Instructions to use Rafeq/donate_cry_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rafeq/donate_cry_classification with Transformers:
# Load model directly from transformers import AutoProcessor, Wav2Vec2ForSpeechClassification processor = AutoProcessor.from_pretrained("Rafeq/donate_cry_classification") model = Wav2Vec2ForSpeechClassification.from_pretrained("Rafeq/donate_cry_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f0b5b4cb388c22ac10c3bb3eb10cc2fc7b23ccfa9a45418eae7b6feb0c43bc41
- Size of remote file:
- 3.58 kB
- SHA256:
- 8630faa6f793d1683e1257a78db954175e447a312dd47f14f999622ed74f3b01
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