sebastjan/reels-app
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
Self-hosted Opus Clip alternative — reels.biba.live
22 Commits 1 Branch 0 Tags 3.3 MiB
Python 77.3%
HTML 22.4%
Dockerfile 0.3%
8512076b91
Go to file
Sebastjan Artič 8512076b91 Major: smart selection pipeline (analyze.py) + audio fade + multi-lang auto-detect 
- New analyze.py: full transcript + energy + structural analysis
- Smart clip range: includes pre-chorus, can exceed 30s up to max_duration (default 45s)
- Audio fade in/out: auto-detected from vocal boundaries
- Instrumental detection: auto-disables subs if vocals < 10% of duration
- Multi-language: auto-detect via Whisper or explicit (DE/SL/HR/BS/SR/EN/IT/ES/FR)
- Frontend: cleaner UX, added bs language, smart selection description
- reframe.py: --fade-in --fade-out args
- clip.py: propagates fade params
- app/main.py: replaces find_chorus.py call with analyze.py
2026-04-29 06:21:35 +00:00
app Major: smart selection pipeline (analyze.py) + audio fade + multi-lang auto-detect 2026-04-29 06:21:35 +00:00
scripts Major: smart selection pipeline (analyze.py) + audio fade + multi-lang auto-detect 2026-04-29 06:21:35 +00:00
templates Major: smart selection pipeline (analyze.py) + audio fade + multi-lang auto-detect 2026-04-29 06:21:35 +00:00
.env.example Initial: reels clipper app 2026-04-28 15:28:22 +00:00
.gitignore Initial: reels clipper app 2026-04-28 15:28:22 +00:00
docker-compose.yml Initial: reels clipper app 2026-04-28 15:28:22 +00:00
Dockerfile Add Deno runtime for yt-dlp YouTube nsig challenge solving 2026-04-28 16:05:09 +00:00
README.md Initial: reels clipper app 2026-04-28 15:28:22 +00:00
requirements.txt Upgrade yt-dlp to nightly for new YouTube nsig algorithm support 2026-04-28 15:48:39 +00:00

README.md

Reels Clipper · biba.live

Self-hosted Opus Clip alternativa za FOLX TV / PTC. Pretvori 16:9 video v 9:16 reels/shorts/tiktok format z auto face tracking, podnapisi (sl/de/en) in avto-detekcijo refrena v glasbenih pesmih.

Features

  • 📤 Drag & drop upload (do 2 GB)
  • 📺 YouTube URL paste (yt-dlp)
  • 🎯 Smart reframe: track (face follow), center, blur (za glasbo)
  • 🎵 Auto-chorus detection (Whisper + energy hibrid)
  • 📝 Burned-in podnapisi (faster-whisper, multi-jezik)
  • 🎨 3 stili podnapisov: reels, yellow (MrBeast), minimal
  • 🔐 HTTP Basic Auth
  • 📊 Real-time progress (Server-Sent Events)
  • 📦 Docker / Coolify ready

Quick start (lokalno)

docker compose up --build
# odpri http://localhost:8000

Default login: sebastjan / nastavi AUTH_PASS v .env.

Coolify deploy

  1. V Coolify ustvari nov projekt → Docker Compose iz tega repoja
  2. Domena: reels.biba.live
  3. Env vars: 
    AUTH_USER=sebastjan
AUTH_PASS=<močno geslo>
MAX_UPLOAD_MB=2000
  4. Volume reels_data se ustvari avtomatsko
  5. Deploy → Coolify postavi Traefik reverse proxy + SSL via Let's Encrypt

Pipeline

Upload / YouTube
       ↓
[ yt_download.py ]    ← samo če YouTube
       ↓
[ find_chorus.py ]    ← samo če auto_chorus=true (Whisper + RMS analiza)
       ↓
[ reframe.py ]        ← 16:9 → 9:16 (track / center / blur)
       ↓
[ subtitle.py ]       ← Whisper transkripcija + burn-in
       ↓
   reel.mp4

API

  • POST /api/upload — multipart file upload, vrne job_id
  • POST /api/youtube — JSON {url, mode, lang, ...}
  • POST /api/process — start processing za uploaded job
  • GET /api/jobs — list vseh
  • GET /api/jobs/{id} — status
  • GET /api/stream/{id} — SSE stream progress
  • GET /api/download/{id} — final reel
  • DELETE /api/jobs/{id} — pobriši

Dependencies

  • FFmpeg (system)
  • faster-whisper (transkripcija)
  • OpenCV (face detection)
  • yt-dlp (YouTube)
  • FastAPI + uvicorn (server)