| --- |
| title: WeeMed AI |
| emoji: π©Ί |
| colorFrom: blue |
| colorTo: green |
| --- |
| |
| # WeeMed AI |
|
|
| **Building a globally standardized, AI-driven digital health and long-term care platform |
| for aging societies.** |
|
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| FHIR-first. Integrated health data, intelligent decision support, personalized preventive |
| medicine β deployed in real clinics, health-screening centers, and community long-term |
| care sites in Taiwan. |
|
|
| > **AI accelerates digitization; it isn't the product.** |
|
|
| A spin-off of the **Medical Image Processing Laboratory (MIPL)**, National Yunlin |
| University of Science and Technology β a lab that has built AI-assisted diagnostic |
| systems with National Taiwan University Hospital Yunlin Branch, National Cheng Kung |
| University Hospital, and Dalin Tzu Chi Hospital since 2003. |
|
|
| --- |
|
|
| ## What we release here |
|
|
| We ship models that fall out of real deployment problems, and we publish the |
| **measurements**, not just the weights. |
|
|
| ### ποΈ [Breeze-ASR-26-edge](https://huggingface.co/weemed/Breeze-ASR-26-edge) |
|
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| Taiwanese Hokkien (ε°θͺ) + Mandarin speech recognition, quantized for the edge, in two |
| runtimes β with a measured benchmark telling you which to pick: |
|
|
| | Runtime | RTF (CPU, 4 threads) | Real-time? | |
| |---|---|---| |
| | CTranslate2 / faster-whisper | **0.18 β 0.23** | β
| |
| | ONNX / sherpa-onnx | 1.17 β 1.76 | β (but runs on Android/iOS/WASM) | |
|
|
| Measured on real multi-speaker meeting audio, not synthetic clips. Derived from |
| [MediaTek-Research/Breeze-ASR-26](https://huggingface.co/MediaTek-Research/Breeze-ASR-26) |
| (Apache-2.0) β all credit for the model to MediaTek Research. |
|
|
| --- |
|
|
| ## Why this matters to us |
|
|
| Taiwan is aging fast, and the people doing the caring β nurses at screening centers, |
| care managers at community sites, elders themselves β mostly do not speak to each other |
| in written Mandarin. They speak **Taigi**, in noisy rooms, on tablets held in one hand. |
|
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| Health tech that only understands clean written Mandarin does not meet them where they |
| are. So we work on the unglamorous end of clinical AI: the languages actually spoken, |
| the devices actually held, the constraints actually present. |
|
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| --- |
|
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| ## How we publish |
|
|
| - **Honest benchmarks.** We report the number that survives scrutiny, including when it |
| kills our own hypothesis. Our ASR card documents the measurement traps we fell into |
| before publishing (short clips and repetitive audio both give flattering, wrong RTFs). |
| - **Named gaps.** What we could not measure is written down as an open gap, not omitted. |
| - **Real audio, real clinics.** Benchmarks on synthetic data tell you about synthetic data. |
|
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| --- |
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| <sub>Apache-2.0 unless stated otherwise Β· Made in Yunlin, Taiwan</sub> |
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