WOLONet: Wave Outlooker for Efficient and High Fidelity Speech Synthesis
This work addresses the need for more efficient and high-fidelity speech synthesis systems, representing an incremental improvement over existing GAN-based vocoders.
The paper tackles the problem of improving speech synthesis quality and efficiency by proposing WOLONet, a lightweight neural vocoder that achieves the best generation quality with fewer parameters compared to state-of-the-art models like HiFiGAN and UnivNet.
Recently, GAN-based neural vocoders such as Parallel WaveGAN, MelGAN, HiFiGAN, and UnivNet have become popular due to their lightweight and parallel structure, resulting in a real-time synthesized waveform with high fidelity, even on a CPU. HiFiGAN and UnivNet are two SOTA vocoders. Despite their high quality, there is still room for improvement. In this paper, motivated by the structure of Vision Outlooker from computer vision, we adopt a similar idea and propose an effective and lightweight neural vocoder called WOLONet. In this network, we develop a novel lightweight block that uses a location-variable, channel-independent, and depthwise dynamic convolutional kernel with sinusoidally activated dynamic kernel weights. To demonstrate the effectiveness and generalizability of our method, we perform an ablation study to verify our novel design and make a subjective and objective comparison with typical GAN-based vocoders. The results show that our WOLONet achieves the best generation quality while requiring fewer parameters than the two neural SOTA vocoders, HiFiGAN and UnivNet.