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Flocksserver
/
whisper-tiny-de-emodb-emotion-classification

Audio Classification
Transformers
Safetensors
German
whisper
Generated from Trainer
Eval Results (legacy)
Model card Files Files and versions
xet
Community

Instructions to use Flocksserver/whisper-tiny-de-emodb-emotion-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Flocksserver/whisper-tiny-de-emodb-emotion-classification with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("audio-classification", model="Flocksserver/whisper-tiny-de-emodb-emotion-classification")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForAudioClassification
    
    processor = AutoProcessor.from_pretrained("Flocksserver/whisper-tiny-de-emodb-emotion-classification")
    model = AutoModelForAudioClassification.from_pretrained("Flocksserver/whisper-tiny-de-emodb-emotion-classification")
  • Notebooks
  • Google Colab
  • Kaggle
whisper-tiny-de-emodb-emotion-classification
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  • 1 contributor
History: 5 commits
Flocksserver's picture
Flocksserver
Delete training_args.bin
9ef7014 verified over 1 year ago
  • .gitattributes
    1.52 kB
    initial commit over 1 year ago
  • README.md
    2.33 kB
    Update README.md over 1 year ago
  • config.json
    1.63 kB
    End of training over 1 year ago
  • model.safetensors
    33.2 MB
    xet
    End of training over 1 year ago
  • preprocessor_config.json
    339 Bytes
    End of training over 1 year ago