Instructions to use stnleyyg/audio_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stnleyyg/audio_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="stnleyyg/audio_classification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("stnleyyg/audio_classification") model = AutoModelForAudioClassification.from_pretrained("stnleyyg/audio_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c32b0ca2cd1e463331b27f35633b1f23da53a04334147be438b319abbb427d31
- Size of remote file:
- 378 MB
- SHA256:
- 883a28174d07b0579585bef5a0fb484fc6800391bc43e526cc64182dd3fa1805
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