yuriyvnv/parakeet-tdt-0.6b-EN-Medical

🩺 Parakeet-TDT-0.6B English Medical 🇬🇧

A fine-tune of `nvidia/parakeet-tdt-0.6b-v3` on the English subset of MultiMed mixed with Common Voice 17 English (train + validation). The mix is the trick: it pushes the model toward medical vocabulary (TAVI, intervertebral disc herniation, drug names, dosing instructions) while keeping the everyday English it already knew.

Outputs cased English text with punctuation. Drop-in for the base Parakeet: same NeMo API, same long-form support, same timestamps.

🔥 Quick start

pip install nemo_toolkit[asr]

import nemo.collections.asr as nemo_asr

asr = nemo_asr.models.ASRModel.from_pretrained("yuriyvnv/parakeet-tdt-0.6b-EN-Medical")
print(asr.transcribe(["audio.wav"])[0].text)

📊 Results

One model, one training mix (MultiMed-en train + Common Voice 17-en train + validation, concatenated and shuffled per epoch — same .nemo for every row below). The two rows are the same checkpoint evaluated on two different held-out test sets: one in-domain (medical) and one out-of-domain (general English). Neither test set was seen during training.

The zero-shot column is the unmodified `nvidia/parakeet-tdt-0.6b-v3`, measured on the same test set with the same evaluator. All numbers are normalized (lowercase + strip punctuation), the standard protocol used by the MultiMed paper and the Open ASR Leaderboard, so they are directly comparable to other published results.

For reference, the MultiMed paper's best published result is Whisper-Small multilingual fine-tune at 16.62% WER (arXiv 2409.14074, Table 6). This model beats that by 2.31 absolute / 14% relative, at 0.6B params.

¹ Both fine-tuned numbers are this single model — the one trained on the MultiMed-en + CV17-en mix.

Two things worth noting:

• —🎯 Medical: 25% relative WER reduction on MultiMed-en test (19.22% → 14.31%), beating the published MultiMed paper SOTA at 0.6B params.
• —🛡️ General English is essentially preserved. On CV17-en test the same model lands at 9.11% WER vs the base Parakeet's 8.58% — a +0.53 absolute / +6.2% relative change. That's the total cost of medical specialisation. A previous medical-only version of this model (trained on MultiMed alone, no CV) scored 13.53% normalized WER on CV17-en — a real catastrophic-forgetting hit. Including CV17-en in the training mix is what made the trade-off this cheap.

🛠️ Training

The natural CV17:MultiMed ratio (~97.5% : ~2.5%) means each batch of 32 contains on average ~0.8 medical clips and the rest general English — strong CV gradient keeps the base distribution intact while medical clips slowly steer the model into the clinical domain.

🎯 Intended use

• —Transcription of English medical speech: clinical consultations, surgical procedures, patient narratives, medical podcasts, healthcare interviews.
• —A drop-in replacement for nvidia/parakeet-tdt-0.6b-v3 in pipelines targeting medical English — picks up clinical vocabulary without breaking general transcription.

📚 Training data

• —leduckhai/MultiMed English subset — multilingual medical ASR dataset (~84h English).
• —fixie-ai/common_voice_17_0 English train + validation splits — Common Voice 17, crowdsourced English speech.

🙏 Acknowledgements

• —NVIDIA NeMo team for releasing Parakeet-TDT-0.6B-v3 and the NeMo toolkit.
• —Khai Le-Duc and the MultiMed authors for releasing the MultiMed multilingual medical ASR dataset.
• —The Mozilla Common Voice community for the crowd-sourced English speech corpus used as the general-English anchor in the training mix.