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cromz22
/
wav2vec2-2-bart-base

Automatic Speech Recognition
Transformers
PyTorch
speech-encoder-decoder
Model card Files Files and versions
xet
Community
1

Instructions to use cromz22/wav2vec2-2-bart-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use cromz22/wav2vec2-2-bart-base with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("automatic-speech-recognition", model="cromz22/wav2vec2-2-bart-base")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSpeechSeq2Seq
    
    tokenizer = AutoTokenizer.from_pretrained("cromz22/wav2vec2-2-bart-base")
    model = AutoModelForSpeechSeq2Seq.from_pretrained("cromz22/wav2vec2-2-bart-base")
  • Notebooks
  • Google Colab
  • Kaggle
wav2vec2-2-bart-base
808 MB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 2 commits
cromz22
upload model files
3665b39 almost 4 years ago
  • .gitattributes
    1.17 kB
    initial commit almost 4 years ago
  • config.json
    7.21 kB
    upload model files almost 4 years ago
  • load_and_save.py
    1.34 kB
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  • merges.txt
    456 kB
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  • preprocessor_config.json
    257 Bytes
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  • pytorch_model.bin

    Detected Pickle imports (3)

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

    What is a pickle import?

    805 MB
    xet
    upload model files almost 4 years ago
  • special_tokens_map.json
    239 Bytes
    upload model files almost 4 years ago
  • tokenizer.json
    2.11 MB
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  • tokenizer_config.json
    393 Bytes
    upload model files almost 4 years ago
  • vocab.json
    798 kB
    upload model files almost 4 years ago