Instructions to use nomsgadded/Audio_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nomsgadded/Audio_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="nomsgadded/Audio_Classification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("nomsgadded/Audio_Classification") model = AutoModelForAudioClassification.from_pretrained("nomsgadded/Audio_Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7cdf951ef9616d9d586d37ad2d914a7cb015e141dd686ddc0dc0f01e8edb6358
- Size of remote file:
- 4.54 kB
- SHA256:
- e30b4cca9576004ec6d339ca97c236a8642ab0f13280fae845177cd2a4e5976c
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