Instructions to use Cesar514/distilhubert-gtzan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cesar514/distilhubert-gtzan with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Cesar514/distilhubert-gtzan")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Cesar514/distilhubert-gtzan") model = AutoModelForAudioClassification.from_pretrained("Cesar514/distilhubert-gtzan", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Cesar514/distilhubert-gtzan: direct link, hf CLI and curl.
- Browser
- Download file 5.78 kB
-
https://huggingface.co/Cesar514/distilhubert-gtzan/resolve/main/training_args.bin
- Command line
-
hf download hf://Cesar514/distilhubert-gtzan/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Cesar514/distilhubert-gtzan/resolve/main/training_args.bin
5.78 kB
- Xet hash:
- ffb1c030b3745ea8f689b786a293aac9162853180f90c0df8a2aa1d046f081bb
- Size of remote file:
- 5.78 kB
- SHA256:
- cb6700ced8a907cb438c682dad22a2059ef6e889617452b5c19c1f577b0f501a
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