YouTube Transcript Pipeline

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.

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):

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