AudioEditor

AudioEditor

AI-powered audio/video editing — transcription, intelligent cut detection, automated editing with crossfades, and optional cloud polish.

Customization

Before executing, check for user customizations at: ${PAI_USER_DIR}/SKILLCUSTOMIZATIONS/AudioEditor/

If this directory exists, load and apply any PREFERENCES.md, configurations, or resources found there. These override default behavior. If the directory does not exist, proceed with skill defaults.

<!-- ## Voice Notification **You MUST send this notification BEFORE doing anything else when this skill is invoked.** 1. **Send voice notification**: ```bash curl -s -X POST http://localhost:8888/notify \ -H "Content-Type: application/json" \ -d '{"message": "Running the WORKFLOWNAME workflow in the AudioEditor skill to ACTION"}' \ > /dev/null 2>&1 & ``` 2. **Output text notification**: ``` Running the **WorkflowName** workflow in the **AudioEditor** skill to ACTION... ``` **This is not optional. Execute this curl command immediately upon skill invocation.** -->

Workflow Routing

Workflow	Trigger	File
Clean	"clean audio", "edit audio", "remove filler words", "clean podcast", "remove ums", "cut dead air", "polish audio"	Workflows/Clean.md

Pipeline Architecture

Audio Input
    |
[Transcribe] Whisper word-level timestamps (insanely-fast-whisper on MPS)
    |
[Analyze] Claude classifies each segment:
    |   KEEP / CUT_FILLER / CUT_FALSE_START / CUT_EDIT_MARKER / CUT_STUTTER / CUT_DEAD_AIR
    |   Distinguishes rhetorical emphasis from accidental repetition
    |
[Edit] ffmpeg executes cuts:
    |   - 40ms qsin crossfades at every edit point
    |   - Room tone extraction and gap filling
    |   - Breath attenuation (50% volume, not removal)
    |
[Polish] (optional) Cleanvoice API final pass:
        - Mouth sound removal
        - Remaining filler detection
        - Loudness normalization

Output: cleaned MP3/WAV

Tools

Tool	Command	Purpose
Transcribe	bun ${CLAUDE_SKILL_DIR}/Tools/Transcribe.ts <file>	Word-level transcription via Whisper
Analyze	bun ${CLAUDE_SKILL_DIR}/Tools/Analyze.ts <transcript.json>	LLM-powered edit classification
Edit	bun ${CLAUDE_SKILL_DIR}/Tools/Edit.ts <file> <edits.json>	Execute cuts with crossfades + room tone
Polish	bun ${CLAUDE_SKILL_DIR}/Tools/Polish.ts <file>	Cleanvoice API cloud polish
Pipeline	bun ${CLAUDE_SKILL_DIR}/Tools/Pipeline.ts <file> [--polish]	Full end-to-end pipeline

API Keys Required

Service	Env Var	Where to Get
Anthropic (for analyze step)	ANTHROPIC_API_KEY	Already set via Claude Code
Cleanvoice (for polish step, optional)	CLEANVOICE_API_KEY	cleanvoice.ai Dashboard Settings API Key

Examples

Example 1: Clean a podcast recording

User: "clean up the audio on this podcast file"
-> Invokes Clean workflow
-> Runs full pipeline: transcribe -> analyze -> edit
-> Outputs cleaned MP3 with filler words, stutters, and dead air removed

Example 2: Preview edits before applying

User: "show me what edits you'd make to this recording"
-> Invokes Clean workflow with --preview flag
-> Transcribes and analyzes, shows proposed edits without modifying audio
-> User reviews edit list, then runs again to apply

Example 3: Aggressive clean with cloud polish

User: "aggressively clean this audio and polish it"
-> Invokes Clean workflow with --aggressive --polish flags
-> Tighter thresholds for filler detection
-> Cleanvoice API pass for mouth sounds and normalization