def main():
parser = ArgumentParser()
parser.add_argument("--vad_model",
type=str,
default="MatchboxNet-VAD-3x2",
required=False,
help="Pass: '******'")
parser.add_argument(
"--dataset",
type=str,
required=True,
help=
"Path of json file of evaluation data. Audio files should have unique names.",
)
parser.add_argument("--out_dir",
type=str,
default="vad_frame",
help="Dir of your vad outputs")
parser.add_argument("--time_length", type=float, default=0.63)
parser.add_argument("--shift_length", type=float, default=0.01)
parser.add_argument("--normalize_audio", type=bool, default=False)
parser.add_argument("--num_workers", type=float, default=20)
parser.add_argument("--split_duration", type=float, default=400)
parser.add_argument(
"--dont_auto_split",
default=False,
action='store_true',
help=
"Whether to automatically split manifest entry by split_duration to avoid potential CUDA out of memory issue.",
)
args = parser.parse_args()
torch.set_grad_enabled(False)
if args.vad_model.endswith('.nemo'):
logging.info(f"Using local VAD model from {args.vad_model}")
vad_model = EncDecClassificationModel.restore_from(
restore_path=args.vad_model)
else:
logging.info(f"Using NGC cloud VAD model {args.vad_model}")
vad_model = EncDecClassificationModel.from_pretrained(
model_name=args.vad_model)
if not os.path.exists(args.out_dir):
os.mkdir(args.out_dir)
# Prepare manifest for streaming VAD
manifest_vad_input = args.dataset
if not args.dont_auto_split:
logging.info("Split long audio file to avoid CUDA memory issue")
logging.debug(
"Try smaller split_duration if you still have CUDA memory issue")
config = {
'manifest_filepath': manifest_vad_input,
'time_length': args.time_length,
'split_duration': args.split_duration,
'num_workers': args.num_workers,
}
manifest_vad_input = prepare_manifest(config)
else:
logging.warning(
"If you encounter CUDA memory issue, try splitting manifest entry by split_duration to avoid it."
)
# setup_test_data
vad_model.setup_test_data(
test_data_config={
'vad_stream': True,
'sample_rate': 16000,
'manifest_filepath': manifest_vad_input,
'labels': [
'infer',
],
'num_workers': args.num_workers,
'shuffle': False,
'time_length': args.time_length,
'shift_length': args.shift_length,
'trim_silence': False,
'normalize_audio': args.normalize_audio,
})
vad_model = vad_model.to(device)
vad_model.eval()
time_unit = int(args.time_length / args.shift_length)
trunc = int(time_unit / 2)
trunc_l = time_unit - trunc
all_len = 0
data = []
for line in open(args.dataset, 'r'):
file = json.loads(line)['audio_filepath'].split("/")[-1]
data.append(file.split(".wav")[0])
logging.info(f"Inference on {len(data)} audio files/json lines!")
status = get_vad_stream_status(data)
for i, test_batch in enumerate(vad_model.test_dataloader()):
test_batch = [x.to(device) for x in test_batch]
with autocast():
log_probs = vad_model(input_signal=test_batch[0],
input_signal_length=test_batch[1])
probs = torch.softmax(log_probs, dim=-1)
pred = probs[:, 1]
if status[i] == 'start':
to_save = pred[:-trunc]
elif status[i] == 'next':
to_save = pred[trunc:-trunc_l]
elif status[i] == 'end':
to_save = pred[trunc_l:]
else:
to_save = pred
all_len += len(to_save)
outpath = os.path.join(args.out_dir, data[i] + ".frame")
with open(outpath, "a") as fout:
for f in range(len(to_save)):
fout.write('{0:0.4f}\n'.format(to_save[f]))
del test_batch
if status[i] == 'end' or status[i] == 'single':
logging.debug(
f"Overall length of prediction of {data[i]} is {all_len}!")
all_len = 0
def diarize(self, paths2audio_files: List[str] = None, batch_size: int = 1):
"""
"""
if paths2audio_files:
self.paths2audio_files = paths2audio_files
else:
if self._cfg.diarizer.paths2audio_files is None:
raise ValueError("Pass path2audio files either through config or to diarize method")
else:
self.paths2audio_files = self._cfg.diarizer.paths2audio_files
if type(self.paths2audio_files) is str and os.path.isfile(self.paths2audio_files):
paths2audio_files = []
with open(self.paths2audio_files, 'r') as path2file:
for audiofile in path2file.readlines():
audiofile = audiofile.strip()
paths2audio_files.append(audiofile)
elif type(self.paths2audio_files) in [list, ListConfig]:
paths2audio_files = list(self.paths2audio_files)
else:
raise ValueError("paths2audio_files must be of type list or path to file containing audio files")
self.AUDIO_RTTM_MAP = audio_rttm_map(paths2audio_files, self._cfg.diarizer.path2groundtruth_rttm_files)
if self.has_vad_model:
logging.info("Performing VAD")
mfst_file = self.path2audio_files_to_manifest(paths2audio_files)
self._dont_auto_split = False
self._split_duration = 50
manifest_vad_input = mfst_file
if not self._dont_auto_split:
logging.info("Split long audio file to avoid CUDA memory issue")
logging.debug("Try smaller split_duration if you still have CUDA memory issue")
config = {
'manifest_filepath': mfst_file,
'time_length': self._vad_window_length_in_sec,
'split_duration': self._split_duration,
'num_workers': self._cfg.num_workers,
}
manifest_vad_input = prepare_manifest(config)
else:
logging.warning(
"If you encounter CUDA memory issue, try splitting manifest entry by split_duration to avoid it."
)
self._setup_vad_test_data(manifest_vad_input)
self._run_vad(manifest_vad_input)
else:
if not os.path.exists(self._speaker_manifest_path):
raise NotFoundError("Oracle VAD based manifest file not found")
self._extract_embeddings(self._speaker_manifest_path)
out_rttm_dir = os.path.join(self._out_dir, 'pred_rttms')
os.makedirs(out_rttm_dir, exist_ok=True)
perform_diarization(
embeddings_file=self._embeddings_file,
reco2num=self._num_speakers,
manifest_path=self._speaker_manifest_path,
sample_rate=self._cfg.sample_rate,
window=self._cfg.diarizer.speaker_embeddings.window_length_in_sec,
shift=self._cfg.diarizer.speaker_embeddings.shift_length_in_sec,
audio_rttm_map=self.AUDIO_RTTM_MAP,
out_rttm_dir=out_rttm_dir,
)