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 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 get_audio_data(file_template, postfix='', ch_range=range(1, 7)):
audio_data = list()
for ch in ch_range:
audio_data.append(audioread(
file_template + '.CH{}{}.wav'.format(ch, postfix))[None, :])
audio_data = np.concatenate(audio_data, axis=0)
audio_data = audio_data.astype(np.float32)
return audio_data
def get_audio_single(file_template, postfix='', ch_range=range(1, 2), fs=16000):
audio_data = list()
for ch in ch_range:
audio_data.append(audioread(
file_template, sample_rate=fs)[None, :])
audio_data = np.concatenate(audio_data, axis=0)
audio_data = audio_data.astype(np.float32)
return audio_data
def get_audio_data(file_template, postfix='', ch_range=range(1, 7)):
audio_data = list()
for ch in ch_range:
audio_data.append(
audioread(file_template +
'.CH{}{}.wav'.format(ch, postfix))[None, :])
audio_data = np.concatenate(audio_data, axis=0)
audio_data = audio_data.astype(np.float32)
return audio_data
def get_audio_nochime(file_template, postfix='', ch_range=range(1, 9), fs=16000):
audio_data = list()
for ch in ch_range:
audio_data.append(audioread(
file_template + '.CH{}{}.wav'.format(ch, postfix), sample_rate=fs)[None, :])
# print("before np: {}".format(audio_data.shape))
audio_data = np.concatenate(audio_data, axis=0)
audio_data = audio_data.astype(np.float32)
# print("after np: ", audio_data.shape)
return audio_data
def get_audio_data(file_template, postfix='', ch_range=range(1, 7)):
audio_data = list()
for ch in ch_range:
audio_data.append(audioread(
file_template + '.CH{}{}.wav'.format(ch, postfix))[None, :])
# print("shape: ", audioread(file_template + '.CH{}{}.wav'.format(ch, postfix)).shape, "size: ",
# sys.getsizeof(audio_data))
# print(file_template + '.CH0.Clean.wav')
audio_data = np.concatenate(audio_data, axis=0)
audio_data = audio_data.astype(np.float32)
return audio_data
def get_audio_data_with_context(embedded_template, t_start, t_end,
ch_range=range(1, 7)):
start_context = max((t_start - 5), 0)
context_samples = (t_start - start_context) * 16000
audio_data = list()
for ch in ch_range:
audio_data.append(audioread(
embedded_template + '.CH{}.wav'.format(ch),
offset=start_context, duration=t_end - start_context)[None, :])
audio_data = np.concatenate(audio_data, axis=0)
audio_data = audio_data.astype(np.float32)
return audio_data, context_samples
def get_audio_data_with_context(embedded_template, t_start, t_end,
ch_range=range(1, 7)):
start_context = max((t_start - 5), 0)
context_samples = (t_start - start_context) * 16000
audio_data = list()
for ch in ch_range:
audio_data.append(audioread(
embedded_template + '.CH{}.wav'.format(ch),
offset=start_context, duration=t_end - start_context)[None, :])
audio_data = np.concatenate(audio_data, axis=0)
audio_data = audio_data.astype(np.float32)
return audio_data, context_samples
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 audio_joiner(path):
chime_data_dir = path
print(path)
flist = [
f for f in listdir(chime_data_dir) if isfile(join(chime_data_dir, f))
]
thefile = open('list.txt', 'w')
y = list()
counter = 0
for item in flist:
audio_file = audioread('{}/{}'.format(path, item), sample_rate=16000)
print(item)
if len(audio_file) < len(y):
c = y.copy()
c[:len(audio_file)] += audio_file
else:
c = audio_file.copy()
c[:len(y)] += y
# y = y + audio_file
audiowrite(c,
'/media/hipo/lento/Dataset/LibriSpeech/test/com.flac',
samplerate=16000)
def load_multichannel_data(prefix):
audio_mat = audioread(prefix)
return np.array(audio_mat).astype(np.float32)
def get_audio_data_1ch(filename):
audio_data = list()
audio_data.append(audioread(filename)[None, :])
audio_data = np.concatenate(audio_data, axis=0)
audio_data = audio_data.astype(np.float32)
return audio_data
N_mask = np.median(N_masks.data, axis=1)
X_mask = np.median(X_masks.data, axis=1)
print("Y: ",
Y.shape,
"N_mask: ",
N_mask.shape,
"X_mask: ",
X_mask.shape,
end="\n")
Y_hat = gev_wrapper_on_masks(Y, N_mask, X_mask)
# audiowrite(istft(Y_hat), "new_dataset_result/2m_feedback_.wav", 48000, True, True)
t_beamform += t.msecs
# second pass beamforming
# second_channel = audioread('AUDIO_RECORDING.CH2.wav', sample_rate=48000)
second_channel = audioread('new_dataset/2m/2m_pub_new.CH5.wav',
sample_rate=48000)
second_channel = np.expand_dims(second_channel, axis=0)
print("second_size", second_channel.shape, end="\n")
second_channel = stft(second_channel, time_dim=1).transpose((1, 0, 2))
print("Y_hat: ", Y_hat.shape, "second_size", second_channel.shape, end="\n")
Y_hat = np.expand_dims(Y_hat, axis=1)
Y_var_second = Variable(np.abs(Y_hat).astype(np.float32), True)
print("Y_hat_second: ", Y_hat.shape)
Y_hat = np.add(Y_hat, second_channel)
print("Y_hat_combined: ", Y_hat.shape)
with Timer() as t:
NN_masks, XX_masks = model.calc_masks(Y_var_second)
def audio_counter(path):
audio_data = audioread(path)
print(audio_data.shape)
audio_data = np.concatenate(audio_data, axis=0)
audio_data = audio_data.astype(np.float32)
print(audio_data.shape)
def load_multichannel_data(prefix):
audio_mat = [
audioread(f) for f in glob.glob('{}.CH[1-6].wav'.format(prefix))
]
return np.array(audio_mat).astype(np.float32)
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)
