Instructions to use dima806/cat_dog_sounds_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/cat_dog_sounds_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="dima806/cat_dog_sounds_classification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("dima806/cat_dog_sounds_classification") model = AutoModelForAudioClassification.from_pretrained("dima806/cat_dog_sounds_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6c7d53593287ca5267618e5e2dfa46196a75e6f23467fd445710131549a738c8
- Size of remote file:
- 3.57 kB
- SHA256:
- d4fba6b704dfb45312fb9c8b716fbccad21fadbd01864cb25088260308dbeef3
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