diff --git a/GPT_SoVITS/inference_webui.py b/GPT_SoVITS/inference_webui.py index 10ebb97..a48d374 100644 --- a/GPT_SoVITS/inference_webui.py +++ b/GPT_SoVITS/inference_webui.py @@ -18,9 +18,10 @@ logging.getLogger("torchaudio._extension").setLevel(logging.ERROR) logging.getLogger("multipart.multipart").setLevel(logging.ERROR) warnings.simplefilter(action='ignore', category=FutureWarning) -import LangSegment, os, re, sys, json +import os, re, sys, json import pdb import torch +from text.LangSegmenter import LangSegmenter try: import gradio.analytics as analytics @@ -380,8 +381,7 @@ def get_phones_and_bert(text,language,version,final=False): if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}: language = language.replace("all_","") if language == "en": - LangSegment.setfilters(["en"]) - formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text)) + formattext = text else: # 因无法区别中日韩文汉字,以用户输入为准 formattext = text @@ -408,19 +408,18 @@ def get_phones_and_bert(text,language,version,final=False): elif language in {"zh", "ja", "ko", "yue", "auto", "auto_yue"}: textlist=[] langlist=[] - LangSegment.setfilters(["zh","ja","en","ko"]) if language == "auto": - for tmp in LangSegment.getTexts(text): + for tmp in LangSegmenter.getTexts(text): langlist.append(tmp["lang"]) textlist.append(tmp["text"]) elif language == "auto_yue": - for tmp in LangSegment.getTexts(text): + for tmp in LangSegmenter.getTexts(text): if tmp["lang"] == "zh": tmp["lang"] = "yue" langlist.append(tmp["lang"]) textlist.append(tmp["text"]) else: - for tmp in LangSegment.getTexts(text): + for tmp in LangSegmenter.getTexts(text): if tmp["lang"] == "en": langlist.append(tmp["lang"]) else: diff --git a/GPT_SoVITS/text/LangSegmenter/__init__.py b/GPT_SoVITS/text/LangSegmenter/__init__.py new file mode 100644 index 0000000..6fe6095 --- /dev/null +++ b/GPT_SoVITS/text/LangSegmenter/__init__.py @@ -0,0 +1 @@ +from .langsegmenter import LangSegmenter \ No newline at end of file diff --git a/GPT_SoVITS/text/LangSegmenter/langsegmenter.py b/GPT_SoVITS/text/LangSegmenter/langsegmenter.py new file mode 100644 index 0000000..6859ddb --- /dev/null +++ b/GPT_SoVITS/text/LangSegmenter/langsegmenter.py @@ -0,0 +1,124 @@ +import logging +import jieba +import re +jieba.setLogLevel(logging.CRITICAL) + +# 更改fast_langdetect大模型位置 +from pathlib import Path +import fast_langdetect +fast_langdetect.ft_detect.infer.CACHE_DIRECTORY = Path(__file__).parent.parent.parent / "pretrained_models" / "fast_langdetect" +import sys +sys.modules["fast_langdetect"] = fast_langdetect + +from split_lang import LangSplitter + + +def full_en(text): + pattern = r'^[A-Za-z0-9\s\u0020-\u007E\u2000-\u206F\u3000-\u303F\uFF00-\uFFEF]+$' + return bool(re.match(pattern, text)) + + +def split_jako(tag_lang,item): + if tag_lang == "ja": + pattern = r"([\u3041-\u3096\u3099\u309A\u30A1-\u30FA\u30FC]+(?:[0-9、-〜。！？.!?… ]+[\u3041-\u3096\u3099\u309A\u30A1-\u30FA\u30FC]*)*)" + else: + pattern = r"([\u1100-\u11FF\u3130-\u318F\uAC00-\uD7AF]+(?:[0-9、-〜。！？.!?… ]+[\u1100-\u11FF\u3130-\u318F\uAC00-\uD7AF]*)*)" + + lang_list: list[dict] = [] + tag = 0 + for match in re.finditer(pattern, item['text']): + if match.start() > tag: + lang_list.append({'lang':item['lang'],'text':item['text'][tag:match.start()]}) + + tag = match.end() + lang_list.append({'lang':tag_lang,'text':item['text'][match.start():match.end()]}) + + if tag < len(item['text']): + lang_list.append({'lang':item['lang'],'text':item['text'][tag:len(item['text'])]}) + + return lang_list + + +def merge_lang(lang_list, item): + if lang_list and item['lang'] == lang_list[-1]['lang']: + lang_list[-1]['text'] += item['text'] + else: + lang_list.append(item) + return lang_list + + +class LangSegmenter(): + # 默认过滤器, 基于gsv目前四种语言 + DEFAULT_LANG_MAP = { + "zh": "zh", + "yue": "zh", # 粤语 + "wuu": "zh", # 吴语 + "zh-cn": "zh", + "zh-tw": "x", # 繁体设置为x + "ko": "ko", + "ja": "ja", + "en": "en", + } + + + def getTexts(text): + lang_splitter = LangSplitter(lang_map=LangSegmenter.DEFAULT_LANG_MAP) + substr = lang_splitter.split_by_lang(text=text) + + lang_list: list[dict] = [] + + for _, item in enumerate(substr): + dict_item = {'lang':item.lang,'text':item.text} + + # 处理短英文被识别为其他语言的问题 + if full_en(dict_item['text']): + dict_item['lang'] = 'en' + lang_list = merge_lang(lang_list,dict_item) + continue + + # 处理非日语夹日文的问题(不包含CJK) + ja_list: list[dict] = [] + if dict_item['lang'] != 'ja': + ja_list = split_jako('ja',dict_item) + + if not ja_list: + ja_list.append(dict_item) + + # 处理非韩语夹韩语的问题(不包含CJK) + ko_list: list[dict] = [] + temp_list: list[dict] = [] + for _, ko_item in enumerate(ja_list): + if ko_item["lang"] != 'ko': + ko_list = split_jako('ko',ko_item) + + if ko_list: + temp_list.extend(ko_list) + else: + temp_list.append(ko_item) + + # 未存在非日韩文夹日韩文 + if len(temp_list) == 1: + # 跳过未知语言 + if dict_item['lang'] == 'x': + continue + else: + lang_list = merge_lang(lang_list,dict_item) + continue + + # 存在非日韩文夹日韩文 + for _, temp_item in enumerate(temp_list): + # 待观察是否会出现带英文或语言为x的中日英韩文 + if temp_item['lang'] == 'x': + continue + + lang_list = merge_lang(lang_list,temp_item) + + return lang_list + + +if __name__ == "__main__": + text = "MyGO?,你也喜欢まいご吗？" + print(LangSegmenter.getTexts(text)) + + + diff --git a/GPT_SoVITS/text/english.py b/GPT_SoVITS/text/english.py index bd5612e..c289ad1 100644 --- a/GPT_SoVITS/text/english.py +++ b/GPT_SoVITS/text/english.py @@ -112,7 +112,7 @@ def replace_phs(phs): def replace_consecutive_punctuation(text): punctuations = ''.join(re.escape(p) for p in punctuation) - pattern = f'([{punctuations}])([{punctuations}])+' + pattern = f'([{punctuations}\s])([{punctuations}])+' result = re.sub(pattern, r'\1', text) return result @@ -233,6 +233,7 @@ def text_normalize(text): # 来自 g2p_en 文本格式化处理 # 增加大写兼容 + # 增加纯大写单词拆分 text = unicode(text) text = normalize_numbers(text) text = ''.join(char for char in unicodedata.normalize('NFD', text) @@ -240,6 +241,7 @@ def text_normalize(text): text = re.sub("[^ A-Za-z'.,?!\-]", "", text) text = re.sub(r"(?i)i\.e\.", "that is", text) text = re.sub(r"(?i)e\.g\.", "for example", text) + text = re.sub(r'(?=0.2.0 +split-lang Faster_Whisper wordsegment rotary_embedding_torch