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The JWT signature verification failed. Check the signing key and the algorithm.
Error code: JWTInvalidSignature
Exception: InvalidSignatureError
Message: Signature verification failed
Traceback: Traceback (most recent call last):
File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
decoded = jwt.decode(
jwt=token,
...<2 lines>...
options=options,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
decoded = self.decode_complete(
jwt,
...<8 lines>...
leeway=leeway,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
decoded = self._jws.decode_complete(
jwt,
...<3 lines>...
detached_payload=detached_payload,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
self._verify_signature(
~~~~~~~~~~~~~~~~~~~~~~^
signing_input,
^^^^^^^^^^^^^^
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options=merged_options,
^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
raise InvalidSignatureError("Signature verification failed")
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Massive YouTube Educational Video Queue
Full metadata and content classification for 4,489,228 YouTube educational videos totaling 3,975,157 hours.
Description
This dataset contains metadata, content categorization, and license risk assessment for ~4.5M YouTube videos identified as potentially educational. It serves as the discovery and processing queue for the massive-yt-edu-transcriptions project, which aims to create the world's largest open educational transcript dataset.
Each video has been classified by content type and assessed for license risk using a 3-tier automated classification system.
Collection Methodology
Video Discovery
Videos were discovered through multiple strategies:
- YouTube Search API — Educational keyword queries across dozens of academic disciplines
- Channel crawling — Snowball discovery from known educational channels (universities, MOOCs, conference organizers)
- Related video traversal — Following YouTube's related video graph from known educational content
- Playlist walking — Extracting full playlists from educational channels and course pages
- Quality filter — Minimum 15 minutes duration, 40+ rejection categories to filter non-educational content
Content Classification (3-tier system)
Tier 1: Channel/Source Name Classification
- 207,000+ YouTube channel and playlist names classified via pattern matching
- Patterns cover: universities (500+ institutions worldwide), conferences (100+ series), research institutes, government agencies, corporate talks, coaching/test prep, religious content, gaming/entertainment, medical/health, museums, tech communities
- Each source mapped to content category and license risk level
Tier 2: Title-Based Classification
- For videos without channel metadata, title analysis using regex patterns
- University detection (institution names, course codes, "Lecture N" patterns)
- Conference paper detection (conference names, "Keynote", year patterns)
- Educational keyword detection (tutorials, courses, crash courses)
- Entertainment/gaming detection for exclusion
- Medical/health content detection
- Religious content detection
- Multi-language support (English, Hindi, Russian, Chinese, Japanese, Korean, Arabic)
Tier 3: Priority-Based Fallback
- Videos with educational priority scores (P8-P9) from discovery classified as
unclassified_educational - Remaining unclassifiable content marked as
unknownwith fair-use-assumed yellow risk
License Risk Assessment
Four risk levels based on content source analysis:
🟢 green: Known Creative Commons or public domain license
- MIT OCW (CC-BY-NC-SA 4.0), Yale OYC (CC-BY-NC-SA), Khan Academy (CC-BY-NC-SA)
- NPTEL/IIT (CC-BY-SA 4.0), Taiwan OCW (CC-BY-NC-SA)
- Library of Congress (public domain), NASA, government agencies
🟡 yellow: Educational/factual content with strong fair use argument
- University lectures (factual educational content)
- Conference talks (meant for public dissemination)
- Tech talks and corporate presentations
- Individual educator tutorials
- Coaching/test prep material
🟠 orange: Uncertain, needs individual review
- Religious content (may be educational but different use case)
- Non-English content where license couldn't be verified
- Mixed educational/entertainment channels
🔴 red: Non-educational or high-risk content (EXCLUDED from processing queue)
- Gaming content, entertainment, reactions, drama
- Music performances, concerts
- News broadcasts
- Content clearly not educational
Fair Use Framework
Our transcription project relies on fair use analysis under 17 U.S.C. § 107:
- Purpose and character of use — Highly transformative: converting audio/video to text for machine learning training and research. The output (text transcripts) serves a fundamentally different purpose than the original (video lectures).
- Nature of the copyrighted work — Factual/educational content strongly favors fair use. Lectures, tutorials, and conference talks are factual works presenting knowledge.
- Amount used — Full transcription of audio (weighs against fair use), though only the audio track is used, not video.
- Effect on market — Text transcripts do not substitute for video content. No one watches a lecture by reading its transcript. The transcript cannot replace the educational experience of the video.
Fields
| Field | Type | Description |
|---|---|---|
video_id |
string | YouTube video ID (11 characters) |
title |
string | Video title as listed on YouTube |
url |
string | Full YouTube URL |
duration_seconds |
int | Video duration in seconds (0 if unknown) |
status |
string | Processing status: pending, completed, rejected, error |
priority |
int | Educational priority score (9=university OCW, 8=lecture, 7=documentary, 5=default) |
source |
string | Channel name, university, or course identifier |
content_category |
string | Content classification category (see below) |
license_risk |
string | License risk level: green, yellow, orange, or red |
Statistics
Total: 4,489,228 videos · 3,975,157 hours
Content Categories
| Category | Count | Hours | % of Total |
|---|---|---|---|
unknown |
1,249,993 | 1,088,480 | 27.8% |
coaching_test_prep |
829,883 | 738,964 | 18.5% |
university_lecture |
688,191 | 584,300 | 15.3% |
individual_educator |
634,423 | 611,780 | 14.1% |
unclassified_educational |
371,400 | 300,047 | 8.3% |
non_english_edu |
183,846 | 182,170 | 4.1% |
conference |
122,628 | 125,967 | 2.7% |
gaming_entertainment |
65,793 | 47,052 | 1.5% |
religious |
59,033 | 67,811 | 1.3% |
corporate_talks |
56,065 | 41,388 | 1.2% |
university_ocw |
43,335 | 27,354 | 1.0% |
tech_community |
33,379 | 44,644 | 0.7% |
individual_creator |
33,078 | 12,283 | 0.7% |
medical_health |
30,656 | 22,809 | 0.7% |
research_institute |
23,325 | 24,050 | 0.5% |
government_public |
21,521 | 21,601 | 0.5% |
museum_cultural |
14,169 | 14,785 | 0.3% |
news_media |
14,111 | 13,259 | 0.3% |
mooc_platform |
10,480 | 2,049 | 0.2% |
public_media |
3,565 | 4,046 | 0.1% |
null |
432 | 377 | 0.0% |
License Risk Distribution
| Risk | Count | Hours | % of Total |
|---|---|---|---|
🟡 yellow |
4,035,222 | 3,589,818 | 89.9% |
🟠 orange |
319,678 | 284,616 | 7.1% |
🔴 red |
71,815 | 54,866 | 1.6% |
🟢 green |
62,159 | 45,538 | 1.4% |
⚪ null |
367 | 340 | 0.0% |
Processing Status
| Status | Count |
|---|---|
pending |
4,266,627 |
rejected |
163,307 |
completed |
57,949 |
error |
1,175 |
timeout |
102 |
processing |
81 |
Priority Distribution
| Priority | Count |
|---|---|
| P9 | 20,117 |
| P8 | 1,549,389 |
| P7 | 11,765 |
| P5 | 2,819,903 |
| P4 | 9 |
| P3 | 12 |
| P0 | 88,111 |
Content Category Descriptions
| Category | Description |
|---|---|
university_lecture |
Lectures from identified universities (MIT, Stanford, IITs, etc.) |
university_ocw |
Official OpenCourseWare with known CC licenses |
individual_educator |
Independent educators, tutorial creators, online teachers |
coaching_test_prep |
Test preparation (GATE, JEE, NEET, GRE, etc.) and exam coaching |
conference |
Academic and tech conference talks (NeurIPS, PyCon, etc.) |
corporate_talks |
Corporate tech talks, cloud platform tutorials |
tech_community |
Open source and developer community content |
research_institute |
Research seminars, colloquia, symposia |
medical_health |
Medical education, clinical lectures, health content |
non_english_edu |
Educational content in non-English languages |
mooc_platform |
MOOC platforms (Coursera, edX channel content) |
museum_cultural |
Museum lectures, cultural institution content |
government_public |
Government agencies, public institutions |
public_media |
Public media educational content |
religious |
Religious lectures, sermons, scripture study |
news_media |
News broadcasts, press conferences |
gaming_entertainment |
Gaming, entertainment (excluded from processing) |
individual_creator |
General content creators (needs review) |
unclassified_educational |
High-priority videos without clear category |
unknown |
Unclassified content, assumed educational |
Related Datasets
- massive-yt-edu-transcriptions — Completed transcriptions from this queue
Code
- github.com/thepowerfuldeez/massive_yt_edu_scraper — Scraper and discovery
- github.com/georgethedeveloper77/million-hour-transcription — Classification and transcription pipeline
License
MIT — this metadata dataset. Individual video content has varying licenses as indicated by the license_risk field.
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