新增VITS批量推理 GPT_SoVITS/TTS_infer_pack/TTS.py

fix some bugs   GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py
	fix some bugs   GPT_SoVITS/TTS_infer_pack/text_segmentation_method.py
	fix some bugs   GPT_SoVITS/inference_webui.py
	fix some bugs   GPT_SoVITS/module/models.py

GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py

@@ -10,7 +10,7 @@ from typing import Dict, List, Tuple
 from text.cleaner import clean_text
 from text import cleaned_text_to_sequence
 from transformers import AutoModelForMaskedLM, AutoTokenizer
-from TTS_infer_pack.text_segmentation_method import splits, get_method as get_seg_method
+from TTS_infer_pack.text_segmentation_method import split_big_text, splits, get_method as get_seg_method
 
 # from tools.i18n.i18n import I18nAuto
 
@@ -39,6 +39,10 @@ def merge_short_text_in_array(texts:str, threshold:int) -> list:
     return result
 
 
+
+
+
+
 class TextPreprocessor:
     def __init__(self, bert_model:AutoModelForMaskedLM, 
                  tokenizer:AutoTokenizer, device:torch.device):
@@ -74,12 +78,18 @@ class TextPreprocessor:
         _texts = text.split("\n")
         _texts = merge_short_text_in_array(_texts, 5)
         texts = []
+    
+
+        
         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[-1] not in splits): text += "。" if lang != "en" else "."
-            texts.append(text)
+            
+            # 解决句子过长导致Bert报错的问题
+            texts.extend(split_big_text(text))
+            
             
         return texts
     
@@ -176,4 +186,8 @@ class TextPreprocessor:
                 dtype=torch.float32,
             ).to(self.device)
 
-        return feature
+        return feature
+    
+
+
+