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realtime-speech
/
s2tlarge

Text-to-Speech
Transformers
PyTorch
seamless_m4t
feature-extraction
SeamlessM4T
Model card Files Files and versions
xet
Community
1

Instructions to use realtime-speech/s2tlarge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use realtime-speech/s2tlarge with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-to-speech", model="realtime-speech/s2tlarge")
    # Load model directly
    from transformers import AutoProcessor, AutoModel
    
    processor = AutoProcessor.from_pretrained("realtime-speech/s2tlarge")
    model = AutoModel.from_pretrained("realtime-speech/s2tlarge")
  • Notebooks
  • Google Colab
  • Kaggle
s2tlarge
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  • 1 contributor
History: 2 commits
realtime-speech's picture
realtime-speech
Uploading model files
776b9af verified about 1 year ago
  • .gitattributes
    1.52 kB
    initial commit about 1 year ago
  • README.md
    6.49 kB
    Uploading model files about 1 year ago
  • added_tokens.json
    2.12 kB
    Uploading model files about 1 year ago
  • config.json
    2.56 kB
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  • generation_config.json
    3.35 kB
    Uploading model files about 1 year ago
  • preprocessor_config.json
    1.78 kB
    Uploading model files about 1 year ago
  • pytorch_model.bin
    9.44 GB
    xet
    Uploading model files about 1 year ago
  • sentencepiece.bpe.model
    5.17 MB
    xet
    Uploading model files about 1 year ago
  • special_tokens_map.json
    1.7 kB
    Uploading model files about 1 year ago
  • tokenizer_config.json
    19.5 kB
    Uploading model files about 1 year ago