Datasets:

AIPeanutman
/

VidDub-Training-Clips

pretty_name: VidDub Training Clips
license: cc-by-nc-4.0
task_categories:
  - video-to-video
language:
  - en
  - zh
size_categories:
  - 10K<n<100K
tags:
  - video-dubbing
  - talking-head
  - audio-driven
  - synthetic

VidDub Training Clips

The processed, model-ready version of the frozen paper-selected VidDub training set. It contains 85,435 synthetic talking-head windows derived from VidDub-Training-Data.

Each sample contains:

video.mp4: 81 RGB frames at 512×512 and 24 FPS;
audio.wav: aligned 16 kHz mono speech;
mask.png: editable lower-face region;
ref.png: appearance reference frame.

The package is approximately 100.1 GB and is split into 86 uncompressed ZIP shards of at most 1,000 clips each. Model-specific latent caches are not included.

Download and extract

huggingface-cli download AIPeanutman/VidDub-Training-Clips \
  --repo-type dataset \
  --local-dir data/VidDub-Training-Clips

cd data/VidDub-Training-Clips
sha256sum -c manifest/checksums.sha256
for archive in zips/*.zip; do unzip -q "$archive"; done

Extracting every shard creates:

clips/<shard>/<clip_id>/
  video.mp4
  audio.wav
  mask.png
  ref.png

manifest/items.jsonl is authoritative for membership and preprocessing metadata. Every ZIP also contains the rows for its own clips at manifest/shard-items-<index>.jsonl.

Relationship to the source dataset

The source dataset stores 45,221 original-resolution generated videos and the exact clip-level selection. This package materializes only those 85,435 selected windows. It does not add identities or samples and remains disjoint from VidDub-Bench.

License and responsible use

VidDub Training Clips is released under CC BY-NC 4.0. Use it for lawful non-commercial research, education, and evaluation of consented video dubbing. Do not use it for impersonation, fraud, harassment, non-consensual sexual content, biometric surveillance, or deceptive political/media manipulation.

All identities are synthetic. Generated media may contain artifacts, stereotypes, demographic imbalance, or inaccurate lip motion. Generation prompts are not verified attributes of real people.

Citation

@article{liu2026viddub,
  title   = {VidDub: Replacement-Aware Distillation for Two-Step Video Dubbing},
  author  = {Liu, Yexin and Lin, Yunlong and Wang, Zehan and He, Guangxin and
             Xu, Xinli and Zha, Jiajun and Liu, Yaofu and Wang, Yueze and
             Ping, Bowen and Li, Yuanming and Wang, Amie and Ghanem, Bernard and
             Li, Bing and Yang, Harry},
  journal = {arXiv preprint arXiv:2611.03404},
  year    = {2026}
}

VidDub Training Clips

Contents

Download and extract

Relationship to the source dataset

License and responsible use

Citation