An AI-powered audio engineering framework supporting transcription, multi-voice synthesis, restoration, stem-splitting, and automated mastering workflows.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ai-audio-processing
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install ai-audio-processing using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is AI Audio Processing Studio?
AI Audio Processing Studio is a highly comprehensive, full-stack audio engineering framework designed to integrate advanced signal processing and neural voice capabilities. Designed to run natively within Openclaw Skills pipelines, this modular interface brings state-of-the-art tools such as Whisper, Bark, OpenVoice, and Demucs directly to automated media workflows.\n\nWhether you are processing large volumes of speech data or producing high-fidelity audio assets, this tool provides developers with a structured API to perform multi-language speech-to-text (ASR), emotionally expressive text-to-speech (TTS), programmatic noise removal, stem separation, and broadcast-standard mastering. It bridges the gap between raw programmatic script execution and full-scale DAW capabilities.
AI Audio Processing Studio Use Cases
- Automated Subtitle & Transcription Pipelines: Transcribe multi-speaker recordings into accurate, timestamp-aligned SRT, VTT, or ASS files across 100+ languages using Whisper via Openclaw Skills.\n- Dynamic Voice Cloning & Dialogue Generation: Synthesize professional narrations, multi-speaker voiceovers, and dynamic dialog lines using only a short 10-second reference voice clip.\n- Automated Podcast Mastering & Publishing: Programmatically clean raw speech inputs, insert dynamic intros/outros, balance loudness levels (LUFS), and export compliant podcast MP3 files automatically.\n- Audio Stem Separation & MIR Analysis: Segment audio tracks into individual parts (vocals, background, drums, bass) for remixing, analytics, and metadata indexing.
How AI Audio Processing Studio Works
- Intake & Demuxing: The workflow begins by taking standard audio inputs and validating formats using ffmpeg under the hood.\n2. Neural Processing Pipelines: Selected tasks are routed to respective PyTorch model pipelines (e.g., Whisper for diarized ASR, Bark/OpenVoice for TTS synthesis, and Demucs for stem splitting).\n3. Post-Processing & Correction: Once output is synthesized or transcribed, internal DSP tools handle noise reduction, LUFS level adjustments, and signal leveling.\n4. Structured Artifact Output: The system outputs highly organized file systems containing mastered tracks, JSON-based speaker timelines, and subtitle files ready for immediate distribution.
AI Audio Processing Studio Setup
To configure this studio tool within your technical stack, ensure you have Python 3.10+, PyTorch, and FFmpeg installed.\n\nFirst, set up your system-level media dependencies:\n\nbash\n# Install FFmpeg on macOS via Homebrew\nbrew install ffmpeg\n\n# Install FFmpeg on Ubuntu/Debian\nsudo apt update && sudo apt install -y ffmpeg\n\n\nThen, install the necessary machine learning runtimes for Openclaw Skills execution:\n\nbash\npip install torch torchaudio --index-url https://download.pytorch.org/whl/cu118\npip install openclaw-audio-studio\n
AI Audio Processing Studio Data Schema & Taxonomy
The integration maps tasks using structured parameters, returning standardized JSON responses alongside raw audio files.\n\n### Configuration Options\n\n| Parameter | Type | Required | Description |\n| :--- | :--- | :--- | :--- |\n| action | String | Yes | Operational mode: transcribe, podcast_pipeline, tts, restore |\n| input | String | Yes | Path to raw source audio file (WAV, MP3, FLAC, etc.) |\n| diarization | Boolean | No | Toggles speaker segmentation during transcription |\n| output | String | Yes | Relative path for primary output storage |\n| chapters | Boolean/String | No | Toggles automatic timestamp detection |\n\n### Directory Structure & Deliverables\nUpon successful execution, the pipeline organizes assets in an export folder:\n- /export/transcripts/ - Contains .srt, .vtt, and raw text transcripts.\n- /export/mastered/ - Standardized .wav files normalized to -14 or -23 LUFS.\n- /export/metadata/ - JSON configurations containing speaker profiles and chapter indexes.
AI Audio Processing Studio Advanced Features
- Zero-Shot Voice Cloning: Programmatically clone target voice profiles using under 10 seconds of clear reference audio input.\n- Dynamic Emotion Modifiers: Infuse synthesized TTS passages with specific sentiments, such as anger, enthusiasm, happiness, or sadness.\n- Text-Based Audio Editing: Edit structural waveforms directly by deleting or re-ordering elements in the transcribed text representation.\n- Reference EQ Matching: Match the tonal profile of an input file directly to professional audio reference templates using programmatic equalizers.
SKILL.md
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