diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py
index 9f98a24..09f3175 100644
--- a/GPT_SoVITS/TTS_infer_pack/TTS.py
+++ b/GPT_SoVITS/TTS_infer_pack/TTS.py
@@ -275,7 +275,7 @@ class TTS:
             prompt_semantic = codes[0, 0].to(self.configs.device)
             self.prompt_cache["prompt_semantic"] = prompt_semantic
     
-    def batch_sequences(self, sequences: List[torch.Tensor], axis: int = 0, pad_value: int = 0):
+    def batch_sequences(self, sequences: List[torch.Tensor], axis: int = 0, pad_value: int = 0, max_length:int=None):
         seq = sequences[0]
         ndim = seq.dim()
         if axis < 0:
@@ -283,7 +283,10 @@ class TTS:
         dtype:torch.dtype = seq.dtype
         pad_value = torch.tensor(pad_value, dtype=dtype)
         seq_lengths = [seq.shape[axis] for seq in sequences]
-        max_length = max(seq_lengths)
+        if max_length is None:
+            max_length = max(seq_lengths)
+        else:
+            max_length = max(seq_lengths) if max_length < max(seq_lengths) else max_length
 
         padded_sequences = []
         for seq, length in zip(sequences, seq_lengths):
@@ -333,6 +336,8 @@ class TTS:
             all_phones_list = []
             all_bert_features_list = []
             norm_text_batch = []
+            bert_max_len = 0
+            phones_max_len = 0
             for item in item_list:
                 if prompt_data is not None:
                     all_bert_features = torch.cat([prompt_data["bert_features"].clone(), item["bert_features"]], 1)
@@ -344,15 +349,18 @@ class TTS:
                     phones = torch.LongTensor(item["phones"])
                     all_phones = phones.clone()
                     # norm_text = item["norm_text"]
-                    
+                bert_max_len = max(bert_max_len, all_bert_features.shape[-1])
+                phones_max_len = max(phones_max_len, phones.shape[-1])
+                
                 phones_list.append(phones)
                 all_phones_list.append(all_phones)
                 all_bert_features_list.append(all_bert_features)
                 norm_text_batch.append(item["norm_text"])
             # phones_batch = phones_list
-            phones_batch = self.batch_sequences(phones_list, axis=0, pad_value=0)
-            all_phones_batch = self.batch_sequences(all_phones_list, axis=0, pad_value=0)
-            all_bert_features_batch = torch.FloatTensor(len(item_list), 1024, all_phones_batch.shape[-1])
+            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)
+            all_phones_batch = self.batch_sequences(all_phones_list, axis=0, pad_value=0, max_length=max_len)
+            all_bert_features_batch = torch.FloatTensor(len(item_list), 1024, max_len)
             all_bert_features_batch.zero_()
 
             for idx, item in enumerate(all_bert_features_list):