def run(args):
stft_kwargs = {
"frame_len": args.frame_len,
"frame_hop": args.frame_hop,
"round_power_of_two": args.round_power_of_two,
"window": args.window,
"center": args.center,
"transpose": False
}
np.random.seed(args.seed)
spectrogram_reader = SpectrogramReader(args.wav_scp, **stft_kwargs)
MaskReader = {"numpy": NumpyReader, "kaldi": ScriptReader}
init_mask_reader = MaskReader[args.fmt](
args.init_mask) if args.init_mask else None
num_done = 0
with NumpyWriter(args.dst_dir) as writer:
dst_dir = Path(args.dst_dir)
for key, stft in spectrogram_reader:
if not (dst_dir / f"{key}.npy").exists():
init_mask = None
if init_mask_reader and key in init_mask_reader:
init_mask = init_mask_reader[key]
# T x F => F x T
if init_mask.ndim == 2:
init_mask = np.transpose(init_mask)
else:
init_mask = np.transpose(init_mask, (0, 2, 1))
logger.info("Using external TF-mask to initialize cgmm")
# stft: N x F x T
trainer = CgmmTrainer(stft,
args.num_classes,
gamma=init_mask,
update_alpha=args.update_alpha)
try:
masks = trainer.train(args.num_iters)
# K x F x T => K x T x F
masks = np.transpose(masks, (0, 2, 1))
num_done += 1
if args.solve_permu:
masks = permu_aligner(masks)
logger.info(
"Permutation alignment done on each frequency")
if args.num_classes == 2:
masks = masks[0]
writer.write(key, masks.astype(np.float32))
logger.info(f"Training utterance {key} ... Done")
except RuntimeError:
logger.warn(f"Training utterance {key} ... Failed")
else:
logger.info(f"Training utterance {key} ... Skip")
logger.info(
f"Train {num_done:d} utterances over {len(spectrogram_reader):d}")
def run(args):
stft_kwargs = {
"frame_len": args.frame_len,
"frame_hop": args.frame_hop,
"round_power_of_two": args.round_power_of_two,
"window": args.window,
"center": args.center,
"transpose": False
}
np.random.seed(args.seed)
spectrogram_reader = SpectrogramReader(args.wav_scp, **stft_kwargs)
MaskReader = {"numpy": NumpyReader, "kaldi": ScriptReader}
init_mask_reader = MaskReader[args.fmt](
args.init_mask) if args.init_mask else None
num_done = 0
with NumpyWriter(args.dst_dir) as writer:
dst_dir = Path(args.dst_dir)
for key, stft in spectrogram_reader:
if not (dst_dir / f"{key}.npy").exists():
# K x F x T
init_mask = None
if init_mask_reader and key in init_mask_reader:
init_mask = init_mask_reader[key]
logger.info("Using external mask to initialize cacgmm")
# stft: N x F x T
trainer = CacgmmTrainer(stft,
args.num_classes,
gamma=init_mask,
cgmm_init=args.cgmm_init)
try:
# EM progress
masks = trainer.train(args.num_epoches)
# align if needed
if not args.cgmm_init or args.num_classes != 2:
# K x F x T => K x T x F
masks = permu_aligner(masks, transpose=True)
logger.info(
"Permutation alignment done on each frequency")
num_done += 1
writer.write(key, masks.astype("float32"))
logger.info(f"Training utterance {key} ... Done")
except np.linalg.LinAlgError:
logger.warn(f"Training utterance {key} ... Failed")
else:
logger.info(f"Training utterance {key} ... Skip")
logger.info(
f"Train {num_done:d} utterances over {len(spectrogram_reader):d}")