src/transformers/models/mctct/feature_extraction_mctct.py/MCTCTFeatureExtractor/__init__
class MCTCTFeatureExtractor:    def __init__(
        self,
        feature_size=80,
        sampling_rate=16000,
        padding_value=0.0,
        hop_length=10,
        win_length=25,
        win_function="hamming_window",
        frame_signal_scale=32768.0,
        preemphasis_coeff=0.97,
        mel_floor=1.0,
        normalize_means=True,
        normalize_vars=True,
        return_attention_mask=False,
        **kwargs,
    ):
        super().__init__(feature_size=feature_size, sampling_rate=sampling_rate, padding_value=padding_value, **kwargs)

        self.feature_size = feature_size
        self.sampling_rate = sampling_rate
        self.padding_value = padding_value
        self.hop_length = hop_length
        self.win_length = win_length
        self.frame_signal_scale = frame_signal_scale
        self.preemphasis_coeff = preemphasis_coeff
        self.mel_floor = mel_floor
        self.normalize_means = normalize_means
        self.normalize_vars = normalize_vars
        self.win_function = win_function
        self.return_attention_mask = return_attention_mask

        self.sample_size = win_length * sampling_rate // 1000
        self.sample_stride = hop_length * sampling_rate // 1000

        self.n_fft = 2 ** int(np.ceil(np.log2(self.sample_size)))
        self.n_freqs = (self.n_fft // 2) + 1