feat(baoyu-youtube-transcript): add title heading, description summary, and cover image to markdown output
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@ -88,7 +88,7 @@ Accepts any of these as video input:
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| Format | Extension | Description |
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|--------|-----------|-------------|
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| `text` | `.md` | Markdown with frontmatter, natural paragraphs, optional timestamps/chapters/speakers |
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| `text` | `.md` | Markdown with frontmatter (incl. `description`), title heading, summary, optional TOC/cover/timestamps/chapters/speakers |
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| `srt` | `.srt` | SubRip subtitle format for video players |
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## Output Directory
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@ -147,7 +147,7 @@ If no chapter timestamps exist in the description, the transcript is output as g
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### Speaker Identification (`--speakers`)
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Speaker identification requires AI processing. The script outputs a raw `.md` file containing:
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- YAML frontmatter with video metadata (title, channel, date, cover, language)
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- YAML frontmatter with video metadata (title, channel, date, cover, description, language)
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- Video description (for speaker name extraction)
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- Chapter list from description (if available)
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- Raw transcript in SRT format (pre-computed start/end timestamps, token-efficient)
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@ -5,9 +5,12 @@ You are an expert transcript specialist. Process the raw transcript file (with Y
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## Output Structure
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Produce a single cohesive markdown file containing:
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1. YAML frontmatter (keep the original frontmatter from the raw file)
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2. Table of Contents
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3. Full chapter-segmented transcript with speaker labels
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1. YAML frontmatter (keep the original frontmatter from the raw file, which includes `description`)
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2. `# Title` heading (from frontmatter title)
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3. Description/summary paragraph (from frontmatter `description`)
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4. Table of Contents
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5. Cover image (if `cover` exists in frontmatter): `` — right after the ToC
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6. Full chapter-segmented transcript with speaker labels
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Use the same language as the transcription for the title and ToC.
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@ -79,13 +82,20 @@ channel: "The Show"
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date: 2024-04-15
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url: "https://www.youtube.com/watch?v=xxx"
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cover: imgs/cover.jpg
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description: "Jane Doe discusses her groundbreaking five-year study on the long-term effects of dietary changes."
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language: en
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---
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# Example Interview
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Jane Doe discusses her groundbreaking five-year study on the long-term effects of dietary changes.
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## Table of Contents
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* [00:00:00] Introduction and Welcome
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* [00:00:12] Overview of the New Research
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## Introduction and Welcome [00:00:00]
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@ -505,17 +505,28 @@ function yamlEscape(s: string): string {
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return s;
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}
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function extractSummary(description: string): string {
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if (!description) return "";
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const firstPara = description.split(/\n\s*\n/)[0].trim();
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const lines = firstPara.split("\n").filter(l => !/^\s*(https?:\/\/|#|@|\d+:\d+)/.test(l) && l.trim());
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return lines.join(" ").slice(0, 300).trim();
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}
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function formatMarkdown(sentences: Sentence[], meta: VideoMeta, opts: { timestamps: boolean; chapters: boolean; speakers: boolean }, snippets?: Snippet[]): string {
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const summary = extractSummary(meta.description);
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let md = "---\n";
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md += `title: ${yamlEscape(meta.title)}\n`;
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md += `channel: ${yamlEscape(meta.channel)}\n`;
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if (meta.publishDate) md += `date: ${meta.publishDate}\n`;
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md += `url: ${yamlEscape(meta.url)}\n`;
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if (meta.coverImage) md += `cover: ${meta.coverImage}\n`;
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if (summary) md += `description: ${yamlEscape(summary)}\n`;
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if (meta.language) md += `language: ${meta.language.code}\n`;
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md += "---\n\n";
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if (opts.speakers) {
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md += `# ${meta.title}\n\n`;
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if (summary) md += `${summary}\n\n`;
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if (meta.description) md += "# Description\n\n" + meta.description.trim() + "\n\n";
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if (meta.chapters.length) {
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md += "# Chapters\n\n";
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@ -527,12 +538,17 @@ function formatMarkdown(sentences: Sentence[], meta: VideoMeta, opts: { timestam
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return md;
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}
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md += `# ${meta.title}\n\n`;
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if (summary) md += `${summary}\n\n`;
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const chapters = opts.chapters ? meta.chapters : [];
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if (chapters.length) {
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md += "## Table of Contents\n\n";
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for (const ch of chapters) md += opts.timestamps ? `* [${ts(ch.start)}] ${ch.title}\n` : `* ${ch.title}\n`;
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md += "\n\n";
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md += "\n";
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if (meta.coverImage) md += `\n\n`;
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md += "\n";
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for (let i = 0; i < chapters.length; i++) {
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const nextStart = i < chapters.length - 1 ? chapters[i + 1].start : Infinity;
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const chSentences = sentences.filter(s => parseTs(s.start) >= chapters[i].start && parseTs(s.start) < nextStart);
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