def infer():
""" CLI function to run pretrained model inference on wav files.
Args:
url_or_path(str): Path to the pretrained model.
files (List(str)): Path to the wav files to separate. Also support list
of filenames, directory names and globs.
force_overwrite (bool): Whether to overwrite output wav files.
output_dir (str): Output directory to save files.
"""
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("url_or_path",
type=str,
help="Path to the pretrained model.")
parser.add_argument(
"--files",
default=None,
type=str,
help=
"Path to the wav files to separate. Also support 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("-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)
file_list = _process_files_as_list(args.files)
for f in file_list:
model.separate(f,
force_overwrite=args.force_overwrite,
output_dir=args.output_dir)
def select_model(model_name,task):
from asteroid.models.base_models import BaseModel
if task == "Speech enhancement":
# if model_name == "ConvTasNet":
# path = "JorisCos/ConvTasNet_Libri1Mix_enhsingle_16k"
if model_name == "DPTNet":
path = "JorisCos/DPTNet_Libri1Mix_enhsingle_16k"
# elif model_name == "DPRNNTasNet":
# path = "JorisCos/DPRNNTasNet-ks2_Libri1Mix_enhsingle_16k"
# elif model_name == "DCCRNet":
# path = "JorisCos/DCCRNet_Libri1Mix_enhsingle_16k"
# elif model_name == "DCUNet":
# path = "JorisCos/DCUNet_Libri1Mix_enhsingle_16k"
elif task == "Source separation":
if model_name == "ConvTasNet":
path = "JorisCos/ConvTasNet_Libri2Mix_sepclean_16k"
if model_name == "DPRNNTasNet":
path = "mpariente/DPRNNTasNet-ks2_WHAM_sepclean"
return BaseModel.from_pretrained(path)
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,
)