+import re
+import urllib.parse
+
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/(?P<id>[0-9]+)'
+ _VALID_URL = r'https?://slideslive\.com/(?:embed/(?:presentation/)?)?(?P<id>[0-9]+)'
_TESTS = [{
- # service_name = yoda
+ # service_name = yoda, only XML slides info
'url': 'https://slideslive.com/38902413/gcc-ia16-backend',
'info_dict': {
'id': '38902413',
'timestamp': 1648189972,
'upload_date': '20220325',
'thumbnail': r're:^https?://.*\.jpg',
+ 'thumbnails': 'count:42',
+ 'chapters': 'count:41',
},
'params': {
'skip_download': 'm3u8',
},
}, {
- # service_name = yoda
+ # service_name = yoda, /v7/ slides
'url': 'https://slideslive.com/38935785',
'info_dict': {
'id': '38935785',
'title': 'Offline Reinforcement Learning: From Algorithms to Practical Challenges',
'upload_date': '20211115',
'timestamp': 1636996003,
- 'thumbnail': r're:^https?://.*\.jpg',
+ 'thumbnail': r're:^https?://.*\.(?:jpg|png)',
+ 'thumbnails': 'count:640',
+ 'chapters': 'count:639',
},
'params': {
'skip_download': 'm3u8',
},
}, {
- # service_name = yoda
+ # service_name = yoda, /v1/ slides
'url': 'https://slideslive.com/38973182/how-should-a-machine-learning-researcher-think-about-ai-ethics',
'info_dict': {
'id': '38973182',
'upload_date': '20220201',
'thumbnail': r're:^https?://.*\.jpg',
'timestamp': 1643728135,
+ 'thumbnails': 'count:3',
+ 'chapters': 'count:2',
},
'params': {
'skip_download': 'm3u8',
},
}, {
- # service_name = youtube
+ # service_name = youtube, only XML slides info
'url': 'https://slideslive.com/38897546/special-metaprednaska-petra-ludwiga-hodnoty-pro-lepsi-spolecnost',
'md5': '8a79b5e3d700837f40bd2afca3c8fa01',
'info_dict': {
'comment_count': int,
'channel_follower_count': int,
'age_limit': 0,
- 'thumbnail': r're:^https?://.*\.jpg',
+ 'thumbnail': r're:^https?://.*\.(?:jpg|webp)',
+ 'thumbnails': 'count:169',
'playable_in_embed': True,
'availability': 'unlisted',
'tags': [],
'categories': ['People & Blogs'],
+ 'chapters': 'count:168',
+ },
+ }, {
+ # 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': 1629671508,
+ 'upload_date': '20210822',
+ 'chapters': 'count:7',
+ },
+ 'params': {
+ 'skip_download': 'm3u8',
},
}, {
- # service_name = youtube
+ # 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': 1654714970,
+ 'upload_date': '20220608',
+ 'chapters': 'count:6',
+ },
+ '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': 1622806321,
+ 'upload_date': '20210604',
+ 'chapters': 'count:15',
+ },
+ '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': 1654714896,
+ 'upload_date': '20220608',
+ 'chapters': 'count:8',
+ },
+ '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': 1654714962,
+ 'thumbnail': r're:^https?://.*\.(?:jpg|png)',
+ 'thumbnails': 'count:22',
+ 'upload_date': '20220608',
+ 'chapters': 'count:21',
+ },
+ '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': 1654714920,
+ 'thumbnail': r're:^https?://.*\.(?:jpg|png)',
+ 'thumbnails': 'count:30',
+ 'upload_date': '20220608',
+ 'chapters': 'count:31',
+ },
+ }, {
+ 'info_dict': {
+ 'id': '38979682-021',
+ 'ext': 'mp4',
+ 'title': 'LoRA: Low-Rank Adaptation of Large Language Models - Slide 021',
+ 'duration': 3,
+ 'timestamp': 1654714920,
+ 'upload_date': '20220608',
+ },
+ }, {
+ 'info_dict': {
+ 'id': '38979682-024',
+ 'ext': 'mp4',
+ 'title': 'LoRA: Low-Rank Adaptation of Large Language Models - Slide 024',
+ 'duration': 4,
+ 'timestamp': 1654714920,
+ 'upload_date': '20220608',
+ },
+ }],
+ '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': 1654714877,
+ 'thumbnail': r're:^https?://.*\.(?:jpg|png)',
+ 'thumbnails': 'count:43',
+ 'upload_date': '20220608',
+ 'chapters': 'count:43',
+ },
+ }, {
+ 'info_dict': {
+ 'id': '38979481-013',
+ 'ext': 'mp4',
+ 'title': 'How to Train Your MAML to Excel in Few-Shot Classification - Slide 013',
+ 'duration': 3,
+ 'timestamp': 1654714877,
+ 'upload_date': '20220608',
+ },
+ }],
+ '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': 'UC62SdArr41t_-_fX40QCLRw',
+ 'uploader_url': 'http://www.youtube.com/channel/UC62SdArr41t_-_fX40QCLRw',
+ 'upload_date': '20200903',
+ 'timestamp': 1602599092,
+ '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',
+ },
+ }, {
+ # service_name = yoda
'url': 'https://slideslive.com/38903721/magic-a-scientific-resurrection-of-an-esoteric-legend',
'only_matching': True,
}, {
- # service_name = url
+ # dead link, service_name = url
'url': 'https://slideslive.com/38922070/learning-transferable-skills-1',
'only_matching': True,
}, {
- # service_name = vimeo
+ # 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': 1629671508,
+ 'upload_date': '20210822',
+ 'chapters': 'count:7',
+ },
+ '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 = {}
'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():
return m3u8_dict
+ def _extract_formats(self, cdn_hostname, path, video_id):
+ formats = []
+ formats.extend(self._extract_m3u8_formats(
+ f'https://{cdn_hostname}/{path}/master.m3u8',
+ video_id, 'mp4', m3u8_id='hls', fatal=False, live=True))
+ formats.extend(self._extract_mpd_formats(
+ f'https://{cdn_hostname}/{path}/master.mpd',
+ video_id, mpd_id='dash', fatal=False))
+ return formats
+
def _real_extract(self, url):
video_id = self._match_id(url)
- webpage = self._download_webpage(url, video_id)
+ 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.geturl()
+ 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,
assert service_name in ('url', 'yoda', 'vimeo', 'youtube')
service_id = player_info['service_id']
+ slides_info_url = None
+ slides, slides_info = [], []
+ if player_info.get('slides_json_url'):
+ slides_info_url = player_info['slides_json_url']
+ slides = traverse_obj(self._download_json(
+ slides_info_url, video_id, fatal=False,
+ note='Downloading slides JSON', errnote=False), 'slides', expected_type=list) or []
+ for slide_id, slide in enumerate(slides, start=1):
+ slides_info.append((
+ slide_id, traverse_obj(slide, ('image', 'name')),
+ int_or_none(slide.get('time'), scale=1000)))
+
+ if not slides and player_info.get('slides_xml_url'):
+ slides_info_url = player_info['slides_xml_url']
+ slides = self._download_xml(
+ slides_info_url, video_id, fatal=False,
+ note='Downloading slides XML', errnote='Failed to download slides info')
+ for slide_id, slide in enumerate(slides.findall('./slide'), start=1):
+ slides_info.append((
+ slide_id, xpath_text(slide, './slideName', 'name'),
+ int_or_none(xpath_text(slide, './timeSec', 'time'))))
+
+ slides_version = int(self._search_regex(
+ r'https?://slides\.slideslive\.com/\d+/v(\d+)/\w+\.(?:json|xml)',
+ slides_info_url, 'slides version', default=0))
+ if slides_version < 4:
+ slide_url_template = 'https://cdn.slideslive.com/data/presentations/%s/slides/big/%s.jpg'
+ else:
+ slide_url_template = 'https://slides.slideslive.com/%s/slides/original/%s.png'
+
+ chapters, thumbnails = [], []
+ if url_or_none(player_info.get('thumbnail')):
+ thumbnails.append({'id': 'cover', 'url': player_info['thumbnail']})
+ for slide_id, slide_path, start_time in slides_info:
+ if slide_path:
+ thumbnails.append({
+ 'id': f'{slide_id:03d}',
+ 'url': slide_url_template % (video_id, slide_path),
+ })
+ 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'))
'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',
- 'thumbnail': url_or_none(player_info.get('thumbnail')),
+ 'thumbnails': thumbnails,
+ 'chapters': chapters,
'subtitles': subtitles,
}
- if service_name in ('url', 'yoda'):
- if service_name == 'url':
- info['url'] = service_id
- else:
- cdn_hostname = player_info['video_servers'][0]
- formats = []
- formats.extend(self._extract_m3u8_formats(
- f'https://{cdn_hostname}/{service_id}/master.m3u8',
- video_id, 'mp4', m3u8_id='hls', fatal=False, live=True))
- formats.extend(self._extract_mpd_formats(
- f'https://{cdn_hostname}/{service_id}/master.mpd',
- video_id, mpd_id='dash', fatal=False))
- info.update({
- 'formats': formats,
- })
+ if service_name == 'url':
+ info['url'] = service_id
+ elif service_name == 'yoda':
+ info['formats'] = self._extract_formats(player_info['video_servers'][0], service_id, video_id)
else:
info.update({
'_type': 'url_transparent',
f'https://player.vimeo.com/video/{service_id}',
{'http_headers': {'Referer': url}})
- return info
+ video_slides = traverse_obj(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(slides, 1):
+ if not 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(cdn_hostname, video_path, video_id)
+ 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'])
projects / yt-dlp.git / commitdiff
