diff --git a/GPT_SoVITS/inference_webui.py b/GPT_SoVITS/inference_webui.py
index 4035915b..e9e84626 100644
--- a/GPT_SoVITS/inference_webui.py
+++ b/GPT_SoVITS/inference_webui.py
@@ -154,83 +154,6 @@ dict_language={
 }
 
 
-def splite_en_inf(sentence, language):
-    pattern = re.compile(r'[a-zA-Z. ]+')
-    textlist = []
-    langlist = []
-    pos = 0
-    for match in pattern.finditer(sentence):
-        start, end = match.span()
-        if start > pos:
-            textlist.append(sentence[pos:start])
-            langlist.append(language)
-        textlist.append(sentence[start:end])
-        langlist.append("en")
-        pos = end
-    if pos < len(sentence):
-        textlist.append(sentence[pos:])
-        langlist.append(language)
-
-    return textlist, langlist
-
-
-def clean_text_inf(text, language):
-    phones, word2ph, norm_text = clean_text(text, language)
-    phones = cleaned_text_to_sequence(phones)
-    
-    return phones, word2ph, norm_text
-    
-
-def get_bert_inf(phones, word2ph, norm_text, language):
-    if language == "zh":
-        bert = get_bert_feature(norm_text, word2ph).to(device)
-    else:
-        bert = torch.zeros(
-                (1024, len(phones)),
-                dtype=torch.float16 if is_half == True else torch.float32,
-            ).to(device)
-        
-    return bert
-
-
-def nonen_clean_text_inf(text, language):
-    textlist, langlist = splite_en_inf(text, language)
-    phones_list = []
-    word2ph_list = []
-    norm_text_list = []
-    for i in range(len(textlist)):
-        lang = langlist[i]
-        phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
-        phones_list.append(phones)
-        if lang=="en" or "ja":
-            pass
-        else:
-            word2ph_list.append(word2ph)
-        norm_text_list.append(norm_text)
-    print(word2ph_list)
-    phones = sum(phones_list, [])
-    word2ph = sum(word2ph_list, [])
-    norm_text = ' '.join(norm_text_list)
-    
-    return phones, word2ph, norm_text
-
-
-def nonen_get_bert_inf(text, language):
-    textlist, langlist = splite_en_inf(text, language)
-    print(textlist)
-    print(langlist)
-    bert_list = []
-    for i in range(len(textlist)):
-        text = textlist[i]
-        lang = langlist[i]
-        phones, word2ph, norm_text = clean_text_inf(text, lang)
-        bert = get_bert_inf(phones, word2ph, norm_text, lang)
-        bert_list.append(bert)
-    bert = torch.cat(bert_list, dim=1)
-    
-    return bert
-
-
 def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language):
     t0 = ttime()
     prompt_text = prompt_text.strip("\n")
@@ -260,32 +183,27 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language)
     t1 = ttime()
     prompt_language = dict_language[prompt_language]
     text_language = dict_language[text_language]
-    if prompt_language == "en":
-        phones1, word2ph1, norm_text1 = clean_text_inf(prompt_text, prompt_language)
-    else:
-        phones1, word2ph1, norm_text1 = nonen_clean_text_inf(prompt_text, prompt_language)
+    phones1, word2ph1, norm_text1 = clean_text(prompt_text, prompt_language)
+    phones1 = cleaned_text_to_sequence(phones1)
     texts = text.split("\n")
     audio_opt = []
     for text in texts:
         # 解决输入目标文本的空行导致报错的问题
         if (len(text.strip()) == 0):
             continue
-            
-        if text_language == "en":
-            phones2, word2ph2, norm_text2 = clean_text_inf(text, text_language)
+        phones2, word2ph2, norm_text2 = clean_text(text, text_language)
+        phones2 = cleaned_text_to_sequence(phones2)
+        if prompt_language == "zh":
+            bert1 = get_bert_feature(norm_text1, word2ph1).to(device)
         else:
-            phones2, word2ph2, norm_text2 = nonen_clean_text_inf(text, text_language)
-
-        if prompt_language == "en":
-            bert1 = get_bert_inf(phones1, word2ph1, norm_text1, prompt_language)
+            bert1 = torch.zeros(
+                (1024, len(phones1)),
+                dtype=torch.float16 if is_half == True else torch.float32,
+            ).to(device)
+        if text_language == "zh":
+            bert2 = get_bert_feature(norm_text2, word2ph2).to(device)
         else:
-            bert1 = nonen_get_bert_inf(prompt_text, prompt_language)
-        
-        if text_language == "en":
-            bert2 = get_bert_inf(phones2, word2ph2, norm_text2, text_language)
-        else:
-            bert2 = nonen_get_bert_inf(text, text_language)
-        
+            bert2 = torch.zeros((1024, len(phones2))).to(bert1)
         bert = torch.cat([bert1, bert2], 1)
 
         all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)