Instructions to use Rafeq/cry_detection_and_classification2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rafeq/cry_detection_and_classification2 with Transformers:
# Load model directly from transformers import AutoProcessor, Wav2Vec2ForSpeechClassification processor = AutoProcessor.from_pretrained("Rafeq/cry_detection_and_classification2") model = Wav2Vec2ForSpeechClassification.from_pretrained("Rafeq/cry_detection_and_classification2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d2b4c8c80334bb9822dffcb1bc29fe02e2a9839f0e3b37467953f0b9a86d8c45
- Size of remote file:
- 1.27 GB
- SHA256:
- 4900aa3529ade6e32a90b44e99afac9c91854a473b7a8cdce74ebb64c042b0d8
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