Instructions to use Avdpro/MLX-Seed-VC-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Avdpro/MLX-Seed-VC-v2 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download Avdpro/MLX-Seed-VC-v2 --local-dir MLX-Seed-VC-v2
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
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Download README.md from Avdpro/MLX-Seed-VC-v2: direct link, hf CLI and curl.
- Browser
- Download file 886 Bytes
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https://huggingface.co/Avdpro/MLX-Seed-VC-v2/resolve/main/README.md
- Command line
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hf download hf://Avdpro/MLX-Seed-VC-v2/README.md
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curl -L -o README.md https://huggingface.co/Avdpro/MLX-Seed-VC-v2/resolve/main/README.md
886 Bytes
| license: gpl-3.0 | |
| pipeline_tag: audio-to-audio | |
| tags: | |
| - mlx | |
| - voice-conversion | |
| - seed-vc | |
| # MLX Seed-VC v2 | |
| Torch-free native MLX conversion of the pinned Seed-VC v2 inference pipeline | |
| for AI2Apps. The composite checkpoint contains the CFM and AR models, both | |
| ASTRAL quantizers, CAMPPlus, HuBERT Large layer 18, and BigVGAN v2. Runtime | |
| weights are stored as safetensors and configuration as JSON. | |
| The default quality profile uses timbre mode with 30 diffusion steps. A 10-step | |
| fast profile and a 30-step AR voice profile are also exposed by the Model | |
| Package. A request must contain both source audio and reference-speaker audio; | |
| reference audio is request-scoped and is not persisted as a Voice Profile. | |
| The implementation is based on `Plachtaa/seed-vc` revision | |
| `51383efd921027683c89e5348211d93ff12ac2a8`. See `NOTICE.md` for all pinned | |
| checkpoint sources and licenses. | |