Instructions to use Ammok/audio_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ammok/audio_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Ammok/audio_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Ammok/audio_classification") model = AutoModelForAudioClassification.from_pretrained("Ammok/audio_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Ammok/audio_classification: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/Ammok/audio_classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Ammok/audio_classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Ammok/audio_classification/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- e49f5ffe58bfb1ff39e73e821cb3dc2929b5d15c170ca0f3d873ff9e6b22402b
- Size of remote file:
- 378 MB
- SHA256:
- 3e60a8a5daa8fb90680a66ccd08fb53446056f44db7bb6f1d53131083c76e778
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