mirror of https://github.com/coqui-ai/TTS.git
81 lines
6.3 KiB
JSON
81 lines
6.3 KiB
JSON
{
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"run_name": "mozilla-tacotron-gst",
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"run_description": "GST with single speaker",
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"audio":{
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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": 22050, // DATASET-RELATED: 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": 0.98, // 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": 0.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
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"mel_fmax": 8000.0, // 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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"distributed":{
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"backend": "nccl",
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"url": "tcp:\/\/localhost:54321"
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},
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"reinit_layers": [],
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"model": "TacotronGST", // one of the model in models/
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"grad_clip": 1, // upper limit for gradients for clipping.
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"epochs": 10000, // total number of epochs to train.
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"lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
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"lr_decay": false, // if true, Noam learning rate decaying is applied through training.
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"warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
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"windowing": false, // Enables attention windowing. Used only in eval mode.
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"memory_size": 5, // ONLY TACOTRON - memory queue size used to queue network predictions to feed autoregressive connection. Useful if r < 5.
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"attention_norm": "sigmoid", // softmax or sigmoid. Suggested to use softmax for Tacotron2 and sigmoid for Tacotron.
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"prenet_type": "original", // "original" or "bn".
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"prenet_dropout": true, // enable/disable dropout at prenet.
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"use_forward_attn": true, // if it uses forward attention. In general, it aligns faster.
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"forward_attn_mask": false, // Apply forward attention mask af inference to prevent bad modes. Try it if your model does not align well.
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"transition_agent": false, // enable/disable transition agent of forward attention.
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"location_attn": false, // enable_disable location sensitive attention. It is enabled for TACOTRON by default.
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"loss_masking": true, // enable / disable loss masking against the sequence padding.
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"enable_eos_bos_chars": false, // enable/disable beginning of sentence and end of sentence chars.
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"stopnet": true, // Train stopnet predicting the end of synthesis.
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"separate_stopnet": true, // Train stopnet seperately if 'stopnet==true'. It prevents stopnet loss to influence the rest of the model. It causes a better model, but it trains SLOWER.
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"tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
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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":16,
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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": 1000, // 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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"batch_group_size": 0, //Number of batches to shuffle after bucketing.
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"run_eval": true,
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"test_delay_epochs": 5, //Until attention is aligned, testing only wastes computation time.
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"test_sentences_file": null,
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"data_path": "/media/erogol/data_ssd/Data/Mozilla/", // DATASET-RELATED: can overwritten from command argument
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"meta_file_train": "metadata_train.txt", // DATASET-RELATED: metafile for training dataloader.
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"meta_file_val": "metadata_val.txt", // DATASET-RELATED: metafile for evaluation dataloader.
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"dataset": "mozilla", // 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": 150, // DATASET-RELATED: maximum text length
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"output_path": "../keep/", // DATASET-RELATED: output path for all training outputs.
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"num_loader_workers": 4, // 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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"phoneme_cache_path": "mozilla_us_phonemes", // phoneme computation is slow, therefore, it caches results in the given folder.
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"use_phonemes": true, // use phonemes instead of raw characters. It is suggested for better pronounciation.
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"phoneme_language": "en-us", // depending on your target language, pick one from https://github.com/bootphon/phonemizer#languages
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"text_cleaner": "phoneme_cleaners"
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}
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