src/transformers/models/speech_to_text/feature_extraction_speech_to_text.py/Speech2TextFeatureExtractor/_extract_fbank_features
class Speech2TextFeatureExtractor:    def _extract_fbank_features(
        self,
        waveform: np.ndarray,
    ) -> np.ndarray:
        """
        Get mel-filter bank features using TorchAudio. Note that TorchAudio requires 16-bit signed integers as inputs
        and hence the waveform should not be normalized before feature extraction.
        """
        waveform = waveform * (2 ** 15)  # Kaldi compliance: 16-bit signed integers
        waveform = torch.from_numpy(waveform).unsqueeze(0)
        features = ta_kaldi.fbank(waveform, num_mel_bins=self.num_mel_bins, sample_frequency=self.sampling_rate)
        return features.numpy()