Mlx MOSS Transcribe Diarize

--- 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.

Source context: 155 downloads · 1 likes · Pipeline automatic-speech-recognition · Library mlx · Repo vanch007/mlx-MOSS-Transcribe-Diarize