Add tags via re

utils.py

@@ -8,6 +8,7 @@ import subprocess
 import numpy as np
 from scipy.io.wavfile import read
 import torch
+import regex as re
 
 MATPLOTLIB_FLAG = False
 
@@ -15,6 +16,137 @@ logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
 logger = logging
 
 
+
+zh_pattern = re.compile(r'[\u4e00-\u9fa5]')
+en_pattern = re.compile(r'[a-zA-Z]')
+jp_pattern = re.compile(r'[\u3040-\u30ff\u31f0-\u31ff]')
+kr_pattern = re.compile(r'[\uac00-\ud7af\u1100-\u11ff\u3130-\u318f\ua960-\ua97f]')
+num_pattern=re.compile(r'[0-9]')
+comma=r"(?<=[.。!！?？；;，,、:：'\"‘“”’()（）《》「」~——])"    #向前匹配但固定长度
+tags={'ZH':'[ZH]','EN':'[EN]','JP':'[JA]','KR':'[KR]'}
+
+def restart_program():
+    python = sys.executable
+    os.execl(python, python, * sys.argv)
+
+def tag_cjke(text):
+    '''为中英日韩加tag,中日正则分不开，故先分句分离中日再识别，以应对大部分情况'''
+    sentences = re.split(r"([.。!！?？；;，,、:：'\"‘“”’()（）【】《》「」~——]+ *(?![0-9]))", text) #分句排除小数点
+    sentences.append("")
+    sentences = ["".join(i) for i in zip(sentences[0::2],sentences[1::2])]
+    # print(sentences)
+    prev_lang=None
+    tagged_text = ""
+    for s in sentences:
+        nu = re.sub(r'[\s\p{P}]+', '', s, flags=re.U).strip()
+        if len(nu)==0:
+            continue
+        s = re.sub(r'[()（）《》「」【】‘“”’]+', '', s)
+        jp=re.findall(jp_pattern, s)
+        if len(jp)>0:  
+            prev_lang,tagged_jke=tag_jke(s,prev_lang)
+            tagged_text +=tagged_jke
+        else:
+            prev_lang,tagged_cke=tag_cke(s,prev_lang)
+            tagged_text +=tagged_cke
+    return tagged_text
+
+def tag_jke(text,prev_sentence=None):
+    '''为英日韩加tag'''
+    # 初始化标记变量
+    tagged_text = ""
+    prev_lang = None
+    tagged=0
+    # 遍历文本
+    for char in text:
+        # 判断当前字符属于哪种语言
+        if jp_pattern.match(char):
+            lang = "JP"
+        elif zh_pattern.match(char):
+            lang = "JP"
+        elif kr_pattern.match(char):
+            lang = "KR"
+        elif en_pattern.match(char):
+            lang = "EN"
+        # elif num_pattern.match(char):
+        #     lang = prev_sentence
+        else:
+            lang = None
+            tagged_text += char
+            continue
+        # 如果当前语言与上一个语言不同，就添加标记
+        if lang != prev_lang:
+            tagged=1
+            if prev_lang==None: # 开头
+                tagged_text =tags[lang]+tagged_text
+            else:
+                tagged_text =tagged_text+tags[prev_lang]+tags[lang]
+
+            # 重置标记变量
+            prev_lang = lang
+
+        # 添加当前字符到标记文本中
+        tagged_text += char
+    
+    # 在最后一个语言的结尾添加对应的标记
+    if prev_lang:
+            tagged_text += tags[prev_lang]
+    if not tagged:
+        prev_lang=prev_sentence
+        tagged_text =tags[prev_lang]+tagged_text+tags[prev_lang]
+
+    return prev_lang,tagged_text
+
+def tag_cke(text,prev_sentence=None):
+    '''为中英韩加tag'''
+    # 初始化标记变量
+    tagged_text = ""
+    prev_lang = None
+    # 是否全略过未标签
+    tagged=0
+    
+    # 遍历文本
+    for char in text:
+        # 判断当前字符属于哪种语言
+        if zh_pattern.match(char):
+            lang = "ZH"
+        elif kr_pattern.match(char):
+            lang = "KR"
+        elif en_pattern.match(char):
+            lang = "EN"
+        # elif num_pattern.match(char):
+        #     lang = prev_sentence
+        else:
+            # 略过
+            lang = None
+            tagged_text += char
+            continue
+
+        # 如果当前语言与上一个语言不同，添加标记
+        if lang != prev_lang:
+            tagged=1
+            if prev_lang==None: # 开头
+                tagged_text =tags[lang]+tagged_text
+            else:
+                tagged_text =tagged_text+tags[prev_lang]+tags[lang]
+
+            # 重置标记变量
+            prev_lang = lang
+
+        # 添加当前字符到标记文本中
+        tagged_text += char
+    
+    # 在最后一个语言的结尾添加对应的标记
+    if prev_lang:
+            tagged_text += tags[prev_lang]
+    # 未标签则继承上一句标签
+    if tagged==0:
+        prev_lang=prev_sentence
+        tagged_text =tags[prev_lang]+tagged_text+tags[prev_lang]
+    return prev_lang,tagged_text
+
+
+
 def load_checkpoint(checkpoint_path, model, optimizer=None, drop_speaker_emb=False):
     assert os.path.isfile(checkpoint_path)
     checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')