def METHOD_NAME(self, *args, **kwargs):
    """
    Collates the audio and text inputs, as well as their targets, into a padded batch.
    Audio inputs are padded by SpeechT5FeatureExtractor's [`~SpeechT5FeatureExtractor.pad`]. Text inputs are padded
    by SpeechT5Tokenizer's [`~SpeechT5Tokenizer.pad`].
    Valid input combinations are:
    - `input_ids` only
    - `input_values` only
    - `labels` only, either log-mel spectrograms or text tokens
    - `input_ids` and log-mel spectrogram `labels`
    - `input_values` and text `labels`
    Please refer to the docstring of the above two methods for more information.
    """
    input_values = kwargs.pop("input_values", None)
    input_ids = kwargs.pop("input_ids", None)
    labels = kwargs.pop("labels", None)
    if input_values is not None and input_ids is not None:
        raise ValueError("Cannot process both `input_values` and `input_ids` inputs.")
    if input_values is None and input_ids is None and labels is None:
        raise ValueError(
            "You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."
        )
    if input_values is not None:
        inputs = self.feature_extractor.METHOD_NAME(input_values, *args, **kwargs)
    elif input_ids is not None:
        inputs = self.tokenizer.METHOD_NAME(input_ids, **kwargs)
    else:
        inputs = None
    if labels is not None:
        if "input_ids" in labels or (isinstance(labels, list) and "input_ids" in labels[0]):
            targets = self.tokenizer.METHOD_NAME(labels, **kwargs)
            labels = targets["input_ids"]
        else:
            feature_size_hack = self.feature_extractor.feature_size
            self.feature_extractor.feature_size = self.feature_extractor.num_mel_bins
            targets = self.feature_extractor.METHOD_NAME(labels, *args, **kwargs)
            self.feature_extractor.feature_size = feature_size_hack
            labels = targets["input_values"]
    else:
        targets = None
    if inputs is None:
        return targets
    if targets is not None:
        inputs["labels"] = labels
        decoder_attention_mask = targets.get("attention_mask")
        if decoder_attention_mask is not None:
            inputs["decoder_attention_mask"] = decoder_attention_mask
    return inputs