def test_separate():
nnet = ConvTasNet(
n_src=2,
n_repeats=2,
n_blocks=3,
bn_chan=16,
hid_chan=4,
skip_chan=8,
n_filters=32,
)
# Test torch input
wav = torch.rand(1, 1, 8000)
model = LambdaOverlapAdd(nnet, None, window_size=1000)
out = separate(model, wav)
def infer():
"""CLI function to run pretrained model inference on wav files."""
parser = argparse.ArgumentParser()
parser.add_argument("url_or_path",
type=str,
help="Path to the pretrained model.")
parser.add_argument(
"--files",
default=None,
required=True,
type=str,
help=
"Path to the wav files to separate. Also supports list of filenames, "
"directory names and globs.",
nargs="+",
)
parser.add_argument(
"-f",
"--force-overwrite",
action="store_true",
help="Whether to overwrite output wav files.",
)
parser.add_argument(
"-r",
"--resample",
action="store_true",
help="Whether to resample wrong sample rate input files.",
)
parser.add_argument(
"-w",
"--ola-window",
type=validate_window_length,
default=None,
help=
"Overlap-add window to use. If not set (default), overlap-add is not used.",
)
parser.add_argument(
"--ola-hop",
type=validate_window_length,
default=None,
help=
"Overlap-add hop length in samples. Defaults to ola-window // 2. Only used if --ola-window is set.",
)
parser.add_argument(
"--ola-window-type",
type=str,
default="hanning",
help=
"Type of overlap-add window to use. Only used if --ola-window is set.",
)
parser.add_argument(
"--ola-no-reorder",
action="store_true",
help=
"Disable automatic reordering of overlap-add chunk. See asteroid.dsp.LambdaOverlapAdd for details. "
"Only used if --ola-window is set.",
)
parser.add_argument("-o",
"--output-dir",
default=None,
type=str,
help="Output directory to save files.")
args = parser.parse_args()
model = BaseModel.from_pretrained(
pretrained_model_conf_or_path=args.url_or_path)
if args.ola_window is not None:
model = LambdaOverlapAdd(
model,
n_src=None,
window_size=args.ola_window,
hop_size=args.ola_hop,
window=args.ola_window_type,
reorder_chunks=not args.ola_no_reorder,
)
file_list = _process_files_as_list(args.files)
for f in file_list:
separate(
model,
f,
force_overwrite=args.force_overwrite,
output_dir=args.output_dir,
resample=args.resample,
)