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from synthesizer.preprocess import create_embeddings | |
from utils.argutils import print_args | |
from pathlib import Path | |
import argparse | |
from synthesizer.preprocess import preprocess_dataset | |
from synthesizer.hparams import hparams | |
from utils.argutils import print_args | |
from pathlib import Path | |
import argparse | |
recognized_datasets = [ | |
"aidatatang_200zh", | |
"magicdata", | |
"aishell3", | |
"data_aishell" | |
] | |
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 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=1, 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("--no_trim", action="store_true", help=\ | |
"Preprocess audio without trimming silences (not recommended).") | |
parser.add_argument("--no_alignments", action="store_true", help=\ | |
"Use this option when dataset does not include alignments\ | |
(these are used to split long audio files into sub-utterances.)") | |
parser.add_argument("-d", "--dataset", type=str, default="aidatatang_200zh", help=\ | |
"Name of the dataset to process, allowing values: magicdata, aidatatang_200zh, aishell3, data_aishell.") | |
parser.add_argument("-e", "--encoder_model_fpath", type=Path, default="encoder/saved_models/pretrained.pt", help=\ | |
"Path your trained encoder model.") | |
parser.add_argument("-ne", "--n_processes_embed", type=int, default=1, help=\ | |
"Number of processes in parallel.An encoder is created for each, so you may need to lower " | |
"this value on GPUs with low memory. Set it to 1 if CUDA is unhappy") | |
args = parser.parse_args() | |
# Process the arguments | |
if not hasattr(args, "out_dir"): | |
args.out_dir = args.datasets_root.joinpath("SV2TTS", "synthesizer") | |
assert args.dataset in recognized_datasets, 'is not supported, please vote for it in https://github.com/babysor/MockingBird/issues/10' | |
# Create directories | |
assert args.datasets_root.exists() | |
args.out_dir.mkdir(exist_ok=True, parents=True) | |
# 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.") | |
encoder_model_fpath = args.encoder_model_fpath | |
del args.no_trim, args.encoder_model_fpath | |
args.hparams = hparams.parse(args.hparams) | |
n_processes_embed = args.n_processes_embed | |
del args.n_processes_embed | |
preprocess_dataset(**vars(args)) | |
create_embeddings(synthesizer_root=args.out_dir, n_processes=n_processes_embed, encoder_model_fpath=encoder_model_fpath) | |