def apply_beamfomer(args):
estimator = MaskEstimator(num_bins)
mask_computer = MaskComputer(estimator, args.model)
flist_name = os.path.basename(args.flist)
sub_dir = flist_name.split('.')[0]
dumps_dir = os.path.join(args.dumps_dir, sub_dir)
func_bf = mvdr_wrapper_on_masks if not args.gev else \
gev_wrapper_on_masks
if not os.path.exists(dumps_dir):
os.makedirs(dumps_dir)
with open(args.flist, 'r') as f:
flist = f.readlines()
for f in flist:
f = f.strip()
tokens = f.split('/')
noisy_samples = load_multichannel_data(f)
noisy_specs = stft(noisy_samples, time_dim=1).transpose((1, 0, 2))
mask_n, mask_x = mask_computer.compute_masks(
np.abs(noisy_specs).astype(np.float32))
mask_n = np.median(mask_n, axis=1)
mask_x = np.median(mask_x, axis=1)
clean_specs = func_bf(noisy_specs, mask_n, mask_x)
clean_samples = istft(clean_specs)
print('dumps to {}/{}.wav'.format(dumps_dir, tokens[-1]))
audiowrite(clean_samples, '{}/{}.wav'.format(dumps_dir, tokens[-1]),
16000, True, True)
def write_wav(magnitude,
phase,
filename,
exponentiate=True,
griffin_lim=False):
if exponentiate:
magnitude = np.exp(magnitude)
complex_spec = magnitude * np.exp(1j * phase)
kwargs = {'size': 512, 'shift': 64, 'window_length': 512}
resynth = istft(complex_spec, **kwargs)
if griffin_lim:
for i in range(10):
complex_spec = magnitude * np.exp(
1j * np.angle(stft(resynth, **kwargs)))
resynth = istft(complex_spec, **kwargs)
audiowrite(resynth, filename)
def apply_beamfomer(args):
estimator = MaskEstimator(num_bins)
mask_computer = MaskComputer(estimator, args.model)
func_bf = mvdr_wrapper_on_masks if not args.gev else \
gev_wrapper_on_masks
f = args.flist.strip()
tokens = f.split('/')
noisy_samples = load_multichannel_data(args.flist)
noisy_specs = stft(noisy_samples, time_dim=1).transpose((1, 0, 2))
mask_n, mask_x = mask_computer.compute_masks(
np.abs(noisy_specs).astype(np.float32))
mask_n = np.median(mask_n, axis=1)
mask_x = np.median(mask_x, axis=1)
clean_specs = func_bf(noisy_specs, mask_n, mask_x)
clean_samples = istft(clean_specs)
#print('dumps to {}/{}.wav'.format(args.dump, tokens[-1]))
audiowrite(clean_samples, '{}/{}'.format(args.dump, tokens[-1]), 16000,
True, True)
def single_normal():
# audio_data = get_audio_nochime('data/new_dataset/216m/2m_pub_new', ch_range=range(1, 9), fs=16000)
# noise_data = get_audio_nochime('data/new_dataset/blstm_noise/noise_124', ch_range=range(1, 9), fs=16000)
# audio_data = get_audio_nochime(args.data_directory, ch_range=range(1, 3), fs=16000)
t_io = 0
t_net = 0
t_beamform = 0
# check execution time
with Timer() as t:
audio_data = get_audio_nochime(args.data_directory,
ch_range=range(1, 3),
fs=16000)
context_samples = 0
print("audio_data: ", audio_data.shape, end="\n")
# for i in range (0, 8):
# print(audio_data[i][1])
t_io += t.msecs
Y = stft(audio_data, time_dim=1).transpose((1, 0, 2))
# N = stft(noise_data, time_dim=1).transpose((1, 0, 2))
Y_phase = np.divide(Y, abs(Y))
print("Y: ", Y.shape, "Y_phase: ", Y_phase.shape, end="\n")
# Y_var with or without chainer Variable class doesn't give any different
Y_var = Variable(np.abs(Y).astype(np.float32))
# N_var = Variable(np.abs(N).astype(np.float32), True)
# blstm_noise = Variable(np.abs(blstm_noise).astype(np.float32), True)
with Timer() as t:
# mask estimation
N_masks, X_masks = model.calc_masks(Y_var)
# Noise_masks = model.calc_mask_noise(N_var)
print("N_masks: ", N_masks.shape, end="\n")
N_masks.to_cpu()
X_masks.to_cpu()
t_net += t.msecs
# Noise_masks.to_cpu()
with Timer() as t:
N_mask = np.median(N_masks.data, axis=1)
X_mask = np.median(X_masks.data, axis=1)
# Noise_mask = np.median(Noise_masks.data, axis=1)
# signal = audioread('data/new_dataset/216m/2m_pub_new' + '.CH{}.wav'.format(ch), sample_rate=16000)
# noise = audioread('data/new_dataset/gevnoise/gevnoise' + '.CH{}.wav'.format(ch), sample_rate=16000)
# signal_ = stft(signal)
# noise_ = stft(noise)
#
# signal_phase = np.divide(signal, abs(signal_))
# noise_masks = model.calc_mask_noise(noise_)
# noise_to = np.multiply(noise_masks.data, signal_)
# noise_to = np.multiply(noise_to, signal_phase)
# audiowrite(istft(noise_to)[context_samples:],
# "/home/hipo/workspace/BeamSaber/result/noise/noise_to_.CH{}.wav".format(ch), 16000, True, True)
Noise = np.multiply(N_masks.data, Y)
Noise = np.multiply(Noise, Y_phase)
# Y_phase_med = np.median(Y_phase, axis=1)
# print(Noise.shape)
# for ch in range(0, 8):
# audiowrite(istft(Noise[:,ch,:])[context_samples:],
# "/home/hipo/workspace/BeamSaber/result/noise/2mnoise_.CH{}.wav".format(ch), 16000, True, True)
Noise = np.median(Noise, axis=1)
# print("N_mask: ", N_mask.shape, "X_mask: ", X_mask.shape, "Y_phase: ", Y_phase.shape, end="\n")
Y_hat = gev_wrapper_on_masks(Y, N_mask, X_mask)
# print(Y_hat.shape)
# print("Noise: ", Noise.shape)
t_beamform += t.msecs
with Timer() as t:
audiowrite(
istft(Noise)[context_samples:],
"/media/hipo/lento/workspace/BeamSaber/tools/enhancement/gev/PublicFOMLSA/sample/{}_noise.wav"
.format(args.exNum), 16000, True, True)
audiowrite(
istft(Y_hat)[context_samples:],
"/media/hipo/lento/workspace/BeamSaber/tools/enhancement/gev/PublicFOMLSA/sample/{}_gev.wav"
.format(args.exNum), 16000, True, True)
t_io += t.msecs
print(
'Timings: I/O: {:.2f}s | Net: {:.2f}s | Beamformer: {:.2f}s | Total: {:.2f}s'
.format(t_io / 1000, t_net / 1000, t_beamform / 1000,
((t_io + t_net + t_beamform) / 1000)))
with Timer() as t:
N_mask = np.median(N_masks.data, axis=1)
X_mask = np.median(X_masks.data, axis=1)
Y_hat_dicts = bf_wrapper_on_masks(Y, N_mask, X_mask, beamformers=beamformers)
t_beamform += t.msecs
if scenario == 'simu':
wsj_name = cur_line.split('/')[-1].split('_')[1]
spk = cur_line.split('/')[-1].split('_')[0]
env = cur_line.split('/')[-1].split('_')[-1]
elif scenario == 'real':
wsj_name = cur_line[3]
spk = cur_line[0].split('/')[-1].split('_')[0]
env = cur_line[0].split('/')[-1].split('_')[-1]
for beamformer, Y_hat in Y_hat_dicts.items():
filename = os.path.join(
args.output_dir,beamformer,
'{}05_{}_{}'.format(stage, env.lower(), scenario),
'{}_{}_{}.wav'.format(spk, wsj_name, env.upper())
)
with Timer() as t:
audiowrite(istft(Y_hat)[context_samples:], filename, 16000, True, True)
t_io += t.msecs
print('Finished')
print('Timings: I/O: {:.2f}s | Net: {:.2f}s | Beamformer: {:.2f}s'.format(
t_io / 1000, t_net / 1000, t_beamform / 1000
))
NN_masks.to_cpu()
XX_masks.to_cpu()
with Timer() as t:
NN_mask = np.median(NN_masks.data, axis=1)
XX_mask = np.median(XX_masks.data, axis=1)
print("Y: ",
Y_hat.shape,
"N_mask: ",
NN_mask.shape,
"X_mask: ",
XX_mask.shape,
end="\n")
# try:
YY_hat = gev_wrapper_on_masks(Y_hat, NN_mask, XX_mask)
# except AttributeError:
# YY_hat = gev_wrapper_on_masks(Y, NN_mask, XX_mask)
with Timer() as t:
audiowrite(
istft(YY_hat),
"new_dataset_result/2m_feedback_{}.wav".format(args.experiments),
48000, True, True)
t_io += t.msecs
print('Finished')
print(
'Timings: I/O: {:.2f}s | Net: {:.2f}s | Beamformer: {:.2f}s | Total Time: {:.2f}s'
.format(t_io / 1000, t_net / 1000, t_beamform / 1000,
((t_io / 1000) + (t_net / 1000) + (t_beamform / 1000))))
X_masks.to_cpu()
t_net += t.msecs
with Timer() as t:
N_mask = np.median(N_masks.data, axis=1)
X_mask = np.median(X_masks.data, axis=1)
Y_hat = gev_wrapper_on_masks(Y, N_mask, X_mask)
t_beamform += t.msecs
if scenario == 'simu':
wsj_name = cur_line.split('/')[-1].split('_')[1]
spk = cur_line.split('/')[-1].split('_')[0]
env = cur_line.split('/')[-1].split('_')[-1]
elif scenario == 'real':
wsj_name = cur_line[3]
spk = cur_line[0].split('/')[-1].split('_')[0]
env = cur_line[0].split('/')[-1].split('_')[-1]
filename = os.path.join(args.output_dir,
'{}05_{}_{}'.format(stage, env.lower(), scenario),
'{}_{}_{}.wav'.format(spk, wsj_name, env.upper()))
with Timer() as t:
audiowrite(
istft(Y_hat, audio_data.shape[1])[context_samples:], filename,
16000, True, True)
t_io += t.msecs
print('Finished')
print('Timings: I/O: {:.2f}s | Net: {:.2f}s | Beamformer: {:.2f}s'.format(
t_io / 1000, t_net / 1000, t_beamform / 1000))
t_net += t.msecs
with Timer() as t:
N_mask = np.median(N_masks.data, axis=1)
X_mask = np.median(X_masks.data, axis=1)
Y_hat = gev_wrapper_on_masks(Y, N_mask, X_mask)
t_beamform += t.msecs
if scenario == 'simu':
wsj_name = cur_line.split('/')[-1].split('_')[1]
spk = cur_line.split('/')[-1].split('_')[0]
env = cur_line.split('/')[-1].split('_')[-1]
elif scenario == 'real':
wsj_name = cur_line[3]
spk = cur_line[0].split('/')[-1].split('_')[0]
env = cur_line[0].split('/')[-1].split('_')[-1]
filename = os.path.join(
args.output_dir,
'{}05_{}_{}'.format(stage, env.lower(), scenario),
'{}_{}_{}.wav'.format(spk, wsj_name, env.upper())
)
with Timer() as t:
audiowrite(istft(Y_hat)[context_samples:], filename, 16000, True, True)
t_io += t.msecs
print('Finished')
print('Timings: I/O: {:.2f}s | Net: {:.2f}s | Beamformer: {:.2f}s'.format(
t_io / 1000, t_net / 1000, t_beamform / 1000
))
if scenario == 'simu' or args.track == 2:
wsj_name = cur_line.split('/')[-1].split('_')[1]
spk = cur_line.split('/')[-1].split('_')[0]
env = cur_line.split('/')[-1].split('_')[-1]
elif scenario == 'real':
wsj_name = cur_line[3]
spk = cur_line[0].split('/')[-1].split('_')[0]
env = cur_line[0].split('/')[-1].split('_')[-1]
filename = os.path.join(
args.output_dir, '{}05_{}_{}'.format(stage, env.lower(), scenario),
'{}_{}_{}.wav'.format(spk, wsj_name, env.upper()))
if args.track == 1:
with Timer() as t:
audiowrite(
istft(Y_hat[:, 0, :])[int(context_samples):], filename, 16000,
True, True)
t_io += t.msecs
elif args.single == 0:
with Timer() as t:
audiowrite(
istft(Y_hat)[int(context_samples):], filename, 16000, True,
True)
t_io += t.msecs
elif args.single >= 1:
ch = args.single
with Timer() as t:
audiowrite(
istft(Y_hat[:, ch - 1, :])[int(context_samples):], filename,
16000, True, True)
t_io += t.msecs