Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Hardware
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

microsoft
/
wavlm-base

Feature Extraction
Transformers
PyTorch
English
wavlm
speech
Model card Files Files and versions
xet
Community
1

Instructions to use microsoft/wavlm-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use microsoft/wavlm-base with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="microsoft/wavlm-base")
    # Load model directly
    from transformers import AutoProcessor, AutoModel
    
    processor = AutoProcessor.from_pretrained("microsoft/wavlm-base")
    model = AutoModel.from_pretrained("microsoft/wavlm-base", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
wavlm-base
378 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 7 commits
patrickvonplaten's picture
patrickvonplaten
Update config.json
d523a0b over 4 years ago
  • .gitattributes
    1.18 kB
    initial commit over 4 years ago
  • README.md
    3.76 kB
    Update README.md over 4 years ago
  • config.json
    2.2 kB
    Update config.json over 4 years ago
  • preprocessor_config.json
    215 Bytes
    upload model over 4 years ago
  • pytorch_model.bin
    378 MB
    xet
    add base model over 4 years ago