Instructions to use kensvin/audio_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kensvin/audio_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="kensvin/audio_classification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("kensvin/audio_classification") model = AutoModelForAudioClassification.from_pretrained("kensvin/audio_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7ffc203c2ba450e2732ccf9e9147f93412d6acbc533ac99dd25f9ce473312f66
- Size of remote file:
- 378 MB
- SHA256:
- 884aed8888e2bcef0aee959551181756c04e424533f8f634d8c666d772d873da
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