Realtime automatic transcription has emerged as a critical technology powering everything from live voice assistants to multilingual conference captioning. The fundamental challenge in this field has always been balancing speed against accuracy while scaling to support multiple concurrent users. Recent breakthroughs are finally solving this dilemma through innovative approaches to model architecture and resource management.
A significant advancement comes from the SWIM system, built on top of OpenAI’s Whisper model, which enables true model-level parallelization for scalable transcription across multiple languages . Unlike traditional approaches that struggle with concurrent audio streams, SWIM introduces a buffer merging strategy that maintains transcription fidelity while ensuring efficient resource usage. In multi-client settings scaling up to twenty concurrent users, this system delivers accurate realtime transcriptions in English, Italian, and Spanish while maintaining latencies around 2.4 seconds—a substantial improvement over previous solutions
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