mirror of https://github.com/coqui-ai/TTS.git
78 lines
4.7 KiB
JSON
78 lines
4.7 KiB
JSON
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{
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"run_name": "speaker_encoder",
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"run_description": "train speaker encoder with VCTK",
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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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"fft_size": 1024, // number of stft frequency levels. Size of the linear spectogram frame.
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"sample_rate": 16000, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
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"win_length": 1024, // stft window length in ms.
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"hop_length": 256, // stft window hop-lengh in ms.
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"frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
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"frame_shift_ms": null, // stft window hop-lengh in ms. If null, 'hop_length' is used.
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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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"stft_pad_mode": "reflect",
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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": true, // move normalization to range [-1, 1]
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"max_norm": 4.0, // 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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"spec_gain": 20.0,
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"do_trim_silence": false, // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
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"trim_db": 60, // threshold for timming silence. Set this according to your dataset.
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"stats_path": null // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
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},
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"reinit_layers": [],
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"loss": "softmaxproto", // "ge2e" to use Generalized End-to-End loss, "angleproto" to use Angular Prototypical loss and "softmaxproto" to use Softmax with Angular Prototypical loss
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"grad_clip": 3.0, // upper limit for gradients for clipping.
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"epochs": 1000, // 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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"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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"steps_plot_stats": 10, // number of steps to plot embeddings.
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// Speakers config
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"num_speakers_in_batch": 108, // Batch size for training.
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"num_utters_per_speaker": 2, //
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"skip_speakers": true, // skip speakers with samples less than "num_utters_per_speaker"
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"voice_len": 2, // number of seconds for each training instance
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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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"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": 20, // Number of steps to log traning on console.
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"output_path": "../../../checkpoints/speaker_encoder/", // DATASET-RELATED: output path for all training outputs.
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"model": {
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"input_dim": 80,
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"proj_dim": 512,
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"lstm_dim": 768,
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"num_lstm_layers": 3,
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"use_lstm_with_projection": true
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},
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"storage": {
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"sample_from_storage_p": 0.66, // the probability with which we'll sample from the DataSet in-memory storage
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"storage_size": 15, // the size of the in-memory storage with respect to a single batch
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"additive_noise": 1e-5 // add very small gaussian noise to the data in order to increase robustness
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},
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"datasets":
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[
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{
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"name": "vctk",
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"path": "/workspace/store/ecasanova/datasets/VCTK-Corpus-removed-silence/",
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"meta_file_train": null,
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"meta_file_val": null
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}
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]
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} |