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rossevine
/
Model_S_P_Wav2Vec2

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
wav2vec2
Generated from Trainer
Model card Files Files and versions
xet
Metrics Training metrics Community
1

Instructions to use rossevine/Model_S_P_Wav2Vec2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use rossevine/Model_S_P_Wav2Vec2 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("automatic-speech-recognition", model="rossevine/Model_S_P_Wav2Vec2")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForCTC
    
    processor = AutoProcessor.from_pretrained("rossevine/Model_S_P_Wav2Vec2")
    model = AutoModelForCTC.from_pretrained("rossevine/Model_S_P_Wav2Vec2")
  • Notebooks
  • Google Colab
  • Kaggle
Model_S_P_Wav2Vec2 / runs
56 kB
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  • 1 contributor
History: 8 commits
rossevine's picture
rossevine
End of training
dca3c6f almost 3 years ago
  • Aug11_21-35-20_hpc-Aquarium2
    add tokenizer almost 3 years ago
  • Aug14_13-49-02_hpc-Aquarium2
    add tokenizer almost 3 years ago
  • Aug14_22-10-37_hpc-Aquarium2
    add tokenizer almost 3 years ago
  • Aug14_22-18-33_hpc-Aquarium2
    Training in progress, step 400 almost 3 years ago
  • Aug14_22-25-09_hpc-Aquarium2
    add tokenizer almost 3 years ago
  • Aug14_22-42-42_hpc-Aquarium2
    End of training almost 3 years ago