Semantic Media & Transcoding Pipeline
An asynchronous audio/video transcoding and transcription pipeline with vector-search indexing for semantic media retrieval.
Year
2023
Services
Architecture Scope
- —Client-side recording streaming
- —Asynchronous transcoding pipeline
- —Automated Whisper transcription
- —Vector embeddings mapping
- —Interactive transcript search
120K+
Video Minutes Processed
Secure cloud hosting
<90s
Average Transcode Latency
Instant cloud encoding
96%
Transcription Accuracy
Whisper speech models
<100ms
Search Index Response
Rapid transcript lookup
The Challenge
Video and audio files are heavy, resource-intensive to process, and hard to search. Processing media uploads on web servers crashes instances under high usage, and generating accurate time-coded transcript search indices requires significant CPU power.
The Solution
We engineered a serverless media processing pipeline. Spoken audio is recorded or uploaded, processed asynchronously to extract audio tracks, and transcribed using neural networks. The transcription timestamps are indexed in a vector database for semantic search and playback synchronization.
System Architecture
Client media streams use pre-signed URLs to upload directly to AWS S3. S3 upload notifications invoke containerized Python workers. These workers run FFmpeg for optimization, extract audio, send segments to Whisper models, and write sentence embeddings to a Pgvector database.
Technology Stack
- Next.js
- FastAPI
- Python
- PostgreSQL
- Whisper API
- AWS S3
- Cloudflare Stream