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aadel4
/
Wav2vec_Classroom_FT

Automatic Speech Recognition
Transformers
Safetensors
wav2vec2
Model card Files Files and versions
xet
Community

Instructions to use aadel4/Wav2vec_Classroom_FT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use aadel4/Wav2vec_Classroom_FT with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("automatic-speech-recognition", model="aadel4/Wav2vec_Classroom_FT")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForCTC
    
    processor = AutoProcessor.from_pretrained("aadel4/Wav2vec_Classroom_FT")
    model = AutoModelForCTC.from_pretrained("aadel4/Wav2vec_Classroom_FT")
  • Notebooks
  • Google Colab
  • Kaggle
Wav2vec_Classroom_FT
1.26 GB
Ctrl+K
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  • 1 contributor
History: 9 commits
aadel4's picture
aadel4
Update README.md
44055ba verified about 1 year ago
  • .gitattributes
    1.52 kB
    initial commit about 1 year ago
  • README.md
    2.77 kB
    Update README.md about 1 year ago
  • config.json
    2.07 kB
    Upload folder using huggingface_hub about 1 year ago
  • model.safetensors
    1.26 GB
    xet
    Upload folder using huggingface_hub about 1 year ago
  • preprocessor_config.json
    254 Bytes
    Upload folder using huggingface_hub about 1 year ago
  • special_tokens_map.json
    96 Bytes
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  • tokenizer.json
    71 Bytes
    Create tokenizer.json about 1 year ago
  • tokenizer_config.json
    1.09 kB
    Upload folder using huggingface_hub about 1 year ago
  • vocab.json
    524 Bytes
    Upload folder using huggingface_hub about 1 year ago