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, # false to comparable with kaldi
"transpose": False # F x T
}
feat_reader = SpectrogramReader(args.wav_scp, **stft_kwargs)
MaskReader = {"numpy": NumpyReader, "kaldi": ScriptReader}
mask_reader = MaskReader[args.fmt](args.mask_scp)
num_bins = nextpow2(args.frame_len) // 2 + 1
beamformer = MvdrBeamformer(num_bins)
df_pair = [tuple(map(int, p.split(","))) for p in args.df_pair.split(";")]
if not len(df_pair):
raise RuntimeError(f"Bad configurations with --pair {args.pair}")
logger.info(f"Compute directional feature with {df_pair}")
num_done = 0
with ArchiveWriter(args.dup_ark, args.scp) as writer:
for key, spect in feat_reader:
if key in mask_reader:
speech_masks = mask_reader[key]
# make sure speech_masks in T x F
_, F, _ = spect.shape
if speech_masks.shape[0] == F:
speech_masks = np.transpose(speech_masks)
speech_masks = np.minimum(speech_masks, 1)
# spectrogram: N x F x T
speech_covar = beamformer.compute_covar_mat(
speech_masks, spect)
sv = beamformer.compute_steer_vector(speech_covar)
df = directional_feats(spect, sv.T, df_pair=df_pair)
writer.write(key, df)
num_done += 1
if not num_done % 1000:
logger.info(f"Processed {num_done:d} utterance...")
else:
logger.warn(f"Missing TF-mask for utterance {key}")
logger.info(f"Processed {num_done:d} utterances over {len(feat_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, # false to comparable with kaldi
"transpose": False # F x T
}
feat_reader = SpectrogramReader(args.wav_scp, **stft_kwargs)
MaskReader = {"numpy": NumpyReader, "kaldi": ScriptReader}
mask_reader = MaskReader[args.fmt](args.mask_scp)
num_bins = nfft(args.frame_len) // 2 + 1
beamformer = MvdrBeamformer(num_bins)
num_done = 0
with ArchiveWriter(args.dup_ark, args.scp) as writer:
for key, spect in feat_reader:
if key in mask_reader:
speech_masks = mask_reader[key]
# make sure speech_masks in T x F
_, F, _ = spect.shape
if speech_masks.shape[0] == F:
speech_masks = np.transpose(speech_masks)
speech_masks = np.minimum(speech_masks, 1)
# spectrogram: N x F x T
speech_covar = beamformer.compute_covar_mat(
speech_masks, spect)
sv = beamformer.compute_steer_vector(speech_covar)
df = directional_feats(spect, sv.T)
writer.write(key, df)
num_done += 1
if not num_done % 1000:
logger.info("Processed {:d} utterance...".format(num_done))
else:
logger.warn("Missing TF-mask for utterance {}".format(key))
logger.info("Processed {:d} utterances over {:d}".format(
num_done, len(feat_reader)))
def run(args):
stft_kwargs = {
"frame_length": args.frame_length,
"frame_shift": args.frame_shift,
"window": args.window,
"center": args.center, # false to comparable with kaldi
"transpose": False # F x T
}
feat_reader = SpectrogramReader(args.wav_scp, **stft_kwargs)
mask_reader = NumpyReader(args.mask_scp) if args.numpy else ScriptReader(
args.mask_scp)
num_bins = nfft(args.frame_length) // 2 + 1
beamformer = MvdrBeamformer(num_bins)
num_done = 0
with ArchiveWriter(args.dup_ark, args.scp) as writer:
for key, spect in feat_reader:
if key in mask_reader:
speech_masks = mask_reader[key]
if args.trans:
speech_masks = np.transpose(speech_masks)
speech_masks = np.minimum(speech_masks, 1)
# spectrogram: N x F x T
speech_covar = beamformer.compute_covar_mat(
speech_masks, spect)
sv = beamformer.compute_steer_vector(speech_covar)
df = directional_feats(spect, sv.T)
writer.write(key, df)
num_done += 1
if not num_done % 1000:
logger.info("Processed {:d} utterance...".format(num_done))
else:
logger.warn("Missing TF-mask for utterance {}".format(key))
logger.info("Processed {:d} utterances over {:d}".format(
num_done, len(feat_reader)))