Offline Real-Time Multilingual Broadcasting Using WebRTC and Local AI Services: Architecture, Performance, and Institutional Evaluation

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Real-time multilingual communication (RTMC) is increasingly important in academic, governmental, defense, and clinical settings, where cloud-based speech processing may raise privacy, data sovereignty, connectivity, and cost concerns. This study presents Yapzek Stream, a fully offline multilingual broadcasting and speech-translation platform that performs speech recognition, translation, and text-to-speech synthesis within institution-owned infrastructure. The system integrates Web Real-Time Communication (WebRTC)-based broadcasting with locally hosted artificial intelligence (AI) services and provides multilingual audio delivery, browser-based access, recording, transcript and subtitle export, and institutional authentication. The platform was evaluated through an implementation-oriented analysis and a university pilot involving two live sessions with 10 and 50 participants and 12 semi-structured interviews. Estimated end-to-end processing latency was 1.8–4.1 s, while pilot observations ranged from approximately 2 s for short utterances to 4.5–5 s for longer speech. The results indicate the feasibility of offline multilingual broadcasting for privacy-preserving and institution-controlled communication. Deployment-specific accuracy and translation evaluation, controlled scalability and GPU-utilization measurements, robustness testing, and larger-scale user-acceptance studies remain for future work.