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在 OpenClaw 中安装
/install video-processor
功能描述
自动提取视频音频，识别生成带时间戳的文字稿，输出SRT/VTT字幕及带字幕的视频，并智能提炼视频标题。
安全使用建议
This skill appears internally consistent for generating subtitles: it uses FFmpeg and Whisper/faster-whisper (expected). Before installing/running: 1) Review the two included scripts yourself (they are the main runtime) and run them on non-sensitive sample videos first. 2) Expect large model downloads (tens to hundreds of MB or more) when using real ASR models — this requires network access and disk space in your user cache (e.g., ~/.cache/huggingface). 3) The docs reference extra modules and optional features (yt-dlp, stable-diffusion) that are NOT present in the package; treat those as optional planned features. 4) No credentials are requested, and the code does not contain obvious exfiltration; nonetheless run it in a sandbox or isolated environment if you are concerned. 5) If you only want offline/safe testing, use the simulated/mock mode (the scripts include a mock transcript path) rather than downloading models.
功能分析
Type: OpenClaw Skill
Name: video-processor
Version: 2.1.1
The video-processor skill bundle is a legitimate tool for automated video subtitling and processing. It utilizes standard libraries such as faster-whisper for speech recognition and subprocess calls to FFmpeg for media manipulation. Analysis of scripts/video_processor.py and scripts/subtitle_processor.py shows no evidence of data exfiltration, malicious persistence, or unauthorized network activity. The instructions in SKILL.md and documentation are consistent with the stated purpose of video processing and do not contain prompt-injection attacks or hidden malicious commands.
能力评估
Purpose & Capability
The name/description (video→subtitles→burn-in→title extraction) align with the included scripts and SKILL.md. The code uses FFmpeg and Whisper/faster-whisper which are appropriate. Minor inconsistency: SKILL.md and README mention several module files (speech_recognition.py, subtitle_generator.py, title_extractor.py, video_renderer.py) in the example file tree that are not present in the provided manifest — only video_processor.py and subtitle_processor.py are included. Documentation also lists optional features (yt-dlp, stable-diffusion) that are not required by the included scripts. These are likely incomplete/overdocumented rather than malicious.
Instruction Scope
Runtime instructions and the scripts instruct extracting audio, running ASR (faster-whisper or simulated mode), generating SRT/VTT, optionally converting Traditional→Simplified, and calling ffmpeg to burn subtitles — all within the stated purpose. The code reads user-provided video/transcript/style files and writes output files; it does not reference unrelated system paths or unexpected remote endpoints. Documentation suggests optionally setting HF_ENDPOINT for model downloads, but the runtime actions that would reach networks are limited to downloading model files (expected for Whisper).
Install Mechanism
No install spec in the registry (instruction-only). Dependencies are installed via pip/OS package manager according to docs (faster-whisper, openai-whisper, ffmpeg, optionally yt-dlp, stable-diffusion). No arbitrary binary downloads or obscure URLs were found in the provided files. Model weights will be downloaded by the ASR library (faster-whisper/Whisper) from standard model hosting (Hugging Face) which is expected but may fetch large files.
Credentials
The skill declares no required environment variables or credentials (good). Documentation mentions HF_ENDPOINT as an optional mirror variable for model downloads; this is optional and not required. There are no requests for unrelated secrets or cloud credentials. Be aware that the ASR library will perform network downloads for model weights and may use the user's home cache directories (e.g., ~/.cache/huggingface/hub).
Persistence & Privilege
The skill does not request persistent/always-on privileges, does not declare always:true, and does not attempt to modify other skills or system-wide agent settings. It runs as invoked and writes output into user-specified output directories only.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install video-processor - 安装完成后，直接呼叫该 Skill 的名称或使用
/video-processor触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v2.1.1
- 完善了字幕样式配置。
- 移除了文件“打包完成报告.md”以简化项目结构。
- 其他代码和文档内容保持不变。
v2.1.0
视频自动上字幕。实现语音识别、自动生成 SRT 字幕、标题提炼、视频合成。支持字幕繁简转换、样式自定义、后处理修正（个别字幕修复后再生成）。
**功能亮点**：
- 🎙️ 语音识别转文字（支持 5 种 Whisper 模型）
- 📝 自动生成 SRT 字幕（时间戳精确同步）
- 🎯 智能提炼视频标题
- 📺 字幕烧录到视频
- 🔄 支持修改文字稿后重新生成
- 🎨 自定义字幕样式配置
🚀 完整工作流
1. 初次处理
python scripts/video_processor.py -i "video.mp4" -m "medium"
↓
2. 编辑文字稿（修正错别字）
用记事本编辑 transcript.txt
↓
3. 配置样式（可选）
复制并编辑 subtitle_style_template.json
↓
4. 重新生成
python scripts/subtitle_processor.py -t "transcript_fixed.txt" -v "video.mp4" -s "my_style.json"
↓
5. 完成
输出带修正字幕和新样式的视频
元数据
常见问题
视频字幕自动生成器——免费的才是最好的 是什么？
自动提取视频音频，识别生成带时间戳的文字稿，输出SRT/VTT字幕及带字幕的视频，并智能提炼视频标题。 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 236 次。
如何安装 视频字幕自动生成器——免费的才是最好的？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install video-processor」即可一键安装，无需额外配置。
视频字幕自动生成器——免费的才是最好的 是免费的吗？
是的，视频字幕自动生成器——免费的才是最好的 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
视频字幕自动生成器——免费的才是最好的 支持哪些平台？
视频字幕自动生成器——免费的才是最好的 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 视频字幕自动生成器——免费的才是最好的？
由 chall2015（@chall2015）开发并维护，当前版本 v2.1.1。
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