mirror of https://github.com/coqui-ai/TTS.git
49 lines
2.7 KiB
JSON
49 lines
2.7 KiB
JSON
{
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"model_name": "TTS-master-_tmp",
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"model_description": "Higher dropout rate for stopnet and disabled custom initialization, pull current mel prediction to stopnet.",
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"audio":{
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"audio_processor": "audio", // to use dictate different audio processors, if available.
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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, // 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": 0.97, // 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": 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": null, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
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"mel_fmax": null // maximum freq level for mel-spec. Tune for dataset!!
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},
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"embedding_size": 256,
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"text_cleaner": "english_cleaners",
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"epochs": 1000,
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"lr": 0.0015,
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"warmup_steps": 4000,
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"batch_size":32,
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"eval_batch_size":32,
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"r": 1,
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"wd": 0.000001,
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"checkpoint": true,
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"save_step": 5000,
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"print_step": 10,
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"run_eval": true,
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"data_path": "../../Data/LJSpeech-1.1/", // can overwritten from command argument
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"meta_file_train": "metadata_train.csv", // metafile for training dataloader
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"meta_file_val": "metadata_val.csv", // metafile for validation dataloader
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"data_loader": "TTSDataset", // dataloader, ["TTSDataset", "TTSDatasetCached", "TTSDatasetMemory"]
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"dataset": "ljspeech", // one of TTS.dataset.preprocessors, only valid id dataloader == "TTSDataset", rest uses "tts_cache" by default.
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"min_seq_len": 0,
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"output_path": "../keep/",
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"num_loader_workers": 8
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} |