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Willy030125
/
Whisper-base-minds14-ForClassification

Audio Classification
Transformers
PyTorch
whisper
Model card Files Files and versions
xet
Community
1

Instructions to use Willy030125/Whisper-base-minds14-ForClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Willy030125/Whisper-base-minds14-ForClassification with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("audio-classification", model="Willy030125/Whisper-base-minds14-ForClassification")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoProcessor, AutoModelForAudioClassification
    
    processor = AutoProcessor.from_pretrained("Willy030125/Whisper-base-minds14-ForClassification")
    model = AutoModelForAudioClassification.from_pretrained("Willy030125/Whisper-base-minds14-ForClassification", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
Whisper-base-minds14-ForClassification
418 MB
Ctrl+K
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  • 1 contributor
History: 5 commits
SFconvertbot's picture
SFconvertbot
Adding `safetensors` variant of this model
e6411dc verified over 1 year ago
  • checkpoint-1820
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  • .gitattributes
    1.52 kB
    initial commit about 2 years ago
  • README.md
    556 Bytes
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  • added_tokens.json
    34.6 kB
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  • config.json
    2.83 kB
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  • merges.txt
    494 kB
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  • model.safetensors
    82.9 MB
    xet
    Adding `safetensors` variant of this model over 1 year ago
  • normalizer.json
    52.7 kB
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  • preprocessor_config.json
    339 Bytes
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  • pytorch_model.bin
    82.9 MB
    xet
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  • special_tokens_map.json
    2.08 kB
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  • tokenizer_config.json
    283 kB
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  • training_args.bin
    3.96 kB
    xet
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  • vocab.json
    1.04 MB
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