Instructions to use J001/distilhubert-finetuned-gtzan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use J001/distilhubert-finetuned-gtzan with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="J001/distilhubert-finetuned-gtzan")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("J001/distilhubert-finetuned-gtzan") model = AutoModelForAudioClassification.from_pretrained("J001/distilhubert-finetuned-gtzan") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b072a3d61bd053ee992b070a82fad53f5a49515e59232b32730a284797ddff67
- Size of remote file:
- 94.8 MB
- SHA256:
- 10b0197f384a4dcae9b8bf292aa79a3a484e2fb16ce0d94f6fd46157cf7966a6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.