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LatestGoogle DeepMindMedASR
Google DeepMindSpeech → Text

Google Releases MedASR for Medical Transcription

The new speech recognition model from DeepMind is trained specifically on medical dictation, aiming for higher accuracy in clinical notes.

Dec 18, 2025
NotableOther
MedASR

Google DeepMind has released MedASR, a new automatic speech recognition (ASR) model designed to accurately transcribe medical dictation. The model targets the specific vocabulary, jargon, and syntax used by clinicians, a domain where general-purpose transcription tools often fall short.

MedASR is specialized for English-language medical speech, with a particular focus on the demanding field of radiology. This narrow training allows it to better handle complex terminology and the rapid dictation style common in clinical settings, aiming to produce cleaner and more reliable electronic health records from spoken notes.

The potential impact of high-accuracy medical ASR is significant. By reducing the time physicians spend manually correcting transcripts, specialized models can help alleviate administrative burdens and allow clinicians to focus more on patient care. Accurate documentation is also critical for billing, insurance, and maintaining a reliable patient history.

While the model is available for exploration, its license carries an important restriction. The weights for MedASR are provided for non-commercial use only, which limits its direct integration into commercial healthcare products. This positions the release primarily as a resource for researchers and developers to build upon for future healthcare applications.

Sources

  • google/medasr

    Hugging Face

    Visit
OlderMiraTTS Brings Qwen2 to Bilingual Speech SynthesisYatharthS · Text → Speech · 10 months agoNewerMiniMax Debuts M2.1, an MoE Model Optimized with FP8MiniMax · Text / LLM · 10 months ago

Get the model

Hugging Face

Specs

Size400 MB
PrecisionFP32
Architecturelasr_ctc
LicenseOTHER
Downloads10.7K
Likes380

Can you run it?

Runs on just about anything.

  • FP32 (as published)

    Any modern laptop · 8 GB RAM

    1.4 GB
  • 16-bit

    Any modern laptop · 8 GB RAM

    1.2 GB
  • FP8

    Any modern laptop · 8 GB RAM

    1.1 GB
Your machine

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Speech → Text

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