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

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.

Model Card for Model ID

Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• Developed by: [More Information Needed]
• Funded by [optional]: [More Information Needed]
• Shared by [optional]: [More Information Needed]
• Model type: [More Information Needed]
• Language(s) (NLP): [More Information Needed]
• License: [More Information Needed]
• Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

• Repository: [More Information Needed]
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

• Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: [More Information Needed]
• Hours used: [More Information Needed]
• Cloud Provider: [More Information Needed]
• Compute Region: [More Information Needed]
• Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]