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RedHatAI
/
whisper-tiny-FP8-Dynamic

Automatic Speech Recognition
Transformers
Safetensors
English
whisper
FP8
vllm
audio
compressed-tensors
Model card Files Files and versions
xet
Community

Instructions to use RedHatAI/whisper-tiny-FP8-Dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use RedHatAI/whisper-tiny-FP8-Dynamic with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("automatic-speech-recognition", model="RedHatAI/whisper-tiny-FP8-Dynamic")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
    
    processor = AutoProcessor.from_pretrained("RedHatAI/whisper-tiny-FP8-Dynamic")
    model = AutoModelForSpeechSeq2Seq.from_pretrained("RedHatAI/whisper-tiny-FP8-Dynamic")
  • Notebooks
  • Google Colab
  • Kaggle
whisper-tiny-FP8-Dynamic
124 MB
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  • 2 contributors
History: 5 commits
nm-research's picture
nm-research
Update README.md
cf233b6 verified about 1 year ago
  • .gitattributes
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  • README.md
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  • added_tokens.json
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  • config.json
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  • generation_config.json
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  • merges.txt
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  • model.safetensors
    122 MB
    xet
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  • normalizer.json
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  • preprocessor_config.json
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  • recipe.yaml
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  • special_tokens_map.json
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  • tokenizer_config.json
    283 kB
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  • vocab.json
    1.04 MB
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