def __call__(
        self,
        raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],
        truncation: bool = True,
        pad_to_multiple_of: Optional[int] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        return_attention_mask: Optional[bool] = None,
        padding: Optional[str] = "max_length",
        max_length: Optional[int] = None,
        sampling_rate: Optional[int] = None,
        do_normalize: Optional[bool] = None,
        **kwargs,
    ) -> BatchFeature:
        """
        Main method to featurize and prepare for the model one or several sequence(s).

        Args:
            raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`):
                The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float
                values, a list of numpy arrays or a list of list of float values. Must be mono channel audio, not
                stereo, i.e. single float per timestep.
            truncation (`bool`, *optional*, default to `True`):
                Activates truncation to cut input sequences longer than *max_length* to *max_length*.
            pad_to_multiple_of (`int`, *optional*, defaults to None):
                If set will pad the sequence to a multiple of the provided value.

                This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
                `>= 7.5` (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128.
            return_attention_mask (`bool`, *optional*):
                Whether to return the attention mask. If left to the default, will return the attention mask according
                to the specific feature_extractor's default.

                [What are attention masks?](../glossary#attention-mask)

                <Tip>

                For Whisper models, `attention_mask` should always be passed for batched inference, to avoid subtle
                bugs.

                </Tip>

            return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return Numpy `np.ndarray` objects.
            sampling_rate (`int`, *optional*):
                The sampling rate at which the `raw_speech` input was sampled. It is strongly recommended to pass
                `sampling_rate` at the forward call to prevent silent errors and allow automatic speech recognition
                pipeline.
            padding_value (`float`, defaults to 0.0):
                The value that is used to fill the padding values / vectors.
            do_normalize (`bool`, *optional*, defaults to `False`):
                Whether or not to zero-mean unit-variance normalize the input. Normalizing can help to significantly
                improve the performance of the model.
        """

        if sampling_rate is not None:
            if sampling_rate != self.sampling_rate:
                raise ValueError(
                    f"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
                    f" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
                    f" was sampled with {self.sampling_rate} and not {sampling_rate}."
                )
        else:
            logger.warning(
                "It is strongly recommended to pass the `sampling_rate` argument to this function. "
                "Failing to do so can result in silent errors that might be hard to debug."
            )

        is_batched_numpy = isinstance(raw_speech, np.ndarray) and len(raw_speech.shape) > 1
        if is_batched_numpy and len(raw_speech.shape) > 2:
            raise ValueError(f"Only mono-channel audio is supported for input to {self}")
        is_batched = is_batched_numpy or (
            isinstance(raw_speech, (list, tuple)) and (isinstance(raw_speech[0], (np.ndarray, tuple, list)))
        )

        if is_batched:
            raw_speech = [np.asarray([speech], dtype=np.float32).T for speech in raw_speech]
        elif not is_batched and not isinstance(raw_speech, np.ndarray):
            raw_speech = np.asarray(raw_speech, dtype=np.float32)
        elif isinstance(raw_speech, np.ndarray) and raw_speech.dtype is np.dtype(np.float64):
            raw_speech = raw_speech.astype(np.float32)

        # always return batch
        if not is_batched:
            raw_speech = [np.asarray([raw_speech]).T]

        batched_speech = BatchFeature({"input_features": raw_speech})

        # convert into correct format for padding

        padded_inputs = self.pad(
            batched_speech,
            padding=padding,
            max_length=max_length if max_length else self.n_samples,
            truncation=truncation,
            pad_to_multiple_of=pad_to_multiple_of,
            return_attention_mask=return_attention_mask or do_normalize,
        )

        # zero-mean and unit-variance normalization
        if do_normalize:
            padded_inputs["input_features"] = self.zero_mean_unit_var_norm(
                padded_inputs["input_features"],
                attention_mask=padded_inputs["attention_mask"],
                padding_value=self.padding_value,
            )
            padded_inputs["input_features"] = np.stack(padded_inputs["input_features"], axis=0)

        # make sure list is in array format
        input_features = padded_inputs.get("input_features").transpose(2, 0, 1)

        input_features = [self._np_extract_fbank_features(waveform) for waveform in input_features[0]]

        if isinstance(input_features[0], List):
            padded_inputs["input_features"] = [np.asarray(feature, dtype=np.float32) for feature in input_features]
        else:
            padded_inputs["input_features"] = input_features

        if return_attention_mask:
            # rescale from sample (48000) to feature (3000)
            padded_inputs["attention_mask"] = padded_inputs["attention_mask"][:, :: self.hop_length]

        if return_tensors is not None:
            padded_inputs = padded_inputs.convert_to_tensors(return_tensors)

        return padded_inputs

        Main method to featurize and prepare for the model one or several sequence(s).

        Args:
            raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`):
                The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float
                values, a list of numpy arrays or a list of list of float values. Must be mono channel audio, not
                stereo, i.e. single float per timestep.
            truncation (`bool`, *optional*, default to `True`):
                Activates truncation to cut input sequences longer than *max_length* to *max_length*.
            pad_to_multiple_of (`int`, *optional*, defaults to None):
                If set will pad the sequence to a multiple of the provided value.

                This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
                `>= 7.5` (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128.
            return_attention_mask (`bool`, *optional*):
                Whether to return the attention mask. If left to the default, will return the attention mask according
                to the specific feature_extractor's default.

                [What are attention masks?](../glossary#attention-mask)

                <Tip>

                For Whisper models, `attention_mask` should always be passed for batched inference, to avoid subtle
                bugs.

                </Tip>

            return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return Numpy `np.ndarray` objects.
            sampling_rate (`int`, *optional*):
                The sampling rate at which the `raw_speech` input was sampled. It is strongly recommended to pass
                `sampling_rate` at the forward call to prevent silent errors and allow automatic speech recognition
                pipeline.
            padding_value (`float`, defaults to 0.0):
                The value that is used to fill the padding values / vectors.
            do_normalize (`bool`, *optional*, defaults to `False`):
                Whether or not to zero-mean unit-variance normalize the input. Normalizing can help to significantly
                improve the performance of the model.
        
dpkd/transformers/src/transformers/models/whisper/feature_extraction_whisper.py