  A demonstration of a real-time and continuous turn-taking prediction system
is presented. The system is based on a voice activity projection (VAP) model,
which directly maps dialogue stereo audio to future voice activities. The VAP
model includes contrastive predictive coding (CPC) and self-attention
transformers, followed by a cross-attention transformer. We examine the effect
of the input context audio length and demonstrate that the proposed system can
operate in real-time with CPU settings, with minimal performance degradation.
