Instructions to use alphacep/vosk-vc-ru with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alphacep/vosk-vc-ru with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("alphacep/vosk-vc-ru", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
ce8c740
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Parent(s): 64d2f17
Update checkpoint
Browse files- D_1642000.pth → D_191000.pth +2 -2
- G_1642000.pth → G_191000.pth +2 -2
- README.md +5 -3
D_1642000.pth → D_191000.pth
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G_1642000.pth → G_191000.pth
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README.md
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@@ -7,7 +7,7 @@ pipeline_tag: audio-to-audio
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# About
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This is a basic zero-shot voice conversion model trained with VITS +
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See:
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<https://github.com/quickvc/QuickVC-VoiceConversion>
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# Speaker Similarity
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Computed with eval.py with Resemblyzer
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```
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Original QuickVC (trained on VCTK) Average: 0.667 Min: 0.477
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New model Average: 0.
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```
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# About
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This is a basic zero-shot voice conversion model trained with VITS + contentvec
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See:
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<https://github.com/quickvc/QuickVC-VoiceConversion>
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<https://github.com/auspicious3000/contentvec>
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# Speaker Similarity
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Computed with eval.py with Resemblyzer
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```
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Original QuickVC (trained on VCTK) Average: 0.667 Min: 0.477
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New model Average: 0.880 Min: 0.712
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```
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