Content Studio
Designed and engineered Content Studio, an AI-powered automated video processing and translation pipeline. Built using Next.js, Python, FFmpeg, and Celery, it automates transcribing, translating, and hard-subtitling video clips. Integrated OpenAI Whisper and GPT-4 APIs for translation and timing alignment. Implemented a distributed task queue with Redis and Celery to manage CPU-heavy render jobs, ensuring 99.9% uptime. Managed project scope by prioritizing core video ingestion and render queue before expanding features.
The Problem
Content agencies spent hours manually transcribing, translating, and editing subtitle files, slowing down production and increasing editing costs by thousands of dollars.
Key Value & Highlights
- ✦AI video translation & captioning pipeline
- ✦Whisper & GPT-4 transcript sync integration
- ✦Reduced post-production video costs by 70%
- ✦Distributed rendering queue via Celery & Redis
Technologies Used
Next.js, Python, Celery, FFmpeg, Redis, OpenAI Whisper, GPT-4, AWS, Docker.
Scale & Performance
Rendered and aligned subtitle overlays for over 10,000 video segments, scaling transcoding nodes dynamically under high demand.
Uptime & Operations
Reduced post-production costs for the agency by 70%. Kept system uptime at 99.9% and avoided scope creep by delivering the core MVP within two weeks, handling new features via clear option-menus.
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