Problem
Lecture capture often stops at raw recordings, leaving transcription, summarization, storage, and retrieval fragmented across separate tools.
Project case study
Lecture Stream Platform
AI transcription and summarization pipeline that converts spoken lectures into structured knowledge artifacts.
Project facts
Theme
Applied AI & Automation Systems
Current state
Research System
My role
Sole architect and pipeline engineer
Core constraint
Event-driven decoupling: Kafka ensures transcription, summarization, and archival stages fail independently without data loss
Outcome
End-to-end pipeline processing audio through transcription and summarization to structured artifacts
Approach
A concise problem → system → evidence structure so the engineering story is easier to inspect.
System design
Built as an event-driven processing pipeline. Producer nodes upload audio into ingest services, Kafka fans work across transcription and summarization workers, archive services persist artifacts, and API/export layers expose transcripts and summaries as reusable outputs.
Producer and consumer modes separate capture from heavy compute.
Kafka events keep transcription, summarization, and archive stages decoupled.
API and export services turn pipeline output into reusable artifacts.
Stack
Kafkafaster-whisperOllamaPython ServicesConsumer APIFile Exporter
Evidence
The system evidence is in the pipeline boundary diagram and the multi-stage processing model rather than a one-screen app demo.
Pipeline architectureWorkflow boundary diagramTerminal processing trace
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