Audio Recognition for Openclaw

A professional-grade Speech-to-Text engine designed to transform complex audio files into structured, speaker-labeled text.

zzhimin
v1.0.0
Apr 3, 2026
0
876
0

Install & Download

1. ClawHub CLI

The fastest way to install a skill directly from the registry.

npx clawhub@latest install audio-recognition

2. Manual Installation

Copy the skill folder to one of these locations

Global
~/.openclaw/skills/
Workspace
<project>/skills/

Priority: Workspace > Local > Bundled

3. Prompt Installation

Copy this prompt to OpenClaw to install it automatically.

Help me install audio-recognition 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 Recognition?

The Audio Recognition skill is a robust solution for developers needing high-fidelity transcription within their AI workflows. By leveraging advanced models like Whisper and Paraformer, this skill ensures that Openclaw Skills can process raw audio into meaningful text with a focus on linguistic accuracy and structural integrity.

Beyond simple transcription, the skill integrates speaker diarization and automated punctuation, making it an essential tool for processing multi-party conversations. It handles the heavy lifting of audio preprocessing, including noise reduction and format normalization, to provide the best possible input for AI models.

Audio Recognition Use Cases

  • Automating meeting minutes and summaries from raw recordings.
  • Generating accurate subtitles and captions for video and audio content.
  • Extracting searchable text data from large audio archives.
  • Facilitating voice command recognition and intent extraction in applications.

How Audio Recognition Works

  1. Audio Preprocessing: The system performs noise reduction, volume normalization, and converts the file into standard 16kHz/16bit PCM or MP3 formats.
  2. Feature Extraction: Acoustic features such as MFCC or FBANK are extracted to serve as inputs for the neural networks.
  3. ASR Recognition: The core Automatic Speech Recognition (ASR) engine (Whisper, WeNet, or Paraformer) processes the audio to generate a draft transcript.
  4. Post-Processing: The draft undergoes error correction, punctuation insertion, and speaker labeling (diarization).
  5. Structured Output: The final result is delivered as clean text, complete with speaker tags and timestamps where required.

Audio Recognition Setup

To integrate this skill into your environment, ensure you have the necessary audio processing libraries installed:

# Install FFmpeg for audio preprocessing
sudo apt update && sudo apt install ffmpeg

# Initialize the Audio Recognition skill
openclaw install audio-recognition

Configure your preferred ASR model (e.g., Whisper) in your skill configuration file to start processing Openclaw Skills compatible audio streams.

Audio Recognition Data Schema & Taxonomy

The skill organizes transcription data into a structured format for easy parsing by downstream AI agents:

Field Type Description
transcript String The full corrected text output
speakers Array List of identified speaker IDs and segments
timestamps Object Start and end times for specific phrases
metadata Object Audio quality metrics and language detection results

Audio Recognition Advanced Features

  • Multi-Speaker Diarization: Automatically distinguishes between different voices in a single recording.
  • Multi-Language Support: Seamlessly recognizes and transcribes diverse global languages and dialects.
  • Real-time & Offline Modes: Flexible processing for either live streams or batch file uploads.
  • Punctuation Optimization: Intelligent sentence breaking and symbol insertion for improved readability.

SKILL.md


Loading

Related Openclaw Skills

METADATA

Github Stars: 0
forks: 0

Featured*