def _process_utterance(out_dir, wav_path, text, hparams): """ Preprocesses a single utterance wav/text pair this writes the mel scale spectogram to disk and return a tuple to write to the train.txt file Args: - mel_dir: the directory to write the mel spectograms into - linear_dir: the directory to write the linear spectrograms into - wav_dir: the directory to write the preprocessed wav into - index: the numeric index to use in the spectogram filename - wav_path: path to the audio file containing the speech input - text: text spoken in the input audio file - hparams: hyper parameters Returns: - A tuple: (audio_filename, mel_filename, linear_filename, time_steps, mel_frames, linear_frames, text) """ try: # Load the audio as numpy array wav = audio.load_wav(wav_path, sr=hparams.sample_rate) except FileNotFoundError: #catch missing wav exception print( 'file {} present in csv metadata is not present in wav folder. skipping!' .format(wav_path)) return None #rescale wav if hparams.rescaling: # hparams.rescale = True wav = wav / np.abs(wav).max() * hparams.rescaling_max #M-AILABS extra silence specific if hparams.trim_silence: # hparams.trim_silence = True wav = audio.trim_silence(wav, hparams) # Trim leading and trailing silence #Mu-law quantize, default 값은 'raw' if hparams.input_type == 'mulaw-quantize': #[0, quantize_channels) out = audio.mulaw_quantize(wav, hparams.quantize_channels) #Trim silences start, end = audio.start_and_end_indices(out, hparams.silence_threshold) wav = wav[start:end] out = out[start:end] constant_values = mulaw_quantize(0, hparams.quantize_channels) out_dtype = np.int16 elif hparams.input_type == 'mulaw': #[-1, 1] out = audio.mulaw(wav, hparams.quantize_channels) constant_values = audio.mulaw(0., hparams.quantize_channels) out_dtype = np.float32 else: # raw #[-1, 1] out = wav constant_values = 0. out_dtype = np.float32 # Compute the mel scale spectrogram from the wav mel_spectrogram = audio.melspectrogram(wav, hparams).astype(np.float32) mel_frames = mel_spectrogram.shape[1] if mel_frames > hparams.max_mel_frames and hparams.clip_mels_length: # hparams.max_mel_frames = 1000, hparams.clip_mels_length = True return None #Compute the linear scale spectrogram from the wav linear_spectrogram = audio.linearspectrogram(wav, hparams).astype(np.float32) linear_frames = linear_spectrogram.shape[1] #sanity check assert linear_frames == mel_frames if hparams.use_lws: # hparams.use_lws = False #Ensure time resolution adjustement between audio and mel-spectrogram fft_size = hparams.fft_size if hparams.win_size is None else hparams.win_size l, r = audio.pad_lr(wav, fft_size, audio.get_hop_size(hparams)) #Zero pad audio signal out = np.pad(out, (l, r), mode='constant', constant_values=constant_values) else: #Ensure time resolution adjustement between audio and mel-spectrogram pad = audio.librosa_pad_lr(wav, hparams.fft_size, audio.get_hop_size(hparams)) #Reflect pad audio signal (Just like it's done in Librosa to avoid frame inconsistency) out = np.pad(out, pad, mode='reflect') assert len(out) >= mel_frames * audio.get_hop_size(hparams) #time resolution adjustement #ensure length of raw audio is multiple of hop size so that we can use #transposed convolution to upsample out = out[:mel_frames * audio.get_hop_size(hparams)] assert len(out) % audio.get_hop_size(hparams) == 0 time_steps = len(out) # Write the spectrogram and audio to disk wav_id = os.path.splitext(os.path.basename(wav_path))[0] # Write the spectrograms to disk: audio_filename = '{}-audio.npy'.format(wav_id) mel_filename = '{}-mel.npy'.format(wav_id) linear_filename = '{}-linear.npy'.format(wav_id) npz_filename = '{}.npz'.format(wav_id) npz_flag = True if npz_flag: # Tacotron 코드와 맞추기 위해, 같은 key를 사용한다. data = { 'audio': out.astype(out_dtype), 'mel': mel_spectrogram.T, 'linear': linear_spectrogram.T, 'time_steps': time_steps, 'mel_frames': mel_frames, 'text': text, 'tokens': text_to_sequence(text), # eos(~)에 해당하는 "1"이 끝에 붙는다. 'loss_coeff': 1 # For Tacotron } np.savez(os.path.join(out_dir, npz_filename), **data, allow_pickle=False) else: np.save(os.path.join(out_dir, audio_filename), out.astype(out_dtype), allow_pickle=False) np.save(os.path.join(out_dir, mel_filename), mel_spectrogram.T, allow_pickle=False) np.save(os.path.join(out_dir, linear_filename), linear_spectrogram.T, allow_pickle=False) # Return a tuple describing this training example return (audio_filename, mel_filename, linear_filename, time_steps, mel_frames, text, npz_filename)
def _process_utterance(out_dir, wav_path, text, hparams): """ Preprocesses a single utterance wav/text pair this writes the mel scale spectogram to disk and return a tuple to write to the train.txt file Args: - mel_dir: the directory to write the mel spectograms into - linear_dir: the directory to write the linear spectrograms into - wav_dir: the directory to write the preprocessed wav into - index: the numeric index to use in the spectogram filename - wav_path: path to the audio file containing the speech input - text: text spoken in the input audio file - hparams: hyper parameters Returns: - A tuple: (audio_filename, mel_filename, linear_filename, time_steps, mel_frames, linear_frames, text) """ try: # Load the audio as numpy array wav = audio.load_wav(wav_path, sr=hparams.sample_rate) #1차원짜리 wav파일 뽑아옴 #Load an audio file as a floating point time series. #Audio will be automatically resampled to the given rate (default sr=22050). #To preserve the native sampling rate of the file, use sr=None. #print('====wav====') #print(wav,wav.shape) (240001,) except FileNotFoundError: #catch missing wav exception print('file {} present in csv metadata is not present in wav folder. skipping!'.format( wav_path)) return None #rescale wav if hparams.rescaling: # hparams.rescale = True wav = wav / np.abs(wav).max() * hparams.rescaling_max #We rescale because it is assumed in Wavenet training that wavs are in [-1, 1] when computing the mixture loss. This is mainly coming from PixelCNN implementation. #https://github.com/Rayhane-mamah/Tacotron-2/issues/69 #M-AILABS extra silence specific if hparams.trim_silence: # hparams.trim_silence = True wav = audio.trim_silence(wav, hparams) # Trim leading and trailing silence #Mu-law quantize, default 값은 'raw' #The quantization noise is from the analog to digital conversion. The mu-law compression actually reduces the noise and increases the dynamic range. #If you search a little bit in the code you will find that the input is always mu-law encoded here. #scalar_input only determines if the model uses a one-hot encoding for every data point of the input waveform, or just uses floating point values for each sample. if hparams.input_type=='mulaw-quantize': #[0, quantize_channels) out = audio.mulaw_quantize(wav, hparams.quantize_channels) #Trim silences start, end = audio.start_and_end_indices(out, hparams.silence_threshold) wav = wav[start: end] out = out[start: end] constant_values = mulaw_quantize(0, hparams.quantize_channels) out_dtype = np.int16 elif hparams.input_type=='mulaw': #[-1, 1] out = audio.mulaw(wav, hparams.quantize_channels) constant_values = mulaw(0., hparams.quantize_channels) out_dtype = np.float32 else: # raw #[-1, 1] out = wav constant_values = 0. out_dtype = np.float32 # Compute the mel scale spectrograFm from the wav mel_spectrogram = audio.melspectrogram(wav, hparams).astype(np.float32) #print('====mel_spectrogram====') #print(mel_spectrogram,mel_spectrogram.shape) #(80,797),(80,801) ... mel_frames = mel_spectrogram.shape[1] #print('===mel frame====') #print(mel_frames) 801, 797 ,... if mel_frames > hparams.max_mel_frames and hparams.clip_mels_length: # hparams.max_mel_frames = 1000, hparams.clip_mels_length = True return None #Compute the linear scale spectrogram from the wav linear_spectrogram = audio.linearspectrogram(wav, hparams).astype(np.float32) #print('====linear_spectrogram====') #print(linear_spectrogram,linear_spectrogram.shape) #(1025,787),(1025,801) linear_frames = linear_spectrogram.shape[1] #sanity check assert linear_frames == mel_frames if hparams.use_lws: # hparams.use_lws = False #Ensure time resolution adjustement between audio and mel-spectrogram fft_size = hparams.fft_size if hparams.win_size is None else hparams.win_size l, r = audio.pad_lr(wav, fft_size, audio.get_hop_size(hparams)) #Zero pad audio signal out = np.pad(out, (l, r), mode='constant', constant_values=constant_values) else: #Ensure time resolution adjustement between audio and mel-spectrogram pad = audio.librosa_pad_lr(wav, hparams.fft_size, audio.get_hop_size(hparams)) #1024 == 2048//2 == fft_size//2 #print('===pad===') #print(pad) #Reflect pad audio signal (Just like it's done in Librosa to avoid frame inconsistency) #print(out,out.shape) #(240001,) out = np.pad(out, pad, mode='reflect') #shape : (242049,) - 패딩 #print(out,out.shape) #(242049,) #print('===out====') #print(out,out.shape) assert len(out) >= mel_frames * audio.get_hop_size(hparams) #time resolution adjustement #ensure length of raw audio is multiple of hop size so that we can use #transposed convolution to upsample out = out[:mel_frames * audio.get_hop_size(hparams)] #240300으로 맞춤(자름) assert len(out) % audio.get_hop_size(hparams) == 0 time_steps = len(out) #print(audio.get_hop_size(hparams)) : 300 #print(out,out.shape) #(240300,) = 801*300 # Write the spectrogram and audio to disk wav_id = os.path.splitext(os.path.basename(wav_path))[0] #확장자 제외하고 파일 이름 얻기 #print('====wav_id====') #print(wav_id) # Write the spectrograms to disk: audio_filename = '{}-audio.npy'.format(wav_id) mel_filename = '{}-mel.npy'.format(wav_id) linear_filename = '{}-linear.npy'.format(wav_id) npz_filename = '{}.npz'.format(wav_id) npz_flag=True if npz_flag: # Tacotron 코드와 맞추기 위해, 같은 key를 사용한다. data = { 'audio': out.astype(out_dtype), 'mel': mel_spectrogram.T, 'linear': linear_spectrogram.T, 'time_steps': time_steps, 'mel_frames': mel_frames, 'text': text, 'tokens': text_to_sequence(text), # eos(~)에 해당하는 "1"이 끝에 붙는다. 'loss_coeff': 1 # For Tacotron } #print('=====data====') #print(data) np.savez(os.path.join(out_dir,npz_filename ), **data, allow_pickle=False) #여러개의 배열을 1개의 압축되지 않은 *.npz 포맷 파일로 저장하기 else: np.save(os.path.join(out_dir, audio_filename), out.astype(out_dtype), allow_pickle=False) np.save(os.path.join(out_dir, mel_filename), mel_spectrogram.T, allow_pickle=False) np.save(os.path.join(out_dir, linear_filename), linear_spectrogram.T, allow_pickle=False) # Return a tuple describing this training example #print('====mel_frames====') #print(mel_frames) #print('====time_steps====') #print(time_steps) return (audio_filename, mel_filename, linear_filename, time_steps, mel_frames, text,npz_filename)
def _process_utterance(out_dir, index, wav_path, text): # Load the audio to a numpy array: wav = audio.load_wav(wav_path) if hparams.rescaling: wav = wav / np.abs(wav).max() * hparams.rescaling_max # Mu-law quantize if is_mulaw_quantize(hparams.input_type): # [0, quantize_channels) out = P.mulaw_quantize(wav, hparams.quantize_channels) # Trim silences start, end = audio.start_and_end_indices(out, hparams.silence_threshold) wav = wav[start:end] out = out[start:end] constant_values = P.mulaw_quantize(0, hparams.quantize_channels) out_dtype = np.int16 elif is_mulaw(hparams.input_type): # [-1, 1] out = P.mulaw(wav, hparams.quantize_channels) constant_values = P.mulaw(0.0, hparams.quantize_channels) out_dtype = np.float32 else: # [-1, 1] out = wav constant_values = 0.0 out_dtype = np.float32 # Compute a mel-scale spectrogram from the trimmed wav: # (N, D) mel_spectrogram = audio.melspectrogram(wav).astype(np.float32).T # lws pads zeros internally before performing stft # this is needed to adjust time resolution between audio and mel-spectrogram l, r = audio.lws_pad_lr(wav, hparams.fft_size, audio.get_hop_size()) # zero pad for quantized signal out = np.pad(out, (l, r), mode="constant", constant_values=constant_values) N = mel_spectrogram.shape[0] assert len(out) >= N * audio.get_hop_size() # time resolution adjustment # ensure length of raw audio is multiple of hop_size so that we can use # transposed convolution to upsample out = out[:N * audio.get_hop_size()] assert len(out) % audio.get_hop_size() == 0 timesteps = len(out) # Write the spectrograms to disk: audio_filename = 'bznsyp-audio-%05d.npy' % index mel_filename = 'bznsyp-mel-%05d.npy' % index np.save(os.path.join(out_dir, audio_filename), out.astype(out_dtype), allow_pickle=False) np.save(os.path.join(out_dir, mel_filename), mel_spectrogram.astype(np.float32), allow_pickle=False) # Return a tuple describing this training example: return (audio_filename, mel_filename, timesteps, text)
def _process_utterance(out_dir, index, audio_filepath, text): # Load the audio to a numpy array: wav_whole = audio.load_wav(audio_filepath) if hparams.rescaling: wav_whole = wav_whole / np.abs(wav_whole).max() * hparams.rescaling_max # This is a librivox source, so the audio files are going to be v. long # compared to a typical 'utterance' : So split the wav into chunks tup_results = [] n_samples = int(8.0 * hparams.sample_rate) # All 8 second utterances n_chunks = wav_whole.shape[0] // n_samples for chunk_idx in range(n_chunks): chunk_start, chunk_end = chunk_idx * n_samples, (chunk_idx + 1) * n_samples if chunk_idx == n_chunks - 1: # This is the last chunk - allow it to extend to the end of the file chunk_end = None wav = wav_whole[chunk_start: chunk_end] # Mu-law quantize if is_mulaw_quantize(hparams.input_type): # [0, quantize_channels) out = P.mulaw_quantize(wav, hparams.quantize_channels) # Trim silences start, end = audio.start_and_end_indices(out, hparams.silence_threshold) wav = wav[start:end] out = out[start:end] constant_values = P.mulaw_quantize(0, hparams.quantize_channels) out_dtype = np.int16 elif is_mulaw(hparams.input_type): # [-1, 1] out = P.mulaw(wav, hparams.quantize_channels) constant_values = P.mulaw(0.0, hparams.quantize_channels) out_dtype = np.float32 else: # [-1, 1] out = wav constant_values = 0.0 out_dtype = np.float32 # Compute a mel-scale spectrogram from the trimmed wav: # (N, D) mel_spectrogram = audio.melspectrogram(wav).astype(np.float32).T # lws pads zeros internally before performing stft # this is needed to adjust time resolution between audio and mel-spectrogram l, r = audio.lws_pad_lr(wav, hparams.fft_size, audio.get_hop_size()) # zero pad for quantized signal out = np.pad(out, (l, r), mode="constant", constant_values=constant_values) N = mel_spectrogram.shape[0] assert len(out) >= N * audio.get_hop_size() # time resolution adjustment # ensure length of raw audio is multiple of hop_size so that we can use # transposed convolution to upsample out = out[:N * audio.get_hop_size()] assert len(out) % audio.get_hop_size() == 0 timesteps = len(out) # Write the spectrograms to disk: audio_filename = 'librivox-audio-%04d-%05d.npy' % (index, chunk_idx,) mel_filename = 'librivox-mel-%04d-%05d.npy' % (index, chunk_idx,) text_idx = '%s - %05d' % (text, chunk_idx,) np.save(os.path.join(out_dir, audio_filename), out.astype(out_dtype), allow_pickle=False) np.save(os.path.join(out_dir, mel_filename), mel_spectrogram.astype(np.float32), allow_pickle=False) # Add results tuple describing this training example: tup_results.append((audio_filename, mel_filename, timesteps, text_idx)) # Return all the audio results tuples (unpack in caller) return tup_results
def _process_utterance(out_dir, index, speaker_id, wav_path, text): sr = hparams.sample_rate # Load the audio to a numpy array. Resampled if needed wav = audio.load_wav(wav_path) lab_path = wav_path.replace("wav/", "lab/").replace(".wav", ".lab") # Trim silence from hts labels if available # TODO if exists(lab_path) and False: labels = hts.load(lab_path) b = int(start_at(labels) * 1e-7 * sr) e = int(end_at(labels) * 1e-7 * sr) wav = wav[b:e] wav, _ = librosa.effects.trim(wav, top_db=20) else: wav, _ = librosa.effects.trim(wav, top_db=20) if hparams.rescaling: wav = wav / np.abs(wav).max() * hparams.rescaling_max # Mu-law quantize if is_mulaw_quantize(hparams.input_type): # [0, quantize_channels) out = P.mulaw_quantize(wav, hparams.quantize_channels) # Trim silences start, end = audio.start_and_end_indices(out, hparams.silence_threshold) wav = wav[start:end] out = out[start:end] constant_values = P.mulaw_quantize(0, hparams.quantize_channels) out_dtype = np.int16 elif is_mulaw(hparams.input_type): # [-1, 1] out = P.mulaw(wav, hparams.quantize_channels) constant_values = P.mulaw(0.0, hparams.quantize_channels) out_dtype = np.float32 else: # [-1, 1] out = wav constant_values = 0.0 out_dtype = np.float32 # Compute a mel-scale spectrogram from the trimmed wav: # (N, D) mel_spectrogram = audio.melspectrogram(wav).astype(np.float32).T # lws pads zeros internally before performing stft # this is needed to adjust time resolution between audio and mel-spectrogram l, r = audio.lws_pad_lr(wav, hparams.fft_size, audio.get_hop_size()) # zero pad for quantized signal out = np.pad(out, (l, r), mode="constant", constant_values=constant_values) N = mel_spectrogram.shape[0] assert len(out) >= N * audio.get_hop_size() # time resolution adjustment # ensure length of raw audio is multiple of hop_size so that we can use # transposed convolution to upsample out = out[:N * audio.get_hop_size()] assert len(out) % audio.get_hop_size() == 0 timesteps = len(out) # Write the spectrograms to disk: audio_filename = 'cmu_arctic-audio-%05d.npy' % index mel_filename = 'cmu_arctic-mel-%05d.npy' % index np.save(os.path.join(out_dir, audio_filename), out.astype(out_dtype), allow_pickle=False) np.save(os.path.join(out_dir, mel_filename), mel_spectrogram.astype(np.float32), allow_pickle=False) # Return a tuple describing this training example: return (audio_filename, mel_filename, timesteps, text, speaker_id)
def _process_utterance(out_dir, index, wav_path, text, silence_threshold, fft_size): '''Preprocesses a single utterance audio/text pair. This writes the mel and linear scale spectrograms to disk and returns a tuple to write to the train.txt file. Args: out_dir: The directory to write the spectrograms into index: The numeric index to use in the spectrogram filenames. wav_path: Path to the audio file containing the speech input text: The text spoken in the input audio file Returns: A (spectrogram_filename, mel_filename, text, mel_len) tuple to write to train.txt ''' # Load the audio to a numpy array: wav = audio.load_wav(wav_path) if hp.rescaling: wav = wav / np.abs(wav).max() * hp.rescaling_max if hp.input_type != "raw": # Mu-law quantize out = P.mulaw_quantize(wav) # Trim silences start, end = audio.start_and_end_indices(out, silence_threshold) out = out[start:end] wav = wav[start:end] constant_value = P.mulaw_quantize(0, 256) out_dtype = np.int16 else: out = wav constant_value = 0. out_dtype = np.float32 # Compute a mel-scale spectrogram from the trimmed wav: # (N, D) mel_spectrogram = audio.melspectrogram(wav).astype(np.float32).T # lws pads zeros internally before performing stft # this is needed to adjust time resolution between audio and mel-spectrogram l, r = audio.lws_pad_lr(wav, fft_size, audio.get_hop_size()) # zero pad for quantized signal out = np.pad(out, (l, r), mode="constant", constant_values=constant_value) mel_len = mel_spectrogram.shape[0] assert len(out) >= mel_len * audio.get_hop_size() # time resolution adjustment # ensure length of raw audio is multiple of hop_size so that we can use # transposed convolution to upsample out = out[:mel_len * audio.get_hop_size()] assert len(out) % audio.get_hop_size() == 0 timesteps = len(out) wav_id = wav_path.split('/')[-1].split('.')[0] # Write the spectrograms to disk: audio_path = os.path.join(out_dir, '{}-audio.npy'.format(wav_id)) mel_path = os.path.join(out_dir, '{}-mel.npy'.format(wav_id)) np.save(audio_path, out.astype(out_dtype), allow_pickle=False) np.save(mel_path, mel_spectrogram.astype(np.float32), allow_pickle=False) # Return a tuple describing this training example: return os.path.abspath(audio_path), os.path.abspath( mel_path), text, timesteps