src/transformers/models/clap/feature_extraction_clap.py/ClapFeatureExtractor/__init__
class ClapFeatureExtractor:    def __init__(
        self,
        feature_size=64,
        sampling_rate=48_000,
        hop_length=480,
        max_length_s=10,
        fft_window_size=1024,
        padding_value=0.0,
        return_attention_mask=False,  # pad inputs to max length with silence token (zero) and no attention mask
        frequency_min: float = 0,
        frequency_max: float = 14_000,
        top_db: int = None,
        truncation: str = "fusion",
        padding: str = "repeatpad",
        **kwargs,
    ):
        super().__init__(
            feature_size=feature_size,
            sampling_rate=sampling_rate,
            padding_value=padding_value,
            return_attention_mask=return_attention_mask,
            **kwargs,
        )
        self.top_db = top_db
        self.truncation = truncation
        self.padding = padding
        self.fft_window_size = fft_window_size
        self.nb_frequency_bins = (fft_window_size >> 1) + 1
        self.hop_length = hop_length
        self.max_length_s = max_length_s
        self.nb_max_samples = max_length_s * sampling_rate
        self.sampling_rate = sampling_rate
        self.frequency_min = frequency_min
        self.frequency_max = frequency_max
        self.mel_filters = get_mel_filter_banks(
            nb_frequency_bins=self.nb_frequency_bins,
            nb_mel_filters=feature_size,
            frequency_min=frequency_min,
            frequency_max=frequency_max,
            sample_rate=sampling_rate,
            norm=None,
            mel_scale="htk",
        )
        self.mel_filters_slaney = get_mel_filter_banks(
            nb_frequency_bins=self.nb_frequency_bins,
            nb_mel_filters=feature_size,
            frequency_min=frequency_min,
            frequency_max=frequency_max,
            sample_rate=sampling_rate,
            norm="slaney",
            mel_scale="slaney",
        )