Problem
Lecture capture often stops at raw recordings, leaving transcription, summarization, storage, and retrieval fragmented across separate tools.
R&D case study
Lecture Stream Platform
Audio-processing pipeline that turns raw recordings into transcripts, summaries, and reusable knowledge outputs.
At a glance
Delivery stage
R&D
Current state
Research System
My role
Sole architect and pipeline engineer
What was built
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.
Result
End-to-end pipeline processing audio through transcription and summarization to structured artifacts
How the system was structured
This section shows the operational logic behind the build, not just the user-facing surface.
Key system pieces
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.
Core constraint
Event-driven decoupling: Kafka ensures transcription, summarization, and archival stages fail independently without data loss
Stack
Kafkafaster-whisperOllamaPython ServicesConsumer APIFile Exporter
Supporting proof
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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