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Self-hosted Opus Clip alternative — reels.biba.live
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Python 77.3%
HTML 22.4%
Dockerfile 0.3%
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Sebastjan Artič 1d6af29a23 Parallel workers (3) + pre-cache Edit assets 
User feedback: 'ne morem nič drugega delat dokler izvaža reel?
a če bi bile večje mašine bi blo bolj?'

Without GPU upgrade, optimize CPU usage:

1. PARALLEL WORKERS:
   - Was: 1 worker thread, processes 1 job at a time
   - Now: NUM_WORKERS=3 parallel threads (configurable via env)
   - Each worker locks its job atomically (set instead of single var)
   - 3 reels render simultaneously instead of sequentially
   - Edit feature usable while other reels render

2. PRE-CACHE EDIT ASSETS:
   - On job done, fire-and-forget ffmpeg subprocess.Popen for:
     * low-q source video (480p) — used in Edit modal video player
     * waveform PNG (2400x72) — used in Edit modal trim bar
   - Both run in background, don't block pipeline
   - When user later clicks Edit, assets already cached → modal instant
   - On-demand fallback still works if precache failed

Result: Edit modal opens instantly even while other reels render.
3 reels can render in parallel = ~3x throughput on multi-core CPU.
2026-04-30 12:55:38 +00:00
app Parallel workers (3) + pre-cache Edit assets 2026-04-30 12:55:38 +00:00
scripts Edit feature: slider + napis edit + recut endpoint 2026-04-30 10:26:25 +00:00
templates Edit modal: zoom + play-from-position + Space toggle 2026-04-30 12:37:06 +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 ACRCloud auto-recognition: never block uploads, fall back to fingerprinting 2026-04-29 14:24:53 +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)