Self-hosted Opus Clip alternative — reels.biba.live

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OpenClaw Agent e350352883 Fix: Gemini 3.1 Pro thinking model needs 32k maxOutputTokens (was 4096 → MAX_TOKENS truncation) Diagnoza: - Gemini 3.x Pro je thinking model (ima internal reasoning, thoughtsTokenCount) - Pri velikih transkriptih (60+ segmentov pesmi): * thoughts ~ 1500-3000 tokens * output JSON s corrected_segments ~ 3000-7000 tokens * total ~ 4500-10000 tokens - Z maxOutputTokens=4096 je bil response prekinjen (finishReason: MAX_TOKENS), JSON odrezan na pol, _parse_llm_response je threw json.JSONDecodeError - Rezultat: 'Gemini vrnil prazen string' v logih Popravki: 1. Gemini maxOutputTokens 4096 → 32768 (dovolj za thinking + dolg JSON) 2. Diagnostika finishReason==MAX_TOKENS in usage tokens v logih 3. Detekcija praznega text-a (ne samo praznega parts array-a) 4. Claude max_tokens 4096 → 8192 (rezerva za dolge pesmi) 5. Claude detekcija stop_reason==max_tokens Test (60 segmentov, 5631 char prompt): - 4096 → finishReason=MAX_TOKENS, thoughts=2594, output=1488, JSON odrezan ❌ - 16384 → finishReason=STOP, thoughts=1445, output=3040, JSON popoln ✅ - 32768 → varen default ✅		2026-04-29 09:03:53 +00:00
app	Auto-resume jobs interrupted by container restart	2026-04-29 08:52:16 +00:00
scripts	Fix: Gemini 3.1 Pro thinking model needs 32k maxOutputTokens (was 4096 → MAX_TOKENS truncation)	2026-04-29 09:03:53 +00:00
templates	Upgrade to Sonnet 4.6 + add Gemini 3.1 Pro support	2026-04-29 08:26:27 +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

V Coolify ustvari nov projekt → Docker Compose iz tega repoja
Domena: reels.biba.live

Env vars:

AUTH_USER=sebastjan
AUTH_PASS=<močno geslo>
MAX_UPLOAD_MB=2000

Volume reels_data se ustvari avtomatsko
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)