切换新的语言分割工具 (#2047)

* Add New LangSegmenter

* New LangSegmenter

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 = text
-            formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(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:

GPT_SoVITS/text/LangSegmenter/__init__.py

@@ -0,0 +1 @@
+from .langsegmenter import LangSegmenter

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))
+
+
+

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'(?<!^)(?<![\s])([A-Z])', r' \1', text)
 
     # 避免重复标点引起的参考泄露
     text = replace_consecutive_punctuation(text)

README.md

@@ -329,7 +329,7 @@ Special thanks to the following projects and contributors:
 - [BigVGAN](https://github.com/NVIDIA/BigVGAN)
 ### Text Frontend for Inference
 - [paddlespeech zh_normalization](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/zh_normalization)
-- [LangSegment](https://github.com/juntaosun/LangSegment)
+- [split-lang](https://github.com/DoodleBears/split-lang)
 - [g2pW](https://github.com/GitYCC/g2pW)
 - [pypinyin-g2pW](https://github.com/mozillazg/pypinyin-g2pW)
 - [paddlespeech g2pw](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/g2pw)

requirements.txt

@@ -23,7 +23,7 @@ PyYAML
 psutil
 jieba_fast
 jieba
-LangSegment>=0.2.0
+split-lang
 Faster_Whisper
 wordsegment
 rotary_embedding_torch