SpeedySpeech: Efficient Neural Speech Synthesis

Jan Vainer; Ondřej Dušek

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arXiv · 2008.03802

SpeedySpeech: Efficient Neural Speech Synthesis

Jan Vainer·Ondřej Dušek

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Abstract

While recent neural sequence-to-sequence models have greatly improved the quality of speech synthesis, there has not been a system capable of fast training, fast inference and high-quality audio synthesis at the same time. We propose a student-teacher network capable of high-quality faster-than-real-time spectrogram synthesis, with low requirements on computational resources and fast training time. We show that self-attention layers are not necessary for generation of high quality audio. We utilize simple convolutional blocks with residual connections in both student and teacher networks and use only a single attention layer in the teacher model. Coupled with a MelGAN vocoder, our model's voice quality was rated significantly higher than Tacotron 2. Our model can be efficiently trained on a single GPU and can run in real time even on a CPU. We provide both our source code and audio samples in our GitHub repository.

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BibTeX RIS

Jan Vainer, Ondřej Dušek. 2020-08-09. SpeedySpeech: Efficient Neural Speech Synthesis. https://arxiv.org/abs/2008.03802

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