Video dubbing is the activity of revoicing a video while offering a viewing experience equivalent to the original video. The revoicing usually comes with a changed script, mostly in a different language, and the revoicing should reproduce the original emotions, coherent with the body language, and lip synchronized. In this project, we aim to build an AD system in three phases: (1) voice-over; (2) emotional voice-over; (3) full dubbing, while enhancing the system with human-in-the-loop capabilities for a higher quality.

Training and testing many possible parameters or model architectures of state-of-the-art machine translation or automatic speech recognition system is a cumbersome task. They usually require a long pipeline of commands reaching from pre-processing the training data to post-processing and evaluating the output.