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 training_args.bin from abelkrw/audio_classification: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/abelkrw/audio_classification/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://abelkrw/audio_classification@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/abelkrw/audio_classification/resolve/refs%2Fpr%2F1/training_args.bin
4.09 kB
- Xet hash:
- f6f7c426be33c644fd7cd57158350a36f517b65e42b67c1c794b4ff19c29e21a
- Size of remote file:
- 4.09 kB
- SHA256:
- 20a8825e756155eabed18b13e78044148ee4f691ea94b8d3e578531ddeb01f2d
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