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gmenon
/
slt-wav2vec2-bart-base

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

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

  • Libraries
  • Transformers

    How to use gmenon/slt-wav2vec2-bart-base with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("automatic-speech-recognition", model="gmenon/slt-wav2vec2-bart-base")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSpeechSeq2Seq
    
    tokenizer = AutoTokenizer.from_pretrained("gmenon/slt-wav2vec2-bart-base")
    model = AutoModelForSpeechSeq2Seq.from_pretrained("gmenon/slt-wav2vec2-bart-base")
  • Notebooks
  • Google Colab
  • Kaggle
slt-wav2vec2-bart-base
1.73 GB
Ctrl+K
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  • 1 contributor
History: 2 commits
gmenon's picture
gmenon
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c081204 over 2 years ago
  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • config.json
    7.18 kB
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  • generation_config.json
    313 Bytes
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  • merges.txt
    456 kB
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  • preprocessor_config.json
    256 Bytes
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  • pytorch_model.bin

    Detected Pickle imports (3)

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

    What is a pickle import?

    1.73 GB
    xet
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  • special_tokens_map.json
    280 Bytes
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  • tokenizer.json
    2.11 MB
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  • tokenizer_config.json
    391 Bytes
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  • vocab.json
    798 kB
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