ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech
arXiv:1807.07281 [cs, eess], vol.
Computer Science - Computation and Language,Computer Science - Artificial Intelligence,Computer Science - Machine Learning,Computer Science - Sound,Electrical Engineering and Systems Science - Audio and Speech Processing
- Wei Ping
- Kainan Peng
- Jitong Chen
In this work, we propose a new solution for parallel wave generation by WaveNet. In contrast to parallel WaveNet (van den Oord et al., 2018), we distill a Gaussian inverse autoregressive flow from the autoregressive WaveNet by minimizing a regularized KL divergence between their highly-peaked output distributions. Our method computes the KL divergence in closed-form, which simplifies the training algorithm and provides very efficient distillation. In addition, we introduce the first text-to-wave neural architecture for speech synthesis, which is fully convolutional and enables fast end-to-end training from scratch. It significantly outperforms the previous pipeline that connects a text-to-spectrogram model to a separately trained WaveNet (Ping et al., 2018). We also successfully distill a parallel waveform synthesizer conditioned on the hidden representation in this end-to-end model.