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jialicheng
/
whisper-tiny-speech_commands

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

Instructions to use jialicheng/whisper-tiny-speech_commands with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use jialicheng/whisper-tiny-speech_commands with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("audio-classification", model="jialicheng/whisper-tiny-speech_commands")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForAudioClassification
    
    processor = AutoProcessor.from_pretrained("jialicheng/whisper-tiny-speech_commands")
    model = AutoModelForAudioClassification.from_pretrained("jialicheng/whisper-tiny-speech_commands")
  • Notebooks
  • Google Colab
  • Kaggle
whisper-tiny-speech_commands
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  • 1 contributor
History: 2 commits
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jialicheng
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  • .gitattributes
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    initial commit about 2 years ago
  • README.md
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  • all_results.json
    620 Bytes
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  • config.json
    2.77 kB
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  • eval_results.json
    204 Bytes
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  • model.safetensors
    33.2 MB
    xet
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  • pred_logit_eval.npy
    326 kB
    xet
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  • pred_logit_test.npy
    148 kB
    xet
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  • pred_logit_train.npy
    2.45 MB
    xet
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  • preprocessor_config.json
    339 Bytes
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  • test_results.json
    205 Bytes
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  • train_results.json
    214 Bytes
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  • trainer_state.json
    5.89 kB
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  • training_args.bin
    5.11 kB
    xet
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