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lfurman
/
whisper-tiny-en

Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
English
whisper
Generated from Trainer
Model card Files Files and versions
xet
Metrics Training metrics Community

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

  • Libraries
  • Transformers

    How to use lfurman/whisper-tiny-en with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("automatic-speech-recognition", model="lfurman/whisper-tiny-en")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
    
    processor = AutoProcessor.from_pretrained("lfurman/whisper-tiny-en")
    model = AutoModelForSpeechSeq2Seq.from_pretrained("lfurman/whisper-tiny-en")
  • Notebooks
  • Google Colab
  • Kaggle
whisper-tiny-en / checkpoint-100
453 MB
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  • 1 contributor
History: 1 commit
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lfurman
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  • config.json
    1.27 kB
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  • generation_config.json
    2.88 kB
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  • model.safetensors
    151 MB
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  • optimizer.pt
    302 MB
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  • preprocessor_config.json
    339 Bytes
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  • rng_state.pth

    Detected Pickle imports (7)

    • "_codecs.encode",
    • "torch.ByteStorage",
    • "numpy.dtype",
    • "torch._utils._rebuild_tensor_v2",
    • "numpy.core.multiarray._reconstruct",
    • "collections.OrderedDict",
    • "numpy.ndarray"

    How to fix it?

    14.2 kB
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  • scheduler.pt

    Pickle imports

    • No problematic imports detected

    What is a pickle import?

    1.06 kB
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  • trainer_state.json
    5.2 kB
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  • training_args.bin
    5.37 kB
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