src/transformers/models/whisper/feature_extraction_whisper.py/WhisperFeatureExtractor/__init__
class WhisperFeatureExtractor:    def __init__(
        self,
        feature_size=80,
        sampling_rate=16000,
        hop_length=160,
        chunk_length=30,
        n_fft=400,
        padding_value=0.0,
        return_attention_mask=False,  # pad inputs to max length with silence token (zero) and no attention mask
        **kwargs,
    ):
        super().__init__(
            feature_size=feature_size,
            sampling_rate=sampling_rate,
            padding_value=padding_value,
            return_attention_mask=return_attention_mask,
            **kwargs,
        )
        self.n_fft = n_fft
        self.hop_length = hop_length
        self.chunk_length = chunk_length
        self.n_samples = chunk_length * sampling_rate
        self.nb_max_frames = self.n_samples // hop_length
        self.sampling_rate = sampling_rate
        self.mel_filters = self.get_mel_filters(sampling_rate, n_fft, n_mels=feature_size)