FreGrad: Lightweight and Fast Frequency-aware Diffusion Vocoder
CoRR(2024)
摘要
The goal of this paper is to generate realistic audio with a lightweight and
fast diffusion-based vocoder, named FreGrad. Our framework consists of the
following three key components: (1) We employ discrete wavelet transform that
decomposes a complicated waveform into sub-band wavelets, which helps FreGrad
to operate on a simple and concise feature space, (2) We design a
frequency-aware dilated convolution that elevates frequency awareness,
resulting in generating speech with accurate frequency information, and (3) We
introduce a bag of tricks that boosts the generation quality of the proposed
model. In our experiments, FreGrad achieves 3.7 times faster training time and
2.2 times faster inference speed compared to our baseline while reducing the
model size by 0.6 times (only 1.78M parameters) without sacrificing the output
quality. Audio samples are available at:
https://mm.kaist.ac.kr/projects/FreGrad.
更多查看译文
关键词
speech synthesis,vocoder,lightweight model,diffusion,fast diffusion
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要