def run(self):
stft_settings = {'window': self.opt['datasets']['audio_setting']['window'],
'nfft': self.opt['datasets']['audio_setting']['nfft'],
'window_length': self.opt['datasets']['audio_setting']['window_length'],
'hop_length': self.opt['datasets']['audio_setting']['hop_length'],
'center': self.opt['datasets']['audio_setting']['center']}
stft_istft = STFT(**stft_settings)
index = 0
for spec_m, spec_l, spec_r in tqdm(self.waves):
# log spk_spectrogram
EPSILON = np.finfo(np.float32).eps
log_spec = np.log(np.maximum(np.abs(spec_m), EPSILON))
# apply cmvn
cmvn = pickle.load(open(self.opt['datasets']['dataloader_setting']['cmvn_file'],'rb'))
cmvn_wave = util.apply_cmvn(log_spec,cmvn)
# calculate non silent
non_silent = util.compute_non_silent(log_spec).astype(np.bool)
target_mask = self._cluster(cmvn_wave, non_silent)
for i in range(len(target_mask)):
name = self.keys[index]
spk_spectrogram_l = target_mask[i] * spec_l
spk_spectrogram_r = target_mask[i] * spec_r
i_stft_l = stft_istft.istft(spk_spectrogram_l)
i_stft_r = stft_istft.istft(spk_spectrogram_r)
i_stft = np.concatenate((np.reshape(i_stft_l,(1,-1)), np.reshape(-1*i_stft_r,(1,-1))), axis=0)
#output_file = os.path.join(
# self.save_file, self.opt['name'], 'spk'+str(i+1))
output_file = self.save_file
os.makedirs(output_file, exist_ok=True)
#librosa.output.write_wav(output_file+'/'+name, i_stft, 8000)
sf.write(output_file+'/'+name[:-4]+'_'+str(i+1)+'.wav', i_stft.T, 8000, 'PCM_16')
index+=1
print('Processing {} utterances'.format(index))
def run(self):
stft_settings = {
'window': self.opt['audio_setting']['window'],
'nfft': self.opt['audio_setting']['nfft'],
'window_length': self.opt['audio_setting']['window_length'],
'hop_length': self.opt['audio_setting']['hop_length'],
'center': self.opt['audio_setting']['center']
}
stft_istft = STFT(**stft_settings)
index = 0
for wave in tqdm(self.waves):
# log spk_spectrogram
EPSILON = np.finfo(np.float32).eps
log_wave = np.log(np.maximum(np.abs(wave), EPSILON))
# apply cmvn
cmvn = pickle.load(open(self.opt['cmvn_file'], 'rb'))
cmvn_wave = util.apply_cmvn(log_wave, cmvn)
# calculate non silent
non_silent = util.compute_non_silent(log_wave).astype(np.bool)
target_mask = self._cluster(cmvn_wave, non_silent)
for i in range(len(target_mask)):
name = self.keys[index]
spk_spectrogram = target_mask[i] * wave
i_stft = stft_istft.istft(spk_spectrogram)
output_file = os.path.join(self.save_file, self.opt['name'],
'spk' + str(i + 1))
os.makedirs(output_file, exist_ok=True)
librosa.output.write_wav(output_file + '/' + name, i_stft,
8000)
index += 1
print('Processing {} utterances'.format(index))