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:
- ea397abd68926e472a8a9d6e7a878bb20b8abb8bccb436d59ef3e2c6df55922a
- Size of remote file:
- 1.27 GB
- SHA256:
- dc5a2fd490e0116af6253b2e1727e0dcb930b307085d8d8ebc235808c97c016e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.