Local Voice Agent

Complete offline voice-to-voice AI assistant for OpenClaw (Whisper.cpp STT + Pocket-TTS). 100% local processing, no cloud APIs, no costs. Use for hands-free operation, voice commands, accessibility, or custom voice cloning.

Voice Agent - OpenClaw Skill

Complete voice-to-voice AI assistant for hands-free operation.

Architecture

text

User Voice → Whisper STT → Text → OpenClaw AI → Text → Pocket-TTS → Voice Response

Prerequisites

1. Whisper.cpp (Speech-to-Text)

bash

# Clone and build
git clone https://github.com/ggerganov/whisper.cpp ~/.local/whisper.cpp
cd ~/.local/whisper.cpp
make -j4

# Download tiny model (fast, low-resource)
bash ./models/download-ggml-model.sh tiny

Test:

bash

./build/bin/whisper-cli -m models/ggml-tiny.bin -f samples/jfk.wav

2. Pocket-TTS (Text-to-Speech)

Option A: Use existing server

bash

export POCKET_TTS_URL="http://localhost:5000"

Option B: Install locally

bash

# Clone your Pocket-TTS server
cd /path/to/pockettts
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python3 -m app.main --host 0.0.0.0 --port 5000

3. FFmpeg (Audio Conversion)

bash

sudo apt-get install -y ffmpeg

Quick Start

Voice Command (One-shot)

bash

# Record → Transcribe → Process → Speak
./bin/voice-agent "What's the weather today?"

Interactive Mode

bash

# Continuous voice conversation
./bin/voice-agent --interactive

Voice File Processing

bash

# Transcribe existing audio file
./bin/voice-to-text recording.wav

# Generate voice from text
./bin/text-to-voice "Hello world!" output.wav

Configuration

Edit config/voices.yaml:

yaml

# Default voices
stt:
  model: tiny  # tiny, small, medium (larger = more accurate, slower)
  language: en  # en, ne, hi, etc.

tts:
  url: http://localhost:5000
  voice: peter voice  # Your custom voice
  format: wav  # wav, mp3

# Performance
performance:
  threads: 4  # CPU threads for Whisper
  realtime: true  # Faster-than-realtime processing

API Endpoints

POST /v1/voice/command

Voice command processing:

bash

curl -X POST "http://localhost:5000/v1/voice/command" \
  -F "audio=@recording.wav" \
  -F "action=openclaw"

Response:

json

{
  "transcription": "What's the weather today?",
  "response_text": "The weather in Kathmandu is partly cloudy, 22 degrees Celsius.",
  "audio_response": "/tmp/response.wav"
}

GET /v1/voices

List available TTS voices:

bash

curl http://localhost:5000/v1/voices

Use Cases

1. Daily Briefings (Voice)

bash

./bin/voice-agent "Give me my morning briefing"

2. Voice Notes

bash

./bin/voice-agent "Remind me to call Peter at 3 PM"

3. Hands-Free Coding

bash

./bin/voice-agent "Show me the status of my git repository"

4. Accessibility

Perfect for users who prefer voice interaction or have mobility constraints.

Scripts

bin/voice-to-text

Convert speech to text:

bash

./bin/voice-to-text input.wav
./bin/voice-to-text input.ogg  # Auto-converts with ffmpeg
./bin/voice-to-text input.mp4  # Extracts audio from video

bin/text-to-voice

Convert text to speech:

bash

./bin/text-to-voice "Hello world!" output.wav
./bin/text-to-voice --voice "usha lama" "Namaste!" greeting.wav

bin/voice-agent

Full voice pipeline:

bash

./bin/voice-agent "What time is it?"
./bin/voice-agent --interactive  # Conversation mode
./bin/voice-agent --file recording.wav  # Process file

Troubleshooting

Whisper.cpp Errors

"failed to read audio file"

• Convert to WAV first: ffmpeg -i input.ogg -ar 16000 -ac 1 output.wav

"model not found"

• Download model: bash models/download-ggml-model.sh tiny

Pocket-TTS Errors

"Connection refused"

• Start TTS server: python3 -m app.main
• Check URL: export POCKET_TTS_URL="http://localhost:5000"

"Voice not found"

• List voices: curl http://localhost:5000/v1/voices
• Clone custom voice if needed

Performance Issues

Slow transcription

• Use smaller model: tiny instead of small
• Reduce audio sample rate: ffmpeg -i input.wav -ar 16000 output.wav

Slow TTS

• Use shorter text
• Generate in background

Examples

See examples/ directory for:

• morning-briefing.sh - Automated voice briefing
• voice-reminder.sh - Voice-based reminders
• conversation-mode.sh - Interactive voice chat

Performance

Model	RAM	Speed (1 min audio)	Accuracy
tiny	500MB	~30 sec	~90%
small	1GB	~60 sec	~95%
medium	2GB	~120 sec	~98%

Recommendation: Start with tiny, upgrade to small if needed.

License

MIT License - See LICENSE file

Credits

• Whisper.cpp by Georgi Gerganov (ggerganov/whisper.cpp)
• Pocket-TTS by Kyutai Labs (kyutai-labs/pocket-tts)
• OpenClaw by OpenClaw Team (openclaw/openclaw)

Support

• GitHub Issues: [Your Repo Link]
• OpenClaw Discord: https://discord.com/invite/clawd
• Documentation: [Your Docs Link]