The global number of Youtube users in was forecast to continuously increase between 2024 and 2029 by in total ***** million users (+***** percent). After the ninth consecutive increasing year, the Youtube user base is estimated to reach *** billion users and therefore a new peak in 2029. Notably, the number of Youtube users of was continuously increasing over the past years.User figures, shown here regarding the platform youtube, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Youtube users in countries like Africa and South America.

As of February 2025, India was the country with the largest YouTube audience by far, with approximately 491 million users engaging with the popular social video platform. The United States followed, with around 253 million YouTube viewers. Brazil came in third, with 144 million users watching content on YouTube. The United Kingdom saw around 54.8 million internet users engaging with the platform in the examined period. What country has the highest percentage of YouTube users? In July 2024, the United Arab Emirates was the country with the highest YouTube penetration worldwide, as around 94 percent of the country's digital population engaged with the service. In 2024, YouTube counted around 100 million paid subscribers for its YouTube Music and YouTube Premium services. YouTube mobile markets In 2024, YouTube was among the most popular social media platforms worldwide. In terms of revenues, the YouTube app generated approximately 28 million U.S. dollars in revenues in the United States in January 2024, as well as 19 million U.S. dollars in Japan.

Use our YouTube profiles dataset to extract both business and non-business information from public channels and filter by channel name, views, creation date, or subscribers. Datapoints include URL, handle, banner image, profile image, name, subscribers, description, video count, create date, views, details, and more. You may purchase the entire dataset or a customized subset, depending on your needs. Popular use cases for this dataset include sentiment analysis, brand monitoring, influencer marketing, and more.

YouTube was created in 2005, with the first video – Me at the Zoo - being uploaded on 23 April 2005. Since then, 1.3 billion people have set up YouTube accounts. In 2018, people watch nearly 5 billion videos each day. People upload 300 hours of video to the site every minute.

According to 2016 research undertaken by Pexeso, music only accounts for 4.3% of YouTube’s content. Yet it makes 11% of the views. Clearly, an awful lot of people watch a comparatively small number of music videos. It should be no surprise, therefore, that the most watched videos of all time on YouTube are predominantly music videos.

On August 13, BTS became the most-viewed artist in YouTube history, accumulating over 26.7 billion views across all their official channels. This count includes all music videos and dance practice videos.

Justin Bieber and Ed Sheeran now hold the records for second and third-highest views, with over 26 billion views each.

Currently, BTS’s most viewed videos are their music videos for “**Boy With Luv**,” “**Dynamite**,” and “**DNA**,” which all have over 1.4 billion views.

Headers of the Dataset Total = Total views (in millions) across all official channels Avg = Current daily average of all videos combined 100M = Number of videos with more than 100 million views

YouTube is the world's largest video-sharing platform, launched in 2005. It allows users to upload, view, and share videos, and has grown to be a central hub for content creators across various fields, including entertainment, education, music, and more. With over 2 billion logged-in users monthly, YouTube has become an essential platform for digital content and marketing.

The Top 1000 YouTube Channels Dataset captures detailed information about the top-performing YouTube channels globally. This dataset includes the following columns:

• Rank : The ranking of the YouTube channel based on its overall popularity and performance.
• Youtuber : The name of the YouTuber or the title of the YouTube channel.
• Subscribers : The total number of subscribers to the channel, indicating its reach and popularity.
• Video Views : The total number of video views the channel has accumulated, reflecting its engagement and audience interaction.
• Video Count : The total number of videos uploaded by the channel, showing the content volume produced.
• Category : The genre or category the channel belongs to, such as music, education, entertainment, etc.
• Started : The year the channel was created, providing insight into its longevity and growth over time.

This dataset is invaluable for analyzing trends, understanding content strategies, and benchmarking channel performances within the YouTube ecosystem.

The number of Youtube users in India was forecast to continuously increase between 2024 and 2029 by in total 222.2 million users (+34.88 percent). After the ninth consecutive increasing year, the Youtube user base is estimated to reach 859.26 million users and therefore a new peak in 2029. Notably, the number of Youtube users of was continuously increasing over the past years.User figures, shown here regarding the platform youtube, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Youtube users in countries like Sri Lanka and Nepal.

Programming youtube videos dataset. Total records extracted more than 300. Last extracted on 24 jan 2022.

Get in touch with crawlfeeds team for large datasets and customized youtube datasets.

In 2021, YouTube's user base in the United States amounts to approximately 203.80 million users. The number of YouTube users in the United States is projected to reach 219.28 million users by 2025. User figures have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

📺 YouTube-Commons 📺

YouTube-Commons is a collection of audio transcripts of 2,063,066 videos shared on YouTube under a CC-By license.

  Content

The collection comprises 22,709,724 original and automatically translated transcripts from 3,156,703 videos (721,136 individual channels). In total, this represents nearly 45 billion words (44,811,518,375). All the videos where shared on YouTube with a CC-BY license: the dataset provide all the necessary provenance information… See the full description on the dataset page: https://huggingface.co/datasets/PleIAs/YouTube-Commons.

The YouTube-100M data set consists of 100 million YouTube videos: 70M training videos, 10M evaluation videos, and 20M validation videos. Videos average 4.6 minutes each for a total of 5.4M training hours. Each of these videos is labeled with 1 or more topic identifiers from a set of 30,871 labels. There are an average of around 5 labels per video. The labels are assigned automatically based on a combination of metadata (title, description, comments, etc.), context, and image content for each video. The labels apply to the entire video and range from very generic (e.g. “Song”) to very specific (e.g. “Cormorant”). Being machine generated, the labels are not 100% accurate and of the 30K labels, some are clearly acoustically relevant (“Trumpet”) and others are less so (“Web Page”). Videos often bear annotations with multiple degrees of specificity. For example, videos labeled with “Trumpet” are often labeled “Entertainment” as well, although no hierarchy is enforced.

This is the statistics for the Top 10 songs of various spotify artists and their YouTube videos. The Creators above generated the data and uploaded it to Kaggle on February 6-7 2023. The license to use this data is "CC0: Public Domain", allowing the data to be copied, modified, distributed, and worked on without having to ask permission. The data is in numerical and textual CSV format as attached. This dataset contains the statistics and attributes of the top 10 songs of various artists in the world. As described by the creators above, it includes 26 variables for each of the songs collected from spotify. These variables are briefly described next:

Track: name of the song, as visible on the Spotify platform. Artist: name of the artist. Url_spotify: the Url of the artist. Album: the album in wich the song is contained on Spotify. Album_type: indicates if the song is relesead on Spotify as a single or contained in an album. Uri: a spotify link used to find the song through the API. Danceability: describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable. Energy: is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy. Key: the key the track is in. Integers map to pitches using standard Pitch Class notation. E.g. 0 = C, 1 = C♯/D♭, 2 = D, and so on. If no key was detected, the value is -1. Loudness: the overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typically range between -60 and 0 db. Speechiness: detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks. Acousticness: a confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic. Instrumentalness: predicts whether a track contains no vocals. "Ooh" and "aah" sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly "vocal". The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0. Liveness: detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live. Valence: a measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry). Tempo: the overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration. Duration_ms: the duration of the track in milliseconds. Stream: number of streams of the song on Spotify. Url_youtube: url of the video linked to the song on Youtube, if it have any. Title: title of the videoclip on youtube. Channel: name of the channel that have published the video. Views: number of views. Likes: number of likes. Comments: number of comments. Description: description of the video on Youtube. Licensed: Indicates whether the video represents licensed content, which means that the content was uploaded to a channel linked to a YouTube content partner and then claimed by that partner. official_video: boolean value that indicates if the video found is the official video of the song. The data was last updated on February 7, 2023.

In 2021, YouTube's user base in the United Kingdom amounts to approximately ***** million users. The number of YouTube users in the United Kingdom is projected to reach ***** million users by 2025. User figures have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

YouTube Dislikes Dataset

This dataset contains information about trending YouTube videos from August 2020 to December 2021 for the USA, Canada, and Great Britain.

Context and Content

Youtube announced the decision to hide the number of dislikes from users around November 2021. However, the official YouTube Data API allowed you to get information about dislikes until December 13, 2021.

This dataset contains the latest possible information about dislikes, which was collected just before December 13. The information was collected by videos that had been trending in the USA, Canada, and Great Britain for a year prior.

The information is aimed at the English audience. In particular, all non-ASCII and non-Latin characters have been removed from the text fields.

The comments were received using the following query and combined into one string:

request = youtube.commentThreads().list(
        part="snippet",
        maxResults=20,
        order="relevance",
        textFormat="plainText",
        videoId=video_id)
response = request.execute()

order=relevance parameter is ignored when videoId is specified, so, basically, it's 20 random comments.

The code used to collect this dataset is available here.

To know more visit this GitLab repo.

Acknowledgements

This dataset was collected using the official YouTube Data API v3. Unique video IDs were extracted from YouTube Trending Video Dataset. Banner image - photo by Alexander Shatov on Unsplash.

Inspiration

Possible uses of this dataset may include a wide range of tasks: - Exploratory Data Analysis and Sentiment Analysis - Clustering YouTube videos - Training neural networks to analyze comments or video descriptions - and so on

This dataset provides a detailed collection of video titles from the popular YouTube channel, 5-Minute Crafts, which is owned by TheSoul Publishing. As of October 2021, the channel was notably the 9th most-subscribed and one of the most-viewed channels on the platform [1]. While known for its DIY-style content, 5-Minute Crafts has faced criticism for unusual or potentially risky 'life hacks' and its heavy use of clickbait [1]. Despite this, the videos consistently achieve a high volume of views [1]. The dataset includes each video's title alongside various meta-features, such as total views, video duration, and the sentiment associated with the title [1]. It is designed for analysis to explore the relationship between words used in titles and views garnered, identify key title features that impact viewership, and examine correlations between title meta-features, total views, duration, and sentiment [1].

Columns

• video_id: A unique identifier for each video [2].
• title: The textual title of the video [2].
• active_since_days: The number of days the video has been active [2].
• duration_seconds: The length of the video in seconds [2].
• total_views: The overall count of views for the video [2].
• num_chars: The total number of characters present in the video title [2].
• num_words: The total count of words within the video title [2].
• num_punctuation: The number of punctuation marks in the title [2].
• num_words_uppercase: The count of words written entirely in uppercase within the title [2].
• num_words_lowercase: The count of words written entirely in lowercase within the title [2].

Distribution

The dataset comprises 4,978 unique video records from the 5-Minute Crafts YouTube channel, with 4,965 unique video titles [2]. * Video Duration: The duration of videos ranges from approximately 1 second to 1,460 seconds (about 24 minutes), with the majority falling between 1022.30 and 1168.20 seconds [3]. * Total Views: View counts range from 4,034 up to 283 million views, with most videos having between 4,034 and 28,306,741.50 views [4, 5]. * Title Characters: Video titles typically contain between 11 and 100 characters, with the most common length being 37.70 to 46.60 characters [5, 6]. * Title Words: Titles usually have between 3 and 20 words, with a peak concentration between 6.40 and 8.10 words [6, 7]. * Punctuation: The number of punctuation marks in titles ranges from 0 to 6, with most titles having very few, specifically between 0 and 0.60 punctuation marks [7]. * Uppercase Words: Titles contain between 0 and 18 uppercase words, with a notable concentration between 5.40 and 7.20 uppercase words [7, 8]. * Lowercase Words: The number of lowercase words in titles ranges from 0 to 12, with the majority of titles having between 0 and 1.20 lowercase words [8].

Usage

This dataset is well-suited for various analytical and modelling tasks, including: * Investigating the correlation between specific words used in titles and the total views generated [1]. * Identifying which features of a video title are most impactful in driving views [1]. * Exploring the relationships between title meta-features (like character or word count), total views, video duration, and sentiment [1]. * Developing predictive models for video performance based on title characteristics. * Performing natural language processing (NLP) tasks on video titles [1].

Coverage

The dataset focuses on videos from the 5-Minute Crafts YouTube channel [2]. * Geographic Scope: The data is globally relevant, reflecting the channel's international reach [9]. * Time Range: The dataset includes an 'active since days' column for each video, indicating its age, though specific calendar dates for data collection are not provided [1, 2].

License

CCO

Who Can Use It

This dataset is ideal for: * Data Scientists and Analysts: For developing and testing models related to content engagement and virality. * Content Creators and Marketers: To gain insights into effective title strategies and audience engagement on YouTube. * Researchers: Studying online media trends, clickbait phenomena, and the dynamics of popular DIY content. * AI/ML Developers: For training and validating NLP models on large-scale text data related to video titles [1].

Dataset Name Suggestions

• 5-Minute Crafts YouTube Performance Data
• YouTube Video Title Analysis Dataset
• Clickbait and Views Dataset
• DIY Content Engagement Metrics
• Online Video Analytics Data

Attributes

Original Data Source: 5-Minute Crafts: Video Clickbait Titles?

This dataset is a daily record of the top trending YouTube videos and it will be updated daily.

Context

YouTube maintains a list of the top trending videos on the platform. According to Variety magazine, “To determine the year’s top-trending videos, YouTube uses a combination of factors including measuring users interactions (number of views, shares, comments and likes). Note that they’re not the most-viewed videos overall for the calendar year”.

Note that this dataset is a structurally improved version of this dataset.

Content

This dataset includes several months (and counting) of data on daily trending YouTube videos. Data is included for the IN, US, GB, DE, CA, FR, RU, BR, MX, KR, and JP regions (India, USA, Great Britain, Germany, Canada, France, Russia, Brazil, Mexico, South Korea, and, Japan respectively), with up to 200 listed trending videos per day.

Each region’s data is in a separate file. Data includes the video title, channel title, publish time, tags, views, likes and dislikes, description, and comment count.

The data also includes a category_id field, which varies between regions. To retrieve the categories for a specific video, find it in the associated JSON. One such file is included for each of the 11 regions in the dataset.

For more information on specific columns in the dataset refer to the column metadata.

Acknowledgements

This dataset was collected using the YouTube API. This dataset is the updated version of Trending YouTube Video Statistics.

Inspiration

Possible uses for this dataset could include: - Sentiment analysis in a variety of forms - Categorizing YouTube videos based on their comments and statistics. - Training ML algorithms like RNNs to generate their own YouTube comments. - Analyzing what factors affect how popular a YouTube video will be. - Statistical analysis over time.

For further inspiration, see the kernels on this dataset!

Context

Dataset was made from 17 professional shogi players' Youtube channels with Youtube Data API. I made a dataset from one of the channels before with Selenium on https://www.kaggle.com/datasets/satoshiss/shogi-channels-data.

If you are interested in Shogi(Japanese Chess), please check any videos listed.

Content

The channel stats file has overall stats for each youtube channel and the video_details file have information on each video including title, views, likes, comment counts, tags, description, and published date.

Creative Commons YouTube

  Description

YouTube is large-scale video-sharing platform where users have the option of uploading content under a CC BY license. To collect high-quality speech-based textual content and combat the rampant license laundering on YouTube, we manually curated a set of over 2,000 YouTube channels that consistently release original openly licensed content containing speech. The resulting collection spans a wide range of genres, including lectures… See the full description on the dataset page: https://huggingface.co/datasets/common-pile/youtube.

If using this dataset, please cite the following paper and the current Zenodo repository.

This dataset is described in detail in the following paper:

[1] Yao, Y., Stebner, A., Tuytelaars, T., Geirnaert, S., & Bertrand, A. (2024). Identifying temporal correlations between natural single-shot videos and EEG signals. Journal of Neural Engineering, 21(1), 016018. doi:10.1088/1741-2552/ad2333

The associated code is available at: https://github.com/YYao-42/Identifying-Temporal-Correlations-Between-Natural-Single-shot-Videos-and-EEG-Signals?tab=readme-ov-file

Introduction

The research work leading to this dataset was conducted at the Department of Electrical Engineering (ESAT), KU Leuven.

This dataset contains electroencephalogram (EEG) data collected from 19 young participants with normal or corrected-to-normal eyesight when they were watching a series of carefully selected YouTube videos. The videos were muted to avoid the confounds introduced by audio. For synchronization, a square box was encoded outside of the original frames and flashed every 30 seconds in the top right corner of the screen. A photosensor, detecting the light changes from this flashing box, was affixed to that region using black tape to ensure that the box did not distract participants. The EEG data was recorded using a BioSemi ActiveTwo system at a sample rate of 2048 Hz. Participants wore a 64-channel EEG cap, and 4 electrooculogram (EOG) sensors were positioned around the eyes to track eye movements.

The dataset includes a total of (19 subjects x 63 min + 9 subjects x 24 min) of data. Further details can be found in the following section.

Content

YouTube Videos: Due to copyright constraints, the dataset includes links to the original YouTube videos along with precise timestamps for the segments used in the experiments. The features proposed in 1 have been extracted and can be downloaded here: https://drive.google.com/file/d/1J1tYrxVizrl1xP-W1imvlA_v-DPzZ2Qh/view?usp=sharing.

Raw EEG Data: Organized by subject ID, the dataset contains EEG segments corresponding to the presented videos. Both EEGLAB .set files (containing metadata) and .fdt files (containing raw data) are provided, which can also be read by popular EEG analysis Python packages such as MNE.

The naming convention links each EEG segment to its corresponding video. E.g., the EEG segment 01_eeg corresponds to video 01_Dance_1, 03_eeg corresponds to video 03_Acrob_1, Mr_eeg corresponds to video Mr_Bean, etc.

The raw data have 68 channels. The first 64 channels are EEG data, and the last 4 channels are EOG data. The position coordinates of the standard BioSemi headcaps can be downloaded here: https://www.biosemi.com/download/Cap_coords_all.xls.

Due to minor synchronization ambiguities, different clocks in the PC and EEG recorder, and missing or extra video frames during video playback (rarely occurred), the length of the EEG data may not perfectly match the corresponding video data. The difference, typically within a few milliseconds, can be resolved by truncating the modality with the excess samples.

Signal Quality Information: A supplementary .txt file detailing potential bad channels. Users can opt to create their own criteria for identifying and handling bad channels.

The dataset is divided into two subsets: Single-shot and MrBean, based on the characteristics of the video stimuli.

Single-shot Dataset

The stimuli of this dataset consist of 13 single-shot videos (63 min in total), each depicting a single individual engaging in various activities such as dancing, mime, acrobatics, and magic shows. All the participants watched this video collection.

Video ID Link Start time (s) End time (s)

01_Dance_1 https://youtu.be/uOUVE5rGmhM 8.54 231.20

03_Acrob_1 https://youtu.be/DjihbYg6F2Y 4.24 231.91

04_Magic_1 https://youtu.be/CvzMqIQLiXE 3.68 348.17

05_Dance_2 https://youtu.be/f4DZp0OEkK4 5.05 227.99

06_Mime_2 https://youtu.be/u9wJUTnBdrs 5.79 347.05

07_Acrob_2 https://youtu.be/kRqdxGPLajs 183.61 519.27

08_Magic_2 https://youtu.be/FUv-Q6EgEFI 3.36 270.62

09_Dance_3 https://youtu.be/LXO-jKksQkM 5.61 294.17

12_Magic_3 https://youtu.be/S84AoWdTq3E 1.76 426.36

13_Dance_4 https://youtu.be/0wc60tA1klw 14.28 217.18

14_Mime_3 https://youtu.be/0Ala3ypPM3M 21.87 386.84

15_Dance_5 https://youtu.be/mg6-SnUl0A0 15.14 233.85

16_Mime_6 https://youtu.be/8V7rhAJF6Gc 31.64 388.61

MrBean Dataset

Additionally, 9 participants watched an extra 24-minute clip from the first episode of Mr. Bean, where multiple (moving) objects may exist and interact, and the camera viewpoint may change. The subject IDs and the signal quality files are inherited from the single-shot dataset.

Video ID Link Start time (s) End time (s)

Mr_Bean https://www.youtube.com/watch?v=7Im2I6STbms 39.77 1495.00

Acknowledgement

This research is funded by the Research Foundation - Flanders (FWO) project No G081722N, junior postdoctoral fellowship fundamental research of the FWO (for S. Geirnaert, No. 1242524N), the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No 802895), the Flemish Government (AI Research Program), and the PDM mandate from KU Leuven (for S. Geirnaert, No PDMT1/22/009).

We also thank the participants for their time and effort in the experiments.

Contact Information

Executive researcher: Yuanyuan Yao, yuanyuan.yao@kuleuven.be

Led by: Prof. Alexander Bertrand, alexander.bertrand@kuleuven.be

This statistic shows a ranking of the estimated number of Youtube users in 2020 in Latin America and the Caribbean, differentiated by country. The user numbers have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

Tamil Youtube

Selected channels from https://www.youtube.com using 'tamil podcast' keyword. With total 121347 audio files, total 11292.83 hours.

  how to download

huggingface-cli download --repo-type dataset
--include '*.z*'
--local-dir './'
malaysia-ai/tamil-youtube

https://gist.githubusercontent.com/huseinzol05/2e26de4f3b29d99e993b349864ab6c10/raw/9b2251f3ff958770215d70c8d82d311f82791b78/unzip.py python3 unzip.py

  Licensing

All the videos, songs, images… See the full description on the dataset page: https://huggingface.co/datasets/malaysia-ai/tamil-youtube.