A comprehensive audio manipulation toolkit for transcribing, generating, and transforming audio files with AI-powered precision.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install iyeque-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 iyeque-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 Audio Processing Skill?
This skill provides a comprehensive suite for audio handling within an AI agent environment. By integrating industry-standard libraries like Whisper for transcription and gTTS for speech generation, it enables seamless audio-to-text and text-to-audio workflows. It is built to be a core part of the Openclaw Skills ecosystem, offering robust features for developers needing to process media files programmatically.
Beyond basic conversion, the skill includes advanced utilities for feature extraction and voice activity detection (VAD). It ensures high performance and security by validating file paths and managing resource-intensive tasks like model loading efficiently. Whether you are building a voice assistant or a media analysis pipeline, this skill provides the necessary primitives for sophisticated audio handling.
Audio Processing Skill Use Cases
- Transcribing long-form audio recordings into text using various Whisper model sizes.
- Generating natural-sounding speech from text strings for automated notifications or content creation.
- Extracting technical audio features like MFCC and RMS for machine learning classification tasks.
- Identifying specific speech segments in noisy audio files through voice activity detection.
- Batch processing audio transformations such as trimming, resampling, and normalization for dataset preparation.
How Audio Processing Skill Works
- The user specifies an action such as transcribe, tts, or transform along with the required parameters like file path or input text.
- The skill performs a security check, resolving absolute paths and ensuring no restricted system directories are accessed.
- Depending on the action, the skill invokes the appropriate backend engine, such as OpenAI Whisper for speech-to-text or FFmpeg for audio filtering.
- For transcription tasks, the skill manages model downloading and execution based on the requested model size (tiny to large).
- Results are returned in a structured JSON format containing the processed data, such as transcribed text or paths to newly generated audio files.
Audio Processing Skill Setup
First, ensure you have the system dependencies installed on your machine:
brew install ffmpeg
Next, install the required Python packages to enable the full functionality of these Openclaw Skills:
pip install openai-whisper gTTS librosa pydub soundfile numpy webrtcvad-wheels
The skill is then ready to be invoked via the audio_tool using the command line.
Audio Processing Skill Data Schema & Taxonomy
The skill uses standard JSON outputs and works with common audio formats like WAV and MP3. Below is the typical structure for transcription and feature extraction results:
| Data Type | Description | Key Fields |
|---|---|---|
| Transcription | Whisper output | text, segments (start, end, text) |
| TTS Result | Metadata for generated audio | file_path, status |
| Audio Features | Technical analysis data | duration, sample_rate, mfcc_mean, rms_mean |
| VAD Segments | Voice detection timestamps | segments (list of start/end pairs) |
All output files are saved to resolved absolute paths provided by the user or auto-generated defaults.
Audio Processing Skill Advanced Features
- Multi-model support for transcription ranging from lightweight tiny models to high-accuracy large models.
- Chained audio transformations allowing multiple operations like trim and normalize in a single JSON-defined pipeline.
- Aggressive Voice Activity Detection (VAD) with configurable sensitivity levels (1-3) using FFmpeg filters.
- High-security file handling protocols that prevent path traversal and restrict access to sensitive system files.
- Automated model management within the Openclaw Skills framework for seamless AI-driven audio processing.
SKILL.md
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