Instructions to use heado/audio_cls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use heado/audio_cls with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="heado/audio_cls")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("heado/audio_cls") model = AutoModelForAudioClassification.from_pretrained("heado/audio_cls", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 07ba7622a45568bb11d7157890cc2196de0895d59561924129ec8981a047ced1
- Size of remote file:
- 5.84 kB
- SHA256:
- 9cdca86c1f7e12e6aa1fadf035f8457b044582eac74804a5ca1eba394fe4637c
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