# 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
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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from modelscope.utils.logger import get_logger
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import logging
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logger = get_logger(log_level=logging.CRITICAL)
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logger.setLevel(logging.CRITICAL)
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import asyncio
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import websockets
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import time
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from queue import Queue
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import threading
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument("--host",
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type=str,
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default="0.0.0.0",
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required=False,
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help="host ip, localhost, 0.0.0.0")
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parser.add_argument("--port",
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type=int,
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default=10095,
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required=False,
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help="grpc server port")
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parser.add_argument("--asr_model",
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type=str,
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default="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
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help="model from modelscope")
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parser.add_argument("--vad_model",
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type=str,
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default="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
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help="model from modelscope")
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parser.add_argument("--punc_model",
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type=str,
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default="",
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help="model from modelscope")
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args = parser.parse_args()
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print("model loading")
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voices = Queue()
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speek = Queue()
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# 创建一个VAD对象
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vad_pipline = pipeline(
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task=Tasks.voice_activity_detection,
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model=args.vad_model,
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model_revision="v1.2.0",
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output_dir=None,
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batch_size=1,
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)
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# 创建一个ASR对象
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param_dict = dict()
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# param_dict["hotword"] = "小五 小五月" # 设置热词，用空格隔开
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inference_pipeline2 = pipeline(
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task=Tasks.auto_speech_recognition,
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model=args.asr_model,
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param_dict=param_dict,
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)
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print("model loaded")
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async def ws_serve(websocket, path):
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global voices
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try:
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async for message in websocket:
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voices.put(message)
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#print("put")
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except websockets.exceptions.ConnectionClosedError as e:
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print('Connection closed with exception:', e)
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except Exception as e:
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print('Exception occurred:', e)
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start_server = websockets.serve(ws_serve, args.host, args.port, subprotocols=["binary"], ping_interval=None)
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def vad(data): # 推理
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global vad_pipline
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#print(type(data))
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segments_result = vad_pipline(audio_in=data)
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#print(segments_result)
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if len(segments_result) == 0:
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return False
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else:
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return True
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def asr(): # 推理
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global inference_pipeline2
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global speek
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while True:
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while not speek.empty():
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audio_in = speek.get()
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speek.task_done()
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rec_result = inference_pipeline2(audio_in=audio_in)
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print(rec_result)
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time.sleep(0.1)
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time.sleep(0.1)
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def main(): # 推理
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frames = [] # 存储所有的帧数据
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buffer = [] # 存储缓存中的帧数据（最多两个片段）
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silence_count = 0 # 统计连续静音的次数
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speech_detected = False # 标记是否检测到语音
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RECORD_NUM = 0
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global voices
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global speek
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while True:
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while not voices.empty():
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data = voices.get()
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#print("队列排队数",voices.qsize())
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voices.task_done()
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buffer.append(data)
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if len(buffer) > 2:
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buffer.pop(0) # 如果缓存超过两个片段，则删除最早的一个
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if speech_detected:
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frames.append(data)
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RECORD_NUM += 1
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if vad(data):
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if not speech_detected:
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print("检测到人声...")
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speech_detected = True # 标记为检测到语音
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frames = []
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frames.extend(buffer) # 把之前2个语音数据快加入
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silence_count = 0 # 重置静音次数
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else:
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silence_count += 1 # 增加静音次数
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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
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print("说话结束或者超过设置最长时间...")
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audio_in = b"".join(frames)
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#asrt = threading.Thread(target=asr,args=(audio_in,))
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#asrt.start()
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speek.put(audio_in)
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#rec_result = inference_pipeline2(audio_in=audio_in) # ASR 模型里跑一跑
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frames = [] # 清空所有的帧数据
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buffer = [] # 清空缓存中的帧数据（最多两个片段）
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silence_count = 0 # 统计连续静音的次数清零
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speech_detected = False # 标记是否检测到语音
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RECORD_NUM = 0
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time.sleep(0.01)
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time.sleep(0.01)
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s = threading.Thread(target=main)
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s.start()
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s = threading.Thread(target=asr)
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s.start()
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asyncio.get_event_loop().run_until_complete(start_server)
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asyncio.get_event_loop().run_forever()
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