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voidful
/
tts_hubert_m2m100

Feature Extraction
Transformers
PyTorch
m2m_100
Model card Files Files and versions
xet
Community
1

Instructions to use voidful/tts_hubert_m2m100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use voidful/tts_hubert_m2m100 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="voidful/tts_hubert_m2m100")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("voidful/tts_hubert_m2m100")
    model = AutoModel.from_pretrained("voidful/tts_hubert_m2m100")
  • Notebooks
  • Google Colab
  • Kaggle
tts_hubert_m2m100
1.95 GB
Ctrl+K
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  • 1 contributor
History: 2 commits
voidful's picture
voidful
init commit
8976b4e over 4 years ago
  • .gitattributes
    1.18 kB
    initial commit over 4 years ago
  • added_tokens.json
    4.11 kB
    init commit over 4 years ago
  • config.json
    912 Bytes
    init commit over 4 years ago
  • pytorch_model.bin
    1.95 GB
    xet
    init commit over 4 years ago
  • sentencepiece.bpe.model
    2.42 MB
    xet
    init commit over 4 years ago
  • special_tokens_map.json
    1.14 kB
    init commit over 4 years ago
  • tokenizer_config.json
    1.4 kB
    init commit over 4 years ago
  • vocab.json
    3.71 MB
    init commit over 4 years ago