AI Transcript Platform
Production transcription pipeline that converts video into clean, WCAG-compliant HTML transcripts for real university programs.
Tech Stack
AI Transcript is a production transcription pipeline built to generate clean, accessible transcript outputs for real university programs. It handles file ingestion, background processing, and output formatting into structured, web-friendly HTML designed for accessibility. The system reduces manual formatting overhead, speeds up transcript turnaround, and standardizes output quality across courses.
Why I Built This
I wanted transcripts to be faster to generate and easier to use — without sacrificing accessibility. The goal was a system that turns raw video into structured, web-friendly HTML that works for everyone, including screen reader users, while reducing the manual formatting load for course teams.
Overview
AI Transcript is a containerized, production-grade pipeline that processes uploaded media, transcribes it via an external ASR provider, and generates WCAG-aligned HTML output. It supports multiple video/audio formats, chunks large files safely, and returns results through an async task system.
What It Delivers
- Uploads → background processing → transcript + accessible HTML output
- WCAG-conscious structure: skip links, landmarks, heading hierarchy, keyboard navigation support
- Authentication using ASU SSO (CAS) + JWT cookies for protected routes
- Cloud-native deployment with CI/CD and service stability checks
Architecture
- Microservices split: Web UI, API, Worker
- Redis used for task queue + session/task state
- S3 used for file storage with hash-based deduplication
- Accessible HTML generated via Jinja2 templates
Accessibility Pipeline
- Landmarks: header/main/footer roles
- Skip link to main content
- ARIA region labels on transcript sections
- Paragraph IDs for deep-linking
- prefers-reduced-motion support
- Keyboard navigation tested
Authentication & Security
- ASU SSO via CAS login → ticket validation → JWT set as httpOnly cookie
- Secure cookie settings in production (secure + samesite lax)
- Security headers and proxy middleware for ALB HTTPS detection
Infrastructure & Deployment
- AWS ECS services behind ALB (path-based routing)
- Images stored in ECR; deploy via GitHub Actions
- Environment separation: local Docker Compose vs production ECS + managed Redis
Research & Evaluation (ASR Quality)
I also worked on ASR evaluation methodology using Word Error Rate (WER) and Character Error Rate (CER) to quantify transcription quality, compare outputs, and guide improvements in chunking, formatting, and post-processing.
Impact
12,760+
Learners Served
100s/term
Manual Hours Saved
TODO
Minutes Processed
TODO
Courses Supported
Screenshots
Screenshot coming soon (UI + transcript output)
Screenshot coming soon (upload + task status)
Interested in this project?
Let's discuss how I built it or explore collaboration opportunities.