| { | |
| "run_name": "Wav2Vec-fine-tuning-TEDx", | |
| "run_description": "Fine tuning TEDx", | |
| "seed": 42, | |
| // AUDIO PARAMS | |
| "sampling_rate": 16000, | |
| // VOCABULARY PARAMETERS | |
| "vocab":{ | |
| "vocab_path": "example/vocab_example_ru.json", // generic vocab for Portuguese | |
| "blank": "<pad>", // blank token for padding | |
| "silence": "|", // token between words | |
| "unk": "<unk>" // unk token | |
| }, | |
| // TRAINING | |
| "batch_size": 16, // Batch size for training. | |
| "mixed_precision": true, // level of optimization with NVIDIA's apex feature for automatic mixed FP16/FP32 precision (AMP), NOTE: currently only O1 is supported, and use "O1" to activate. | |
| "early_stop_epochs": 10, // If 0 disabled else Number of epochs for stop training with validation loss dont decrease | |
| "preprocess_dataset": false, // if true, the dataset will be pre-processed and saved in disk, otherwise the audio files will be loaded in each step. Preprocessing makes training faster, but requires much more disk space. | |
| // OPTIMIZER | |
| "epochs": 140, // total number of epochs to train. | |
| "lr": 0.00003, // Initial learning rate. | |
| "gradient_accumulation_steps": 12, | |
| // LOGGING | |
| "logging_steps": 100, // Number of steps to plot. | |
| "load_best_model_at_end": true, | |
| "save_total_limit": 3, | |
| "warmup_ratio": 0.04761904762142857, // 0 disable Ratio of total training steps used for a linear warmup from 0 to learning_rate | |
| "warmup_steps": 0, // 0 disable Number of steps used for a linear warmup from 0 to learning_rate | |
| // DATA LOADING | |
| "num_loader_workers": 4, // number of training data loader processes. Don't set it too big. 4-8 are goo | |
| // MODEL | |
| "freeze_feature_extractor": true, // Whether to freeze the feature extractor layers of the model. | |
| "attention_dropout": 0.1, // The dropout ratio for the attention probabilities. | |
| "activation_dropout": 0.1, // The dropout ratio for activations inside the fully connected layer. | |
| "hidden_dropout": 0.1, // The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler. | |
| "feat_proj_dropout": 0.1, // The dropout probabilitiy for all 1D convolutional layers in feature extractor. | |
| "mask_time_prob": 0.05, // Propability of each feature vector along the time axis to be chosen as the start of the vector span to be masked. | |
| "layerdrop": 0.0, // The LayerDrop probability. | |
| "gradient_checkpointing": true, // If True, use gradient checkpointing to save memory at the expense of slower backward pass. | |
| // ToDo: Implement Time mask and Frequency Mask | |
| "audio_augmentation":[ | |
| // additive noise and room impulse response (RIR) simulation similar to: https://arxiv.org/pdf/2009.14153.pdf | |
| { | |
| "name": "additive", | |
| "sounds_path":"/raid/datasets/DA/musan/speech/", // download: https://www.openslr.org/17/ | |
| "lru_cache_size": 32, // Maximum size of the LRU cache for storing noise files in memory | |
| "min_snr_in_db": 13.0, | |
| "max_snr_in_db": 20.0, | |
| // "sample_rate": 16000, | |
| "p": 0.25 | |
| }, | |
| { | |
| "name": "additive", | |
| "sounds_path":"/raid/datasets/DA/musan/music/", // download: https://www.openslr.org/17/ | |
| "lru_cache_size": 32, // Maximum size of the LRU cache for storing noise files in memory | |
| "min_snr_in_db": 5.0, | |
| "max_snr_in_db": 15.0, | |
| // "sample_rate": 16000, | |
| "p": 0.25 | |
| }, | |
| { | |
| "name": "additive", | |
| "sounds_path":"/raid/datasets/DA/musan/noise/", // download: https://www.openslr.org/17/ | |
| "lru_cache_size": 32, // Maximum size of the LRU cache for storing noise files in memory | |
| "min_snr_in_db": 0.0, | |
| "max_snr_in_db": 15.0, | |
| // "sample_rate": 16000, | |
| "p": 0.25 | |
| }, | |
| // rir filter proposed by: https://ieeexplore.ieee.org/document/7953152 | |
| { | |
| "name": "rir", | |
| "ir_path": "/raid/datasets/DA/RIRS_NOISES/simulated_rirs/", // download: https://www.openslr.org/28/ | |
| "lru_cache_size": 128, // Maximum size of the LRU cache for storing noise files in memory | |
| // "sample_rate": 16000, | |
| "p": 0.25 | |
| } | |
| , | |
| // { | |
| // "name": "gain", | |
| // "min_gain_in_db": -18.0, | |
| // "max_gain_in_db": 6, | |
| // "p": 0.25 // propability of apply this method, 0 is disable | |
| // }, | |
| { | |
| "name": "pitch_shift", | |
| "min_semitones": -4, | |
| "max_semitones": 4, | |
| "p": 0.25 // propability of apply this method, 0 is disable | |
| }, | |
| { | |
| "name": "gaussian", | |
| "min_amplitude": 0.0001, | |
| "max_amplitude": 0.001, | |
| "p": 0.25 // propability of apply this method, 0 is disable | |
| } | |
| ], | |
| // PATHS | |
| "output_path": "../checkpoints/YourTTS2ASR/Wav2Vec-voxpopuli/one-speaker/just-TTS/RU/140-epoch-high-bs/", | |
| // CACHE | |
| "dataset_cache": "../datasets/ru-cache-high-bs/", | |
| // DATASETS | |
| "datasets":{ | |
| "files_path": "/raid/datasets/Mailabs/ru/", // relative path for audios It's will be join with the CS | |
| "train": | |
| [ | |
| // this dicts is pass directly for the load dataset see the documentation: https://huggingface.co/docs/datasets/package_reference/loading_methods.html#datasets.load_dataset | |
| { | |
| "name": "csv", | |
| "path": "csv", | |
| "data_files": ["/raid/datasets/Mailabs/ru/train_converted.csv"], // csv files | |
| "text_column": "text", | |
| "path_column": "file_path" | |
| } | |
| ] | |
| , | |
| "devel": | |
| [ | |
| { | |
| "name": "csv", | |
| "path": "csv", | |
| "data_files": ["/raid/datasets/Mailabs/ru/dev_converted.csv"], // csv files | |
| "text_column": "text", | |
| "path_column": "file_path" | |
| } | |
| ] | |
| , | |
| "test": | |
| { | |
| "name": "csv", | |
| "path": "csv", | |
| "data_files": ["/raid/datasets/DA/Common_Voice/cv-corpus-7.0-2021-07-21/ru/test_converted.csv"], // csv files | |
| "text_column": "text", | |
| "path_column": "file_path" | |
| } | |
| }//, | |
| // used only for test | |
| // "KenLM":{ | |
| // "kenlm_model_path": "../../kenLM/binaries/subtitle/4-gram/lm.binary", // Path for KenLM model | |
| // "lexicon_path": "example/lexicon.lst", // file with all words for limit the decoder search | |
| // "beam": 2048, | |
| // "nbest": 1, | |
| // "beam_threshold": 25, | |
| // "lm_weight": 1, | |
| // "word_score": -1, | |
| // "sil_weight": 0 | |
| // } | |
| } | |