ANIM-400K: A Large-Scale Dataset for Automated End-To-End Dubbing of
  Video
Kevin Cai Chonghua Liu David M. Chan
The Internet's wealth of content, with up to 60% published in English,
starkly contrasts the global population, where only 18.8% are English speakers,
and just 5.1% consider it their native language, leading to disparities in
online information access. Unfortunately, automated processes for dubbing of
video - replacing the audio track of a video with a translated alternative -
remains a complex and challenging task due to pipelines, necessitating precise
timing, facial movement synchronization, and prosody matching. While end-to-end
dubbing offers a solution, data scarcity continues to impede the progress of
both end-to-end and pipeline-based methods. In this work, we introduce
Anim-400K, a comprehensive dataset of over 425K aligned animated video segments
in Japanese and English supporting various video-related tasks, including
automated dubbing, simultaneous translation, guided video summarization, and
genre/theme/style classification. Our dataset is made publicly available for
research purposes at https://github.com/davidmchan/Anim400K.