| | 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() |
| |
|
| | |
| | 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 |
| |
|
| | |
| | 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) |
| |
|
| | |
| | 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) |
| |
|
