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56 lines
3.7 KiB
JSON
56 lines
3.7 KiB
JSON
{
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"model_name": "TTS-phoneme",
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"model_description": "Training with phonemes, dropout rate 0.5",
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"audio":{
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"audio_processor": "audio", // to use dictate different audio processors, if available.
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// Audio processing parameters
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"num_mels": 80, // size of the mel spec frame.
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"num_freq": 1025, // number of stft frequency levels. Size of the linear spectogram frame.
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"sample_rate": 16000, // wav sample-rate. If different than the original data, it is resampled.
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"frame_length_ms": 50, // stft window length in ms.
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"frame_shift_ms": 12.5, // stft window hop-lengh in ms.
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"preemphasis": 0.97, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
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"min_level_db": -100, // normalization range
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"ref_level_db": 20, // reference level db, theoretically 20db is the sound of air.
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"power": 1.5, // value to sharpen wav signals after GL algorithm.
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"griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
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// Normalization parameters
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"signal_norm": true, // normalize the spec values in range [0, 1]
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"symmetric_norm": false, // move normalization to range [-1, 1]
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"max_norm": 1, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
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"clip_norm": true, // clip normalized values into the range.
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"mel_fmin": null, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
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"mel_fmax": null, // maximum freq level for mel-spec. Tune for dataset!!
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"do_trim_silence": true // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
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},
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"embedding_size": 256,
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"text_cleaner": "phoneme_cleaners",
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"epochs": 1000,
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"lr": 0.0001,
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"lr_decay": false,
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"warmup_steps": 4000,
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"batch_size": 32, // Batch size for training. Lower values than 32 might cause hard to learn attention.
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"eval_batch_size":32,
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"r": 5, // Number of frames to predict for step.
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"wd": 0.000001, // Weight decay weight.
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"checkpoint": true, // If true, it saves checkpoints per "save_step"
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"save_step": 5000, // Number of training steps expected to save traning stats and checkpoints.
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"print_step": 10, // Number of steps to log traning on console.
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"tb_model_param_stats": true, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
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"run_eval": true,
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"data_path": "../../Data/LJSpeech-1.1/", // DATASET-RELATED: can overwritten from command argument
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"meta_file_train": "transcript_train.txt", // DATASET-RELATED: metafile for training dataloader.
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"meta_file_val": "transcript_val.txt", // DATASET-RELATED: metafile for evaluation dataloader.
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"dataset": "tweb", // DATASET-RELATED: one of TTS.dataset.preprocessors depending on your target dataset. Use "tts_cache" for pre-computed dataset by extract_features.py
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"min_seq_len": 0, // DATASET-RELATED: minimum text length to use in training
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"max_seq_len": 300, // DATASET-RELATED: maximum text length
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"output_path": "/media/erogol/data_ssd/Data/models/tweb_models/", // DATASET-RELATED: output path for all training outputs.
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"num_loader_workers": 8, // number of training data loader processes. Don't set it too big. 4-8 are good values.
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"num_val_loader_workers": 4 // number of evaluation data loader processes.
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}
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