MedASR
June 8, 2026 · View on GitHub
Google's google/medasr ported to transcribe.cpp. 105M-parameter encoder-CTC for medical-dictation English ASR. 17-layer Conformer encoder with RoPE attention (rope_theta=10000), macaron FFNs (residual scalars [1.5, 0.5]), BatchNorm conv module (kernel=32, residual scalars [2.0, 1.0]), and a Linear 512→512 CTC head over a SentencePiece BPE vocabulary.
What it's for
Offline English speech-to-text optimized for medical dictation (radiology, internal medicine, family medicine). Decoder is greedy CTC; no language model, no beam search. This matches what HuggingFace transformers' AutoModelForCTC does by default.
Trained on ~5,000 hours of de-identified physician dictations on top of a LibriHeavy 50k-hour pretrain. The upstream model card flags lower accuracy on non-native accents and a male-skewed speaker distribution.
Licensed under the Health AI Developer Foundations terms. The upstream repo is gated; you must accept the HF terms before download.
Ported from upstream commit ae1e484, pinned 2026-06-04.
Input limits
MedASR is trained for audio up to about 400 seconds (~6.7 min) — the
encoder's rotary-position window. Longer audio is accepted, but the library logs
a WARN and accuracy may degrade past that window; it is not rejected. Segment
long recordings for best results. See the
input-length contract.
Download
| Quantization | Download | Size | WER (LibriSpeech test-clean) |
|---|---|---|---|
| F32 | medasr-F32.gguf | 417 MB | 17.88% |
| F16 | medasr-F16.gguf | 202 MB | 17.88% |
| Q8_0 | medasr-Q8_0.gguf | 122 MB | 17.86% |
| Q6_K | medasr-Q6_K.gguf | 101 MB | 17.93% |
| Q5_K_M | medasr-Q5_K_M.gguf | 90 MB | 17.91% |
| Q4_K_M | medasr-Q4_K_M.gguf | 79 MB | 18.14% |
Recommended default: Q8_0. Smallest preset with no statistically detectable WER degradation versus F32 (122 MB; +0.00 pp within bootstrap CI). Q4_K_M shows a real +0.26 pp degradation on LibriSpeech and is shipped for completeness but not recommended — prefer Q5_K_M if you need smaller than Q8_0.
WER measured on the full LibriSpeech test-clean split (2,620 utterances) with greedy CTC decoding and no external LM. F32 reference baseline (HuggingFace transformers, Mac MPS): 17.88%; transcribe.cpp F32 matches exactly. Absolute WER is higher than general-purpose ASR (e.g. Whisper-base ≈ 5%) because the model is fine-tuned for medical dictation — on the publisher's internal RAD-DICT / GENERAL-DICT / FM-DICT datasets the model scores 6.6%–9.3%, but those datasets are not publicly reproducible. See reports/wer/medasr.test-clean.summary.md for the full sweep.
Quick Start
cmake -B build
cmake --build build
build/bin/transcribe-cli \
-m models/medasr/medasr-Q8_0.gguf \
--language en \
samples/jfk.wav
If your audio is not already 16 kHz mono WAV, convert it first:
ffmpeg -i input.mp3 -ar 16000 -ac 1 output.wav
Performance
Cells are wall-clock latency (mean over 3 iterations after 1 warmup), with speedup over realtime in parentheses. Units: ms below 1 s, s above (2 decimal places).
Apple M4 Max
| Backend | Sample | Q8_0 | Q4_K_M |
|---|---|---|---|
| Metal | jfk (11.0 s) | 38 ms (290×) | 44 ms (248×) |
| Metal | dots (35.3 s) | 84 ms (419×) | 90 ms (394×) |
| CPU | jfk (11.0 s) | 161 ms (68×) | 180 ms (61×) |
| CPU | dots (35.3 s) | 558 ms (63×) | 623 ms (57×) |
macOS 26.5, transcribe.cpp 8139a4b. Metal device: Apple M4 Max. Mel pipeline uses the shared MelFrontend (Accelerate vDSP fp64 FFT + cblas_sgemm); encoder is the conformer + RoPE + BatchNorm-conv graph in src/arch/medasr/encoder.cpp.
AMD Ryzen 7 4750U Pro
| Backend | Sample | Q8_0 | Q4_K_M |
|---|---|---|---|
| Vulkan | jfk (11.0 s) | 163 ms (68×) | 174 ms (63×) |
| Vulkan | dots (35.3 s) | 481 ms (74×) | 495 ms (71×) |
| CPU | jfk (11.0 s) | 543 ms (20×) | 488 ms (23×) |
| CPU | dots (35.3 s) | 1.84 s (19×) | 1.63 s (22×) |
Fedora 43, transcribe.cpp 79d139a. Vulkan device: AMD Radeon Graphics (RADV RENOIR).
Benchmark reproduction:
uv run scripts/bench/run.py \
--models medasr --quants q8_0,q4_k_m --samples jfk,dots \
--backends metal,cpu,vulkan --iters 3 --warmup 1 --name medasr-publication
Numerical Validation
transcribe.cpp is validated tensor-by-tensor against the upstream HuggingFace Transformers reference on samples/jfk.wav via scripts/validate.py. The family-level forward map at reports/porting/medasr/forward-map.md documents the per-stage divergence sources (fp64 vDSP STFT, BatchNorm fusion, CUDA fp16-accumulator workarounds in the macaron + conv residual stack).
| Field | Value |
|---|---|
| Reference | transformers @ 65dc2615 (dev commit; v5.0.0 not yet released), AutoModelForCTC.from_pretrained("google/medasr") device=mps fp32 |
| Dump script | scripts/dump_reference_medasr_transformers.py |
| Manifest | tests/golden/medasr/medasr.manifest.json |
| Command | uv run scripts/validate.py all --family medasr --variant medasr |
Reproduction
Convert
uv run --project scripts/envs/medasr \
scripts/convert-medasr.py google/medasr
Quantize
build/bin/transcribe-quantize \
models/medasr/medasr-F32.gguf \
models/medasr/medasr-Q8_0.gguf \
--quant Q8_0
Validate
uv run scripts/validate.py all --family medasr --variant medasr