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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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