Dataset Card for "TALI"

Dataset Description

Abstract

TALI is a large-scale, tetramodal dataset designed to facilitate a shift from unimodal and duomodal to tetramodal research in deep learning. It aligns text, video, images, and audio, providing a rich resource for innovative self-supervised learning tasks and multimodal research. TALI enables exploration of how different modalities and data/model scaling affect downstream performance, with the aim of inspiring diverse research ideas and enhancing understanding of model capabilities and robustness in deep learning.

Brief Description

TALI (Temporally and semantically Aligned Audio, Language and Images) is a dataset that uses the Wikipedia Image Text (WIT) captions and article titles to search Youtube for videos that match the captions. It then downloads the video, audio, and subtitles from these videos. The result is a rich multimodal dataset that has multiple caption types related to both the WiT Images, and the Youtube videos. This enables learning to take place between either temporally or semantically aligned text, images, audio and video.

Dataset Information

Modalities

The TALI dataset consists of the following modalities:

1. Image:
2. Wikipedia caption image
3. Randomly sampled image from youtube video
4. Text
5. Wikipedia Caption Text
6. Wikipedia Title Text
7. Wikipedia Main Body Text
8. YouTube Subtitle Text
9. YouTube Description Text
10. YouTube Title Text
11. Audio
12. YouTube Content Audio
13. Video
14. YouTube Content Video

Usage:

To get started with TALI, you can load the dataset via Hugging Face's datasets library through our helper functions. The reason we don't use datasets directly is because we found huggingface_hub downloads much faster and reliable. For a full set of possible configurations look at examples.py. Here's a basic usage example:

First install the tali package:

Installation

For the default install use:

pip install git+https://github.com/AntreasAntoniou/TALI

For the dev install use:

pip install git+https://github.com/AntreasAntoniou/TALI[dev]

Then use the dataset using:

Examples

Import relevant helper functions

  import pathlib
  from enum import Enum
  
  import torch
  from tqdm.auto import tqdm
  
  from tali.data import (
      SubModalityTypes,
      TALIBaseTransform,
      TALIBaseTransformConfig,
      VideoFramesFormat,
      default_transforms,
      load_dataset_via_hub,
  )

TALI with default transforms (CLIP and Whisper) and no streaming

  def tali_with_transforms_no_streaming(
    dataset_storage_path: pathlib.Path | str,
):
    if isinstance(dataset_storage_path, str):
        dataset_storage_path = pathlib.Path(dataset_storage_path)

    dataset = load_dataset_via_hub(
        dataset_storage_path, dataset_name="Antreas/TALI"
    )["train"]

    (
        image_transforms,
        text_transforms,
        audio_transforms,
        video_transforms,
    ) = default_transforms()

    preprocessing_transform = TALIBaseTransform(
        cache_dir=dataset_storage_path / "cache",
        text_tokenizer=text_transforms,
        image_tokenizer=image_transforms,
        audio_tokenizer=audio_transforms,
        video_tokenizer=video_transforms,
        config=TALIBaseTransformConfig(
            root_filepath=dataset_storage_path,
            modality_list=[
                SubModalityTypes.youtube_content_video,
                SubModalityTypes.youtube_content_audio,
                SubModalityTypes.youtube_random_video_frame,
                SubModalityTypes.youtube_subtitle_text,
                SubModalityTypes.youtube_description_text,
                SubModalityTypes.youtube_title_text,
                SubModalityTypes.wikipedia_caption_image,
                SubModalityTypes.wikipedia_caption_text,
                SubModalityTypes.wikipedia_main_body_text,
                SubModalityTypes.wikipedia_title_text,
            ],
            video_frames_format=VideoFramesFormat.PIL,
        ),
    )

    for sample in tqdm(dataset):
        sample = preprocessing_transform(sample)
        print(list(sample.keys()))
        for key, value in sample.items():
            if hasattr(value, "shape"):
                print(key, value.shape)
            elif isinstance(value, torch.Tensor):
                print(key, value.shape)
            elif hasattr(value, "__len__"):
                print(key, len(value))
            print(key, type(value))

        break

    

TALI with no transforms and no streaming, returning text as text, images as PIL images, videos as a list of PIL images, and audio as a sequence of floats

def tali_without_transforms_no_streaming(
    dataset_storage_path: pathlib.Path | str,
):
    if isinstance(dataset_storage_path, str):
        dataset_storage_path = pathlib.Path(dataset_storage_path)

    dataset = load_dataset_via_hub(
        dataset_storage_path, dataset_name="Antreas/TALI"
    )["train"]

    preprocessing_transform = TALIBaseTransform(
        cache_dir=dataset_storage_path / "cache",
        text_tokenizer=None,
        image_tokenizer=None,
        audio_tokenizer=None,
        video_tokenizer=None,
        config=TALIBaseTransformConfig(
            root_filepath=dataset_storage_path,
            modality_list=[
                SubModalityTypes.youtube_content_video,
                SubModalityTypes.youtube_content_audio,
                SubModalityTypes.youtube_random_video_frame,
                SubModalityTypes.youtube_subtitle_text,
                SubModalityTypes.youtube_description_text,
                SubModalityTypes.youtube_title_text,
                SubModalityTypes.wikipedia_caption_image,
                SubModalityTypes.wikipedia_caption_text,
                SubModalityTypes.wikipedia_main_body_text,
                SubModalityTypes.wikipedia_title_text,
            ],
            video_frames_format=VideoFramesFormat.PIL,
        ),
    )

    for sample in tqdm(dataset):
        sample = preprocessing_transform(sample)
        print(list(sample.keys()))
        for key, value in sample.items():
            if hasattr(value, "shape"):
                print(key, value.shape)
            elif isinstance(value, torch.Tensor):
                print(key, value.shape)
            elif hasattr(value, "__len__"):
                print(key, len(value))
            print(key, type(value))

        break

TALI with default transforms and streaming

  def tali_with_transforms_streaming(
    dataset_storage_path: pathlib.Path | str,
):
    if isinstance(dataset_storage_path, str):
        dataset_storage_path = pathlib.Path(dataset_storage_path)

    dataset = load_dataset_via_hub(
        dataset_storage_path, dataset_name="Antreas/TALI", streaming=True
    )["train"]

    (
        image_transforms,
        text_transforms,
        audio_transforms,
        video_transforms,
    ) = default_transforms()

    preprocessing_transform = TALIBaseTransform(
        cache_dir=dataset_storage_path / "cache",
        text_tokenizer=text_transforms,
        image_tokenizer=image_transforms,
        audio_tokenizer=audio_transforms,
        video_tokenizer=video_transforms,
        config=TALIBaseTransformConfig(
            root_filepath=dataset_storage_path,
            modality_list=[
                SubModalityTypes.youtube_content_video,
                SubModalityTypes.youtube_content_audio,
                SubModalityTypes.youtube_random_video_frame,
                SubModalityTypes.youtube_subtitle_text,
                SubModalityTypes.youtube_description_text,
                SubModalityTypes.youtube_title_text,
                SubModalityTypes.wikipedia_caption_image,
                SubModalityTypes.wikipedia_caption_text,
                SubModalityTypes.wikipedia_main_body_text,
                SubModalityTypes.wikipedia_title_text,
            ],
            video_frames_format=VideoFramesFormat.PIL,
        ),
    )

    for sample in tqdm(dataset):
        sample = preprocessing_transform(sample)
        print(list(sample.keys()))
        for key, value in sample.items():
            if hasattr(value, "shape"):
                print(key, value.shape)
            elif isinstance(value, torch.Tensor):
                print(key, value.shape)
            elif hasattr(value, "__len__"):
                print(key, len(value))
            print(key, type(value))

        break

TALI with no transforms and streaming, returning text as text, images as PIL images, videos as a list of PIL images, and audio as a sequence of floats

  def tali_without_transforms_streaming(
    dataset_storage_path: pathlib.Path | str,
):
    if isinstance(dataset_storage_path, str):
        dataset_storage_path = pathlib.Path(dataset_storage_path)

    dataset = load_dataset_via_hub(
        dataset_storage_path, dataset_name="Antreas/TALI", streaming=True
    )["train"]

    preprocessing_transform = TALIBaseTransform(
        cache_dir=dataset_storage_path / "cache",
        text_tokenizer=None,
        image_tokenizer=None,
        audio_tokenizer=None,
        video_tokenizer=None,
        config=TALIBaseTransformConfig(
            root_filepath=dataset_storage_path,
            modality_list=[
                SubModalityTypes.youtube_content_video,
                SubModalityTypes.youtube_content_audio,
                SubModalityTypes.youtube_random_video_frame,
                SubModalityTypes.youtube_subtitle_text,
                SubModalityTypes.youtube_description_text,
                SubModalityTypes.youtube_title_text,
                SubModalityTypes.wikipedia_caption_image,
                SubModalityTypes.wikipedia_caption_text,
                SubModalityTypes.wikipedia_main_body_text,
                SubModalityTypes.wikipedia_title_text,
            ],
            video_frames_format=VideoFramesFormat.PIL,
        ),
    )

    for sample in tqdm(dataset):
        sample = preprocessing_transform(sample)
        print(list(sample.keys()))
        for key, value in sample.items():
            if hasattr(value, "shape"):
                print(key, value.shape)
            elif isinstance(value, torch.Tensor):
                print(key, value.shape)
            elif hasattr(value, "__len__"):
                print(key, len(value))
            print(key, type(value))

        break

Dataset Statistics

TBA

Dataset Creation

The TALI dataset was created by starting from the WiT dataset and using either the context_page_description or page_title as a source-query to search YouTube for video that were creative commons opted-in, and, not age restricted. The top 100 result titles were returned and compared with the source-query using the CLIP text embeddings of the largest CLIP model available. The top-1 title’s video based on the CLIP ranking was chosen and downloaded. The video was broken into 30-second segments and the top-10 segments for eachvideo were chosen based on the distance between the CLIP image embedding of the first image of each segment and the video’s title text. The image, audio, and subtitle frames were extracted from these segments. At sampling time, one of these 10 segments is randomly selected, and a 10-second segment is chosen out of the 30-second clip. The result is 200 video frames (spread throughout the 10-second segment), and 160000 audio frames (10 seconds).

Dataset Use

TALI is designed for use in a wide range of multimodal research tasks, including but not limited to:

• Multimodal understanding and reasoning
• Self-supervised learning
• Multimodal alignment and translation
• Multimodal summarization
• Multimodal question answering

Dataset Curators: Antreas Antoniou

Citation Information: TBA Contributions: Thanks to all contributors including data curators, annotators, and software developers.

More Information needed