Content
# 🎬 Klaket
**Turn any video into LLM-ready data.**
[](LICENSE)
[](CONTRIBUTING.md)
[](#quick-start)
> 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.
```bash
pip install klaket
klaket ingest "https://youtube.com/watch?v=..." --wait
```
```jsonc
{
"transcript": [
{ "start": 14.32, "end": 19.80, "speaker": "S1", "text": "So let's deploy this with docker compose..." }
],
"scenes": [
{ "start": 190.0, "end": 342.5, "keyframes": ["scene_004_01.jpg"] }
],
"chapters": [...],
"summary": "..."
}
```
## 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
```python
# pip install klaket
from klaket import Klaket
result = Klaket().process("https://youtube.com/watch?v=...", num_speakers=2)
```
```ts
// npm i klaket-sdk
import { Klaket } from "klaket-sdk";
const result = await new Klaket().process("https://youtube.com/watch?v=...");
```
## Give your agent eyes
```bash
# Claude Code
claude mcp add klaket -- npx klaket-mcp # KLAKET_API_URL defaults to localhost:8484
```
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
```bash
git clone https://github.com/huseyinstif/klaket.git && cd klaket
docker compose up --build
# API on :8484, dashboard on :5180
curl -X POST localhost:8484/v1/ingest \
-H "Content-Type: application/json" \
-d '{"url": "https://youtube.com/watch?v=..."}'
```
That's it — no API keys, no GPUs required. `make help` lists developer shortcuts (`make up`, `make test`, `make e2e`).
## Architecture
```
client ──► Go API ──► Redis queue ──► Python worker (ffmpeg · faster-whisper · scenedetect)
│ │
dashboard ◄────────────────────┘ /data/jobs/<id>/result.json
```
- `apps/api` — Go, job orchestration
- `apps/worker` — Python, media pipeline
- `apps/dashboard` — React dashboard
## Self-host vs Cloud
Klaket is open source (AGPL-3.0) and fully self-hostable. A hosted, pay-per-minute cloud API with managed GPUs is planned — join the waitlist (coming soon).
## Status
🚧 v0.7 — pre-1.0, moving fast. Star the repo to follow along.
## License
[AGPL-3.0](LICENSE). SDKs and clients will be MIT.
## Contact
Built by Hüseyin Tıntaş — [X (@1337stif)](https://x.com/1337stif) · [LinkedIn](https://www.linkedin.com/in/huseyintintas/)
