Instructions to use midoiv/Audio_Class_CREMA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use midoiv/Audio_Class_CREMA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="midoiv/Audio_Class_CREMA")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("midoiv/Audio_Class_CREMA") model = AutoModelForAudioClassification.from_pretrained("midoiv/Audio_Class_CREMA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from midoiv/Audio_Class_CREMA: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/midoiv/Audio_Class_CREMA/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://midoiv/Audio_Class_CREMA/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/midoiv/Audio_Class_CREMA/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 8e7ee1b8fcb36eae2e8eb85b4b3517b0a46903ac5ebfb048b32004a14298125a
- Size of remote file:
- 378 MB
- SHA256:
- b5af220afbe4ff506d6a0049a3b7bb6cb4f092bc6f647d82acae5a2d8c0d8b0d
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