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
Proof status
This page separates what is already visible from what is still being prepared, so the proof layer can grow without pretending unfinished artifacts already exist.
Proof surfaces
Real artifacts stay visible. Missing artifacts are labeled directly so this page stays honest and ready for stronger proof later.
System Walkthrough
Available nowThe current walkthrough is the pipeline boundary and processing flow rather than a public interface demo.
- Producer-to-consumer processing path shows how audio becomes reusable artifacts.
- The system can be explained as staged pipeline logic instead of a single black-box service.
Architecture / Flow
Available nowThe boundary diagram on this page is the clearest proof artifact for how the pipeline is structured.
- Kafka separates ingestion from transcription and summarization workers.
- Archive and export layers preserve artifacts for later reuse.
- API surfaces expose transcripts and summaries without coupling them to processing workers.
Operational Surfaces
Available nowEven as a research system, the pipeline has explicit surfaces for capture, processing, and output handling.
- Producer node for raw audio intake.
- Worker stages for transcription and summarization.
- API/export surface for structured outputs.
Artifacts & Evidence
Available nowThe current evidence is process-oriented and technical rather than public-facing.
- Pipeline boundary diagram on this page.
- Workflow model for capture, processing, and export.
- Terminal processing traces available for later inclusion.
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