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FAcodec trained on 50k hours speech data, with more timbre diversity and better at reconstructing speakers from podcasts, videos, games or animations. This is a separate decoder designed and trained based on the...
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FAcodec trained on 50k hours speech data, with more timbre diversity and better at reconstructing speakers from podcasts, videos, games or animations.
This is a separate decoder designed and trained based on the pretrained encoder specifically for voice conversion task.
It is capable of zero-shot voice conversion, stream voice conversion and has outstanding timbre generalization ability.
See for example usages.
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1 sourceVérifié 18 sept.
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1 artefactExtraits de sources
2 extraits--- license: mit pipeline_tag: audio-to-audio --- [FAcodec](https://arxiv.org/pdf/2403.03100) trained on 50k hours speech data, with more timbre diversity and better at reconstructing speakers from podcasts, videos, games or animations. This is a separate decoder designed and trained based on the pretrained [encoder](https://huggingface.co/Plachta/FAcodec) specifically for voice conversion task. It is capable of zero-shot voice conversion, stream voice conversion and has outstanding timbre generalization ability. See [main repository](https://github.com/Plachtaa/FAcodec) for example usages.
Source context: 0 downloads · 10 likes · Pipeline audio-to-audio · Repo Plachta/FAcodec-redecoder
