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官方说明7 个来源MCP 服务8 天前
huseyinstif/klaket

huseyinstif/klaket

Local video understanding: word-timest
在 GitHub 查看 ↗直链 · 无中转页
配置模板(请按你的客户端调整)
claude mcp add klaket -- npx -y klaket
这是根据仓库名生成的可粘贴骨架,命令行参数与包名请以该项目 README 为准。
★ 数取值
3 ★ · huseyinstif/klaket · 取自 GitHub 搜索 API · (2026-10-16 20:00:20 核对)
来源明细
收录自 punkpeye/awesome-mcp-servers 榜单(我们从中收集的条目之一) · 条目类型:MCP 服务
收录与活跃
收录于 2026-10-09 · 最近见到 2026-10-16 · 共见到 32 次 · 被 7 个来源列出
评分构成
综合分 703 · 3 ★ · 7 个来源 · mcp
常见问题(答案都来自我们的记录)
怎么安装?
我们【没有】从该仓库取到安装命令(我们的 install 覆盖率只有约 4%)。请看它的 README 或 releases 页。
它适合谁 / 用来做什么?
我们把它归在这些标签下: MCP 服务。标签来自条目自身,不是我们强加的分类。
谁维护它、还在更新吗?
仓库 huseyinstif/klaket;我们收录于 2026-10-09;最近见到 2026-10-16。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。
第三方质量标记

🎬 Klaket

以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →

**Turn any video into LLM-ready data.**
> A *klaket* is a clapperboard — the tool that syncs sound and image on a film set. **Klaket syncs video with LLMs.**
LLMs read text. The web became readable with scrapers — but video, the largest store of human knowledge, is still locked away. Klaket unlocks it: give it a video URL or file, get back structured, timestamped, LLM-ready data.
Features
- **📝 Transcript** — timestamped speech-to-text in **~100 languages** (auto-detected) with **word-level timestamps**; pick the model per job ("model": "medium")
- **🎙️ Podcasts too** — pass an audio file/URL (mp3, m4a…) and Klaket skips the visual stages, deriving chapters from speech pauses
- **🗣️ Speaker diarization** — who said what (S1/S2/…), local & keyless (sherpa-onnx)
- **💬 Subtitles** — ready-to-use .srt / .vtt files with speaker labels
- **🎞️ Scene detection** — content-aware scene boundaries + keyframes per scene
- **🔎 On-screen text (OCR)** — reads slides, terminals and captions per scene, local & keyless
- **🧩 One JSON timeline** — transcript, scenes, frames and on-screen text aligned on a single timeline
- **🔌 Works offline, no API key required** — the core pipeline uses zero LLM calls
- **🧠 Pluggable model layer** — optional scene descriptions via local VLMs (Ollama) or any OpenAI-compatible endpoint (KLAKET_VLM=off by default)
- **🤖 MCP server** — let coding agents "watch" any video and find moments inside it
- **🔍 In-video search** — GET /v1/jobs/{id}/search?q=… finds the exact moment
- **▶️ Playground** — the dashboard plays the video with a click-to-seek, live-highlighted transcript
SDKs
Give your agent eyes
Then: *"Watch https://youtube.com/watch?v=… and summarize the commands the presenter runs."*
The agent gets klaket_ingest, klaket_job_status and klaket_get_result tools.
Quick start
That's it — no API keys, no GPUs required. make help lists developer shortcuts (make up, make test, make e2e).
Architecture
- apps/api — Go, job orchestration
- apps/worker — Python, media pipeline
- apps/dashboard — React dashboard
Self-host vs Cloud
来源
awesome · https://github.com/huseyinstif/klaket
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