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PaddleSpeech/paddlespeech/t2s/frontend/mix_frontend.py

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# 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 Dict
from typing import List
import paddle
from paddlespeech.t2s.frontend import English
from paddlespeech.t2s.frontend.zh_frontend import Frontend
class MixFrontend():
def __init__(self,
g2p_model="pypinyin",
phone_vocab_path=None,
tone_vocab_path=None):
self.zh_frontend = Frontend(
phone_vocab_path=phone_vocab_path, tone_vocab_path=tone_vocab_path)
self.en_frontend = English(phone_vocab_path=phone_vocab_path)
self.SENTENCE_SPLITOR = re.compile(r'([:、,;。?!,;?!][”’]?)')
self.sp_id = self.zh_frontend.vocab_phones["sp"]
self.sp_id_tensor = paddle.to_tensor([self.sp_id])
def is_chinese(self, char):
if char >= '\u4e00' and char <= '\u9fa5':
return True
else:
return False
def is_alphabet(self, char):
if (char >= '\u0041' and char <= '\u005a') or (char >= '\u0061' and
char <= '\u007a'):
return True
else:
return False
def is_number(self, char):
if char >= '\u0030' and char <= '\u0039':
return True
else:
return False
def is_other(self, char):
if not (self.is_chinese(char) or self.is_number(char) or
self.is_alphabet(char)):
return True
else:
return False
def is_end(self, before_char, after_char) -> bool:
if ((self.is_alphabet(before_char) or before_char == " ") and
(self.is_alphabet(after_char) or after_char == " ")):
return True
else:
return False
def _replace(self, text: str) -> str:
new_text = ""
# get "." indexs
point = "."
point_indexs = []
index = -1
for i in range(text.count(point)):
index = text.find(".", index + 1, len(text))
point_indexs.append(index)
# replace "." -> "。" when English sentence ending
if len(point_indexs) == 0:
new_text = text
elif len(point_indexs) == 1:
point_index = point_indexs[0]
if point_index == 0 or point_index == len(text) - 1:
new_text = text
else:
if not self.is_end(text[point_index - 1], text[point_index +
1]):
new_text = text
else:
new_text = text[:point_index] + "" + text[point_index + 1:]
elif len(point_indexs) == 2:
first_index = point_indexs[0]
end_index = point_indexs[1]
# first
if first_index != 0:
if not self.is_end(text[first_index - 1], text[first_index +
1]):
new_text += (text[:first_index] + ".")
else:
new_text += (text[:first_index] + "")
else:
new_text += "."
# last
if end_index != len(text) - 1:
if not self.is_end(text[end_index - 1], text[end_index + 1]):
new_text += text[point_indexs[-2] + 1:]
else:
new_text += (text[point_indexs[-2] + 1:end_index] + "" +
text[end_index + 1:])
else:
new_text += "."
else:
first_index = point_indexs[0]
end_index = point_indexs[-1]
# first
if first_index != 0:
if not self.is_end(text[first_index - 1], text[first_index +
1]):
new_text += (text[:first_index] + ".")
else:
new_text += (text[:first_index] + "")
else:
new_text += "."
# middle
for j in range(1, len(point_indexs) - 1):
point_index = point_indexs[j]
if not self.is_end(text[point_index - 1], text[point_index +
1]):
new_text += (
text[point_indexs[j - 1] + 1:point_index] + ".")
else:
new_text += (
text[point_indexs[j - 1] + 1:point_index] + "")
# last
if end_index != len(text) - 1:
if not self.is_end(text[end_index - 1], text[end_index + 1]):
new_text += text[point_indexs[-2] + 1:]
else:
new_text += (text[point_indexs[-2] + 1:end_index] + "" +
text[end_index + 1:])
else:
new_text += "."
return new_text
def _split(self, text: str) -> List[str]:
text = re.sub(r'[《》【】<=>{}()#&@“”^_|…\\]', '', text)
# 替换英文句子的句号 "." --> "。" 用于后续分句
text = self._replace(text)
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 _distinguish(self, text: str) -> List[str]:
# sentence --> [ch_part, en_part, ch_part, ...]
segments = []
types = []
flag = 0
temp_seg = ""
temp_lang = ""
# Determine the type of each character. type: blank, chinese, alphabet, number, unk and point.
for ch in text:
if ch == ".":
types.append("point")
elif self.is_chinese(ch):
types.append("zh")
elif self.is_alphabet(ch):
types.append("en")
elif ch == " ":
types.append("blank")
elif self.is_number(ch):
types.append("num")
else:
types.append("unk")
assert len(types) == len(text)
for i in range(len(types)):
# find the first char of the seg
if flag == 0:
# 首个字符是中文,英文或者数字
if types[i] == "zh" or types[i] == "en" or types[i] == "num":
temp_seg += text[i]
temp_lang = types[i]
flag = 1
else:
# 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
if types[i] == temp_lang or types[i] == "num" or types[
i] == "point":
temp_seg += text[i]
# 数字与后面的任意字符都拼接
elif temp_lang == "num":
temp_seg += text[i]
if types[i] == "zh" or types[i] == "en":
temp_lang = types[i]
# 如果是空格则与前面字符拼接
elif types[i] == "blank":
temp_seg += text[i]
elif types[i] == "unk":
pass
else:
segments.append((temp_seg, temp_lang))
if types[i] == "zh" or types[i] == "en":
temp_seg = text[i]
temp_lang = types[i]
flag = 1
else:
flag = 0
temp_seg = ""
temp_lang = ""
segments.append((temp_seg, temp_lang))
return segments
def get_input_ids(self,
sentence: str,
merge_sentences: bool=False,
get_tone_ids: bool=False,
add_sp: bool=True,
to_tensor: bool=True) -> Dict[str, List[paddle.Tensor]]:
sentences = self._split(sentence)
phones_list = []
result = {}
for text in sentences:
phones_seg = []
segments = self._distinguish(text)
for seg in segments:
content = seg[0]
lang = seg[1]
if content != '':
if lang == "en":
input_ids = self.en_frontend.get_input_ids(
content, merge_sentences=True, to_tensor=to_tensor)
else:
input_ids = self.zh_frontend.get_input_ids(
content,
merge_sentences=True,
get_tone_ids=get_tone_ids,
to_tensor=to_tensor)
phones_seg.append(input_ids["phone_ids"][0])
if add_sp:
phones_seg.append(self.sp_id_tensor)
if phones_seg == []:
phones_seg.append(self.sp_id_tensor)
phones = paddle.concat(phones_seg)
phones_list.append(phones)
if merge_sentences:
merge_list = paddle.concat(phones_list)
# rm the last 'sp' to avoid the noise at the end
# cause in the training data, no 'sp' in the end
if merge_list[-1] == self.sp_id_tensor:
merge_list = merge_list[:-1]
phones_list = []
phones_list.append(merge_list)
result["phone_ids"] = phones_list
return result