Publish moonshine-tiny OpenASR packs (fp16/q8_0/q4_k)

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+moonshine-tiny-fp16.oasr filter=lfs diff=lfs merge=lfs -text
+moonshine-tiny-q8_0.oasr filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,115 @@
+---
+license: mit
+base_model: UsefulSensors/moonshine-tiny
+pipeline_tag: automatic-speech-recognition
+library_name: openasr
+tags:
+  - automatic-speech-recognition
+  - speech-to-text
+  - openasr
+  - oasr
+  - moonshine
+---
+
+<div align="center">
+
+# Moonshine Tiny · OpenASR
+
+**Tiny 27M-parameter English ASR built for real-time, on-device transcription**
+
+[![License](https://img.shields.io/badge/license-MIT-2563eb.svg)](https://huggingface.co/UsefulSensors/moonshine-tiny)
+[![Format](https://img.shields.io/badge/format-.oasr-7c3aed.svg)](https://github.com/QuintinShaw/OpenASR)
+[![Runtime](https://img.shields.io/badge/runtime-OpenASR-111827.svg)](https://openasr.org)
+[![Base model](https://img.shields.io/badge/base-moonshine--tiny-f59e0b.svg)](https://huggingface.co/UsefulSensors/moonshine-tiny)
+
+Native speech-to-text in the **[OpenASR](https://github.com/QuintinShaw/OpenASR)** runtime —
+engineered for peak performance on CPU & GPU, **no Python at inference time**.
+
+</div>
+
+---
+
+## ✨ Highlights
+
+- 🪶 **Just 27M parameters** — the smallest Moonshine, sized for memory- and compute-constrained edge hardware
+- ⚡ **Real-time on-device** — engineered by Useful Sensors for live transcription and voice commands on low-cost devices
+- 🎯 **Accurate for its size** — beats similarly-sized ASR systems on standard English benchmarks (per the Moonshine paper)
+- 🗣️ **English speech-to-text** — sequence-to-sequence ASR trained on 200K hours of audio
+- 🦀 **Native in OpenASR** — `.oasr` packs run with no Python at inference, engineered for peak performance on CPU & GPU
+
+## 🚀 Quickstart
+
+```bash
+# 1. Install the OpenASR CLI  ·  https://openasr.org
+# 2. Pull a build (pick a quant — see the table below)
+openasr pull moonshine:tiny-q8
+
+# 3. Transcribe
+openasr transcribe audio.wav --model moonshine-tiny
+```
+
+All builds for this model:
+
+```bash
+openasr pull moonshine:tiny-fp16
+openasr pull moonshine:tiny-q8
+```
+
+## 📦 Available builds
+
+| Quant | File (`.oasr`) | Size | RAM peak | RTF · M1 CPU | RTF · M1 GPU | JFK ΔWER vs fp16 |
+|:------|:---------------|-----:|---------:|-------------:|-------------:|-----------------:|
+| fp16 | `moonshine-tiny-fp16.oasr` | 109 MB | 322 MB | 0.02× | 0.03× | 0.0% |
+| q8_0 | `moonshine-tiny-q8_0.oasr` | 34 MB | 305 MB | 0.02× | 0.03× | 0.0% |
+
+<sub>RTF = real-time factor on the fixed 11s JFK clip (**lower is faster**); RAM peak measured per pack
+in an isolated subprocess. JFK ΔWER compares each quantized build's JFK transcript to this model's
+fp16 JFK transcript, so it measures quantization drift rather than absolute recognition accuracy.
+**q8_0** is the recommended default — near-reference quality at a fraction of the
+footprint.</sub>
+
+## 🧠 About Moonshine Tiny
+
+Moonshine Tiny is the smallest model in Useful Sensors' **Moonshine** family — a 27M-parameter,
+sequence-to-sequence English speech-recognition model designed for **real-time, on-device
+transcription** on hardware that is severely constrained in memory and compute. Trained on 200,000
+hours of audio, it transcribes English speech to text and, despite its size, reports greater accuracy
+than existing ASR systems of comparable scale on standard benchmarks. It targets developers building
+live transcription and voice-command experiences on low-cost devices. Like other autoregressive ASR
+models it can occasionally hallucinate or repeat on very short or clipped segments, so robust
+in-domain evaluation is recommended before deployment. This OpenASR repo repackages the original
+weights as `.oasr` packs that run natively in the OpenASR runtime — no Python at inference time. The
+**q8_0** build is the recommended default (near-reference accuracy at roughly a third of the
+footprint); **fp16** is for verification or maximum fidelity.
+
+## ⚙️ How these packs were made
+
+Converted from [UsefulSensors/moonshine-tiny](https://huggingface.co/UsefulSensors/moonshine-tiny) with the OpenASR importer:
+
+```bash
+openasr model-pack import-moonshine-local <src> <out>.oasr \
+  --package-id moonshine-tiny --quantization {fp16,q8-0,q4-k}
+```
+
+The `.oasr` container is GGUF-backed; packs use zero-copy mmap weight binding and graph
+buffer reuse to keep peak memory low.
+
+## ⚖️ License
+
+These packs **inherit the upstream model's license: MIT**
+([source](https://huggingface.co/UsefulSensors/moonshine-tiny)). OpenASR packaging retains the upstream copyright and
+NOTICE; the only modifications are format conversion and quantization.
+
+## 🙏 Acknowledgements
+
+This pack is a redistribution of **Moonshine Tiny**, created and open-sourced by **Useful Sensors**
+([UsefulSensors/moonshine-tiny](https://huggingface.co/UsefulSensors/moonshine-tiny)). All credit for
+the original architecture, training, and weights belongs to them; the license is inherited from and
+identical to the upstream model (MIT). Thank you to the Moonshine authors — Nat Jeffries, Evan King,
+Manjunath Kudlur, Guy Nicholson, James Wang, and Pete Warden — for releasing their work openly.
+
+## 🔗 Links
+
+- 🦀 **OpenASR** — <https://github.com/QuintinShaw/OpenASR>
+- 🌐 **Website** — <https://openasr.org>
+- 🤗 **Upstream model** — [UsefulSensors/moonshine-tiny](https://huggingface.co/UsefulSensors/moonshine-tiny)

moonshine-tiny-fp16.oasr

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:08dc692fcfaec26ce597bb21189406052a68d6c428ea5c38e72a4e73afd3a21d
+size 108862976

moonshine-tiny-q8_0.oasr

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5df858db11aca1aca8d4bb8ba1230e9e683da8292a46137f0993981b89c655a3
+size 34360832