diff --git a/GPT_SoVITS/AR/models/t2s_model.py b/GPT_SoVITS/AR/models/t2s_model.py
index f8f6582..5b76c50 100644
--- a/GPT_SoVITS/AR/models/t2s_model.py
+++ b/GPT_SoVITS/AR/models/t2s_model.py
@@ -629,11 +629,6 @@ class Text2SemanticDecoder(nn.Module):
         
         xy_mask = torch.concat([x_mask, y_mask], dim=0).view(1 , src_len, src_len).repeat(bsz, 1, 1).to(x.device)
         _xy_padding_mask = xy_padding_mask.view(bsz, 1, src_len).repeat(1, src_len, 1)
-        
-        for i in range(bsz):
-            l = x_lens[i]
-            _xy_padding_mask[i,l:max_len,:]=True
-            
         xy_attn_mask = xy_mask.logical_or(_xy_padding_mask)
         xy_attn_mask = xy_attn_mask.unsqueeze(1).expand(-1, self.num_head, -1, -1)
         xy_attn_mask = xy_attn_mask.bool()
@@ -645,7 +640,7 @@ class Text2SemanticDecoder(nn.Module):
         idx_list = [None]*y.shape[0]
         for idx in tqdm(range(1500)):
             if idx == 0:
-                xy_dec, k_cache, v_cache = self.t2s_transformer.process_prompt(xy_pos, xy_attn_mask, xy_padding_mask, False)
+                xy_dec, k_cache, v_cache = self.t2s_transformer.process_prompt(xy_pos, xy_attn_mask, None, False)
             else:
                 xy_dec, k_cache, v_cache = self.t2s_transformer.decode_next_token(xy_pos, k_cache, v_cache, xy_attn_mask, False)
             logits = self.ar_predict_layer(