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")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("abdelhalim/Shower_Sound_Recognition") model = AutoModelForAudioClassification.from_pretrained("abdelhalim/Shower_Sound_Recognition") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ff16ce3db3e70bbcc53811761875cdb965d833e61de44b75c91baaefc21d4b42
- Size of remote file:
- 378 MB
- SHA256:
- 2d538874b60c4d885d08ef144a1a76bbdf0daebc4cd10154302bf555326b10d5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.