subtitle-generator
当用户需要生成字幕、制作字幕、字幕对齐、ASR识别、语音转文字时使用此技能。触发词:生成字幕, 字幕生成, 制作字幕, 视频字幕, 语音识别, ASR字幕, Whisper字幕, 字幕制作, 视频转字幕, 字幕对齐, 音频转字幕, 语音转文字, 自动字幕, ASR识别, faster-whisper, subtitle, subtitles, caption, transcription, speech to text
Best use case
subtitle-generator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
当用户需要生成字幕、制作字幕、字幕对齐、ASR识别、语音转文字时使用此技能。触发词:生成字幕, 字幕生成, 制作字幕, 视频字幕, 语音识别, ASR字幕, Whisper字幕, 字幕制作, 视频转字幕, 字幕对齐, 音频转字幕, 语音转文字, 自动字幕, ASR识别, faster-whisper, subtitle, subtitles, caption, transcription, speech to text
Teams using subtitle-generator should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/subtitle-generator/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How subtitle-generator Compares
| Feature / Agent | subtitle-generator | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
当用户需要生成字幕、制作字幕、字幕对齐、ASR识别、语音转文字时使用此技能。触发词:生成字幕, 字幕生成, 制作字幕, 视频字幕, 语音识别, ASR字幕, Whisper字幕, 字幕制作, 视频转字幕, 字幕对齐, 音频转字幕, 语音转文字, 自动字幕, ASR识别, faster-whisper, subtitle, subtitles, caption, transcription, speech to text
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Subtitle Generator / 字幕生成器
基于可插拔 ASR 引擎的多语言字幕生成技能,支持 Faster-Whisper(优先)和 OpenAI Whisper(兜底),自动检测 GPU 加速,支持 99+ 语言。
## ⚠️ 执行规则(强制,AI 必须遵守)
### 1. 必须后台直接执行,禁止委托子代理
字幕生成是纯执行任务,**禁止**使用 `sessions_spawn` 或子代理来执行命令,原因:
- 子代理消耗额外 tokens,纯属浪费
- 直接 `exec background:true` 效率最高
### 2. 执行模板
```bash
exec background:true command:"python3 ~/.openclaw/workspace/skills/subtitle-generator/scripts/main.py <视频文件> [srt|vtt] [语言] --notify"
```
**⚠️ 必须使用 `background:true`,禁止前台执行阻塞主窗口。**
### 3. 执行流程(强制顺序)
**步骤 0(首次自动):** engines/__init__.py 在首次导入时自动检测 ~/.whisper-venv 是否存在,不存在时自动创建 venv 并安装依赖(faster-whisper 优先,openai-whisper 兜底),GPU 可用时自动启用 CUDA 加速。AI 无需手动干预。
1. 收到任务后**立即回复**用户:「🎬 字幕生成已启动(后台),完成后我会通知你。」
2. 使用 `exec background:true` 启动任务
3. 任务完成后(进程退出码为 0 或出错)**必须 kill 相关进程**
4. 通过 `openclaw system event --mode now` 唤醒 AI,再由 AI 使用 `message` 工具发送完成通知给用户
- ⚠️ `openclaw system event` 只唤醒 AI,不发送 Telegram/Discord 等消息
- ✅ 必须用 `message` 工具才能触达用户
5. 将字幕文件复制到视频同目录
6. 清理 `/tmp` 中的临时文件
### 4. 禁止行为
- ❌ `sessions_spawn` / 子代理执行此任务
- ❌ 前台执行 `exec command:"..."`(无 `background:true`)
- ❌ 不发送通知就结束会话
- ❌ 任务完成后不 kill 相关进程
### 5. AI 回复模板
任务启动时:
```
🎬 字幕生成已启动(后台),完成后我会通知你。
文件:xxx.mp4 | 格式:SRT | 语言:中文
```
Python 脚本输出 `【字幕生成完成】` 或 `【字幕生成失败】` 标记后,AI 被唤醒时使用 `message` 工具发送:
```python
message(
action="send",
# 不传 channel 参数,自动路由到用户当前所在的聊天平台
message="✅ 字幕生成完成\n文件:xxx.srt | 条数:356\n路径:C:\\Users\\xxx\\Videos\\temp\\xxx.srt"
)
```
⚠️ **必须省略 `channel` 参数**,这样才能兼容所有 OpenClaw 支持的聊天工具(Telegram / Discord / WhatsApp / Slack 等)。
## 使用方法 / Usage
```bash
python ~/.openclaw/workspace/skills/subtitle-generator/scripts/main.py <video> [srt|vtt] [language] [--notify]
```
### 示例 / Examples
```bash
# English video, SRT subtitles
python ~/.openclaw/workspace/skills/subtitle-generator/scripts/main.py video.mp4 srt en --notify
# Japanese video, VTT subtitles
python ~/.openclaw/workspace/skills/subtitle-generator/scripts/main.py video.mp4 vtt ja --notify
# Chinese video, auto language detection
python ~/.openclaw/workspace/skills/subtitle-generator/scripts/main.py video.mp4 srt zh --notify
# Auto-detect any Whisper-supported language
python ~/.openclaw/workspace/skills/subtitle-generator/scripts/main.py video.mp4 srt --notify
```
### 支持语言 / Supported Languages
Whisper 支持 99+ 语言。常用语言代码:
| Code | Language |
|------|----------|
| `en` | English |
| `zh` | 中文 (Chinese) |
| `ja` | 日本語 (Japanese) |
| `ko` | 한국어 (Korean) |
| `fr` | Français |
| `de` | Deutsch |
| `es` | Español |
| `ru` | Русский |
不传语言参数 = 自动检测。
## GPU 加速 / GPU Acceleration
faster-whisper 使用 CTranslate2 作为推理引擎,运行时自动检测并启用 GPU:
| 系统 | GPU | 加速方式 | 说明 |
|------|-----|----------|------|
| Windows + NVIDIA | CUDA | cuDNN + CUDA | CTranslate2 自动加载 |
| macOS M1/M2/M3/M4 | Metal | Apple GPU | 需安装 Metal 版 CTranslate2(见下) |
| macOS Intel | CPU | — | 慢但可用 |
| Linux + NVIDIA | CUDA | cuDNN + CUDA | CTranslate2 自动加载 |
| CPU-only | — | 降级运行 | 所有平台均支持 |
### Apple Silicon (M系列) 安装 Metal 加速版
faster-whisper 默认不包含 Metal 加速,手动安装 Metal 版可获得显著提速:
```bash
# 先激活 venv,再安装 Metal 版 CTranslate2
~/.whisper-venv/bin/pip install \
--extra-index-url https://download.pytorch.org/whl/metal \
faster-whisper
```
安装后运行时日志会显示 `Using device Metal`,表示 Metal 加速已启用。
## 引擎优先级 / Engine Priority
| Priority | Engine | Notes |
|----------|--------|-------|
| 1 (preferred) | faster-whisper | CTranslate2, 2-4x faster, lower RAM |
| 2 (fallback) | openai-whisper | PyTorch, better compatibility |
Engine is auto-selected. No manual configuration needed.
## 输出 / Output
字幕文件生成在视频同目录:
```
video.mp4
video.srt ← auto-generated
```
## 依赖项 / Dependencies
- `faster-whisper` or `openai-whisper` (auto-selected, auto-installed)
- `ffmpeg` (系统级,必须提前安装)
- `ffmpeg-python` (Python binding, auto-installed)
### ffmpeg 安装 / ffmpeg Installation
| 系统 | 安装命令 |
|------|----------|
| Windows | `winget install ffmpeg` 或 https://ffmpeg.org/download.html |
| macOS | `brew install ffmpeg` |
| Linux | `sudo apt install ffmpeg` 或 `sudo yum install ffmpeg` |
> ⚠️ ffmpeg 缺失时脚本会报错并给出对应系统的安装提示。Related Skills
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