---

license: mit

base_model: UsefulSensors/moonshine-base-ar

base_model_relation: quantized

library_name: transcribe.cpp

pipeline_tag: automatic-speech-recognition

language:

- ar

tags:

- gguf

- transcribe.cpp

- asr

- speech-to-text

- moonshine

- useful-sensors

- encoder-decoder

transcribe_cpp:

wer_fleurs_ar:

f32: 24.45

f16: 24.45

q8_0: 24.5

rtf_m4_max:

metal: 79.5

cpu: 80.5

rtf_ryzen_4750u:

vulkan: 34.5

cpu: 22

streaming: false

translate: false

lang_detect: false

timestamps: none

---

Provenance

Byte-identical re-hosting of handy-computer/moonshine-base-ar-gguf at revision

1ae85af52b16, serving as the primary

model source for the Panita desktop app.

• GGUF conversion by handy-computer

(transcribe.cpp project) — re-hosted unmodified.

• Upstream model: UsefulSensors/moonshine-base-ar.
• License: mit — inherited from the upstream model; see the original

model card below.

Every file's sha256 matches the source repository; the app verifies each

download against the catalog's pinned hashes.

moonshine-base-ar: transcribe.cpp GGUF

GGUF conversions of UsefulSensors/moonshine-base-ar for use

with transcribe.cpp.

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.

Downloads

| Quantization | Download | Size | WER (FLEURS ar test) |

| --- | --- | ---: | ---: |

| F32 | moonshine-base-ar-F32.gguf | 236 MB | 24.45% |

| F16 | moonshine-base-ar-F16.gguf | 126 MB | 24.45% |

| Q8_0 | moonshine-base-ar-Q8_0.gguf | 74 MB | 24.50% |

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).

Usage

Build transcribe.cpp from source:

git clone git@github.com: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-ar-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.

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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