def test_delay_zero_cancels_all(self):
delay = 0
X_hat = wpe.wpe(self.Y, self.K, delay=delay)
# Beginning is never zero. It is a copy of input signal.
tc.assert_allclose(X_hat[:, delay + self.K - 1:],
np.zeros_like(X_hat[:, delay + self.K - 1:]),
atol=1e-10)
def __call__(self, xs):
"""Return enhanced
:param np.ndarray xs: (Time, Channel, Frequency)
:return: enhanced_xs
:rtype: np.ndarray
"""
# nara_wpe.wpe: (F, C, T)
xs = wpe(xs.transpose((2, 1, 0)),
taps=self.taps,
delay=self.delay,
iterations=self.iterations,
psd_context=self.psd_context,
statistics_mode=self.statistics_mode)
return xs.transpose(2, 1, 0)
input_files = args.files[:len(args.files) // 2]
output_files = args.files[len(args.files) // 2:]
out_dir = os.path.dirname(output_files[0])
try:
os.makedirs(out_dir)
except OSError as e:
if e.errno != errno.EEXIST:
raise
stft_options = dict(size=512,
shift=128,
window_length=None,
fading=True,
pad=True,
symmetric_window=False)
sampling_rate = 16000
delay = 3
iterations = 5
taps = 10
signal_list = [sf.read(f)[0] for f in input_files]
y = np.stack(signal_list, axis=0)
Y = stft(y, **stft_options).transpose(2, 0, 1)
Z = wpe(Y, iterations=iterations, statistics_mode='full').transpose(1, 2, 0)
z = istft(Z, size=stft_options['size'], shift=stft_options['shift'])
for d in range(len(signal_list)):
sf.write(output_files[d], z[d, :], sampling_rate)
#!/usr/bin/python3
import numpy as np
import soundfile as sf
from tqdm import tqdm
from nara_wpe.wpe import wpe
from nara_wpe.wpe import get_power
from nara_wpe.utils import stft, istft, get_stft_center_frequencies
import sys
basename = sys.argv[1]
x = int(sys.argv[2])
print("Starting single-channel WPE")
stft_options = dict(size=512, shift=128)
for i in range(x):
y, fs = sf.read('single_channel/' + basename + '.CH' + str(i + 1) + '.wav')
Y = np.expand_dims(y, axis=0)
Y = stft(Y, size=512, shift=128)
Y = Y.transpose(2, 0, 1)
Z = wpe(Y)
z_np = istft(Z.transpose(1, 2, 0),
size=stft_options['size'],
shift=stft_options['shift'])
sf.write(
'single_dereverb/wpe/' + sys.argv[1] + '.CH' + str(i + 1) + '.wav',
z_np.T, fs)
#WPEの繰り返し回数
n_wpe_iterations=20
#残響除去のパラメータ
D=2
Lh=5
#過去のマイクロホン入力信号
x_bar=make_x_bar(stft_data,D,Lh)
#WPEで残響除去
x_dereverb_wpe,cost_buff_wpe=dereverberation_wpe(stft_data,x_bar,n_wpe_iterations)
#nara WPEで残響除去
x_dereverb_nara_wpe=wpe.wpe(np.transpose(stft_data,(1,0,2)),taps=Lh,delay=D,iterations=n_wpe_iterations)
x_dereverb_nara_wpe=np.transpose(x_dereverb_nara_wpe,(1,0,2))[0,...]
#x:入力信号( M, Nk, Lt)
t,x_dereverb_wpe=sp.istft(x_dereverb_wpe,fs=sample_rate,window="hann",nperseg=N,noverlap=N-Nshift)
t,x_dereverb_nara_wpe=sp.istft(x_dereverb_nara_wpe,fs=sample_rate,window="hann",nperseg=N,noverlap=N-Nshift)
snr_pre=calculate_snr(multi_conv_data_no_reverb[0,...],multi_conv_data[0,...])
snr_wpe_post=calculate_snr(multi_conv_data_no_reverb[0,...],x_dereverb_wpe)
snr_nara_wpe_post=calculate_snr(multi_conv_data_no_reverb[0,...],x_dereverb_nara_wpe)
write_file_from_time_signal(x_dereverb_wpe[:wave_len]*np.iinfo(np.int16).max/20.,"./dereverb_wpe_{}_{}.wav".format(Lh,D),sample_rate)
write_file_from_time_signal(x_dereverb_nara_wpe[:wave_len]*np.iinfo(np.int16).max/20.,"./dereverb_nara_wpe.wav",sample_rate)
os.makedirs(out_dir)
except OSError as e:
if e.errno != errno.EEXIST:
raise
stft_options = dict(
size=512,
shift=128,
window_length=None,
fading=True,
pad=True,
symmetric_window=False
)
sampling_rate = 16000
delay = 3
iterations = 5
taps = 10
signal_list = [
sf.read(f)[0]
for f in input_files
]
y = np.stack(signal_list, axis=0)
Y = stft(y, **stft_options).transpose(2, 0, 1)
Z = wpe(Y, iterations=iterations, statistics_mode='full').transpose(1, 2, 0)
z = istft(Z, size=stft_options['size'], shift=stft_options['shift'])
for d in range(len(signal_list)):
sf.write(output_files[d], z[d,:], sampling_rate)
def wpe_worker(
wav_scp,
processing_id=None,
processing_num=None,
data_root="MISP_121h",
output_root="MISP_121h_WPE_",
):
sampling_rate = 16000
iterations = 5
stft_options = dict(
size=512,
shift=128,
window_length=None,
fading=True,
pad=True,
symmetric_window=False,
)
with codecs.open(wav_scp, "r") as handle:
lines_content = handle.readlines()
wav_lines = [
*map(lambda x: x[:-1] if x[-1] in ["\n"] else x, lines_content)
]
for wav_idx in tqdm(
range(len(wav_lines)),
leave=True,
desc="0" if processing_id is None else str(processing_id),
):
if processing_id is None:
processing_token = True
else:
if wav_idx % processing_num == processing_id:
processing_token = True
else:
processing_token = False
if processing_token:
file_list = wav_lines[wav_idx].split(" ")
name, wav_list = file_list[0], file_list[1:]
signal_list = []
for f in wav_list:
_, data = wf.read(f)
if data.dtype == np.int16:
data = np.float32(data) / 32768
signal_list.append(data)
print("wait to process {} : {}".format(wav_idx, wav_list[0]))
min_len = len(signal_list[0])
max_len = len(signal_list[0])
for i in range(1, len(signal_list)):
min_len = min(min_len, len(signal_list[i]))
max_len = max(max_len, len(signal_list[i]))
if min_len != max_len:
for i in range(len(signal_list)):
signal_list[i] = signal_list[i][:min_len]
y = np.stack(signal_list, axis=0)
Y = stft(y, **stft_options).transpose(2, 0, 1)
Z = wpe(Y, iterations=iterations,
statistics_mode="full").transpose(1, 2, 0)
z = istft(Z,
size=stft_options["size"],
shift=stft_options["shift"])
for d in range(len(signal_list)):
store_path = wav_list[d].replace(data_root, output_root)
if not os.path.exists(os.path.split(store_path)[0]):
os.makedirs(os.path.split(store_path)[0], exist_ok=True)
tmpwav = np.int16(z[d, :] * 32768)
wf.write(store_path, sampling_rate, tmpwav)
return None
import numpy as np
import soundfile as sf
from nara_wpe.utils import istft, stft
from nara_wpe.wpe import wpe
parser = argparse.ArgumentParser(description='de-reverb audio wave.')
parser.add_argument('inwave', metavar='InWaveFile', type=str)
parser.add_argument('outwave', metavar='OutWaveFile', type=str)
args = parser.parse_args()
stft_options = dict(size=512, shift=128)
delay = 3
iterations = 5
taps = 10
data, sampling_rate = sf.read(args.inwave)
signal_list = [data]
y = np.stack(signal_list, axis=0)
Y = stft(y, **stft_options).transpose(2, 0, 1)
Z = wpe(Y,
taps=taps,
delay=delay,
iterations=iterations,
statistics_mode='full').transpose(1, 2, 0)
z = istft(Z, size=stft_options['size'], shift=stft_options['shift'])
sf.write(args.outwave, z[0], sampling_rate)
