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,
            log_mel="log10",
        )
        log_spec = log_spec[:, :-1]
        log_spec = np.maximum(log_spec, log_spec.max() - 8.0)
        log_spec = (log_spec + 4.0) / 4.0
        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/whisper/feature_extraction_whisper.py