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UMUTeam
/
w2v-bert-emotion-en

Audio Classification
Transformers
Safetensors
English
wav2vec2-bert
emotion-recognition
speech-emotion-recognition
speech-processing
english
affective-computing
umuteam
Eval Results (legacy)
Model card Files Files and versions
xet
Community
1

Instructions to use UMUTeam/w2v-bert-emotion-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use UMUTeam/w2v-bert-emotion-en with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("audio-classification", model="UMUTeam/w2v-bert-emotion-en")
    # Load model directly
    from transformers import AutoProcessor, CustomAudioClassification
    
    processor = AutoProcessor.from_pretrained("UMUTeam/w2v-bert-emotion-en")
    model = CustomAudioClassification.from_pretrained("UMUTeam/w2v-bert-emotion-en", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
w2v-bert-emotion-en
4.66 GB
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  • 2 contributors
History: 3 commits
tomasBernal's picture
tomasBernal
Create README.md
3b72926 verified 4 months ago
  • .gitattributes
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    initial commit 9 months ago
  • README.md
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  • config.json
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  • model.bin
    2.33 GB
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  • model.safetensors
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  • preprocessor_config.json
    275 Bytes
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  • training_args.bin
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