LAION-BVD: A 10-Million-Hour Open Video Dataset for Multimodal Pre-training

We present LAION-BVD (LAION - Big Video Dataset), a large-scale open video dataset for multimodal learning, containing _1.3B platform-specific video URLs_ collected from CommonCrawl. From these, we download 80M videos with a total duration of _10 million hours_. The dataset is designed for multimodal pre-training across video, audio, and image modalities. Using content-aware scene detection we extract clips for which we synthetically generate video and audio captions. Models trained on these data achieve competitive performance on standard video-text and audio-text benchmarks, with consistent improvements as training or model scale increases. Additionally, we explore video frames as a new source of image-text data by extracting scene-changing frames. These frames exhibit a visual distribution distinct from standard web image corpora, and models trained on this dataset achieve strong image-text retrieval performance. Overall, LAION-BVD significantly expands open access to multimodal videos at unprecedented scale and will be released to the research community for multimodal research