Context
HAM radio communities generate large volumes of audio recordings that are difficult to navigate manually.
Finding specific call signs, technical discussions, or historical references required listening through long archives and slowed down practical research.
Solution
- Implemented an AI pipeline for transcription and summarization, using speech recognition plus LLM-based narrative summaries.
- Added full-text retrieval across transcripts, so users can search specific keywords or topics across the full archive.
- Delivered a web interface for selecting recordings, reading summaries, and jumping directly to relevant transcript context.
Results
- Faster information discovery across previously unstructured audio archives.
- Reduced manual effort for operators and researchers who need targeted technical details.
- Created a reusable structured dataset that supports future analytics and AI workflows.