---

license: mit

language:

• en

pipeline_tag: automatic-speech-recognition

tags:

• audio
• speech-recognition
• transcription
• gguf
• moonshine
• lightweight

library_name: ggml

base_model: UsefulSensors/moonshine-tiny

---

Moonshine Tiny -- GGUF

GGUF conversions and quantisations of UsefulSensors/moonshine-tiny for use with CrispStrobe/CrispASR.

Available variants

| File | Quant | Size | Notes |

|---|---|---|---|

| moonshine-tiny.gguf | F32 | 104 MB | Full precision |

| moonshine-tiny-q8_0.gguf | Q8_0 | 33 MB | High quality |

| moonshine-tiny-q4_k.gguf | Q4_K | 21 MB | Best size/quality tradeoff |

All variants produce correct transcription on test audio.

Model details

• Architecture: Conv1d stem + 6L transformer encoder + 6L transformer decoder (288d, 8 heads, partial RoPE, SiLU/GELU)
• Parameters: 27M
• Languages: English only
• WER: 4.55% (LibriSpeech clean), 11.68% (Other)
• Performance: 11.2x realtime on CPU (F32)
• License: MIT
• Source: moonshine.cpp (MIT)

Usage with CrispASR

./build/bin/crispasr -m moonshine-tiny-q4_k.gguf -f audio.wav
./build/bin/crispasr --backend moonshine -m moonshine-tiny-q4_k.gguf -f audio.wav -osrt

Provenance and EU AI Act Art. 53 note

• Upstream model: UsefulSensors/moonshine-tiny — published by UsefulSensors.
• Upstream licence: mit. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
• What was done here: format conversion and/or quantisation only (GGUF/GGML). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
• Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
• Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.