Instructions to use midoiv/Audio_CREMA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use midoiv/Audio_CREMA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="midoiv/Audio_CREMA")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("midoiv/Audio_CREMA") model = AutoModelForAudioClassification.from_pretrained("midoiv/Audio_CREMA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 2
Browse files
pytorch_model.bin
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runs/Apr21_03-12-11_0d2a4bff3178/events.out.tfevents.1713669144.0d2a4bff3178.17.0
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