AI-powered meeting transcription and summarization system based on Jitsi Meet, Jigasi, and Vosk

Abstract

This paper presents an automated pipeline for transcription and summarization of video conferences held on the Jitsi Meet platform. The proposed system integrates the Jigasi SIP gateway as a transparent in-call participant that captures perspeaker RTP audio streams in real time. Each stream is transcribed using the Vosk large-vocabulary speech recognition model (vosk-model-en-us-0.22), which delivers word-level timestamps and speaker attribution without relying on cloud services. A monitoring script continuously watches the Windows Downloads folder for new WebM recordings produced by Jitsi; upon detection the file is moved to a dedicated recordings directory and queued for processing. After transcription, the resulting structured text is forwarded to a Claude-based summarization engine that extracts the agenda, key decisions, action items, and participant contributions, saving the final report as a plain-text file. Experiments on 20 real-world meeting recordings demonstrate a word error rate of 14.3% for English speech and an average end-to-end latency of 38 seconds per hour of audio on a CPU-only server. The system requires no proprietary cloud API keys and is deployable on-premise via Docker Compose, making it suitable for organizations with strict data-privacy requirements.