[extractor/slideslive] Support embeds and slides (#5784)

Authored by: bashonly, Grub4K, pukkandan

yt_dlp/extractor/slideslive.py

@@ -1,16 +1,24 @@
+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',
@@ -19,12 +27,14 @@ class SlidesLiveIE(InfoExtractor):
             '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',
@@ -32,13 +42,15 @@ class SlidesLiveIE(InfoExtractor):
             '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',
@@ -47,12 +59,14 @@ class SlidesLiveIE(InfoExtractor):
             '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': {
@@ -76,26 +90,253 @@ class SlidesLiveIE(InfoExtractor):
             '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 = {}
 
@@ -108,6 +349,8 @@ class SlidesLiveIE(InfoExtractor):
             '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():
@@ -126,9 +369,33 @@ class SlidesLiveIE(InfoExtractor):
 
         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,
@@ -139,6 +406,50 @@ class SlidesLiveIE(InfoExtractor):
         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'))
@@ -154,25 +465,15 @@ class SlidesLiveIE(InfoExtractor):
             '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':
-            if service_name == 'url':
+            info['url'] = service_id
-                info['url'] = service_id
+        elif service_name == 'yoda':
-            else:
+            info['formats'] = self._extract_formats(player_info['video_servers'][0], service_id, video_id)
-                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,
-                })
         else:
             info.update({
                 '_type': 'url_transparent',
@@ -185,4 +486,37 @@ class SlidesLiveIE(InfoExtractor):
                     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'])