Instructions to use rmarcosg/bark-detection-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rmarcosg/bark-detection-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="rmarcosg/bark-detection-model")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("rmarcosg/bark-detection-model") model = AutoModelForAudioClassification.from_pretrained("rmarcosg/bark-detection-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e8f10f2569e644e95caa7779eac4c0c9f1f3058f1254aa384bb445fb2605f418
- Size of remote file:
- 4.92 kB
- SHA256:
- 7143379d4f8d9c57e8a8f460d5a4d6ed864bd4d19538151144ea5b3e63ca8768
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