Datasets:

jamescalam
/

youtube-transcriptions

Tasks:

Question Answering

Visual Question Answering

Sub-tasks: open-domain-qa extractive-qa document-retrieval

Languages: English

Multilinguality: monolingual

Size Categories: 10K<n<100K

Language Creators: found

Annotations Creators: no-annotation

Source Datasets: original

Tags: youtube technical speech to text speech video video search

License:

Dataset card Viewer Files Files and versions Community

jamescalam commited on Oct 21, 2022

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@@ -35,7 +35,7 @@ task_ids:
 - visual-question-answering
 ---
-The YouTube transcriptions dataset contains technical tutorials (currently only from [YouTube.com/c/JamesBriggs](https://www.youtube.com/c/jamesbriggs)) transcribed using [OpenAI's Whisper](https://huggingface.co/openai/whisper-large) (large). Each row represents roughly a sentence-length chunk of text alongside the video URL and timestamp.
 Note that each item in the dataset contains just a short chunk of text. For most use cases you will likely need to merge multiple rows to create more substantial chunks of text, if you need to do that, this code snippet will help:

 - visual-question-answering
 ---
+The YouTube transcriptions dataset contains technical tutorials (currently from [James Briggs](https://www.youtube.com/c/jamesbriggs), [Daniel Bourke](https://www.youtube.com/channel/UCr8O8l5cCX85Oem1d18EezQ), and [AI Coffee Break](https://www.youtube.com/c/aicoffeebreak)) transcribed using [OpenAI's Whisper](https://huggingface.co/openai/whisper-large) (large). Each row represents roughly a sentence-length chunk of text alongside the video URL and timestamp.
 Note that each item in the dataset contains just a short chunk of text. For most use cases you will likely need to merge multiple rows to create more substantial chunks of text, if you need to do that, this code snippet will help: