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optimum
/
whisper-tiny.en

Automatic Speech Recognition
Transformers
ONNX
English
whisper
audio
hf-asr-leaderboard
Model card Files Files and versions
xet
Community

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

  • Libraries
  • Transformers

    How to use optimum/whisper-tiny.en with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("automatic-speech-recognition", model="optimum/whisper-tiny.en")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
    
    processor = AutoProcessor.from_pretrained("optimum/whisper-tiny.en")
    model = AutoModelForSpeechSeq2Seq.from_pretrained("optimum/whisper-tiny.en")
  • Notebooks
  • Google Colab
  • Kaggle
whisper-tiny.en
Ctrl+K
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  • 2 contributors
History: 2 commits
Félix Marty
add model
310872f over 3 years ago
  • .gitattributes
    1.48 kB
    initial commit over 3 years ago
  • README.md
    368 Bytes
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  • added_tokens.json
    2.08 kB
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  • config.json
    1.91 kB
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  • decoder_model.onnx
    198 MB
    xet
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  • decoder_with_past_model.onnx
    193 MB
    xet
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  • encoder_model.onnx
    32.9 MB
    xet
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  • merges.txt
    456 kB
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  • normalizer.json
    52.7 kB
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  • preprocessor_config.json
    185 kB
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  • special_tokens_map.json
    1.72 kB
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  • tokenizer_config.json
    874 Bytes
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  • vocab.json
    999 kB
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