diff --git a/funasr/runtime/python/vad_asr_websocket_client/vad_asr_websocket_client.py b/funasr/runtime/python/vad_asr_websocket_client/vad_asr_websocket_client.py
new file mode 100644
index 000000000..c5096cb5c
--- /dev/null
+++ b/funasr/runtime/python/vad_asr_websocket_client/vad_asr_websocket_client.py
@@ -0,0 +1,197 @@
+#""" from https://github.com/cgisky1980/550W_AI_Assistant """
+
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+from modelscope.utils.logger import get_logger
+import logging
+logger = get_logger(log_level=logging.CRITICAL)
+logger.setLevel(logging.CRITICAL)
+import websocket
+import pyaudio
+import time
+import json
+import threading
+
+
+# ---------WebsocketClient相关  主要处理 on_message on_open  已经做了断线重连处理
+class WebsocketClient(object):
+    def __init__(self, address, message_callback=None):
+        super(WebsocketClient, self).__init__()
+        self.address = address
+        self.message_callback = None
+
+    def on_message(self, ws, message):
+        try:
+            messages = json.loads(
+                (message.encode("raw_unicode_escape")).decode()
+            )  # 收到WS消息后的处理
+            if messages.get("type") == "ping":
+                self.ws.send('{"type":"pong"}')
+        except json.JSONDecodeError as e:
+            print(f"JSONDecodeError: {e}")
+        except KeyError:
+            print("KeyError!")
+
+    def on_error(self, ws, error):
+        print("client error:", error)
+
+    def on_close(self, ws):
+        print("### client closed ###")
+        self.ws.close()
+        self.is_running = False
+
+    def on_open(self, ws):  # 连上ws后发布登录信息
+        self.is_running = True
+        self.ws.send(
+            '{"type":"login","uid":"asr","pwd":"tts9102093109"}'
+        )  # WS链接上后的登陆处理
+
+    def close_connect(self):
+        self.ws.close()
+
+    def send_message(self, message):
+        try:
+            self.ws.send(message)
+        except BaseException as err:
+            pass
+
+    def run(self):  # WS初始化
+        websocket.enableTrace(True)
+        self.ws = websocket.WebSocketApp(
+            self.address,
+            on_message=lambda ws, message: self.on_message(ws, message),
+            on_error=lambda ws, error: self.on_error(ws, error),
+            on_close=lambda ws: self.on_close(ws),
+        )
+        websocket.enableTrace(False)  # 要看ws调试信息，请把这行注释掉
+        self.ws.on_open = lambda ws: self.on_open(ws)
+        self.is_running = False
+        # WS断线重连判断
+        while True:  
+            if not self.is_running:
+                self.ws.run_forever()
+            time.sleep(3)  # 3秒检测一次
+
+
+class WSClient(object):
+    def __init__(self, address, call_back):
+        super(WSClient, self).__init__()
+        self.client = WebsocketClient(address, call_back)
+        self.client_thread = None
+
+    def run(self):
+        self.client_thread = threading.Thread(target=self.run_client)
+        self.client_thread.start()
+
+    def run_client(self):
+        self.client.run()
+
+    def send_message(self, message):
+        self.client.send_message(message)
+
+
+def vad(data):  # VAD推理
+    segments_result = vad_pipline(audio_in=data)
+    if segments_result["text"] == "[]":
+        return False
+    else:
+        return True
+
+
+# 创建一个VAD对象
+vad_pipline = pipeline(
+    task=Tasks.voice_activity_detection,
+    model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
+    model_revision="v1.2.0",
+    output_dir=None,
+    batch_size=1,
+)
+
+param_dict = dict()
+param_dict["hotword"] = "小五 小五月"  # 设置热词，用空格隔开
+
+
+# 创建一个ASR对象
+inference_pipeline2 = pipeline(
+    task=Tasks.auto_speech_recognition,
+    model="damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404",
+    param_dict=param_dict,
+)
+
+# 创建一个PyAudio对象
+p = pyaudio.PyAudio()
+
+# 定义一些参数
+FORMAT = pyaudio.paInt16  # 采样格式
+CHANNELS = 1  # 单声道
+RATE = 16000  # 采样率
+CHUNK = int(RATE / 1000 * 300)  # 每个片段的帧数（300毫秒）
+RECORD_NUM = 0 # 录制时长（片段）
+
+# 打开输入流
+stream = p.open(
+    format=FORMAT,
+    channels=CHANNELS,
+    rate=RATE,
+    input=True,
+    frames_per_buffer=CHUNK,
+)
+
+print("开始...")
+
+# 创建一个WS连接
+ws_client = WSClient("ws://localhost:7272", None)
+ws_client.run()
+
+frames = []  # 存储所有的帧数据
+buffer = []  # 存储缓存中的帧数据（最多两个片段）
+silence_count = 0  # 统计连续静音的次数
+speech_detected = False  # 标记是否检测到语音
+
+# 循环读取输入流中的数据
+while True:
+    data = stream.read(CHUNK)  # 读取一个片段的数据
+    buffer.append(data)  # 将当前数据添加到缓存中
+
+    if len(buffer) > 2:
+        buffer.pop(0)  # 如果缓存超过两个片段，则删除最早的一个
+
+    if speech_detected:
+        frames.append(data)
+        RECORD_NUM += 1
+        # print(str(RECORD_NUM)+ "\r")
+
+    if vad(data):  # VAD 判断是否有声音
+        if not speech_detected:
+            print("开始录音...")
+            speech_detected = True  # 标记为检测到语音
+            frames = []
+            frames.extend(buffer)  # 把之前2个语音数据快加入
+        silence_count = 0  # 重置静音次数
+
+    else:
+        silence_count += 1  # 增加静音次数
+        #检测静音次数4次  或者已经录了50个数据块，则录音停止
+        if speech_detected and (silence_count > 4 or RECORD_NUM > 50):  
+            print("停止录音...")
+            audio_in = b"".join(frames)
+            rec_result = inference_pipeline2(audio_in=audio_in)  # ws播报数据
+            rec_result["type"] = "nlp"  # 添加ws播报数据
+            ws_client.send_message(
+                json.dumps(rec_result, ensure_ascii=False)
+            )  # ws发送到服务端
+            print(rec_result)
+            frames = []  # 清空所有的帧数据
+            buffer = []  # 清空缓存中的帧数据（最多两个片段）
+            silence_count = 0  # 统计连续静音的次数清零
+            speech_detected = False  # 标记是否检测到语音
+            # RECORD_NUM = 0
+
+print("结束录制...")
+
+# 停止并关闭输入流
+stream.stop_stream()
+stream.close()
+
+# 关闭PyAudio对象
+p.terminate()