def _process_utterance(mel_dir, linear_dir, wav_dir, index, wav_path, text, hparams):
"""
<<<<<<< HEAD
Preprocesses a single utterance wavs/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 wavs 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:rescaling_max
- 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 wavs exception
print('file {} present in csv metadata is not present in wavs folder. skipping!'.format(
wav_path))
return None
# rescale wavs
if hparams.rescale:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
# M-AILABS extra silence specific
if hparams.trim_silence:
wav = audio.trim_silence(wav, hparams)
# Mu-law quantize
if is_mulaw_quantize(hparams.input_type):
# [0, quantize_channels)
out = 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 is_mulaw(hparams.input_type):
# [-1, 1]
out = mulaw(wav, hparams.quantize_channels)
constant_values = mulaw(0., hparams.quantize_channels)
out_dtype = np.float32
else:
# [-1, 1]
out = wav
constant_values = 0.
out_dtype = np.float32
def _process_utterance(mel_dir, linear_dir, wav_dir, index, wav_path, text):
try:
# Load the audio as numpy array
wav = audio.load_wav(wav_path)
except:
print('file {} present in csv not in folder'.format(wav_path))
return None
if hparams.rescale:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
if hparams.trim_silence:
wav = audio.trim_silence(wav)
out = mulaw_quantize(wav, hparams.quantize_channels)
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
mel_spectrogram = audio.melspectrogram(wav).astype(np.float32)
mel_frames = mel_spectrogram.shape[1]
linear_spectrogram = audio.linearspectrogram(wav).astype(np.float32)
linear_frames = linear_spectrogram.shape[1]
assert linear_frames == mel_frames
l, r = audio.pad_lr(wav, hparams.fft_size, audio.get_hop_size())
out = np.pad(out, (l, r), mode='constant', constant_values=constant_values)
time_steps = len(out)
assert time_steps >= mel_frames * audio.get_hop_size()
out = out[:mel_frames * audio.get_hop_size()]
assert time_steps % audio.get_hop_size() == 0
audio_filename = 'speech-audio-{:05d}.npy'.format(index)
mel_filename = 'speech-mel-{:05d}.npy'.format(index)
linear_filename = 'speech-linear-{:05d}.npy'.format(index)
np.save(os.path.join(wav_dir, audio_filename),
out.astype(out_dtype),
allow_pickle=False)
np.save(os.path.join(mel_dir, mel_filename),
mel_spectrogram.T,
allow_pickle=False)
np.save(os.path.join(linear_dir, linear_filename),
linear_spectrogram.T,
allow_pickle=False)
return (audio_filename, mel_filename, linear_filename, time_steps,
mel_frames, text)
def _process_utterance(mel_dir, linear_dir, wav_dir, index, 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.rescale:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
#M-AILABS extra silence specific
if hparams.trim_silence:
wav = audio.trim_silence(wav, hparams)
#Mu-law quantize
if is_mulaw_quantize(hparams.input_type):
#[0, quantize_channels)
out = 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 is_mulaw(hparams.input_type):
#[-1, 1]
out = mulaw(wav, hparams.quantize_channels)
constant_values = mulaw(0., hparams.quantize_channels)
out_dtype = np.float32
else:
#[-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:
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
#Ensure time resolution adjustement between audio and mel-spectrogram
fft_size = hparams.n_fft 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 for quantized signal
out = np.pad(out, (l, r), mode='constant', constant_values=constant_values)
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
audio_filename = 'audio-{}.npy'.format(index)
mel_filename = 'mel-{}.npy'.format(index)
linear_filename = 'linear-{}.npy'.format(index)
np.save(os.path.join(wav_dir, audio_filename),
out.astype(out_dtype),
allow_pickle=False)
np.save(os.path.join(mel_dir, mel_filename),
mel_spectrogram.T,
allow_pickle=False)
np.save(os.path.join(linear_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)
def _process_utterance(mel_dir, wav_dir, index, wav_path, hparams, speaker_id):
"""
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 spectrogram 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.rescale:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
#M-AILABS extra silence specific
if hparams.trim_silence:
wav = audio.trim_silence(wav, hparams)
#Mu-law quantize
if is_mulaw_quantize(hparams.input_type):
#[0, quantize_channels)
out = 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 is_mulaw(hparams.input_type):
#[-1, 1]
out = mulaw(wav, hparams.quantize_channels)
constant_values = mulaw(0., hparams.quantize_channels)
out_dtype = np.float32
else:
#[-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:
return None
if hparams.use_lws:
#Ensure time resolution adjustement between audio and mel-spectrogram
fft_size = hparams.n_fft 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.n_fft,
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
audio_filename = os.path.join(wav_dir, 'audio-{}.npy'.format(index))
mel_filename = os.path.join(mel_dir, 'mel-{}.npy'.format(index))
np.save(audio_filename, out.astype(out_dtype), allow_pickle=False)
np.save(mel_filename, mel_spectrogram.T, allow_pickle=False)
#global condition features
if hparams.gin_channels > 0:
# raise RuntimeError('When activating global conditions, please set your speaker_id rules in line 129 of datasets/wavenet_preprocessor.py to use them during training')
speaker_id = speaker_id #put the rule to determine how to assign speaker ids (using file names maybe? file basenames are available in "index" variable)
else:
speaker_id = speaker_id
# Return a tuple describing this training example
return (audio_filename, mel_filename, '_', speaker_id, time_steps,
mel_frames)
def _process_utterance(mel_dir, linear_dir, wav_dir, spkid, uttid, 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
#Trim lead/trail silences
if hparams.trim_silence:
wav = audio.trim_silence(wav, hparams)
#Pre-emphasize
preem_wav = audio.preemphasis(wav, hparams.preemphasis,
hparams.preemphasize)
#rescale wav
if hparams.rescale:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
preem_wav = preem_wav / np.abs(preem_wav).max() * hparams.rescaling_max
#Assert all audio is in [-1, 1]
if (wav > 1.).any() or (wav < -1.).any():
raise RuntimeError('wav has invalid value: {}'.format(wav_path))
if (preem_wav > 1.).any() or (preem_wav < -1.).any():
raise RuntimeError('wav has invalid value: {}'.format(wav_path))
#Mu-law quantize
if is_mulaw_quantize(hparams.input_type):
#[0, quantize_channels)
out = mulaw_quantize(wav, hparams.quantize_channels)
#Trim silences
start, end = audio.start_and_end_indices(out,
hparams.silence_threshold)
wav = wav[start:end]
preem_wav = preem_wav[start:end]
out = out[start:end]
constant_values = mulaw_quantize(0, hparams.quantize_channels)
out_dtype = np.int16
elif is_mulaw(hparams.input_type):
#[-1, 1]
out = mulaw(wav, hparams.quantize_channels)
constant_values = mulaw(0., hparams.quantize_channels)
out_dtype = np.float32
else:
#[-1, 1]
out = wav
constant_values = 0.
out_dtype = np.float32
# Compute the mel scale spectrogram from the wav
mel_spectrogram = audio.melspectrogram(preem_wav,
hparams).astype(np.float32)
mel_frames = mel_spectrogram.shape[1]
if mel_frames > hparams.max_mel_frames and hparams.clip_mels_length:
return None
#Compute the linear scale spectrogram from the wav
linear_spectrogram = audio.linearspectrogram(preem_wav,
hparams).astype(np.float32)
linear_frames = linear_spectrogram.shape[1]
#sanity check
assert linear_frames == mel_frames
if hparams.use_lws:
#Ensure time resolution adjustement between audio and mel-spectrogram
fft_size = hparams.n_fft 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
l_pad, r_pad = audio.librosa_pad_lr(wav, hparams.n_fft,
audio.get_hop_size(hparams),
hparams.wavenet_pad_sides)
#Reflect pad audio signal on the right (Just like it's done in Librosa to avoid frame inconsistency)
out = np.pad(out, (l_pad, r_pad),
mode='constant',
constant_values=constant_values)
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
sub_wav_dir = os.path.join(wav_dir, spkid)
sub_mel_dir = os.path.join(mel_dir, spkid)
sub_linear_dir = os.path.join(linear_dir, spkid)
os.makedirs(sub_wav_dir, exist_ok=True)
os.makedirs(sub_mel_dir, exist_ok=True)
os.makedirs(sub_linear_dir, exist_ok=True)
audio_filename = 'audio-{}.npy'.format(uttid)
mel_filename = 'mel-{}.npy'.format(uttid)
linear_filename = 'linear-{}.npy'.format(uttid)
np.save(os.path.join(sub_wav_dir, audio_filename),
out.astype(out_dtype),
allow_pickle=False)
np.save(os.path.join(sub_mel_dir, mel_filename),
mel_spectrogram.T,
allow_pickle=False)
np.save(os.path.join(sub_linear_dir, linear_filename),
linear_spectrogram.T,
allow_pickle=False)
# Return a tuple describing this training example
return (spkid, audio_filename, mel_filename, linear_filename, time_steps,
mel_frames, text)
def _process_utterance(out_dir, index, wav_path, pinyin, hparams):
'''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
pinyin: The pinyin of Chinese spoken in the input audio file
Returns:
A (spectrogram_filename, mel_filename, n_frames, text) tuple to write to train.txt
'''
mel_dir = out_dir + "/mels"
linear_dir = out_dir + "/linear"
wav_dir = out_dir + "/audio"
# Load the audio to a numpy array:
wav = audio.load_wav(wav_path, sr=hparams.sample_rate)
print("debug wav_path:", wav_path)
#rescale wav
if hparams.rescale:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
#M-AILABS extra silence specific
if hparams.trim_silence:
wav = audio.trim_silence(wav, hparams)
#Mu-law quantize
if is_mulaw_quantize(hparams.input_type):
#[0, quantize_channels)
out = 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 is_mulaw(hparams.input_type):
#[-1, 1]
out = mulaw(wav, hparams.quantize_channels)
constant_values = mulaw(0., hparams.quantize_channels)
out_dtype = np.float32
else:
#[-1, 1]
out = wav
constant_values = 0.
out_dtype = np.float32
# Compute a mel-scale spectrogram from the wav:
#mel_spectrogram = audio.melspectrogram(wav).astype(np.float32)
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:
print("debug --- drop wav_path:", wav_path, "mel_frames:", mel_frames)
return None
# Compute the linear-scale spectrogram from the wav:
#spectrogram = audio.spectrogram(wav).astype(np.float32)
#n_frames = spectrogram.shape[1]
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:
#Ensure time resolution adjustement between audio and mel-spectrogram
fft_size = hparams.n_fft 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.n_fft,
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 spectrograms to disk:
#spectrogram_filename = 'thchs30-spec-%05d.npy' % index
#mel_filename = 'thchs30-mel-%05d.npy' % index
#np.save(os.path.join(out_dir, spectrogram_filename), spectrogram.T, allow_pickle=False)
#np.save(os.path.join(out_dir, mel_filename), mel_spectrogram.T, allow_pickle=False)
audio_filename = 'audio-{}.npy'.format(index)
mel_filename = 'mel-{}.npy'.format(index)
linear_filename = 'linear-{}.npy'.format(index)
np.save(os.path.join(wav_dir, audio_filename),
out.astype(out_dtype),
allow_pickle=False)
np.save(os.path.join(mel_dir, mel_filename),
mel_spectrogram.T,
allow_pickle=False)
np.save(os.path.join(linear_dir, linear_filename),
linear_spectrogram.T,
allow_pickle=False)
print("debug save wav file:", os.path.join(wav_dir, audio_filename))
# Return a tuple describing this training example:
return (audio_filename, mel_filename, linear_filename, time_steps,
mel_frames, pinyin)
def _process_utterance(mel_dir, linear_dir, wav_dir, index, 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.rescale:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
#M-AILABS extra silence specific
if hparams.trim_silence:
wav = audio.trim_silence(wav, hparams)
#Mu-law quantize
if is_mulaw_quantize(hparams.input_type):
#[0, quantize_channels)
out = 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 is_mulaw(hparams.input_type):
#[-1, 1]
out = mulaw(wav, hparams.quantize_channels)
constant_values = mulaw(0., hparams.quantize_channels)
out_dtype = np.float32
else:
#[-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:
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
#Ensure time resolution adjustement between audio and mel-spectrogram
fft_size = hparams.n_fft 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 for quantized signal
out = np.pad(out, (l, r), mode='constant', constant_values=constant_values)
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
audio_filename = 'speech-audio-{:05d}.npy'.format(index)
mel_filename = 'speech-mel-{:05d}.npy'.format(index)
linear_filename = 'speech-linear-{:05d}.npy'.format(index)
np.save(os.path.join(wav_dir, audio_filename), out.astype(out_dtype), allow_pickle=False)
np.save(os.path.join(mel_dir, mel_filename), mel_spectrogram.T, allow_pickle=False)
np.save(os.path.join(linear_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)
def _process_utterance(mel_dir, linear_dir, wav_dir, index, wav_path, text):
"""
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
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)
except :
print('file {} present in csv not in folder'.format(
wav_path))
return None
if hparams.rescale:
wav = wav / np.abs(wav).max() * hparams.rescaling_max
#M-AILABS extra silence specific
if hparams.trim_silence:
wav = audio.trim_silence(wav)
#[0, quantize_channels)
out = 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
# Compute the mel scale spectrogram from the wav
mel_spectrogram = audio.melspectrogram(wav).astype(np.float32)
mel_frames = mel_spectrogram.shape[1]
#Compute the linear scale spectrogram from the wav
linear_spectrogram = audio.linearspectrogram(wav).astype(np.float32)
linear_frames = linear_spectrogram.shape[1]
#sanity check
assert linear_frames == mel_frames
#Ensure time resolution adjustement between audio and mel-spectrogram
l, r = audio.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)
time_steps = len(out)
assert time_steps >= mel_frames * audio.get_hop_size()
#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()]
assert time_steps % audio.get_hop_size() == 0
# Write the spectrogram and audio to disk
audio_filename = 'speech-audio-{:05d}.npy'.format(index)
mel_filename = 'speech-mel-{:05d}.npy'.format(index)
linear_filename = 'speech-linear-{:05d}.npy'.format(index)
np.save(os.path.join(wav_dir, audio_filename), out.astype(out_dtype), allow_pickle=False)
np.save(os.path.join(mel_dir, mel_filename), mel_spectrogram.T, allow_pickle=False)
np.save(os.path.join(linear_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)
def _process_utterance(mel_dir, linear_dir, wav_dir, index, wav_path, text,
hparams):
'''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 tuple: (audio_filename, mel_filename, linear_filename, time_steps, mel_frames, linear_frames, text)
'''
# Load the audio to a numpy array:
wav = audio.load_wav(wav_path, sr=hparams.sample_rate)
# Mu-law quantize
if is_mulaw_quantize(hparams.input_type):
# [0, quantize_channels)
out = 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 is_mulaw(hparams.input_type):
# [-1, 1]
out = mulaw(wav, hparams.quantize_channels)
constant_values = mulaw(0., hparams.quantize_channels)
out_dtype = np.float32
else:
# [-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:
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
# Ensure time resolution adjustement between audio and mel-spectrogram
fft_size = hparams.n_fft 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 for quantized signal
out = np.pad(out, (l, r), mode='constant', constant_values=constant_values)
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
audio_filename = 'speech-audio-{:05d}.npy'.format(index)
mel_filename = 'speech-mel-{:05d}.npy'.format(index)
linear_filename = 'speech-linear-{:05d}.npy'.format(index)
np.save(os.path.join(wav_dir, audio_filename),
out.astype(out_dtype),
allow_pickle=False)
np.save(os.path.join(mel_dir, mel_filename),
mel_spectrogram.T,
allow_pickle=False)
np.save(os.path.join(linear_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)
def _process_utterance(mel_dir, linear_dir, wav_dir, index, wav_path, text,
hparams):
wav = _trim_wav(audio.load_wav(wav_path, sr=hparams.sample_rate))
# Mu-law quantize
if is_mulaw_quantize(hparams.input_type):
# [0, quantize_channels)
out = 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 is_mulaw(hparams.input_type):
# [-1, 1]
out = mulaw(wav, hparams.quantize_channels)
constant_values = mulaw(0., hparams.quantize_channels)
out_dtype = np.float32
else:
# [-1, 1]
out = wav
constant_values = 0.
out_dtype = np.float32
mel_spectrogram = audio.melspectrogram(wav, hparams).astype(np.float32)
name = os.path.splitext(os.path.basename(wav_path))[0]
speaker_id = _speaker_re.match(name).group(1)
mel_frames = mel_spectrogram.shape[1]
if mel_frames > hparams.max_mel_frames and hparams.clip_mels_length:
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
# Ensure time resolution adjustement between audio and mel-spectrogram
fft_size = hparams.n_fft 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 for quantized signal
out = np.pad(out, (l, r), mode='constant', constant_values=constant_values)
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
audio_filename = 'speech-audio-{:05d}.npy'.format(index)
mel_filename = 'speech-mel-{:05d}.npy'.format(index)
linear_filename = 'speech-linear-{:05d}.npy'.format(index)
np.save(os.path.join(wav_dir, audio_filename),
out.astype(out_dtype),
allow_pickle=False)
np.save(os.path.join(mel_dir, mel_filename),
mel_spectrogram.T,
allow_pickle=False)
np.save(os.path.join(linear_dir, linear_filename),
linear_spectrogram.T,
allow_pickle=False)
return (audio_filename, mel_filename, linear_filename, time_steps,
mel_frames, speaker_id, text)