metadata
license: apache-2.0
library_name: mlx
tags:
- mlx
- automatic-speech-recognition
- speaker-diarization
- audio
- moss
base_model:
- OpenMOSS-Team/MOSS-Transcribe-Diarize
pipeline_tag: automatic-speech-recognition
mlx-MOSS-Transcribe-Diarize
Converted MLX safetensors for OpenMOSS-Team/MOSS-Transcribe-Diarize.
This repository contains the already-converted Apple Silicon MLX model. Users do not need to download the original HF model or run conversion locally.
Use
Install the project:
git clone https://github.com/OpenMOSS/MOSS-Transcribe-Diarize.git mlx-MOSS-Transcribe-Diarize
cd mlx-MOSS-Transcribe-Diarize
python -m pip install -e ".[mlx-runtime]"
Run transcription:
python -m moss_transcribe_diarize.mlx.cli /path/to/input.wav \
--model vanch007/mlx-MOSS-Transcribe-Diarize \
--out-dir runs/mlx_example
Python:
from moss_transcribe_diarize.mlx import load_model
model = load_model("vanch007/mlx-MOSS-Transcribe-Diarize", strict=True)
result = model.generate("/path/to/input.wav", max_tokens=2048, temperature=0.0)
print(result.text)
Output Format
[start_time][Sxx]transcribed speech[end_time]
Example:
[0.06][S01] Hello world. This is a local MLX smoke test.[3.12]
Conversion
Converted from OpenMOSS-Team/MOSS-Transcribe-Diarize with:
python -m moss_transcribe_diarize.mlx.convert \
--source pretrained/moss-transcribe-diarize-hf \
--output pretrained/mlx-moss-transcribe-diarize \
--overwrite
See mlx_conversion.json for conversion metadata.