src/transformers/pipelines/automatic_speech_recognition.py/AutomaticSpeechRecognitionPipeline/__call__
class AutomaticSpeechRecognitionPipeline:    def __call__(
        self,
        inputs: Union[np.ndarray, bytes, str],
        **kwargs,
    ):
        """
        Transcribe the audio sequence(s) given as inputs to text. See the [`AutomaticSpeechRecognitionPipeline`]
        documentation for more information.

        Args:
            inputs (`np.ndarray` or `bytes` or `str` or `dict`):
                The inputs is either :
                    - `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.
                    - `bytes` it is supposed to be the content of an audio file and is interpreted by *ffmpeg* in the
                      same way.
                    - (`np.ndarray` of shape (n, ) of type `np.float32` or `np.float64`)
                        Raw audio at the correct sampling rate (no further check will be done)
                    - `dict` form can be used to pass raw audio sampled at arbitrary `sampling_rate` and let this
                      pipeline do the resampling. The dict must be in the format `{"sampling_rate": int, "raw":
                      np.array}` with optionally a `"stride": (left: int, right: int)` than can ask the pipeline to
                      treat the first `left` samples and last `right` samples to be ignored in decoding (but used at
                      inference to provide more context to the model). Only use `stride` with CTC models.
            return_timestamps (*optional*, `str`):
                Only available for pure CTC models. If set to `"char"`, the pipeline will return `timestamps` along the
                text for every character in the text. For instance if you get `[{"text": "h", "timestamps": (0.5,0.6),
                {"text": "i", "timestamps": (0.7, .9)}]`, then it means the model predicts that the letter "h" was
                pronounced after `0.5` and before `0.6` seconds. If set to `"word"`, the pipeline will return
                `timestamps` along the text for every word in the text. For instance if you get `[{"text": "hi ",
                "timestamps": (0.5,0.9), {"text": "there", "timestamps": (1.0, .1.5)}]`, then it means the model
                predicts that the word "hi" was pronounced after `0.5` and before `0.9` seconds.
            generate_kwargs (`dict`, *optional*):
                The dictionary of ad-hoc parametrization of `generate_config` to be used for the generation call. For a
                complete overview of generate, check the [following
                guide](https://huggingface.co/docs/transformers/en/main_classes/text_generation).
            max_new_tokens (`int`, *optional*):
                The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt.

        Return:
            `Dict`: A dictionary with the following keys:
                - **text** (`str` ) -- The recognized text.
                - **chunks** (*optional(, `List[Dict]`)
                        When using `return_timestamps`, the `chunks` will become a list containing all the various text
                        chunks identified by the model, *e.g.* `[{"text": "hi ", "timestamps": (0.5,0.9), {"text":
                        "there", "timestamps": (1.0, 1.5)}]`. The original full text can roughly be recovered by doing
                        `"".join(chunk["text"] for chunk in output["chunks"])`.
        """
        return super().__call__(inputs, **kwargs)