4cfad5ae0f
- 全新ui - 全面优化websocket逻辑，提高数字人和ui连接的稳定性及资源开销 - 全面优化唤醒逻辑，提供稳定的普通唤醒模式和前置词唤醒模式 - 优化拾音质量，支持多声道麦克风拾音 - 优化自动播放服务器的对接机制，提供稳定和兼容旧版ue工程的对接模式 - 数字人接口输出机器人表情，以适应新fay ui及单片机的数字人表情输出 - 使用更高级的音频时长计算方式，可以更精准控制音频播放完成后的逻辑 - 修复点击关闭按钮会导致程序退出的bug - 修复没有麦克风的设备开启麦克风会出错的问题 - 为服务器主机地址提供配置项，以方便服务器部署
101 lines
3.2 KiB
Python
101 lines
3.2 KiB
Python
import asyncio
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import websockets
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import argparse
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import json
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from funasr import AutoModel
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from funasr.utils.postprocess_utils import rich_transcription_postprocess
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import os
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# 设置日志级别
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import logging
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.CRITICAL)
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# 解析命令行参数
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parser = argparse.ArgumentParser()
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parser.add_argument("--host", type=str, default="0.0.0.0", help="host ip, localhost, 0.0.0.0")
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parser.add_argument("--port", type=int, default=10197, help="grpc server port")
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parser.add_argument("--ngpu", type=int, default=1, help="0 for cpu, 1 for gpu")
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args = parser.parse_args()
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# 初始化模型
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print("model loading")
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model_dir = "iic/SenseVoiceSmall"
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asr_model = AutoModel(
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model=model_dir,
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trust_remote_code=True,
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remote_code="./model.py",
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vad_model="fsmn-vad",
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vad_kwargs={"max_single_segment_time": 30000},
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device="cuda:1",
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)
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print("model loaded")
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websocket_users = {}
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task_queue = asyncio.Queue()
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async def ws_serve(websocket, path):
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global websocket_users
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user_id = id(websocket)
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websocket_users[user_id] = websocket
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try:
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async for message in websocket:
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if isinstance(message, str):
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data = json.loads(message)
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if 'url' in data:
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await task_queue.put((websocket, data['url']))
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except websockets.exceptions.ConnectionClosed as e:
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logger.info(f"Connection closed: {e.reason}")
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except Exception as e:
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logger.error(f"Unexpected error: {e}")
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finally:
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logger.info(f"Cleaning up connection for user {user_id}")
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if user_id in websocket_users:
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del websocket_users[user_id]
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await websocket.close()
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logger.info("WebSocket closed")
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async def worker():
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while True:
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websocket, url = await task_queue.get()
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if websocket.open:
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await process_wav_file(websocket, url)
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else:
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logger.info("WebSocket connection is already closed when trying to process file")
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task_queue.task_done()
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async def process_wav_file(websocket, url):
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#热词
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param_dict = {"sentence_timestamp": False}
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with open("data/hotword.txt", "r", encoding="utf-8") as f:
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lines = f.readlines()
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lines = [line.strip() for line in lines]
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hotword = " ".join(lines)
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print(f"热词：{hotword}")
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param_dict["hotword"] = hotword
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wav_path = url
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try:
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res = asr_model.generate(input=wav_path, cache={},
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language="auto", # "zh", "en", "yue", "ja", "ko", "nospeech"
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use_itn=True,
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batch_size_s=60,
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merge_vad=True, #
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merge_length_s=15, **param_dict)
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if res:
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if 'text' in res[0] and websocket.open:
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text = rich_transcription_postprocess(res[0]["text"])
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await websocket.send(text)
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except Exception as e:
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print(f"Error during model.generate: {e}")
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finally:
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if os.path.exists(wav_path):
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os.remove(wav_path)
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async def main():
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start_server = websockets.serve(ws_serve, args.host, args.port, subprotocols=["binary"], ping_interval=10)
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await start_server
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worker_task = asyncio.create_task(worker())
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await worker_task
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# 使用 asyncio 运行主函数
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asyncio.run(main()) |