mirror of
https://github.com/yt-dlp/yt-dlp.git
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5ab3534d44
* Fix slides/thumbnails extraction * Extract duration to fix issues w/ `--embed-chapters`, `--split-chapters` * Add `InfoExtractor._extract_mpd_vod_duration` method * Expand applicability of `InfoExtractor._parse_m3u8_vod_duration` method Authored by: bashonly
567 lines
22 KiB
Python
567 lines
22 KiB
Python
import re
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import urllib.parse
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from .common import InfoExtractor
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from ..utils import (
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ExtractorError,
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int_or_none,
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parse_qs,
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smuggle_url,
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traverse_obj,
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unified_timestamp,
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update_url_query,
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url_or_none,
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xpath_text,
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)
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class SlidesLiveIE(InfoExtractor):
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_VALID_URL = r'https?://slideslive\.com/(?:embed/(?:presentation/)?)?(?P<id>[0-9]+)'
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_TESTS = [{
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# service_name = yoda, only XML slides info
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'url': 'https://slideslive.com/38902413/gcc-ia16-backend',
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'info_dict': {
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'id': '38902413',
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'ext': 'mp4',
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'title': 'GCC IA16 backend',
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'timestamp': 1648189972,
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'upload_date': '20220325',
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'thumbnail': r're:^https?://.*\.jpg',
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'thumbnails': 'count:42',
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'chapters': 'count:41',
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'duration': 1638,
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},
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'params': {
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'skip_download': 'm3u8',
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},
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}, {
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# service_name = yoda, /v7/ slides
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'url': 'https://slideslive.com/38935785',
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'info_dict': {
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'id': '38935785',
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'ext': 'mp4',
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'title': 'Offline Reinforcement Learning: From Algorithms to Practical Challenges',
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'upload_date': '20211115',
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'timestamp': 1636996003,
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'thumbnail': r're:^https?://.*\.(?:jpg|png)',
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'thumbnails': 'count:640',
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'chapters': 'count:639',
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'duration': 9832,
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},
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'params': {
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'skip_download': 'm3u8',
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},
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}, {
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# service_name = yoda, /v1/ slides
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'url': 'https://slideslive.com/38973182/how-should-a-machine-learning-researcher-think-about-ai-ethics',
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'info_dict': {
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'id': '38973182',
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'ext': 'mp4',
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'title': 'How Should a Machine Learning Researcher Think About AI Ethics?',
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'upload_date': '20220201',
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'thumbnail': r're:^https?://.*\.jpg',
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'timestamp': 1643728135,
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'thumbnails': 'count:3',
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'chapters': 'count:2',
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'duration': 5889,
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},
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'params': {
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'skip_download': 'm3u8',
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},
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}, {
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# service_name = youtube, only XML slides info
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'url': 'https://slideslive.com/38897546/special-metaprednaska-petra-ludwiga-hodnoty-pro-lepsi-spolecnost',
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'md5': '8a79b5e3d700837f40bd2afca3c8fa01',
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'info_dict': {
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'id': 'jmg02wCJD5M',
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'display_id': '38897546',
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'ext': 'mp4',
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'title': 'SPECIÁL: Meta-přednáška Petra Ludwiga - Hodnoty pro lepší společnost',
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'description': 'Watch full version of this video at https://slideslive.com/38897546.',
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'channel_url': 'https://www.youtube.com/channel/UCZWdAkNYFncuX0khyvhqnxw',
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'channel': 'SlidesLive Videos - G1',
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'channel_id': 'UCZWdAkNYFncuX0khyvhqnxw',
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'uploader_id': 'UCZWdAkNYFncuX0khyvhqnxw',
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'uploader': 'SlidesLive Videos - G1',
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'uploader_url': 'http://www.youtube.com/channel/UCZWdAkNYFncuX0khyvhqnxw',
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'live_status': 'not_live',
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'upload_date': '20160710',
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'timestamp': 1618786715,
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'duration': 6827,
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'like_count': int,
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'view_count': int,
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'comment_count': int,
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'channel_follower_count': int,
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'age_limit': 0,
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'thumbnail': r're:^https?://.*\.(?:jpg|webp)',
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'thumbnails': 'count:169',
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'playable_in_embed': True,
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'availability': 'unlisted',
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'tags': [],
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'categories': ['People & Blogs'],
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'chapters': 'count:168',
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},
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}, {
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# embed-only presentation, only XML slides info
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'url': 'https://slideslive.com/embed/presentation/38925850',
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'info_dict': {
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'id': '38925850',
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'ext': 'mp4',
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'title': 'Towards a Deep Network Architecture for Structured Smoothness',
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'thumbnail': r're:^https?://.*\.jpg',
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'thumbnails': 'count:8',
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'timestamp': 1629671508,
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'upload_date': '20210822',
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'chapters': 'count:7',
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'duration': 326,
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},
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'params': {
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'skip_download': 'm3u8',
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},
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}, {
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# embed-only presentation, only JSON slides info, /v5/ slides (.png)
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'url': 'https://slideslive.com/38979920/',
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'info_dict': {
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'id': '38979920',
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'ext': 'mp4',
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'title': 'MoReL: Multi-omics Relational Learning',
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'thumbnail': r're:^https?://.*\.(?:jpg|png)',
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'thumbnails': 'count:7',
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'timestamp': 1654714970,
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'upload_date': '20220608',
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'chapters': 'count:6',
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'duration': 171,
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},
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'params': {
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'skip_download': 'm3u8',
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},
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}, {
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# /v2/ slides (.jpg)
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'url': 'https://slideslive.com/38954074',
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'info_dict': {
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'id': '38954074',
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'ext': 'mp4',
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'title': 'Decentralized Attribution of Generative Models',
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'thumbnail': r're:^https?://.*\.jpg',
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'thumbnails': 'count:16',
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'timestamp': 1622806321,
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'upload_date': '20210604',
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'chapters': 'count:15',
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'duration': 306,
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},
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'params': {
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'skip_download': 'm3u8',
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},
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}, {
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# /v4/ slides (.png)
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'url': 'https://slideslive.com/38979570/',
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'info_dict': {
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'id': '38979570',
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'ext': 'mp4',
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'title': 'Efficient Active Search for Combinatorial Optimization Problems',
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'thumbnail': r're:^https?://.*\.(?:jpg|png)',
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'thumbnails': 'count:9',
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'timestamp': 1654714896,
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'upload_date': '20220608',
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'chapters': 'count:8',
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'duration': 295,
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},
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'params': {
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'skip_download': 'm3u8',
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},
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}, {
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# /v10/ slides
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'url': 'https://slideslive.com/embed/presentation/38979880?embed_parent_url=https%3A%2F%2Fedit.videoken.com%2F',
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'info_dict': {
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'id': '38979880',
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'ext': 'mp4',
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'title': 'The Representation Power of Neural Networks',
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'timestamp': 1654714962,
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'thumbnail': r're:^https?://.*\.(?:jpg|png)',
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'thumbnails': 'count:22',
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'upload_date': '20220608',
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'chapters': 'count:21',
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'duration': 294,
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},
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'params': {
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'skip_download': 'm3u8',
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},
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}, {
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# /v7/ slides, 2 video slides
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'url': 'https://slideslive.com/embed/presentation/38979682?embed_container_origin=https%3A%2F%2Fedit.videoken.com',
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'playlist_count': 3,
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'info_dict': {
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'id': '38979682-playlist',
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'title': 'LoRA: Low-Rank Adaptation of Large Language Models',
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},
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'playlist': [{
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'info_dict': {
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'id': '38979682',
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'ext': 'mp4',
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'title': 'LoRA: Low-Rank Adaptation of Large Language Models',
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'timestamp': 1654714920,
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'thumbnail': r're:^https?://.*\.(?:jpg|png)',
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'thumbnails': 'count:30',
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'upload_date': '20220608',
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'chapters': 'count:31',
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'duration': 272,
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},
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}, {
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'info_dict': {
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'id': '38979682-021',
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'ext': 'mp4',
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'title': 'LoRA: Low-Rank Adaptation of Large Language Models - Slide 021',
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'duration': 3,
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'timestamp': 1654714920,
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'upload_date': '20220608',
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},
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}, {
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'info_dict': {
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'id': '38979682-024',
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'ext': 'mp4',
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'title': 'LoRA: Low-Rank Adaptation of Large Language Models - Slide 024',
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'duration': 4,
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'timestamp': 1654714920,
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'upload_date': '20220608',
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},
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}],
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'params': {
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'skip_download': 'm3u8',
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},
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}, {
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# /v6/ slides, 1 video slide, edit.videoken.com embed
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'url': 'https://slideslive.com/38979481/',
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'playlist_count': 2,
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'info_dict': {
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'id': '38979481-playlist',
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'title': 'How to Train Your MAML to Excel in Few-Shot Classification',
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},
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'playlist': [{
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'info_dict': {
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'id': '38979481',
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'ext': 'mp4',
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'title': 'How to Train Your MAML to Excel in Few-Shot Classification',
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'timestamp': 1654714877,
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'thumbnail': r're:^https?://.*\.(?:jpg|png)',
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'thumbnails': 'count:43',
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'upload_date': '20220608',
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'chapters': 'count:43',
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'duration': 315,
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},
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}, {
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'info_dict': {
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'id': '38979481-013',
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'ext': 'mp4',
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'title': 'How to Train Your MAML to Excel in Few-Shot Classification - Slide 013',
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'duration': 3,
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'timestamp': 1654714877,
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'upload_date': '20220608',
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},
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}],
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'params': {
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'skip_download': 'm3u8',
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},
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}, {
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# /v3/ slides, .jpg and .png, service_name = youtube
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'url': 'https://slideslive.com/embed/38932460/',
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'info_dict': {
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'id': 'RTPdrgkyTiE',
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'display_id': '38932460',
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'ext': 'mp4',
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'title': 'Active Learning for Hierarchical Multi-Label Classification',
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'description': 'Watch full version of this video at https://slideslive.com/38932460.',
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'channel': 'SlidesLive Videos - A',
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'channel_id': 'UC62SdArr41t_-_fX40QCLRw',
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'channel_url': 'https://www.youtube.com/channel/UC62SdArr41t_-_fX40QCLRw',
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'uploader': 'SlidesLive Videos - A',
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'uploader_id': 'UC62SdArr41t_-_fX40QCLRw',
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'uploader_url': 'http://www.youtube.com/channel/UC62SdArr41t_-_fX40QCLRw',
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'upload_date': '20200903',
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'timestamp': 1602599092,
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'duration': 942,
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'age_limit': 0,
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'live_status': 'not_live',
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'playable_in_embed': True,
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'availability': 'unlisted',
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'categories': ['People & Blogs'],
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'tags': [],
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'channel_follower_count': int,
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'like_count': int,
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'view_count': int,
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'thumbnail': r're:^https?://.*\.(?:jpg|png|webp)',
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'thumbnails': 'count:21',
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'chapters': 'count:20',
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},
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'params': {
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'skip_download': 'm3u8',
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},
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}, {
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# /v3/ slides, .png only, service_name = yoda
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'url': 'https://slideslive.com/38983994',
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'info_dict': {
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'id': '38983994',
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'ext': 'mp4',
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'title': 'Zero-Shot AutoML with Pretrained Models',
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'timestamp': 1662384834,
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'upload_date': '20220905',
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'thumbnail': r're:^https?://.*\.(?:jpg|png)',
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'thumbnails': 'count:23',
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'chapters': 'count:22',
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'duration': 295,
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},
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'params': {
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'skip_download': 'm3u8',
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},
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}, {
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# service_name = yoda
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'url': 'https://slideslive.com/38903721/magic-a-scientific-resurrection-of-an-esoteric-legend',
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'only_matching': True,
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}, {
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# dead link, service_name = url
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'url': 'https://slideslive.com/38922070/learning-transferable-skills-1',
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'only_matching': True,
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}, {
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# dead link, service_name = vimeo
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'url': 'https://slideslive.com/38921896/retrospectives-a-venue-for-selfreflection-in-ml-research-3',
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'only_matching': True,
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}]
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_WEBPAGE_TESTS = [{
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# only XML slides info
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'url': 'https://iclr.cc/virtual_2020/poster_Hklr204Fvr.html',
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'info_dict': {
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'id': '38925850',
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'ext': 'mp4',
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'title': 'Towards a Deep Network Architecture for Structured Smoothness',
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'thumbnail': r're:^https?://.*\.jpg',
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'thumbnails': 'count:8',
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'timestamp': 1629671508,
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'upload_date': '20210822',
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'chapters': 'count:7',
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'duration': 326,
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},
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'params': {
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'skip_download': 'm3u8',
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},
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}]
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@classmethod
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def _extract_embed_urls(cls, url, webpage):
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# Reference: https://slideslive.com/embed_presentation.js
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for embed_id in re.findall(r'(?s)new\s+SlidesLiveEmbed\s*\([^)]+\bpresentationId:\s*["\'](\d+)["\']', webpage):
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url_parsed = urllib.parse.urlparse(url)
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origin = f'{url_parsed.scheme}://{url_parsed.netloc}'
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yield update_url_query(
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f'https://slideslive.com/embed/presentation/{embed_id}', {
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'embed_parent_url': url,
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'embed_container_origin': origin,
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})
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def _download_embed_webpage_handle(self, video_id, headers):
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return self._download_webpage_handle(
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f'https://slideslive.com/embed/presentation/{video_id}', video_id,
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headers=headers, query=traverse_obj(headers, {
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'embed_parent_url': 'Referer',
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'embed_container_origin': 'Origin',
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}))
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def _extract_custom_m3u8_info(self, m3u8_data):
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m3u8_dict = {}
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lookup = {
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'PRESENTATION-TITLE': 'title',
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'PRESENTATION-UPDATED-AT': 'timestamp',
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'PRESENTATION-THUMBNAIL': 'thumbnail',
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'PLAYLIST-TYPE': 'playlist_type',
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'VOD-VIDEO-SERVICE-NAME': 'service_name',
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'VOD-VIDEO-ID': 'service_id',
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'VOD-VIDEO-SERVERS': 'video_servers',
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'VOD-SUBTITLES': 'subtitles',
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'VOD-SLIDES-JSON-URL': 'slides_json_url',
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'VOD-SLIDES-XML-URL': 'slides_xml_url',
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}
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for line in m3u8_data.splitlines():
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if not line.startswith('#EXT-SL-'):
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continue
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tag, _, value = line.partition(':')
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key = lookup.get(tag.lstrip('#EXT-SL-'))
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if not key:
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continue
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m3u8_dict[key] = value
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# Some values are stringified JSON arrays
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for key in ('video_servers', 'subtitles'):
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if key in m3u8_dict:
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m3u8_dict[key] = self._parse_json(m3u8_dict[key], None, fatal=False) or []
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return m3u8_dict
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def _extract_formats_and_duration(self, cdn_hostname, path, video_id, skip_duration=False):
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formats, duration = [], None
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hls_formats = self._extract_m3u8_formats(
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f'https://{cdn_hostname}/{path}/master.m3u8',
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video_id, 'mp4', m3u8_id='hls', fatal=False, live=True)
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if hls_formats:
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if not skip_duration:
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duration = self._extract_m3u8_vod_duration(
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hls_formats[0]['url'], video_id, note='Extracting duration from HLS manifest')
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formats.extend(hls_formats)
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dash_formats = self._extract_mpd_formats(
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f'https://{cdn_hostname}/{path}/master.mpd', video_id, mpd_id='dash', fatal=False)
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if dash_formats:
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if not duration and not skip_duration:
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duration = self._extract_mpd_vod_duration(
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f'https://{cdn_hostname}/{path}/master.mpd', video_id,
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note='Extracting duration from DASH manifest')
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formats.extend(dash_formats)
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return formats, duration
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def _real_extract(self, url):
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video_id = self._match_id(url)
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webpage, urlh = self._download_embed_webpage_handle(
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video_id, headers=traverse_obj(parse_qs(url), {
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'Referer': ('embed_parent_url', -1),
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'Origin': ('embed_container_origin', -1)}))
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redirect_url = urlh.geturl()
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if 'domain_not_allowed' in redirect_url:
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domain = traverse_obj(parse_qs(redirect_url), ('allowed_domains[]', ...), get_all=False)
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if not domain:
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raise ExtractorError(
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'This is an embed-only presentation. Try passing --referer', expected=True)
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webpage, _ = self._download_embed_webpage_handle(video_id, headers={
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'Referer': f'https://{domain}/',
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'Origin': f'https://{domain}',
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})
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player_token = self._search_regex(r'data-player-token="([^"]+)"', webpage, 'player token')
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player_data = self._download_webpage(
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f'https://ben.slideslive.com/player/{video_id}', video_id,
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note='Downloading player info', query={'player_token': player_token})
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|
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'])
|