es and fat mutton pieces to be ladled out in thick, peppered flour-fatten sauce.'}
    ```

    Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)

    Arguments:
        model ([`PreTrainedModel`] or [`TFPreTrainedModel`]):
            The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
            [`PreTrainedModel`] for PyTorch and [`TFPreTrainedModel`] for TensorFlow.
        tokenizer ([`PreTrainedTokenizer`]):
            The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
            [`PreTrainedTokenizer`].
        feature_extractor ([`SequenceFeatureExtractor`]):
            The feature extractor that will be used by the pipeline to encode waveform for the model.
        chunk_length_s (`float`, *optional*, defaults to 0):
            The input length for in each chunk. If `chunk_length_s = 0` then chunking is disabled (default). Only
            available for CTC models, e.g. [`Wav2Vec2ForCTC`].

            <Tip>

            For more information on how to effectively use `chunk_length_s`, please have a look at the [ASR chunking
            blog post](https://huggingface.co/blog/asr-chunking).

            </Tip>

        stride_length_s (`float`, *optional*, defaults to `chunk_length_s / 6`):
            The length of stride on the left and right of each chunk. Used only with `chunk_length_s > 0`. This enables
            the model to *see* more context and infer letters better than without this context but the pipeline
            discards the stride bits at the end to make the final reconstitution as perfect as possible.

            <Tip>

            For more information on how to effectively use `stride_length_s`, please have a look at the [ASR chunking
            blog post](https://huggingface.co/blog/asr-chunking).

            </Tip>

        framework (`str`, *optional*):
            The framework to use, either `"pt"` for PyTorch or `"tf"` for TensorFlow. The specified framework must be
            installed. If no framework is specified, will default to the one currently installed. If no framework is
            specified and both frameworks are installed, will default to the framework of the `model`, or to PyTorch if
            no model is provided.
        device (Union[`int`, `torch.device`], *optional*):
            Device ordinal for CPU/GPU supports. Setting this to `None` will leverage CPU, a positive will run the
            model on the associated CUDA device id.
        decoder (`pyctcdecode.BeamSearchDecoderCTC`, *optional*):
            [PyCTCDecode's
            BeamSearchDecoderCTC](https://github.com/kensho-technologies/pyctcdecode/blob/2fd33dc37c4111417e08d89ccd23d28e9b308d19/pyctcdecode/decoder.py#L180)
            can be passed for language model boosted decoding. See [`Wav2Vec2ProcessorWithLM`] for more information.

    N)