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| from encoder.preprocess import preprocess_librispeech, preprocess_voxceleb1, preprocess_voxceleb2 | |
| from utils.argutils import print_args | |
| from pathlib import Path | |
| import argparse | |
| if __name__ == "__main__": | |
| class MyFormatter(argparse.ArgumentDefaultsHelpFormatter, argparse.RawDescriptionHelpFormatter): | |
| pass | |
| parser = argparse.ArgumentParser( | |
| description="Preprocesses audio files from datasets, encodes them as mel spectrograms and " | |
| "writes them to the disk. This will allow you to train the encoder. The " | |
| "datasets required are at least one of VoxCeleb1, VoxCeleb2 and LibriSpeech. " | |
| "Ideally, you should have all three. You should extract them as they are " | |
| "after having downloaded them and put them in a same directory, e.g.:\n" | |
| "-[datasets_root]\n" | |
| " -LibriSpeech\n" | |
| " -train-other-500\n" | |
| " -VoxCeleb1\n" | |
| " -wav\n" | |
| " -vox1_meta.csv\n" | |
| " -VoxCeleb2\n" | |
| " -dev", | |
| formatter_class=MyFormatter | |
| ) | |
| parser.add_argument("datasets_root", type=Path, help=\ | |
| "Path to the directory containing your LibriSpeech/TTS and VoxCeleb datasets.") | |
| parser.add_argument("-o", "--out_dir", type=Path, default=argparse.SUPPRESS, help=\ | |
| "Path to the output directory that will contain the mel spectrograms. If left out, " | |
| "defaults to <datasets_root>/SV2TTS/encoder/") | |
| parser.add_argument("-d", "--datasets", type=str, | |
| default="librispeech_other,voxceleb1,voxceleb2", help=\ | |
| "Comma-separated list of the name of the datasets you want to preprocess. Only the train " | |
| "set of these datasets will be used. Possible names: librispeech_other, voxceleb1, " | |
| "voxceleb2.") | |
| parser.add_argument("-s", "--skip_existing", action="store_true", help=\ | |
| "Whether to skip existing output files with the same name. Useful if this script was " | |
| "interrupted.") | |
| parser.add_argument("--no_trim", action="store_true", help=\ | |
| "Preprocess audio without trimming silences (not recommended).") | |
| args = parser.parse_args() | |
| # Verify webrtcvad is available | |
| if not args.no_trim: | |
| try: | |
| import webrtcvad | |
| except: | |
| raise ModuleNotFoundError("Package 'webrtcvad' not found. This package enables " | |
| "noise removal and is recommended. Please install and try again. If installation fails, " | |
| "use --no_trim to disable this error message.") | |
| del args.no_trim | |
| # Process the arguments | |
| args.datasets = args.datasets.split(",") | |
| if not hasattr(args, "out_dir"): | |
| args.out_dir = args.datasets_root.joinpath("SV2TTS", "encoder") | |
| assert args.datasets_root.exists() | |
| args.out_dir.mkdir(exist_ok=True, parents=True) | |
| # Preprocess the datasets | |
| print_args(args, parser) | |
| preprocess_func = { | |
| "librispeech_other": preprocess_librispeech, | |
| "voxceleb1": preprocess_voxceleb1, | |
| "voxceleb2": preprocess_voxceleb2, | |
| } | |
| args = vars(args) | |
| for dataset in args.pop("datasets"): | |
| print("Preprocessing %s" % dataset) | |
| preprocess_func[dataset](**args) | |