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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)
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