Fine-Tuning OpenAI Whisper-Small for Domain-Specific Medical Speech Recognition within a Microservice Architecture - Moussa, Alaeddine; Drine, Noursene (Informatica, 2026)
We fine-tune Whisper-small (244M parameters) on 8.5 hours of in-domain medical audio and evaluatewith word error rate (WER). Compared to an unadapted Whisper-small baseline, our fine-tuned modelreduces WER from ∼63% to ∼32%. While the relative gain is substantial, this accuracy is not suitablefor unsupervised clinical use; we position the system as a clinician-in-the-loop assistant. We also describedeployment as an on-premise microservice and report latency/throughput considerations.