audio-pipeline

name	audio-pipeline
description	Audio pipeline: transcription (Whisper), TTS (ElevenLabs), audio extraction (ffmpeg), and YouTube transcript via MCP. [WHAT] Audio skill for transcribe, TTS, voice-over, podcast workflow. Whisper for transcription (default OpenAI Whisper, configurable per-language), ElevenLabs for TTS, ffmpeg for extraction, YouTube-transcript MCP for video. [WHEN] Use when: transcribe, audio, TTS, voice-over, podcast, YouTube transcript, generate speech, read aloud, dictation audio, mp3, wav, m4a. [LANGUAGE] Configurable. TTS voice configurable per project.
allowed-tools	Bash, Read, Write, mcp__youtube-transcript__get_transcript, mcp__elevenlabs__Text_To_Speech

Audio Pipeline

Role: unified audio workflow for transcribe, TTS, extract.

Components

1. Transcription

Default: OpenAI Whisper (works for most languages).

Optional: language-specific models if better quality is needed (configure per project).

# Default OpenAI Whisper
~/.claude/scripts/transcribe input.mp3 --output transcript.md

# With language hint
~/.claude/scripts/transcribe input.mp3 --language en --output transcript.md

# Long file → chunked
~/.claude/scripts/transcribe long.m4a --chunk 600 --output transcript.md

Output: markdown with timestamps every ~60 seconds, speaker turns if detectable.

2. TTS (text-to-speech)

Default: ElevenLabs (configure voice ID in ~/.claude/.env).

# ElevenLabs default voice
~/.claude/scripts/tts "Text to speak" --output speech.mp3

# Specific voice ID
~/.claude/scripts/tts "Text" --voice $VOICE_ID --output speech.mp3

# From file
~/.claude/scripts/tts --input script.txt --output speech.mp3

3. Audio extraction

ffmpeg for extracting audio from video, converting formats, splitting.

# Extract audio from video
ffmpeg -i video.mp4 -vn -acodec mp3 audio.mp3

# Convert m4a → mp3
ffmpeg -i input.m4a -acodec mp3 output.mp3

# Split into chunks
ffmpeg -i long.mp3 -f segment -segment_time 600 -c copy chunk_%03d.mp3

# Speed up 1.25x without pitch change
ffmpeg -i input.mp3 -filter:a "atempo=1.25" output.mp3

4. YouTube transcripts

Via MCP (no browser, no scraping):

mcp__youtube-transcript__get_transcript URL

Returns transcript with timestamps. Use directly or pipe to extract-wisdom skill.

Common workflows

Workflow 1: podcast → notes

1. Get audio: dictation file or extract from video
2. Transcribe
3. Run extract-wisdom skill on transcript
4. Save to vault via archive-to-vault

Workflow 2: written text → audio

1. User has script (chronicle, talking points, etc.)
2. Run TTS
3. Output mp3 ready for review/listen

Workflow 3: meeting recording → meeting notes

1. Extract audio from video meeting
2. Transcribe with speaker diarisation if available
3. Run meeting-processor agent on transcript

Output paths

Configure per project. Common defaults:

• Transcripts: ~/Documents/transcripts/{YYYY-MM-DD}_{title}.md
• Audio output: ~/Documents/audio/{YYYY-MM-DD}_{title}.mp3

Anti-patterns

• Transcribing without language hint when known (worse accuracy)
• TTS-ing very long texts in one call (chunks are more reliable)
• Missing audio metadata (always include duration, source, date)

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🎯 COMPLETED: [SKILL:audio-pipeline] [audio operation completed] 🗣️ CUSTOM COMPLETED: [SKILL:audio-pipeline] [Audio done]