import re
import urllib.parse
+import xml.etree.ElementTree
from .common import InfoExtractor
from ..utils import (
'id': '38902413',
'ext': 'mp4',
'title': 'GCC IA16 backend',
- 'timestamp': 1648189972,
- 'upload_date': '20220325',
+ 'timestamp': 1697793372,
+ 'upload_date': '20231020',
'thumbnail': r're:^https?://.*\.jpg',
'thumbnails': 'count:42',
'chapters': 'count:41',
'id': '38935785',
'ext': 'mp4',
'title': 'Offline Reinforcement Learning: From Algorithms to Practical Challenges',
- 'upload_date': '20211115',
- 'timestamp': 1636996003,
+ 'upload_date': '20231020',
+ 'timestamp': 1697807002,
'thumbnail': r're:^https?://.*\.(?:jpg|png)',
'thumbnails': 'count:640',
'chapters': 'count:639',
'id': '38973182',
'ext': 'mp4',
'title': 'How Should a Machine Learning Researcher Think About AI Ethics?',
- 'upload_date': '20220201',
+ 'upload_date': '20231020',
'thumbnail': r're:^https?://.*\.jpg',
- 'timestamp': 1643728135,
+ 'timestamp': 1697822521,
'thumbnails': 'count:3',
'chapters': 'count:2',
'duration': 5889,
'skip_download': 'm3u8',
},
}, {
- # service_name = youtube, only XML slides info
+ # formerly youtube, converted to native
'url': 'https://slideslive.com/38897546/special-metaprednaska-petra-ludwiga-hodnoty-pro-lepsi-spolecnost',
'md5': '8a79b5e3d700837f40bd2afca3c8fa01',
'info_dict': {
- 'id': 'jmg02wCJD5M',
- 'display_id': '38897546',
+ 'id': '38897546',
'ext': 'mp4',
'title': 'SPECIÁL: Meta-přednáška Petra Ludwiga - Hodnoty pro lepší společnost',
- 'description': 'Watch full version of this video at https://slideslive.com/38897546.',
- 'channel_url': 'https://www.youtube.com/channel/UCZWdAkNYFncuX0khyvhqnxw',
- 'channel': 'SlidesLive Videos - G1',
- 'channel_id': 'UCZWdAkNYFncuX0khyvhqnxw',
- 'uploader_id': 'UCZWdAkNYFncuX0khyvhqnxw',
- 'uploader': 'SlidesLive Videos - G1',
- 'uploader_url': 'http://www.youtube.com/channel/UCZWdAkNYFncuX0khyvhqnxw',
- 'live_status': 'not_live',
- 'upload_date': '20160710',
- 'timestamp': 1618786715,
- 'duration': 6827,
- 'like_count': int,
- 'view_count': int,
- 'comment_count': int,
- 'channel_follower_count': int,
- 'age_limit': 0,
- 'thumbnail': r're:^https?://.*\.(?:jpg|webp)',
+ 'thumbnail': r're:^https?://.*\.jpg',
+ 'upload_date': '20231029',
+ 'timestamp': 1698588144,
'thumbnails': 'count:169',
- 'playable_in_embed': True,
- 'availability': 'unlisted',
- 'tags': [],
- 'categories': ['People & Blogs'],
'chapters': 'count:168',
+ 'duration': 6827,
+ },
+ 'params': {
+ 'skip_download': 'm3u8',
},
}, {
# embed-only presentation, only XML slides info
'title': 'Towards a Deep Network Architecture for Structured Smoothness',
'thumbnail': r're:^https?://.*\.jpg',
'thumbnails': 'count:8',
- 'timestamp': 1629671508,
- 'upload_date': '20210822',
+ 'timestamp': 1697803109,
+ 'upload_date': '20231020',
'chapters': 'count:7',
'duration': 326,
},
'title': 'MoReL: Multi-omics Relational Learning',
'thumbnail': r're:^https?://.*\.(?:jpg|png)',
'thumbnails': 'count:7',
- 'timestamp': 1654714970,
- 'upload_date': '20220608',
+ 'timestamp': 1697824939,
+ 'upload_date': '20231020',
'chapters': 'count:6',
'duration': 171,
},
'title': 'Decentralized Attribution of Generative Models',
'thumbnail': r're:^https?://.*\.jpg',
'thumbnails': 'count:16',
- 'timestamp': 1622806321,
- 'upload_date': '20210604',
+ 'timestamp': 1697814901,
+ 'upload_date': '20231020',
'chapters': 'count:15',
'duration': 306,
},
'title': 'Efficient Active Search for Combinatorial Optimization Problems',
'thumbnail': r're:^https?://.*\.(?:jpg|png)',
'thumbnails': 'count:9',
- 'timestamp': 1654714896,
- 'upload_date': '20220608',
+ 'timestamp': 1697824757,
+ 'upload_date': '20231020',
'chapters': 'count:8',
'duration': 295,
},
'id': '38979880',
'ext': 'mp4',
'title': 'The Representation Power of Neural Networks',
- 'timestamp': 1654714962,
+ 'timestamp': 1697824919,
'thumbnail': r're:^https?://.*\.(?:jpg|png)',
'thumbnails': 'count:22',
- 'upload_date': '20220608',
+ 'upload_date': '20231020',
'chapters': 'count:21',
'duration': 294,
},
'id': '38979682',
'ext': 'mp4',
'title': 'LoRA: Low-Rank Adaptation of Large Language Models',
- 'timestamp': 1654714920,
+ 'timestamp': 1697824815,
'thumbnail': r're:^https?://.*\.(?:jpg|png)',
'thumbnails': 'count:30',
- 'upload_date': '20220608',
+ 'upload_date': '20231020',
'chapters': 'count:31',
'duration': 272,
},
'ext': 'mp4',
'title': 'LoRA: Low-Rank Adaptation of Large Language Models - Slide 021',
'duration': 3,
- 'timestamp': 1654714920,
- 'upload_date': '20220608',
+ 'timestamp': 1697824815,
+ 'upload_date': '20231020',
},
}, {
'info_dict': {
'ext': 'mp4',
'title': 'LoRA: Low-Rank Adaptation of Large Language Models - Slide 024',
'duration': 4,
- 'timestamp': 1654714920,
- 'upload_date': '20220608',
+ 'timestamp': 1697824815,
+ 'upload_date': '20231020',
},
}],
'params': {
'id': '38979481',
'ext': 'mp4',
'title': 'How to Train Your MAML to Excel in Few-Shot Classification',
- 'timestamp': 1654714877,
+ 'timestamp': 1697824716,
'thumbnail': r're:^https?://.*\.(?:jpg|png)',
'thumbnails': 'count:43',
- 'upload_date': '20220608',
+ 'upload_date': '20231020',
'chapters': 'count:43',
'duration': 315,
},
'ext': 'mp4',
'title': 'How to Train Your MAML to Excel in Few-Shot Classification - Slide 013',
'duration': 3,
- 'timestamp': 1654714877,
- 'upload_date': '20220608',
+ 'timestamp': 1697824716,
+ 'upload_date': '20231020',
},
}],
'params': {
'channel_id': 'UC62SdArr41t_-_fX40QCLRw',
'channel_url': 'https://www.youtube.com/channel/UC62SdArr41t_-_fX40QCLRw',
'uploader': 'SlidesLive Videos - A',
- 'uploader_id': 'UC62SdArr41t_-_fX40QCLRw',
- 'uploader_url': 'http://www.youtube.com/channel/UC62SdArr41t_-_fX40QCLRw',
+ 'uploader_id': '@slideslivevideos-a6075',
+ 'uploader_url': 'https://www.youtube.com/@slideslivevideos-a6075',
'upload_date': '20200903',
- 'timestamp': 1602599092,
+ 'timestamp': 1697805922,
'duration': 942,
'age_limit': 0,
'live_status': 'not_live',
'id': '38983994',
'ext': 'mp4',
'title': 'Zero-Shot AutoML with Pretrained Models',
- 'timestamp': 1662384834,
- 'upload_date': '20220905',
+ 'timestamp': 1697826708,
+ 'upload_date': '20231020',
'thumbnail': r're:^https?://.*\.(?:jpg|png)',
'thumbnails': 'count:23',
'chapters': 'count:22',
'title': 'Towards a Deep Network Architecture for Structured Smoothness',
'thumbnail': r're:^https?://.*\.jpg',
'thumbnails': 'count:8',
- 'timestamp': 1629671508,
- 'upload_date': '20210822',
+ 'timestamp': 1697803109,
+ 'upload_date': '20231020',
'chapters': 'count:7',
'duration': 326,
},
if not line.startswith('#EXT-SL-'):
continue
tag, _, value = line.partition(':')
- key = lookup.get(tag.lstrip('#EXT-SL-'))
+ key = lookup.get(tag[8:])
if not key:
continue
m3u8_dict[key] = value
video_id, headers=traverse_obj(parse_qs(url), {
'Referer': ('embed_parent_url', -1),
'Origin': ('embed_container_origin', -1)}))
- redirect_url = urlh.geturl()
+ redirect_url = urlh.url
if 'domain_not_allowed' in redirect_url:
domain = traverse_obj(parse_qs(redirect_url), ('allowed_domains[]', ...), get_all=False)
if not domain:
slides = self._download_xml(
player_info['slides_xml_url'], video_id, fatal=False,
note='Downloading slides XML', errnote='Failed to download slides info')
- slide_url_template = 'https://cdn.slideslive.com/data/presentations/%s/slides/big/%s%s'
- for slide_id, slide in enumerate(slides.findall('./slide') if slides else [], 1):
- slides_info.append((
- slide_id, xpath_text(slide, './slideName', 'name'), '.jpg',
- int_or_none(xpath_text(slide, './timeSec', 'time'))))
+ if isinstance(slides, xml.etree.ElementTree.Element):
+ slide_url_template = 'https://cdn.slideslive.com/data/presentations/%s/slides/big/%s%s'
+ for slide_id, slide in enumerate(slides.findall('./slide')):
+ slides_info.append((
+ slide_id, xpath_text(slide, './slideName', 'name'), '.jpg',
+ int_or_none(xpath_text(slide, './timeSec', 'time'))))
chapters, thumbnails = [], []
if url_or_none(player_info.get('thumbnail')):
if service_name == 'vimeo':
info['url'] = smuggle_url(
f'https://player.vimeo.com/video/{service_id}',
- {'http_headers': {'Referer': url}})
+ {'referer': url})
video_slides = traverse_obj(slides, ('slides', ..., 'video', 'id'))
if not video_slides:
}, note='Downloading video slides info', errnote='Failed to download video slides info') or {}
for slide_id, slide in enumerate(traverse_obj(slides, ('slides', ...)), 1):
- if not traverse_obj(slide, ('video', 'service')) == 'yoda':
+ if traverse_obj(slide, ('video', 'service')) != 'yoda':
continue
video_path = traverse_obj(slide, ('video', 'id'))
cdn_hostname = traverse_obj(service_data, (
projects / yt-dlp.git / blobdiff