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yt-dlp/yt_dlp/extractor/slideslive.py
sepro add96eb9f8
[cleanup] Add more ruff rules (#10149) 
Authored by: seproDev

Reviewed-by: bashonly <88596187+bashonly@users.noreply.github.com>
Reviewed-by: Simon Sawicki <contact@grub4k.xyz>
2024-06-12 01:09:58 +02:00

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21 KiB
Python
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import re
import urllib.parse
import xml.etree.ElementTree
from .common import InfoExtractor
from ..utils import (
ExtractorError,
int_or_none,
parse_qs,
smuggle_url,
traverse_obj,
unified_timestamp,
update_url_query,
url_or_none,
xpath_text,
)
class SlidesLiveIE(InfoExtractor):
_VALID_URL = r'https?://slideslive\.com/(?:embed/(?:presentation/)?)?(?P<id>[0-9]+)'
_TESTS = [{
# service_name = yoda, only XML slides info
'url': 'https://slideslive.com/38902413/gcc-ia16-backend',
'info_dict': {
'id': '38902413',
'ext': 'mp4',
'title': 'GCC IA16 backend',
'timestamp': 1697793372,
'upload_date': '20231020',
'thumbnail': r're:^https?://.*\.jpg',
'thumbnails': 'count:42',
'chapters': 'count:41',
'duration': 1638,
},
'params': {
'skip_download': 'm3u8',
},
}, {
# service_name = yoda, /v7/ slides
'url': 'https://slideslive.com/38935785',
'info_dict': {
'id': '38935785',
'ext': 'mp4',
'title': 'Offline Reinforcement Learning: From Algorithms to Practical Challenges',
'upload_date': '20231020',
'timestamp': 1697807002,
'thumbnail': r're:^https?://.*\.(?:jpg|png)',
'thumbnails': 'count:640',
'chapters': 'count:639',
'duration': 9832,
},
'params': {
'skip_download': 'm3u8',
},
}, {
# service_name = yoda, /v1/ slides
'url': 'https://slideslive.com/38973182/how-should-a-machine-learning-researcher-think-about-ai-ethics',
'info_dict': {
'id': '38973182',
'ext': 'mp4',
'title': 'How Should a Machine Learning Researcher Think About AI Ethics?',
'upload_date': '20231020',
'thumbnail': r're:^https?://.*\.jpg',
'timestamp': 1697822521,
'thumbnails': 'count:3',
'chapters': 'count:2',
'duration': 5889,
},
'params': {
'skip_download': 'm3u8',
},
}, {
# formerly youtube, converted to native
'url': 'https://slideslive.com/38897546/special-metaprednaska-petra-ludwiga-hodnoty-pro-lepsi-spolecnost',
'md5': '8a79b5e3d700837f40bd2afca3c8fa01',
'info_dict': {
'id': '38897546',
'ext': 'mp4',
'title': 'SPECIÁL: Meta-přednáška Petra Ludwiga - Hodnoty pro lepší společnost',
'thumbnail': r're:^https?://.*\.jpg',
'upload_date': '20231029',
'timestamp': 1698588144,
'thumbnails': 'count:169',
'chapters': 'count:168',
'duration': 6827,
},
'params': {
'skip_download': 'm3u8',
},
}, {
# embed-only presentation, only XML slides info
'url': 'https://slideslive.com/embed/presentation/38925850',
'info_dict': {
'id': '38925850',
'ext': 'mp4',
'title': 'Towards a Deep Network Architecture for Structured Smoothness',
'thumbnail': r're:^https?://.*\.jpg',
'thumbnails': 'count:8',
'timestamp': 1697803109,
'upload_date': '20231020',
'chapters': 'count:7',
'duration': 326,
},
'params': {
'skip_download': 'm3u8',
},
}, {
# embed-only presentation, only JSON slides info, /v5/ slides (.png)
'url': 'https://slideslive.com/38979920/',
'info_dict': {
'id': '38979920',
'ext': 'mp4',
'title': 'MoReL: Multi-omics Relational Learning',
'thumbnail': r're:^https?://.*\.(?:jpg|png)',
'thumbnails': 'count:7',
'timestamp': 1697824939,
'upload_date': '20231020',
'chapters': 'count:6',
'duration': 171,
},
'params': {
'skip_download': 'm3u8',
},
}, {
# /v2/ slides (.jpg)
'url': 'https://slideslive.com/38954074',
'info_dict': {
'id': '38954074',
'ext': 'mp4',
'title': 'Decentralized Attribution of Generative Models',
'thumbnail': r're:^https?://.*\.jpg',
'thumbnails': 'count:16',
'timestamp': 1697814901,
'upload_date': '20231020',
'chapters': 'count:15',
'duration': 306,
},
'params': {
'skip_download': 'm3u8',
},
}, {
# /v4/ slides (.png)
'url': 'https://slideslive.com/38979570/',
'info_dict': {
'id': '38979570',
'ext': 'mp4',
'title': 'Efficient Active Search for Combinatorial Optimization Problems',
'thumbnail': r're:^https?://.*\.(?:jpg|png)',
'thumbnails': 'count:9',
'timestamp': 1697824757,
'upload_date': '20231020',
'chapters': 'count:8',
'duration': 295,
},
'params': {
'skip_download': 'm3u8',
},
}, {
# /v10/ slides
'url': 'https://slideslive.com/embed/presentation/38979880?embed_parent_url=https%3A%2F%2Fedit.videoken.com%2F',
'info_dict': {
'id': '38979880',
'ext': 'mp4',
'title': 'The Representation Power of Neural Networks',
'timestamp': 1697824919,
'thumbnail': r're:^https?://.*\.(?:jpg|png)',
'thumbnails': 'count:22',
'upload_date': '20231020',
'chapters': 'count:21',
'duration': 294,
},
'params': {
'skip_download': 'm3u8',
},
}, {
# /v7/ slides, 2 video slides
'url': 'https://slideslive.com/embed/presentation/38979682?embed_container_origin=https%3A%2F%2Fedit.videoken.com',
'playlist_count': 3,
'info_dict': {
'id': '38979682-playlist',
'title': 'LoRA: Low-Rank Adaptation of Large Language Models',
},
'playlist': [{
'info_dict': {
'id': '38979682',
'ext': 'mp4',
'title': 'LoRA: Low-Rank Adaptation of Large Language Models',
'timestamp': 1697824815,
'thumbnail': r're:^https?://.*\.(?:jpg|png)',
'thumbnails': 'count:30',
'upload_date': '20231020',
'chapters': 'count:31',
'duration': 272,
},
}, {
'info_dict': {
'id': '38979682-021',
'ext': 'mp4',
'title': 'LoRA: Low-Rank Adaptation of Large Language Models - Slide 021',
'duration': 3,
'timestamp': 1697824815,
'upload_date': '20231020',
},
}, {
'info_dict': {
'id': '38979682-024',
'ext': 'mp4',
'title': 'LoRA: Low-Rank Adaptation of Large Language Models - Slide 024',
'duration': 4,
'timestamp': 1697824815,
'upload_date': '20231020',
},
}],
'params': {
'skip_download': 'm3u8',
},
}, {
# /v6/ slides, 1 video slide, edit.videoken.com embed
'url': 'https://slideslive.com/38979481/',
'playlist_count': 2,
'info_dict': {
'id': '38979481-playlist',
'title': 'How to Train Your MAML to Excel in Few-Shot Classification',
},
'playlist': [{
'info_dict': {
'id': '38979481',
'ext': 'mp4',
'title': 'How to Train Your MAML to Excel in Few-Shot Classification',
'timestamp': 1697824716,
'thumbnail': r're:^https?://.*\.(?:jpg|png)',
'thumbnails': 'count:43',
'upload_date': '20231020',
'chapters': 'count:43',
'duration': 315,
},
}, {
'info_dict': {
'id': '38979481-013',
'ext': 'mp4',
'title': 'How to Train Your MAML to Excel in Few-Shot Classification - Slide 013',
'duration': 3,
'timestamp': 1697824716,
'upload_date': '20231020',
},
}],
'params': {
'skip_download': 'm3u8',
},
}, {
# /v3/ slides, .jpg and .png, service_name = youtube
'url': 'https://slideslive.com/embed/38932460/',
'info_dict': {
'id': 'RTPdrgkyTiE',
'display_id': '38932460',
'ext': 'mp4',
'title': 'Active Learning for Hierarchical Multi-Label Classification',
'description': 'Watch full version of this video at https://slideslive.com/38932460.',
'channel': 'SlidesLive Videos - A',
'channel_id': 'UC62SdArr41t_-_fX40QCLRw',
'channel_url': 'https://www.youtube.com/channel/UC62SdArr41t_-_fX40QCLRw',
'uploader': 'SlidesLive Videos - A',
'uploader_id': '@slideslivevideos-a6075',
'uploader_url': 'https://www.youtube.com/@slideslivevideos-a6075',
'upload_date': '20200903',
'timestamp': 1697805922,
'duration': 942,
'age_limit': 0,
'live_status': 'not_live',
'playable_in_embed': True,
'availability': 'unlisted',
'categories': ['People & Blogs'],
'tags': [],
'channel_follower_count': int,
'like_count': int,
'view_count': int,
'thumbnail': r're:^https?://.*\.(?:jpg|png|webp)',
'thumbnails': 'count:21',
'chapters': 'count:20',
},
'params': {
'skip_download': 'm3u8',
},
}, {
# /v3/ slides, .png only, service_name = yoda
'url': 'https://slideslive.com/38983994',
'info_dict': {
'id': '38983994',
'ext': 'mp4',
'title': 'Zero-Shot AutoML with Pretrained Models',
'timestamp': 1697826708,
'upload_date': '20231020',
'thumbnail': r're:^https?://.*\.(?:jpg|png)',
'thumbnails': 'count:23',
'chapters': 'count:22',
'duration': 295,
},
'params': {
'skip_download': 'm3u8',
},
}, {
# service_name = yoda
'url': 'https://slideslive.com/38903721/magic-a-scientific-resurrection-of-an-esoteric-legend',
'only_matching': True,
}, {
# dead link, service_name = url
'url': 'https://slideslive.com/38922070/learning-transferable-skills-1',
'only_matching': True,
}, {
# dead link, service_name = vimeo
'url': 'https://slideslive.com/38921896/retrospectives-a-venue-for-selfreflection-in-ml-research-3',
'only_matching': True,
}]
_WEBPAGE_TESTS = [{
# only XML slides info
'url': 'https://iclr.cc/virtual_2020/poster_Hklr204Fvr.html',
'info_dict': {
'id': '38925850',
'ext': 'mp4',
'title': 'Towards a Deep Network Architecture for Structured Smoothness',
'thumbnail': r're:^https?://.*\.jpg',
'thumbnails': 'count:8',
'timestamp': 1697803109,
'upload_date': '20231020',
'chapters': 'count:7',
'duration': 326,
},
'params': {
'skip_download': 'm3u8',
},
}]
@classmethod
def _extract_embed_urls(cls, url, webpage):
# Reference: https://slideslive.com/embed_presentation.js
for embed_id in re.findall(r'(?s)new\s+SlidesLiveEmbed\s*\([^)]+\bpresentationId:\s*["\'](\d+)["\']', webpage):
url_parsed = urllib.parse.urlparse(url)
origin = f'{url_parsed.scheme}://{url_parsed.netloc}'
yield update_url_query(
f'https://slideslive.com/embed/presentation/{embed_id}', {
'embed_parent_url': url,
'embed_container_origin': origin,
})
def _download_embed_webpage_handle(self, video_id, headers):
return self._download_webpage_handle(
f'https://slideslive.com/embed/presentation/{video_id}', video_id,
headers=headers, query=traverse_obj(headers, {
'embed_parent_url': 'Referer',
'embed_container_origin': 'Origin',
}))
def _extract_custom_m3u8_info(self, m3u8_data):
m3u8_dict = {}
lookup = {
'PRESENTATION-TITLE': 'title',
'PRESENTATION-UPDATED-AT': 'timestamp',
'PRESENTATION-THUMBNAIL': 'thumbnail',
'PLAYLIST-TYPE': 'playlist_type',
'VOD-VIDEO-SERVICE-NAME': 'service_name',
'VOD-VIDEO-ID': 'service_id',
'VOD-VIDEO-SERVERS': 'video_servers',
'VOD-SUBTITLES': 'subtitles',
'VOD-SLIDES-JSON-URL': 'slides_json_url',
'VOD-SLIDES-XML-URL': 'slides_xml_url',
}
for line in m3u8_data.splitlines():
if not line.startswith('#EXT-SL-'):
continue
tag, _, value = line.partition(':')
key = lookup.get(tag[8:])
if not key:
continue
m3u8_dict[key] = value
# Some values are stringified JSON arrays
for key in ('video_servers', 'subtitles'):
if key in m3u8_dict:
m3u8_dict[key] = self._parse_json(m3u8_dict[key], None, fatal=False) or []
return m3u8_dict
def _extract_formats_and_duration(self, cdn_hostname, path, video_id, skip_duration=False):
formats, duration = [], None
hls_formats = self._extract_m3u8_formats(
f'https://{cdn_hostname}/{path}/master.m3u8',
video_id, 'mp4', m3u8_id='hls', fatal=False, live=True)
if hls_formats:
if not skip_duration:
duration = self._extract_m3u8_vod_duration(
hls_formats[0]['url'], video_id, note='Extracting duration from HLS manifest')
formats.extend(hls_formats)
dash_formats = self._extract_mpd_formats(
f'https://{cdn_hostname}/{path}/master.mpd', video_id, mpd_id='dash', fatal=False)
if dash_formats:
if not duration and not skip_duration:
duration = self._extract_mpd_vod_duration(
f'https://{cdn_hostname}/{path}/master.mpd', video_id,
note='Extracting duration from DASH manifest')
formats.extend(dash_formats)
return formats, duration
def _real_extract(self, url):
video_id = self._match_id(url)
webpage, urlh = self._download_embed_webpage_handle(
video_id, headers=traverse_obj(parse_qs(url), {
'Referer': ('embed_parent_url', -1),
'Origin': ('embed_container_origin', -1)}))
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:
raise ExtractorError(
'This is an embed-only presentation. Try passing --referer', expected=True)
webpage, _ = self._download_embed_webpage_handle(video_id, headers={
'Referer': f'https://{domain}/',
'Origin': f'https://{domain}',
})
player_token = self._search_regex(r'data-player-token="([^"]+)"', webpage, 'player token')
player_data = self._download_webpage(
f'https://ben.slideslive.com/player/{video_id}', video_id,
note='Downloading player info', query={'player_token': player_token})
player_info = self._extract_custom_m3u8_info(player_data)
service_name = player_info['service_name'].lower()
assert service_name in ('url', 'yoda', 'vimeo', 'youtube')
service_id = player_info['service_id']
slide_url_template = 'https://slides.slideslive.com/%s/slides/original/%s%s'
slides, slides_info = {}, []
if player_info.get('slides_json_url'):
slides = self._download_json(
player_info['slides_json_url'], video_id, fatal=False,
note='Downloading slides JSON', errnote=False) or {}
slide_ext_default = '.png'
slide_quality = traverse_obj(slides, ('slide_qualities', 0))
if slide_quality:
slide_ext_default = '.jpg'
slide_url_template = f'https://cdn.slideslive.com/data/presentations/%s/slides/{slide_quality}/%s%s'
for slide_id, slide in enumerate(traverse_obj(slides, ('slides', ...), expected_type=dict), 1):
slides_info.append((
slide_id, traverse_obj(slide, ('image', 'name')),
traverse_obj(slide, ('image', 'extname'), default=slide_ext_default),
int_or_none(slide.get('time'), scale=1000)))
if not slides and player_info.get('slides_xml_url'):
slides = self._download_xml(
player_info['slides_xml_url'], video_id, fatal=False,
note='Downloading slides XML', errnote='Failed to download slides info')
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')):
thumbnails.append({'id': 'cover', 'url': player_info['thumbnail']})
for slide_id, slide_path, slide_ext, start_time in slides_info:
if slide_path:
thumbnails.append({
'id': f'{slide_id:03d}',
'url': slide_url_template % (video_id, slide_path, slide_ext),
})
chapters.append({
'title': f'Slide {slide_id:03d}',
'start_time': start_time,
})
subtitles = {}
for sub in traverse_obj(player_info, ('subtitles', ...), expected_type=dict):
webvtt_url = url_or_none(sub.get('webvtt_url'))
if not webvtt_url:
continue
subtitles.setdefault(sub.get('language') or 'en', []).append({
'url': webvtt_url,
'ext': 'vtt',
})
info = {
'id': video_id,
'title': player_info.get('title') or self._html_search_meta('title', webpage, default=''),
'timestamp': unified_timestamp(player_info.get('timestamp')),
'is_live': player_info.get('playlist_type') != 'vod',
'thumbnails': thumbnails,
'chapters': chapters,
'subtitles': subtitles,
}
if service_name == 'url':
info['url'] = service_id
elif service_name == 'yoda':
formats, duration = self._extract_formats_and_duration(
player_info['video_servers'][0], service_id, video_id)
info.update({
'duration': duration,
'formats': formats,
})
else:
info.update({
'_type': 'url_transparent',
'url': service_id,
'ie_key': service_name.capitalize(),
'display_id': video_id,
})
if service_name == 'vimeo':
info['url'] = smuggle_url(
f'https://player.vimeo.com/video/{service_id}',
{'referer': url})
video_slides = traverse_obj(slides, ('slides', ..., 'video', 'id'))
if not video_slides:
return info
def entries():
yield info
service_data = self._download_json(
f'https://ben.slideslive.com/player/{video_id}/slides_video_service_data',
video_id, fatal=False, query={
'player_token': player_token,
'videos': ','.join(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 traverse_obj(slide, ('video', 'service')) != 'yoda':
continue
video_path = traverse_obj(slide, ('video', 'id'))
cdn_hostname = traverse_obj(service_data, (
video_path, 'video_servers', ...), get_all=False)
if not cdn_hostname or not video_path:
continue
formats, _ = self._extract_formats_and_duration(
cdn_hostname, video_path, video_id, skip_duration=True)
if not formats:
continue
yield {
'id': f'{video_id}-{slide_id:03d}',
'title': f'{info["title"]} - Slide {slide_id:03d}',
'timestamp': info['timestamp'],
'duration': int_or_none(traverse_obj(slide, ('video', 'duration_ms')), scale=1000),
'formats': formats,
}
return self.playlist_result(entries(), f'{video_id}-playlist', info['title'])
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