Automatic Speech Recognition
Safetensors
MLX
mlx-audio
qwen3_asr
speech-to-text
speech
transcription
asr
stt
4-bit precision
Instructions to use runfuture/Mega-ASR-MLX-Q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use runfuture/Mega-ASR-MLX-Q4 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Mega-ASR-MLX-Q4 runfuture/Mega-ASR-MLX-Q4
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
metadata
license: apache-2.0
tags:
- mlx
- speech-to-text
- speech
- transcription
- asr
- stt
- mlx-audio
library_name: mlx-audio
pipeline_tag: automatic-speech-recognition
base_model:
- zhifeixie/Mega-ASR
Mega-ASR MLX Q4
This is a private MLX conversion of zhifeixie/Mega-ASR.
The checkpoint was produced by merging the mega-asr-merged LoRA adapter from
zhifeixie/Mega-ASR into the bundled Qwen3-ASR-1.7B base checkpoint, then
converting the merged weights to the mlx-audio Qwen3-ASR layout.
Conversion
- Base/source repo:
zhifeixie/Mega-ASR - Adapter:
mega-asr-merged - Format: MLX /
mlx-audio - Quantization: affine Q4,
group_size=64,bits=4 - Text model and token embedding are quantized; audio tower remains full precision.
Use With mlx-audio
pip install -U mlx-audio
python -m mlx_audio.stt.generate --model runfuture/Mega-ASR-MLX-Q4 --audio audio.wav