Instructions to use harriskr14/audio_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harriskr14/audio_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="harriskr14/audio_classification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("harriskr14/audio_classification") model = AutoModelForAudioClassification.from_pretrained("harriskr14/audio_classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 47bd824f5e6d26e131f48c2dd8af71127894e728e51d126e36d8a2c52884257d
- Size of remote file:
- 5.71 kB
- SHA256:
- d2a11d99bcfb89cebcbea79b4a6a9e8af5cc7268b1ff728556fc248c5b942b32
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