diff --git a/egs/speech_llm/ASR_LLM/whisper_llm_zh/decode.py b/egs/speech_llm/ASR_LLM/whisper_llm_zh/decode.py
index 6efee5e4d..7bdfc57dd 100755
--- a/egs/speech_llm/ASR_LLM/whisper_llm_zh/decode.py
+++ b/egs/speech_llm/ASR_LLM/whisper_llm_zh/decode.py
@@ -290,11 +290,9 @@ def decode_one_batch(
     )
 
     generated_ids = model.decode(feature, input_ids.to(device, dtype=torch.long), attention_mask.to(device))
-    hyps = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
-    hyps = tokenizer.batch_decode(generated_ids, skip_special_tokens=False)[0]
-    # hyps = remove_punctuation(hyps)
-    # hyps = to_simple(hyps)
-    # hyps = [params.normalizer.normalize(hyp) for hyp in hyps]
+    hyps = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+    hyps = tokenizer.batch_decode(generated_ids, skip_special_tokens=False)
+
     print(hyps)
     texts = batch["supervisions"]["text"]
     for i, text in enumerate(texts):
@@ -381,7 +379,7 @@ def decode_dataset(
 
         for lm_scale, hyps in hyps_dict.items():
             this_batch = []
-            # assert len(hyps) == len(texts)
+            assert len(hyps) == len(texts)
             for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
                 ref_text = normalize_text_alimeeting(ref_text)
                 ref_words = ref_text.split()