Instructions to use Zarakun/tmp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Zarakun/tmp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Zarakun/tmp")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Zarakun/tmp") model = AutoModelForAudioClassification.from_pretrained("Zarakun/tmp") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9183d92b4f54f7c5a9109d023abcc9f456e3d1c4450676f008587e830d537140
- Size of remote file:
- 378 MB
- SHA256:
- 871e2f4a98188f2783c332358a87593cf6182d6b3f1ec61dd9d4bc4278edd56d
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