Datasets:

Antreas
/

TALI

	---
	configs:
	- config_name: default
	data_files:
	- split: train
	path: data/train-*
	- split: val
	path: data/val-*
	- split: test
	path: data/test-*
	dataset_info:
	features:
	- name: image
	dtype: image
	- name: image_url
	dtype: string
	- name: item_idx
	dtype: int64
	- name: wit_features
	struct:
	- name: attribution_passes_lang_id
	sequence: bool
	- name: caption_alt_text_description
	sequence: string
	- name: caption_reference_description
	sequence: string
	- name: caption_title_and_reference_description
	sequence: string
	- name: context_page_description
	sequence: string
	- name: context_section_description
	sequence: string
	- name: hierarchical_section_title
	sequence: string
	- name: is_main_image
	sequence: bool
	- name: language
	sequence: string
	- name: page_changed_recently
	sequence: bool
	- name: page_title
	sequence: string
	- name: page_url
	sequence: string
	- name: section_title
	sequence: string
	- name: wit_idx
	dtype: int64
	- name: youtube_title_text
	dtype: string
	- name: youtube_description_text
	dtype: string
	- name: youtube_video_content
	dtype: binary
	- name: youtube_video_starting_time
	dtype: string
	- name: youtube_subtitle_text
	dtype: string
	- name: youtube_video_size
	dtype: int64
	- name: youtube_video_file_path
	dtype: string
	splits:
	- name: train
	num_bytes: 1902638101655.625
	num_examples: 1052915
	- name: val
	num_bytes: 104485442867.25
	num_examples: 57958
	- name: test
	num_bytes: 111107332347.375
	num_examples: 61389
	download_size: 2058391040534
	dataset_size: 2118230876870.25
	license: cc-by-4.0
	task_categories:
	- zero-shot-classification
	tags:
	- video
	- audio
	- text
	- image
	- tetramodal
	- multimodal
	- youtube
	- wikipedia
	pretty_name: TALI
	size_categories:
	- 1M<n<10M
	---
	# Dataset Card for "TALI"

	## Table of Contents
	1. Dataset Description
	1. Abstract
	2. Brief Description
	2. Dataset Information
	1. Modalities
	2. Dataset Variants
	3. Dataset Statistics
	4. Data Fields
	5. Data Splits
	3. Dataset Creation
	4. Dataset Use
	5. Additional Information

	## 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:
	1. Wikipedia caption image
	2. Randomly sampled image from youtube video
	2. Text
	1. Wikipedia Caption Text
	2. Wikipedia Title Text
	3. Wikipedia Main Body Text
	4. YouTube Subtitle Text
	5. YouTube Description Text
	6. YouTube Title Text
	3. Audio
	1. YouTube Content Audio
	4. Video
	1. 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](examples.py). Here's a basic usage example:

	First install the tali package:

	### Installation

	For the default install use:

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

	For the dev install use:

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

	Then use the dataset using:

	### Examples
	Import relevant helper functions
	```python
	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

	```python
	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

	```python
	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

	```python
	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
	```python
	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](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)