  Recent advances in text-to-speech (TTS) synthesis, such as Tacotron and
WaveRNN, have made it possible to construct a fully neural network based TTS
system, by coupling the two components together. Such a system is conceptually
simple as it only takes grapheme or phoneme input, uses Mel-spectrogram as an
intermediate feature, and directly generates speech samples. The system
achieves quality equal or close to natural speech. However, the high
computational cost of the system and issues with robustness have limited their
usage in real-world speech synthesis applications and products. In this paper,
we present key modeling improvements and optimization strategies that enable
deploying these models, not only on GPU servers, but also on mobile devices.
The proposed system can generate high-quality 24 kHz speech at 5x faster than
real time on server and 3x faster than real time on mobile devices.
