diff --git a/runtime/python/onnxruntime/funasr_onnx/utils/frontend.py b/runtime/python/onnxruntime/funasr_onnx/utils/frontend.py
index 7b38f8b76..f4cc8fff8 100644
--- a/runtime/python/onnxruntime/funasr_onnx/utils/frontend.py
+++ b/runtime/python/onnxruntime/funasr_onnx/utils/frontend.py
@@ -52,12 +52,12 @@ class WavFrontend:
 
     def fbank(self, waveform: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
         waveform = waveform * (1 << 15)
-        self.fbank_fn = knf.OnlineFbank(self.opts)
-        self.fbank_fn.accept_waveform(self.opts.frame_opts.samp_freq, waveform.tolist())
-        frames = self.fbank_fn.num_frames_ready
+        fbank_fn = knf.OnlineFbank(self.opts)
+        fbank_fn.accept_waveform(self.opts.frame_opts.samp_freq, waveform.tolist())
+        frames = fbank_fn.num_frames_ready
         mat = np.empty([frames, self.opts.mel_opts.num_bins])
         for i in range(frames):
-            mat[i, :] = self.fbank_fn.get_frame(i)
+            mat[i, :] = fbank_fn.get_frame(i)
         feat = mat.astype(np.float32)
         feat_len = np.array(mat.shape[0]).astype(np.int32)
         return feat, feat_len
diff --git a/runtime/python/onnxruntime/funasr_onnx/vad_bin.py b/runtime/python/onnxruntime/funasr_onnx/vad_bin.py
index 3f21004bf..af4663a7d 100644
--- a/runtime/python/onnxruntime/funasr_onnx/vad_bin.py
+++ b/runtime/python/onnxruntime/funasr_onnx/vad_bin.py
@@ -69,7 +69,7 @@ class Fsmn_vad:
             model_file, device_id, intra_op_num_threads=intra_op_num_threads
         )
         self.batch_size = batch_size
-        self.vad_scorer = E2EVadModel(config["model_conf"])
+        self.vad_scorer_config = config["model_conf"]
         self.max_end_sil = (
             max_end_sil if max_end_sil is not None else config["model_conf"]["max_end_silence_time"]
         )
@@ -90,10 +90,9 @@ class Fsmn_vad:
         waveform_list = self.load_data(audio_in, self.frontend.opts.frame_opts.samp_freq)
         waveform_nums = len(waveform_list)
         is_final = kwargs.get("kwargs", False)
-
         segments = [[]] * self.batch_size
         for beg_idx in range(0, waveform_nums, self.batch_size):
-
+            vad_scorer = E2EVadModel(self.vad_scorer_config)
             end_idx = min(waveform_nums, beg_idx + self.batch_size)
             waveform = waveform_list[beg_idx:end_idx]
             feats, feats_len = self.extract_feat(waveform)
@@ -122,7 +121,7 @@ class Fsmn_vad:
                     inputs.extend(in_cache)
                     scores, out_caches = self.infer(inputs)
                     in_cache = out_caches
-                    segments_part = self.vad_scorer(
+                    segments_part = vad_scorer(
                         scores,
                         waveform_package,
                         is_final=is_final,