  We present a lightweight adaptable neural TTS system with high quality
output. The system is composed of three separate neural network blocks: prosody
prediction, acoustic feature prediction and Linear Prediction Coding Net as a
neural vocoder. This system can synthesize speech with close to natural quality
while running 3 times faster than real-time on a standard CPU. The modular
setup of the system allows for simple adaptation to new voices with a small
amount of data. We first demonstrate the ability of the system to produce high
quality speech when trained on large, high quality datasets. Following that, we
demonstrate its adaptability by mimicking unseen voices using 5 to 20 minutes
long datasets with lower recording quality. Large scale Mean Opinion Score
quality and similarity tests are presented, showing that the system can adapt
to unseen voices with quality gap of 0.12 and similarity gap of 3% compared to
natural speech for male voices and quality gap of 0.35 and similarity of gap of
9 % for female voices.
