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65 lines
3.0 KiB
Python
65 lines
3.0 KiB
Python
import argparse
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from pathlib import Path
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from models.encoder.preprocess import (preprocess_aidatatang_200zh,
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preprocess_librispeech, preprocess_voxceleb1,
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preprocess_voxceleb2)
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from utils.argutils import print_args
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if __name__ == "__main__":
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class MyFormatter(argparse.ArgumentDefaultsHelpFormatter, argparse.RawDescriptionHelpFormatter):
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pass
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parser = argparse.ArgumentParser(
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description="Preprocesses audio files from datasets, encodes them as mel spectrograms and "
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"writes them to the disk. This will allow you to train the encoder. The "
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"datasets required are at least one of LibriSpeech, VoxCeleb1, VoxCeleb2, aidatatang_200zh. ",
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formatter_class=MyFormatter
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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 and VoxCeleb 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. If left out, "
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"defaults to <datasets_root>/SV2TTS/encoder/")
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parser.add_argument("-d", "--datasets", type=str,
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default="librispeech_other,voxceleb1,aidatatang_200zh", help=\
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"Comma-separated list of the name of the datasets you want to preprocess. Only the train "
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"set of these datasets will be used. Possible names: librispeech_other, voxceleb1, "
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"voxceleb2.")
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parser.add_argument("-s", "--skip_existing", action="store_true", help=\
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"Whether to skip existing output files with the same name. Useful if this script was "
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"interrupted.")
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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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args = parser.parse_args()
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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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del args.no_trim
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# Process the arguments
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args.datasets = args.datasets.split(",")
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if not hasattr(args, "out_dir"):
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args.out_dir = args.datasets_root.joinpath("SV2TTS", "encoder")
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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 datasets
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print_args(args, parser)
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preprocess_func = {
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"librispeech_other": preprocess_librispeech,
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"voxceleb1": preprocess_voxceleb1,
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"voxceleb2": preprocess_voxceleb2,
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"aidatatang_200zh": preprocess_aidatatang_200zh,
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}
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args = vars(args)
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for dataset in args.pop("datasets"):
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print("Preprocessing %s" % dataset)
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preprocess_func[dataset](**args)
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