0

Low-latency Real-time Voice Conversion on CPU

A low-latency, low-resource voice conversion model using generative adversarial architecture and knowledge distillation achieves state-of-the-art performance in real-time audio manipulation.

Year
2023
Venue
arXiv 2023
Authors
3
Hosting
Abstract onlyARXIV-DEFAULT

Cite

Notes

Only stored in your browser.

Attribution

Abstract & full text
arxiv.org/abs/2311.00873ARXIV-DEFAULT
TL;DR
Semantic Scholar
Attribution policy →

Abstract

We adapt the architectures of previous audio manipulation and generation neural networks to the task of real-time any-to-one voice conversion. Our resulting model, LLVC ($\textbf{L}$ow-latency $\textbf{L}$ow-resource $\textbf{V}$oice $\textbf{C}$onversion), has a latency of under 20ms at a bitrate of 16kHz and runs nearly 2.8x faster than real-time on a consumer CPU. LLVC uses both a generative adversarial architecture as well as knowledge distillation in order to attain this performance. To our knowledge LLVC achieves both the lowest resource usage as well as the lowest latency of any open-source voice conversion model. We provide open-source samples, code, and pretrained model weights at https://github.com/KoeAI/LLVC.

Authors

3