Instructions to use doggy8088/Breeze-ASR-26-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use doggy8088/Breeze-ASR-26-MLX with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Breeze-ASR-26-MLX doggy8088/Breeze-ASR-26-MLX
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
Breeze-ASR-26-MLX
這是 MediaTek-Research/Breeze-ASR-26 的 MLX 轉換版本。原始模型是以 Whisper Large-v2 為基礎微調的語音辨識模型。
使用方式
安裝 mlx-whisper:
pip install mlx-whisper
使用 CLI 轉錄音訊:
mlx_whisper audio.wav --model doggy8088/Breeze-ASR-26-MLX --language zh
或在 Python 中使用:
import mlx_whisper
result = mlx_whisper.transcribe(
"audio.wav",
path_or_hf_repo="doggy8088/Breeze-ASR-26-MLX",
language="zh",
)
print(result["text"])
轉換方式
此模型是從 Hugging Face Transformers checkpoint 轉換成 mlx-whisper 格式,權重使用 fp16。
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