Instructions to use juniorjukeko/audio-class-minds14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use juniorjukeko/audio-class-minds14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="juniorjukeko/audio-class-minds14")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("juniorjukeko/audio-class-minds14") model = AutoModelForAudioClassification.from_pretrained("juniorjukeko/audio-class-minds14", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from juniorjukeko/audio-class-minds14: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/juniorjukeko/audio-class-minds14/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://juniorjukeko/audio-class-minds14/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/juniorjukeko/audio-class-minds14/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- d018671eba1daf533b39a3fc443483ca0ae2ba306eb90d2d2979195c2ea477bd
- Size of remote file:
- 378 MB
- SHA256:
- d5a898e078d56a31654d5a8fd458963d50a917bce58650fb4615486848bc5ec6
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