CelebChat / rtvc /synthesizer_preprocess_audio.py
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from synthesizer.preprocess import preprocess_librispeech, preprocess_vctk
from synthesizer.hparams import syn_hparams
from utils.argutils import print_args
from pathlib import Path
import argparse
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Preprocesses audio files from datasets, encodes them as mel spectrograms "
"and writes them to the disk. Audio files are also saved, to be used by the "
"vocoder for training.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument("datasets_root", type=Path, help=\
"Path to the directory containing your LibriSpeech/TTS datasets.")
parser.add_argument("-o", "--out_dir", type=Path, default=argparse.SUPPRESS, help=\
"Path to the output directory that will contain the mel spectrograms, the audios and the "
"embeds. Defaults to <datasets_root>/SV2TTS/synthesizer/")
parser.add_argument("-n", "--n_processes", type=int, default=4, help=\
"Number of processes in parallel.")
parser.add_argument("-s", "--skip_existing", action="store_true", help=\
"Whether to overwrite existing files with the same name. Useful if the preprocessing was "
"interrupted.")
parser.add_argument("--hparams", type=str, default="", help=\
"Hyperparameter overrides as a comma-separated list of name-value pairs")
parser.add_argument("--datasets_names", type=list, default=["LibriSpeech","VCTK"], help=\
"Name of the dataset directory to process.")
parser.add_argument("--all_subfolders", type=list, default=["train-clean-100,train-clean-360,dev-clean", "wav48_silence_trimmed"], help=\
"Comma-separated list of subfolders to process inside your dataset directory")
args = parser.parse_args()
# Process the arguments
if not hasattr(args, "out_dir"):
args.out_dir = args.datasets_root.joinpath("SV2TTS", "synthesizer")
# Create directories
assert args.datasets_root.exists()
args.out_dir.mkdir(exist_ok=True, parents=True)
# Preprocess the dataset
print_args(args, parser)
args.hparams = syn_hparams.parse(args.hparams)
preprocess_func = {
"LibriSpeech": preprocess_librispeech,
"VCTK": preprocess_vctk,
}
args = vars(args)
for i in range(len(args["datasets_names"])):
dataset = args["datasets_names"][i]
subfolders = args["all_subfolders"][i]
print("Preprocessing %s" % dataset)
preprocess_func[dataset](datasets_root=args["datasets_root"], out_dir=args["out_dir"], n_processes=args["n_processes"], skip_existing=args["skip_existing"], hparams=args["hparams"],
datasets_name=dataset, subfolders=subfolders)