def parse_audio(self, audio_path: str, augment_method: int) -> Tensor:
"""
Parses audio.
Args:
audio_path (str): path of audio file
augment_method (int): flag indication which augmentation method to use.
Returns: feature_vector
- **feature_vector** (torch.FloatTensor): feature from audio file.
"""
signal = load_audio(audio_path, self.del_silence)
if augment_method == SpectrogramParser.NOISE_INJECTION or augment_method == SpectrogramParser.HYBRID_AUGMENT:
signal = self.noise_injector(signal)
feature_vector = self.transforms(signal)
if self.normalize:
feature_vector -= feature_vector.mean()
feature_vector /= np.std(feature_vector)
# Refer to "Sequence to Sequence Learning with Neural Network" paper
if self.input_reverse:
feature_vector = feature_vector[:, ::-1]
feature_vector = FloatTensor(np.ascontiguousarray(np.swapaxes(feature_vector, 0, 1)))
else:
feature_vector = FloatTensor(feature_vector).transpose(0, 1)
if augment_method == SpectrogramParser.SPEC_AUGMENT or augment_method == SpectrogramParser.HYBRID_AUGMENT:
feature_vector = self.spec_augment(feature_vector)
return feature_vector
def parse_audio(audio_path: str, del_silence: bool = True) -> Tensor:
signal = load_audio(audio_path, del_silence)
feature_vector = torchaudio.compliance.kaldi.fbank(
Tensor(signal).unsqueeze(0),
num_mel_bins=80,
frame_length=20,
frame_shift=10,
window_type='hamming').transpose(0, 1).numpy()
feature_vector -= feature_vector.mean()
feature_vector = Tensor(feature_vector).transpose(0, 1)
return feature_vector
def parse_audio(audio_path: str, del_silence: bool = True) -> Tensor:
signal = load_audio(audio_path, del_silence)
mfcc = librosa.feature.mfcc(y=signal,
sr=16000,
n_mfcc=40,
n_fft=320,
hop_length=160)
mfcc -= mfcc.mean()
mfcc = Tensor(mfcc).transpose(0, 1)
mfcc = mfcc[:, ::-1]
mfcc = torch.FloatTensor(np.ascontiguousarray(np.swapaxes(mfcc, 0, 1)))
return mfcc
def parse_audio(audio_path: str,
del_silence: bool = False,
audio_extension: str = 'pcm') -> Tensor:
signal = load_audio(audio_path, del_silence, extension=audio_extension)
feature = torchaudio.compliance.kaldi.fbank(
waveform=Tensor(signal).unsqueeze(0),
num_mel_bins=80,
frame_length=20,
frame_shift=10,
window_type='hamming').transpose(0, 1).numpy()
feature -= feature.mean()
feature /= np.std(feature)
return torch.FloatTensor(feature).transpose(0, 1)
def parse_audio(self, audio_path: str, augment_method: int) -> Tensor:
"""
Parses audio.
Args:
audio_path (str): path of audio file
augment_method (int): flag indication which augmentation method to use.
Returns: feature_vector
- **feature_vector** (torch.FloatTensor): feature from audio file.
"""
signal = load_audio(audio_path,
self.del_silence,
extension=self.audio_extension)
if signal is None:
logger.info("Audio is None : {0}".format(audio_path))
return None
feature = self.transforms(signal)
if self.normalize:
feature -= feature.mean()
feature /= np.std(feature)
# Refer to "Sequence to Sequence Learning with Neural Network" paper
if self.input_reverse:
feature = feature[:, ::-1]
feature = FloatTensor(
np.ascontiguousarray(np.swapaxes(feature, 0, 1)))
else:
feature = FloatTensor(feature).transpose(0, 1)
if augment_method == SpectrogramParser.SPEC_AUGMENT:
feature = self.spec_augment(feature)
return feature