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midoiv
/
Audio_Class_CREMA

Audio Classification
Transformers
PyTorch
wav2vec2
Generated from Trainer
Model card Files Files and versions
xet
Community
1

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")
    # 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
Audio_Class_CREMA
378 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
midoiv's picture
midoiv
update model card README.md
c8e3249 over 2 years ago
  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • .gitignore
    13 Bytes
    End of training over 2 years ago
  • README.md
    1.05 kB
    update model card README.md over 2 years ago
  • config.json
    2.54 kB
    End of training over 2 years ago
  • preprocessor_config.json
    215 Bytes
    End of training over 2 years ago
  • pytorch_model.bin
    378 MB
    xet
    End of training over 2 years ago
  • training_args.bin
    3.06 kB
    xet
    End of training over 2 years ago