, e.g. `.generate(inputs, num_beams=4, do_sample=True)`.

For an overview of generation strategies and code examples, check out the [following
guide](./generation_strategies).

</Tip>

Parameters:
    input_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_banks)`):
        Input audio features. This should be returned by the [`SeamlessM4TFeatureExtractor`] class or the
        [`SeamlessM4TProcessor`] class. See [`SeamlessM4TFeatureExtractor.__call__`] for details.

    tgt_lang (`str`, *optional*):
        The language to use as target language for translation.
    generation_config (`~generation.GenerationConfig`, *optional*):
        The generation configuration to be used as base parametrization for the generation call. `**kwargs`
        passed to generate matching the attributes of `generation_config` will override them. If
        `generation_config` is not provided, the default will be used, which had the following loading
        priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model
        configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s
        default values, whose documentation should be checked to parameterize generation.
    logits_processor (`LogitsProcessorList`, *optional*):
        Custom logits processors that complement the default logits processors built from arguments and
        generation config. If a logit processor is passed that is already created with the arguments or a
        generation config an error is thrown. This feature is intended for advanced users.
    stopping_criteria (`StoppingCriteriaList`, *optional*):
        Custom stopping criteria that complement the default stopping criteria built from arguments and a
        generation config. If a stopping criteria is passed that is already created with the arguments or a
        generation config an error is thrown. This feature is intended for advanced users.
    prefix_allowed_tokens_fn (`Callable[[int, torch.Tensor], list[int]]`, *optional*):
        If provided, this function constraints the beam search to allowed tokens only at each step. If not
        provided no constraint is applied. This function takes 2 arguments: the batch ID `batch_id` and
        `input_ids`. It has to return a list with the allowed tokens for the next generation step conditioned
        on the batch ID `batch_id` and the previously generated tokens `inputs_ids`. This argument is useful
        for constrained generation conditioned on the prefix, as described in [Autoregressive Entity
        Retrieval](https://huggingface.co/papers/2010.00904).
    synced_gpus (`bool`, *optional*, defaults to `False`):
        Whether to continue running the while loop until max_length (needed to avoid deadlocking with
        `FullyShardedDataParallel` and DeepSpeed ZeRO Stage 3).
    kwargs (`dict[str, Any]`, *optional*):
        Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be
        forwarded to the `forward` function of the model.

Return:
    [`~utils.ModelOutput`] or `torch.LongTensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True`
    or when `config.return_dict_in_generate=True`) or a `torch.FloatTensor`. The possible
    [`~utils.ModelOutput`] types are:
        - [`~generation.GenerateEncoderDecoderOutput`],
        - [`~generation.GenerateBeamEncoderDecoderOutput`]
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