Instructions to use sfabriece/whisper-large-v3-atco2-asr-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use sfabriece/whisper-large-v3-atco2-asr-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir whisper-large-v3-atco2-asr-mlx sfabriece/whisper-large-v3-atco2-asr-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
whisper-large-v3-atco2-asr-mlx
MLX (float16) build of jlvdoorn/whisper-large-v3-atco2-asr for Apple Silicon inference via mlx-whisper.
The base model is an aviation-fine-tuned Whisper-large-v3 that reports ~17% WER on ATCO2 vs ~37โ55% WER for stock large-v3 on the same domain. No public MLX conversion existed at conversion time (2026-04), so this repo exists to unblock MPS evaluation.
Usage
import mlx_whisper
result = mlx_whisper.transcribe(
"audio.wav",
path_or_hf_repo="sfabriece/whisper-large-v3-atco2-asr-mlx",
language="en",
)
print(result["text"])
Conversion
Converted with the mlx-examples/whisper/convert.py script:
python convert.py \
--torch-name-or-path jlvdoorn/whisper-large-v3-atco2-asr \
--mlx-path ./whisper-large-v3-atco2-asr-mlx \
--dtype float16
# then rename model.safetensors โ weights.safetensors (mlx-whisper loader expects this name)
License
Apache-2.0 (inherited from the base model).
Attribution
- Base model:
jlvdoorn/whisper-large-v3-atco2-asrโ aviation fine-tune on top of OpenAI Whisper large-v3 (MIT). - Conversion tooling:
ml-explore/mlx-examples/whisper.
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Hardware compatibility
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Quantized
Model tree for sfabriece/whisper-large-v3-atco2-asr-mlx
Base model
openai/whisper-large-v3 Finetuned
jlvdoorn/whisper-large-v3-atco2-asr