Upload 46 files

.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+venv/
+env/
+ENV/
+.env
+
+# Temporary files
+temp/*
+backend/temp/*
+!temp/.gitkeep
+!backend/temp/.gitkeep
+
+# Development and testing files
+app_test.py
+test_simple.py
+*_test.py
+*_TEST.py
+
+# Documentation (optional - 可以选择包含或排除)
+MIGRATION_SUMMARY.md
+DOCKER_PERFORMANCE_ANALYSIS.md
+HF_SPACES_UPLOAD_CHECKLIST.md
+GIT_DEPLOYMENT_GUIDE.md
+
+# Development tools
+.kiro/
+backend/test_e2e.py
+backend/E2E_TESTS_README.md
+backend/verify_*.py
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Chrome Extension 构建产物（保留源码）
+extension/build/
+extension/dist/
+
+# Logs
+*.log
+
+# Distribution
+dist/
+build/
+*.egg-info/
+
+# HuggingFace cache
+.cache/

Dockerfile

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+# Universal Fast Dubbing - FastAPI Docker 配置（性能优化版）
+FROM python:3.11-slim
+
+# 设置工作目录
+WORKDIR /app
+
+# 安装系统依赖（优化版本）
+RUN apt-get update && apt-get install -y \
+    ffmpeg \
+    --no-install-recommends \
+    && rm -rf /var/lib/apt/lists/* \
+    && apt-get clean
+
+# 复制并安装Python依赖（利用Docker缓存）
+COPY backend/requirements.txt .
+RUN pip install --no-cache-dir --upgrade pip \
+    && pip install --no-cache-dir -r requirements.txt
+
+# 复制应用代码
+COPY . .
+
+# 创建临时目录
+RUN mkdir -p temp/gateway temp/processing temp/tts temp/sync temp/downloads
+
+# 设置环境变量（性能优化）
+ENV PYTHONPATH=/app/backend
+ENV PYTHONUNBUFFERED=1
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV UVICORN_WORKERS=1
+ENV UVICORN_WORKER_CLASS=uvicorn.workers.UvicornWorker
+
+# 暴露端口
+EXPOSE 7860
+
+# 健康检查（使用现有的API端点）
+HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
+    CMD curl -f http://localhost:7860/api/status || exit 1
+
+# 启动命令（使用完整版应用）
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860", "--workers", "1", "--access-log"]

LICENSE

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README.md

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+---
+title: Universal Fast Dubbing
+emoji: 🎬
+colorFrom: blue
+colorTo: purple
+sdk: docker
+app_port: 7860
+pinned: false
+---
+
+# 🎬 Universal Fast Dubbing v3.0
+
+全网通用AI配音插件 - 支持YouTube、Bilibili、Netflix、TikTok等全平台视频
+
+## 功能特点
+
+- 🎯 **智能语音识别**: 基于 Whisper V3 的高精度语音转文字
+- 🌐 **多语言翻译**: 使用 Llama 3 进行智能翻译和角色识别
+- 🎤 **自然语音合成**: Edge-TTS / SiliconFlow 高质量配音
+- ⚡ **音画同步对齐**: 精确到 ±0.3秒 的音频同步
+- 🚀 **双模式支持**: URL直接处理 + 录制模式
+
+## 支持平台
+
+| 平台 | URL模式 | 录制模式 |
+|------|---------|----------|
+| YouTube | ✓ | ✓ |
+| Bilibili | ✓ | ✓ |
+| Netflix | ✗ | ✓ |
+| TikTok | ✓ | ✓ |
+| Twitter/X | ✓ | ✓ |
+
+## 快速开始
+
+### 1. 配置 API 密钥
+
+在 Space Settings 中添加以下 Secrets：
+
+```bash
+# 必需（二选一或同时配置）
+GROQ_API_KEY=gsk_xxxxxxxxxxxxxxxxxxxxxxxx
+SILICONFLOW_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxx
+
+# 可选配置
+API_PROVIDER=auto  # auto, groq, siliconflow
+TTS_PROVIDER=edge-tts  # edge-tts, siliconflow
+```
+
+### 2. 使用 Web 界面
+
+1. 选择处理模式（自动检测/URL/录制）
+2. 输入视频URL 或 上传录制的音频
+3. 点击"开始配音"按钮
+4. 等待处理完成，下载配音结果
+
+### 3. 配合 Chrome 扩展使用
+
+安装配套的 Chrome 扩展，可以直接在视频网站上一键配音：
+
+1. 下载扩展代码
+2. 在 Chrome 中加载 `extension/` 文件夹
+3. 配置后端地址为此 Space 的 URL
+4. 在视频页面点击扩展图标开始配音
+
+## API 使用
+
+### 配音处理 API
+
+```python
+import requests
+
+# 处理视频URL
+response = requests.post(
+    "https://your-space.hf.space/api/process",
+    json={
+        "mode": "url",
+        "url": "https://www.youtube.com/watch?v=xxxxx"
+    }
+)
+
+result = response.json()
+audio_url = result["audio_url"]
+```
+
+### 获取系统状态
+
+```python
+response = requests.get("https://your-space.hf.space/api/status")
+status = response.json()
+print(f"系统健康: {status['healthy']}")
+print(f"内存使用: {status['performance']['memory_mb']} MB")
+```
+
+### 获取后端配置
+
+```python
+response = requests.get("https://your-space.hf.space/api/config")
+config = response.json()
+print(f"API提供商: {config['api_provider']}")
+print(f"TTS提供商: {config['tts_provider']}")
+```
+
+## 技术架构
+
+### 后端技术栈
+
+- **Web框架**: FastAPI + Uvicorn
+- **模板引擎**: Jinja2 + Tailwind CSS
+- **语音识别**: Groq Whisper V3 / SiliconFlow SenseVoice
+- **翻译引擎**: Groq Llama 3
+- **语音合成**: Edge-TTS / SiliconFlow (Fish Speech, CosyVoice2, MOSS-TTSD)
+- **音频处理**: FFmpeg + Pydub + Librosa
+- **视频下载**: yt-dlp
+
+### 处理流程
+
+```
+视频URL/录制音频
+    ↓
+音频提取 (yt-dlp / 直接上传)
+    ↓
+语音识别 (Whisper V3 带时间戳)
+    ↓
+翻译 + 角色识别 (Llama 3)
+    ↓
+语音合成 (Edge-TTS / SiliconFlow)
+    ↓
+音频同步对齐 (时间戳匹配)
+    ↓
+配音音频输出
+```
+
+## 配置说明
+
+### API 提供商选择
+
+#### Groq (推荐用于精确时间戳)
+
+- **优势**: Whisper V3 带精确时间戳，免费额度充足
+- **限制**: 每分钟 20 次 ASR 请求
+- **获取**: [console.groq.com](https://console.groq.com)
+
+```bash
+GROQ_API_KEY=your_key
+API_PROVIDER=groq
+TTS_PROVIDER=edge-tts
+```
+
+#### SiliconFlow (推荐用于高质量中文TTS)
+
+- **优势**: 多种高质量 TTS 模型，支持多角色对话
+- **限制**: ASR 无时间戳（需后处理）
+- **获取**: [cloud.siliconflow.cn](https://cloud.siliconflow.cn)
+
+```bash
+SILICONFLOW_API_KEY=your_key
+API_PROVIDER=siliconflow
+TTS_PROVIDER=siliconflow
+SILICONFLOW_TTS_MODEL=fishaudio/fish-speech-1.5
+```
+
+#### 混合模式 (最佳质量)
+
+```bash
+GROQ_API_KEY=your_groq_key
+SILICONFLOW_API_KEY=your_sf_key
+API_PROVIDER=groq  # ASR 用 Groq
+TTS_PROVIDER=siliconflow  # TTS 用 SiliconFlow
+SILICONFLOW_TTS_MODEL=FunAudioLLM/CosyVoice2-0.5B
+```
+
+### 性能优化配置
+
+```bash
+# 启用低码率音频（加速处理）
+USE_LOW_QUALITY_AUDIO=true
+
+# 并发处理数（根据硬件调整）
+MAX_CONCURRENT_WORKERS=3
+
+# 缓存时长（秒）
+CACHE_DURATION=3600
+
+# 最大同时会话数
+MAX_SESSIONS=10
+```
+
+## 限制说明
+
+- **视频时长**: 建议 15 分钟以内
+- **同步精度**: ±0.3 秒
+- **支持语言**: 英语、日语 → 中文
+- **并发处理**: 根据硬件配置自动调整
+
+## 常见问题
+
+### Q: 为什么某些网站不支持 URL 模式？
+
+A: Netflix、Amazon Prime 等平台有 DRM 保护，无法直接下载音频。请使用录制模式。
+
+### Q: 如何提高处理速度？
+
+A: 
+1. 启用 `USE_LOW_QUALITY_AUDIO=true`
+2. 减少并发数 `MAX_CONCURRENT_WORKERS=2`
+3. 处理较短的视频片段
+
+### Q: API 调用失败怎么办？
+
+A:
+1. 检查 API Key 是否正确配置
+2. 确认账户配额未用尽
+3. 尝试切换 `API_PROVIDER` 到另一个提供商
+
+### Q: 音频同步不准确？
+
+A:
+1. 确保使用 Groq Whisper V3（带时间戳）
+2. 检查原视频音频质量
+3. 调整 `SYNC_THRESHOLD` 参数
+
+## 项目结构
+
+```
+universal-fast-dubbing/
+├── backend/                 # Python后端服务
+│   ├── app.py              # Gradio主入口
+│   ├── requirements.txt    # Python依赖
+│   ├── packages.txt        # 系统依赖 (ffmpeg)
+│   ├── modules/            # 核心处理模块
+│   │   ├── gateway.py      # API网关
+│   │   ├── groq_client.py  # Groq API客户端
+│   │   ├── siliconflow_client.py  # SiliconFlow客户端
+│   │   ├── processor.py    # 配音处理器
+│   │   ├── segmenter.py    # 音频分段器
+│   │   ├── tts_generator.py  # TTS生成器
+│   │   ├── audio_sync.py   # 音频同步
+│   │   ├── router.py       # API路由
+│   │   └── ...
+│   └── temp/               # 临时文件目录
+│
+└── extension/              # Chrome扩展（需单独安装）
+    ├── manifest.json       # 扩展配置
+    ├── background/         # Background Service Worker
+    ├── content/            # Content Scripts
+    └── popup/              # Popup界面
+```
+
+## 开发指南
+
+### 本地开发
+
+```bash
+# 克隆项目
+git clone <your-repo-url>
+cd universal-fast-dubbing/backend
+
+# 创建虚拟环境
+python -m venv venv
+source venv/bin/activate  # Windows: venv\Scripts\activate
+
+# 安装依赖
+pip install -r requirements.txt
+
+# 配置环境变量
+cp .env.example .env
+# 编辑 .env 文件，填入 API 密钥
+
+# 启动服务
+python app.py
+```
+
+### 部署到 Hugging Face Spaces
+
+1. Fork 本项目
+2. 在 HF 创建新 Space，选择 Gradio SDK
+3. 连接 Git 仓库或上传文件
+4. 在 Settings 中配置 Secrets（API 密钥）
+5. 等待构建完成
+
+详细部署说明请查看 [DEPLOYMENT.md](./DEPLOYMENT.md)
+
+## 性能监控
+
+系统内置性能监控，可在"系统状态"标签页查看：
+
+- **内存使用**: 实时内存占用
+- **CPU使用率**: 处理器负载
+- **成功率**: API调用成功率
+- **活跃会话**: 当前处理中的任务数
+- **缓存统计**: 缓存命中率
+
+## 许可证
+
+MIT License
+
+## 致谢
+
+- [Groq](https://groq.com) - 提供高速 LPU 推理
+- [SiliconFlow](https://siliconflow.cn) - 提供多样化 AI 模型
+- [Edge-TTS](https://github.com/rany2/edge-tts) - 微软 Edge 语音合成
+- [yt-dlp](https://github.com/yt-dlp/yt-dlp) - 视频下载工具
+- [Gradio](https://gradio.app) - Web 界面框架
+
+## 技术支持
+
+如遇到问题，请：
+1. 查看"系统状态"标签页的错误信息
+2. 检查 Space Logs 中的详细日志
+3. 提交 Issue 并附上错误信息
+
+---
+
+**注意**: 本项目仅供学习和研究使用，请遵守各视频平台的服务条款。

app.py

@@ -0,0 +1,754 @@
+"""
+Universal Fast Dubbing - FastAPI 主应用
+
+使用 FastAPI + Jinja2 模板替代 Gradio
+提供更好的性能和控制能力
+
+新增功能：
+- WebSocket 流式处理支持
+- SSE (Server-Sent Events) 流式处理（HF Spaces 推荐）
+- 分段并行处理
+- 实时进度反馈
+- 10秒内开始播放优化
+"""
+
+import os
+import json
+import asyncio
+import base64
+from pathlib import Path
+from contextlib import asynccontextmanager
+from fastapi import FastAPI, Request, Form, File, UploadFile, WebSocket, WebSocketDisconnect
+from fastapi.templating import Jinja2Templates
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import HTMLResponse, JSONResponse, StreamingResponse
+from typing import Optional, AsyncGenerator
+import sys
+
+# 将 backend 目录添加到 Python 路径
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'backend'))
+
+# 导入后端模块
+from modules.gateway import GradioAPIGateway, GatewayConfig
+from modules.groq_client import GroqConfig
+from modules.logging_config import setup_logging, get_component_logger, Component
+
+# 配置日志
+setup_logging()
+logger = get_component_logger(Component.SYSTEM)
+
+from contextlib import asynccontextmanager
+
+# 全局网关实例
+gateway: Optional[GradioAPIGateway] = None
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """应用生命周期管理"""
+    global gateway
+    
+    # 启动时初始化
+    logger.info("初始化 Universal Fast Dubbing 后端...")
+    
+    # 配置 Groq
+    groq_api_key = os.getenv("GROQ_API_KEY")
+    groq_config = GroqConfig(
+        api_key=groq_api_key,
+        asr_model=os.getenv("ASR_MODEL", "whisper-large-v3"),
+        llm_model=os.getenv("LLM_MODEL", "llama3-8b-8192")
+    ) if groq_api_key else None
+    
+    # 配置网关
+    gateway_config = GatewayConfig(
+        temp_dir=os.getenv("TEMP_DIR", "temp/gateway"),
+        cache_duration=int(os.getenv("CACHE_DURATION", "3600")),
+        max_sessions=int(os.getenv("MAX_SESSIONS", "10")),
+        use_low_quality_audio=os.getenv("USE_LOW_QUALITY_AUDIO", "true").lower() == "true"
+    )
+    
+    gateway = GradioAPIGateway(config=gateway_config, groq_config=groq_config)
+    await gateway.initialize()
+    
+    logger.info("后端初始化完成")
+    
+    yield  # 应用运行期间
+    
+    # 关闭时清理
+    if gateway:
+        # 这里可以添加清理逻辑
+        pass
+
+# 创建 FastAPI 应用
+app = FastAPI(
+    title="Universal Fast Dubbing", 
+    version="3.0.0",
+    lifespan=lifespan  # 使用新的生命周期管理
+)
+
+# 设置模板和静态文件目录
+templates = Jinja2Templates(directory="templates")
+app.mount("/static", StaticFiles(directory="static"), name="static")
+
+@app.get("/", response_class=HTMLResponse)
+async def home(request: Request):
+    """主页面"""
+    return templates.TemplateResponse("index.html", {
+        "request": request,
+        "title": "Universal Fast Dubbing",
+        "version": "3.0.0"
+    })
+
+@app.get("/api/status")
+async def get_status():
+    """获取系统状态 API"""
+    try:
+        # 获取性能监控数据
+        from modules.performance_monitor import get_performance_monitor
+        perf_monitor = get_performance_monitor()
+        perf_stats = perf_monitor.get_statistics()
+        health = perf_monitor.is_healthy()
+        
+        # 获取网关状态
+        gateway_status = {}
+        if gateway and gateway.is_initialized:
+            gateway_status = {
+                "active_sessions": len(gateway.get_active_sessions()),
+                "cache_stats": gateway.get_cache_stats()
+            }
+        
+        return {
+            "timestamp": "2025-12-21T00:00:00Z",
+            "healthy": health["healthy"],
+            "issues": health["issues"],
+            "performance": {
+                "memory_mb": perf_stats["current_memory_mb"],
+                "cpu_percent": perf_stats["current_cpu_percent"],
+                "success_rate": perf_stats["success_rate"],
+                "total_operations": perf_stats["total_operations"]
+            },
+            "gateway": gateway_status
+        }
+    except Exception as e:
+        logger.error(f"获取系统状态失败: {e}")
+        return {"error": str(e), "healthy": False}
+
+@app.get("/api/config")
+async def get_config():
+    """获取后端配置 API"""
+    try:
+        return {
+            "api_provider": os.getenv("API_PROVIDER", "auto"),
+            "has_groq_key": bool(os.getenv("GROQ_API_KEY")),
+            "has_siliconflow_key": bool(os.getenv("SILICONFLOW_API_KEY")),
+            "tts_provider": os.getenv("TTS_PROVIDER", "edge-tts"),
+            "siliconflow_tts_model": os.getenv("SILICONFLOW_TTS_MODEL", "fishaudio/fish-speech-1.5"),
+            "use_low_quality_audio": os.getenv("USE_LOW_QUALITY_AUDIO", "true").lower() == "true",
+            "max_concurrent_workers": int(os.getenv("MAX_CONCURRENT_WORKERS", "3")),
+            "cache_duration": int(os.getenv("CACHE_DURATION", "3600")),
+            "asr_model": os.getenv("ASR_MODEL", "whisper-large-v3"),
+            "llm_model": os.getenv("LLM_MODEL", "llama3-8b-8192"),
+            "version": "3.0.0",
+            "debug": os.getenv("DEBUG", "false").lower() == "true",
+        }
+    except Exception as e:
+        logger.error(f"获取后端配置失败: {e}")
+        return {"error": str(e)}
+
+@app.post("/api/ping")
+async def ping(client_config: str = Form(default="{}")):
+    """统一连接测试 API"""
+    try:
+        # 解析客户端配置
+        ext_config = {}
+        if client_config and client_config != "{}":
+            try:
+                ext_config = json.loads(client_config)
+                logger.info(f"收到客户端配置: {ext_config}")
+            except json.JSONDecodeError as e:
+                return {
+                    "success": False,
+                    "error": f"客户端配置解析失败: {e}",
+                    "message": "配置格式错误"
+                }
+        
+        # 获取后端配置
+        backend_config = await get_config()
+        
+        return {
+            "success": True,
+            "message": "连接成功",
+            "backend_config": backend_config,
+            "client_config_received": ext_config if ext_config else None
+        }
+        
+    except Exception as e:
+        logger.error(f"ping 失败: {e}")
+        return {
+            "success": False,
+            "error": str(e),
+            "message": "连接失败"
+        }
+
+@app.post("/api/save_config")
+async def save_client_config(client_config: str = Form(...)):
+    """保存客户端配置 API"""
+    try:
+        # 解析客户端配置
+        config_data = {}
+        if client_config and client_config != "{}":
+            try:
+                config_data = json.loads(client_config)
+                logger.info(f"保存客户端配置: {config_data}")
+            except json.JSONDecodeError as e:
+                return {
+                    "success": False,
+                    "error": f"配置解析失败: {e}",
+                    "message": "配置格式错误"
+                }
+        
+        # 这里可以将配置保存到文件或数据库
+        # 目前只是记录日志，实际项目中可以持久化存储
+        logger.info(f"[配置保存] 客户端配置已接收并记录: {config_data}")
+        
+        # 如果有网关实例，可以将配置传递给网关
+        if gateway and gateway.is_initialized:
+            # 将客户端配置传递给网关，用于后续处理
+            gateway.update_client_config(config_data)
+        
+        return {
+            "success": True,
+            "message": "配置保存成功",
+            "saved_config": config_data
+        }
+        
+    except Exception as e:
+        logger.error(f"保存配置失败: {e}")
+        return {
+            "success": False,
+            "error": str(e),
+            "message": "配置保存失败"
+        }
+
+@app.post("/api/process")
+async def process_dubbing(
+    mode: str = Form(...),
+    url: Optional[str] = Form(None),
+    audio_file: Optional[UploadFile] = File(None),
+    client_config: str = Form(default="{}")
+):
+    """配音处理 API"""
+    try:
+        if not gateway:
+            return {"success": False, "error": "后端未初始化"}
+        
+        # 解析客户端配置
+        ext_config = {}
+        if client_config and client_config != "{}":
+            try:
+                ext_config = json.loads(client_config)
+            except json.JSONDecodeError:
+                pass
+        
+        # 准备处理数据
+        data = {"client_config": ext_config}
+        
+        if mode == "url" and url:
+            data["url"] = url
+            actual_mode = "url"
+        elif mode == "record" and audio_file:
+            # 保存上传的音频文件
+            audio_content = await audio_file.read()
+            data["audio_data"] = audio_content
+            actual_mode = "record"
+        else:
+            return {"success": False, "error": "无效的输入参数"}
+        
+        # 处理请求
+        result = None
+        async for update in gateway.process_request(actual_mode, data):
+            if update.get("state") == "completed":
+                result = update.get("result", {})
+                break
+            elif update.get("state") == "failed":
+                return {"success": False, "error": update.get("message", "处理失败")}
+        
+        if result:
+            return {
+                "success": True,
+                "audio_url": result.get("audio_url"),
+                "processing_time": result.get("processing_time", 0),
+                "segments_processed": result.get("segments_processed", 0)
+            }
+        else:
+            return {"success": False, "error": "处理未返回结果"}
+            
+    except Exception as e:
+        logger.error(f"配音处理失败: {e}")
+        return {"success": False, "error": str(e)}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)
+
+
+@app.get("/api/capabilities")
+async def get_capabilities():
+    """
+    获取后端能力信息
+    用于客户端判断支持的功能
+    """
+    return {
+        "websocket_supported": True,
+        "sse_supported": True,  # SSE 在 HF Spaces 上更稳定
+        "segmented_processing": True,
+        "range_download": True,  # 支持分段下载
+        "parallel_tts": True,
+        "max_segment_duration": 120,  # 每段最大2分钟
+        "first_segment_target": 10,  # 目标10秒内开始播放
+        "supported_modes": ["url", "record"],
+        "version": "3.1.0"
+    }
+
+
+@app.post("/api/process_stream")
+async def process_stream_sse(
+    mode: str = Form(...),
+    url: Optional[str] = Form(None),
+    audio: Optional[UploadFile] = File(None),
+    segment_duration: int = Form(default=120),
+    client_config: str = Form(default="{}")
+):
+    """
+    SSE 流式处理端点（推荐用于 HF Spaces）
+    
+    比 WebSocket 更稳定，HF Spaces 代理对 SSE 支持更好。
+    支持两种输入方式：
+    1. URL模式：提供视频URL
+    2. 上传模式：上传音频文件（来自扩展端拦截或录制）
+    
+    参数:
+        mode: 处理模式 (url/upload)
+        url: 视频URL（URL模式）
+        audio: 音频文件（上传模式）
+        segment_duration: 每段时长（秒），默认120秒
+        client_config: 客户端配置JSON
+    
+    返回:
+        SSE 事件流，包含:
+        - progress: 进度更新
+        - segment_ready: 分段完成
+        - complete: 处理完成
+        - error: 错误信息
+    """
+    
+    async def event_generator() -> AsyncGenerator[str, None]:
+        """SSE 事件生成器"""
+        try:
+            if not gateway or not gateway.is_initialized:
+                yield f"data: {json.dumps({'type': 'error', 'message': '后端未初始化'})}\n\n"
+                return
+            
+            # 解析客户端配置
+            ext_config = {}
+            if client_config and client_config != "{}":
+                try:
+                    ext_config = json.loads(client_config)
+                except json.JSONDecodeError:
+                    pass
+            
+            # 准备处理数据
+            process_data = {
+                "client_config": ext_config,
+                "segment_duration": segment_duration
+            }
+            
+            # 根据模式准备数据
+            actual_mode = mode
+            if mode == "url" and url:
+                process_data["url"] = url
+                logger.info(f"[SSE] URL模式处理: {url[:50]}..., 分段时长: {segment_duration}秒")
+            elif mode == "upload" and audio:
+                # 读取上传的音频
+                audio_content = await audio.read()
+                process_data["audio_data"] = audio_content
+                actual_mode = "upload"
+                logger.info(f"[SSE] 上传模式处理: {len(audio_content)} bytes, 分段时长: {segment_duration}秒")
+            else:
+                yield f"data: {json.dumps({'type': 'error', 'message': '缺少视频URL或音频文件'})}\n\n"
+                return
+            
+            # 发送初始进度
+            yield f"data: {json.dumps({'type': 'progress', 'progress': 5, 'message': '正在分析音频...'})}\n\n"
+            
+            # 使用分段处理模式
+            segment_index = 0
+            total_duration = 0
+            
+            async for update in gateway.process_request_segmented(actual_mode, process_data):
+                update_type = update.get("type", "progress")
+                
+                if update_type == "progress":
+                    yield f"data: {json.dumps({'type': 'progress', 'progress': update.get('progress', 0), 'message': update.get('message', '')})}\n\n"
+                    
+                elif update_type == "segment_ready":
+                    segment_data = update.get("segment", {})
+                    audio_data = segment_data.get("audio_data")
+                    
+                    # 二进制数据转base64
+                    if isinstance(audio_data, bytes):
+                        audio_data = base64.b64encode(audio_data).decode('utf-8')
+                    
+                    yield f"data: {json.dumps({'type': 'segment_ready', 'index': segment_index, 'startTime': segment_data.get('start_time', total_duration), 'duration': segment_data.get('duration', segment_duration), 'audioData': audio_data})}\n\n"
+                    
+                    total_duration += segment_data.get("duration", segment_duration)
+                    segment_index += 1
+                    
+                elif update_type == "complete":
+                    yield f"data: {json.dumps({'type': 'complete', 'totalSegments': segment_index, 'totalDuration': total_duration, 'processingTime': update.get('processing_time', 0)})}\n\n"
+                    break
+                    
+                elif update_type == "error":
+                    yield f"data: {json.dumps({'type': 'error', 'message': update.get('message', '处理失败')})}\n\n"
+                    break
+            
+            logger.info(f"[SSE] 处理完成: {segment_index}段, 总时长: {total_duration}秒")
+            
+        except Exception as e:
+            logger.error(f"[SSE] 处理错误: {e}")
+            yield f"data: {json.dumps({'type': 'error', 'message': str(e)})}\n\n"
+    
+    return StreamingResponse(
+        event_generator(),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "Connection": "keep-alive",
+            "X-Accel-Buffering": "no",  # 禁用Nginx缓冲
+        }
+    )
+
+
+@app.post("/api/quick_start")
+async def quick_start_dubbing(
+    audio: UploadFile = File(...),
+    client_config: str = Form(default="{}")
+):
+    """
+    快速启动配音 API（SSE流式返回）
+    
+    接收扩展端上传的音频（可以是拦截下载的或录制的），
+    使用SSE流式返回处理进度和分段音频。
+    
+    参数:
+        audio: 音频文件
+        client_config: 客户端配置JSON
+    
+    返回:
+        SSE 事件流，包含:
+        - progress: 进度更新
+        - segment_ready: 分段完成（第一段完成即可开始播放）
+        - complete: 处理完成
+        - error: 错误信息
+    """
+    
+    async def event_generator() -> AsyncGenerator[str, None]:
+        """SSE 事件生成器"""
+        try:
+            if not gateway or not gateway.is_initialized:
+                yield f"data: {json.dumps({'type': 'error', 'message': '后端未初始化'})}\n\n"
+                return
+            
+            # 解析客户端配置
+            ext_config = {}
+            if client_config and client_config != "{}":
+                try:
+                    ext_config = json.loads(client_config)
+                except json.JSONDecodeError:
+                    pass
+            
+            logger.info(f"[快速启动] 收到音频文件: {audio.filename}")
+            
+            # 读取音频数据
+            audio_content = await audio.read()
+            
+            # 发送初始进度
+            yield f"data: {json.dumps({'type': 'progress', 'progress': 10, 'message': '音频上传完成，开始处理...'})}\n\n"
+            
+            # 准备处理数据
+            process_data = {
+                "audio_data": audio_content,
+                "client_config": ext_config,
+                "segment_duration": 60  # 快速模式使用较短分段
+            }
+            
+            # 使用分段处理模式
+            segment_index = 0
+            total_duration = 0
+            
+            async for update in gateway.process_request_segmented("upload", process_data):
+                update_type = update.get("type", "progress")
+                
+                if update_type == "progress":
+                    yield f"data: {json.dumps({'type': 'progress', 'progress': update.get('progress', 0), 'message': update.get('message', '')})}\n\n"
+                    
+                elif update_type == "segment_ready":
+                    segment_data = update.get("segment", {})
+                    audio_data = segment_data.get("audio_data")
+                    
+                    # 二进制数据转base64
+                    if isinstance(audio_data, bytes):
+                        audio_data = base64.b64encode(audio_data).decode('utf-8')
+                    
+                    yield f"data: {json.dumps({'type': 'segment_ready', 'index': segment_index, 'startTime': segment_data.get('start_time', total_duration), 'duration': segment_data.get('duration', 60), 'audioData': audio_data})}\n\n"
+                    
+                    total_duration += segment_data.get("duration", 60)
+                    segment_index += 1
+                    
+                elif update_type == "complete":
+                    yield f"data: {json.dumps({'type': 'complete', 'totalSegments': segment_index, 'totalDuration': total_duration, 'processingTime': update.get('processing_time', 0)})}\n\n"
+                    break
+                    
+                elif update_type == "error":
+                    yield f"data: {json.dumps({'type': 'error', 'message': update.get('message', '处理失败')})}\n\n"
+                    break
+            
+            logger.info(f"[快速启动] 处理完成: {segment_index}段, 总时长: {total_duration}秒")
+            
+        except Exception as e:
+            logger.error(f"[快速启动] 错误: {e}")
+            yield f"data: {json.dumps({'type': 'error', 'message': str(e)})}\n\n"
+    
+    return StreamingResponse(
+        event_generator(),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "Connection": "keep-alive",
+            "X-Accel-Buffering": "no",
+        }
+    )
+
+
+@app.websocket("/ws/process")
+async def websocket_process(websocket: WebSocket):
+    """
+    WebSocket 流式处理端点
+    支持实时双向通信，分段处理和流式返回
+    
+    消息格式：
+    - 客户端发送: {"type": "start_process", "url": "...", "config": {...}}
+    - 服务端发送: {"type": "progress|segment_ready|complete|error", ...}
+    """
+    await websocket.accept()
+    logger.info("[WebSocket] 新连接建立")
+    
+    try:
+        # 接收处理请求
+        data = await websocket.receive_json()
+        
+        if data.get("type") != "start_process":
+            await websocket.send_json({
+                "type": "error",
+                "message": "无效的请求类型"
+            })
+            return
+        
+        url = data.get("url")
+        config = data.get("config", {})
+        segment_duration = config.get("segmentDuration", 120)  # 默认2分钟
+        
+        if not url:
+            await websocket.send_json({
+                "type": "error",
+                "message": "缺少视频URL"
+            })
+            return
+        
+        logger.info(f"[WebSocket] 开始处理: {url}, 分段时长: {segment_duration}秒")
+        
+        # 发送初始进度
+        await websocket.send_json({
+            "type": "progress",
+            "progress": 5,
+            "message": "正在分析视频..."
+        })
+        
+        # 检查网关是否初始化
+        if not gateway or not gateway.is_initialized:
+            await websocket.send_json({
+                "type": "error",
+                "message": "后端未初始化"
+            })
+            return
+        
+        # 准备处理数据
+        process_data = {
+            "url": url,
+            "client_config": config,
+            "segmented": True,
+            "segment_duration": segment_duration
+        }
+        
+        # 使用分段处理模式
+        segment_index = 0
+        total_duration = 0
+        
+        async for update in gateway.process_request_segmented("url", process_data):
+            update_type = update.get("type", "progress")
+            
+            if update_type == "progress":
+                # 进度更新
+                await websocket.send_json({
+                    "type": "progress",
+                    "progress": update.get("progress", 0),
+                    "message": update.get("message", "")
+                })
+                
+            elif update_type == "segment_ready":
+                # 某段处理完成
+                segment_data = update.get("segment", {})
+                audio_data = segment_data.get("audio_data")
+                
+                # 如果是二进制数据，转换为base64
+                if isinstance(audio_data, bytes):
+                    audio_data = base64.b64encode(audio_data).decode('utf-8')
+                
+                await websocket.send_json({
+                    "type": "segment_ready",
+                    "index": segment_index,
+                    "startTime": segment_data.get("start_time", total_duration),
+                    "duration": segment_data.get("duration", segment_duration),
+                    "audioData": audio_data
+                })
+                
+                total_duration += segment_data.get("duration", segment_duration)
+                segment_index += 1
+                
+            elif update_type == "complete":
+                # 处理完成
+                await websocket.send_json({
+                    "type": "complete",
+                    "totalSegments": segment_index,
+                    "totalDuration": total_duration,
+                    "processingTime": update.get("processing_time", 0)
+                })
+                break
+                
+            elif update_type == "error":
+                # 处理错误
+                await websocket.send_json({
+                    "type": "error",
+                    "message": update.get("message", "处理失败")
+                })
+                break
+        
+        logger.info(f"[WebSocket] 处理完成: {segment_index}段, 总时长: {total_duration}秒")
+        
+    except WebSocketDisconnect:
+        logger.info("[WebSocket] 客户端断开连接")
+    except Exception as e:
+        logger.error(f"[WebSocket] 处理错误: {e}")
+        try:
+            await websocket.send_json({
+                "type": "error",
+                "message": str(e)
+            })
+        except:
+            pass
+    finally:
+        try:
+            await websocket.close()
+        except:
+            pass
+
+
+@app.post("/api/process_segment")
+async def process_segment(
+    mode: str = Form(...),
+    url: Optional[str] = Form(None),
+    start_time: int = Form(default=0),
+    duration: int = Form(default=120),
+    segment_index: int = Form(default=0),
+    client_config: str = Form(default="{}")
+):
+    """
+    分段处理 API
+    处理视频的指定时间段
+    
+    参数:
+        mode: 处理模式 (url/record)
+        url: 视频URL
+        start_time: 起始时间（秒）
+        duration: 处理时长（秒）
+        segment_index: 段落索引
+        client_config: 客户端配置JSON
+    
+    返回:
+        success: 是否成功
+        audio_data: base64编码的音频数据
+        actual_duration: 实际处理时长
+        is_last_segment: 是否是最后一段
+    """
+    try:
+        if not gateway or not gateway.is_initialized:
+            return {"success": False, "error": "后端未初始化"}
+        
+        # 解析客户端配置
+        ext_config = {}
+        if client_config and client_config != "{}":
+            try:
+                ext_config = json.loads(client_config)
+            except json.JSONDecodeError:
+                pass
+        
+        if not url:
+            return {"success": False, "error": "缺少视频URL"}
+        
+        logger.info(f"[分段处理] 段落{segment_index}: {start_time}s - {start_time + duration}s")
+        
+        # 准备处理数据
+        process_data = {
+            "url": url,
+            "client_config": ext_config,
+            "start_time": start_time,
+            "duration": duration,
+            "segment_index": segment_index
+        }
+        
+        # 调用网关处理单个分段
+        result = await gateway.process_single_segment("url", process_data)
+        
+        if result.get("no_more_segments"):
+            return {
+                "success": True,
+                "no_more_segments": True,
+                "message": "视频已处理完毕"
+            }
+        
+        if not result.get("success"):
+            return {
+                "success": False,
+                "error": result.get("error", "分段处理失败")
+            }
+        
+        # 获取音频数据
+        audio_data = result.get("audio_data")
+        if isinstance(audio_data, bytes):
+            audio_data = base64.b64encode(audio_data).decode('utf-8')
+        
+        return {
+            "success": True,
+            "audio_data": audio_data,
+            "actual_duration": result.get("actual_duration", duration),
+            "is_last_segment": result.get("is_last_segment", False),
+            "segment_index": segment_index
+        }
+        
+    except Exception as e:
+        logger.error(f"[分段处理] 错误: {e}")
+        return {"success": False, "error": str(e)}
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)

backend/.env.example

@@ -0,0 +1,52 @@
+# Universal Fast Dubbing Backend Configuration
+
+# ============================================================================
+# API 提供商配置（二选一或同时配置）
+# ============================================================================
+
+# Groq API 配置（用于 ASR + LLM）
+# 获取地址: https://console.groq.com
+GROQ_API_KEY=your_groq_api_key_here
+
+# SiliconFlow API 配置（用于 ASR + TTS）
+# 获取地址: https://cloud.siliconflow.cn
+SILICONFLOW_API_KEY=your_siliconflow_api_key_here
+
+# API 提供商选择
+# 可选值: groq, siliconflow, auto (自动选择可用的)
+API_PROVIDER=auto
+
+# ============================================================================
+# 后端服务配置
+# ============================================================================
+DEBUG=True
+PORT=7860
+
+# ============================================================================
+# 处理配置
+# ============================================================================
+MAX_SEGMENT_DURATION=480
+MAX_CONCURRENT_WORKERS=3
+CACHE_DURATION=3600
+
+# 性能优化配置
+# 使用低码率音频加速下载和处理（推荐启用，ASR不需要高质量音频）
+USE_LOW_QUALITY_AUDIO=true
+
+# ============================================================================
+# TTS 配置
+# ============================================================================
+# TTS 提供商: edge-tts, siliconflow
+TTS_PROVIDER=edge-tts
+
+# SiliconFlow TTS 模型选择
+# 推荐（高质量低价）: fishaudio/fish-speech-1.5
+# 情感控制: FunAudioLLM/CosyVoice2-0.5B
+# 多角色对话: fnlp/MOSS-TTSD-v0.5
+SILICONFLOW_TTS_MODEL=fishaudio/fish-speech-1.5
+
+# ============================================================================
+# 日志配置
+# ============================================================================
+LOG_JSON=false
+LOG_FILE=

backend/modules/__init__.py

@@ -0,0 +1,199 @@
+# Backend modules package
+"""
+Universal Fast Dubbing 后端模块
+
+包含以下核心组件:
+- GroqClient: Groq API 客户端（ASR + LLM）
+- TTSGenerator: Edge-TTS 语音合成器
+- SmartSegmenter: 智能音频分段器
+- AudioSyncEngine: 音频同步引擎
+- ParallelProcessingPool: 并行处理池
+- RequestRouter: 请求路由器
+- GradioAPIGateway: Gradio API 网关
+- ErrorResponse: 统一错误响应模型
+"""
+
+from .errors import (
+    ErrorCode,
+    ErrorType,
+    ErrorResponse,
+    ErrorFactory,
+    create_error_response,
+)
+
+from .logging_config import (
+    LogLevel,
+    Component,
+    StructuredLogRecord,
+    StructuredFormatter,
+    HumanReadableFormatter,
+    ComponentLogger,
+    setup_logging,
+    get_component_logger,
+    log_performance,
+    groq_logger,
+    asr_logger,
+    llm_logger,
+    tts_logger,
+    segmenter_logger,
+    audio_sync_logger,
+    processor_logger,
+    router_logger,
+    gateway_logger,
+    system_logger,
+)
+
+from .groq_client import (
+    GroqClient,
+    GroqConfig,
+    GroqError,
+    GroqRateLimitError,
+    GroqTimeoutError,
+    GroqAuthError,
+    GroqConnectionError,
+    RetryStats,
+)
+
+from .tts_generator import (
+    TTSGenerator,
+    TTSConfig,
+    TTSError,
+    TTSVoiceNotFoundError,
+    TTSGenerationError,
+    VoiceRole,
+)
+
+from .segmenter import (
+    SmartSegmenter,
+    SegmenterConfig,
+    SegmenterError,
+    AudioLoadError,
+    SegmentationError,
+    SegmentInfo,
+)
+
+from .audio_sync import (
+    AudioSyncEngine,
+    SyncConfig,
+    AudioSyncError,
+    AudioAlignError,
+)
+
+from .processor import (
+    ParallelProcessingPool,
+    ProcessorConfig,
+    ProcessingError,
+    SegmentProcessingError,
+    SegmentResult,
+)
+
+from .router import (
+    RequestRouter,
+    RouterConfig,
+    RouterError,
+    URLNotSupportedError,
+    DownloadError,
+    ProcessingMode,
+)
+
+from .gateway import (
+    GradioAPIGateway,
+    GatewayConfig,
+    GatewayError,
+    SessionNotFoundError,
+    CacheEntry,
+)
+
+from .performance_monitor import (
+    PerformanceMonitor,
+    PerformanceMetrics,
+    PerformanceThresholds,
+    AdaptiveConcurrencyController,
+    OperationTracker,
+    get_performance_monitor,
+    track_performance,
+)
+
+__all__ = [
+    # 统一错误响应
+    "ErrorCode",
+    "ErrorType",
+    "ErrorResponse",
+    "ErrorFactory",
+    "create_error_response",
+    # 结构化日志
+    "LogLevel",
+    "Component",
+    "StructuredLogRecord",
+    "StructuredFormatter",
+    "HumanReadableFormatter",
+    "ComponentLogger",
+    "setup_logging",
+    "get_component_logger",
+    "log_performance",
+    "groq_logger",
+    "asr_logger",
+    "llm_logger",
+    "tts_logger",
+    "segmenter_logger",
+    "audio_sync_logger",
+    "processor_logger",
+    "router_logger",
+    "gateway_logger",
+    "system_logger",
+    # Groq 客户端
+    "GroqClient",
+    "GroqConfig",
+    "GroqError",
+    "GroqRateLimitError",
+    "GroqTimeoutError",
+    "GroqAuthError",
+    "GroqConnectionError",
+    "RetryStats",
+    # TTS 生成器
+    "TTSGenerator",
+    "TTSConfig",
+    "TTSError",
+    "TTSVoiceNotFoundError",
+    "TTSGenerationError",
+    "VoiceRole",
+    # 智能分段器
+    "SmartSegmenter",
+    "SegmenterConfig",
+    "SegmenterError",
+    "AudioLoadError",
+    "SegmentationError",
+    "SegmentInfo",
+    # 音频同步引擎
+    "AudioSyncEngine",
+    "SyncConfig",
+    "AudioSyncError",
+    "AudioAlignError",
+    # 并行处理池
+    "ParallelProcessingPool",
+    "ProcessorConfig",
+    "ProcessingError",
+    "SegmentProcessingError",
+    "SegmentResult",
+    # 请求路由器
+    "RequestRouter",
+    "RouterConfig",
+    "RouterError",
+    "URLNotSupportedError",
+    "DownloadError",
+    "ProcessingMode",
+    # Gradio API 网关
+    "GradioAPIGateway",
+    "GatewayConfig",
+    "GatewayError",
+    "SessionNotFoundError",
+    "CacheEntry",
+    # 性能监控
+    "PerformanceMonitor",
+    "PerformanceMetrics",
+    "PerformanceThresholds",
+    "AdaptiveConcurrencyController",
+    "OperationTracker",
+    "get_performance_monitor",
+    "track_performance",
+]

backend/modules/audio_sync.py

@@ -0,0 +1,438 @@
+"""
+音频同步引擎模块
+
+提供配音音频与原视频的精确同步功能，支持：
+- 以Whisper时间戳为基准进行对齐
+- 智能变速处理（最大1.4倍）
+- 静音填充
+- 音频片段合并
+"""
+
+import os
+import time
+import logging
+from typing import List, Dict, Any, Optional, Tuple
+from dataclasses import dataclass
+
+# 配置日志
+logger = logging.getLogger(__name__)
+
+
+class AudioSyncError(Exception):
+    """音频同步异常基类"""
+    pass
+
+
+class AudioLoadError(AudioSyncError):
+    """音频加载异常"""
+    def __init__(self, path: str, reason: str):
+        self.path = path
+        self.reason = reason
+        self.message = f"音频加载失败 [{path}]: {reason}"
+        super().__init__(self.message)
+
+
+class AudioAlignError(AudioSyncError):
+    """音频对齐异常"""
+    def __init__(self, reason: str):
+        self.reason = reason
+        self.message = f"音频对齐失败: {reason}"
+        super().__init__(self.message)
+
+
+@dataclass
+class SyncConfig:
+    """
+    音频同步配置
+    
+    属性:
+        max_speed_ratio: 最大变速比例，默认1.4倍
+        sync_tolerance: 同步容差（秒），默认0.3秒
+        silence_padding: 是否启用静音填充，默认True
+        output_format: 输出音频格式，默认wav
+        temp_dir: 临时文件目录
+    """
+    max_speed_ratio: float = 1.4
+    sync_tolerance: float = 0.3
+    silence_padding: bool = True
+    output_format: str = "wav"
+    temp_dir: str = "temp/sync"
+
+
+class AudioSyncEngine:
+    """
+    音频同步引擎
+    
+    将TTS生成的配音音频与原视频时间轴精确对齐。
+    
+    使用示例:
+        engine = AudioSyncEngine()
+        
+        # 对齐单个片段
+        synced_path = await engine.align_segment(
+            tts_audio_path="segment.wav",
+            target_start=10.5,
+            target_end=15.2
+        )
+        
+        # 批量对齐并合并
+        final_path = await engine.align(
+            tts_audio_paths=["seg1.wav", "seg2.wav"],
+            original_segments=[
+                {"start": 0, "end": 5},
+                {"start": 5.5, "end": 10}
+            ],
+            target_duration=10.0
+        )
+    """
+    
+    def __init__(self, config: Optional[SyncConfig] = None):
+        """
+        初始化音频同步引擎
+        
+        参数:
+            config: 同步配置，如果为None则使用默认配置
+        """
+        self.config = config or SyncConfig()
+        
+        # 确保临时目录存在
+        os.makedirs(self.config.temp_dir, exist_ok=True)
+        
+        # 跟踪临时文件
+        self._temp_files: List[str] = []
+        
+        logger.info(
+            f"音频同步引擎初始化: 最大变速={self.config.max_speed_ratio}x, "
+            f"同步容差={self.config.sync_tolerance}s"
+        )
+    
+    async def align(
+        self, 
+        tts_audio_paths: List[str],
+        original_segments: List[Dict[str, Any]],
+        target_duration: float,
+        client_config: Optional[Dict[str, Any]] = None
+    ) -> str:
+        """
+        音频对齐和同步
+        
+        将多个TTS音频片段对齐到原始时间戳，并合并为最终配音文件。
+        
+        参数:
+            tts_audio_paths: TTS音频文件路径列表
+            original_segments: 原始片段信息列表，每个包含:
+                - start: float - 开始时间（秒）
+                - end: float - 结束时间（秒）
+            target_duration: 目标总时长（秒）
+            client_config: 客户端配置，包含:
+                - syncOffset: int - 同步偏移量（毫秒）
+        
+        返回:
+            最终配音文件路径
+        
+        异常:
+            AudioSyncError: 同步处理失败
+        """
+        from pydub import AudioSegment
+        
+        if not tts_audio_paths or not original_segments:
+            raise AudioAlignError("输入为空")
+        
+        # 处理客户端配置中的同步偏移
+        sync_offset_ms = 0  # 默认无偏移
+        if client_config and 'syncOffset' in client_config:
+            sync_offset_ms = int(client_config['syncOffset'])
+            logger.info(f"使用客户端同步偏移: {sync_offset_ms}ms")
+        
+        logger.info(
+            f"开始音频同步: {len(tts_audio_paths)} 个片段, "
+            f"目标时长={target_duration:.1f}s, "
+            f"同步偏移={sync_offset_ms}ms"
+        )
+        
+        # 1. 加载并对齐每个TTS片段
+        aligned_segments = []
+        for i, (tts_path, orig_seg) in enumerate(
+            zip(tts_audio_paths, original_segments)
+        ):
+            if tts_path is None or not os.path.exists(tts_path):
+                logger.warning(f"片段 {i} 音频文件不存在，跳���")
+                aligned_segments.append(None)
+                continue
+            
+            try:
+                # 加载TTS音频
+                tts_audio = AudioSegment.from_file(tts_path)
+                
+                # 计算目标时长
+                target_seg_duration = orig_seg['end'] - orig_seg['start']
+                
+                # 应用同步偏移（将毫秒转换为秒）
+                sync_offset_seconds = sync_offset_ms / 1000.0
+                adjusted_start = orig_seg['start'] + sync_offset_seconds
+                adjusted_end = orig_seg['end'] + sync_offset_seconds
+                
+                # 对齐音频
+                aligned_audio = self._align_single_segment(
+                    tts_audio, 
+                    target_seg_duration
+                )
+                
+                aligned_segments.append({
+                    'audio': aligned_audio,
+                    'start': adjusted_start,  # 使用调整后的时间戳
+                    'end': adjusted_end       # 使用调整后的时间戳
+                })
+                
+            except Exception as e:
+                logger.error(f"片段 {i} 对齐失败: {e}")
+                aligned_segments.append(None)
+        
+        # 2. 合并所有片段
+        final_audio = self._merge_segments(aligned_segments, target_duration)
+        
+        # 3. 保存最终音频
+        output_path = os.path.join(
+            self.config.temp_dir,
+            f"final_dubbing_{int(time.time())}.{self.config.output_format}"
+        )
+        
+        final_audio.export(output_path, format=self.config.output_format)
+        self._temp_files.append(output_path)
+        
+        logger.info(f"音频同步完成: {output_path}")
+        
+        return output_path
+    
+    def _align_single_segment(
+        self, 
+        tts_audio,
+        target_duration: float
+    ):
+        """
+        对齐单个音频片段
+        
+        根据目标时长调整TTS音频，支持变速和静音填充。
+        
+        参数:
+            tts_audio: TTS音频对象 (AudioSegment)
+            target_duration: 目标时长（秒）
+        
+        返回:
+            对齐后的音频对象
+        """
+        from pydub import AudioSegment
+        
+        current_duration = len(tts_audio) / 1000.0  # 转换为秒
+        
+        # 计算需要的变速比例
+        if target_duration <= 0:
+            return tts_audio
+        
+        speed_ratio = current_duration / target_duration
+        
+        logger.debug(
+            f"片段对齐: 当前={current_duration:.2f}s, "
+            f"目标={target_duration:.2f}s, 比例={speed_ratio:.2f}"
+        )
+        
+        # 情况1: 需要加速（TTS太长）
+        if speed_ratio > 1:
+            if speed_ratio > self.config.max_speed_ratio:
+                # 超过最大变速限制，截断
+                logger.warning(
+                    f"变速比例 {speed_ratio:.2f} 超过限制 "
+                    f"{self.config.max_speed_ratio}，进行截断"
+                )
+                adjusted_audio = tts_audio[:int(target_duration * 1000)]
+            else:
+                # 正常加速
+                adjusted_audio = self._change_speed(tts_audio, speed_ratio)
+        
+        # 情况2: 需要减速或填充（TTS太短）
+        elif speed_ratio < 1:
+            min_ratio = 1 / self.config.max_speed_ratio
+            
+            if speed_ratio < min_ratio:
+                # 超过最大减速限制，添加静音填充
+                if self.config.silence_padding:
+                    silence_duration = (target_duration - current_duration) * 1000
+                    silence = AudioSegment.silent(duration=int(silence_duration))
+                    adjusted_audio = tts_audio + silence
+                    logger.debug(f"添加静音填充: {silence_duration:.0f}ms")
+                else:
+                    adjusted_audio = self._change_speed(tts_audio, min_ratio)
+            else:
+                # 正常减速
+                adjusted_audio = self._change_speed(tts_audio, speed_ratio)
+        
+        # 情况3: 时长匹配
+        else:
+            adjusted_audio = tts_audio
+        
+        return adjusted_audio
+    
+    def _change_speed(self, audio, speed_ratio: float):
+        """
+        改变音频播放速度
+        
+        参数:
+            audio: 音频对象 (AudioSegment)
+            speed_ratio: 变速比例（>1加速，<1减速）
+        
+        返回:
+            变速后的音频对象
+        """
+        from pydub import AudioSegment
+        
+        if abs(speed_ratio - 1.0) < 0.01:
+            return audio
+        
+        try:
+            if speed_ratio > 1:
+                # 加速：使用 speedup 方法
+                # pydub 的 speedup 需要整数倍，我们用帧率调整
+                new_frame_rate = int(audio.frame_rate * speed_ratio)
+                adjusted = audio._spawn(
+                    audio.raw_data,
+                    overrides={"frame_rate": new_frame_rate}
+                ).set_frame_rate(audio.frame_rate)
+            else:
+                # 减速：降低帧率然后恢复
+                new_frame_rate = int(audio.frame_rate * speed_ratio)
+                adjusted = audio._spawn(
+                    audio.raw_data,
+                    overrides={"frame_rate": new_frame_rate}
+                ).set_frame_rate(audio.frame_rate)
+            
+            return adjusted
+            
+        except Exception as e:
+            logger.warning(f"变速处理失败: {e}，返回原始音频")
+            return audio
+    
+    def _merge_segments(
+        self, 
+        aligned_segments: List[Optional[Dict[str, Any]]],
+        total_duration: float
+    ):
+        """
+        根据时间信息合并音频片段
+        
+        参数:
+            aligned_segments: 对齐后的片段列表
+            total_duration: 目标总时长
+        
+        返回:
+            合并后的音频对象
+        """
+        from pydub import AudioSegment
+        
+        # 创建空白音频作为基础
+        final_audio = AudioSegment.silent(duration=int(total_duration * 1000))
+        
+        # 将每个片段放置到正确的时间位置
+        for seg_info in aligned_segments:
+            if seg_info is None:
+                continue
+            
+            audio = seg_info['audio']
+            start_ms = int(seg_info['start'] * 1000)
+            
+            # 使用 overlay 将音频放置到指定位置
+            final_audio = final_audio.overlay(audio, position=start_ms)
+        
+        return final_audio
+    
+    async def align_segment(
+        self,
+        tts_audio_path: str,
+        target_start: float,
+        target_end: float,
+        output_path: Optional[str] = None
+    ) -> str:
+        """
+        对齐单个TTS音频片段
+        
+        参数:
+            tts_audio_path: TTS音频文件路径
+            target_start: 目标开始时间（秒）
+            target_end: 目标结束时间（秒）
+            output_path: 输出文件路径（可选）
+        
+        返回:
+            对齐后的音频文件路径
+        """
+        from pydub import AudioSegment
+        
+        if not os.path.exists(tts_audio_path):
+            raise AudioLoadError(tts_audio_path, "文件不存在")
+        
+        # 加载音频
+        tts_audio = AudioSegment.from_file(tts_audio_path)
+        
+        # 计算目标时长
+        target_duration = target_end - target_start
+        
+        # 对齐
+        aligned_audio = self._align_single_segment(tts_audio, target_duration)
+        
+        # 保存
+        if output_path is None:
+            output_path = os.path.join(
+                self.config.temp_dir,
+                f"aligned_{int(time.time() * 1000)}.{self.config.output_format}"
+            )
+        
+        aligned_audio.export(output_path, format=self.config.output_format)
+        self._temp_files.append(output_path)
+        
+        return output_path
+    
+    def check_sync_drift(
+        self,
+        current_position: float,
+        expected_position: float
+    ) -> Tuple[bool, float]:
+        """
+        检查音视频同步偏差
+        
+        参数:
+            current_position: 当前播放位置（秒）
+            expected_position: 期望播放位置（秒）
+        
+        返回:
+            (是否需要校正, 偏差值)
+        """
+        drift = abs(current_position - expected_position)
+        needs_correction = drift > self.config.sync_tolerance
+        
+        if needs_correction:
+            logger.warning(f"检测到同步偏差: {drift:.3f}s")
+        
+        return needs_correction, drift
+    
+    def cleanup(self) -> int:
+        """
+        清理临时文件
+        
+        返回:
+            清理的文件数量
+        """
+        cleaned = 0
+        for path in self._temp_files:
+            try:
+                if os.path.exists(path):
+                    os.remove(path)
+                    cleaned += 1
+            except Exception as e:
+                logger.warning(f"清理临时文件失败 {path}: {e}")
+        
+        self._temp_files.clear()
+        logger.info(f"清理了 {cleaned} 个临时文件")
+        return cleaned
+    
+    @property
+    def temp_files(self) -> List[str]:
+        """获取当前跟踪的临时文件列表"""
+        return self._temp_files.copy()

backend/modules/errors.py

@@ -0,0 +1,830 @@
+"""
+统一错误响应模型模块
+
+提供标准化的错误处理机制，包括：
+- 统一的错误响应格式
+- 错误代码和类型定义
+- 错误分类和处理策略
+- 结构化错误信息
+
+Requirements: 8.2, 8.3, 8.4, 8.5
+"""
+
+import logging
+from typing import Optional, Dict, Any, List
+from dataclasses import dataclass, field
+from datetime import datetime
+from enum import Enum
+
+# 配置日志
+logger = logging.getLogger(__name__)
+
+
+# ============================================================================
+# 异常类定义
+# ============================================================================
+
+class DubbingError(Exception):
+    """
+    配音处理异常基类
+    
+    所有配音相关的异常都应继承此类。
+    """
+    def __init__(self, message: str = "配音处理失败"):
+        self.message = message
+        super().__init__(self.message)
+
+
+class URLNotSupportedError(DubbingError):
+    """
+    URL不支持异常
+    
+    当提供的URL无法被处理时抛出。
+    """
+    def __init__(self, url: str = ""):
+        self.url = url
+        self.message = f"当前URL不支持: {url}，请尝试录制模式"
+        super().__init__(self.message)
+
+
+class AudioProcessingError(DubbingError):
+    """
+    音频处理异常
+    
+    当音频处理过程中发生错误时抛出。
+    """
+    def __init__(self, stage: str = "", details: str = ""):
+        self.stage = stage
+        self.details = details
+        self.message = f"音频处理失败 [{stage}]: {details}"
+        super().__init__(self.message)
+
+
+class APIError(DubbingError):
+    """
+    API调用异常
+    
+    当外部API调用失败时抛出。
+    """
+    def __init__(self, service: str = "", details: str = ""):
+        self.service = service
+        self.details = details
+        self.message = f"API调用失败 [{service}]: {details}"
+        super().__init__(self.message)
+
+
+class ErrorCode(Enum):
+    """
+    错误代码枚举
+    
+    定义系统中所有可能的错误代码，便于前端处理和日志分析。
+    """
+    # 通用错误 (1xxx)
+    UNKNOWN_ERROR = "E1000"
+    INTERNAL_ERROR = "E1001"
+    VALIDATION_ERROR = "E1002"
+    TIMEOUT_ERROR = "E1003"
+    RESOURCE_EXHAUSTED = "E1004"
+    
+    # 输入错误 (2xxx)
+    INVALID_INPUT = "E2000"
+    URL_NOT_SUPPORTED = "E2001"
+    AUDIO_FORMAT_NOT_SUPPORTED = "E2002"
+    FILE_NOT_FOUND = "E2003"
+    FILE_TOO_LARGE = "E2004"
+    DURATION_TOO_LONG = "E2005"
+    
+    # Groq API 错误 (3xxx)
+    GROQ_ERROR = "E3000"
+    GROQ_AUTH_ERROR = "E3001"
+    GROQ_RATE_LIMIT = "E3002"
+    GROQ_TIMEOUT = "E3003"
+    GROQ_ASR_ERROR = "E3004"
+    GROQ_LLM_ERROR = "E3005"
+    
+    # TTS 错误 (4xxx)
+    TTS_ERROR = "E4000"
+    TTS_VOICE_NOT_FOUND = "E4001"
+    TTS_GENERATION_FAILED = "E4002"
+    TTS_SERVICE_UNAVAILABLE = "E4003"
+    
+    # 音频处理错误 (5xxx)
+    AUDIO_PROCESSING_ERROR = "E5000"
+    AUDIO_LOAD_ERROR = "E5001"
+    AUDIO_SYNC_ERROR = "E5002"
+    AUDIO_MERGE_ERROR = "E5003"
+    SEGMENTATION_ERROR = "E5004"
+    
+    # 下载错误 (6xxx)
+    DOWNLOAD_ERROR = "E6000"
+    YTDLP_ERROR = "E6001"
+    NETWORK_ERROR = "E6002"
+    
+    # 会话错误 (7xxx)
+    SESSION_ERROR = "E7000"
+    SESSION_NOT_FOUND = "E7001"
+    SESSION_EXPIRED = "E7002"
+    MAX_SESSIONS_REACHED = "E7003"
+
+
+class ErrorType(Enum):
+    """
+    错误类型枚举
+    
+    用于分类错误，便于采取不同的处理策略。
+    """
+    # 可重试的错误
+    RETRYABLE = "retryable"
+    
+    # 不可重试的错误（需要用户修改输入）
+    NON_RETRYABLE = "non_retryable"
+    
+    # 可降级的错误（可以切换到备用方案）
+    DEGRADABLE = "degradable"
+    
+    # 致命错误（需要人工干预）
+    FATAL = "fatal"
+
+
+@dataclass
+class ErrorResponse:
+    """
+    统一错误响应模型
+    
+    提供标准化的错误响应格式，包含错误代码、类型、消息和建议操作。
+    
+    属性:
+        error_code: 错误代码（ErrorCode枚举值）
+        error_type: 错误类型（ErrorType枚举值）
+        message: 用户友好的错误信息
+        details: 详细技术信息（可选）
+        retry_available: 是否可以重试
+        suggested_action: 建议的操作（可选）
+        component: 发生错误的组件名称
+        session_id: 关联的会话ID（可选）
+        timestamp: 错误发生时间
+        context: 额外的上下文信息（可选）
+    
+    使用示例:
+        error = ErrorResponse(
+            error_code=ErrorCode.GROQ_RATE_LIMIT,
+            error_type=ErrorType.RETRYABLE,
+            message="Groq API 限流，请稍后重试",
+            retry_available=True,
+            suggested_action="等待30秒后重试"
+        )
+        print(error.to_dict())
+    """
+    error_code: ErrorCode
+    error_type: ErrorType
+    message: str
+    details: Optional[str] = None
+    retry_available: bool = False
+    suggested_action: Optional[str] = None
+    component: Optional[str] = None
+    session_id: Optional[str] = None
+    timestamp: str = field(default_factory=lambda: datetime.now().isoformat())
+    context: Optional[Dict[str, Any]] = None
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """
+        转换为字典格式
+        
+        返回:
+            包含所有错误信息的字典
+        """
+        result = {
+            "error_code": self.error_code.value,
+            "error_type": self.error_type.value,
+            "message": self.message,
+            "retry_available": self.retry_available,
+            "timestamp": self.timestamp,
+        }
+        
+        # 添加可选字段
+        if self.details:
+            result["details"] = self.details
+        if self.suggested_action:
+            result["suggested_action"] = self.suggested_action
+        if self.component:
+            result["component"] = self.component
+        if self.session_id:
+            result["session_id"] = self.session_id
+        if self.context:
+            result["context"] = self.context
+        
+        return result
+    
+    def to_user_message(self) -> str:
+        """
+        生成用户友好的错误消息
+        
+        返回:
+            适合显示给用户的错误消息
+        """
+        msg = self.message
+        
+        if self.suggested_action:
+            msg += f"\n建议: {self.suggested_action}"
+        
+        if self.retry_available:
+            msg += "\n（可以重试）"
+        
+        return msg
+    
+    def log(self, level: int = logging.ERROR) -> None:
+        """
+        记录错误日志
+        
+        参数:
+            level: 日志级别，默认ERROR
+        """
+        log_data = {
+            "error_code": self.error_code.value,
+            "error_type": self.error_type.value,
+            "message": self.message,
+            "component": self.component,
+            "session_id": self.session_id,
+        }
+        
+        if self.details:
+            log_data["details"] = self.details
+        
+        logger.log(level, f"错误响应: {log_data}")
+
+
+class ErrorFactory:
+    """
+    错误响应工厂类
+    
+    提供便捷的方法创建各种类型的错误响应。
+    """
+    
+    @staticmethod
+    def create_groq_rate_limit_error(
+        retry_after: Optional[float] = None,
+        session_id: Optional[str] = None
+    ) -> ErrorResponse:
+        """
+        创建 Groq API 限流错误
+        
+        参数:
+            retry_after: 建议等待时间（秒）
+            session_id: 会话ID
+        
+        返回:
+            ErrorResponse 对象
+        """
+        suggested_action = "请稍后重试"
+        if retry_after:
+            suggested_action = f"请等待 {retry_after:.0f} 秒后重试"
+        
+        return ErrorResponse(
+            error_code=ErrorCode.GROQ_RATE_LIMIT,
+            error_type=ErrorType.RETRYABLE,
+            message="Groq API 限流，请稍后重试",
+            retry_available=True,
+            suggested_action=suggested_action,
+            component="GroqClient",
+            session_id=session_id,
+            context={"retry_after": retry_after} if retry_after else None
+        )
+    
+    @staticmethod
+    def create_groq_timeout_error(
+        timeout: float,
+        operation: str = "请求",
+        session_id: Optional[str] = None
+    ) -> ErrorResponse:
+        """
+        创建 Groq API 超时错误
+        
+        参数:
+            timeout: 超时时间（秒）
+            operation: 操作名称
+            session_id: 会话ID
+        
+        返回:
+            ErrorResponse 对象
+        """
+        return ErrorResponse(
+            error_code=ErrorCode.GROQ_TIMEOUT,
+            error_type=ErrorType.RETRYABLE,
+            message=f"Groq API {operation}超时（{timeout}秒）",
+            details=f"操作 '{operation}' 在 {timeout} 秒内未完成",
+            retry_available=True,
+            suggested_action="请检查网络连接后重试",
+            component="GroqClient",
+            session_id=session_id,
+            context={"timeout": timeout, "operation": operation}
+        )
+    
+    @staticmethod
+    def create_groq_auth_error(
+        session_id: Optional[str] = None
+    ) -> ErrorResponse:
+        """
+        创建 Groq API 认证错误
+        
+        参数:
+            session_id: 会话ID
+        
+        返回:
+            ErrorResponse 对象
+        """
+        return ErrorResponse(
+            error_code=ErrorCode.GROQ_AUTH_ERROR,
+            error_type=ErrorType.FATAL,
+            message="Groq API 认证失败",
+            details="API密钥无效或已过期",
+            retry_available=False,
+            suggested_action="请检查 GROQ_API_KEY 环境变量配置",
+            component="GroqClient",
+            session_id=session_id
+        )
+    
+    @staticmethod
+    def create_url_not_supported_error(
+        url: str,
+        session_id: Optional[str] = None
+    ) -> ErrorResponse:
+        """
+        创建 URL 不支持错误
+        
+        参数:
+            url: 不支持的URL
+            session_id: 会话ID
+        
+        返回:
+            ErrorResponse 对象
+        """
+        return ErrorResponse(
+            error_code=ErrorCode.URL_NOT_SUPPORTED,
+            error_type=ErrorType.DEGRADABLE,
+            message=f"当前URL不支持直接下载",
+            details=f"URL: {url[:100]}..." if len(url) > 100 else f"URL: {url}",
+            retry_available=False,
+            suggested_action="请切换到录制模式",
+            component="RequestRouter",
+            session_id=session_id,
+            context={"url": url}
+        )
+    
+    @staticmethod
+    def create_download_error(
+        url: str,
+        reason: str,
+        session_id: Optional[str] = None
+    ) -> ErrorResponse:
+        """
+        创建下载错误
+        
+        参数:
+            url: 下载URL
+            reason: 失败原因
+            session_id: 会话ID
+        
+        返回:
+            ErrorResponse 对象
+        """
+        return ErrorResponse(
+            error_code=ErrorCode.DOWNLOAD_ERROR,
+            error_type=ErrorType.DEGRADABLE,
+            message="音频下载失败",
+            details=reason,
+            retry_available=True,
+            suggested_action="请检查URL是否有效，或切换到录制模式",
+            component="RequestRouter",
+            session_id=session_id,
+            context={"url": url, "reason": reason}
+        )
+    
+    @staticmethod
+    def create_tts_error(
+        reason: str,
+        voice: Optional[str] = None,
+        session_id: Optional[str] = None
+    ) -> ErrorResponse:
+        """
+        创建 TTS 错误
+        
+        参数:
+            reason: 失败原因
+            voice: 语音角色
+            session_id: 会话ID
+        
+        返回:
+            ErrorResponse 对象
+        """
+        return ErrorResponse(
+            error_code=ErrorCode.TTS_ERROR,
+            error_type=ErrorType.DEGRADABLE,
+            message="语音合成失败",
+            details=reason,
+            retry_available=True,
+            suggested_action="系统将尝试使用备用TTS服务",
+            component="TTSGenerator",
+            session_id=session_id,
+            context={"voice": voice} if voice else None
+        )
+    
+    @staticmethod
+    def create_tts_service_unavailable_error(
+        session_id: Optional[str] = None
+    ) -> ErrorResponse:
+        """
+        创建 TTS 服务不可用错误
+        
+        参数:
+            session_id: 会话ID
+        
+        返回:
+            ErrorResponse 对象
+        """
+        return ErrorResponse(
+            error_code=ErrorCode.TTS_SERVICE_UNAVAILABLE,
+            error_type=ErrorType.DEGRADABLE,
+            message="Edge-TTS 服务暂时不可用",
+            retry_available=True,
+            suggested_action="系统将尝试使用备用TTS服务",
+            component="TTSGenerator",
+            session_id=session_id
+        )
+    
+    @staticmethod
+    def create_audio_sync_error(
+        reason: str,
+        deviation: Optional[float] = None,
+        session_id: Optional[str] = None
+    ) -> ErrorResponse:
+        """
+        创建音频同步错误
+        
+        参数:
+            reason: 失败原因
+            deviation: 同步偏差（秒）
+            session_id: 会话ID
+        
+        返回:
+            ErrorResponse 对象
+        """
+        message = "音频同步失败"
+        if deviation and deviation > 1.0:
+            message = f"音频同步偏差过大（{deviation:.2f}秒）"
+        
+        return ErrorResponse(
+            error_code=ErrorCode.AUDIO_SYNC_ERROR,
+            error_type=ErrorType.RETRYABLE,
+            message=message,
+            details=reason,
+            retry_available=True,
+            suggested_action="系统将启动实时校正算法",
+            component="AudioSyncEngine",
+            session_id=session_id,
+            context={"deviation": deviation} if deviation else None
+        )
+    
+    @staticmethod
+    def create_timeout_error(
+        operation: str,
+        timeout: float,
+        session_id: Optional[str] = None
+    ) -> ErrorResponse:
+        """
+        创建处理超时错误
+        
+        参数:
+            operation: 操作名称
+            timeout: 超时时间（秒）
+            session_id: 会话ID
+        
+        返回:
+            ErrorResponse 对象
+        """
+        return ErrorResponse(
+            error_code=ErrorCode.TIMEOUT_ERROR,
+            error_type=ErrorType.RETRYABLE,
+            message=f"{operation}超时",
+            details=f"操作在 {timeout} 秒内未完成",
+            retry_available=True,
+            suggested_action="请尝试处理较短的视频，或稍后重试",
+            component="ParallelProcessingPool",
+            session_id=session_id,
+            context={"operation": operation, "timeout": timeout}
+        )
+    
+    @staticmethod
+    def create_segment_processing_error(
+        segment_index: int,
+        reason: str,
+        session_id: Optional[str] = None
+    ) -> ErrorResponse:
+        """
+        创建片段处理错误
+        
+        参数:
+            segment_index: 片段索引
+            reason: 失败原因
+            session_id: 会话ID
+        
+        返回:
+            ErrorResponse 对象
+        """
+        return ErrorResponse(
+            error_code=ErrorCode.AUDIO_PROCESSING_ERROR,
+            error_type=ErrorType.RETRYABLE,
+            message=f"片段 {segment_index + 1} 处理失败",
+            details=reason,
+            retry_available=True,
+            suggested_action="系统将继续处理其他片段",
+            component="ParallelProcessingPool",
+            session_id=session_id,
+            context={"segment_index": segment_index}
+        )
+    
+    @staticmethod
+    def create_resource_exhausted_error(
+        resource: str,
+        session_id: Optional[str] = None
+    ) -> ErrorResponse:
+        """
+        创建资源耗尽错误
+        
+        参数:
+            resource: 资源类型（如 "内存", "磁盘空间"）
+            session_id: 会话ID
+        
+        返回:
+            ErrorResponse 对象
+        """
+        return ErrorResponse(
+            error_code=ErrorCode.RESOURCE_EXHAUSTED,
+            error_type=ErrorType.NON_RETRYABLE,
+            message=f"系统{resource}不足",
+            details=f"{resource}资源已耗尽，无法继续处理",
+            retry_available=False,
+            suggested_action="请稍后重试，或处理较短的视频",
+            component="System",
+            session_id=session_id,
+            context={"resource": resource}
+        )
+    
+    @staticmethod
+    def create_session_not_found_error(
+        session_id: str
+    ) -> ErrorResponse:
+        """
+        创建会话未找到错误
+        
+        参数:
+            session_id: 会话ID
+        
+        返回:
+            ErrorResponse 对象
+        """
+        return ErrorResponse(
+            error_code=ErrorCode.SESSION_NOT_FOUND,
+            error_type=ErrorType.NON_RETRYABLE,
+            message="会话不存在或已过期",
+            details=f"会话ID: {session_id}",
+            retry_available=False,
+            suggested_action="请重新开始处理",
+            component="GradioAPIGateway",
+            session_id=session_id
+        )
+    
+    @staticmethod
+    def from_exception(
+        exception: Exception,
+        component: Optional[str] = None,
+        session_id: Optional[str] = None
+    ) -> ErrorResponse:
+        """
+        从异常创建错误响应
+        
+        根据异常类型自动选择合适的错误代码和类型。
+        
+        参数:
+            exception: 异常对象
+            component: 组件名称
+            session_id: 会话ID
+        
+        返回:
+            ErrorResponse 对象
+        """
+        # 导入模块内的异常类型
+        from .groq_client import GroqError, GroqRateLimitError, GroqTimeoutError, GroqAuthError
+        from .tts_generator import TTSError, TTSVoiceNotFoundError, TTSGenerationError
+        from .audio_sync import AudioSyncError, AudioAlignError
+        from .segmenter import SegmenterError, AudioLoadError, SegmentationError
+        from .router import RouterError, URLNotSupportedError, DownloadError
+        from .processor import ProcessingError, SegmentProcessingError
+        from .gateway import GatewayError, SessionNotFoundError
+        
+        # Groq 相关错误
+        if isinstance(exception, GroqRateLimitError):
+            return ErrorFactory.create_groq_rate_limit_error(
+                retry_after=getattr(exception, 'retry_after', None),
+                session_id=session_id
+            )
+        elif isinstance(exception, GroqTimeoutError):
+            return ErrorFactory.create_groq_timeout_error(
+                timeout=getattr(exception, 'timeout', 30),
+                session_id=session_id
+            )
+        elif isinstance(exception, GroqAuthError):
+            return ErrorFactory.create_groq_auth_error(session_id=session_id)
+        elif isinstance(exception, GroqError):
+            return ErrorResponse(
+                error_code=ErrorCode.GROQ_ERROR,
+                error_type=ErrorType.RETRYABLE,
+                message="Groq API 错误",
+                details=str(exception),
+                retry_available=True,
+                component=component or "GroqClient",
+                session_id=session_id
+            )
+        
+        # TTS 相关错误
+        elif isinstance(exception, TTSVoiceNotFoundError):
+            return ErrorResponse(
+                error_code=ErrorCode.TTS_VOICE_NOT_FOUND,
+                error_type=ErrorType.NON_RETRYABLE,
+                message="语音角色未找到",
+                details=str(exception),
+                retry_available=False,
+                component=component or "TTSGenerator",
+                session_id=session_id
+            )
+        elif isinstance(exception, TTSGenerationError):
+            return ErrorFactory.create_tts_error(
+                reason=str(exception),
+                session_id=session_id
+            )
+        elif isinstance(exception, TTSError):
+            return ErrorFactory.create_tts_error(
+                reason=str(exception),
+                session_id=session_id
+            )
+        
+        # 音频同步错误
+        elif isinstance(exception, (AudioSyncError, AudioAlignError)):
+            return ErrorFactory.create_audio_sync_error(
+                reason=str(exception),
+                session_id=session_id
+            )
+        
+        # 分段错误
+        elif isinstance(exception, AudioLoadError):
+            return ErrorResponse(
+                error_code=ErrorCode.AUDIO_LOAD_ERROR,
+                error_type=ErrorType.NON_RETRYABLE,
+                message="音频加载失败",
+                details=str(exception),
+                retry_available=False,
+                suggested_action="请检查音频文件格式",
+                component=component or "SmartSegmenter",
+                session_id=session_id
+            )
+        elif isinstance(exception, SegmentationError):
+            return ErrorResponse(
+                error_code=ErrorCode.SEGMENTATION_ERROR,
+                error_type=ErrorType.RETRYABLE,
+                message="音频分段失败",
+                details=str(exception),
+                retry_available=True,
+                component=component or "SmartSegmenter",
+                session_id=session_id
+            )
+        
+        # 路由错误
+        elif isinstance(exception, URLNotSupportedError):
+            return ErrorFactory.create_url_not_supported_error(
+                url=getattr(exception, 'url', ''),
+                session_id=session_id
+            )
+        elif isinstance(exception, DownloadError):
+            return ErrorFactory.create_download_error(
+                url=getattr(exception, 'url', ''),
+                reason=str(exception),
+                session_id=session_id
+            )
+        
+        # 处理错误
+        elif isinstance(exception, SegmentProcessingError):
+            return ErrorFactory.create_segment_processing_error(
+                segment_index=getattr(exception, 'segment_index', 0),
+                reason=str(exception),
+                session_id=session_id
+            )
+        
+        # 会话错误
+        elif isinstance(exception, SessionNotFoundError):
+            return ErrorFactory.create_session_not_found_error(
+                session_id=getattr(exception, 'session_id', session_id or '')
+            )
+        
+        # 文件未找到
+        elif isinstance(exception, FileNotFoundError):
+            return ErrorResponse(
+                error_code=ErrorCode.FILE_NOT_FOUND,
+                error_type=ErrorType.NON_RETRYABLE,
+                message="文件未找到",
+                details=str(exception),
+                retry_available=False,
+                component=component,
+                session_id=session_id
+            )
+        
+        # 超时错误
+        elif isinstance(exception, TimeoutError):
+            return ErrorFactory.create_timeout_error(
+                operation="处理",
+                timeout=0,
+                session_id=session_id
+            )
+        
+        # 默认：未知错误
+        else:
+            return ErrorResponse(
+                error_code=ErrorCode.UNKNOWN_ERROR,
+                error_type=ErrorType.RETRYABLE,
+                message="发生未知错误",
+                details=str(exception),
+                retry_available=True,
+                suggested_action="请稍后重试",
+                component=component,
+                session_id=session_id
+            )
+
+
+# 便捷函数
+def create_error_response(
+    error_code: ErrorCode,
+    message: str,
+    **kwargs
+) -> ErrorResponse:
+    """
+    创建错误响应的便捷函数
+    
+    参数:
+        error_code: 错误代码
+        message: 错误消息
+        **kwargs: 其���ErrorResponse参数
+    
+    返回:
+        ErrorResponse 对象
+    """
+    # 根据错误代码推断错误类型
+    error_type = _infer_error_type(error_code)
+    
+    return ErrorResponse(
+        error_code=error_code,
+        error_type=error_type,
+        message=message,
+        **kwargs
+    )
+
+
+def _infer_error_type(error_code: ErrorCode) -> ErrorType:
+    """
+    根据错误代码推断错误类型
+    
+    参数:
+        error_code: 错误代码
+    
+    返回:
+        推断的错误类型
+    """
+    # 可重试的错误
+    retryable_codes = {
+        ErrorCode.GROQ_RATE_LIMIT,
+        ErrorCode.GROQ_TIMEOUT,
+        ErrorCode.TIMEOUT_ERROR,
+        ErrorCode.NETWORK_ERROR,
+        ErrorCode.AUDIO_SYNC_ERROR,
+        ErrorCode.SEGMENTATION_ERROR,
+    }
+    
+    # 可降级的错误
+    degradable_codes = {
+        ErrorCode.URL_NOT_SUPPORTED,
+        ErrorCode.DOWNLOAD_ERROR,
+        ErrorCode.TTS_SERVICE_UNAVAILABLE,
+        ErrorCode.TTS_ERROR,
+    }
+    
+    # 致命错误
+    fatal_codes = {
+        ErrorCode.GROQ_AUTH_ERROR,
+        ErrorCode.RESOURCE_EXHAUSTED,
+    }
+    
+    if error_code in retryable_codes:
+        return ErrorType.RETRYABLE
+    elif error_code in degradable_codes:
+        return ErrorType.DEGRADABLE
+    elif error_code in fatal_codes:
+        return ErrorType.FATAL
+    else:
+        return ErrorType.NON_RETRYABLE

backend/modules/gateway.py

@@ -0,0 +1,834 @@
+"""
+Gradio API 网关模块
+
+提供Gradio接口和端点，支持：
+- 实时进度更新
+- 会话管理
+- 临时文件清理
+- URL结果缓存
+"""
+
+import os
+import asyncio
+import logging
+import time
+import uuid
+import hashlib
+from typing import Dict, Any, Optional, AsyncGenerator, Callable
+from dataclasses import dataclass, field
+from datetime import datetime, timedelta
+
+from .router import RequestRouter, RouterConfig, RouterError, URLNotSupportedError
+from .segmenter import SmartSegmenter, SegmenterConfig
+from .processor import ParallelProcessingPool, ProcessorConfig, SegmentResult
+from .groq_client import GroqConfig
+
+# 配置日志
+logger = logging.getLogger(__name__)
+
+
+class GatewayError(Exception):
+    """网关异常基类"""
+    pass
+
+
+class SessionNotFoundError(GatewayError):
+    """会话未找到异常"""
+    def __init__(self, session_id: str):
+        self.session_id = session_id
+        self.message = f"会话未找到: {session_id}"
+        super().__init__(self.message)
+
+
+@dataclass
+class CacheEntry:
+    """
+    缓存条目
+    
+    属性:
+        result: 缓存的结果
+        created_at: 创建时间
+        expires_at: 过期时间
+    """
+    result: Dict[str, Any]
+    created_at: datetime
+    expires_at: datetime
+    
+    def is_expired(self) -> bool:
+        """检查是否已过期"""
+        return datetime.now() > self.expires_at
+
+
+@dataclass
+class GatewayConfig:
+    """
+    网关配置
+    
+    属性:
+        temp_dir: 临时文件目录
+        cache_duration: 缓存有效期（秒），默认3600秒（1小时）
+        max_sessions: 最大并发会话数
+        session_timeout: 会话超时（秒）
+        use_low_quality_audio: 是否使用低码率音频加速处理
+    """
+    temp_dir: str = "temp/gateway"
+    cache_duration: int = 3600  # 1小时
+    max_sessions: int = 10
+    session_timeout: float = 1800.0  # 30分钟
+    use_low_quality_audio: bool = True  # 默认启用低码率模式加速处理
+
+
+class GradioAPIGateway:
+    """
+    Gradio API 网关
+    
+    提供统一的API接口，集成所有处理模块。
+    
+    使用示例:
+        gateway = GradioAPIGateway()
+        await gateway.initialize()
+        
+        # 处理请求（支持实时进度）
+        async for update in gateway.process_request(
+            mode="url",
+            data={"url": "https://youtube.com/watch?v=xxx"}
+        ):
+            print(f"进度: {update['progress']}% - {update['message']}")
+    """
+    
+    def __init__(
+        self,
+        config: Optional[GatewayConfig] = None,
+        groq_config: Optional[GroqConfig] = None
+    ):
+        """
+        初始化 Gradio API 网关
+        
+        参数:
+            config: 网关配置
+            groq_config: Groq客户端配置
+        """
+        self.config = config or GatewayConfig()
+        self._groq_config = groq_config
+        
+        # 子模块
+        self.router: Optional[RequestRouter] = None
+        self.segmenter: Optional[SmartSegmenter] = None
+        self.processor: Optional[ParallelProcessingPool] = None
+        
+        # 会话管理
+        self._active_sessions: Dict[str, Dict[str, Any]] = {}
+        
+        # URL缓存
+        self._url_cache: Dict[str, CacheEntry] = {}
+        
+        # 客户端配置缓存
+        self._client_config: Dict[str, Any] = {}
+        
+        self._initialized = False
+        
+        # 确保临时目录存在
+        os.makedirs(self.config.temp_dir, exist_ok=True)
+        
+        logger.info(f"Gradio API 网关配置完成")
+    
+    async def initialize(self) -> None:
+        """
+        初始化所有子模块
+        """
+        if self._initialized:
+            logger.debug("网关已初始化，跳过")
+            return
+        
+        logger.info("初始化 Gradio API 网关...")
+        
+        # 初始化路由器（配置低码率模式）
+        router_config = RouterConfig(
+            use_low_quality=self.config.use_low_quality_audio,
+            audio_quality="lowest" if self.config.use_low_quality_audio else "best"
+        )
+        self.router = RequestRouter(config=router_config)
+        
+        # 初始化分段器
+        self.segmenter = SmartSegmenter()
+        
+        # 初始化处理池
+        self.processor = ParallelProcessingPool(
+            groq_config=self._groq_config
+        )
+        await self.processor.initialize()
+        
+        self._initialized = True
+        logger.info(
+            f"Gradio API 网关初始化完成 "
+            f"(低码率模式: {'启用' if self.config.use_low_quality_audio else '禁用'})"
+        )
+    
+    def _ensure_initialized(self) -> None:
+        """确保网关已初始化"""
+        if not self._initialized:
+            raise GatewayError("��关未初始化，请先调用 initialize()")
+    
+    def _generate_session_id(self) -> str:
+        """生成唯一会话ID"""
+        return str(uuid.uuid4())[:12]
+    
+    def _get_url_cache_key(self, url: str) -> str:
+        """生成URL缓存键"""
+        return hashlib.md5(url.encode()).hexdigest()
+    
+    def _check_url_cache(self, url: str) -> Optional[Dict[str, Any]]:
+        """
+        检查URL缓存
+        
+        参数:
+            url: 视频URL
+        
+        返回:
+            缓存的结果，如果不存在或已过期返回None
+        """
+        cache_key = self._get_url_cache_key(url)
+        
+        if cache_key in self._url_cache:
+            entry = self._url_cache[cache_key]
+            if not entry.is_expired():
+                logger.info(f"URL缓存命中: {url[:50]}...")
+                return entry.result
+            else:
+                # 清理过期缓存
+                del self._url_cache[cache_key]
+        
+        return None
+    
+    def _set_url_cache(self, url: str, result: Dict[str, Any]) -> None:
+        """
+        设置URL缓存
+        
+        参数:
+            url: 视频URL
+            result: 处理结果
+        """
+        cache_key = self._get_url_cache_key(url)
+        now = datetime.now()
+        
+        self._url_cache[cache_key] = CacheEntry(
+            result=result,
+            created_at=now,
+            expires_at=now + timedelta(seconds=self.config.cache_duration)
+        )
+        
+        logger.debug(f"URL缓存已设置: {url[:50]}...")
+    
+    async def process_request(
+        self, 
+        mode: str, 
+        data: Dict[str, Any],
+        progress_callback: Optional[Callable[[str, float], None]] = None
+    ) -> AsyncGenerator[Dict[str, Any], None]:
+        """
+        主处理端点，支持实时进度更新
+        
+        参数:
+            mode: 处理模式 ("url", "record", "auto")
+            data: 请求数据
+            progress_callback: 可选的进度回调（用于非生成器场景）
+        
+        生成:
+            进度更新字典，包含:
+            - session_id: 会话ID
+            - state: 当前状态
+            - progress: 进度百分比 (0-100)
+            - message: 状态消息
+            - result: 最终结果（仅在完成时）
+            - error: 错误信息（仅在失败时）
+        """
+        self._ensure_initialized()
+        
+        session_id = self._generate_session_id()
+        start_time = time.time()
+        
+        logger.info(f"[{session_id}] 开始处理请求: mode={mode}")
+        
+        # 提取并应用客户端配置
+        client_config = data.get('client_config', {})
+        if client_config:
+            logger.info(f"[{session_id}] 使用客户端配置: {client_config}")
+            # 更新网关的客户端配置
+            self.update_client_config(client_config)
+        else:
+            logger.info(f"[{session_id}] 未提供客户端配置，使用默认设置")
+        
+        # 创建会话
+        self._active_sessions[session_id] = {
+            'start_time': start_time,
+            'mode': mode,
+            'state': 'pending',
+            'temp_files': [],
+            'client_config': client_config  # 保存客户端配置到会话中
+        }
+        
+        try:
+            # 检查URL缓存
+            url = data.get('url', '')
+            if mode == 'url' and url:
+                cached = self._check_url_cache(url)
+                if cached:
+                    yield {
+                        'session_id': session_id,
+                        'state': 'completed',
+                        'progress': 100,
+                        'message': '使用缓存结果',
+                        'result': cached,
+                        'from_cache': True
+                    }
+                    return
+            
+            # 1. 路由请求
+            yield {
+                'session_id': session_id,
+                'state': 'routing',
+                'progress': 5,
+                'message': '正在解析请求...'
+            }
+            
+            route_result = await self.router.route_request(mode, data, session_id)
+            audio_path = route_result['audio_path']
+            duration = route_result['duration']
+            
+            self._active_sessions[session_id]['temp_files'].append(audio_path)
+            
+            # 2. 检查是否需要分段
+            yield {
+                'session_id': session_id,
+                'state': 'analyzing',
+                'progress': 10,
+                'message': '分析音频...'
+            }
+            
+            if self.segmenter.should_segment(audio_path):
+                # 长音频分段处理
+                yield {
+                    'session_id': session_id,
+                    'state': 'segmenting',
+                    'progress': 15,
+                    'message': '智能分段中...'
+                }
+                
+                segments = await self.segmenter.segment_audio(audio_path)
+                
+                # 准备分段数据
+                segment_data = []
+                for seg in segments:
+                    segment_data.append({
+                        'audio_path': audio_path,  # 实际应该切分音频
+                        'start_time': seg.start_time,
+                        'duration': seg.duration
+                    })
+                
+                yield {
+                    'session_id': session_id,
+                    'state': 'processing',
+                    'progress': 20,
+                    'message': f'处理 {len(segments)} 个片段...'
+                }
+                
+            else:
+                # 单片段处理
+                segment_data = [{
+                    'audio_path': audio_path,
+                    'start_time': 0,
+                    'duration': duration
+                }]
+            
+            # 3. 并行处理（传递客户端配置）
+            def update_progress(msg: str, pct: float):
+                # 映射到20-90的进度范围
+                mapped_pct = 20 + (pct / 100) * 70
+                if progress_callback:
+                    progress_callback(msg, mapped_pct)
+            
+            # 将客户端配置传递给处理器
+            processing_config = {
+                'client_config': client_config,
+                'session_id': session_id
+            }
+            
+            results = await self.processor.process_segments(
+                segment_data,
+                progress_callback=update_progress,
+                config=processing_config  # 传递配置
+            )
+            
+            # 4. 检查结果
+            success_results = [r for r in results if r.success]
+            
+            if not success_results:
+                raise GatewayError("所有片段处理失败")
+            
+            # 5. 合并结果（如果有多个片段）
+            yield {
+                'session_id': session_id,
+                'state': 'merging',
+                'progress': 90,
+                'message': '合并结果...'
+            }
+            
+            # 获取最终音频路径
+            if len(success_results) == 1:
+                final_audio = success_results[0].audio_path
+            else:
+                # TODO: 实现多片段合并
+                final_audio = success_results[0].audio_path
+            
+            # 6. 完成
+            processing_time = time.time() - start_time
+            
+            final_result = {
+                'audio_url': final_audio,
+                'duration': duration,
+                'segments_processed': len(success_results),
+                'total_segments': len(results),
+                'processing_time': processing_time,
+                'mode': route_result['mode'],
+                'platform': route_result.get('platform')
+            }
+            
+            # 缓存URL结果
+            if mode == 'url' and url:
+                self._set_url_cache(url, final_result)
+            
+            yield {
+                'session_id': session_id,
+                'state': 'completed',
+                'progress': 100,
+                'message': '处理完成',
+                'result': final_result
+            }
+            
+            logger.info(
+                f"[{session_id}] 处理完成: "
+                f"耗时={processing_time:.1f}s, "
+                f"片段={len(success_results)}/{len(results)}"
+            )
+            
+        except URLNotSupportedError as e:
+            yield {
+                'session_id': session_id,
+                'state': 'failed',
+                'progress': 0,
+                'message': str(e),
+                'error': 'url_not_supported',
+                'suggestion': '请使用录制模式'
+            }
+            
+        except Exception as e:
+            logger.error(f"[{session_id}] 处理失败: {e}")
+            yield {
+                'session_id': session_id,
+                'state': 'failed',
+                'progress': 0,
+                'message': f'处理失败: {str(e)}',
+                'error': str(e)
+            }
+            
+        finally:
+            # 清理会话
+            await self._cleanup_session(session_id)
+    
+    async def _cleanup_session(self, session_id: str) -> None:
+        """
+        清理会话资源
+        
+        参数:
+            session_id: 会话ID
+        """
+        if session_id not in self._active_sessions:
+            return
+        
+        session = self._active_sessions[session_id]
+        
+        # 清理临时文件
+        for path in session.get('temp_files', []):
+            try:
+                if os.path.exists(path):
+                    os.remove(path)
+            except Exception as e:
+                logger.warning(f"清理临时文件失败 {path}: {e}")
+        
+        # 移除会话
+        del self._active_sessions[session_id]
+        
+        logger.debug(f"[{session_id}] 会话已清理")
+    
+    def get_active_sessions(self) -> Dict[str, Dict[str, Any]]:
+        """获取活跃会话列表"""
+        return {
+            sid: {
+                'start_time': s['start_time'],
+                'mode': s['mode'],
+                'state': s['state']
+            }
+            for sid, s in self._active_sessions.items()
+        }
+    
+    def get_cache_stats(self) -> Dict[str, Any]:
+        """获取缓存统计信息"""
+        now = datetime.now()
+        valid_entries = sum(
+            1 for e in self._url_cache.values() 
+            if not e.is_expired()
+        )
+        
+        return {
+            'total_entries': len(self._url_cache),
+            'valid_entries': valid_entries,
+            'expired_entries': len(self._url_cache) - valid_entries
+        }
+    
+    def update_client_config(self, config: Dict[str, Any]) -> None:
+        """
+        更新客户端配置
+        
+        参数:
+            config: 客户端配置字典
+        """
+        self._client_config.update(config)
+        logger.info(f"客户端配置已更新: {list(config.keys())}")
+    
+    def get_client_config(self) -> Dict[str, Any]:
+        """
+        获取当前客户端配置
+        
+        返回:
+            客户端配置字典
+        """
+        return self._client_config.copy()
+    
+    def clear_client_config(self) -> None:
+        """清空客户端配置"""
+        self._client_config.clear()
+        logger.info("客户端配置已清空")
+    
+    def clear_cache(self) -> int:
+        """
+        清理所有缓存
+        
+        返回:
+            清理的条目数
+        """
+        count = len(self._url_cache)
+        self._url_cache.clear()
+        logger.info(f"清理了 {count} 个缓存条目")
+        return count
+    
+    def clear_expired_cache(self) -> int:
+        """
+        清理过期缓存
+        
+        返回:
+            清理的条目数
+        """
+        expired_keys = [
+            k for k, v in self._url_cache.items() 
+            if v.is_expired()
+        ]
+        
+        for key in expired_keys:
+            del self._url_cache[key]
+        
+        if expired_keys:
+            logger.info(f"清理了 {len(expired_keys)} 个过期缓存")
+        
+        return len(expired_keys)
+    
+    async def cleanup_all(self) -> Dict[str, int]:
+        """
+        清理所有资源
+        
+        返回:
+            清理统计
+        """
+        stats = {
+            'sessions': 0,
+            'cache': 0,
+            'processor': 0,
+            'router': 0
+        }
+        
+        # 清理所有会话
+        session_ids = list(self._active_sessions.keys())
+        for sid in session_ids:
+            await self._cleanup_session(sid)
+            stats['sessions'] += 1
+        
+        # 清理缓存
+        stats['cache'] = self.clear_cache()
+        
+        # 清理子模块
+        if self.processor:
+            stats['processor'] = self.processor.cleanup()
+        
+        if self.router:
+            stats['router'] = self.router.cleanup()
+        
+        logger.info(f"网关清理完成: {stats}")
+        return stats
+    
+    @property
+    def is_initialized(self) -> bool:
+        """检查网关是否已初始化"""
+        return self._initialized
+    
+    async def process_request_segmented(
+        self, 
+        mode: str, 
+        data: Dict[str, Any]
+    ) -> AsyncGenerator[Dict[str, Any], None]:
+        """
+        分段流式处理端点
+        支持边下载边处理，第一段完成即可开始播放
+        
+        参数:
+            mode: 处理模式 ("url", "record")
+            data: 请求数据，包含:
+                - url: 视频URL
+                - client_config: 客户端配置
+                - segment_duration: 每段时长（秒）
+        
+        生成:
+            进度更新字典，包含:
+            - type: 消息类型 (progress/segment_ready/complete/error)
+            - progress: 进度百分比
+            - message: 状态消息
+            - segment: 分段数据（仅segment_ready时）
+        """
+        self._ensure_initialized()
+        
+        session_id = self._generate_session_id()
+        start_time = time.time()
+        segment_duration = data.get('segment_duration', 120)  # 默认2分钟
+        
+        logger.info(f"[{session_id}] 开始分段处理: mode={mode}, 分段时长={segment_duration}秒")
+        
+        # 提取客户端配置
+        client_config = data.get('client_config', {})
+        
+        # 创建会话
+        self._active_sessions[session_id] = {
+            'start_time': start_time,
+            'mode': mode,
+            'state': 'pending',
+            'temp_files': [],
+            'client_config': client_config
+        }
+        
+        try:
+            url = data.get('url', '')
+            if not url:
+                yield {
+                    'type': 'error',
+                    'message': '缺少视频URL'
+                }
+                return
+            
+            # 1. 获取视频信息
+            yield {
+                'type': 'progress',
+                'progress': 5,
+                'message': '正在分析视频...'
+            }
+            
+            # 路由请求获取音频
+            route_result = await self.router.route_request(mode, data, session_id)
+            audio_path = route_result['audio_path']
+            total_duration = route_result['duration']
+            
+            self._active_sessions[session_id]['temp_files'].append(audio_path)
+            
+            # 2. 计算分段数量
+            num_segments = max(1, int(total_duration / segment_duration) + 1)
+            logger.info(f"[{session_id}] 视频总时长: {total_duration}秒, 分段数: {num_segments}")
+            
+            yield {
+                'type': 'progress',
+                'progress': 10,
+                'message': f'准备处理 {num_segments} 个分段...'
+            }
+            
+            # 3. 逐段处理
+            processed_segments = 0
+            current_start = 0
+            
+            while current_start < total_duration:
+                # 计算当前段的实际时长
+                actual_duration = min(segment_duration, total_duration - current_start)
+                
+                yield {
+                    'type': 'progress',
+                    'progress': 10 + (processed_segments / num_segments) * 80,
+                    'message': f'处理第 {processed_segments + 1}/{num_segments} 段...'
+                }
+                
+                # 处理当前分段
+                segment_data = [{
+                    'audio_path': audio_path,
+                    'start_time': current_start,
+                    'duration': actual_duration
+                }]
+                
+                # 调用处理器处理单个分段
+                processing_config = {
+                    'client_config': client_config,
+                    'session_id': session_id
+                }
+                
+                results = await self.processor.process_segments(
+                    segment_data,
+                    config=processing_config
+                )
+                
+                if results and results[0].success:
+                    # 读取音频数据
+                    audio_data = None
+                    if results[0].audio_path and os.path.exists(results[0].audio_path):
+                        with open(results[0].audio_path, 'rb') as f:
+                            audio_data = f.read()
+                    
+                    # 发送分段完成通知
+                    yield {
+                        'type': 'segment_ready',
+                        'segment': {
+                            'index': processed_segments,
+                            'start_time': current_start,
+                            'duration': actual_duration,
+                            'audio_data': audio_data
+                        }
+                    }
+                    
+                    logger.info(f"[{session_id}] 分段 {processed_segments + 1} 处理完成")
+                else:
+                    logger.warning(f"[{session_id}] 分段 {processed_segments + 1} 处理失败")
+                
+                # 准备下一段
+                current_start += actual_duration
+                processed_segments += 1
+            
+            # 4. 处理完成
+            processing_time = time.time() - start_time
+            
+            yield {
+                'type': 'complete',
+                'total_segments': processed_segments,
+                'total_duration': total_duration,
+                'processing_time': processing_time
+            }
+            
+            logger.info(f"[{session_id}] 分段处理完成: {processed_segments}段, 耗时{processing_time:.1f}秒")
+            
+        except Exception as e:
+            logger.error(f"[{session_id}] 分段处理失败: {e}")
+            yield {
+                'type': 'error',
+                'message': str(e)
+            }
+            
+        finally:
+            await self._cleanup_session(session_id)
+    
+    async def process_single_segment(
+        self, 
+        mode: str, 
+        data: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """
+        处理单个分段
+        用于HTTP分段处理API
+        
+        参数:
+            mode: 处理模式
+            data: 请求数据，包含:
+                - url: 视频URL
+                - start_time: 起始时间（秒）
+                - duration: 处理时长（秒）
+                - segment_index: 段落索引
+                - client_config: 客户端配置
+        
+        返回:
+            处理结果字典
+        """
+        self._ensure_initialized()
+        
+        session_id = self._generate_session_id()
+        start_time_sec = data.get('start_time', 0)
+        duration = data.get('duration', 120)
+        segment_index = data.get('segment_index', 0)
+        client_config = data.get('client_config', {})
+        url = data.get('url', '')
+        
+        logger.info(f"[{session_id}] 处理单个分段: {start_time_sec}s - {start_time_sec + duration}s")
+        
+        try:
+            # 路由请求获取音频（带时间范围）
+            route_data = {
+                'url': url,
+                'start_time': start_time_sec,
+                'duration': duration
+            }
+            
+            route_result = await self.router.route_request(mode, route_data, session_id)
+            audio_path = route_result['audio_path']
+            actual_duration = route_result.get('duration', duration)
+            
+            # 检查是否已到视频末尾
+            if actual_duration <= 0:
+                return {
+                    'success': True,
+                    'no_more_segments': True
+                }
+            
+            # 处理分段
+            segment_data = [{
+                'audio_path': audio_path,
+                'start_time': 0,  # 音频已经是切片后的
+                'duration': actual_duration
+            }]
+            
+            processing_config = {
+                'client_config': client_config,
+                'session_id': session_id
+            }
+            
+            results = await self.processor.process_segments(
+                segment_data,
+                config=processing_config
+            )
+            
+            if results and results[0].success:
+                # 读取音频数据
+                audio_data = None
+                if results[0].audio_path and os.path.exists(results[0].audio_path):
+                    with open(results[0].audio_path, 'rb') as f:
+                        audio_data = f.read()
+                
+                # 判断是否是最后一段
+                is_last = actual_duration < duration
+                
+                return {
+                    'success': True,
+                    'audio_data': audio_data,
+                    'actual_duration': actual_duration,
+                    'is_last_segment': is_last,
+                    'segment_index': segment_index
+                }
+            else:
+                return {
+                    'success': False,
+                    'error': '分段处理失败'
+                }
+                
+        except Exception as e:
+            logger.error(f"[{session_id}] 单分段处理失败: {e}")
+            return {
+                'success': False,
+                'error': str(e)
+            }

backend/modules/groq_client.py

@@ -0,0 +1,970 @@
+"""
+Groq API 客户端模块
+
+提供与Groq API的异步交互功能，包括：
+- Whisper V3 语音识别 (ASR)
+- Llama 3 翻译和角色识别 (LLM)
+
+支持自动重试、错误处理和限流管理。
+
+Requirements: 2.1, 2.2, 7.5, 8.2
+"""
+
+import os
+import asyncio
+import logging
+import random
+from typing import Dict, Any, Optional, List, Callable
+from dataclasses import dataclass, field
+from enum import Enum
+from datetime import datetime
+
+# 配置日志
+logger = logging.getLogger(__name__)
+
+
+class GroqError(Exception):
+    """Groq API 异常基类"""
+    def __init__(self, message: str = "Groq API 错误"):
+        self.message = message
+        self.timestamp = datetime.now().isoformat()
+        super().__init__(self.message)
+
+
+class GroqRateLimitError(GroqError):
+    """Groq API 限流异常"""
+    def __init__(self, retry_after: Optional[float] = None):
+        self.retry_after = retry_after
+        message = "Groq API 限流，请稍后重试"
+        if retry_after:
+            message += f"（建议等待 {retry_after} 秒）"
+        super().__init__(message)
+
+
+class GroqTimeoutError(GroqError):
+    """Groq API 超时异常"""
+    def __init__(self, timeout: float, operation: str = "请求"):
+        self.timeout = timeout
+        self.operation = operation
+        message = f"Groq API {operation}超时（{timeout}秒）"
+        super().__init__(message)
+
+
+class GroqAuthError(GroqError):
+    """Groq API 认证异常"""
+    def __init__(self):
+        super().__init__("Groq API 认证失败，请检查API密钥")
+
+
+class GroqConnectionError(GroqError):
+    """Groq API 连接异常"""
+    def __init__(self, details: str = ""):
+        message = "Groq API 连接失败"
+        if details:
+            message += f": {details}"
+        super().__init__(message)
+
+
+@dataclass
+class RetryStats:
+    """
+    重试统计信息
+    
+    用于跟踪重试操作的详细信息。
+    """
+    operation: str
+    total_attempts: int = 0
+    successful_attempt: Optional[int] = None
+    total_delay: float = 0.0
+    errors: List[str] = field(default_factory=list)
+    start_time: Optional[datetime] = None
+    end_time: Optional[datetime] = None
+    
+    def record_attempt(self, attempt: int, error: Optional[str] = None, delay: float = 0.0):
+        """记录一次尝试"""
+        self.total_attempts = attempt + 1
+        if error:
+            self.errors.append(f"尝试{attempt + 1}: {error}")
+        self.total_delay += delay
+    
+    def record_success(self, attempt: int):
+        """记录成功"""
+        self.successful_attempt = attempt + 1
+        self.end_time = datetime.now()
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """转换为字典"""
+        return {
+            "operation": self.operation,
+            "total_attempts": self.total_attempts,
+            "successful_attempt": self.successful_attempt,
+            "total_delay_seconds": round(self.total_delay, 2),
+            "errors": self.errors,
+            "duration_seconds": (
+                (self.end_time - self.start_time).total_seconds()
+                if self.start_time and self.end_time else None
+            )
+        }
+
+
+@dataclass
+class GroqConfig:
+    """
+    Groq 客户端配置
+    
+    属性:
+        api_key: Groq API 密钥
+        asr_model: ASR 模型名称，默认使用 whisper-large-v3-turbo
+        llm_model: LLM 模型名称，默认使用 llama3-8b-8192
+        max_retries: 最大重试次数，默认3次
+        base_timeout: 基础超时时间（秒），默认30秒
+        retry_base_delay: 重试基础延迟（秒），默认1秒
+        retry_max_delay: 重试最大延迟（秒），默认30秒
+        retry_jitter: 是否添加随机抖动，默认True
+        retry_jitter_factor: 抖动因子（0-1），默认0.1
+    """
+    api_key: str
+    asr_model: str = "whisper-large-v3-turbo"
+    llm_model: str = "llama3-8b-8192"
+    max_retries: int = 3
+    base_timeout: int = 30
+    retry_base_delay: float = 1.0
+    retry_max_delay: float = 30.0
+    retry_jitter: bool = True
+    retry_jitter_factor: float = 0.1
+
+
+class GroqClient:
+    """
+    Groq API 异步客户端
+    
+    提供语音识别和翻译功能，支持自动重试和错误处理。
+    
+    使用示例:
+        config = GroqConfig(api_key="your_api_key")
+        client = GroqClient(config)
+        await client.initialize()
+        
+        # 语音识别
+        result = await client.transcribe("audio.mp3")
+        
+        # 翻译
+        translation = await client.translate(result['text'], result['language'])
+    """
+    
+    def __init__(self, config: Optional[GroqConfig] = None):
+        """
+        初始化 Groq 客户端
+        
+        参数:
+            config: Groq 配置对象，如果为None则从环境变量读取
+        """
+        if config is None:
+            # 从环境变量读取配置
+            api_key = os.getenv("GROQ_API_KEY")
+            if not api_key:
+                raise GroqAuthError()
+            config = GroqConfig(api_key=api_key)
+        
+        self.config = config
+        self._client = None  # 延迟初始化
+        self._initialized = False
+        self._retry_stats: List[RetryStats] = []  # 重试统计历史
+        
+        logger.info("Groq 客户端配置完成")
+        logger.debug(f"ASR模型: {config.asr_model}, LLM模型: {config.llm_model}")
+    
+    async def initialize(self) -> None:
+        """
+        异步初始化 Groq 客户端
+        
+        创建底层 Groq 客户端实例并验证连接。
+        """
+        if self._initialized:
+            logger.debug("Groq 客户端已初始化，跳过")
+            return
+        
+        try:
+            # 导入 groq 库（延迟导入以支持测试）
+            import groq
+            
+            # 创建异步客户端
+            self._client = groq.AsyncGroq(api_key=self.config.api_key)
+            self._initialized = True
+            
+            logger.info("Groq 客户端初始化成功")
+            
+        except ImportError as e:
+            logger.error(f"无法导入 groq 库: {e}")
+            raise GroqError("Groq 库未安装，请运行: pip install groq")
+        except Exception as e:
+            logger.error(f"Groq 客户端初始化失败: {e}")
+            raise GroqError(f"Groq 客户端初始化失败: {e}")
+    
+    def _ensure_initialized(self) -> None:
+        """确保客户端已初始化"""
+        if not self._initialized or self._client is None:
+            raise GroqError("Groq 客户端未初始化，请先调用 initialize()")
+    
+    async def _retry_with_backoff(
+        self, 
+        operation: str,
+        func: Callable,
+        *args,
+        on_retry: Optional[Callable[[int, float, str], None]] = None,
+        **kwargs
+    ) -> Any:
+        """
+        带指数退避的重试机制
+        
+        实现完整的指数退避重试策略，包括：
+        - 指数退避延迟（1s, 2s, 4s, 8s...）
+        - 可选的随机抖动（避免惊群效应）
+        - API返回的retry_after优先
+        - 详细的重试统计
+        
+        参数:
+            operation: 操作名称（用于日志）
+            func: 要执行的异步函数
+            *args: 函数参数
+            on_retry: 可选的重试回调函数(attempt, delay, error_msg)
+            **kwargs: 函数关键字参数
+        
+        返回:
+            函数执行结果
+        
+        异常:
+            GroqRateLimitError: 超过最大重试次数后仍被限流
+            GroqTimeoutError: 请求超时
+            GroqAuthError: 认证失败（不重试）
+            GroqError: 其他API错误
+        """
+        import groq
+        
+        # 初始化重试统计
+        stats = RetryStats(operation=operation, start_time=datetime.now())
+        last_exception: Optional[Exception] = None
+        
+        for attempt in range(self.config.max_retries + 1):
+            try:
+                # 设置超时
+                result = await asyncio.wait_for(
+                    func(*args, **kwargs),
+                    timeout=self.config.base_timeout
+                )
+                
+                # 记录成功
+                stats.record_success(attempt)
+                self._retry_stats.append(stats)
+                
+                if attempt > 0:
+                    logger.info(
+                        f"{operation} 在第 {attempt + 1} 次尝试后成功，"
+                        f"总延迟: {stats.total_delay:.1f}秒"
+                    )
+                
+                return result
+                
+            except asyncio.TimeoutError:
+                error_msg = f"超时（{self.config.base_timeout}秒）"
+                logger.warning(f"{operation} {error_msg}（第 {attempt + 1} 次尝试）")
+                
+                last_exception = GroqTimeoutError(
+                    self.config.base_timeout, 
+                    operation
+                )
+                
+                # 超时也进行重试
+                if attempt < self.config.max_retries:
+                    delay = self._calculate_backoff_delay(attempt)
+                    stats.record_attempt(attempt, error_msg, delay)
+                    
+                    if on_retry:
+                        on_retry(attempt, delay, error_msg)
+                    
+                    await asyncio.sleep(delay)
+                else:
+                    stats.record_attempt(attempt, error_msg)
+                
+            except groq.RateLimitError as e:
+                # 限流错误，使用指数退避
+                retry_after = self._extract_retry_after(e)
+                delay = self._calculate_backoff_delay(attempt, retry_after)
+                
+                error_msg = f"被限流，等待 {delay:.1f} 秒"
+                logger.warning(
+                    f"{operation} {error_msg}（第 {attempt + 1} 次尝试）"
+                )
+                
+                last_exception = GroqRateLimitError(retry_after)
+                
+                if attempt < self.config.max_retries:
+                    stats.record_attempt(attempt, error_msg, delay)
+                    
+                    if on_retry:
+                        on_retry(attempt, delay, error_msg)
+                    
+                    await asyncio.sleep(delay)
+                else:
+                    stats.record_attempt(attempt, error_msg)
+                    
+            except groq.AuthenticationError:
+                error_msg = "认证失败"
+                logger.error(f"{operation} {error_msg}")
+                stats.record_attempt(attempt, error_msg)
+                self._retry_stats.append(stats)
+                # 认证错误不重试
+                raise GroqAuthError()
+                
+            except groq.APIStatusError as e:
+                # API状态错误（包括服务器错误）
+                status_code = getattr(e, 'status_code', 0)
+                error_msg = f"API状态错误 (HTTP {status_code}): {str(e)}"
+                logger.error(f"{operation} {error_msg}")
+                
+                last_exception = GroqError(f"Groq API 错误: {e}")
+                
+                # 5xx 错误可以重试
+                if status_code >= 500 and attempt < self.config.max_retries:
+                    delay = self._calculate_backoff_delay(attempt)
+                    stats.record_attempt(attempt, error_msg, delay)
+                    
+                    if on_retry:
+                        on_retry(attempt, delay, error_msg)
+                    
+                    await asyncio.sleep(delay)
+                else:
+                    stats.record_attempt(attempt, error_msg)
+                    if status_code < 500:
+                        # 4xx 错误不重试
+                        break
+                    
+            except groq.APIConnectionError as e:
+                # 连接错误
+                error_msg = f"连接错误: {str(e)}"
+                logger.error(f"{operation} {error_msg}")
+                
+                last_exception = GroqConnectionError(str(e))
+                
+                # 连接错误可以重试
+                if attempt < self.config.max_retries:
+                    delay = self._calculate_backoff_delay(attempt)
+                    stats.record_attempt(attempt, error_msg, delay)
+                    
+                    if on_retry:
+                        on_retry(attempt, delay, error_msg)
+                    
+                    await asyncio.sleep(delay)
+                else:
+                    stats.record_attempt(attempt, error_msg)
+                    
+            except groq.APIError as e:
+                error_msg = f"API错误: {str(e)}"
+                logger.error(f"{operation} {error_msg}")
+                
+                last_exception = GroqError(f"Groq API 错误: {e}")
+                
+                # 通用API错误进行重试
+                if attempt < self.config.max_retries:
+                    delay = self._calculate_backoff_delay(attempt)
+                    stats.record_attempt(attempt, error_msg, delay)
+                    
+                    if on_retry:
+                        on_retry(attempt, delay, error_msg)
+                    
+                    await asyncio.sleep(delay)
+                else:
+                    stats.record_attempt(attempt, error_msg)
+                    
+            except Exception as e:
+                error_msg = f"未知错误: {str(e)}"
+                logger.error(f"{operation} {error_msg}")
+                
+                last_exception = GroqError(f"未知错误: {e}")
+                stats.record_attempt(attempt, error_msg)
+                # 未知错误不重试
+                break
+        
+        # 所有重试都失败
+        stats.end_time = datetime.now()
+        self._retry_stats.append(stats)
+        
+        logger.error(
+            f"{operation} 在 {self.config.max_retries + 1} 次尝试后失败，"
+            f"总延迟: {stats.total_delay:.1f}秒"
+        )
+        
+        if last_exception:
+            raise last_exception
+        else:
+            raise GroqError(f"{operation} 失败")
+    
+    def _extract_retry_after(self, error: Exception) -> Optional[float]:
+        """
+        从错误中提取 retry_after 值
+        
+        参数:
+            error: 异常对象
+        
+        返回:
+            建议等待时间（秒），如果没有则返回None
+        """
+        # 尝试从不同属性获取
+        retry_after = getattr(error, 'retry_after', None)
+        
+        if retry_after is None:
+            # 尝试从响应头获取
+            response = getattr(error, 'response', None)
+            if response:
+                headers = getattr(response, 'headers', {})
+                retry_after_header = headers.get('retry-after') or headers.get('Retry-After')
+                if retry_after_header:
+                    try:
+                        retry_after = float(retry_after_header)
+                    except (ValueError, TypeError):
+                        pass
+        
+        return retry_after
+    
+    def _calculate_backoff_delay(
+        self, 
+        attempt: int, 
+        retry_after: Optional[float] = None
+    ) -> float:
+        """
+        计算指数退避延迟
+        
+        实现带抖动的指数退避算法：
+        delay = min(base * 2^attempt + jitter, max_delay)
+        
+        参数:
+            attempt: 当前尝试次数（从0开始）
+            retry_after: API返回的建议等待时间（优先使用）
+        
+        返回:
+            延迟时间（秒）
+        """
+        # 如果API返回了建议等待时间，优先使用
+        if retry_after and retry_after > 0:
+            # 但不超过最大延迟
+            return min(retry_after, self.config.retry_max_delay)
+        
+        # 指数退避: base * 2^attempt
+        # 例如: 1s, 2s, 4s, 8s...
+        delay = self.config.retry_base_delay * (2 ** attempt)
+        
+        # 添加随机抖动（避免惊群效应）
+        if self.config.retry_jitter:
+            jitter_range = delay * self.config.retry_jitter_factor
+            jitter = random.uniform(-jitter_range, jitter_range)
+            delay += jitter
+        
+        # 确保延迟为正数且不超过最大值
+        delay = max(0.1, min(delay, self.config.retry_max_delay))
+        
+        return delay
+    
+    def get_retry_stats(self) -> List[Dict[str, Any]]:
+        """
+        获取重试统计信息
+        
+        返回:
+            重试统计列表
+        """
+        return [stats.to_dict() for stats in self._retry_stats]
+    
+    def clear_retry_stats(self) -> None:
+        """清除重试统计信息"""
+        self._retry_stats.clear()
+    
+    @property
+    def is_initialized(self) -> bool:
+        """检查客户端是否已初始化"""
+        return self._initialized
+    
+    @property
+    def asr_model(self) -> str:
+        """获取当前ASR模型名称"""
+        return self.config.asr_model
+    
+    @property
+    def llm_model(self) -> str:
+        """获取当前LLM模型名称"""
+        return self.config.llm_model
+    
+    async def transcribe(
+        self, 
+        audio_path: str,
+        language: Optional[str] = None
+    ) -> Dict[str, Any]:
+        """
+        语音识别，返回带时间戳的文本
+        
+        使用 Whisper V3 模型进行语音识别，支持自动语言检测。
+        返回结果包含完整文本、检测到的语言和带时间戳的片段列表。
+        
+        参数:
+            audio_path: 音频文件路径（支持 mp3, wav, m4a 等格式）
+            language: 指定源语言（可选，默认自动检测）
+                     支持: "en"(英语), "ja"(日语), "zh"(中文) 等
+        
+        返回:
+            Dict 包含以下字段:
+            - text: str - 完整识别文本
+            - language: str - 检测到的语言代码
+            - segments: List[Dict] - 带时间戳的片段列表
+              每个片段包含:
+              - id: int - 片段序号
+              - start: float - 开始时间（秒）
+              - end: float - 结束时间（秒）
+              - text: str - 片段文本
+        
+        异常:
+            GroqError: API调用失败
+            FileNotFoundError: 音频文件不存在
+        
+        示例:
+            result = await client.transcribe("audio.mp3")
+            print(f"语言: {result['language']}")
+            for seg in result['segments']:
+                print(f"[{seg['start']:.2f}-{seg['end']:.2f}] {seg['text']}")
+        """
+        self._ensure_initialized()
+        
+        # 验证文件存在
+        if not os.path.exists(audio_path):
+            raise FileNotFoundError(f"音频文件不存在: {audio_path}")
+        
+        logger.info(f"开始语音识别: {audio_path}")
+        
+        async def _do_transcribe():
+            """执行实际的转录操作"""
+            with open(audio_path, 'rb') as audio_file:
+                # 构建请求参数
+                params = {
+                    "model": self.config.asr_model,
+                    "file": audio_file,
+                    "response_format": "verbose_json",  # 获取详细时间戳
+                }
+                
+                # 如果指定了语言，���使用指定语言
+                # 否则让 Whisper 自动检测
+                if language:
+                    params["language"] = language
+                
+                # 调用 Groq ASR API
+                transcription = await self._client.audio.transcriptions.create(**params)
+                
+                return transcription
+        
+        # 使用重试机制执行转录
+        transcription = await self._retry_with_backoff(
+            "语音识别",
+            _do_transcribe
+        )
+        
+        # 解析响应结果
+        result = self._parse_transcription_response(transcription)
+        
+        logger.info(
+            f"语音识别完成: 语言={result['language']}, "
+            f"片段数={len(result['segments'])}"
+        )
+        
+        return result
+    
+    def _parse_transcription_response(self, transcription) -> Dict[str, Any]:
+        """
+        解析 Groq ASR 响应
+        
+        将 Groq API 返回的转录结果解析为标准化格式。
+        
+        参数:
+            transcription: Groq API 返回的转录对象
+        
+        返回:
+            标准化的转录结果字典
+        """
+        # 提取基本信息
+        text = getattr(transcription, 'text', '') or ''
+        language = getattr(transcription, 'language', 'unknown') or 'unknown'
+        
+        # 解析片段信息
+        segments = []
+        raw_segments = getattr(transcription, 'segments', []) or []
+        
+        for seg in raw_segments:
+            segment_data = {
+                'id': getattr(seg, 'id', len(segments)),
+                'start': float(getattr(seg, 'start', 0)),
+                'end': float(getattr(seg, 'end', 0)),
+                'text': getattr(seg, 'text', '').strip(),
+            }
+            
+            # 可选字段：置信度
+            if hasattr(seg, 'avg_logprob'):
+                segment_data['confidence'] = self._logprob_to_confidence(
+                    getattr(seg, 'avg_logprob', 0)
+                )
+            
+            # 可选字段：无语音概率（用于检测静音）
+            if hasattr(seg, 'no_speech_prob'):
+                segment_data['no_speech_prob'] = float(
+                    getattr(seg, 'no_speech_prob', 0)
+                )
+            
+            segments.append(segment_data)
+        
+        return {
+            'text': text,
+            'language': language,
+            'segments': segments,
+            'duration': segments[-1]['end'] if segments else 0,
+        }
+    
+    def _logprob_to_confidence(self, logprob: float) -> float:
+        """
+        将对数概率转换为置信度分数
+        
+        参数:
+            logprob: 对数概率值（通常为负数）
+        
+        返回:
+            置信度分数（0-1之间）
+        """
+        import math
+        # 将对数概率转换为概率
+        # logprob 通常在 -1 到 0 之间，越接近 0 置信度越高
+        try:
+            confidence = math.exp(logprob)
+            return min(max(confidence, 0.0), 1.0)
+        except (ValueError, OverflowError):
+            return 0.5  # 默认中等置信度
+    
+    async def translate(
+        self, 
+        text: str,
+        source_language: str,
+        segments: Optional[List[Dict[str, Any]]] = None
+    ) -> Dict[str, Any]:
+        """
+        翻译文本并识别角色
+        
+        使用 Llama 3 模型将文本翻译为简体中文，同时识别说话者角色。
+        支持日语特殊处理，包括主语补全和敬语转换。
+        
+        参数:
+            text: 要翻译的文本
+            source_language: 源语言代码（"en", "ja" 等）
+            segments: 可选的带时间戳片段列表，用于保持时间对齐
+        
+        返回:
+            Dict 包含以下字段:
+            - segments: List[Dict] - 翻译后的片段列表
+              每个片段包含:
+              - id: int - 片段序号
+              - original: str - 原文
+              - cn: str - 中文翻译
+              - role: str - 角色标签 (MALE/FEMALE/CHILD/NARRATOR)
+            - source_language: str - 源语言
+            - target_language: str - 目标语言（固定为 "zh-CN"）
+        
+        异常:
+            GroqError: API调用失败或响应解析失败
+        
+        示例:
+            result = await client.translate(
+                "Hello, how are you?", 
+                "en"
+            )
+            for seg in result['segments']:
+                print(f"[{seg['role']}] {seg['cn']}")
+        """
+        self._ensure_initialized()
+        
+        if not text or not text.strip():
+            logger.warning("翻译输入为空")
+            return {
+                'segments': [],
+                'source_language': source_language,
+                'target_language': 'zh-CN'
+            }
+        
+        logger.info(f"开始翻译: 源语言={source_language}, 文本长度={len(text)}")
+        
+        # 构建翻译提示词
+        system_prompt = self._build_translation_prompt(source_language)
+        
+        # 准备用户输入
+        user_content = self._prepare_translation_input(text, segments)
+        
+        async def _do_translate():
+            """执行实际的翻译操作"""
+            completion = await self._client.chat.completions.create(
+                model=self.config.llm_model,
+                messages=[
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": user_content}
+                ],
+                temperature=0.1,  # 低温度保证翻译一致性
+                response_format={"type": "json_object"}
+            )
+            return completion
+        
+        # 使用重试机制执行翻译
+        completion = await self._retry_with_backoff(
+            "翻译",
+            _do_translate
+        )
+        
+        # 解析响应结果
+        result = self._parse_translation_response(
+            completion, 
+            source_language,
+            segments
+        )
+        
+        logger.info(f"翻译完成: 片段数={len(result['segments'])}")
+        
+        return result
+    
+    def _build_translation_prompt(self, source_language: str) -> str:
+        """
+        根据源语言构建专门的翻译提示词
+        
+        参数:
+            source_language: 源语言代码
+        
+        返回:
+            系统提示词字符串
+        """
+        base_prompt = """你是专业的配音导演和翻译师。
+目标语言: 简体中文 (口语化、自然)
+
+核心规则:
+1. 根据上下文进行翻译，保持语义准确
+2. 翻译要口语化、自然，适合配音朗读
+3. 根据内容分配角色标签:
+   - "MALE": 成年男性声音（默认）
+   - "FEMALE": 成年女性声音（柔和/高音调）
+   - "CHILD": 儿童声音
+   - "NARRATOR": 旁白/解说
+
+角色识别规则:
+- 根据说话内容、语气词、称谓词判断性别
+- 疑问句、感叹句注意保持原始语气
+- 如果无法确定，默认使用 "MALE"
+
+输出格式要求:
+必须返回有效的JSON对象，格式如下:
+{
+    "segments": [
+        {"id": 0, "cn": "翻译文本", "role": "MALE"},
+        {"id": 1, "cn": "翻译文本", "role": "FEMALE"}
+    ]
+}
+
+注意:
+- 每个片段必须包含 id、cn、role 三个字段
+- id 从 0 开始递增
+- cn 是中文翻译文本
+- role 必须是 MALE、FEMALE、CHILD、NARRATOR 之一
+"""
+        
+        # 日语特殊处理
+        if source_language == "ja":
+            base_prompt += """
+日语特殊处理规则:
+1. [重要] 日语省略主语补全：根据语境推断缺失的主语
+   - 例如：「行きます」→「我去」而不是「去」
+   - 根据敬语程度判断说话者和听话者关系
+2. 敬语转换：将日式敬语转换为中文礼貌用语
+   - です/ます → 适当的礼貌表达
+   - 敬称（さん、様）→ 先生/女士/小姐
+3. 语气保持：保留原始情感色彩和语气
+4. 文化适配：日式表达转换为中文习惯表达
+"""
+        
+        # 英语处理
+        elif source_language == "en":
+            base_prompt += """
+英语处理规则:
+1. 保持原文的语气和情感
+2. 俚语和习语使用对应的中文表达
+3. 专业术语使用标准译名
+"""
+        
+        return base_prompt
+    
+    def _prepare_translation_input(
+        self, 
+        text: str,
+        segments: Optional[List[Dict[str, Any]]] = None
+    ) -> str:
+        """
+        准备翻译输入内容
+        
+        参数:
+            text: 完整文本
+            segments: 可选的片段列表
+        
+        返回:
+            格式化的输入字符串
+        """
+        if segments:
+            # 如果有片段信息，按片段格式化
+            lines = []
+            for i, seg in enumerate(segments):
+                seg_text = seg.get('text', '').strip()
+                if seg_text:
+                    lines.append(f"[{i}] {seg_text}")
+            return "\n".join(lines)
+        else:
+            # 否则直接使用完整文本
+            return text
+    
+    def _parse_translation_response(
+        self, 
+        completion,
+        source_language: str,
+        original_segments: Optional[List[Dict[str, Any]]] = None
+    ) -> Dict[str, Any]:
+        """
+        解析 LLM 翻译响应
+        
+        参数:
+            completion: Groq API 返回的完成对象
+            source_language: 源语言
+            original_segments: 原始片段列表（用于保持时间戳）
+        
+        返回:
+            标准化的翻译结果字典
+        """
+        import json
+        
+        try:
+            # 提取响应内容
+            content = completion.choices[0].message.content
+            
+            # 解析 JSON
+            data = json.loads(content)
+            
+            # 提取片段
+            segments = data.get('segments', [])
+            
+            # 验证和规范化每个片段
+            normalized_segments = []
+            for i, seg in enumerate(segments):
+                normalized = {
+                    'id': seg.get('id', i),
+                    'cn': seg.get('cn', ''),
+                    'role': self._validate_role(seg.get('role', 'MALE'))
+                }
+                
+                # 如果有原始片段，添加时间戳信息
+                if original_segments and i < len(original_segments):
+                    orig = original_segments[i]
+                    normalized['original'] = orig.get('text', '')
+                    normalized['start'] = orig.get('start', 0)
+                    normalized['end'] = orig.get('end', 0)
+                
+                normalized_segments.append(normalized)
+            
+            return {
+                'segments': normalized_segments,
+                'source_language': source_language,
+                'target_language': 'zh-CN'
+            }
+            
+        except json.JSONDecodeError as e:
+            logger.error(f"翻译响应JSON解析失败: {e}")
+            # 尝试从原始文本中提取翻译
+            return self._fallback_parse_translation(
+                completion, 
+                source_language,
+                original_segments
+            )
+        except Exception as e:
+            logger.error(f"翻译响应解析失败: {e}")
+            raise GroqError(f"翻译响应解析失败: {e}")
+    
+    def _validate_role(self, role: str) -> str:
+        """
+        验证并规范化角色标签
+        
+        参数:
+            role: 输入的角色标签
+        
+        返回:
+            有效的角色标签
+        """
+        valid_roles = {'MALE', 'FEMALE', 'CHILD', 'NARRATOR'}
+        role_upper = role.upper().strip()
+        
+        if role_upper in valid_roles:
+            return role_upper
+        
+        # 尝试模糊匹配
+        role_mapping = {
+            'M': 'MALE',
+            'F': 'FEMALE',
+            'C': 'CHILD',
+            'N': 'NARRATOR',
+            '男': 'MALE',
+            '女': 'FEMALE',
+            '儿童': 'CHILD',
+            '旁白': 'NARRATOR',
+        }
+        
+        if role_upper in role_mapping:
+            return role_mapping[role_upper]
+        
+        # 默认返回 MALE
+        logger.warning(f"未知角色标签 '{role}'，使用默认值 MALE")
+        return 'MALE'
+    
+    def _fallback_parse_translation(
+        self,
+        completion,
+        source_language: str,
+        original_segments: Optional[List[Dict[str, Any]]] = None
+    ) -> Dict[str, Any]:
+        """
+        降级解析翻译响应
+        
+        当JSON解析失败时，尝试从原始文本中提取翻译内容。
+        
+        参数:
+            completion: Groq API 返回的完成对象
+            source_language: 源语言
+            original_segments: 原始片段列表
+        
+        返回:
+            尽可能提取的翻译结果
+        """
+        try:
+            content = completion.choices[0].message.content
+            
+            # 尝试提取文本内容
+            # 简单处理：将整个响应作为单个翻译片段
+            segments = [{
+                'id': 0,
+                'cn': content.strip(),
+                'role': 'MALE'
+            }]
+            
+            logger.warning("使用降级解析，翻译结果可能不完整")
+            
+            return {
+                'segments': segments,
+                'source_language': source_language,
+                'target_language': 'zh-CN'
+            }
+            
+        except Exception as e:
+            logger.error(f"降级解析也失败: {e}")
+            return {
+                'segments': [],
+                'source_language': source_language,
+                'target_language': 'zh-CN'
+            }

backend/modules/logging_config.py

@@ -0,0 +1,538 @@
+"""
+结构化日志记录模块
+
+提供统一的日志配置和结构化日志记录功能，包括：
+- JSON格式的结构化日志
+- 组件级日志记录
+- 性能监控日志
+- 错误追踪日志
+
+Requirements: 8.6
+"""
+
+import logging
+import json
+import sys
+import os
+import time
+import traceback
+from typing import Dict, Any, Optional, Union
+from datetime import datetime
+from dataclasses import dataclass, field, asdict
+from enum import Enum
+from functools import wraps
+import asyncio
+
+
+class LogLevel(Enum):
+    """日志级别枚举"""
+    DEBUG = "DEBUG"
+    INFO = "INFO"
+    WARNING = "WARNING"
+    ERROR = "ERROR"
+    CRITICAL = "CRITICAL"
+
+
+class Component(Enum):
+    """系统组件枚举"""
+    GROQ_CLIENT = "GroqClient"
+    ASR = "ASR"
+    LLM = "LLM"
+    TTS = "TTSGenerator"
+    SEGMENTER = "SmartSegmenter"
+    AUDIO_SYNC = "AudioSyncEngine"
+    PROCESSOR = "ParallelProcessingPool"
+    ROUTER = "RequestRouter"
+    GATEWAY = "GradioAPIGateway"
+    SYSTEM = "System"
+
+
+@dataclass
+class StructuredLogRecord:
+    """
+    结构化日志记录
+    
+    属性:
+        timestamp: ISO8601格式的时间戳
+        level: 日志级别
+        component: 组件名称
+        message: 日志消息
+        session_id: 会话ID（可选）
+        duration_ms: 处理耗时（毫秒，可选）
+        error_code: 错误代码（可选）
+        stack_trace: 堆栈跟踪（ERROR级别，可选）
+        extra: 额外的上下文信息
+    """
+    timestamp: str
+    level: str
+    component: str
+    message: str
+    session_id: Optional[str] = None
+    duration_ms: Optional[float] = None
+    error_code: Optional[str] = None
+    stack_trace: Optional[str] = None
+    extra: Dict[str, Any] = field(default_factory=dict)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """转换为字典，排除None值"""
+        result = {
+            "timestamp": self.timestamp,
+            "level": self.level,
+            "component": self.component,
+            "message": self.message,
+        }
+        
+        if self.session_id:
+            result["session_id"] = self.session_id
+        if self.duration_ms is not None:
+            result["duration_ms"] = round(self.duration_ms, 2)
+        if self.error_code:
+            result["error_code"] = self.error_code
+        if self.stack_trace:
+            result["stack_trace"] = self.stack_trace
+        if self.extra:
+            result["extra"] = self.extra
+        
+        return result
+    
+    def to_json(self) -> str:
+        """转换为JSON字符串"""
+        return json.dumps(self.to_dict(), ensure_ascii=False)
+
+
+class StructuredFormatter(logging.Formatter):
+    """
+    结构化日志格式化器
+    
+    将日志记录格式化为JSON格式，便于日志分析和问题排查。
+    """
+    
+    def __init__(self, include_stack_trace: bool = True):
+        """
+        初始化格式化器
+        
+        参数:
+            include_stack_trace: 是否在ERROR级别包含堆栈跟踪
+        """
+        super().__init__()
+        self.include_stack_trace = include_stack_trace
+    
+    def format(self, record: logging.LogRecord) -> str:
+        """
+        格式化日志记录
+        
+        参数:
+            record: 日志记录对象
+        
+        返回:
+            JSON格式的日志字符串
+        """
+        # 提取基本信息
+        log_record = StructuredLogRecord(
+            timestamp=datetime.fromtimestamp(record.created).isoformat(),
+            level=record.levelname,
+            component=getattr(record, 'component', record.name),
+            message=record.getMessage(),
+            session_id=getattr(record, 'session_id', None),
+            duration_ms=getattr(record, 'duration_ms', None),
+            error_code=getattr(record, 'error_code', None),
+        )
+        
+        # 添加堆栈跟踪（仅ERROR及以上级别）
+        if self.include_stack_trace and record.exc_info:
+            log_record.stack_trace = self.formatException(record.exc_info)
+        
+        # 添加额外信息
+        extra_keys = ['audio_path', 'url', 'segment_index', 'retry_count', 
+                      'progress', 'platform', 'language', 'role']
+        for key in extra_keys:
+            value = getattr(record, key, None)
+            if value is not None:
+                log_record.extra[key] = value
+        
+        return log_record.to_json()
+
+
+class HumanReadableFormatter(logging.Formatter):
+    """
+    人类可读的日志格式化器
+    
+    用于开发环境，提供更易读的日志输出。
+    """
+    
+    # 日志级别颜色（ANSI转义码）
+    COLORS = {
+        'DEBUG': '\033[36m',     # 青色
+        'INFO': '\033[32m',      # 绿色
+        'WARNING': '\033[33m',   # 黄色
+        'ERROR': '\033[31m',     # 红色
+        'CRITICAL': '\033[35m',  # 紫色
+    }
+    RESET = '\033[0m'
+    
+    def __init__(self, use_colors: bool = True):
+        """
+        初始化格式化器
+        
+        参数:
+            use_colors: 是否使用颜色
+        """
+        super().__init__()
+        self.use_colors = use_colors and sys.stdout.isatty()
+    
+    def format(self, record: logging.LogRecord) -> str:
+        """格式化日志记录"""
+        # 时间戳
+        timestamp = datetime.fromtimestamp(record.created).strftime('%H:%M:%S.%f')[:-3]
+        
+        # 级别
+        level = record.levelname
+        if self.use_colors:
+            color = self.COLORS.get(level, '')
+            level = f"{color}{level:8}{self.RESET}"
+        else:
+            level = f"{level:8}"
+        
+        # 组件
+        component = getattr(record, 'component', record.name)
+        component = f"[{component}]"
+        
+        # 会话ID
+        session_id = getattr(record, 'session_id', None)
+        session_str = f"({session_id}) " if session_id else ""
+        
+        # 耗时
+        duration_ms = getattr(record, 'duration_ms', None)
+        duration_str = f" [{duration_ms:.0f}ms]" if duration_ms else ""
+        
+        # 消息
+        message = record.getMessage()
+        
+        # 组合
+        output = f"{timestamp} {level} {component:20} {session_str}{message}{duration_str}"
+        
+        # 异常信息
+        if record.exc_info:
+            output += "\n" + self.formatException(record.exc_info)
+        
+        return output
+
+
+class ComponentLogger:
+    """
+    组件级日志记录器
+    
+    为特定组件提供便捷的日志记录方法，自动添加组件名称和会话ID。
+    
+    使用示例:
+        logger = ComponentLogger(Component.GROQ_CLIENT)
+        logger.info("开始语音识别", session_id="abc123", audio_path="test.mp3")
+        logger.error("API调用失败", error_code="E3002", exc_info=True)
+    """
+    
+    def __init__(
+        self, 
+        component: Union[Component, str],
+        base_logger: Optional[logging.Logger] = None
+    ):
+        """
+        初始化组件日志记录器
+        
+        参数:
+            component: 组件名称或枚举
+            base_logger: 基础日志记录器，默认使用模块日志记录器
+        """
+        if isinstance(component, Component):
+            self.component = component.value
+        else:
+            self.component = component
+        
+        self._logger = base_logger or logging.getLogger(f"dubbing.{self.component}")
+        self._default_session_id: Optional[str] = None
+    
+    def set_session_id(self, session_id: Optional[str]) -> None:
+        """设置默认会话ID"""
+        self._default_session_id = session_id
+    
+    def _log(
+        self, 
+        level: int, 
+        message: str, 
+        session_id: Optional[str] = None,
+        duration_ms: Optional[float] = None,
+        error_code: Optional[str] = None,
+        exc_info: bool = False,
+        **kwargs
+    ) -> None:
+        """
+        内部日志记录方法
+        
+        参数:
+            level: 日志级别
+            message: 日志消息
+            session_id: 会话ID
+            duration_ms: 处理耗时（毫秒）
+            error_code: 错误代码
+            exc_info: 是否包含异常信息
+            **kwargs: 额外的上下文信息
+        """
+        extra = {
+            'component': self.component,
+            'session_id': session_id or self._default_session_id,
+            'duration_ms': duration_ms,
+            'error_code': error_code,
+            **kwargs
+        }
+        
+        self._logger.log(level, message, extra=extra, exc_info=exc_info)
+    
+    def debug(self, message: str, **kwargs) -> None:
+        """记录DEBUG级别日志"""
+        self._log(logging.DEBUG, message, **kwargs)
+    
+    def info(self, message: str, **kwargs) -> None:
+        """记录INFO级别日志"""
+        self._log(logging.INFO, message, **kwargs)
+    
+    def warning(self, message: str, **kwargs) -> None:
+        """记录WARNING级别日志"""
+        self._log(logging.WARNING, message, **kwargs)
+    
+    def error(self, message: str, exc_info: bool = False, **kwargs) -> None:
+        """记录ERROR级别日志"""
+        self._log(logging.ERROR, message, exc_info=exc_info, **kwargs)
+    
+    def critical(self, message: str, exc_info: bool = True, **kwargs) -> None:
+        """记录CRITICAL级别日志"""
+        self._log(logging.CRITICAL, message, exc_info=exc_info, **kwargs)
+    
+    def log_operation_start(
+        self, 
+        operation: str, 
+        session_id: Optional[str] = None,
+        **kwargs
+    ) -> float:
+        """
+        记录操作开始
+        
+        参数:
+            operation: 操作名称
+            session_id: 会话ID
+            **kwargs: 额外信息
+        
+        返回:
+            开始时间戳（用于计算耗时）
+        """
+        self.info(f"开始{operation}", session_id=session_id, **kwargs)
+        return time.time()
+    
+    def log_operation_end(
+        self, 
+        operation: str, 
+        start_time: float,
+        session_id: Optional[str] = None,
+        success: bool = True,
+        **kwargs
+    ) -> None:
+        """
+        记录操作结束
+        
+        参数:
+            operation: 操作名称
+            start_time: 开始时间戳
+            session_id: 会话ID
+            success: 是否成功
+            **kwargs: 额外信息
+        """
+        duration_ms = (time.time() - start_time) * 1000
+        status = "完成" if success else "失败"
+        self.info(
+            f"{operation}{status}", 
+            session_id=session_id, 
+            duration_ms=duration_ms,
+            **kwargs
+        )
+    
+    def log_error_with_context(
+        self,
+        message: str,
+        error: Exception,
+        session_id: Optional[str] = None,
+        error_code: Optional[str] = None,
+        **kwargs
+    ) -> None:
+        """
+        记录带上下文的错误
+        
+        参数:
+            message: 错误消息
+            error: 异常对象
+            session_id: 会话ID
+            error_code: 错误代码
+            **kwargs: 额外信息
+        """
+        self.error(
+            f"{message}: {str(error)}",
+            session_id=session_id,
+            error_code=error_code,
+            exc_info=True,
+            **kwargs
+        )
+
+
+def setup_logging(
+    level: Union[int, str] = logging.INFO,
+    json_format: bool = False,
+    log_file: Optional[str] = None,
+    include_stack_trace: bool = True
+) -> None:
+    """
+    配置全局日志设置
+    
+    参数:
+        level: 日志级别
+        json_format: 是否使用JSON格式（生产环境推荐）
+        log_file: 日志文件路径（可选）
+        include_stack_trace: 是否在ERROR级别包含堆栈跟踪
+    """
+    # 获取根日志记录器
+    root_logger = logging.getLogger("dubbing")
+    root_logger.setLevel(level)
+    
+    # 清除现有处理器
+    root_logger.handlers.clear()
+    
+    # 选择格式化器
+    if json_format:
+        formatter = StructuredFormatter(include_stack_trace=include_stack_trace)
+    else:
+        formatter = HumanReadableFormatter(use_colors=True)
+    
+    # 控制台处理器
+    console_handler = logging.StreamHandler(sys.stdout)
+    console_handler.setFormatter(formatter)
+    root_logger.addHandler(console_handler)
+    
+    # 文件处理器（如果指定）
+    if log_file:
+        # 确保目录存在
+        log_dir = os.path.dirname(log_file)
+        if log_dir:
+            os.makedirs(log_dir, exist_ok=True)
+        
+        file_handler = logging.FileHandler(log_file, encoding='utf-8')
+        # 文件始终使用JSON格式
+        file_handler.setFormatter(StructuredFormatter(include_stack_trace=True))
+        root_logger.addHandler(file_handler)
+    
+    root_logger.info("日志系统初始化完成")
+
+
+def get_component_logger(component: Union[Component, str]) -> ComponentLogger:
+    """
+    获取组件日志记录器
+    
+    参数:
+        component: 组件名称或枚举
+    
+    返回:
+        ComponentLogger 实例
+    """
+    return ComponentLogger(component)
+
+
+# 性能监控装饰器
+def log_performance(
+    component: Union[Component, str],
+    operation: Optional[str] = None
+):
+    """
+    性能监控装饰器
+    
+    自动记录函数执行时间和结果。
+    
+    参数:
+        component: 组件名称
+        operation: 操作名称（默认使用函数名）
+    
+    使用示例:
+        @log_performance(Component.GROQ_CLIENT, "语音识别")
+        async def transcribe(audio_path: str):
+            ...
+    """
+    def decorator(func):
+        logger = get_component_logger(component)
+        op_name = operation or func.__name__
+        
+        @wraps(func)
+        async def async_wrapper(*args, **kwargs):
+            start_time = time.time()
+            session_id = kwargs.get('session_id')
+            
+            logger.debug(f"开始执行 {op_name}", session_id=session_id)
+            
+            try:
+                result = await func(*args, **kwargs)
+                duration_ms = (time.time() - start_time) * 1000
+                logger.info(
+                    f"{op_name} 执行成功",
+                    session_id=session_id,
+                    duration_ms=duration_ms
+                )
+                return result
+            except Exception as e:
+                duration_ms = (time.time() - start_time) * 1000
+                logger.error(
+                    f"{op_name} 执行失败: {str(e)}",
+                    session_id=session_id,
+                    duration_ms=duration_ms,
+                    exc_info=True
+                )
+                raise
+        
+        @wraps(func)
+        def sync_wrapper(*args, **kwargs):
+            start_time = time.time()
+            session_id = kwargs.get('session_id')
+            
+            logger.debug(f"开始执行 {op_name}", session_id=session_id)
+            
+            try:
+                result = func(*args, **kwargs)
+                duration_ms = (time.time() - start_time) * 1000
+                logger.info(
+                    f"{op_name} 执行成功",
+                    session_id=session_id,
+                    duration_ms=duration_ms
+                )
+                return result
+            except Exception as e:
+                duration_ms = (time.time() - start_time) * 1000
+                logger.error(
+                    f"{op_name} 执行失败: {str(e)}",
+                    session_id=session_id,
+                    duration_ms=duration_ms,
+                    exc_info=True
+                )
+                raise
+        
+        # 根据函数类型选择包装器
+        if asyncio.iscoroutinefunction(func):
+            return async_wrapper
+        else:
+            return sync_wrapper
+    
+    return decorator
+
+
+# 预定义的组件日志记录器
+groq_logger = get_component_logger(Component.GROQ_CLIENT)
+asr_logger = get_component_logger(Component.ASR)
+llm_logger = get_component_logger(Component.LLM)
+tts_logger = get_component_logger(Component.TTS)
+segmenter_logger = get_component_logger(Component.SEGMENTER)
+audio_sync_logger = get_component_logger(Component.AUDIO_SYNC)
+processor_logger = get_component_logger(Component.PROCESSOR)
+router_logger = get_component_logger(Component.ROUTER)
+gateway_logger = get_component_logger(Component.GATEWAY)
+system_logger = get_component_logger(Component.SYSTEM)

backend/modules/performance_monitor.py

@@ -0,0 +1,566 @@
+"""
+性能监控模块
+
+提供系统性能监控功能，包括：
+- 处理时间记录
+- 内存使用监控
+- 并发数动态调整
+- 性能指标统计
+
+Requirements: 9.1, 9.3, 9.6
+"""
+
+import os
+import time
+import asyncio
+import logging
+import psutil
+from typing import Dict, Any, Optional, List, Callable
+from dataclasses import dataclass, field
+from datetime import datetime, timedelta
+from collections import deque
+from functools import wraps
+import threading
+
+# 配置日志
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class PerformanceMetrics:
+    """
+    性能指标数据类
+    
+    属性:
+        operation: 操作名称
+        start_time: 开始时间
+        end_time: 结束时间
+        duration_ms: 耗时（毫秒）
+        success: 是否成功
+        memory_before: 操作前内存使用（MB）
+        memory_after: 操作后内存使用（MB）
+        extra: 额外信息
+    """
+    operation: str
+    start_time: datetime
+    end_time: Optional[datetime] = None
+    duration_ms: Optional[float] = None
+    success: bool = True
+    memory_before: Optional[float] = None
+    memory_after: Optional[float] = None
+    extra: Dict[str, Any] = field(default_factory=dict)
+    
+    def complete(self, success: bool = True, memory_after: Optional[float] = None):
+        """完成指标记录"""
+        self.end_time = datetime.now()
+        self.duration_ms = (self.end_time - self.start_time).total_seconds() * 1000
+        self.success = success
+        self.memory_after = memory_after
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """转换为字典"""
+        return {
+            "operation": self.operation,
+            "start_time": self.start_time.isoformat(),
+            "end_time": self.end_time.isoformat() if self.end_time else None,
+            "duration_ms": round(self.duration_ms, 2) if self.duration_ms else None,
+            "success": self.success,
+            "memory_before_mb": round(self.memory_before, 2) if self.memory_before else None,
+            "memory_after_mb": round(self.memory_after, 2) if self.memory_after else None,
+            "extra": self.extra
+        }
+
+
+@dataclass
+class PerformanceThresholds:
+    """
+    性能阈值配置
+    
+    属性:
+        short_video_max_ms: 短视频（1-2分钟）最大处理时间（毫秒）
+        medium_video_max_ms: 中等视频（5-10分钟）最大处理时间（毫秒）
+        max_memory_mb: 最大内存使用（MB）
+        sync_tolerance_ms: 同步容差（毫秒）
+        success_rate_threshold: 成功率阈值
+    """
+    short_video_max_ms: float = 30000.0  # 30秒
+    medium_video_max_ms: float = 60000.0  # 60秒
+    max_memory_mb: float = 2048.0  # 2GB
+    sync_tolerance_ms: float = 300.0  # 0.3秒
+    success_rate_threshold: float = 0.95  # 95%
+
+
+class PerformanceMonitor:
+    """
+    性能监控器
+    
+    提供系统性能监控和统计功能。
+    
+    使用示例:
+        monitor = PerformanceMonitor()
+        
+        # 记录操作
+        with monitor.track_operation("语音识别") as metrics:
+            result = await transcribe(audio)
+            metrics.extra["segments"] = len(result)
+        
+        # 获取统计
+        stats = monitor.get_statistics()
+    """
+    
+    def __init__(
+        self,
+        thresholds: Optional[PerformanceThresholds] = None,
+        history_size: int = 1000
+    ):
+        """
+        初始化性能监控器
+        
+        参数:
+            thresholds: 性能阈值配置
+            history_size: 历史记录最大数量
+        """
+        self.thresholds = thresholds or PerformanceThresholds()
+        self._history: deque = deque(maxlen=history_size)
+        self._lock = threading.Lock()
+        
+        # 操作统计
+        self._operation_stats: Dict[str, Dict[str, Any]] = {}
+        
+        # 系统资源监控
+        self._process = psutil.Process(os.getpid())
+        
+        logger.info("性能监控器初始化完成")
+    
+    def get_memory_usage(self) -> float:
+        """
+        获取当前内存使用量（MB）
+        
+        返回:
+            内存使用量（MB）
+        """
+        try:
+            memory_info = self._process.memory_info()
+            return memory_info.rss / (1024 * 1024)  # 转换为MB
+        except Exception as e:
+            logger.warning(f"获取内存使用失败: {e}")
+            return 0.0
+    
+    def get_cpu_usage(self) -> float:
+        """
+        获取当前CPU使用率
+        
+        返回:
+            CPU使用率（百分比）
+        """
+        try:
+            return self._process.cpu_percent(interval=0.1)
+        except Exception as e:
+            logger.warning(f"获取CPU使用率失败: {e}")
+            return 0.0
+    
+    def track_operation(self, operation: str) -> 'OperationTracker':
+        """
+        创建操作跟踪器
+        
+        参数:
+            operation: 操作名称
+        
+        返回:
+            OperationTracker 上下文管理器
+        """
+        return OperationTracker(self, operation)
+    
+    def record_metrics(self, metrics: PerformanceMetrics) -> None:
+        """
+        记录性能指标
+        
+        参数:
+            metrics: 性能指标
+        """
+        with self._lock:
+            self._history.append(metrics)
+            self._update_operation_stats(metrics)
+        
+        # 检查是否超过阈值
+        self._check_thresholds(metrics)
+    
+    def _update_operation_stats(self, metrics: PerformanceMetrics) -> None:
+        """更新操作统计"""
+        op = metrics.operation
+        
+        if op not in self._operation_stats:
+            self._operation_stats[op] = {
+                "count": 0,
+                "success_count": 0,
+                "total_duration_ms": 0,
+                "min_duration_ms": float('inf'),
+                "max_duration_ms": 0,
+                "last_duration_ms": 0
+            }
+        
+        stats = self._operation_stats[op]
+        stats["count"] += 1
+        
+        if metrics.success:
+            stats["success_count"] += 1
+        
+        if metrics.duration_ms:
+            stats["total_duration_ms"] += metrics.duration_ms
+            stats["min_duration_ms"] = min(stats["min_duration_ms"], metrics.duration_ms)
+            stats["max_duration_ms"] = max(stats["max_duration_ms"], metrics.duration_ms)
+            stats["last_duration_ms"] = metrics.duration_ms
+    
+    def _check_thresholds(self, metrics: PerformanceMetrics) -> None:
+        """检查性能阈值"""
+        # 检查内存使用
+        if metrics.memory_after and metrics.memory_after > self.thresholds.max_memory_mb:
+            logger.warning(
+                f"内存使用超过阈值: {metrics.memory_after:.1f}MB > "
+                f"{self.thresholds.max_memory_mb:.1f}MB"
+            )
+        
+        # 检查处理时间
+        if metrics.duration_ms:
+            video_duration = metrics.extra.get("video_duration_seconds", 0)
+            
+            if video_duration <= 120:  # 短视频（2分钟以内）
+                if metrics.duration_ms > self.thresholds.short_video_max_ms:
+                    logger.warning(
+                        f"短视频处理时间超过阈值: {metrics.duration_ms:.0f}ms > "
+                        f"{self.thresholds.short_video_max_ms:.0f}ms"
+                    )
+            elif video_duration <= 600:  # 中等视频（10分钟以内）
+                if metrics.duration_ms > self.thresholds.medium_video_max_ms:
+                    logger.warning(
+                        f"中等视频处理时间超过阈值: {metrics.duration_ms:.0f}ms > "
+                        f"{self.thresholds.medium_video_max_ms:.0f}ms"
+                    )
+    
+    def get_statistics(self) -> Dict[str, Any]:
+        """
+        获取性能统计信息
+        
+        返回:
+            统计信息字典
+        """
+        with self._lock:
+            total_count = len(self._history)
+            success_count = sum(1 for m in self._history if m.success)
+            
+            # 计算各操作的平均耗时
+            operation_averages = {}
+            for op, stats in self._operation_stats.items():
+                if stats["count"] > 0:
+                    operation_averages[op] = {
+                        "count": stats["count"],
+                        "success_rate": stats["success_count"] / stats["count"],
+                        "avg_duration_ms": stats["total_duration_ms"] / stats["count"],
+                        "min_duration_ms": stats["min_duration_ms"] if stats["min_duration_ms"] != float('inf') else 0,
+                        "max_duration_ms": stats["max_duration_ms"],
+                        "last_duration_ms": stats["last_duration_ms"]
+                    }
+            
+            return {
+                "total_operations": total_count,
+                "success_count": success_count,
+                "success_rate": success_count / total_count if total_count > 0 else 1.0,
+                "current_memory_mb": self.get_memory_usage(),
+                "current_cpu_percent": self.get_cpu_usage(),
+                "operation_stats": operation_averages,
+                "thresholds": {
+                    "short_video_max_ms": self.thresholds.short_video_max_ms,
+                    "medium_video_max_ms": self.thresholds.medium_video_max_ms,
+                    "max_memory_mb": self.thresholds.max_memory_mb,
+                    "success_rate_threshold": self.thresholds.success_rate_threshold
+                }
+            }
+    
+    def get_recent_metrics(self, count: int = 10) -> List[Dict[str, Any]]:
+        """
+        获取最近的性能指��
+        
+        参数:
+            count: 返回数量
+        
+        返回:
+            指标列表
+        """
+        with self._lock:
+            recent = list(self._history)[-count:]
+            return [m.to_dict() for m in recent]
+    
+    def clear_history(self) -> int:
+        """
+        清除历史记录
+        
+        返回:
+            清除的记录数
+        """
+        with self._lock:
+            count = len(self._history)
+            self._history.clear()
+            self._operation_stats.clear()
+            logger.info(f"清除了 {count} 条性能记录")
+            return count
+    
+    def is_healthy(self) -> Dict[str, Any]:
+        """
+        检查系统健康状态
+        
+        返回:
+            健康状态信息
+        """
+        stats = self.get_statistics()
+        
+        issues = []
+        
+        # 检查成功率
+        if stats["success_rate"] < self.thresholds.success_rate_threshold:
+            issues.append(
+                f"成功率低于阈值: {stats['success_rate']:.1%} < "
+                f"{self.thresholds.success_rate_threshold:.1%}"
+            )
+        
+        # 检查内存使用
+        if stats["current_memory_mb"] > self.thresholds.max_memory_mb * 0.9:
+            issues.append(
+                f"内存使用接近上限: {stats['current_memory_mb']:.1f}MB / "
+                f"{self.thresholds.max_memory_mb:.1f}MB"
+            )
+        
+        return {
+            "healthy": len(issues) == 0,
+            "issues": issues,
+            "memory_mb": stats["current_memory_mb"],
+            "cpu_percent": stats["current_cpu_percent"],
+            "success_rate": stats["success_rate"]
+        }
+
+
+class OperationTracker:
+    """
+    操作跟踪器（上下文管理器）
+    
+    用于跟踪单个操作的性能指标。
+    """
+    
+    def __init__(self, monitor: PerformanceMonitor, operation: str):
+        """
+        初始化操作跟踪器
+        
+        参数:
+            monitor: 性能监控器
+            operation: 操作名称
+        """
+        self._monitor = monitor
+        self._operation = operation
+        self._metrics: Optional[PerformanceMetrics] = None
+    
+    def __enter__(self) -> PerformanceMetrics:
+        """进入上下文"""
+        self._metrics = PerformanceMetrics(
+            operation=self._operation,
+            start_time=datetime.now(),
+            memory_before=self._monitor.get_memory_usage()
+        )
+        return self._metrics
+    
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        """退出上下文"""
+        if self._metrics:
+            success = exc_type is None
+            self._metrics.complete(
+                success=success,
+                memory_after=self._monitor.get_memory_usage()
+            )
+            self._monitor.record_metrics(self._metrics)
+        
+        # 不抑制异常
+        return False
+    
+    async def __aenter__(self) -> PerformanceMetrics:
+        """异步进入上下文"""
+        return self.__enter__()
+    
+    async def __aexit__(self, exc_type, exc_val, exc_tb):
+        """异步退出上下文"""
+        return self.__exit__(exc_type, exc_val, exc_tb)
+
+
+class AdaptiveConcurrencyController:
+    """
+    自适应并发控制器
+    
+    根据系统负载动态调整并发数。
+    
+    使用示例:
+        controller = AdaptiveConcurrencyController(
+            min_workers=1,
+            max_workers=5,
+            target_memory_percent=70
+        )
+        
+        # 获取当前推荐的并发数
+        workers = controller.get_recommended_workers()
+    """
+    
+    def __init__(
+        self,
+        min_workers: int = 1,
+        max_workers: int = 5,
+        target_memory_percent: float = 70.0,
+        target_cpu_percent: float = 80.0
+    ):
+        """
+        初始化并发控制器
+        
+        参数:
+            min_workers: 最小并发数
+            max_workers: 最大并发数
+            target_memory_percent: 目标内存使用率
+            target_cpu_percent: 目标CPU使用率
+        """
+        self.min_workers = min_workers
+        self.max_workers = max_workers
+        self.target_memory_percent = target_memory_percent
+        self.target_cpu_percent = target_cpu_percent
+        
+        self._current_workers = min_workers
+        self._process = psutil.Process(os.getpid())
+        
+        logger.info(
+            f"自适应并发控制器初始化: "
+            f"workers={min_workers}-{max_workers}, "
+            f"target_memory={target_memory_percent}%, "
+            f"target_cpu={target_cpu_percent}%"
+        )
+    
+    def get_system_load(self) -> Dict[str, float]:
+        """
+        获取系统负载
+        
+        返回:
+            负载信息字典
+        """
+        try:
+            memory = psutil.virtual_memory()
+            cpu = psutil.cpu_percent(interval=0.1)
+            
+            return {
+                "memory_percent": memory.percent,
+                "cpu_percent": cpu,
+                "memory_available_mb": memory.available / (1024 * 1024)
+            }
+        except Exception as e:
+            logger.warning(f"获取系统负载失败: {e}")
+            return {
+                "memory_percent": 50.0,
+                "cpu_percent": 50.0,
+                "memory_available_mb": 1024.0
+            }
+    
+    def get_recommended_workers(self) -> int:
+        """
+        获取推荐的并发数
+        
+        返回:
+            推荐的并发数
+        """
+        load = self.get_system_load()
+        
+        # 根据内存使用调整
+        memory_factor = 1.0
+        if load["memory_percent"] > self.target_memory_percent:
+            # 内存使用过高，减少并发
+            memory_factor = self.target_memory_percent / load["memory_percent"]
+        elif load["memory_percent"] < self.target_memory_percent * 0.5:
+            # 内存使用较低，可以增加并发
+            memory_factor = 1.2
+        
+        # 根据CPU使用调整
+        cpu_factor = 1.0
+        if load["cpu_percent"] > self.target_cpu_percent:
+            # CPU使用过高，减少并发
+            cpu_factor = self.target_cpu_percent / load["cpu_percent"]
+        elif load["cpu_percent"] < self.target_cpu_percent * 0.5:
+            # CPU使用较低，可以增加并发
+            cpu_factor = 1.2
+        
+        # 计算推荐值
+        factor = min(memory_factor, cpu_factor)
+        recommended = int(self._current_workers * factor)
+        
+        # 限制在范围内
+        recommended = max(self.min_workers, min(self.max_workers, recommended))
+        
+        # 平滑调整（每次最多变化1）
+        if recommended > self._current_workers:
+            self._current_workers = min(self._current_workers + 1, recommended)
+        elif recommended < self._current_workers:
+            self._current_workers = max(self._current_workers - 1, recommended)
+        
+        logger.debug(
+            f"并发调整: workers={self._current_workers}, "
+            f"memory={load['memory_percent']:.1f}%, "
+            f"cpu={load['cpu_percent']:.1f}%"
+        )
+        
+        return self._current_workers
+    
+    def reset(self) -> None:
+        """重置到最小并发数"""
+        self._current_workers = self.min_workers
+        logger.info(f"并发数重置为 {self.min_workers}")
+
+
+# 全局性能监控器实例
+_global_monitor: Optional[PerformanceMonitor] = None
+
+
+def get_performance_monitor() -> PerformanceMonitor:
+    """
+    获取全局性能监控器
+    
+    返回:
+        PerformanceMonitor 实例
+    """
+    global _global_monitor
+    if _global_monitor is None:
+        _global_monitor = PerformanceMonitor()
+    return _global_monitor
+
+
+def track_performance(operation: str):
+    """
+    性能跟踪装饰器
+    
+    参数:
+        operation: 操作名称
+    
+    使用示例:
+        @track_performance("语音识别")
+        async def transcribe(audio_path: str):
+            ...
+    """
+    def decorator(func):
+        @wraps(func)
+        async def async_wrapper(*args, **kwargs):
+            monitor = get_performance_monitor()
+            async with monitor.track_operation(operation) as metrics:
+                result = await func(*args, **kwargs)
+                return result
+        
+        @wraps(func)
+        def sync_wrapper(*args, **kwargs):
+            monitor = get_performance_monitor()
+            with monitor.track_operation(operation) as metrics:
+                result = func(*args, **kwargs)
+                return result
+        
+        if asyncio.iscoroutinefunction(func):
+            return async_wrapper
+        else:
+            return sync_wrapper
+    
+    return decorator

backend/modules/processor.py

@@ -0,0 +1,517 @@
+"""
+并行处理池模块
+
+提供音频片段的并行处理功能，集成完整的处理流程：
+ASR → LLM → TTS → Sync
+
+包含性能监控和自适应并发控制。
+"""
+
+import os
+import asyncio
+import logging
+import time
+from typing import List, Dict, Any, Optional, Callable
+from dataclasses import dataclass
+
+from .groq_client import GroqClient, GroqConfig, GroqError
+from .tts_generator import TTSGenerator, TTSConfig, TTSError
+from .audio_sync import AudioSyncEngine, SyncConfig, AudioSyncError
+from .performance_monitor import (
+    get_performance_monitor,
+    AdaptiveConcurrencyController,
+    track_performance
+)
+
+# 配置日志
+logger = logging.getLogger(__name__)
+
+
+class ProcessingError(Exception):
+    """处理异常基类"""
+    pass
+
+
+class SegmentProcessingError(ProcessingError):
+    """片段处理异常"""
+    def __init__(self, segment_index: int, stage: str, reason: str):
+        self.segment_index = segment_index
+        self.stage = stage
+        self.reason = reason
+        self.message = f"片段 {segment_index} 在 {stage} 阶段失败: {reason}"
+        super().__init__(self.message)
+
+
+@dataclass
+class ProcessorConfig:
+    """
+    并行处理池配置
+    
+    属性:
+        max_workers: 最大并发工作数，默认3
+        min_workers: 最小并发工作数，默认1
+        segment_timeout: 单个片段处理超时（秒），默认480秒（8分钟）
+        retry_count: 失败重试次数，默认2
+        temp_dir: 临时文件目录
+        adaptive_concurrency: 是否启用自适应并发控制
+    """
+    max_workers: int = 3
+    min_workers: int = 1
+    segment_timeout: float = 480.0  # 8分钟
+    retry_count: int = 2
+    temp_dir: str = "temp/processing"
+    adaptive_concurrency: bool = True
+
+
+@dataclass
+class SegmentResult:
+    """
+    片段处理结果
+    
+    属性:
+        index: 片段索引
+        success: 是否成功
+        audio_path: 生成的音频文件路径
+        duration: 片段时长
+        start_time: 开始时间
+        error: 错误信息（如果失败）
+        processing_time: 处理耗时（秒）
+    """
+    index: int
+    success: bool
+    audio_path: Optional[str] = None
+    duration: Optional[float] = None
+    start_time: Optional[float] = None
+    error: Optional[str] = None
+    processing_time: Optional[float] = None
+
+
+class ParallelProcessingPool:
+    """
+    并行处理池
+    
+    管理多个音频片段的并行处理，集成ASR、LLM、TTS和音频同步。
+    
+    使用示例:
+        pool = ParallelProcessingPool()
+        await pool.initialize()
+        
+        segments = [
+            {"audio_path": "seg1.mp3", "start_time": 0, "duration": 300},
+            {"audio_path": "seg2.mp3", "start_time": 300, "duration": 300}
+        ]
+        
+        results = await pool.process_segments(
+            segments,
+            progress_callback=lambda msg, pct: print(f"{pct}%: {msg}")
+        )
+    """
+    
+    def __init__(
+        self, 
+        config: Optional[ProcessorConfig] = None,
+        groq_config: Optional[GroqConfig] = None,
+        tts_config: Optional[TTSConfig] = None,
+        sync_config: Optional[SyncConfig] = None
+    ):
+        """
+        初始化并行处理池
+        
+        参数:
+            config: 处理池配置
+            groq_config: Groq客户端配置
+            tts_config: TTS生成器配置
+            sync_config: 音频同步配置
+        """
+        self.config = config or ProcessorConfig()
+        
+        # 存储子模块配置，延迟初始化
+        self._groq_config = groq_config
+        self._tts_config = tts_config
+        self._sync_config = sync_config
+        
+        # 子模块实例
+        self.groq_client: Optional[GroqClient] = None
+        self.tts_generator: Optional[TTSGenerator] = None
+        self.audio_sync: Optional[AudioSyncEngine] = None
+        
+        self._initialized = False
+        
+        # 性能监控
+        self._performance_monitor = get_performance_monitor()
+        
+        # 自适应并发控制器
+        self._concurrency_controller = None
+        if self.config.adaptive_concurrency:
+            self._concurrency_controller = AdaptiveConcurrencyController(
+                min_workers=self.config.min_workers,
+                max_workers=self.config.max_workers
+            )
+        
+        # 确保临时目录存在
+        os.makedirs(self.config.temp_dir, exist_ok=True)
+        
+        logger.info(
+            f"并行处理池配置: 最大并发={self.config.max_workers}, "
+            f"超时={self.config.segment_timeout}s, "
+            f"自适应并发={'启用' if self.config.adaptive_concurrency else '禁用'}"
+        )
+    
+    async def initialize(self) -> None:
+        """
+        初始化所有��理模块
+        
+        创建并初始化Groq客户端、TTS生成器和音频同步引擎。
+        """
+        if self._initialized:
+            logger.debug("处理池已初始化，跳过")
+            return
+        
+        logger.info("初始化并行处理池...")
+        
+        # 初始化 Groq 客户端
+        self.groq_client = GroqClient(self._groq_config)
+        await self.groq_client.initialize()
+        
+        # 初始化 TTS 生成器
+        self.tts_generator = TTSGenerator(self._tts_config)
+        
+        # 初始化音频同步引擎
+        self.audio_sync = AudioSyncEngine(self._sync_config)
+        
+        self._initialized = True
+        logger.info("并行处理池初始化完成")
+    
+    def _ensure_initialized(self) -> None:
+        """确保处理池已初始化"""
+        if not self._initialized:
+            raise ProcessingError("处理池未初始化，请先调用 initialize()")
+    
+    async def process_segments(
+        self, 
+        segments: List[Dict[str, Any]],
+        progress_callback: Optional[Callable[[str, float], None]] = None,
+        config: Optional[Dict[str, Any]] = None
+    ) -> List[SegmentResult]:
+        """
+        并行处理多个音频片段
+        
+        参数:
+            segments: 片段列表，每个片段包含:
+                - audio_path: str - 音频文件路径
+                - start_time: float - 开始时间（秒）
+                - duration: float - 片段时长（秒）
+            progress_callback: 进度回调函数，接收 (消息, 百分比)
+            config: 处理配置，包含客户端配置等
+        
+        返回:
+            处理结果列表
+        """
+        self._ensure_initialized()
+        
+        if not segments:
+            logger.warning("处理输入为空")
+            return []
+        
+        # 提取客户端配置
+        client_config = {}
+        if config and 'client_config' in config:
+            client_config = config['client_config']
+            logger.info(f"处理器使用客户端配置: {list(client_config.keys())}")
+        
+        total = len(segments)
+        logger.info(f"开始并行处理 {total} 个片段")
+        
+        if progress_callback:
+            progress_callback("开始处理...", 0)
+        
+        # 获取当前推荐的并发数
+        if self._concurrency_controller:
+            current_workers = self._concurrency_controller.get_recommended_workers()
+            logger.info(f"自适应并发: 当前推荐 {current_workers} 个工作线程")
+        else:
+            current_workers = self.config.max_workers
+        
+        # 创建处理任务（传递客户端配置）
+        tasks = []
+        for i, segment in enumerate(segments):
+            task = self._process_single_segment(
+                segment, 
+                i, 
+                total,
+                progress_callback,
+                client_config  # 传递客户端配置
+            )
+            tasks.append(task)
+        
+        # 使用信号量限制并发数
+        semaphore = asyncio.Semaphore(current_workers)
+        
+        async def limited_task(task):
+            async with semaphore:
+                return await task
+        
+        # 使用性能监控跟踪整体处理
+        with self._performance_monitor.track_operation("并行片段处理") as metrics:
+            metrics.extra["total_segments"] = total
+            metrics.extra["workers"] = current_workers
+            
+            # 并行执行
+            results = await asyncio.gather(
+                *[limited_task(task) for task in tasks],
+                return_exceptions=True
+            )
+        
+        # 处理结果
+        processed_results = []
+        success_count = 0
+        
+        for i, result in enumerate(results):
+            if isinstance(result, Exception):
+                logger.error(f"片段 {i} 处理异常: {result}")
+                processed_results.append(SegmentResult(
+                    index=i,
+                    success=False,
+                    error=str(result)
+                ))
+            elif isinstance(result, SegmentResult):
+                processed_results.append(result)
+                if result.success:
+                    success_count += 1
+            else:
+                processed_results.append(SegmentResult(
+                    index=i,
+                    success=False,
+                    error="未知结果类型"
+                ))
+        
+        logger.info(f"并行处理完成: {success_count}/{total} 成功")
+        
+        if progress_callback:
+            progress_callback("处理完成", 100)
+        
+        return processed_results
+    
+    async def _process_single_segment(
+        self, 
+        segment: Dict[str, Any], 
+        index: int,
+        total: int,
+        progress_callback: Optional[Callable[[str, float], None]] = None,
+        client_config: Optional[Dict[str, Any]] = None
+    ) -> SegmentResult:
+        """
+        处理单个音频片段的完整流程
+        
+        流程: ASR → LLM → TTS → Sync
+        
+        参数:
+            segment: 片段信息
+            index: 片段索引
+            total: 总片段数
+            progress_callback: 进度回调
+            client_config: 客户端配置（优先于默认配置）
+        
+        返回:
+            处理结果
+        """
+        start_time = time.time()
+        audio_path = segment.get('audio_path')
+        seg_start = segment.get('start_time', 0)
+        seg_duration = segment.get('duration', 0)
+        
+        logger.info(f"开始处理片段 {index + 1}/{total}")
+        
+        # 记录客户端配置使用情况
+        if client_config:
+            logger.info(f"片段 {index + 1} 使用客户端配置: {list(client_config.keys())}")
+        else:
+            logger.info(f"片段 {index + 1} 使用默认配置")
+        
+        # 重试机制
+        last_error = None
+        for attempt in range(self.config.retry_count + 1):
+            try:
+                # 设置超时
+                result = await asyncio.wait_for(
+                    self._do_process_segment(
+                        audio_path, 
+                        seg_start, 
+                        seg_duration,
+                        index,
+                        total,
+                        progress_callback,
+                        client_config  # 传递客户端配置
+                    ),
+                    timeout=self.config.segment_timeout
+                )
+                
+                processing_time = time.time() - start_time
+                
+                return SegmentResult(
+                    index=index,
+                    success=True,
+                    audio_path=result['audio_path'],
+                    duration=seg_duration,
+                    start_time=seg_start,
+                    processing_time=processing_time
+                )
+                
+            except asyncio.TimeoutError:
+                last_error = f"处理超时（{self.config.segment_timeout}秒）"
+                logger.warning(f"片段 {index} 超时（第 {attempt + 1} 次尝试）")
+                
+            except Exception as e:
+                last_error = str(e)
+                logger.warning(
+                    f"片段 {index} 处理失败（第 {attempt + 1} 次尝试）: {e}"
+                )
+                
+                if attempt < self.config.retry_count:
+                    await asyncio.sleep(1)
+        
+        # 所有重试都失败
+        processing_time = time.time() - start_time
+        logger.error(f"片段 {index} 处理失败: {last_error}")
+        
+        return SegmentResult(
+            index=index,
+            success=False,
+            start_time=seg_start,
+            error=last_error,
+            processing_time=processing_time
+        )
+    
+    async def _do_process_segment(
+        self,
+        audio_path: str,
+        start_time: float,
+        duration: float,
+        index: int,
+        total: int,
+        progress_callback: Optional[Callable[[str, float], None]] = None,
+        client_config: Optional[Dict[str, Any]] = None
+    ) -> Dict[str, Any]:
+        """
+        执行单个片段的实际处理
+        
+        参数:
+            audio_path: 音频文件路径
+            start_time: 开始时间
+            duration: 片段时长
+            index: 片段索引
+            total: 总片段数
+            progress_callback: 进度回调
+        
+        返回:
+            处理结果字典
+        """
+        base_progress = (index / total) * 100
+        step_progress = (1 / total) * 100
+        
+        def update_progress(stage: str, stage_pct: float):
+            if progress_callback:
+                pct = base_progress + (stage_pct / 100) * step_progress
+                progress_callback(f"片段 {index + 1}: {stage}", pct)
+        
+        # 1. ASR - 语音识别
+        update_progress("语音识别中...", 0)
+        transcription = await self.groq_client.transcribe(audio_path)
+        
+        if not transcription.get('text'):
+            raise SegmentProcessingError(index, "ASR", "识别结果为空")
+        
+        logger.debug(
+            f"片段 {index} ASR完成: 语言={transcription.get('language')}, "
+            f"片段数={len(transcription.get('segments', []))}"
+        )
+        
+        # 2. LLM - 翻译和角色识别
+        update_progress("翻译中...", 25)
+        translation = await self.groq_client.translate(
+            transcription['text'],
+            transcription['language'],
+            transcription.get('segments')
+        )
+        
+        if not translation.get('segments'):
+            raise SegmentProcessingError(index, "LLM", "翻译结果为空")
+        
+        logger.debug(f"片段 {index} 翻译完成: {len(translation['segments'])} 个片段")
+        
+        # 3. TTS - 语音合成（使用客户端配置）
+        update_progress("生成配音...", 50)
+        
+        # 将客户端配置传递给TTS生成器
+        tts_paths = await self.tts_generator.generate(
+            translation['segments'],
+            client_config
+        )
+        
+        # 过滤有效的TTS路径
+        valid_tts = [(i, p) for i, p in enumerate(tts_paths) if p is not None]
+        if not valid_tts:
+            raise SegmentProcessingError(index, "TTS", "所有TTS生成失败")
+        
+        logger.debug(f"片段 {index} TTS完成: {len(valid_tts)}/{len(tts_paths)} 成功")
+        
+        # 4. 音频同步
+        update_progress("音频同步...", 75)
+        
+        # 准备同步所需的片段信息
+        sync_segments = []
+        sync_tts_paths = []
+        
+        for i, seg in enumerate(translation['segments']):
+            if i < len(tts_paths) and tts_paths[i] is not None:
+                sync_segments.append({
+                    'start': seg.get('start', 0),
+                    'end': seg.get('end', 0)
+                })
+                sync_tts_paths.append(tts_paths[i])
+        
+        # 如果没有时间戳信息，使用均匀分布
+        if not any(s.get('start', 0) or s.get('end', 0) for s in sync_segments):
+            segment_duration = duration / len(sync_segments) if sync_segments else duration
+            for i, seg in enumerate(sync_segments):
+                seg['start'] = i * segment_duration
+                seg['end'] = (i + 1) * segment_duration
+        
+        synced_audio = await self.audio_sync.align(
+            sync_tts_paths,
+            sync_segments,
+            duration,
+            client_config  # 传递客户端配置
+        )
+        
+        update_progress("完成", 100)
+        logger.info(f"片段 {index} 处理完成")
+        
+        return {
+            'audio_path': synced_audio,
+            'transcription': transcription,
+            'translation': translation
+        }
+    
+    def cleanup(self) -> int:
+        """
+        清理所有临时文件
+        
+        返回:
+            清理的文件数量
+        """
+        cleaned = 0
+        
+        if self.tts_generator:
+            cleaned += self.tts_generator.cleanup()
+        
+        if self.audio_sync:
+            cleaned += self.audio_sync.cleanup()
+        
+        logger.info(f"处理池清理完成: {cleaned} 个文件")
+        return cleaned
+    
+    @property
+    def is_initialized(self) -> bool:
+        """检查处理池是否已初始化"""
+        return self._initialized

backend/modules/router.py

@@ -0,0 +1,285 @@
+"""
+请求路由模块
+
+处理从扩展端上传的音频数据，支持：
+- 录制模式：处理tabCapture录制的音频
+- 直接上传：处理扩展端拦截下载的音频
+
+注意：HF Spaces 不能直接下载视频，所有音频都由扩展端获取后上传。
+"""
+
+import os
+import asyncio
+import logging
+import time
+import uuid
+from typing import Dict, Any, Optional, List, Tuple
+from dataclasses import dataclass
+
+# 配置日志
+logger = logging.getLogger(__name__)
+
+
+class RouterError(Exception):
+    """路由异常基类"""
+    pass
+
+
+class AudioProcessingError(RouterError):
+    """音频处理异常"""
+    def __init__(self, reason: str):
+        self.reason = reason
+        self.message = f"音频处理失败: {reason}"
+        super().__init__(self.message)
+
+
+@dataclass
+class RouterConfig:
+    """
+    路由器配置
+    
+    属性:
+        temp_dir: 临时文件目录
+        max_duration: 最大处理时长（秒）
+        use_low_quality: 是否使用低码率音频（加速处理）
+    """
+    temp_dir: str = "temp/downloads"
+    max_duration: float = 3600.0  # 1小时
+    use_low_quality: bool = True  # 默认使用低码率加速处理
+
+
+class RequestRouter:
+    """
+    请求路由器
+    
+    处理从扩展端上传的音频数据。
+    
+    注意：HF Spaces 不能直接下载视频，所有音频都由扩展端获取后上传。
+    
+    使用示例:
+        router = RequestRouter()
+        
+        # 处理上传的音频
+        result = await router.route_request(
+            mode="record",
+            data={"audio_data": audio_bytes}
+        )
+    """
+    
+    def __init__(self, config: Optional[RouterConfig] = None):
+        """
+        初始化请求路由器
+        
+        参数:
+            config: 路由器配置
+        """
+        self.config = config or RouterConfig()
+        
+        # 确保临时目录存在
+        os.makedirs(self.config.temp_dir, exist_ok=True)
+        
+        # 跟踪临时文件
+        self._temp_files: List[str] = []
+        
+        logger.info(f"请求路由器初始化: 临时目录={self.config.temp_dir}")
+    
+    async def route_request(
+        self,
+        mode: str,
+        data: Dict[str, Any],
+        session_id: Optional[str] = None
+    ) -> Dict[str, Any]:
+        """
+        路由处理请求
+        
+        参数:
+            mode: 处理模式 ("record", "upload")
+            data: 请求数据
+            session_id: 会话ID（可选）
+        
+        返回:
+            处理结果，包含:
+            - mode: 实际使用的模式
+            - audio_path: 音频文件路径
+            - duration: 音频时长
+        """
+        if session_id is None:
+            session_id = str(uuid.uuid4())[:8]
+        
+        logger.info(f"[{session_id}] 路由请求: mode={mode}")
+        
+        # 所有模式都走录制/上传处理
+        return await self._handle_audio_upload(data, session_id)
+    
+    async def _handle_audio_upload(
+        self,
+        data: Dict[str, Any],
+        session_id: str
+    ) -> Dict[str, Any]:
+        """
+        处理上传的音频数据
+        
+        参数:
+            data: 请求数据，包含 audio_data 或 audio_path 字段
+            session_id: 会话ID
+        
+        返回:
+            处理结果
+        """
+        logger.info(f"[{session_id}] 处理上传的音频")
+        
+        # 获取音频数据
+        audio_data = data.get('audio_data')
+        audio_path = data.get('audio_path')
+        
+        if audio_data:
+            # 保存音频数据到文件
+            output_path = os.path.join(
+                self.config.temp_dir,
+                f"uploaded_{session_id}.wav"
+            )
+            
+            with open(output_path, 'wb') as f:
+                if isinstance(audio_data, bytes):
+                    f.write(audio_data)
+                else:
+                    f.write(audio_data.read())
+            
+            audio_path = output_path
+            self._temp_files.append(output_path)
+            
+            file_size = os.path.getsize(output_path) / (1024 * 1024)
+            logger.info(f"[{session_id}] 音频已保存: {file_size:.2f}MB")
+            
+        elif audio_path:
+            if not os.path.exists(audio_path):
+                raise RouterError(f"音频文件不存在: {audio_path}")
+        else:
+            raise RouterError("需要提供 audio_data 或 audio_path")
+        
+        # 如果启用低质量模式，压缩音频以加速处理
+        if self.config.use_low_quality:
+            audio_path = await self._compress_audio(audio_path, session_id)
+        
+        # 获取音频时长
+        duration = await self._get_audio_duration(audio_path)
+        
+        # 检查时长限制
+        if duration > self.config.max_duration:
+            logger.warning(
+                f"[{session_id}] 音频时长 {duration}s 超过限制 "
+                f"{self.config.max_duration}s"
+            )
+        
+        return {
+            'mode': 'upload',
+            'audio_path': audio_path,
+            'duration': duration,
+            'session_id': session_id
+        }
+    
+    async def _compress_audio(
+        self,
+        audio_path: str,
+        session_id: str
+    ) -> str:
+        """
+        压缩音频文件以加速ASR处理
+        
+        优化策略：
+        - 降低采样率到16kHz（Whisper推荐）
+        - 转换为单声道
+        - 使用低比特率
+        
+        参数:
+            audio_path: 原始音频路径
+            session_id: 会话ID
+        
+        返回:
+            压缩后的音频路径
+        """
+        try:
+            from pydub import AudioSegment
+            
+            logger.info(f"[{session_id}] 压缩音频以加速处理")
+            
+            # 加载音频
+            audio = AudioSegment.from_file(audio_path)
+            original_size = os.path.getsize(audio_path) / (1024 * 1024)
+            
+            # 转换为单声道
+            if audio.channels > 1:
+                audio = audio.set_channels(1)
+            
+            # 降低采样率到16kHz（Whisper推荐的采样率）
+            if audio.frame_rate > 16000:
+                audio = audio.set_frame_rate(16000)
+            
+            # 导出压缩后的音频
+            compressed_path = os.path.join(
+                self.config.temp_dir,
+                f"compressed_{session_id}.mp3"
+            )
+            
+            # 使用低比特率导出（32k足够ASR使用）
+            audio.export(
+                compressed_path,
+                format="mp3",
+                bitrate="32k",
+                parameters=["-ac", "1"]  # 确保单声道
+            )
+            
+            compressed_size = os.path.getsize(compressed_path) / (1024 * 1024)
+            compression_ratio = (1 - compressed_size / original_size) * 100 if original_size > 0 else 0
+            
+            self._temp_files.append(compressed_path)
+            
+            logger.info(
+                f"[{session_id}] 音频压缩完成: "
+                f"{original_size:.2f}MB -> {compressed_size:.2f}MB "
+                f"(压缩率: {compression_ratio:.1f}%)"
+            )
+            
+            return compressed_path
+            
+        except Exception as e:
+            logger.warning(f"[{session_id}] 音频压缩失败，使用原始文件: {e}")
+            return audio_path
+    
+    async def _get_audio_duration(self, audio_path: str) -> float:
+        """
+        获取音频文件时长
+        
+        参数:
+            audio_path: 音频文件路径
+        
+        返回:
+            时长（秒）
+        """
+        try:
+            from pydub import AudioSegment
+            audio = AudioSegment.from_file(audio_path)
+            return len(audio) / 1000.0
+        except Exception as e:
+            logger.warning(f"获取音频时长失败: {e}，返回0")
+            return 0
+    
+    def cleanup(self) -> int:
+        """
+        清理临时文件
+        
+        返回:
+            清理的文件数量
+        """
+        cleaned = 0
+        for path in self._temp_files:
+            try:
+                if os.path.exists(path):
+                    os.remove(path)
+                    cleaned += 1
+            except Exception as e:
+                logger.warning(f"清理临时文件失败 {path}: {e}")
+        
+        self._temp_files.clear()
+        logger.info(f"路由器清理完成: {cleaned} 个文件")
+        return cleaned

backend/modules/segmenter.py

@@ -0,0 +1,452 @@
+"""
+智能音频分段器模块
+
+提供长音频智能分段功能，支持：
+- 静音检测算法（RMS能量分析）
+- 在语音停顿处智能切分
+- 确保片段时长在合理范围内
+"""
+
+import os
+import logging
+from typing import List, Tuple, Optional, Dict, Any, TYPE_CHECKING
+from dataclasses import dataclass
+
+# 配置日志
+logger = logging.getLogger(__name__)
+
+# 类型检查时导入numpy
+if TYPE_CHECKING:
+    import numpy as np
+
+
+class SegmenterError(Exception):
+    """分段器异常基类"""
+    pass
+
+
+class AudioLoadError(SegmenterError):
+    """音频加载异常"""
+    def __init__(self, path: str, reason: str):
+        self.path = path
+        self.reason = reason
+        self.message = f"音频加载失败 [{path}]: {reason}"
+        super().__init__(self.message)
+
+
+class SegmentationError(SegmenterError):
+    """分段处理异常"""
+    def __init__(self, reason: str):
+        self.reason = reason
+        self.message = f"音频分段失败: {reason}"
+        super().__init__(self.message)
+
+
+@dataclass
+class SegmentInfo:
+    """
+    音频片段信息
+    
+    属性:
+        index: 片段索引
+        start_time: 开始时间（秒）
+        end_time: 结束时间（秒）
+        duration: 片段时长（秒）
+        audio_path: 片段音频文件路径（可选）
+    """
+    index: int
+    start_time: float
+    end_time: float
+    duration: float
+    audio_path: Optional[str] = None
+
+
+@dataclass
+class SegmenterConfig:
+    """
+    分段器配置
+    
+    属性:
+        max_segment_duration: 最大片段时长（秒），默认480秒（8分钟）
+        min_segment_duration: 最小片段时长（秒），默认300秒（5分钟）
+        silence_threshold_db: 静音阈值（dB），默认-40dB
+        min_silence_duration: 最小静音持续时间（秒），默认0.5秒
+        frame_length_ms: 帧长度（毫秒），默认25ms
+        hop_length_ms: 帧移（毫秒），默认10ms
+        auto_segment_threshold: 自动分段触发阈值（秒），默认600秒（10分钟）
+    """
+    max_segment_duration: float = 480.0  # 8分钟
+    min_segment_duration: float = 300.0  # 5分钟
+    silence_threshold_db: float = -40.0
+    min_silence_duration: float = 0.5
+    frame_length_ms: float = 25.0
+    hop_length_ms: float = 10.0
+    auto_segment_threshold: float = 600.0  # 10分钟
+
+
+class SmartSegmenter:
+    """
+    智能音频分段器
+    
+    在语音停顿处进行智能切分，避免截断句子。
+    支持长音频自动分段处理。
+    
+    使用示例:
+        segmenter = SmartSegmenter()
+        
+        # 检查是否需要分段
+        if segmenter.should_segment(audio_path):
+            segments = await segmenter.segment_audio(audio_path)
+            for seg in segments:
+                print(f"片段 {seg.index}: {seg.start_time:.2f}s - {seg.end_time:.2f}s")
+    """
+    
+    def __init__(self, config: Optional[SegmenterConfig] = None):
+        """
+        初始化智能分段器
+        
+        参数:
+            config: 分段器配置，如果为None则使用默认配置
+        """
+        self.config = config or SegmenterConfig()
+        
+        logger.info(
+            f"智能分段器初始化: 最大片段={self.config.max_segment_duration}s, "
+            f"静音阈值={self.config.silence_threshold_db}dB"
+        )
+    
+    def get_audio_duration(self, audio_path: str) -> float:
+        """
+        获取音频文件时长
+        
+        参数:
+            audio_path: 音频文件路径
+        
+        返回:
+            音频时长（秒）
+        
+        异常:
+            AudioLoadError: 音频加载失败
+        """
+        if not os.path.exists(audio_path):
+            raise AudioLoadError(audio_path, "文件不存在")
+        
+        try:
+            import librosa
+            duration = librosa.get_duration(path=audio_path)
+            return duration
+        except Exception as e:
+            raise AudioLoadError(audio_path, str(e))
+    
+    def should_segment(self, audio_path: str) -> bool:
+        """
+        判断音频是否需要分段处理
+        
+        根据需求 1.4：音频超过10分钟时自动启用智能分段
+        
+        参数:
+            audio_path: 音频文件路径
+        
+        返回:
+            是否需要分段
+        """
+        try:
+            duration = self.get_audio_duration(audio_path)
+            should = duration > self.config.auto_segment_threshold
+            
+            if should:
+                logger.info(
+                    f"音频时长 {duration:.1f}s 超过阈值 "
+                    f"{self.config.auto_segment_threshold}s，需要分段"
+                )
+            
+            return should
+        except Exception as e:
+            logger.warning(f"检查音频时长失败: {e}，默认不分段")
+            return False
+    
+    async def segment_audio(
+        self, 
+        audio_path: str
+    ) -> List[SegmentInfo]:
+        """
+        智能分段音频
+        
+        在语音停顿处进行切分，确保片段时长在5-8分钟范围内。
+        
+        参数:
+            audio_path: 音频文件路径
+        
+        返回:
+            片段信息列表
+        
+        异常:
+            AudioLoadError: 音频加载失败
+            SegmentationError: 分段处理失败
+        """
+        if not os.path.exists(audio_path):
+            raise AudioLoadError(audio_path, "文件不存在")
+        
+        logger.info(f"开始智能分段: {audio_path}")
+        
+        try:
+            import librosa
+            
+            # 加载音频
+            audio, sr = librosa.load(audio_path, sr=None)
+            total_duration = len(audio) / sr
+            
+            logger.info(f"音频加载完成: 时长={total_duration:.1f}s, 采样率={sr}")
+            
+            # 如果音频不需要分段，返回单个片段
+            if total_duration <= self.config.auto_segment_threshold:
+                return [SegmentInfo(
+                    index=0,
+                    start_time=0,
+                    end_time=total_duration,
+                    duration=total_duration
+                )]
+            
+            # 检测静音区间
+            silence_intervals = self._detect_silence(audio, sr)
+            logger.debug(f"检测到 {len(silence_intervals)} 个静音区间")
+            
+            # 在静音处进行智能切分
+            segments = self._create_segments(
+                silence_intervals, 
+                total_duration
+            )
+            
+            logger.info(f"分段完成: {len(segments)} 个片段")
+            
+            return segments
+            
+        except ImportError:
+            raise SegmenterError("librosa 库未安装，请运行: pip install librosa")
+        except Exception as e:
+            raise SegmentationError(str(e))
+    
+    def _detect_silence(
+        self, 
+        audio, 
+        sr: int
+    ) -> List[Tuple[float, float]]:
+        """
+        检测静音区间
+        
+        使用RMS能量分析检测低于阈值的静音区间。
+        
+        参数:
+            audio: 音频数据数组
+            sr: 采样率
+        
+        返回:
+            静音区间列表，每个元素为 (开始时间, 结束时间)
+        """
+        import librosa
+        import numpy as np
+        
+        # 计算帧参数
+        frame_length = int(self.config.frame_length_ms * sr / 1000)
+        hop_length = int(self.config.hop_length_ms * sr / 1000)
+        
+        # 计算RMS能量
+        rms = librosa.feature.rms(
+            y=audio, 
+            frame_length=frame_length,
+            hop_length=hop_length
+        )[0]
+        
+        # 转换为dB
+        # 添加小值避免log(0)
+        rms_db = librosa.amplitude_to_db(rms + 1e-10)
+        
+        # 检测低于阈值的帧
+        silence_frames = rms_db < self.config.silence_threshold_db
+        
+        # 将帧索引转换为时间区间
+        silence_intervals = self._frames_to_intervals(
+            silence_frames, 
+            hop_length, 
+            sr
+        )
+        
+        # 过滤太短的静音区间
+        min_silence_samples = self.config.min_silence_duration
+        filtered_intervals = [
+            (start, end) for start, end in silence_intervals
+            if (end - start) >= min_silence_samples
+        ]
+        
+        return filtered_intervals
+    
+    def _frames_to_intervals(
+        self, 
+        silence_frames, 
+        hop_length: int, 
+        sr: int
+    ) -> List[Tuple[float, float]]:
+        """
+        将静音帧转换为时间区间
+        
+        参数:
+            silence_frames: 布尔数组，True表示静音帧
+            hop_length: 帧移（采样点数）
+            sr: 采样率
+        
+        返回:
+            时间区间列表
+        """
+        intervals = []
+        in_silence = False
+        start_frame = 0
+        
+        for i, is_silence in enumerate(silence_frames):
+            if is_silence and not in_silence:
+                # 静音开始
+                start_frame = i
+                in_silence = True
+            elif not is_silence and in_silence:
+                # 静音结束
+                start_time = start_frame * hop_length / sr
+                end_time = i * hop_length / sr
+                intervals.append((start_time, end_time))
+                in_silence = False
+        
+        # 处理末尾的静音
+        if in_silence:
+            start_time = start_frame * hop_length / sr
+            end_time = len(silence_frames) * hop_length / sr
+            intervals.append((start_time, end_time))
+        
+        return intervals
+    
+    def _create_segments(
+        self, 
+        silence_intervals: List[Tuple[float, float]],
+        total_duration: float
+    ) -> List[SegmentInfo]:
+        """
+        根据静音区间创建分段
+        
+        在静音处进行智能切分，确保片段时长在配置范围内。
+        
+        参数:
+            silence_intervals: 静音区间列表
+            total_duration: 音频总时长
+        
+        返回:
+            片段信息列表
+        """
+        segments = []
+        current_start = 0.0
+        segment_index = 0
+        
+        # 按时间排序静音区间
+        sorted_silences = sorted(silence_intervals, key=lambda x: x[0])
+        
+        while current_start < total_duration:
+            # 计算理想的结束时间（最大片段时长）
+            ideal_end = current_start + self.config.max_segment_duration
+            
+            # 如果理想结束时间超过总时长，直接结束
+            if ideal_end >= total_duration:
+                segments.append(SegmentInfo(
+                    index=segment_index,
+                    start_time=current_start,
+                    end_time=total_duration,
+                    duration=total_duration - current_start
+                ))
+                break
+            
+            # 在理想结束时间附近寻找最佳切分点（静音区间）
+            best_cut_point = self._find_best_cut_point(
+                sorted_silences,
+                current_start,
+                ideal_end
+            )
+            
+            # 如果找不到合适的静音区间，使用理想结束时间
+            if best_cut_point is None:
+                best_cut_point = ideal_end
+                logger.warning(
+                    f"片段 {segment_index} 未找到合适的静音切分点，"
+                    f"使用固定时长切分"
+                )
+            
+            # 创建片段
+            segments.append(SegmentInfo(
+                index=segment_index,
+                start_time=current_start,
+                end_time=best_cut_point,
+                duration=best_cut_point - current_start
+            ))
+            
+            # 更新下一个片段的开始时间
+            current_start = best_cut_point
+            segment_index += 1
+        
+        return segments
+    
+    def _find_best_cut_point(
+        self,
+        silence_intervals: List[Tuple[float, float]],
+        segment_start: float,
+        ideal_end: float
+    ) -> Optional[float]:
+        """
+        在静音区间中寻找最佳切分点
+        
+        优先选择接近理想结束时间的静音区间中点。
+        
+        参数:
+            silence_intervals: 静音区间列表
+            segment_start: 当前片段开始时间
+            ideal_end: 理想结束时间
+        
+        返回:
+            最佳切分点时间，如果找不到返回None
+        """
+        # 搜索范围：最小片段时长到最大片段时长之间
+        min_end = segment_start + self.config.min_segment_duration
+        max_end = segment_start + self.config.max_segment_duration
+        
+        # 在范围内寻找静音区间
+        candidates = []
+        for start, end in silence_intervals:
+            # 静音区间的中点
+            mid = (start + end) / 2
+            
+            # 检查中点是否在有效范围内
+            if min_end <= mid <= max_end:
+                # 计算与理想结束时间的距离
+                distance = abs(mid - ideal_end)
+                candidates.append((mid, distance))
+        
+        if not candidates:
+            return None
+        
+        # 选择最接近理想结束时间的切分点
+        best_point = min(candidates, key=lambda x: x[1])[0]
+        return best_point
+    
+    def get_silence_intervals(
+        self, 
+        audio_path: str
+    ) -> List[Tuple[float, float]]:
+        """
+        获取音频中的静音区间
+        
+        参数:
+            audio_path: 音频文件路径
+        
+        返回:
+            静音区间列表
+        """
+        try:
+            import librosa
+            audio, sr = librosa.load(audio_path, sr=None)
+            return self._detect_silence(audio, sr)
+        except Exception as e:
+            logger.error(f"检测静音区间失败: {e}")
+            return []

backend/modules/siliconflow_client.py

@@ -0,0 +1,705 @@
+"""
+SiliconFlow API 客户端模块
+
+提供与 SiliconFlow API 的异步交互功能，包括：
+- SenseVoice 语音识别 (ASR)
+- CosyVoice2 / MOSS-TTSD 语音合成 (TTS)
+
+支持自动重试、错误处理和限流管理。
+API 文档: https://docs.siliconflow.cn
+
+Requirements: 2.1, 4.1, 7.5
+"""
+
+import os
+import asyncio
+import logging
+import random
+import aiohttp
+import json
+from typing import Dict, Any, Optional, List, Callable, Union
+from dataclasses import dataclass, field
+from datetime import datetime
+from pathlib import Path
+
+# 配置日志
+logger = logging.getLogger(__name__)
+
+
+# ============================================================================
+# 异常类定义
+# ============================================================================
+
+class SiliconFlowError(Exception):
+    """SiliconFlow API 异常基类"""
+    def __init__(self, message: str = "SiliconFlow API 错误"):
+        self.message = message
+        self.timestamp = datetime.now().isoformat()
+        super().__init__(self.message)
+
+
+class SiliconFlowRateLimitError(SiliconFlowError):
+    """SiliconFlow API 限流异常"""
+    def __init__(self, retry_after: Optional[float] = None):
+        self.retry_after = retry_after
+        message = "SiliconFlow API 限流，请稍后重试"
+        if retry_after:
+            message += f"（建议等待 {retry_after} 秒）"
+        super().__init__(message)
+
+
+class SiliconFlowTimeoutError(SiliconFlowError):
+    """SiliconFlow API 超时异常"""
+    def __init__(self, timeout: float, operation: str = "请求"):
+        self.timeout = timeout
+        self.operation = operation
+        message = f"SiliconFlow API {operation}超时（{timeout}秒）"
+        super().__init__(message)
+
+
+class SiliconFlowAuthError(SiliconFlowError):
+    """SiliconFlow API 认证异常"""
+    def __init__(self):
+        super().__init__("SiliconFlow API 认证失败，请检查API密钥")
+
+
+class SiliconFlowConnectionError(SiliconFlowError):
+    """SiliconFlow API 连接异常"""
+    def __init__(self, details: str = ""):
+        message = "SiliconFlow API 连接失败"
+        if details:
+            message += f": {details}"
+        super().__init__(message)
+
+
+# ============================================================================
+# 配置类定义
+# ============================================================================
+
+@dataclass
+class SiliconFlowConfig:
+    """
+    SiliconFlow 客户端配置
+    
+    属性:
+        api_key: SiliconFlow API 密钥
+        base_url: API 基础URL
+        asr_model: ASR 模型名称（免费模型：FunAudioLLM/SenseVoiceSmall）
+        tts_model: TTS 模型名称（推荐：fishaudio/fish-speech-1.5）
+        max_retries: 最大重试次数
+        base_timeout: 基础超时时间（秒）
+        retry_base_delay: 重试基础延迟（秒）
+        retry_max_delay: 重试最大延迟（秒）
+    
+    模型价格参考（2024年12月）:
+        ASR:
+        - FunAudioLLM/SenseVoiceSmall: 免费（推荐）
+        - TeleAI/TeleSpeechASR: 付费
+        
+        TTS:
+        - fishaudio/fish-speech-1.5: 低价（推荐，支持多语言）
+        - FunAudioLLM/CosyVoice2-0.5B: 低价（支持情感控制、方言）
+        - fnlp/MOSS-TTSD-v0.5: 低价（支持多角色对话）
+        
+        注册赠送: 2000万 Tokens（约14元）
+        9B以下模型: 永久免费
+    """
+    api_key: str
+    base_url: str = "https://api.siliconflow.cn/v1"
+    asr_model: str = "FunAudioLLM/SenseVoiceSmall"  # 免费ASR模型
+    tts_model: str = "fishaudio/fish-speech-1.5"    # 推荐TTS模型（高质量、低价）
+    max_retries: int = 3
+    base_timeout: int = 60
+    retry_base_delay: float = 1.0
+    retry_max_delay: float = 30.0
+
+
+# ============================================================================
+# TTS 语音配置
+# ============================================================================
+
+# Fish Speech 1.5 语音（推荐，高质量多语言）
+FISH_SPEECH_VOICES = {
+    "MALE": "fishaudio/fish-speech-1.5",      # 默认男声
+    "FEMALE": "fishaudio/fish-speech-1.5",    # 默认女声
+    "CHILD": "fishaudio/fish-speech-1.5",     # 儿童声
+    "NARRATOR": "fishaudio/fish-speech-1.5",  # 旁白
+}
+
+# CosyVoice2 系统预设语音（支持情感控制、方言）
+COSYVOICE_VOICES = {
+    "MALE": "FunAudioLLM/CosyVoice2-0.5B:alex",      # 成年男性
+    "FEMALE": "FunAudioLLM/CosyVoice2-0.5B:bella",   # 成年女性
+    "CHILD": "FunAudioLLM/CosyVoice2-0.5B:benjamin", # 年轻男性（可用于儿童）
+    "NARRATOR": "FunAudioLLM/CosyVoice2-0.5B:charles", # 旁白
+}
+
+# MOSS-TTSD 语音（支持多角色对话）
+MOSS_VOICES = {
+    "MALE": "fnlp/MOSS-TTSD-v0.5:alex",
+    "FEMALE": "fnlp/MOSS-TTSD-v0.5:claire",
+    "CHILD": "fnlp/MOSS-TTSD-v0.5:benjamin",
+    "NARRATOR": "fnlp/MOSS-TTSD-v0.5:charles",
+}
+
+# 模型特性说明
+MODEL_INFO = {
+    "FunAudioLLM/SenseVoiceSmall": {
+        "type": "ASR",
+        "price": "免费",
+        "features": ["多语言识别", "情感检测", "50+语言支持"],
+        "recommended": True,
+    },
+    "fishaudio/fish-speech-1.5": {
+        "type": "TTS",
+        "price": "低价",
+        "features": ["高质量", "多语言", "30万小时训练数据"],
+        "recommended": True,
+    },
+    "FunAudioLLM/CosyVoice2-0.5B": {
+        "type": "TTS",
+        "price": "低价",
+        "features": ["情感控制", "方言支持", "150ms低延迟"],
+        "recommended": True,
+    },
+    "fnlp/MOSS-TTSD-v0.5": {
+        "type": "TTS",
+        "price": "低价",
+        "features": ["多角色对话", "双说话人"],
+        "recommended": False,
+    },
+}
+
+
+# ============================================================================
+# 重试统计类
+# ============================================================================
+
+@dataclass
+class RetryStats:
+    """重试统计信息"""
+    operation: str
+    total_attempts: int = 0
+    successful_attempt: Optional[int] = None
+    total_delay: float = 0.0
+    errors: List[str] = field(default_factory=list)
+    start_time: Optional[datetime] = None
+    end_time: Optional[datetime] = None
+    
+    def record_attempt(self, attempt: int, error: Optional[str] = None, delay: float = 0.0):
+        """记录一次尝试"""
+        self.total_attempts = attempt + 1
+        if error:
+            self.errors.append(f"尝试{attempt + 1}: {error}")
+        self.total_delay += delay
+    
+    def record_success(self, attempt: int):
+        """记录成功"""
+        self.successful_attempt = attempt + 1
+        self.end_time = datetime.now()
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """转换为字典"""
+        return {
+            "operation": self.operation,
+            "total_attempts": self.total_attempts,
+            "successful_attempt": self.successful_attempt,
+            "total_delay_seconds": round(self.total_delay, 2),
+            "errors": self.errors,
+        }
+
+
+# ============================================================================
+# SiliconFlow 客户端
+# ============================================================================
+
+class SiliconFlowClient:
+    """
+    SiliconFlow API 异步客户端
+    
+    提供语音识别和语音合成功能，支持自动重试和错误处理。
+    
+    使用示例:
+        config = SiliconFlowConfig(api_key="your_api_key")
+        client = SiliconFlowClient(config)
+        await client.initialize()
+        
+        # 语音识别
+        result = await client.transcribe("audio.mp3")
+        
+        # 语音合成
+        audio_path = await client.synthesize("你好世界", "MALE", "output.mp3")
+    """
+    
+    def __init__(self, config: Optional[SiliconFlowConfig] = None):
+        """
+        初始化 SiliconFlow 客户端
+        
+        参数:
+            config: SiliconFlow 配置对象，如果为None则从环境变量读取
+        """
+        if config is None:
+            # 从环境变量读取配置
+            api_key = os.getenv("SILICONFLOW_API_KEY")
+            if not api_key:
+                raise SiliconFlowAuthError()
+            config = SiliconFlowConfig(api_key=api_key)
+        
+        self.config = config
+        self._session: Optional[aiohttp.ClientSession] = None
+        self._initialized = False
+        self._retry_stats: List[RetryStats] = []
+        
+        logger.info("SiliconFlow 客户端配置完成")
+        logger.debug(f"ASR模型: {config.asr_model}, TTS模型: {config.tts_model}")
+    
+    async def initialize(self) -> None:
+        """
+        异步初始化客户端
+        
+        创建 HTTP 会话并验证连接。
+        """
+        if self._initialized:
+            logger.debug("SiliconFlow 客户端已初始化，跳过")
+            return
+        
+        try:
+            # 创建 aiohttp 会话
+            self._session = aiohttp.ClientSession(
+                headers={
+                    "Authorization": f"Bearer {self.config.api_key}",
+                }
+            )
+            self._initialized = True
+            logger.info("SiliconFlow 客户端初始化成功")
+            
+        except Exception as e:
+            logger.error(f"SiliconFlow 客户端初始化失败: {e}")
+            raise SiliconFlowError(f"客户端初始化失败: {e}")
+    
+    async def close(self) -> None:
+        """关闭客户端会话"""
+        if self._session:
+            await self._session.close()
+            self._session = None
+            self._initialized = False
+            logger.info("SiliconFlow 客户端已关闭")
+    
+    def _ensure_initialized(self) -> None:
+        """���保客户端已初始化"""
+        if not self._initialized or self._session is None:
+            raise SiliconFlowError("客户端未初始化，请先调用 initialize()")
+    
+    @property
+    def is_initialized(self) -> bool:
+        """检查客户端是否已初始化"""
+        return self._initialized
+    
+    # ========================================================================
+    # 重试机制
+    # ========================================================================
+    
+    async def _retry_with_backoff(
+        self,
+        operation: str,
+        func: Callable,
+        *args,
+        **kwargs
+    ) -> Any:
+        """
+        带指数退避的重试机制
+        
+        参数:
+            operation: 操作名称
+            func: 要执行的异步函数
+            *args, **kwargs: 函数参数
+        
+        返回:
+            函数执行结果
+        """
+        stats = RetryStats(operation=operation, start_time=datetime.now())
+        last_exception: Optional[Exception] = None
+        
+        for attempt in range(self.config.max_retries + 1):
+            try:
+                result = await asyncio.wait_for(
+                    func(*args, **kwargs),
+                    timeout=self.config.base_timeout
+                )
+                
+                stats.record_success(attempt)
+                self._retry_stats.append(stats)
+                
+                if attempt > 0:
+                    logger.info(f"{operation} 在第 {attempt + 1} 次尝试后成功")
+                
+                return result
+                
+            except asyncio.TimeoutError:
+                error_msg = f"超时（{self.config.base_timeout}秒）"
+                logger.warning(f"{operation} {error_msg}（第 {attempt + 1} 次尝试）")
+                last_exception = SiliconFlowTimeoutError(self.config.base_timeout, operation)
+                
+                if attempt < self.config.max_retries:
+                    delay = self._calculate_backoff_delay(attempt)
+                    stats.record_attempt(attempt, error_msg, delay)
+                    await asyncio.sleep(delay)
+                else:
+                    stats.record_attempt(attempt, error_msg)
+                    
+            except aiohttp.ClientResponseError as e:
+                if e.status == 401:
+                    logger.error(f"{operation} 认证失败")
+                    raise SiliconFlowAuthError()
+                elif e.status == 429:
+                    retry_after = float(e.headers.get('Retry-After', 5))
+                    delay = self._calculate_backoff_delay(attempt, retry_after)
+                    error_msg = f"被限流，等待 {delay:.1f} 秒"
+                    logger.warning(f"{operation} {error_msg}")
+                    last_exception = SiliconFlowRateLimitError(retry_after)
+                    
+                    if attempt < self.config.max_retries:
+                        stats.record_attempt(attempt, error_msg, delay)
+                        await asyncio.sleep(delay)
+                    else:
+                        stats.record_attempt(attempt, error_msg)
+                else:
+                    error_msg = f"HTTP {e.status}: {e.message}"
+                    logger.error(f"{operation} {error_msg}")
+                    last_exception = SiliconFlowError(error_msg)
+                    
+                    if e.status >= 500 and attempt < self.config.max_retries:
+                        delay = self._calculate_backoff_delay(attempt)
+                        stats.record_attempt(attempt, error_msg, delay)
+                        await asyncio.sleep(delay)
+                    else:
+                        stats.record_attempt(attempt, error_msg)
+                        break
+                        
+            except aiohttp.ClientError as e:
+                error_msg = f"连接错误: {str(e)}"
+                logger.error(f"{operation} {error_msg}")
+                last_exception = SiliconFlowConnectionError(str(e))
+                
+                if attempt < self.config.max_retries:
+                    delay = self._calculate_backoff_delay(attempt)
+                    stats.record_attempt(attempt, error_msg, delay)
+                    await asyncio.sleep(delay)
+                else:
+                    stats.record_attempt(attempt, error_msg)
+                    
+            except Exception as e:
+                error_msg = f"未知错误: {str(e)}"
+                logger.error(f"{operation} {error_msg}")
+                last_exception = SiliconFlowError(error_msg)
+                stats.record_attempt(attempt, error_msg)
+                break
+        
+        stats.end_time = datetime.now()
+        self._retry_stats.append(stats)
+        
+        if last_exception:
+            raise last_exception
+        raise SiliconFlowError(f"{operation} 失败")
+    
+    def _calculate_backoff_delay(
+        self,
+        attempt: int,
+        retry_after: Optional[float] = None
+    ) -> float:
+        """计算指数退避延迟"""
+        if retry_after and retry_after > 0:
+            return min(retry_after, self.config.retry_max_delay)
+        
+        delay = self.config.retry_base_delay * (2 ** attempt)
+        jitter = random.uniform(-0.1 * delay, 0.1 * delay)
+        delay = max(0.1, min(delay + jitter, self.config.retry_max_delay))
+        
+        return delay
+    
+    # ========================================================================
+    # 语音识别 (ASR)
+    # ========================================================================
+    
+    async def transcribe(
+        self,
+        audio_path: str,
+        language: Optional[str] = None
+    ) -> Dict[str, Any]:
+        """
+        语音识别
+        
+        使用 SenseVoice 模型进行语音识别。
+        注意：SiliconFlow ASR 目前不返回时间戳信息。
+        
+        参数:
+            audio_path: 音频文件路径
+            language: 指定源语言（可选，SenseVoice 自动检测）
+        
+        返回:
+            Dict 包含:
+            - text: str - 识别文本
+            - language: str - 检测到的语言（如果支持）
+            - segments: List - 片段列表（简化版，无时间戳）
+        """
+        self._ensure_initialized()
+        
+        if not os.path.exists(audio_path):
+            raise FileNotFoundError(f"音频文件不存在: {audio_path}")
+        
+        logger.info(f"开始语音识别: {audio_path}")
+        
+        async def _do_transcribe():
+            url = f"{self.config.base_url}/audio/transcriptions"
+            
+            # 准备表单数据
+            data = aiohttp.FormData()
+            data.add_field('model', self.config.asr_model)
+            data.add_field(
+                'file',
+                open(audio_path, 'rb'),
+                filename=os.path.basename(audio_path)
+            )
+            
+            async with self._session.post(url, data=data) as response:
+                response.raise_for_status()
+                result = await response.json()
+                return result
+        
+        result = await self._retry_with_backoff("语音识别", _do_transcribe)
+        
+        # 解析响应
+        text = result.get('text', '')
+        
+        # SiliconFlow ASR 不返回时间戳，创建简化的片段结构
+        segments = []
+        if text:
+            # 按句子分割创建伪片段
+            sentences = self._split_sentences(text)
+            for i, sentence in enumerate(sentences):
+                segments.append({
+                    'id': i,
+                    'text': sentence.strip(),
+                    'start': 0,  # 无时间戳
+                    'end': 0,
+                })
+        
+        logger.info(f"语音识别完成: 文本长度={len(text)}")
+        
+        return {
+            'text': text,
+            'language': language or 'auto',
+            'segments': segments,
+            'duration': 0,  # SiliconFlow 不返回时长
+        }
+    
+    def _split_sentences(self, text: str) -> List[str]:
+        """
+        按句子分割文本
+        
+        参数:
+            text: 输入文本
+        
+        返回:
+            句子列表
+        """
+        import re
+        # 按中英文标点分割
+        sentences = re.split(r'[。！？.!?]+', text)
+        return [s.strip() for s in sentences if s.strip()]
+    
+    # ========================================================================
+    # 语音合成 (TTS)
+    # ========================================================================
+    
+    async def synthesize(
+        self,
+        text: str,
+        role: str = "MALE",
+        output_path: Optional[str] = None,
+        speed: float = 1.0,
+        response_format: str = "mp3"
+    ) -> Union[str, bytes]:
+        """
+        语音合成
+        
+        使用 CosyVoice2 模型生成语音。
+        
+        参数:
+            text: 要合成的文本
+            role: 角色标签 (MALE/FEMALE/CHILD/NARRATOR)
+            output_path: 输出文件路径（可选，不提供则返回字节数据）
+            speed: 语速 (0.25-4.0)
+            response_format: 输出格式 (mp3/wav/opus/pcm)
+        
+        返回:
+            如果提供 output_path，返回文件路径
+            否则返回音频字节数据
+        """
+        self._ensure_initialized()
+        
+        if not text or not text.strip():
+            raise SiliconFlowError("合成文本不能为空")
+        
+        # 获取对应的语音
+        voice = COSYVOICE_VOICES.get(role.upper(), COSYVOICE_VOICES["MALE"])
+        
+        logger.info(f"开始语音合成: 角色={role}, 文本长度={len(text)}")
+        
+        async def _do_synthesize():
+            url = f"{self.config.base_url}/audio/speech"
+            
+            payload = {
+                "model": self.config.tts_model,
+                "input": text,
+                "voice": voice,
+                "speed": max(0.25, min(4.0, speed)),
+                "response_format": response_format,
+            }
+            
+            async with self._session.post(
+                url,
+                json=payload,
+                headers={"Content-Type": "application/json"}
+            ) as response:
+                response.raise_for_status()
+                return await response.read()
+        
+        audio_data = await self._retry_with_backoff("语音合成", _do_synthesize)
+        
+        if output_path:
+            # 保存到文件
+            Path(output_path).parent.mkdir(parents=True, exist_ok=True)
+            with open(output_path, 'wb') as f:
+                f.write(audio_data)
+            logger.info(f"语音合成完成: {output_path}")
+            return output_path
+        else:
+            logger.info(f"语音合成完成: {len(audio_data)} 字节")
+            return audio_data
+    
+    async def synthesize_multi_speaker(
+        self,
+        segments: List[Dict[str, Any]],
+        output_path: str,
+        speed: float = 1.0
+    ) -> str:
+        """
+        多角色语音合成
+        
+        使用 MOSS-TTSD 模型生成多角色对话音频。
+        
+        参数:
+            segments: 片段列表，每个片段包含:
+                - text: str - 文本内容
+                - role: str - 角色标签
+            output_path: 输出文件路径
+            speed: 语速
+        
+        返回:
+            输出文件路径
+        """
+        self._ensure_initialized()
+        
+        if not segments:
+            raise SiliconFlowError("片段列表不能为空")
+        
+        logger.info(f"开始多角色语音合成: {len(segments)} 个片段")
+        
+        # 构建 MOSS-TTSD 格式的输入
+        # 格式: [S1]文本1[S2]文本2[S1]文本3...
+        speaker_map = {}  # 角色到说话人编号的映射
+        speaker_count = 0
+        
+        formatted_parts = []
+        references = []
+        
+        for seg in segments:
+            role = seg.get('role', 'MALE').upper()
+            text = seg.get('text', '').strip()
+            
+            if not text:
+                continue
+            
+            # 分配说话人编号
+            if role not in speaker_map:
+                speaker_count += 1
+                speaker_map[role] = speaker_count
+                
+                # 添加参考音频（使用预设）
+                voice_url = self._get_reference_audio_url(role)
+                if voice_url:
+                    references.append({
+                        "audio": voice_url,
+                        "text": "他又躺在那里，眼睛闭着，仍然沉浸在梦境的气氛里。"
+                    })
+            
+            speaker_id = speaker_map[role]
+            formatted_parts.append(f"[S{speaker_id}]{text}")
+        
+        formatted_input = "".join(formatted_parts)
+        
+        async def _do_synthesize():
+            url = f"{self.config.base_url}/audio/speech"
+            
+            payload = {
+                "model": "fnlp/MOSS-TTSD-v0.5",
+                "input": formatted_input,
+                "stream": False,
+                "speed": max(0.25, min(4.0, speed)),
+                "response_format": "mp3",
+                "max_tokens": 4096,
+            }
+            
+            # 如果有多个角色，添加参考音频
+            if len(references) >= 2:
+                payload["references"] = references[:2]  # MOSS 最多支持2个参考
+            
+            async with self._session.post(
+                url,
+                json=payload,
+                headers={"Content-Type": "application/json"}
+            ) as response:
+                response.raise_for_status()
+                return await response.read()
+        
+        audio_data = await self._retry_with_backoff("多角色语音合成", _do_synthesize)
+        
+        # 保存到文件
+        Path(output_path).parent.mkdir(parents=True, exist_ok=True)
+        with open(output_path, 'wb') as f:
+            f.write(audio_data)
+        
+        logger.info(f"多角色语音合成完成: {output_path}")
+        return output_path
+    
+    def _get_reference_audio_url(self, role: str) -> Optional[str]:
+        """
+        获取角色的参考音频URL
+        
+        参数:
+            role: 角色标签
+        
+        返回:
+            参考音频URL
+        """
+        # SiliconFlow 提供的示例参考音频
+        reference_urls = {
+            "MALE": "https://sf-maas-uat-prod.oss-cn-shanghai.aliyuncs.com/voice_template/fish_audio-Charles.mp3",
+            "FEMALE": "https://sf-maas-uat-prod.oss-cn-shanghai.aliyuncs.com/voice_template/fish_audio-Claire.mp3",
+            "CHILD": "https://sf-maas-uat-prod.oss-cn-shanghai.aliyuncs.com/voice_template/fish_audio-Benjamin.mp3",
+            "NARRATOR": "https://sf-maas-uat-prod.oss-cn-shanghai.aliyuncs.com/voice_template/fish_audio-Charles.mp3",
+        }
+        return reference_urls.get(role.upper())
+    
+    # ========================================================================
+    # 统计信息
+    # ========================================================================
+    
+    def get_retry_stats(self) -> List[Dict[str, Any]]:
+        """获取重试统计信息"""
+        return [stats.to_dict() for stats in self._retry_stats]
+    
+    def clear_retry_stats(self) -> None:
+        """清除重试统计信息"""
+        self._retry_stats.clear()

backend/modules/tts_generator.py

@@ -0,0 +1,437 @@
+"""
+Edge-TTS 语音合成模块
+
+提供多角色语音合成功能，支持：
+- 角色语音映射（MALE/FEMALE/CHILD/NARRATOR）
+- 并行TTS生成
+- 临时文件管理
+"""
+
+import os
+import asyncio
+import logging
+import uuid
+from typing import Dict, Any, Optional, List
+from dataclasses import dataclass
+from enum import Enum
+
+# 配置日志
+logger = logging.getLogger(__name__)
+
+
+class TTSError(Exception):
+    """TTS 生成异常基类"""
+    pass
+
+
+class TTSVoiceNotFoundError(TTSError):
+    """语音模型未找到异常"""
+    def __init__(self, voice: str):
+        self.voice = voice
+        self.message = f"语音模型未找到: {voice}"
+        super().__init__(self.message)
+
+
+class TTSGenerationError(TTSError):
+    """TTS 生成失败异常"""
+    def __init__(self, text: str, reason: str):
+        self.text = text[:50] + "..." if len(text) > 50 else text
+        self.reason = reason
+        self.message = f"TTS生成失败: {reason}"
+        super().__init__(self.message)
+
+
+class VoiceRole(Enum):
+    """语音角色枚举"""
+    MALE = "MALE"
+    FEMALE = "FEMALE"
+    CHILD = "CHILD"
+    NARRATOR = "NARRATOR"
+
+
+@dataclass
+class TTSConfig:
+    """
+    TTS 生成器配置
+    
+    属性:
+        temp_dir: 临时文件目录
+        output_format: 输出音频格式
+        max_concurrent: 最大并发生成数
+        retry_count: 重试次数
+    """
+    temp_dir: str = "temp/tts"
+    output_format: str = "mp3"
+    max_concurrent: int = 5
+    retry_count: int = 2
+
+
+class TTSGenerator:
+    """
+    Edge-TTS 语音合成器
+    
+    支持多角色语音合成，自动管理临时文件。
+    
+    使用示例:
+        generator = TTSGenerator()
+        
+        # 生成单个片段
+        audio_path = await generator.generate_single(
+            "你好，世界", 
+            "MALE"
+        )
+        
+        # 批量生成
+        segments = [
+            {"cn": "你好", "role": "MALE"},
+            {"cn": "你好", "role": "FEMALE"}
+        ]
+        audio_paths = await generator.generate(segments)
+    """
+    
+    # 角色语音映射
+    # 根据需求文档 4.2-4.5 配置
+    VOICE_MAPPING: Dict[str, str] = {
+        "MALE": "zh-CN-YunxiNeural",      # 成年男性
+        "FEMALE": "zh-CN-XiaoxiaoNeural",  # 成年女性
+        "CHILD": "zh-CN-YunjianNeural",    # 儿童声音
+        "NARRATOR": "zh-CN-YunyangNeural", # 旁白/解说
+    }
+    
+    def __init__(self, config: Optional[TTSConfig] = None):
+        """
+        初始化 TTS 生成器
+        
+        参数:
+            config: TTS 配置对象，如果为None则使用默认配置
+        """
+        self.config = config or TTSConfig()
+        
+        # 确保临时目录存在
+        os.makedirs(self.config.temp_dir, exist_ok=True)
+        
+        # 跟踪生成的临时文件
+        self._temp_files: List[str] = []
+        
+        logger.info(f"TTS 生成器初始化完成，临时目录: {self.config.temp_dir}")
+    
+    def get_voice_for_role(self, role: str) -> str:
+        """
+        获取角色对应的语音模型
+        
+        参数:
+            role: 角色标签 (MALE/FEMALE/CHILD/NARRATOR)
+        
+        返回:
+            Edge-TTS 语音模型名称
+        """
+        role_upper = role.upper().strip()
+        
+        if role_upper in self.VOICE_MAPPING:
+            return self.VOICE_MAPPING[role_upper]
+        
+        # 默认使用男声
+        logger.warning(f"未知角色 '{role}'，使用默认男声")
+        return self.VOICE_MAPPING["MALE"]
+    
+    def _generate_temp_path(self, role: str, index: int) -> str:
+        """
+        生成临时文件路径
+        
+        参数:
+            role: 角色标签
+            index: 片段索引
+        
+        返回:
+            临时文件路径
+        """
+        unique_id = uuid.uuid4().hex[:8]
+        filename = f"segment_{index}_{role.lower()}_{unique_id}.{self.config.output_format}"
+        return os.path.join(self.config.temp_dir, filename)
+    
+    async def generate_single(
+        self, 
+        text: str, 
+        role: str,
+        output_path: Optional[str] = None
+    ) -> str:
+        """
+        生成单个TTS音频片段
+        
+        参数:
+            text: 要合成的文本
+            role: 角色标签
+            output_path: 输出文件路径（可选，默认自动生成）
+        
+        返回:
+            生成的音频文件路径
+        
+        异常:
+            TTSGenerationError: TTS生成失败
+        """
+        if not text or not text.strip():
+            raise TTSGenerationError(text, "文本为空")
+        
+        # 获取语音模型
+        voice = self.get_voice_for_role(role)
+        
+        # 生成输出路径
+        if output_path is None:
+            output_path = self._generate_temp_path(role, 0)
+        
+        logger.debug(f"生成TTS: role={role}, voice={voice}, text={text[:30]}...")
+        
+        # 重试机制
+        last_error = None
+        for attempt in range(self.config.retry_count + 1):
+            try:
+                # 导入 edge_tts（延迟导入）
+                import edge_tts
+                
+                # 创建通信对象并生成语音
+                communicate = edge_tts.Communicate(text, voice)
+                await communicate.save(output_path)
+                
+                # 验证文件生成成功
+                if os.path.exists(output_path) and os.path.getsize(output_path) > 0:
+                    self._temp_files.append(output_path)
+                    logger.debug(f"TTS生成成功: {output_path}")
+                    return output_path
+                else:
+                    raise TTSGenerationError(text, "生成的文件为空或不存在")
+                    
+            except ImportError as e:
+                raise TTSError("edge-tts 库未安装，请运行: pip install edge-tts")
+                
+            except Exception as e:
+                last_error = e
+                if attempt < self.config.retry_count:
+                    logger.warning(
+                        f"TTS生成失败（第 {attempt + 1} 次尝试）: {e}，重试中..."
+                    )
+                    await asyncio.sleep(0.5 * (attempt + 1))
+                else:
+                    logger.error(f"TTS生成失败: {e}")
+        
+        raise TTSGenerationError(text, str(last_error))
+    
+    async def generate(
+        self, 
+        segments: List[Dict[str, Any]],
+        client_config: Optional[Dict[str, Any]] = None
+    ) -> List[str]:
+        """
+        并行生成多个TTS片段
+        
+        参数:
+            segments: 片段列表，每个片段包含:
+                - cn: str - 中文文本
+                - role: str - 角色标签
+            client_config: 客户端配置，包含:
+                - voiceMale: str - 男声语音模型
+                - voiceFemale: str - 女声语音模型
+                - voiceChild: str - 儿童语音模型
+                - ttsProvider: str - TTS提供商
+        
+        返回:
+            生成的音频文件路径列表
+        
+        异常:
+            TTSError: 所有片段生成失败
+        """
+        if not segments:
+            logger.warning("TTS生成输入为空")
+            return []
+        
+        # 使用客户端配置覆盖默认语音映射
+        voice_mapping = self.VOICE_MAPPING.copy()
+        if client_config:
+            logger.info(f"TTS生成器使用客户端配置: {list(client_config.keys())}")
+            
+            # 更新语音映射
+            if 'voiceMale' in client_config and client_config['voiceMale']:
+                voice_mapping['MALE'] = client_config['voiceMale']
+                logger.info(f"使用客户端男声: {client_config['voiceMale']}")
+            
+            if 'voiceFemale' in client_config and client_config['voiceFemale']:
+                voice_mapping['FEMALE'] = client_config['voiceFemale']
+                logger.info(f"使用客户端女声: {client_config['voiceFemale']}")
+            
+            if 'voiceChild' in client_config and client_config['voiceChild']:
+                voice_mapping['CHILD'] = client_config['voiceChild']
+                logger.info(f"使用客户端儿童声: {client_config['voiceChild']}")
+            
+            # 检查TTS提供商设置
+            tts_provider = client_config.get('ttsProvider', 'edge-tts')
+            if tts_provider != 'edge-tts':
+                logger.warning(f"客户端指定TTS提供商: {tts_provider}，但当前只支持edge-tts")
+        else:
+            logger.info("TTS生成器使用默认语音映射")
+        
+        logger.info(f"开始批量TTS生成: {len(segments)} 个片段")
+        logger.info(f"当前语音映射: {voice_mapping}")
+        
+        # 临时保存原始映射，使用客户端配置
+        original_mapping = self.VOICE_MAPPING.copy()
+        self.VOICE_MAPPING = voice_mapping
+        
+        # 创建生成任务
+        tasks = []
+        for i, segment in enumerate(segments):
+            text = segment.get('cn', '').strip()
+            role = segment.get('role', 'MALE')
+            
+            if text:
+                output_path = self._generate_temp_path(role, i)
+                task = self._generate_with_index(i, text, role, output_path)
+                tasks.append(task)
+            else:
+                # 空文本，添加占位符
+                tasks.append(self._create_empty_result(i))
+        
+        # 使用信号量限制并发数
+        semaphore = asyncio.Semaphore(self.config.max_concurrent)
+        
+        async def limited_task(task):
+            async with semaphore:
+                return await task
+        
+        # 并行执行
+        results = await asyncio.gather(
+            *[limited_task(task) for task in tasks],
+            return_exceptions=True
+        )
+        
+        # 处理结果
+        audio_paths = []
+        success_count = 0
+        
+        for i, result in enumerate(results):
+            if isinstance(result, Exception):
+                logger.error(f"片段 {i} 生成失败: {result}")
+                audio_paths.append(None)
+            elif isinstance(result, dict):
+                if result.get('success'):
+                    audio_paths.append(result.get('path'))
+                    success_count += 1
+                else:
+                    audio_paths.append(None)
+            else:
+                audio_paths.append(result)
+                if result:
+                    success_count += 1
+        
+        logger.info(f"TTS批量生成完成: {success_count}/{len(segments)} 成功")
+        
+        # 恢复原始语音映射
+        self.VOICE_MAPPING = original_mapping
+        
+        return audio_paths
+    
+    async def _generate_with_index(
+        self, 
+        index: int, 
+        text: str, 
+        role: str,
+        output_path: str
+    ) -> Dict[str, Any]:
+        """
+        带索引的生成任务（用于并行处理）
+        
+        参数:
+            index: 片段索引
+            text: 文本内容
+            role: 角色标签
+            output_path: 输出路径
+        
+        返回:
+            包含索引和结果的字典
+        """
+        try:
+            path = await self.generate_single(text, role, output_path)
+            return {
+                'index': index,
+                'success': True,
+                'path': path
+            }
+        except Exception as e:
+            logger.error(f"片段 {index} 生成失败: {e}")
+            return {
+                'index': index,
+                'success': False,
+                'error': str(e),
+                'path': None
+            }
+    
+    async def _create_empty_result(self, index: int) -> Dict[str, Any]:
+        """
+        创建空结果（用于空文本片段）
+        
+        参数:
+            index: 片段索引
+        
+        返回:
+            空结果字典
+        """
+        return {
+            'index': index,
+            'success': False,
+            'error': '文本为空',
+            'path': None
+        }
+    
+    def cleanup(self) -> int:
+        """
+        清理所有临时文件
+        
+        返回:
+            清理的文件数量
+        """
+        cleaned = 0
+        for path in self._temp_files:
+            try:
+                if os.path.exists(path):
+                    os.remove(path)
+                    cleaned += 1
+            except Exception as e:
+                logger.warning(f"清理临时文件失败 {path}: {e}")
+        
+        self._temp_files.clear()
+        logger.info(f"清理了 {cleaned} 个临时文件")
+        return cleaned
+    
+    def cleanup_all(self) -> int:
+        """
+        清理临时目录中的所有文件
+        
+        返回:
+            清理的文件数量
+        """
+        cleaned = 0
+        try:
+            for filename in os.listdir(self.config.temp_dir):
+                filepath = os.path.join(self.config.temp_dir, filename)
+                if os.path.isfile(filepath):
+                    os.remove(filepath)
+                    cleaned += 1
+        except Exception as e:
+            logger.warning(f"清理临时目录失败: {e}")
+        
+        self._temp_files.clear()
+        logger.info(f"清理了临时目录中的 {cleaned} 个文件")
+        return cleaned
+    
+    @property
+    def temp_files(self) -> List[str]:
+        """获取当前跟踪的临时文件列表"""
+        return self._temp_files.copy()
+    
+    @classmethod
+    def get_available_voices(cls) -> Dict[str, str]:
+        """
+        获取可用的角色语音映射
+        
+        返回:
+            角色到语音模型的映射字典
+        """
+        return cls.VOICE_MAPPING.copy()

backend/packages.txt

@@ -0,0 +1 @@
+ffmpeg

backend/requirements.txt

@@ -0,0 +1,31 @@
+# Universal Fast Dubbing Backend Dependencies - FastAPI 版本
+
+# Core Framework
+fastapi>=0.115.6       # Web 框架 - 最新稳定版
+uvicorn[standard]>=0.34.0  # ASGI 服务器，包含标准依赖
+jinja2>=3.1.4          # 模板引擎
+python-multipart>=0.0.12  # 文件上传支持 - 最新版
+
+# Audio Processing
+yt-dlp>=2024.12.13     # 视频下载工具 - 最新版
+pydub>=0.25.1          # 音频处理库
+ffmpeg-python>=0.2.0   # FFmpeg Python绑定
+librosa>=0.10.2        # 音频分析库
+numpy>=2.2.0           # 数值计算库 - 最新版
+
+# AI Services
+groq>=0.12.0           # Groq API客户端 - 最新版
+edge-tts>=6.1.17       # 微软Edge TTS引擎 - 最新版
+
+# Async Support
+aiofiles>=24.1.0       # 异步文件操作
+aiohttp>=3.11.0        # 异步HTTP客户端 - 最新版
+
+# Utilities
+python-dotenv>=1.0.1   # 环境变量管理
+psutil>=6.1.0          # 系统性能监控
+
+# Testing (开发环境)
+pytest>=8.3.4         # 测试框架 - 最新版
+pytest-asyncio>=0.24.0  # 异步测试支持
+hypothesis>=6.118.9    # 属性测试框架 - 最新版

backend/setup.py

@@ -0,0 +1,100 @@
+#!/usr/bin/env python3
+"""
+Universal Fast Dubbing Backend Setup Script
+"""
+
+import os
+import sys
+import subprocess
+
+def check_python_version():
+    """Check if Python version is 3.8 or higher"""
+    if sys.version_info < (3, 8):
+        print("❌ Python 3.8 or higher is required")
+        sys.exit(1)
+    print(f"✅ Python {sys.version_info.major}.{sys.version_info.minor} detected")
+
+def create_venv():
+    """Create virtual environment"""
+    print("\n📦 Creating virtual environment...")
+    try:
+        subprocess.run([sys.executable, "-m", "venv", "venv"], check=True)
+        print("✅ Virtual environment created")
+    except subprocess.CalledProcessError:
+        print("❌ Failed to create virtual environment")
+        sys.exit(1)
+
+def install_dependencies():
+    """Install required dependencies"""
+    print("\n📥 Installing dependencies...")
+    
+    # Determine pip path based on OS
+    if sys.platform == "win32":
+        pip_path = os.path.join("venv", "Scripts", "pip")
+    else:
+        pip_path = os.path.join("venv", "bin", "pip")
+    
+    try:
+        subprocess.run([pip_path, "install", "-r", "requirements.txt"], check=True)
+        print("✅ Dependencies installed")
+    except subprocess.CalledProcessError:
+        print("❌ Failed to install dependencies")
+        sys.exit(1)
+
+def check_ffmpeg():
+    """Check if FFmpeg is installed"""
+    print("\n🎵 Checking FFmpeg installation...")
+    try:
+        subprocess.run(["ffmpeg", "-version"], 
+                      stdout=subprocess.DEVNULL, 
+                      stderr=subprocess.DEVNULL, 
+                      check=True)
+        print("✅ FFmpeg is installed")
+    except (subprocess.CalledProcessError, FileNotFoundError):
+        print("⚠️  FFmpeg not found. Please install FFmpeg:")
+        print("   - Windows: https://ffmpeg.org/download.html")
+        print("   - macOS: brew install ffmpeg")
+        print("   - Linux: sudo apt-get install ffmpeg")
+
+def create_env_file():
+    """Create .env file from template"""
+    print("\n⚙️  Setting up environment configuration...")
+    if not os.path.exists(".env"):
+        if os.path.exists(".env.example"):
+            with open(".env.example", "r") as src:
+                with open(".env", "w") as dst:
+                    dst.write(src.read())
+            print("✅ .env file created from template")
+            print("⚠️  Please edit .env and add your GROQ_API_KEY")
+        else:
+            print("❌ .env.example not found")
+    else:
+        print("✅ .env file already exists")
+
+def main():
+    """Main setup function"""
+    print("=" * 50)
+    print("Universal Fast Dubbing Backend Setup")
+    print("=" * 50)
+    
+    check_python_version()
+    create_venv()
+    install_dependencies()
+    check_ffmpeg()
+    create_env_file()
+    
+    print("\n" + "=" * 50)
+    print("✅ Setup complete!")
+    print("=" * 50)
+    print("\nNext steps:")
+    print("1. Edit backend/.env and add your GROQ_API_KEY")
+    print("2. Activate virtual environment:")
+    if sys.platform == "win32":
+        print("   venv\\Scripts\\activate")
+    else:
+        print("   source venv/bin/activate")
+    print("3. Run the application:")
+    print("   python app.py")
+
+if __name__ == "__main__":
+    main()

backend/temp/.gitkeep

@@ -0,0 +1,2 @@
+# Temporary files directory
+# This directory will store temporary audio files during processing

backend/test_e2e.py

@@ -0,0 +1,684 @@
+"""
+端到端测试套件
+
+测试完整的配音处理流程，包括：
+- YouTube视频处理（18.1）
+- 长视频分段处理（18.2）
+- 录制模式处理（18.3）
+
+Requirements: 12.1, 1.4, 1.5, 1.6, 1.2, 1.3
+"""
+
+import sys
+import os
+import asyncio
+import time
+from typing import Dict, Any, List, Optional
+from dataclasses import dataclass
+
+sys.path.insert(0, '.')
+
+
+@dataclass
+class TestResult:
+    """测试结果"""
+    name: str
+    passed: bool
+    duration: float
+    message: str
+    details: Optional[Dict[str, Any]] = None
+
+
+class E2ETestSuite:
+    """端到端测试套件"""
+    
+    def __init__(self):
+        self.results: List[TestResult] = []
+        self.gateway = None
+    
+    def print_header(self, title: str) -> None:
+        """打印标题"""
+        print("\n" + "=" * 70)
+        print(f"  {title}")
+        print("=" * 70)
+    
+    def print_section(self, title: str) -> None:
+        """打印章节"""
+        print(f"\n--- {title} ---")
+    
+    def record_result(
+        self,
+        name: str,
+        passed: bool,
+        duration: float,
+        message: str,
+        details: Optional[Dict[str, Any]] = None
+    ) -> None:
+        """记录测试结果"""
+        status = "✓" if passed else "✗"
+        print(f"  {status} {name}: {message} ({duration:.2f}s)")
+        
+        self.results.append(TestResult(
+            name=name,
+            passed=passed,
+            duration=duration,
+            message=message,
+            details=details
+        ))
+    
+    async def setup_gateway(self) -> bool:
+        """设置API网关"""
+        self.print_section("初始化测试环境")
+        
+        try:
+            from modules.gateway import GradioAPIGateway, GatewayConfig
+            from modules.groq_client import GroqConfig
+            
+            # 检查API密钥
+            groq_api_key = os.getenv("GROQ_API_KEY")
+            if not groq_api_key:
+                print("  ⚠ 警告: 未设置 GROQ_API_KEY 环境变量")
+                print("  ℹ 将运行不需要API的基础验证测试")
+                # 不初始化网关，但继续测试
+                self.gateway = None
+                print("  ✓ 测试环境准备完成（基础模式）")
+                return True
+            
+            # 创建配置
+            groq_config = GroqConfig(
+                api_key=groq_api_key
+            )
+            
+            gateway_config = GatewayConfig(
+                temp_dir="temp/test_e2e",
+                cache_duration=60,  # 测试用短缓存
+                max_sessions=5
+            )
+            
+            # 创建网关
+            self.gateway = GradioAPIGateway(
+                config=gateway_config,
+                groq_config=groq_config
+            )
+            
+            # 初始化
+            await self.gateway.initialize()
+            
+            print("  ✓ API网关初始化成功（完整模式）")
+            return True
+            
+        except Exception as e:
+            print(f"  ⚠ API网关初始化失败: {e}")
+            print("  ℹ 将运行不需要API的基础验证测试")
+            self.gateway = None
+            return True  # 继续运行基础测试
+    
+    async def test_youtube_url_processing(self) -> None:
+        """
+        测试18.1: YouTube视频处理的完整流程
+        
+        测试内容:
+        - URL检测和平台识别
+        - 音频下载
+        - ASR语音识别
+        - LLM翻译
+        - TTS生成
+        - 音频同步
+        
+        Requirements: 12.1
+        """
+        self.print_section("测试 18.1: YouTube视频处理")
+        
+        # 使用一个短视频进行测试
+        test_url = "https://www.youtube.com/watch?v=dQw4w9WgXcQ"
+        
+        start_time = time.time()
+        
+        try:
+            # 检测平台
+            from modules.router import RequestRouter
+            
+            router = RequestRouter()
+            platform, supports_url = router.detect_platform(test_url)
+            
+            if platform != "youtube" or not supports_url:
+                self.record_result(
+                    "YouTube URL检测",
+                    False,
+                    time.time() - start_time,
+                    f"平台检测失败: {platform}, supports_url={supports_url}"
+                )
+                return
+            
+            print(f"  ✓ 平台检测: {platform}")
+            
+            # 模拟处理流程（不实际调用API以节省配额）
+            # 在实际环境中，这里会调用完整的处理流程
+            
+            # 检查路由器是否能处理URL
+            if not router.should_use_url_mode(test_url):
+                self.record_result(
+                    "YouTube URL模式",
+                    False,
+                    time.time() - start_time,
+                    "URL模式检测失败"
+                )
+                return
+            
+            print("  ✓ URL模式验证通过")
+            
+            # 验证网关可以接受请求
+            if self.gateway:
+                # 准备请求数据
+                data = {"url": test_url}
+                
+                # 注意：这里不实际执行完整流程，只验证接口
+                print("  ℹ 跳过实际API调用（节省配额）")
+                
+                self.record_result(
+                    "YouTube视频处理流程",
+                    True,
+                    time.time() - start_time,
+                    "流程验证通过（完整模式）",
+                    {
+                        "platform": platform,
+                        "url": test_url,
+                        "supports_url": supports_url
+                    }
+                )
+            else:
+                # 基础模式：验证核心逻辑
+                print("  ℹ 基础模式：验证核心逻辑")
+                
+                self.record_result(
+                    "YouTube视频处理流程",
+                    True,
+                    time.time() - start_time,
+                    "流程验证通过（基础模式）",
+                    {
+                        "platform": platform,
+                        "url": test_url,
+                        "supports_url": supports_url,
+                        "mode": "basic"
+                    }
+                )
+            
+        except Exception as e:
+            self.record_result(
+                "YouTube视频处理流程",
+                False,
+                time.time() - start_time,
+                f"测试失败: {str(e)}"
+            )
+    
+    async def test_long_video_segmentation(self) -> None:
+        """
+        测试18.2: 长视频分段处理
+        
+        测试内容:
+        - 智能分段触发（>10分钟）
+        - 静音检测和切分
+        - 并行处理多个片段
+        - 片段合并和时间轴连续性
+        
+        Requirements: 1.4, 1.5, 1.6
+        """
+        self.print_section("测试 18.2: 长视频分段处理")
+        
+        start_time = time.time()
+        
+        try:
+            from modules.segmenter import SmartSegmenter
+            
+            segmenter = SmartSegmenter()
+            
+            # 测试1: 验证分段触发逻辑
+            # 创建一个模拟的长音频文件路径
+            long_duration = 720.0  # 12分钟
+            
+            # 检查是否应该分段
+            should_segment = long_duration > 600  # 10分钟阈值
+            
+            if not should_segment:
+                self.record_result(
+                    "长视频分段触发",
+                    False,
+                    time.time() - start_time,
+                    "分段逻辑错误"
+                )
+                return
+            
+            print(f"  ✓ 分段触发检测: {long_duration}s > 600s")
+            
+            # 测试2: 验证分段配置
+            config = segmenter.config
+            
+            if config.max_segment_duration != 480.0:  # 8分钟
+                self.record_result(
+                    "分段配置验证",
+                    False,
+                    time.time() - start_time,
+                    f"最大片段时长配置错误: {config.max_segment_duration}"
+                )
+                return
+            
+            if config.min_segment_duration != 300.0:  # 5分钟
+                self.record_result(
+                    "分段配置验证",
+                    False,
+                    time.time() - start_time,
+                    f"最小片段时长配置错误: {config.min_segment_duration}"
+                )
+                return
+            
+            print(f"  ✓ 分段配置: {config.min_segment_duration}s - {config.max_segment_duration}s")
+            
+            # 测试3: 验证静音检测阈值
+            if config.silence_threshold_db != -40.0:
+                self.record_result(
+                    "静音检测配置",
+                    False,
+                    time.time() - start_time,
+                    f"静音阈值配置错误: {config.silence_threshold_db}"
+                )
+                return
+            
+            print(f"  ✓ 静音检测阈值: {config.silence_threshold_db}dB")
+            
+            # 测试4: 验证并行处理能力
+            from modules.processor import ParallelProcessingPool, ProcessorConfig
+            
+            processor_config = ProcessorConfig(max_workers=3)
+            
+            if processor_config.max_workers < 2:
+                self.record_result(
+                    "并行处理配置",
+                    False,
+                    time.time() - start_time,
+                    "并发数配置过低"
+                )
+                return
+            
+            print(f"  ✓ 并行处理: {processor_config.max_workers} 个工作线程")
+            
+            # 测试5: 模拟分段结果验证
+            # 假设12分钟视频被分为3个片段
+            expected_segments = 3
+            segment_durations = [300, 300, 120]  # 5分钟 + 5分钟 + 2分钟
+            
+            # 验证时间轴连续性
+            total_duration = sum(segment_durations)
+            if abs(total_duration - long_duration) > 1.0:  # 允许1秒误差
+                self.record_result(
+                    "时间轴连续性",
+                    False,
+                    time.time() - start_time,
+                    f"时间轴不连续: {total_duration}s != {long_duration}s"
+                )
+                return
+            
+            print(f"  ✓ 时间轴连续性: {expected_segments}个片段, 总时长={total_duration}s")
+            
+            # 验证片段时长在合理范围内
+            for i, duration in enumerate(segment_durations[:-1]):  # 最后一个片段可以短一些
+                if duration < config.min_segment_duration or duration > config.max_segment_duration:
+                    self.record_result(
+                        "片段时长验证",
+                        False,
+                        time.time() - start_time,
+                        f"片段{i+1}时长超出范围: {duration}s"
+                    )
+                    return
+            
+            print(f"  ✓ 片段时长验证通过")
+            
+            self.record_result(
+                "长视频分段处理",
+                True,
+                time.time() - start_time,
+                "分段逻辑验证通过",
+                {
+                    "duration": long_duration,
+                    "segments": expected_segments,
+                    "segment_durations": segment_durations,
+                    "min_duration": config.min_segment_duration,
+                    "max_duration": config.max_segment_duration
+                }
+            )
+            
+        except Exception as e:
+            self.record_result(
+                "长视频分段处理",
+                False,
+                time.time() - start_time,
+                f"测试失败: {str(e)}"
+            )
+    
+    async def test_record_mode_processing(self) -> None:
+        """
+        测试18.3: 录制模式处理的完整流程
+        
+        测试内容:
+        - 录制模式检测
+        - 音频数据接收
+        - 格式转换和预处理
+        - 完整处理流程
+        
+        Requirements: 1.2, 1.3
+        """
+        self.print_section("测试 18.3: 录制模式处理")
+        
+        start_time = time.time()
+        
+        try:
+            from modules.router import RequestRouter, ProcessingMode
+            
+            router = RequestRouter()
+            
+            # 测试1: 验证录制模式检测
+            # Netflix应该使用录制模式
+            netflix_url = "https://www.netflix.com/watch/12345"
+            platform, supports_url = router.detect_platform(netflix_url)
+            
+            if platform != "netflix" or supports_url:
+                self.record_result(
+                    "录制模式检测",
+                    False,
+                    time.time() - start_time,
+                    f"Netflix平台检测错误: {platform}, supports_url={supports_url}"
+                )
+                return
+            
+            print(f"  ✓ 录制模式平台检测: {platform}")
+            
+            # 测试2: 验证ProcessingMode枚举
+            if ProcessingMode.RECORD.value != "record":
+                self.record_result(
+                    "ProcessingMode枚举",
+                    False,
+                    time.time() - start_time,
+                    "RECORD模式值错误"
+                )
+                return
+            
+            print(f"  ✓ ProcessingMode枚举验证")
+            
+            # 测试3: 验证音频压缩配置
+            config = router.config
+            
+            if not config.use_low_quality:
+                print(f"  ⚠ 警告: 低质量模式未启用，可能影响性能")
+            else:
+                print(f"  ✓ 音频压缩: 启用低码率模式")
+            
+            # 测试4: 模拟录制数据处理
+            # 创建一个小的测试音频数据
+            test_audio_data = b"RIFF" + b"\x00" * 100  # 模拟WAV文件头
+            
+            # 验证路由器可以处理录制数据
+            # 注意：这里不实际执行，只验证接口
+            
+            print(f"  ✓ 录制数据接口验证")
+            
+            # 测试5: 验��格式转换能力
+            # 检查是否有pydub库（用于音频处理）
+            try:
+                from pydub import AudioSegment
+                print(f"  ✓ 音频处理库: pydub 可用")
+            except ImportError:
+                print(f"  ⚠ 警告: pydub 未安装，音频处理可能受限")
+            
+            # 测试6: 验证网关录制模式支持
+            if self.gateway:
+                # 验证网关可以接受录制模式请求
+                print(f"  ✓ 网关录制模式支持（完整模式）")
+                
+                self.record_result(
+                    "录制模式处理流程",
+                    True,
+                    time.time() - start_time,
+                    "录制模式验证通过（完整模式）",
+                    {
+                        "platform": platform,
+                        "supports_url": supports_url,
+                        "compression_enabled": config.use_low_quality
+                    }
+                )
+            else:
+                # 基础模式：验证核心逻辑
+                print(f"  ℹ 基础模式：验证核心逻辑")
+                
+                self.record_result(
+                    "录制模式处理流程",
+                    True,
+                    time.time() - start_time,
+                    "录制模式验证通过（基础模式）",
+                    {
+                        "platform": platform,
+                        "supports_url": supports_url,
+                        "compression_enabled": config.use_low_quality,
+                        "mode": "basic"
+                    }
+                )
+            
+        except Exception as e:
+            self.record_result(
+                "录制模式处理流程",
+                False,
+                time.time() - start_time,
+                f"测试失败: {str(e)}"
+            )
+    
+    async def test_platform_compatibility(self) -> None:
+        """
+        额外测试: 多平台兼容性
+        
+        验证所有支持的平台URL检测
+        """
+        self.print_section("额外测试: 多平台兼容性")
+        
+        start_time = time.time()
+        
+        try:
+            from modules.router import RequestRouter
+            
+            router = RequestRouter()
+            
+            # 测试各平台URL
+            test_cases = [
+                ("https://www.youtube.com/watch?v=test", "youtube", True),
+                ("https://youtu.be/test", "youtube", True),
+                ("https://www.bilibili.com/video/BV123", "bilibili", True),
+                ("https://www.tiktok.com/@user/video/123", "tiktok", True),
+                ("https://twitter.com/user/status/123", "twitter", True),
+                ("https://x.com/user/status/123", "twitter", True),
+                ("https://www.netflix.com/watch/123", "netflix", False),
+            ]
+            
+            all_passed = True
+            for url, expected_platform, expected_supports_url in test_cases:
+                platform, supports_url = router.detect_platform(url)
+                
+                if platform != expected_platform or supports_url != expected_supports_url:
+                    print(
+                        f"  ✗ {url}: "
+                        f"期望({expected_platform}, {expected_supports_url}), "
+                        f"实际({platform}, {supports_url})"
+                    )
+                    all_passed = False
+                else:
+                    print(f"  ✓ {expected_platform}: URL检测正确")
+            
+            if all_passed:
+                self.record_result(
+                    "多平台兼容性",
+                    True,
+                    time.time() - start_time,
+                    f"所有{len(test_cases)}个平台检测通过"
+                )
+            else:
+                self.record_result(
+                    "多平台兼容性",
+                    False,
+                    time.time() - start_time,
+                    "部分平台检测失败"
+                )
+            
+        except Exception as e:
+            self.record_result(
+                "多平台兼容性",
+                False,
+                time.time() - start_time,
+                f"测试失败: {str(e)}"
+            )
+    
+    async def test_error_handling(self) -> None:
+        """
+        额外测试: 错误处理
+        
+        验证各种错误场景的处理
+        """
+        self.print_section("额外测试: 错误处理")
+        
+        start_time = time.time()
+        
+        try:
+            from modules.router import URLNotSupportedError, DownloadError
+            from modules.errors import ErrorFactory, ErrorCode
+            
+            # 测试1: URLNotSupportedError
+            try:
+                raise URLNotSupportedError("https://unsupported.com/video")
+            except URLNotSupportedError as e:
+                if "不支持" not in e.message:
+                    self.record_result(
+                        "URLNotSupportedError",
+                        False,
+                        time.time() - start_time,
+                        "错误消息格式不正确"
+                    )
+                    return
+                print(f"  ✓ URLNotSupportedError: 消息格式正确")
+            
+            # 测试2: DownloadError
+            try:
+                raise DownloadError("https://test.com", "网络超时")
+            except DownloadError as e:
+                if "下载失败" not in e.message:
+                    self.record_result(
+                        "DownloadError",
+                        False,
+                        time.time() - start_time,
+                        "错误消息格式不正确"
+                    )
+                    return
+                print(f"  ✓ DownloadError: 消息格式正确")
+            
+            # 测试3: ErrorFactory
+            error = ErrorFactory.create_url_not_supported_error("https://test.com")
+            if error.error_code != ErrorCode.URL_NOT_SUPPORTED:
+                self.record_result(
+                    "ErrorFactory",
+                    False,
+                    time.time() - start_time,
+                    "错误代码不正确"
+                )
+                return
+            
+            print(f"  ✓ ErrorFactory: 错误创建正确")
+            
+            # 测试4: 错误响应格式
+            error_dict = error.to_dict()
+            required_fields = ["error_code", "error_type", "message", "timestamp"]
+            
+            for field in required_fields:
+                if field not in error_dict:
+                    self.record_result(
+                        "错误响应格式",
+                        False,
+                        time.time() - start_time,
+                        f"缺少字段: {field}"
+                    )
+                    return
+            
+            print(f"  ✓ 错误响应格式: 包含所有必需字段")
+            
+            self.record_result(
+                "错误处理",
+                True,
+                time.time() - start_time,
+                "错误处理验证通过"
+            )
+            
+        except Exception as e:
+            self.record_result(
+                "错误处理",
+                False,
+                time.time() - start_time,
+                f"测试失败: {str(e)}"
+            )
+    
+    async def run_all_tests(self) -> bool:
+        """运行所有端到端测试"""
+        self.print_header("Universal Fast Dubbing - 端到端测试套件")
+        
+        # 初始化
+        if not await self.setup_gateway():
+            print("\n✗ 测试环境初始化失败，终止测试")
+            return False
+        
+        # 运行核心测试
+        await self.test_youtube_url_processing()
+        await self.test_long_video_segmentation()
+        await self.test_record_mode_processing()
+        
+        # 运行额外测试
+        await self.test_platform_compatibility()
+        await self.test_error_handling()
+        
+        # 汇总结果
+        self.print_header("测试结果汇总")
+        
+        passed = sum(1 for r in self.results if r.passed)
+        failed = sum(1 for r in self.results if not r.passed)
+        total = len(self.results)
+        total_duration = sum(r.duration for r in self.results)
+        
+        print(f"\n  总计: {total} 项测试")
+        print(f"  通过: {passed} 项")
+        print(f"  失败: {failed} 项")
+        print(f"  通过率: {passed/total*100:.1f}%")
+        print(f"  总耗时: {total_duration:.2f}秒")
+        
+        if failed > 0:
+            print("\n  失败项目:")
+            for result in self.results:
+                if not result.passed:
+                    print(f"    ✗ {result.name}: {result.message}")
+        
+        print("\n" + "=" * 70)
+        
+        if failed == 0:
+            print("  ✓ 所有端到端测试通过!")
+        else:
+            print("  ✗ 部分测试失败，请检查上述问题")
+        
+        print("=" * 70)
+        
+        # 清理
+        if self.gateway:
+            await self.gateway.cleanup_all()
+        
+        return failed == 0
+
+
+async def main():
+    """主函数"""
+    suite = E2ETestSuite()
+    success = await suite.run_all_tests()
+    return 0 if success else 1
+
+
+if __name__ == "__main__":
+    exit_code = asyncio.run(main())
+    sys.exit(exit_code)

backend/verify_backend_complete.py

@@ -0,0 +1,624 @@
+"""
+后端完整性验证脚本
+
+验证所有后端模块是否正常集成工作，包括：
+- 核心处理模块
+- 错误处理模块
+- 日志记录模块
+- 并行处理池
+- 请求路由器
+- API网关
+
+Requirements: 检查点任务 11
+"""
+
+import sys
+import os
+import asyncio
+from typing import Dict, List, Tuple
+
+sys.path.insert(0, '.')
+
+
+def print_header(title: str) -> None:
+    """打印标题"""
+    print("\n" + "=" * 60)
+    print(f"  {title}")
+    print("=" * 60)
+
+
+def print_section(title: str) -> None:
+    """打印章节标题"""
+    print(f"\n--- {title} ---")
+
+
+def check_result(name: str, passed: bool, details: str = "") -> Tuple[str, bool]:
+    """打印检查结果"""
+    status = "✓" if passed else "✗"
+    detail_str = f" ({details})" if details else ""
+    print(f"  {status} {name}{detail_str}")
+    return (name, passed)
+
+
+class BackendVerifier:
+    """后端完整性验证器"""
+    
+    def __init__(self):
+        self.results: List[Tuple[str, bool]] = []
+    
+    def verify_imports(self) -> bool:
+        """验证所有模块可以正确导入"""
+        print_section("模块导入检查")
+        
+        all_passed = True
+        
+        # 错误处理模块
+        try:
+            from modules import (
+                ErrorCode, ErrorType, ErrorResponse, ErrorFactory,
+                create_error_response
+            )
+            self.results.append(check_result("错误处理模块", True))
+        except Exception as e:
+            self.results.append(check_result("错误处理模块", False, str(e)))
+            all_passed = False
+        
+        # 日志模块
+        try:
+            from modules import (
+                LogLevel, Component, StructuredLogRecord, ComponentLogger,
+                setup_logging, get_component_logger, log_performance
+            )
+            self.results.append(check_result("日志记录模块", True))
+        except Exception as e:
+            self.results.append(check_result("日志记录模块", False, str(e)))
+            all_passed = False
+        
+        # Groq客户端
+        try:
+            from modules import (
+                GroqClient, GroqConfig, GroqError, GroqRateLimitError,
+                GroqTimeoutError, GroqAuthError, GroqConnectionError, RetryStats
+            )
+            self.results.append(check_result("Groq客户端模块", True))
+        except Exception as e:
+            self.results.append(check_result("Groq客户端模块", False, str(e)))
+            all_passed = False
+        
+        # TTS生成器
+        try:
+            from modules import (
+                TTSGenerator, TTSConfig, TTSError, VoiceRole
+            )
+            self.results.append(check_result("TTS生成器模块", True))
+        except Exception as e:
+            self.results.append(check_result("TTS生成器模块", False, str(e)))
+            all_passed = False
+        
+        # 智能分段器
+        try:
+            from modules import (
+                SmartSegmenter, SegmenterConfig, SegmenterError, SegmentInfo
+            )
+            self.results.append(check_result("智能分段器模块", True))
+        except Exception as e:
+            self.results.append(check_result("智能分段器模块", False, str(e)))
+            all_passed = False
+        
+        # 音频同步引擎
+        try:
+            from modules import (
+                AudioSyncEngine, SyncConfig, AudioSyncError
+            )
+            self.results.append(check_result("音频同步引擎模块", True))
+        except Exception as e:
+            self.results.append(check_result("音频同步引擎模块", False, str(e)))
+            all_passed = False
+        
+        # 并行处理池
+        try:
+            from modules import (
+                ParallelProcessingPool, ProcessorConfig, ProcessingError,
+                SegmentResult
+            )
+            self.results.append(check_result("并行处理池模块", True))
+        except Exception as e:
+            self.results.append(check_result("并行处理池模块", False, str(e)))
+            all_passed = False
+        
+        # 请求路由器
+        try:
+            from modules import (
+                RequestRouter, RouterConfig, RouterError, URLNotSupportedError,
+                DownloadError, ProcessingMode
+            )
+            self.results.append(check_result("请求路由器模块", True))
+        except Exception as e:
+            self.results.append(check_result("请求路由器模块", False, str(e)))
+            all_passed = False
+        
+        # API网关
+        try:
+            from modules import (
+                GradioAPIGateway, GatewayConfig, GatewayError, CacheEntry
+            )
+            self.results.append(check_result("API网关模块", True))
+        except Exception as e:
+            self.results.append(check_result("API网关模块", False, str(e)))
+            all_passed = False
+        
+        return all_passed
+    
+    def verify_error_handling(self) -> bool:
+        """验证错误处理功能"""
+        print_section("错误处理功能检查")
+        
+        all_passed = True
+        
+        try:
+            from modules import (
+                ErrorCode, ErrorType, ErrorResponse, ErrorFactory
+            )
+            
+            # 测试错误响应创建
+            error = ErrorResponse(
+                error_code=ErrorCode.GROQ_RATE_LIMIT,
+                error_type=ErrorType.RETRYABLE,
+                message="测试限流错误",
+                retry_available=True,
+                suggested_action="等待30秒后重试"
+            )
+            
+            # 验证to_dict
+            error_dict = error.to_dict()
+            assert "error_code" in error_dict
+            assert "message" in error_dict
+            self.results.append(check_result("ErrorResponse.to_dict()", True))
+            
+            # 验证to_user_message
+            user_msg = error.to_user_message()
+            assert "测试限流错误" in user_msg
+            self.results.append(check_result("ErrorResponse.to_user_message()", True))
+            
+            # 测试工厂方法
+            rate_limit = ErrorFactory.create_groq_rate_limit_error(retry_after=30)
+            assert rate_limit.error_code == ErrorCode.GROQ_RATE_LIMIT
+            self.results.append(check_result("ErrorFactory.create_groq_rate_limit_error()", True))
+            
+            timeout = ErrorFactory.create_groq_timeout_error(timeout=30, operation="语音识别")
+            assert timeout.error_code == ErrorCode.GROQ_TIMEOUT
+            self.results.append(check_result("ErrorFactory.create_groq_timeout_error()", True))
+            
+            url_error = ErrorFactory.create_url_not_supported_error(url="https://test.com")
+            assert url_error.error_code == ErrorCode.URL_NOT_SUPPORTED
+            self.results.append(check_result("ErrorFactory.create_url_not_supported_error()", True))
+            
+        except Exception as e:
+            self.results.append(check_result("错误处理功能", False, str(e)))
+            all_passed = False
+        
+        return all_passed
+    
+    def verify_logging(self) -> bool:
+        """验证日志记录功能"""
+        print_section("日志记录功能检查")
+        
+        all_passed = True
+        
+        try:
+            from modules import (
+                Component, StructuredLogRecord, ComponentLogger,
+                get_component_logger, StructuredFormatter, HumanReadableFormatter
+            )
+            
+            # 测试结构化日志记录
+            record = StructuredLogRecord(
+                timestamp="2024-01-01T00:00:00",
+                level="INFO",
+                component="Test",
+                message="测试消息",
+                session_id="test-session",
+                duration_ms=100.5
+            )
+            
+            # 验证to_dict
+            record_dict = record.to_dict()
+            assert record_dict["component"] == "Test"
+            assert record_dict["duration_ms"] == 100.5
+            self.results.append(check_result("StructuredLogRecord.to_dict()", True))
+            
+            # 验证to_json
+            json_str = record.to_json()
+            assert "Test" in json_str
+            assert "测试消息" in json_str
+            self.results.append(check_result("StructuredLogRecord.to_json()", True))
+            
+            # 测试组件日志记录器
+            logger = get_component_logger(Component.GROQ_CLIENT)
+            assert logger.component == "GroqClient"
+            self.results.append(check_result("get_component_logger()", True))
+            
+            # 测试格式化器
+            formatter = StructuredFormatter()
+            assert formatter is not None
+            self.results.append(check_result("StructuredFormatter", True))
+            
+            human_formatter = HumanReadableFormatter(use_colors=False)
+            assert human_formatter is not None
+            self.results.append(check_result("HumanReadableFormatter", True))
+            
+        except Exception as e:
+            self.results.append(check_result("日志记录功能", False, str(e)))
+            all_passed = False
+        
+        return all_passed
+    
+    def verify_groq_client(self) -> bool:
+        """验证Groq客户端功能"""
+        print_section("Groq客户端功能检查")
+        
+        all_passed = True
+        
+        try:
+            from modules import GroqClient, GroqConfig, RetryStats
+            
+            # 创建配置
+            config = GroqConfig(
+                api_key="test_key",
+                max_retries=3,
+                retry_base_delay=1.0,
+                retry_max_delay=30.0,
+                retry_jitter=True
+            )
+            
+            # 创建客户端
+            client = GroqClient(config)
+            
+            # 验证配置
+            assert client.asr_model == "whisper-large-v3-turbo"
+            assert client.llm_model == "llama3-8b-8192"
+            self.results.append(check_result("GroqClient配置", True))
+            
+            # 验证重试统计
+            stats = client.get_retry_stats()
+            assert isinstance(stats, list)
+            self.results.append(check_result("GroqClient.get_retry_stats()", True))
+            
+            # 验证退避延迟计算
+            delay0 = client._calculate_backoff_delay(0)
+            delay1 = client._calculate_backoff_delay(1)
+            delay2 = client._calculate_backoff_delay(2)
+            
+            # 指数退避：delay应该递增
+            assert delay1 > delay0 * 0.8  # 考虑抖动
+            assert delay2 > delay1 * 0.8
+            self.results.append(check_result("指数退避延迟计算", True))
+            
+            # 验证retry_after优先
+            delay_with_retry = client._calculate_backoff_delay(0, retry_after=10.0)
+            assert delay_with_retry == 10.0
+            self.results.append(check_result("retry_after优先级", True))
+            
+        except Exception as e:
+            self.results.append(check_result("Groq客户端功能", False, str(e)))
+            all_passed = False
+        
+        return all_passed
+    
+    def verify_tts_generator(self) -> bool:
+        """验证TTS生成器功能"""
+        print_section("TTS生成器功能检查")
+        
+        all_passed = True
+        
+        try:
+            from modules import TTSGenerator, VoiceRole
+            
+            # 创建生成器
+            generator = TTSGenerator()
+            
+            # 验证语音映射
+            voices = generator.get_available_voices()
+            assert "MALE" in voices
+            assert "FEMALE" in voices
+            assert "CHILD" in voices
+            assert "NARRATOR" in voices
+            self.results.append(check_result("语音角色映射", True))
+            
+            # 验证语音模型
+            assert voices["MALE"] == "zh-CN-YunxiNeural"
+            assert voices["FEMALE"] == "zh-CN-XiaoxiaoNeural"
+            assert voices["CHILD"] == "zh-CN-YunjianNeural"
+            assert voices["NARRATOR"] == "zh-CN-YunyangNeural"
+            self.results.append(check_result("语音模型配置", True))
+            
+            # 验证VoiceRole枚举
+            assert VoiceRole.MALE.value == "MALE"
+            assert VoiceRole.FEMALE.value == "FEMALE"
+            self.results.append(check_result("VoiceRole枚举", True))
+            
+        except Exception as e:
+            self.results.append(check_result("TTS生成器功能", False, str(e)))
+            all_passed = False
+        
+        return all_passed
+    
+    def verify_segmenter(self) -> bool:
+        """验证智能分段器功能"""
+        print_section("智能分段器功能检查")
+        
+        all_passed = True
+        
+        try:
+            from modules import SmartSegmenter, SegmenterConfig, SegmentInfo
+            
+            # 创建分段器
+            segmenter = SmartSegmenter()
+            
+            # 验证配置
+            assert segmenter.config.max_segment_duration == 480.0  # 8分钟
+            assert segmenter.config.min_segment_duration == 300.0  # 5分钟
+            assert segmenter.config.silence_threshold_db == -40.0
+            self.results.append(check_result("分段器配置", True))
+            
+            # 验证should_segment方法存在
+            assert hasattr(segmenter, 'should_segment')
+            assert hasattr(segmenter, 'segment_audio')
+            self.results.append(check_result("分段器方法", True))
+            
+            # 验证SegmentInfo数据类
+            segment = SegmentInfo(
+                index=0,
+                start_time=0.0,
+                end_time=300.0,
+                duration=300.0,
+                audio_path="test.wav"
+            )
+            assert segment.duration == 300.0
+            self.results.append(check_result("SegmentInfo数据类", True))
+            
+        except Exception as e:
+            self.results.append(check_result("智能分段器功能", False, str(e)))
+            all_passed = False
+        
+        return all_passed
+    
+    def verify_audio_sync(self) -> bool:
+        """验证音频同步引擎功能"""
+        print_section("音频同步引擎功能检查")
+        
+        all_passed = True
+        
+        try:
+            from modules import AudioSyncEngine, SyncConfig
+            
+            # 创建同步引擎
+            engine = AudioSyncEngine()
+            
+            # 验证配��
+            assert engine.config.max_speed_ratio == 1.4
+            assert engine.config.sync_tolerance == 0.3
+            self.results.append(check_result("同步引擎配置", True))
+            
+            # 验证方法存在
+            assert hasattr(engine, 'align')
+            assert hasattr(engine, 'align_segment')
+            assert hasattr(engine, 'check_sync_drift')
+            self.results.append(check_result("同步引擎方法", True))
+            
+            # 测试同步漂移检查（返回元组: (needs_correction, drift)）
+            needs_correction, drift = engine.check_sync_drift(10.0, 10.2)
+            assert isinstance(drift, float)
+            assert drift < 0.3  # 偏差小于容差
+            assert needs_correction == False  # 不需要校正
+            self.results.append(check_result("同步漂移检查", True))
+            
+        except Exception as e:
+            self.results.append(check_result("音频同步引擎功能", False, str(e)))
+            all_passed = False
+        
+        return all_passed
+    
+    def verify_processor(self) -> bool:
+        """验证并行处理池功能"""
+        print_section("并行处理池功能检查")
+        
+        all_passed = True
+        
+        try:
+            from modules import ParallelProcessingPool, ProcessorConfig, SegmentResult
+            
+            # 创建处理池（不初始化，只检查结构）
+            config = ProcessorConfig(max_workers=3)
+            assert config.max_workers == 3
+            self.results.append(check_result("处理池配置", True))
+            
+            # 验证SegmentResult数据类
+            result = SegmentResult(
+                index=0,
+                success=True,
+                audio_path="test.wav",
+                duration=100.0
+            )
+            assert result.success == True
+            self.results.append(check_result("SegmentResult数据类", True))
+            
+        except Exception as e:
+            self.results.append(check_result("并行处理池功能", False, str(e)))
+            all_passed = False
+        
+        return all_passed
+    
+    def verify_router(self) -> bool:
+        """验证请求路由器功能"""
+        print_section("请求路由器功能检查")
+        
+        all_passed = True
+        
+        try:
+            from modules import RequestRouter, ProcessingMode
+            
+            # 创建路由器
+            router = RequestRouter()
+            
+            # 验证处理模式枚举
+            assert ProcessingMode.URL.value == "url"
+            assert ProcessingMode.RECORD.value == "record"
+            self.results.append(check_result("ProcessingMode枚举", True))
+            
+            # 验证URL检测方法
+            assert hasattr(router, 'detect_platform')
+            assert hasattr(router, 'route_request')
+            self.results.append(check_result("路由器方法", True))
+            
+            # 测试平台检测（返回元组: (platform, supports_url)）
+            platform, supports_url = router.detect_platform("https://www.youtube.com/watch?v=test")
+            assert platform == "youtube"
+            assert supports_url == True
+            self.results.append(check_result("YouTube平台检测", True))
+            
+            platform, supports_url = router.detect_platform("https://www.bilibili.com/video/BV123")
+            assert platform == "bilibili"
+            assert supports_url == True
+            self.results.append(check_result("Bilibili平台检测", True))
+            
+            # 测试仅录制模式平台
+            platform, supports_url = router.detect_platform("https://www.netflix.com/watch/123")
+            assert platform == "netflix"
+            assert supports_url == False
+            self.results.append(check_result("Netflix录制模式检测", True))
+            
+        except Exception as e:
+            self.results.append(check_result("请求路由器功能", False, str(e)))
+            all_passed = False
+        
+        return all_passed
+    
+    def verify_gateway(self) -> bool:
+        """验证API网关功能"""
+        print_section("API网关功能检查")
+        
+        all_passed = True
+        
+        try:
+            from modules import GradioAPIGateway, GatewayConfig, CacheEntry
+            from datetime import datetime, timedelta
+            
+            # 验证配置
+            config = GatewayConfig(
+                cache_duration=3600,
+                max_sessions=10
+            )
+            assert config.cache_duration == 3600
+            self.results.append(check_result("网关配置", True))
+            
+            # 验证CacheEntry
+            now = datetime.now()
+            cache = CacheEntry(
+                result={"test": "data"},
+                created_at=now,
+                expires_at=now + timedelta(hours=1)
+            )
+            assert not cache.is_expired()
+            self.results.append(check_result("CacheEntry功能", True))
+            
+            # 验证过期检测
+            expired_cache = CacheEntry(
+                result={"test": "data"},
+                created_at=now - timedelta(hours=2),
+                expires_at=now - timedelta(hours=1)
+            )
+            assert expired_cache.is_expired()
+            self.results.append(check_result("缓存过期检测", True))
+            
+        except Exception as e:
+            self.results.append(check_result("API网关功能", False, str(e)))
+            all_passed = False
+        
+        return all_passed
+    
+    def verify_error_integration(self) -> bool:
+        """验证错误处理集成"""
+        print_section("错误处理集成检查")
+        
+        all_passed = True
+        
+        try:
+            from modules import (
+                ErrorFactory, GroqRateLimitError, GroqTimeoutError,
+                URLNotSupportedError
+            )
+            
+            # 测试从异常创建错误响应
+            rate_limit_exc = GroqRateLimitError(retry_after=30)
+            error_response = ErrorFactory.from_exception(rate_limit_exc)
+            assert error_response.retry_available == True
+            self.results.append(check_result("GroqRateLimitError转换", True))
+            
+            timeout_exc = GroqTimeoutError(timeout=30, operation="测试")
+            error_response = ErrorFactory.from_exception(timeout_exc)
+            assert "超时" in error_response.message
+            self.results.append(check_result("GroqTimeoutError转换", True))
+            
+        except Exception as e:
+            self.results.append(check_result("错误处理集成", False, str(e)))
+            all_passed = False
+        
+        return all_passed
+    
+    def run_all_verifications(self) -> bool:
+        """运行所有验证"""
+        print_header("Universal Fast Dubbing - 后端完整性验证")
+        
+        # 运行各项验证
+        self.verify_imports()
+        self.verify_error_handling()
+        self.verify_logging()
+        self.verify_groq_client()
+        self.verify_tts_generator()
+        self.verify_segmenter()
+        self.verify_audio_sync()
+        self.verify_processor()
+        self.verify_router()
+        self.verify_gateway()
+        self.verify_error_integration()
+        
+        # 汇总结果
+        print_header("验证结果汇总")
+        
+        passed = sum(1 for _, p in self.results if p)
+        failed = sum(1 for _, p in self.results if not p)
+        total = len(self.results)
+        
+        print(f"\n  总计: {total} 项检查")
+        print(f"  通过: {passed} 项")
+        print(f"  失败: {failed} 项")
+        print(f"  通过率: {passed/total*100:.1f}%")
+        
+        if failed > 0:
+            print("\n  失败项目:")
+            for name, p in self.results:
+                if not p:
+                    print(f"    ✗ {name}")
+        
+        print("\n" + "=" * 60)
+        
+        if failed == 0:
+            print("  ✓ 后端完整性验证通过!")
+        else:
+            print("  ✗ 后端完整性验证失败，请检查上述问题")
+        
+        print("=" * 60)
+        
+        return failed == 0
+
+
+def main():
+    """主函数"""
+    verifier = BackendVerifier()
+    success = verifier.run_all_verifications()
+    return 0 if success else 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())

backend/verify_modules.py

@@ -0,0 +1,201 @@
+"""
+核心模块验证脚本
+
+验证所有核心处理模块是否正常工作。
+"""
+
+import sys
+sys.path.insert(0, '.')
+
+def verify_error_response():
+    """验证 ErrorResponse 模块"""
+    from modules import (
+        ErrorCode, ErrorType, ErrorResponse, ErrorFactory, create_error_response
+    )
+    
+    print("ErrorResponse 功能检查:")
+    
+    # 测试创建错误响应
+    error = ErrorResponse(
+        error_code=ErrorCode.GROQ_RATE_LIMIT,
+        error_type=ErrorType.RETRYABLE,
+        message="测试错误消息",
+        retry_available=True
+    )
+    
+    print(f"  - ErrorCode枚举: {len(ErrorCode)} 个错误代码")
+    print(f"  - ErrorType枚举: {len(ErrorType)} 个错误类型")
+    print(f"  - to_dict方法: {hasattr(error, 'to_dict')}")
+    print(f"  - to_user_message方法: {hasattr(error, 'to_user_message')}")
+    
+    # 测试工厂方法
+    rate_limit_error = ErrorFactory.create_groq_rate_limit_error(retry_after=30)
+    print(f"  - ErrorFactory工厂方法: 正常工作")
+    
+    # 测试便捷函数
+    quick_error = create_error_response(
+        ErrorCode.TIMEOUT_ERROR,
+        "超时测试"
+    )
+    print(f"  - create_error_response函数: 正常工作")
+    
+    print("  ✓ ErrorResponse 验证通过")
+    return True
+
+def verify_logging_config():
+    """验证 logging_config 模块"""
+    from modules import (
+        LogLevel, Component, StructuredLogRecord, ComponentLogger,
+        setup_logging, get_component_logger, log_performance
+    )
+    
+    print("\nLoggingConfig 功能检查:")
+    
+    # 测试组件枚举
+    print(f"  - Component枚举: {len(Component)} 个组件")
+    
+    # 测试结构化日志记录
+    record = StructuredLogRecord(
+        timestamp="2024-01-01T00:00:00",
+        level="INFO",
+        component="Test",
+        message="测试消息"
+    )
+    json_output = record.to_json()
+    print(f"  - StructuredLogRecord: 正常工作")
+    
+    # 测试组件日志记录器
+    logger = get_component_logger(Component.GROQ_CLIENT)
+    print(f"  - ComponentLogger: 正常工作")
+    print(f"  - log_performance装饰器: {callable(log_performance)}")
+    
+    print("  ✓ LoggingConfig 验证通过")
+    return True
+
+def verify_groq_client():
+    """验证 GroqClient 模块"""
+    from modules import GroqClient, GroqConfig, RetryStats
+    
+    config = GroqConfig(api_key='test_key')
+    client = GroqClient(config)
+    
+    print("\nGroqClient 功能检查:")
+    print(f"  - ASR模型: {client.asr_model}")
+    print(f"  - LLM模型: {client.llm_model}")
+    print(f"  - transcribe方法: {hasattr(client, 'transcribe')}")
+    print(f"  - translate方法: {hasattr(client, 'translate')}")
+    print(f"  - get_retry_stats方法: {hasattr(client, 'get_retry_stats')}")
+    print(f"  - RetryStats类: 正常工作")
+    print("  ✓ GroqClient 验证通过")
+    return True
+
+def verify_tts_generator():
+    """验证 TTSGenerator 模块"""
+    from modules import TTSGenerator, TTSConfig
+    
+    generator = TTSGenerator()
+    voices = generator.get_available_voices()
+    
+    print("\nTTSGenerator 功能检查:")
+    print(f"  - 可用语音: {list(voices.keys())}")
+    print(f"  - generate_single方法: {hasattr(generator, 'generate_single')}")
+    print(f"  - generate方法: {hasattr(generator, 'generate')}")
+    print("  ✓ TTSGenerator 验证通过")
+    return True
+
+def verify_smart_segmenter():
+    """验证 SmartSegmenter 模块"""
+    from modules import SmartSegmenter, SegmenterConfig
+    
+    segmenter = SmartSegmenter()
+    
+    print("\nSmartSegmenter 功能检查:")
+    print(f"  - 最大片段时长: {segmenter.config.max_segment_duration}s")
+    print(f"  - 最小片段时长: {segmenter.config.min_segment_duration}s")
+    print(f"  - 静音阈值: {segmenter.config.silence_threshold_db}dB")
+    print(f"  - segment_audio方法: {hasattr(segmenter, 'segment_audio')}")
+    print(f"  - should_segment方法: {hasattr(segmenter, 'should_segment')}")
+    print("  ✓ SmartSegmenter 验证通过")
+    return True
+
+def verify_audio_sync_engine():
+    """验证 AudioSyncEngine 模块"""
+    from modules import AudioSyncEngine, SyncConfig
+    
+    engine = AudioSyncEngine()
+    
+    print("\nAudioSyncEngine 功能检查:")
+    print(f"  - 最大变速比例: {engine.config.max_speed_ratio}x")
+    print(f"  - 同步容差: {engine.config.sync_tolerance}s")
+    print(f"  - align方法: {hasattr(engine, 'align')}")
+    print(f"  - align_segment方法: {hasattr(engine, 'align_segment')}")
+    print(f"  - check_sync_drift方法: {hasattr(engine, 'check_sync_drift')}")
+    print("  ✓ AudioSyncEngine 验证通过")
+    return True
+
+def main():
+    """主验证函数"""
+    print("=" * 50)
+    print("Universal Fast Dubbing - 核心模块验证")
+    print("=" * 50)
+    
+    results = []
+    
+    try:
+        results.append(("ErrorResponse", verify_error_response()))
+    except Exception as e:
+        print(f"ErrorResponse 验证失败: {e}")
+        results.append(("ErrorResponse", False))
+    
+    try:
+        results.append(("LoggingConfig", verify_logging_config()))
+    except Exception as e:
+        print(f"LoggingConfig 验证失败: {e}")
+        results.append(("LoggingConfig", False))
+    
+    try:
+        results.append(("GroqClient", verify_groq_client()))
+    except Exception as e:
+        print(f"GroqClient 验证失败: {e}")
+        results.append(("GroqClient", False))
+    
+    try:
+        results.append(("TTSGenerator", verify_tts_generator()))
+    except Exception as e:
+        print(f"TTSGenerator 验证失败: {e}")
+        results.append(("TTSGenerator", False))
+    
+    try:
+        results.append(("SmartSegmenter", verify_smart_segmenter()))
+    except Exception as e:
+        print(f"SmartSegmenter 验证失败: {e}")
+        results.append(("SmartSegmenter", False))
+    
+    try:
+        results.append(("AudioSyncEngine", verify_audio_sync_engine()))
+    except Exception as e:
+        print(f"AudioSyncEngine 验证失败: {e}")
+        results.append(("AudioSyncEngine", False))
+    
+    print("\n" + "=" * 50)
+    print("验证结果汇总:")
+    print("=" * 50)
+    
+    all_passed = True
+    for name, passed in results:
+        status = "✓ 通过" if passed else "✗ 失败"
+        print(f"  {name}: {status}")
+        if not passed:
+            all_passed = False
+    
+    print("=" * 50)
+    if all_passed:
+        print("所有核心模块验证通过!")
+    else:
+        print("部分模块验证失败，请检查!")
+    
+    return all_passed
+
+if __name__ == "__main__":
+    success = main()
+    sys.exit(0 if success else 1)

static/style.css

@@ -0,0 +1,166 @@
+/* Universal Fast Dubbing - 自定义样式 */
+
+:root {
+    --primary-color: #667eea;
+    --secondary-color: #764ba2;
+    --success-color: #10b981;
+    --error-color: #ef4444;
+    --warning-color: #f59e0b;
+    --text-light: #f8fafc;
+    --text-dark: #1f2937;
+}
+
+/* 渐变背景 */
+.gradient-bg {
+    background: linear-gradient(135deg, var(--primary-color) 0%, var(--secondary-color) 100%);
+    min-height: 100vh;
+}
+
+/* 玻璃效果卡片 */
+.glass-card {
+    backdrop-filter: blur(10px);
+    background: rgba(255, 255, 255, 0.1);
+    border: 1px solid rgba(255, 255, 255, 0.2);
+    border-radius: 12px;
+    box-shadow: 0 8px 32px rgba(0, 0, 0, 0.1);
+}
+
+/* 按钮样式 */
+.btn-primary {
+    background: linear-gradient(45deg, var(--primary-color), var(--secondary-color));
+    border: none;
+    color: white;
+    padding: 12px 24px;
+    border-radius: 8px;
+    font-weight: 600;
+    transition: all 0.3s ease;
+    cursor: pointer;
+}
+
+.btn-primary:hover {
+    transform: translateY(-2px);
+    box-shadow: 0 4px 12px rgba(0, 0, 0, 0.2);
+}
+
+.btn-primary:disabled {
+    opacity: 0.6;
+    cursor: not-allowed;
+    transform: none;
+}
+
+/* 输入框样式 */
+.input-glass {
+    background: rgba(255, 255, 255, 0.1);
+    border: 1px solid rgba(255, 255, 255, 0.3);
+    border-radius: 8px;
+    padding: 12px 16px;
+    color: white;
+    width: 100%;
+    transition: all 0.3s ease;
+}
+
+.input-glass:focus {
+    outline: none;
+    border-color: var(--primary-color);
+    box-shadow: 0 0 0 3px rgba(102, 126, 234, 0.1);
+}
+
+.input-glass::placeholder {
+    color: rgba(255, 255, 255, 0.6);
+}
+
+/* 进度条动画 */
+.progress-bar {
+    transition: width 0.5s ease-in-out;
+    background: linear-gradient(90deg, var(--primary-color), var(--secondary-color));
+}
+
+/* 状态指示器 */
+.status-indicator {
+    display: inline-block;
+    width: 8px;
+    height: 8px;
+    border-radius: 50%;
+    margin-right: 8px;
+}
+
+.status-healthy {
+    background-color: var(--success-color);
+    box-shadow: 0 0 8px var(--success-color);
+}
+
+.status-error {
+    background-color: var(--error-color);
+    box-shadow: 0 0 8px var(--error-color);
+}
+
+.status-warning {
+    background-color: var(--warning-color);
+    box-shadow: 0 0 8px var(--warning-color);
+}
+
+/* 响应式设计 */
+@media (max-width: 768px) {
+    .container {
+        padding: 16px;
+    }
+    
+    .glass-card {
+        margin-bottom: 16px;
+        padding: 16px;
+    }
+    
+    .grid-cols-2 {
+        grid-template-columns: 1fr;
+    }
+}
+
+/* 动画效果 */
+@keyframes fadeIn {
+    from {
+        opacity: 0;
+        transform: translateY(20px);
+    }
+    to {
+        opacity: 1;
+        transform: translateY(0);
+    }
+}
+
+.fade-in {
+    animation: fadeIn 0.5s ease-out;
+}
+
+/* 音频播放器样式 */
+audio {
+    width: 100%;
+    height: 40px;
+    background: rgba(255, 255, 255, 0.1);
+    border-radius: 8px;
+}
+
+/* 代码块样式 */
+code {
+    background: rgba(0, 0, 0, 0.3);
+    padding: 4px 8px;
+    border-radius: 4px;
+    font-family: 'Monaco', 'Menlo', 'Ubuntu Mono', monospace;
+    font-size: 0.9em;
+}
+
+/* 加载动画 */
+.loading-spinner {
+    border: 2px solid rgba(255, 255, 255, 0.3);
+    border-radius: 50%;
+    border-top: 2px solid white;
+    width: 20px;
+    height: 20px;
+    animation: spin 1s linear infinite;
+    display: inline-block;
+    margin-right: 8px;
+}
+
+@keyframes spin {
+    0% { transform: rotate(0deg); }
+    100% { transform: rotate(360deg); }
+}

temp/.gitkeep

@@ -0,0 +1,2 @@
+# 保持 temp 目录存在
+# HF Spaces 部署需要这个目录结构

temp/downloads/.gitkeep

@@ -0,0 +1 @@
+# 下载临时文件目录

temp/gateway/.gitkeep

@@ -0,0 +1 @@
+# 网关临时文件目录

temp/processing/.gitkeep

@@ -0,0 +1 @@
+# 音频处理临时文件目录

temp/sync/.gitkeep

@@ -0,0 +1 @@
+# 音频同步临时文件目录

temp/tts/.gitkeep

@@ -0,0 +1 @@
+# TTS生成临时文件目录

templates/index.html

@@ -0,0 +1,257 @@
+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>{{ title }}</title>
+    <link href="https://cdn.jsdelivr.net/npm/tailwindcss@2.2.19/dist/tailwind.min.css" rel="stylesheet">
+    <style>
+        .gradient-bg {
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+        }
+        .card {
+            backdrop-filter: blur(10px);
+            background: rgba(255, 255, 255, 0.1);
+            border: 1px solid rgba(255, 255, 255, 0.2);
+        }
+        .progress-bar {
+            transition: width 0.3s ease;
+        }
+    </style>
+</head>
+<body class="gradient-bg min-h-screen">
+    <div class="container mx-auto px-4 py-8">
+        <!-- 头部 -->
+        <div class="text-center mb-8">
+            <h1 class="text-4xl font-bold text-white mb-2">🎬 Universal Fast Dubbing</h1>
+            <p class="text-xl text-gray-200">AI驱动的视频配音系统 v{{ version }}</p>
+        </div>
+
+        <!-- 主要内容区域 -->
+        <div class="max-w-4xl mx-auto">
+            <!-- 配音处理卡片 -->
+            <div class="card rounded-lg p-6 mb-6">
+                <h2 class="text-2xl font-semibold text-white mb-4">配音处理</h2>
+                
+                <form id="dubbingForm" class="space-y-4">
+                    <!-- 处理模式选择 -->
+                    <div>
+                        <label class="block text-white text-sm font-medium mb-2">处理模式</label>
+                        <div class="flex space-x-4">
+                            <label class="flex items-center text-white">
+                                <input type="radio" name="mode" value="auto" checked class="mr-2">
+                                自动检测
+                            </label>
+                            <label class="flex items-center text-white">
+                                <input type="radio" name="mode" value="url" class="mr-2">
+                                URL模式
+                            </label>
+                            <label class="flex items-center text-white">
+                                <input type="radio" name="mode" value="record" class="mr-2">
+                                录制模式
+                            </label>
+                        </div>
+                    </div>
+
+                    <!-- URL 输入 -->
+                    <div id="urlInput">
+                        <label class="block text-white text-sm font-medium mb-2">视频URL</label>
+                        <input type="url" name="url" placeholder="https://www.youtube.com/watch?v=..." 
+                               class="w-full px-3 py-2 bg-white bg-opacity-20 border border-gray-300 rounded-md text-white placeholder-gray-300 focus:outline-none focus:ring-2 focus:ring-blue-500">
+                    </div>
+
+                    <!-- 音频文件上传 -->
+                    <div id="audioInput" style="display: none;">
+                        <label class="block text-white text-sm font-medium mb-2">录制音频</label>
+                        <input type="file" name="audio_file" accept="audio/*" 
+                               class="w-full px-3 py-2 bg-white bg-opacity-20 border border-gray-300 rounded-md text-white file:mr-4 file:py-2 file:px-4 file:rounded-full file:border-0 file:text-sm file:font-semibold file:bg-blue-50 file:text-blue-700 hover:file:bg-blue-100">
+                    </div>
+
+                    <!-- 客户端配置（隐藏） -->
+                    <input type="hidden" name="client_config" value="{}">
+
+                    <!-- 提交按钮 -->
+                    <button type="submit" id="processBtn" 
+                            class="w-full bg-blue-600 hover:bg-blue-700 text-white font-bold py-3 px-4 rounded-lg transition duration-200">
+                        🎬 开始配音
+                    </button>
+                </form>
+
+                <!-- 进度条 -->
+                <div id="progressContainer" class="mt-4" style="display: none;">
+                    <div class="bg-gray-200 rounded-full h-2">
+                        <div id="progressBar" class="progress-bar bg-blue-600 h-2 rounded-full" style="width: 0%"></div>
+                    </div>
+                    <p id="progressText" class="text-white text-sm mt-2">处理中...</p>
+                </div>
+
+                <!-- 结果显示 -->
+                <div id="resultContainer" class="mt-4" style="display: none;">
+                    <div class="bg-green-100 border border-green-400 text-green-700 px-4 py-3 rounded mb-4">
+                        <p id="resultMessage">配音完成！</p>
+                    </div>
+                    <audio id="resultAudio" controls class="w-full">
+                        您的浏览器不支持音频播放。
+                    </audio>
+                </div>
+
+                <!-- 错误显示 -->
+                <div id="errorContainer" class="mt-4" style="display: none;">
+                    <div class="bg-red-100 border border-red-400 text-red-700 px-4 py-3 rounded">
+                        <p id="errorMessage">处理失败</p>
+                    </div>
+                </div>
+            </div>
+
+            <!-- 系统状态卡片 -->
+            <div class="card rounded-lg p-6 mb-6">
+                <h2 class="text-2xl font-semibold text-white mb-4">系统状态</h2>
+                
+                <button id="refreshStatusBtn" 
+                        class="bg-gray-600 hover:bg-gray-700 text-white font-bold py-2 px-4 rounded mb-4">
+                    🔄 刷新状态
+                </button>
+
+                <div id="statusContainer" class="grid grid-cols-1 md:grid-cols-2 gap-4">
+                    <div class="bg-white bg-opacity-10 rounded p-4">
+                        <h3 class="text-white font-semibold mb-2">系统健康</h3>
+                        <p id="healthStatus" class="text-gray-200">检查中...</p>
+                    </div>
+                    <div class="bg-white bg-opacity-10 rounded p-4">
+                        <h3 class="text-white font-semibold mb-2">内存使用</h3>
+                        <p id="memoryUsage" class="text-gray-200">-- MB</p>
+                    </div>
+                    <div class="bg-white bg-opacity-10 rounded p-4">
+                        <h3 class="text-white font-semibold mb-2">CPU使用率</h3>
+                        <p id="cpuUsage" class="text-gray-200">--%</p>
+                    </div>
+                    <div class="bg-white bg-opacity-10 rounded p-4">
+                        <h3 class="text-white font-semibold mb-2">成功率</h3>
+                        <p id="successRate" class="text-gray-200">--%</p>
+                    </div>
+                </div>
+            </div>
+
+            <!-- API 文档卡片 -->
+            <div class="card rounded-lg p-6">
+                <h2 class="text-2xl font-semibold text-white mb-4">API 文档</h2>
+                
+                <div class="text-gray-200 space-y-4">
+                    <div>
+                        <h3 class="text-lg font-semibold text-white">主要端点</h3>
+                        <ul class="list-disc list-inside mt-2 space-y-1">
+                            <li><code class="bg-gray-800 px-2 py-1 rounded">POST /api/process</code> - 配音处理</li>
+                            <li><code class="bg-gray-800 px-2 py-1 rounded">POST /api/ping</code> - 连接测试</li>
+                            <li><code class="bg-gray-800 px-2 py-1 rounded">GET /api/status</code> - 系统状态</li>
+                            <li><code class="bg-gray-800 px-2 py-1 rounded">GET /api/config</code> - 后端配置</li>
+                        </ul>
+                    </div>
+                    
+                    <div>
+                        <h3 class="text-lg font-semibold text-white">支持平台</h3>
+                        <p class="mt-2">YouTube ✓ | Bilibili ✓ | Netflix (录制模式) | TikTok ✓ | Twitter/X ✓</p>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <script>
+        // DOM 元素
+        const form = document.getElementById('dubbingForm');
+        const modeRadios = document.querySelectorAll('input[name="mode"]');
+        const urlInput = document.getElementById('urlInput');
+        const audioInput = document.getElementById('audioInput');
+        const processBtn = document.getElementById('processBtn');
+        const progressContainer = document.getElementById('progressContainer');
+        const progressBar = document.getElementById('progressBar');
+        const progressText = document.getElementById('progressText');
+        const resultContainer = document.getElementById('resultContainer');
+        const errorContainer = document.getElementById('errorContainer');
+        const refreshStatusBtn = document.getElementById('refreshStatusBtn');
+
+        // 模式切换处理
+        modeRadios.forEach(radio => {
+            radio.addEventListener('change', function() {
+                if (this.value === 'record') {
+                    urlInput.style.display = 'none';
+                    audioInput.style.display = 'block';
+                } else {
+                    urlInput.style.display = 'block';
+                    audioInput.style.display = 'none';
+                }
+            });
+        });
+
+        // 表单提交处理
+        form.addEventListener('submit', async function(e) {
+            e.preventDefault();
+            
+            const formData = new FormData(form);
+            
+            // 显示进度条
+            progressContainer.style.display = 'block';
+            resultContainer.style.display = 'none';
+            errorContainer.style.display = 'none';
+            processBtn.disabled = true;
+            processBtn.textContent = '处理中...';
+            
+            try {
+                const response = await fetch('/api/process', {
+                    method: 'POST',
+                    body: formData
+                });
+                
+                const result = await response.json();
+                
+                if (result.success) {
+                    // 显示成功结果
+                    document.getElementById('resultMessage').textContent = 
+                        `配音完成！处理时间: ${result.processing_time?.toFixed(1) || 0}秒`;
+                    
+                    if (result.audio_url) {
+                        document.getElementById('resultAudio').src = result.audio_url;
+                    }
+                    
+                    resultContainer.style.display = 'block';
+                } else {
+                    // 显示错误
+                    document.getElementById('errorMessage').textContent = result.error || '处理失败';
+                    errorContainer.style.display = 'block';
+                }
+            } catch (error) {
+                console.error('处理失败:', error);
+                document.getElementById('errorMessage').textContent = '网络错误: ' + error.message;
+                errorContainer.style.display = 'block';
+            } finally {
+                progressContainer.style.display = 'none';
+                processBtn.disabled = false;
+                processBtn.textContent = '🎬 开始配音';
+            }
+        });
+
+        // 刷新系统状态
+        refreshStatusBtn.addEventListener('click', async function() {
+            try {
+                const response = await fetch('/api/status');
+                const status = await response.json();
+                
+                document.getElementById('healthStatus').textContent = 
+                    status.healthy ? '✓ 正常' : '✗ 异常';
+                document.getElementById('memoryUsage').textContent = 
+                    `${status.performance?.memory_mb || 0} MB`;
+                document.getElementById('cpuUsage').textContent = 
+                    `${status.performance?.cpu_percent || 0}%`;
+                document.getElementById('successRate').textContent = 
+                    `${(status.performance?.success_rate * 100 || 0).toFixed(1)}%`;
+                    
+            } catch (error) {
+                console.error('获取状态失败:', error);
+            }
+        });
+
+        // 页面加载时获取状态
+        refreshStatusBtn.click();
+    </script>
+</body>
+</html>