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UMUTeam
/
w2v-bert-beto-concat-emotion-en

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

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

  • Libraries
  • Transformers

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

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("audio-classification", model="UMUTeam/w2v-bert-beto-concat-emotion-en")
    # Load model directly
    from transformers import AutoProcessor, CustomAudioClassificationConcat
    
    processor = AutoProcessor.from_pretrained("UMUTeam/w2v-bert-beto-concat-emotion-en")
    model = CustomAudioClassificationConcat.from_pretrained("UMUTeam/w2v-bert-beto-concat-emotion-en")
  • Notebooks
  • Google Colab
  • Kaggle
w2v-bert-beto-concat-emotion-en
1.52 kB
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  • 2 contributors
History: 1 commit
Rhpan's picture
Rhpan
initial commit
756a15a verified 7 months ago
  • .gitattributes
    1.52 kB
    initial commit 7 months ago