Instructions to use ridwansukri/audio_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ridwansukri/audio_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="ridwansukri/audio_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("ridwansukri/audio_classification") model = AutoModelForAudioClassification.from_pretrained("ridwansukri/audio_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ridwansukri/audio_classification: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/ridwansukri/audio_classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ridwansukri/audio_classification/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/ridwansukri/audio_classification/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- fd7c930d55cb6ec410fd909ce271f05cbe82fe61bea8a654f8b4229760b2298f
- Size of remote file:
- 378 MB
- SHA256:
- 35a304dce5931c45ed8e6f2f9b3923f05223fadf4d8d2e111353bd0ae3e67f7c
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