def _np_extract_fbank_features(self, waveform: np.array) -> np.ndarray:
        """
        Compute the log-mel spectrogram of the provided audio, gives similar results to Whisper's original torch
        implementation with 1e-5 tolerance.
        """
        log_spec = spectrogram(
            waveform,
            window_function(self.n_fft, "hann"),
            frame_length=self.n_fft,
            hop_length=self.hop_length,
            power=2.0,
            mel_filters=self.mel_filters.T,
            log_mel="dB",
            db_range=80.0,
        )
        log_spec = log_spec[:, :-1]
        log_spec = log_spec - 20.0
        log_spec = np.clip(log_spec / 40.0, -2.0, 0.0) + 1.0
        return log_spec

        Compute the log-mel spectrogram of the provided audio, gives similar results to Whisper's original torch
        implementation with 1e-5 tolerance.
        
dpkd/transformers/src/transformers/models/tvlt/feature_extraction_tvlt.py