Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
no-annotation
Source Datasets:
original
License:
| annotations_creators: | |
| - no-annotation | |
| language: | |
| - en | |
| language_creators: | |
| - found | |
| license: | |
| - afl-3.0 | |
| multilinguality: | |
| - monolingual | |
| pretty_name: Youtube Transcriptions | |
| size_categories: | |
| - 10K<n<100K | |
| source_datasets: | |
| - original | |
| tags: | |
| - youtube | |
| - technical | |
| - speech to text | |
| - speech | |
| - video | |
| - video search | |
| - audio | |
| - audio search | |
| task_categories: | |
| - conversational | |
| - question-answering | |
| - text-retrieval | |
| - visual-question-answering | |
| task_ids: | |
| - open-domain-qa | |
| - extractive-qa | |
| - document-retrieval | |
| - 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: | |
| ```python | |
| from datasets import load_dataset | |
| # first download the dataset | |
| data = load_dataset( | |
| 'jamescalam/youtube-transcriptions', | |
| split='train' | |
| ) | |
| new_data = [] # this will store adjusted data | |
| window = 6 # number of sentences to combine | |
| stride = 3 # number of sentences to 'stride' over, used to create overlap | |
| for i in range(0, len(data), stride): | |
| i_end = min(len(data)-1, i+window) | |
| if data[i]['title'] != data[i_end]['title']: | |
| # in this case we skip this entry as we have start/end of two videos | |
| continue | |
| # create larger text chunk | |
| text = ' '.join(data[i:i_end]['text']) | |
| # add to adjusted data list | |
| new_data.append({ | |
| 'start': data[i]['start'], | |
| 'end': data[i_end]['end'], | |
| 'title': data[i]['title'], | |
| 'text': text, | |
| 'id': data[i]['id'], | |
| 'url': data[i]['url'], | |
| 'published': data[i]['published'] | |
| }) | |
| ``` |