def prepare_training_data(chime_data_dir, dest_dir):
for stage in ['tr', 'dt']:
flist = gen_flist_simu(chime_data_dir, stage, ext=True)
export_flist = list()
# mkdir_p(os.path.join(dest_dir, stage))
for idx in range(len(flist)):
f = flist[idx]
clean_audio = get_audio_data(f, '.Clean')
noise_audio = get_audio_data(f, '.Noise')
X = stft(clean_audio, time_dim=1, size=256, shift=128).transpose(
(1, 0, 2))
N = stft(noise_audio, time_dim=1, size=256, shift=128).transpose(
(1, 0, 2))
IBM_X, IBM_N = estimate_IBM(X, N)
Y_abs = np.abs(X + N)
export_dict = {
'IBM_X': IBM_X.astype(np.float32),
'IBM_N': IBM_N.astype(np.float32),
'Y_abs': Y_abs.astype(np.float32)
}
export_name = os.path.join(dest_dir, stage, f.split('/')[-1])
with open(export_name, 'wb') as fid:
pickle.dump(export_dict, fid)
export_flist.append(os.path.join(stage, f.split('/')[-1]))
with open(os.path.join(dest_dir, 'flist_{}.json'.format(stage)),
'w') as fid:
json.dump(export_flist, fid, indent=4)
def prepare_training_data(chime_data_dir, dest_dir, suffix_id):
for stage in ['tr', 'dt']:
fpath, flist = gen_flist_simu(chime_data_dir, stage, suffix_id)
export_flist = list()
mkdir_p(os.path.join(dest_dir, stage))
for f in tqdm.tqdm(flist, desc='Generating data for {}'.format(fpath)):
clean_audio = get_audio_data(f, '_clean')
noise_audio = get_audio_data(f, '_noise')
X = stft(clean_audio, time_dim=1).transpose((1, 0, 2))
N = stft(noise_audio, time_dim=1).transpose((1, 0, 2))
IBM_X, IBM_N = estimate_IBM(X, N)
Y_abs = np.abs(X + N)
export_dict = {
'IBM_X': IBM_X.astype(np.float32),
'IBM_N': IBM_N.astype(np.float32),
'Y_abs': Y_abs.astype(np.float32)
}
export_name = os.path.join(dest_dir, stage, f.split('/')[-1])
with open(export_name, 'wb') as fid:
pickle.dump(export_dict, fid)
export_flist.append(os.path.join(stage, f.split('/')[-1]))
with open(
os.path.join(dest_dir,
'flist_{}_{}.json'.format(stage, suffix_id)),
'w') as fid:
json.dump(export_flist, fid, indent=4)
def audio_manipulation(self):
print("longto")
audio_file = audioread('new_dataset/chime_ex.wav', sample_rate=16000)
babble_file = audioread('new_dataset/babble_16.wav', sample_rate=16000)
print("len chime: ", audio_file.shape)
print("len chime: ", babble_file.shape)
audio_shape = audio_file.shape[0]
babble_shape = babble_file.shape[0]
split = int(babble_shape / audio_shape)
# y = list()
start = 0
end = audio_file.shape[0]
for i in range(1, 7):
print("start = ", start, "end = ", end)
y = babble_file[start:end]
start = end + 1
end = end + audio_file.shape[0]
audiowrite(y, "new_dataset/babble_noise/babble.CH{}.wav".format(i))
# audiowrite(y, "y.wav")
# np.split(babble_file, 2)
print("split into: ", split, "babble shape: ", babble_file.shape, "y: ",
sys.getsizeof(y))
audio_stft = stft(audio_file)
babble_stft = stft(y)
print(audio_stft.shape)
print(babble_stft.shape)
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 prepare_clean_training_data(chime_data_dir, dest_dir):
start = 0
# print("sdsd")
for stage in ['tr', 'dt']:
reset_counter = 0
flist = gen_flist_simu(chime_data_dir, stage, ext=True)
# print(flist)
export_flist = list()
mkdir_p(os.path.join(dest_dir, stage))
clean_data = audioread('/media/hipo/Mega Store/Dataset/single file/Chinese_tai_clean.wav')
print("clean_data size:", clean_data.shape[0])
for f in tqdm.tqdm(flist, desc='Generating data for {}'.format(stage)):
# clean_audio = get_audio_data(f, '.Clean')
noise_audio = get_audio_data(f, '.Noise')
# print(chime_data_dir)
chime_size = audioread('{}.CH{}{}.Noise.wav'.format(f, 1, ''))
clean_files = list()
end = chime_size.shape[0] + start
if end > clean_data.shape[0]:
print("reset counter: ", reset_counter + 1)
start = 0
end = chime_size.shape[0] + start
for i in range(1, 7):
y = clean_data[start:end]
start = end
clean_files.append(y[None, :])
clean_files = np.concatenate(clean_files, axis=0)
clean_files = clean_files.astype(np.float32)
clean_audio = clean_files
X = stft(clean_audio, time_dim=1).transpose((1, 0, 2))
N = stft(noise_audio, time_dim=1).transpose((1, 0, 2))
IBM_X, IBM_N = estimate_IBM(X, N)
Y_abs = np.abs(X + N)
export_dict = {
'IBM_X': IBM_X.astype(np.float32),
'IBM_N': IBM_N.astype(np.float32),
'Y_abs': Y_abs.astype(np.float32)
}
export_name = os.path.join(dest_dir, stage, f.split('/')[-1])
with open(export_name, 'wb') as fid:
pickle.dump(export_dict, fid)
export_flist.append(os.path.join(stage, f.split('/')[-1]))
with open(os.path.join(dest_dir, 'flist_{}.json'.format(stage)),
'w') as fid:
json.dump(export_flist, fid, indent=4)
def prepare_training_data(chime_data_dir, dest_dir):
for stage in ['tr', 'dt']:
flist = gen_flist_simu(chime_data_dir, stage, ext=True)
export_flist = list()
mkdir_p(os.path.join(dest_dir, stage))
for f in tqdm.tqdm(flist, desc='Generating data for {}'.format(stage)):
clean_audio = get_audio_data(f, '.Clean')
noise_audio = get_audio_data(f, '.Noise')
X = stft(clean_audio, time_dim=1).transpose((1, 0, 2))
N = stft(noise_audio, time_dim=1).transpose((1, 0, 2))
IBM_X, IBM_N = estimate_IBM(X, N)
Y_abs = np.abs(X + N)
export_dict = {
'IBM_X': IBM_X.astype(np.float32),
'IBM_N': IBM_N.astype(np.float32),
'Y_abs': Y_abs.astype(np.float32)
}
export_name = os.path.join(dest_dir, stage, f.split('/')[-1])
with open(export_name, 'wb') as fid:
pickle.dump(export_dict, fid)
export_flist.append(os.path.join(stage, f.split('/')[-1]))
with open(os.path.join(dest_dir, 'flist_{}.json'.format(stage)),
'w') as fid:
json.dump(export_flist, fid, indent=4)
def load_arrays_from_wav(base_dir, fname, idx, delay=0, divisor=16):
kwargs = {'time_dim': 1, 'size': 512, 'shift': 160, 'window_length': 400}
filename = os.path.join(base_dir, fname[idx])
audio = np.expand_dims(audioread(filename), axis=0)
if delay > 0:
audio = np.roll(audio, delay, axis=-1)
if audio.ndim == 3:
complex_spec = stft(audio[:, 0], **kwargs)
feats = complex_spec / 2
feats += stft(audio[:, 1], **kwargs) / 2
else:
complex_spec = stft(audio, **kwargs)
feats = complex_spec
# multiple-of-16-ify
if divisor > 1:
feats = feats[:, :, :-(feats.shape[-1] % divisor)]
pad = ((0, 0), (0, divisor - feats.shape[1] % divisor), (0, 0))
feats = np.pad(feats, pad, 'edge')
return feats.astype(np.complex64)
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 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 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)))
)))
t_io = 0
t_net = 0
t_beamform = 0
# Beamform loop
for cur_line in tqdm(flist):
with Timer() as t:
if scenario == 'simu':
audio_data = get_audio_data(cur_line)
context_samples = 0
elif scenario == 'real':
audio_data, context_samples = get_audio_data_with_context(
cur_line[0], cur_line[1], cur_line[2])
t_io += t.msecs
Y = stft(audio_data, time_dim=1).transpose((1, 0, 2))
Y_var = Variable(np.abs(Y).astype(np.float32))
if args.gpu >= 0:
Y_var.to_gpu(args.gpu)
with Timer() as t:
N_masks, X_masks = model.calc_masks(Y_var)
N_masks.to_cpu()
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_dicts = bf_wrapper_on_masks(Y, N_mask, X_mask, beamformers=beamformers)
t_beamform += t.msecs
t_io = 0
t_net = 0
t_beamform = 0
with Timer() as t:
audio_data = get_audio_nochime('new_dataset/2m/2m_pub_new',
ch_range=range(1, 9),
fs=48000)
# audio_data = get_audio_nochime('new_dataset/new_audio/AUDIO_RECORDING', ch_range=range(1, 9), fs=49000)
# calculate the time for load the audio files
t_io += t.msecs
# change the audio files into frequency domain
Y = stft(audio_data, time_dim=1).transpose((1, 0, 2))
print(audio_data.shape, type(audio_data))
Y_var = Variable(np.abs(Y).astype(np.float32), True)
# mask estimation
with Timer() as t:
N_masks, X_masks = model.calc_masks(Y_var)
N_masks.to_cpu()
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)
print("Y: ",
def prepare_other_training_data(train_dir, dest_dir):
start = 0
chime_data_dir = os.path.join(train_dir[:-1], 'tr')
print(chime_data_dir)
for stage in ['tr', 'dt']:
if stage is 'dt':
chime_data_dir = os.path.join(train_dir[:-1], 'dt')
print(chime_data_dir)
reset_counter = 0
# flist = gen_flist_simu(chime_data_dir, stage, ext=True)
flist = [f for f in listdir(chime_data_dir) if isfile(join(chime_data_dir, f))]
# print(flist)
export_flist = list()
mkdir_p(os.path.join(dest_dir, stage))
noise_data = audioread('/media/hipo/lento/Dataset/single file/noise_files/all_noise.wav')
print("noise_data size:", noise_data.shape[0])
for f in tqdm.tqdm(flist, desc='Generating data for {}'.format(stage)):
# clean_audio = get_audio_data(f)
path = os.path.join(chime_data_dir, f)
clean_audio = get_audio_single(path)
# clean_audioa = audioread(path)
# clean_audiob = audioread(path)
# multi_track = list()
# multi_track.append(clean_audioa[None, :])
# multi_track.append(clean_audiob[None, :])
# multi_track = np.concatenate(multi_track, axis=0)
# multi_track = multi_track.astype(np.float32)
# print(multi_track.shape)
chime_size = audioread(path)
noise_files = list()
end = chime_size.shape[0] + start
if end > noise_data.shape[0]:
print("reset counter: ", reset_counter + 1)
start = 0
end = chime_size.shape[0] + start
for i in range(1, 2):
y = noise_data[start:end]
start = end
noise_files.append(y[None, :])
noise_files = np.concatenate(noise_files, axis=0)
noise_files = noise_files.astype(np.float32)
noise_audio = noise_files
# print("speech size: ", multi_track.shape, "noise size: ", noise_audio.shape)
X = stft(clean_audio, time_dim=1).transpose((1, 0, 2))
N = stft(noise_audio, time_dim=1).transpose((1, 0, 2))
IBM_X, IBM_N = estimate_IBM(X, N)
Y_abs = np.abs(X + N)
export_dict = {
'IBM_X': IBM_X.astype(np.float32),
'IBM_N': IBM_N.astype(np.float32),
'Y_abs': Y_abs.astype(np.float32)
}
export_name = os.path.join(dest_dir, stage, f.split('/')[-1])
with open(export_name, 'wb') as fid:
pickle.dump(export_dict, fid)
export_flist.append(os.path.join(stage, f.split('/')[-1]))
with open(os.path.join(dest_dir, 'flist_{}.json'.format(stage)),
'w') as fid:
json.dump(export_flist, fid, indent=4)
)))
t_io = 0
t_net = 0
t_beamform = 0
# Beamform loop
for cur_line in tqdm(flist):
with Timer() as t:
if scenario == 'simu':
audio_data = get_audio_data(cur_line)
context_samples = 0
elif scenario == 'real':
audio_data, context_samples = get_audio_data_with_context(
cur_line[0], cur_line[1], cur_line[2])
t_io += t.msecs
Y = stft(audio_data, time_dim=1).transpose((1, 0, 2))
Y_var = Variable(np.abs(Y).astype(np.float32), True)
if args.gpu >= 0:
Y_var.to_gpu(args.gpu)
with Timer() as t:
N_masks, X_masks = model.calc_masks(Y_var)
N_masks.to_cpu()
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