Instructions to use abelkrw/audio_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abelkrw/audio_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="abelkrw/audio_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("abelkrw/audio_classification") model = AutoModelForAudioClassification.from_pretrained("abelkrw/audio_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from abelkrw/audio_classification: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/abelkrw/audio_classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://abelkrw/audio_classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/abelkrw/audio_classification/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- af3264ebb5bc2e157b564fb56e462dab2c9eb9d0e6e890baa20ed08709fcc238
- Size of remote file:
- 378 MB
- SHA256:
- 58bcb0da08849dcb25a02ce6c2af773f0d5c4a74433839ddc31da52f9c115806
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