--- license: cc-by-sa-4.0 dataset_info: features: - name: video_id dtype: string - name: chunk_idx dtype: int64 - name: chunk_text dtype: string - name: video_metadata dtype: string - name: video_language dtype: string - name: chunk_media dtype: string splits: - name: shard_10339 num_bytes: 1997009 num_examples: 631 - name: shard_10400 num_bytes: 2638827 num_examples: 722 - name: shard_10324 num_bytes: 1700655 num_examples: 515 - name: shard_10418 num_bytes: 3034319 num_examples: 947 - name: shard_1045 num_bytes: 2042334 num_examples: 648 - name: shard_10428 num_bytes: 2314345 num_examples: 706 - name: shard_10435 num_bytes: 2300183 num_examples: 677 - name: shard_10424 num_bytes: 1839226 num_examples: 552 - name: shard_10442 num_bytes: 1543285 num_examples: 419 - name: shard_10411 num_bytes: 2005599 num_examples: 604 - name: shard_10344 num_bytes: 1796239 num_examples: 589 - name: shard_10439 num_bytes: 1780546 num_examples: 567 - name: shard_10351 num_bytes: 2156111 num_examples: 677 - 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name: shard_10813 num_bytes: 2399853 num_examples: 736 - name: shard_10935 num_bytes: 3427617 num_examples: 848 - name: shard_10864 num_bytes: 2676369 num_examples: 731 - name: shard_11010 num_bytes: 1311128 num_examples: 452 - name: shard_10998 num_bytes: 1936638 num_examples: 516 - name: shard_11003 num_bytes: 3035444 num_examples: 850 - name: shard_10490 num_bytes: 2059200 num_examples: 636 - name: shard_10886 num_bytes: 1592390 num_examples: 462 - name: shard_10948 num_bytes: 2410338 num_examples: 766 - name: shard_11032 num_bytes: 2392294 num_examples: 661 - name: shard_1090 num_bytes: 1729394 num_examples: 518 - name: shard_10973 num_bytes: 1874095 num_examples: 543 - name: shard_10893 num_bytes: 2438998 num_examples: 752 - name: shard_10671 num_bytes: 2373850 num_examples: 581 - name: shard_10496 num_bytes: 2672925 num_examples: 772 - name: shard_10871 num_bytes: 2666803 num_examples: 744 - name: shard_10960 num_bytes: 2586483 num_examples: 789 - name: shard_10941 num_bytes: 2750219 num_examples: 937 - name: shard_11047 num_bytes: 1940080 num_examples: 545 - name: shard_11025 num_bytes: 1900405 num_examples: 569 - name: shard_10967 num_bytes: 4183264 num_examples: 996 - name: shard_10206 num_bytes: 3714862 num_examples: 891 - name: shard_11018 num_bytes: 3019471 num_examples: 871 - name: shard_10914 num_bytes: 1980877 num_examples: 552 - name: shard_10992 num_bytes: 1580255 num_examples: 484 - name: shard_10606 num_bytes: 2584262 num_examples: 640 - name: shard_10999 num_bytes: 1950388 num_examples: 551 - name: shard_10921 num_bytes: 2477762 num_examples: 743 - name: shard_10315 num_bytes: 2911312 num_examples: 743 - name: shard_10419 num_bytes: 2586408 num_examples: 667 - name: shard_11054 num_bytes: 2011864 num_examples: 594 - name: shard_10289 num_bytes: 3470407 num_examples: 963 - name: shard_10835 num_bytes: 3026775 num_examples: 806 - name: shard_10298 num_bytes: 2823620 num_examples: 791 - name: shard_10455 num_bytes: 3275368 num_examples: 750 - name: shard_10311 num_bytes: 4072154 num_examples: 1148 - name: shard_11090 num_bytes: 3270282 num_examples: 858 - name: shard_11035 num_bytes: 1447049 num_examples: 458 - name: shard_11022 num_bytes: 2185853 num_examples: 652 - name: shard_11111 num_bytes: 2068341 num_examples: 615 - name: shard_11119 num_bytes: 1970512 num_examples: 580 - name: shard_11126 num_bytes: 1703981 num_examples: 523 download_size: 314533805 dataset_size: 506056044 configs: - config_name: default data_files: - split: train path: data/*.parquet - split: shard_10339 path: data/shard_10339-* - split: shard_10400 path: data/shard_10400-* - split: shard_10424 path: data/shard_10424-* - split: shard_10324 path: data/shard_10324-* - split: shard_10428 path: data/shard_10428-* - split: shard_10258 path: data/shard_10258-* - split: shard_10396 path: data/shard_10396-* - split: shard_10411 path: data/shard_10411-* - split: shard_10418 path: data/shard_10418-* - split: shard_10206 path: data/shard_10206-* - split: shard_10442 path: data/shard_10442-* - split: shard_1045 path: data/shard_1045-* - split: shard_10289 path: data/shard_10289-* - split: shard_10298 path: data/shard_10298-* - split: shard_10344 path: data/shard_10344-* - split: shard_10435 path: data/shard_10435-* - split: shard_10311 path: data/shard_10311-* - split: shard_10405 path: data/shard_10405-* - split: shard_10464 path: data/shard_10464-* - split: shard_10457 path: data/shard_10457-* - split: shard_10439 path: data/shard_10439-* - split: shard_10351 path: data/shard_10351-* - split: shard_10446 path: data/shard_10446-* - split: shard_10315 path: data/shard_10315-* - split: shard_10471 path: data/shard_10471-* - split: shard_1035 path: data/shard_1035-* - split: shard_10456 path: data/shard_10456-* - split: shard_10486 path: data/shard_10486-* - split: shard_10430 path: data/shard_10430-* - split: shard_10469 path: data/shard_10469-* - split: shard_10360 path: data/shard_10360-* - split: shard_10443 path: data/shard_10443-* - split: shard_10453 path: data/shard_10453-* - split: shard_10462 path: data/shard_10462-* - split: shard_10481 path: data/shard_10481-* - split: shard_10482 path: data/shard_10482-* - split: shard_10365 path: data/shard_10365-* - split: shard_10475 path: data/shard_10475-* - split: shard_10444 path: data/shard_10444-* - split: shard_10493 path: data/shard_10493-* - split: shard_10433 path: data/shard_10433-* - split: shard_1037 path: data/shard_1037-* - split: shard_1049 path: data/shard_1049-* - split: shard_10507 path: data/shard_10507-* - split: shard_10521 path: data/shard_10521-* - split: shard_10479 path: data/shard_10479-* - split: shard_10543 path: data/shard_10543-* - split: shard_10494 path: data/shard_10494-* - split: shard_10565 path: data/shard_10565-* - split: shard_10558 path: data/shard_10558-* - split: shard_10506 path: data/shard_10506-* - split: shard_10497 path: data/shard_10497-* - split: shard_10503 path: data/shard_10503-* - split: shard_10488 path: data/shard_10488-* - split: shard_1050 path: data/shard_1050-* - split: shard_10379 path: data/shard_10379-* - split: shard_10366 path: data/shard_10366-* - split: shard_10512 path: data/shard_10512-* - split: shard_10529 path: data/shard_10529-* - split: shard_10477 path: data/shard_10477-* - split: shard_10510 path: data/shard_10510-* - split: shard_10518 path: data/shard_10518-* - split: shard_10514 path: data/shard_10514-* - split: shard_10383 path: data/shard_10383-* - split: shard_10550 path: data/shard_10550-* - split: shard_10525 path: data/shard_10525-* - split: shard_10536 path: data/shard_10536-* - split: shard_10531 path: data/shard_10531-* - split: shard_10538 path: data/shard_10538-* - split: shard_10532 path: data/shard_10532-* - split: shard_10382 path: data/shard_10382-* - split: shard_10509 path: data/shard_10509-* - split: shard_10572 path: data/shard_10572-* - split: shard_1058 path: data/shard_1058-* - split: shard_10455 path: data/shard_10455-* - split: shard_10594 path: data/shard_10594-* - split: shard_10587 path: data/shard_10587-* - split: shard_1054 path: data/shard_1054-* - split: shard_10608 path: data/shard_10608-* - split: shard_10554 path: data/shard_10554-* - split: shard_10563 path: data/shard_10563-* - split: shard_10600 path: data/shard_10600-* - split: shard_10399 path: data/shard_10399-* - split: shard_10409 path: data/shard_10409-* - split: shard_10583 path: data/shard_10583-* - split: shard_10397 path: data/shard_10397-* - split: shard_10595 path: data/shard_10595-* - split: shard_10414 path: data/shard_10414-* - split: shard_10589 path: data/shard_10589-* - split: shard_10622 path: data/shard_10622-* - split: shard_1042 path: data/shard_1042-* - split: shard_10613 path: data/shard_10613-* - split: shard_1062 path: data/shard_1062-* - split: shard_10626 path: data/shard_10626-* - split: shard_10590 path: data/shard_10590-* - split: shard_10659 path: data/shard_10659-* - split: shard_10651 path: data/shard_10651-* - split: shard_10607 path: data/shard_10607-* - split: shard_10637 path: data/shard_10637-* - split: shard_10552 path: data/shard_10552-* - split: shard_10436 path: data/shard_10436-* - split: shard_10645 path: data/shard_10645-* - split: shard_10604 path: data/shard_10604-* - split: shard_10632 path: data/shard_10632-* - split: shard_10611 path: data/shard_10611-* - split: shard_1063 path: data/shard_1063-* - split: shard_10673 path: data/shard_10673-* - split: shard_10419 path: data/shard_10419-* - split: shard_10633 path: data/shard_10633-* - split: shard_10670 path: data/shard_10670-* - split: shard_10680 path: data/shard_10680-* - split: shard_10639 path: data/shard_10639-* - split: shard_10574 path: data/shard_10574-* - split: shard_10658 path: data/shard_10658-* - split: shard_10664 path: data/shard_10664-* - split: shard_10640 path: data/shard_10640-* - split: shard_10648 path: data/shard_10648-* - split: shard_10688 path: data/shard_10688-* - split: shard_10701 path: data/shard_10701-* - split: shard_10677 path: data/shard_10677-* - split: shard_10596 path: data/shard_10596-* - split: shard_10441 path: data/shard_10441-* - split: shard_10683 path: data/shard_10683-* - split: shard_10684 path: data/shard_10684-* - split: shard_10696 path: data/shard_10696-* - split: shard_10606 path: data/shard_10606-* - split: shard_10733 path: data/shard_10733-* - split: shard_10691 path: data/shard_10691-* - split: shard_10699 path: data/shard_10699-* - split: shard_10716 path: data/shard_10716-* - split: shard_10752 path: data/shard_10752-* - split: shard_10447 path: data/shard_10447-* - split: shard_1076 path: data/shard_1076-* - split: shard_10727 path: data/shard_10727-* - split: shard_10746 path: data/shard_10746-* - split: shard_1074 path: data/shard_1074-* - split: shard_10662 path: data/shard_10662-* - split: shard_10714 path: data/shard_10714-* - split: shard_10655 path: data/shard_10655-* - split: shard_10767 path: data/shard_10767-* - split: shard_10720 path: data/shard_10720-* - split: shard_10745 path: data/shard_10745-* - split: shard_1072 path: data/shard_1072-* - split: shard_10774 path: data/shard_10774-* - split: shard_10781 path: data/shard_10781-* - split: shard_10789 path: data/shard_10789-* - split: shard_10796 path: data/shard_10796-* - split: shard_10778 path: data/shard_10778-* - split: shard_10734 path: data/shard_10734-* - split: shard_10741 path: data/shard_10741-* - split: shard_10771 path: data/shard_10771-* - split: shard_1081 path: data/shard_1081-* - split: shard_10831 path: data/shard_10831-* - split: shard_10705 path: data/shard_10705-* - split: shard_10784 path: data/shard_10784-* - split: shard_10846 path: data/shard_10846-* - split: shard_10824 path: data/shard_10824-* - split: shard_10809 path: data/shard_10809-* - split: shard_10756 path: data/shard_10756-* - split: shard_10853 path: data/shard_10853-* - split: shard_10797 path: data/shard_10797-* - split: shard_10868 path: data/shard_10868-* - split: shard_10815 path: data/shard_10815-* - split: shard_10860 path: data/shard_10860-* - split: shard_10763 path: data/shard_10763-* - split: shard_10875 path: data/shard_10875-* - 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split: shard_11111 path: data/shard_11111-* - split: shard_11119 path: data/shard_11119-* - split: shard_11126 path: data/shard_11126-* --- ![VALID Dataset](https://huggingface.co/datasets/ontocord/VALID/resolve/main/banner1-1.webp) # VALID (Video-Audio Large Interleaved Dataset) ## Overview The **VALID (Video-Audio Large Interleaved Dataset)** is a multimodal dataset comprising approximately 720,000 [Creative Commons licensed](https://creativecommons.org/share-your-work/cclicenses/) videos crawled from YouTube, and processed into audio-video-text data records for machine learning research. The dataset provides a unique opportunity for training models to understand relationships between modalities such as video frames, audio clips, and multilingual textual data, making it suitable for applications like multimodal representation learning. - **Please note the current version is a PREVIEW version. We are still in the process of uploading. Please be patient.** ## Features - Audio-Video-Text Format: A combination of: ``` English text ``` - The non-text multimodal portion begins the data item and can include multiple media. Some snippets may have more than one audio, and more than one video. Others may have only images/videos or only audio paired with English text. Each video contains multiple frames stored as images, and text captions for each image. There can also be standalone images interleaved as well. Even though each audio video snippets are no more than 10 seconds, a data record may span over more than 10 secs (e.g., if a data item has two 10 second videos, then the corresponding English text corresponds roughly to 20 seconds of video). The intention for this format is to teach a model to associate multiple modalities with each other, and understand multiple audio-video elements in an interleaved fashion. - Data Components: - **Images**: PNG format, phashed to ensure variability, with 0–10 images per audio snippet. Each image includes a caption created with Florence-2. - **Audio**: OGG format, multilingual, ~10 seconds per snippet, with shorter sound or music snippets (1–3 seconds) to minimize copyright issues. Each audio snippet is transcribed either with Whisper for non-English, or with the original Youtube ASR for English. - **Text**: Not including the captions and transcripts, the “text” portion is a concatenation of Youtube’s original English transcripts associated with the above media of around 1–40 words per data record. - Dataset Size: - **About 7,000,000 records.** - **About 15,000,000 images, each captioned with FLorence-2.** - **About 30,000,000 audio snippets, about half of which transcribed with Whisper-large, and half with Youtube ASR.** - **Divided into about 12K shards of about 600 records, each in a parquet file and a corresponding .tar.gz file for the media.** - **About 14TB in total.** ## File Organization - Each data entry follows the `