Skill: bluebirdback/youtube-transcript-pipeline (version 1.0.0)

--- name: youtube-transcript-pipeline description: Generate, clean, correct, translate, and package YouTube interview transcripts with speaker-attributed timestamps into reusable deliverables. Use for workflows involving Deepgram transcription, diarization correction, bilingual output, and structured folder packaging for handoff. --- # YouTube Transcript Pipeline ## Overview This skill turns a YouTube link into a complete transcript package: - speaker-labeled, timestamped transcript - cleaned/interviewer-vs-main speaker correction passes - optional Chinese translation preserving line format - optional image/download + workspace packaging Use this when user asks for: transcript production, diarization fixes, interview cleanup, Chinese translation of transcript, or reusable handoff folders. ## Quick Start ### Inputs - YouTube URL - Deepgram API key (Nova-3 for best results) - preferred speakers (main/interviewer defaults if known) ### Outputs Place all outputs in a structured folder: - `transcripts/` (raw / cleaned / translated) - `artifacts/` (audio + thumbnails) - `scripts/` (workflow helpers) - `snippets/` (small JSONs, helper files) - `MANIFEST.txt` ## Workflow ### 1) Prepare assets 1. download audio from YouTube 2. split into manageable chunks (e.g., 180s) 3. run Deepgram chunked transcription with `nova-3`, word-level output if possible Suggested command (adapt paths): ```bash python3 resilient_dg_transcribe_resume.py \ --audio-dir /tmp/dg_chunks_work2 \ --max 0 --chunk-sec 180 --model nova-3 --timeout 240 ``` ### 2) Produce base transcripts - keep all baseline artifacts (`transcript_*.json`, `transcript_speakers_*.txt`) - do not delete intermediates; they are useful for audit ### 3) Fix speaker attribution - apply conservative correction pass first (prefering **main speaker default**) - review and accept swaps only for clear interviewer-style short questions/interjections - keep line/timestamp count unchanged ### 4) Translate (optional) - preserve format: `[$TS] Speaker: text` - keep timestamps + speaker tags unchanged - keep technical terms unless explicitly requested otherwise ### 5) Package for delivery - create workbench folder with clear naming and manifest - add short summary files (e.g., `summary_30s_zh.txt`, `summary_1min_zh.txt`) - optional git init + commit for reproducibility ## Quality Rules (important) - Preserve structure exactly unless user asks otherwise. - Keep format and timestamps consistent across all versions. - Use conservative speaker reassignment to avoid over-attributing interviewer lines. - Append changes in memory logs for durable state (per workspace conventions). ## Resource Mapping ### scripts/ - `create_youtube_transcript_workbench.sh`: creates standardized output folders and manifest, optionally copies key files - `translate_transcript_google.py`: batch-translates timestamped transcript while preserving format - `rebuild_from_words.py`: optional word-level rebuild/reconciliation utility for diarization consistency ### references/ - `workflow.md`: detailed decision points for conservative speaker fixes and translation fallback handling


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