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from encoder.preprocess import preprocess_librispeech, preprocess_voxceleb1, preprocess_voxceleb2, preprocess_aidatatang_200zh | |
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 LibriSpeech, VoxCeleb1, VoxCeleb2, aidatatang_200zh. ", | |
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,aidatatang_200zh", 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, | |
"aidatatang_200zh": preprocess_aidatatang_200zh, | |
} | |
args = vars(args) | |
for dataset in args.pop("datasets"): | |
print("Preprocessing %s" % dataset) | |
preprocess_func[dataset](**args) | |