def main(cfg):
preprocess_linear_specs_dataset(**cfg.model.preprocessor)
trainer = pl.Trainer(**cfg.trainer)
exp_manager(trainer, cfg.get("exp_manager", None))
model = EDMel2SpecModel(cfg=cfg.model, trainer=trainer)
epoch_time_logger = LogEpochTimeCallback()
trainer.callbacks.extend([epoch_time_logger])
trainer.fit(model)
def main():
parser = argparse.ArgumentParser(
description=
'Create dataset fitted for training and validating deep griffin iteration from wavefiles'
)
parser.add_argument(
"-v",
"--valid_filelist",
help=
"Filelist for validation set, with all validation audio files listed",
required=True,
default=None,
type=str,
)
parser.add_argument(
"-t",
"--train_filelist",
help="Filelist for train set, with all train audio files listed",
required=True,
default=None,
type=str,
)
parser.add_argument(
"-n",
"--n_fft",
help=
"Value for the n_fft parameter, and the filter length for the STFT",
default=512,
type=int,
)
parser.add_argument("--hop_length",
help="STFT parameter",
default=256,
type=int)
parser.add_argument(
"-d",
"--destination",
help="Destination to save the preprocessed data set to",
default="/tmp",
type=str)
parser.add_argument(
"-s",
"--num_snr",
help=
"Number of distinctive noisy samples to generate for each clear sample at the file list",
default=1,
type=int,
)
args = parser.parse_args()
preprocess_linear_specs_dataset(args.valid_filelist, args.train_filelist,
args.n_fft, args.hop_length, args.num_snr,
args.destination)