Instructions to use krishnareddy/audio_classification_example with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use krishnareddy/audio_classification_example with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="krishnareddy/audio_classification_example")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("krishnareddy/audio_classification_example") model = AutoModelForAudioClassification.from_pretrained("krishnareddy/audio_classification_example", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e9dc17327471eabf0506fdbc5932b275e0f0958d3aae0e3090e8d69591c9626b
- Size of remote file:
- 4.73 kB
- SHA256:
- ce284c7eb46b92f048e0d29a835c326862f0536f4110857de628a23443168802
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