README.md

---
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](https://huggingface.co/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:

```bash
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:

```bash
python -m moss_transcribe_diarize.mlx.cli /path/to/input.wav \
  --model vanch007/mlx-MOSS-Transcribe-Diarize \
  --out-dir runs/mlx_example
```

Python:

```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

```text
[start_time][Sxx]transcribed speech[end_time]
```

Example:

```text
[0.06][S01] Hello world. This is a local MLX smoke test.[3.12]
```

## Conversion

Converted from `OpenMOSS-Team/MOSS-Transcribe-Diarize` with:

```bash
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.