metadata
license: mit
language:
- en
pipeline_tag: automatic-speech-recognition
tags:
- audio
- speech-recognition
- transcription
- gguf
- moonshine
- streaming
- lightweight
library_name: ggml
base_model: UsefulSensors/moonshine-streaming-small
Moonshine Streaming Small -- GGUF
GGUF conversions and quantisations of UsefulSensors/moonshine-streaming-small for use with CrispStrobe/CrispASR.
Available variants
| File | Quant | Size | Notes |
|---|---|---|---|
moonshine-streaming-small.gguf |
F32 | 535 MB | Full precision |
moonshine-streaming-small-q4_k.gguf |
Q4_K | 243 MB | Quantized |
Model details
- Architecture: Streaming encoder-decoder ASR. Raw-waveform audio frontend (no mel) + sliding-window transformer encoder (10L, 620d) + autoregressive transformer decoder (10L, 512d, SiLU-gated MLP, partial RoPE)
- Parameters: 123M
- Languages: English
- License: MIT
- Source:
UsefulSensors/moonshine-streaming-small - Designed for: Low-latency streaming ASR on edge devices
Usage with CrispASR
./build/bin/crispasr --backend moonshine-streaming -m moonshine-streaming-small-q4_k.gguf -f audio.wav
Notes
- Tokenizer (
tokenizer.bin) must be in the same directory as the model file - Streaming architecture: sliding-window attention with 80ms lookahead
- Audio frontend processes raw waveform (no mel spectrogram needed)
Provenance and EU AI Act Art. 53 note
- Upstream model: UsefulSensors/moonshine-streaming-small — 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.