âParallel WaveGAN: A Fast Waveform Generation Model Based on Generative Adversarial Networks With Multi-Resolution Spectrogramâ, 2019-10-25 (; similar)â :
We propose Parallel WaveGAN, a distillation-free, fast, and small-footprint waveform generation method using a generative adversarial network.
In the proposed method, a non-autoregressive WaveNet is trained by jointly optimizing multi-resolution spectrogram and adversarial loss functions, which can effectively capture the time-frequency distribution of the realistic speech waveform. As our method does not require density distillation [knowledge distillation] used in the conventional teacher-student framework, the entire model can be easily trained. Furthermore, our model is able to generate high-fidelity speech even with its compact architecture.
In particular, the proposed Parallel WaveGAN has only 1.44 million parameters and can generate 24 kHz speech waveform 28.68Ă faster than real-time on a single GPU environment.
Perceptual listening test results verify that our proposed method achieves 4.16 mean opinion score within a Transformer-based text-to-speech framework, which is comparative to the best distillation-based Parallel WaveNet system.