Omi Med STT v1
Omi Med STT v1 is an English medical speech-to-text model for clinical dialogue, built from NVIDIA Parakeet TDT 0.6B v2.
It is designed for local transcription of GP-style consultations, medication
review, clinical dictation, and procedure/device/test language. The canonical
checkpoint is this NeMo .nemo model. Mac and CPU exports are available in the
companion repos below.
Why Use It
- Open-weight, 0.6B-class medical ASR model
- Runs locally through the
omi-med-sttCLI - Stronger medical transcription than base Parakeet v2 in Omi's internal evaluation
- Runtime options for Apple Silicon, NVIDIA CUDA, and Linux/Windows CPU
Quickstart
Recommended CLI:
pip install -U omi-med-stt
omi-med-stt audio.wav
Apple Silicon:
pip install -U "omi-med-stt[mlx]"
omi-med-stt audio.wav
NVIDIA CUDA / canonical NeMo checkpoint:
pip install -U "omi-med-stt[nemo]"
omi-med-stt audio.wav --runtime nemo
Linux or Windows CPU:
pip install -U omi-med-stt
omi-med-stt install-cpp --cpp-backend cpu
omi-med-stt audio.wav --runtime cpp
Direct NeMo use:
from huggingface_hub import hf_hub_download
import nemo.collections.asr as nemo_asr
nemo_path = hf_hub_download("omi-health/omi-med-stt-v1", filename="omimedstt-v1.nemo")
model = nemo_asr.models.ASRModel.restore_from(nemo_path)
print(model.transcribe(["audio_16k_mono.wav"])[0])
Which Repo Should I Use?
| Use case | Repo | Notes |
|---|---|---|
| Best quality / NVIDIA GPU | omi-health/omi-med-stt-v1 | Canonical NeMo checkpoint |
| Apple Silicon default | omi-health/omi-med-stt-v1-mlx-q8 | Smaller MLX q8 export |
| Apple Silicon full precision | omi-health/omi-med-stt-v1-mlx | Larger MLX export |
| Linux/Windows CPU | omi-health/omi-med-stt-v1-gguf | GGUF q8_0 via parakeet.cpp |
Evaluation
The public weights are unchanged. These September 2026 results come from the current documented runtime recipes on the same frozen 1,513-clip, 7.18-hour medical benchmark and scorer. No dictionary, custom vocabulary, contextual bias, reference-aware selection, or transcript correction was used.
| Runtime artifact | Platform | WER | M-WER | Drug M-WER | Medical recall |
|---|---|---|---|---|---|
| NeMo canonical | NVIDIA CUDA (L4, BF16) | 6.54% | 2.23% | 4.75% | 97.77% |
| MLX q8 | Apple Silicon (M4 Max) | 6.65% | 2.12% | 4.52% | 97.88% |
| GGUF q8_0 | Linux/Windows CPU | 7.10% | 2.16% | 4.30% | 97.84% |
Lower is better for WER, M-WER, and Drug M-WER; higher is better for medical recall. GPU is the quality and throughput path, MLX q8 is the selected Apple runtime, and CPU is the portable fallback. The CPU row has the lowest observed occurrence counts in this draw, but paired tests do not establish it as clinically better than GPU or MLX.
Compared with the open-model rows on Omi's standing 30-system board, the CUDA and MLX q8 runtimes have the lowest observed WER, while MLX q8 has the second-lowest observed M-WER. These are positions in this benchmark draw, not a universal ranking.
The previous model-card figures were produced by older runtime/scorer paths. The numbers above supersede them as runtime results; they do not represent a new training run or changed model weights.
Notes
- The model is English-only.
- The CLI converts audio to 16 kHz mono automatically.
- The CPU GGUF path is for portability; use MLX or NeMo when available.
Safety
Omi Med STT v1 is speech-to-text only. It is not a diagnostic, triage, prescribing, or clinical decision model, and it is not clinically validated. Transcripts must be reviewed before any clinical use.
License And Attribution
Weights are released under CC-BY-4.0, matching the base model.
Omi Med STT v1 is a derivative of
nvidia/parakeet-tdt-0.6b-v2.
It is not an NVIDIA model.
Runtime: Omi-Health/omi-med-stt-runtime under MIT.
Built by Omi Health. Related product: Omi Scribe.