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78 lines
4.0 KiB
Python
78 lines
4.0 KiB
Python
from models.synthesizer.preprocess import create_embeddings, preprocess_dataset, create_emo
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from models.synthesizer.hparams import hparams
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from pathlib import Path
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import argparse
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recognized_datasets = [
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"aidatatang_200zh",
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"aidatatang_200zh_s",
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"magicdata",
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"aishell3",
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"data_aishell"
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]
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#TODO: add for emotional data
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="Preprocesses audio files from datasets, encodes them as mel spectrograms "
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"and writes them to the disk. Audio files are also saved, to be used by the "
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"vocoder for training.",
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
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)
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parser.add_argument("datasets_root", type=Path, help=\
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"Path to the directory containing your datasets.")
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parser.add_argument("-o", "--out_dir", type=Path, default=argparse.SUPPRESS, help=\
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"Path to the output directory that will contain the mel spectrograms, the audios and the "
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"embeds. Defaults to <datasets_root>/SV2TTS/synthesizer/")
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parser.add_argument("-n", "--n_processes", type=int, default=1, help=\
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"Number of processes in parallel.")
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parser.add_argument("-s", "--skip_existing", action="store_true", help=\
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"Whether to overwrite existing files with the same name. Useful if the preprocessing was "
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"interrupted. ")
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parser.add_argument("--hparams", type=str, default="", help=\
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"Hyperparameter overrides as a comma-separated list of name-value pairs")
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parser.add_argument("--no_trim", action="store_true", help=\
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"Preprocess audio without trimming silences (not recommended).")
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parser.add_argument("--no_alignments", action="store_true", help=\
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"Use this option when dataset does not include alignments\
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(these are used to split long audio files into sub-utterances.)")
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parser.add_argument("-d", "--dataset", type=str, default="aidatatang_200zh", help=\
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"Name of the dataset to process, allowing values: magicdata, aidatatang_200zh, aishell3, data_aishell.")
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parser.add_argument("-e", "--encoder_model_fpath", type=Path, default="data/ckpt/encoder/pretrained.pt", help=\
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"Path your trained encoder model.")
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parser.add_argument("-ne", "--n_processes_embed", type=int, default=1, help=\
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"Number of processes in parallel.An encoder is created for each, so you may need to lower "
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"this value on GPUs with low memory. Set it to 1 if CUDA is unhappy")
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parser.add_argument("-ee","--emotion_extract", action="store_true", help=\
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"Preprocess audio to extract emotional numpy (for emotional vits).")
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args = parser.parse_args()
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# Process the arguments
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if not hasattr(args, "out_dir"):
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args.out_dir = args.datasets_root.joinpath("SV2TTS", "synthesizer")
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assert args.dataset in recognized_datasets, 'is not supported, please vote for it in https://github.com/babysor/MockingBird/issues/10'
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# Create directories
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assert args.datasets_root.exists()
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args.out_dir.mkdir(exist_ok=True, parents=True)
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# Verify webrtcvad is available
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if not args.no_trim:
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try:
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import webrtcvad
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except:
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raise ModuleNotFoundError("Package 'webrtcvad' not found. This package enables "
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"noise removal and is recommended. Please install and try again. If installation fails, "
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"use --no_trim to disable this error message.")
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encoder_model_fpath = args.encoder_model_fpath
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del args.no_trim
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args.hparams = hparams.parse(args.hparams)
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n_processes_embed = args.n_processes_embed
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del args.n_processes_embed
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preprocess_dataset(**vars(args))
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create_embeddings(synthesizer_root=args.out_dir, n_processes=n_processes_embed, encoder_model_fpath=encoder_model_fpath, skip_existing=args.skip_existing)
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if args.emotion_extract:
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create_emo(synthesizer_root=args.out_dir, n_processes=n_processes_embed, skip_existing=args.skip_existing, hparams=args.hparams)
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