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arXiv · 2507.10860

WhisperKit: On-device Real-time ASR with Billion-Scale Transformers

Atila Orhon·Arda Okan·Berkin Durmus·Zach Nagengast·Eduardo Pacheco
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Abstract

Real-time Automatic Speech Recognition (ASR) is a fundamental building block for many commercial applications of ML, including live captioning, dictation, meeting transcriptions, and medical scribes. Accuracy and latency are the most important factors when companies select a system to deploy. We present WhisperKit, an optimized on-device inference system for real-time ASR that significantly outperforms leading cloud-based systems. We benchmark against server-side systems that deploy a diverse set of models, including a frontier model (OpenAI gpt-4o-transcribe), a proprietary model (Deepgram nova-3), and an open-source model (Fireworks large-v3-turbo).Our results show that WhisperKit matches the lowest latency at 0.46s while achieving the highest accuracy 2.2% WER. The optimizations behind the WhisperKit system are described in detail in this paper.

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Atila Orhon, Arda Okan, Berkin Durmus, Zach Nagengast, Eduardo Pacheco. 2025-07-14. WhisperKit: On-device Real-time ASR with Billion-Scale Transformers. https://arxiv.org/abs/2507.10860

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