mirror of https://github.com/coqui-ai/TTS.git
update test config
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@ -13,10 +13,12 @@
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// Audio processing parameters
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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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"preemphasis": 0.0, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
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"ref_level_db": 0, // reference level db, theoretically 20db is the sound of air.
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"ref_level_db": 20, // reference level db, theoretically 20db is the sound of air.
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"log_func": "np.log10",
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"do_sound_norm": true,
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// Silence trimming
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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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"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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// MelSpectrogram parameters
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@ -31,7 +33,7 @@
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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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"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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"stats_path": null
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},
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// DISTRIBUTED TRAINING
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@ -126,6 +128,11 @@
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"wd": 0.0, // Weight decay weight.
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"gen_clip_grad": -1, // Generator gradient clipping threshold. Apply gradient clipping if > 0
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"disc_clip_grad": -1, // Discriminator gradient clipping threshold.
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"optimizer": "AdamW",
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"optimizer_params":{
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"betas": [0.8, 0.99],
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"weight_decay": 0.0
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},
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"lr_scheduler_gen": "MultiStepLR", // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
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"lr_scheduler_gen_params": {
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"gamma": 0.5,
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