def run(args):
stft_kwargs = {
"frame_len": args.frame_len,
"frame_hop": args.frame_hop,
"window": args.window,
"center": args.center,
"transpose": False
}
topo = list(map(float, args.linear_topo.split(",")))
doa = args.doa if args.doa > 0 else 180 + args.doa
if doa < 0 or doa > 180:
raise RuntimeError("Illegal value for DoA: {:.2f}".format(args.doa))
spectrogram_reader = SpectrogramReader(
args.wav_scp,
round_power_of_two=args.round_power_of_two,
**stft_kwargs)
beamformer = DSBeamformer(topo)
logger.info("Initialize {:d} channel DSBeamformer".format(len(topo)))
with WaveWriter(args.dst_dir, fs=args.fs) as writer:
for key, stft_src in spectrogram_reader:
stft_enh = beamformer.run(
doa, stft_src, c=args.speed, sample_rate=args.fs)
power = spectrogram_reader.power(key)
samps = istft(stft_enh, **stft_kwargs, power=power)
writer.write(key, samps)
logger.info("Processed {:d} utterances".format(len(spectrogram_reader)))
def run(args):
stft_kwargs = {
"frame_len": args.frame_len,
"frame_hop": args.frame_hop,
"window": args.window,
"center": args.center, # false to comparable with kaldi
"transpose": False # F x T
}
spectrogram_reader = SpectrogramReader(
args.wav_scp,
round_power_of_two=args.round_power_of_two,
**stft_kwargs)
MaskReader = {"numpy": NumpyReader, "kaldi": ScriptReader}
tgt_mask_reader = MaskReader[args.fmt](args.tgt_mask)
itf_mask_reader = MaskReader[args.fmt](
args.tgt_mask) if args.itf_mask else None
if itf_mask_reader is not None:
logger.info(f"Using interfering masks from {args.itf_mask}")
online = False
num_bins = nextpow2(args.frame_len) // 2 + 1
supported_beamformer = {
"mvdr":
MvdrBeamformer(num_bins),
"mpdr":
MpdrBeamformer(num_bins),
"mpdr-whiten":
MpdrBeamformer(num_bins, whiten=True),
"gevd":
GevdBeamformer(num_bins),
"pmwf-0":
PmwfBeamformer(num_bins,
beta=0,
ref_channel=args.pmwf_ref,
rank1_appro=args.rank1_appro),
"pmwf-1":
PmwfBeamformer(num_bins,
beta=1,
ref_channel=args.pmwf_ref,
rank1_appro=args.rank1_appro)
}
supported_online_beamformer = {
"mvdr": OnlineMvdrBeamformer(num_bins, args.channels, args.alpha),
"gevd": OnlineGevdBeamformer(num_bins, args.channels, args.alpha),
}
if args.chunk_size <= 0:
logger.info(f"Using offline {args.beamformer} beamformer")
beamformer = supported_beamformer[args.beamformer]
else:
if args.chunk_size < 32:
raise RuntimeError(f"Seems chunk size({args.chunk_size:.2f}) " +
"too small for online beamformer")
beamformer = supported_online_beamformer[args.beamformer]
online = True
logger.info(f"Using online {args.beamformer} beamformer, " +
f"chunk size = {args.chunk_size:d}")
num_done = 0
with WaveWriter(args.dst_dir, sr=args.sr) as writer:
for key, stft_mat in spectrogram_reader:
if key in tgt_mask_reader:
power = spectrogram_reader.power(key)
norm = spectrogram_reader.maxabs(key)
logger.info(
f"Processing utterance {key}, " +
f"signal power {10 * np.log10(power + 1e-5):.2f}...")
# prefer T x F
speech_mask = tgt_mask_reader[key]
# constraint [0, 1]
if itf_mask_reader is None:
speech_mask = np.minimum(speech_mask, 1)
interf_mask = None
else:
interf_mask = itf_mask_reader[key]
# make sure speech_mask at shape T x F
_, F, _ = stft_mat.shape
# if in F x T
if speech_mask.shape[0] == F and speech_mask.shape[1] != F:
speech_mask = np.transpose(speech_mask)
if interf_mask is not None:
interf_mask = np.transpose(interf_mask)
if 0.5 < args.vad_proportion < 1:
vad_mask, N = compute_vad_masks(stft_mat[0],
args.vad_proportion)
logger.info(f"Filtering {N} TF-masks...")
speech_mask = np.where(vad_mask, 1.0e-4, speech_mask)
if interf_mask is not None:
interf_mask = np.where(vad_mask, 1.0e-4, interf_mask)
# stft_enh, stft_mat: (N) x F x T
try:
if not online:
stft_enh = beamformer.run(speech_mask,
stft_mat,
mask_n=interf_mask,
ban=args.ban)
else:
stft_enh = do_online_beamform(beamformer, speech_mask,
interf_mask, stft_mat,
args)
except np.linalg.LinAlgError:
logger.error(f"Raise linalg error: {key}")
continue
# masking beamformer output if necessary
if args.mask:
stft_enh = stft_enh * np.transpose(speech_mask)
samps = inverse_stft(stft_enh, norm=norm, **stft_kwargs)
writer.write(key, samps)
num_done += 1
logger.info(f"Processed {num_done:d} utterances " +
f"out of {len(spectrogram_reader):d}")
def run(args):
stft_kwargs = {
"frame_len": args.frame_len,
"frame_hop": args.frame_hop,
"window": args.window,
"center": args.center, # false to comparable with kaldi
"transpose": False # F x T
}
spectrogram_reader = SpectrogramReader(
args.wav_scp,
round_power_of_two=args.round_power_of_two,
**stft_kwargs)
MaskReader = {"numpy": NumpyReader, "kaldi": ScriptReader}
mask_reader = MaskReader[args.fmt](args.mask_scp)
online = False
num_bins = nfft(args.frame_len) // 2 + 1
supported_beamformer = {
"mvdr": MvdrBeamformer(num_bins),
"gevd": GevdBeamformer(num_bins),
"pmwf": PmwfBeamformer(num_bins)
}
supported_online_beamformer = {
"mvdr": OnlineMvdrBeamformer(num_bins, args.channels, args.alpha),
"gevd": OnlineGevdBeamformer(num_bins, args.channels, args.alpha),
}
if args.chunk_size <= 0:
logger.info("Using offline {} beamformer".format(args.beamformer))
beamformer = supported_beamformer[args.beamformer]
else:
if args.chunk_size < 32:
raise RuntimeError(
"Seems chunk size({:.2f}) too small for online beamformer".
format(args.chunk_size))
beamformer = supported_online_beamformer[args.beamformer]
online = True
logger.info("Using online {} beamformer, chunk size = {:d}".format(
args.beamformer, args.chunk_size))
num_done = 0
with WaveWriter(args.dst_dir, fs=args.samp_freq) as writer:
for key, stft_mat in spectrogram_reader:
if key in mask_reader:
num_done += 1
power = spectrogram_reader.power(key)
logger.info(
"Processing utterance {}, signal power {:.2f}...".format(
key, 10 * np.log10(power + 1e-5)))
# prefer T x F
speech_mask = mask_reader[key]
# constraint [0, 1]
speech_mask = np.minimum(speech_mask, 1)
# make sure speech_mask at shape T x F
_, F, _ = stft_mat.shape
# if in F x T
if speech_mask.shape[0] == F:
speech_mask = np.transpose(speech_mask)
# stft_enh, stft_mat: (N) x F x T
if not online:
stft_enh = beamformer.run(speech_mask,
stft_mat,
normalize=args.ban)
else:
stft_enh = do_online_beamform(beamformer, speech_mask,
stft_mat, args)
# masking beamformer output if necessary
if args.mask:
stft_enh = stft_enh * np.transpose(speech_mask)
samps = istft(stft_enh, power=power, **stft_kwargs)
writer.write(key, samps)
logger.info("Processed {:d} utterances out of {:d}".format(
num_done, len(spectrogram_reader)))
