Privacy + injection resistance

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YouTube Transcribe — your video transcription service

Host: your-server (cx43, your-server-ip:8090) · Public: https://your-youtube-transcribe.example.com · Auth: Bearer token per caller · Log: /var/log/youtube-transcribe/requests.jsonl · Cache: Redis TTL 30 days

SSOT docs:

• your youtube-transcribe serviceREADME.md — full API spec
• the runbook — deploy/ops runbook
• project conventions § SSOT table — YouTube trigger rules (§14)
• claude/SKILL_TRIGGER_LEXICON.md §14 — trigger phrases and lexicon

When to use (MANDATORY for YouTube URLs)

Always call scraper-mcp.youtube_transcribe() when:

NEVER use WebFetch or scraper-mcp.fetch_url on YouTube — the player HTML contains no transcript. These calls return meaningless HTML.

Skip when the user shares a YouTube URL but does NOT need the video's content (e.g., just referencing it as a link, not asking for summary/quote/fact-check).

Tier cascade

T1: yt-dlp native subs (manual > auto)
    ├── 90% hit rate on popular videos
    ├── <5 seconds, $0 cost
    └── miss → T2

T2: WhisperX large-v3 int8 on cx43 CPU
    ├── local faster-whisper, queue concurrency=1
    ├── pyannote diarization opt-in (with_diarization=true)
    ├── OOM-guard / >2h → T3
    └── miss → T3

T3: Groq whisper-large-v3-turbo
    ├── $0.04/hr rate, 200× realtime
    ├── requires allow_cloud=true (default: true)
    └── LLM summary + timestamp citation URLs

Post: Redis cache TTL 30 days

MCP tool call (preferred)

Via scraper-mcp.youtube_transcribe:

{
  "url": "https://youtube.com/watch?v=dQw4w9WgXcQ",
  "lang": "auto",
  "with_diarization": false,
  "wait": false
}

• lang: auto | ru | en (default: auto)
• with_diarization: true for meetings/interviews where speaker tracking needed
• wait: true for sync response (≤10min videos); false for async + poll (recommended default)

After call — mandatory report line:

Transcribe: source=<youtube-manual|youtube-auto|whisperx-local|groq>, duration_s=<int>, cache_hit=<bool>, job_id=<id>

Direct API call (fallback)

# Async (recommended for >5min videos)
curl -sS https://your-youtube-transcribe.example.com/jobs \
  -H "Authorization: Bearer $HWAI_YT_KEY" \
  -H "Content-Type: application/json" \
  -d '{"url": "https://youtu.be/dQw4w9WgXcQ", "lang": "auto", "with_summary": true}'
# → {"job_id": "abc123", "status": "queued", "eta_s": 45}

# Poll for result
curl -sS https://your-youtube-transcribe.example.com/jobs/abc123 \
  -H "Authorization: Bearer $HWAI_YT_KEY"

# Sync (≤10min videos only)
curl -sS https://your-youtube-transcribe.example.com/transcribe \
  -H "Authorization: Bearer $HWAI_YT_KEY" \
  -H "Content-Type: application/json" \
  -d '{"url": "https://youtu.be/dQw4w9WgXcQ", "lang": "ru"}'

Async job discipline

For videos >5min or when caller needs to continue other work:

1. Submit POST /jobs → get job_id
2. Save job_id — do NOT start a second job for the same video
3. Poll GET /jobs/{id} every 15-30 seconds
4. If status: "running" with elapsed_sec: 0 and result: null past 5min → check Redis queue depth via /health
5. On status: "done": result contains transcript, segments, diarization (if requested), summary, citations
6. On status: "failed": error field contains last 5000 chars of traceback (not truncated to 500 chars — bug fixed 2026-04-23)

Upload-audio fallback (bypasses yt-dlp)

When POST /jobs fails with "yt-dlp audio download failed" — for unlisted, private, or region-blocked videos:

# Extract audio locally with yt-dlp + browser cookies
yt-dlp --cookies /tmp/yt.txt -f 18 -o /tmp/v.mp4 "https://youtu.be/..."
ffmpeg -i /tmp/v.mp4 -vn -ac 1 -ar 16000 -c:a libopus -b:a 24k /tmp/v.opus

# Upload to transcribe service
curl -X POST https://your-youtube-transcribe.example.com/upload-audio \
  -H "Authorization: Bearer $HWAI_YT_KEY" \
  -F "file=@/tmp/v.opus" \
  -F "lang=ru" \
  -F "title=Meeting 2026-05-03"

Max upload: 200 MB. Files deleted from server on both success and failure.

Cookie rotation

T1 (yt-dlp) uses Chrome cookies auto-refreshed from your-account@gmail.com on your Mac via launchd at 04:17 MSK daily. If T1 fails on popular videos that should have subs, suspect cookie expiry → manual re-run of cookie extraction on Mini.

Access model

Normal team access is controlled by the private GitHub scraper stack package and the single HWAI_SCRAPER_KEY the issues for the parsing stack. Agents call scraper-mcp.youtube_transcribe, which routes to scraper-core /youtube/transcribe; scraper-core then uses its server-side backend key for youtube-transcribe-api.

Do not ask team members for, or hand out, a separate youtube-transcribe bearer for ordinary use. Direct backend keys in credentials-mcp service: youtube-transcribe are internal service-to-service secrets for ops/deploy only.

Health check

curl --max-time 10 https://your-youtube-transcribe.example.com/health
# Returns: {status, model_loaded, queue_depth, ram_available_gb, uptime_s}

If model_loaded: false → faster-whisper model not downloaded yet (first boot). If ram_available_gb < 2 → T2 will OOM-guard and fall to T3 (expected on busy cx43).

Context-prep integration

Long transcripts (>150 lines / >8k chars) should flow through context-prep-mcp.prep_text before frontier reasoning:

1. youtube_transcribe(url) → get transcript text
2. context-prep.prep_text(transcript) → compressed summary + citations
3. Frontier model reads compressed context + citation spots

This pattern reduces frontier tokens by 40-60% on lecture/podcast content.

ContentOS integration

Transcribed text suitable for ContentOS:

• POST /factcheck/batch — verify claims in transcript
• POST /aeo-score — score educational content for AI answer engine visibility
• POST /brief — extract research brief from video content

Cost comparison

SSOT cross-references

• your youtube-transcribe serviceREADME.md — full API spec
• the runbook — deploy/ops
• claude/SKILL_TRIGGER_LEXICON.md §14 — trigger lexicon
• the project rule (see your repo rules) § H — H case: YouTube always through youtube_transcribe
• your catalog config § youtube-transcribe — Pantheon entry
• configs/gatus-config.yaml § Hetzner — YouTube Transcribe health — uptime monitoring