diff --git a/GPT_SoVITS/AR/models/t2s_model.py b/GPT_SoVITS/AR/models/t2s_model.py
index b49bcfb..dad2440 100644
--- a/GPT_SoVITS/AR/models/t2s_model.py
+++ b/GPT_SoVITS/AR/models/t2s_model.py
@@ -297,7 +297,8 @@ class Text2SemanticDecoder(nn.Module):
             (0, y_len),
             value=True,
         )
-        
+        # 取消对y[0]的mask,以防止复读，详见https://github.com/RVC-Boss/GPT-SoVITS/issues/965
+        x_attn_mask[:, x_len]=False
         y_attn_mask = F.pad(
             torch.triu(
                 torch.ones(y_len, y_len, dtype=torch.bool, device=x.device),
@@ -393,6 +394,8 @@ class Text2SemanticDecoder(nn.Module):
             (0, y_len),
             value=True,
         )
+        # 取消对y[0]的mask,以防止复读，详见https://github.com/RVC-Boss/GPT-SoVITS/issues/965
+        x_attn_mask[:, x_len]=False 
         y_attn_mask = F.pad(
             torch.triu(
                 torch.ones(y_len, y_len, dtype=torch.bool, device=x.device),
@@ -458,7 +461,7 @@ class Text2SemanticDecoder(nn.Module):
                 value=True,
             )
             y_attn_mask = F.pad(
-                torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1),
+                torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=0),# diagonal必须为0，否则会导致batch_size>1时的复读情况
                 (x_len, 0),
                 value=False,
             )
@@ -504,29 +507,29 @@ class Text2SemanticDecoder(nn.Module):
 
     def infer_panel_batch_infer_with_flash_attn(
         self,
-        x:List[torch.LongTensor],  #####全部文本token
+        x:torch.LongTensor,  #####全部文本token
         x_lens:torch.LongTensor,
         prompts:torch.LongTensor,  ####参考音频token
-        bert_feature:List[torch.LongTensor],
+        bert_feature:torch.LongTensor,
         top_k: int = -100,
         top_p: int = 100,
         early_stop_num: int = -1,
         temperature: float = 1.0,
     ):
-        # 先对phones进行embedding、对bert_features进行project，再pad到相同长度，以缓解复读问题。（可能还有其他因素导致复读）
-        max_len = 0
-        for x_item, bert_item in zip(x, bert_feature):
-            max_len = max(max_len, x_item.shape[0], bert_item.shape[1])
-        x_list = [self.ar_text_embedding(item) for item in x]
-        x_list = [F.pad(item,(0,0,0,max_len-item.shape[0]),value=0) if item.shape[0]<max_len else item for item in x_list]
-        x = torch.stack(x_list, dim=0)
+        ## 先对phones进行embedding、对bert_features进行project，再pad到相同长度（padding策略会影响T2S模型生成的结果，但不直接影响复读概率。影响复读概率的主要因素是mask的策略）
+        # max_len = 0
+        # for x_item, bert_item in zip(x, bert_feature):
+        #     max_len = max(max_len, x_item.shape[0], bert_item.shape[1])
+        # x_list = [self.ar_text_embedding(item) for item in x]
+        # x_list = [F.pad(item,(0,0,0,max_len-item.shape[0]),value=0) if item.shape[0]<max_len else item for item in x_list]
+        # x = torch.stack(x_list, dim=0)
 
-        bert_features_list = [self.bert_proj(item.transpose(0, 1)) for item in bert_feature]
-        bert_features_list = [F.pad(item,(0,0,0,max_len-item.shape[0]), value=0) if item.shape[0]<max_len else item for item in bert_features_list]
-        bert_feature = torch.stack(bert_features_list, dim=0)
+        # bert_features_list = [self.bert_proj(item.transpose(0, 1)) for item in bert_feature]
+        # bert_features_list = [F.pad(item,(0,0,0,max_len-item.shape[0]), value=0) if item.shape[0]<max_len else item for item in bert_features_list]
+        # bert_feature = torch.stack(bert_features_list, dim=0)
         
-        # bert_feature = self.bert_proj(bert_feature.transpose(1, 2))
-        # x = self.ar_text_embedding(x)
+        bert_feature = self.bert_proj(bert_feature.transpose(1, 2))
+        x = self.ar_text_embedding(x)
         x = x + bert_feature 
         x = self.ar_text_position(x)
 
@@ -573,8 +576,8 @@ class Text2SemanticDecoder(nn.Module):
             (0, y_len),  ###xx的纯0扩展到xx纯0+xy纯1，(x,x+y)
             value=True,
         )
-        y_mask = F.pad(  ###yy的右上1扩展到左边xy的0,(y,x+y)
-            torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1),
+        y_mask = F.pad(  ###yy的右上0扩展到左边xy的0,(y,x+y)
+            torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=0), # diagonal必须为0，否则会导致batch_size>1时的复读情况
             (x_len, 0),
             value=False,
         )
@@ -669,29 +672,29 @@ class Text2SemanticDecoder(nn.Module):
     
     def infer_panel_batch_only(
         self,
-        x:List[torch.LongTensor],  #####全部文本token
+        x:torch.LongTensor,  #####全部文本token
         x_lens:torch.LongTensor,
         prompts:torch.LongTensor,  ####参考音频token
-        bert_feature:List[torch.LongTensor],
+        bert_feature:torch.LongTensor,
         top_k: int = -100,
         top_p: int = 100,
         early_stop_num: int = -1,
         temperature: float = 1.0,
     ):
-        # 先对phones进行embedding、对bert_features进行project，再pad到相同长度，以缓解复读问题。（可能还有其他因素导致复读）
-        max_len = 0
-        for x_item, bert_item in zip(x, bert_feature):
-            max_len = max(max_len, x_item.shape[0], bert_item.shape[1])
-        x_list = [self.ar_text_embedding(item) for item in x]
-        x_list = [F.pad(item,(0,0,0,max_len-item.shape[0]),value=0) if item.shape[0]<max_len else item for item in x_list]
-        x = torch.stack(x_list, dim=0)
+        ## 先对phones进行embedding、对bert_features进行project，再pad到相同长度（padding策略会影响T2S模型生成的结果，但不直接影响复读概率。影响复读概率的主要因素是mask的策略）
+        # max_len = 0
+        # for x_item, bert_item in zip(x, bert_feature):
+        #     max_len = max(max_len, x_item.shape[0], bert_item.shape[1])
+        # x_list = [self.ar_text_embedding(item) for item in x]
+        # x_list = [F.pad(item,(0,0,0,max_len-item.shape[0]),value=0) if item.shape[0]<max_len else item for item in x_list]
+        # x = torch.stack(x_list, dim=0)
 
-        bert_features_list = [self.bert_proj(item.transpose(0, 1)) for item in bert_feature]
-        bert_features_list = [F.pad(item,(0,0,0,max_len-item.shape[0]), value=0) if item.shape[0]<max_len else item for item in bert_features_list]
-        bert_feature = torch.stack(bert_features_list, dim=0)
+        # bert_features_list = [self.bert_proj(item.transpose(0, 1)) for item in bert_feature]
+        # bert_features_list = [F.pad(item,(0,0,0,max_len-item.shape[0]), value=0) if item.shape[0]<max_len else item for item in bert_features_list]
+        # bert_feature = torch.stack(bert_features_list, dim=0)
         
-        # bert_feature = self.bert_proj(bert_feature.transpose(1, 2))
-        # x = self.ar_text_embedding(x)
+        bert_feature = self.bert_proj(bert_feature.transpose(1, 2))
+        x = self.ar_text_embedding(x)
         x = x + bert_feature 
         x = self.ar_text_position(x)
 
@@ -747,7 +750,7 @@ class Text2SemanticDecoder(nn.Module):
             value=True,
         )
         y_mask = F.pad(  ###yy的右上1扩展到左边xy的0,(y,x+y)
-            torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1),
+            torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=0), # diagonal必须为0，否则会导致batch_size>1时的复读情况
             (x_len, 0),
             value=False,
         )
diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py
index b875151..a6f2541 100644
--- a/GPT_SoVITS/TTS_infer_pack/TTS.py
+++ b/GPT_SoVITS/TTS_infer_pack/TTS.py
@@ -515,16 +515,16 @@ class TTS:
             all_bert_features_batch = all_bert_features_list
             
             
-            # max_len = max(bert_max_len, phones_max_len)
+            max_len = max(bert_max_len, phones_max_len)
             # phones_batch = self.batch_sequences(phones_list, axis=0, pad_value=0, max_length=max_len)
-            #### 直接对phones和bert_features进行pad，会增大复读概率。
-            # all_phones_batch = self.batch_sequences(all_phones_list, axis=0, pad_value=0, max_length=max_len)
-            # all_bert_features_batch = all_bert_features_list
-            # all_bert_features_batch = torch.zeros(len(item_list), 1024, max_len, dtype=precision, device=device)
-            # for idx, item in enumerate(all_bert_features_list):
-            #     all_bert_features_batch[idx, :, : item.shape[-1]] = item
+            #### 直接对phones和bert_features进行pad。（padding策略会影响T2S模型生成的结果，但不直接影响复读概率。影响复读概率的主要因素是mask的策略）
+            all_phones_batch = self.batch_sequences(all_phones_list, axis=0, pad_value=0, max_length=max_len)
+            all_bert_features_batch = all_bert_features_list
+            all_bert_features_batch = torch.zeros(len(item_list), 1024, max_len, dtype=precision, device=device)
+            for idx, item in enumerate(all_bert_features_list):
+                all_bert_features_batch[idx, :, : item.shape[-1]] = item
             
-            # #### 先对phones进行embedding、对bert_features进行project，再pad到相同长度，以缓解复读问题。（可能还有其他因素导致复读）
+            # #### 先对phones进行embedding、对bert_features进行project，再pad到相同长度，（padding策略会影响T2S模型生成的结果，但不直接影响复读概率。影响复读概率的主要因素是mask的策略）
             # all_phones_list = [self.t2s_model.model.ar_text_embedding(item.to(self.t2s_model.device)) for item in all_phones_list]
             # all_phones_list = [F.pad(item,(0,0,0,max_len-item.shape[0]),value=0) for item in all_phones_list]
             # all_phones_batch = torch.stack(all_phones_list, dim=0)
@@ -734,17 +734,18 @@ class TTS:
                         continue
 
                 batch_phones:List[torch.LongTensor] = item["phones"]
+                # batch_phones:torch.LongTensor = item["phones"]
                 batch_phones_len:torch.LongTensor = item["phones_len"]
-                all_phoneme_ids:List[torch.LongTensor] = item["all_phones"]
+                all_phoneme_ids:torch.LongTensor = item["all_phones"]
                 all_phoneme_lens:torch.LongTensor  = item["all_phones_len"]
-                all_bert_features:List[torch.LongTensor] = item["all_bert_features"]
+                all_bert_features:torch.LongTensor = item["all_bert_features"]
                 norm_text:str = item["norm_text"]
 
                 print(i18n("前端处理后的文本(每句):"), norm_text)
                 if no_prompt_text :
                     prompt = None
                 else:
-                    prompt = self.prompt_cache["prompt_semantic"].expand(len(all_phoneme_ids), -1).to(self.configs.device)
+                    prompt = self.prompt_cache["prompt_semantic"].expand(all_phoneme_ids.shape[0], -1).to(self.configs.device)
 
 
                 pred_semantic_list, idx_list = self.t2s_model.model.infer_panel(