src/transformers/models/speech_to_text/feature_extraction_speech_to_text.py/Speech2TextFeatureExtractor/__call__
class Speech2TextFeatureExtractor:    def __call__(
        self,
        raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],
        padding: Union[bool, str, PaddingStrategy] = False,
        max_length: Optional[int] = None,
        truncation: bool = False,
        pad_to_multiple_of: Optional[int] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        sampling_rate: Optional[int] = None,
        return_attention_mask: Optional[bool] = None,
        **kwargs
    ) -> BatchFeature:
        """
        Main method to featurize and prepare for the model one or several sequence(s). sequences.

        Args:
            raw_speech (:obj:`np.ndarray`, :obj:`List[float]`, :obj:`List[np.ndarray]`, :obj:`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.
            padding (:obj:`bool`, :obj:`str` or :class:`~transformers.file_utils.PaddingStrategy`, `optional`, defaults to :obj:`True`):
                Select a strategy to pad the returned sequences (according to the model's padding side and padding
                index) among:

                * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a
                  single sequence if provided).
                * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
                  maximum acceptable input length for the model if that argument is not provided.
                * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
                  different lengths).
            max_length (:obj:`int`, `optional`):
                Maximum length of the returned list and optionally padding length (see above).
            truncation (:obj:`bool`):
                Activates truncation to cut input sequences longer than `max_length` to `max_length`.
            pad_to_multiple_of (:obj:`int`, `optional`):
                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 (:obj:`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.html#attention-mask>`__

                .. note::

                    For Speech2TextTransoformer models, :obj:`attention_mask` should alwys be passed for batched
                    inference, to avoid subtle bugs.

            return_tensors (:obj:`str` or :class:`~transformers.file_utils.TensorType`, `optional`):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                * :obj:`'tf'`: Return TensorFlow :obj:`tf.constant` objects.
                * :obj:`'pt'`: Return PyTorch :obj:`torch.Tensor` objects.
                * :obj:`'np'`: Return Numpy :obj:`np.ndarray` objects.
            sampling_rate (:obj:`int`, `optional`):
                The sampling rate at which the :obj:`raw_speech` input was sampled. It is strongly recommended to pass
                :obj:`sampling_rate` at the forward call to prevent silent errors.
            padding_value (:obj:`float`, defaults to 0.0):
                The value that is used to fill the padding values / vectors.
        """

        if sampling_rate is not None:
            if sampling_rate != self.sampling_rate:
                raise ValueError(
                    f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of {self.sampling_rate}."
                    f"Please make sure that the provided `raw_speech` input 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 = bool(
            isinstance(raw_speech, (list, tuple))
            and (isinstance(raw_speech[0], np.ndarray) or isinstance(raw_speech[0], (tuple, list)))
        )

        if is_batched:
            raw_speech = [np.asarray(speech, dtype=np.float32) 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 = [raw_speech]

        # extract fbank features
        features = [self._extract_fbank_features(waveform) for waveform in raw_speech]

        # convert into correct format for padding
        encoded_inputs = BatchFeature({"input_features": features})

        padded_inputs = self.pad(
            encoded_inputs,
            padding=padding,
            max_length=max_length,
            truncation=truncation,
            pad_to_multiple_of=pad_to_multiple_of,
            return_attention_mask=return_attention_mask,
            **kwargs,
        )

        # make sure list is in array format
        input_features = padded_inputs.get("input_features")
        if isinstance(input_features[0], list):
            padded_inputs["input_features"] = [np.asarray(feature, dtype=np.float32) for feature in input_features]

        attention_mask = padded_inputs.get("attention_mask")
        if attention_mask is not None:
            padded_inputs["attention_mask"] = [np.asarray(array, dtype=np.int32) for array in attention_mask]

        # Utterance-level cepstral mean and variance normalization
        if self.do_ceptral_normalize:
            attention_mask = (
                np.array(attention_mask, dtype=np.int32)
                if self._get_padding_strategies(padding, max_length=max_length) is not PaddingStrategy.DO_NOT_PAD
                else None
            )
            padded_inputs["input_features"] = self.normalize(
                padded_inputs["input_features"], attention_mask=attention_mask
            )

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

        return padded_inputs