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- Move GPU wait_for_result to Celery background task (lipsync_gpu_process_async)
POST /lipsync/jobs now returns <1s instead of blocking 200s+
- Rewrite musetalk_server.py: MuseTalk receives full audio directly
(v2 architecture) — no pre-looping video before inference
Output video length = audio length, mux is fast stream copy
- Frontend polls GET /lipsync/jobs/{id} for status updates
- refresh_job_status: GPU async path (processing + no mediakit_task_id)
skips MediaKit polling; stale jobs (>30min) auto-marked failed
- 21 unit tests pass (11 GPU integration + 10 musetalk audio mux)
Co-Authored-By: Coze <coze-opensource@bytedance.com>
226 lines
9.1 KiB
Markdown
226 lines
9.1 KiB
Markdown
# MuseTalk GPU Worker 部署指南
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本目录包含两个组件:
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1. **gpu_worker.py**:反向轮询客户端,部署在 RTX2060 本地,轮询 SaaS API 拉取口型任务,调用本地 MuseTalk 服务推理,上传结果回 SaaS。
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2. **musetalk_server.py**:MuseTalk Flask HTTP 服务端,接收 gpu_worker.py 的推理请求,调用 MuseTalk 模型生成口型同步视频。
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---
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## 一、环境准备
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### 1.1 硬件要求
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- GPU: NVIDIA RTX 2060 或更高(显存 ≥ 6GB)
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- CUDA: 11.8+
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- Python: 3.10+
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- ffmpeg: 需安装并加入 PATH
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### 1.2 安装依赖
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```bash
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cd deploy/gpu_worker
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python3 -m venv venv
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source venv/bin/activate
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pip install -r requirements.txt
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```
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---
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## 二、MuseTalk 服务端部署(musetalk_server.py)
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### 2.1 配置环境变量
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复制 `.env.example` 为 `.env`,修改配置:
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```bash
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cp .env.example .env
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vim .env
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```
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关键配置:
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| 变量 | 说明 | 默认值 |
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|------|------|--------|
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| `MUSE_PORT` | 监听端口 | `7861` |
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| `MUSE_INFERENCE_TIMEOUT` | 推理超时秒数 | `600` |
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| `MUSE_VIDEO_MAX_MB` | 视频上传大小限制 MB | `100` |
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| `MUSE_AUDIO_MAX_MB` | 音频上传大小限制 MB | `20` |
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| `MUSE_DEFAULT_FPS` | 视频 fps 兜底值 | `25.0` |
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| `MUSE_TEMP_DIR` | 临时文件目录 | `/tmp/musetalk_$$` |
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| `MUSE_VIDEO_ENCODER` | 兜底循环视频时的编码器:`auto`(优先 h264_nvenc,失败回退 libx264)/`h264_nvenc`/`libx264` | `auto` |
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### 2.2 更新部署(v2 性能修复,必做)
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> ⚠️ 2026-09-20 v2 架构:修复 16 倍性能回归。旧版在推理前 loop 视频导致 MuseTalk 处理帧数翻倍、RTX2060 推理 >200s、nginx 504。**必须重新拉取并重启**:
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```bash
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# 在 RTX2060 上备份旧文件并拉取新版本
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cp ~/projects/MuseTalk/musetalk_server.py ~/projects/MuseTalk/musetalk_server.py.bak
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wget -O ~/projects/MuseTalk/musetalk_server.py \
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"https://git.xiaoxiajianji.com/xiaoxia/xiaoxia-saas/raw/branch/develop/deploy/gpu_worker/musetalk_server.py"
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# 重启服务
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sudo systemctl restart musetalk-server
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sudo systemctl status musetalk-server
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curl http://127.0.0.1:7861/health
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```
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v2 架构核心变化:
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- **MuseTalk 直传全量音频**:不再在推理前用 ffmpeg 循环视频。MuseTalk 原生支持长音频输入,内部自动循环视频帧。推理时间不变(~14s/5s 视频)
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- **ffmpeg 只做快速封装**:`-c:v copy -c:a aac -shortest`,秒级完成,不重编码
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- **循环仅兜底**:仅当 MuseTalk 输出画面短于音频时(极端情况),才 `-stream_loop` + NVENC 兜底
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- **删除 `MUSE_ENABLE_VIDEO_LOOP`**:不再需要此开关,MuseTalk 原生处理
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### 2.3 启动服务
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```bash
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# 前台运行(调试用)
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python musetalk_server.py
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# 后台运行(生产用 systemd)
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sudo systemctl start musetalk-server
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sudo systemctl enable musetalk-server
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```
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### 2.4 验证健康检查
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```bash
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curl http://127.0.0.1:7861/health
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```
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应返回:
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```json
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{
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"status": "healthy",
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"gpu": {
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"gpu_name": "NVIDIA GeForce RTX 2060",
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"memory_total_mb": 6144,
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"memory_used_mb": 1024,
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"memory_free_mb": 5120
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},
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"current_task": {
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"task_id": null,
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"running": false,
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"elapsed_seconds": 0.0
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},
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"timestamp": 1700000000.0
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}
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```
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---
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## 三、GPU Worker 客户端部署(gpu_worker.py)
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### 3.1 配置环境变量
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复制 `.env.example` 为 `.env`,修改配置:
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```bash
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cp .env.example .env
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vim .env
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```
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关键配置:
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| 变量 | 说明 | 默认值 |
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|------|------|--------|
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| `API_BASE_URL` | SaaS API 基础 URL | `https://staging-api.xiaoxiajianji.com` |
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| `GPU_WORKER_TOKEN` | 长期 API Token(与服务端一致) | - |
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| `MUSE_TALK_URL` | 本地 MuseTalk 服务地址 | `http://127.0.0.1:7861` |
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| `POLL_INTERVAL` | 轮询间隔秒 | `5` |
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| `HEARTBEAT_INTERVAL` | 空闲心跳间隔秒 | `15` |
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| `REQUEST_TIMEOUT` | HTTP 请求超时秒 | `900` |
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| `TASK_MAX_RETRY` | 本地最大重试次数 | `1` |
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| `TASK_HEARTBEAT_INTERVAL` | 推理期间任务心跳间隔秒 | `30` |
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| `MIN_VIDEO_DURATION_SECONDS` | 最短输入视频时长秒 | `3` |
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### 3.2 启动 Worker
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```bash
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# 前台运行(调试用)
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python gpu_worker.py
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# 后台运行(生产用 systemd)
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sudo systemctl start xiaoxia-gpu-worker
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sudo systemctl enable xiaoxia-gpu-worker
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```
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### 3.3 验证启动日志
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应看到:
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```
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============================================================
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MuseTalk GPU Worker 启动
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worker_id = rtx2060-xxxx
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api_base = https://staging-api.xiaoxiajianji.com
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muse_talk = http://127.0.0.1:7861
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poll = 5.0s / heartbeat = 15.0s
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============================================================
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MuseTalk 健康检查通过: {...}
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注册/心跳成功
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```
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---
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## 四、常见问题排查
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| 现象 | 可能原因 / 排查 |
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| 日志 401 `Invalid GPU worker token` | `.env` 的 `GPU_WORKER_TOKEN` 与服务端不一致 |
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| 日志 `MuseTalk 健康检查未通过` | 本地 MuseTalk 没启动,或端口不是 7861;`curl http://127.0.0.1:7861/health` 验证 |
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| 任务长时间不被拉取 | Worker 和服务端连不上;检查 API_BASE_URL 是否可达、Token 是否正确 |
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| 推理后上传 OSS 失败 | 本地出口网络被防火墙拦截 OSS 域名(oss-cn-hangzhou.aliyuncs.com) |
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| 服务端看到任务回退到 pending 重试 | 任务心跳真正超时(默认 900s):Worker 进程崩溃/断网,或推理彻底卡死;正常长推理期间心跳线程每 30s 续期,不会回退 |
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| 日志 `MuseTalk 推理超时或连接失败` | 视频太长或显存不足;可临时调大 REQUEST_TIMEOUT(服务端 GPU_TASK_TIMEOUT_SECONDS 需同步调大),或限制输入视频时长 |
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| 日志 `视频过短(x.xxs < 3s)` | 输入视频不足 3s,MuseTalk 对短视频会 division by zero,已在本地直接上报失败;可用 MIN_VIDEO_DURATION_SECONDS 调整阈值 |
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| MuseTalk 服务端 503 `GPU 正在处理其他任务` | 并发请求被锁拒绝,等当前推理完成即可 |
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| MuseTalk 服务端 504 `推理超时` | 推理超过 MUSE_INFERENCE_TIMEOUT,客户端会调 /cancel 终止服务端任务 |
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---
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## 五、安全注意事项
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- `.env` 包含长期 Token,文件权限设为 600(`chmod 600 .env`)
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- Token 泄露要立即在服务端更换 `GPU_WORKER_TOKEN` 并重启 Worker
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- Worker 只需要出站访问 SaaS API 和 OSS,不需要开放任何入站端口
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- MuseTalk 服务端只监听本地 127.0.0.1(或 0.0.0.0 但通过防火墙限制),不暴露到公网
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- 临时文件自动清理(推理完成/失败后),无需手动维护
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---
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## 六、工程改进记录(musetalk_server.py)
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相比原 `worker.py`,修复了以下 8 个 bug:
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1. **Flask 单线程阻塞**:`app.run(threaded=True)`,推理时 `/health` 仍可响应
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2. **fps=0 除零崩溃**:`_get_video_fps()` 兜底 `MUSE_DEFAULT_FPS`
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3. **ffmpeg 不检查返回码**:`subprocess.run(check=True)` + 超时检查,失败立即报错
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4. **无并发锁**:`threading.Lock` 控制并发,第二请求立即 503
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5. **无推理超时**:线程 join timeout,超时返回 504 并调 `/cancel`
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6. **结果文件不清理**:推理完成/失败后自动删除临时目录
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7. **无人脸检测兜底**:MuseTalk 推理内部处理(TODO: 可在 `_run_inference` 前置检查)
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8. **上传无大小限制**:`_check_file_size()` 校验,超限返回 413
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新增:
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- `/cancel` 端点:终止当前推理任务,清理临时文件
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- `/health` 端点:返回 GPU 显存信息和当前任务状态
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2026-09-20 追加修复(音轨正确性,上线阻断级):
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9. **音轨未替换(严重)**:旧最终封装让 ffmpeg 默认选流,结果保留了源视频自带音轨(与画面相关系数 0.9998,与 TTS 无关)。改为 `_mux_video_with_audio()` 统一封装,强制 `-map 0:v:0 -map 1:a:0`,画面取 MuseTalk 无声产物、音轨只取驱动音频
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10. **音视频时长不对齐**:TTS 长于原视频时 `-shortest` 会截短语音。改为探测双方时长,音频更长时 `-stream_loop -1` 循环画面 + `h264_nvenc` 硬件重编码(`MUSE_VIDEO_ENCODER=auto`,失败回退 libx264)+ `-t <音频时长>`;不循环时 `-c:v copy` 秒封装
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- 开关 `MUSE_ENABLE_VIDEO_LOOP=0` 可关闭循环;请求也支持 form 参数 `enable_video_loop` 单任务覆盖
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2026-09-20 v2 架构重构(性能回归修复,上线阻断级):
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11. **16 倍性能回归**:#9/#10 的实现虽然音轨正确,但在某些集成场景下(推理前 loop 视频再喂 MuseTalk)导致推理帧数 ×2.2 + 叠加 ffmpeg 软编码预处理,5s 视频 +11s 音频推理 >200s,nginx 60s 超时 504
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- **正确架构**:MuseTalk 原生支持长音频输入,内部自动循环视频帧。把【原视频】+【全量音频】直传 MuseTalk,输出时长=音频时长
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- **ffmpeg 后置快速封装**:`-c:v copy -c:a aac -shortest` 秒级完成,不重编码
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- **循环仅兜底**:仅当 MuseTalk 输出画面短于音频时(极端情况),才 `-stream_loop` + NVENC 兜底补齐
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- **业务侧异步化**:POST /lipsync/jobs 创建 GPU 任务后立即返回 `job.status="processing"`,Celery 异步等待结果回写。前端 GET /jobs/{id} 轮询。避免同步阻塞 HTTP 请求 >200s
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- **删除 `MUSE_ENABLE_VIDEO_LOOP`**:不再需要此开关
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