fix

九耳

2023-02-05 86d65112abcaa6b41352c7d2774e82c5c5618b6c

fix

 funasr/datasets/preprocessor.py

@@ -538,3 +538,96 @@
                data[text_name] = np.array(text_ints, dtype=np.int64)
        assert check_return_type(data)
        return data

class CodeMixTokenizerCommonPreprocessor(CommonPreprocessor):
    def __init__(
            self,
            train: bool,
            token_type: str = None,
            token_list: Union[Path, str, Iterable[str]] = None,
            bpemodel: Union[Path, str, Iterable[str]] = None,
            text_cleaner: Collection[str] = None,
            g2p_type: str = None,
            unk_symbol: str = "<unk>",
            space_symbol: str = "<space>",
            non_linguistic_symbols: Union[Path, str, Iterable[str]] = None,
            delimiter: str = None,
            rir_scp: str = None,
            rir_apply_prob: float = 1.0,
            noise_scp: str = None,
            noise_apply_prob: float = 1.0,
            noise_db_range: str = "3_10",
            speech_volume_normalize: float = None,
            speech_name: str = "speech",
            text_name: str = "text",
            split_text_name: str = "split_text",
            split_with_space: bool = False,
            seg_dict_file: str = None,
    ):
        super().__init__(
            train=train,
            # Force to use word.
            token_type="word",
            token_list=token_list,
            bpemodel=bpemodel,
            text_cleaner=text_cleaner,
            g2p_type=g2p_type,
            unk_symbol=unk_symbol,
            space_symbol=space_symbol,
            non_linguistic_symbols=non_linguistic_symbols,
            delimiter=delimiter,
            speech_name=speech_name,
            text_name=text_name,
            rir_scp=rir_scp,
            rir_apply_prob=rir_apply_prob,
            noise_scp=noise_scp,
            noise_apply_prob=noise_apply_prob,
            noise_db_range=noise_db_range,
            speech_volume_normalize=speech_volume_normalize,
            split_with_space=split_with_space,
            seg_dict_file=seg_dict_file,
        )
        # The data field name for split text.
        self.split_text_name = split_text_name

    @classmethod
    def split_words(cls, text: str):
        words = []
        segs = text.split()
        for seg in segs:
            # There is no space in seg.
            current_word = ""
            for c in seg:
                if len(c.encode()) == 1:
                    # This is an ASCII char.
                    current_word += c
                else:
                    # This is a Chinese char.
                    if len(current_word) > 0:
                        words.append(current_word)
                        current_word = ""
                    words.append(c)
            if len(current_word) > 0:
                words.append(current_word)
        return words

    def __call__(
            self, uid: str, data: Dict[str, Union[list, str, np.ndarray]]
    ) -> Dict[str, Union[list, np.ndarray]]:
        assert check_argument_types()
        # Split words.
        if isinstance(data[self.text_name], str):
            split_text = self.split_words(data[self.text_name])
        else:
            split_text = data[self.text_name]
        data[self.text_name] = " ".join(split_text)
        data = self._speech_process(data)
        data = self._text_process(data)
        data[self.split_text_name] = split_text
        return data

    def pop_split_text_data(self, data: Dict[str, Union[str, np.ndarray]]):
        result = data[self.split_text_name]
        del data[self.split_text_name]
        return result