diff --git a/funasr/runtime/python/onnxruntime/demo_vad.py b/funasr/runtime/python/onnxruntime/demo_vad.py
index ae033cc5b..2e171978c 100644
--- a/funasr/runtime/python/onnxruntime/demo_vad.py
+++ b/funasr/runtime/python/onnxruntime/demo_vad.py
@@ -1,12 +1,30 @@
-
+import soundfile
 from funasr_onnx import Fsmn_vad
 
 
 model_dir = "/Users/zhifu/Downloads/speech_fsmn_vad_zh-cn-16k-common-pytorch"
-
+wav_path = "/Users/zhifu/Downloads/speech_fsmn_vad_zh-cn-16k-common-pytorch/example/vad_example.wav"
 model = Fsmn_vad(model_dir)
 
-wav_path = "/Users/zhifu/Downloads/speech_fsmn_vad_zh-cn-16k-common-pytorch/example/vad_example.wav"
+#offline vad
+# result = model(wav_path)
+# print(result)
+
+#online vad
+speech, sample_rate = soundfile.read(wav_path)
+speech_length = speech.shape[0]
+
+sample_offset = 0
+step = 160 * 10
+param_dict = {'in_cache': []}
+for sample_offset in range(0, speech_length, min(step, speech_length - sample_offset)):
+    if sample_offset + step >= speech_length - 1:
+        step = speech_length - sample_offset
+        is_final = True
+    else:
+        is_final = False
+    param_dict['is_final'] = is_final
+    segments_result = model(audio_in=speech[sample_offset: sample_offset + step],
+                            param_dict=param_dict)
+    print(segments_result)
 
-result = model(wav_path)
-print(result)
\ No newline at end of file
diff --git a/funasr/runtime/python/onnxruntime/funasr_onnx/vad_bin.py b/funasr/runtime/python/onnxruntime/funasr_onnx/vad_bin.py
index 0b7ecffc0..cdd4578d1 100644
--- a/funasr/runtime/python/onnxruntime/funasr_onnx/vad_bin.py
+++ b/funasr/runtime/python/onnxruntime/funasr_onnx/vad_bin.py
@@ -53,13 +53,13 @@ class Fsmn_vad():
 		proj_dim = self.encoder_conf["proj_dim"]
 		lorder = self.encoder_conf["lorder"]
 		for i in range(fsmn_layers):
-			cache = np.zeros(1, proj_dim, lorder-1, 1).astype(np.float32)
+			cache = np.zeros((1, proj_dim, lorder-1, 1)).astype(np.float32)
 			in_cache.append(cache)
 		return in_cache
 		
 	
-	def __call__(self, wav_content: Union[str, np.ndarray, List[str]], **kwargs) -> List:
-		waveform_list = self.load_data(wav_content, self.frontend.opts.frame_opts.samp_freq)
+	def __call__(self, audio_in: Union[str, np.ndarray, List[str]], **kwargs) -> List:
+		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)
 
@@ -70,13 +70,13 @@ class Fsmn_vad():
 			waveform = waveform_list[beg_idx:end_idx]
 			feats, feats_len = self.extract_feat(waveform)
 			param_dict = kwargs.get('param_dict', dict())
-			in_cache = param_dict.get('cache', list())
+			in_cache = param_dict.get('in_cache', list())
 			in_cache = self.prepare_cache(in_cache)
 			try:
 				inputs = [feats]
 				inputs.extend(in_cache)
 				scores, out_caches = self.infer(inputs)
-				param_dict['cache'] = out_caches
+				param_dict['in_cache'] = out_caches
 				segments = self.vad_scorer(scores, waveform[0][None, :], is_final=is_final, max_end_sil=self.max_end_sil)
 				
 			except ONNXRuntimeError: