src/transformers/models/whisper/tokenization_whisper.py/WhisperTokenizer/decode
class WhisperTokenizer:    def decode(
        self,
        token_ids,
        skip_special_tokens: bool = False,
        clean_up_tokenization_spaces: bool = True,
        output_offsets: bool = False,
        time_precision=0.02,
        decode_with_timestamps: 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.
            kwargs (additional keyword arguments, *optional*):
                Will be passed to the underlying model specific decode method.
            output_offsets (`bool`, *optional*, defaults to `False`):
                Whether or not to output the offsets of the tokens. This should only be set if the model predicted
                timestamps.
            decode_with_timestamps (`bool`, *optional*, defaults to `False`):
                WHether or not to decode with timestamps included in the raw text.
        Returns:
            `str`: The decoded sentence.
        """
        text = super().decode(
            token_ids,
            skip_special_tokens=skip_special_tokens,
            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
            **kwargs,
        )
        if decode_with_timestamps:
            text = self._decode_with_timestamps(token_ids, time_precision=time_precision)
        # retrieve offsets
        if output_offsets:
            offsets = None
            offsets = self._compute_offsets(token_ids, time_precision=time_precision)
            return {"text": text, "offsets": offsets}
        return text