python/FunASR-XL.git

			@@ -9,10 +9,14 @@
			from modelscope.utils.constant import Tasks
			from modelscope.utils.logger import get_logger
			import logging
			import tracemalloc
			tracemalloc.start()

			logger = get_logger(log_level=logging.CRITICAL)
			logger.setLevel(logging.CRITICAL)


			websocket_users = set() #维护客户端列表

			parser = argparse.ArgumentParser()
			parser.add_argument("--host",
			@@ -46,8 +50,7 @@
			args = parser.parse_args()

			print("model loading")
			voices = Queue()
			speek = Queue()


			# vad
			inference_pipeline_vad = pipeline(
			@@ -86,20 +89,76 @@


			async def ws_serve(websocket, path):
			global voices
			#speek = Queue()
			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）
			RECORD_NUM = 0
			global websocket_users
			speech_start, speech_end = False, False
			# 调用asr函数
			websocket.speek = Queue() #websocket 添加进队列对象让asr读取语音数据包
			websocket.send_msg = Queue() #websocket 添加个队列对象让ws发送消息到客户端
			websocket_users.add(websocket)
			ss = threading.Thread(target=asr, args=(websocket,))
			ss.start()

			try:
			async for message in websocket:
			voices.put(message)
			#voices.put(message)
			#print("put")
			except websockets.exceptions.ConnectionClosedError as e:
			print('Connection closed with exception:', e)
			#await websocket.send("123")
			buffer.append(message)
			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_start:
			frames.append(message)
			RECORD_NUM += 1
			speech_start_i, speech_end_i = vad(message)
			#print(speech_start_i, speech_end_i)
			if speech_start_i:
			speech_start = speech_start_i
			frames = []
			frames.extend(buffer) # 把之前2个语音数据快加入
			if speech_end_i or RECORD_NUM > 300:
			speech_start = False
			audio_in = b"".join(frames)
			websocket.speek.put(audio_in)
			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）
			RECORD_NUM = 0
			if not websocket.send_msg.empty():
			await websocket.send(websocket.send_msg.get())
			websocket.send_msg.task_done()


			except websockets.ConnectionClosed:
			print("ConnectionClosed...", websocket_users) # 链接断开
			websocket_users.remove(websocket)
			except websockets.InvalidState:
			print("InvalidState...") # 无效状态
			except Exception as e:
			print('Exception occurred:', e)
			print("Exception:", e)


			start_server = websockets.serve(ws_serve, args.host, args.port, subprotocols=["binary"], ping_interval=None)
			def asr(websocket): # ASR推理
			global inference_pipeline2
			global param_dict_punc
			global websocket_users
			while websocket in websocket_users:
			if not websocket.speek.empty():
			audio_in = websocket.speek.get()
			websocket.speek.task_done()
			if len(audio_in) > 0:
			rec_result = inference_pipeline_asr(audio_in=audio_in)
			if inference_pipeline_punc is not None and 'text' in rec_result:
			rec_result = inference_pipeline_punc(text_in=rec_result['text'], param_dict=param_dict_punc)
			results = (rec_result["text"] if "text" in rec_result else rec_result)
			websocket.send_msg.put(results) # 存入发送队列直接调用send发送不了

			time.sleep(0.1)


			def vad(data): # 推理
			def vad(data): # VAD推理
			global vad_pipline, param_dict_vad
			#print(type(data))
			# print(param_dict_vad)
			@@ -117,79 +176,7 @@
			speech_end = True
			return speech_start, speech_end

			def asr(): # 推理
			global inference_pipeline2
			global speek, param_dict_punc
			while True:
			while not speek.empty():
			audio_in = speek.get()
			speek.task_done()
			if len(audio_in) > 0:
			rec_result = inference_pipeline_asr(audio_in=audio_in)
			if inference_pipeline_punc is not None and 'text' in rec_result:
			rec_result = inference_pipeline_punc(text_in=rec_result['text'], param_dict=param_dict_punc)
			print(rec_result["text"] if "text" in rec_result else 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
			speech_start, speech_end = False, False
			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_start:
			frames.append(data)
			RECORD_NUM += 1
			speech_start_i, speech_end_i = vad(data)
			# print(speech_start_i, speech_end_i)
			if speech_start_i:
			speech_start = speech_start_i
			# 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
			if speech_end_i or RECORD_NUM > 300:
			# silence_count += 1 # 增加静音次数
			# speech_end = speech_end_i
			speech_start = False
			# if RECORD_NUM > 300: #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()


			start_server = websockets.serve(ws_serve, args.host, args.port, subprotocols=["binary"], ping_interval=None)
			asyncio.get_event_loop().run_until_complete(start_server)
			asyncio.get_event_loop().run_forever()