diff --git a/funasr/models/llm_asr/model.py b/funasr/models/llm_asr/model.py
index f6d230b9f..319e1a88d 100644
--- a/funasr/models/llm_asr/model.py
+++ b/funasr/models/llm_asr/model.py
@@ -2982,10 +2982,10 @@ class LLMASRXvecSlotTTS(nn.Module):
         text = "".join(normed_text)
 
         cur_token, feat, wav = None, None, None
-        t_size = len(self.tts_text_tokenizer.text2tokens(text))
-        if (t_size - last_t_size) >= tts_text_chunk_size:
-            _text = f"<|endofprompt|><|sil|>{text}" + ("<|sil|>" if is_last else "")
-            text_token = self.tts_tokenizer_warpper(_text)
+        _text = f"<|endofprompt|><|sil|>{text}" + ("<|sil|>" if is_last else "")
+        text_token = self.tts_tokenizer_warpper(_text)
+        t_size = len(text_token)
+        if (t_size - last_t_size) >= tts_text_chunk_size or is_last:
             text_token = torch.tensor([text_token], dtype=torch.long, device=device)
             text_token_len = torch.tensor([text_token.shape[1]], dtype=torch.long, device=device)
             cur_token, feat = self.tts_model.streaming_one_step(
@@ -3000,7 +3000,7 @@ class LLMASRXvecSlotTTS(nn.Module):
                 sampling="threshold_1e-6",
                 chunk_idx=chunk_idx,
             )
-            if cur_token is not None:
+            if cur_token is not None and cur_token.shape[1] > 0 and feat.shape[2] > 0:
                 # process first package, token in B,T,D, feat in B,F,T
                 if prompt_token[0] is None:
                     prompt_token = [cur_token, torch.tensor([cur_token.shape[1]], dtype=torch.long, device=device)]
@@ -3012,6 +3012,8 @@ class LLMASRXvecSlotTTS(nn.Module):
                     prompt_audio[0] = torch.concat([prompt_audio[0], feat.transpose(1, 2)], dim=1)
                 wav = self.vocoder.inference(feat.transpose(1, 2))
                 chunk_idx += 1
+            else:
+                cur_token, feat, wav = None, None, None
 
             # post process
             last_t_size = t_size
@@ -3042,15 +3044,15 @@ class LLMASRXvecSlotTTS(nn.Module):
         token_list, feat_list, wav_list = [], [], []
         prompt_token, prompt_audio = [None, None], [None, None]
         new_text, last_t_size, chunk_idx = "", 0, 0
-        i = 0
-        while i < preds.shape[1]:
-            chunk_size = int(llm_token_num_per_call / (given_rtf ** min(i, 2)))
+        st, count = 0, 0
+        while st < preds.shape[1]:
+            chunk_size = int(llm_token_num_per_call / (given_rtf ** min(count, 2)))
             _resp = llm_tokenizer.batch_decode(
-                preds[:, i:i + chunk_size],
+                preds[:, st:st + chunk_size],
                 add_special_tokens=False,
                 skip_special_tokens=True,
             )[0]
-            is_last = (i + chunk_size >= preds.shape[1])
+            is_last = (st + chunk_size >= preds.shape[1])
 
             new_text = new_text + _resp
             rt_value, states = self.generate_speech_one_step(
@@ -3063,12 +3065,13 @@ class LLMASRXvecSlotTTS(nn.Module):
             cur_token, feat, wav = rt_value
             new_text, last_t_size, prompt_token, prompt_audio, chunk_idx = states
             # save results
-            if cur_token is not None:
+            if cur_token is not None and feat is not None and wav is not None:
                 token_list.append(cur_token)
                 feat_list.append(feat)
                 wav_list.append(wav)
 
-            i += chunk_size
+            st += chunk_size
+            count += 1
 
         speech_tokens = torch.cat(token_list, dim=1)
         mel_feats = torch.cat(feat_list, dim=2)
diff --git a/funasr/models/llm_asr/tts_models/e2e_model.py b/funasr/models/llm_asr/tts_models/e2e_model.py
index e682445c1..d8f9a3204 100644
--- a/funasr/models/llm_asr/tts_models/e2e_model.py
+++ b/funasr/models/llm_asr/tts_models/e2e_model.py
@@ -911,10 +911,7 @@ class UCTDXvecSlotModel(UpsampleCtcTokenDiffModel):
         # streaming related config
         chunk_size = kwargs.get("streaming_chunk_size", 4)
         chunk_size_maxium = kwargs.get("chunk_size_maxium", 16)
-        try:
-            lookahead_size = self.am_model.encoder.pre_lookahead_len
-        except AttributeError:
-            lookahead_size = 0
+        lookahead_size = self.am_model.encoder.pre_lookahead_len
         hint_once(f"chunk_size={chunk_size}, chunk_size_maxium={chunk_size_maxium}, "
                   f"pre lookahead size={lookahead_size}.",
                   "pre_lookahead_len")