CoolFace
Modelpublic

handy-computer/moonshine-base-ja-gguf

sourceHugging Facemitupdated 8d agoView on Hugging Face
0likes893downloads
Model Card

moonshine-base-ja: transcribe.cpp GGUF

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.

Downloads

QuantizationDownloadSizeCER (FLEURS ja test)
F32moonshine-base-ja-F32.gguf248 MB10.84%
F16moonshine-base-ja-F16.gguf132 MB10.81%
Q8_0moonshine-base-ja-Q8_0.gguf77 MB10.53%

CER on the full FLEURS ja split (650 utterances), batch size 1, timestamps none. Figures without a commit were published before provenance was recorded.

Decoded with the transcribe.cpp defaults (greedy, numbeams=1, maxlength=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).

Usage

Build transcribe.cpp from source:

bash
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:

bash
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:

bash
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-ja at commit f9d4e6a for offline reference. The upstream card is the authoritative source.

Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

Model Details

Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a ๐Ÿค— transformers model that has been pushed on the Hub. This model card has been automatically generated.

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

Model Sources [optional]

<!-- Provide the basic links for the model. -->

  • โ€”Repository: [More Information Needed]
  • โ€”Paper [optional]: [More Information Needed]
  • โ€”Demo [optional]: [More Information Needed]

Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters
  • โ€”Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

Testing Data, Factors & Metrics

Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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

  • โ€”Hardware Type: [More Information Needed]
  • โ€”Hours used: [More Information Needed]
  • โ€”Cloud Provider: [More Information Needed]
  • โ€”Compute Region: [More Information Needed]
  • โ€”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]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]