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48 lines
2.3 KiB
Python
48 lines
2.3 KiB
Python
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from synthesizer.preprocess import preprocess_dataset
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from synthesizer.hparams import hparams
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from utils.argutils import print_args
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from pathlib import Path
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import argparse
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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 LibriSpeech/TTS 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=4, 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_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("--datasets_name", type=str, default="LibriSpeech", help=\
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"Name of the dataset directory to process.")
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parser.add_argument("--subfolders", type=str, default="train-clean-100,train-clean-360,dev-clean", help=\
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"Comma-separated list of subfolders to process inside your dataset directory")
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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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# 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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# Preprocess the dataset
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print_args(args, parser)
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args.hparams = hparams.parse(args.hparams)
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preprocess_dataset(**vars(args))
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