File size: 6,994 Bytes
1cf1e13
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
import logging

import regex as re

from tools.classify_language import classify_language, split_alpha_nonalpha


def check_is_none(item) -> bool:
    """none -> True, not none -> False"""
    return (
        item is None
        or (isinstance(item, str) and str(item).isspace())
        or str(item) == ""
    )


def markup_language(text: str, target_languages: list = None) -> str:
    pattern = (
        r"[\!\"\#\$\%\&\'\(\)\*\+\,\-\.\/\:\;\<\>\=\?\@\[\]\{\}\\\\\^\_\`"
        r"\!?。"#$%&'()*+,-/:;<=>@[\]^_`{|}~⦅⦆「」、、〃》「」"
        r"『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘\'\‛\“\”\„\‟…‧﹏.]+"
    )
    sentences = re.split(pattern, text)

    pre_lang = ""
    p = 0

    if target_languages is not None:
        sorted_target_languages = sorted(target_languages)
        if sorted_target_languages in [["en", "zh"], ["en", "ja"], ["en", "ja", "zh"]]:
            new_sentences = []
            for sentence in sentences:
                new_sentences.extend(split_alpha_nonalpha(sentence))
            sentences = new_sentences

    for sentence in sentences:
        if check_is_none(sentence):
            continue

        lang = classify_language(sentence, target_languages)

        if pre_lang == "":
            text = text[:p] + text[p:].replace(
                sentence, f"[{lang.upper()}]{sentence}", 1
            )
            p += len(f"[{lang.upper()}]")
        elif pre_lang != lang:
            text = text[:p] + text[p:].replace(
                sentence, f"[{pre_lang.upper()}][{lang.upper()}]{sentence}", 1
            )
            p += len(f"[{pre_lang.upper()}][{lang.upper()}]")
        pre_lang = lang
        p += text[p:].index(sentence) + len(sentence)
    text += f"[{pre_lang.upper()}]"

    return text


def split_by_language(text: str, target_languages: list = None) -> list:
    pattern = (
        r"[\!\"\#\$\%\&\'\(\)\*\+\,\-\.\/\:\;\<\>\=\?\@\[\]\{\}\\\\\^\_\`"
        r"\!?\。"#$%&'()*+,-/:;<=>@[\]^_`{|}~⦅⦆「」、、〃》「」"
        r"『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘\'\‛\“\”\„\‟…‧﹏.]+"
    )
    sentences = re.split(pattern, text)

    pre_lang = ""
    start = 0
    end = 0
    sentences_list = []

    if target_languages is not None:
        sorted_target_languages = sorted(target_languages)
        if sorted_target_languages in [["en", "zh"], ["en", "ja"], ["en", "ja", "zh"]]:
            new_sentences = []
            for sentence in sentences:
                new_sentences.extend(split_alpha_nonalpha(sentence))
            sentences = new_sentences

    for sentence in sentences:
        if check_is_none(sentence):
            continue

        lang = classify_language(sentence, target_languages)

        end += text[end:].index(sentence)
        if pre_lang != "" and pre_lang != lang:
            sentences_list.append((text[start:end], pre_lang))
            start = end
        end += len(sentence)
        pre_lang = lang
    sentences_list.append((text[start:], pre_lang))

    return sentences_list


def sentence_split(text: str, max: int) -> list:
    pattern = r"[!(),—+\-.:;??。,、;:]+"
    sentences = re.split(pattern, text)
    discarded_chars = re.findall(pattern, text)

    sentences_list, count, p = [], 0, 0

    # 按被分割的符号遍历
    for i, discarded_chars in enumerate(discarded_chars):
        count += len(sentences[i]) + len(discarded_chars)
        if count >= max:
            sentences_list.append(text[p : p + count].strip())
            p += count
            count = 0

    # 加入最后剩余的文本
    if p < len(text):
        sentences_list.append(text[p:])

    return sentences_list


def sentence_split_and_markup(text, max=50, lang="auto", speaker_lang=None):
    # 如果该speaker只支持一种语言
    if speaker_lang is not None and len(speaker_lang) == 1:
        if lang.upper() not in ["AUTO", "MIX"] and lang.lower() != speaker_lang[0]:
            logging.debug(
                f'lang "{lang}" is not in speaker_lang {speaker_lang},automatically set lang={speaker_lang[0]}'
            )
        lang = speaker_lang[0]

    sentences_list = []
    if lang.upper() != "MIX":
        if max <= 0:
            sentences_list.append(
                markup_language(text, speaker_lang)
                if lang.upper() == "AUTO"
                else f"[{lang.upper()}]{text}[{lang.upper()}]"
            )
        else:
            for i in sentence_split(text, max):
                if check_is_none(i):
                    continue
                sentences_list.append(
                    markup_language(i, speaker_lang)
                    if lang.upper() == "AUTO"
                    else f"[{lang.upper()}]{i}[{lang.upper()}]"
                )
    else:
        sentences_list.append(text)

    for i in sentences_list:
        logging.debug(i)

    return sentences_list


if __name__ == "__main__":
    text = "这几天心里颇不宁静。今晚在院子里坐着乘凉,忽然想起日日走过的荷塘,在这满月的光里,总该另有一番样子吧。月亮渐渐地升高了,墙外马路上孩子们的欢笑,已经听不见了;妻在屋里拍着闰儿,迷迷糊糊地哼着眠歌。我悄悄地披了大衫,带上门出去。"
    print(markup_language(text, target_languages=None))
    print(sentence_split(text, max=50))
    print(sentence_split_and_markup(text, max=50, lang="auto", speaker_lang=None))

    text = "你好,这是一段用来测试自动标注的文本。こんにちは,これは自動ラベリングのテスト用テキストです.Hello, this is a piece of text to test autotagging.你好!今天我们要介绍VITS项目,其重点是使用了GAN Duration predictor和transformer flow,并且接入了Bert模型来提升韵律。Bert embedding会在稍后介绍。"
    print(split_by_language(text, ["zh", "ja", "en"]))

    text = "vits和Bert-VITS2是tts模型。花费3days.花费3天。Take 3 days"

    print(split_by_language(text, ["zh", "ja", "en"]))
    # output: [('vits', 'en'), ('和', 'ja'), ('Bert-VITS', 'en'), ('2是', 'zh'), ('tts', 'en'), ('模型。花费3', 'zh'), ('days.', 'en'), ('花费3天。', 'zh'), ('Take 3 days', 'en')]

    print(split_by_language(text, ["zh", "en"]))
    # output: [('vits', 'en'), ('和', 'zh'), ('Bert-VITS', 'en'), ('2是', 'zh'), ('tts', 'en'), ('模型。花费3', 'zh'), ('days.', 'en'), ('花费3天。', 'zh'), ('Take 3 days', 'en')]

    text = "vits 和 Bert-VITS2 是 tts 模型。花费 3 days. 花费 3天。Take 3 days"
    print(split_by_language(text, ["zh", "en"]))
    # output: [('vits ', 'en'), ('和 ', 'zh'), ('Bert-VITS2 ', 'en'), ('是 ', 'zh'), ('tts ', 'en'), ('模型。花费 ', 'zh'), ('3 days. ', 'en'), ('花费 3天。', 'zh'), ('Take 3 days', 'en')]