def decode_one_wav(sess, model, wavedata): x_spec_t = spectrum_tool.magnitude_spectrum_librosa_stft( wavedata, PARAM.NFFT, PARAM.OVERLAP) x_phase_t = spectrum_tool.phase_spectrum_librosa_stft( wavedata, PARAM.NFFT, PARAM.OVERLAP) length = np.shape(x_spec_t)[0] x_spec = np.array([x_spec_t], dtype=np.float32) x_theta = np.array([x_phase_t], dtype=np.float32) lengths = np.array([length], dtype=np.int32) y_theta_est, y_mag_estimation, mask, x_mag, = sess.run([ model.y_theta_estimation, model.y_mag_estimation, model.mask, model._x_mag_spec, ], feed_dict={ model.x_mag: x_spec, model.lengths: lengths, model.x_theta: x_theta, }) y_mag_estimation = np.array(y_mag_estimation[0]) mask = np.array(mask[0]) # print(np.shape(y_mag_estimation), np.shape(mask)) if PARAM.RESTORE_PHASE == 'MIXED': y_mag_estimation = y_mag_estimation * np.exp( 1j * spectrum_tool.phase_spectrum_librosa_stft( wavedata, PARAM.NFFT, PARAM.OVERLAP)) reY = spectrum_tool.librosa_istft(y_mag_estimation, PARAM.NFFT, PARAM.OVERLAP) elif PARAM.RESTORE_PHASE == 'GRIFFIN_LIM': reY = spectrum_tool.griffin_lim(y_mag_estimation, PARAM.NFFT, PARAM.OVERLAP, PARAM.GRIFFIN_ITERNUM, wavedata) elif PARAM.RESTORE_PHASE == 'ESTIMATE': if y_theta_est is None: print('Model cannot estimate y_theta.') exit(-1) y_mag_estimation = y_mag_estimation * np.exp(1j * y_theta_est) reY = spectrum_tool.librosa_istft(y_mag_estimation, PARAM.NFFT, PARAM.OVERLAP) else: print('RESTORE_PHASE error.') exit(-1) # print(np.shape(mask), np.max(mask), np.min(mask)) # print(np.shape(x_mag), np.max(x_mag), np.min(x_mag)) # print(np.shape(norm_x_mag), np.max(norm_x_mag), np.min(norm_x_mag)) # print(np.shape(norm_logmag), np.max(norm_logmag), np.min(norm_logmag)) # print(np.shape(y_mag_estimation), np.max(y_mag_estimation), np.min(y_mag_estimation)) # spectrum_tool.picture_spec(mask[0],"233") return reY, mask
def decode_one_wav(sess, model, wavedata): x_spec_t = spectrum_tool.magnitude_spectrum_librosa_stft( wavedata, PARAM.NFFT, PARAM.OVERLAP) length = np.shape(x_spec_t)[0] x_spec = np.array([x_spec_t], dtype=np.float32) lengths = np.array([length], dtype=np.int32) cleaned, mask, x_mag, norm_x_mag, norm_logmag = sess.run([ model.cleaned, model.mask, model._x_mag_spec, model._norm_x_mag_spec, model._norm_x_logmag_spec ], feed_dict={ model.inputs: x_spec, model.lengths: lengths, }) cleaned = np.array(cleaned) if PARAM.RESTORE_PHASE == 'MIXED': cleaned = cleaned * np.exp( 1j * spectrum_tool.phase_spectrum_librosa_stft( wavedata, PARAM.NFFT, PARAM.OVERLAP)) reY = spectrum_tool.librosa_istft(cleaned, PARAM.NFFT, PARAM.OVERLAP) elif PARAM.RESTORE_PHASE == 'GRIFFIN_LIM': reY = spectrum_tool.griffin_lim(cleaned, PARAM.NFFT, PARAM.OVERLAP, PARAM.GRIFFIN_ITERNUM, wavedata) # print(np.shape(mask), np.max(mask), np.min(mask)) # print(np.shape(x_mag), np.max(x_mag), np.min(x_mag)) # print(np.shape(norm_x_mag), np.max(norm_x_mag), np.min(norm_x_mag)) # print(np.shape(norm_logmag), np.max(norm_logmag), np.min(norm_logmag)) # spectrum_tool.picture_spec(mask[0],"233") return reY
def _addnoise_and_decoder_one_batch(i_p, speaker_id, sub_process_speaker_num, waves_dir, noise_dir, sess, model): """ x_wav, y_wav_est """ s_time = time.time() noise_dir_list = [ os.path.join(noise_dir, _dir) for _dir in os.listdir(noise_dir) ] n_noise = len(noise_dir_list) wave_dir_list = [ os.path.join(waves_dir, _dir) for _dir in os.listdir(waves_dir) ] # print(len(wave_dir_list), os.path.dirname(wave_dir_list[0])) # mix && get input # x_batch = [] # [n_wav, time, 257] # x_theta_batch = [] # [n_wav, time, 257] # x_lengths = [] # [n_wav] batch_size = 0 for wav_dir in wave_dir_list: batch_size += 1 y_wave, sr_y = audio_tool.read_audio(wav_dir) if y_wave.ndim != 1: # aishell has 2 channel wav y_wave = y_wave.T[0] + y_wave.T[1] noise_id = np.random.randint(n_noise) noise_wave, sr_n = audio_tool.read_audio(noise_dir_list[noise_id]) noise_wave = audio_tool.repeat_to_len(noise_wave, len(y_wave)) x_wave, alpha = audio_tool._mix_wav_by_randomSNR(y_wave, noise_wave) assert sr_y == sr_n and sr_y == 16000, 'sr error sr_y:%d, sr_n %d' % ( sr_y, sr_n) x_wav_dir = wav_dir.replace('wav', addnoise_dir_name, 1) x_wav_dir = x_wav_dir.replace(root_dir, new_root_dir, 1) x_wav_father_dir = os.path.dirname(x_wav_dir) if not os.path.exists(x_wav_father_dir): os.makedirs(x_wav_father_dir) audio_tool.write_audio(x_wav_dir, x_wave, sr_y) x_spec_t = spectrum_tool.magnitude_spectrum_librosa_stft( x_wave, # [time, 257] PARAM.NFFT, PARAM.OVERLAP) x_phase_t = spectrum_tool.phase_spectrum_librosa_stft( x_wave, PARAM.NFFT, PARAM.OVERLAP) # x_batch.append(x_spec_t) # x_theta_batch.append(x_phase_t) # x_lengths.append(np.shape(x_spec_t)[0]) x_batch = np.array([x_spec_t], dtype=np.float32) x_theta_batch = np.array([x_phase_t], dtype=np.float32) x_lengths = np.array([np.shape(x_spec_t)[0]], dtype=np.int32) # enhance y_mag_est = sess.run(model.y_mag_estimation, feed_dict={ model.x_mag: x_batch, model.x_theta: x_theta_batch, model.lengths: x_lengths, }) # istf && save if PARAM.RESTORE_PHASE != 'MIXED': raise ValueError('Please set PARAM.RESTORE_PHASE=MIXED.') # istft y_mag_est = y_mag_est * np.exp(1j * x_phase_t) reY = spectrum_tool.librosa_istft(y_mag_est, PARAM.NFFT, PARAM.OVERLAP) y_wav_dir = wav_dir.replace('wav', enhanced_dir_name, 1) y_wav_dir = y_wav_dir.replace(root_dir, new_root_dir, 1) y_wav_father_dir = os.path.dirname(y_wav_dir) if not os.path.exists(y_wav_father_dir): os.makedirs(y_wav_father_dir) audio_tool.write_audio(y_wav_dir, reY, PARAM.FS) max_len = np.max(x_lengths) e_time = time.time() print("\n----------------\n" "%d workers\n" "%s\n" "Worker_id %03d, rate of progress: %d/%d\n" "time_step_max_len: %d\n" "batch_sie: %d\n" 'batch_cost_time: %ds\n' % (num_process, time.ctime(), i_p + 1, speaker_id, sub_process_speaker_num, max_len, batch_size, e_time - s_time), flush=True)
def _extract_phase(data): theta = spectrum_tool.phase_spectrum_librosa_stft(data, PARAM.NFFT, PARAM.OVERLAP) return theta
def addnoise_and_decoder_one_batch(waves_dir, noise_dir, sess, model): """ x_wav, y_wav_est """ s_time = time.time() global speaker_n speaker_n += 1 print("\n----------------\n","%d/%d"%(speaker_n,all_speaker)) sys.stdout.flush() noise_dir_list = [os.path.join(noise_dir, _dir) for _dir in os.listdir(noise_dir)] n_noise = len(noise_dir_list) wave_dir_list = [os.path.join(waves_dir, _dir) for _dir in os.listdir(waves_dir)] # print(len(wave_dir_list), os.path.dirname(wave_dir_list[0])) # mix && get input x_batch = [] # [n_wav, time, 257] x_theta_batch = [] # [n_wav, time, 257] x_lengths = [] # [n_wav] for wav_dir in wave_dir_list: y_wave, sr_y = audio_tool.read_audio(wav_dir) if y_wave.ndim != 1: # aishell has 2 channel wav y_wave = y_wave.T[0]+y_wave.T[1] noise_id = np.random.randint(n_noise) noise_wave, sr_n = audio_tool.read_audio(noise_dir_list[noise_id]) noise_wave = audio_tool.repeat_to_len(noise_wave, len(y_wave)) x_wave, alpha = audio_tool._mix_wav_by_randomSNR(y_wave, noise_wave) assert sr_y == sr_n and sr_y == 16000, 'sr error sr_y:%d, sr_n %d' % (sr_y, sr_n) x_wav_dir = wav_dir.replace('wav', addnoise_dir_name, 1) x_wav_father_dir = os.path.dirname(x_wav_dir) if not os.path.exists(x_wav_father_dir): os.makedirs(x_wav_father_dir) audio_tool.write_audio(x_wav_dir, x_wave, sr_y) x_spec_t = spectrum_tool.magnitude_spectrum_librosa_stft(x_wave, # [time, 257] PARAM.NFFT, PARAM.OVERLAP) x_phase_t = spectrum_tool.phase_spectrum_librosa_stft(x_wave, PARAM.NFFT, PARAM.OVERLAP) x_batch.append(x_spec_t) x_theta_batch.append(x_phase_t) x_lengths.append(np.shape(x_spec_t)[0]) max_len = np.max(x_lengths) print("time_step_max_len:",max_len) sys.stdout.flush() x_batch_mat = [] x_theta_batch_mat = [] for x_spec, x_theta, length in zip(x_batch, x_theta_batch, x_lengths): x_spec_mat = np.pad(x_spec, ((0,max_len-length),(0,0)), 'constant', constant_values=((0,0),(0,0))) x_theta_mat = np.pad(x_theta, ((0,max_len-length),(0,0)), 'constant', constant_values=((0,0),(0,0))) x_batch_mat.append(x_spec_mat) x_theta_batch_mat.append(x_theta_mat) x_batch = np.array(x_batch_mat, dtype=np.float32) x_theta_batch = np.array(x_theta_batch_mat, dtype=np.float32) x_lengths = np.array(x_lengths, dtype=np.int32) # enhance y_mag_est_batch = sess.run( model.y_mag_estimation, feed_dict={ model.x_mag: x_batch, model.x_theta: x_theta_batch, model.lengths: x_lengths, }) # istf && save print(np.shape(y_mag_est_batch), np.shape(x_theta_batch), np.shape(x_lengths)) sys.stdout.flush() for y_mag_est, x_theta, length, wav_dir in zip(y_mag_est_batch, x_theta_batch, x_lengths, wave_dir_list): if PARAM.RESTORE_PHASE != 'MIXED': raise ValueError('Please set PARAM.RESTORE_PHASE=MIXED.') # cat padding y_mag_est = y_mag_est[:length,:] x_theta = x_theta[:length,:] # istft y_mag_est = y_mag_est*np.exp(1j*x_theta) reY = spectrum_tool.librosa_istft(y_mag_est, PARAM.NFFT, PARAM.OVERLAP) y_wav_dir = wav_dir.replace('wav', enhanced_dir_name, 1) y_wav_father_dir = os.path.dirname(y_wav_dir) if not os.path.exists(y_wav_father_dir): os.makedirs(y_wav_father_dir) audio_tool.write_audio(y_wav_dir, reY, PARAM.FS) e_time = time.time() print('batch_cost_time: %ds' % (e_time-s_time), flush=True)