PaddleSpeech/paddlespeech/t2s/frontend/zh_normalization/text_normlization.py

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from typing import List

from .char_convert import tranditional_to_simplified
from .chronology import RE_DATE
from .chronology import RE_DATE2
from .chronology import RE_TIME
from .chronology import RE_TIME_RANGE
from .chronology import replace_date
from .chronology import replace_date2
from .chronology import replace_time
from .constants import F2H_ASCII_LETTERS
from .constants import F2H_DIGITS
from .constants import F2H_SPACE
from .num import RE_DECIMAL_NUM
from .num import RE_DEFAULT_NUM
from .num import RE_FRAC
from .num import RE_INTEGER
from .num import RE_NUMBER
from .num import RE_PERCENTAGE
from .num import RE_POSITIVE_QUANTIFIERS
from .num import RE_RANGE
from .num import replace_default_num
from .num import replace_frac
from .num import replace_negative_num
from .num import replace_number
from .num import replace_percentage
from .num import replace_positive_quantifier
from .num import replace_range
from .phonecode import RE_MOBILE_PHONE
from .phonecode import RE_NATIONAL_UNIFORM_NUMBER
from .phonecode import RE_TELEPHONE
from .phonecode import replace_mobile
from .phonecode import replace_phone
from .quantifier import RE_TEMPERATURE
from .quantifier import replace_temperature


class TextNormalizer():
    def __init__(self):
        self.SENTENCE_SPLITOR = re.compile(r'([：、，；。？！,;?!][”’]?)')

    def _split(self, text: str, lang="zh") -> List[str]:
        """Split long text into sentences with sentence-splitting punctuations.
        Parameters
        ----------
        text : str
            The input text.
        Returns
        -------
        List[str]
            Sentences.
        """
        # Only for pure Chinese here
        if lang == "zh":
            text = text.replace(" ", "")
        text = self.SENTENCE_SPLITOR.sub(r'\1\n', text)
        text = text.strip()
        sentences = [sentence.strip() for sentence in re.split(r'\n+', text)]
        return sentences

    def normalize_sentence(self, sentence: str) -> str:
        # basic character conversions
        sentence = tranditional_to_simplified(sentence)
        sentence = sentence.translate(F2H_ASCII_LETTERS).translate(
            F2H_DIGITS).translate(F2H_SPACE)

        # number related NSW verbalization
        sentence = RE_DATE.sub(replace_date, sentence)
        sentence = RE_DATE2.sub(replace_date2, sentence)

        # range first
        sentence = RE_TIME_RANGE.sub(replace_time, sentence)
        sentence = RE_TIME.sub(replace_time, sentence)

        sentence = RE_TEMPERATURE.sub(replace_temperature, sentence)
        sentence = RE_FRAC.sub(replace_frac, sentence)
        sentence = RE_PERCENTAGE.sub(replace_percentage, sentence)
        sentence = RE_MOBILE_PHONE.sub(replace_mobile, sentence)

        sentence = RE_TELEPHONE.sub(replace_phone, sentence)
        sentence = RE_NATIONAL_UNIFORM_NUMBER.sub(replace_phone, sentence)

        sentence = RE_RANGE.sub(replace_range, sentence)
        sentence = RE_INTEGER.sub(replace_negative_num, sentence)
        sentence = RE_DECIMAL_NUM.sub(replace_number, sentence)
        sentence = RE_POSITIVE_QUANTIFIERS.sub(replace_positive_quantifier,
                                               sentence)
        sentence = RE_DEFAULT_NUM.sub(replace_default_num, sentence)
        sentence = RE_NUMBER.sub(replace_number, sentence)

        return sentence

    def normalize(self, text: str) -> List[str]:
        sentences = self._split(text)

        sentences = [self.normalize_sentence(sent) for sent in sentences]
        return sentences