Ported from upstream commit
264cc18,
pinned 2026-05-12.
Validated against the transformers reference at transcribe.cpp commit
90bf720
on 2026-05-12.
UsefulSensors' Moonshine base fine-tuned on Arabic. 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
(ar); no translation, no language detection, no timestamps.
WER measured on the FLEURS-ar test split (428
utterances) using the transcribe.cpp default decode (greedy,
num_beams=1, max_length=192 — matching the upstream generation_config).
UsefulSensors does not publish a per-language WER number
for this variant. As a comparable baseline we ran the Transformers F32
reference (MoonshineForConditionalGeneration, fp32 on MPS) on the
same manifest: 24.51% WER. 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).
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.
License
Inherited from the base model: MIT. See the
upstream model card for full terms.
Original Model Card
The section below is reproduced from
UsefulSensors/moonshine-base-ar at commit
264cc18 for offline reference. The upstream card is the
authoritative source.
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