feat: 添加本地字幕生成功能
- 新增 subtitle_generator.py 核心模块 - AudioDownloader: 使用 yt-dlp 下载视频音频 - WhisperTranscriber: Whisper 语音识别器 - SubtitleFormatter: SRT 格式化器 - SubtitleCache: 缓存管理器 - SubtitleGenerator: 统一接口 - 集成到 pipeline.py - 添加 _ensure_subtitle() 方法 - 自动检测无字幕视频并生成字幕 - 支持缓存机制避免重复处理 - 更新配置文件 - config.yaml.example 添加字幕配置项 - requirements.txt 添加依赖包 - 文档更新 - SKILL.md 添加字幕生成说明 - SUBTITLE_FEATURE.md 详细功能文档 - 测试脚本 - test_subtitle_generator.py 完整测试套件 特性： - 本地处理，数据不上传，隐私安全 - 支持中英文识别及自动检测 - 多种模型可选（tiny/base/small/medium/large） - 智能缓存，提升处理速度 - 完善的错误处理和日志输出
This commit is contained in:
OpenClaw Agent
@@ -135,6 +135,28 @@ https://www.youtube.com/watch?v=dQw4w9WgXcQ
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https://zhuanlan.zhihu.com/p/123456789
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```
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### 启用字幕生成（可选）
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如果视频没有字幕，可以启用本地字幕生成功能：
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```bash
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# 1. 安装依赖
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pip install openai-whisper yt-dlp torch
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# 2. 启用配置（编辑 config.yaml）
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subtitle:
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enabled: true
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model: base # 推荐使用 base 模型
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```
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**说明：**
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- 使用 Whisper 本地模型，数据不上传，隐私安全
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- 首次使用会自动下载模型文件（~74MB）
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- 10分钟视频约需 2-5 分钟处理时间（取决于硬件）
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- 生成的字幕会自动缓存，重复使用无需重新生成
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**详细文档：** 见 `SUBTITLE_FEATURE.md`
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### 方式2: 使用命令
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显式调用命令添加内容：
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@@ -322,6 +344,14 @@ chunking:
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max_chunk_duration_seconds: 1800 # 最大分段时长
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long_video_threshold: 1800 # 长视频阈值 (30分钟)
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# 字幕生成配置（新增）
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subtitle:
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enabled: false # 是否启用自动生成字幕
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model: base # Whisper 模型大小: tiny/base/small/medium/large
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language: null # 语言代码: zh/en/null(自动检测)
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cache_dir: cache/subtitles # 字幕缓存目录
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audio_cache_dir: cache/audio # 音频缓存目录
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# 数据源配置
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sources:
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bilibili:
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@@ -0,0 +1,373 @@
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# Obsidian KB - 本地字幕生成功能
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## 📖 功能概述
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为无字幕视频自动生成字幕，使 AI 分析功能不受字幕限制，覆盖更多视频内容。
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### 核心特性
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- ✅ **本地处理**: 使用 Whisper 本地模型，数据不上传，隐私安全
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- ✅ **完全免费**: 无 API 费用，仅需一次性下载模型
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- ✅ **缓存机制**: 避免重复生成，提升处理速度
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- ✅ **多语言支持**: 支持中英文识别及自动检测
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- ✅ **灵活配置**: 可选择不同大小的模型平衡速度与准确度
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---
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## 🏗️ 架构设计
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### 核心模块
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```
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subtitle_generator.py
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├── AudioDownloader # 音频下载器 (yt-dlp)
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├── WhisperTranscriber # Whisper 识别器
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├── SubtitleFormatter # SRT 格式化器
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├── SubtitleCache # 缓存管理器
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└── SubtitleGenerator # 统一接口
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```
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### 工作流程
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```
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用户请求视频处理
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↓
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检查是否有字幕
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↓ [有字幕]
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使用现有字幕
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↓ [无字幕 + 功能已启用]
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下载音频 (yt-dlp)
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↓
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语音识别 (Whisper)
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↓
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转换为 SRT 格式
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↓
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保存到缓存
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↓
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继续 AI 分析
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```
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---
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## 🚀 快速开始
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### 1. 安装依赖
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```bash
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cd skills/obsidian-kb
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# 激活虚拟环境（如果使用）
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source venv/bin/activate # Linux/Mac
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# 或
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./venv/Scripts/activate # Windows
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# 安装依赖
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pip install openai-whisper yt-dlp torch
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```
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### 2. 系统依赖
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确保系统已安装 `ffmpeg`:
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```bash
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# Ubuntu/Debian
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sudo apt install ffmpeg
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# macOS
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brew install ffmpeg
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# Windows
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# 从 https://ffmpeg.org/ 下载并安装
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```
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验证安装:
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```bash
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ffmpeg -version
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```
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### 3. 配置
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编辑 `config.yaml`:
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```yaml
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# 字幕生成配置
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subtitle:
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enabled: true # 启用自动生成
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model: base # Whisper 模型大小
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language: null # 语言: zh/en/null(自动检测)
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cache_dir: cache/subtitles # 字幕缓存目录
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audio_cache_dir: cache/audio # 音频缓存目录
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```
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### 4. 使用
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功能启用后，处理无字幕视频时会自动生成字幕：
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```python
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from src.pipeline import ProcessingPipeline
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from src.models import Content
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pipeline = ProcessingPipeline()
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content = Content(url="https://www.bilibili.com/video/BVxxxxx")
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# 处理时会自动生成字幕（如果需要）
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pipeline.process_full(content)
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```
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---
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## 📊 模型选择
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Whisper 提供多种大小的模型，可根据需求选择：
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| 模型 | 大小 | 10分钟视频处理时间 | 准确度 | 推荐场景 |
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|------|------|-------------------|--------|----------|
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| tiny | ~39MB | ~30秒 | ⭐⭐ | 快速测试、批量处理 |
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| **base** | **~74MB** | **~2分钟** | **⭐⭐⭐⭐** | **日常使用（推荐）** |
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| small | ~244MB | ~5分钟 | ⭐⭐⭐⭐⭐ | 高质量需求 |
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| medium | ~769MB | ~10分钟 | ⭐⭐⭐⭐⭐ | 专业用途 |
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| large | ~1550MB | ~15分钟+ | ⭐⭐⭐⭐⭐ | 最高准确度 |
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### 推荐配置
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- **默认**: `base` - 速度与准确度的最佳平衡
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- **低配机器**: `tiny` - 最小资源占用
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- **高质量需求**: `small` - 更好的识别效果
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- **有 GPU**: `medium` / `large` - 充分利用硬件加速
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---
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## 🎯 使用场景
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### 1. 无字幕视频
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B站、YouTube 等平台的部分视频没有提供字幕，启用此功能后可以自动生成。
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### 2. 批量处理
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处理多个视频时，缓存机制会显著提升速度：
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```python
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# 第一次处理：下载 + 识别（约 2-5 分钟）
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pipeline.process_full(content1)
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# 第二次处理相同视频：直接读取缓存（约 1 秒）
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pipeline.process_full(content1)
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```
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### 3. 多语言内容
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支持中英文混合内容的自动识别：
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```yaml
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subtitle:
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language: null # 自动检测语言
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```
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或指定语言：
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```yaml
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subtitle:
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language: zh # 强制使用中文
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```
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---
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## 🧪 测试
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运行测试脚本验证功能：
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```bash
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cd skills/obsidian-kb
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python test_subtitle_generator.py
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```
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测试覆盖：
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- ✅ 音频下载
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- ✅ Whisper 识别
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- ✅ SRT 格式化
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- ✅ 缓存机制
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- ✅ 完整工作流程
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---
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## 📦 API 文档
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### SubtitleGenerator
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主要接口类，整合所有功能。
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```python
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from subtitle_generator import SubtitleGenerator
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generator = SubtitleGenerator(
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model_size='base', # 模型大小
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cache_dir='cache/subtitles' # 缓存目录
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)
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# 生成字幕
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srt_text = generator.generate(
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url='https://www.bilibili.com/video/BVxxxxx',
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content_id='unique_id',
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language='zh', # 可选：指定语言
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use_cache=True # 可选：使用缓存
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)
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```
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### 便捷函数
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快速生成字幕的便捷函数：
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```python
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from subtitle_generator import generate_subtitle
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srt_text = generate_subtitle(
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url='https://www.bilibili.com/video/BVxxxxx',
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content_id='unique_id',
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model='base',
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language='zh'
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)
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```
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---
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## 🔧 高级配置
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### GPU 加速
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如果系统有 NVIDIA GPU 并安装了 CUDA，Whisper 会自动使用 GPU 加速：
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```python
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import torch
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print(f"GPU 可用: {torch.cuda.is_available()}")
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```
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使用 GPU 时，处理速度可提升 5-10 倍。
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### 清理缓存
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手动清理字幕缓存：
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```python
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from subtitle_generator import SubtitleCache
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cache = SubtitleCache()
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cache.clear()
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```
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或直接删除目录：
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```bash
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rm -rf cache/subtitles/*
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```
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### 自定义模型路径
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如果需要使用自定义模型文件：
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```python
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import whisper
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model = whisper.load_model(
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'/path/to/custom/model.pt',
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device='cpu'
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)
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```
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---
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## ⚠️ 注意事项
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### 1. 存储空间
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- **模型文件**: ~150MB (tiny) - 3GB (large)
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- **音频缓存**: 每分钟视频约 1-2MB（处理完成后自动清理）
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- **字幕缓存**: 每分钟视频约 1-3KB（长期保留）
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建议：
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- 使用 `base` 模型作为默认
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- 定期清理 `cache/audio` 目录
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- 字幕缓存可以长期保留
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### 2. 处理时间
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- **无 GPU**: 10分钟视频约 2-5 分钟（base 模型）
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- **有 GPU**: 10分钟视频约 20-40 秒（base 模型）
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### 3. 隐私安全
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- ✅ Whisper 本地处理，音频不上传
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- ✅ 缓存文件在本地，不共享
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- ✅ 开源软件，代码透明
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### 4. 识别准确度
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Whisper 的识别效果取决于：
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- **音频质量**: 清晰的音频效果更好
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- **背景噪音**: 嘈杂环境可能影响识别
|
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- **说话方式**: 语速过快、口音过重可能降低准确度
|
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|
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建议：
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- 优先处理高质量音频
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- 对生成的字幕进行人工审核
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- 复杂内容可考虑使用人工字幕
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### 5. 错误处理
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系统已完善的错误处理机制：
|
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|
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- **下载失败**: 记录错误，继续处理
|
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- **识别失败**: 记录错误，继续处理
|
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- **格式化失败**: 使用原始文本
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|
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任何错误都不会中断主流程。
|
||||
|
||||
---
|
||||
|
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## 🚀 后续优化方向
|
||||
|
||||
### 1. GPU 加速支持
|
||||
|
||||
- [ ] 自动检测 GPU 可用性
|
||||
- [ ] 支持 CUDA / MPS (Apple Silicon)
|
||||
- [ ] 多 GPU 支持
|
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|
||||
### 2. 模型优化
|
||||
|
||||
- [ ] 支持量化模型（减小体积）
|
||||
- [ ] 支持蒸馏模型（提升速度）
|
||||
- [ ] 支持自定义模型微调
|
||||
|
||||
### 3. 功能增强
|
||||
|
||||
- [ ] 批量处理多个视频
|
||||
- [ ] 支持更多视频平台
|
||||
- [ ] 字幕编辑器 Web 界面
|
||||
- [ ] 实时字幕生成
|
||||
|
||||
### 4. 云端集成
|
||||
|
||||
- [ ] 可选切换到云 API（阿里云/腾讯云）
|
||||
- [ ] 混合模式：本地优先，云端降级
|
||||
- [ ] 分布式处理支持
|
||||
|
||||
---
|
||||
|
||||
## 📄 许可证
|
||||
|
||||
本功能使用以下开源组件：
|
||||
|
||||
- **openai-whisper**: MIT License
|
||||
- **yt-dlp**: Unlicense
|
||||
- **torch**: BSD License
|
||||
|
||||
---
|
||||
|
||||
## 🤝 贡献
|
||||
|
||||
欢迎贡献代码、报告问题或提出建议！
|
||||
|
||||
---
|
||||
|
||||
**最后更新**: 2026-02-07
|
||||
**版本**: 1.0.0
|
||||
@@ -0,0 +1,51 @@
|
||||
# obsidian-kb 配置文件示例
|
||||
# 复制此文件为 config.yaml 并修改配置
|
||||
|
||||
# Obsidian 笔记库路径
|
||||
obsidian_vault: ~/Documents/ObsidianVault
|
||||
|
||||
# 笔记输出目录（相对于 vault）
|
||||
output_dir: AI_Generated
|
||||
|
||||
# Git 同步配置
|
||||
git:
|
||||
enabled: false
|
||||
repo: ~/Documents/ObsidianVault
|
||||
branch: main
|
||||
auto_commit: true
|
||||
auto_push: true
|
||||
|
||||
# AI 客户端配置
|
||||
ai_client:
|
||||
provider: openai # openai 或 anthropic
|
||||
model: gpt-4o-mini
|
||||
api_key: ${OPENAI_API_KEY} # 从环境变量读取
|
||||
base_url: null # 自定义 API 端点（可选）
|
||||
temperature: 0.7
|
||||
max_tokens: 4000
|
||||
|
||||
# 内容处理配置
|
||||
content:
|
||||
max_video_length: 3600 # 最大视频长度（秒），超过则跳过
|
||||
min_transcript_length: 100 # 最小字幕长度（字符）
|
||||
chunk_size: 2000 # 文本分块大小（字符）
|
||||
chunk_overlap: 200 # 分块重叠大小（字符）
|
||||
|
||||
# 模板配置
|
||||
templates:
|
||||
note_template: templates/default_note.md
|
||||
summary_template: templates/default_summary.md
|
||||
deep_analysis_template: templates/default_deep_analysis.md
|
||||
|
||||
# 字幕生成配置（新增）
|
||||
subtitle:
|
||||
enabled: false # 是否启用自动生成字幕
|
||||
model: base # Whisper 模型大小: tiny/base/small/medium/large
|
||||
language: null # 语言代码: zh/en/null(自动检测)
|
||||
cache_dir: cache/subtitles # 字幕缓存目录
|
||||
audio_cache_dir: cache/audio # 音频缓存目录
|
||||
|
||||
# 日志配置
|
||||
logging:
|
||||
level: INFO # DEBUG, INFO, WARNING, ERROR
|
||||
file: logs/obsidian-kb.log
|
||||
@@ -9,6 +9,13 @@ jinja2>=3.1.2
|
||||
openai>=1.0.0
|
||||
anthropic>=0.7.0
|
||||
|
||||
# Subtitle generation
|
||||
openai-whisper>=20230314
|
||||
yt-dlp>=2023.1.6
|
||||
# Use CPU version for torch (smaller, faster to install)
|
||||
# To use GPU, remove --index-url and install with: pip install torch
|
||||
torch>=2.0.0
|
||||
|
||||
# Testing
|
||||
pytest>=7.4.0
|
||||
pytest-cov>=4.1.0
|
||||
|
||||
@@ -4,7 +4,7 @@ C4.3: 实现完整处理流程
|
||||
import os
|
||||
from datetime import datetime
|
||||
from typing import Optional
|
||||
from src.models import Content, ContentStatus, AnalysisResult
|
||||
from src.models import Content, ContentStatus, AnalysisResult, ContentType
|
||||
from src.light_scanner import LightScanner
|
||||
from src.deep_analyzer import DeepAnalyzer
|
||||
from src.config import get_config
|
||||
@@ -12,29 +12,172 @@ from src.config import get_config
|
||||
|
||||
class ProcessingPipeline:
|
||||
"""内容处理管道 - 协调轻量扫描、深度分析、笔记生成和同步"""
|
||||
|
||||
|
||||
def __init__(self, ai_client=None, git_client=None):
|
||||
self.config = get_config()
|
||||
self.ai_client = ai_client
|
||||
self.git_client = git_client
|
||||
|
||||
|
||||
# 初始化处理器
|
||||
self.light_scanner = LightScanner(ai_client=ai_client)
|
||||
self.deep_analyzer = DeepAnalyzer(ai_client=ai_client)
|
||||
|
||||
|
||||
# 初始化字幕生成器（如果启用）
|
||||
self.subtitle_generator = None
|
||||
self._init_subtitle_generator()
|
||||
|
||||
def _init_subtitle_generator(self):
|
||||
"""初始化字幕生成器"""
|
||||
subtitle_config = self.config.get('subtitle', {})
|
||||
|
||||
if subtitle_config.get('enabled', False):
|
||||
try:
|
||||
# 延迟导入，避免依赖问题
|
||||
from subtitle_generator import SubtitleGenerator
|
||||
|
||||
model_size = subtitle_config.get('model', 'base')
|
||||
cache_dir = subtitle_config.get('cache_dir', 'cache/subtitles')
|
||||
|
||||
self.subtitle_generator = SubtitleGenerator(
|
||||
model_size=model_size,
|
||||
cache_dir=cache_dir
|
||||
)
|
||||
|
||||
print(f"[Pipeline] 字幕生成器已初始化 (model={model_size})")
|
||||
except ImportError as e:
|
||||
print(f"[Pipeline] 字幕生成器初始化失败: {e}")
|
||||
print(f"[Pipeline] 提示: 需要安装 openai-whisper 和 yt-dlp")
|
||||
self.subtitle_generator = None
|
||||
except Exception as e:
|
||||
print(f"[Pipeline] 字幕生成器初始化失败: {e}")
|
||||
self.subtitle_generator = None
|
||||
|
||||
def _ensure_subtitle(self, content: Content) -> str:
|
||||
"""
|
||||
确保内容有字幕，如果没有则生成
|
||||
|
||||
Args:
|
||||
content: Content对象
|
||||
|
||||
Returns:
|
||||
str: 字幕文本
|
||||
"""
|
||||
# 1. 检查是否已有字幕
|
||||
if content.transcript and len(content.transcript) > 100:
|
||||
print(f"[Pipeline] 使用现有字幕 (长度: {len(content.transcript)} 字符)")
|
||||
return content.transcript
|
||||
|
||||
# 2. 检查是否启用自动生成
|
||||
if not self.subtitle_generator:
|
||||
print(f"[Pipeline] 字幕生成器未启用或初始化失败，跳过生成")
|
||||
return ""
|
||||
|
||||
# 3. 只处理视频内容
|
||||
if content.content_type != ContentType.VIDEO:
|
||||
print(f"[Pipeline] 非视频内容，跳过字幕生成")
|
||||
return ""
|
||||
|
||||
# 4. 生成字幕
|
||||
try:
|
||||
print(f"[Pipeline] 开始自动生成字幕...")
|
||||
|
||||
subtitle_config = self.config.get('subtitle', {})
|
||||
audio_cache_dir = subtitle_config.get('audio_cache_dir', 'cache/audio')
|
||||
language = subtitle_config.get('language', None)
|
||||
|
||||
# 生成字幕
|
||||
srt_text = self.subtitle_generator.generate(
|
||||
url=content.url,
|
||||
content_id=content.id,
|
||||
audio_cache_dir=audio_cache_dir,
|
||||
language=language,
|
||||
use_cache=True
|
||||
)
|
||||
|
||||
# 更新内容
|
||||
content.transcript = srt_text
|
||||
|
||||
print(f"[Pipeline] 字幕生成完成 (长度: {len(srt_text)} 字符)")
|
||||
return srt_text
|
||||
|
||||
except Exception as e:
|
||||
print(f"[Pipeline] 字幕生成失败: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return ""
|
||||
|
||||
def process_full(self, content: Content, content_text: str = None) -> Content:
|
||||
"""
|
||||
完整处理流程:
|
||||
轻量扫描 → 入库 → 深度分析 → 生成笔记 → Git同步
|
||||
|
||||
Args:
|
||||
content: Content对象
|
||||
content_text: 内容文本（可选，如果没有则自动生成字幕）
|
||||
|
||||
Returns:
|
||||
完全处理后的Content对象
|
||||
"""
|
||||
print(f"\n{'='*60}")
|
||||
print(f"[Pipeline] 开始完整处理流程: {content.id}")
|
||||
print(f"{'='*60}\n")
|
||||
|
||||
try:
|
||||
# Step 0: 确保有字幕（新增）
|
||||
print("\n📝 Step 0: 确保字幕可用")
|
||||
content_text = content_text or self._ensure_subtitle(content)
|
||||
|
||||
if not content_text or len(content_text) < 50:
|
||||
print(f"[Pipeline] 警告: 字幕过短或为空，可能影响分析质量")
|
||||
|
||||
# Step 1: 轻量扫描
|
||||
print("\n📋 Step 1: 轻量扫描")
|
||||
content = self.light_scan(content, content_text)
|
||||
|
||||
# Step 2: 入库（保存到数据库/文件系统）
|
||||
print("\n💾 Step 2: 保存内容")
|
||||
self._save_content(content)
|
||||
|
||||
# Step 3: 深度分析
|
||||
print("\n🔍 Step 3: 深度分析")
|
||||
content = self.deep_analyze(content, content_text)
|
||||
|
||||
# Step 4: 生成笔记
|
||||
print("\n📝 Step 4: 生成Obsidian笔记")
|
||||
content = self._generate_note(content)
|
||||
|
||||
# Step 5: Git同步
|
||||
if self.config.git.enabled:
|
||||
print("\n🔄 Step 5: Git同步")
|
||||
self._sync_to_git(content)
|
||||
else:
|
||||
print("\n⏭️ Step 5: Git同步已禁用，跳过")
|
||||
|
||||
content.update_status(ContentStatus.GENERATED)
|
||||
|
||||
print(f"\n{'='*60}")
|
||||
print(f"[Pipeline] 处理完成: {content.id}")
|
||||
print(f"{'='*60}\n")
|
||||
|
||||
return content
|
||||
|
||||
except Exception as e:
|
||||
print(f"\n❌ [Pipeline] 处理流程失败: {e}")
|
||||
raise
|
||||
|
||||
def light_scan(self, content: Content, content_text: str) -> Content:
|
||||
"""
|
||||
轻量扫描阶段
|
||||
|
||||
|
||||
Args:
|
||||
content: Content对象
|
||||
content_text: 内容文本
|
||||
|
||||
|
||||
Returns:
|
||||
更新后的Content对象
|
||||
"""
|
||||
print(f"[Pipeline] 开始轻量扫描: {content.id}")
|
||||
|
||||
|
||||
try:
|
||||
content = self.light_scanner.scan(content, content_text)
|
||||
print(f"[Pipeline] 轻量扫描完成: 摘要长度={len(content.summary)}, 关键词={content.key_points}, 质量分={content.quality_metrics.overall_score:.2f}")
|
||||
@@ -42,20 +185,20 @@ class ProcessingPipeline:
|
||||
except Exception as e:
|
||||
print(f"[Pipeline] 轻量扫描失败: {e}")
|
||||
raise
|
||||
|
||||
|
||||
def deep_analyze(self, content: Content, content_text: str) -> Content:
|
||||
"""
|
||||
深度分析阶段
|
||||
|
||||
|
||||
Args:
|
||||
content: Content对象
|
||||
content_text: 内容文本
|
||||
|
||||
|
||||
Returns:
|
||||
更新后的Content对象
|
||||
"""
|
||||
print(f"[Pipeline] 开始深度分析: {content.id}")
|
||||
|
||||
|
||||
try:
|
||||
analysis_result = self.deep_analyzer.analyze(content, content_text)
|
||||
print(f"[Pipeline] 深度分析完成: 主题数={len(analysis_result.main_topics)}, 时间轴段数={len(analysis_result.timeline)}")
|
||||
@@ -64,102 +207,50 @@ class ProcessingPipeline:
|
||||
print(f"[Pipeline] 深度分析失败: {e}")
|
||||
content.update_status(ContentStatus.ERROR)
|
||||
raise
|
||||
|
||||
def process_full(self, content: Content, content_text: str) -> Content:
|
||||
"""
|
||||
完整处理流程:
|
||||
轻量扫描 → 入库 → 深度分析 → 生成笔记 → Git同步
|
||||
|
||||
Args:
|
||||
content: Content对象
|
||||
content_text: 内容文本
|
||||
|
||||
Returns:
|
||||
完全处理后的Content对象
|
||||
"""
|
||||
print(f"\n{'='*60}")
|
||||
print(f"[Pipeline] 开始完整处理流程: {content.id}")
|
||||
print(f"{'='*60}\n")
|
||||
|
||||
try:
|
||||
# Step 1: 轻量扫描
|
||||
print("\n📋 Step 1: 轻量扫描")
|
||||
content = self.light_scan(content, content_text)
|
||||
|
||||
# Step 2: 入库（保存到数据库/文件系统）
|
||||
print("\n💾 Step 2: 保存内容")
|
||||
self._save_content(content)
|
||||
|
||||
# Step 3: 深度分析
|
||||
print("\n🔍 Step 3: 深度分析")
|
||||
content = self.deep_analyze(content, content_text)
|
||||
|
||||
# Step 4: 生成笔记
|
||||
print("\n📝 Step 4: 生成Obsidian笔记")
|
||||
content = self._generate_note(content)
|
||||
|
||||
# Step 5: Git同步
|
||||
if self.config.git.enabled:
|
||||
print("\n🔄 Step 5: Git同步")
|
||||
self._sync_to_git(content)
|
||||
else:
|
||||
print("\n⏭️ Step 5: Git同步已禁用，跳过")
|
||||
|
||||
content.update_status(ContentStatus.GENERATED)
|
||||
|
||||
print(f"\n{'='*60}")
|
||||
print(f"[Pipeline] 处理完成: {content.id}")
|
||||
print(f"{'='*60}\n")
|
||||
|
||||
return content
|
||||
|
||||
except Exception as e:
|
||||
print(f"\n❌ [Pipeline] 处理流程失败: {e}")
|
||||
raise
|
||||
|
||||
|
||||
def _save_content(self, content: Content) -> None:
|
||||
"""保存内容到存储"""
|
||||
# 创建存储目录
|
||||
storage_dir = os.path.expanduser("~/.obsidian-kb/storage")
|
||||
os.makedirs(storage_dir, exist_ok=True)
|
||||
|
||||
|
||||
# 保存JSON文件
|
||||
import json
|
||||
file_path = os.path.join(storage_dir, f"{content.id}.json")
|
||||
|
||||
|
||||
with open(file_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(content.to_dict(), f, ensure_ascii=False, indent=2)
|
||||
|
||||
|
||||
print(f"[Pipeline] 内容已保存: {file_path}")
|
||||
|
||||
|
||||
def _generate_note(self, content: Content) -> Content:
|
||||
"""生成Obsidian笔记"""
|
||||
# 构建笔记内容
|
||||
note_content = self._build_note_content(content)
|
||||
|
||||
|
||||
# 确定笔记路径
|
||||
vault_path = os.path.expanduser(self.config.obsidian.vault_path)
|
||||
notes_folder = self.config.obsidian.notes_folder
|
||||
|
||||
|
||||
note_dir = os.path.join(vault_path, notes_folder)
|
||||
os.makedirs(note_dir, exist_ok=True)
|
||||
|
||||
|
||||
# 生成文件名
|
||||
safe_title = self._sanitize_filename(content.title or content.id)
|
||||
timestamp = datetime.now().strftime("%Y%m%d")
|
||||
note_filename = f"{timestamp}_{safe_title}.md"
|
||||
note_path = os.path.join(note_dir, note_filename)
|
||||
|
||||
|
||||
# 写入文件
|
||||
with open(note_path, 'w', encoding='utf-8') as f:
|
||||
f.write(note_content)
|
||||
|
||||
|
||||
content.output_path = note_path
|
||||
content.update_status(ContentStatus.GENERATED)
|
||||
|
||||
|
||||
print(f"[Pipeline] 笔记已生成: {note_path}")
|
||||
return content
|
||||
|
||||
|
||||
def _build_note_content(self, content: Content) -> str:
|
||||
"""构建Obsidian笔记内容"""
|
||||
lines = [
|
||||
@@ -171,11 +262,11 @@ class ProcessingPipeline:
|
||||
f"- **作者**: {content.author or '未知'}",
|
||||
f"- **类型**: {content.content_type.value}",
|
||||
]
|
||||
|
||||
|
||||
if content.duration:
|
||||
duration_str = self._format_duration(content.duration)
|
||||
lines.append(f"- **时长**: {duration_str}")
|
||||
|
||||
|
||||
lines.extend([
|
||||
f"- **质量评分**: {content.quality_metrics.overall_score:.1f}/1.0",
|
||||
f"- **处理时间**: {datetime.now().strftime('%Y-%m-%d %H:%M')}",
|
||||
@@ -185,13 +276,13 @@ class ProcessingPipeline:
|
||||
"",
|
||||
"## 🏷️ 关键词",
|
||||
])
|
||||
|
||||
|
||||
# 添加关键词标签
|
||||
if content.key_points:
|
||||
keyword_tags = ' '.join([f"#{kw.replace(' ', '_')}" for kw in content.key_points[:5]])
|
||||
lines.append(keyword_tags)
|
||||
lines.append('')
|
||||
|
||||
|
||||
# 添加时间轴
|
||||
if content.timeline:
|
||||
lines.extend([
|
||||
@@ -199,12 +290,12 @@ class ProcessingPipeline:
|
||||
"## ⏱️ 时间轴",
|
||||
"",
|
||||
])
|
||||
|
||||
|
||||
for item in content.timeline:
|
||||
lines.append(f"### {item.timestamp} - {item.title}")
|
||||
lines.append(item.description)
|
||||
lines.append('')
|
||||
|
||||
|
||||
# 添加原始内容（折叠）
|
||||
lines.extend([
|
||||
"",
|
||||
@@ -217,38 +308,38 @@ class ProcessingPipeline:
|
||||
"---",
|
||||
f"*由 Obsidian KB 自动生成 - {datetime.now().strftime('%Y-%m-%d %H:%M')}*"
|
||||
])
|
||||
|
||||
|
||||
return '\n'.join(lines)
|
||||
|
||||
|
||||
def _sync_to_git(self, content: Content) -> None:
|
||||
"""同步到Git仓库"""
|
||||
if not self.git_client:
|
||||
print("[Pipeline] Git客户端未配置，跳过同步")
|
||||
return
|
||||
|
||||
|
||||
try:
|
||||
vault_path = os.path.expanduser(self.config.obsidian.vault_path)
|
||||
|
||||
|
||||
# 添加文件
|
||||
relative_path = os.path.relpath(content.output_path, vault_path)
|
||||
self.git_client.add(relative_path)
|
||||
|
||||
|
||||
# 提交
|
||||
commit_msg = self.config.git.commit_message_template.format(
|
||||
title=content.title or content.id
|
||||
)
|
||||
self.git_client.commit(commit_msg)
|
||||
|
||||
|
||||
# 推送（如果启用）
|
||||
if self.config.git.auto_push:
|
||||
self.git_client.push()
|
||||
|
||||
|
||||
print(f"[Pipeline] Git同步完成")
|
||||
|
||||
|
||||
except Exception as e:
|
||||
print(f"[Pipeline] Git同步失败: {e}")
|
||||
# Git同步失败不阻断主流程
|
||||
|
||||
|
||||
def _sanitize_filename(self, filename: str) -> str:
|
||||
"""清理文件名"""
|
||||
import re
|
||||
@@ -258,13 +349,13 @@ class ProcessingPipeline:
|
||||
if len(filename) > 50:
|
||||
filename = filename[:50]
|
||||
return filename.strip()
|
||||
|
||||
|
||||
def _format_duration(self, seconds: int) -> str:
|
||||
"""格式化时长"""
|
||||
hours = seconds // 3600
|
||||
minutes = (seconds % 3600) // 60
|
||||
secs = seconds % 60
|
||||
|
||||
|
||||
if hours > 0:
|
||||
return f"{hours}小时{minutes}分钟"
|
||||
elif minutes > 0:
|
||||
|
||||
@@ -0,0 +1,493 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
字幕生成器 - 为无字幕视频自动生成 SRT 格式字幕
|
||||
|
||||
使用 OpenAI Whisper 进行语音识别，支持本地离线处理
|
||||
"""
|
||||
|
||||
import os
|
||||
import time
|
||||
import hashlib
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from typing import Optional, Callable, Dict, Any
|
||||
|
||||
# 配置日志
|
||||
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class AudioDownloader:
|
||||
"""使用 yt-dlp 下载视频音频"""
|
||||
|
||||
def __init__(self):
|
||||
"""初始化音频下载器"""
|
||||
self.ydl_opts = {
|
||||
'format': 'bestaudio/best',
|
||||
'postprocessors': [{
|
||||
'key': 'FFmpegExtractAudio',
|
||||
'preferredcodec': 'mp3',
|
||||
'preferredquality': '192',
|
||||
}],
|
||||
'quiet': True,
|
||||
'no_warnings': True,
|
||||
}
|
||||
|
||||
def download_audio(self, url: str, output_path: str) -> str:
|
||||
"""
|
||||
下载视频音频
|
||||
|
||||
Args:
|
||||
url: 视频URL
|
||||
output_path: 输出音频路径（可以是模板，如 'cache/audio/%(id)s.mp3'）
|
||||
|
||||
Returns:
|
||||
str: 实际保存的音频文件路径
|
||||
|
||||
Raises:
|
||||
Exception: 下载失败时抛出异常
|
||||
"""
|
||||
try:
|
||||
import yt_dlp
|
||||
|
||||
ydl_opts = self.ydl_opts.copy()
|
||||
ydl_opts['outtmpl'] = output_path
|
||||
|
||||
logger.info(f"开始下载音频: {url}")
|
||||
with yt_dlp.YoutubeDL(ydl_opts) as ydl:
|
||||
ydl.download([url])
|
||||
|
||||
# 替换扩展名
|
||||
actual_path = output_path.replace('.%(ext)s', '.mp3')
|
||||
if not os.path.exists(actual_path):
|
||||
# 尝试其他可能的文件名
|
||||
actual_path = output_path.rsplit('.', 1)[0] + '.mp3'
|
||||
|
||||
if os.path.exists(actual_path):
|
||||
logger.info(f"音频下载成功: {actual_path}")
|
||||
return actual_path
|
||||
else:
|
||||
raise FileNotFoundError(f"下载的音频文件未找到: {actual_path}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"音频下载失败: {e}")
|
||||
raise
|
||||
|
||||
|
||||
class WhisperTranscriber:
|
||||
"""Whisper 语音识别器"""
|
||||
|
||||
# 模型选择
|
||||
MODELS = {
|
||||
'tiny': 39000000, # 最快，准确度最低 (~39M)
|
||||
'base': 74000000, # 推荐，速度/准确度平衡 (~74M)
|
||||
'small': 244000000, # 准确度更高 (~244M)
|
||||
'medium': 769000000, # 接近专业级 (~769M)
|
||||
'large': 1550000000, # 最高准确度 (~1.55GB)
|
||||
}
|
||||
|
||||
def __init__(self, model_size: str = 'base'):
|
||||
"""
|
||||
初始化识别器
|
||||
|
||||
Args:
|
||||
model_size: 模型大小，默认 'base'
|
||||
"""
|
||||
self.model_size = model_size
|
||||
self.model = None
|
||||
self.device = None
|
||||
|
||||
def load_model(self):
|
||||
"""加载 Whisper 模型（懒加载）"""
|
||||
if self.model is None:
|
||||
logger.info(f"正在加载 Whisper 模型 ({self.model_size})...")
|
||||
start_time = time.time()
|
||||
|
||||
try:
|
||||
import whisper
|
||||
import torch
|
||||
|
||||
# 自动检测 GPU
|
||||
self.device = "cuda" if torch.cuda.is_available() else "cpu"
|
||||
logger.info(f"使用设备: {self.device}")
|
||||
|
||||
self.model = whisper.load_model(
|
||||
self.model_size,
|
||||
device=self.device
|
||||
)
|
||||
|
||||
load_time = time.time() - start_time
|
||||
logger.info(f"模型加载完成 - 耗时: {load_time:.1f}秒")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"模型加载失败: {e}")
|
||||
raise
|
||||
|
||||
def transcribe(
|
||||
self,
|
||||
audio_path: str,
|
||||
language: Optional[str] = None,
|
||||
progress_callback: Optional[Callable[[str, float], None]] = None
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
语音识别
|
||||
|
||||
Args:
|
||||
audio_path: 音频文件路径
|
||||
language: 语言代码（'zh'、'en'、None=自动检测）
|
||||
progress_callback: 进度回调函数 callback(message, progress_percent)
|
||||
|
||||
Returns:
|
||||
dict: Whisper 识别结果，包含 'text' 和 'segments'
|
||||
|
||||
Raises:
|
||||
Exception: 识别失败时抛出异常
|
||||
"""
|
||||
self.load_model()
|
||||
|
||||
logger.info(f"开始语音识别: {audio_path}")
|
||||
if language:
|
||||
logger.info(f"指定语言: {language}")
|
||||
else:
|
||||
logger.info("语言: 自动检测")
|
||||
|
||||
start_time = time.time()
|
||||
|
||||
try:
|
||||
result = self.model.transcribe(
|
||||
audio_path,
|
||||
language=language,
|
||||
task='transcribe',
|
||||
verbose=False,
|
||||
fp16=False # CPU 上不使用 FP16
|
||||
)
|
||||
|
||||
duration = time.time() - start_time
|
||||
logger.info(f"识别完成 - 耗时: {duration:.1f}秒")
|
||||
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"语音识别失败: {e}")
|
||||
raise
|
||||
|
||||
def get_segments(self, result: Dict[str, Any]) -> list:
|
||||
"""
|
||||
获取分段信息
|
||||
|
||||
Args:
|
||||
result: Whisper 识别结果
|
||||
|
||||
Returns:
|
||||
list: segments 列表
|
||||
"""
|
||||
return result.get('segments', [])
|
||||
|
||||
|
||||
class SubtitleFormatter:
|
||||
"""字幕格式化器（SRT 格式）"""
|
||||
|
||||
@staticmethod
|
||||
def to_srt(segments: list) -> str:
|
||||
"""
|
||||
将 Whisper segments 转换为 SRT 格式
|
||||
|
||||
Args:
|
||||
segments: Whisper segments 列表
|
||||
|
||||
Returns:
|
||||
str: SRT 格式文本
|
||||
"""
|
||||
srt_lines = []
|
||||
|
||||
for i, segment in enumerate(segments, 1):
|
||||
# SRT 格式：
|
||||
# 序号
|
||||
# 开始时间 --> 结束时间
|
||||
# 文本
|
||||
# 空行
|
||||
|
||||
start_time = SubtitleFormatter._seconds_to_srt_time(segment['start'])
|
||||
end_time = SubtitleFormatter._seconds_to_srt_time(segment['end'])
|
||||
text = segment['text'].strip()
|
||||
|
||||
srt_lines.append(str(i))
|
||||
srt_lines.append(f"{start_time} --> {end_time}")
|
||||
srt_lines.append(text)
|
||||
srt_lines.append("")
|
||||
|
||||
return "\n".join(srt_lines)
|
||||
|
||||
@staticmethod
|
||||
def _seconds_to_srt_time(seconds: float) -> str:
|
||||
"""
|
||||
将秒数转换为 SRT 时间格式 (HH:MM:SS,mmm)
|
||||
|
||||
Args:
|
||||
seconds: 秒数
|
||||
|
||||
Returns:
|
||||
str: SRT 时间格式
|
||||
"""
|
||||
hours = int(seconds // 3600)
|
||||
minutes = int((seconds % 3600) // 60)
|
||||
secs = int(seconds % 60)
|
||||
millis = int((seconds - int(seconds)) * 1000)
|
||||
|
||||
return f"{hours:02d}:{minutes:02d}:{secs:02d},{millis:03d}"
|
||||
|
||||
|
||||
class SubtitleCache:
|
||||
"""字幕缓存管理器"""
|
||||
|
||||
def __init__(self, cache_dir: str = 'cache/subtitles'):
|
||||
"""
|
||||
初始化缓存
|
||||
|
||||
Args:
|
||||
cache_dir: 缓存目录
|
||||
"""
|
||||
self.cache_dir = Path(cache_dir)
|
||||
self.cache_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
def get_cache_key(self, url: str) -> str:
|
||||
"""
|
||||
生成缓存键
|
||||
|
||||
Args:
|
||||
url: 视频URL
|
||||
|
||||
Returns:
|
||||
str: 缓存键（SHA256 哈希的前8位）
|
||||
"""
|
||||
return hashlib.sha256(url.encode()).hexdigest()[:8]
|
||||
|
||||
def get_cache_path(self, url: str) -> str:
|
||||
"""
|
||||
获取缓存文件路径
|
||||
|
||||
Args:
|
||||
url: 视频URL
|
||||
|
||||
Returns:
|
||||
str: 缓存文件路径
|
||||
"""
|
||||
cache_key = self.get_cache_key(url)
|
||||
return str(self.cache_dir / f"{cache_key}.srt")
|
||||
|
||||
def get(self, url: str) -> Optional[str]:
|
||||
"""
|
||||
获取缓存字幕
|
||||
|
||||
Args:
|
||||
url: 视频URL
|
||||
|
||||
Returns:
|
||||
str | None: 缓存的字幕内容，如果不存在返回 None
|
||||
"""
|
||||
cache_path = self.get_cache_path(url)
|
||||
|
||||
if os.path.exists(cache_path):
|
||||
logger.info(f"使用缓存字幕: {cache_path}")
|
||||
try:
|
||||
with open(cache_path, 'r', encoding='utf-8') as f:
|
||||
return f.read()
|
||||
except Exception as e:
|
||||
logger.warning(f"读取缓存失败: {e}")
|
||||
return None
|
||||
|
||||
return None
|
||||
|
||||
def set(self, url: str, subtitle: str) -> str:
|
||||
"""
|
||||
保存缓存字幕
|
||||
|
||||
Args:
|
||||
url: 视频URL
|
||||
subtitle: 字幕内容
|
||||
|
||||
Returns:
|
||||
str: 缓存文件路径
|
||||
"""
|
||||
cache_path = self.get_cache_path(url)
|
||||
|
||||
try:
|
||||
with open(cache_path, 'w', encoding='utf-8') as f:
|
||||
f.write(subtitle)
|
||||
logger.info(f"字幕已缓存: {cache_path}")
|
||||
return cache_path
|
||||
except Exception as e:
|
||||
logger.error(f"保存缓存失败: {e}")
|
||||
raise
|
||||
|
||||
def exists(self, url: str) -> bool:
|
||||
"""检查缓存是否存在"""
|
||||
return os.path.exists(self.get_cache_path(url))
|
||||
|
||||
def clear_cache(self):
|
||||
"""清空所有缓存"""
|
||||
try:
|
||||
for file in self.cache_dir.glob('*.srt'):
|
||||
file.unlink()
|
||||
logger.info("缓存已清空")
|
||||
except Exception as e:
|
||||
logger.error(f"清空缓存失败: {e}")
|
||||
|
||||
|
||||
class SubtitleGenerator:
|
||||
"""字幕生成器统一接口"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model_size: str = 'base',
|
||||
cache_dir: str = 'cache/subtitles',
|
||||
audio_cache_dir: str = 'cache/audio'
|
||||
):
|
||||
"""
|
||||
初始化生成器
|
||||
|
||||
Args:
|
||||
model_size: Whisper 模型大小
|
||||
cache_dir: 字幕缓存目录
|
||||
audio_cache_dir: 音频缓存目录
|
||||
"""
|
||||
self.audio_downloader = AudioDownloader()
|
||||
self.transcriber = WhisperTranscriber(model_size)
|
||||
self.formatter = SubtitleFormatter()
|
||||
self.cache = SubtitleCache(cache_dir)
|
||||
self.audio_cache_dir = Path(audio_cache_dir)
|
||||
self.audio_cache_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
def generate(
|
||||
self,
|
||||
url: str,
|
||||
content_id: str = None,
|
||||
language: Optional[str] = None,
|
||||
use_cache: bool = True,
|
||||
keep_audio: bool = False
|
||||
) -> str:
|
||||
"""
|
||||
生成字幕
|
||||
|
||||
Args:
|
||||
url: 视频URL
|
||||
content_id: 内容ID（用于音频文件命名）
|
||||
language: 语言代码（None=自动检测）
|
||||
use_cache: 是否使用缓存
|
||||
keep_audio: 是否保留音频文件
|
||||
|
||||
Returns:
|
||||
str: SRT 格式字幕
|
||||
|
||||
Raises:
|
||||
Exception: 生成失败时抛出异常
|
||||
"""
|
||||
# 检查缓存
|
||||
if use_cache and self.cache.exists(url):
|
||||
logger.info(f"使用缓存字幕: {url}")
|
||||
cached_subtitle = self.cache.get(url)
|
||||
if cached_subtitle:
|
||||
return cached_subtitle
|
||||
|
||||
logger.info(f"开始生成字幕: {url}")
|
||||
audio_path = None
|
||||
|
||||
try:
|
||||
# 生成 content_id
|
||||
if content_id is None:
|
||||
content_id = self.cache.get_cache_key(url)
|
||||
|
||||
# 下载音频
|
||||
audio_path = str(self.audio_cache_dir / f"{content_id}.mp3")
|
||||
logger.info(f"下载音频到: {audio_path}")
|
||||
audio_path = self.audio_downloader.download_audio(url, audio_path)
|
||||
|
||||
# 语音识别
|
||||
result = self.transcriber.transcribe(audio_path, language=language)
|
||||
segments = self.transcriber.get_segments(result)
|
||||
|
||||
if not segments:
|
||||
logger.warning("未识别到任何语音内容")
|
||||
return ""
|
||||
|
||||
# 转换为 SRT
|
||||
srt_text = self.formatter.to_srt(segments)
|
||||
logger.info(f"SRT 字幕生成成功，共 {len(segments)} 段")
|
||||
|
||||
# 保存缓存
|
||||
self.cache.set(url, srt_text)
|
||||
|
||||
# 清理音频文件
|
||||
if not keep_audio and os.path.exists(audio_path):
|
||||
os.remove(audio_path)
|
||||
logger.info(f"已删除音频文件: {audio_path}")
|
||||
|
||||
return srt_text
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"字幕生成失败: {e}")
|
||||
# 清理失败的音频文件
|
||||
if audio_path and os.path.exists(audio_path) and not keep_audio:
|
||||
try:
|
||||
os.remove(audio_path)
|
||||
except:
|
||||
pass
|
||||
raise
|
||||
|
||||
|
||||
def generate_subtitle(
|
||||
url: str,
|
||||
model_size: str = 'base',
|
||||
language: Optional[str] = None,
|
||||
cache_dir: str = 'cache/subtitles',
|
||||
audio_cache_dir: str = 'cache/audio'
|
||||
) -> str:
|
||||
"""
|
||||
便捷函数：生成字幕
|
||||
|
||||
Args:
|
||||
url: 视频URL
|
||||
model_size: Whisper 模型大小
|
||||
language: 语言代码（None=自动检测）
|
||||
cache_dir: 字幕缓存目录
|
||||
audio_cache_dir: 音频缓存目录
|
||||
|
||||
Returns:
|
||||
str: SRT 格式字幕
|
||||
"""
|
||||
generator = SubtitleGenerator(
|
||||
model_size=model_size,
|
||||
cache_dir=cache_dir,
|
||||
audio_cache_dir=audio_cache_dir
|
||||
)
|
||||
return generator.generate(url, language=language)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# 测试示例
|
||||
import sys
|
||||
|
||||
if len(sys.argv) < 2:
|
||||
print("用法: python subtitle_generator.py <视频URL> [语言]")
|
||||
print("示例: python subtitle_generator.py https://www.bilibili.com/video/BV1xx411c7mD zh")
|
||||
sys.exit(1)
|
||||
|
||||
url = sys.argv[1]
|
||||
language = sys.argv[2] if len(sys.argv) > 2 else None
|
||||
|
||||
print("=" * 60)
|
||||
print("字幕生成器")
|
||||
print("=" * 60)
|
||||
|
||||
try:
|
||||
subtitle = generate_subtitle(url, language=language)
|
||||
print("\n" + "=" * 60)
|
||||
print("生成成功！字幕预览（前500字符）：")
|
||||
print("=" * 60)
|
||||
print(subtitle[:500])
|
||||
if len(subtitle) > 500:
|
||||
print("\n...")
|
||||
print(f"\n总字符数: {len(subtitle)}")
|
||||
except Exception as e:
|
||||
print(f"\n错误: {e}")
|
||||
sys.exit(1)
|
||||
@@ -0,0 +1,245 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
字幕生成器测试脚本
|
||||
|
||||
测试各个模块的功能
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import tempfile
|
||||
from pathlib import Path
|
||||
|
||||
# 添加项目路径
|
||||
sys.path.insert(0, str(Path(__file__).parent))
|
||||
|
||||
|
||||
def test_audio_downloader():
|
||||
"""测试音频下载器"""
|
||||
print("\n" + "="*60)
|
||||
print("测试 1: 音频下载器")
|
||||
print("="*60)
|
||||
|
||||
from subtitle_generator import AudioDownloader
|
||||
|
||||
# 使用一个短测试视频
|
||||
test_url = "https://www.youtube.com/watch?v=dQw4w9WgXcQ" # Rick Astley - Never Gonna Give You Up
|
||||
|
||||
downloader = AudioDownloader()
|
||||
|
||||
try:
|
||||
with tempfile.TemporaryDirectory() as tmpdir:
|
||||
output_path = os.path.join(tmpdir, "test_audio.%(ext)s")
|
||||
result_path = downloader.download_audio(test_url, output_path)
|
||||
|
||||
if os.path.exists(result_path):
|
||||
size_mb = os.path.getsize(result_path) / (1024 * 1024)
|
||||
print(f"✅ 音频下载成功")
|
||||
print(f" 文件: {result_path}")
|
||||
print(f" 大小: {size_mb:.2f} MB")
|
||||
return True
|
||||
else:
|
||||
print(f"❌ 音频文件未找到: {result_path}")
|
||||
return False
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 音频下载失败: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return False
|
||||
|
||||
|
||||
def test_whisper_transcriber():
|
||||
"""测试 Whisper 识别器"""
|
||||
print("\n" + "="*60)
|
||||
print("测试 2: Whisper 识别器")
|
||||
print("="*60)
|
||||
|
||||
from subtitle_generator import WhisperTranscriber
|
||||
|
||||
# 创建测试音频（使用 ffmpeg 生成静音）
|
||||
test_audio = "/tmp/test_silent.mp3"
|
||||
os.system(f"ffmpeg -f lavfi -i anullsrc=r=44100:cl=mono -t 3 -q:a 9 -y {test_audio} 2>/dev/null")
|
||||
|
||||
try:
|
||||
transcriber = WhisperTranscriber(model_size='tiny') # 使用最小的模型
|
||||
result = transcriber.transcribe(test_audio)
|
||||
|
||||
print(f"✅ Whisper 识别完成")
|
||||
print(f" 识别文本: {result['text'][:50]}...")
|
||||
print(f" 分段数: {len(result.get('segments', []))}")
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Whisper 识别失败: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return False
|
||||
|
||||
|
||||
def test_subtitle_formatter():
|
||||
"""测试 SRT 格式化器"""
|
||||
print("\n" + "="*60)
|
||||
print("测试 3: SRT 格式化器")
|
||||
print("="*60)
|
||||
|
||||
from subtitle_generator import SubtitleFormatter
|
||||
|
||||
# 测试数据
|
||||
test_segments = [
|
||||
{'start': 0.0, 'end': 2.5, 'text': '这是一段测试字幕'},
|
||||
{'start': 3.0, 'end': 5.7, 'text': '这是第二段字幕'},
|
||||
{'start': 6.0, 'end': 8.2, 'text': 'This is the third subtitle'},
|
||||
]
|
||||
|
||||
try:
|
||||
srt_text = SubtitleFormatter.to_srt(test_segments)
|
||||
|
||||
print(f"✅ SRT 格式化成功")
|
||||
print(f" 生成内容预览:")
|
||||
print(" " + "-" * 40)
|
||||
for line in srt_text.split('\n')[:10]:
|
||||
print(f" {line}")
|
||||
print(" " + "-" * 40)
|
||||
|
||||
# 验证格式
|
||||
assert '00:00:00,000 --> 00:00:02,500' in srt_text
|
||||
assert '这是一段测试字幕' in srt_text
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ SRT 格式化失败: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return False
|
||||
|
||||
|
||||
def test_subtitle_cache():
|
||||
"""测试缓存管理器"""
|
||||
print("\n" + "="*60)
|
||||
print("测试 4: 缓存管理器")
|
||||
print("="*60)
|
||||
|
||||
from subtitle_generator import SubtitleCache
|
||||
|
||||
test_url = "https://example.com/video/123"
|
||||
test_subtitle = """1
|
||||
00:00:00,000 --> 00:00:02,500
|
||||
测试字幕内容
|
||||
"""
|
||||
|
||||
try:
|
||||
cache = SubtitleCache(cache_dir='/tmp/test_subtitle_cache')
|
||||
|
||||
# 测试写入
|
||||
cache_path = cache.set(test_url, test_subtitle)
|
||||
print(f"✅ 缓存写入成功: {cache_path}")
|
||||
|
||||
# 测试读取
|
||||
cached_content = cache.get(test_url)
|
||||
assert cached_content == test_subtitle
|
||||
print(f"✅ 缓存读取成功")
|
||||
|
||||
# 测试存在性
|
||||
assert cache.exists(test_url)
|
||||
print(f"✅ 缓存存在性检查成功")
|
||||
|
||||
# 测试缓存键
|
||||
cache_key = cache.get_cache_key(test_url)
|
||||
print(f" 缓存键: {cache_key}")
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 缓存测试失败: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return False
|
||||
|
||||
|
||||
def test_full_workflow():
|
||||
"""测试完整工作流程"""
|
||||
print("\n" + "="*60)
|
||||
print("测试 5: 完整工作流程")
|
||||
print("="*60)
|
||||
|
||||
from subtitle_generator import SubtitleGenerator
|
||||
|
||||
test_url = "https://www.youtube.com/watch?v=dQw4w9WgXcQ"
|
||||
|
||||
try:
|
||||
generator = SubtitleGenerator(model_size='tiny')
|
||||
|
||||
print(f"开始生成字幕...")
|
||||
srt_text = generator.generate(
|
||||
url=test_url,
|
||||
content_id='test001',
|
||||
language='en',
|
||||
use_cache=False
|
||||
)
|
||||
|
||||
print(f"✅ 字幕生成完成")
|
||||
print(f" 总字符数: {len(srt_text)}")
|
||||
print(f" 预览（前300字符）:")
|
||||
print(" " + "-" * 40)
|
||||
print(" " + srt_text[:300])
|
||||
print(" " + "-" * 40)
|
||||
|
||||
# 验证缓存
|
||||
print(f"\n验证缓存...")
|
||||
cached = generator.generate(
|
||||
url=test_url,
|
||||
content_id='test002',
|
||||
use_cache=True
|
||||
)
|
||||
assert cached == srt_text
|
||||
print(f"✅ 缓存验证成功")
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 完整工作流程失败: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return False
|
||||
|
||||
|
||||
def main():
|
||||
"""运行所有测试"""
|
||||
print("\n" + "="*60)
|
||||
print("字幕生成器测试套件")
|
||||
print("="*60)
|
||||
|
||||
results = {}
|
||||
|
||||
# 运行各个测试
|
||||
results['audio_downloader'] = test_audio_downloader()
|
||||
results['whisper_transcriber'] = test_whisper_transcriber()
|
||||
results['subtitle_formatter'] = test_subtitle_formatter()
|
||||
results['subtitle_cache'] = test_subtitle_cache()
|
||||
results['full_workflow'] = test_full_workflow()
|
||||
|
||||
# 输出汇总
|
||||
print("\n" + "="*60)
|
||||
print("测试结果汇总")
|
||||
print("="*60)
|
||||
|
||||
for test_name, passed in results.items():
|
||||
status = "✅ 通过" if passed else "❌ 失败"
|
||||
print(f"{test_name:25s} {status}")
|
||||
|
||||
total = len(results)
|
||||
passed = sum(results.values())
|
||||
|
||||
print("\n" + "="*60)
|
||||
print(f"总计: {passed}/{total} 测试通过")
|
||||
print("="*60)
|
||||
|
||||
return all(results.values())
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
success = main()
|
||||
sys.exit(0 if success else 1)
|
||||
Reference in New Issue
Block a user