GAN vocoders are currently one of the state-of-the-art methods for building high-quality neural waveform generative models. However, most of their architectures require dozens of billion floating-point operations per second (GFLOPS) to generate speech waveforms in samplewise manner. This makes GAN vocoders still challenging to run on normal CPUs without accelerators or parallel computers. In this work, we propose a new architecture for GAN vocoders that mainly depends on recurrent and fully-connected networks to directly generate the time domain signal in framewise manner. This results in considerable reduction of the computational cost and enables very fast generation on both GPUs and low-complexity CPUs. Experimental results show that our Framewise WaveGAN vocoder achieves significantly higher quality than auto-regressive maximum-likelihood vocoders such as LPCNet at a very low complexity of 1.2 GFLOPS. This makes GAN vocoders more practical on edge and low-power devices.

翻译：GAN声码器是当前构建高质量神经波形生成模型的最先进方法之一。然而，大多数架构需要每秒数十亿次浮点运算（GFLOPS）才能以逐样本方式生成语音波形，这使得GAN声码器在没有加速器或并行计算机的普通CPU上运行仍具挑战性。本文提出一种新型GAN声码器架构，主要依赖循环网络和全连接网络以逐帧方式直接生成时域信号。这一设计显著降低了计算成本，并在GPU和低复杂度CPU上均可实现极高速生成。实验结果表明，我们的Framewise WaveGAN声码器在1.2 GFLOPS的超低复杂度下，其质量显著优于LPCNet等自回归最大似然声码器。这使得GAN声码器在边缘设备和低功耗设备上的应用更具实用性。