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") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:8fcfd4bc17b79303626c974db18e8d2ec64c6f710e8b032d8230073eaa4bd379
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size 1266030648
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