Instructions to use heado/kws with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use heado/kws with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="heado/kws")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("heado/kws") model = AutoModelForAudioClassification.from_pretrained("heado/kws", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0f20399c89e4ee3935371ad60355af7388a1d78e45b77def13dce51b37ddda24
- Size of remote file:
- 5.84 kB
- SHA256:
- 9b1752f31f0f741564374411f5b8e472b09ef67989464c00170d0f6a7177d6eb
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