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mlr2000
/
vocoder-large-speaker-encoder

Feature Extraction
Transformers
Safetensors
vocbulwark_speaker_encoder
audio
speaker-recognition
speaker-embedding
speaker-verification
wav2vec2
vocbulwark
custom_code
Model card Files Files and versions
xet
Community

Instructions to use mlr2000/vocoder-large-speaker-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use mlr2000/vocoder-large-speaker-encoder with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="mlr2000/vocoder-large-speaker-encoder", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("mlr2000/vocoder-large-speaker-encoder", trust_remote_code=True, device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
vocoder-large-speaker-encoder
74.7 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
mlr2000's picture
mlr2000
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  • .gitattributes
    1.52 kB
    initial commit 15 days ago
  • README.md
    3.16 kB
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  • __init__.py
    205 Bytes
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  • config.json
    4.28 kB
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  • configuration_speaker.py
    661 Bytes
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  • example_roundtrip.ipynb
    5 kB
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  • ge2e_loss.py
    4.14 kB
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  • model.safetensors
    74.7 MB
    xet
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  • modeling_speaker.py
    3.35 kB
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  • speaker_embedding_architecture.py
    10.5 kB
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  • speaker_embedding_config.py
    1.25 kB
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