def extract_fbank_features(
waveform,
sample_rate: int,
output_path: Optional[Path] = None,
n_mel_bins: int = 80,
overwrite: bool = False,
):
if output_path is not None and output_path.is_file() and not overwrite:
return
_waveform = waveform * (2**15) # Kaldi compliance: 16-bit signed integers
_waveform = _waveform.squeeze().numpy()
features = _get_kaldi_fbank(_waveform, sample_rate, n_mel_bins)
if features is None:
features = _get_torchaudio_fbank(_waveform, sample_rate, n_mel_bins)
if features is None:
raise ImportError(
"Please install pyKaldi or torchaudio to enable fbank feature extraction"
)
if output_path is not None:
np.save(output_path.as_posix(), features)
else:
return features
def extract_fbank_features(
waveform,
sample_rate,
output_path=None,
n_mel_bins=80,
apply_utterance_cmvn=True,
overwrite=False,
):
if output_path is not None and op.exists(output_path) and not overwrite:
return
_waveform = waveform * (2**15) # Kaldi compliance: 16-bit signed integers
_waveform = _waveform.squeeze().numpy()
features = _get_kaldi_fbank(_waveform, sample_rate, n_mel_bins)
if features is None:
features = _get_torchaudio_fbank(_waveform, sample_rate, n_mel_bins)
if features is None:
raise ImportError("Please install pyKaldi or torchaudio to enable "
"online filterbank feature extraction")
if apply_utterance_cmvn:
cmvn = UtteranceCMVN(norm_means=True, norm_vars=True)
features = cmvn(features)
if output_path is not None:
np.save(output_path, features)
else:
return features
