diff --git a/egs/speech_llm/SPEECH2SPEECH/qwen_omni/train_tts.py b/egs/speech_llm/SPEECH2SPEECH/qwen_omni/train_tts.py
index 6d35ed6a9..e505c0700 100755
--- a/egs/speech_llm/SPEECH2SPEECH/qwen_omni/train_tts.py
+++ b/egs/speech_llm/SPEECH2SPEECH/qwen_omni/train_tts.py
@@ -189,71 +189,6 @@ def data_collator(batch):
         "lang": lang,
     }
 
-def data_collator_concate_items(batch, concat_items_num: int = 3):
-    """Concatenate *concat_items_num* consecutive dataset items into one.
-
-    The function groups the incoming ``batch`` (a list of dataset items)
-    into non-overlapping chunks of *concat_items_num*.  For each group it
-    concatenates the textual fields and speech codec tokens so that the
-    model generates one longer utterance instead of several short ones.
-
-    Any remainder (when ``len(batch)`` is not divisible by
-    *concat_items_num*) is also kept as a smaller group.
-    """
-
-    grouped_speech_tokens, grouped_messages, grouped_durations = [], [], []
-    grouped_ids, grouped_lang = [], []
-
-    # Iterate over the batch in strides of *concat_items_num*
-    for start_idx in range(0, len(batch), concat_items_num):
-        group = batch[start_idx : start_idx + concat_items_num]
-        if not group:
-            continue
-
-        # 1) Speech tokens --------------------------------------------------
-        # ``item['code']`` can be a list[int] or a 1-D tensor.  Use the first
-        # element to decide how to concatenate.
-        first_code = group[0]["code"]
-        if isinstance(first_code, torch.Tensor):
-            concat_code = torch.cat([item["code"] for item in group], dim=0)
-        else:
-            # assume list / iterable of ints
-            concat_code = []
-            for item in group:
-                concat_code.extend(item["code"])
-
-        # 2) Text -----------------------------------------------------------
-        concat_text = "".join([item["text"] for item in group])
-
-        # 3) Build chat template messages -----------------------------------
-        message_list_item = [
-            {
-                "role": "user",
-                "content": f"Generate a speech from the following text:\n\n{concat_text}{DEFAULT_SPEECH_TOKEN}",
-            },
-            {"role": "assistant", "content": concat_text},
-        ]
-
-        # 4) Misc meta fields ----------------------------------------------
-        total_duration = sum(item["duration"] for item in group)
-        group_ids = [item.get("index", item.get("id")) for item in group]
-        language = group[0].get("language", "")
-
-        # 5) Append to output lists ----------------------------------------
-        grouped_speech_tokens.append(concat_code)
-        grouped_messages.append(message_list_item)
-        grouped_durations.append(total_duration)
-        grouped_ids.append(group_ids)
-        grouped_lang.append(language)
-
-    return {
-        "speech_tokens": grouped_speech_tokens,
-        "messages": grouped_messages,
-        "durations": grouped_durations,
-        "ids": grouped_ids,
-        "lang": grouped_lang,
-    }
-
 def data_collator_ultra_chat(batch):
     speech_tokens, messages, durations, ids, lang, dnsmos = [], [], [], [], [], []
     for i, item in enumerate(batch):
@@ -550,7 +485,7 @@ def run(rank, world_size, args):
     logging.info(f"Device: {device}")
     model.to(device)
 
-    assert params.deepspeed and world_size > 1
+    # assert params.deepspeed and world_size > 1
     logging.info("Using DeepSpeed")
     model, optimizer, _, scheduler = deepspeed.initialize(
         args=params, model=model, model_parameters=model.parameters()