Automatic Speech Recognition
MLX
Safetensors
moss_transcribe_diarize
int8
speaker-diarization
custom_code
Instructions to use plaincompute/mlx-MOSS-Transcribe-Diarize-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use plaincompute/mlx-MOSS-Transcribe-Diarize-8bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir mlx-MOSS-Transcribe-Diarize-8bit plaincompute/mlx-MOSS-Transcribe-Diarize-8bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
File size: 660 Bytes
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license: apache-2.0
library_name: mlx
tags:
- mlx
- int8
- automatic-speech-recognition
- speaker-diarization
base_model:
- vanch007/mlx-MOSS-Transcribe-Diarize
pipeline_tag: automatic-speech-recognition
---
# mlx-MOSS-Transcribe-Diarize 8bit
8bit MLX weight-only quantized variant of `vanch007/mlx-MOSS-Transcribe-Diarize`.
Quantization uses MLX affine quantization with `bits=8` and `group_size=64` on the text backbone, excluding the Whisper audio encoder and VQ adaptor.
```bash
python -m moss_transcribe_diarize.mlx.cli /path/to/input.wav \
--model plaincompute/mlx-MOSS-Transcribe-Diarize-8bit \
--out-dir runs/mlx_8bit_example
```
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