Python源码示例：hparams.hparams.silence_threshold()
示例1
def adjust_time_resolution(quantized, mel):
"""Adjust time resolution by repeating features
Args:
quantized (ndarray): (T,)
mel (ndarray): (N, D)
Returns:
tuple: Tuple of (T,) and (T, D)
"""
assert len(quantized.shape) == 1
assert len(mel.shape) == 2
upsample_factor = quantized.size // mel.shape[0]
mel = np.repeat(mel, upsample_factor, axis=0)
n_pad = quantized.size - mel.shape[0]
if n_pad != 0:
assert n_pad > 0
mel = np.pad(mel, [(0, n_pad), (0, 0)], mode="constant", constant_values=0)
# trim
start, end = start_and_end_indices(quantized, hparams.silence_threshold)
return quantized[start:end], mel[start:end, :]
示例2
def adjust_time_resolution(quantized, mel):
"""Adjust time resolution by repeating features
Args:
quantized (ndarray): (T,)
mel (ndarray): (N, D)
Returns:
tuple: Tuple of (T,) and (T, D)
"""
assert len(quantized.shape) == 1
assert len(mel.shape) == 2
upsample_factor = quantized.size // mel.shape[0]
mel = np.repeat(mel, upsample_factor, axis=0)
n_pad = quantized.size - mel.shape[0]
if n_pad != 0:
assert n_pad > 0
mel = np.pad(mel, [(0, n_pad), (0, 0)], mode="constant", constant_values=0)
# trim
start, end = start_and_end_indices(quantized, hparams.silence_threshold)
return quantized[start:end], mel[start:end, :]
示例3
def adjust_time_resolution(quantized, mel):
"""Adjust time resolution by repeating features
Args:
quantized (ndarray): (T,)
mel (ndarray): (N, D)
Returns:
tuple: Tuple of (T,) and (T, D)
"""
assert len(quantized.shape) == 1
assert len(mel.shape) == 2
upsample_factor = quantized.size // mel.shape[0]
mel = np.repeat(mel, upsample_factor, axis=0)
n_pad = quantized.size - mel.shape[0]
if n_pad != 0:
assert n_pad > 0
mel = np.pad(mel, [(0, n_pad), (0, 0)], mode="constant", constant_values=0)
# trim
start, end = start_and_end_indices(quantized, hparams.silence_threshold)
return quantized[start:end], mel[start:end, :]
示例4
def adjust_time_resolution(quantized, mel):
"""Adjust time resolution by repeating features
Args:
quantized (ndarray): (T,)
mel (ndarray): (N, D)
Returns:
tuple: Tuple of (T,) and (T, D)
"""
assert len(quantized.shape) == 1
assert len(mel.shape) == 2
upsample_factor = quantized.size // mel.shape[0]
mel = np.repeat(mel, upsample_factor, axis=0)
n_pad = quantized.size - mel.shape[0]
if n_pad != 0:
assert n_pad > 0
mel = np.pad(mel, [(0, n_pad), (0, 0)], mode="constant", constant_values=0)
# trim
start, end = start_and_end_indices(quantized, hparams.silence_threshold)
return quantized[start:end], mel[start:end, :]
示例5
def trim(quantized):
start, end = start_and_end_indices(quantized, hparams.silence_threshold)
return quantized[start:end]
示例6
def start_and_end_indices(quantized, silence_threshold=2):
for start in range(quantized.size):
if abs(quantized[start] - 127) > silence_threshold:
break
for end in range(quantized.size - 1, 1, -1):
if abs(quantized[end] - 127) > silence_threshold:
break
assert abs(quantized[start] - 127) > silence_threshold
assert abs(quantized[end] - 127) > silence_threshold
return start, end
示例7
def trim(quantized):
start, end = start_and_end_indices(quantized, hparams.silence_threshold)
return quantized[start:end]
示例8
def start_and_end_indices(quantized, silence_threshold=2):
for start in range(quantized.size):
if abs(quantized[start] - 127) > silence_threshold:
break
for end in range(quantized.size - 1, 1, -1):
if abs(quantized[end] - 127) > silence_threshold:
break
assert abs(quantized[start] - 127) > silence_threshold
assert abs(quantized[end] - 127) > silence_threshold
return start, end
示例9
def trim(quantized):
start, end = start_and_end_indices(quantized, hparams.silence_threshold)
return quantized[start:end]
示例10
def trim(quantized):
start, end = start_and_end_indices(quantized, hparams.silence_threshold)
return quantized[start:end]
示例11
def start_and_end_indices(quantized, silence_threshold=2):
for start in range(quantized.size):
if abs(quantized[start] - 127) > silence_threshold:
break
for end in range(quantized.size - 1, 1, -1):
if abs(quantized[end] - 127) > silence_threshold:
break
assert abs(quantized[start] - 127) > silence_threshold
assert abs(quantized[end] - 127) > silence_threshold
return start, end
示例12
def trim(quantized):
start, end = start_and_end_indices(quantized, hparams.silence_threshold)
return quantized[start:end]
示例13
def start_and_end_indices(quantized, silence_threshold=2):
for start in range(quantized.size):
if abs(quantized[start] - 127) > silence_threshold:
break
for end in range(quantized.size - 1, 1, -1):
if abs(quantized[end] - 127) > silence_threshold:
break
assert abs(quantized[start] - 127) > silence_threshold
assert abs(quantized[end] - 127) > silence_threshold
return start, end
示例14
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 = 'ljspeech-audio-%05d.npy' % index
mel_filename = 'ljspeech-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)
示例15
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 = 'ljspeech-audio-%05d.npy' % index
mel_filename = 'ljspeech-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)
