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Diffstat (limited to 'yt_dlp/extractor/slideslive.py')
| -rw-r--r-- | yt_dlp/extractor/slideslive.py | 567 |
1 files changed, 567 insertions, 0 deletions
diff --git a/yt_dlp/extractor/slideslive.py b/yt_dlp/extractor/slideslive.py new file mode 100644 index 0000000..3d36edb --- /dev/null +++ b/yt_dlp/extractor/slideslive.py @@ -0,0 +1,567 @@ +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/(?: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': 1648189972, + 'upload_date': '20220325', + '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': '20211115', + 'timestamp': 1636996003, + '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': '20220201', + 'thumbnail': r're:^https?://.*\.jpg', + 'timestamp': 1643728135, + 'thumbnails': 'count:3', + 'chapters': 'count:2', + 'duration': 5889, + }, + 'params': { + 'skip_download': 'm3u8', + }, + }, { + # service_name = youtube, only XML slides info + 'url': 'https://slideslive.com/38897546/special-metaprednaska-petra-ludwiga-hodnoty-pro-lepsi-spolecnost', + 'md5': '8a79b5e3d700837f40bd2afca3c8fa01', + 'info_dict': { + 'id': 'jmg02wCJD5M', + 'display_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)', + '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', + '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': 1654714970, + 'upload_date': '20220608', + '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': 1622806321, + 'upload_date': '20210604', + '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': 1654714896, + 'upload_date': '20220608', + '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': 1654714962, + 'thumbnail': r're:^https?://.*\.(?:jpg|png)', + 'thumbnails': 'count:22', + 'upload_date': '20220608', + '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': 1654714920, + 'thumbnail': r're:^https?://.*\.(?:jpg|png)', + 'thumbnails': 'count:30', + 'upload_date': '20220608', + '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': 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', + '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': 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', + }, + }, { + # /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': 1662384834, + 'upload_date': '20220905', + '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': 1629671508, + 'upload_date': '20210822', + '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.lstrip('#EXT-SL-')) + 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.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, + 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') + 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')))) + + 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}', + {'http_headers': {'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']) |