websocket runtime

funasr/runtime/websocket/ASR_client.py

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+import pyaudio
+import websocket #区别服务端这里是 websocket-client库
+import time
+import websockets
+import asyncio
+from queue import Queue
+import threading
+voices = Queue()
+async def hello():
+    global ws # 定义一个全局变量ws，用于保存websocket连接对象
+    uri = "ws://localhost:8899"
+    ws = await websockets.connect(uri, subprotocols=["binary"]) # 创建一个长连接
+    ws.max_size = 1024 * 1024 * 20
+    print("connected ws server")
+async def send(data):
+    global ws # 引用全局变量ws
+    try:
+        await ws.send(data) # 通过ws对象发送数据
+    except Exception as e:
+        print('Exception occurred:', e)
+    
+
+
+asyncio.get_event_loop().run_until_complete(hello()) # 启动协程  
+
+
+# 其他函数可以通过调用send(data)来发送数据，例如：
+async def test():
+    #print("2")
+    global voices
+    FORMAT = pyaudio.paInt16
+    CHANNELS = 1
+    RATE = 16000
+    CHUNK = int(RATE / 1000 * 300)
+
+    p = pyaudio.PyAudio()
+
+    stream = p.open(format=FORMAT,
+                    channels=CHANNELS,
+                    rate=RATE,
+                    input=True,
+                    frames_per_buffer=CHUNK)
+
+    while True:
+
+        data = stream.read(CHUNK)
+        
+        voices.put(data)
+        #print(voices.qsize())
+        await asyncio.sleep(0.01)
+    
+      
+
+
+
+async def ws_send():
+    global voices
+    print("started to sending data!")
+    while True:
+        while not voices.empty():
+            data = voices.get()
+            voices.task_done()
+            await send(data)
+            await asyncio.sleep(0.01)
+        await asyncio.sleep(0.01)
+
+async def main():
+    task = asyncio.create_task(test()) # 创建一个后台任务
+    task2 = asyncio.create_task(ws_send()) # 创建一个后台任务
+     
+    await asyncio.gather(task, task2)
+
+asyncio.run(main())

funasr/runtime/websocket/ASR_server.py

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+# server.py   注意本例仅处理单个clent发送的语音数据，并未对多client连接进行判断和处理
+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 asyncio
+import websockets  #区别客户端这里是 websockets库
+import time
+from queue import Queue
+import  threading
+
+print("model loading")
+voices = Queue()
+speek = Queue()
+# 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,
+)
+  
+# 创建一个ASR对象
+param_dict = dict()
+param_dict["hotword"] = "小五 小五月"  # 设置热词，用空格隔开
+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,
+)
+print("model loaded")
+
+
+
+async def echo(websocket, path):
+    global voices
+    try:
+        async for message in websocket:
+            voices.put(message)
+            #print("put")
+    except websockets.exceptions.ConnectionClosedError as e:
+        print('Connection closed with exception:', e)
+    except Exception as e:
+        print('Exception occurred:', e)
+
+start_server = websockets.serve(echo, "localhost", 8899, subprotocols=["binary"],ping_interval=None)
+
+
+def vad(data):  # 推理
+    global vad_pipline
+    #print(type(data))
+    segments_result = vad_pipline(audio_in=data)
+    #print(segments_result)
+    if len(segments_result) == 0:
+        return False
+    else:
+        return True
+
+def asr():  # 推理
+    global inference_pipeline2
+    global speek
+    while True:
+        while not speek.empty():
+            audio_in = speek.get()
+            speek.task_done()
+            rec_result = inference_pipeline2(audio_in=audio_in)
+            print(rec_result)
+            time.sleep(0.1)
+        time.sleep(0.1)    
+
+
+def main():  # 推理
+    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
+    RECORD_NUM = 0
+    global voices 
+    global speek
+    while True:
+        while not voices.empty():
+            
+            data = voices.get()
+            #print("队列排队数",voices.qsize())
+            voices.task_done()
+            buffer.append(data)
+            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    
+            
+            if  vad(data):
+                if not speech_detected:
+                    print("检测到人声...")
+                    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
+
+                if speech_detected and (silence_count > 4 or RECORD_NUM > 50): #这里 50 可根据需求改为合适的数据快数量
+                    print("说话结束或者超过设置最长时间...")
+                    audio_in = b"".join(frames)
+                    #asrt = threading.Thread(target=asr,args=(audio_in,))
+                    #asrt.start()
+                    speek.put(audio_in)
+                    #rec_result = inference_pipeline2(audio_in=audio_in)  # ASR 模型里跑一跑
+                    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
+            time.sleep(0.01)
+        time.sleep(0.01)
+            
+
+
+s = threading.Thread(target=main)
+s.start()
+s = threading.Thread(target=asr)
+s.start()
+
+asyncio.get_event_loop().run_until_complete(start_server)
+asyncio.get_event_loop().run_forever()
+
+
+ 
+
+
+
+
+
+ 
+
+        
+