Instructions to use Jurabek/AudioClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jurabek/AudioClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Jurabek/AudioClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Jurabek/AudioClassification") model = AutoModelForAudioClassification.from_pretrained("Jurabek/AudioClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Jurabek/AudioClassification: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/Jurabek/AudioClassification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Jurabek/AudioClassification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Jurabek/AudioClassification/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- d752fee520316aac781df7e41c7469175bc903f3ff8c4a71f7232afba2910a5a
- Size of remote file:
- 378 MB
- SHA256:
- 590a99a5755ba7529c930e56daad1201a0ef208bac93c6aec8e53e0a261f86fb
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