Datasets:

thepowerfuldeez
/

massive-yt-edu-transcriptions

Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 2 new columns ({'license_risk', 'content_category'})

This happened while the json dataset builder was generating data using

hf://datasets/thepowerfuldeez/massive-yt-edu-transcriptions/train-00001.jsonl (at revision 54ad78baed4e66b5a6a8b9f9def2653973df5e3e), [/tmp/hf-datasets-cache/medium/datasets/30933230930908-config-parquet-and-info-thepowerfuldeez-massive-y-77cde49d/hub/datasets--thepowerfuldeez--massive-yt-edu-transcriptions/snapshots/54ad78baed4e66b5a6a8b9f9def2653973df5e3e/train-00000.jsonl (origin=hf://datasets/thepowerfuldeez/massive-yt-edu-transcriptions@54ad78baed4e66b5a6a8b9f9def2653973df5e3e/train-00000.jsonl), /tmp/hf-datasets-cache/medium/datasets/30933230930908-config-parquet-and-info-thepowerfuldeez-massive-y-77cde49d/hub/datasets--thepowerfuldeez--massive-yt-edu-transcriptions/snapshots/54ad78baed4e66b5a6a8b9f9def2653973df5e3e/train-00001.jsonl (origin=hf://datasets/thepowerfuldeez/massive-yt-edu-transcriptions@54ad78baed4e66b5a6a8b9f9def2653973df5e3e/train-00001.jsonl)]

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1887, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 675, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              video_id: string
              title: string
              text: string
              duration_seconds: int64
              source: string
              url: string
              priority: int64
              speed_ratio: double
              content_category: string
              license_risk: string
              to
              {'video_id': Value('string'), 'title': Value('string'), 'text': Value('string'), 'duration_seconds': Value('float64'), 'source': Value('string'), 'url': Value('string'), 'priority': Value('int64'), 'speed_ratio': Value('float64')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 884, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1736, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1889, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 2 new columns ({'license_risk', 'content_category'})
              
              This happened while the json dataset builder was generating data using
              
              hf://datasets/thepowerfuldeez/massive-yt-edu-transcriptions/train-00001.jsonl (at revision 54ad78baed4e66b5a6a8b9f9def2653973df5e3e), [/tmp/hf-datasets-cache/medium/datasets/30933230930908-config-parquet-and-info-thepowerfuldeez-massive-y-77cde49d/hub/datasets--thepowerfuldeez--massive-yt-edu-transcriptions/snapshots/54ad78baed4e66b5a6a8b9f9def2653973df5e3e/train-00000.jsonl (origin=hf://datasets/thepowerfuldeez/massive-yt-edu-transcriptions@54ad78baed4e66b5a6a8b9f9def2653973df5e3e/train-00000.jsonl), /tmp/hf-datasets-cache/medium/datasets/30933230930908-config-parquet-and-info-thepowerfuldeez-massive-y-77cde49d/hub/datasets--thepowerfuldeez--massive-yt-edu-transcriptions/snapshots/54ad78baed4e66b5a6a8b9f9def2653973df5e3e/train-00001.jsonl (origin=hf://datasets/thepowerfuldeez/massive-yt-edu-transcriptions@54ad78baed4e66b5a6a8b9f9def2653973df5e3e/train-00001.jsonl)]
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

video_id string	title string	text string	duration_seconds float64	priority int64	speed_ratio float64
vidCX_dMCu0	Lec 02. How to Train a Neural Net	Okay, so welcome to lecture two of deep learning. Today we're going to talk about how you actually train a neural network. And I imagine that some of this might be a review for some of you but hopefully kind of the perspective or the way we look talk about the process of training the neural network might be a slightly ...	4,773.952	9	67.6
8zzfcYIELdo	Lec 15. Generative Models: Representation Learning Meets Generative Modeling	We're going to continue on today and this week with generative models. So today we'll be on variational auto-encoders. And we're going to give a perspective that generative modeling and representation learning are very tightly coupled. If you can solve the generative modeling problem, it should help you solve the repre...	4,839.317333	9	63.7
7hbf4klU3ks	Lec 20. Scaling Laws	Okay, so today we're going to talk about scaling loss. And this lecture is, you know, we're at the final few weeks of the semester. So this is, we're moving into territory, which is much more kind of ongoing science. Like I'm gonna talk about recent papers and results that are empirical in nature for which we don't kno...	2,302.037333	9	69.9
IiHknRHA-Gk	Lec 10. Architectures: Memory	"So today we're going to be doing our last of this mini series on machine learning architectures or (...TRUNCATED)	4,407.616	9	70.7
bxVkZ4M-hIE	Lec 04. Architectures: Grids	"So today we're going to start off the first of a series of lectures on machine learning architectur(...TRUNCATED)	5,036.309333	9	65.9
zaMcHuJwe1w	Lec 16. Generative Models: Conditional Models	"OK, so welcome. Happy Halloween. Nice to see a few little costumes. OK, so today we're going to tal(...TRUNCATED)	4,891.285333	9	63.1
-eC0-5mXHQg	Lec 13. Representation Learning: Theory	"Okay, thank you everyone for coming to the lecture. So this was the example on the, do people remem(...TRUNCATED)	4,520.106667	9	73.7
QxOzQRtd440	Lec 11. Representation Learning: Reconstruction-Based	"Good. So welcome to today's lecture. And today we're going to talk about representation learning. S(...TRUNCATED)	4,863.616	9	65.7
RUdQMHV-7KM	Lec 19. Transfer Learning: Data	"We're going to finish up our short series on transfer learning today. And the other sort of logisti(...TRUNCATED)	4,543.104	9	68.2
hJlrAHqGOS8	Lec 14. Generative Models: Basics	"Okay, yeah, welcome everyone. So now we're going to get coming to the, um, another section of the c(...TRUNCATED)	4,877.909333	9	67.3

End of preview.

Massive YouTube Educational Transcriptions

Large-scale educational content transcribed from YouTube using distil-whisper/distil-large-v3.5.

Stats

Videos: 59,355
Characters: 1,539,022,925 (~384M tokens)
Audio hours: 35,890
Model: faster-whisper (CTranslate2) with distil-large-v3.5
Hardware: 2x RTX 5090 + 2x RTX 4090 at 165-185x realtime

Fields

Field	Description
`video_id`	YouTube video ID
`title`	Video title
`text`	Full transcript
`duration_seconds`	Original audio duration (seconds)
`source`	Discovery source (channel/course/playlist)
`url`	YouTube URL
`priority`	Educational priority (9=university, 8=lecture, 7=documentary, 5=general)
`speed_ratio`	Transcription speed (realtime multiplier)
`content_category`	Content type (university_lecture, conference, individual_educator, etc.)
`license_risk`	License risk level (green/yellow/orange/red)

Content Categories

green: CC-licensed or public domain (NPTEL, Khan Academy, MIT OCW, Yale OYC)
yellow: Standard YouTube license, fair use for research
orange: Commercial content, needs review
red: Non-educational, excluded from transcription

Methodology

Discovery: Channel crawling, related video walking, CC-focused search
Quality filter: 40+ reject categories, duration >= 15min, 3-tier priority scoring
Transcription: faster-whisper CTranslate2, 1.2x audio speedup, beam=1, no VAD
Classification: Channel name -> title regex -> priority fallback

Code

github.com/thepowerfuldeez/massive_yt_edu_scraper

License

MIT

Downloads last month: 24

Total file size:

2.31 GB