diff --git a/lightllm/models/qwen3_omni_moe_thinker/audio_process.py b/lightllm/models/qwen3_omni_moe_thinker/audio_process.py
index 194914d45..58b223d57 100644
--- a/lightllm/models/qwen3_omni_moe_thinker/audio_process.py
+++ b/lightllm/models/qwen3_omni_moe_thinker/audio_process.py
@@ -102,7 +102,7 @@ def zero_mean_unit_var_norm(
     def _preprocess(
         self,
         raw_speech: Union[np.ndarray, list[float], list[np.ndarray], list[list[float]]],
-        truncation: bool = True,
+        truncation: bool = False,
         pad_to_multiple_of: Optional[int] = None,
         return_tensors: Optional[Union[str, TensorType]] = None,
         return_attention_mask: Optional[bool] = None,
diff --git a/lightllm/models/qwen3_omni_moe_thinker/model.py b/lightllm/models/qwen3_omni_moe_thinker/model.py
index 1b8fa0110..bee15e3d2 100644
--- a/lightllm/models/qwen3_omni_moe_thinker/model.py
+++ b/lightllm/models/qwen3_omni_moe_thinker/model.py
@@ -59,12 +59,7 @@ def init_audioitem_extral_params(
         return
 
     def get_audio_token_length(self, audio: AudioItem):
-        # 这里得处理对应奖语音长度按照 30 进行限制，后续处理中，超过30的会被截断。
-        if audio.audio_length > self.n_samples:
-            logger.warning(f"audio length {audio.audio_length} exceed max length {self.n_samples}, will be truncated.")
-
-        length = min(audio.audio_length, int(self.n_samples))
-        token_num = self._caclu_audio_token_num(length)
+        token_num = self._caclu_audio_token_num(audio.audio_length)
         # print(f"token_num is {token_num}  n_samples is {self.n_samples} hop_length is {self.hop_length}")
         return token_num
 
diff --git a/lightllm/models/qwen3_omni_moe_thinker/qwen3_omni_audio.py b/lightllm/models/qwen3_omni_moe_thinker/qwen3_omni_audio.py
index 03c57126f..ff49ab160 100644
--- a/lightllm/models/qwen3_omni_moe_thinker/qwen3_omni_audio.py
+++ b/lightllm/models/qwen3_omni_moe_thinker/qwen3_omni_audio.py
@@ -10,10 +10,15 @@
 from transformers.activations import ACT2FN
 
 from lightllm.server.multimodal_params import AudioItem
+from lightllm.utils.log_utils import init_logger
 from lightllm.common.basemodel.layer_infer.cache_tensor_manager import g_cache_manager
 from lightllm.models.vit.triton_kernel.flashattention_nopad import flash_attention_fwd
 from lightllm.models.qwen3_omni_moe_thinker.audio_process import WhisperFeatureExtractor
 
+QWEN3_OMNI_CONV_CHUNKSIZE = int(os.getenv("LIGHTLLM_QWEN3_OMNI_CONV_CHUNKSIZE", 500))
+
+logger = init_logger(__name__)
+
 
 def _get_feat_extract_output_lengths(input_lengths):
     """
@@ -156,7 +161,7 @@ def __init__(
         activation_function="gelu",
         output_dim=2048,
         n_window_infer=800,
-        conv_chunksize=500,
+        conv_chunksize=QWEN3_OMNI_CONV_CHUNKSIZE,
         encoder_attention_heads=20,
         attention_dropout=0,
         activation_dropout=0,
@@ -259,6 +264,7 @@ def load_model(self, weight_dir, config):
 
         self.load_state_dict(weight_dict)
 
+    @torch.inference_mode()
     def forward(
         self,
         input_features,
@@ -327,6 +333,7 @@ def forward(
         hidden_states = self.proj2(hidden_states)
         return hidden_states
 
+    @torch.inference_mode()
     def encode(self, audio_items: List[AudioItem]):
         uuids = []
         items: List[AudioItem] = []
@@ -363,3 +370,23 @@ def encode(self, audio_items: List[AudioItem]):
             all_embeds.append(cur_embed)
 
         return all_embeds, audio_items
+
+    @torch.inference_mode()
+    def check_long_audio_infer(self):
+        """Exercise forward with mel length chosen so the conv loop runs once with batch dim == conv_chunksize."""
+        params = next(self.parameters())
+        device = params.device
+        dtype = params.dtype
+        frame_len = self.conv_chunksize * (self.n_window * 2)
+        logger.info(
+            "check_long_audio_infer: start frame_len=%s conv_chunksize=%s n_window=%s device=%s dtype=%s",
+            frame_len,
+            self.conv_chunksize,
+            self.n_window,
+            device,
+            dtype,
+        )
+        input_features = torch.zeros(self.num_mel_bins, frame_len, device=device, dtype=dtype)
+        feature_lens = torch.tensor([frame_len], device=device, dtype=torch.long)
+        out = self.forward(input_features, feature_lens=feature_lens)
+        logger.info("check_long_audio_infer: done output_shape=%s", tuple(out.shape))
diff --git a/lightllm/models/whisper/whisper_audio.py b/lightllm/models/whisper/whisper_audio.py
index aaa29e1c7..8a984d29a 100644
--- a/lightllm/models/whisper/whisper_audio.py
+++ b/lightllm/models/whisper/whisper_audio.py
@@ -223,3 +223,6 @@ def encode(self, audio_items: List[AudioItem]):
             ans_embeds.append(cur_embed)
 
         return ans_embeds, audio_items
+
+    def check_long_audio_infer(self):
+        pass
diff --git a/lightllm/server/audioserver/model_infer/model_rpc.py b/lightllm/server/audioserver/model_infer/model_rpc.py
index 39a7e06ac..82919856d 100644
--- a/lightllm/server/audioserver/model_infer/model_rpc.py
+++ b/lightllm/server/audioserver/model_infer/model_rpc.py
@@ -51,6 +51,7 @@ def exposed_init_model(self, kvargs):
 
             self.model.load_model(weight_dir, model_cfg)
             self.model = self.model.cuda()
+            self.model.check_long_audio_infer()
 
             self.cache_client = rpyc.connect("localhost", self.cache_port, config={"allow_pickle": True})
             self.cache_client._channel.stream.sock.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)