T

sebastjan 49a80599e1 Word-level extension: lookback to capture full phrase before clip

Bug: Claude picked clip start at 78.19s (0.3s before segment 'tepe' at
78.4s). Word-level extension then found word 'me' (77.88-78.16s) right
before clip start, extended to 77.73s. But the FULL phrase was 'Žena me'
where 'Žena' [76.88-77.74] precedes 'me' [77.88-78.16] in the same
breath/speech burst (gap 0.14s, not a real pause).

Fix: when extending back via word-level, do a lookback through earlier
words. Stop only when finding a real pause (gap >= 0.5s between words).
This captures the entire connected phrase before clip start.

Now: clip start 78.19s → finds 'me' at 78.16s → looks back: 'Žena' at
77.74s (gap to 'me' = 0.14s, < 0.5s) → continue. Earlier 'verjet.' at
76.78s (gap to 'Žena' = 0.10s) → also captured if connected... actually
'verjet.' is part of previous verse, but anchor stops at next pause >= 0.5s.
For the Žena case, anchor will be at 'Žena' (or earlier if no big pause).

This makes the extension MUCH more robust for cases where multiple words
of the chorus opening fall in the previous transcript segment.

2026-04-29 16:52:44 +00:00

app

Fix SRT subtitles: word-level clipping for partial segments

2026-04-29 16:48:39 +00:00

scripts

Word-level extension: lookback to capture full phrase before clip

2026-04-29 16:52:44 +00:00

templates

Fix: live preview blocks right-side preview buttons

2026-04-29 15:37:51 +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

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)