diff --git a/GPT_SoVITS/inference_webui.py b/GPT_SoVITS/inference_webui.py
index fdee8d9..574b9d8 100644
--- a/GPT_SoVITS/inference_webui.py
+++ b/GPT_SoVITS/inference_webui.py
@@ -4,6 +4,9 @@ logging.getLogger("urllib3").setLevel(logging.ERROR)
 logging.getLogger("httpcore").setLevel(logging.ERROR)
 logging.getLogger("httpx").setLevel(logging.ERROR)
 logging.getLogger("asyncio").setLevel(logging.ERROR)
+
+logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
+logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
 import pdb
 
 if os.path.exists("./gweight.txt"):
@@ -75,7 +78,7 @@ def get_bert_feature(text, word2ph):
     with torch.no_grad():
         inputs = tokenizer(text, return_tensors="pt")
         for i in inputs:
-            inputs[i] = inputs[i].to(device)  #####输入是long不用管精度问题，精度随bert_model
+            inputs[i] = inputs[i].to(device)
         res = bert_model(**inputs, output_hidden_states=True)
         res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
     assert len(word2ph) == len(text)
@@ -182,6 +185,83 @@ 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")
@@ -211,30 +291,32 @@ 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]
-    phones1, word2ph1, norm_text1 = clean_text(prompt_text, prompt_language)
-    phones1 = cleaned_text_to_sequence(phones1)
+
+    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)
     texts = text.split("\n")
     audio_opt = []
-    
-    if prompt_language == "zh":
-        bert1 = get_bert_feature(norm_text1, word2ph1).to(device)
+    if prompt_language == "en":
+        bert1 = get_bert_inf(phones1, word2ph1, norm_text1, prompt_language)
     else:
-        bert1 = torch.zeros(
-            (1024, len(phones1)),
-            dtype=torch.float16 if is_half == True else torch.float32,
-        ).to(device)
+        bert1 = nonen_get_bert_inf(prompt_text, prompt_language)
     
     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
-        phones2, word2ph2, norm_text2 = clean_text(text, text_language)
-        phones2 = cleaned_text_to_sequence(phones2)
-
-        if text_language == "zh":
-            bert2 = get_bert_feature(norm_text2, word2ph2).to(device)
+        if text_language == "en":
+            phones2, word2ph2, norm_text2 = clean_text_inf(text, text_language)
         else:
-            bert2 = torch.zeros((1024, len(phones2))).to(bert1)
+            phones2, word2ph2, norm_text2 = nonen_clean_text_inf(text, text_language)
+
+        if text_language == "en":
+            bert2 = get_bert_inf(phones2, word2ph2, norm_text2, text_language)
+        else:
+            bert2 = nonen_get_bert_inf(text, text_language)
+
         bert = torch.cat([bert1, bert2], 1)
 
         all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)