Instructions to use Developer9215/audio_cls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Developer9215/audio_cls with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Developer9215/audio_cls")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Developer9215/audio_cls") model = AutoModelForAudioClassification.from_pretrained("Developer9215/audio_cls") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d15568d7cd8433752af605969873b7bf021976baa2805572aad02c1cbfb24283
- Size of remote file:
- 378 MB
- SHA256:
- f237626b83cc9111b5e84bd331bcc08f6cd03133c2fb07e5b5ad5a78ce1e7901
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