Parallel Neural Text-to-Speech
Kainan Peng, Wei Ping, Zhao Song, Kexin Zhao
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In this work, we first propose ParaNet, a non-autoregressive seq2seq model that converts text to spectrogram. It is fully convolutional and obtains 46.7 times speed-up over Deep Voice 3 at synthesis while maintaining comparable speech quality using a WaveNet vocoder. ParaNet also produces stable alignment between text and speech on the challenging test sentences by iteratively improving the attention in a layer-by-layer manner. Based on ParaNet, we build the first fully parallel neural text-to-speech system using parallel neural vocoders, which can synthesize speech from text through a single feed-forward pass. We investigate several parallel vocoders within the TTS system, including variants of IAF vocoders and bipartite flow vocoder.