Instructions to use abdelhalim/Shower_Sound_Recognition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abdelhalim/Shower_Sound_Recognition with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="abdelhalim/Shower_Sound_Recognition")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("abdelhalim/Shower_Sound_Recognition") model = AutoModelForAudioClassification.from_pretrained("abdelhalim/Shower_Sound_Recognition", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from abdelhalim/Shower_Sound_Recognition: direct link, hf CLI and curl.
- Browser
- Download file 757 MB
-
https://huggingface.co/abdelhalim/Shower_Sound_Recognition/resolve/refs%2Fpr%2F1/optimizer.pt
- Command line
-
hf download hf://abdelhalim/Shower_Sound_Recognition@refs/pr/1/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/abdelhalim/Shower_Sound_Recognition/resolve/refs%2Fpr%2F1/optimizer.pt
757 MB
- Xet hash:
- 1366039bebf5406c232b04759869faf2867a63eb35860b6a456a3883f41f5711
- Size of remote file:
- 757 MB
- SHA256:
- 7c39f74e4653fe114a1d6593a535236a3cdc99a305c686356dc699f6b22b391f
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