FeatherWave: An efficient high-fidelity neural vocoder with multi-band linear prediction
Abstract
In this paper, we propose the FeatherWave, yet another variant of WaveRNN vocoder combining the multi-band signal processing and the linear predictive coding. The LPCNet, a recently proposed neural vocoder which utilized the linear predictive characteristic of speech signal in the WaveRNN architecture, can generate high quality speech with a speed faster than real-time on a single CPU core. However, LPCNet is still not efficient enough for online speech generation tasks. To address this issue, we adopt the multi-band linear predictive coding for WaveRNN vocoder. The multi-band method enables the model to generate several speech samples in parallel at one step. Therefore, it can significantly improve the efficiency of speech synthesis. The proposed model with 4 sub-bands needs less than 1.6 GFLOPS for speech generation. In our experiments, it can generate 24 kHz high-fidelity audio 9x faster than real-time on a single CPU, which is much faster than the LPCNet vocoder. Furthermore, our subjective listening test shows that the FeatherWave can generate speech with better quality than LPCNet.
Keywords
Cite
@article{arxiv.2005.05551,
title = {FeatherWave: An efficient high-fidelity neural vocoder with multi-band linear prediction},
author = {Qiao Tian and Zewang Zhang and Heng Lu and Ling-Hui Chen and Shan Liu},
journal= {arXiv preprint arXiv:2005.05551},
year = {2020}
}
Comments
Accepted by INTERSPEECH 2020