Instructions to use Hemg/AudioclassDesktop with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hemg/AudioclassDesktop with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Hemg/AudioclassDesktop")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Hemg/AudioclassDesktop") model = AutoModelForAudioClassification.from_pretrained("Hemg/AudioclassDesktop") - Notebooks
- Google Colab
- Kaggle
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End of training
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README.md
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### Framework versions
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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| No log | 0.8 | 3 | 2.6479 | 0.0531 |
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### Framework versions
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