src/transformers/models/wav2vec2_phoneme/tokenization_wav2vec2_phoneme.py/Wav2Vec2PhonemeCTCTokenizer/decode
class Wav2Vec2PhonemeCTCTokenizer:    def decode(
        self,
        token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"],
        skip_special_tokens: bool = False,
        clean_up_tokenization_spaces: bool = True,
        output_char_offsets: bool = False,
        **kwargs,
    ) -> str:
        """
        Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
        tokens and clean up tokenization spaces.

        Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`.

        Args:
            token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`):
                List of tokenized input ids. Can be obtained using the `__call__` method.
            skip_special_tokens (`bool`, *optional*, defaults to `False`):
                Whether or not to remove special tokens in the decoding.
            clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
                Whether or not to clean up the tokenization spaces.
            output_char_offsets (`bool`, *optional*, defaults to `False`):
                Whether or not to output character offsets. Character offsets can be used in combination with the
                sampling rate and model downsampling rate to compute the time-stamps of transcribed characters.

                <Tip>

                Please take a look at the Example of [`~models.wav2vec2.tokenization_wav2vec2.decode`] to better
                understand how to make use of `output_word_offsets`.
                [`~model.wav2vec2_phoneme.tokenization_wav2vec2_phoneme.batch_decode`] works the same way with
                phonemes.

                </Tip>

            kwargs (additional keyword arguments, *optional*):
                Will be passed to the underlying model specific decode method.

        Returns:
            `str` or [`~models.wav2vec2.tokenization_wav2vec2_phoneme.Wav2Vec2PhonemeCTCTokenizerOutput`]: The decoded
            sentence. Will be a [`~models.wav2vec2.tokenization_wav2vec2_phoneme.Wav2Vec2PhonemeCTCTokenizerOutput`]
            when `output_char_offsets == True`.
        """
        # Convert inputs to python lists
        token_ids = to_py_obj(token_ids)

        return self._decode(
            token_ids=token_ids,
            skip_special_tokens=skip_special_tokens,
            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
            output_char_offsets=output_char_offsets,
            **kwargs,
        )