+import re
+import urllib.parse
+import xml.etree.ElementTree
+
from .common import InfoExtractor
from ..utils import (
- bool_or_none,
+ ExtractorError,
+ int_or_none,
+ parse_qs,
smuggle_url,
- try_get,
+ 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]+)'
- _WORKING = False
+ _VALID_URL = r'https?://slideslive\.com/(?:embed/(?:presentation/)?)?(?P<id>[0-9]+)'
_TESTS = [{
- # video_service_name = YOUTUBE
+ # service_name = yoda, only XML slides info
'url': 'https://slideslive.com/38902413/gcc-ia16-backend',
- 'md5': 'b29fcd6c6952d0c79c5079b0e7a07e6f',
'info_dict': {
- 'id': 'LMtgR8ba0b0',
+ 'id': '38902413',
'ext': 'mp4',
'title': 'GCC IA16 backend',
- 'description': 'Watch full version of this video at https://slideslive.com/38902413.',
- 'uploader': 'SlidesLive Videos - A',
- 'uploader_id': 'UC62SdArr41t_-_fX40QCLRw',
- 'timestamp': 1597615266,
- 'upload_date': '20170925',
- }
+ 'timestamp': 1697793372,
+ 'upload_date': '20231020',
+ 'thumbnail': r're:^https?://.*\.jpg',
+ 'thumbnails': 'count:42',
+ 'chapters': 'count:41',
+ 'duration': 1638,
+ },
+ 'params': {
+ 'skip_download': 'm3u8',
+ },
}, {
- # video_service_name = yoda
+ # service_name = yoda, /v7/ slides
'url': 'https://slideslive.com/38935785',
- 'md5': '575cd7a6c0acc6e28422fe76dd4bcb1a',
'info_dict': {
- 'id': 'RMraDYN5ozA_',
+ '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',
},
}, {
- # video_service_name = youtube
+ # /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,
}, {
- # video_service_name = url
+ # dead link, service_name = url
'url': 'https://slideslive.com/38922070/learning-transferable-skills-1',
'only_matching': True,
}, {
- # video_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': 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)
- video_data = self._download_json(
- 'https://ben.slideslive.com/player/' + video_id, video_id)
- service_name = video_data['video_service_name'].lower()
+ 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 = video_data['video_service_id']
+ 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 try_get(video_data, lambda x: x['subtitles'], list) or []:
- if not isinstance(sub, dict):
- continue
+ 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
- lang = sub.get('language') or 'en'
- subtitles.setdefault(lang, []).append({
+ subtitles.setdefault(sub.get('language') or 'en', []).append({
'url': webvtt_url,
+ 'ext': 'vtt',
})
+
info = {
'id': video_id,
- 'thumbnail': video_data.get('thumbnail'),
- 'is_live': bool_or_none(video_data.get('is_live')),
+ '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 in ('url', 'yoda'):
- info['title'] = video_data['title']
- if service_name == 'url':
- info['url'] = service_id
- else:
- formats = []
- _MANIFEST_PATTERN = 'https://01.cdn.yoda.slideslive.com/%s/master.%s'
- # use `m3u8` entry_protocol until EXT-X-MAP is properly supported by `m3u8_native` entry_protocol
- formats.extend(self._extract_m3u8_formats(
- _MANIFEST_PATTERN % (service_id, 'm3u8'),
- service_id, 'mp4', m3u8_id='hls', fatal=False))
- formats.extend(self._extract_mpd_formats(
- _MANIFEST_PATTERN % (service_id, 'mpd'), service_id,
- mpd_id='dash', fatal=False))
- info.update({
- 'id': service_id,
- 'formats': formats,
- })
+
+ 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(),
- 'title': video_data.get('title'),
+ 'display_id': video_id,
})
if service_name == 'vimeo':
info['url'] = smuggle_url(
- 'https://player.vimeo.com/video/' + service_id,
- {'http_headers': {'Referer': url}})
- return info
+ 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 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_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'])
projects / yt-dlp.git / blobdiff