Instructions to use Jurabek/Audio_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jurabek/Audio_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Jurabek/Audio_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Jurabek/Audio_classification") model = AutoModelForAudioClassification.from_pretrained("Jurabek/Audio_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Jurabek/Audio_classification: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/Jurabek/Audio_classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Jurabek/Audio_classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Jurabek/Audio_classification/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 3e3ad47e1477c4d1e375015437673ad025418fb8576ff1b1dd88e83f3ccbd8b9
- Size of remote file:
- 378 MB
- SHA256:
- 0085b3cd2831d377e4da67d728525e5e9471a1cea780e0ea6893390a82d56e1c
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