|
|
# 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 _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
|
|
|
point = "."
|
|
|
point_indexs = []
|
|
|
index = -1
|
|
|
for i in range(text.count(point)):
|
|
|
index = text.find(".", index + 1, len(text))
|
|
|
point_indexs.append(index)
|
|
|
|
|
|
print(point_indexs)
|
|
|
|
|
|
for point_index in point_indexs:
|
|
|
# 如果点在最开始或者最末尾的位置，不处理
|
|
|
if point_index == 0 or point_index == len(text) - 1:
|
|
|
pass
|
|
|
else:
|
|
|
if ((self.is_alphabet(text[point_index - 1]) or
|
|
|
text[point_index - 1] == " ") and
|
|
|
(self.is_alphabet(text[point_index + 1]) or
|
|
|
text[point_index + 1] == " ")):
|
|
|
text = text.replace(text[point_index], "。")
|
|
|
|
|
|
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=True,
|
|
|
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 lang == "zh":
|
|
|
input_ids = self.zh_frontend.get_input_ids(
|
|
|
content,
|
|
|
merge_sentences=True,
|
|
|
get_tone_ids=get_tone_ids,
|
|
|
to_tensor=to_tensor)
|
|
|
|
|
|
elif lang == "en":
|
|
|
input_ids = self.en_frontend.get_input_ids(
|
|
|
content, merge_sentences=True, to_tensor=to_tensor)
|
|
|
|
|
|
phones_seg.append(input_ids["phone_ids"][0])
|
|
|
if add_sp:
|
|
|
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
|
