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dolphinnlp
/
wav2vec2vncskh

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

Instructions to use dolphinnlp/wav2vec2vncskh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use dolphinnlp/wav2vec2vncskh with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("automatic-speech-recognition", model="dolphinnlp/wav2vec2vncskh")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForCTC
    
    processor = AutoProcessor.from_pretrained("dolphinnlp/wav2vec2vncskh")
    model = AutoModelForCTC.from_pretrained("dolphinnlp/wav2vec2vncskh")
  • Notebooks
  • Google Colab
  • Kaggle
wav2vec2vncskh
378 MB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 18 commits
infi
End of training
1b71ca2 over 2 years ago
  • runs
    End of training over 2 years ago
  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • README.md
    1.54 kB
    End of training over 2 years ago
  • added_tokens.json
    64 Bytes
    Upload tokenizer over 2 years ago
  • config.json
    2.08 kB
    End of training over 2 years ago
  • model.safetensors
    378 MB
    xet
    End of training over 2 years ago
  • preprocessor_config.json
    257 Bytes
    Upload processor over 2 years ago
  • special_tokens_map.json
    96 Bytes
    Upload tokenizer over 2 years ago
  • tokenizer_config.json
    1.1 kB
    Upload processor over 2 years ago
  • training_args.bin
    4.66 kB
    xet
    End of training over 2 years ago
  • vocab.json
    1.29 kB
    Upload tokenizer over 2 years ago