Fine-tuned Parakeet 0.6B medical ASR model released as open weights
u/MajesticAd2862, founder of Omi Health, fine-tuned NVIDIA's Parakeet TDT 0.6B v2 into Omi Med STT v1, a CC-BY-4.0 medical ASR model that runs locally on Mac, Windows, and Linux and benchmarks competitively against cloud transcription APIs.
Score breakdown
Omi Med STT v1 is the best-performing locally-running open model on this benchmark, achieving cloud-competitive M-WER at 0.6B parameters while keeping patient audio entirely on-device.
- 01Omi Med STT v1 is a fine-tune of NVIDIA's Parakeet TDT 0.6B v2, released under CC-BY-4.0.
- 02Installable via `pip install omi-med-stt`; auto-selects MLX (Apple Silicon), NeMo (CUDA), or GGUF/parakeet.cpp (CPU) backends.
- 03Benchmark: 1,513 clips / 7.18 hours of held-out medical audio, scored by M-WER (errors on clinical terms only) and RTFx.
u/MajesticAd2862, founder of Omi Health, fine-tuned NVIDIA's Parakeet TDT 0.6B v2 on clinical speech and released the result as Omi Med STT v1 under a CC-BY-4.0 license. The model is distributed via `pip install omi-med-stt` and ships with a runtime for Mac, Windows, and Linux that automatically selects the appropriate backend: MLX on Apple Silicon, NeMo on CUDA, and GGUF/parakeet.cpp on CPU. The default quantization is q8; a q4 variant was benchmarked but not shipped because drug-name accuracy regressed too much. Training used approximately 127 hours of audio, roughly 71% real and 29% synthetic, drawn from licensed, openly-available, and custom synthetic sources targeting hard-to-source medical speech.
Evaluation was conducted on a locked held-out split of 1,513 clips totaling 7.18 hours, scored using medical word error rate (M-WER), which counts errors on clinical terms only, and RTFx (× realtime speed).
Evaluation was conducted on a locked held-out split of 1,513 clips totaling 7.18 hours, scored using medical word error rate (M-WER), which counts errors on clinical terms only, and RTFx (× realtime speed). Omi Med STT v1 achieved an M-WER of 2.37%, WER of 8.30%, and drug M-WER of 4.75% at 145× RTFx on an A10 GPU. Among open/local models, only VibeVoice-ASR 9B edges it on M-WER (1.78%), but that model is approximately 15× larger, ran on an H100 in the evaluation, and posted a higher overall WER of 11.10% versus Omi's 8.30%. Against cloud APIs, Omi Med STT v1 sits ahead of Deepgram Nova-3 Medical and Corti Transcripts on M-WER, and is competitive with general-purpose cloud scribes, while keeping audio on-device. A notable benchmark finding: both Gemini 3.1 Pro Preview and Gemini 3.5 Flash exhibited a hallucination failure mode — on a stress lane of 420 benign, non-diagnostic clips, they fabricated entire fake consultations (3.1 Pro on 33/420 clips, 3.5 Flash on 87/420), while every other dedicated ASR model scored 0 on that lane.
Drug-name accuracy is identified as the primary weakness and the top priority for v2. Planned future work includes a streaming version and a multilingual variant.
Key facts
- 01Omi Med STT v1 is a fine-tune of NVIDIA's Parakeet TDT 0.6B v2, released under CC-BY-4.0.
- 02Installable via `pip install omi-med-stt`; auto-selects MLX (Apple Silicon), NeMo (CUDA), or GGUF/parakeet.cpp (CPU) backends.
- 03Benchmark: 1,513 clips / 7.18 hours of held-out medical audio, scored by M-WER (errors on clinical terms only) and RTFx.
- 04Achieved M-WER of 2.37% and 145× RTFx on an A10 GPU — cutting the base Parakeet TDT 0.6B v2 M-WER of 8.36% by ~3.5×.
- 05Spurious drug mentions dropped from 131 to 9 versus the base model.
- 06Gemini 3.1 Pro Preview and Gemini 3.5 Flash fabricated entire fake consultations on 33/420 and 87/420 benign clips respectively; all other dedicated ASR models scored 0 on that lane.
- 07Training used ~127 hours of audio (~71% real, ~29% synthetic); a q4 quantization was tested but not shipped due to drug-name accuracy regression.
Topics
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