def decode(
        self,
        token_ids,
        skip_special_tokens: bool = False,
        clean_up_tokenization_spaces: bool = None,
        output_offsets: bool = False,
        time_precision=0.02,
        decode_with_timestamps: bool = False,
        normalize: bool = False,
        basic_normalize: bool = False,
        remove_diacritics: 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*):
                Whether or not to clean up the tokenization spaces. If `None`, will default to
                `self.clean_up_tokenization_spaces` (available in the `tokenizer_config`).
            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.
            time_precision (`float`, `optional`, defaults to 0.02):
                The time ratio to convert from token to time.
            decode_with_timestamps (`bool`, *optional*, defaults to `False`):
                Whether or not to decode with timestamps included in the raw text.
            normalize (`bool`, *optional*, defaults to `False`):
                Whether or not to apply the English text normalizer to the decoded text. Only applicable when the
                target text is in English. Otherwise, the basic text normalizer should be applied.
            basic_normalize (`bool`, *optional*, defaults to `False`):
                Whether or not to apply the Basic text normalizer to the decoded text. Applicable to multilingual
                target text.
            remove_diacritics (`bool`, *optional*, defaults to `False`):
                Whether or not to remove diacritics when applying the Basic text normalizer. Removing diacritics may
                destroy information in the decoded text, hence it should be used with caution.
            kwargs (additional keyword arguments, *optional*):
                Will be passed to the underlying model specific decode method.
        Returns:
            `str`: The decoded sentence.
        """
        filtered_ids = self._preprocess_token_ids(
            token_ids,
            skip_special_tokens=skip_special_tokens,
        )

        text = super().decode(
            filtered_ids,
            skip_special_tokens=skip_special_tokens,
            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
            normalize=normalize,
            basic_normalize=basic_normalize,
            remove_diacritics=remove_diacritics,
            **kwargs,
        )
        if decode_with_timestamps:
            # legacy method to decode timestamps when not included in the tokenizer vocabulary
            text = self._decode_with_timestamps(
                filtered_ids, time_precision=time_precision, skip_special_tokens=skip_special_tokens
            )
        else:
            text = self._filter_timestamp_ids(text)

        # retrieve offsets
        if output_offsets:
            offsets = self._compute_offsets(token_ids, time_precision=time_precision)
            return {"text": text, "offsets": offsets}
        return text

        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*):
                Whether or not to clean up the tokenization spaces. If `None`, will default to
                `self.clean_up_tokenization_spaces` (available in the `tokenizer_config`).
            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.
            time_precision (`float`, `optional`, defaults to 0.02):
                The time ratio to convert from token to time.
            decode_with_timestamps (`bool`, *optional*, defaults to `False`):
                Whether or not to decode with timestamps included in the raw text.
            normalize (`bool`, *optional*, defaults to `False`):
                Whether or not to apply the English text normalizer to the decoded text. Only applicable when the
                target text is in English. Otherwise, the basic text normalizer should be applied.
            basic_normalize (`bool`, *optional*, defaults to `False`):
                Whether or not to apply the Basic text normalizer to the decoded text. Applicable to multilingual
                target text.
            remove_diacritics (`bool`, *optional*, defaults to `False`):
                Whether or not to remove diacritics when applying the Basic text normalizer. Removing diacritics may
                destroy information in the decoded text, hence it should be used with caution.
            kwargs (additional keyword arguments, *optional*):
                Will be passed to the underlying model specific decode method.
        Returns:
            `str`: The decoded sentence.
        
dpkd/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py