Fast and Lightweight On-Device TTS with Tacotron2 and LPCNet

By Vadim Popov, Stanislav Kamenev, Mikhail Kudinov, Sergey Repyevsky, Tasnima Sadekova, Vitalii Bushaev, Vladimir Mikhailovich Kryzhanovskiy, Denis Parkhomenko. 2020

9 citations recorded by the source corpus.

We present a fast and lightweight on-device text-to-speech system based on state-of-art methods of feature and speech generation i.e. Tacotron2 and LPCNet. We show that modifica-tion of the basic pipeline combined with hardware-specific optimizations and extensive usage of parallelization enables running TTS service even on low-end devices with faster than real-time waveform generation. Moreover, the system preserves high quality of speech without noticeable degradation of Mean Opinion Score compared to the non-optimized baseline. While the system is mostly oriented on low-to-mid range hardware we believe that it can also be used in any CPU-based environment.

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Xu Tan
2. Tao Qin
3. Tie-Yan Liu
4. Frank K. Soong
5. Yi Ren
6. Olivier Chapelle
7. Yanqing Liu
8. Rongjie Huang

Citations in the corpus

• A Survey on Neural Speech Synthesis (2021, methodology): “[268] propose frame splitting and cross-fading to synthesize some parts of the waveform in parallel and then concatenate the synthesized waveforms together to ensure fast synthesis on low-end devices.”