mirror of https://github.com/coqui-ai/TTS.git
25 lines
1.5 KiB
JSON
25 lines
1.5 KiB
JSON
{
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"audio":{
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"num_mels": 80, // size of the mel spec frame.
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"num_freq": 513, // number of stft frequency levels. Size of the linear spectogram frame.
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"sample_rate": 22050, // wav sample-rate. If different than the original data, it is resampled.
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"frame_length_ms": null, // stft window length in ms.
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"frame_shift_ms": null, // stft window hop-lengh in ms.
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"hop_length": 256,
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"win_length": 1024,
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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": 30,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
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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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"clip_norm": true, // clip normalized values into the range.
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"max_norm": 4, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
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"mel_fmin": 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, // maximum freq level for mel-spec. Tune for dataset!!
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"do_trim_silence": false
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}
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}
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