 - CH UW0 - EY1 - SH AH0 N
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+SELF-PORTRAIT  S EH1 L F - P AO1 R - T R AH0 T
+SELF-PROFESSED  S EH1 L F - P R AH0 - F EH1 S T
+SELF-SUBSISTENCE  S EH1 L F - S AH0 B - S IH1 - S T AH0 N S
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+SELF-SUFFICIENT  S EH1 L F - S AH0 - F IH1 - SH AH0 N T
+SELF-SUFFICIENT(2)  S EH1 L F - S AH0 - F IH1 - SH IH0 N T
+SELF-TORMENT  S EH1 L F - T AO1 R - M EH2 N T
+SELF-TORMENTS  S EH1 L F - T AO1 R - M EH2 N T S
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+SEMI-COLON  S EH1 - M IY0 - K OW1 - L AH0 N
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+SEMIAUTOMATIC(3)  S EH2 - M IH0 - AO2 - T AH0 - M AE1 - T IH0 K
+SEMICIRCULAR  S EH2 - M IY0 - S ER1 - K Y AH0 - L ER0
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+SEMICONDUCTOR'S  S EH2 - M IY0 - K AH0 N - D AH1 K - T ER0 Z
+SEMICONDUCTOR'S(2)  S EH2 - M IH0 - K AH0 N - D AH1 K - T ER0 Z
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+SEMICONDUCTORS(3)  S EH2 - M AY0 - K AH0 N - D AH1 K - T ER0 Z
+SEMICYLINDRICAL  S EH2 - M IY0 - S AH0 - L IH1 N - D R IH0 - K AH0 L
+SEMICYLINDRICAL(2)  S EH2 - M IH0 - S AH0 - L IH1 N - D R IH0 - K AH0 L
+SEMICYLINDRICAL(3)  S EH2 - M AY0 - S AH0 - L IH1 N - D R IH0 - K AH0 L
+SEMIDRY  S EH2 - M IY0 - D R AY1
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+SEMIFINAL  S EH2 - M IY0 - F AY1 - N AH0 L
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+SEMIFINAL(3)  S EH2 - M AY0 - F AY1 - N AH0 L
+SEMIFINALIST  S EH2 - M IY0 - F AY1 - N AH0 L - IH0 S T
+SEMIFINALIST(2)  S EH2 - M IH0 - F AY1 - N AH0 - L IH0 S T
+SEMIFINALIST(3)  S EH2 - M AY0 - F AY1 - N AH0 - L IH0 S T
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+SEMIFINALISTS(2)  S EH2 - M IY0 - F AY1 - N AH0 L - IH0 S S
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+SEMIFINALISTS(4)  S EH2 - M AY0 - F AY1 - N AH0 - L IH0 S T S
+SEMIFINALISTS(5)  S EH2 - M AY0 - F AY1 - N AH0 L - IH0 S S
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+SEMIFINALS(2)  S EH2 - M IH0 - F AY1 - N AH0 L Z
+SEMIFINALS(3)  S EH2 - M AY0 - F AY1 - N AH0 L Z
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+SEMIFINISH(2)  S EH2 - M AY0 - F IH1 - N IH0 SH
+SEMIFINISH(3)  S EH2 - M IH0 - F IH1 - N IH0 SH
+SEMIFINISHED  S EH2 - M IY0 - F IH1 - N IH0 SH T
+SEMIFINISHED(2)  S EH2 - M AY0 - F IH1 - N IH0 SH T
+SEMIFINISHED(3)  S EH2 - M IH0 - F IH1 - N IH0 SH T
+SEMIGLOSS  S EH2 - M IY0 - G L AA1 S
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+SEMILEGENDARY  S EH2 - M IY0 - L EH1 - JH AH0 N - D EH2 - R IY0
+SEMILEGENDARY(2)  S EH2 - M IH0 - L EH1 - JH AH0 N - D EH2 - R IY0
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+SEMIPERMANENT(3)  S EH2 - M AY0 - P ER1 - M AH0 - N AH0 N T
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+SEMIPRECIOUS(2)  S EH2 - M IH0 - P R EH1 - SH AH0 S
+SEMIPRECIOUS(3)  S EH2 - M AY0 - P R EH1 - SH AH0 S
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+SEMITROPICAL(3)  S EH2 - M AY0 - T R AA1 - P IH0 - K AH0 L
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+SHEEK  SH IY1 K
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+SHIPMAN  SH IH1 P - M AH0 N
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+SHOBE  SH OW1 B
+SHOBER  SH OW1 - B ER0
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+SHOCKED  SH AA1 K T
+SHOCKER  SH AA1 - K ER0
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+SHOCKING  SH AA1 - K IH0 NG
+SHOCKINGLY  SH AA1 - K IH0 NG - L IY0
+SHOCKLEY  SH AA1 K - L IY0
+SHOCKS  SH AA1 K S
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+SHOFF  SH AO1 F
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+SHONK  SH AA1 NG K
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+SHOOTIN'  SH UW1 - T IH0 N
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+SHOOTOUT  SH UW1 T - AW0 T
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+SHOP  SH AA1 P
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+SHORTAGE  SH AO1 R - T AH0 JH
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+SHORTAGES  SH AO1 R - T AH0 - JH AH0 Z
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+SHORTALL  SH AO1 R - T AH0 L
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+SHORTCHANGING  SH AO2 R T - CH EY1 N - JH IH0 NG
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+SHORTELL  SH AO0 R - T EH1 L
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+SHORTENS  SH AO1 R - T AH0 N Z
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+SHORTFALL  SH AO1 R T - F AO2 L
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+SHORTGRASS  SH AO1 R T - G R AE2 S
+SHORTHAIR  SH AO1 R T - HH EH2 R
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+SHORTLY  SH AO1 R T - L IY0
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+SHUNTED  SH AH1 N - T IH0 D
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+SPROUTED  S P R AW1 - T AH0 D
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+SPROW  S P R AW1
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+SPURIOUS  S P Y UH1 - R IY0 - AH0 S
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+SPYCHALSKI  S P IH0 - HH AA1 L S - K IY0
+SPYGLASS  S P AY1 - G L AE2 S
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+SQUABBLE  S K W AA1 - B AH0 L
+SQUABBLED  S K W AA1 - B AH0 L D
+SQUABBLES  S K W AA1 - B AH0 L Z
+SQUABBLING  S K W AA1 - B AH0 L - IH0 NG
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+SQUAD  S K W AA1 D
+SQUAD'S  S K W AA1 D Z
+SQUADRON  S K W AA1 - D R AH0 N
+SQUADRONS  S K W AA1 - D R AH0 N Z
+SQUADS  S K W AA1 D Z
+SQUALID  S K W AA1 - L AH0 D
+SQUALL  S K W AO1 L
+SQUALLS  S K W AO1 L Z
+SQUALOR  S K W AA1 - L ER0
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+SQUANDERING  S K W AA1 N - D ER0 - IH0 NG
+SQUARE  S K W EH1 R
+SQUARED  S K W EH1 R D
+SQUARELY  S K W EH1 R - L IY0
+SQUARES  S K W EH1 R Z
+SQUARING  S K W EH1 - R IH0 NG
+SQUASH  S K W AA1 SH
+SQUASHED  S K W AA1 SH T
+SQUASHING  S K W AA1 - SH IH0 NG
+SQUASHY  S K W AA1 - SH IY0
+SQUAT  S K W AA1 T
+SQUATS  S K W AA1 T S
+SQUATTER  S K W AA1 - T ER0
+SQUATTERS  S K W AA1 - T ER0 Z
+SQUATTING  S K W AA1 - T IH0 NG
+SQUAWK  S K W AO1 K
+SQUAWKING  S K W AO1 - K IH0 NG
+SQUAWKS  S K W AO1 K S
+SQUEAK  S K W IY1 K
+SQUEAKED  S K W IY1 K T
+SQUEAKER  S K W IY1 - K ER0
+SQUEAKERS  S K W IY1 - K ER0 Z
+SQUEAKING  S K W IY1 - K IH0 NG
+SQUEAKS  S K W IY1 K S
+SQUEAKY  S K W IY1 - K IY0
+SQUEAL  S K W IY1 L
+SQUEALING  S K W IY1 - L IH0 NG
+SQUEALS  S K W IY1 L Z
+SQUEAMISH  S K W IY1 - M IH0 SH
+SQUEAMISHNESS  S K W IY1 - M IH0 SH - N AH0 S
+SQUEEGEE  S K W IY1 - JH IY0
+SQUEEZE  S K W IY1 Z
+SQUEEZED  S K W IY1 Z D
+SQUEEZES  S K W IY1 - Z IH0 Z
+SQUEEZING  S K W IY1 - Z IH0 NG
+SQUELCH  S K W EH1 L CH
+SQUELCHED  S K W EH1 L CH T
+SQUELCHING  S K W EH1 L - CH IH0 NG
+SQUIBB  S K W IH1 B
+SQUIBB'S  S K W IH1 B Z
+SQUID  S K W IH1 D
+SQUIER  S K W AY1 - ER0
+SQUIERS  S K W AY1 - ER0 Z
+SQUIGGLE  S K W IH1 - G AH0 L
+SQUIGGLES  S K W IH1 - G AH0 L Z
+SQUILLACE  S K W IH1 - L AH0 S
+SQUILLANTE  S K W IY0 - L AA1 N - T EY0
+SQUINT  S K W IH1 N T
+SQUINTING  S K W IH1 N - T IH0 NG
+SQUIRE  S K W AY1 R
+SQUIRES  S K W AY1 - ER0 Z
+SQUIRM  S K W ER1 M
+SQUIRMING  S K W ER1 - M IH0 NG
+SQUIRMS  S K W ER1 M Z
+SQUIRREL  S K W ER1 - AH0 L
+SQUIRRELED  S K W ER1 - AH0 L D
+SQUIRRELS  S K W ER1 - AH0 L Z
+SQUIRT  S K W ER1 T
+SQUIRTED  S K W ER1 - T AH0 D
+SQUIRTED(2)  S K W ER1 - T IH0 D
+SQUIRTING  S K W ER1 - T IH0 NG
+SQUIRTS  S K W ER1 T S
+SQUISHY  S K W IH1 - SH IY0
+SQUITIERI  S K W IY0 - T IH1 - R IY0
+SQUYRES  S K W AY1 R Z
+SR  S IY1 - N Y ER0
+SR(2)  S IH1 - S T ER0
+SRADER  SH R EY1 - D ER0
+SRAM  SH R AE1 M
+SRAM(2)  EH1 - S R AE1 M
+SRAMEK  SH R AE1 - M IH0 K
+SRDAN  S ER1 - D AE2 N
+SREBRENICA  S R EY2 - B R EH0 - N IY1 T - S AH0
+SREBRENICA'S  S R EY2 - B R EH0 - N IY1 T - S AH0 Z
+SREBRENICA'S(2)  SH R EY2 - B R AH0 - N IY1 T - S AH0 Z
+SREBRENICA(2)  SH R EY2 - B R AH0 - N IY1 T - S AH0
+SREMAC  S R IY1 - M AE2 K
+SRI  SH R IY1
+SRI(2)  EH1 - S AA1 - R AY1
+SRI(3)  S R IY1
+SRI-LANKA  S R IY1 - L AE1 NG - K AH0
+SRI-LANKA(2)  SH R IY1 - L AE1 NG - K AH0
+SRINAGAR  S R IY1 - N AH0 - G AA2 R
+SRINAGAR(2)  SH R IY1 - N AH0 - G AA2 R
+SRINAGARB  S R IY1 - N AH0 - G AA2 R B
+SRINAGARB(2)  SH R IY1 - N AH0 - G AA2 R B
+SRINIVAS  S R IY1 - N IH0 - V AA2 S
+SRINIVASAN  S R IY0 - N IY0 - V AA0 - S AA1 N
+SRINIVASAN(2)  SH R IY0 - N IY0 - V AA0 - S AA1 N
+SRIRAM  S R IY1 - R AA2 M
+SRIRAM(2)  SH R IY1 - R AA2 M
+SRIVASTAVA  S R IY0 - V AA0 - S T AA1 - V AH0
+SRIVASTAVA(2)  SH R IY0 - V AA0 - S T AA1 - V AH0
+SROCK  SH R AA1 K
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+SROUFE  SH R OW1 F
+SSANGYONG  S AE1 NG - Y AO2 NG
+ST  S T R IY1 T
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+STA  S T AH1
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+STAAB  S T AA1 B
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+STABENOW  S T AE1 - B IH0 - N OW0
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+STABILIZATION  S T EY2 - B AH0 - L AH0 - Z EY1 - SH AH0 N
+STABILIZE  S T EY1 - B AH0 - L AY2 Z
+STABILIZED  S T EY1 - B AH0 - L AY2 Z D
+STABILIZER  S T EY1 - B AH0 - L AY2 - Z ER0
+STABILIZERS  S T EY1 - B AH0 - L AY2 - Z ER0 Z
+STABILIZES  S T EY1 - B AH0 - L AY2 - Z AH0 Z
+STABILIZES(2)  S T EY1 - B AH0 - L AY2 - Z IH0 Z
+STABILIZING  S T EY1 - B AH0 - L AY2 - Z IH0 NG
+STABLE  S T EY1 - B AH0 L
+STABLER  S T EY1 - B AH0 L - ER0
+STABLER(2)  S T EY1 - B L ER0
+STABLES  S T EY1 - B AH0 L Z
+STABLEST  S T EY1 - B AH0 - L AH0 S T
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+STACCATO  S T AH0 - K AA1 - T OW2
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+STACH  S T AE1 CH
+STACHNIK  S T AE1 K - N IH0 K
+STACHOWIAK  S T AH0 - HH AW1 - IY0 - AE0 K
+STACHOWICZ  S T AA1 - HH AH0 - V IH0 CH
+STACHOWSKI  S T AH0 - HH AO1 F S - K IY0
+STACHURA  S T AA0 - K UH1 - R AH0
+STACHURSKI  S T AH0 - HH ER1 S - K IY0
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+STACIE  S T AE1 - K IY0
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+STADIA  S T EY1 - D IY0 - AH0
+STADIUM  S T EY1 - D IY0 - AH0 M
+STADIUM'S  S T EY1 - D IY0 - AH0 M Z
+STADIUMS  S T EY1 - D IY0 - AH0 M Z
+STADLER  S T AE1 D - L ER0
+STADNIK  S T AE1 D - N IH0 K
+STADT  S T AE1 D T
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+STADTMILLER  SH T AE1 T - M IH1 - L ER0
+STAEBELL  S T EH1 - B EH0 L
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+STAEHLE  S T EY1 L
+STAFF  S T AE1 F
+STAFF'S  S T AE1 F S
+STAFFA  S T AA1 - F AH0
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+STAFFIERI  S T AA0 - F IH1 - R IY0
+STAFFING  S T AE1 - F IH0 NG
+STAFFORD  S T AE1 - F ER0 D
+STAFFORD'S  S T AE1 - F ER0 D Z
+STAFFORDSHIRE  S T AE1 - F ER0 D - SH ER0
+STAFFORDSHIRE'S  S T AE1 - F ER0 D - SH ER0 Z
+STAFFORDSHIRE'S(2)  S T AE1 - F ER0 D - SH AY2 - ER0 Z
+STAFFORDSHIRE(2)  S T AE1 - F ER0 D - SH AY2 - ER0
+STAFFS  S T AE1 F S
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+STAGFLATE  S T AE2 G - F L EY1 T
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+STAGNANT  S T AE1 G - N AH0 N T
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+STAHL  S T AA1 L
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+STAIRWAYS  S T EH1 R - W EY2 Z
+STAIRWELL  S T EH1 R - W EH2 L
+STAIRWELLS  S T EH1 R - W EH2 L Z
+STAKE  S T EY1 K
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+STAKED  S T EY1 K T
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+STAKEOUT  S T EY1 K - AW2 T
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+STALCUP  S T AO1 L K - AH2 P
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+STALE  S T EY1 L
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+STALEY  S T EY1 - L IY0
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+STALIN  S T AA1 - L AH0 N
+STALIN'S  S T AA1 - L IH0 N Z
+STALINGRAD  S T AE1 - L IH0 N - G R AE2 D
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+STALINIST  S T AA1 - L IH0 - N IH0 S T
+STALINISTIC  S T AA2 - L IH0 - N IH1 - S T IH0 K
+STALINISTS  S T AE1 - L IH0 - N IH0 S T S
+STALINISTS(2)  S T AE1 - L IH0 - N IH0 S S
+STALINISTS(3)  S T AE1 - L IH0 - N IH0 S
+STALINIZATION  S T AE2 - L IH0 - N IH0 - Z EY1 - SH AH0 N
+STALINIZE  S T AE1 - L IH0 - N AY2 Z
+STALK  S T AO1 K
+STALKED  S T AO1 K T
+STALKER  S T AO1 - K ER0
+STALKERS  S T AO1 - K ER0 Z
+STALKING  S T AO1 - K IH0 NG
+STALKS  S T AO1 K S
+STALKY  S T AO1 - K IY0
+STALL  S T AO1 L
+STALLARD  S T AE1 - L ER0 D
+STALLCUP  S T AO1 L K - AH2 P
+STALLED  S T AO1 L D
+STALLER  S T AO1 - L ER0
+STALLIBRASS  S T AE1 - L IH0 - B R AE0 S
+STALLING  S T AO1 - L IH0 NG
+STALLINGS  S T AO1 - L IH0 NG Z
+STALLION  S T AE1 - L Y AH0 N
+STALLIONS  S T AE1 - L Y AH0 N Z
+STALLKAMP  S T AO1 L - K AE2 M P
+STALLMAN  S T AO1 L - M AH0 N
+STALLONE  S T AH0 - L OW1 N
+STALLONE'S  S T AH0 - L OW1 N Z
+STALLS  S T AO1 L Z
+STALLSMITH  S T AO1 L - S M IH2 TH
+STALLWORTH  S T AO1 L - W ER2 TH
+STALNAKER  S T AE1 L - N AH0 - K ER0
+STALOFF  S T AE1 - L AO0 F
+STALON  S T EY1 - L AH0 N
+STALOWA  S T AH0 - L OW1 - AH0
+STALTER  S T AO1 L - T ER0
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+STALWART  S T AO1 L - W ER0 T
+STALWARTS  S T AO1 L - W ER0 T S
+STALZER  S T EY1 L - Z ER0
+STAM  S T AE1 M
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+STAMANT  S T AE1 - M AH0 N T
+STAMAS  S T AA1 - M AH0 Z
+STAMATY  S T AE1 - M AH0 - T IY0
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+STAMINA  S T AE1 - M AH0 - N AH0
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+STAMM  S T AE1 M
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+STAMOUR  S T AH0 - M UH1 R
+STAMP  S T AE1 M P
+STAMPED  S T AE1 M P T
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+STAMPEDING  S T AE0 M - P IY1 - D IH0 NG
+STAMPER  S T AE1 M - P ER0
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+STAMPING  S T AE1 M - P IH0 NG
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+STAN  S T AE1 N
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+STANALAND  S T AE1 - N AH0 - L AH0 N D
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+STANBERRY  S T AE1 N - B EH2 - R IY0
+STANBERY  S T AE1 N - B ER0 - IY0
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+STANCE  S T AE1 N S
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+STANCH  S T AE1 N CH
+STANCHED  S T AE1 N CH T
+STANCHFIELD  S T AE1 N CH - F IY0 L D
+STANCHING  S T AE1 N - CH IH0 NG
+STANCIK  S T AE1 N - S IH0 K
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+STANCLIFF  S T AE1 N - K L IH0 F
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+STANCZAK  S T AE1 N - CH AE0 K
+STANCZYK  S T AE1 N - CH IH0 K
+STAND  S T AE1 N D
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+STANDARD'S  S T AE1 N - D ER0 D Z
+STANDARD-BEARER  S T AE1 N - D ER0 D - B EH1 - R ER0
+STANDARD-BEARERS  S T AE1 N - D ER0 D - B EH1 - R ER0 Z
+STANDARDIZATION  S T AE0 N - D ER0 - D IH0 - Z EY1 - SH AH0 N
+STANDARDIZE  S T AE1 N - D ER0 - D AY2 Z
+STANDARDIZED  S T AE1 N - D ER0 - D AY2 Z D
+STANDARDIZING  S T AE1 N - D ER0 - D AY2 - Z IH0 NG
+STANDARDS  S T AE1 N - D ER0 D Z
+STANDBY  S T AE1 N D - B AY1
+STANDBYS  S T AE1 N D - B AY2 Z
+STANDEFER  S T AE1 N - D IH0 - F ER0
+STANDEN  S T AE1 N - D AH0 N
+STANDER  S T AE1 N - D ER0
+STANDERFER  S T AE1 N - D ER0 - F ER0
+STANDERFORD  S T AE1 N - D ER0 - F ER0 D
+STANDEX  S T AE1 N - D AH0 K S
+STANDFIELD  S T AE1 N D - F IY2 L D
+STANDIFER  S T AE1 N - D IH0 - F ER0
+STANDIFORD  S T AE1 N - D IH0 - F ER0 D
+STANDIN'  S T AE1 N - D IH0 N
+STANDING  S T AE1 N - D IH0 NG
+STANDINGS  S T AE1 N - D IH0 NG Z
+STANDISH  S T AE1 N - D IH0 SH
+STANDLEE  S T AE1 N D - L IY2
+STANDLEY  S T AE1 N D - L IY0
+STANDOFF  S T AE1 N - D AO2 F
+STANDOFFS  S T AE1 N - D AO2 F S
+STANDOUT  S T AE1 N D - AW2 T
+STANDOUTS  S T AE1 N D - AW2 T S
+STANDPOINT  S T AE1 N D - P OY2 N T
+STANDPOINTS  S T AE1 N D - P OY2 N T S
+STANDRE  S T AE1 N - D ER0
+STANDRIDGE  S T AE1 N - D R IH0 JH
+STANDS  S T AE1 N D Z
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+STANDUP  S T AE1 N D - AH2 P
+STANEK  S T AE1 - N IH0 K
+STANFIELD  S T AE1 N - F IY2 L D
+STANFILL  S T AE1 N - F AH0 L
+STANFORD  S T AE1 N - F ER0 D
+STANFORD'S  S T AE1 N - F ER0 D Z
+STANFORTH  S T AE1 N - F ER0 TH
+STANG  S T AE1 NG
+STANGA  S T AA1 NG - G AH0
+STANGE  S T AE1 N JH
+STANGEL  S T EY1 NG - G AH0 L
+STANGELAND  S T EY1 NG - G IH0 - L AH0 N D
+STANGELAND(2)  S T EY1 NG - G L AH0 N D
+STANGELO  S T AA0 NG - G EH1 - L OW0
+STANGER  S T AE1 - NG ER0
+STANGL  S T AE1 NG - G AH0 L
+STANGLAND  S T AE1 NG - G L AH0 N D
+STANGLE  S T AE1 NG - G AH0 L
+STANGLER  S T AE1 NG - G AH0 - L ER0
+STANGLER(2)  S T AE1 NG - G L ER0
+STANGO  S T AA1 NG - G OW0
+STANHOPE  S T AE1 - N AH0 P
+STANIAR  S T EY1 - N IY0 - AA0 R
+STANICH  S T AE1 - N IH0 CH
+STANIFER  S T AE1 - N IH0 - F ER0
+STANIS  S T AE1 - N IH0 S
+STANISH  S T AE1 - N IH0 SH
+STANISLAS  S T AH0 - N IH1 S - L AH0 S
+STANISLAV  S T AE1 - N IH0 - S L AA0 V
+STANISLAW  S T AE1 - N IH0 S - L AO2
+STANISLAWSKI  S T AH0 - N IH0 S - L AA1 F S - K IY0
+STANISZEWSKI  S T AH0 - N IH0 - SH EH1 F S - K IY0
+STANK  S T AE1 NG K
+STANKE  S T AE1 NG K
+STANKEVICH  S T AE1 NG - K AH0 - V IH2 CH
+STANKEY  S T AE1 NG - K IY0
+STANKIEWICZ  S T AE1 N - K AH0 - V IH0 CH
+STANKO  S T AE1 NG - K OW0
+STANKOVICH  S T AE1 NG - K AH0 - V IH0 CH
+STANKOWSKI  S T AH0 NG - K AO1 F S - K IY0
+STANKUS  S T AE1 NG - K AH0 S
+STANLEIGH  S T AE1 N - L AH0
+STANLEY  S T AE1 N - L IY0
+STANLEY'S  S T AE1 N - L IY0 Z
+STANLEYTOWN  S T AE1 N - L IY0 - T AW2 N
+STANLINE  S T AE1 N - L AY2 N
+STANLY  S T AE1 N - L IY0
+STANMORE  S T AE1 N - M AO0 R
+STANN  S T AE1 N
+STANNARD  S T AE1 - N ER0 D
+STANNIE  S T AE1 - N IY0
+STANO  S T AA1 - N OW0
+STANPHILL  S T AE1 N P - HH IH2 L
+STANPHILL(2)  S T AE1 M P - HH IH2 L
+STANSBERRY  S T AE1 N S - B EH2 - R IY0
+STANSBURY  S T AE1 N S - B EH0 - R IY0
+STANSEL  S T AE1 N - S AH0 L
+STANSELL  S T AE1 N - S AH0 L
+STANSFIELD  S T AE1 N S - F IY0 L D
+STANSKY  S T AE1 N S - K IY0
+STANT  S T AE1 N T
+STANTON  S T AE1 N - T AH0 N
+STANWAY  S T AE1 N - W EY2
+STANWICK  S T AE1 N - W IH0 K
+STANWOOD  S T AE1 N - W UH2 D
+STANZA  S T AE1 N - Z AH0
+STANZAS  S T AE1 N - Z AH0 Z
+STANZIONE  S T AA0 N - Z IY0 - OW1 - N IY0
+STAPEL  S T AE1 - P AH0 L
+STAPF  S T AE1 P F
+STAPLE  S T EY1 - P AH0 L
+STAPLED  S T EY1 - P AH0 L D
+STAPLER  S T EY1 - P AH0 - L ER0
+STAPLER(2)  S T EY1 P - L ER0
+STAPLERS  S T EY1 - P AH0 - L ER0 Z
+STAPLERS(2)  S T EY1 P - L ER0 Z
+STAPLES  S T EY1 - P AH0 L Z
+STAPLETON  S T EY1 - P AH0 L - T AH0 N
+STAPLEY  S T AE1 P - L IY0
+STAPLING  S T EY1 - P AH0 L - IH0 NG
+STAPLING(2)  S T EY1 - P L IH0 NG
+STAPP  S T AE1 P
+STAR  S T AA1 R
+STAR'S  S T AA1 R Z
+STARACE  S T AA0 - R AA1 - CH IY0
+STARBIRD  S T AA1 R - B ER2 D
+STARBOARD  S T AA1 R - B ER0 D
+STARBOARDS  S T AA1 R - B ER0 R D Z
+STARBOARDS(2)  S T AA1 R - B AO2 R D Z
+STARBUCK  S T AA1 R - B AH2 K
+STARBUCK'S  S T AA1 R - B AH2 K S
+STARBUCKS  S T AA1 R - B AH2 K S
+STARCEVICH  S T AA1 R - S IH0 - V IH0 CH
+STARCH  S T AA1 R CH
+STARCHED  S T AA1 R CH T
+STARCHER  S T AA1 R - CH ER0
+STARCHES  S T AA1 R - CH IH0 Z
+STARCHLIKE  S T AA1 R CH - L AY2 K
+STARCHY  S T AA1 R - CH IY0
+STARCK  S T AA1 R K
+STARCKMANN  S T AA1 R K - M AH0 N
+STARCRAFT  S T AA1 R - K R AE2 F T
+STARCROSS  S T AA1 R - K R AO2 S
+STARDEL  S T AA1 R - D EH2 L
+STARDENT  S T AA1 R - D EH2 N T
+STARDOM  S T AA1 R - D AH0 M
+STARDUST  S T AA1 R - D AH2 S T
+STARE  S T EH1 R
+STARED  S T EH1 R D
+STARER  S T EH1 - R ER0
+STARES  S T EH1 R Z
+STARFISH  S T AA1 R - F IH2 SH
+STARGATE  S T AA1 R - G EY2 T
+STARGATES  S T AA1 R - G EY2 T S
+STARGAZER  S T AA1 R - G EY2 - Z ER0
+STARGAZERS  S T AA1 R - G EY2 - Z ER0 Z
+STARIN  S T AE1 - R IH0 N
+STARING  S T EH1 - R IH0 NG
+STARK  S T AA1 R K
+STARK'S  S T AA1 R K S
+STARKE  S T AA1 R K
+STARKEL  S T AA1 R - K AH0 L
+STARKER  S T AA1 R - K ER0
+STARKES  S T AA1 R K S
+STARKEY  S T AA1 R - K IY2
+STARKIST  S T AA1 R - K IH0 S T
+STARKLY  S T AA1 R K - L IY0
+STARKMAN  S T AA1 R K - M AH0 N
+STARKNESS  S T AA1 R K - N IH0 S
+STARKOVICH  S T AA1 R - K AH0 - V IH0 CH
+STARKS  S T AA1 R K S
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+STARLET  S T AA1 R - L AH0 T
+STARLETS  S T AA1 R - L AH0 T S
+STARLIGHT  S T AA1 R - L AY2 T
+STARLIKE  S T AA1 R - L AY2 K
+STARLIN  S T AA1 R - L IH0 N
+STARLING  S T AA1 R - L IH0 NG
+STARLIPER  S T AA1 R - L IH0 - P ER0
+STARLIT  S T AA1 R - L IH0 T
+STARMAN  S T AA1 R - M AH0 N
+STARMER  S T AA1 R - M ER0
+STARN  S T AA1 R N
+STARNER  S T AA1 R - N ER0
+STARNES  S T AA1 R N Z
+STARNS  S T AA1 R N Z
+STARODUBSTEV  S T AA2 - R OW0 - D AH1 B - S T EH0 V
+STARON  S T AE1 - R AH0 N
+STARPLEX  S T AA1 R - P L EH2 K S
+STARPOINTE  S T AA1 R - P OY2 N T
+STARR  S T AA1 R
+STARR'S  S T AA1 R Z
+STARRED  S T AA1 R D
+STARRETT  S T AE1 - R IH0 T
+STARRING  S T AA1 - R IH0 NG
+STARRS  S T AA1 R Z
+STARRY  S T AA1 - R IY0
+STARS  S T AA1 R Z
+STARS'  S T AA1 R Z
+STARSHIP  S T AA1 R - SH IH2 P
+STARSIGHT  S T AA1 R - S AY2 T
+STARSTREAM  S T AA1 R - S T R IY2 M
+STARSTRUCK  S T AA1 R - S T R AH2 K
+START  S T AA1 R T
+START-UP  S T AA1 R T - AH2 P
+START-UPS  S T AA1 R T - AH1 P S
+STARTED  S T AA1 R - T AH0 D
+STARTED(2)  S T AA1 R - T IH0 D
+STARTER  S T AA1 R - T ER0
+STARTERS  S T AA1 R - T ER0 Z
+STARTING  S T AA1 R - T IH0 NG
+STARTLE  S T AA1 R - T AH0 L
+STARTLED  S T AA1 R - T AH0 L D
+STARTLES  S T AA1 R - T AH0 L Z
+STARTLING  S T AA1 R T - L IH0 NG
+STARTLINGLY  S T AA1 R T - L IH0 NG - L IY0
+STARTS  S T AA1 R T S
+STARTUP  S T AA1 R T - AH2 P
+STARTUPS  S T AA1 R T - AH2 P S
+STARVATION  S T AA0 R - V EY1 - SH AH0 N
+STARVE  S T AA1 R V
+STARVED  S T AA1 R V D
+STARVING  S T AA1 R - V IH0 NG
+STARWALT  S T AA1 R - W AH0 L T
+STARWAVE  S T AA1 R - W EY2 V
+STARY  S T EH1 - R IY0
+STARZYK  S T AA1 R - Z IH0 K
+STASH  S T AE1 SH
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+STASI  S T AA1 - S IY0
+STASIAK  S T AA1 - S IY0 - AE0 K
+STASIK  S T AA1 - S IH0 K
+STASIO  S T AA1 - S IY0 - OW0
+STASKO  S T AA1 - S K OW0
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+STASNY  S T AE1 S - N IY0
+STASSEN  S T AE1 - S AH0 N
+STASSI  S T AE1 - S IY0
+STASTNY  S T AE1 S T - N IY0
+STASZAK  S T AA1 - SH AH0 K
+STASZEWSKI  S T AH0 - SH EH1 F S - K IY0
+STAT  S T AE1 T
+STATE  S T EY1 T
+STATE'S  S T EY1 T S
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+STATED  S T EY1 - T AH0 D
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+STATEHOOD  S T EY1 T - HH UH2 D
+STATEHOUSE  S T EY1 T - HH AW2 S
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+STATELESS  S T EY1 T - L IH0 S
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+STATEMENT'S  S T EY1 T - M AH0 N T S
+STATEMENTS  S T EY1 T - M AH0 N T S
+STATEN  S T AE1 - T AH0 N
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+STATER'S  S T EY1 - T ER0 Z
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+STATES  S T EY1 T S
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+STATESMANSHIP  S T EY1 T S - M AH0 N - SH IH2 P
+STATESMEN  S T EY1 T S - M IH0 N
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+STATION'S  S T EY1 - SH AH0 N Z
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+STATIONED  S T EY1 - SH AH0 N D
+STATIONER  S T EY1 - SH AH0 N - ER0
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+STATIONERY  S T EY1 - SH AH0 N - EH2 - R IY0
+STATIONING  S T EY1 - SH AH0 N - IH0 NG
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+STATISM  S T EY1 - T IH2 - Z AH0 M
+STATIST  S T EY1 - T IH0 S T
+STATISTIC  S T AH0 - T IH1 - S T IH0 K
+STATISTICAL  S T AH0 - T IH1 - S T IH0 - K AH0 L
+STATISTICALLY  S T AH0 - T IH1 - S T IH0 - K AH0 - L IY0
+STATISTICALLY(2)  S T AH0 - T IH1 - S T IH0 K - L IY0
+STATISTICIAN  S T AE2 - T AH0 - S T IH1 - SH AH0 N
+STATISTICIANS  S T AE2 - T IH0 - S T IH1 - SH AH0 N Z
+STATISTICS  S T AH0 - T IH1 - S T IH0 K S
+STATISTICS'  S T AH0 - T IH1 - S T IH0 K S
+STATISTS  S T EY1 - T IH0 S T S
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+STATLER  S T AE1 T - L ER0
+STATOIL  S T AH0 - T OY1 L
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+STATUM  S T AE1 - T AH0 M
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+STATUTORY  S T AE1 - CH AH0 - T AO2 - R IY0
+STATZ  S T AE1 T S
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+STAUB  S T AO1 B
+STAUBER  S T AW1 - B ER0
+STAUBIN  S T AW1 - B IH0 N
+STAUBS  S T AO1 B Z
+STAUCH  S T AO1 CH
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+STAUDE  S T AO1 D
+STAUDER  S T AW1 - D ER0
+STAUDINGER  S T AW1 - D IH0 - NG ER0
+STAUDT  S T AO1 D T
+STAUFF  S T AO1 F
+STAUFFACHER  S T AW1 - F AH0 - K ER0
+STAUFFER  S T AO1 - F ER0
+STAUFFER'S  S T AO1 - F ER0 Z
+STAUNCH  S T AO1 N CH
+STAUNCHEST  S T AO1 N - CH AH0 S T
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+STAUNTON  S T AO1 N - T AH0 N
+STAUP  S T AO1 P
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+STAUTER  S T AW1 - T ER0
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+STAVER  S T EY1 - V ER0
+STAVES  S T EY1 V Z
+STAVING  S T EY1 - V IH0 NG
+STAVINOHA  S T AE0 - V IH0 - N OW1 - HH AH0
+STAVOLA  S T AA0 - V OW1 - L AH0
+STAVROPOULOS  S T AH0 - V R AA1 - P AH0 - L IH0 S
+STAVROS  S T AH0 - V R OW1 Z
+STAWICKI  S T AA0 - V IH1 T S - K IY0
+STAWSKI  S T AA1 F S - K IY0
+STAY  S T EY1
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+STAYOVER  S T EY1 - OW2 - V ER0
+STAYOVERS  S T EY1 - OW2 - V ER0 Z
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+STDS  EH1 - S T IY1 - D IY1 Z
+STDS(2)  EH1 - S T IY1 - D IY1 - EH1 S
+STEAD  S T EH1 D
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+STEAGLE  S T IY1 - G AH0 L
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+STECKLER  S T EH1 K - L ER0
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+STECKMAN  S T EH1 K - M AH0 N
+STEDMAN  S T EH1 D - M AH0 N
+STEEB  S T IY1 B
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+STEED  S T IY1 D
+STEED'S  S T IY1 D Z
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+STEEG  S T IY1 G
+STEEGE  S T IY1 JH
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+STEELHEAD  S T IY1 L - HH EH2 D
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+STEELMAKER'S  S T IY1 L - M EY2 - K ER0 Z
+STEELMAKERS  S T IY1 L - M EY2 - K ER0 Z
+STEELMAKERS'  S T IY1 L - M AH0 - K ER0 Z
+STEELMAKING  S T IY1 L - M EY2 - K IH0 NG
+STEELMAN  S T IY1 L - M AH0 N
+STEELS  S T IY1 L Z
+STEELWORKER  S T IY1 L - W ER2 - K ER0
+STEELWORKERS  S T IY1 L - W ER2 - K ER0 Z
+STEELWORKERS'  S T IY1 L - W ER0 - K ER0 Z
+STEELWORKS  S T IY1 L - W ER2 K S
+STEELY  S T IY1 - L IY0
+STEEN  S T IY1 N
+STEENBERGEN  S T IY1 N - B ER0 - G AH0 N
+STEENBURGEN  S T IY1 N - B ER0 - G AH0 N
+STEENKAMP  S T IY1 N - K AE2 M P
+STEENKISTE  S T IY1 N - K IH2 S T
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+STEENSMA  S T IY1 N Z - M AH0
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+STEEP  S T IY1 P
+STEEPED  S T IY1 P T
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+STEEPEST  S T IY1 - P AH0 S T
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+STEEPLECHASE  S T IY1 - P AH0 L - CH EY2 S
+STEEPLY  S T IY1 P - L IY0
+STEEPNESS  S T IY1 P - N AH0 S
+STEER  S T IH1 R
+STEERE  S T IH1 R
+STEERED  S T IH1 R D
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+STEERS  S T IH1 R Z
+STEES  S T IY1 Z
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+STEEVES  S T IY1 V Z
+STEFA  S T EH1 - F AH0
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+STEFANELLI  S T EH0 - F AA0 N - EH1 - L IY0
+STEFANI  S T EH1 - F AH0 - N IY0
+STEFANI'S  S T EH1 - F AH0 - N IY0 Z
+STEFANIAK  S T IH0 - F AE1 - N IY0 - AE0 K
+STEFANIC  S T IH0 - F AE1 - N IH0 K
+STEFANICH  S T EH1 - F AH0 - N IH0 CH
+STEFANICK  S T EH1 - F AH0 N - IH0 K
+STEFANIE  S T EH1 - F AH0 - N IY0
+STEFANIK  S T IH0 - F AE1 - N IH0 K
+STEFANKO  S T IH0 - F AE1 NG - K OW0
+STEFANO  S T EH1 - F AH0 - N OW0
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+STEFANSKI  S T IH0 - F AE1 N S - K IY0
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+STEFFE  S T EH1 F
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+STEFFEL  S T EH1 - F AH0 L
+STEFFEN  S T EH1 - F AH0 N
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+STEFFENS  S T EH1 - F AH0 N Z
+STEFFENSEN  S T EH1 - F IH0 N - S AH0 N
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+STEFFENSON  S T EH1 - F IH0 N - S AH0 N
+STEFFES  S T EH1 F S
+STEFFEY  S T EH1 - F IY0
+STEFFI  S T EH1 - F IY0
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+STEFL  S T EH1 - F AH0 L
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+STEGE  S T IY1 JH
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+STEGMAN  S T EH1 G - M AH0 N
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+STEIG  S T IY1 G
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+STEIN  S T AY1 N
+STEIN'S  S T AY1 N Z
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+STEINBACH  S T AY1 N - B AA2 K
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+STEINBAUGH  S T AY1 N - B AW0
+STEINBECK  S T AY1 N - B EH2 K
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+STEINBERG  S T AY1 N - B ER0 G
+STEINBERG'S  S T AY1 N - B ER0 G Z
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+STEINBRINK  S T AY1 N - B R IH2 NG K
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+STORIES  S T AO1 - R IY0 Z
+STORING  S T AO1 - R IH0 NG
+STORK  S T AO1 R K
+STORLIE  S T AO1 R - L IY0
+STORM  S T AO1 R M
+STORM'S  S T AO1 R M Z
+STORMED  S T AO1 R M D
+STORMENT  S T AO1 R - M AH0 N T
+STORMER  S T AO1 R - M ER0
+STORMES  S T AO1 R M Z
+STORMIEST  S T AO1 R - M IY0 - IH0 S T
+STORMING  S T AO1 R - M IH0 NG
+STORMONT  S T AO1 R - M OW0 N T
+STORMS  S T AO1 R M Z
+STORMY  S T AO1 R - M IY0
+STORR  S T AO1 R
+STORROW  S T AA1 - R OW0
+STORRS  S T AO1 R Z
+STORTI  S T AO1 R - T IY0
+STORTING  S T AO1 R - T IH0 NG
+STORTS  S T AO1 R T S
+STORTZ  S T AO1 R T S
+STORY  S T AO1 - R IY0
+STORY'S  S T AO1 - R IY0 Z
+STORYBOARD  S T AO1 - R IY0 - B AO2 R D
+STORYBOOK  S T AO1 - R IY0 - B UH2 K
+STORYBOOKS  S T AO1 - R IY0 - B UH2 K S
+STORYLINE  S T AO1 - R IY0 - L AY2 N
+STORYLINES  S T AO1 - R IY0 - L AY2 N Z
+STORYTELLER  S T AO1 - R IY0 - T EH2 - L ER0
+STORYTELLERS  S T AO1 - R IY0 - T EH2 - L ER0 Z
+STORYTELLING  S T AO1 - R IY0 - T EH2 - L IH0 NG
+STORZ  S T AO1 R Z
+STOSSEL  S T AA1 - S AH0 L
+STOSSEL'S  S T AA1 - S AH0 L Z
+STOTLER  S T AA1 T - L ER0
+STOTT  S T AA1 T
+STOTTLEMYER  S T AA1 - T AH0 L - M AY0 - ER0
+STOTTS  S T AA1 T S
+STOTZ  S T AA1 T S
+STOUDEMIRE  S T UW1 - D AH0 - M AY2 R
+STOUDENMIRE  S T UW1 - D AH0 N - M AY2 R
+STOUDER  S T AH1 - D ER0
+STOUDT  S T AH1 D T
+STOUFFER  S T AH1 - F ER0
+STOUFFER(2)  S T OW1 - F ER0
+STOUFFS  S T AH1 F S
+STOUFFS(2)  S T OW1 F S
+STOUGH  S T AH1 F
+STOUGHTON  S T OW1 - T AH0 N
+STOUP  S T UW1 P
+STOUT  S T AW1 T
+STOUTE  S T AW1 T
+STOUTENBURG  S T AW1 - T AH0 N - B ER0 G
+STOUTLY  S T AW1 T - L IY0
+STOUTNESS  S T AW1 T - N AH0 S
+STOVALL  S T OW1 - V AA0 L
+STOVE  S T OW1 V
+STOVER  S T OW1 - V ER0
+STOVES  S T OW1 V Z
+STOW  S T OW1
+STOWE  S T OW1
+STOWED  S T OW1 D
+STOWELL  S T AA1 - W EH0 L
+STOWER  S T OW1 - ER0
+STOWERS  S T OW1 - ER0 Z
+STOY  S T OY1
+STOYER  S T OY1 - ER0
+STRACENER  S T R AE1 - S IY0 - N ER0
+STRACHAN  S T R AE1 - CH AH0 N
+STRACK  S T R AE1 K
+STRACKE  S T R AE1 K
+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
+STRADER  S T R EY1 - D ER0
+STRADFORD  S T R AE1 D - F ER0 D
+STRADIVARIUS  S T R AE2 - D IH0 - V EH1 - R IY0 - AH0 S
+STRADLEY  S T R AE1 D - L IY0
+STRADLING  S T R AE1 D - L IH0 NG
+STRAFE  S T R EY1 F
+STRAFING  S T R EY1 - F IH0 NG
+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
+STRAHAN  S T R AE1 - HH AH0 N
+STRAHL  S T R AA1 L
+STRAHLE  S T R EY1 - HH AH0 L
+STRAHLER  S T R AA1 - L ER0
+STRAHM  S T R AA1 M
+STRAIGHT  S T R EY1 T
+STRAIGHTAWAY  S T R EY1 T - AH0 - W EY2
+STRAIGHTEDGE  S T R EY1 - T EH2 JH
+STRAIGHTEN  S T R EY1 - T AH0 N
+STRAIGHTENED  S T R EY1 - T AH0 N D
+STRAIGHTENING  S T R EY1 - T AH0 N - IH0 NG
+STRAIGHTENING(2)  S T R EY1 T - N IH0 NG
+STRAIGHTENS  S T R EY1 - T AH0 N Z
+STRAIGHTER  S T R EY1 - T ER0
+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
+STRAIGHTS  S T R EY1 T S
+STRAIN  S T R EY1 N
+STRAINED  S T R EY1 N D
+STRAINING  S T R EY1 - N IH0 NG
+STRAINS  S T R EY1 N Z
+STRAIT  S T R EY1 T
+STRAITJACKET  S T R EY1 T - JH AE2 - K AH0 T
+STRAITS  S T R EY1 T S
+STRAKA  S T R AA1 - K AH0
+STRAKER  S T R EY1 - K ER0
+STRALEY  S T R AE1 - L IY0
+STRAM  S T R AE1 M
+STRANAHAN  S T R AE1 - N AH0 - HH AE0 N
+STRAND  S T R AE1 N D
+STRANDBERG  S T R AE1 N D - B ER0 G
+STRANDE  S T R AE1 N D
+STRANDED  S T R AE1 N - D AH0 D
+STRANDED(2)  S T R AE1 N - D IH0 D
+STRANDING  S T R AE1 N - D IH0 NG
+STRANDLINE  S T R AE1 N D - L AY2 N
+STRANDLINE(2)  S T R AE1 N - L AY2 N
+STRANDLINES  S T R AE1 N D - L AY2 N Z
+STRANDLINES(2)  S T R AE1 N - L AY2 N Z
+STRANDS  S T R AE1 N D Z
+STRANG  S T R AE1 NG
+STRANGE  S T R EY1 N JH
+STRANGELOVE  S T R EY1 N - JH L AH2 V
+STRANGELY  S T R EY1 N JH - L IY0
+STRANGENESS  S T R EY1 N JH - N AH0 S
+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
+STRANGEST  S T R EY1 N - JH IH0 S T
+STRANGIS  S T R AE1 N - JH IH0 S
+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
+STRANGLING  S T R AE1 NG - G AH0 - L IH0 NG
+STRANGLING(2)  S T R AE1 NG - G L IH0 NG
+STRANGULATE  S T R AE1 NG - G Y AH0 - L EY2 T
+STRANGULATION  S T R AE2 NG - G Y AH0 - L EY1 - SH AH0 N
+STRANGULATIONS  S T R AE2 NG - G Y AH0 - L EY1 - SH AH0 N Z
+STRANGWAYES  S T R AE1 NG - W EY2 Z
+STRANO  S T R AA1 - N OW0
+STRANSKY  S T R AE1 N S - K IY0
+STRAP  S T R AE1 P
+STRAPPED  S T R AE1 P T
+STRAPPING  S T R AE1 - P IH0 NG
+STRAPS  S T R AE1 P S
+STRASBERG  S T R AE1 S - B ER0 G
+STRASBOURG  S T R AE1 S - B AO2 R G
+STRASBURG  S T R AE1 S - B ER0 G
+STRASBURGER  S T R AE1 S - B ER0 - G ER0
+STRASSBURG  S T R AE1 S - B ER0 G
+STRASSBURGER  S T R AE1 S - B ER0 - G ER0
+STRASSER  S T R AE1 - S ER0
+STRASSMAN  S T R AE1 S - M AH0 N
+STRASSNER  S T R AE1 S - N ER0
+STRASZHEIM  S T R AE1 S - HH AY2 M
+STRATA  S T R AE1 - T AH0
+STRATACOM  S T R AE1 - T AH0 - K AA0 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
+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
+STRATEGISTS(2)  S T R AE1 - T IH0 - JH IH0 S S
+STRATEGISTS(3)  S T R AE1 - T IH0 - JH IH0 S
+STRATEGIZE  S T R AE1 - T AH0 - JH AY0 Z
+STRATEGIZING  S T R AE1 - T AH0 - JH AY0 - Z IH0 NG
+STRATEGY  S T R AE1 - T AH0 - JH IY0
+STRATEGY'S  S T R AE1 - T AH0 - JH IY0 Z
+STRATER  S T R EY1 - T ER0
+STRATFORD  S T R AE1 T - F ER0 D
+STRATHMAN  S T R AE1 TH - M AH0 N
+STRATIFIED  S T R AE1 - T AH0 - F AY2 D
+STRATIFY  S T R AE1 - T AH0 - F AY2
+STRATIGRAPHIC  S T R AE2 - T AH0 - G R AE1 - F IH0 K
+STRATMAN  S T R AE1 T - M AH0 N
+STRATMANN  S T R AE1 T - M AH0 N
+STRATOCASTER  S T R AE1 - T OW0 - K AE2 - S T ER0
+STRATOFLEX  S T R AE1 - T OW0 - F L EH2 K S
+STRATOSPHERE  S T R AE1 - T AH0 - S F IH2 R
+STRATOSPHERIC  S T R AE2 - T AH0 - S F IH1 - R IH0 K
+STRATTON  S T R AE1 - T AH0 N
+STRATUM  S T R AE1 - T AH0 M
+STRATUS  S T R AE1 - T AH0 S
+STRATUS'S  S T R AE1 - T AH0 - S IH0 Z
+STRAUB  S T R AW1 B
+STRAUBE  S T R AW1 B
+STRAUCH  S T R AW1 K
+STRAUGHAN  S T R AO1 - AH0 N
+STRAUGHN  S T R AO1 N
+STRAUGHTER  S T R AO1 - T ER0
+STRAUM  S T R AW1 M
+STRAUM(2)  S T R AA1 M
+STRAUS  S T R AW1 S
+STRAUSBAUGH  S T R AW1 S - B AW0
+STRAUSE  S T R AW1 S
+STRAUSER  S T R AW1 - S ER0
+STRAUSS  S T R AW1 S
+STRAUSS'S  S T R AW1 - S IH0 Z
+STRAUSSER  S T R AW1 - S ER0
+STRAVINSKY  S T R AH0 - V IH1 N - S K IY0
+STRAVINSKY'S  S T R AH0 - V IH1 N - S K IY0 Z
+STRAW  S T R AO1
+STRAWBERRIES  S T R AO1 - B EH2 - R IY0 Z
+STRAWBERRY  S T R AO1 - B EH2 - R IY0
+STRAWBRIDGE  S T R AO1 - B R IH2 JH
+STRAWDER  S T R AO1 - D ER0
+STRAWDERMAN  S T R AO1 - D ER0 - M AH0 N
+STRAWN  S T R AO1 N
+STRAWS  S T R AO1 Z
+STRAWSER  S T R AO1 - Z ER0
+STRAY  S T R EY1
+STRAYED  S T R EY1 D
+STRAYER  S T R EY1 - ER0
+STRAYHORN  S T R EY1 - HH ER0 N
+STRAYING  S T R EY1 - IH0 NG
+STRAYS  S T R EY1 Z
+STREAK  S T R IY1 K
+STREAKED  S T R IY1 K T
+STREAKER  S T R IY1 - K ER0
+STREAKING  S T R IY1 - K IH0 NG
+STREAKS  S T R IY1 K S
+STREAM  S T R IY1 M
+STREAMED  S T R IY1 M D
+STREAMER  S T R IY1 - M ER0
+STREAMERS  S T R IY1 - M ER0 Z
+STREAMING  S T R IY1 - M IH0 NG
+STREAMLINE  S T R IY1 M - L AY2 N
+STREAMLINED  S T R IY1 M - L AY2 N D
+STREAMLINING  S T R IY1 M - L AY2 - N IH0 NG
+STREAMS  S T R IY1 M Z
+STREATER  S T R IY1 - T ER0
+STREB  S T R EH1 B
+STREBE  S T R IY1 B
+STREBECK  S T R IY1 - B EH0 K
+STREBEL  S T R EH1 - B AH0 L
+STRECK  S T R EH1 K
+STRECKER  S T R EH1 - K ER0
+STREED  S T R IY1 D
+STREEP  S T R IY1 P
+STREEPER  S T R IY1 - P ER0
+STREET  S T R IY1 T
+STREET'S  S T R IY1 T S
+STREETCAR  S T R IY1 T - K AA2 R
+STREETER  S T R IY1 - T ER0
+STREETERS  S T R IY1 - T ER0 Z
+STREETMAN  S T R IY1 T - M AH0 N
+STREETS  S T R IY1 T S
+STREETT  S T R IY1 T
+STREETWISE  S T R IY1 T - W AY2 Z
+STREETY  S T R IY1 - T IY0
+STREFF  S T R EH1 F
+STREGE  S T R IY1 JH
+STREHL  S T R EH1 L
+STREHLE  S T R EH1 L
+STREHLOW  S T R EH1 - L OW0
+STREIB  S T R AY1 B
+STREIBER  S T R AY1 - B ER0
+STREICH  S T R AY1 K
+STREICHER  S T R AY1 - K ER0
+STREIFF  S T R AY1 F
+STREIGHT  S T R EY1 T
+STREIKER  S T R AY1 - K ER0
+STREISAND  S T R AY1 - Z AH0 N D
+STREISAND'S  S T R AY1 - Z AH0 N D Z
+STREISAND'S(2)  S T R AY1 - S AE2 N D Z
+STREISAND(2)  S T R AY1 - S AE2 N D
+STREIT  S T R AY1 T
+STREITMATTER  S T R AY1 T - M AH0 - T ER0
+STRELOW  S T R EH1 - L OW0
+STRENG  S T R EH1 NG
+STRENGER  S T R EH1 NG - G ER0
+STRENGTH  S T R EH1 NG K TH
+STRENGTH(2)  S T R EH1 NG TH
+STRENGTHEN  S T R EH1 NG - TH AH0 N
+STRENGTHENED  S T R EH1 NG - TH AH0 N D
+STRENGTHENING  S T R EH1 NG - TH AH0 N - IH0 NG
+STRENGTHENS  S T R EH1 NG - TH AH0 N Z
+STRENGTHS  S T R EH1 NG K TH S
+STRENGTHS(2)  S T R EH1 NG TH S
+STRENIO  S T R IY1 - N IY0 - OW0
+STRENUOUS  S T R EH1 - N Y UW0 - AH0 S
+STRENUOUSLY  S T R EH1 - N Y UW0 - AH0 S - L IY0
+STREP  S T R EH1 P
+STREPS  S T R EH1 P S
+STREPTOCOCCUS  S T R EH2 P - T AH0 - K AO1 - K AH0 S
+STREPTOKINASE  S T R EH2 P - T AH0 - K AY1 - N EY2 S
+STREPTOKINASE(2)  S T R EH2 P - T OW0 - K AY1 - N EY2 Z
+STRESS  S T R EH1 S
+STRESSED  S T R EH1 S T
+STRESSES  S T R EH1 - S AH0 Z
+STRESSES(2)  S T R EH1 - S IH0 Z
+STRESSFUL  S T R EH1 S - F AH0 L
+STRESSING  S T R EH1 - S IH0 NG
+STRESSOR  S T R EH1 - S ER0
+STRESSORS  S T R EH1 - S ER0 Z
+STRETCH  S T R EH1 CH
+STRETCHED  S T R EH1 CH T
+STRETCHER  S T R EH1 - CH ER0
+STRETCHER-BEARER  S T R EH1 - CH ER0 - B EH1 - R ER0
+STRETCHER-BEARERS  S T R EH1 - CH ER0 - B EH1 - R ER0 Z
+STRETCHERS  S T R EH1 - CH ER0 Z
+STRETCHES  S T R EH1 - CH AH0 Z
+STRETCHES(2)  S T R EH1 - CH IH0 Z
+STRETCHING  S T R EH1 - CH IH0 NG
+STRETTO  S T R EH1 - T OW2
+STREVIG  S T R EH1 - V IH0 G
+STREW  S T R UW1
+STREWN  S T R UW1 N
+STREY  S T R EY1
+STRIAR  S T R AY1 R
+STRIBLING  S T ER1 - AH0 - B AH0 L - IH0 NG
+STRIBLING(2)  S T R IH1 - B L IH0 NG
+STRICK  S T R IH1 K
+STRICKEN  S T R IH1 - K AH0 N
+STRICKER  S T R IH1 - K ER0
+STRICKLAND  S T R IH1 - K L AH0 N D
+STRICKLEN  S T R IH1 - K AH0 - L AH0 N
+STRICKLER  S T R IH1 - K L ER0
+STRICKLIN  S T R IH1 - K L IH0 N
+STRICKLING  S T R IH1 - K L IH0 NG
+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
+STRICTLY  S T R IH1 K T - L IY0
+STRICTURE  S T R IH1 K - CH ER0
+STRICTURES  S T R IH1 K - CH ER0 Z
+STRIDE  S T R AY1 D
+STRIDENCY  S T R AY1 - D AH0 N - S IY0
+STRIDENT  S T R AY1 - D AH0 N T
+STRIDENTLY  S T R AY1 - D AH0 N T - L IY0
+STRIDER  S T R AY1 - D ER0
+STRIDES  S T R AY1 D Z
+STRIDING  S T R AY1 - D IH0 NG
+STRIEBER  S T R AY1 - B ER0
+STRIEGEL  S T R IY1 - G AH0 L
+STRIEKER  S T R IY1 - K ER0
+STRIEKER'S  S T R IY1 - K ER0 Z
+STRIETER  S T R IY1 - T ER0
+STRIFE  S T R AY1 F
+STRIFES  S T R AY1 F S
+STRIKE  S T R AY1 K
+STRIKE'S  S T R AY1 K S
+STRIKEBREAKER  S T R AY1 K - B R EY2 - K ER0
+STRIKEBREAKERS  S T R AY1 K - B R EY2 - K ER0 Z
+STRIKEOUT  S T R AY1 K - AW2 T
+STRIKEOUTS  S T R AY1 K - AW2 T S
+STRIKER  S T R AY1 - K ER0
+STRIKERS  S T R AY1 - K ER0 Z
+STRIKERS'  S T R AY1 - K ER0 Z
+STRIKES  S T R AY1 K S
+STRIKES'  S T R AY1 K S
+STRIKING  S T R AY1 - K IH0 NG
+STRIKINGLY  S T R AY1 - K IH0 NG - L IY0
+STRIMPLE  S T R IH1 M - P AH0 L
+STRINDEN  S T R IH1 N - D AH0 N
+STRINE  S T R AY1 N
+STRING  S T R IH1 NG
+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
+STRINGERS  S T R IH1 - NG ER0 Z
+STRINGFELLOW  S T R IH1 NG - F EH2 - L OW0
+STRINGFIELD  S T R IH1 NG - F IY2 L D
+STRINGHAM  S T R IH1 NG - HH AE2 M
+STRINGING  S T R IH1 - NG IH0 NG
+STRINGS  S T R IH1 NG Z
+STRINGY  S T R IH1 - NG IY0
+STRIP  S T R IH1 P
+STRIP'S  S T R IH1 P S
+STRIPE  S T R AY1 P
+STRIPED  S T R AY1 P T
+STRIPER  S T R AY1 - P ER0
+STRIPERS  S T R AY1 - P ER0 Z
+STRIPES  S T R AY1 P S
+STRIPLIN  S T R IH1 - P L IH0 N
+STRIPLING  S T R IH1 - P L IH0 NG
+STRIPPED  S T R IH1 P T
+STRIPPER  S T R IH1 - P ER0
+STRIPPERS  S T R IH1 - P ER0 Z
+STRIPPING  S T R IH1 - P IH0 NG
+STRIPS  S T R IH1 P S
+STRIPTEASE  S T R IH1 P - T IY2 Z
+STRITE  S T R AY1 T
+STRITTMATTER  S T R IH1 T - M AH0 - T ER0
+STRIVE  S T R AY1 V
+STRIVEN  S T R IH1 - V AH0 N
+STRIVES  S T R AY1 V Z
+STRIVING  S T R AY1 - V IH0 NG
+STRIVINGS  S T R AY1 - V IH0 NG Z
+STRNAD  S T ER1 - N AE0 D
+STROBE  S T R OW1 B
+STROBEL  S T R OW1 - B AH0 L
+STROBEL'S  S T R OW1 - B AH0 L Z
+STROBER  S T R OW1 - B ER0
+STROBL  S T R AA1 - B AH0 L
+STROBLE  S T R OW1 - B AH0 L
+STROBRIDGE  S T R AA1 - B R IH0 JH
+STROCK  S T R AA1 K
+STRODE  S T R OW1 D
+STRODER  S T R OW1 - D ER0
+STROEBEL  S T R OW1 - B AH0 L
+STROESSNER  S T R OW1 S - N ER0
+STROGANOFF  S T R OW1 - G AH0 - N AO2 F
+STROH  S T R OW1
+STROHECKER  S T R OW1 - IH0 - K ER0
+STROHL  S T R OW1 L
+STROHM  S T R OW1 M
+STROHMAIER  S T R OW1 - M AY0 - ER0
+STROHMAN  S T R OW1 - M AH0 N
+STROHMEIER  S T R OW1 - M AY0 - ER0
+STROHMEYER  S T R OW1 - M AY0 - ER0
+STROIK  S T R OY1 K
+STROJNY  S T R OW1 Y - N IY0
+STROKE  S T R OW1 K
+STROKED  S T R OW1 K T
+STROKES  S T R OW1 K S
+STROKING  S T R OW1 - K IH0 NG
+STROLE  S T R OW1 L
+STROLL  S T R OW1 L
+STROLLED  S T R OW1 L D
+STROLLER  S T R OW1 - L ER0
+STROLLERS  S T R OW1 - L ER0 Z
+STROLLING  S T R OW1 - L IH0 NG
+STROLLO  S T R AA1 - L OW0
+STROLLS  S T R OW1 L Z
+STROM  S T R AA1 M
+STROMA  S T R OW1 - M AH0
+STROMAIN  S T R AA1 - M AY0 N
+STROMAN  S T R OW1 - M AH0 N
+STROMBECK  S T R AA1 M - B EH2 K
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+STUDENT  S T UW1 - D AH0 N T
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+STUDENTS  S T UW1 - D AH0 N T S
+STUDENTS'  S T UW1 - D AH0 N T S
+STUDER  S T UW1 - D ER0
+STUDIED  S T AH1 - D IY0 D
+STUDIES  S T AH1 - D IY0 Z
+STUDIO  S T UW1 - D IY0 - OW2
+STUDIO'S  S T UW1 - D IY0 - OW2 Z
+STUDIOS  S T UW1 - D IY0 - OW2 Z
+STUDIOS'  S T UW1 - D IY0 - OW2 Z
+STUDIOUS  S T UW1 - D IY0 - AH0 S
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+STUDLEY  S T AH1 D - L IY0
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+STUFF  S T AH1 F
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+STUFFED  S T AH1 F T
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+STUFFLEBEAM  S T AH1 - F AH0 L - B IY2 M
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+STUKEY  S T AH1 - K IY0
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+STULTIFYING  S T AH1 L - T AH0 - F AY2 - IH0 NG
+STULTS  S T AH1 L T S
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+STUPAY  S T UW0 - P EY1
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+STUPP  S T AH1 P
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+STURROCK  S T AO1 - R AH0 K
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+STUTES  S T UW1 T S
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+STUTHEIT  S T AH1 - TH AY0 T
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+STUTTGART  S T AH1 T - G ER0 T
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+STUTTS  S T AH1 T S
+STUTZ  S T AH1 T S
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+STUVE  S T UW1 V
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+STUYVESANT  S T AY1 - V AH0 - S AH0 N T
+STY  S T AY1
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+STYERS  S T AY1 - ER0 Z
+STYLE  S T AY1 L
+STYLED  S T AY1 L D
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+STYLISHLY  S T AY1 - L IH0 SH - L IY0
+STYLIST  S T AY1 - L IH0 S T
+STYLISTIC  S T AY0 - L IH1 - S T IH0 K
+STYLISTICALLY  S T AY0 - L IH1 - S T IH0 K - L IY0
+STYLISTS  S T AY1 - L IH0 S T S
+STYLISTS(2)  S T AY1 - L IH0 S S
+STYLISTS(3)  S T AY1 - L IH0 S
+STYLITES  S T IH0 - L AY1 T S
+STYLIZE  S T AY1 - L AY2 Z
+STYLIZE(2)  S T AY1 - AH0 - L AY2 Z
+STYLIZED  S T AY1 - L AY2 Z D
+STYLUS  S T AY1 - L AH0 S
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+STYMIES  S T AY1 - M IY0 Z
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+STYX  S T IH1 K S
+ST_CHARLES  S EY1 N T - CH AA1 - R AH0 L Z
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+ST_DENIS  S EY1 N T - D EH1 - N IH0 S
+ST_DENNIS  S EY1 N T - D EH1 - N IH0 S
+ST_GEORGE  S EY1 N T - JH AO1 R JH
+ST_GERMAIN  S EY1 N T - JH ER2 - M EY1 N
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+ST_GERMAINE(2)  S AA1 N - ZH ER2 - M EY1 N
+ST_HILAIRE  S EY1 N T - HH IH0 - L EY1 R
+ST_JACQUES  S EY1 N T - JH AA1 K S
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+ST_LAURENT  S EY1 N T - L AO1 - R AH0 N T
+ST_LAURENT(2)  S AA1 N - L AO2 - R AO1 N T
+ST_LAWRENCE  S EY1 N T - L AO1 - R AH0 N S
+ST_LOUIS  S EY1 N T - L UW1 - AH0 S
+ST_LOUIS(2)  S EY1 N T - L UW1 - IY0
+ST_LUCIA  S EY1 N T - L UW1 - SH AH0
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+ST_MARIE  S EY1 N T - M ER0 - IY1
+ST_MARTIN  S EY1 N T - M AA1 R - T IH0 N
+ST_MARY  S EY1 N T - M EH1 - R IY0
+ST_PETER  S EY1 N T - P IY1 - T ER0
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+ST_PIERRE  S EY1 N T - P IY0 - EH1 R
+ST_THOMAS  S EY1 N T - T AA1 - M AH0 S
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+SUAVE  S W AA1 V
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+SUB  S AH1 B
+SUB'S  S AH1 B Z
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+SUBA  S UW1 - B AH0
+SUBACUTE  S AH1 - B AH0 - K Y UW1 T
+SUBANDRIO  S AH0 - B AE1 N - D R IY0 - OW0
+SUBARU  S UW1 - B ER0 - UW0
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+SUBASSEMBLY  S AH2 - B AH0 - S EH1 M - B L IY0
+SUBATOMIC  S AH2 B - AH0 - T AA1 - M IH0 K
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+SUBCHAPTER  S AH1 B - CH AE1 P - T ER0
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+SUBCOMMANDER'S  S AH1 B - K AH0 - M AE2 N - D ER0 Z
+SUBCOMMANDERS  S AH1 B - K AH0 - M AE2 N - D ER0 Z
+SUBCOMMITTEE  S AH1 B - K AH0 - M IH1 - T IY0
+SUBCOMMITTEE'S  S AH1 B - K AH0 - M IH1 - T IY0 Z
+SUBCOMMITTEES  S AH1 B - K AH0 - M IH1 - T IY0 Z
+SUBCOMPACT  S AH0 B - K AA1 M - P AE0 K T
+SUBCOMPACTS  S AH0 B - K AA1 M - P AE0 K T S
+SUBCONSCIOUS  S AH0 B - K AA1 N - SH AH0 S
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+SUBCONTINENT  S AH0 B - K AA1 N - T IH0 - N AH0 N T
+SUBCONTINENTS  S AH0 B - K AA1 N - T IH0 - N AH0 N T S
+SUBCONTRACT  S AH0 B - K AA1 N - T R AE2 K T
+SUBCONTRACTED  S AH0 B - K AA1 N - T R AE0 K - T IH0 D
+SUBCONTRACTING  S AH2 B - K AH0 N - T R AE1 K - T IH0 NG
+SUBCONTRACTOR  S AH0 B - K AA1 N - T R AE2 K - T ER0
+SUBCONTRACTORS  S AH0 B - K AA1 N - T R AE0 K - T ER0 Z
+SUBCONTRACTS  S AH0 B - K AA1 N - T R AE2 K T S
+SUBCULTURE  S AH1 B - K AH2 L - CH ER0
+SUBCULTURES  S AH1 B - K AH2 L - CH ER0 Z
+SUBDIVIDE  S AH2 B - D AH0 - V AY1 D
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+SUBDIVISION  S AH1 B - D IH0 - V IH2 - ZH AH0 N
+SUBDIVISIONS  S AH1 B - D IH0 - V IH2 - ZH AH0 N Z
+SUBDUE  S AH0 B - D UW1
+SUBDUED  S AH0 B - D UW1 D
+SUBDUING  S AH0 B - D UW1 - IH0 NG
+SUBER  S UW1 - B ER0
+SUBFAMILIES  S AH1 B - F AE2 - M AH0 - L IY0 Z
+SUBFAMILY  S AH1 B - F AE2 - M AH0 - L IY0
+SUBGROUP  S AH1 B - G R UW2 P
+SUBGROUPS  S AH1 B - G R UW2 P S
+SUBHLOK  S AH1 B - L AA2 K
+SUBHUMAN  S AH2 B - HH Y UW1 - M AH0 N
+SUBIA  S UW0 - B IY1 - AH0
+SUBIC  S UW1 - B IH0 K
+SUBJECT  S AH0 B - JH EH1 K T
+SUBJECT'S  S AH1 B - JH IH0 K T S
+SUBJECT(2)  S AH1 B - JH IH0 K T
+SUBJECTED  S AH0 B - JH EH1 K - T IH0 D
+SUBJECTING  S AH0 B - JH EH1 K - T IH0 NG
+SUBJECTIVE  S AH0 B - JH EH1 K - T IH0 V
+SUBJECTIVITY  S AH0 B - JH EH0 K - T IH1 - V IH0 - T IY0
+SUBJECTS  S AH1 B - JH IH0 K T S
+SUBJECTS'  S AH1 B - JH EH0 K T S
+SUBJECTS'(2)  S AH1 B - JH EH0 K S
+SUBJECTS(2)  S AH0 B - JH EH1 K T S
+SUBJECTS(3)  S AH0 B - JH EH1 K S
+SUBJUGATE  S AH1 B - JH AH0 - G EY2 T
+SUBJUGATED  S AH1 B - JH AH0 - G EY2 - T IH0 D
+SUBKINGDOM  S AH0 B - K IH1 NG - D AH0 M
+SUBLEASE  S AH1 B - L IY2 S
+SUBLEASING  S AH0 B - L IY1 - S IH0 NG
+SUBLET  S AH1 - B L EH2 T
+SUBLETT  S UW1 - B L IH0 T
+SUBLIME  S AH0 - B L AY1 M
+SUBLIMINAL  S AH0 B - L IH1 - M IH0 - N AH0 L
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+SUBLUXATION  S AH0 - B L AH0 K - S EY1 - SH AH0 N
+SUBLUXATIONS  S AH0 - B L AH0 K - S EY1 - SH AH0 N Z
+SUBMACHINE  S AH2 B - M AH0 - SH IY1 N
+SUBMARINE  S AH1 B - M ER0 - IY2 N
+SUBMARINE'S  S AH1 B - M ER0 - IY2 N Z
+SUBMARINE'S(2)  S AH0 B - M ER0 - IY1 N Z
+SUBMARINE(2)  S AH2 B - M ER0 - IY1 N
+SUBMARINES  S AH1 B - M ER0 - IY2 N Z
+SUBMARINES'S  S AH1 B - M ER0 - IY2 N - Z IH0 Z
+SUBMARINES(2)  S AH0 B - M ER0 - IY1 N Z
+SUBMERGE  S AH0 B - M ER1 JH
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+SUBMERGENCE  S AH0 B - M ER1 - JH AH0 N S
+SUBMERSE  S AH0 B - M ER1 S
+SUBMERSED  S AH0 B - M ER1 S T
+SUBMERSIBLE  S AH0 B - M ER1 - S IH0 - B AH0 L
+SUBMERSION  S AH0 B - M ER1 - ZH AH0 N
+SUBMINIMUM  S AH0 B - M IH1 - N IH0 - M AH0 M
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+SUBMITS  S AH2 B - M IH1 T S
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+SUBNOTEBOOK  S AH1 B - N OW1 T - B UH2 K
+SUBORDINATE  S AH0 - B AO1 R - D AH0 - N EY2 T
+SUBORDINATE(2)  S AH0 - B AO1 R - D AH0 N - AH0 T
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+SUBORDINATES(2)  S AH0 - B AO1 R - D AH0 N - AH0 T S
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+SUBORDINATION  S AH0 - B AO2 R - D AH0 - N EY1 - SH AH0 N
+SUBOTNICK  S AH0 - B AA1 T - N IH0 K
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+SUBPLOTS  S AH1 B - P L AA0 T S
+SUBPOENA  S AH0 - P IY1 - N AH0
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+SUBRAMANIAN  S UW2 - B R AH0 - M AA1 - N IY0 - AH0 N
+SUBROTO  S UW0 - B R OW1 - T OW0
+SUBS  S AH1 B Z
+SUBS'S  S AH1 B - Z IH0 Z
+SUBSAHARAN  S AH2 B - S AH0 - HH EH1 - R AH0 N
+SUBSCRIBE  S AH0 B - S K R AY1 B
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+SUBSCRIBER'S  S AH0 B - S K R AY1 - B ER0 Z
+SUBSCRIBERS  S AH0 B - S K R AY1 - B ER0 Z
+SUBSCRIBERS'  S AH0 B - S K R AY1 - B ER0 Z
+SUBSCRIBES  S AH0 B - S K R AY1 B Z
+SUBSCRIBING  S AH0 B - S K R AY1 - B IH0 NG
+SUBSCRIPTION  S AH0 B - S K R IH1 P - SH AH0 N
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+SUBSIDES  S AH0 B - S AY1 D Z
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+SUBSIDIARY  S AH0 B - S IH1 - D IY0 - EH2 - R IY0
+SUBSIDIARY'S  S AH0 B - S IH1 - D IY0 - EH2 - R IY0 Z
+SUBSIDIES  S AH1 B - S AH0 - D IY0 Z
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+SUBSIDING  S AH0 B - S AY1 - D IH0 NG
+SUBSIDIZATION  S AH2 B - S IH0 - D IH0 - Z EY1 - SH AH0 N
+SUBSIDIZE  S AH1 B - S IH0 - D AY2 Z
+SUBSIDIZED  S AH1 B - S IH0 - D AY2 Z D
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+SUBSIDIZING  S AH1 B - S IH0 - D AY2 - Z IH0 NG
+SUBSIDY  S AH1 B - S IH0 - D IY0
+SUBSIST  S AH0 B - S IH1 S T
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+SUBSTANTIALLY(2)  S AH0 B - S T AE1 N - CH AH0 - L IY0
+SUBSTANTIATE  S AH0 B - S T AE1 N - CH IY0 - EY2 T
+SUBSTANTIATE(2)  S AH0 B - S T AE1 N - SH IY0 - EY2 T
+SUBSTANTIATED  S AH0 B - S T AE1 N - SH IY0 - EY2 - T IH0 D
+SUBSTANTIATED(2)  S AH0 B - S T AE1 N - CH IY0 - EY2 - T IH0 D
+SUBSTANTIATES  S AH0 B - S T AE1 N - CH IY0 - EY2 T S
+SUBSTANTIATES(2)  S AH0 B - S T AE1 N - SH IY0 - EY2 T S
+SUBSTANTIATION  S AH0 B - S T AE2 N - CH IY0 - EY1 - SH AH0 N
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+SUBSTANTIVE  S AH1 B - S T AH0 N - T IH0 V
+SUBSTANTIVELY  S AH1 B - S T AH0 N - T IH0 V - L IY0
+SUBSTATION  S AH1 B - S T EY2 - SH AH0 N
+SUBSTITUTE  S AH1 B - S T AH0 - T UW2 T
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+SUBSTITUTION  S AH2 B - S T IH0 - T UW1 - SH AH0 N
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+SUBSTRATES  S AH1 B - S T R EY2 T S
+SUBSURFACE  S AH1 B - S ER2 - F AH0 S
+SUBSYSTEM  S AH1 B - S IH2 - S T AH0 M
+SUBSYSTEMS  S AH1 B - S IH2 - S T AH0 M Z
+SUBTERFUGE  S AH1 B - T ER0 - F Y UW2 JH
+SUBTERRANEAN  S AH0 B - T ER0 - EY1 - N IY0 - AH0 N
+SUBTEXT  S AH1 B - T EH2 K S T
+SUBTITLE  S AH1 B - T AY2 - T AH0 L
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+SUBTITLES  S AH1 B - T AY2 - T AH0 L Z
+SUBTLE  S AH1 - T AH0 L
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+SUBTLER(2)  S AH1 T - L ER0
+SUBTLETIES  S AH1 - T AH0 L - T IY0 Z
+SUBTLETY  S AH1 - T AH0 L - T IY0
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+SUBTRACT  S AH0 B - T R AE1 K T
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+SUBTYPE  S AH1 B - T AY2 P
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+SUBURB'S  S AH1 - B ER0 B Z
+SUBURBAN  S AH0 - B ER1 - B AH0 N
+SUBURBANITE  S AH0 - B ER1 - B AH0 - N AY2 T
+SUBURBANITES  S AH0 - B ER1 - B AH0 - N AY2 T S
+SUBURBANIZATION  S AH0 - B ER2 - B AH0 - N IH0 - Z EY1 - SH AH0 N
+SUBURBANIZE  S AH0 - B ER1 - B AH0 - N AY2 Z
+SUBURBANS  S AH0 - B ER1 - B AH0 N Z
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+SUBURBS  S AH1 - B ER0 B Z
+SUBVERSION  S AH0 B - V ER1 - ZH AH0 N
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+SUBVERSIVES  S AH0 B - V ER1 - S IH0 V Z
+SUBVERT  S AH0 B - V ER1 T
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+SUBVERTS  S AH0 B - V ER1 T S
+SUBVOLCANIC  S AH2 B - V AA0 L - K AE1 - N IH0 K
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+SUBZERO(2)  S AH2 B - Z IY1 - R OW0
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+SUCCEEDED  S AH0 K - S IY1 - D AH0 D
+SUCCEEDED(2)  S AH0 K - S IY1 - D IH0 D
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+SUCCEEDS  S AH0 K - S IY1 D Z
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+SUCCULENT  S AH1 - K Y AH0 - L IH0 N T
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+SUCCUMBS  S AH0 - K AH1 M Z
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+SUCHAN  S AH1 - CH AH0 N
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+SUCHINDA  S UW2 - CH IH1 N - D AH0
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+SUCRALOSE  S UW1 - K R AH0 - L OW2 S
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+SUCTION  S AH1 K - SH AH0 N
+SUDA  S UW1 - D AH0
+SUDAFED  S UW1 - D AH0 - F EH2 D
+SUDAN  S UW0 - D AE1 N
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+SUDANESE  S UW2 - D AH0 - N IY1 Z
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+SUDDRETH  S AH1 - D R IH0 TH
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+SUEDE  S W EY1 D
+SUEDES  S W EY1 D Z
+SUEKER  S UW1 - K ER0
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+SUEZ  S UW1 - EH0 Z
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+SUFFER  S AH1 - F ER0
+SUFFERED  S AH1 - F ER0 D
+SUFFERER  S AH1 - F ER0 - ER0
+SUFFERERS  S AH1 - F ER0 - ER0 Z
+SUFFERING  S AH1 - F ER0 - IH0 NG
+SUFFERING(2)  S AH1 - F R IH0 NG
+SUFFERINGS  S AH1 - F ER0 - IH0 NG Z
+SUFFERINGS(2)  S AH1 - F R IH0 NG Z
+SUFFERN  S AH1 - F ER0 N
+SUFFERS  S AH1 - F ER0 Z
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+SUFFICIENT  S AH0 - F IH1 - SH AH0 N T
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+SUFFIELD  S AH1 - F IY0 L D
+SUFFIELD'S  S AH1 - F IY0 L D Z
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+SUFFOCATED  S AH1 - F AH0 - K EY2 - T IH0 D
+SUFFOCATING  S AH1 - F AH0 - K EY2 - T IH0 NG
+SUFFOCATION  S AH2 - F AH0 - K EY1 - SH AH0 N
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+SUFFRAGETTES  S AH2 - F R AH0 - JH EH1 T S
+SUFFRAGIST  S AH1 - F R AH0 - JH IH0 S T
+SUFFRAGISTS  S AH1 - F R AH0 - JH IH0 S T S
+SUFFRAGISTS(2)  S AH1 - F R AH0 - JH IH0 S S
+SUFFRAGISTS(3)  S AH1 - F R AH0 - JH IH0 S
+SUFFUSE  S AH0 - F Y UW1 Z
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+SUGGS  S AH1 G Z
+SUGIHARA  S UW0 - G IY0 - HH AA1 - R AH0
+SUGIMOTO  S UW0 - G IY0 - M OW1 - T OW0
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+SUI(2)  S W IY0
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+SUISSE  S W IH1 S
+SUISSE'S  S W IH1 - S IH0 Z
+SUISSE'S(2)  S W IY1 - S IH0 Z
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+SUIT'S  S UW1 T S
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+SUITT  S UW1 T
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+SULK  S AH1 L K
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+SULLIVANS  S AH1 - L IH0 - V AH0 N Z
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+TOUCH  T AH1 CH
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+TOUCHE  T UW1 SH
+TOUCHED  T AH1 CH T
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+TOUCHING  T AH1 - CH IH0 NG
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+TOUGHED  T AH1 F T
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+TOURISTS  T UH1 - R AH0 S T S
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+TOURNAMENT  T UH1 R - N AH0 - M AH0 N T
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+TOW  T OW1
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+TOWARDS  T AH0 - W AO1 R D Z
+TOWARDS(2)  T AO1 R D Z
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+TOWBOAT  T OW1 - B OW2 T
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+TOWED  T OW1 D
+TOWEL  T AW1 - AH0 L
+TOWEL(2)  T AW1 L
+TOWELING  T AW1 - AH0 L - IH0 NG
+TOWELING(2)  T AW1 - L IH0 NG
+TOWELL  T AA1 - W EH0 L
+TOWELS  T AW1 - AH0 L Z
+TOWELS(2)  T AW1 L Z
+TOWER  T AW1 - ER0
+TOWER'S  T AW1 - ER0 Z
+TOWERED  T AW1 - ER0 D
+TOWERING  T AW1 - ER0 - IH0 NG
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+TOWERS'  T AW1 - ER0 Z
+TOWERY  T OW0 - ER1 - IY0
+TOWEY  T OW1 - IY0
+TOWING  T OW1 - IH0 NG
+TOWLE  T AW1 L
+TOWLE'S  T AW1 L Z
+TOWLER  T OW1 - L ER0
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+TOWN  T AW1 N
+TOWN'S  T AW1 N Z
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+TOWNERS  T AW1 - N ER0 Z
+TOWNES  T AW1 N Z
+TOWNHOUSE  T AW1 N - HH AW2 S
+TOWNHOUSES  T AW1 N - HH AW2 - S IH0 Z
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+TOWNSHEND  T AW1 N - SH EH2 N D
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+TOWNSHIPS  T AW1 N - SH IH0 P S
+TOWNSLEY  T AW1 N S - L IY0
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+TOWNSON  T AW1 N - S AH0 N
+TOWNSPEOPLE  T AW1 N Z - P IY2 - P AH0 L
+TOWRY  T AO1 - R IY0
+TOWS  T OW1 Z
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+TOXICITY  T AA0 K - S IH1 - S AH0 - T IY0
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+TOXICOLOGIST  T AA2 K - S IH0 - K AA1 - L AH0 - JH IH0 S T
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+TOXICOLOGISTS(2)  T AA2 K - S IH0 - K AA1 - L AH0 - JH IH0 S S
+TOXICOLOGISTS(3)  T AA2 K - S IH0 - K AA1 - L AH0 - JH IH0 S
+TOXICOLOGY  T AA2 K - S IH0 - K AA1 - L AH0 - JH IY0
+TOXICS  T AA1 K - S IH0 K S
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+TOY  T OY1
+TOY'S  T OY1 Z
+TOYA  T OY1 - AH0
+TOYAMA  T OW0 - Y AA1 - M AH0
+TOYE  T OY1
+TOYED  T OY1 D
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+TOYMAKER  T OY1 - M EY2 - K ER0
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+TOYO  T OW1 - Y OW0
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+TOYOTA  T OW0 - Y OW1 - T AH0
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+TOYS  T OY1 Z
+TOYS'  T OY1 Z
+TOYSTORE  T OY1 - S T AO2 R
+TOYSTORES  T OY1 - S T AO2 R Z
+TOZER  T OW1 - Z ER0
+TOZIER  T OW1 - Z IY0 - ER0
+TOZZI  T AA1 - Z IY0
+TRABER  T R EY1 - B ER0
+TRABERT  T R AE1 - B ER0 T
+TRABUCCO  T R AA0 - B UW1 - K OW0
+TRABUE  T R AA1 B - W EH0
+TRAC  T R AE1 K
+TRACE  T R EY1 S
+TRACEABLE  T R EY1 - S AH0 - B AH0 L
+TRACED  T R EY1 S T
+TRACER  T R EY1 - S ER0
+TRACERS  T R EY1 - S ER0 Z
+TRACES  T R EY1 - S AH0 Z
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+TRACEY  T R EY1 - S IY0
+TRACHEA  T R EY1 - K IY0 - AH0
+TRACHEAL  T R EY1 - K IY0 - AH0 L
+TRACHEOPHYTE  T R EY1 - K IY0 - AH0 - F AY0 T
+TRACHEOPHYTES  T R EY1 - K IY0 - AH0 - F AY0 T S
+TRACHSEL  T R AE1 K - S AH0 L
+TRACHT  T R AE1 K T
+TRACHTENBERG  T R AE1 K - T AH0 N - B ER0 G
+TRACIE  T R EY1 - S IY0
+TRACINDA  T R AH0 - S IH1 N - D AH0
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+TRACING  T R EY1 - S IH0 NG
+TRACK  T R AE1 K
+TRACK'S  T R AE1 K S
+TRACKAGE  T R AE1 - K IH0 JH
+TRACKBALL  T R AE1 K - B AO2 L
+TRACKBALLS  T R AE1 K - B AO2 L Z
+TRACKED  T R AE1 K T
+TRACKER  T R AE1 - K ER0
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+TRACKS  T R AE1 K S
+TRACOR  T R EY1 - S ER0
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+TRACOR(3)  T R EY1 - K AO2 R
+TRACOR(4)  T R AE1 - K AO2 R
+TRACT  T R AE1 K T
+TRACTABLE  T R AE1 K - T AH0 - B AH0 L
+TRACTEBEL  T R AE1 K - T AH0 - B AH0 L
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+TRACTOR  T R AE1 K - T ER0
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+TRACTS  T R AE1 K T S
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+TRACY'S  T R EY1 - S IY0 Z
+TRACZ  T R AA1 CH
+TRACZYK  T R AA1 - CH IH2 K
+TRADABLE  T R EY1 - D AH0 - B AH0 L
+TRADE  T R EY1 D
+TRADE'S  T R EY1 D Z
+TRADEABLE  T R EY1 - D AH0 - B AH0 L
+TRADED  T R EY1 - D IH0 D
+TRADEMARK  T R EY1 D - M AA2 R K
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+TRADEMARKS  T R EY1 D - M AA2 R K S
+TRADEOFF  T R EY1 - D AO2 F
+TRADEOFFS  T R EY1 - D AO2 F S
+TRADER  T R EY1 - D ER0
+TRADER'S  T R EY1 - D ER0 Z
+TRADERS  T R EY1 - D ER0 Z
+TRADERS'  T R EY1 - D ER0 Z
+TRADES  T R EY1 D Z
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+TRADING  T R EY1 - D IH0 NG
+TRADING'S  T R EY1 - D IH0 NG Z
+TRADINGS  T R EY1 - D IH0 NG Z
+TRADITION  T R AH0 - D IH1 - SH AH0 N
+TRADITIONAL  T R AH0 - D IH1 - SH AH0 - N AH0 L
+TRADITIONALIST  T R AH0 - D IH1 - SH AH0 N - AH0 - L IH0 S T
+TRADITIONALISTS  T R AH0 - D IH1 SH - N AH0 - L AH0 S T S
+TRADITIONALLY  T R AH0 - D IH1 - SH AH0 N - AH0 - L IY0
+TRADITIONALLY(2)  T R AH0 - D IH1 SH - N AH0 - L IY0
+TRADITIONS  T R AH0 - D IH1 - SH AH0 N Z
+TRAEGER  T R EH1 - G ER0
+TRAER  T R EH1 R
+TRAFALGAR  T R AH0 - F AE1 L - G ER0
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+TRAFFORD  T R AE1 - F ER0 D
+TRAFICANT  T R AE1 - F IH0 - K AH0 N T
+TRAFICANTE  T R AA0 - F IY0 - K AA1 N - T IY0
+TRAFILLIO  T R AH0 - F IY1 - L IY0 - OW0
+TRAFILLIO'S  T R AH0 - F IY1 - L IY0 - OW0 Z
+TRAFTON  T R AE1 F - T AH0 N
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+TRAGEDY  T R AE1 - JH AH0 - D IY0
+TRAGER  T R EY1 - G ER0
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+TRAGIC  T R AE1 - JH IH0 K
+TRAGICALLY  T R AE1 - JH IH0 K - L IY0
+TRAGICOMIC  T R AE2 - JH IH0 - K AA1 - M IH0 K
+TRAGOS  T R AE1 - G OW0 Z
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+TRAHERN  T R AE1 - HH ER0 N
+TRAIL  T R EY1 L
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+TRAILBLAZERS  T R EY1 L - B L EY2 - Z ER0 Z
+TRAILED  T R EY1 L D
+TRAILER  T R EY1 - L ER0
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+TRAILHEAD  T R EY1 L - HH EH2 D
+TRAILING  T R EY1 - L IH0 NG
+TRAILS  T R EY1 L Z
+TRAILS'  T R EY1 L Z
+TRAILWAYS  T R EY1 L - W EY2 Z
+TRAILWAYS'  T R EY1 L - W EY2 Z
+TRAIN  T R EY1 N
+TRAIN'S  T R EY1 N Z
+TRAINA  T R EY1 - N AH0
+TRAINABLE  T R EY1 - N AH0 - B AH0 L
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+TRAINEES  T R EY1 - N IY1 Z
+TRAINER  T R EY1 - N ER0
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+TRAINING  T R EY1 - N IH0 NG
+TRAINMEN  T R EY1 N - M AH0 N
+TRAINOR  T R EY1 - N ER0
+TRAINS  T R EY1 N Z
+TRAIPSE  T R EY1 P S
+TRAIPSING  T R EY1 P - S IH0 NG
+TRAISTER  T R EY1 - S T ER0
+TRAIT  T R EY1 T
+TRAITOR  T R EY1 - T ER0
+TRAITOROUS  T R EY1 - T ER0 - AH0 S
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+TRAITS  T R EY1 T S
+TRAJAN  T R EY1 - JH AH0 N
+TRAJAN'S  T R EY1 - JH AH0 N Z
+TRAJECTORY  T R AH0 - JH EH1 K - T ER0 - IY0
+TRAK  T R AE1 K
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+TRAM  T R AE1 M
+TRAMBLE  T R AE1 M - B AH0 L
+TRAMCO  T R AE1 M - K OW0
+TRAMEL  T R AE1 - M AH0 L
+TRAMELL  T R AA0 - M EY1 L
+TRAMIEL  T R AE1 - M IY0 - AH0 L
+TRAMMEL  T R AE1 - M AH0 L
+TRAMMELL  T R AE1 - M AH0 L
+TRAMONTANA  T R AA0 - M OW0 N - T AE1 - N AH0
+TRAMONTANO  T R AA0 - M OW0 N - T AA1 - N OW0
+TRAMONTE  T R AA0 - M OW1 N - T IY0
+TRAMONTIN  T R AH0 - M AA1 N - T IH0 N
+TRAMP  T R AE1 M P
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+TRAMPING  T R AE1 M - P IH0 NG
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+TRAMPLED  T R AE1 M - P AH0 L D
+TRAMPLES  T R AE1 M - P AH0 L Z
+TRAMPLING  T R AE1 M - P L IH0 NG
+TRAMPOLINE  T R AE2 M - P AH0 - L IY1 N
+TRAMPS  T R AE1 M P Z
+TRAMS  T R AE1 M Z
+TRAN  T R AE1 N
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+TRANCE  T R AE1 N S
+TRANCHE  T R AE1 N CH
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+TRANCHINA  T R AA0 N - K IY1 - N AH0
+TRANE  T R EY1 N
+TRANG  T R AE1 NG
+TRANI  T R AA1 - N IY0
+TRANQUIL  T R AE1 NG - K W AH0 L
+TRANQUIL(2)  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
+TRANQUILIZE(2)  T R AE1 NG - K W AH0 - L AY2 Z
+TRANQUILIZER  T R AE1 NG - K W AH0 - L AY2 - Z ER0
+TRANQUILIZERS  T R AE1 NG - K W AH0 - L AY2 - Z ER0 Z
+TRANQUILIZING  T R AE1 NG - K W AH0 - L AY2 - Z IH0 NG
+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(2)  T R AE0 N - Z AE1 K - T IH0 D
+TRANSACTION  T R AE0 N - Z AE1 K - SH AH0 N
+TRANSACTION'S  T R AE0 N - Z AE1 K - SH AH0 N Z
+TRANSACTIONS  T R AE0 N - Z AE1 K - SH AH0 N Z
+TRANSAFRICA  T R AE2 N - Z AE1 - F R IH0 - K AH0
+TRANSALASKA  T R AE2 N - Z AH0 - L AE1 S - K AH0
+TRANSALTA  T R AE2 N - Z AO1 L - T AH0
+TRANSAMERICA  T R AE2 N - S AH0 - M EH1 - R IH0 - K AH0
+TRANSAMERICA'S  T R AE2 N - Z AH0 - M EH1 - R IH0 - K AH0 Z
+TRANSAMERICAN  T R AE2 N - Z AH0 - M EH1 - R IH0 - K AH0 N
+TRANSAMERICAN'S  T R AE2 N - Z AH0 - M EH1 - R IH0 - K AH0 N Z
+TRANSATLANTIC  T R AE2 N Z - AH0 T - L AE1 N - T IH0 K
+TRANSATLANTIC(2)  T R AE2 N Z - AH0 T - L AE1 - N IH0 K
+TRANSCANADA  T R AE2 N Z - K AE1 - N AH0 - T AH0
+TRANSCANADA'S  T R AE2 N Z - K AE1 - N AH0 - D AH0 Z
+TRANSCAPITAL  T R AE2 N Z - K AE1 - P IH0 - T AH0 L
+TRANSCEND  T R AE0 N - S EH1 N D
+TRANSCENDED  T R AE0 N - S EH1 N - D IH0 D
+TRANSCENDENCE  T R AE0 N - S EH1 N - D AH0 N S
+TRANSCENDENT  T R AE0 N - S EH1 N - D AH0 N T
+TRANSCENDENTAL  T R AE2 N - S AH0 N - D EH1 N - T AH0 L
+TRANSCENDENTAL(2)  T R AE2 N - S AH0 N - D EH1 - N AH0 L
+TRANSCENDING  T R AE0 N - S EH1 N - D IH0 NG
+TRANSCENDS  T R AE0 N - S EH1 N D Z
+TRANSCHANNEL  T R AH1 N S - CH AE1 - N AH0 L
+TRANSCHANNEL'S  T R AH1 N S - CH AE1 - N AH0 L Z
+TRANSCISCO  T R AE2 N - S IH1 - S K OW0
+TRANSCO  T R AE1 N - S K OW0
+TRANSCON  T R AE1 N Z - K AA0 N
+TRANSCONTINENTAL  T R AE2 N Z - K AA2 N - T IH0 - N EH1 N - T AH0 L
+TRANSCONTINENTAL'S  T R AE2 N Z - K AA2 N - T IH0 - N EH1 N - T AH0 L Z
+TRANSCONTINENTAL'S(2)  T R AE2 N Z - K AA2 - N IH0 - N EH1 N - T AH0 L Z
+TRANSCONTINENTAL'S(3)  T R AE2 N Z - K AA2 N - T IH0 - N EH1 - N AH0 L Z
+TRANSCONTINENTAL'S(4)  T R AE2 N Z - K AA2 - N IH0 - N EH1 - N AH0 L Z
+TRANSCONTINENTAL(2)  T R AE2 N Z - K AA2 - N IH0 - N EH1 N - T AH0 L
+TRANSCONTINENTAL(3)  T R AE2 N Z - K AA2 N - T IH0 - N EH1 - N AH0 L
+TRANSCONTINENTAL(4)  T R AE2 N Z - K AA2 - N IH0 - N EH1 - N 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
+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
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+TRANSCRIPTS  T R AE1 N - S K R IH2 P T S
+TRANSDUCER  T R AE0 N S - D UW1 - S ER0
+TRANSDUCERS  T R AE0 N S - D UW1 - S ER0 Z
+TRANSECT  T R AE1 N - S EH2 K T
+TRANSECTED  T R AE1 N - S EH2 K - T IH0 D
+TRANSECTION  T R AE1 N - S EH2 K - SH AH0 N
+TRANSFER  T R AE0 N S - F ER1
+TRANSFER(2)  T R AE1 N S - F ER0
+TRANSFERABILITY  T R AE2 N S - F ER0 - AH0 - B IH1 - L IH0 - T IY0
+TRANSFERABLE  T R AE0 N S - F ER1 - AH0 - B AH0 L
+TRANSFERED  T R AE0 N S - F ER1 D
+TRANSFERENCE  T R AE0 N S - F ER1 - AH0 N S
+TRANSFERING  T R AE0 N S - F ER1 - IH0 NG
+TRANSFERRABLE  T R AE2 N S - F ER1 - AH0 - B AH0 L
+TRANSFERRED  T R AE0 N S - F ER1 D
+TRANSFERRED(2)  T R AE1 N S - F ER0 D
+TRANSFERRING  T R AE0 N S - F ER1 - IH0 NG
+TRANSFERS  T R AE0 N S - F ER1 Z
+TRANSFERS(2)  T R AE1 N S - F ER0 Z
+TRANSFIX  T R AE0 N S - F IH1 K S
+TRANSFIXED  T R AE0 N S - F IH1 K S T
+TRANSFORM  T R AE0 N S - F AO1 R M
+TRANSFORM(2)  T R AE1 N S - F AO0 R M
+TRANSFORMATION  T R AE2 N S - F ER0 - M EY1 - SH AH0 N
+TRANSFORMATIONAL  T R AE2 N S - F ER0 - M EY1 - SH AH0 - N AH0 L
+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
+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
+TRANSFUSION  T R AE0 N S - F Y UW1 - ZH AH0 N
+TRANSFUSIONS  T R AE0 N S - F Y UW1 - ZH AH0 N Z
+TRANSGENIC  T R AE2 N Z - JH EH1 - N IH0 K
+TRANSGRESS  T R AE0 N Z - G R EH1 S
+TRANSGRESSED  T R AE0 N Z - G R EH1 S T
+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
+TRANSIENCE  T R AE1 N - Z IY0 - AH0 N S
+TRANSIENT  T R AE1 N - ZH AH0 N T
+TRANSIENTS  T R AE1 N - Z IY0 - AH0 N T S
+TRANSILLUMINATION  T R AE2 N - Z AH0 - L UW2 - M AH0 - N EY1 - SH AH0 N
+TRANSIMAGE  T R AE2 N - Z IH1 - M IH0 JH
+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 AH0 T
+TRANSITION  T R AE0 N - Z IH1 - SH AH0 N
+TRANSITIONAL  T R AE0 N - S IH1 - SH AH0 - N AH0 L
+TRANSITIONAL(2)  T R AE0 N - Z IH1 - SH AH0 - N AH0 L
+TRANSITIONING  T R AE0 N - Z IH1 - SH AH0 N - IH0 NG
+TRANSITIONS  T R AE0 N - Z IH1 - SH AH0 N Z
+TRANSITORY  T R AE1 N - Z AH0 - T AO2 - R IY0
+TRANSKEI  T R AE1 N Z - K EY2
+TRANSLATE  T R AE0 N Z - L EY1 T
+TRANSLATE(2)  T R AE0 N S - L EY1 T
+TRANSLATED  T R AE0 N Z - L EY1 - T AH0 D
+TRANSLATED(2)  T R AE0 N S - L EY1 - T IH0 D
+TRANSLATES  T R AE0 N Z - L EY1 T S
+TRANSLATES(2)  T R AE1 N S - L EY2 T S
+TRANSLATING  T R AE0 N Z - L EY1 - T IH0 NG
+TRANSLATING(2)  T R AE1 N S - L EY2 - T IH0 NG
+TRANSLATION  T R AE0 N Z - L EY1 - SH AH0 N
+TRANSLATION(2)  T R AE0 N S - L EY1 - SH AH0 N
+TRANSLATIONS  T R AE0 N Z - L EY1 - SH AH0 N Z
+TRANSLATIONS(2)  T R AE0 N S - L EY1 - SH AH0 N Z
+TRANSLATOR  T R AE0 N S - L EY1 - T ER0
+TRANSLATOR(2)  T R AE0 N Z - L EY1 - T ER0
+TRANSLATORS  T R AE0 N S - L EY1 - T ER0 Z
+TRANSLATORS(2)  T R AE0 N Z - L EY1 - T ER0 Z
+TRANSLOGIC  T R AE2 N Z - L AA1 - JH IH0 K
+TRANSLUCENT  T R AE0 N S - L UW1 - S AH0 N T
+TRANSMARK  T R AE1 N Z - M AA2 R K
+TRANSMEDIA  T R AE2 N Z - M IY1 - D IY0 - AH0
+TRANSMISSION  T R AE0 N S - M IH1 - SH AH0 N
+TRANSMISSION(2)  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(2)  T R AE0 N Z - M IH1 - T AH0 D
+TRANSMITTER  T R AE0 N S - M IH1 - T ER0
+TRANSMITTERS  T R AE0 N S - M IH1 - T ER0 Z
+TRANSMITTING  T R AE0 N S - M IH1 - 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
+TRANSOHIO  T R AE2 N Z - OW0 - HH AY1 - OW0
+TRANSOM  T R AE1 N - S AH0 M
+TRANSOMS  T R AE1 N - S AH0 M Z
+TRANSOU  T R AE1 N - Z UW2
+TRANSPAC  T R AE1 N Z - P AE2 K
+TRANSPACIFIC  T R AE2 N S - P AH0 - S IH1 - F IH0 K
+TRANSPARENCIES  T R AE0 N - S P EH1 - R AH0 N - S IY0 Z
+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
+TRANSPARK  T R AE1 N S - P AA1 R K
+TRANSPIRE  T R AE0 N - S P AY1 - ER0
+TRANSPIRED  T R AE0 N - S P AY1 - ER0 D
+TRANSPIRES  T R AE0 N - S P AY1 - ER0 Z
+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
+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
+TRANSPONDER  T R AE0 N S - P AA1 N - D ER0
+TRANSPONDERS  T R AE0 N S - P AA1 N - D ER0 Z
+TRANSPORT  T R AE0 N S - P AO1 R T
+TRANSPORT(2)  T R AE1 N S - P AO0 R T
+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
+TRANSPORTATION'S  T R AE2 N S - P ER0 - T EY1 - SH AH0 N Z
+TRANSPORTED  T R AE0 N S - P AO1 R - T AH0 D
+TRANSPORTER  T R AE0 N S - P AO1 R - T ER0
+TRANSPORTERS  T R AE0 N S - P AO1 R - T ER0 Z
+TRANSPORTING  T R AE0 N S - P AO1 R - T IH0 NG
+TRANSPORTS  T R AE0 N S - P AO1 R T S
+TRANSPORTS(2)  T R AE1 N S - P AO0 R T S
+TRANSPOSE  T R AE0 N S - P OW1 Z
+TRANSPOSED  T R AE0 N S - P OW1 Z D
+TRANSRACIAL  T R AE2 N Z - R EY1 - SH AH0 L
+TRANSRAPID  T R AE1 N Z - R AE1 - P IH0 D
+TRANSSEXUAL  T R AE0 N - S EH1 K - SH Y UW0 - AH0 L
+TRANSSEXUALS  T R AE0 N - S EH1 K - SH Y UW0 - AH0 L Z
+TRANSTAR  T R AE0 N S - T AA1 R
+TRANSTECHNOLOGY  T R AE2 N Z - T AH0 K - N AA1 - L AH0 - JH IY0
+TRANSTECTOR  T R AE2 N Z - T EH1 K - T ER0
+TRANSUE  T R AE1 N - Z UW0
+TRANSVAAL  T R AE0 N Z - V AA1 L
+TRANSVERSE  T R AE0 N Z - V ER1 S
+TRANSVESTITE  T R AE0 N Z - V EH1 - S T AY0 T
+TRANSVESTITES  T R AE0 N Z - V EH1 - S T AY0 T S
+TRANSWAY  T R AE1 N Z - W EY2
+TRANSWESTERN  T R AE2 N Z - W EH1 - S T ER0 N
+TRANSWORLD  T R AE0 N S - W ER1 L D
+TRANSYLVANIA  T R AE2 N - Z IY0 L - V EY1 - N IY0 - AH0
+TRANSYLVANIA(2)  T R AE2 N - S IH0 L - V EY1 - N Y AH0
+TRANT  T R AE1 N T
+TRANTER  T R AE1 N - T ER0
+TRANTHAM  T R AE1 N - TH AH0 M
+TRANUM  T R AE1 - N AH0 M
+TRANZONIC  T R AE0 N - Z AA1 - N IH0 K
+TRAP  T R AE1 P
+TRAPANI  T R AA0 - P AA1 - N IY0
+TRAPASSO  T R AA0 - P AA1 - S OW0
+TRAPELO  T R AH0 - P EH1 - L OW0
+TRAPEZE  T R AH0 - P IY1 Z
+TRAPHAGEN  T R AE1 - F AH0 - G AH0 N
+TRAPNELL  T R AE1 P - N AH0 L
+TRAPP  T R AE1 P
+TRAPPE  T R AE1 P
+TRAPPED  T R AE1 P T
+TRAPPERS  T R AE1 - P ER0 Z
+TRAPPING  T R AE1 - P IH0 NG
+TRAPPINGS  T R AE1 - P IH0 NG Z
+TRAPPIST  T R AE1 - P IH0 S T
+TRAPS  T R AE1 P S
+TRASH  T R AE1 SH
+TRASHED  T R AE1 SH T
+TRASHES  T R AE1 - SH IH0 Z
+TRASHING  T R AE1 - SH IH0 NG
+TRASHY  T R AE1 - SH IY0
+TRASK  T R AE1 S K
+TRAUB  T R AO1 B
+TRAUDT  T R AO1 D T
+TRAUGER  T R AW1 - G ER0
+TRAUGH  T R AO1
+TRAUGHBER  T R AO1 - B ER0
+TRAUGOTT  T R AW1 - G AH0 T
+TRAUM  T R AO1 M
+TRAUMA  T R AO1 - M AH0
+TRAUMAS  T R AO1 - M AH0 Z
+TRAUMATIC  T R AO0 - M AE1 - T IH0 K
+TRAUMATIZE  T R AO1 - M AH0 - T AY2 Z
+TRAUMATIZED  T R AO1 - M AH0 - T AY2 Z D
+TRAUSCH  T R AW1 SH
+TRAUT  T R AO1 T
+TRAUTH  T R AO1 TH
+TRAUTMAN  T R AW1 T - M AH0 N
+TRAUTMANN  T R AW1 T - M AH0 N
+TRAUTNER  T R AW1 T - N ER0
+TRAUTWEIN  T R AW1 T - W AY2 N
+TRAVAGLINI  T R AA0 - V AA0 G - L IY1 - N IY0
+TRAVAIL  T R AH0 - V EY1 L
+TRAVAILS  T R AH0 - V EY1 L Z
+TRAVEL  T R AE1 - V AH0 L
+TRAVELDAY  T R AE1 - V AH0 L - D EY2
+TRAVELDAYS  T R AE1 - V AH0 L - D EY2 Z
+TRAVELED  T R AE1 - V AH0 L D
+TRAVELER  T R AE1 - V AH0 - L ER0
+TRAVELER'S  T R AE1 - V AH0 - L ER0 Z
+TRAVELER(2)  T R AE1 V - L ER0
+TRAVELERS  T R AE1 - V AH0 - L ER0 Z
+TRAVELERS'  T R AE1 - V AH0 - L ER0 Z
+TRAVELERS(2)  T R AE1 V - L ER0 Z
+TRAVELGATE  T R AE1 - V AH0 L - G EY2 T
+TRAVELING  T R AE1 - V AH0 L - IH0 NG
+TRAVELING(2)  T R AE1 - V L IH0 NG
+TRAVELLED  T R AE1 - V AH0 L D
+TRAVELLER  T R AE1 - V AH0 - L ER0
+TRAVELLERS  T R AE1 - V AH0 - L ER0 Z
+TRAVELLING  T R AE1 - V AH0 L - IH0 NG
+TRAVELLING(2)  T R AE1 - V L IH0 NG
+TRAVELODGE  T R AE1 - V AH0 - L AA1 JH
+TRAVELOGUE  T R AE1 - V AH0 - L AO2 G
+TRAVELS  T R AE1 - V AH0 L Z
+TRAVELSTEAD  T R AE1 - V AH0 L - S T EH2 D
+TRAVENOL  T R AE1 - V AH0 - N AH0 L
+TRAVER  T R EY1 - V ER0
+TRAVERS  T R AE1 - V ER0 Z
+TRAVERSE  T R AE1 - V ER0 S
+TRAVERSE(2)  T R AH0 - V ER1 S
+TRAVERSED  T R AE1 - V ER0 S T
+TRAVERSED(2)  T R AH0 - V ER1 S T
+TRAVERSING  T R AH0 - V ER1 - S IH0 NG
+TRAVERSO  T R AA0 - V EH1 R - S OW0
+TRAVESTIES  T R AE1 - V AH0 - S T IY0 Z
+TRAVESTY  T R AE1 - V AH0 - S T IY0
+TRAVIATA  T R AA0 - V IY0 - AA1 - T AH0
+TRAVIESO  T R AA0 - V IY1 - S OW0
+TRAVIS  T R AE1 - V IH0 S
+TRAVISANO  T R AE2 - V IH0 - S AA1 - N OW0
+TRAVNIK  T R AE1 V - N IH0 K
+TRAVOLTA  T R AH0 - V OW1 L - T AH0
+TRAVOLTA'S  T R AH0 - V OW1 L - T AH0 Z
+TRAVOLTAS  T R AH0 - V OW1 L - T AH0 Z
+TRAWEEK  T R AO1 - IY2 K
+TRAWICK  T R AO1 - IH0 K
+TRAWLER  T R AO1 - L ER0
+TRAWLERS  T R AO1 - L ER0 Z
+TRAX  T R AE1 K S
+TRAXLER  T R AE1 K S - L ER0
+TRAY  T R EY1
+TRAYER  T R EY1 - ER0
+TRAYLOR  T R EY1 - L ER0
+TRAYNHAM  T R EY1 N - HH AH0 M
+TRAYNOR  T R EY1 - N ER0
+TRAYS  T R EY1 Z
+TRAYWICK  T R EY1 - W IH2 K
+TRBOVICH  T ER0 - B AA1 - V IH0 CH
+TREACHEROUS  T R EH1 - CH ER0 - AH0 S
+TREACHERY  T R EH1 - CH ER0 - IY0
+TREACY  T R EY1 - S IY0
+TREAD  T R EH1 D
+TREADAWAY  T R EH1 D - AH0 - W EY2
+TREADING  T R EH1 - D IH0 NG
+TREADMILL  T R EH1 D - M IH2 L
+TREADMILLS  T R EH1 D - M IH2 L Z
+TREADS  T R EH1 D Z
+TREADWAY  T R EH1 D - W EY2
+TREADWELL  T R EH1 D - W EH2 L
+TREADWHEEL  T R EH1 D - W IY2 L
+TREANOR  T R IY1 - N ER0
+TREASE  T R IY1 Z
+TREASON  T R IY1 - Z AH0 N
+TREASTER  T R IY1 - S T ER0
+TREASURE  T R EH1 - ZH ER0
+TREASURED  T R EH1 - ZH ER0 D
+TREASURER  T R EH1 - ZH ER0 - ER0
+TREASURER'S  T R EH1 - ZH ER0 - ER0 Z
+TREASURERS  T R EH1 - ZH ER0 - ER0 Z
+TREASURES  T R EH1 - ZH ER0 Z
+TREASURIES  T R EH1 - ZH ER0 - IY0 Z
+TREASURY  T R EH1 - ZH ER0 - IY0
+TREASURY'S  T R EH1 - ZH ER0 - IY0 Z
+TREASURYS  T R EH1 - ZH ER0 - IY0 Z
+TREAT  T R IY1 T
+TREATABLE  T R IY1 - T AH0 - B AH0 L
+TREATED  T R IY1 - T AH0 D
+TREATED(2)  T R IY1 - T IH0 D
+TREATER  T R IY1 - T ER0
+TREATERS  T R IY1 - T ER0 Z
+TREATIES  T R IY1 - T IY0 Z
+TREATING  T R IY1 - T IH0 NG
+TREATISE  T R IY1 - T AH0 S
+TREATISES  T R IY1 - T AH0 - S AH0 Z
+TREATMENT  T R IY1 T - M AH0 N T
+TREATMENTS  T R IY1 T - M AH0 N T S
+TREATS  T R IY1 T S
+TREATY  T R IY1 - T IY0
+TREATY'S  T R IY1 - T IY0 Z
+TREBILCOCK  T R IH0 - B IH1 L - K AH0 K
+TREBLE  T R EH1 - B AH0 L
+TREBLED  T R EH1 - B AH0 L D
+TREBLINKA  T R EH0 B - L IH1 NG - K ER0
+TREBLINKA(2)  T R EH0 B - L IH1 NG - K AH0
+TRECKER  T R EH1 - K ER0
+TREDER  T R IY1 - D ER0
+TREDWAY  T R EH1 D - W EY2
+TREE  T R IY1
+TREECE  T R IY1 S
+TREELESS  T R IY1 - L AH0 S
+TREEN  T R IY1 N
+TREES  T R IY1 Z
+TREESE  T R IY1 Z
+TREESH  T R IY1 SH
+TREESWEET  T R IY1 S - W IY2 T
+TREETOP  T R IY1 - T AO2 P
+TREETOPS  T R IY1 - T AO2 P S
+TREFETHEN  T R EH1 - F IH0 - TH AH0 N
+TREFGARNE  T R EH1 F - G AA0 R N
+TREFRY  T R EH1 - F R IY0
+TREFZ  T R EH1 F Z
+TREGLIA  T R EH1 G - L IY0 - AH0
+TREGO  T R EH1 - G OW0
+TREGONING  T R EH1 - G AH0 - N IH0 NG
+TREGRE  T R EH1 - G ER0
+TREGURTHA  T R EH0 - G ER1 - TH AH0
+TREHARNE  T R EH1 - HH AA0 R N
+TREIBER  T R AY1 - B ER0
+TREICHEL  T R AY1 - K AH0
+TREICHLER  T R AY1 - K AH0 - L ER0
+TREICHLER(2)  T R AY1 - K L ER0
+TREINEN  T R AY1 - N AH0 N
+TREJO  T R EY1 - Y OW0
+TREK  T R EH1 K
+TREKKED  T R EH1 K T
+TREKKING  T R EH1 - K IH0 NG
+TREKS  T R EH1 K S
+TRELA  T R EH1 - L AH0
+TRELLA  T R EH1 - L AH0
+TRELLEBORG  T R EH1 - L AH0 - B AO0 R G
+TRELLEBORG'S  T R EH1 - L AH0 - B AO0 R G Z
+TRELLIS  T R EH1 - L AH0 S
+TRELOAR  T R EH1 - L AO0 R
+TREMAIN  T R EH1 - M AY0 N
+TREMAINE  T R IH0 - M EY1 N
+TREMAYNE  T R EH1 - M EY0 N
+TREMBATH  T R EH1 M - B AH0 TH
+TREMBLAY  T R EH1 M - B L EY0
+TREMBLE  T R EH1 M - B AH0 L
+TREMBLED  T R EH1 M - B AH0 L D
+TREMBLEY  T R EH1 M - B L IY0
+TREMBLING  T R EH1 M - B AH0 L - IH0 NG
+TREMBLING(2)  T R EH1 M - B L IH0 NG
+TREMBLY  T R EH1 M - B L IY0
+TREMEL  T R EH1 - M AH0 L
+TREMENDOUS  T R AH0 - M EH1 N - D AH0 S
+TREMENDOUS(2)  T R IH0 - M EH1 N - D AH0 S
+TREMENDOUSLY  T R AH0 - M EH1 N - D AH0 S - L IY0
+TREMENDOUSLY(2)  T R IH0 - M EH1 N - D AH0 S - L IY0
+TREMENS  T R EH1 - M AH0 N Z
+TREML  T R EH1 - M AH0 L
+TREMMEL  T R EH1 - M AH0 L
+TREMONT  T R EH1 - M AH0 N T
+TREMOR  T R EH1 - M ER0
+TREMORS  T R EH1 - M ER0 Z
+TREMPER  T R EH1 M - P ER0
+TREMULOUS  T R EH1 - M Y AH0 - L AH0 S
+TREMULOUSLY  T R EH1 - M Y AH0 - L AH0 S - L IY0
+TRENARY  T R EH1 - N EH0 - R IY0
+TRENCH  T R EH1 N CH
+TRENCHANT  T R EH1 N - CH AH0 N T
+TRENCHARD  T R EH1 NG - K ER0 D
+TRENCHER  T R EH1 N - CH ER0
+TRENCHES  T R EH1 N - CH IH0 Z
+TREND  T R EH1 N D
+TRENDED  T R EH1 N - D IH0 D
+TRENDIER  T R EH1 N - D Y ER0
+TRENDIER(2)  T R EH1 N - D IY0 - ER0
+TRENDIEST  T R EH0 N - D IY1 S T
+TRENDIEST(2)  T R EH1 N - D IY0 - AH0 S T
+TRENDING  T R EH1 N - D IH0 NG
+TRENDLESS  T R EH1 N D - L AH0 S
+TRENDLINE  T R EH1 N D - L AY2 N
+TRENDS  T R EH1 N D Z
+TRENDS(2)  T R EH1 N Z
+TRENDSETTER  T R EH1 N D - S EH2 - T ER0
+TRENDY  T R EH1 N - D IY0
+TRENHOLM  T R EH1 N - HH OW2 L M
+TRENKAMP  T R EH1 N - K AE2 M P
+TRENKLE  T R EH1 NG - K AH0 L
+TRENT  T R EH1 N T
+TRENT'S  T R EH1 N T S
+TRENTE-ET-QUARANTE  T R EY1 N - T EY0 - K W AA2 - R EH1 N - T EY0
+TRENTHAM  T R EH1 N - TH AH0 M
+TRENTMAN  T R EH1 N T - M AH0 N
+TRENTON  T R EH1 N - T AH0 N
+TRENTON'S  T R EH1 N - T AH0 N Z
+TREON  T R IY1 - AH0 N
+TREPAGNIER  T R EH1 - P AH0 G - N IY0 - ER0
+TREPANIER  T R EH1 - P AH0 - N IY0 - ER0
+TREPIDATION  T R EH2 - P IH0 - D EY1 - SH AH0 N
+TREPPEL  T R EH1 - P AH0 L
+TREPTOW  T R EH1 P - T OW0
+TRESCH  T R EH1 SH
+TRESCOTT  T R EH1 S - K AH0 T
+TRESPASS  T R EH1 S - P AE2 S
+TRESPASS(2)  T R EH1 S - P AH0 S
+TRESPASSING  T R EH1 S - P AE2 - S IH0 NG
+TRESPASSING(2)  T R EH1 S - P AH0 - S IH0 NG
+TRESS  T R EH1 S
+TRESSEL  T R EH1 - S AH0 L
+TRESSES  T R EH1 - S IH0 Z
+TRESSLER  T R EH1 S - L ER0
+TREST  T R EH1 S T
+TRESTER  T R EH1 - S T ER0
+TRESTLE  T R EH1 - S AH0 L
+TRETHEWEY  T R EH1 - TH Y UW0 - IY0
+TRETINOIN  T R EH1 - T IH0 - N OY2 N
+TRETTEL  T R EH1 - T AH0 L
+TRETTER  T R EH1 - T ER0
+TRETTIN  T R EH1 - T IH0 N
+TREU  T R UW1
+TREUHAND  T R UW1 - HH AE2 N D
+TREUHANDANSTALT  T R UW2 - HH AE1 N - D AH0 N - S T AA2 L T
+TREURNICHT  T R UW1 R - N IH0 K T
+TREVATHAN  T R EH1 - V AH0 - TH AH0 N
+TREVELYAN  T R AH0 - V EH1 L - Y AH0 N
+TREVINO  T R AH0 - V IY1 - N OW0
+TREVISO  T R EH0 - V IY1 - S OW0
+TREVIZO  T R EH0 - V IY1 - Z OW0
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+TREW  T R UW1
+TREXLER  T R EH1 K S - L ER0
+TREY  T R EY1
+TREYBIG  T R EY1 - B IH0 G
+TREZISE  T R EH1 - Z AY0 Z
+TREZZA  T R EH1 - Z AH0
+TRI  T R AY1
+TRI-STATE  T R AY1 - S T EY1 T
+TRIAD  T R AY1 - AE2 D
+TRIAD'S  T R AY1 - AE2 D Z
+TRIADS  T R AY1 - AE2 D Z
+TRIAGE  T R AY1 - IH0 JH
+TRIAL  T R AY1 - AH0 L
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+TRIALS  T R AY1 - AH0 L Z
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+TRIANA  T R IY0 - AE1 - N AH0
+TRIANGLE  T R AY1 - AE2 NG - G AH0 L
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+TRIANGLES  T R AY1 - AE2 NG - G AH0 L Z
+TRIANGULAR  T R AY0 - AE1 NG - G Y AH0 - L ER0
+TRIANGULATION  T R AY0 - AE2 NG - G Y UW0 - L EY1 - SH AH0 N
+TRIANO  T R IY0 - AA1 - N OW0
+TRIARC  T R AY1 - AA2 R K
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+TRIB  T R IH1 B
+TRIBAL  T R AY1 - B AH0 L
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+TRIBBETT  T R IH1 - B IH0 T
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+TRIBE  T R AY1 B
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+TRIBES  T R AY1 B Z
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+TRIBESMEN  T R AY1 B Z - M IH0 N
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+TRIBULATIONS  T R IH2 - B Y AH0 - L EY1 - SH AH0 N Z
+TRIBULL  T R IH1 - B AH0 L
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+TRIBUNALS  T R AY2 - B Y UW1 - N AH0 L Z
+TRIBUNE  T R IH1 - B Y UW0 N
+TRIBUNE'S  T R IH1 - B Y UW0 N Z
+TRIBUTARIES  T R IH1 - B Y AH0 - T EH2 - R IY0 Z
+TRIBUTARY  T R IH1 - B Y AH0 - T EH2 - R IY0
+TRIBUTE  T R IH1 - B Y UW0 T
+TRIBUTES  T R IH1 - B Y UW0 T S
+TRICARICO  T R IY0 - K AA0 - R IY1 - K OW0
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+TRICENTROL  T R AY2 - S EH1 N - T R AA0 L
+TRICENTROL'S  T R AY2 - S EH1 N - T R AA0 L Z
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+TRICKED  T R IH1 K T
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+TRICKETT  T R IH1 - K IH0 T
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+TRICKIEST  T R IH1 - K IY0 - AH0 S T
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+TRICKLES  T R IH1 - K AH0 L Z
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+TRIOLO  T R IY0 - OW1 - L OW0
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+WHEATEN(2)  HH W IY1 - T AH0 N
+WHEATIE  W IY1 - T IY0
+WHEATIE(2)  HH W IY1 - T IY0
+WHEATIES  W IY1 - T IY0 Z
+WHEATIES(2)  HH W IY1 - T IY0 Z
+WHEATLEY  W IY1 T - L IY0
+WHEATLEY(2)  HH W IY1 T - L IY0
+WHEATLY  W IY1 T - L IY0
+WHEATLY(2)  HH W IY1 T - L IY0
+WHEATON  W IY1 - T AH0 N
+WHEATON(2)  HH W IY1 - T AH0 N
+WHEDBEE  W EH1 D - B IY2
+WHEDBEE(2)  HH W EH1 D - B IY2
+WHEDON  W EH1 - D AH0 N
+WHEDON(2)  HH W EH1 - D AH0 N
+WHEEL  W IY1 L
+WHEEL(2)  HH W IY1 L
+WHEELABRATOR  W IY2 - L AH0 - B R EY1 - T ER0
+WHEELABRATOR(2)  HH W IY2 - L AH0 - B R EY1 - T ER0
+WHEELAN  W IY1 - L AH0 N
+WHEELAN(2)  HH W IY1 - L AH0 N
+WHEELAND  W IY1 - L AH0 N D
+WHEELAND(2)  HH W IY1 - L AH0 N D
+WHEELBARROW  W IY1 L - B EH2 - R OW0
+WHEELBARROW(2)  HH W IY1 L - B EH2 - R OW0
+WHEELBARROWS  W IY1 L - B EH2 - R OW0 Z
+WHEELBARROWS(2)  HH W IY1 L - B EH2 - R OW0 Z
+WHEELBASE  W IY1 L - B EY2 S
+WHEELBASE(2)  HH W IY1 L - B EY2 S
+WHEELCHAIR  W IY1 L - CH EH2 R
+WHEELCHAIR(2)  HH W IY1 L - CH EH2 R
+WHEELCHAIRS  W IY1 L - CH EH2 R Z
+WHEELCHAIRS(2)  HH W IY1 L - CH EH2 R Z
+WHEELDON  W IY1 L - D AH0 N
+WHEELDON(2)  HH W IY1 L - D AH0 N
+WHEELED  W IY1 L D
+WHEELED(2)  HH W IY1 L D
+WHEELER  W IY1 - L ER0
+WHEELER'S  W IY1 - L ER0 Z
+WHEELER'S(2)  HH W IY1 - L ER0 Z
+WHEELER(2)  HH W IY1 - L ER0
+WHEELERS  W IY1 - L ER0 Z
+WHEELERS(2)  HH W IY1 - L ER0 Z
+WHEELESS  W IY1 - L AH0 S
+WHEELESS(2)  HH W IY1 - L AH0 S
+WHEELING  W IY1 - L IH0 NG
+WHEELING'S  W IY1 - L IH0 NG Z
+WHEELING'S(2)  HH W IY1 - L IH0 NG Z
+WHEELING(2)  HH W IY1 - L IH0 NG
+WHEELIS  W IY1 - L IH0 S
+WHEELIS(2)  HH W IY1 - L IH0 S
+WHEELOCK  W IY1 - L AA2 K
+WHEELOCK(2)  HH W IY1 - L AA2 K
+WHEELON  W IY1 - L AH0 N
+WHEELON(2)  HH W IY1 - L AH0 N
+WHEELS  W IY1 L Z
+WHEELS(2)  HH W IY1 L Z
+WHEELUS  W IY1 - L AH0 S
+WHEELUS(2)  HH W IY1 - L AH0 S
+WHEELWRIGHT  W IY1 L - R AY2 T
+WHEELWRIGHT(2)  HH W IY1 L - R AY2 T
+WHEELWRITER  W IY1 L - R AY2 - T ER0
+WHEELWRITER(2)  HH W IY1 L - R AY2 - T ER0
+WHEEZE  W IY1 Z
+WHEEZE(2)  HH W IY1 Z
+WHEEZES  W IY1 - Z AH0 Z
+WHEEZES(2)  HH W IY1 - Z AH0 Z
+WHEEZES(3)  W IY1 - Z IH0 Z
+WHEEZING  W IY1 - Z IH0 NG
+WHEEZING(2)  HH W IY1 - Z IH0 NG
+WHELAN  W EH1 - L AH0 N
+WHELAN(2)  HH W EH1 - L AH0 N
+WHELAN(3)  HH W IY1 - L AH0 N
+WHELAN(4)  W IY1 - L AH0 N
+WHELCHEL  W EH1 L - CH AH0 L
+WHELCHEL(2)  HH W EH1 L - CH AH0 L
+WHELESS  W IY1 - L IH0 S
+WHELESS(2)  HH W IY1 - L IH0 S
+WHELPLEY  W EH1 L P - L IY0
+WHELPLEY(2)  HH W EH1 L P - L IY0
+WHELTON  W EH1 L - T AH0 N
+WHELTON(2)  HH W EH1 L - T AH0 N
+WHEN  W EH1 N
+WHEN'LL  W EH1 - N AH0 L
+WHEN'LL(2)  HH W EH1 - N AH0 L
+WHEN'S  W EH1 N Z
+WHEN'S(2)  HH W EH1 N Z
+WHEN(2)  HH W EH1 N
+WHEN(3)  W IH1 N
+WHEN(4)  HH W IH1 N
+WHENCE  W EH1 N S
+WHENCE(2)  HH W EH1 N S
+WHENEVER  W EH0 N - EH1 - V ER0
+WHENEVER(2)  HH W EH0 - N EH1 - V ER0
+WHERE  W EH1 R
+WHERE'D  W EH1 R D
+WHERE'D(2)  HH W EH1 R D
+WHERE'S  W EH1 R Z
+WHERE'S(2)  HH W EH1 R Z
+WHERE(2)  HH W EH1 R
+WHEREABOUTS  W EH1 - R AH0 - B AW2 T S
+WHEREABOUTS(2)  HH W EH1 - R AH0 - B AW2 T S
+WHEREAS  W EH0 - R AE1 Z
+WHEREAS(2)  HH W EH0 - R AE1 Z
+WHEREBY  W EH0 R - B AY1
+WHEREBY(2)  HH W EH0 R - B AY1
+WHEREIN  W EH0 - R IH1 N
+WHEREIN(2)  HH W EH0 - R IH1 N
+WHEREUPON  W EH1 - R AH0 - P AA1 N
+WHEREUPON(2)  HH W EH1 - R AH0 - P AA1 N
+WHEREVER  W EH0 - R EH1 - V ER0
+WHEREVER(2)  HH W EH0 - R EH1 - V ER0
+WHEREWITHAL  W EH1 R - W IH0 - DH AO2 L
+WHEREWITHAL(2)  HH W EH1 R - W IH0 - DH AO2 L
+WHERLEY  W ER1 - L IY0
+WHERLEY(2)  HH W ER1 - L IY0
+WHERRY  W EH1 - R IY0
+WHERRY(2)  HH W EH1 - R IY0
+WHET  W EH1 T
+WHET(2)  HH W EH1 T
+WHETHER  W EH1 - DH ER0
+WHETHER(2)  HH W EH1 - DH ER0
+WHETSEL  W EH1 T - S AH0 L
+WHETSEL(2)  HH W EH1 T - S AH0 L
+WHETSELL  W EH1 T - S AH0 L
+WHETSELL(2)  HH W EH1 T - S AH0 L
+WHETSTINE  W EH1 T - S T IY0 N
+WHETSTINE(2)  HH W EH1 T - S T IY0 N
+WHETSTONE  W EH1 T - S T OW2 N
+WHETSTONE(2)  HH W EH1 T - S T OW2 N
+WHETTED  W EH1 - T IH0 D
+WHETTED(2)  HH W EH1 - T IH0 D
+WHETZEL  W EH1 T - Z AH0 L
+WHETZEL(2)  HH W EH1 T - Z AH0 L
+WHEW  W UW1
+WHEW(2)  HH W UW1
+WHEW(3)  HH Y UW1
+WHEY  W EY1
+WHEY(2)  HH W EY1
+WHICH  W IH1 CH
+WHICH'RE  W IH1 - CH ER0
+WHICH'RE(2)  HH W IH1 - CH ER0
+WHICH'S  W IH1 - CH IH0 Z
+WHICH'S(2)  HH W IH1 - CH IH0 Z
+WHICH(2)  HH W IH1 CH
+WHICHARD  W IH1 - CH ER0 D
+WHICHARD(2)  HH W IH1 - CH ER0 D
+WHICHEVER  W IH0 CH - EH1 - V ER0
+WHICHEVER(2)  HH W IH0 CH - EH1 - V ER0
+WHICKER  W IH1 - K ER0
+WHICKER(2)  HH W IH1 - K ER0
+WHIDBY  W IH1 D - B IY0
+WHIDBY(2)  HH W IH1 D - B IY0
+WHIDDEN  W IH1 - D AH0 N
+WHIDDEN(2)  HH W IH1 - D AH0 N
+WHIDDON  W IH1 - D AH0 N
+WHIDDON(2)  HH W IH1 - D AH0 N
+WHIFF  W IH1 F
+WHIFF(2)  HH W IH1 F
+WHIG  W IH1 G
+WHIG(2)  HH W IH1 G
+WHIGHAM  W IH1 - G AH0 M
+WHIGHAM(2)  HH W IH1 - G AH0 M
+WHIGS  W IH1 G Z
+WHIGS(2)  HH W IH1 G Z
+WHILDEN  W AY1 L - D AH0 N
+WHILDEN(2)  HH W AY1 L - D AH0 N
+WHILE  W AY1 L
+WHILE(2)  HH W AY1 L
+WHILES  W AY1 L Z
+WHILES(2)  HH W AY1 L Z
+WHILST  W AY1 L S T
+WHIM  W IH1 M
+WHIM(2)  HH W IH1 M
+WHIMPER  W IH1 M - P ER0
+WHIMPER(2)  HH W IH1 M - P ER0
+WHIMPERING  W IH1 M - P ER0 - IH0 NG
+WHIMPERING(2)  HH W IH1 M - P ER0 - IH0 NG
+WHIMS  W IH1 M Z
+WHIMS(2)  HH W IH1 M Z
+WHIMSICAL  W IH1 M - Z IH0 - K AH0 L
+WHIMSICAL(2)  HH W IH1 M - Z IH0 - K AH0 L
+WHIMSY  W IH1 M - S IY0
+WHIMSY(2)  HH W IH1 M - S IY0
+WHINE  W AY1 N
+WHINE(2)  HH W AY1 N
+WHINER  W AY1 - N ER0
+WHINER(2)  HH W AY1 - N ER0
+WHINERS  W AY1 - N ER0 Z
+WHINERS(2)  HH W AY1 - N ER0 Z
+WHINERY  W AY1 - N ER0 - IY0
+WHINERY(2)  HH W AY1 - N ER0 - IY0
+WHINES  W AY1 N Z
+WHINES(2)  HH W AY1 N Z
+WHINING  W AY1 - N IH0 NG
+WHINING(2)  HH W AY1 - N IH0 NG
+WHINNERY  W IH1 - N ER0 - IY0
+WHINNERY(2)  HH W IH1 - N ER0 - IY0
+WHINNEY  W IH1 - N IY0
+WHINNEY(2)  HH W IH1 - N IY0
+WHINY  W AY1 - N IY0
+WHINY(2)  HH W AY1 - N IY0
+WHIP  W IH1 P
+WHIP(2)  HH W IH1 P
+WHIPKEY  W IH1 P - K IY2
+WHIPKEY(2)  HH W IH1 P - K IY2
+WHIPLASH  W IH1 P - L AE2 SH
+WHIPLASH(2)  HH W IH1 P - L AE2 SH
+WHIPLASHES  W IH1 P - L AE2 - SH AH0 Z
+WHIPLASHES(2)  HH W IH1 P - L AE2 - SH AH0 Z
+WHIPP  W IH1 P
+WHIPP(2)  HH W IH1 P
+WHIPPANY  W IH1 - P AH0 - N IY0
+WHIPPANY(2)  HH W IH1 - P AH0 - N IY0
+WHIPPED  W IH1 P T
+WHIPPED(2)  HH W IH1 P T
+WHIPPING  W IH1 - P IH0 NG
+WHIPPING(2)  HH W IH1 - P IH0 NG
+WHIPPLE  W IH1 - P AH0 L
+WHIPPLE'S  W IH1 - P AH0 L Z
+WHIPPLE'S(2)  HH W IH1 - P AH0 L Z
+WHIPPLE(2)  HH W IH1 - P AH0 L
+WHIPPOORWILLS  W IH1 P - ER0 - W IH2 L Z
+WHIPPOORWILLS(2)  HH W IH1 P - ER0 - W IH2 L Z
+WHIPPS  W IH1 P S
+WHIPPS(2)  HH W IH1 P S
+WHIPS  W IH1 P S
+WHIPS(2)  HH W IH1 P S
+WHIPSAW  W IH1 P - S AO2
+WHIPSAW(2)  HH W IH1 P - S AO2
+WHIPSAWED  W IH1 P - S AO2 D
+WHIPSAWED(2)  HH W IH1 P - S AO2 D
+WHIPSAWING  W IH1 P - S AO2 - IH0 NG
+WHIPSAWING(2)  HH W IH1 P - S AO2 - IH0 NG
+WHIRL  W ER1 L
+WHIRL(2)  HH W ER1 L
+WHIRLED  W ER1 L D
+WHIRLED(2)  HH W ER1 L D
+WHIRLEY  W ER1 - L IY0
+WHIRLEY(2)  HH W ER1 - L IY0
+WHIRLING  W ER1 - L IH0 NG
+WHIRLING(2)  HH W ER1 - L IH0 NG
+WHIRLPOOL  W ER1 L - P UW2 L
+WHIRLPOOL'S  W ER1 L - P UW2 L Z
+WHIRLPOOL'S(2)  HH W ER1 L - P UW2 L Z
+WHIRLPOOL(2)  HH W ER1 L - P UW2 L
+WHIRLPOOLS  W ER1 L - P UW2 L Z
+WHIRLPOOLS(2)  HH W ER1 L - P UW2 L Z
+WHIRLWIND  W ER1 L - W IH2 N D
+WHIRLWIND(2)  HH W ER1 L - W IH2 N D
+WHIRRING  W ER1 - IH0 NG
+WHIRRING(2)  HH W ER1 - IH0 NG
+WHISENAND  W IH1 - S IH0 - N AE0 N D
+WHISENAND(2)  HH W IH1 - S IH0 - N AE0 N D
+WHISENANT  W IH1 - S IH0 - N AH0 N T
+WHISENANT(2)  HH W IH1 - S IH0 - N AH0 N T
+WHISENHUNT  W AY1 - Z AH0 N - HH AH2 N T
+WHISENHUNT(2)  HH W AY1 - Z AH0 N - HH AH2 N T
+WHISK  W IH1 S K
+WHISK(2)  HH W IH1 S K
+WHISKED  W IH1 S K T
+WHISKED(2)  HH W IH1 S K T
+WHISKER  W IH1 - S K ER0
+WHISKER(2)  HH W IH1 - S K ER0
+WHISKERS  W IH1 - S K ER0 Z
+WHISKERS(2)  HH W IH1 - S K ER0 Z
+WHISKEY  W IH1 S - K IY0
+WHISKEY(2)  HH W IH1 S - K IY0
+WHISKEYS  W IH1 - S K IY0 Z
+WHISKEYS(2)  HH W IH1 - S K IY0 Z
+WHISKS  W IH1 S K S
+WHISKS(2)  HH W IH1 S K S
+WHISKY  W IH1 S - K IY0
+WHISKY(2)  HH W IH1 S - K IY0
+WHISLER  W IH1 S - L ER0
+WHISLER(2)  HH W IH1 S - L ER0
+WHISMAN  W IH1 S - M AH0 N
+WHISMAN(2)  HH W IH1 S - M AH0 N
+WHISNANT  W IH1 S - N AH0 N T
+WHISNANT(2)  HH W IH1 S - N AH0 N T
+WHISNER  W IH1 S - N ER0
+WHISNER(2)  HH W IH1 S - N ER0
+WHISONANT  W IH1 - S AH0 - N AH0 N T
+WHISPER  W IH1 - S P ER0
+WHISPER(2)  HH W IH1 - S P ER0
+WHISPERED  W IH1 - S P ER0 D
+WHISPERED(2)  HH W IH1 - S P ER0 D
+WHISPERING  W IH1 - S P ER0 - IH0 NG
+WHISPERING(2)  HH W IH1 - S P ER0 - IH0 NG
+WHISPERS  W IH1 - S P ER0 Z
+WHISPERS(2)  HH W IH1 - S P ER0 Z
+WHISTLE  W IH1 - S AH0 L
+WHISTLE(2)  HH W IH1 - S AH0 L
+WHISTLEBLOWER  W IH1 - S AH0 L - B L OW2 - ER0
+WHISTLEBLOWER(2)  HH W IH1 - S AH0 L - B L OW2 - ER0
+WHISTLEBLOWERS  W IH1 - S AH0 L - B L OW2 - ER0 Z
+WHISTLEBLOWERS(2)  HH W IH1 - S AH0 L - B L OW2 - ER0 Z
+WHISTLED  W IH1 - S AH0 L D
+WHISTLED(2)  HH W IH1 - S AH0 L D
+WHISTLER  W IH1 S - L ER0
+WHISTLER(2)  HH W IH1 S - L ER0
+WHISTLERS  W IH1 S - L ER0 Z
+WHISTLERS(2)  HH W IH1 S - L ER0 Z
+WHISTLES  W IH1 - S AH0 L Z
+WHISTLES(2)  HH W IH1 - S AH0 L Z
+WHISTLING  W IH1 - S L IH0 NG
+WHISTLING(2)  HH W IH1 - S L IH0 NG
+WHISTON  W IH1 - S T AH0 N
+WHISTON(2)  HH W IH1 - S T AH0 N
+WHIT  W IH1 T
+WHIT(2)  HH W IH1 T
+WHITACRE  W IH1 - T AH0 - K ER0
+WHITACRE(2)  HH W IH1 - T AH0 - K ER0
+WHITAKER  W IH1 - T AH0 - K ER0
+WHITAKER(2)  HH W IH1 - T AH0 - K ER0
+WHITBECK  W IH1 T - B EH2 K
+WHITBECK(2)  HH W IH1 T - B EH2 K
+WHITBREAD  W IH1 T - B R EH2 D
+WHITBREAD(2)  HH W IH1 T - B R EH2 D
+WHITBY  W IH1 T - B IY0
+WHITBY(2)  HH W IH1 T - B IY0
+WHITCHER  W IH1 - CH ER0
+WHITCHER(2)  HH W IH1 - CH ER0
+WHITCHURCH  W IH1 T - CH ER2 CH
+WHITCHURCH(2)  HH W IH1 T - CH ER2 CH
+WHITCOMB  W IH1 T - K AH0 M
+WHITCOMB(2)  HH W IH1 T - K AH0 M
+WHITCRAFT  W IH1 T - K R AE2 F T
+WHITCRAFT(2)  HH W IH1 T - K R AE2 F T
+WHITE  W AY1 T
+WHITE'S  W AY1 T S
+WHITE'S(2)  HH W AY1 T S
+WHITE(2)  HH W AY1 T
+WHITEAKER  W IH1 - T AH0 - K ER0
+WHITEAKER(2)  HH W IH1 - T AH0 - K ER0
+WHITEBREAD  W AY1 T - B R EH2 D
+WHITEBREAD(2)  HH W AY1 T - B R EH2 D
+WHITECOTTON  W AY1 T - K AA2 - T AH0 N
+WHITECOTTON(2)  HH W AY1 T - K AA2 - T AH0 N
+WHITED  W AY1 - T IH0 D
+WHITED(2)  HH W AY1 - T IH0 D
+WHITEFIELD  W AY1 T - F IY2 L D
+WHITEFIELD(2)  HH W AY1 T - F IY2 L D
+WHITEFISH  W AY1 T - F IH2 SH
+WHITEFISH(2)  HH W AY1 T - F IH2 SH
+WHITEFORD  W AY1 T - F AO0 R D
+WHITEFORD(2)  HH W AY1 T - F AO0 R D
+WHITEHAIR  W AY1 T - HH EH1 R
+WHITEHAIR(2)  HH W AY1 T - HH EH1 R
+WHITEHALL  W AY1 T - HH AO2 L
+WHITEHALL'S  W AY1 T - HH AO2 L Z
+WHITEHALL'S(2)  HH W AY1 T - HH AO2 L Z
+WHITEHALL(2)  HH W AY1 T - HH AO2 L
+WHITEHEAD  W AY1 T - HH EH2 D
+WHITEHEAD'S  W AY1 T - HH EH2 D Z
+WHITEHEAD'S(2)  HH W AY1 T - HH EH2 D Z
+WHITEHEAD(2)  HH W AY1 T - HH EH2 D
+WHITEHILL  W AY1 T - HH IH2 L
+WHITEHILL(2)  HH W AY1 T - HH IH2 L
+WHITEHORN  W AY1 T - HH AO2 R N
+WHITEHORN(2)  HH W AY1 T - HH AO2 R N
+WHITEHORSE  W AY1 T - HH AO2 R S
+WHITEHORSE(2)  HH W AY1 T - HH AO2 R S
+WHITEHOUSE  W AY1 T - HH AW2 S
+WHITEHOUSE(2)  HH W AY1 T - HH AW2 S
+WHITEHURST  W AY1 T - HH ER2 S T
+WHITEHURST'S  W AY1 T - HH ER2 S T
+WHITEHURST(2)  HH W AY1 T - HH ER2 S T
+WHITELAW  W AY1 T - L AO2
+WHITELAW(2)  HH W AY1 T - L AO2
+WHITELEY  W AY1 T - L IY0
+WHITELEY(2)  HH W AY1 T - L IY0
+WHITELOCK  W AY1 T - L AA2 K
+WHITELOCK(2)  HH W AY1 T - L AA2 K
+WHITELY  W AY1 T - L IY0
+WHITELY(2)  HH W AY1 T - L IY0
+WHITEMAN  W AY1 T - M AH0 N
+WHITEMAN(2)  HH W AY1 T - M AH0 N
+WHITEMONT  W AY1 T - M AA2 N T
+WHITEMONT(2)  HH W AY1 T - M AA2 N T
+WHITEN  W AY1 - T AH0 N
+WHITEN(2)  HH W AY1 - T AH0 N
+WHITENACK  W AY1 T - N AE2 K
+WHITENACK(2)  HH W AY1 T - N AE2 K
+WHITENEIR  W AY1 T - N IH2 R
+WHITENEIR(2)  HH W AY1 T - N IH2 R
+WHITENER  W AY1 T - N ER0
+WHITENER(2)  HH W AY1 T - N ER0
+WHITENESS  W AY1 T - N AH0 S
+WHITENESS(2)  HH W AY1 T - N AH0 S
+WHITENIGHT  W AY1 T - N AY2 T
+WHITENIGHT(2)  HH W AY1 T - N AY2 T
+WHITENING  W AY1 - T IH0 - N IH0 NG
+WHITENING(2)  W AY1 T - N IH0 NG
+WHITENING(3)  HH W AY1 - T IH0 - N IH0 NG
+WHITENING(4)  HH W AY1 T - N IH0 NG
+WHITER  W AY1 - T ER0
+WHITER(2)  HH W AY1 - T ER0
+WHITES  W AY1 T S
+WHITES(2)  HH W AY1 T S
+WHITESCARVER  W AY1 T - S K AA2 R - V ER0
+WHITESCARVER(2)  HH W AY1 T - S K AA2 R - V ER0
+WHITESEL  W AY1 T - S EH2 L
+WHITESEL(2)  HH W AY1 T - S EH2 L
+WHITESELL  W AY1 T - S EH2 L
+WHITESELL(2)  HH W AY1 T - S EH2 L
+WHITESIDE  W AY1 T - S AY2 D
+WHITESIDE(2)  HH W AY1 T - S AY2 D
+WHITESIDES  W AY1 T - S AY2 D Z
+WHITESIDES(2)  HH W AY1 T - S AY2 D Z
+WHITEST  W AY1 - T IH0 S T
+WHITEST(2)  HH W AY1 - T IH0 S T
+WHITESTONE  W AY1 T - S T OW2 N
+WHITESTONE(2)  HH W AY1 T - S T OW2 N
+WHITETAIL  W AY1 T - T EY2 L
+WHITETAIL(2)  HH W AY1 T - T EY2 L
+WHITETAIL(3)  HH W AY1 - T EY2 L
+WHITEWASH  W AY1 T - W AA2 SH
+WHITEWASH(2)  HH W AY1 T - W AA2 SH
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+ZIKA  Z IH1 - K AH0
+ZIKE  Z AY1 K
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+ZILLMER  Z IH1 L - M ER0
+ZILMER  Z IH1 L - M ER0
+ZILVIA  Z IH1 L - V IY0 - AH0
+ZIM  Z IH1 M
+ZIMA  Z IY1 - M AH0
+ZIMBABWE  Z IH0 M - B AA1 B - W EY0
+ZIMBABWE'S  Z IH0 M - B AA1 B - W EY0 Z
+ZIMBABWEAN  Z IH0 M - B AA1 B - W IY0 - AH0 N
+ZIMBALIST  Z IH1 M - B AH0 - L IH0 S T
+ZIMBELMAN  Z IH1 M - B AH0 L - M AH0 N
+ZIMDARS  Z IH1 M - D ER0 Z
+ZIMERMAN  Z AY1 - M ER0 - M AH0 N
+ZIMMER  Z IH1 - M ER0
+ZIMMERER  Z IH1 - M ER0 - ER0
+ZIMMERLE  Z IH1 - M ER0 - AH0 L
+ZIMMERLY  Z IH1 - M ER0 - L IY0
+ZIMMERMAN  Z IH1 - M ER0 - M AH0 N
+ZIMMERMANN  Z IH1 - M ER0 - M AH0 N
+ZIMMERS  Z IH1 - M ER0 Z
+ZIMNY  Z IH1 M - N IY0
+ZIMPFER  Z IH1 M P - F ER0
+ZINC  Z IH1 NG K
+ZINCK  Z IH1 NG K
+ZINDA  Z IH1 N - D AH0
+ZINDER  Z IH1 N - D ER0
+ZINE  Z AY1 N
+ZINES  Z AY1 N Z
+ZINFANDEL  Z IH1 N - F AH0 N - D EH2 L
+ZING  Z IH1 NG
+ZINGA  Z IH1 - NG AH0
+ZINGALE  Z IH0 NG - G AA1 - L IY0
+ZINGARO  Z IH0 NG - G AA1 - R OW0
+ZINGER  Z IH1 - NG ER0
+ZINGG  Z IH1 NG G
+ZINGLER  Z IH1 NG - G AH0 - L ER0
+ZINGLER(2)  Z IH1 NG - G L ER0
+ZINIA  Z IY1 - N IY0 - AH0
+ZINK  Z IH1 NG K
+ZINKE  Z IH1 NG K
+ZINMAN  Z IH1 N - M AH0 N
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+ZINNIA  Z IH1 - N IY0 - AH0
+ZINNIAS  Z IH1 - N IY0 - AH0 Z
+ZINNO  Z IH1 - N OW0
+ZINS  Z IH1 N Z
+ZINSER  Z IH1 N - S ER0
+ZINSMEISTER  Z IH1 N S - M AY0 - S T ER0
+ZINTER  Z IH1 N - T ER0
+ZIOBRO  Z IY0 - OW1 - B R OW0
+ZIOLKOWSKI  Z IY0 - OW0 L - K AO1 F S - K IY0
+ZIOMEK  Z IY0 - OW1 - M EH0 K
+ZION  Z AY1 - AH0 N
+ZIONISM  Z AY1 - AH0 - N IH2 - Z AH0 M
+ZIONIST  Z AY1 - AH0 - N IH0 S T
+ZIONISTS  Z AY1 - AH0 - N IH0 S T S
+ZIONS  Z AY1 - AH0 N Z
+ZIP  Z IH1 P
+ZIPF  Z IH1 P F
+ZIPFEL  Z IH1 P - F AH0 L
+ZIPKIN  Z IH1 P - K IH0 N
+ZIPLESS  Z IH1 P - L AH0 S
+ZIPP  Z IH1 P
+ZIPPED  Z IH1 P T
+ZIPPER  Z IH1 - P ER0
+ZIPPERED  Z IH1 - P ER0 D
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+ZIRCONIUMS  Z ER0 - K OW1 - N IY0 - AH0 M Z
+ZIRCONS  Z ER1 - K AA0 N Z
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+ZITO  Z IY1 - T OW0
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+ZIYANG(2)  ZH IY0 - AA1 NG
+ZIZZO  Z IH1 - Z OW0
+ZLATA  Z L AA1 - T AH2
+ZLATA'S  Z L AA1 - T AH2 Z
+ZLATA'S(2)  Z L AA1 - T AH0 Z
+ZLATA(2)  Z L AA1 - T AH0
+ZLIN  Z L IH1 N
+ZLOBIN  Z L AO1 - B IH0 N
+ZLOGAR  Z L OW1 - G ER0
+ZLOMSOWITCH  Z L AA1 M - S OW0 - IH0 CH
+ZLOTNICK  Z L AA1 T - N IH0 K
+ZLOTY  Z L AO1 - T IY0
+ZLOTYS  Z L AO1 - T IY0 Z
+ZMIJEWSKI  Z M IY0 - UW1 - S K IY0
+ZOBEL  Z OW1 - B AH0 L
+ZOBRIST  Z AA1 - B R IH0 S T
+ZOCH  Z AA1 K
+ZOCOR  Z OW1 - K ER0
+ZOCOR(2)  Z OW1 - K AO2 R
+ZODIAC  Z OW1 - D IY0 - AE2 K
+ZODY  Z OW1 - D IY0
+ZOE  Z OW1 - IY0
+ZOELLER  Z OW1 - L ER0
+ZOELLICK  Z OW1 - L IH0 K
+ZOELLNER  Z OW1 L - N ER0
+ZOETE  Z OW1 - T IY0
+ZOFAGART  Z OW1 - F AH0 - G AA0 R T
+ZOFRAN  Z OW1 - F R AE2 N
+ZOGBY  Z AO1 G - B IY0
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+ZOGHBY  Z OW1 - B IY0
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+ZOLMAN  Z AA1 L - M AH0 N
+ZOLOFT  Z AA1 - L AA0 F T
+ZOLOFT(2)  Z OW1 - L AA0 F T
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+ZOLTAN  Z OW1 L - T AH0 N
+ZOMBIE  Z AA1 M - B IY0
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+ZONING  Z OW1 - N IH0 NG
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+ZOO'S  Z UW1 Z
+ZOOK  Z UH1 K
+ZOOLOGICAL  Z UW2 - L AA1 - JH IH0 - K AH0 L
+ZOOLOGIST  Z OW0 - AA1 - L AH0 - JH AH0 S T
+ZOOLOGY  Z OW0 - AA1 - L AH0 - JH IY0
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+ZUCCO  Z UW1 - K OW0
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+ZUCHOWSKI  Z AH0 - HH AO1 F S - K IY0
+ZUCHOWSKI(2)  Z UW0 - K AO1 F S - K IY0
+ZUCK  Z AH1 K
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+ZUCKER'S  Z AH1 - K ER0 Z
+ZUCKER'S(2)  Z UW1 - K ER0 Z
+ZUCKER(2)  Z UW1 - K ER0
+ZUCKERMAN  Z AH1 - K ER0 - M AH0 N
+ZUCKERMAN'S  Z AH1 - K ER0 - M AH0 N Z
+ZUCKERMAN'S(2)  Z UW1 - K ER0 - M AH0 N Z
+ZUCKERMAN(2)  Z UW1 - K ER0 - M AH0 N
+ZUE  Z UW1
+ZUE'S  Z UW1 Z
+ZUEGE  Z UW1 JH
+ZUEHLKE  Z UH1 L K
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+ZUFELT  Z AH1 - F IH0 L T
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+ZUGANOV'S  Z UW1 - G AH0 - N AO2 V S
+ZUGANOV'S(2)  Z UW1 - G AH0 - N AO2 F S
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+ZUHLKE  Z UW1 L K
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+ZUK  Z AH1 K
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+ZUKIN  Z UW1 - K IH0 N
+ZUKOWSKI  Z AH0 - K AO1 F S - K IY0
+ZULAUF  Z UW1 - L AW0 F
+ZULEGER  Z UW1 - L IH0 - G ER0
+ZULFIKAR  Z AH2 L - F IH0 - K AA1 R
+ZULLI  Z UW1 - L IY0
+ZULLO  Z UW1 - L OW0
+ZULU  Z UW1 - L UW2
+ZULUAGA  Z UW2 - L UW0 - AA1 - G AH0
+ZULUS  Z UW1 - L UW0 Z
+ZUMBRO  Z AH1 M - B R OW0
+ZUMBRUN  Z AH1 M - B R AH0 N
+ZUMEL  Z UW2 - M EH1 L
+ZUMMO  Z UW1 - M OW0
+ZUMPANO  Z UW0 M - P AA1 - N OW0
+ZUMSTEIN  Z AH1 M - S T IY2 N
+ZUMSTEIN(2)  Z AH1 M - S T AY2 N
+ZUMWALT  Z AH1 M - W AH0 L T
+ZUNDEL  Z AH1 N - D AH0 L
+ZUNGU  Z AH2 - NG UW1
+ZUNI  Z UW1 - N IY2
+ZUNIGA  Z UW0 - N IY1 - G AH0
+ZUNINO  Z UW0 - N IY1 - N OW0
+ZUNKER  Z AH1 NG - K ER0
+ZUPAN  Z UW1 - P AH0 N
+ZUPANCIC  Z AH0 - P AE1 NG - K IH0 K
+ZUPANJA  Z AH0 - P AE1 N - JH AH0
+ZUPKO  Z AH1 P - K OW0
+ZURAWSKI  Z ER0 - AA1 F S - K IY0
+ZURCHER  Z ER1 - K ER0
+ZUREK  Z UH1 - R EH0 K
+ZURFLUH  Z ER1 - F L UW0
+ZURI  Z UH1 - R IY0
+ZURICH  Z UH1 - R IH0 K
+ZURICH'S  Z UH1 - R IH0 K S
+ZURITA  Z ER0 - AY1 - T AH0
+ZURKUHLEN  Z ER0 - K Y UW1 - L AH0 N
+ZURN  Z ER1 N
+ZUROWSKI  Z ER0 - AO1 F S - K IY0
+ZUVER  Z UW1 - V ER0
+ZUZANA  Z UW2 - Z AA1 - N AH0
+ZVORNIK  Z V AO1 R - N IH0 K
+ZWACK  Z W AO1 K
+ZWAHLEN  Z W AA1 - L AH0 N
+ZWART  Z W AO1 R T
+ZWEBER  Z W IY1 - B ER0
+ZWEIBEL  Z W AY1 - B AH0 L
+ZWEIFEL  Z W AY1 - F AH0 L
+ZWEIG  Z W AY1 G
+ZWERDLING  Z W ER1 D - L IH0 NG
+ZWERDLING'S  Z W ER1 D - L IH0 NG Z
+ZWETCHKENBAUM  Z W EH1 CH - K AH0 N - B AA0 M
+ZWICK  Z W IH1 K
+ZWICKER  Z W IH1 - K ER0
+ZWICKY  Z W IH1 - K IY0
+ZWIEBEL  Z W IY1 - B AH0 L
+ZWIEFELHOFER  Z W IY1 - F AH0 L - HH AA2 - F ER0
+ZWIEG  Z W IY1 G
+ZWILLING  Z W IH1 - L IH0 NG
+ZWOLINSKI  Z V AH0 - L IH1 N - S K IY0
+ZYCAD  Z IH1 - K AE2 D
+ZYCH  Z AY1 CH
+ZYCHER  Z IH1 - K ER0
+ZYDECO  Z AY2 - D EH1 - K OW2
+ZYDECO(2)  Z IH1 - D AH0 - K OW2
+ZYDECO(3)  Z AY1 - D AH0 - K OW2
+ZYGMUNT  Z IH1 G - M AH0 N T
+ZYGOTE  Z AY1 - G OW0 T
+ZYLA  Z IH1 - L AH0
+ZYLKA  Z IH1 L - K AH0
+ZYLSTRA  Z IH1 L - S T R AH0
+ZYMAN  Z AY1 - M AH0 N
+ZYNDA  Z IH1 N - D AH0
+ZYSK  Z AY1 S K
+ZYSKOWSKI  Z IH0 - S K AO1 F S - K IY0
+ZYUGANOV  Z Y UW1 - G AA0 - N AA0 V
+ZYUGANOV'S  Z Y UW1 - G AA0 - N AA0 V Z
+ZYUGANOV'S(2)  Z Y UW1 - G AA0 - N AA0 F S
+ZYUGANOV'S(3)  Z UW1 - G AA0 - N AA0 V Z
+ZYUGANOV'S(4)  Z UW1 - G AA0 - N AA0 F S
+ZYUGANOV(2)  Z Y UW1 - G AA0 - N AA0 F
+ZYUGANOV(3)  Z UW1 - G AA0 - N AA0 V
+ZYUGANOV(4)  Z UW1 - G AA0 - N AA0 F
+ZYWICKI  Z IH0 - W IH1 - K IY0
+ZZZZ  Z IY0 Z
+ZZZZ(2)  Z Z
+{BRACE  B R EY1 S
+{LEFT-BRACE  L EH1 F T - B R EY1 S
+}CLOSE-BRACE  K L OW1 Z - B R EY1 S
+}RIGHT-BRACE  R AY1 T - B R EY1 S
diff --git a/style_bert_vits2/nlp/english/cmudict_cache.pickle b/style_bert_vits2/nlp/english/cmudict_cache.pickle
new file mode 100644
index 0000000000000000000000000000000000000000..cab7f7d732affe0bd8d860a81e770b9bf1f72064
--- /dev/null
+++ b/style_bert_vits2/nlp/english/cmudict_cache.pickle
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b9b21b20325471934ba92f2e4a5976989e7d920caa32e7a286eacb027d197949
+size 6212655
diff --git a/style_bert_vits2/nlp/english/g2p.py b/style_bert_vits2/nlp/english/g2p.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e3f9b329b40bd6a2aa462cebfe9efb71a3ad661
--- /dev/null
+++ b/style_bert_vits2/nlp/english/g2p.py
@@ -0,0 +1,230 @@
+import re
+
+from g2p_en import G2p
+
+from style_bert_vits2.constants import Languages
+from style_bert_vits2.nlp import bert_models
+from style_bert_vits2.nlp.english.cmudict import get_dict
+from style_bert_vits2.nlp.symbols import PUNCTUATIONS, SYMBOLS
+
+
+# Initialize global variables once
+ARPA = {
+    "AH0",
+    "S",
+    "AH1",
+    "EY2",
+    "AE2",
+    "EH0",
+    "OW2",
+    "UH0",
+    "NG",
+    "B",
+    "G",
+    "AY0",
+    "M",
+    "AA0",
+    "F",
+    "AO0",
+    "ER2",
+    "UH1",
+    "IY1",
+    "AH2",
+    "DH",
+    "IY0",
+    "EY1",
+    "IH0",
+    "K",
+    "N",
+    "W",
+    "IY2",
+    "T",
+    "AA1",
+    "ER1",
+    "EH2",
+    "OY0",
+    "UH2",
+    "UW1",
+    "Z",
+    "AW2",
+    "AW1",
+    "V",
+    "UW2",
+    "AA2",
+    "ER",
+    "AW0",
+    "UW0",
+    "R",
+    "OW1",
+    "EH1",
+    "ZH",
+    "AE0",
+    "IH2",
+    "IH",
+    "Y",
+    "JH",
+    "P",
+    "AY1",
+    "EY0",
+    "OY2",
+    "TH",
+    "HH",
+    "D",
+    "ER0",
+    "CH",
+    "AO1",
+    "AE1",
+    "AO2",
+    "OY1",
+    "AY2",
+    "IH1",
+    "OW0",
+    "L",
+    "SH",
+}
+_g2p = G2p()
+eng_dict = get_dict()
+
+
+def g2p(text: str) -> tuple[list[str], list[int], list[int]]:
+    phones = []
+    tones = []
+    phone_len = []
+    words = __text_to_words(text)
+
+    for word in words:
+        temp_phones, temp_tones = [], []
+        if len(word) > 1 and "'" in word:
+            word = ["".join(word)]
+
+        for w in word:
+            if w in PUNCTUATIONS:
+                temp_phones.append(w)
+                temp_tones.append(0)
+                continue
+            if w.upper() in eng_dict:
+                phns, tns = __refine_syllables(eng_dict[w.upper()])
+                temp_phones += [__post_replace_ph(i) for i in phns]
+                temp_tones += tns
+            else:
+                phone_list = list(filter(lambda p: p != " ", _g2p(w)))
+                phns, tns = [], []
+                for ph in phone_list:
+                    if ph in ARPA:
+                        ph, tn = __refine_ph(ph)
+                        phns.append(ph)
+                        tns.append(tn)
+                    else:
+                        phns.append(ph)
+                        tns.append(0)
+                temp_phones += [__post_replace_ph(i) for i in phns]
+                temp_tones += tns
+
+        phones += temp_phones
+        tones += temp_tones
+        phone_len.append(len(temp_phones))
+
+    word2ph = []
+    for token, pl in zip(words, phone_len):
+        word_len = len(token)
+        word2ph += __distribute_phone(pl, word_len)
+
+    phones = ["_"] + phones + ["_"]
+    tones = [0] + tones + [0]
+    word2ph = [1] + word2ph + [1]
+    assert len(phones) == len(tones), text
+    assert len(phones) == sum(word2ph), text
+
+    return phones, tones, word2ph
+
+
+def __post_replace_ph(ph: str) -> str:
+    REPLACE_MAP = {
+        "：": ",",
+        "；": ",",
+        "，": ",",
+        "。": ".",
+        "！": "!",
+        "？": "?",
+        "\n": ".",
+        "·": ",",
+        "、": ",",
+        "…": "...",
+        "···": "...",
+        "・・・": "...",
+        "v": "V",
+    }
+    if ph in REPLACE_MAP:
+        ph = REPLACE_MAP[ph]
+    if ph in SYMBOLS:
+        return ph
+    return "UNK"
+
+
+def __refine_ph(phn: str) -> tuple[str, int]:
+    tone = 0
+    if re.search(r"\d$", phn):
+        tone = int(phn[-1]) + 1
+        phn = phn[:-1]
+    else:
+        tone = 3
+    return phn.lower(), tone
+
+
+def __refine_syllables(syllables: list[list[str]]) -> tuple[list[str], list[int]]:
+    tones = []
+    phonemes = []
+    for phn_list in syllables:
+        for phn in phn_list:
+            phn, tone = __refine_ph(phn)
+            phonemes.append(phn)
+            tones.append(tone)
+    return phonemes, tones
+
+
+def __distribute_phone(n_phone: int, n_word: int) -> list[int]:
+    phones_per_word = [0] * n_word
+    for task in range(n_phone):
+        min_tasks = min(phones_per_word)
+        min_index = phones_per_word.index(min_tasks)
+        phones_per_word[min_index] += 1
+    return phones_per_word
+
+
+def __text_to_words(text: str) -> list[list[str]]:
+    tokenizer = bert_models.load_tokenizer(Languages.EN)
+    tokens = tokenizer.tokenize(text)
+    words = []
+    for idx, t in enumerate(tokens):
+        if t.startswith("▁"):
+            words.append([t[1:]])
+        elif t in PUNCTUATIONS:
+            if idx == len(tokens) - 1:
+                words.append([f"{t}"])
+            elif (
+                not tokens[idx + 1].startswith("▁")
+                and tokens[idx + 1] not in PUNCTUATIONS
+            ):
+                if idx == 0:
+                    words.append([])
+                words[-1].append(f"{t}")
+            else:
+                words.append([f"{t}"])
+        else:
+            if idx == 0:
+                words.append([])
+            words[-1].append(f"{t}")
+    return words
+
+
+if __name__ == "__main__":
+    # print(get_dict())
+    # print(eng_word_to_phoneme("hello"))
+    print(g2p("In this paper, we propose 1 DSPGAN, a GAN-based universal vocoder."))
+    # all_phones = set()
+    # eng_dict = get_dict()
+    # for k, syllables in eng_dict.items():
+    #     for group in syllables:
+    #         for ph in group:
+    #             all_phones.add(ph)
+    # print(all_phones)
diff --git a/style_bert_vits2/nlp/english/normalizer.py b/style_bert_vits2/nlp/english/normalizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..88cec023a6183d333eb238ec9353346291184ebb
--- /dev/null
+++ b/style_bert_vits2/nlp/english/normalizer.py
@@ -0,0 +1,132 @@
+import re
+
+import inflect
+
+
+__INFLECT = inflect.engine()
+__COMMA_NUMBER_PATTERN = re.compile(r"([0-9][0-9\,]+[0-9])")
+__DECIMAL_NUMBER_PATTERN = re.compile(r"([0-9]+\.[0-9]+)")
+__POUNDS_PATTERN = re.compile(r"£([0-9\,]*[0-9]+)")
+__DOLLARS_PATTERN = re.compile(r"\$([0-9\.\,]*[0-9]+)")
+__ORDINAL_PATTERN = re.compile(r"[0-9]+(st|nd|rd|th)")
+__NUMBER_PATTERN = re.compile(r"[0-9]+")
+
+
+def normalize_text(text: str) -> str:
+    text = __normalize_numbers(text)
+    text = replace_punctuation(text)
+    text = re.sub(r"([,;.\?\!])([\w])", r"\1 \2", text)
+    return text
+
+
+def replace_punctuation(text: str) -> str:
+    REPLACE_MAP = {
+        "：": ",",
+        "；": ",",
+        "，": ",",
+        "。": ".",
+        "！": "!",
+        "？": "?",
+        "\n": ".",
+        "．": ".",
+        "…": "...",
+        "···": "...",
+        "・・・": "...",
+        "·": ",",
+        "・": ",",
+        "、": ",",
+        "$": ".",
+        "“": "'",
+        "”": "'",
+        '"': "'",
+        "‘": "'",
+        "’": "'",
+        "（": "'",
+        "）": "'",
+        "(": "'",
+        ")": "'",
+        "《": "'",
+        "》": "'",
+        "【": "'",
+        "】": "'",
+        "[": "'",
+        "]": "'",
+        "—": "-",
+        "−": "-",
+        "～": "-",
+        "~": "-",
+        "「": "'",
+        "」": "'",
+    }
+    pattern = re.compile("|".join(re.escape(p) for p in REPLACE_MAP))
+    replaced_text = pattern.sub(lambda x: REPLACE_MAP[x.group()], text)
+    # replaced_text = re.sub(
+    #     r"[^\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\u3005"
+    #     + "".join(punctuation)
+    #     + r"]+",
+    #     "",
+    #     replaced_text,
+    # )
+    return replaced_text
+
+
+def __normalize_numbers(text: str) -> str:
+    text = re.sub(__COMMA_NUMBER_PATTERN, __remove_commas, text)
+    text = re.sub(__POUNDS_PATTERN, r"\1 pounds", text)
+    text = re.sub(__DOLLARS_PATTERN, __expand_dollars, text)
+    text = re.sub(__DECIMAL_NUMBER_PATTERN, __expand_decimal_point, text)
+    text = re.sub(__ORDINAL_PATTERN, __expand_ordinal, text)
+    text = re.sub(__NUMBER_PATTERN, __expand_number, text)
+    return text
+
+
+def __expand_dollars(m: re.Match[str]) -> str:
+    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 __remove_commas(m: re.Match[str]) -> str:
+    return m.group(1).replace(",", "")
+
+
+def __expand_ordinal(m: re.Match[str]) -> str:
+    return __INFLECT.number_to_words(m.group(0))  # type: ignore
+
+
+def __expand_number(m: re.Match[str]) -> str:
+    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)  # type: ignore
+        elif num % 100 == 0:
+            return __INFLECT.number_to_words(num // 100) + " hundred"  # type: ignore
+        else:
+            return __INFLECT.number_to_words(
+                num, andword="", zero="oh", group=2  # type: ignore
+            ).replace(
+                ", ", " "
+            )  # type: ignore
+    else:
+        return __INFLECT.number_to_words(num, andword="")  # type: ignore
+
+
+def __expand_decimal_point(m: re.Match[str]) -> str:
+    return m.group(1).replace(".", " point ")
diff --git a/style_bert_vits2/nlp/japanese/__init__.py b/style_bert_vits2/nlp/japanese/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/style_bert_vits2/nlp/japanese/bert_feature.py b/style_bert_vits2/nlp/japanese/bert_feature.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6e214b9ad10f61f609d976ce1100dbb324c5a9f
--- /dev/null
+++ b/style_bert_vits2/nlp/japanese/bert_feature.py
@@ -0,0 +1,73 @@
+from typing import Optional
+
+import torch
+
+from style_bert_vits2.constants import Languages
+from style_bert_vits2.nlp import bert_models
+from style_bert_vits2.nlp.japanese.g2p import text_to_sep_kata
+
+
+def extract_bert_feature(
+    text: str,
+    word2ph: list[int],
+    device: str,
+    assist_text: Optional[str] = None,
+    assist_text_weight: float = 0.7,
+) -> torch.Tensor:
+    """
+    日本語のテキストから BERT の特徴量を抽出する
+
+    Args:
+        text (str): 日本語のテキスト
+        word2ph (list[int]): 元のテキストの各文字に音素が何個割り当てられるかを表すリスト
+        device (str): 推論に利用するデバイス
+        assist_text (Optional[str], optional): 補助テキスト (デフォルト: None)
+        assist_text_weight (float, optional): 補助テキストの重み (デフォルト: 0.7)
+
+    Returns:
+        torch.Tensor: BERT の特徴量
+    """
+
+    # 各単語が何文字かを作る `word2ph` を使う必要があるので、読めない文字は必ず無視する
+    # でないと `word2ph` の結果とテキストの文字数結果が整合性が取れない
+    text = "".join(text_to_sep_kata(text, raise_yomi_error=False)[0])
+    if assist_text:
+        assist_text = "".join(text_to_sep_kata(assist_text, raise_yomi_error=False)[0])
+
+    if device == "cuda" and not torch.cuda.is_available():
+        device = "cpu"
+    model = bert_models.load_model(Languages.JP).to(device)  # type: ignore
+
+    style_res_mean = None
+    with torch.no_grad():
+        tokenizer = bert_models.load_tokenizer(Languages.JP)
+        inputs = tokenizer(text, return_tensors="pt")
+        for i in inputs:
+            inputs[i] = inputs[i].to(device)  # type: ignore
+        res = model(**inputs, output_hidden_states=True)
+        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
+        if assist_text:
+            style_inputs = tokenizer(assist_text, return_tensors="pt")
+            for i in style_inputs:
+                style_inputs[i] = style_inputs[i].to(device)  # type: ignore
+            style_res = model(**style_inputs, output_hidden_states=True)
+            style_res = torch.cat(style_res["hidden_states"][-3:-2], -1)[0].cpu()
+            style_res_mean = style_res.mean(0)
+
+    assert len(word2ph) == len(text) + 2, text
+    word2phone = word2ph
+    phone_level_feature = []
+    for i in range(len(word2phone)):
+        if assist_text:
+            assert style_res_mean is not None
+            repeat_feature = (
+                res[i].repeat(word2phone[i], 1) * (1 - assist_text_weight)
+                + style_res_mean.repeat(word2phone[i], 1) * assist_text_weight
+            )
+        else:
+            repeat_feature = res[i].repeat(word2phone[i], 1)
+        phone_level_feature.append(repeat_feature)
+
+    phone_level_feature = torch.cat(phone_level_feature, dim=0)
+
+    return phone_level_feature.T
diff --git a/style_bert_vits2/nlp/japanese/g2p.py b/style_bert_vits2/nlp/japanese/g2p.py
new file mode 100644
index 0000000000000000000000000000000000000000..dab3dbf98d45491ffe3d74653ccb3163c63689e6
--- /dev/null
+++ b/style_bert_vits2/nlp/japanese/g2p.py
@@ -0,0 +1,721 @@
+import re
+from typing import TypedDict
+
+from style_bert_vits2.constants import Languages
+from style_bert_vits2.logging import logger
+from style_bert_vits2.nlp import bert_models
+from style_bert_vits2.nlp.japanese import pyopenjtalk_worker as pyopenjtalk
+from style_bert_vits2.nlp.japanese.mora_list import MORA_KATA_TO_MORA_PHONEMES, VOWELS
+from style_bert_vits2.nlp.japanese.normalizer import replace_punctuation
+from style_bert_vits2.nlp.symbols import PUNCTUATIONS
+
+
+def g2p(
+    norm_text: str, use_jp_extra: bool = True, raise_yomi_error: bool = False
+) -> tuple[list[str], list[int], list[int]]:
+    """
+    他で使われるメインの関数。`normalize_text()` で正規化された `norm_text` を受け取り、
+    - phones: 音素のリスト（ただし `!` や `,` や `.` など punctuation が含まれうる）
+    - tones: アクセントのリスト、0（低）と1（高）からなり、phones と同じ長さ
+    - word2ph: 元のテキストの各文字に音素が何個割り当てられるかを表すリスト
+    のタプルを返す。
+    ただし `phones` と `tones` の最初と終わりに `_` が入り、応じて `word2ph` の最初と最後に 1 が追加される。
+
+    Args:
+        norm_text (str): 正規化されたテキスト
+        use_jp_extra (bool, optional): False の場合、「ん」の音素を「N」ではなく「n」とする。Defaults to True.
+        raise_yomi_error (bool, optional): False の場合、読めない文字が「'」として発音される。Defaults to False.
+
+    Returns:
+        tuple[list[str], list[int], list[int]]: 音素のリスト、アクセントのリスト、word2ph のリスト
+    """
+
+    # pyopenjtalk のフルコンテキストラベルを使ってアクセントを取り出すと、punctuation の位置が消えてしまい情報が失われてしまう：
+    # 「こんにちは、世界。」と「こんにちは！世界。」と「こんにちは！！！？？？世界……。」は全て同じになる。
+    # よって、まず punctuation 無しの音素とアクセントのリストを作り、
+    # それとは別に pyopenjtalk.run_frontend() で得られる音素リスト（こちらは punctuation が保持される）を使い、
+    # アクセント割当をしなおすことによって punctuation を含めた音素とアクセントのリストを作る。
+
+    # punctuation がすべて消えた、音素とアクセントのタプルのリスト（「ん」は「N」）
+    phone_tone_list_wo_punct = __g2phone_tone_wo_punct(norm_text)
+
+    # sep_text: 単語単位の単語のリスト
+    # sep_kata: 単語単位の単語のカタカナ読みのリスト、読めない文字は raise_yomi_error=True なら例外、False なら読めない文字を「'」として返ってくる
+    sep_text, sep_kata = text_to_sep_kata(norm_text, raise_yomi_error=raise_yomi_error)
+
+    # sep_phonemes: 各単語ごとの音素のリストのリスト
+    sep_phonemes = __handle_long([__kata_to_phoneme_list(i) for i in sep_kata])
+
+    # phone_w_punct: sep_phonemes を結合した、punctuation を元のまま保持した音素列
+    phone_w_punct: list[str] = []
+    for i in sep_phonemes:
+        phone_w_punct += i
+
+    # punctuation 無しのアクセント情報を使って、punctuation を含めたアクセント情報を作る
+    phone_tone_list = __align_tones(phone_w_punct, phone_tone_list_wo_punct)
+    # logger.debug(f"phone_tone_list:\n{phone_tone_list}")
+
+    # word2ph は厳密な解答は不可能なので（「今日」「眼鏡」等の熟字訓が存在）、
+    # Bert-VITS2 では、単語単位の分割を使って、単語の文字ごとにだいたい均等に音素を分配する
+
+    # sep_text から、各単語を1文字1文字分割して、文字のリスト（のリスト）を作る
+    sep_tokenized: list[list[str]] = []
+    for i in sep_text:
+        if i not in PUNCTUATIONS:
+            sep_tokenized.append(
+                bert_models.load_tokenizer(Languages.JP).tokenize(i)
+            )  # ここでおそらく`i`が文字単位に分割される
+        else:
+            sep_tokenized.append([i])
+
+    # 各単語について、音素の数と文字の数を比較して、均等っぽく分配する
+    word2ph = []
+    for token, phoneme in zip(sep_tokenized, sep_phonemes):
+        phone_len = len(phoneme)
+        word_len = len(token)
+        word2ph += __distribute_phone(phone_len, word_len)
+
+    # 最初と最後に `_` 記号を追加、アクセントは 0（低）、word2ph もそれに合わせて追加
+    phone_tone_list = [("_", 0)] + phone_tone_list + [("_", 0)]
+    word2ph = [1] + word2ph + [1]
+
+    phones = [phone for phone, _ in phone_tone_list]
+    tones = [tone for _, tone in phone_tone_list]
+
+    assert len(phones) == sum(word2ph), f"{len(phones)} != {sum(word2ph)}"
+
+    # use_jp_extra でない場合は「N」を「n」に変換
+    if not use_jp_extra:
+        phones = [phone if phone != "N" else "n" for phone in phones]
+
+    return phones, tones, word2ph
+
+
+def text_to_sep_kata(
+    norm_text: str, raise_yomi_error: bool = False
+) -> tuple[list[str], list[str]]:
+    """
+    `normalize_text` で正規化済みの `norm_text` を受け取り、それを単語分割し、
+    分割された単語リストとその読み（カタカナ or 記号1文字）のリストのタプルを返す。
+    単語分割結果は、`g2p()` の `word2ph` で1文字あたりに割り振る音素記号の数を決めるために使う。
+    例:
+    `私はそう思う!って感じ?` →
+    ["私", "は", "そう", "思う", "!", "って", "感じ", "?"], ["ワタシ", "ワ", "ソー", "オモウ", "!", "ッテ", "カンジ", "?"]
+
+    Args:
+        norm_text (str): 正規化されたテキスト
+        raise_yomi_error (bool, optional): False の場合、読めない文字が「'」として発音される。Defaults to False.
+
+    Returns:
+        tuple[list[str], list[str]]: 分割された単語リストと、その読み（カタカナ or 記号1文字）のリスト
+    """
+
+    # parsed: OpenJTalkの解析結果
+    parsed = pyopenjtalk.run_frontend(norm_text)
+    sep_text: list[str] = []
+    sep_kata: list[str] = []
+
+    for parts in parsed:
+        # word: 実際の単語の文字列
+        # yomi: その読み、但し無声化サインの`’`は除去
+        word, yomi = replace_punctuation(parts["string"]), parts["pron"].replace(
+            "’", ""
+        )
+        """
+        ここで `yomi` の取りうる値は以下の通りのはず。
+        - `word` が通常単語 → 通常の読み（カタカナ）
+            （カタカナからなり、長音記号も含みうる、`アー` 等）
+        - `word` が `ー` から始まる → `ーラー` や `ーーー` など
+        - `word` が句読点や空白等 → `、`
+        - `word` が punctuation の繰り返し → 全角にしたもの
+        基本的に punctuation は1文字ずつ分かれるが、何故かある程度連続すると1つにまとまる。
+        他にも `word` が読めないキリル文字アラビア文字等が来ると `、` になるが、正規化でこの場合は起きないはず。
+        また元のコードでは `yomi` が空白の場合の処理があったが、これは起きないはず。
+        処理すべきは `yomi` が `、` の場合のみのはず。
+        """
+        assert yomi != "", f"Empty yomi: {word}"
+        if yomi == "、":
+            # word は正規化されているので、`.`, `,`, `!`, `'`, `-`, `--` のいずれか
+            if not set(word).issubset(set(PUNCTUATIONS)):  # 記号繰り返しか判定
+                # ここは pyopenjtalk が読めない文字等のときに起こる
+                ## 例外を送出する場合
+                if raise_yomi_error:
+                    raise YomiError(f"Cannot read: {word} in:\n{norm_text}")
+                ## 例外を送出しない場合
+                ## 読めない文字は「'」として扱う
+                logger.warning(
+                    f'Cannot read: {word} in:\n{norm_text}, replaced with "\'"'
+                )
+                # word の文字数分「'」を追加
+                yomi = "'" * len(word)
+            else:
+                # yomi は元の記号のままに変更
+                yomi = word
+        elif yomi == "？":
+            assert word == "?", f"yomi `？` comes from: {word}"
+            yomi = "?"
+        sep_text.append(word)
+        sep_kata.append(yomi)
+
+    return sep_text, sep_kata
+
+
+def adjust_word2ph(
+    word2ph: list[int],
+    generated_phone: list[str],
+    given_phone: list[str],
+) -> list[int]:
+    """
+    `g2p()` で得られた `word2ph` を、generated_phone と given_phone の差分情報を使っていい感じに調整する。
+    generated_phone は正規化された読み上げテキストから生成された読みの情報だが、
+    given_phone で 同じ読み上げテキストに異なる読みが与えられた場合、正規化された読み上げテキストの各文字に
+    音素が何文字割り当てられるかを示す word2ph の合計値が given_phone の長さ (音素数) と一致しなくなりうる
+    そこで generated_phone と given_phone の差分を取り変更箇所に対応する word2ph の要素の値だけを増減させ、
+    アクセントへの影響を最低限に抑えつつ word2ph の合計値を given_phone の長さ (音素数) に一致させる。
+
+    Args:
+        word2ph (list[int]): 単語ごとの音素の数のリスト
+        generated_phone (list[str]): 生成された音素のリスト
+        given_phone (list[str]): 与えられた音素のリスト
+
+    Returns:
+        list[int]: 修正された word2ph のリスト
+    """
+
+    # word2ph・generated_phone・given_phone 全ての先頭と末尾にダミー要素が入っているので、処理の都合上それらを削除
+    # word2ph は先頭と末尾に 1 が入っている (返す際に再度追加する)
+    word2ph = word2ph[1:-1]
+    generated_phone = generated_phone[1:-1]
+    given_phone = given_phone[1:-1]
+
+    class DiffDetail(TypedDict):
+        begin_index: int
+        end_index: int
+        value: list[str]
+
+    class Diff(TypedDict):
+        generated: DiffDetail
+        given: DiffDetail
+
+    def extract_differences(
+        generated_phone: list[str], given_phone: list[str]
+    ) -> list[Diff]:
+        """
+        最長共通部分列を基にして、二つのリストの異なる部分を抽出する。
+        """
+
+        def longest_common_subsequence(
+            X: list[str], Y: list[str]
+        ) -> list[tuple[int, int]]:
+            """
+            二つのリストの最長共通部分列のインデックスのペアを返す。
+            """
+            m, n = len(X), len(Y)
+            L = [[0] * (n + 1) for _ in range(m + 1)]
+            # LCSの長さを構築
+            for i in range(1, m + 1):
+                for j in range(1, n + 1):
+                    if X[i - 1] == Y[j - 1]:
+                        L[i][j] = L[i - 1][j - 1] + 1
+                    else:
+                        L[i][j] = max(L[i - 1][j], L[i][j - 1])
+            # LCSを逆方向にトレースしてインデックスのペアを取得
+            index_pairs = []
+            i, j = m, n
+            while i > 0 and j > 0:
+                if X[i - 1] == Y[j - 1]:
+                    index_pairs.append((i - 1, j - 1))
+                    i -= 1
+                    j -= 1
+                elif L[i - 1][j] >= L[i][j - 1]:
+                    i -= 1
+                else:
+                    j -= 1
+            index_pairs.reverse()
+            return index_pairs
+
+        differences = []
+        common_indices = longest_common_subsequence(generated_phone, given_phone)
+        prev_x, prev_y = -1, -1
+
+        # 共通部分のインデックスを基にして差分を抽出
+        for x, y in common_indices:
+            diff_X = {
+                "begin_index": prev_x + 1,
+                "end_index": x,
+                "value": generated_phone[prev_x + 1 : x],
+            }
+            diff_Y = {
+                "begin_index": prev_y + 1,
+                "end_index": y,
+                "value": given_phone[prev_y + 1 : y],
+            }
+            if diff_X or diff_Y:
+                differences.append({"generated": diff_X, "given": diff_Y})
+            prev_x, prev_y = x, y
+        # 最後の非共通部分を追加
+        if prev_x < len(generated_phone) - 1 or prev_y < len(given_phone) - 1:
+            differences.append(
+                {
+                    "generated": {
+                        "begin_index": prev_x + 1,
+                        "end_index": len(generated_phone) - 1,
+                        "value": generated_phone[prev_x + 1 : len(generated_phone) - 1],
+                    },
+                    "given": {
+                        "begin_index": prev_y + 1,
+                        "end_index": len(given_phone) - 1,
+                        "value": given_phone[prev_y + 1 : len(given_phone) - 1],
+                    },
+                }
+            )
+        # generated.value と given.value の両方が空の要素を diffrences から削除
+        for diff in differences[:]:
+            if (
+                len(diff["generated"]["value"]) == 0
+                and len(diff["given"]["value"]) == 0
+            ):
+                differences.remove(diff)
+
+        return differences
+
+    # 二つのリストの差分を抽出
+    differences = extract_differences(generated_phone, given_phone)
+
+    # word2ph をもとにして新しく作る word2ph のリスト
+    ## 長さは word2ph と同じだが、中身は 0 で初期化されている
+    adjusted_word2ph: list[int] = [0] * len(word2ph)
+    # 現在処理中の generated_phone のインデックス
+    current_generated_index = 0
+
+    # word2ph の要素数 (=正規化された読み上げテキストの文字数) を維持しながら、差分情報を使って word2ph を修正
+    ## 音素数が generated_phone と given_phone で異なる場合にこの align_word2ph() が呼び出される
+    ## word2ph は正規化された読み上げテキストの文字数に対応しているので、要素数はそのまま given_phone で増減した音素数に合わせて各要素の値を増減する
+    for word2ph_element_index, word2ph_element in enumerate(word2ph):
+        # ここの word2ph_element は、正規化された読み上げテキストの各文字に割り当てられる音素の数を示す
+        # 例えば word2ph_element が 2 ならば、その文字には 2 つの音素 (例: "k", "a") が割り当てられる
+        # 音素の数だけループを回す
+        for _ in range(word2ph_element):
+            # difference の中に 処理中の generated_phone から始まる差分があるかどうかを確認
+            current_diff: Diff | None = None
+            for diff in differences:
+                if diff["generated"]["begin_index"] == current_generated_index:
+                    current_diff = diff
+                    break
+            # current_diff が None でない場合、generated_phone から始まる差分がある
+            if current_diff is not None:
+                # generated から given で変わった音素数の差分を取得 (2増えた場合は +2 だし、2減った場合は -2)
+                diff_in_phonemes = \
+                    len(current_diff["given"]["value"]) - len(current_diff["generated"]["value"])  # fmt: skip
+                # adjusted_word2ph[(読み上げテキストの各文字のインデックス)] に上記差分を反映
+                adjusted_word2ph[word2ph_element_index] += diff_in_phonemes
+            # adjusted_word2ph[(読み上げテキストの各文字のインデックス)] に処理が完了した分の音素として 1 を加える
+            adjusted_word2ph[word2ph_element_index] += 1
+            # 処理中の generated_phone のインデックスを進める
+            current_generated_index += 1
+
+    # この時点で given_phone の長さと adjusted_word2ph に記録されている音素数の合計が一致しているはず
+    assert len(given_phone) == sum(adjusted_word2ph), f"{len(given_phone)} != {sum(adjusted_word2ph)}"  # fmt: skip
+
+    # generated_phone から given_phone の間で音素が減った場合 (例: a, sh, i, t, a -> a, s, u) 、
+    # adjusted_word2ph の要素の値が 1 未満になることがあるので、1 になるように値を増やす
+    ## この時、adjusted_word2ph に記録されている音素数の合計を変えないために、
+    ## 値を 1 にした分だけ右隣の要素から増やした分の差分を差し引く
+    for adjusted_word2ph_element_index, adjusted_word2ph_element in enumerate(adjusted_word2ph):  # fmt: skip
+        # もし現在の要素が 1 未満ならば
+        if adjusted_word2ph_element < 1:
+            # 値を 1 にするためにどれだけ足せばいいかを計算
+            diff = 1 - adjusted_word2ph_element
+            # adjusted_word2ph[(読み上げテキストの各文字のインデックス)] を 1 にする
+            # これにより、当該文字に最低ラインとして 1 つの音素が割り当てられる
+            adjusted_word2ph[adjusted_word2ph_element_index] = 1
+            # 次の要素のうち、一番近くてかつ 1 以上の要素から diff を引く
+            # この時、diff を引いた結果引いた要素が 1 未満になる場合は、その要素の次の要素の中から一番近くてかつ 1 以上の要素から引く
+            # 上記を繰り返していって、diff が 0 になるまで続ける
+            for i in range(1, len(adjusted_word2ph)):
+                if adjusted_word2ph_element_index + i >= len(adjusted_word2ph):
+                    break  # adjusted_word2ph の最後に達した場合は諦める
+                if adjusted_word2ph[adjusted_word2ph_element_index + i] - diff >= 1:
+                    adjusted_word2ph[adjusted_word2ph_element_index + i] -= diff
+                    break
+                else:
+                    diff -= adjusted_word2ph[adjusted_word2ph_element_index + i] - 1
+                    adjusted_word2ph[adjusted_word2ph_element_index + i] = 1
+                    if diff == 0:
+                        break
+
+    # 逆に、generated_phone から given_phone の間で音素が増えた場合 (例: a, s, u -> a, sh, i, t, a) 、
+    # 1文字あたり7音素以上も割り当てられてしまう場合があるので、最大6音素にした上で削った分の差分を次の要素に加える
+    # 次の要素に差分を加えた結果7音素以上になってしまう場合は、その差分をさらに次の要素に加える
+    for adjusted_word2ph_element_index, adjusted_word2ph_element in enumerate(adjusted_word2ph):  # fmt: skip
+        if adjusted_word2ph_element > 6:
+            diff = adjusted_word2ph_element - 6
+            adjusted_word2ph[adjusted_word2ph_element_index] = 6
+            for i in range(1, len(adjusted_word2ph)):
+                if adjusted_word2ph_element_index + i >= len(adjusted_word2ph):
+                    break  # adjusted_word2ph の最後に達した場合は諦める
+                if adjusted_word2ph[adjusted_word2ph_element_index + i] + diff <= 6:
+                    adjusted_word2ph[adjusted_word2ph_element_index + i] += diff
+                    break
+                else:
+                    diff -= 6 - adjusted_word2ph[adjusted_word2ph_element_index + i]
+                    adjusted_word2ph[adjusted_word2ph_element_index + i] = 6
+                    if diff == 0:
+                        break
+
+    # この時点で given_phone の長さと adjusted_word2ph に記録されている音素数の合計が一致していない場合、
+    # 正規化された読み上げテキストと given_phone が著しく乖離していることを示す
+    # このとき、この関数の呼び出し元の get_text() にて InvalidPhoneError が送出される
+
+    # 最初に削除した前後のダミー要素を追加して返す
+    return [1] + adjusted_word2ph + [1]
+
+
+def __g2phone_tone_wo_punct(text: str) -> list[tuple[str, int]]:
+    """
+    テキストに対して、音素とアクセント（0か1）のペアのリストを返す。
+    ただし「!」「.」「?」等の非音素記号 (punctuation) は全て消える（ポーズ記号も残さない）。
+    非音素記号を含める処理は `align_tones()` で行われる。
+    また「っ」は「q」に、「ん」は「N」に変換される。
+    例: "こんにちは、世界ー。。元気？！" →
+    [('k', 0), ('o', 0), ('N', 1), ('n', 1), ('i', 1), ('ch', 1), ('i', 1), ('w', 1), ('a', 1), ('s', 1), ('e', 1), ('k', 0), ('a', 0), ('i', 0), ('i', 0), ('g', 1), ('e', 1), ('N', 0), ('k', 0), ('i', 0)]
+
+    Args:
+        text (str): テキスト
+
+    Returns:
+        list[tuple[str, int]]: 音素とアクセントのペアのリスト
+    """
+
+    prosodies = __pyopenjtalk_g2p_prosody(text, drop_unvoiced_vowels=True)
+    # logger.debug(f"prosodies: {prosodies}")
+    result: list[tuple[str, int]] = []
+    current_phrase: list[tuple[str, int]] = []
+    current_tone = 0
+
+    for i, letter in enumerate(prosodies):
+        # 特殊記号の処理
+
+        # 文頭記号、無視する
+        if letter == "^":
+            assert i == 0, "Unexpected ^"
+        # アクセント句の終わりに来る記号
+        elif letter in ("$", "?", "_", "#"):
+            # 保持しているフレーズを、アクセント数値を 0-1 に修正し結果に追加
+            result.extend(__fix_phone_tone(current_phrase))
+            # 末尾に来る終了記号、無視（文中の疑問文は `_` になる）
+            if letter in ("$", "?"):
+                assert i == len(prosodies) - 1, f"Unexpected {letter}"
+            # あとは "_"（ポーズ）と "#"（アクセント句の境界）のみ
+            # これらは残さず、次のアクセント句に備える。
+            current_phrase = []
+            # 0 を基準点にしてそこから上昇・下降する（負の場合は上の `fix_phone_tone` で直る）
+            current_tone = 0
+        # アクセント上昇記号
+        elif letter == "[":
+            current_tone = current_tone + 1
+        # アクセント下降記号
+        elif letter == "]":
+            current_tone = current_tone - 1
+        # それ以外は通常の音素
+        else:
+            if letter == "cl":  # 「っ」の処理
+                letter = "q"
+            # elif letter == "N":  # 「ん」の処理
+            #     letter = "n"
+            current_phrase.append((letter, current_tone))
+
+    return result
+
+
+__PYOPENJTALK_G2P_PROSODY_A1_PATTERN = re.compile(r"/A:([0-9\-]+)\+")
+__PYOPENJTALK_G2P_PROSODY_A2_PATTERN = re.compile(r"\+(\d+)\+")
+__PYOPENJTALK_G2P_PROSODY_A3_PATTERN = re.compile(r"\+(\d+)/")
+__PYOPENJTALK_G2P_PROSODY_E3_PATTERN = re.compile(r"!(\d+)_")
+__PYOPENJTALK_G2P_PROSODY_F1_PATTERN = re.compile(r"/F:(\d+)_")
+__PYOPENJTALK_G2P_PROSODY_P3_PATTERN = re.compile(r"\-(.*?)\+")
+
+
+def __pyopenjtalk_g2p_prosody(
+    text: str, drop_unvoiced_vowels: bool = True
+) -> list[str]:
+    """
+    ESPnet の実装から引用、概ね変更点無し。「ん」は「N」なことに注意。
+    ref: https://github.com/espnet/espnet/blob/master/espnet2/text/phoneme_tokenizer.py
+    ------------------------------------------------------------------------------------------
+
+    Extract phoneme + prosody symbol sequence from input full-context labels.
+
+    The algorithm is based on `Prosodic features control by symbols as input of
+    sequence-to-sequence acoustic modeling for neural TTS`_ with some r9y9's tweaks.
+
+    Args:
+        text (str): Input text.
+        drop_unvoiced_vowels (bool): whether to drop unvoiced vowels.
+
+    Returns:
+        List[str]: List of phoneme + prosody symbols.
+
+    Examples:
+        >>> from espnet2.text.phoneme_tokenizer import pyopenjtalk_g2p_prosody
+        >>> pyopenjtalk_g2p_prosody("こんにちは。")
+        ['^', 'k', 'o', '[', 'N', 'n', 'i', 'ch', 'i', 'w', 'a', '$']
+
+    .. _`Prosodic features control by symbols as input of sequence-to-sequence acoustic
+        modeling for neural TTS`: https://doi.org/10.1587/transinf.2020EDP7104
+    """
+
+    def _numeric_feature_by_regex(pattern: re.Pattern[str], s: str) -> int:
+        match = pattern.search(s)
+        if match is None:
+            return -50
+        return int(match.group(1))
+
+    labels = pyopenjtalk.make_label(pyopenjtalk.run_frontend(text))
+    N = len(labels)
+
+    phones = []
+    for n in range(N):
+        lab_curr = labels[n]
+
+        # current phoneme
+        p3 = __PYOPENJTALK_G2P_PROSODY_P3_PATTERN.search(lab_curr).group(1)  # type: ignore
+        # deal unvoiced vowels as normal vowels
+        if drop_unvoiced_vowels and p3 in "AEIOU":
+            p3 = p3.lower()
+
+        # deal with sil at the beginning and the end of text
+        if p3 == "sil":
+            assert n == 0 or n == N - 1
+            if n == 0:
+                phones.append("^")
+            elif n == N - 1:
+                # check question form or not
+                e3 = _numeric_feature_by_regex(
+                    __PYOPENJTALK_G2P_PROSODY_E3_PATTERN, lab_curr
+                )
+                if e3 == 0:
+                    phones.append("$")
+                elif e3 == 1:
+                    phones.append("?")
+            continue
+        elif p3 == "pau":
+            phones.append("_")
+            continue
+        else:
+            phones.append(p3)
+
+        # accent type and position info (forward or backward)
+        a1 = _numeric_feature_by_regex(__PYOPENJTALK_G2P_PROSODY_A1_PATTERN, lab_curr)
+        a2 = _numeric_feature_by_regex(__PYOPENJTALK_G2P_PROSODY_A2_PATTERN, lab_curr)
+        a3 = _numeric_feature_by_regex(__PYOPENJTALK_G2P_PROSODY_A3_PATTERN, lab_curr)
+
+        # number of mora in accent phrase
+        f1 = _numeric_feature_by_regex(__PYOPENJTALK_G2P_PROSODY_F1_PATTERN, lab_curr)
+
+        a2_next = _numeric_feature_by_regex(
+            __PYOPENJTALK_G2P_PROSODY_A2_PATTERN, labels[n + 1]
+        )
+        # accent phrase border
+        if a3 == 1 and a2_next == 1 and p3 in "aeiouAEIOUNcl":
+            phones.append("#")
+        # pitch falling
+        elif a1 == 0 and a2_next == a2 + 1 and a2 != f1:
+            phones.append("]")
+        # pitch rising
+        elif a2 == 1 and a2_next == 2:
+            phones.append("[")
+
+    return phones
+
+
+def __fix_phone_tone(phone_tone_list: list[tuple[str, int]]) -> list[tuple[str, int]]:
+    """
+    `phone_tone_list` の tone（アクセントの値）を 0 か 1 の範囲に修正する。
+    例: [(a, 0), (i, -1), (u, -1)] → [(a, 1), (i, 0), (u, 0)]
+
+    Args:
+        phone_tone_list (list[tuple[str, int]]): 音素とアクセントのペアのリスト
+
+    Returns:
+        list[tuple[str, int]]: 修正された音素とアクセントのペアのリスト
+    """
+
+    tone_values = set(tone for _, tone in phone_tone_list)
+    if len(tone_values) == 1:
+        assert tone_values == {0}, tone_values
+        return phone_tone_list
+    elif len(tone_values) == 2:
+        if tone_values == {0, 1}:
+            return phone_tone_list
+        elif tone_values == {-1, 0}:
+            return [
+                (letter, 0 if tone == -1 else 1) for letter, tone in phone_tone_list
+            ]
+        else:
+            raise ValueError(f"Unexpected tone values: {tone_values}")
+    else:
+        raise ValueError(f"Unexpected tone values: {tone_values}")
+
+
+def __handle_long(sep_phonemes: list[list[str]]) -> list[list[str]]:
+    """
+    フレーズごとに分かれた音素（長音記号がそのまま）のリストのリスト `sep_phonemes` を受け取り、
+    その長音記号を処理して、音素のリストのリストを返す。
+    基本的には直前の音素を伸ばすが、直前の音素が母音でない場合もしくは冒頭の場合は、
+    おそらく長音記号とダッシュを勘違いしていると思われるので、ダッシュに対応する音素 `-` に変換する。
+
+    Args:
+        sep_phonemes (list[list[str]]): フレーズごとに分かれた音素のリストのリスト
+
+    Returns:
+        list[list[str]]: 長音記号を処理した音素のリストのリスト
+    """
+
+    for i in range(len(sep_phonemes)):
+        if len(sep_phonemes[i]) == 0:
+            # 空白文字等でリストが空の場合
+            continue
+        if sep_phonemes[i][0] == "ー":
+            if i != 0:
+                prev_phoneme = sep_phonemes[i - 1][-1]
+                if prev_phoneme in VOWELS:
+                    # 母音と「ん」のあとの伸ばし棒なので、その母音に変換
+                    sep_phonemes[i][0] = sep_phonemes[i - 1][-1]
+                else:
+                    # 「。ーー」等おそらく予期しない長音記号
+                    # ダッシュの勘違いだと思われる
+                    sep_phonemes[i][0] = "-"
+            else:
+                # 冒頭に長音記号が来ていおり、これはダッシュの勘違いと思われる
+                sep_phonemes[i][0] = "-"
+        if "ー" in sep_phonemes[i]:
+            for j in range(len(sep_phonemes[i])):
+                if sep_phonemes[i][j] == "ー":
+                    sep_phonemes[i][j] = sep_phonemes[i][j - 1][-1]
+
+    return sep_phonemes
+
+
+__KATAKANA_PATTERN = re.compile(r"[\u30A0-\u30FF]+")
+__MORA_PATTERN = re.compile(
+    "|".join(
+        map(re.escape, sorted(MORA_KATA_TO_MORA_PHONEMES.keys(), key=len, reverse=True))
+    )
+)
+__LONG_PATTERN = re.compile(r"(\w)(ー*)")
+
+
+def __kata_to_phoneme_list(text: str) -> list[str]:
+    """
+    原則カタカナの `text` を受け取り、それをそのままいじらずに音素記号のリストに変換。
+    注意点：
+    - punctuation かその繰り返しが来た場合、punctuation たちをそのままリストにして返す。
+    - 冒頭に続く「ー」はそのまま「ー」のままにする（`handle_long()` で処理される）
+    - 文中の「ー」は前の音素記号の最後の音素記号に変換される。
+    例：
+    `ーーソーナノカーー` → ["ー", "ー", "s", "o", "o", "n", "a", "n", "o", "k", "a", "a", "a"]
+    `?` → ["?"]
+    `!?!?!?!?!` → ["!", "?", "!", "?", "!", "?", "!", "?", "!"]
+
+    Args:
+        text (str): カタカナのテキスト
+
+    Returns:
+        list[str]: 音素記号のリスト
+    """
+
+    if set(text).issubset(set(PUNCTUATIONS)):
+        return list(text)
+    # `text` がカタカナ（`ー`含む）のみからなるかどうかをチェック
+    if __KATAKANA_PATTERN.fullmatch(text) is None:
+        raise ValueError(f"Input must be katakana only: {text}")
+
+    def mora2phonemes(mora: str) -> str:
+        consonant, vowel = MORA_KATA_TO_MORA_PHONEMES[mora]
+        if consonant is None:
+            return f" {vowel}"
+        return f" {consonant} {vowel}"
+
+    spaced_phonemes = __MORA_PATTERN.sub(lambda m: mora2phonemes(m.group()), text)
+
+    # 長音記号「ー」の処理
+    long_replacement = lambda m: m.group(1) + (" " + m.group(1)) * len(m.group(2))  # type: ignore
+    spaced_phonemes = __LONG_PATTERN.sub(long_replacement, spaced_phonemes)
+
+    return spaced_phonemes.strip().split(" ")
+
+
+def __align_tones(
+    phones_with_punct: list[str], phone_tone_list: list[tuple[str, int]]
+) -> list[tuple[str, int]]:
+    """
+    例: …私は、、そう思う。
+    phones_with_punct:
+        [".", ".", ".", "w", "a", "t", "a", "sh", "i", "w", "a", ",", ",", "s", "o", "o", "o", "m", "o", "u", "."]
+    phone_tone_list:
+        [("w", 0), ("a", 0), ("t", 1), ("a", 1), ("sh", 1), ("i", 1), ("w", 1), ("a", 1), ("_", 0), ("s", 0), ("o", 0), ("o", 1), ("o", 1), ("m", 1), ("o", 1), ("u", 0))]
+    Return:
+        [(".", 0), (".", 0), (".", 0), ("w", 0), ("a", 0), ("t", 1), ("a", 1), ("sh", 1), ("i", 1), ("w", 1), ("a", 1), (",", 0), (",", 0), ("s", 0), ("o", 0), ("o", 1), ("o", 1), ("m", 1), ("o", 1), ("u", 0), (".", 0)]
+
+    Args:
+        phones_with_punct (list[str]): punctuation を含む音素のリスト
+        phone_tone_list (list[tuple[str, int]]): punctuation を含まない音素とアクセントのペアのリスト
+
+    Returns:
+        list[tuple[str, int]]: punctuation を含む音素とアクセントのペアのリスト
+    """
+
+    result: list[tuple[str, int]] = []
+    tone_index = 0
+    for phone in phones_with_punct:
+        if tone_index >= len(phone_tone_list):
+            # 余った punctuation がある場合 → (punctuation, 0) を追加
+            result.append((phone, 0))
+        elif phone == phone_tone_list[tone_index][0]:
+            # phone_tone_list の現在の音素と一致する場合 → tone をそこから取得、(phone, tone) を追加
+            result.append((phone, phone_tone_list[tone_index][1]))
+            # 探す index を1つ進める
+            tone_index += 1
+        elif phone in PUNCTUATIONS:
+            # phone が punctuation の場合 → (phone, 0) を追加
+            result.append((phone, 0))
+        else:
+            logger.debug(f"phones: {phones_with_punct}")
+            logger.debug(f"phone_tone_list: {phone_tone_list}")
+            logger.debug(f"result: {result}")
+            logger.debug(f"tone_index: {tone_index}")
+            logger.debug(f"phone: {phone}")
+            raise ValueError(f"Unexpected phone: {phone}")
+
+    return result
+
+
+def __distribute_phone(n_phone: int, n_word: int) -> list[int]:
+    """
+    左から右に 1 ずつ振り分け、次にまた左から右に1ずつ増やし、というふうに、
+    音素の数 `n_phone` を単語の数 `n_word` に分配する。
+
+    Args:
+        n_phone (int): 音素の数
+        n_word (int): 単語の数
+
+    Returns:
+        list[int]: 単語ごとの音素の数のリスト
+    """
+
+    phones_per_word = [0] * n_word
+    for _ in range(n_phone):
+        min_tasks = min(phones_per_word)
+        min_index = phones_per_word.index(min_tasks)
+        phones_per_word[min_index] += 1
+
+    return phones_per_word
+
+
+class YomiError(Exception):
+    """
+    OpenJTalk で、読みが正しく取得できない箇所があるときに発生する例外。
+    基本的に「学習の前処理のテキスト処理時」には発生させ、そうでない場合は、
+    ignore_yomi_error=True にしておいて、この例外を発生させないようにする。
+    """
diff --git a/style_bert_vits2/nlp/japanese/g2p_utils.py b/style_bert_vits2/nlp/japanese/g2p_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce0b049a45075edefea0e1826834dc5abd785b08
--- /dev/null
+++ b/style_bert_vits2/nlp/japanese/g2p_utils.py
@@ -0,0 +1,84 @@
+from style_bert_vits2.nlp.japanese.g2p import g2p
+from style_bert_vits2.nlp.japanese.mora_list import (
+    CONSONANTS,
+    MORA_KATA_TO_MORA_PHONEMES,
+    MORA_PHONEMES_TO_MORA_KATA,
+)
+from style_bert_vits2.nlp.symbols import PUNCTUATIONS
+
+
+def g2kata_tone(norm_text: str) -> list[tuple[str, int]]:
+    """
+    テキストからカタカナとアクセントのペアのリストを返す。
+    推論時のみに使われる関数のため、常に `raise_yomi_error=False` を指定して g2p() を呼ぶ仕様になっている。
+
+    Args:
+        norm_text: 正規化されたテキスト。
+
+    Returns:
+        カタカナと音高のリスト。
+    """
+
+    phones, tones, _ = g2p(norm_text, use_jp_extra=True, raise_yomi_error=False)
+    return phone_tone2kata_tone(list(zip(phones, tones)))
+
+
+def phone_tone2kata_tone(phone_tone: list[tuple[str, int]]) -> list[tuple[str, int]]:
+    """
+    phone_tone の phone 部分をカタカナに変換する。ただし最初と最後の ("_", 0) は無視する。
+
+    Args:
+        phone_tone: 音素と音高のリスト。
+
+    Returns:
+        カタカナと音高のリスト。
+    """
+
+    phone_tone = phone_tone[1:]  # 最初の("_", 0)を無視
+    phones = [phone for phone, _ in phone_tone]
+    tones = [tone for _, tone in phone_tone]
+    result: list[tuple[str, int]] = []
+    current_mora = ""
+    for phone, next_phone, tone, next_tone in zip(phones, phones[1:], tones, tones[1:]):
+        # zip の関係で最後の ("_", 0) は無視されている
+        if phone in PUNCTUATIONS:
+            result.append((phone, tone))
+            continue
+        if phone in CONSONANTS:  # n以外の子音の場合
+            assert current_mora == "", f"Unexpected {phone} after {current_mora}"
+            assert tone == next_tone, f"Unexpected {phone} tone {tone} != {next_tone}"
+            current_mora = phone
+        else:
+            # phoneが母音もしくは「N」
+            current_mora += phone
+            result.append((MORA_PHONEMES_TO_MORA_KATA[current_mora], tone))
+            current_mora = ""
+
+    return result
+
+
+def kata_tone2phone_tone(kata_tone: list[tuple[str, int]]) -> list[tuple[str, int]]:
+    """
+    `phone_tone2kata_tone()` の逆の変換を行う。
+
+    Args:
+        kata_tone: カタカナと音高のリスト。
+
+    Returns:
+        音素と音高のリスト。
+    """
+
+    result: list[tuple[str, int]] = [("_", 0)]
+    for mora, tone in kata_tone:
+        if mora in PUNCTUATIONS:
+            result.append((mora, tone))
+        else:
+            consonant, vowel = MORA_KATA_TO_MORA_PHONEMES[mora]
+            if consonant is None:
+                result.append((vowel, tone))
+            else:
+                result.append((consonant, tone))
+                result.append((vowel, tone))
+    result.append(("_", 0))
+
+    return result
diff --git a/style_bert_vits2/nlp/japanese/mora_list.py b/style_bert_vits2/nlp/japanese/mora_list.py
new file mode 100644
index 0000000000000000000000000000000000000000..db69c3495af87db2acededa4a15c193dfa3439e2
--- /dev/null
+++ b/style_bert_vits2/nlp/japanese/mora_list.py
@@ -0,0 +1,248 @@
+"""
+以下のコードは VOICEVOX のソースコードからお借りし最低限の改造を行ったもの。
+https://github.com/VOICEVOX/voicevox_engine/blob/master/voicevox_engine/tts_pipeline/mora_list.py
+"""
+
+"""
+以下のモーラ対応表は OpenJTalk のソースコードから取得し、
+カタカナ表記とモーラが一対一対応するように改造した。
+ライセンス表記：
+-----------------------------------------------------------------
+          The Japanese TTS System "Open JTalk"
+          developed by HTS Working Group
+          http://open-jtalk.sourceforge.net/
+-----------------------------------------------------------------
+
+ Copyright (c) 2008-2014  Nagoya Institute of Technology
+                          Department of Computer Science
+
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or
+without modification, are permitted provided that the following
+conditions are met:
+
+- Redistributions of source code must retain the above copyright
+  notice, this list of conditions and the following disclaimer.
+- Redistributions in binary form must reproduce the above
+  copyright notice, this list of conditions and the following
+  disclaimer in the documentation and/or other materials provided
+  with the distribution.
+- Neither the name of the HTS working group nor the names of its
+  contributors may be used to endorse or promote products derived
+  from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
+CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
+INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
+MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS
+BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
+TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
+OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
+"""
+
+from typing import Optional
+
+
+# (カタカナ, 子音, 母音)の順。子音がない場合は None を入れる。
+# 但し「ン」と「ッ」は母音のみという扱いで、「ン」は「N」、「ッ」は「q」とする。
+# （元々「ッ」は「cl」）
+# また「デェ = dy e」は pyopenjtalk の出力（de e）と合わないため削除
+__MORA_LIST_MINIMUM: list[tuple[str, Optional[str], str]] = [
+    ("ヴォ", "v", "o"),
+    ("ヴェ", "v", "e"),
+    ("ヴィ", "v", "i"),
+    ("ヴァ", "v", "a"),
+    ("ヴ", "v", "u"),
+    ("ン", None, "N"),
+    ("ワ", "w", "a"),
+    ("ロ", "r", "o"),
+    ("レ", "r", "e"),
+    ("ル", "r", "u"),
+    ("リョ", "ry", "o"),
+    ("リュ", "ry", "u"),
+    ("リャ", "ry", "a"),
+    ("リェ", "ry", "e"),
+    ("リ", "r", "i"),
+    ("ラ", "r", "a"),
+    ("ヨ", "y", "o"),
+    ("ユ", "y", "u"),
+    ("ヤ", "y", "a"),
+    ("モ", "m", "o"),
+    ("メ", "m", "e"),
+    ("ム", "m", "u"),
+    ("ミョ", "my", "o"),
+    ("ミュ", "my", "u"),
+    ("ミャ", "my", "a"),
+    ("ミェ", "my", "e"),
+    ("ミ", "m", "i"),
+    ("マ", "m", "a"),
+    ("ポ", "p", "o"),
+    ("ボ", "b", "o"),
+    ("ホ", "h", "o"),
+    ("ペ", "p", "e"),
+    ("ベ", "b", "e"),
+    ("ヘ", "h", "e"),
+    ("プ", "p", "u"),
+    ("ブ", "b", "u"),
+    ("フォ", "f", "o"),
+    ("フェ", "f", "e"),
+    ("フィ", "f", "i"),
+    ("ファ", "f", "a"),
+    ("フ", "f", "u"),
+    ("ピョ", "py", "o"),
+    ("ピュ", "py", "u"),
+    ("ピャ", "py", "a"),
+    ("ピェ", "py", "e"),
+    ("ピ", "p", "i"),
+    ("ビョ", "by", "o"),
+    ("ビュ", "by", "u"),
+    ("ビャ", "by", "a"),
+    ("ビェ", "by", "e"),
+    ("ビ", "b", "i"),
+    ("ヒョ", "hy", "o"),
+    ("ヒュ", "hy", "u"),
+    ("ヒャ", "hy", "a"),
+    ("ヒェ", "hy", "e"),
+    ("ヒ", "h", "i"),
+    ("パ", "p", "a"),
+    ("バ", "b", "a"),
+    ("ハ", "h", "a"),
+    ("ノ", "n", "o"),
+    ("ネ", "n", "e"),
+    ("ヌ", "n", "u"),
+    ("ニョ", "ny", "o"),
+    ("ニュ", "ny", "u"),
+    ("ニャ", "ny", "a"),
+    ("ニェ", "ny", "e"),
+    ("ニ", "n", "i"),
+    ("ナ", "n", "a"),
+    ("ドゥ", "d", "u"),
+    ("ド", "d", "o"),
+    ("トゥ", "t", "u"),
+    ("ト", "t", "o"),
+    ("デョ", "dy", "o"),
+    ("デュ", "dy", "u"),
+    ("デャ", "dy", "a"),
+    # ("デェ", "dy", "e"),
+    ("ディ", "d", "i"),
+    ("デ", "d", "e"),
+    ("テョ", "ty", "o"),
+    ("テュ", "ty", "u"),
+    ("テャ", "ty", "a"),
+    ("ティ", "t", "i"),
+    ("テ", "t", "e"),
+    ("ツォ", "ts", "o"),
+    ("ツェ", "ts", "e"),
+    ("ツィ", "ts", "i"),
+    ("ツァ", "ts", "a"),
+    ("ツ", "ts", "u"),
+    ("ッ", None, "q"),  # 「cl」から「q」に変更
+    ("チョ", "ch", "o"),
+    ("チュ", "ch", "u"),
+    ("チャ", "ch", "a"),
+    ("チェ", "ch", "e"),
+    ("チ", "ch", "i"),
+    ("ダ", "d", "a"),
+    ("タ", "t", "a"),
+    ("ゾ", "z", "o"),
+    ("ソ", "s", "o"),
+    ("ゼ", "z", "e"),
+    ("セ", "s", "e"),
+    ("ズィ", "z", "i"),
+    ("ズ", "z", "u"),
+    ("スィ", "s", "i"),
+    ("ス", "s", "u"),
+    ("ジョ", "j", "o"),
+    ("ジュ", "j", "u"),
+    ("ジャ", "j", "a"),
+    ("ジェ", "j", "e"),
+    ("ジ", "j", "i"),
+    ("ショ", "sh", "o"),
+    ("シュ", "sh", "u"),
+    ("シャ", "sh", "a"),
+    ("シェ", "sh", "e"),
+    ("シ", "sh", "i"),
+    ("ザ", "z", "a"),
+    ("サ", "s", "a"),
+    ("ゴ", "g", "o"),
+    ("コ", "k", "o"),
+    ("ゲ", "g", "e"),
+    ("ケ", "k", "e"),
+    ("グヮ", "gw", "a"),
+    ("グ", "g", "u"),
+    ("クヮ", "kw", "a"),
+    ("ク", "k", "u"),
+    ("ギョ", "gy", "o"),
+    ("ギュ", "gy", "u"),
+    ("ギャ", "gy", "a"),
+    ("ギェ", "gy", "e"),
+    ("ギ", "g", "i"),
+    ("キョ", "ky", "o"),
+    ("キュ", "ky", "u"),
+    ("キャ", "ky", "a"),
+    ("キェ", "ky", "e"),
+    ("キ", "k", "i"),
+    ("ガ", "g", "a"),
+    ("カ", "k", "a"),
+    ("オ", None, "o"),
+    ("エ", None, "e"),
+    ("ウォ", "w", "o"),
+    ("ウェ", "w", "e"),
+    ("ウィ", "w", "i"),
+    ("ウ", None, "u"),
+    ("イェ", "y", "e"),
+    ("イ", None, "i"),
+    ("ア", None, "a"),
+]
+__MORA_LIST_ADDITIONAL: list[tuple[str, Optional[str], str]] = [
+    ("ヴョ", "by", "o"),
+    ("ヴュ", "by", "u"),
+    ("ヴャ", "by", "a"),
+    ("ヲ", None, "o"),
+    ("ヱ", None, "e"),
+    ("ヰ", None, "i"),
+    ("ヮ", "w", "a"),
+    ("ョ", "y", "o"),
+    ("ュ", "y", "u"),
+    ("ヅ", "z", "u"),
+    ("ヂ", "j", "i"),
+    ("ヶ", "k", "e"),
+    ("ャ", "y", "a"),
+    ("ォ", None, "o"),
+    ("ェ", None, "e"),
+    ("ゥ", None, "u"),
+    ("ィ", None, "i"),
+    ("ァ", None, "a"),
+]
+
+# モーラの音素表記とカタカナの対応表
+# 例: "vo" -> "ヴォ", "a" -> "ア"
+MORA_PHONEMES_TO_MORA_KATA: dict[str, str] = {
+    (consonant or "") + vowel: kana for [kana, consonant, vowel] in __MORA_LIST_MINIMUM
+}
+
+# モーラのカタカナ表記と音素の対応表
+# 例: "ヴォ" -> ("v", "o"), "ア" -> (None, "a")
+MORA_KATA_TO_MORA_PHONEMES: dict[str, tuple[Optional[str], str]] = {
+    kana: (consonant, vowel)
+    for [kana, consonant, vowel] in __MORA_LIST_MINIMUM + __MORA_LIST_ADDITIONAL
+}
+
+# 子音の集合
+CONSONANTS = set(
+    [
+        consonant
+        for consonant, _ in MORA_KATA_TO_MORA_PHONEMES.values()
+        if consonant is not None
+    ]
+)
+
+# 母音の集合 (便宜上「ん」を含める)
+VOWELS = {"a", "i", "u", "e", "o", "N"}
diff --git a/style_bert_vits2/nlp/japanese/normalizer.py b/style_bert_vits2/nlp/japanese/normalizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ecbfb6f0b36ff1620ede243d25112ce0dda6424
--- /dev/null
+++ b/style_bert_vits2/nlp/japanese/normalizer.py
@@ -0,0 +1,164 @@
+import re
+import unicodedata
+
+from num2words import num2words
+
+from style_bert_vits2.nlp.symbols import PUNCTUATIONS
+
+
+# 記号類の正規化マップ
+__REPLACE_MAP = {
+    "：": ",",
+    "；": ",",
+    "，": ",",
+    "。": ".",
+    "！": "!",
+    "？": "?",
+    "\n": ".",
+    "．": ".",
+    "…": "...",
+    "···": "...",
+    "・・・": "...",
+    "·": ",",
+    "・": ",",
+    "、": ",",
+    "$": ".",
+    "“": "'",
+    "”": "'",
+    '"': "'",
+    "‘": "'",
+    "’": "'",
+    "（": "'",
+    "）": "'",
+    "(": "'",
+    ")": "'",
+    "《": "'",
+    "》": "'",
+    "【": "'",
+    "】": "'",
+    "[": "'",
+    "]": "'",
+    # NFKC 正規化後のハイフン・ダッシュの変種を全て通常半角ハイフン - \u002d に変換
+    "\u02d7": "\u002d",  # ˗, Modifier Letter Minus Sign
+    "\u2010": "\u002d",  # ‐, Hyphen,
+    # "\u2011": "\u002d",  # ‑, Non-Breaking Hyphen, NFKC により \u2010 に変換される
+    "\u2012": "\u002d",  # ‒, Figure Dash
+    "\u2013": "\u002d",  # –, En Dash
+    "\u2014": "\u002d",  # —, Em Dash
+    "\u2015": "\u002d",  # ―, Horizontal Bar
+    "\u2043": "\u002d",  # ⁃, Hyphen Bullet
+    "\u2212": "\u002d",  # −, Minus Sign
+    "\u23af": "\u002d",  # ⎯, Horizontal Line Extension
+    "\u23e4": "\u002d",  # ⏤, Straightness
+    "\u2500": "\u002d",  # ─, Box Drawings Light Horizontal
+    "\u2501": "\u002d",  # ━, Box Drawings Heavy Horizontal
+    "\u2e3a": "\u002d",  # ⸺, Two-Em Dash
+    "\u2e3b": "\u002d",  # ⸻, Three-Em Dash
+    # "～": "-",  # これは長音記号「ー」として扱うよう変更
+    # "~": "-",  # これも長音記号「ー」として扱うよう変更
+    "「": "'",
+    "」": "'",
+}
+# 記号類の正規化パターン
+__REPLACE_PATTERN = re.compile("|".join(re.escape(p) for p in __REPLACE_MAP))
+# 句読点等の正規化パターン
+__PUNCTUATION_CLEANUP_PATTERN = re.compile(
+    # ↓ ひらがな、カタカナ、漢字
+    r"[^\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\u3400-\u4DBF\u3005"
+    # ↓ 半角アルファベット（大文字と小文字）
+    + r"\u0041-\u005A\u0061-\u007A"
+    # ↓ 全角アルファベット（大文字と小文字）
+    + r"\uFF21-\uFF3A\uFF41-\uFF5A"
+    # ↓ ギリシャ文字
+    + r"\u0370-\u03FF\u1F00-\u1FFF"
+    # ↓ "!", "?", "…", ",", ".", "'", "-", 但し`…`はすでに`...`に変換されている
+    + "".join(PUNCTUATIONS) + r"]+",  # fmt: skip
+)
+# 数字・通貨記号の正規化パターン
+__CURRENCY_MAP = {"$": "ドル", "¥": "円", "£": "ポンド", "€": "ユーロ"}
+__CURRENCY_PATTERN = re.compile(r"([$¥£€])([0-9.]*[0-9])")
+__NUMBER_PATTERN = re.compile(r"[0-9]+(\.[0-9]+)?")
+__NUMBER_WITH_SEPARATOR_PATTERN = re.compile("[0-9]{1,3}(,[0-9]{3})+")
+
+
+def normalize_text(text: str) -> str:
+    """
+    日本語のテキストを正規化する。
+    結果は、ちょうど次の文字のみからなる：
+    - ひらがな
+    - カタカナ（全角長音記号「ー」が入る！）
+    - 漢字
+    - 半角アルファベット（大文字と小文字）
+    - ギリシャ文字
+    - `.` （句点`。`や`…`の一部や改行等）
+    - `,` （読点`、`や`:`等）
+    - `?` （疑問符`？`）
+    - `!` （感嘆符`！`）
+    - `'` （`「`や`」`等）
+    - `-` （`―`（ダッシュ、長音記号ではない）や`-`等）
+
+    注意点:
+    - 三点リーダー`…`は`...`に変換される（`なるほど…。` → `なるほど....`）
+    - 数字は漢字に変換される（`1,100円` → `千百円`、`52.34` → `五十二点三四`）
+    - 読点や疑問符等の位置・個数等は保持される（`??あ、、！！！` → `??あ,,!!!`）
+
+    Args:
+        text (str): 正規化するテキスト
+
+    Returns:
+        str: 正規化されたテキスト
+    """
+
+    res = unicodedata.normalize("NFKC", text)  # ここでアルファベットは半角になる
+    res = __convert_numbers_to_words(res)  # 「100円」→「百円」等
+    # 「～」と「〜」と「~」も長音記号として扱う
+    res = res.replace("~", "ー")
+    res = res.replace("～", "ー")
+    res = res.replace("〜", "ー")
+
+    res = replace_punctuation(res)  # 句読点等正規化、読めない文字を削除
+
+    # 結合文字の濁点・半濁点を削除
+    # 通常の「ば」等はそのままのこされる、「あ゛」は上で「あ゙」になりここで「あ」になる
+    res = res.replace("\u3099", "")  # 結合文字の濁点を削除、る゙ → る
+    res = res.replace("\u309A", "")  # 結合文字の半濁点を削除、な゚ → な
+    return res
+
+
+def replace_punctuation(text: str) -> str:
+    """
+    句読点等を「.」「,」「!」「?」「'」「-」に正規化し、OpenJTalk で読みが取得できるもののみ残す：
+    漢字・平仮名・カタカナ、アルファベット、ギリシャ文字
+
+    Args:
+        text (str): 正規化するテキスト
+
+    Returns:
+        str: 正規化されたテキスト
+    """
+
+    # 句読点を辞書で置換
+    replaced_text = __REPLACE_PATTERN.sub(lambda x: __REPLACE_MAP[x.group()], text)
+
+    # 上述以外の文字を削除
+    replaced_text = __PUNCTUATION_CLEANUP_PATTERN.sub("", replaced_text)
+
+    return replaced_text
+
+
+def __convert_numbers_to_words(text: str) -> str:
+    """
+    記号や数字を日本語の文字表現に変換する。
+
+    Args:
+        text (str): 変換するテキスト
+
+    Returns:
+        str: 変換されたテキスト
+    """
+
+    res = __NUMBER_WITH_SEPARATOR_PATTERN.sub(lambda m: m[0].replace(",", ""), text)
+    res = __CURRENCY_PATTERN.sub(lambda m: m[2] + __CURRENCY_MAP.get(m[1], m[1]), res)
+    res = __NUMBER_PATTERN.sub(lambda m: num2words(m[0], lang="ja"), res)
+
+    return res
diff --git a/style_bert_vits2/nlp/japanese/pyopenjtalk_worker/__init__.py b/style_bert_vits2/nlp/japanese/pyopenjtalk_worker/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d86646787a550aa8785b3554342be8483fb1924a
--- /dev/null
+++ b/style_bert_vits2/nlp/japanese/pyopenjtalk_worker/__init__.py
@@ -0,0 +1,160 @@
+"""
+Run the pyopenjtalk worker in a separate process
+to avoid user dictionary access error
+"""
+
+from typing import Any, Optional
+
+from style_bert_vits2.logging import logger
+from style_bert_vits2.nlp.japanese.pyopenjtalk_worker.worker_client import WorkerClient
+from style_bert_vits2.nlp.japanese.pyopenjtalk_worker.worker_common import WORKER_PORT
+
+
+WORKER_CLIENT: Optional[WorkerClient] = None
+
+
+# pyopenjtalk interface
+# g2p(): not used
+
+
+def run_frontend(text: str) -> list[dict[str, Any]]:
+    if WORKER_CLIENT is not None:
+        ret = WORKER_CLIENT.dispatch_pyopenjtalk("run_frontend", text)
+        assert isinstance(ret, list)
+        return ret
+    else:
+        # without worker
+        import pyopenjtalk
+
+        return pyopenjtalk.run_frontend(text)
+
+
+def make_label(njd_features: Any) -> list[str]:
+    if WORKER_CLIENT is not None:
+        ret = WORKER_CLIENT.dispatch_pyopenjtalk("make_label", njd_features)
+        assert isinstance(ret, list)
+        return ret
+    else:
+        # without worker
+        import pyopenjtalk
+
+        return pyopenjtalk.make_label(njd_features)
+
+
+def mecab_dict_index(path: str, out_path: str, dn_mecab: Optional[str] = None) -> None:
+    if WORKER_CLIENT is not None:
+        WORKER_CLIENT.dispatch_pyopenjtalk("mecab_dict_index", path, out_path, dn_mecab)
+    else:
+        # without worker
+        import pyopenjtalk
+
+        pyopenjtalk.mecab_dict_index(path, out_path, dn_mecab)
+
+
+def update_global_jtalk_with_user_dict(path: str) -> None:
+    if WORKER_CLIENT is not None:
+        WORKER_CLIENT.dispatch_pyopenjtalk("update_global_jtalk_with_user_dict", path)
+    else:
+        # without worker
+        import pyopenjtalk
+
+        pyopenjtalk.update_global_jtalk_with_user_dict(path)
+
+
+def unset_user_dict() -> None:
+    if WORKER_CLIENT is not None:
+        WORKER_CLIENT.dispatch_pyopenjtalk("unset_user_dict")
+    else:
+        # without worker
+        import pyopenjtalk
+
+        pyopenjtalk.unset_user_dict()
+
+
+# initialize module when imported
+
+
+def initialize_worker(port: int = WORKER_PORT) -> None:
+    import atexit
+    import signal
+    import socket
+    import sys
+    import time
+
+    global WORKER_CLIENT
+    if WORKER_CLIENT:
+        return
+
+    client = None
+    try:
+        client = WorkerClient(port)
+    except (OSError, socket.timeout):
+        logger.debug("try starting pyopenjtalk worker server")
+        import os
+        import subprocess
+
+        worker_pkg_path = os.path.relpath(
+            os.path.dirname(__file__), os.getcwd()
+        ).replace(os.sep, ".")
+        args = [sys.executable, "-m", worker_pkg_path, "--port", str(port)]
+        # new session, new process group
+        if sys.platform.startswith("win"):
+            cf = subprocess.CREATE_NEW_CONSOLE | subprocess.CREATE_NEW_PROCESS_GROUP  # type: ignore
+            si = subprocess.STARTUPINFO()  # type: ignore
+            si.dwFlags |= subprocess.STARTF_USESHOWWINDOW  # type: ignore
+            si.wShowWindow = subprocess.SW_HIDE  # type: ignore
+            subprocess.Popen(args, creationflags=cf, startupinfo=si)
+        else:
+            # align with Windows behavior
+            # start_new_session is same as specifying setsid in preexec_fn
+            subprocess.Popen(
+                args,
+                stdout=subprocess.DEVNULL,
+                stderr=subprocess.DEVNULL,
+                start_new_session=True,
+            )
+
+        # wait until server listening
+        count = 0
+        while True:
+            try:
+                client = WorkerClient(port)
+                break
+            except OSError:
+                time.sleep(0.5)
+                count += 1
+                # 20: max number of retries
+                if count == 20:
+                    raise TimeoutError("サーバーに接続できませんでした")
+
+    logger.debug("pyopenjtalk worker server started")
+    WORKER_CLIENT = client
+    atexit.register(terminate_worker)
+
+    # when the process is killed
+    def signal_handler(signum: int, frame: Any):
+        terminate_worker()
+
+    try:
+        signal.signal(signal.SIGTERM, signal_handler)
+    except ValueError:
+        # signal only works in main thread
+        pass
+
+
+# top-level declaration
+def terminate_worker() -> None:
+    logger.debug("pyopenjtalk worker server terminated")
+    global WORKER_CLIENT
+    if not WORKER_CLIENT:
+        return
+
+    # prepare for unexpected errors
+    try:
+        if WORKER_CLIENT.status() == 1:
+            WORKER_CLIENT.quit_server()
+    except Exception as e:
+        logger.error(e)
+
+    WORKER_CLIENT.close()
+    WORKER_CLIENT = None
diff --git a/style_bert_vits2/nlp/japanese/pyopenjtalk_worker/__main__.py b/style_bert_vits2/nlp/japanese/pyopenjtalk_worker/__main__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2452a16438108c2e2b463827dbcb6b2dd542cf3e
--- /dev/null
+++ b/style_bert_vits2/nlp/japanese/pyopenjtalk_worker/__main__.py
@@ -0,0 +1,16 @@
+import argparse
+
+from style_bert_vits2.nlp.japanese.pyopenjtalk_worker.worker_common import WORKER_PORT
+from style_bert_vits2.nlp.japanese.pyopenjtalk_worker.worker_server import WorkerServer
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--port", type=int, default=WORKER_PORT)
+    args = parser.parse_args()
+    server = WorkerServer()
+    server.start_server(port=args.port)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/style_bert_vits2/nlp/japanese/pyopenjtalk_worker/worker_client.py b/style_bert_vits2/nlp/japanese/pyopenjtalk_worker/worker_client.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4c5606ea83cbadd3626586fa287273804abac14
--- /dev/null
+++ b/style_bert_vits2/nlp/japanese/pyopenjtalk_worker/worker_client.py
@@ -0,0 +1,60 @@
+import socket
+from typing import Any, cast
+
+from style_bert_vits2.logging import logger
+from style_bert_vits2.nlp.japanese.pyopenjtalk_worker.worker_common import (
+    RequestType,
+    receive_data,
+    send_data,
+)
+
+
+class WorkerClient:
+    """pyopenjtalk worker client"""
+
+    def __init__(self, port: int) -> None:
+        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+        # timeout: 60 seconds
+        sock.settimeout(60)
+        sock.connect((socket.gethostname(), port))
+        self.sock = sock
+
+    def __enter__(self) -> "WorkerClient":
+        return self
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        self.close()
+
+    def close(self) -> None:
+        self.sock.close()
+
+    def dispatch_pyopenjtalk(self, func: str, *args: Any, **kwargs: Any) -> Any:
+        data = {
+            "request-type": RequestType.PYOPENJTALK,
+            "func": func,
+            "args": args,
+            "kwargs": kwargs,
+        }
+        logger.trace(f"client sends request: {data}")
+        send_data(self.sock, data)
+        logger.trace("client sent request successfully")
+        response = receive_data(self.sock)
+        logger.trace(f"client received response: {response}")
+        return response.get("return")
+
+    def status(self) -> int:
+        data = {"request-type": RequestType.STATUS}
+        logger.trace(f"client sends request: {data}")
+        send_data(self.sock, data)
+        logger.trace("client sent request successfully")
+        response = receive_data(self.sock)
+        logger.trace(f"client received response: {response}")
+        return cast(int, response.get("client-count"))
+
+    def quit_server(self) -> None:
+        data = {"request-type": RequestType.QUIT_SERVER}
+        logger.trace(f"client sends request: {data}")
+        send_data(self.sock, data)
+        logger.trace("client sent request successfully")
+        response = receive_data(self.sock)
+        logger.trace(f"client received response: {response}")
diff --git a/style_bert_vits2/nlp/japanese/pyopenjtalk_worker/worker_common.py b/style_bert_vits2/nlp/japanese/pyopenjtalk_worker/worker_common.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d79a5aaaae9e8455e3582f21818d5e6eb0e4735
--- /dev/null
+++ b/style_bert_vits2/nlp/japanese/pyopenjtalk_worker/worker_common.py
@@ -0,0 +1,45 @@
+import json
+import socket
+from enum import IntEnum, auto
+from typing import Any, Final
+
+
+WORKER_PORT: Final[int] = 7861
+HEADER_SIZE: Final[int] = 4
+
+
+class RequestType(IntEnum):
+    STATUS = auto()
+    QUIT_SERVER = auto()
+    PYOPENJTALK = auto()
+
+
+class ConnectionClosedException(Exception):
+    pass
+
+
+# socket communication
+
+
+def send_data(sock: socket.socket, data: dict[str, Any]):
+    json_data = json.dumps(data).encode()
+    header = len(json_data).to_bytes(HEADER_SIZE, byteorder="big")
+    sock.sendall(header + json_data)
+
+
+def __receive_until(sock: socket.socket, size: int):
+    data = b""
+    while len(data) < size:
+        part = sock.recv(size - len(data))
+        if part == b"":
+            raise ConnectionClosedException("接続が閉じられました")
+        data += part
+
+    return data
+
+
+def receive_data(sock: socket.socket) -> dict[str, Any]:
+    header = __receive_until(sock, HEADER_SIZE)
+    data_length = int.from_bytes(header, byteorder="big")
+    body = __receive_until(sock, data_length)
+    return json.loads(body.decode())
diff --git a/style_bert_vits2/nlp/japanese/pyopenjtalk_worker/worker_server.py b/style_bert_vits2/nlp/japanese/pyopenjtalk_worker/worker_server.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed0d4e7a71cb2d30c5c29cb7c57d6a1f7f3ab724
--- /dev/null
+++ b/style_bert_vits2/nlp/japanese/pyopenjtalk_worker/worker_server.py
@@ -0,0 +1,123 @@
+import select
+import socket
+import time
+from typing import Any, cast
+
+import pyopenjtalk
+
+from style_bert_vits2.logging import logger
+from style_bert_vits2.nlp.japanese.pyopenjtalk_worker.worker_common import (
+    ConnectionClosedException,
+    RequestType,
+    receive_data,
+    send_data,
+)
+
+
+# To make it as fast as possible
+# Probably faster than calling getattr every time
+PYOPENJTALK_FUNC_DICT = {
+    "run_frontend": pyopenjtalk.run_frontend,
+    "make_label": pyopenjtalk.make_label,
+    "mecab_dict_index": pyopenjtalk.mecab_dict_index,
+    "update_global_jtalk_with_user_dict": pyopenjtalk.update_global_jtalk_with_user_dict,
+    "unset_user_dict": pyopenjtalk.unset_user_dict,
+}
+
+
+class WorkerServer:
+    """pyopenjtalk worker server"""
+
+    def __init__(self) -> None:
+        self.client_count: int = 0
+        self.quit: bool = False
+
+    def handle_request(self, request: dict[str, Any]) -> dict[str, Any]:
+        request_type = None
+        try:
+            request_type = RequestType(cast(int, request.get("request-type")))
+        except Exception:
+            return {
+                "success": False,
+                "reason": "request-type is invalid",
+            }
+
+        response: dict[str, Any] = {}
+        if request_type:
+            if request_type == RequestType.STATUS:
+                response = {
+                    "success": True,
+                    "client-count": self.client_count,
+                }
+            elif request_type == RequestType.QUIT_SERVER:
+                self.quit = True
+                response = {"success": True}
+            elif request_type == RequestType.PYOPENJTALK:
+                func_name = request.get("func")
+                assert isinstance(func_name, str)
+                func = PYOPENJTALK_FUNC_DICT[func_name]
+                args = request.get("args")
+                kwargs = request.get("kwargs")
+                assert isinstance(args, list)
+                assert isinstance(kwargs, dict)
+                ret = func(*args, **kwargs)
+                response = {"success": True, "return": ret}
+            else:
+                # NOT REACHED
+                response = request
+
+        return response
+
+    def start_server(self, port: int, no_client_timeout: int = 30) -> None:
+        logger.info("start pyopenjtalk worker server")
+        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as server_socket:
+            server_socket.bind((socket.gethostname(), port))
+            server_socket.listen()
+            sockets = [server_socket]
+            no_client_since = time.time()
+            while True:
+                if self.client_count == 0:
+                    if no_client_since is None:
+                        no_client_since = time.time()
+                    elif (time.time() - no_client_since) > no_client_timeout:
+                        logger.info("quit because there is no client")
+                        return
+                else:
+                    no_client_since = None
+
+                ready_sockets, _, _ = select.select(sockets, [], [], 0.1)
+                for sock in ready_sockets:
+                    if sock is server_socket:
+                        logger.info("new client connected")
+                        client_socket, _ = server_socket.accept()
+                        sockets.append(client_socket)
+                        self.client_count += 1
+                    else:
+                        # client
+                        try:
+                            request = receive_data(sock)
+                        except Exception as e:
+                            sock.close()
+                            sockets.remove(sock)
+                            self.client_count -= 1
+                            # unexpected disconnections
+                            if not isinstance(e, ConnectionClosedException):
+                                logger.error(e)
+
+                            logger.info("close connection")
+                            continue
+
+                        logger.trace(f"server received request: {request}")
+
+                        response = self.handle_request(request)
+                        logger.trace(f"server sends response: {response}")
+                        try:
+                            send_data(sock, response)
+                            logger.trace("server sent response successfully")
+                        except Exception:
+                            logger.warning(
+                                "an exception occurred during sending responce"
+                            )
+                        if self.quit:
+                            logger.info("quit pyopenjtalk worker server")
+                            return
diff --git a/style_bert_vits2/nlp/japanese/user_dict/README.md b/style_bert_vits2/nlp/japanese/user_dict/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..3e7a0c03485ad887d1979ef8dfaaf5c11a4ccb81
--- /dev/null
+++ b/style_bert_vits2/nlp/japanese/user_dict/README.md
@@ -0,0 +1,27 @@
+
+## ユーザー辞書関連のコードについて
+
+このフォルダに含まれるユーザー辞書関連のコードは、[VOICEVOX ENGINE](https://github.com/VOICEVOX/voicevox_engine) プロジェクトのコードを改変したものを使用しています。  
+VOICEVOX プロジェクトのチームに深く感謝し、その貢献を尊重します。
+
+### 元のコード
+
+- [voicevox_engine/user_dict/](https://github.com/VOICEVOX/voicevox_engine/tree/f181411ec69812296989d9cc583826c22eec87ae/voicevox_engine/user_dict)
+- [voicevox_engine/model.py](https://github.com/VOICEVOX/voicevox_engine/blob/f181411ec69812296989d9cc583826c22eec87ae/voicevox_engine/model.py#L207)
+
+### 改変の詳細
+
+- ファイル名の書き換えおよびそれに伴う import 文の書き換え。
+- VOICEVOX 固有の部分をコメントアウト。
+- mutex を使用している部分をコメントアウト。
+- 参照している pyopenjtalk の違いによるメソッド名の書き換え。
+- UserDictWord の mora_count のデフォルト値を None に指定。
+- `model.py` のうち、必要な Pydantic モデルのみを抽出。
+
+### ライセンス
+
+元の VOICEVOX ENGINE のリポジトリのコードは、LGPL v3 と、ソースコードの公開が不要な別ライセンスのデュアルライセンスの下で使用されています。  
+当プロジェクトにおけるこのモジュールも LGPL ライセンスの下にあります。
+
+詳細については、プロジェクトのルートディレクトリにある [LGPL_LICENSE](/LGPL_LICENSE) ファイルをご参照ください。  
+また、元の VOICEVOX ENGINE プロジェクトのライセンスについては、[こちら](https://github.com/VOICEVOX/voicevox_engine/blob/master/LICENSE) をご覧ください。
diff --git a/style_bert_vits2/nlp/japanese/user_dict/__init__.py b/style_bert_vits2/nlp/japanese/user_dict/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..53ea63fdcc46b744efa6ca8ee57c6a0a46ec1ae4
--- /dev/null
+++ b/style_bert_vits2/nlp/japanese/user_dict/__init__.py
@@ -0,0 +1,472 @@
+"""
+このファイルは、VOICEVOX プロジェクトの VOICEVOX ENGINE からお借りしています。
+引用元: https://github.com/VOICEVOX/voicevox_engine/blob/f181411ec69812296989d9cc583826c22eec87ae/voicevox_engine/user_dict/user_dict.py
+ライセンス: LGPL-3.0
+詳しくは、このファイルと同じフォルダにある README.md を参照してください。
+"""
+
+import json
+import sys
+import traceback
+from pathlib import Path
+from typing import Dict, List, Optional
+from uuid import UUID, uuid4
+
+import numpy as np
+from fastapi import HTTPException
+
+from style_bert_vits2.constants import DEFAULT_USER_DICT_DIR
+from style_bert_vits2.nlp.japanese import pyopenjtalk_worker as pyopenjtalk
+from style_bert_vits2.nlp.japanese.user_dict.part_of_speech_data import (
+    MAX_PRIORITY,
+    MIN_PRIORITY,
+    part_of_speech_data,
+)
+from style_bert_vits2.nlp.japanese.user_dict.word_model import UserDictWord, WordTypes
+
+
+# root_dir = engine_root()
+# save_dir = get_save_dir()
+
+# if not save_dir.is_dir():
+#     save_dir.mkdir(parents=True)
+
+default_dict_path = (
+    DEFAULT_USER_DICT_DIR / "default.csv"
+)  # VOICEVOXデフォルト辞書ファイルのパス
+user_dict_path = DEFAULT_USER_DICT_DIR / "user_dict.json"  # ユーザー辞書ファイルのパス
+compiled_dict_path = (
+    DEFAULT_USER_DICT_DIR / "user.dic"
+)  # コンパイル済み辞書ファイルのパス
+
+
+# # 同時書き込みの制御
+# mutex_user_dict = threading.Lock()
+# mutex_openjtalk_dict = threading.Lock()
+
+
+# @mutex_wrapper(mutex_user_dict)
+def _write_to_json(user_dict: Dict[str, UserDictWord], user_dict_path: Path) -> None:
+    """
+    ユーザー辞書ファイルへのユーザー辞書データ書き込み
+    Parameters
+    ----------
+    user_dict : Dict[str, UserDictWord]
+        ユーザー辞書データ
+    user_dict_path : Path
+        ユーザー辞書ファイルのパス
+    """
+    converted_user_dict = {}
+    for word_uuid, word in user_dict.items():
+        word_dict = word.model_dump()
+        word_dict["cost"] = _priority2cost(
+            word_dict["context_id"], word_dict["priority"]
+        )
+        del word_dict["priority"]
+        converted_user_dict[word_uuid] = word_dict
+    # 予めjsonに変換できることを確かめる
+    user_dict_json = json.dumps(converted_user_dict, ensure_ascii=False)
+
+    # ユーザー辞書ファイルへの書き込み
+    user_dict_path.write_text(user_dict_json, encoding="utf-8")
+
+
+# @mutex_wrapper(mutex_openjtalk_dict)
+def update_dict(
+    default_dict_path: Path = default_dict_path,
+    user_dict_path: Path = user_dict_path,
+    compiled_dict_path: Path = compiled_dict_path,
+) -> None:
+    """
+    辞書の更新
+    Parameters
+    ----------
+    default_dict_path : Path
+        デフォルト辞書ファイルのパス
+    user_dict_path : Path
+        ユーザー辞書ファイルのパス
+    compiled_dict_path : Path
+        コンパイル済み辞書ファイルのパス
+    """
+
+    random_string = uuid4()
+    tmp_csv_path = compiled_dict_path.with_suffix(
+        f".dict_csv-{random_string}.tmp"
+    )  # csv形式辞書データの一時保存ファイル
+    tmp_compiled_path = compiled_dict_path.with_suffix(
+        f".dict_compiled-{random_string}.tmp"
+    )  # コンパイル済み辞書データの一時保存ファイル
+
+    try:
+        # 辞書.csvを作成
+        csv_text = ""
+
+        # デフォルト辞書データの追加
+        if not default_dict_path.is_file():
+            print("Warning: Cannot find default dictionary.", file=sys.stderr)
+            return
+        default_dict = default_dict_path.read_text(encoding="utf-8")
+        if default_dict == default_dict.rstrip():
+            default_dict += "\n"
+        csv_text += default_dict
+
+        # ユーザー辞書データの追加
+        user_dict = read_dict(user_dict_path=user_dict_path)
+        for word_uuid in user_dict:
+            word = user_dict[word_uuid]
+            csv_text += (
+                "{surface},{context_id},{context_id},{cost},{part_of_speech},"
+                + "{part_of_speech_detail_1},{part_of_speech_detail_2},"
+                + "{part_of_speech_detail_3},{inflectional_type},"
+                + "{inflectional_form},{stem},{yomi},{pronunciation},"
+                + "{accent_type}/{mora_count},{accent_associative_rule}\n"
+            ).format(
+                surface=word.surface,
+                context_id=word.context_id,
+                cost=_priority2cost(word.context_id, word.priority),
+                part_of_speech=word.part_of_speech,
+                part_of_speech_detail_1=word.part_of_speech_detail_1,
+                part_of_speech_detail_2=word.part_of_speech_detail_2,
+                part_of_speech_detail_3=word.part_of_speech_detail_3,
+                inflectional_type=word.inflectional_type,
+                inflectional_form=word.inflectional_form,
+                stem=word.stem,
+                yomi=word.yomi,
+                pronunciation=word.pronunciation,
+                accent_type=word.accent_type,
+                mora_count=word.mora_count,
+                accent_associative_rule=word.accent_associative_rule,
+            )
+        # 辞書データを辞書.csv へ一時保存
+        tmp_csv_path.write_text(csv_text, encoding="utf-8")
+
+        # 辞書.csvをOpenJTalk用にコンパイル
+        # pyopenjtalk.create_user_dict(str(tmp_csv_path), str(tmp_compiled_path))
+        pyopenjtalk.mecab_dict_index(str(tmp_csv_path), str(tmp_compiled_path))
+        if not tmp_compiled_path.is_file():
+            raise RuntimeError("辞書のコンパイル時にエラーが発生しました。")
+
+        # コンパイル済み辞書の置き換え・読み込み
+        pyopenjtalk.unset_user_dict()
+        tmp_compiled_path.replace(compiled_dict_path)
+        if compiled_dict_path.is_file():
+            # pyopenjtalk.set_user_dict(str(compiled_dict_path.resolve(strict=True)))
+            pyopenjtalk.update_global_jtalk_with_user_dict(str(compiled_dict_path))
+
+    except Exception as e:
+        print("Error: Failed to update dictionary.", file=sys.stderr)
+        traceback.print_exc(file=sys.stderr)
+        raise e
+
+    finally:
+        # 後処理
+        if tmp_csv_path.exists():
+            tmp_csv_path.unlink()
+        if tmp_compiled_path.exists():
+            tmp_compiled_path.unlink()
+
+
+# @mutex_wrapper(mutex_user_dict)
+def read_dict(user_dict_path: Path = user_dict_path) -> Dict[str, UserDictWord]:
+    """
+    ユーザー辞書の読み出し
+    Parameters
+    ----------
+    user_dict_path : Path
+        ユーザー辞書ファイルのパス
+    Returns
+    -------
+    result : Dict[str, UserDictWord]
+        ユーザー辞書
+    """
+    # 指定ユーザー辞書が存在しない場合、空辞書を返す
+    if not user_dict_path.is_file():
+        return {}
+
+    with user_dict_path.open(encoding="utf-8") as f:
+        result: Dict[str, UserDictWord] = {}
+        for word_uuid, word in json.load(f).items():
+            # cost2priorityで変換を行う際にcontext_idが必要となるが、
+            # 0.12以前の辞書は、context_idがハードコーディングされていたためにユーザー辞書内に保管されていない
+            # ハードコーディングされていたcontext_idは固有名詞を意味するものなので、固有名詞のcontext_idを補完する
+            if word.get("context_id") is None:
+                word["context_id"] = part_of_speech_data[
+                    WordTypes.PROPER_NOUN
+                ].context_id
+            word["priority"] = _cost2priority(word["context_id"], word["cost"])
+            del word["cost"]
+            result[str(UUID(word_uuid))] = UserDictWord(**word)
+
+    return result
+
+
+def _create_word(
+    surface: str,
+    pronunciation: str,
+    accent_type: int,
+    word_type: Optional[WordTypes] = None,
+    priority: Optional[int] = None,
+) -> UserDictWord:
+    """
+    単語オブジェクトの生成
+    Parameters
+    ----------
+    surface : str
+        単語情報
+    pronunciation : str
+        単語情報
+    accent_type : int
+        単語情報
+    word_type : Optional[WordTypes]
+        品詞
+    priority : Optional[int]
+        優先度
+    Returns
+    -------
+    : UserDictWord
+        単語オブジェクト
+    """
+    if word_type is None:
+        word_type = WordTypes.PROPER_NOUN
+    if word_type not in part_of_speech_data.keys():
+        raise HTTPException(status_code=422, detail="不明な品詞です")
+    if priority is None:
+        priority = 5
+    if not MIN_PRIORITY <= priority <= MAX_PRIORITY:
+        raise HTTPException(status_code=422, detail="優先度の値が無効です")
+    pos_detail = part_of_speech_data[word_type]
+    return UserDictWord(
+        surface=surface,
+        context_id=pos_detail.context_id,
+        priority=priority,
+        part_of_speech=pos_detail.part_of_speech,
+        part_of_speech_detail_1=pos_detail.part_of_speech_detail_1,
+        part_of_speech_detail_2=pos_detail.part_of_speech_detail_2,
+        part_of_speech_detail_3=pos_detail.part_of_speech_detail_3,
+        inflectional_type="*",
+        inflectional_form="*",
+        stem="*",
+        yomi=pronunciation,
+        pronunciation=pronunciation,
+        accent_type=accent_type,
+        accent_associative_rule="*",
+    )
+
+
+def apply_word(
+    surface: str,
+    pronunciation: str,
+    accent_type: int,
+    word_type: Optional[WordTypes] = None,
+    priority: Optional[int] = None,
+    user_dict_path: Path = user_dict_path,
+    compiled_dict_path: Path = compiled_dict_path,
+) -> str:
+    """
+    新規単語の追加
+    Parameters
+    ----------
+    surface : str
+        単語情報
+    pronunciation : str
+        単語情報
+    accent_type : int
+        単語情報
+    word_type : Optional[WordTypes]
+        品詞
+    priority : Optional[int]
+        優先度
+    user_dict_path : Path
+        ユーザー辞書ファイルのパス
+    compiled_dict_path : Path
+        コンパイル済み辞書ファイルのパス
+    Returns
+    -------
+    word_uuid : UserDictWord
+        追加された単語に発行されたUUID
+    """
+    # 新規単語の追加による辞書データの更新
+    word = _create_word(
+        surface=surface,
+        pronunciation=pronunciation,
+        accent_type=accent_type,
+        word_type=word_type,
+        priority=priority,
+    )
+    user_dict = read_dict(user_dict_path=user_dict_path)
+    word_uuid = str(uuid4())
+    user_dict[word_uuid] = word
+
+    # 更新された辞書データの保存と適用
+    _write_to_json(user_dict, user_dict_path)
+    update_dict(user_dict_path=user_dict_path, compiled_dict_path=compiled_dict_path)
+
+    return word_uuid
+
+
+def rewrite_word(
+    word_uuid: str,
+    surface: str,
+    pronunciation: str,
+    accent_type: int,
+    word_type: Optional[WordTypes] = None,
+    priority: Optional[int] = None,
+    user_dict_path: Path = user_dict_path,
+    compiled_dict_path: Path = compiled_dict_path,
+) -> None:
+    """
+    既存単語の上書き更新
+    Parameters
+    ----------
+    word_uuid : str
+        単語UUID
+    surface : str
+        単語情報
+    pronunciation : str
+        単語情報
+    accent_type : int
+        単語情報
+    word_type : Optional[WordTypes]
+        品詞
+    priority : Optional[int]
+        優先度
+    user_dict_path : Path
+        ユーザー辞書ファイルのパス
+    compiled_dict_path : Path
+        コンパイル済み辞書ファイルのパス
+    """
+    word = _create_word(
+        surface=surface,
+        pronunciation=pronunciation,
+        accent_type=accent_type,
+        word_type=word_type,
+        priority=priority,
+    )
+
+    # 既存単語の上書きによる辞書データの更新
+    user_dict = read_dict(user_dict_path=user_dict_path)
+    if word_uuid not in user_dict:
+        raise HTTPException(
+            status_code=422, detail="UUIDに該当するワードが見つかりませんでした"
+        )
+    user_dict[word_uuid] = word
+
+    # 更新された辞書データの保存と適用
+    _write_to_json(user_dict, user_dict_path)
+    update_dict(user_dict_path=user_dict_path, compiled_dict_path=compiled_dict_path)
+
+
+def delete_word(
+    word_uuid: str,
+    user_dict_path: Path = user_dict_path,
+    compiled_dict_path: Path = compiled_dict_path,
+) -> None:
+    """
+    単語の削除
+    Parameters
+    ----------
+    word_uuid : str
+        単語UUID
+    user_dict_path : Path
+        ユーザー辞書ファイルのパス
+    compiled_dict_path : Path
+        コンパイル済み辞書ファイルのパス
+    """
+    # 既存単語の削除による辞書データの更新
+    user_dict = read_dict(user_dict_path=user_dict_path)
+    if word_uuid not in user_dict:
+        raise HTTPException(
+            status_code=422, detail="IDに該当するワードが見つかりませんでした"
+        )
+    del user_dict[word_uuid]
+
+    # 更新された辞書データの保存と適用
+    _write_to_json(user_dict, user_dict_path)
+    update_dict(user_dict_path=user_dict_path, compiled_dict_path=compiled_dict_path)
+
+
+def import_user_dict(
+    dict_data: Dict[str, UserDictWord],
+    override: bool = False,
+    user_dict_path: Path = user_dict_path,
+    default_dict_path: Path = default_dict_path,
+    compiled_dict_path: Path = compiled_dict_path,
+) -> None:
+    """
+    ユーザー辞書のインポート
+    Parameters
+    ----------
+    dict_data : Dict[str, UserDictWord]
+        インポートするユーザー辞書のデータ
+    override : bool
+        重複したエントリがあった場合、上書きするかどうか
+    user_dict_path : Path
+        ユーザー辞書ファイルのパス
+    default_dict_path : Path
+        デフォルト辞書ファイルのパス
+    compiled_dict_path : Path
+        コンパイル済み辞書ファイルのパス
+    """
+    # インポートする辞書データのバリデーション
+    for word_uuid, word in dict_data.items():
+        UUID(word_uuid)
+        assert isinstance(word, UserDictWord)
+        for pos_detail in part_of_speech_data.values():
+            if word.context_id == pos_detail.context_id:
+                assert word.part_of_speech == pos_detail.part_of_speech
+                assert (
+                    word.part_of_speech_detail_1 == pos_detail.part_of_speech_detail_1
+                )
+                assert (
+                    word.part_of_speech_detail_2 == pos_detail.part_of_speech_detail_2
+                )
+                assert (
+                    word.part_of_speech_detail_3 == pos_detail.part_of_speech_detail_3
+                )
+                assert (
+                    word.accent_associative_rule in pos_detail.accent_associative_rules
+                )
+                break
+        else:
+            raise ValueError("対応していない品詞です")
+
+    # 既存辞書の読み出し
+    old_dict = read_dict(user_dict_path=user_dict_path)
+
+    # 辞書データの更新
+    # 重複エントリの上書き
+    if override:
+        new_dict = {**old_dict, **dict_data}
+    # 重複エントリの保持
+    else:
+        new_dict = {**dict_data, **old_dict}
+
+    # 更新された辞書データの保存と適用
+    _write_to_json(user_dict=new_dict, user_dict_path=user_dict_path)
+    update_dict(
+        default_dict_path=default_dict_path,
+        user_dict_path=user_dict_path,
+        compiled_dict_path=compiled_dict_path,
+    )
+
+
+def _search_cost_candidates(context_id: int) -> List[int]:
+    for value in part_of_speech_data.values():
+        if value.context_id == context_id:
+            return value.cost_candidates
+    raise HTTPException(status_code=422, detail="品詞IDが不正です")
+
+
+def _cost2priority(context_id: int, cost: int) -> int:
+    assert -32768 <= cost <= 32767
+    cost_candidates = _search_cost_candidates(context_id)
+    # cost_candidatesの中にある値で最も近い値を元にpriorityを返す
+    # 参考: https://qiita.com/Krypf/items/2eada91c37161d17621d
+    # この関数とpriority2cost関数によって、辞書ファイルのcostを操作しても最も近いpriorityのcostに上書きされる
+    return MAX_PRIORITY - np.argmin(np.abs(np.array(cost_candidates) - cost)).item()
+
+
+def _priority2cost(context_id: int, priority: int) -> int:
+    assert MIN_PRIORITY <= priority <= MAX_PRIORITY
+    cost_candidates = _search_cost_candidates(context_id)
+    return cost_candidates[MAX_PRIORITY - priority]
diff --git a/style_bert_vits2/nlp/japanese/user_dict/part_of_speech_data.py b/style_bert_vits2/nlp/japanese/user_dict/part_of_speech_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..a48f0c5896a2086653cec6513feb0c075dac5d30
--- /dev/null
+++ b/style_bert_vits2/nlp/japanese/user_dict/part_of_speech_data.py
@@ -0,0 +1,152 @@
+"""
+このファイルは、VOICEVOX プロジェクトの VOICEVOX ENGINE からお借りしています。
+引用元: https://github.com/VOICEVOX/voicevox_engine/blob/f181411ec69812296989d9cc583826c22eec87ae/voicevox_engine/user_dict/part_of_speech_data.py
+ライセンス: LGPL-3.0
+詳しくは、このファイルと同じフォルダにある README.md を参照してください。
+"""
+
+from typing import Dict
+
+from style_bert_vits2.nlp.japanese.user_dict.word_model import (
+    USER_DICT_MAX_PRIORITY,
+    USER_DICT_MIN_PRIORITY,
+    PartOfSpeechDetail,
+    WordTypes,
+)
+
+
+MIN_PRIORITY = USER_DICT_MIN_PRIORITY
+MAX_PRIORITY = USER_DICT_MAX_PRIORITY
+
+part_of_speech_data: Dict[WordTypes, PartOfSpeechDetail] = {
+    WordTypes.PROPER_NOUN: PartOfSpeechDetail(
+        part_of_speech="名詞",
+        part_of_speech_detail_1="固有名詞",
+        part_of_speech_detail_2="一般",
+        part_of_speech_detail_3="*",
+        context_id=1348,
+        cost_candidates=[
+            -988,
+            3488,
+            4768,
+            6048,
+            7328,
+            8609,
+            8734,
+            8859,
+            8984,
+            9110,
+            14176,
+        ],
+        accent_associative_rules=[
+            "*",
+            "C1",
+            "C2",
+            "C3",
+            "C4",
+            "C5",
+        ],
+    ),
+    WordTypes.COMMON_NOUN: PartOfSpeechDetail(
+        part_of_speech="名詞",
+        part_of_speech_detail_1="一般",
+        part_of_speech_detail_2="*",
+        part_of_speech_detail_3="*",
+        context_id=1345,
+        cost_candidates=[
+            -4445,
+            49,
+            1473,
+            2897,
+            4321,
+            5746,
+            6554,
+            7362,
+            8170,
+            8979,
+            15001,
+        ],
+        accent_associative_rules=[
+            "*",
+            "C1",
+            "C2",
+            "C3",
+            "C4",
+            "C5",
+        ],
+    ),
+    WordTypes.VERB: PartOfSpeechDetail(
+        part_of_speech="動詞",
+        part_of_speech_detail_1="自立",
+        part_of_speech_detail_2="*",
+        part_of_speech_detail_3="*",
+        context_id=642,
+        cost_candidates=[
+            3100,
+            6160,
+            6360,
+            6561,
+            6761,
+            6962,
+            7414,
+            7866,
+            8318,
+            8771,
+            13433,
+        ],
+        accent_associative_rules=[
+            "*",
+        ],
+    ),
+    WordTypes.ADJECTIVE: PartOfSpeechDetail(
+        part_of_speech="形容詞",
+        part_of_speech_detail_1="自立",
+        part_of_speech_detail_2="*",
+        part_of_speech_detail_3="*",
+        context_id=20,
+        cost_candidates=[
+            1527,
+            3266,
+            3561,
+            3857,
+            4153,
+            4449,
+            5149,
+            5849,
+            6549,
+            7250,
+            10001,
+        ],
+        accent_associative_rules=[
+            "*",
+        ],
+    ),
+    WordTypes.SUFFIX: PartOfSpeechDetail(
+        part_of_speech="名詞",
+        part_of_speech_detail_1="接尾",
+        part_of_speech_detail_2="一般",
+        part_of_speech_detail_3="*",
+        context_id=1358,
+        cost_candidates=[
+            4399,
+            5373,
+            6041,
+            6710,
+            7378,
+            8047,
+            9440,
+            10834,
+            12228,
+            13622,
+            15847,
+        ],
+        accent_associative_rules=[
+            "*",
+            "C1",
+            "C2",
+            "C3",
+            "C4",
+            "C5",
+        ],
+    ),
+}
diff --git a/style_bert_vits2/nlp/japanese/user_dict/word_model.py b/style_bert_vits2/nlp/japanese/user_dict/word_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..43420da7103811f000d79d40df969b59d86eca8e
--- /dev/null
+++ b/style_bert_vits2/nlp/japanese/user_dict/word_model.py
@@ -0,0 +1,132 @@
+"""
+このファイルは、VOICEVOX プロジェクトの VOICEVOX ENGINE からお借りしています。
+引用元: https://github.com/VOICEVOX/voicevox_engine/blob/f181411ec69812296989d9cc583826c22eec87ae/voicevox_engine/model.py#L207
+ライセンス: LGPL-3.0
+詳しくは、このファイルと同じフォルダにある README.md を参照してください。
+"""
+
+from enum import Enum
+from re import findall, fullmatch
+from typing import List, Optional
+
+from pydantic import BaseModel, Field, validator
+
+
+USER_DICT_MIN_PRIORITY = 0
+USER_DICT_MAX_PRIORITY = 10
+
+
+class UserDictWord(BaseModel):
+    """
+    辞書のコンパイルに使われる情報
+    """
+
+    surface: str = Field(title="表層形")
+    priority: int = Field(
+        title="優先度", ge=USER_DICT_MIN_PRIORITY, le=USER_DICT_MAX_PRIORITY
+    )
+    context_id: int = Field(title="文脈ID", default=1348)
+    part_of_speech: str = Field(title="品詞")
+    part_of_speech_detail_1: str = Field(title="品詞細分類1")
+    part_of_speech_detail_2: str = Field(title="品詞細分類2")
+    part_of_speech_detail_3: str = Field(title="品詞細分類3")
+    inflectional_type: str = Field(title="活用型")
+    inflectional_form: str = Field(title="活用形")
+    stem: str = Field(title="原形")
+    yomi: str = Field(title="読み")
+    pronunciation: str = Field(title="発音")
+    accent_type: int = Field(title="アクセント型")
+    mora_count: Optional[int] = Field(title="モーラ数", default=None)
+    accent_associative_rule: str = Field(title="アクセント結合規則")
+
+    class Config:
+        validate_assignment = True
+
+    @validator("surface")
+    def convert_to_zenkaku(cls, surface):
+        return surface.translate(
+            str.maketrans(
+                "".join(chr(0x21 + i) for i in range(94)),
+                "".join(chr(0xFF01 + i) for i in range(94)),
+            )
+        )
+
+    @validator("pronunciation", pre=True)
+    def check_is_katakana(cls, pronunciation):
+        if not fullmatch(r"[ァ-ヴー]+", pronunciation):
+            raise ValueError("発音は有効なカタカナでなくてはいけません。")
+        sutegana = ["ァ", "ィ", "ゥ", "ェ", "ォ", "ャ", "ュ", "ョ", "ヮ", "ッ"]
+        for i in range(len(pronunciation)):
+            if pronunciation[i] in sutegana:
+                # 「キャット」のように、捨て仮名が連続する可能性が考えられるので、
+                # 「ッ」に関しては「ッ」そのものが連続している場合と、「ッ」の後にほかの捨て仮名が連続する場合のみ無効とする
+                if i < len(pronunciation) - 1 and (
+                    pronunciation[i + 1] in sutegana[:-1]
+                    or (
+                        pronunciation[i] == sutegana[-1]
+                        and pronunciation[i + 1] == sutegana[-1]
+                    )
+                ):
+                    raise ValueError("無効な発音です。(捨て仮名の連続)")
+            if pronunciation[i] == "ヮ":
+                if i != 0 and pronunciation[i - 1] not in ["ク", "グ"]:
+                    raise ValueError(
+                        "無効な発音です。(「くゎ」「ぐゎ」以外の「ゎ」の使用)"
+                    )
+        return pronunciation
+
+    @validator("mora_count", pre=True, always=True)
+    def check_mora_count_and_accent_type(cls, mora_count, values):
+        if "pronunciation" not in values or "accent_type" not in values:
+            # 適切な場所でエラーを出すようにする
+            return mora_count
+
+        if mora_count is None:
+            rule_others = (
+                "[イ][ェ]|[ヴ][ャュョ]|[トド][ゥ]|[テデ][ィャュョ]|[デ][ェ]|[クグ][ヮ]"
+            )
+            rule_line_i = "[キシチニヒミリギジビピ][ェャュョ]"
+            rule_line_u = "[ツフヴ][ァ]|[ウスツフヴズ][ィ]|[ウツフヴ][ェォ]"
+            rule_one_mora = "[ァ-ヴー]"
+            mora_count = len(
+                findall(
+                    f"(?:{rule_others}|{rule_line_i}|{rule_line_u}|{rule_one_mora})",
+                    values["pronunciation"],
+                )
+            )
+
+        if not 0 <= values["accent_type"] <= mora_count:
+            raise ValueError(
+                "誤ったアクセント型です({})。 expect: 0 <= accent_type <= {}".format(
+                    values["accent_type"], mora_count
+                )
+            )
+        return mora_count
+
+
+class PartOfSpeechDetail(BaseModel):
+    """
+    品詞ごとの情報
+    """
+
+    part_of_speech: str = Field(title="品詞")
+    part_of_speech_detail_1: str = Field(title="品詞細分類1")
+    part_of_speech_detail_2: str = Field(title="品詞細分類2")
+    part_of_speech_detail_3: str = Field(title="品詞細分類3")
+    # context_idは辞書の左・右文脈IDのこと
+    # https://github.com/VOICEVOX/open_jtalk/blob/427cfd761b78efb6094bea3c5bb8c968f0d711ab/src/mecab-naist-jdic/_left-id.def
+    context_id: int = Field(title="文脈ID")
+    cost_candidates: List[int] = Field(title="コストのパーセンタイル")
+    accent_associative_rules: List[str] = Field(title="アクセント結合規則の一覧")
+
+
+class WordTypes(str, Enum):
+    """
+    fastapiでword_type引数を検証する時に使用するクラス
+    """
+
+    PROPER_NOUN = "PROPER_NOUN"
+    COMMON_NOUN = "COMMON_NOUN"
+    VERB = "VERB"
+    ADJECTIVE = "ADJECTIVE"
+    SUFFIX = "SUFFIX"
diff --git a/style_bert_vits2/nlp/symbols.py b/style_bert_vits2/nlp/symbols.py
new file mode 100644
index 0000000000000000000000000000000000000000..e3a650f717518fd2ee6b2d83630e4fe3b778d367
--- /dev/null
+++ b/style_bert_vits2/nlp/symbols.py
@@ -0,0 +1,194 @@
+# Punctuations
+PUNCTUATIONS = ["!", "?", "…", ",", ".", "'", "-"]
+
+# Punctuations and special tokens
+PUNCTUATION_SYMBOLS = PUNCTUATIONS + ["SP", "UNK"]
+
+# Padding
+PAD = "_"
+
+# Chinese symbols
+ZH_SYMBOLS = [
+    "E",
+    "En",
+    "a",
+    "ai",
+    "an",
+    "ang",
+    "ao",
+    "b",
+    "c",
+    "ch",
+    "d",
+    "e",
+    "ei",
+    "en",
+    "eng",
+    "er",
+    "f",
+    "g",
+    "h",
+    "i",
+    "i0",
+    "ia",
+    "ian",
+    "iang",
+    "iao",
+    "ie",
+    "in",
+    "ing",
+    "iong",
+    "ir",
+    "iu",
+    "j",
+    "k",
+    "l",
+    "m",
+    "n",
+    "o",
+    "ong",
+    "ou",
+    "p",
+    "q",
+    "r",
+    "s",
+    "sh",
+    "t",
+    "u",
+    "ua",
+    "uai",
+    "uan",
+    "uang",
+    "ui",
+    "un",
+    "uo",
+    "v",
+    "van",
+    "ve",
+    "vn",
+    "w",
+    "x",
+    "y",
+    "z",
+    "zh",
+    "AA",
+    "EE",
+    "OO",
+]
+NUM_ZH_TONES = 6
+
+# Japanese
+JP_SYMBOLS = [
+    "N",
+    "a",
+    "a:",
+    "b",
+    "by",
+    "ch",
+    "d",
+    "dy",
+    "e",
+    "e:",
+    "f",
+    "g",
+    "gy",
+    "h",
+    "hy",
+    "i",
+    "i:",
+    "j",
+    "k",
+    "ky",
+    "m",
+    "my",
+    "n",
+    "ny",
+    "o",
+    "o:",
+    "p",
+    "py",
+    "q",
+    "r",
+    "ry",
+    "s",
+    "sh",
+    "t",
+    "ts",
+    "ty",
+    "u",
+    "u:",
+    "w",
+    "y",
+    "z",
+    "zy",
+]
+NUM_JP_TONES = 2
+
+# English
+EN_SYMBOLS = [
+    "aa",
+    "ae",
+    "ah",
+    "ao",
+    "aw",
+    "ay",
+    "b",
+    "ch",
+    "d",
+    "dh",
+    "eh",
+    "er",
+    "ey",
+    "f",
+    "g",
+    "hh",
+    "ih",
+    "iy",
+    "jh",
+    "k",
+    "l",
+    "m",
+    "n",
+    "ng",
+    "ow",
+    "oy",
+    "p",
+    "r",
+    "s",
+    "sh",
+    "t",
+    "th",
+    "uh",
+    "uw",
+    "V",
+    "w",
+    "y",
+    "z",
+    "zh",
+]
+NUM_EN_TONES = 4
+
+# Combine all symbols
+NORMAL_SYMBOLS = sorted(set(ZH_SYMBOLS + JP_SYMBOLS + EN_SYMBOLS))
+SYMBOLS = [PAD] + NORMAL_SYMBOLS + PUNCTUATION_SYMBOLS
+SIL_PHONEMES_IDS = [SYMBOLS.index(i) for i in PUNCTUATION_SYMBOLS]
+
+# Combine all tones
+NUM_TONES = NUM_ZH_TONES + NUM_JP_TONES + NUM_EN_TONES
+
+# Language maps
+LANGUAGE_ID_MAP = {"ZH": 0, "JP": 1, "EN": 2}
+NUM_LANGUAGES = len(LANGUAGE_ID_MAP.keys())
+
+# Language tone start map
+LANGUAGE_TONE_START_MAP = {
+    "ZH": 0,
+    "JP": NUM_ZH_TONES,
+    "EN": NUM_ZH_TONES + NUM_JP_TONES,
+}
+
+
+if __name__ == "__main__":
+    a = set(ZH_SYMBOLS)
+    b = set(EN_SYMBOLS)
+    print(sorted(a & b))
diff --git a/style_bert_vits2/tts_model.py b/style_bert_vits2/tts_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..6df8394aef662cc545142b07c2ee94dc24dd440b
--- /dev/null
+++ b/style_bert_vits2/tts_model.py
@@ -0,0 +1,503 @@
+from pathlib import Path
+from typing import Any, Optional, Union
+
+import numpy as np
+import torch
+from numpy.typing import NDArray
+from pydantic import BaseModel
+
+from style_bert_vits2.constants import (
+    DEFAULT_ASSIST_TEXT_WEIGHT,
+    DEFAULT_LENGTH,
+    DEFAULT_LINE_SPLIT,
+    DEFAULT_NOISE,
+    DEFAULT_NOISEW,
+    DEFAULT_SDP_RATIO,
+    DEFAULT_SPLIT_INTERVAL,
+    DEFAULT_STYLE,
+    DEFAULT_STYLE_WEIGHT,
+    Languages,
+)
+from style_bert_vits2.logging import logger
+from style_bert_vits2.models.hyper_parameters import HyperParameters
+from style_bert_vits2.models.infer import get_net_g, infer
+from style_bert_vits2.models.models import SynthesizerTrn
+from style_bert_vits2.models.models_jp_extra import (
+    SynthesizerTrn as SynthesizerTrnJPExtra,
+)
+from style_bert_vits2.voice import adjust_voice
+
+
+# Gradio の import は重いため、ここでは型チェック時のみ import する
+# ライブラリとしての利用を考慮し、TTSModelHolder の _for_gradio() 系メソッド以外では Gradio に依存しないようにする
+# _for_gradio() 系メソッドの戻り値の型アノテーションを文字列としているのは、Gradio なしで実行できるようにするため
+# if TYPE_CHECKING:
+#     import gradio as gr
+
+
+class TTSModel:
+    """
+    Style-Bert-Vits2 の音声合成モデルを操作するクラス。
+    モデル/ハイパーパラメータ/スタイルベクトルのパスとデバイスを指定して初期化し、model.infer() メソッドを呼び出すと音声合成を行える。
+    """
+
+    def __init__(
+        self,
+        model_path: Path,
+        config_path: Union[Path, HyperParameters],
+        style_vec_path: Union[Path, NDArray[Any]],
+        device: str,
+    ) -> None:
+        """
+        Style-Bert-Vits2 の音声合成モデルを初期化する。
+        この時点ではモデルはロードされていない (明示的にロードしたい場合は model.load() を呼び出す)。
+
+        Args:
+            model_path (Path): モデル (.safetensors) のパス
+            config_path (Union[Path, HyperParameters]): ハイパーパラメータ (config.json) のパス (直接 HyperParameters を指定することも可能)
+            style_vec_path (Union[Path, NDArray[Any]]): スタイルベクトル (style_vectors.npy) のパス (直接 NDArray を指定することも可能)
+            device (str): 音声合成時に利用するデバイス (cpu, cuda, mps など)
+        """
+
+        self.model_path: Path = model_path
+        self.device: str = device
+
+        # ハイパーパラメータの Pydantic モデルが直接指定された
+        if isinstance(config_path, HyperParameters):
+            self.config_path: Path = Path("")  # 互換性のため空の Path を設定
+            self.hyper_parameters: HyperParameters = config_path
+        # ハイパーパラメータのパスが指定された
+        else:
+            self.config_path: Path = config_path
+            self.hyper_parameters: HyperParameters = HyperParameters.load_from_json(
+                self.config_path
+            )
+
+        # スタイルベクトルの NDArray が直接指定された
+        if isinstance(style_vec_path, np.ndarray):
+            self.style_vec_path: Path = Path("")  # 互換性のため空の Path を設定
+            self.__style_vectors: NDArray[Any] = style_vec_path
+        # スタイルベクトルのパスが指定された
+        else:
+            self.style_vec_path: Path = style_vec_path
+            self.__style_vectors: NDArray[Any] = np.load(self.style_vec_path)
+
+        self.spk2id: dict[str, int] = self.hyper_parameters.data.spk2id
+        self.id2spk: dict[int, str] = {v: k for k, v in self.spk2id.items()}
+
+        num_styles: int = self.hyper_parameters.data.num_styles
+        if hasattr(self.hyper_parameters.data, "style2id"):
+            self.style2id: dict[str, int] = self.hyper_parameters.data.style2id
+        else:
+            self.style2id: dict[str, int] = {str(i): i for i in range(num_styles)}
+        if len(self.style2id) != num_styles:
+            raise ValueError(
+                f"Number of styles ({num_styles}) does not match the number of style2id ({len(self.style2id)})"
+            )
+
+        if self.__style_vectors.shape[0] != num_styles:
+            raise ValueError(
+                f"The number of styles ({num_styles}) does not match the number of style vectors ({self.__style_vectors.shape[0]})"
+            )
+        self.__style_vector_inference: Optional[Any] = None
+
+        self.__net_g: Union[SynthesizerTrn, SynthesizerTrnJPExtra, None] = None
+
+    def load(self) -> None:
+        """
+        音声合成モデルをデバイスにロードする。
+        """
+        self.__net_g = get_net_g(
+            model_path=str(self.model_path),
+            version=self.hyper_parameters.version,
+            device=self.device,
+            hps=self.hyper_parameters,
+        )
+
+    def __get_style_vector(self, style_id: int, weight: float = 1.0) -> NDArray[Any]:
+        """
+        スタイルベクトルを取得する。
+
+        Args:
+            style_id (int): スタイル ID (0 から始まるインデックス)
+            weight (float, optional): スタイルベクトルの重み. Defaults to 1.0.
+
+        Returns:
+            NDArray[Any]: スタイルベクトル
+        """
+        mean = self.__style_vectors[0]
+        style_vec = self.__style_vectors[style_id]
+        style_vec = mean + (style_vec - mean) * weight
+        return style_vec
+
+    def __get_style_vector_from_audio(
+        self, audio_path: str, weight: float = 1.0
+    ) -> NDArray[Any]:
+        """
+        音声からスタイルベクトルを推論する。
+
+        Args:
+            audio_path (str): 音声ファイルのパス
+            weight (float, optional): スタイルベクトルの重み. Defaults to 1.0.
+        Returns:
+            NDArray[Any]: スタイルベクトル
+        """
+
+        if self.__style_vector_inference is None:
+
+            # pyannote.audio は scikit-learn などの大量の重量級ライブラリに依存しているため、
+            # TTSModel.infer() に reference_audio_path を指定し音声からスタイルベクトルを推論する場合のみ遅延 import する
+            try:
+                import pyannote.audio
+            except ImportError:
+                raise ImportError(
+                    "pyannote.audio is required to infer style vector from audio"
+                )
+
+            # スタイルベクトルを取得するための推論モデルを初期化
+            self.__style_vector_inference = pyannote.audio.Inference(
+                model=pyannote.audio.Model.from_pretrained(
+                    "pyannote/wespeaker-voxceleb-resnet34-LM"
+                ),
+                window="whole",
+            )
+            self.__style_vector_inference.to(torch.device(self.device))
+
+        # 音声からスタイルベクトルを推論
+        xvec = self.__style_vector_inference(audio_path)
+        mean = self.__style_vectors[0]
+        xvec = mean + (xvec - mean) * weight
+        return xvec
+
+    def __convert_to_16_bit_wav(self, data: NDArray[Any]) -> NDArray[Any]:
+        """
+        音声データを 16-bit int 形式に変換する。
+        gradio.processing_utils.convert_to_16_bit_wav() を移植したもの。
+
+        Args:
+            data (NDArray[Any]): 音声データ
+
+        Returns:
+            NDArray[Any]: 16-bit int 形式の音声データ
+        """
+        # Based on: https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.wavfile.write.html
+        if data.dtype in [np.float64, np.float32, np.float16]:  # type: ignore
+            data = data / np.abs(data).max()
+            data = data * 32767
+            data = data.astype(np.int16)
+        elif data.dtype == np.int32:
+            data = data / 65536
+            data = data.astype(np.int16)
+        elif data.dtype == np.int16:
+            pass
+        elif data.dtype == np.uint16:
+            data = data - 32768
+            data = data.astype(np.int16)
+        elif data.dtype == np.uint8:
+            data = data * 257 - 32768
+            data = data.astype(np.int16)
+        elif data.dtype == np.int8:
+            data = data * 256
+            data = data.astype(np.int16)
+        else:
+            raise ValueError(
+                "Audio data cannot be converted automatically from "
+                f"{data.dtype} to 16-bit int format."
+            )
+        return data
+
+    def infer(
+        self,
+        text: str,
+        language: Languages = Languages.JP,
+        speaker_id: int = 0,
+        reference_audio_path: Optional[str] = None,
+        sdp_ratio: float = DEFAULT_SDP_RATIO,
+        noise: float = DEFAULT_NOISE,
+        noise_w: float = DEFAULT_NOISEW,
+        length: float = DEFAULT_LENGTH,
+        line_split: bool = DEFAULT_LINE_SPLIT,
+        split_interval: float = DEFAULT_SPLIT_INTERVAL,
+        assist_text: Optional[str] = None,
+        assist_text_weight: float = DEFAULT_ASSIST_TEXT_WEIGHT,
+        use_assist_text: bool = False,
+        style: str = DEFAULT_STYLE,
+        style_weight: float = DEFAULT_STYLE_WEIGHT,
+        given_phone: Optional[list[str]] = None,
+        given_tone: Optional[list[int]] = None,
+        pitch_scale: float = 1.0,
+        intonation_scale: float = 1.0,
+    ) -> tuple[int, NDArray[Any]]:
+        """
+        テキストから音声を合成する。
+
+        Args:
+            text (str): 読み上げるテキスト
+            language (Languages, optional): 言語. Defaults to Languages.JP.
+            speaker_id (int, optional): 話者 ID. Defaults to 0.
+            reference_audio_path (Optional[str], optional): 音声スタイルの参照元の音声ファイルのパス. Defaults to None.
+            sdp_ratio (float, optional): DP と SDP の混合比。0 で DP のみ、1で SDP のみを使用 (値を大きくするとテンポに緩急がつく). Defaults to DEFAULT_SDP_RATIO.
+            noise (float, optional): DP に与えられるノイズ. Defaults to DEFAULT_NOISE.
+            noise_w (float, optional): SDP に与えられるノイズ. Defaults to DEFAULT_NOISEW.
+            length (float, optional): 生成音声の長さ（話速）のパラメータ。大きいほど生成音声が長くゆっくり、小さいほど短く早くなる。 Defaults to DEFAULT_LENGTH.
+            line_split (bool, optional): テキストを改行ごとに分割して生成するかどうか (True の場合 given_phone/given_tone は無視される). Defaults to DEFAULT_LINE_SPLIT.
+            split_interval (float, optional): 改行ごとに分割する場合の無音 (秒). Defaults to DEFAULT_SPLIT_INTERVAL.
+            assist_text (Optional[str], optional): 感情表現の参照元の補助テキスト. Defaults to None.
+            assist_text_weight (float, optional): 感情表現の補助テキストを適用する強さ. Defaults to DEFAULT_ASSIST_TEXT_WEIGHT.
+            use_assist_text (bool, optional): 音声合成時に感情表現の補助テキストを使用するかどうか. Defaults to False.
+            style (str, optional): 音声スタイル (Neutral, Happy など). Defaults to DEFAULT_STYLE.
+            style_weight (float, optional): 音声スタイルを適用する強さ. Defaults to DEFAULT_STYLE_WEIGHT.
+            given_phone (Optional[list[int]], optional): 読み上げテキストの読みを表す音素列。指定する場合は given_tone も別途指定が必要. Defaults to None.
+            given_tone (Optional[list[int]], optional): アクセントのトーンのリスト. Defaults to None.
+            pitch_scale (float, optional): ピッチの高さ (1.0 から変更すると若干音質が低下する). Defaults to 1.0.
+            intonation_scale (float, optional): 抑揚の平均からの変化幅 (1.0 から変更すると若干音質が低下する). Defaults to 1.0.
+
+        Returns:
+            tuple[int, NDArray[Any]]: サンプリングレートと音声データ (16bit PCM)
+        """
+
+        logger.info(f"Start generating audio data from text:\n{text}")
+        if language != "JP" and self.hyper_parameters.version.endswith("JP-Extra"):
+            raise ValueError(
+                "The model is trained with JP-Extra, but the language is not JP"
+            )
+        if reference_audio_path == "":
+            reference_audio_path = None
+        if assist_text == "" or not use_assist_text:
+            assist_text = None
+
+        if self.__net_g is None:
+            self.load()
+        assert self.__net_g is not None
+        if reference_audio_path is None:
+            style_id = self.style2id[style]
+            style_vector = self.__get_style_vector(style_id, style_weight)
+        else:
+            style_vector = self.__get_style_vector_from_audio(
+                reference_audio_path, style_weight
+            )
+        if not line_split:
+            with torch.no_grad():
+                audio = infer(
+                    text=text,
+                    sdp_ratio=sdp_ratio,
+                    noise_scale=noise,
+                    noise_scale_w=noise_w,
+                    length_scale=length,
+                    sid=speaker_id,
+                    language=language,
+                    hps=self.hyper_parameters,
+                    net_g=self.__net_g,
+                    device=self.device,
+                    assist_text=assist_text,
+                    assist_text_weight=assist_text_weight,
+                    style_vec=style_vector,
+                    given_phone=given_phone,
+                    given_tone=given_tone,
+                )
+        else:
+            texts = text.split("\n")
+            texts = [t for t in texts if t != ""]
+            audios = []
+            with torch.no_grad():
+                for i, t in enumerate(texts):
+                    audios.append(
+                        infer(
+                            text=t,
+                            sdp_ratio=sdp_ratio,
+                            noise_scale=noise,
+                            noise_scale_w=noise_w,
+                            length_scale=length,
+                            sid=speaker_id,
+                            language=language,
+                            hps=self.hyper_parameters,
+                            net_g=self.__net_g,
+                            device=self.device,
+                            assist_text=assist_text,
+                            assist_text_weight=assist_text_weight,
+                            style_vec=style_vector,
+                        )
+                    )
+                    if i != len(texts) - 1:
+                        audios.append(np.zeros(int(44100 * split_interval)))
+                audio = np.concatenate(audios)
+        logger.info("Audio data generated successfully")
+        if not (pitch_scale == 1.0 and intonation_scale == 1.0):
+            _, audio = adjust_voice(
+                fs=self.hyper_parameters.data.sampling_rate,
+                wave=audio,
+                pitch_scale=pitch_scale,
+                intonation_scale=intonation_scale,
+            )
+        audio = self.__convert_to_16_bit_wav(audio)
+        return (self.hyper_parameters.data.sampling_rate, audio)
+
+
+class TTSModelInfo(BaseModel):
+    name: str
+    files: list[str]
+    styles: list[str]
+    speakers: list[str]
+
+
+class TTSModelHolder:
+    """
+    Style-Bert-Vits2 の音声合成モデルを管理するクラス。
+    model_holder.models_info から指定されたディレクトリ内にある音声合成モデルの一覧を取得できる。
+    """
+
+    def __init__(self, model_root_dir: Path, device: str) -> None:
+        """
+        Style-Bert-Vits2 の音声合成モデルを管理するクラスを初期化する。
+        音声合成モデルは下記のように配置されていることを前提とする (.safetensors のファイル名は自由) 。
+        ```
+        model_root_dir
+        ├── model-name-1
+        │   ├── config.json
+        │   ├── model-name-1_e160_s14000.safetensors
+        │   └── style_vectors.npy
+        ├── model-name-2
+        │   ├── config.json
+        │   ├── model-name-2_e160_s14000.safetensors
+        │   └── style_vectors.npy
+        └── ...
+        ```
+
+        Args:
+            model_root_dir (Path): 音声合成モデルが配置されているディレクトリのパス
+            device (str): 音声合成時に利用するデバイス (cpu, cuda, mps など)
+        """
+
+        self.root_dir: Path = model_root_dir
+        self.device: str = device
+        self.model_files_dict: dict[str, list[Path]] = {}
+        self.current_model: Optional[TTSModel] = None
+        self.model_names: list[str] = []
+        self.models_info: list[TTSModelInfo] = []
+        self.refresh()
+
+    def refresh(self) -> None:
+        """
+        音声合成モデルの一覧を更新する。
+        """
+
+        self.model_files_dict = {}
+        self.model_names = []
+        self.current_model = None
+        self.models_info = []
+
+        model_dirs = [d for d in self.root_dir.iterdir() if d.is_dir()]
+        for model_dir in model_dirs:
+            model_files = [
+                f
+                for f in model_dir.iterdir()
+                if f.suffix in [".pth", ".pt", ".safetensors"]
+            ]
+            if len(model_files) == 0:
+                logger.warning(f"No model files found in {model_dir}, so skip it")
+                continue
+            config_path = model_dir / "config.json"
+            if not config_path.exists():
+                logger.warning(
+                    f"Config file {config_path} not found, so skip {model_dir}"
+                )
+                continue
+            self.model_files_dict[model_dir.name] = model_files
+            self.model_names.append(model_dir.name)
+            hyper_parameters = HyperParameters.load_from_json(config_path)
+            style2id: dict[str, int] = hyper_parameters.data.style2id
+            styles = list(style2id.keys())
+            spk2id: dict[str, int] = hyper_parameters.data.spk2id
+            speakers = list(spk2id.keys())
+            self.models_info.append(
+                TTSModelInfo(
+                    name=model_dir.name,
+                    files=[str(f) for f in model_files],
+                    styles=styles,
+                    speakers=speakers,
+                )
+            )
+
+    def get_model(self, model_name: str, model_path_str: str) -> TTSModel:
+        """
+        指定された音声合成モデルのインスタンスを取得する。
+        この時点ではモデルはロードされていない (明示的にロードしたい場合は model.load() を呼び出す)。
+
+        Args:
+            model_name (str): 音声合成モデルの名前
+            model_path_str (str): 音声合成モデルのファイルパス (.safetensors)
+
+        Returns:
+            TTSModel: 音声合成モデルのインスタンス
+        """
+
+        model_path = Path(model_path_str)
+        if model_name not in self.model_files_dict:
+            raise ValueError(f"Model `{model_name}` is not found")
+        if model_path not in self.model_files_dict[model_name]:
+            raise ValueError(f"Model file `{model_path}` is not found")
+        if self.current_model is None or self.current_model.model_path != model_path:
+            self.current_model = TTSModel(
+                model_path=model_path,
+                config_path=self.root_dir / model_name / "config.json",
+                style_vec_path=self.root_dir / model_name / "style_vectors.npy",
+                device=self.device,
+            )
+
+        return self.current_model
+
+    def get_model_for_gradio(self, model_name: str, model_path_str: str):
+        import gradio as gr
+
+        model_path = Path(model_path_str)
+        if model_name not in self.model_files_dict:
+            raise ValueError(f"Model `{model_name}` is not found")
+        if model_path not in self.model_files_dict[model_name]:
+            raise ValueError(f"Model file `{model_path}` is not found")
+        if (
+            self.current_model is not None
+            and self.current_model.model_path == model_path
+        ):
+            # Already loaded
+            speakers = list(self.current_model.spk2id.keys())
+            styles = list(self.current_model.style2id.keys())
+            return (
+                gr.Dropdown(choices=styles, value=styles[0]),  # type: ignore
+                gr.Button(interactive=True, value="音声合成"),
+                gr.Dropdown(choices=speakers, value=speakers[0]),  # type: ignore
+            )
+        self.current_model = TTSModel(
+            model_path=model_path,
+            config_path=self.root_dir / model_name / "config.json",
+            style_vec_path=self.root_dir / model_name / "style_vectors.npy",
+            device=self.device,
+        )
+        speakers = list(self.current_model.spk2id.keys())
+        styles = list(self.current_model.style2id.keys())
+        return (
+            gr.Dropdown(choices=styles, value=styles[0]),  # type: ignore
+            gr.Button(interactive=True, value="音声合成"),
+            gr.Dropdown(choices=speakers, value=speakers[0]),  # type: ignore
+        )
+
+    def update_model_files_for_gradio(self, model_name: str):
+        import gradio as gr
+
+        model_files = [str(f) for f in self.model_files_dict[model_name]]
+        return gr.Dropdown(choices=model_files, value=model_files[0])  # type: ignore
+
+    def update_model_names_for_gradio(
+        self,
+    ):
+        import gradio as gr
+
+        self.refresh()
+        initial_model_name = self.model_names[0]
+        initial_model_files = [
+            str(f) for f in self.model_files_dict[initial_model_name]
+        ]
+        return (
+            gr.Dropdown(choices=self.model_names, value=initial_model_name),  # type: ignore
+            gr.Dropdown(choices=initial_model_files, value=initial_model_files[0]),  # type: ignore
+            gr.Button(interactive=False),  # For tts_button
+        )
diff --git a/style_bert_vits2/utils/__init__.py b/style_bert_vits2/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/style_bert_vits2/utils/stdout_wrapper.py b/style_bert_vits2/utils/stdout_wrapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..174d1fd267ed5f431acb9db44eb1d72c571b7807
--- /dev/null
+++ b/style_bert_vits2/utils/stdout_wrapper.py
@@ -0,0 +1,42 @@
+import sys
+import tempfile
+from typing import TextIO
+
+
+class StdoutWrapper(TextIO):
+    """
+    `sys.stdout` wrapper for both Google Colab and local environment.
+    """
+
+    def __init__(self) -> None:
+        self.temp_file = tempfile.NamedTemporaryFile(
+            mode="w+", delete=False, encoding="utf-8"
+        )
+        self.original_stdout = sys.stdout
+
+    def write(self, message: str) -> int:
+        result = self.temp_file.write(message)
+        self.temp_file.flush()
+        print(message, end="", file=self.original_stdout)
+        return result
+
+    def flush(self) -> None:
+        self.temp_file.flush()
+
+    def read(self, n: int = -1) -> str:
+        self.temp_file.seek(0)
+        return self.temp_file.read(n)
+
+    def close(self) -> None:
+        self.temp_file.close()
+
+    def fileno(self) -> int:
+        return self.temp_file.fileno()
+
+
+try:
+    import google.colab  # type: ignore
+
+    SAFE_STDOUT = StdoutWrapper()
+except ImportError:
+    SAFE_STDOUT = sys.stdout
diff --git a/style_bert_vits2/utils/strenum.py b/style_bert_vits2/utils/strenum.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2e2e3c288aabd28eefad7423352200f645801e2
--- /dev/null
+++ b/style_bert_vits2/utils/strenum.py
@@ -0,0 +1,37 @@
+import enum
+
+
+class StrEnum(str, enum.Enum):
+    """
+    Enum where members are also (and must be) strings (backported from Python 3.11).
+    """
+
+    def __new__(cls, *values: str) -> "StrEnum":
+        "values must already be of type `str`"
+        if len(values) > 3:
+            raise TypeError("too many arguments for str(): %r" % (values,))
+        if len(values) == 1:
+            # it must be a string
+            if not isinstance(values[0], str):  # type: ignore
+                raise TypeError("%r is not a string" % (values[0],))
+        if len(values) >= 2:
+            # check that encoding argument is a string
+            if not isinstance(values[1], str):  # type: ignore
+                raise TypeError("encoding must be a string, not %r" % (values[1],))
+        if len(values) == 3:
+            # check that errors argument is a string
+            if not isinstance(values[2], str):  # type: ignore
+                raise TypeError("errors must be a string, not %r" % (values[2]))
+        value = str(*values)
+        member = str.__new__(cls, value)
+        member._value_ = value
+        return member
+
+    @staticmethod
+    def _generate_next_value_(
+        name: str, start: int, count: int, last_values: list[str]
+    ) -> str:
+        """
+        Return the lower-cased version of the member name.
+        """
+        return name.lower()
diff --git a/style_bert_vits2/utils/subprocess.py b/style_bert_vits2/utils/subprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f159a20fc2885853902f2bdc30a0e55aae32d5b
--- /dev/null
+++ b/style_bert_vits2/utils/subprocess.py
@@ -0,0 +1,59 @@
+import subprocess
+import sys
+from typing import Any, Callable
+
+from style_bert_vits2.logging import logger
+from style_bert_vits2.utils.stdout_wrapper import SAFE_STDOUT
+
+
+def run_script_with_log(
+    cmd: list[str], ignore_warning: bool = False
+) -> tuple[bool, str]:
+    """
+    指定されたコマンドを実行し、そのログを記録する。
+
+    Args:
+        cmd: 実行するコマンドのリスト
+        ignore_warning: 警告を無視するかどうかのフラグ
+
+    Returns:
+        tuple[bool, str]: 実行が成功したかどうかのブール値と、エラーまたは警告のメッセージ（ある場合）
+    """
+
+    logger.info(f"Running: {' '.join(cmd)}")
+    result = subprocess.run(
+        [sys.executable] + cmd,
+        stdout=SAFE_STDOUT,
+        stderr=subprocess.PIPE,
+        text=True,
+        encoding="utf-8",
+        check=False,
+    )
+    if result.returncode != 0:
+        logger.error(f"Error: {' '.join(cmd)}\n{result.stderr}")
+        return False, result.stderr
+    elif result.stderr and not ignore_warning:
+        logger.warning(f"Warning: {' '.join(cmd)}\n{result.stderr}")
+        return True, result.stderr
+    logger.success(f"Success: {' '.join(cmd)}")
+
+    return True, ""
+
+
+def second_elem_of(
+    original_function: Callable[..., tuple[Any, Any]]
+) -> Callable[..., Any]:
+    """
+    与えられた関数をラップし、その戻り値の 2 番目の要素のみを返す関数を生成する。
+
+    Args:
+        original_function (Callable[..., tuple[Any, Any]])): ラップする元の関数
+
+    Returns:
+        Callable[..., Any]: 元の関数の戻り値の 2 番目の要素のみを返す関数
+    """
+
+    def inner_function(*args, **kwargs) -> Any:  # type: ignore
+        return original_function(*args, **kwargs)[1]
+
+    return inner_function
diff --git a/style_bert_vits2/voice.py b/style_bert_vits2/voice.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0584195dd4a8b3fdab096ed35bab1f5cdd7988b
--- /dev/null
+++ b/style_bert_vits2/voice.py
@@ -0,0 +1,52 @@
+from typing import Any
+
+import numpy as np
+import pyworld
+from numpy.typing import NDArray
+
+
+def adjust_voice(
+    fs: int,
+    wave: NDArray[Any],
+    pitch_scale: float = 1.0,
+    intonation_scale: float = 1.0,
+) -> tuple[int, NDArray[Any]]:
+    """
+    音声のピッチと抑揚を調整する。
+    変更すると若干音質が劣化するので、どちらも初期値のままならそのまま返す。
+
+    Args:
+        fs (int): 音声のサンプリング周波数
+        wave (NDArray[Any]): 音声データ
+        pitch_scale (float, optional): ピッチの高さ. Defaults to 1.0.
+        intonation_scale (float, optional): 抑揚の平均からの変更比率. Defaults to 1.0.
+
+    Returns:
+        tuple[int, NDArray[Any]]: 調整後の音声データのサンプリング周波数と音声データ
+    """
+
+    if pitch_scale == 1.0 and intonation_scale == 1.0:
+        # 初期値の場合は、音質劣化を避けるためにそのまま返す
+        return fs, wave
+
+    # pyworld で f0 を加工して合成
+    # pyworld よりもよいのがあるかもしれないが……
+
+    wave = wave.astype(np.double)
+
+    # 質が高そうだしとりあえずharvestにしておく
+    f0, t = pyworld.harvest(wave, fs)
+
+    sp = pyworld.cheaptrick(wave, f0, t, fs)
+    ap = pyworld.d4c(wave, f0, t, fs)
+
+    non_zero_f0 = [f for f in f0 if f != 0]
+    f0_mean = sum(non_zero_f0) / len(non_zero_f0)
+
+    for i, f in enumerate(f0):
+        if f == 0:
+            continue
+        f0[i] = pitch_scale * f0_mean + intonation_scale * (f - f0_mean)
+
+    wave = pyworld.synthesize(f0, sp, ap, fs)
+    return fs, wave