Instructions to use ridwansukri/audio_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ridwansukri/audio_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="ridwansukri/audio_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("ridwansukri/audio_classification") model = AutoModelForAudioClassification.from_pretrained("ridwansukri/audio_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ridwansukri/audio_classification: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/ridwansukri/audio_classification/resolve/main/training_args.bin
- Command line
-
hf download hf://ridwansukri/audio_classification/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/ridwansukri/audio_classification/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- dababf69c8d86f72fbe8ee57fc50065b6cf121f4e3daf04ae6f3b73f2b9447a8
- Size of remote file:
- 4.09 kB
- SHA256:
- 90019cc15f0efd980bc844b2cadd901c19ba9bcacfa751939ab9b73d3fe0c756
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