video-summarizer
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@video-summarizersummarize the meeting recording at /Users/me/recordings/team-sync.mp4"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
video-summarizer
Turn videos / audio / meeting recordings into structured transcripts & summaries — multi-backend ASR with auto-fallback, zero hard dependencies, and MCP support.
对 tmeet-skill(腾讯会议专属)的二创与泛化:去掉对单一会议平台的强依赖, 做成任意视频/音频都能用的总结工具。同一个核心代码,既能当命令行工具跑,也能当 MCP 服务器 被 Claude / Cursor / WorkBuddy 等任意 AI 客户端直接调用。
✨ 特性
多后端 ASR,自动降级:本地 Whisper → 阿里云百炼 → OpenAI,按可用性自动选;挂了无缝下一个。
零硬依赖:没装 ffmpeg / whisper 也不崩,给出清晰安装指引。
外部转写直读:已有
.txt / .srt / .vtt(含腾讯会议导出的转写)直接读,零依赖跑通。结构化输出:
transcript.txt(纯文本)、transcript.json(带元数据)、transcript.srt(字幕)。双形态:命令行工具 + MCP 服务器,一份代码两种接入方式。
开闭原则:新增 ASR 后端只需实现一个函数并注册,主流程一行不动。
Related MCP server: Augent
🔧 支持的转写后端
后端 | 参数值 | 是否联网 | 成本 | 依赖 | 数据出境 |
本地 Whisper |
| 否(首下模型需网) | 免费 |
| 否(离线) |
阿里云百炼 |
| 是 | 低 |
| 是(国内合规更稳) |
OpenAI API |
| 是 | 按量 |
| 是 |
外部转写文件 |
| 否 | 免费 | 无 | 否 |
auto(默认)顺序:local → aliyun → openai;都不行则提示用 --transcript。
📦 安装
git clone https://github.com/yangmiao3/video-summarizer.git
cd video-summarizer
pip install -r requirements.txt核心逻辑零硬依赖;上面会一并装
mcp/dashscope/openai/faster-whisper以便用满所有后端。 只想用"外部转写直读"的话,不装任何东西也能跑。
🚀 快速开始
# 1) 已有转写文件(如腾讯会议导出)→ 零依赖直读
python -m video_summarizer.pipeline 会议.mp4 --transcript 转写.txt
# 2) 直接丢视频/音频,自动选后端转写(需配好至少一个 ASR 后端 + ffmpeg)
python -m video_summarizer.pipeline 会议录制.mp4 --backend auto --language zh
# 3) 指定后端
python -m video_summarizer.pipeline lecture.m4a --backend aliyun --language zh输出在源文件旁的 <stem>_transcript/ 目录:transcript.txt / transcript.json / transcript.srt。
拿到转写后,交给任意 LLM(或你的总结 Skill)按模板生成概览 / 章节 / 核心要点 / 行动项即可。
🔌 作为 MCP 服务器使用(跨客户端复用)
pip install mcp
python -m video_summarizer.mcp_server客户端配置(以 WorkBuddy 为例,编辑 ~/.workbuddy/mcp.json):
{
"mcpServers": {
"video-summarizer": {
"command": "python",
"args": ["-m", "video_summarizer.mcp_server"]
}
}
}配置后,AI 客户端就能直接调用 transcribe_audio / summarize_video / list_backends 三个工具。
📁 项目结构
video-summarizer/
├── README.md
├── LICENSE
├── requirements.txt
├── .gitignore
├── SKILL.md # 兼顾 WorkBuddy 技能用法
├── src/video_summarizer/
│ ├── __init__.py
│ ├── asr.py # 多后端转写(核心,开闭原则)
│ ├── pipeline.py # 抽音频 + 转写 + 落盘(CLI 入口)
│ └── mcp_server.py # MCP 封装(FastMCP)
├── assets/summary_template.md # 总结模板
└── examples/sample_transcript.txt🧩 扩展:加一个新后端(开闭原则演示)
只需在 asr.py 实现一个函数并注册,不用改任何主流程:
def _try_my_backend(audio: Path, language: str) -> Transcript | None:
key = os.environ.get("MY_KEY")
if not key:
return None
# ... 调用你的 ASR ...
return Transcript(text=..., segments=..., backend="my-backend")
BACKENDS["my"] = _try_my_backend # 一行注册,transcribe() 自动支持 --backend my📄 许可证
MIT —— 可自由用于学习与商业项目。
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