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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...
Model source
Source description
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.
Sources
1 sourceVerified Sep 18
Model artifacts
1 artifactSource excerpts
2 excerpts--- 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
