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handy-computer/moonshine-base-ja-gguf
moonshine-base-ja-gguf is a automatic speech recognition model from handy-computer. Use it when you need speech turned into text. It is set up for transcribe.cpp. The card lists the license as other.
GGUF conversions of UsefulSensors/moonshine-base-ja for use with transcribe.cpp.
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From the Hugging Face model README
GGUF conversions of UsefulSensors/moonshine-base-ja for use with transcribe.cpp.
Ported from upstream commit f9d4e6a, pinned 2026-05-12. Validated against the transformers reference at transcribe.cpp commit 90bf720 on 2026-05-12.
UsefulSensors' Moonshine base fine-tuned on Japanese. Same encoder-decoder transformer architecture as moonshine-base (62M parameters): consumes 16 kHz raw PCM via a three-layer Conv1d stem (no STFT, no mel filterbank) and emits transcript-only output. Single-language (ja); no translation, no language detection, no timestamps.
| Quantization | Download | Size | CER (FLEURS ja) |
|---|---|---|---|
| F32 | moonshine-base-ja-F32.gguf | 248 MB | 10.84% |
| F16 | moonshine-base-ja-F16.gguf | 132 MB | 10.81% |
| Q8_0 | moonshine-base-ja-Q8_0.gguf | 77 MB | 11.11% |
CER on the full FLEURS ja split (650 utterances), batch sizes 1 and 8, timestamps none. Figures without a commit were published before provenance was recorded.
Decoded with the transcribe.cpp defaults (greedy, num_beams=1, max_length=192, matching the upstream generation_config).
UsefulSensors does not publish a per-language CER number
for this variant. As a comparable baseline we ran the Transformers F32
reference (MoonshineForConditionalGeneration, fp32 on MPS) on the
same manifest: 10.69% CER. The C++ F32/F16 numbers
above match the reference within bootstrap-CI noise; Q8_0 introduces
a small additional drift from F16 (typically within 0.1pp).
Build transcribe.cpp from source:
git clone [email protected]:handy-computer/transcribe.cpp.git
cd transcribe.cpp
cmake -B build && cmake --build build
Run on a 16 kHz mono WAV:
build/bin/transcribe-cli \
-m moonshine-base-ja-Q8_0.gguf \
input.wav
If your audio isn't already 16 kHz mono WAV, convert it first:
ffmpeg -i input.mp3 -ar 16000 -ac 1 output.wav
See the transcribe.cpp model page for performance numbers, numerical validation, and reproduction steps.
Inherited from the base model: Moonshine AI Community License. See the
upstream model card for full terms.
This repository includes the license text
(LICENSE) and a
NOTICE recording
how these files were derived from the original.
Free for research and non-commercial use. Commercial use requires registering with Moonshine AI and is limited to organizations under US$1M annual revenue; read the license before using it. Moonshine AI releases its English and streaming models under MIT; only these legacy language fine-tunes carry this license.
Powered by Moonshine AI.
The section below is reproduced from UsefulSensors/moonshine-base-ja at commit
f9d4e6afor offline reference. The upstream card is the authoritative source.
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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