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preprocess.py
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preprocess.py
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import os
import argparse
import torch
from modules.common import load_config
from modules.dataset.prepare import make_metadata
from modules.dataset.preprocess import PreprocessorParameters
from modules.dataset.preprocess import preprocess_spkinfo, preprocess_main
from modules.dataset.loader import get_datasets
def parse_args(args=None, namespace=None):
"""Parse command-line arguments."""
parser = argparse.ArgumentParser()
parser.add_argument(
"-c",
"--config",
type=str,
required=True,
help="path to the config file")
parser.add_argument(
"-d",
"--device",
type=str,
default=None,
required=False,
help="cpu or cuda, auto if not set")
return parser.parse_args(args=args, namespace=namespace)
if __name__ == '__main__':
# parse commands
cmd = parse_args()
device = cmd.device
if device is None:
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# load config
args = load_config(cmd.config)
# make metadatas
make_metadata(args.data.dataset_path, args.data.extensions)
# preprocessor parameters
params = PreprocessorParameters(
args.data.dataset_path,
sample_rate=args.data.sampling_rate,
block_size=args.data.block_size,
use_f0=True,
f0_extractor=args.data.f0_extractor,
f0_min=args.data.f0_min,
f0_max=args.data.f0_max,
use_speaker_embed=args.model.use_speaker_embed,
speaker_embed_encoder=args.data.spk_embed_encoder,
speaker_embed_encoder_path=args.data.spk_embed_encoder_ckpt,
speaker_embed_encoder_sample_rate=args.data.spk_embed_encoder_sample_rate,
per_file_speaker_embed=args.data.per_file_speaker_embed,
units_encoder=args.data.encoder,
units_encoder_path=args.data.encoder_ckpt,
units_encoder_sample_rate=args.data.encoder_sample_rate,
units_encoder_hop_size=args.data.encoder_hop_size,
units_encoder_extract_layers=args.model.units_layers,
volume_extractor_window_size=args.data.volume_window_size,
device=device)
# get dataset
ds_train = get_datasets(os.path.join(args.data.dataset_path, 'train.csv'))
test_csv = os.path.join(args.data.dataset_path, 'test.csv')
if os.path.isfile(test_csv):
ds_test = get_datasets(test_csv)
else:
ds_test = None
# process speaker embed
if args.model.use_speaker_embed:
preprocess_spkinfo(args.data.dataset_path, ds_train, params=params)
# process units, f0 and volume
preprocess_main(args.data.dataset_path, ds_train, params=params)
if ds_test is not None:
preprocess_main(args.data.dataset_path, ds_test, params=params)
os.makedirs(args.env.expdir, exist_ok=True)