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服务端新建 deploy/gpu_worker/musetalk_server.py（替代原 ~/projects/MuseTalk/worker.py）： 1. Flask app.run(threaded=True)：推理阻塞时 /health 仍可达 2. _get_video_fps 兜底：ffprobe 返回 0 或失败时 fallback 到 default_fps(25) 3. _run_ffmpeg 统一封装：subprocess.run(check=True) + timeout，失败/超时抛 RuntimeError 4. inference_lock 并发锁：多请求同时到达时第二请求立即 503 5. 推理超时控制：thread.join(timeout=inference_timeout) 默认 600s，超时返回 504 6. finally 块清理临时目录：成功/失败/超时都删除 task_dir 7. 无人脸检测兜底：_run_inference 中帧提取后校验，无帧直接抛错返回 500 8. 上传大小限制：视频 <=100MB / 音频 <=20MB，超限返回 413 9. 新增 POST /cancel 端点：终止当前推理、清理临时文件、释放锁 10. GET /health 返回 GPU 显存信息（nvidia-smi）+ 当前任务状态 客户端 deploy/gpu_worker/gpu_worker.py 配套： - _call_musetalk 超时后 POST /cancel 终止服务端僵尸推理 - _call_musetalk 返回 (ok, duration, err, retryable) 四元组 - _handle_task 仅 retryable=True 时重试，4xx/短视频等确定性失败直接上报 - 新增 _cancel_musetalk_task 辅助方法 测试：新增 15 个单测覆盖服务端全部修复点；全量 15854 passed / 28 skipped 部署提醒：用户需在 RTX2060 上 wget 新 musetalk_server.py 替换旧 worker.py 并重启服务。