Edge TTS Voice System

Local voice system for OpenClaw using faster-whisper for inbound transcription and Edge TTS for outbound replies. Use when you need private voice workflows, offline transcription, cached reply audio, or voice message handling after OpenClaw updates.

A complete, privacy-focused voice system for OpenClaw that works entirely offline. No internet required, no data leaves your machine.

Features

• Outbound replies: Edge TTS with cached audio output
• Accurate STT: faster-whisper base model for speech recognition
• Fully offline: No internet connection required
• Privacy-focused: All processing happens locally
• Easy integration: Ready-to-use Python and bash scripts
• Voice conversations: Natural back-and-forth voice interactions

Quick Start

Installation

bash

# Install the skill
clawhub install lessac_offline_voice_system

# Or manually from this directory
./scripts/install.sh

Basic Usage

python

from scripts.voice_handler import VoiceHandler

handler = VoiceHandler()

# Transcribe audio to text
text = handler.audio_to_text("voice_message.ogg")
print(f"You said: {text}")

# Generate voice response
audio_file = handler.text_to_audio("Hello, this is a voice response.")

Command Line

bash

# Transcribe audio
./scripts/voice_integration.sh transcribe voice_message.ogg

# Generate TTS
./scripts/voice_integration.sh tts "Hello world" output.wav

# Full voice processing
./scripts/voice_integration.sh process voice_message.ogg

Components

1. Text-to-Speech (TTS)

• Voice: Edge-supported voice (default en-IE-ConnorNeural)
• Library: Edge TTS (edge-tts)
• Quality: Natural speech with cached output
• Sample rate: provider-defined

2. Speech-to-Text (STT)

• Model: faster-whisper base
• Accuracy: High, comparable to cloud services
• Languages: Multi-language support (auto-detected)
• Speed: ~2 seconds for typical audio

3. Audio Processing

• Formats: OGG/Opus, WAV, MP3 (via ffmpeg)
• Conversion: Automatic format handling
• Quality: 16kHz mono for optimal recognition

Performance

• TTS Load time: ~2 seconds (one-time)
• TTS Generation: ~3-4 seconds
• STT Transcription: ~2 seconds
• Total response time: 5-7 seconds

Integration with OpenClaw

Automatic Voice Processing

When installed, the skill can be configured to automatically:

1. Detect incoming voice messages
2. Transcribe them silently
3. Generate AI responses
4. Convert responses to voice
5. Send voice replies back

OpenClaw reply TTS configuration

The built-in OpenClaw reply TTS path is not the local voice pipeline used by this skill. This skill now uses a local Edge TTS reply path instead, with cached output stored under /root/.openclaw/tts/cache.

Default outbound voice:

• en-IE-ConnorNeural

Relevant files:

• tts_edge_wrapper.py
• voice_handler.py
• voice_integration.sh
• scripts/install.sh

If you need to change the voice, set:

bash

export OPENCLAW_EDGE_TTS_VOICE="en-IE-ConnorNeural"

or replace it with another Edge-supported voice.

Reinstall after OpenClaw updates

After an OpenClaw system update, rerun the installer to restore the voice stack:

bash

cd /root/.openclaw/workspace/skills/lessac_offline_voice_system
./scripts/install.sh

This refreshes:

• the Python venv dependencies (faster-whisper, edge-tts, soundfile)
• the runtime cache directory
• the local voice wrappers
• the config file under /root/.openclaw/tts/config.json

Manual Integration

python

# In your OpenClaw agent or custom script
import sys
sys.path.append("/path/to/skill/scripts")
from voice_handler import VoiceHandler

class YourAgent:
    def __init__(self):
        self.voice = VoiceHandler()
    
    def handle_voice_message(self, audio_file):
        # Transcribe
        text = self.voice.audio_to_text(audio_file)
        
        # Generate response (your AI logic here)
        response = self.generate_response(text)
        
        # Convert to voice
        voice_response = self.voice.text_to_audio(response)
        
        return voice_response

Configuration

Voice Model Selection

The skill uses Edge TTS by default. To use a different voice:

1. Set OPENCLAW_EDGE_TTS_VOICE to a supported Edge voice
2. Re-run the installer to refresh the cache and wrappers

STT Model Selection

Change the faster-whisper model size in scripts/voice_handler.py:

• "tiny": Fastest, lower accuracy
• "base": Default, good balance
• "small": Higher accuracy, slower
• "medium": Best accuracy, slowest

Troubleshooting

Common Issues

1. "No module named 'piper'"

   bash

   pip install piper-tts
   

2. "ffmpeg not found"

   bash

   sudo apt-get install ffmpeg
   

3. Out of memory with large models

   • Use "tiny" or "base" STT model
   • Use a different Edge voice if needed

4. Slow TTS generation

   • First generation loads model (~2s)
   • Subsequent generations are faster (~0.3s per sentence)

Debug Mode

Enable debug output:

bash

export VOICE_DEBUG=1
./scripts/voice_integration.sh process audio.ogg

Files

• scripts/install.sh - Installation script
• scripts/voice_handler.py - Main Python handler
• scripts/piper_tts.py - Edge TTS wrapper
• scripts/voice_integration.sh - Bash interface
• references/voice_models.md - Voice model information
• assets/ - Voice model files (downloaded during install)

Dependencies

• Python 3.8+
• ffmpeg
• Python packages (installed automatically):
  • faster-whisper
  • piper-tts
  • soundfile

License

Open source. See included LICENSE file.

Support

For issues or questions:

1. Check the troubleshooting section
2. Review the references/ directory
3. Open an issue on the skill repository