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lsb
/
wav2vec2-large-pem123-960h-la

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

Instructions to use lsb/wav2vec2-large-pem123-960h-la with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use lsb/wav2vec2-large-pem123-960h-la with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("automatic-speech-recognition", model="lsb/wav2vec2-large-pem123-960h-la")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForCTC
    
    processor = AutoProcessor.from_pretrained("lsb/wav2vec2-large-pem123-960h-la")
    model = AutoModelForCTC.from_pretrained("lsb/wav2vec2-large-pem123-960h-la", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
wav2vec2-large-pem123-960h-la
3.81 GB
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  • 1 contributor
History: 5 commits
lsb's picture
lsb
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a856e0c over 4 years ago
  • checkpoint-128583
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  • checkpoint-171444
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  • runs
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  • .gitattributes
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  • config.json
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  • preprocessor_config.json
    215 Bytes
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  • pytorch_model.bin

    Detected Pickle imports (3)

    • "torch.FloatStorage",
    • "collections.OrderedDict",
    • "torch._utils._rebuild_tensor_v2"

    What is a pickle import?

    1.26 GB
    xet
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  • special_tokens_map.json
    85 Bytes
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  • tokenizer_config.json
    217 Bytes
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  • vocab.json
    291 Bytes
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