diff --git a/egs/speech_llm/SPEECH2SPEECH/slam_omni/train.py b/egs/speech_llm/SPEECH2SPEECH/slam_omni/train.py
index f05e5c1ac..f79e5b64c 100755
--- a/egs/speech_llm/SPEECH2SPEECH/slam_omni/train.py
+++ b/egs/speech_llm/SPEECH2SPEECH/slam_omni/train.py
@@ -347,42 +347,6 @@ def compute_loss(
 
         return input_ids, attention_mask, target_ids
 
-    def normalize_text_alimeeting(text: str, normalize: str = "m2met") -> str:
-        """
-        Text normalization similar to M2MeT challenge baseline.
-        See: https://github.com/yufan-aslp/AliMeeting/blob/main/asr/local/text_normalize.pl
-        """
-        if normalize == "none":
-            return text
-        elif normalize == "m2met":
-            import re
-
-            text = text.replace(" ", "")
-            text = text.replace("<sil>", "")
-            text = text.replace("<%>", "")
-            text = text.replace("<->", "")
-            text = text.replace("<$>", "")
-            text = text.replace("<#>", "")
-            text = text.replace("<_>", "")
-            text = text.replace("<space>", "")
-            text = text.replace("`", "")
-            text = text.replace("&", "")
-            text = text.replace(",", "")
-            if re.search("[a-zA-Z]", text):
-                text = text.upper()
-            text = text.replace("Ａ", "A")
-            text = text.replace("ａ", "A")
-            text = text.replace("ｂ", "B")
-            text = text.replace("ｃ", "C")
-            text = text.replace("ｋ", "K")
-            text = text.replace("ｔ", "T")
-            text = text.replace("，", "")
-            text = text.replace("丶", "")
-            text = text.replace("。", "")
-            text = text.replace("、", "")
-            text = text.replace("？", "")
-            return text
-
     max_frames = params.max_duration * 1000 // params.frame_shift_ms
     allowed_max_frames = int(max_frames * (1.0 + params.allowed_excess_duration_ratio))
     batch = filter_uneven_sized_batch(batch, allowed_max_frames)
@@ -400,15 +364,15 @@ def compute_loss(
     questions_with_history = [cut.custom["question"] for cut in batch["supervisions"]["cut"]]
     answer_cosyvoice_speech_token = [cut.custom["answer_cosyvoice_speech_token"] for cut in batch["supervisions"]["cut"]]
     last_questions = [question.split('<USER>: ')[-1].strip() for question in questions_with_history]
-
-    texts = batch["supervisions"]["text"]
-    # remove spaces in texts
-    texts = [normalize_text_alimeeting(text) for text in texts]
+    history_contexts = [question.rsplit('<USER>:', 1)[0].strip() for question in questions_with_history]
+    # USER: 生成一个关于夏天的诗歌。 ASSISTANT: 夏日炎炎，万物生长，阳光明媚，享受着夏日的美好时光。 USER: 给我列举一些新闻头条。 ASSISTANT: 当今社会的新闻永远不会停。<USER>: 告诉我如何烹饪鸡肉 
+    #  <USER>: 对以下句子进行鉴赏：他心地善良。输出结果为“他是一个有善心的人。
 
     messages = []
     for i, text in enumerate(texts):
+        history_context = history_contexts[i]
         message = [
-            {"role": "user", "content": f"{DEFAULT_SPEECH_TOKEN}请转写音频为文字"},
+            {"role": "user", "content": f"{history_context}{DEFAULT_SPEECH_TOKEN}请转写音频为文字"},
             {"role": "assistant", "content": text},
         ]
         messages.append(message)