Instructions to use MU-NLPC/whisper-small-audio-captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MU-NLPC/whisper-small-audio-captioning with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, WhisperForAudioCaptioning processor = AutoProcessor.from_pretrained("MU-NLPC/whisper-small-audio-captioning") model = WhisperForAudioCaptioning.from_pretrained("MU-NLPC/whisper-small-audio-captioning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Update model_class.py
Verified signaturea75f70c -
update model_class.py to be compatible with new transformers (transformers==4.36.2)
Verified signaturee8e231a
-
Update model_class.py (#5)
Verified signature- Update model_class.py (c0a6c8d4ea3905485ea20f4f16f6a87c09538893) Co-authored-by: weilai <geekweilai@users.noreply.huggingface.co>53d0b79
-
Create model_class.py
50b22ff