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OpenWhistleNeurIPS26
/
OpenWhistle-Wav2Vec2.0

Feature Extraction
Transformers
Safetensors
wav2vec2
bioacoustics
audio
self-supervised-learning
dolphin
bottlenose-dolphin
whistle
openwhistle
Model card Files Files and versions
xet
Community

Instructions to use OpenWhistleNeurIPS26/OpenWhistle-Wav2Vec2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use OpenWhistleNeurIPS26/OpenWhistle-Wav2Vec2.0 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="OpenWhistleNeurIPS26/OpenWhistle-Wav2Vec2.0")
    # Load model directly
    from transformers import AutoProcessor, Wav2vec2ForPreTraining_randommask
    
    processor = AutoProcessor.from_pretrained("OpenWhistleNeurIPS26/OpenWhistle-Wav2Vec2.0")
    model = Wav2vec2ForPreTraining_randommask.from_pretrained("OpenWhistleNeurIPS26/OpenWhistle-Wav2Vec2.0")
  • Notebooks
  • Google Colab
  • Kaggle
OpenWhistle-Wav2Vec2.0
404 MB
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  • 1 contributor
History: 2 commits
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OpenWhistle
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  • .gitattributes
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  • README.md
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  • config.json
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  • model.safetensors
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  • preprocessor_config.json
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  • trainer_state.json
    24.2 MB
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