mirror of https://github.com/coqui-ai/TTS.git
70 lines
3.4 KiB
JSON
70 lines
3.4 KiB
JSON
{
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"audio":{
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"audio_processor": "audio", // to use dictate different audio processors, if available.
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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": 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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"spec_gain": 20
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},
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"characters":{
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"pad": "_",
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"eos": "~",
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"bos": "^",
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"characters": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz!'(),-.:;? ",
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"punctuations":"!'(),-.:;? ",
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"phonemes":"iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻʘɓǀɗǃʄǂɠǁʛpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟˈˌːˑʍwɥʜʢʡɕʑɺɧɚ˞ɫʲ"
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},
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"hidden_size": 128,
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"embedding_size": 256,
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"text_cleaner": "english_cleaners",
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"epochs": 2000,
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"lr": 0.003,
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"lr_patience": 5,
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"lr_decay": 0.5,
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"batch_size": 2,
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"r": 5,
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"mk": 1.0,
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"num_loader_workers": 0,
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"memory_size": 5,
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"save_step": 200,
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"data_path": "tests/data/ljspeech/",
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"output_path": "result",
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"min_seq_len": 0,
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"max_seq_len": 300,
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"log_dir": "tests/outputs/",
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// MULTI-SPEAKER and GST
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"use_speaker_embedding": false, // use speaker embedding to enable multi-speaker learning.
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"use_gst": true, // use global style tokens
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"gst": { // gst parameter if gst is enabled
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"gst_style_input": null, // Condition the style input either on a
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// -> wave file [path to wave] or
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// -> dictionary using the style tokens {'token1': 'value', 'token2': 'value'} example {"0": 0.15, "1": 0.15, "5": -0.15}
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// with the dictionary being len(dict) <= len(gst_style_tokens).
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"gst_use_speaker_embedding": true, // if true pass speaker embedding in attention input GST.
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"gst_embedding_dim": 512,
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"gst_num_heads": 4,
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"gst_style_tokens": 10
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}
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}
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