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bgstud
/
whisper-tiny-libirClean-vs-commonNative

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
Model card Files Files and versions
xet
Metrics Training metrics Community
1

Instructions to use bgstud/whisper-tiny-libirClean-vs-commonNative with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use bgstud/whisper-tiny-libirClean-vs-commonNative with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("automatic-speech-recognition", model="bgstud/whisper-tiny-libirClean-vs-commonNative")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
    
    processor = AutoProcessor.from_pretrained("bgstud/whisper-tiny-libirClean-vs-commonNative")
    model = AutoModelForSpeechSeq2Seq.from_pretrained("bgstud/whisper-tiny-libirClean-vs-commonNative")
  • Notebooks
  • Google Colab
  • Kaggle
whisper-tiny-libirClean-vs-commonNative
151 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 6 commits
bgstud's picture
bgstud
Training in progress, step 750
2763087 over 3 years ago
  • runs
    Training in progress, step 750 over 3 years ago
  • .gitattributes
    1.48 kB
    initial commit over 3 years ago
  • .gitignore
    13 Bytes
    Training in progress, step 250 over 3 years ago
  • config.json
    1.02 kB
    Training in progress, step 250 over 3 years ago
  • preprocessor_config.json
    185 kB
    Training in progress, step 250 over 3 years ago
  • pytorch_model.bin
    151 MB
    xet
    Training in progress, step 750 over 3 years ago
  • training_args.bin
    3.57 kB
    xet
    Training in progress, step 250 over 3 years ago