A specialized skill for clawbot that decodes WeChat SILK audio files and transcribes them into text using local Whisper models.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install wechat-voice-decode
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 wechat-voice-decode 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 WeChat Voice Parsing?
WeChat Voice is a technical utility designed to bridge the gap between WeChat's proprietary audio formats and actionable AI data. This skill enables an agent to intercept inbound audio attachments, identify the specific SILK_V3 header, and convert the content into standard WAV format.
By leveraging Openclaw Skills, this tool ensures that your AI agent can 'hear' and understand voice messages natively. It uses a local CPU-based Whisper implementation to provide fast, privacy-focused transcriptions without relying on external cloud APIs, making it ideal for responsive and secure WeChat bot integrations.
WeChat Voice Parsing Use Cases
- Transcribing voice notes from WeChat users into text for AI processing.
- Analyzing audio attachments to determine if they are silent, too short, or unclear.
- Providing automated text-based replies based on the content of a voice message.
- Converting legacy SILK audio files into standard WAV format for archival or debugging.
How WeChat Voice Parsing Works
- The agent retrieves the local file path for an inbound audio attachment from the conversation context.
- It performs a byte-level inspection to detect the #!SILK_V3 header.
- The skill invokes a bundled Python script to decode the SILK stream into a WAV file.
- Faster-Whisper processes the WAV file locally on the CPU to generate a text transcript.
- The system returns the recognized text or a NO_SEGMENTS flag if the audio is unusable.
- The agent generates a user-facing response based on the resulting transcript.
WeChat Voice Parsing Setup
This skill requires Python 3 and FFmpeg to be installed on the host system. To install the necessary dependencies for these Openclaw Skills, run:
python3 -m pip install --user silk-python faster-whisper
Ensure that the bundled transcription script is accessible within your workspace path.
WeChat Voice Parsing Data Schema & Taxonomy
The skill manages audio data and metadata through the following structure:
| Data Type | Description |
|---|---|
| Input File | Raw WeChat SILK audio (detected via hex header #!SILK_V3) |
| Intermediate | Temporary WAV file generated during the decoding phase |
| Output | Plaintext transcript or standardized status code (NO_SEGMENTS) |
| Scripts | transcribe_wechat_voice.py serves as the primary execution entry point |
WeChat Voice Parsing Advanced Features
- Local CPU Optimization: Uses faster-whisper to avoid heavy GPU/CUDA dependencies while maintaining high accuracy.
- Format Validation: Includes byte-level header inspection to prevent processing errors on non-SILK files.
- Error Resilience: Explicit handling for silent or extremely short clips to provide better user feedback.
- Integrated Debugging: Supports manual hex inspection and byte-size verification for troubleshooting attachment issues.
SKILL.md
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