src/transformers/pipelines/automatic_speech_recognition.py/AutomaticSpeechRecognitionPipeline/preprocess
class AutomaticSpeechRecognitionPipeline:    def preprocess(self, inputs, chunk_length_s=0, stride_length_s=None, ignore_warning=False):
        if isinstance(inputs, str):
            if inputs.startswith("http://") or inputs.startswith("https://"):
                # We need to actually check for a real protocol, otherwise it's impossible to use a local file
                # like http_huggingface_co.png
                inputs = requests.get(inputs).content
            else:
                with open(inputs, "rb") as f:
                    inputs = f.read()

        if isinstance(inputs, bytes):
            inputs = ffmpeg_read(inputs, self.feature_extractor.sampling_rate)

        stride = None
        extra = {}
        if isinstance(inputs, dict):
            stride = inputs.pop("stride", None)
            # Accepting `"array"` which is the key defined in `datasets` for
            # better integration
            if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
                raise ValueError(
                    "When passing a dictionary to AutomaticSpeechRecognitionPipeline, the dict needs to contain a "
                    '"raw" key containing the numpy array representing the audio and a "sampling_rate" key, '
                    "containing the sampling_rate associated with that array"
                )

            _inputs = inputs.pop("raw", None)
            if _inputs is None:
                # Remove path which will not be used from `datasets`.
                inputs.pop("path", None)
                _inputs = inputs.pop("array", None)
            in_sampling_rate = inputs.pop("sampling_rate")
            extra = inputs
            inputs = _inputs
            if in_sampling_rate != self.feature_extractor.sampling_rate:
                import torch
                from torchaudio import functional as F

                inputs = F.resample(
                    torch.from_numpy(inputs), in_sampling_rate, self.feature_extractor.sampling_rate
                ).numpy()
                ratio = self.feature_extractor.sampling_rate / in_sampling_rate
            else:
                ratio = 1
            if stride is not None:
                if stride[0] + stride[1] > inputs.shape[0]:
                    raise ValueError("Stride is too large for input")

                # Stride needs to get the chunk length here, it's going to get
                # swallowed by the `feature_extractor` later, and then batching
                # can add extra data in the inputs, so we need to keep track
                # of the original length in the stride so we can cut properly.
                stride = (inputs.shape[0], int(round(stride[0] * ratio)), int(round(stride[1] * ratio)))
        if not isinstance(inputs, np.ndarray):
            raise ValueError(f"We expect a numpy ndarray as input, got `{type(inputs)}`")
        if len(inputs.shape) != 1:
            raise ValueError("We expect a single channel audio input for AutomaticSpeechRecognitionPipeline")

        if chunk_length_s:
            if self.type == "seq2seq" and not ignore_warning:
                logger.warning(
                    "Using `chunk_length_s` is very experimental with seq2seq models. The results will not necessarily"
                    " be entirely accurate and will have caveats. More information:"
                    " https://github.com/huggingface/transformers/pull/20104. Ignore this warning with pipeline(...,"
                    " ignore_warning=True)"
                )
                self._preprocess_params["ignore_warning"] = True
            if stride_length_s is None:
                stride_length_s = chunk_length_s / 6

            if isinstance(stride_length_s, (int, float)):
                stride_length_s = [stride_length_s, stride_length_s]

            # XXX: Carefuly, this variable will not exist in `seq2seq` setting.
            # Currently chunking is not possible at this level for `seq2seq` so
            # it's ok.
            align_to = getattr(self.model.config, "inputs_to_logits_ratio", 1)
            chunk_len = int(round(chunk_length_s * self.feature_extractor.sampling_rate / align_to) * align_to)
            stride_left = int(round(stride_length_s[0] * self.feature_extractor.sampling_rate / align_to) * align_to)
            stride_right = int(round(stride_length_s[1] * self.feature_extractor.sampling_rate / align_to) * align_to)

            if chunk_len < stride_left + stride_right:
                raise ValueError("Chunk length must be superior to stride length")

            rescale = self.type != "seq2seq_whisper"
            # make sure that
            for item in chunk_iter(
                inputs, self.feature_extractor, chunk_len, stride_left, stride_right, rescale, self.torch_dtype
            ):
                yield item
        else:
            processed = self.feature_extractor(
                inputs, sampling_rate=self.feature_extractor.sampling_rate, return_tensors="pt"
            )
            if self.torch_dtype is not None:
                processed = processed.to(dtype=self.torch_dtype)
            if stride is not None:
                if self.type == "seq2seq":
                    raise ValueError("Stride is only usable with CTC models, try removing it !")

                processed["stride"] = stride
            yield {"is_last": True, **processed, **extra}