{ "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.json", // generic vocab for Portuguese "blank": "", // blank token for padding "silence": "|", // token between words "unk": "" // unk token }, // TRAINING "batch_size": 8, // 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": 24, // LOGGING "logging_steps": 100, // Number of steps to plot. "load_best_model_at_end": true, "save_total_limit": 3, "warmup_ratio": 0.06666666667, // 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": 8, // 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/PT/140-epoch-high-bs/", // CACHE "dataset_cache": "../datasets/", // DATASETS "datasets":{ "files_path": "/raid/datasets/TTS-Portuguese-Corpus/", // 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/TTS-Portuguese-Corpus/train_TTS-Portuguese_Corpus_metadata_converted_to_ASR.csv"], // csv files "text_column": "text", "path_column": "file_path" } ] , "devel": [ { "name": "csv", "path": "csv", "data_files": ["/raid/datasets/TTS-Portuguese-Corpus/eval_TTS-Portuguese_Corpus_metadata_converted_to_ASR.csv"], // csv files "text_column": "text", "path_column": "file_path" } ] , "test": { "name": "csv", "path": "csv", "data_files": ["/raid/datasets/Common_Voice/cv-corpus-7.0-2021-07-21/pt/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 // } }