AI System for Automated Video Editing
Editing takes 30–60% of post-production time. For high-frequency content — YouTube, Reels, corporate video — this is a bottleneck. AI editing does not reproduce Godard's artistic decisions, but handles repetitive tasks (interview cutting, removing pauses, music synchronization, highlights assembly) faster than humans.
What Gets Automated
Pause and Filler Word Removal:
- STT (Whisper large-v3) for transcription with time stamps
- Automatic detection and removal: "um", "uh", pauses >0.5 sec, repetitions
- Result: 60-minute interview processed in 5–8 minutes
B-roll Selection:
- CLIP-based semantic search across footage library
- Automatic B-roll insertion under key words from transcript
- Scene detection for breaking footage into clips
Highlights & Short-form:
- SaliencyMap + Audio Energy for identifying "hot" moments
- Auto-assembly of Reels/Shorts format from long video (16:9 → 9:16)
- Smart Reframe via object tracking + face detection
Music Synchronization:
- Beat detection (librosa, madmom)
- Automatic cut placement by rhythm
- Dynamic color grading synchronized with track energy
Technical Stack
FFmpeg + Python pipeline, Adobe Premiere Pro API (for existing workflow integration), DaVinci Resolve Scripting API, Runway Gen-2 API for AI transitions. Whisper for transcription, CLIP + FAISS for semantic search across footage.
Development: 4–6 weeks
Depends on integration depth with existing workflow and quantity of automatable tasks.
| Parameter | Value |
|---|---|
| Editing Time Savings | 40–70% |
| Pause Removal Accuracy | >96% |
| Processing Speed (1h video) | 8–15 min |
| Input Formats | MP4, MOV, AVI, MXF, R3D |