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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import re
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from typing import Dict
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from typing import List
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import paddle
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from paddlespeech.t2s.frontend import English
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from paddlespeech.t2s.frontend.zh_frontend import Frontend
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class MixFrontend():
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def __init__(self,
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g2p_model="pypinyin",
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phone_vocab_path=None,
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tone_vocab_path=None):
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self.zh_frontend = Frontend(
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phone_vocab_path=phone_vocab_path, tone_vocab_path=tone_vocab_path)
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self.en_frontend = English(phone_vocab_path=phone_vocab_path)
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self.SENTENCE_SPLITOR = re.compile(r'([:、,;。?!,;?!][”’]?)')
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self.sp_id = self.zh_frontend.vocab_phones["sp"]
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self.sp_id_tensor = paddle.to_tensor([self.sp_id])
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def is_chinese(self, char):
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if char >= '\u4e00' and char <= '\u9fa5':
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return True
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else:
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return False
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def is_alphabet(self, char):
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if (char >= '\u0041' and char <= '\u005a') or (char >= '\u0061' and
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char <= '\u007a'):
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return True
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else:
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return False
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def is_number(self, char):
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if char >= '\u0030' and char <= '\u0039':
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return True
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else:
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return False
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def is_other(self, char):
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if not (self.is_chinese(char) or self.is_number(char) or
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self.is_alphabet(char)):
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return True
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else:
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return False
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def is_end(self, before_char, after_char) -> bool:
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if ((self.is_alphabet(before_char) or before_char == " ") and
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(self.is_alphabet(after_char) or after_char == " ")):
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return True
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else:
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return False
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def _replace(self, text: str) -> str:
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new_text = ""
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# get "." indexs
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point = "."
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point_indexs = []
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index = -1
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for i in range(text.count(point)):
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index = text.find(".", index + 1, len(text))
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point_indexs.append(index)
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# replace "." -> "。" when English sentence ending
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if len(point_indexs) == 0:
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new_text = text
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elif len(point_indexs) == 1:
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point_index = point_indexs[0]
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if point_index == 0 or point_index == len(text) - 1:
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new_text = text
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else:
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if not self.is_end(text[point_index - 1], text[point_index +
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1]):
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new_text = text
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else:
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new_text = text[:point_index] + "。" + text[point_index + 1:]
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elif len(point_indexs) == 2:
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first_index = point_indexs[0]
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end_index = point_indexs[1]
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# first
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if first_index != 0:
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if not self.is_end(text[first_index - 1], text[first_index +
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1]):
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new_text += (text[:first_index] + ".")
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else:
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new_text += (text[:first_index] + "。")
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else:
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new_text += "."
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# last
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if end_index != len(text) - 1:
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if not self.is_end(text[end_index - 1], text[end_index + 1]):
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new_text += text[point_indexs[-2] + 1:]
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else:
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new_text += (text[point_indexs[-2] + 1:end_index] + "。" +
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text[end_index + 1:])
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else:
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new_text += "."
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else:
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first_index = point_indexs[0]
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end_index = point_indexs[-1]
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# first
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if first_index != 0:
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if not self.is_end(text[first_index - 1], text[first_index +
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1]):
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new_text += (text[:first_index] + ".")
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else:
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new_text += (text[:first_index] + "。")
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else:
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new_text += "."
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# middle
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for j in range(1, len(point_indexs) - 1):
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point_index = point_indexs[j]
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if not self.is_end(text[point_index - 1], text[point_index +
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1]):
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new_text += (
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text[point_indexs[j - 1] + 1:point_index] + ".")
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else:
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new_text += (
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text[point_indexs[j - 1] + 1:point_index] + "。")
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# last
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if end_index != len(text) - 1:
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if not self.is_end(text[end_index - 1], text[end_index + 1]):
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new_text += text[point_indexs[-2] + 1:]
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else:
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new_text += (text[point_indexs[-2] + 1:end_index] + "。" +
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text[end_index + 1:])
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else:
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new_text += "."
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return new_text
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def _split(self, text: str) -> List[str]:
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text = re.sub(r'[《》【】<=>{}()()#&@“”^_|…\\]', '', text)
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# 替换英文句子的句号 "." --> "。" 用于后续分句
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text = self._replace(text)
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text = self.SENTENCE_SPLITOR.sub(r'\1\n', text)
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text = text.strip()
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sentences = [sentence.strip() for sentence in re.split(r'\n+', text)]
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return sentences
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def _distinguish(self, text: str) -> List[str]:
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# sentence --> [ch_part, en_part, ch_part, ...]
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segments = []
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types = []
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flag = 0
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temp_seg = ""
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temp_lang = ""
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# Determine the type of each character. type: blank, chinese, alphabet, number, unk and point.
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for ch in text:
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if ch == ".":
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types.append("point")
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elif self.is_chinese(ch):
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types.append("zh")
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elif self.is_alphabet(ch):
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types.append("en")
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elif ch == " ":
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types.append("blank")
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elif self.is_number(ch):
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types.append("num")
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else:
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types.append("unk")
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assert len(types) == len(text)
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for i in range(len(types)):
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# find the first char of the seg
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if flag == 0:
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# 首个字符是中文,英文或者数字
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if types[i] == "zh" or types[i] == "en" or types[i] == "num":
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temp_seg += text[i]
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temp_lang = types[i]
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flag = 1
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else:
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# 数字和小数点均与前面的字符合并,类型属于前面一个字符的类型
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if types[i] == temp_lang or types[i] == "num" or types[
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i] == "point":
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temp_seg += text[i]
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# 数字与后面的任意字符都拼接
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elif temp_lang == "num":
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temp_seg += text[i]
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if types[i] == "zh" or types[i] == "en":
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temp_lang = types[i]
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# 如果是空格则与前面字符拼接
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elif types[i] == "blank":
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temp_seg += text[i]
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elif types[i] == "unk":
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pass
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else:
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segments.append((temp_seg, temp_lang))
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if types[i] == "zh" or types[i] == "en":
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temp_seg = text[i]
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temp_lang = types[i]
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flag = 1
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else:
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flag = 0
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temp_seg = ""
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temp_lang = ""
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segments.append((temp_seg, temp_lang))
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return segments
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def get_input_ids(self,
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sentence: str,
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merge_sentences: bool=False,
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get_tone_ids: bool=False,
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add_sp: bool=True,
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to_tensor: bool=True) -> Dict[str, List[paddle.Tensor]]:
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sentences = self._split(sentence)
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phones_list = []
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result = {}
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for text in sentences:
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phones_seg = []
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segments = self._distinguish(text)
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for seg in segments:
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content = seg[0]
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lang = seg[1]
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if content != '':
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if lang == "en":
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input_ids = self.en_frontend.get_input_ids(
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content, merge_sentences=True, to_tensor=to_tensor)
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else:
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input_ids = self.zh_frontend.get_input_ids(
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content,
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merge_sentences=True,
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get_tone_ids=get_tone_ids,
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to_tensor=to_tensor)
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phones_seg.append(input_ids["phone_ids"][0])
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if add_sp:
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phones_seg.append(self.sp_id_tensor)
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if phones_seg == []:
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phones_seg.append(self.sp_id_tensor)
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phones = paddle.concat(phones_seg)
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phones_list.append(phones)
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if merge_sentences:
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merge_list = paddle.concat(phones_list)
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# rm the last 'sp' to avoid the noise at the end
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# cause in the training data, no 'sp' in the end
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if merge_list[-1] == self.sp_id_tensor:
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merge_list = merge_list[:-1]
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phones_list = []
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phones_list.append(merge_list)
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result["phone_ids"] = phones_list
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return result
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