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_0
num_bytes: 2152532
num_examples: 694
- name: shard_1
num_bytes: 2039321
num_examples: 628
- name: shard_10
num_bytes: 1711625
num_examples: 502
- name: shard_100
num_bytes: 1879092
num_examples: 608
- name: shard_1000
num_bytes: 2554377
num_examples: 631
- name: shard_10000
num_bytes: 1436826
num_examples: 409
- name: shard_10001
num_bytes: 2566374
num_examples: 919
- name: shard_10002
num_bytes: 1441850
num_examples: 416
- name: shard_10003
num_bytes: 1479331
num_examples: 453
- name: shard_10004
num_bytes: 2304946
num_examples: 665
- name: shard_10005
num_bytes: 2326767
num_examples: 765
- name: shard_10008
num_bytes: 2405272
num_examples: 769
- name: shard_10006
num_bytes: 2272052
num_examples: 667
- name: shard_10007
num_bytes: 2369366
num_examples: 632
- name: shard_10009
num_bytes: 2081310
num_examples: 626
- name: shard_1001
num_bytes: 2383462
num_examples: 664
- name: shard_10010
num_bytes: 4633710
num_examples: 1011
- name: shard_10011
num_bytes: 2031992
num_examples: 572
- name: shard_10016
num_bytes: 1524141
num_examples: 440
- name: shard_10027
num_bytes: 2009449
num_examples: 561
- name: shard_1004
num_bytes: 2236232
num_examples: 679
- name: shard_10015
num_bytes: 1936158
num_examples: 651
- name: shard_10022
num_bytes: 1375721
num_examples: 381
- name: shard_10020
num_bytes: 1851431
num_examples: 572
- name: shard_10024
num_bytes: 2066917
num_examples: 621
- name: shard_10012
num_bytes: 2046815
num_examples: 626
- name: shard_10013
num_bytes: 2377377
num_examples: 691
- name: shard_10014
num_bytes: 1775675
num_examples: 492
- name: shard_10017
num_bytes: 3541944
num_examples: 1225
- name: shard_1002
num_bytes: 2343929
num_examples: 603
- name: shard_10039
num_bytes: 2087969
num_examples: 600
- name: shard_10033
num_bytes: 2335915
num_examples: 676
- name: shard_10031
num_bytes: 1783883
num_examples: 478
- name: shard_10036
num_bytes: 1701763
num_examples: 490
- name: shard_10026
num_bytes: 1930478
num_examples: 585
- name: shard_10060
num_bytes: 2259114
num_examples: 677
- name: shard_1005
num_bytes: 2555364
num_examples: 580
- name: shard_10035
num_bytes: 1755575
num_examples: 572
- name: shard_10021
num_bytes: 2182556
num_examples: 599
- name: shard_10025
num_bytes: 1763936
num_examples: 547
- name: shard_10057
num_bytes: 1655171
num_examples: 514
- name: shard_10071
num_bytes: 2342632
num_examples: 668
- name: shard_10046
num_bytes: 1849419
num_examples: 521
- name: shard_10082
num_bytes: 2396177
num_examples: 690
- name: shard_10093
num_bytes: 1926455
num_examples: 618
download_size: 120269273
dataset_size: 95682401
configs:
- config_name: default
data_files:
- split: shard_0
path: data/shard_0-*
- split: shard_1
path: data/shard_1-*
- split: shard_10
path: data/shard_10-*
- split: shard_100
path: data/shard_100-*
- split: shard_1000
path: data/shard_1000-*
- split: shard_10000
path: data/shard_10000-*
- split: shard_10001
path: data/shard_10001-*
- split: shard_10002
path: data/shard_10002-*
- split: shard_10003
path: data/shard_10003-*
- split: shard_10004
path: data/shard_10004-*
- split: shard_10005
path: data/shard_10005-*
- split: shard_10011
path: data/shard_10011-*
- split: shard_10008
path: data/shard_10008-*
- split: shard_10010
path: data/shard_10010-*
- split: shard_10013
path: data/shard_10013-*
- split: shard_10006
path: data/shard_10006-*
- split: shard_10012
path: data/shard_10012-*
- split: shard_10007
path: data/shard_10007-*
- split: shard_10009
path: data/shard_10009-*
- split: shard_1001
path: data/shard_1001-*
- split: shard_10014
path: data/shard_10014-*
- split: shard_10016
path: data/shard_10016-*
- split: shard_10015
path: data/shard_10015-*
- split: shard_10022
path: data/shard_10022-*
- split: shard_10025
path: data/shard_10025-*
- split: shard_10020
path: data/shard_10020-*
- split: shard_10027
path: data/shard_10027-*
- split: shard_10031
path: data/shard_10031-*
- split: shard_10024
path: data/shard_10024-*
- split: shard_10046
path: data/shard_10046-*
- split: shard_1004
path: data/shard_1004-*
- split: shard_10039
path: data/shard_10039-*
- split: shard_10033
path: data/shard_10033-*
- split: shard_10017
path: data/shard_10017-*
- split: shard_1002
path: data/shard_1002-*
- split: shard_10036
path: data/shard_10036-*
- split: shard_1005
path: data/shard_1005-*
- split: shard_10026
path: data/shard_10026-*
- split: shard_10060
path: data/shard_10060-*
- split: shard_10035
path: data/shard_10035-*
- split: shard_10021
path: data/shard_10021-*
- split: shard_10057
path: data/shard_10057-*
- split: shard_10071
path: data/shard_10071-*
- split: shard_10082
path: data/shard_10082-*
- split: shard_10093
path: data/shard_10093-*
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 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.
Features
- Audio-Video-Text Format: A combination of:
<video>
<caption><image> the caption </caption>
<caption><image> the caption </caption>
<caption><image> the caption </caption>
</video>
<transcript> <audio> multi-lingual transcript </transcript>
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 (e.g., if a data item has two 10 second videos, then the corresponding English
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 15,000,000 images.
- About 30,000,000 audio snippets.
File Organization
- Each data entry follows the
<video><image(s)><audio><text>structure as described above. - Metadata includes timestamps and alignment between modalities.
Multimodal Details
- Audio-Video Alignment: Snippets allow learning temporal relationships between audio and visual elements.
- Text Annotations: Text descriptions, including captions and contextual keywords, provide linguistic alignment.
Preprocessing
- Phashing for Images: Ensures that images within the dataset are dynamic and non-static.
- Audio Snippet Lengths: Music and sound effects are clipped to 1–3 seconds to minimize copyright concerns.
Licenses
All videos in VALID are CC BY, as declared by their original uploaders on YouTube. We publish the snippets of these videos here under these rights and under the principles of fair use. However, we cannot guarantee that original uploaders had the rights to share the content. [Todo: Put in AS-IS WHERE-AS usage disclaimer]
Intended Uses
- Primary Use Case: Training models for multimodal understanding, such as contrastive multimodal learning (e.g., CLIP).
- Not Recommended For: Generation tasks, as the dataset's quality may not meet generative model requirements.
Dataset Limitations
- Quality: Images and audio are sourced from YouTube and may vary in resolution and clarity.
- Rights Uncertainty: While videos are marked as CC-BY by the third party authors of the videos, original rights may not be verifiable.
- Biases: The dataset's multilingual audio paired with English-only text may introduce linguistic biases. The large variety of videos may introduce bias.
Ethical Considerations
The dataset was built under the principles of fair use and CC BY-SA licensing. Its creation strives to align with the spirit of the EU AI Act, emphasizing transparency and safety in AI model development. Users must exercise caution and adhere to copyright and licensing rules when using VALID.
Policy for Managing Video Deletion Requests
Our goal is to establish a clear process for removing videos from our dataset when requested by users or required by external factors, while balancing the rights of content owners, compliance with CC-BY licenses, and the community's ability to utilize the dataset for training and research purposes.
1. Respecting Content Owners' Rights: All videos in the dataset are under the CC-BY license. As such, proper attribution will always be maintained as required by the license. If a content owner requests the removal of a video from the dataset, we will balance this request with the community's ability to train on the data, considering the original intent of the CC-BY license.
2. Deletion Request Process:
- Content owners or users can request the removal of a video by FIRST requesting it be removed from Youtube: Here and Here.
- Then verifying that it has been removed from YouTube and providing this feedback to us Here.
- Requests must demonstrate that the video is no longer publicly available on YouTube.
- We will remove the confirmed videos in the next release of this dataset.
3. Verification and Balancing Interests: All deletion requests will be verified by checking YouTube to ensure the video is no longer available. We may also remove a video in our sole discretion. Decisions on video removal will take into account: The rights and wishes of content owners, including their ability to remove their videos from public availability. The community's need for robust datasets for training and research. The spirit of the CC-BY license, which permits redistribution and use with proper attribution.
4. Responsibilities for Derivative Datasets: Users creating derivative datasets must ensure compliance by deleting videos listed in
delete_these_videos.json.5. Proactive Deletion: Videos may be removed proactively under the following circumstances:
Requests from the hosting provider (e.g., Hugging Face).
Legal requirements or enforcement actions.
Internal decisions.
6. Community Considerations:
The community is encouraged to respect the balance between individual content owners’ wishes and the public benefit derived from open access datasets.
Efforts will be made to keep the dataset robust while honoring legitimate requests for content removal.
7. Updates: Users are encouraged to check the
delete_these_videos.json, from time to time to ensure their copy of the dataset is up to date.
Related Materials:
- If you are looking for CC-BY Youtube transcripts of videos, check out PleIAs’ https://huggingface.co/datasets/PleIAs/YouTube-Commons.
- Also, Huggingface has created an excellent CC-BY Youtube video dataset here: https://huggingface.co/datasets/HuggingFaceFV/finevideo
Acknowledgement and Thanks
This dataset was built by Ontocord.AI in cooperation with Grass and LAION.AI. It was created as part of the EUHPC grant EUHPC_E03_068 for the Leonardo supercomputers resources in order to build safe multimodal models that comply with the EU AI Act. This dataset was built on a subset of the Grass Video Repository, a massive video dataset of creative commons videos. We deeply thank EuroHPC and Cineca, as well as Huggingface and the open source community for their support.
About the Contributors:
- Grass is committed to making the public web accessible again. Through its network of millions of globally distributed nodes, it is capable of collecting petabyte-scale datasets for a variety of use cases, including training AI models. The network is run exclusively by users who have downloaded an application to their devices, allowing them to contribute their unused internet bandwidth to the network. On X: @getgrass_io
- LAION, is a non-profit organization, that provides datasets, tools and models to liberate machine learning research. By doing so, we encourage open public education and a more environment-friendly use of resources by reusing existing datasets and models.
- Ontocord is a technology company focused on legally compliant AI. Our mission is to make our AGI future lawful and accessible to everyone.
- Alignment Lab AI: Our mission is to build a future leveraging AI as a force for good and as a tool that enhances human lives. We believe everyone deserves to harness the power of personal intelligence.
- And many others ...
Citation
@misc{Huu2024VALID,
title = {VALID (Video-Audio Large Interleaved Dataset)},
author = {Huu Nguyen, Ken Tsui, Andrej Radonjic, Christoph Schuhmann},
year = {2024}
url = {https://huggingface.co/datasets/ontocord/VALID},
}