Automated Video Clipping and Captioning for Short-Form ReelsAutomated Video Clipping and Captioning for Short-Form Reels
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Automating viral shorts: Turning 60-minute raw video feeds into cut-and-captioned reels programmatically 🎬⚡
Most content agencies still spend 4+ hours per video manually scrubbing timelines, finding hooks, and syncing subtitle keyframes.
Currently deploying an automated multi-modal clipping engine combining computer vision, audio processing, and programmatic rendering:
Audio-Visual Hook Detection: Runs Whisper for high-precision word-level timestamps and audio frequency analysis to pinpoint emotional peaks, loud reactions, and punchlines.
Auto-Reframing (Face Tracking): Uses OpenCV / MediaPipe face tracking to dynamically crop 16:9 widescreen video into vertical 9:16, keeping speakers centered across active camera cuts.
Programmatic FFmpeg Rendering: Bypasses sluggish UI video editors by chaining headless FFmpeg commands for seamless transitions, auto-zooms, and b-roll overlays.
Dynamic Kinetic Captions: Generates styled, word-by-word animated subtitles directly baked into video layers at 60 FPS.
Turning raw customer webinars and podcasts into 10 ready-to-post short-form clips in under 3 minutes per run.
Pipeline architecture and rendering speed benchmarks below! 👇
#VideoAutomation #FFmpeg #Python #ComputerVision #ContentOps #GenerativeAI #BuildInPublic
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