Instructions to use Rafeq/cry_detection_and_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rafeq/cry_detection_and_classification with Transformers:
# Load model directly from transformers import AutoProcessor, Wav2Vec2ForSpeechClassification processor = AutoProcessor.from_pretrained("Rafeq/cry_detection_and_classification") model = Wav2Vec2ForSpeechClassification.from_pretrained("Rafeq/cry_detection_and_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c2169ac25f8b75b2d13e6df47904f64ad7569b7c8c1e04c43c73f41f6ae89148
- Size of remote file:
- 3.64 kB
- SHA256:
- f1ad8e035674c9486798c9d1ff63ff038ab967349d5c2e0200e5cea5884540bf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.