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slplab
/
asd_pron_w2v_fairseq_balanced_500

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

Instructions to use slplab/asd_pron_w2v_fairseq_balanced_500 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use slplab/asd_pron_w2v_fairseq_balanced_500 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("automatic-speech-recognition", model="slplab/asd_pron_w2v_fairseq_balanced_500")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForCTC
    
    processor = AutoProcessor.from_pretrained("slplab/asd_pron_w2v_fairseq_balanced_500")
    model = AutoModelForCTC.from_pretrained("slplab/asd_pron_w2v_fairseq_balanced_500", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
asd_pron_w2v_fairseq_balanced_500
1.26 GB
Ctrl+K
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  • 2 contributors
History: 2 commits
lsnoo's picture
lsnoo
During training ... Accuracy=85%
2712024 almost 3 years ago
  • logs
    During training ... Accuracy=85% almost 3 years ago
  • .gitattributes
    1.52 kB
    initial commit almost 3 years ago
  • README.md
    21 Bytes
    initial commit almost 3 years ago
  • config.json
    1.8 kB
    During training ... Accuracy=85% almost 3 years ago
  • preprocessor_config.json
    212 Bytes
    During training ... Accuracy=85% almost 3 years ago
  • pytorch_model.bin
    1.26 GB
    xet
    During training ... Accuracy=85% almost 3 years ago
  • special_tokens_map.json
    85 Bytes
    During training ... Accuracy=85% almost 3 years ago
  • tokenizer_config.json
    181 Bytes
    During training ... Accuracy=85% almost 3 years ago
  • vocab.json
    69 Bytes
    During training ... Accuracy=85% almost 3 years ago