  Virtual humans have gained considerable attention in numerous industries,
e.g., entertainment and e-commerce. As a core technology, synthesizing
photorealistic face frames from target speech and facial identity has been
actively studied with generative adversarial networks. Despite remarkable
results of modern talking-face generation models, they often entail high
computational burdens, which limit their efficient deployment. This study aims
to develop a lightweight model for speech-driven talking-face synthesis. We
build a compact generator by removing the residual blocks and reducing the
channel width from Wav2Lip, a popular talking-face generator. We also present a
knowledge distillation scheme to stably yet effectively train the
small-capacity generator without adversarial learning. We reduce the number of
parameters and MACs by 28$\times$ while retaining the performance of the
original model. Moreover, to alleviate a severe performance drop when
converting the whole generator to INT8 precision, we adopt a selective
quantization method that uses FP16 for the quantization-sensitive layers and
INT8 for the other layers. Using this mixed precision, we achieve up to a
19$\times$ speedup on edge GPUs without noticeably compromising the generation
quality.
