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wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-plus-data-augmentation-russian

Automatic Speech Recognition
Transformers
PyTorch
Portuguese
wav2vec2
audio
speech
Russian-speech-corpus
PyTorch
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wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-plus-data-augmentation-russian / config_train.json
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{
"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": 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": 100, // 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": 0.04761904762142857, // 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": 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":"/workspace/scratch/ecasanova/datasets/YourTTS2ASR/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":"/workspace/scratch/ecasanova/datasets/YourTTS2ASR/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":"/workspace/scratch/ecasanova/datasets/YourTTS2ASR/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": "/workspace/scratch/ecasanova/datasets/YourTTS2ASR/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/Wav2Vec-voxpopuli/one-speaker/Final-paper/GEN/RU/100-epoch/",
// CACHE
"dataset_cache": "../datasets/",
// DATASETS
"datasets":{
"files_path": "/workspace/scratch/ecasanova/datasets/YourTTS2ASR/Common_Voice/cv-corpus-7.0-2021-07-21/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": ["/workspace/scratch/ecasanova/datasets/YourTTS2ASR/Common_Voice/cv-corpus-7.0-2021-07-21/ru/train_converted_copy_generated_en_speakers.csv"], // csv files
"text_column": "text",
"path_column": "file_path"
},
{
"name": "csv",
"path": "csv",
"data_files": ["/workspace/scratch/ecasanova/datasets/YourTTS2ASR/M-AILABS/ru_RU/train_converted_copy_generated_VC_en_speakers_5_speakers_per_text_fixed.csv"], // csv files
"text_column": "text",
"path_column": "file_path"
},
{
"name": "csv",
"path": "csv",
"data_files": ["/workspace/scratch/ecasanova/datasets/YourTTS2ASR/M-AILABS/ru_RU/train_converted.csv"], // csv files
"text_column": "text",
"path_column": "file_path"
}
]
,
"devel":
[
{
"name": "csv",
"path": "csv",
"data_files": ["/workspace/scratch/ecasanova/datasets/YourTTS2ASR/Common_Voice/cv-corpus-7.0-2021-07-21/ru/dev_converted_copy_generated_en_speakers_500-samples.csv"], // csv files
"text_column": "text",
"path_column": "file_path"
},
{
"name": "csv",
"path": "csv",
"data_files": ["/workspace/scratch/ecasanova/datasets/YourTTS2ASR/M-AILABS/ru_RU/dev_converted.csv"], // csv files
"text_column": "text",
"path_column": "file_path"
},
{
"name": "csv",
"path": "csv",
"data_files": ["/workspace/scratch/ecasanova/datasets/YourTTS2ASR/M-AILABS/ru_RU/dev_converted_VC_5_speakers_500-samples.csv"], // csv files
"text_column": "text",
"path_column": "file_path"
}
]
,
"test":
{
"name": "csv",
"path": "csv",
"data_files": ["/workspace/scratch/ecasanova/datasets/YourTTS2ASR/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
// }
}