Feature Extraction
MLX
resnet34_embedding
speaker-recognition
speaker-embedding
speaker-diarization
audio
resnet
apple-silicon
Instructions to use mlx-community/wespeaker-voxceleb-resnet34-LM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/wespeaker-voxceleb-resnet34-LM with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir wespeaker-voxceleb-resnet34-LM mlx-community/wespeaker-voxceleb-resnet34-LM
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
Upload README.md with huggingface_hub
Browse files
README.md
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# WeSpeaker ResNet34 Speaker Embedding Model (MLX)
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This is an MLX port of the [pyannote/wespeaker-voxceleb-resnet34-LM](https://huggingface.co/pyannote/wespeaker-voxceleb-resnet34-LM) speaker embedding model from the WeSpeaker toolkit.
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---
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library_name: mlx
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tags:
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- speaker-recognition
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- speaker-embedding
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- speaker-diarization
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- audio
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- resnet
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- mlx
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- apple-silicon
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base_model: pyannote/wespeaker-voxceleb-resnet34-LM
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license: mit
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pipeline_tag: feature-extraction
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---
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# WeSpeaker ResNet34 Speaker Embedding Model (MLX)
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This is an MLX port of the [pyannote/wespeaker-voxceleb-resnet34-LM](https://huggingface.co/pyannote/wespeaker-voxceleb-resnet34-LM) speaker embedding model from the WeSpeaker toolkit.
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