src/transformers/pipelines/audio_classification.py/AudioClassificationPipeline/__call__
class AudioClassificationPipeline:    def __call__(
        self,
        inputs: Union[np.ndarray, bytes, str],
        **kwargs,
    ):
        """
        Classify the sequence(s) given as inputs. See the :obj:`~transformers.AutomaticSpeechRecognitionPipeline`
        documentation for more information.

        Args:
            inputs (:obj:`np.ndarray` or :obj:`bytes` or :obj:`str`):
                The inputs is either a raw waveform (:obj:`np.ndarray` of shape (n, ) of type :obj:`np.float32` or
                :obj:`np.float64`) at the correct sampling rate (no further check will be done) or a :obj:`str` that is
                the filename of the audio file, the file will be read at the correct sampling rate to get the waveform
                using `ffmpeg`. This requires `ffmpeg` to be installed on the system. If `inputs` is :obj:`bytes` it is
                supposed to be the content of an audio file and is interpreted by `ffmpeg` in the same way.
            top_k (:obj:`int`, `optional`, defaults to None):
                The number of top labels that will be returned by the pipeline. If the provided number is `None` or
                higher than the number of labels available in the model configuration, it will default to the number of
                labels.

        Return:
            A list of :obj:`dict` with the following keys:

            - **label** (:obj:`str`) -- The label predicted.
            - **score** (:obj:`float`) -- The corresponding probability.
        """
        return super().__call__(inputs, **kwargs)