Merge commit 'f5c01ad83a43dd6495b7906bee63a0652c9d1100'

feat(speech): enhance speech processing with advanced audio setup and detection
- Add audio setup script for PulseAudio configuration - Improve wake word detection with advanced noise filtering - Implement continuous transcription and command processing - Update speech Dockerfile with additional audio dependencies - Enhance logging and error handling in wake word detector
2025-02-04 22:51:11 +01:00 · 2025-02-04 22:51:06 +01:00 · 2025-02-04 21:42:50 +01:00 · 2025-02-04 20:29:52 +00:00 · 2025-02-04 20:29:51 +00:00 · 2025-02-04 20:18:46 +01:00
22 changed files with 2243 additions and 270 deletions
--- a/.dockerignore
+++ b/.dockerignore
@@ -73,7 +73,6 @@ temp/
 .cloud/
 *.db
 *.db-* 
 bun.lockb
 .cursor/
 .cursor*
 .cursorconfig
--- a/.env.example
+++ b/.env.example
@@ -101,4 +101,11 @@ VERSION=0.1.0
 TEST_HASS_HOST=http://localhost:8123
 TEST_HASS_TOKEN=test_token
 TEST_HASS_SOCKET_URL=ws://localhost:8123/api/websocket
-TEST_PORT=3001
+TEST_PORT=3001
 # Speech Features Configuration
 ENABLE_SPEECH_FEATURES=false
 ENABLE_WAKE_WORD=true
 ENABLE_SPEECH_TO_TEXT=true
 WHISPER_MODEL_PATH=/models
 WHISPER_MODEL_TYPE=base
--- a/69
+++ b/69
@@ -1,23 +1,70 @@
-# Use Bun as the base image
+# Use Node.js as base for building
-FROM oven/bun:1.0.25
+FROM node:20-slim as builder
 # Set working directory
 WORKDIR /app
-# Copy package files
+# Install bun
 RUN npm install -g bun@1.0.25
 # Install only the minimal dependencies needed and clean up in the same layer
 RUN apt-get update && apt-get install -y --no-install-recommends \
    ca-certificates \
    curl \
    && rm -rf /var/lib/apt/lists/* \
    && apt-get clean \
    && rm -rf /var/cache/apt/*
 # Set build-time environment variables
 ENV NODE_ENV=production \
    NODE_OPTIONS="--max-old-space-size=2048" \
    BUN_INSTALL_CACHE=0
 # Copy only package files first
 COPY package.json ./
-# Install dependencies
+# Install dependencies with a clean slate
-RUN bun install
+RUN rm -rf node_modules .bun bun.lockb && \
    bun install --no-save
-# Copy source code
+# Copy source files and build
-COPY . .
+COPY src ./src
 COPY tsconfig*.json ./
 RUN bun build ./src/index.ts --target=bun --minify --outdir=./dist
-# Build TypeScript
+# Create a smaller production image
-RUN bun run build
+FROM node:20-slim as runner
 # Install bun in production image
 RUN npm install -g bun@1.0.25
 # Set production environment variables
 ENV NODE_ENV=production \
    NODE_OPTIONS="--max-old-space-size=1024"
 # Create a non-root user
 RUN addgroup --system --gid 1001 nodejs && \
    adduser --system --uid 1001 bunjs
 WORKDIR /app
 # Copy only the necessary files from builder
 COPY --from=builder --chown=bunjs:nodejs /app/dist ./dist
 COPY --from=builder --chown=bunjs:nodejs /app/node_modules ./node_modules
 COPY --chown=bunjs:nodejs package.json ./
 # Create logs directory with proper permissions
 RUN mkdir -p /app/logs && chown -R bunjs:nodejs /app/logs
 # Switch to non-root user
 USER bunjs
 # Health check
 HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
    CMD curl -f http://localhost:4000/health || exit 1
 # Expose port
 EXPOSE 4000
-# Start the application
+# Start the application with optimized flags
-CMD ["bun", "run", "start"] 
+CMD ["bun", "--smol", "run", "start"] 
--- a/README.md
+++ b/README.md
@@ -1,311 +1,296 @@
-# 🚀 Model Context Protocol (MCP) Server for Home Assistant
+# 🚀 MCP Server for Home Assistant - Bringing AI-Powered Smart Homes to Life!
-The **Model Context Protocol (MCP) Server** is a robust, secure, and high-performance bridge that integrates Home Assistant with Language Learning Models (LLMs), enabling natural language control and real-time monitoring of your smart home devices. Unlock advanced automation, control, and analytics for your Home Assistant ecosystem.
+[![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)  
-
+[![Bun](https://img.shields.io/badge/bun-%3E%3D1.0.26-black)](https://bun.sh)  
-![License](https://img.shields.io/badge/license-MIT-blue.svg)
+[![TypeScript](https://img.shields.io/badge/typescript-%5E5.0.0-blue.svg)](https://www.typescriptlang.org)  
-![Bun](https://img.shields.io/badge/bun-%3E%3D1.0.26-black)
+[![Test Coverage](https://img.shields.io/badge/coverage-95%25-brightgreen.svg)](#)  
-![TypeScript](https://img.shields.io/badge/typescript-%5E5.0.0-blue.svg)
+[![Documentation](https://img.shields.io/badge/docs-github.io-blue.svg)](https://jango-blockchained.github.io/homeassistant-mcp/)  
-![Test Coverage](https://img.shields.io/badge/coverage-95%25-brightgreen.svg)
+[![Docker](https://img.shields.io/badge/docker-%3E%3D20.10.8-blue)](https://www.docker.com)
 [![Documentation](https://img.shields.io/badge/docs-github.io-blue.svg)](https://jango-blockchained.github.io/homeassistant-mcp/)
 ![Docker](https://img.shields.io/badge/docker-%3E%3D20.10.8-blue)
 ## 🌟 Key Benefits
 ### 🎮 Device Control & Monitoring
 - **Voice-like Control:** "Dim living room lights to 50%" 🌇
 - **Real-time Updates:** WebSocket/SSE with <100ms latency ⚡
 - **Cross-Device Automation:** Create scene-based rules 🎭
 ### 🤖 AI-Powered Features
 - Natural language processing for commands
 - Predictive automation suggestions
 - Anomaly detection in device behavior
 ## 🏗 Architecture Overview
 ```mermaid
 graph TD
    A[User Interface] --> B{MCP Server}
    B --> C[Home Assistant]
    B --> D[LLM Integration]
    B --> E[Cache Layer]
    E --> F[Redis]
    B --> G[Security Middleware]
    C --> H[Smart Devices]
 ```
 ## 🛠 Installation
 ### 🐳 Docker Setup (Recommended)
 ```bash
 # 1. Clone repo with caching
 git clone --depth 1 https://github.com/jango-blockchained/homeassistant-mcp.git
 # 2. Configure environment
 cp .env.example .env  # Edit with your HA details 🔧
 # 3. Start with compose
 docker compose up -d --build  # Auto-scaling enabled 📈
 # View real-time logs 📜
 docker compose logs -f --tail=50
 ```
 ### 📦 Bare Metal Installation
 ```bash
 # Install Bun (if missing)
 curl -fsSL https://bun.sh/install | bash  # 🐇 Fast runtime
 # Install dependencies with cache
 bun install --frozen-lockfile  # ♻️ Reliable dep tree
 # Start in dev mode with hot-reload 🔥
 bun run dev --watch
 ```   |
 ## 💡 Example Usage
 ```javascript
 // Real-time device monitoring 🌐
 const ws = new WebSocket('wss://mcp.yourha.com/ws');
 ws.onmessage = ({ data }) => {
  const update = JSON.parse(data);
  if(update.entity_id === 'light.kitchen') {
    smartBulb(update.state);  // 🎛️ Update UI
  }
 };
 ```
 ## 🔄 Update Strategy
 ```bash
 # Zero-downtime updates 🕒
 docker compose pull
 docker compose up -d --build
 docker system prune  # Clean old images 🧹
 ```
 ## 🛡 Security Features
 - JWT authentication with refresh tokens 🔑
 - Automatic request sanitization 🧼
 - IP-based rate limiting with fail2ban integration 🚫
 - End-to-end encryption support 🔒
 ## 🌍 Community & Support
 | Platform       | Link                          | Response Time |
 |----------------|-------------------------------|---------------|
 | 📚 Docs        | [API Reference](docs/api.md)  | Instant       |
 | 🐛 GitHub      | [Issues](#)                   | <24hr         |
 ## 🚧 Troubleshooting Guide
 ```bash
 # Check service health 🩺
 docker compose ps
 # Test API endpoints 🔌
 curl -I http://localhost:3000/healthcheck  # Should return 200 ✅
 # Inspect cache status 💾
 docker exec mcp_redis redis-cli info memory
 ```
 ## 🔮 Roadmap Highlights
 - [ ] **AI Assistant Integration** (Q4 2024) 🤖
 - [ ] **Predictive Automation** (Q1 2025) 🔮
 - [x] **Real-time Analytics** (Shipped! 🚀) 
 - [ ] **Energy Optimization** (Q3 2024) 🌱
 ## 🤝 Contributing
 We love community input! Here's how to help:
 1. 🍴 Fork the repository
 2. 🌿 Create a feature branch
 3. 💻 Make your changes
 4. 🧪 Run tests: `bun test --coverage`
 5. 📦 Commit using [Conventional Commits](https://www.conventionalcommits.org)
 6. 🔀 Open a Pull Request
 **Pro Tip:** Check our [Good First Issues](https://github.com/jango-blockchained/homeassistant-mcp/contribute) for starter tasks! 🎯
 ---
-**📢 Note:** This project adheres to [Semantic Versioning](https://semver.org). Always check breaking changes in release notes before upgrading! ⚠️
+## Overview 🌐
-## Table of Contents
+Welcome to the **Model Context Protocol (MCP) Server for Home Assistant**! This robust platform bridges Home Assistant with cutting-edge Language Learning Models (LLMs), enabling natural language interactions and real-time automation of your smart devices. Imagine entering your home, saying:  
- [Overview](#overview)
+> “Hey MCP, dim the lights and start my evening playlist,”  
 - [Key Features](#key-features)
 - [Architecture & Design](#architecture--design)
 - [Installation](#installation)
  - [Basic Setup](#basic-setup)
  - [Docker Setup (Recommended)](#docker-setup-recommended)
 - [Usage](#usage)
 - [API & Documentation](#api--documentation)
 - [Development](#development)
 - [Roadmap & Future Plans](#roadmap--future-plans)
 - [Community & Support](#community--support)
 - [Contributing](#contributing)
 - [Troubleshooting & FAQ](#troubleshooting--faq)
 - [License](#license)
-## Overview
+and watching your home transform instantly—that's the magic that MCP Server delivers!
-The MCP Server bridges Home Assistant with advanced LLM integrations to deliver intuitive control, automation, and state monitoring. Leveraging a high-performance runtime and real-time communication protocols, MCP offers a seamless experience for managing your smart home.
+---
-## Key Features
+## Key Benefits ✨
-### Device Control & Monitoring
+### 🎮 Device Control & Monitoring
- **Smart Device Control:** Manage lights, climate, covers, switches, sensors, media players, fans, locks, vacuums, and cameras using natural language commands.
+- **Voice-Controlled Automation:**  
- **Real-time Updates:** Receive instant notifications and updates via Server-Sent Events (SSE).
+  Use simple commands like "Turn on the kitchen lights" or "Set the thermostat to 22°C" without touching a switch.  
  **Real-World Example:**  
  In the morning, say "Good morning! Open the blinds and start the coffee machine" to kickstart your day automatically.
-### System & Automation Management
+- **Real-Time Communication:**  
- **Automation Engine:** Create, modify, and trigger custom automation rules with ease.
+  Experience sub-100ms latency updates via Server-Sent Events (SSE) or WebSocket connections, ensuring your dashboard is always current.  
- **Add-on & Package Management:** Integrates with HACS for deploying custom integrations, themes, scripts, and applications.
+  **Real-World Example:**  
- **Robust System Management:** Features advanced state monitoring, error handling, and security safeguards.
+  Monitor energy usage instantly during peak hours and adjust remotely for efficient consumption.
-## Architecture & Design
+- **Seamless Automation:**  
  Create scene-based rules to synchronize multiple devices effortlessly.  
  **Real-World Example:**  
  For movie nights, have MCP dim the lights, adjust the sound system, and launch your favorite streaming app with just one command.
-The MCP Server is built with scalability, resilience, and security in mind:
+### 🤖 AI-Powered Enhancements
 - **Natural Language Processing (NLP):**  
  Convert everyday speech into actionable commands—just say, "Prepare the house for dinner," and MCP will adjust lighting, temperature, and even play soft background music.
- **High-Performance Runtime:** Powered by Bun for fast startup, efficient memory utilization, and native TypeScript support.
+- **Predictive Automation & Suggestions:**  
- **Real-time Communication:** Employs Server-Sent Events (SSE) for continuous, real-time data updates.
+  Receive proactive recommendations based on usage habits and environmental trends.  
- **Modular & Extensible:** Designed to support plugins, add-ons, and custom automation scripts, allowing for easy expansion.
+  **Real-World Example:**  
- **Secure API Integration:** Implements token-based authentication, rate limiting, and adherence to best security practices.
+  When home temperature fluctuates unexpectedly, MCP suggests an optimal setting and notifies you immediately.
-For a deeper dive into the system architecture, please refer to our [Architecture Documentation](docs/architecture.md).
+- **Anomaly Detection:**  
  Continuously monitor device activity and alert you to unusual behavior, helping prevent malfunctions or potential security breaches.
-## Usage
+---
-Once the server is running, open your browser at [http://localhost:3000](http://localhost:3000). For real-time device updates, integrate the SSE endpoint in your application:
+## Architectural Overview 🏗
 Our architecture is engineered for performance, scalability, and security. The following Mermaid diagram illustrates the data flow and component interactions:
 ```mermaid
 graph TD
    subgraph Client
       A[Client Application<br/>(Web / Mobile / Voice)]
    end
    subgraph CDN
       B[CDN / Cache]
    end
    subgraph Server
       C[Bun Native Server]
       E[NLP Engine<br/>& Language Processing Module]
    end
    subgraph Integration
       D[Home Assistant<br/>(Devices, Lights, Thermostats)]
    end
    A -->|HTTP Request| B
    B -- Cache Miss --> C
    C -->|Interpret Command| E
    E -->|Determine Action| D
    D -->|Return State/Action| C
    C -->|Response| B
    B -->|Cached/Processed Response| A
 ```
 Learn more about our architecture in the [Architecture Documentation](docs/architecture.md).
 ---
 ## Technical Stack 🔧
 Our solution is built on a modern, high-performance stack that powers every feature:
 - **Bun:**  
  A next-generation JavaScript runtime offering rapid startup times, native TypeScript support, and high performance.  
  👉 [Learn about Bun](https://bun.sh)
 - **Bun Native Server:**  
  Utilizes Bun's built-in HTTP server to efficiently process API requests with sub-100ms response times.  
  👉 See the [Installation Guide](docs/getting-started/installation.md) for details.
 - **Natural Language Processing (NLP) & LLM Integration:**  
  Processes and interprets natural language commands using state-of-the-art LLMs and custom NLP modules.  
  👉 Find API usage details in the [API Documentation](docs/api.md).
 - **Home Assistant Integration:**  
  Provides seamless connectivity with Home Assistant, ensuring flawless communication with your smart devices.  
  👉 Refer to the [Usage Guide](docs/usage.md) for more information.
 - **Redis Cache:**  
  Enables rapid data retrieval and session persistence essential for real-time updates.
 - **TypeScript:**  
  Enhances type safety and developer productivity across the entire codebase.
 - **JWT & Security Middleware:**  
  Protects your ecosystem with JWT-based authentication, request sanitization, rate-limiting, and encryption.
 - **Containerization with Docker:**  
  Enables scalable, isolated deployments for production environments.
 For further technical details, check out our [Documentation Index](docs/index.md).
 ---
 ## Installation 🛠
 ### Installing via Smithery
 To install Home Assistant MCP Server for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@jango-blockchained/advanced-homeassistant-mcp):
 ```bash
 npx -y @smithery/cli install @jango-blockchained/advanced-homeassistant-mcp --client claude
 ```
 ### 🐳 Docker Setup (Recommended)
 For a hassle-free, containerized deployment:
 ```bash
 # 1. Clone the repository (using a shallow copy for efficiency)
 git clone --depth 1 https://github.com/jango-blockchained/homeassistant-mcp.git
 # 2. Configure your environment: copy the example file and edit it with your Home Assistant credentials
 cp .env.example .env  # Modify .env with your Home Assistant host, tokens, etc.
 # 3. Build and run the Docker containers
 docker compose up -d --build
 # 4. View real-time logs (last 50 log entries)
 docker compose logs -f --tail=50
 ```
 👉 Refer to our [Installation Guide](docs/getting-started/installation.md) for full details.
 ### 💻 Bare Metal Installation
 For direct deployment on your host machine:
 ```bash
 # 1. Install Bun (if not already installed)
 curl -fsSL https://bun.sh/install | bash
 # 2. Install project dependencies with caching support
 bun install --frozen-lockfile
 # 3. Launch the server in development mode with hot-reload enabled
 bun run dev --watch
 ```
 ---
 ## Real-World Usage Examples 🔍
 ### 📱 Smart Home Dashboard Integration
 Integrate MCP's real-time updates into your custom dashboard for a dynamic smart home experience:
 ```javascript
 const eventSource = new EventSource('http://localhost:3000/subscribe_events?token=YOUR_TOKEN&domain=light');
 eventSource.onmessage = (event) => {
-  const data = JSON.parse(event.data);
+    const data = JSON.parse(event.data);
-  console.log('Update received:', data);
+    console.log('Real-time update:', data);
    // Update your UI dashboard, e.g., refresh a light intensity indicator.
 };
 ```
-## API & Documentation
+### 🏠 Voice-Activated Control
 Utilize voice commands to trigger actions with minimal effort:
-Access comprehensive API details and guides in the docs directory:
+```javascript
 // Establish a WebSocket connection for real-time command processing
 const ws = new WebSocket('wss://mcp.yourha.com/ws');
- **API Reference:** [API Documentation](docs/api.md)
+ws.onmessage = ({ data }) => {
- **SSE Documentation:** [SSE API](docs/sse-api.md)
+    const update = JSON.parse(data);
- **Troubleshooting Guide:** [Troubleshooting](docs/troubleshooting.md)
+    if (update.entity_id === 'light.living_room') {
- **Architecture Details:** [Architecture Documentation](docs/architecture.md)
+        console.log('Adjusting living room lighting based on voice command...');
        // Additional logic to update your UI or trigger further actions can go here.
    }
 };
-## Development
+// Simulate processing a voice command
 function simulateVoiceCommand(command) {
    console.log("Processing voice command:", command);
    // Integrate with your actual voice-to-text system as needed.
 }
-### Running in Development Mode
+simulateVoiceCommand("Turn off all the lights for bedtime");
 ```bash
 bun run dev
 ```
-### Running Tests
+👉 Learn more in our [Usage Guide](docs/usage.md).
- Execute all tests:
+---
  ```bash
  bun test
  ```
- Run tests with coverage:
+## Update Strategy 🔄
  ```bash
  bun test --coverage
  ```
-### Production Build & Start
+Maintain a seamless operation with zero downtime updates:
 ```bash
-bun run build
+# 1. Pull the latest Docker images
-bun start
+docker compose pull
 # 2. Rebuild and restart containers smoothly
 docker compose up -d --build
 # 3. Clean up unused Docker images to free up space
 docker system prune -f
 ```
-## Roadmap & Future Plans
+For more details, review our [Troubleshooting & Updates](docs/troubleshooting.md).
-The MCP Server is under active development and improvement. Planned enhancements include:
+---
- **Advanced Automation Capabilities:** Introducing more complex automation rules and conditional logic.
+## Security Features 🔐
 - **Enhanced Security Features:** Additional authentication layers, encryption enhancements, and security monitoring tools.
 - **User Interface Improvements:** Development of a more intuitive web dashboard for easier device management.
 - **Expanded Integrations:** Support for a wider array of smart home devices and third-party services.
 - **Performance Optimizations:** Continued efforts to reduce latency and improve resource efficiency.
-For additional details, check out our [Roadmap](docs/roadmap.md).
+We prioritize the security of your smart home with multiple layers of defense:
 - **JWT Authentication 🔑:** Secure, token-based API access to prevent unauthorized usage.
 - **Request Sanitization 🧼:** Automatic filtering and validation of API requests to combat injection attacks.
 - **Rate Limiting & Fail2Ban 🚫:** Monitors requests to prevent brute force and DDoS attacks.
 - **End-to-End Encryption 🔒:** Ensures that your commands and data remain private during transmission.
-## Community & Support
+---
-Join our community to stay updated, share ideas, and get help:
+## Contributing 🤝
- **GitHub Issues:** Report bugs or suggest features on our [GitHub Issues Page](https://github.com/jango-blockchained/homeassistant-mcp/issues).
+We value community contributions! Here's how you can help improve MCP Server:
- **Discussion Forums:** Connect with other users and contributors in our community forums.
+1. **Fork the Repository 🍴**  
- **Chat Platforms:** Join our real-time discussions on [Discord](#) or [Slack](#).
+   Create your own copy of the project.
 2. **Create a Feature Branch 🌿**
    ```bash
    git checkout -b feature/your-feature-name
    ```
 3. **Install Dependencies & Run Tests 🧪**
    ```bash
    bun install
    bun test --coverage
    ```
 4. **Make Your Changes & Commit 📝**  
   Follow the [Conventional Commits](https://www.conventionalcommits.org) guidelines.
 5. **Open a Pull Request 🔀**  
   Submit your changes for review.
-## Contributing
+Read more in our [Contribution Guidelines](docs/contributing.md).
-We welcome your contributions! To get started:
+---
-1. Fork the repository.
+## Roadmap & Future Enhancements 🔮
 2. Create your feature branch:
   ```bash
   git checkout -b feature/your-feature-name
   ```
 3. Install dependencies:
   ```bash
   bun install
   ```
 4. Make your changes and run tests:
   ```bash
   bun test
   ```
 5. Commit and push your changes, then open a Pull Request.
-For detailed guidelines, see [Contributing Guide](docs/contributing.md).
+We're continuously evolving MCP Server. Upcoming features include:
 - **AI Assistant Integration (Q4 2024):**  
  Smarter, context-aware voice commands and personalized automation.
 - **Predictive Automation (Q1 2025):**  
  Enhanced scheduling capabilities powered by advanced AI.
 - **Enhanced Security (Q2 2024):**  
  Introduction of multi-factor authentication, advanced monitoring, and rigorous encryption methods.
 - **Performance Optimizations (Q3 2024):**  
  Reducing latency further, optimizing caching, and improving load balancing.
-## Troubleshooting & FAQ
+For more details, see our [Roadmap](docs/roadmap.md).
-### Common Issues
+---
- **Connection Problems:** Ensure that your `HASS_HOST`, authentication token, and WebSocket URL are correctly configured.
+## Community & Support 🌍
 - **Docker Deployment:** Confirm that Docker is running and that your `.env` file contains the correct settings.
 - **Automation Errors:** Verify entity availability and review your automation configurations for potential issues.
-For more troubleshooting details, refer to [Troubleshooting Guide](docs/troubleshooting.md).
+Your feedback and collaboration are vital! Join our community:
 - **GitHub Issues:** Report bugs or request features via our [Issues Page](https://github.com/jango-blockchained/homeassistant-mcp/issues).
 - **Discord & Slack:** Connect with fellow users and developers in real-time.
 - **Documentation:** Find comprehensive guides on the [MCP Documentation Website](https://jango-blockchained.github.io/homeassistant-mcp/).
-### Frequently Asked Questions
+---
-**Q: What platforms does MCP Server support?**
+## License 📜
-A: MCP Server runs on Linux, macOS, and Windows (Docker is recommended for Windows environments).
+This project is licensed under the MIT License. See [LICENSE](LICENSE) for full details.
-**Q: How do I report a bug or request a feature?**
+---
-A: Please use our [GitHub Issues Page](https://github.com/jango-blockchained/homeassistant-mcp/issues) to report bugs or request new features.
+🔋 Batteries included.
 **Q: Can I contribute to the project?**
 A: Absolutely! We welcome contributions from the community. See the [Contributing](#contributing) section for more details.
 ## License
 This project is licensed under the MIT License. See [LICENSE](LICENSE) for the full license text.
 ## Documentation
 Full documentation is available at: [https://jango-blockchained.github.io/homeassistant-mcp/](https://jango-blockchained.github.io/homeassistant-mcp/)
 ## Quick Start
 ## Installation
 ## Usage
--- a/bun.lock
+++ b/bun.lock
@@ -0,0 +1,570 @@
 {
  "lockfileVersion": 0,
  "workspaces": {
    "": {
      "dependencies": {
        "@elysiajs/cors": "^1.2.0",
        "@elysiajs/swagger": "^1.2.0",
        "@types/jsonwebtoken": "^9.0.5",
        "@types/node": "^20.11.24",
        "@types/sanitize-html": "^2.9.5",
        "@types/ws": "^8.5.10",
        "dotenv": "^16.4.5",
        "elysia": "^1.2.11",
        "helmet": "^7.1.0",
        "jsonwebtoken": "^9.0.2",
        "node-fetch": "^3.3.2",
        "sanitize-html": "^2.11.0",
        "typescript": "^5.3.3",
        "winston": "^3.11.0",
        "winston-daily-rotate-file": "^5.0.0",
        "ws": "^8.16.0",
        "zod": "^3.22.4",
      },
      "devDependencies": {
        "@types/uuid": "^10.0.0",
        "@typescript-eslint/eslint-plugin": "^7.1.0",
        "@typescript-eslint/parser": "^7.1.0",
        "bun-types": "^1.2.2",
        "eslint": "^8.57.0",
        "eslint-config-prettier": "^9.1.0",
        "eslint-plugin-prettier": "^5.1.3",
        "husky": "^9.0.11",
        "prettier": "^3.2.5",
        "supertest": "^6.3.3",
        "uuid": "^11.0.5",
      },
    },
  },
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  }
 }
--- a/bun.lockb
+++ b/bun.lockb
--- a/docker-build.sh
+++ b/docker-build.sh
@@ -0,0 +1,64 @@
 #!/bin/bash
 # Enable error handling
 set -euo pipefail
 # Function to clean up on script exit
 cleanup() {
    echo "Cleaning up..."
    docker builder prune -f --filter until=24h
    docker image prune -f
 }
 trap cleanup EXIT
 # Clean up Docker system
 echo "Cleaning up Docker system..."
 docker system prune -f --volumes
 # Set build arguments for better performance
 export DOCKER_BUILDKIT=1
 export COMPOSE_DOCKER_CLI_BUILD=1
 export BUILDKIT_PROGRESS=plain
 # Calculate available memory and CPU
 TOTAL_MEM=$(free -m | awk '/^Mem:/{print $2}')
 BUILD_MEM=$(( TOTAL_MEM / 2 ))  # Use half of available memory
 CPU_COUNT=$(nproc)
 CPU_QUOTA=$(( CPU_COUNT * 50000 ))  # Allow 50% CPU usage per core
 echo "Building with ${BUILD_MEM}MB memory limit and CPU quota ${CPU_QUOTA}"
 # Remove any existing lockfile
 rm -f bun.lockb
 # Build with resource limits, optimizations, and timeout
 echo "Building Docker image..."
 DOCKER_BUILDKIT=1 docker build \
    --memory="${BUILD_MEM}m" \
    --memory-swap="${BUILD_MEM}m" \
    --cpu-quota="${CPU_QUOTA}" \
    --build-arg BUILDKIT_INLINE_CACHE=1 \
    --build-arg DOCKER_BUILDKIT=1 \
    --build-arg NODE_ENV=production \
    --progress=plain \
    --no-cache \
    --compress \
    -t homeassistant-mcp:latest \
    -t homeassistant-mcp:$(date +%Y%m%d) \
    .
 # Check if build was successful
 BUILD_EXIT_CODE=$?
 if [ $BUILD_EXIT_CODE -eq 124 ]; then
    echo "Build timed out after 15 minutes!"
    exit 1
 elif [ $BUILD_EXIT_CODE -ne 0 ]; then
    echo "Build failed with exit code ${BUILD_EXIT_CODE}!"
    exit 1
 else
    echo "Build completed successfully!"
    # Show image size and layers
    docker image ls homeassistant-mcp:latest --format "Image size: {{.Size}}"
    echo "Layer count: $(docker history homeassistant-mcp:latest | wc -l)"
 fi 
--- a/docker/speech/Dockerfile
+++ b/docker/speech/Dockerfile
@@ -0,0 +1,68 @@
 # Use Python slim image as builder
 FROM python:3.10-slim as builder
 # Install build dependencies
 RUN apt-get update && apt-get install -y \
    git \
    build-essential \
    portaudio19-dev \
    && rm -rf /var/lib/apt/lists/*
 # Create and activate virtual environment
 RUN python -m venv /opt/venv
 ENV PATH="/opt/venv/bin:$PATH"
 # Install Python dependencies with specific versions and CPU-only variants
 RUN pip install --no-cache-dir "numpy>=1.24.3,<2.0.0" && \
    pip install --no-cache-dir torch==2.1.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cpu && \
    pip install --no-cache-dir faster-whisper==0.10.0 openwakeword==0.4.0 pyaudio==0.2.14 sounddevice==0.4.6 requests==2.31.0 && \
    pip freeze > /opt/venv/requirements.txt
 # Create final image
 FROM python:3.10-slim
 # Copy virtual environment from builder
 COPY --from=builder /opt/venv /opt/venv
 ENV PATH="/opt/venv/bin:$PATH"
 # Install audio dependencies
 RUN apt-get update && apt-get install -y \
    portaudio19-dev \
    python3-pyaudio \
    alsa-utils \
    libasound2 \
    libasound2-plugins \
    pulseaudio \
    && rm -rf /var/lib/apt/lists/*
 # Create necessary directories
 RUN mkdir -p /models/wake_word /audio
 # Set working directory
 WORKDIR /app
 # Copy the wake word detection script
 COPY wake_word_detector.py .
 # Set environment variables
 ENV WHISPER_MODEL_PATH=/models \
    WAKEWORD_MODEL_PATH=/models/wake_word \
    PYTHONUNBUFFERED=1 \
    ASR_MODEL=base.en \
    ASR_MODEL_PATH=/models
 # Add resource limits to Python
 ENV PYTHONMALLOC=malloc \
    MALLOC_TRIM_THRESHOLD_=100000 \
    PYTHONDEVMODE=1
 # Add healthcheck
 HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
    CMD ps aux | grep '[p]ython' || exit 1
 # Copy audio setup script
 COPY setup-audio.sh /setup-audio.sh
 RUN chmod +x /setup-audio.sh
 # Start command
 CMD ["/bin/bash", "-c", "/setup-audio.sh && python -u wake_word_detector.py"] 
--- a/docker/speech/setup-audio.sh
+++ b/docker/speech/setup-audio.sh
@@ -0,0 +1,16 @@
 #!/bin/bash
 # Wait for PulseAudio to be ready
 sleep 2
 # Mute the monitor to prevent feedback
 pactl set-source-mute alsa_output.pci-0000_00_1b.0.analog-stereo.monitor 1
 # Set microphone sensitivity to 65%
 pactl set-source-volume alsa_input.pci-0000_00_1b.0.analog-stereo 65%
 # Set speaker volume to 40%
 pactl set-sink-volume alsa_output.pci-0000_00_1b.0.analog-stereo 40%
 # Make the script executable
 chmod +x /setup-audio.sh 
--- a/docker/speech/wake_word_detector.py
+++ b/docker/speech/wake_word_detector.py
@@ -0,0 +1,415 @@
 import os
 import json
 import queue
 import threading
 import numpy as np
 import sounddevice as sd
 from openwakeword import Model
 from datetime import datetime
 import wave
 from faster_whisper import WhisperModel
 import requests
 import logging
 import time
 # Set up logging
 logging.basicConfig(
    level=logging.DEBUG,
    format='%(asctime)s - %(levelname)s - %(message)s'
 )
 logger = logging.getLogger(__name__)
 # Configuration
 SAMPLE_RATE = 16000
 CHANNELS = 1
 CHUNK_SIZE = 1024
 BUFFER_DURATION = 10  # seconds to keep in buffer
 DETECTION_THRESHOLD = 0.5
 CONTINUOUS_TRANSCRIPTION_INTERVAL = 3  # seconds between transcriptions
 MAX_MODEL_LOAD_RETRIES = 3
 MODEL_LOAD_RETRY_DELAY = 5  # seconds
 MODEL_DOWNLOAD_TIMEOUT = 600  # 10 minutes timeout for model download
 # Audio processing parameters
 NOISE_THRESHOLD = 0.08  # Increased threshold for better noise filtering
 MIN_SPEECH_DURATION = 2.0  # Longer minimum duration to avoid fragments
 SILENCE_DURATION = 1.0  # Longer silence duration
 MAX_REPETITIONS = 1  # More aggressive repetition filtering
 ECHO_THRESHOLD = 0.75  # More sensitive echo detection
 MIN_SEGMENT_DURATION = 1.0  # Longer minimum segment duration
 FEEDBACK_WINDOW = 5  # Window size for feedback detection in seconds
 # Feature flags from environment
 WAKE_WORD_ENABLED = os.environ.get('ENABLE_WAKE_WORD', 'false').lower() == 'true'
 SPEECH_ENABLED = os.environ.get('ENABLE_SPEECH_FEATURES', 'true').lower() == 'true'
 # Wake word models to use (only if wake word is enabled)
 WAKE_WORDS = ["alexa"]  # Using 'alexa' as temporary replacement for 'gaja'
 WAKE_WORD_ALIAS = "gaja"  # What we print when wake word is detected
 # Home Assistant Configuration
 HASS_HOST = os.environ.get('HASS_HOST', 'http://homeassistant.local:8123')
 HASS_TOKEN = os.environ.get('HASS_TOKEN')
 def initialize_asr_model():
    """Initialize the ASR model with retries and timeout"""
    model_path = os.environ.get('ASR_MODEL_PATH', '/models')
    model_name = os.environ.get('ASR_MODEL', 'large-v3')
    start_time = time.time()
    for attempt in range(MAX_MODEL_LOAD_RETRIES):
        try:
            if time.time() - start_time > MODEL_DOWNLOAD_TIMEOUT:
                logger.error("Model download timeout exceeded")
                raise TimeoutError("Model download took too long")
            logger.info(f"Loading ASR model (attempt {attempt + 1}/{MAX_MODEL_LOAD_RETRIES})")
            model = WhisperModel(
                model_size_or_path=model_name,
                device="cpu",
                compute_type="int8",
                download_root=model_path,
                num_workers=1  # Reduce concurrent downloads
            )
            logger.info("ASR model loaded successfully")
            return model
        except Exception as e:
            logger.error(f"Failed to load ASR model (attempt {attempt + 1}): {e}")
            if attempt < MAX_MODEL_LOAD_RETRIES - 1:
                logger.info(f"Retrying in {MODEL_LOAD_RETRY_DELAY} seconds...")
                time.sleep(MODEL_LOAD_RETRY_DELAY)
            else:
                logger.error("Failed to load ASR model after all retries")
                raise
 # Initialize the ASR model with retries
 try:
    asr_model = initialize_asr_model()
 except Exception as e:
    logger.error(f"Critical error initializing ASR model: {e}")
    raise
 def send_command_to_hass(domain, service, entity_id):
    """Send command to Home Assistant"""
    if not HASS_TOKEN:
        logger.error("Error: HASS_TOKEN not set")
        return False
    headers = {
        "Authorization": f"Bearer {HASS_TOKEN}",
        "Content-Type": "application/json",
    }
    url = f"{HASS_HOST}/api/services/{domain}/{service}"
    data = {"entity_id": entity_id}
    try:
        response = requests.post(url, headers=headers, json=data)
        response.raise_for_status()
        logger.info(f"Command sent: {domain}.{service} for {entity_id}")
        return True
    except Exception as e:
        logger.error(f"Error sending command to Home Assistant: {e}")
        return False
 def is_speech(audio_data, threshold=NOISE_THRESHOLD):
    """Detect if audio segment contains speech based on amplitude and frequency content"""
    # Calculate RMS amplitude
    rms = np.sqrt(np.mean(np.square(audio_data)))
    # Calculate signal energy in speech frequency range (100-4000 Hz)
    fft = np.fft.fft(audio_data)
    freqs = np.fft.fftfreq(len(audio_data), 1/SAMPLE_RATE)
    speech_mask = (np.abs(freqs) >= 100) & (np.abs(freqs) <= 4000)
    speech_energy = np.sum(np.abs(fft[speech_mask])) / len(audio_data)
    # Enhanced echo detection
    # 1. Check for periodic patterns in the signal
    autocorr = np.correlate(audio_data, audio_data, mode='full')
    autocorr = autocorr[len(autocorr)//2:]  # Use only positive lags
    peaks = np.where(autocorr > ECHO_THRESHOLD * np.max(autocorr))[0]
    peak_spacing = np.diff(peaks)
    has_periodic_echo = len(peak_spacing) > 2 and np.std(peak_spacing) < 0.1 * np.mean(peak_spacing)
    # 2. Check for sudden amplitude changes
    amplitude_envelope = np.abs(audio_data)
    amplitude_changes = np.diff(amplitude_envelope)
    has_feedback_spikes = np.any(np.abs(amplitude_changes) > threshold * 2)
    # 3. Check frequency distribution
    freq_magnitudes = np.abs(fft)[:len(fft)//2]
    peak_freqs = freqs[:len(fft)//2][np.argsort(freq_magnitudes)[-3:]]
    has_feedback_freqs = np.any((peak_freqs > 2000) & (peak_freqs < 4000))
    # Combine all criteria
    is_valid_speech = (
        rms > threshold and
        speech_energy > threshold and
        not has_periodic_echo and
        not has_feedback_spikes and
        not has_feedback_freqs
    )
    return is_valid_speech
 def process_command(text):
    """Process the transcribed command and execute appropriate action"""
    text = text.lower().strip()
    # Skip if text is too short or contains numbers (likely noise)
    if len(text) < 5 or any(char.isdigit() for char in text):
        logger.debug("Text too short or contains numbers, skipping")
        return
    # Enhanced noise pattern detection
    noise_patterns = ["lei", "los", "und", "aber", "nicht mehr", "das das", "und und"]
    for pattern in noise_patterns:
        if text.count(pattern) > 1:  # More aggressive pattern filtering
            logger.debug(f"Detected noise pattern '{pattern}', skipping")
            return
    # More aggressive repetition detection
    words = text.split()
    if len(words) >= 2:
        # Check for immediate word repetitions
        for i in range(len(words)-1):
            if words[i] == words[i+1]:
                logger.debug(f"Detected immediate word repetition: '{words[i]}', skipping")
                return
        # Check for phrase repetitions
        phrases = [' '.join(words[i:i+2]) for i in range(len(words)-1)]
        phrase_counts = {}
        for phrase in phrases:
            phrase_counts[phrase] = phrase_counts.get(phrase, 0) + 1
            if phrase_counts[phrase] > MAX_REPETITIONS:
                logger.debug(f"Skipping due to excessive repetition: '{phrase}'")
                return
    # German command mappings
    commands = {
        "ausschalten": "turn_off",
        "einschalten": "turn_on",
        "an": "turn_on",
        "aus": "turn_off"
    }
    rooms = {
        "wohnzimmer": "living_room",
        "küche": "kitchen",
        "schlafzimmer": "bedroom",
        "bad": "bathroom"
    }
    # Detect room
    detected_room = None
    for german_room, english_room in rooms.items():
        if german_room in text:
            detected_room = english_room
            break
    # Detect command
    detected_command = None
    for german_cmd, english_cmd in commands.items():
        if german_cmd in text:
            detected_command = english_cmd
            break
    if detected_room and detected_command:
        # Construct entity ID (assuming light)
        entity_id = f"light.{detected_room}"
        # Send command to Home Assistant
        if send_command_to_hass("light", detected_command, entity_id):
            logger.info(f"Executed: {detected_command} for {entity_id}")
        else:
            logger.error("Failed to execute command")
    else:
        logger.debug(f"No command found in text: '{text}'")
 class AudioProcessor:
    def __init__(self):
        logger.info("Initializing AudioProcessor...")
        self.audio_buffer = queue.Queue()
        self.recording = False
        self.buffer = np.zeros(SAMPLE_RATE * BUFFER_DURATION)
        self.buffer_lock = threading.Lock()
        self.last_transcription_time = 0
        self.stream = None
        self.speech_detected = False
        self.silence_frames = 0
        self.speech_frames = 0
        # Initialize wake word detection only if enabled
        if WAKE_WORD_ENABLED:
            try:
                logger.info("Initializing wake word model...")
                self.wake_word_model = Model(vad_threshold=0.5)
                self.last_prediction = None
                logger.info("Wake word model initialized successfully")
            except Exception as e:
                logger.error(f"Failed to initialize wake word model: {e}")
                raise
        else:
            self.wake_word_model = None
            self.last_prediction = None
            logger.info("Wake word detection disabled")
    def should_transcribe(self):
        """Determine if we should transcribe based on mode and timing"""
        current_time = datetime.now().timestamp()
        if not WAKE_WORD_ENABLED:
            # Check if enough time has passed since last transcription
            time_since_last = current_time - self.last_transcription_time
            if time_since_last >= CONTINUOUS_TRANSCRIPTION_INTERVAL:
                # Only transcribe if we detect speech
                frames_per_chunk = CHUNK_SIZE
                min_speech_frames = int(MIN_SPEECH_DURATION * SAMPLE_RATE / frames_per_chunk)
                if self.speech_frames >= min_speech_frames:
                    self.last_transcription_time = current_time
                    self.speech_frames = 0  # Reset counter
                    return True
        return False
    def audio_callback(self, indata, frames, time, status):
        """Callback for audio input"""
        if status:
            logger.warning(f"Audio callback status: {status}")
        # Convert to mono if necessary
        if CHANNELS > 1:
            audio_data = np.mean(indata, axis=1)
        else:
            audio_data = indata.flatten()
        # Check for speech
        if is_speech(audio_data):
            self.speech_frames += 1
            self.silence_frames = 0
        else:
            self.silence_frames += 1
            frames_per_chunk = CHUNK_SIZE
            silence_frames_threshold = int(SILENCE_DURATION * SAMPLE_RATE / frames_per_chunk)
            if self.silence_frames >= silence_frames_threshold:
                self.speech_frames = 0
        # Update circular buffer
        with self.buffer_lock:
            self.buffer = np.roll(self.buffer, -len(audio_data))
            self.buffer[-len(audio_data):] = audio_data
        if WAKE_WORD_ENABLED:
            # Process for wake word detection
            self.last_prediction = self.wake_word_model.predict(audio_data)
            # Check if wake word detected
            for wake_word in WAKE_WORDS:
                confidence = self.last_prediction[wake_word]
                if confidence > DETECTION_THRESHOLD:
                    logger.info(
                        f"Wake word: {WAKE_WORD_ALIAS} (confidence: {confidence:.2f})"
                    )
                    self.process_audio()
                    break
        else:
            # Continuous transcription mode
            if self.should_transcribe():
                self.process_audio()
    def process_audio(self):
        """Process the current audio buffer (save and transcribe)"""
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        filename = f"/audio/audio_segment_{timestamp}.wav"
        # Save the audio buffer to a WAV file
        with wave.open(filename, 'wb') as wf:
            wf.setnchannels(CHANNELS)
            wf.setsampwidth(2)  # 16-bit audio
            wf.setframerate(SAMPLE_RATE)
            # Convert float32 to int16
            audio_data = (self.buffer * 32767).astype(np.int16)
            wf.writeframes(audio_data.tobytes())
        logger.info(f"Saved audio segment to {filename}")
        # Transcribe the audio with German language preference
        try:
            segments, info = asr_model.transcribe(
                filename,
                language="de",  # Set German as preferred language
                beam_size=5,
                temperature=0
            )
            # Get the full transcribed text
            transcribed_text = " ".join(segment.text for segment in segments)
            logger.info(f"Transcribed text: {transcribed_text}")
            # Process the command
            process_command(transcribed_text)
        except Exception as e:
            logger.error(f"Error during transcription or processing: {e}")
    def start(self):
        """Start audio processing"""
        try:
            logger.info("Starting audio processor...")
            # Log configuration
            logger.debug(f"Sample Rate: {SAMPLE_RATE}")
            logger.debug(f"Channels: {CHANNELS}")
            logger.debug(f"Chunk Size: {CHUNK_SIZE}")
            logger.debug(f"Buffer Duration: {BUFFER_DURATION}")
            logger.debug(f"Wake Word Enabled: {WAKE_WORD_ENABLED}")
            logger.debug(f"Speech Enabled: {SPEECH_ENABLED}")
            logger.debug(f"ASR Model: {os.environ.get('ASR_MODEL')}")
            if WAKE_WORD_ENABLED:
                logger.info("Initializing wake word detection...")
                logger.info(f"Loaded wake words: {', '.join(WAKE_WORDS)}")
            else:
                logger.info("Starting continuous transcription mode...")
                interval = CONTINUOUS_TRANSCRIPTION_INTERVAL
                logger.info(f"Will transcribe every {interval} seconds")
            try:
                logger.debug("Setting up audio input stream...")
                with sd.InputStream(
                    channels=CHANNELS,
                    samplerate=SAMPLE_RATE,
                    blocksize=CHUNK_SIZE,
                    callback=self.audio_callback
                ):
                    logger.info("Audio input stream started successfully")
                    logger.info("Listening for audio input...")
                    logger.info("Press Ctrl+C to stop")
                    while True:
                        sd.sleep(1000)  # Sleep for 1 second
            except sd.PortAudioError as e:
                logger.error(f"Error setting up audio stream: {e}")
                logger.error("Check if microphone is connected and accessible")
                raise
            except Exception as e:
                logger.error(f"Unexpected error in audio stream: {e}")
                raise
        except KeyboardInterrupt:
            logger.info("\nStopping audio processing...")
        except Exception as e:
            logger.error("Critical error in audio processing", exc_info=True)
            raise
 if __name__ == "__main__":
    try:
        logger.info("Initializing AudioProcessor...")
        processor = AudioProcessor()
        processor.start()
    except Exception as e:
        logger.error("Failed to start AudioProcessor", exc_info=True)
        raise 
--- a/examples/README.md
+++ b/examples/README.md
@@ -0,0 +1,91 @@
 # Speech-to-Text Examples
 This directory contains examples demonstrating how to use the speech-to-text integration with wake word detection.
 ## Prerequisites
 1. Make sure you have Docker installed and running
 2. Build and start the services:
   ```bash
   docker-compose up -d
   ```
 ## Running the Example
 1. Install dependencies:
   ```bash
   npm install
   ```
 2. Run the example:
   ```bash
   npm run example:speech
   ```
   Or using `ts-node` directly:
   ```bash
   npx ts-node examples/speech-to-text-example.ts
   ```
 ## Features Demonstrated
 1. **Wake Word Detection**
   - Listens for wake words: "hey jarvis", "ok google", "alexa"
   - Automatically saves audio when wake word is detected
   - Transcribes the detected speech
 2. **Manual Transcription**
   - Example of how to transcribe audio files manually
   - Supports different models and configurations
 3. **Event Handling**
   - Wake word detection events
   - Transcription results
   - Progress updates
   - Error handling
 ## Example Output
 When a wake word is detected, you'll see output like this:
 ```
 🎤 Wake word detected!
  Timestamp: 20240203_123456
  Audio file: /path/to/audio/wake_word_20240203_123456.wav
  Metadata file: /path/to/audio/wake_word_20240203_123456.wav.json
 📝 Transcription result:
  Full text: This is what was said after the wake word.
  Segments:
    1. [0.00s - 1.52s] (95.5% confidence)
       "This is what was said"
    2. [1.52s - 2.34s] (98.2% confidence)
       "after the wake word."
 ```
 ## Customization
 You can customize the behavior by:
 1. Changing the wake word models in `docker/speech/Dockerfile`
 2. Modifying transcription options in the example file
 3. Adding your own event handlers
 4. Implementing different audio processing logic
 ## Troubleshooting
 1. **Docker Issues**
   - Make sure Docker is running
   - Check container logs: `docker-compose logs fast-whisper`
   - Verify container is up: `docker ps`
 2. **Audio Issues**
   - Check audio device permissions
   - Verify audio file format (WAV files recommended)
   - Check audio file permissions
 3. **Performance Issues**
   - Try using a smaller model (tiny.en or base.en)
   - Adjust beam size and patience parameters
   - Consider using GPU acceleration if available 
--- a/examples/speech-to-text-example.ts
+++ b/examples/speech-to-text-example.ts
@@ -0,0 +1,91 @@
 import { SpeechToText, TranscriptionResult, WakeWordEvent } from '../src/speech/speechToText';
 import path from 'path';
 async function main() {
    // Initialize the speech-to-text service
    const speech = new SpeechToText('fast-whisper');
    // Check if the service is available
    const isHealthy = await speech.checkHealth();
    if (!isHealthy) {
        console.error('Speech service is not available. Make sure Docker is running and the fast-whisper container is up.');
        console.error('Run: docker-compose up -d');
        process.exit(1);
    }
    console.log('Speech service is ready!');
    console.log('Listening for wake words: "hey jarvis", "ok google", "alexa"');
    console.log('Press Ctrl+C to exit');
    // Set up event handlers
    speech.on('wake_word', (event: WakeWordEvent) => {
        console.log('\n🎤 Wake word detected!');
        console.log('  Timestamp:', event.timestamp);
        console.log('  Audio file:', event.audioFile);
        console.log('  Metadata file:', event.metadataFile);
    });
    speech.on('transcription', (event: { audioFile: string; result: TranscriptionResult }) => {
        console.log('\n📝 Transcription result:');
        console.log('  Full text:', event.result.text);
        console.log('\n  Segments:');
        event.result.segments.forEach((segment, index) => {
            console.log(`    ${index + 1}. [${segment.start.toFixed(2)}s - ${segment.end.toFixed(2)}s] (${(segment.confidence * 100).toFixed(1)}% confidence)`);
            console.log(`       "${segment.text}"`);
        });
    });
    speech.on('progress', (event: { type: string; data: string }) => {
        if (event.type === 'stderr' && !event.data.includes('Loading model')) {
            console.error('❌ Error:', event.data);
        }
    });
    speech.on('error', (error: Error) => {
        console.error('❌ Error:', error.message);
    });
    // Example of manual transcription
    async function transcribeFile(filepath: string) {
        try {
            console.log(`\n🎯 Manually transcribing: ${filepath}`);
            const result = await speech.transcribeAudio(filepath, {
                model: 'base.en',  // You can change this to tiny.en, small.en, medium.en, or large-v2
                language: 'en',
                temperature: 0,
                beamSize: 5
            });
            console.log('\n📝 Transcription result:');
            console.log('  Text:', result.text);
        } catch (error) {
            console.error('❌ Transcription failed:', error instanceof Error ? error.message : error);
        }
    }
    // Create audio directory if it doesn't exist
    const audioDir = path.join(__dirname, '..', 'audio');
    if (!require('fs').existsSync(audioDir)) {
        require('fs').mkdirSync(audioDir, { recursive: true });
    }
    // Start wake word detection
    speech.startWakeWordDetection(audioDir);
    // Example: You can also manually transcribe files
    // Uncomment the following line and replace with your audio file:
    // await transcribeFile('/path/to/your/audio.wav');
    // Keep the process running
    process.on('SIGINT', () => {
        console.log('\nStopping speech service...');
        speech.stopWakeWordDetection();
        process.exit(0);
    });
 }
 // Run the example
 main().catch(error => {
    console.error('Fatal error:', error);
    process.exit(1);
 }); 
--- a/package.json
+++ b/package.json
@@ -21,7 +21,7 @@
    "profile": "bun --inspect src/index.ts",
    "clean": "rm -rf dist .bun coverage",
    "typecheck": "bun x tsc --noEmit",
-    "preinstall": "bun install --frozen-lockfile"
+    "example:speech": "bun run examples/speech-to-text-example.ts"
  },
  "dependencies": {
    "@elysiajs/cors": "^1.2.0",
@@ -37,6 +37,8 @@
    "node-fetch": "^3.3.2",
    "sanitize-html": "^2.11.0",
    "typescript": "^5.3.3",
    "winston": "^3.11.0",
    "winston-daily-rotate-file": "^5.0.0",
    "ws": "^8.16.0",
    "zod": "^3.22.4"
  },
--- a/smithery.yaml
+++ b/smithery.yaml
@@ -0,0 +1,21 @@
 # Smithery configuration file: https://smithery.ai/docs/config#smitheryyaml
 startCommand:
  type: stdio
  configSchema:
    # JSON Schema defining the configuration options for the MCP.
    type: object
    required:
      - hassToken
    properties:
      hassToken:
        type: string
        description: The token for connecting to Home Assistant API.
      port:
        type: number
        default: 4000
        description: The port on which the MCP server will run.
  commandFunction:
    # A function that produces the CLI command to start the MCP on stdio.
    |-
    config => ({command: 'bun', args: ['--smol', 'run', 'start'], env: { HASS_TOKEN: config.hassToken, PORT: config.port.toString() }})
--- a/src/config/app.config.ts
+++ b/src/config/app.config.ts
@@ -24,7 +24,7 @@ config({ path: resolve(process.cwd(), envFile) });
 */
 export const AppConfigSchema = z.object({
  /** Server Configuration */
-  PORT: z.number().default(4000),
+  PORT: z.coerce.number().default(4000),
  NODE_ENV: z
    .enum(["development", "production", "test"])
    .default("development"),
@@ -33,6 +33,21 @@ export const AppConfigSchema = z.object({
  HASS_HOST: z.string().default("http://192.168.178.63:8123"),
  HASS_TOKEN: z.string().optional(),
  /** Speech Features Configuration */
  SPEECH: z.object({
    ENABLED: z.boolean().default(false),
    WAKE_WORD_ENABLED: z.boolean().default(false),
    SPEECH_TO_TEXT_ENABLED: z.boolean().default(false),
    WHISPER_MODEL_PATH: z.string().default("/models"),
    WHISPER_MODEL_TYPE: z.string().default("base"),
  }).default({
    ENABLED: false,
    WAKE_WORD_ENABLED: false,
    SPEECH_TO_TEXT_ENABLED: false,
    WHISPER_MODEL_PATH: "/models",
    WHISPER_MODEL_TYPE: "base",
  }),
  /** Security Configuration */
  JWT_SECRET: z.string().default("your-secret-key"),
  RATE_LIMIT: z.object({
@@ -113,4 +128,11 @@ export const APP_CONFIG = AppConfigSchema.parse({
    LOG_REQUESTS: process.env.LOG_REQUESTS === "true",
  },
  VERSION: "0.1.0",
  SPEECH: {
    ENABLED: process.env.ENABLE_SPEECH_FEATURES === "true",
    WAKE_WORD_ENABLED: process.env.ENABLE_WAKE_WORD === "true",
    SPEECH_TO_TEXT_ENABLED: process.env.ENABLE_SPEECH_TO_TEXT === "true",
    WHISPER_MODEL_PATH: process.env.WHISPER_MODEL_PATH || "/models",
    WHISPER_MODEL_TYPE: process.env.WHISPER_MODEL_TYPE || "base",
  },
 });
--- a/src/index.ts
+++ b/src/index.ts
@@ -25,6 +25,8 @@ import {
  climateCommands,
  type Command,
 } from "./commands.js";
 import { speechService } from "./speech/index.js";
 import { APP_CONFIG } from "./config/app.config.js";
 // Load environment variables based on NODE_ENV
 const envFile =
@@ -129,8 +131,19 @@ app.get("/health", () => ({
  status: "ok",
  timestamp: new Date().toISOString(),
  version: "0.1.0",
  speech_enabled: APP_CONFIG.SPEECH.ENABLED,
  wake_word_enabled: APP_CONFIG.SPEECH.WAKE_WORD_ENABLED,
  speech_to_text_enabled: APP_CONFIG.SPEECH.SPEECH_TO_TEXT_ENABLED,
 }));
 // Initialize speech service if enabled
 if (APP_CONFIG.SPEECH.ENABLED) {
  console.log("Initializing speech service...");
  speechService.initialize().catch((error) => {
    console.error("Failed to initialize speech service:", error);
  });
 }
 // Create API endpoints for each tool
 tools.forEach((tool) => {
  app.post(`/api/tools/${tool.name}`, async ({ body }: { body: Record<string, unknown> }) => {
@@ -145,7 +158,12 @@ app.listen(PORT, () => {
 });
 // Handle server shutdown
-process.on("SIGTERM", () => {
+process.on("SIGTERM", async () => {
  console.log("Received SIGTERM. Shutting down gracefully...");
  if (APP_CONFIG.SPEECH.ENABLED) {
    await speechService.shutdown().catch((error) => {
      console.error("Error shutting down speech service:", error);
    });
  }
  process.exit(0);
 });
--- a/src/speech/tests/fixtures/test.wav
+++ b/src/speech/tests/fixtures/test.wav
--- a/src/speech/tests/speechToText.test.ts
+++ b/src/speech/tests/speechToText.test.ts
@@ -0,0 +1,116 @@
 import { SpeechToText, WakeWordEvent, TranscriptionError } from '../speechToText';
 import fs from 'fs';
 import path from 'path';
 describe('SpeechToText', () => {
    let speechToText: SpeechToText;
    const testAudioDir = path.join(__dirname, 'test_audio');
    beforeEach(() => {
        speechToText = new SpeechToText('fast-whisper');
        // Create test audio directory if it doesn't exist
        if (!fs.existsSync(testAudioDir)) {
            fs.mkdirSync(testAudioDir, { recursive: true });
        }
    });
    afterEach(() => {
        speechToText.stopWakeWordDetection();
        // Clean up test files
        if (fs.existsSync(testAudioDir)) {
            fs.rmSync(testAudioDir, { recursive: true, force: true });
        }
    });
    describe('checkHealth', () => {
        it('should handle Docker not being available', async () => {
            const isHealthy = await speechToText.checkHealth();
            expect(isHealthy).toBeDefined();
            expect(isHealthy).toBe(false);
        });
    });
    describe('wake word detection', () => {
        it('should detect new audio files and emit wake word events', (done) => {
            const testFile = path.join(testAudioDir, 'wake_word_test_123456.wav');
            const testMetadata = `${testFile}.json`;
            speechToText.startWakeWordDetection(testAudioDir);
            speechToText.on('wake_word', (event: WakeWordEvent) => {
                expect(event).toBeDefined();
                expect(event.audioFile).toBe(testFile);
                expect(event.metadataFile).toBe(testMetadata);
                expect(event.timestamp).toBe('123456');
                done();
            });
            // Create a test audio file to trigger the event
            fs.writeFileSync(testFile, 'test audio content');
        }, 1000);
        it('should handle transcription errors when Docker is not available', (done) => {
            const testFile = path.join(testAudioDir, 'wake_word_test_123456.wav');
            let errorEmitted = false;
            let wakeWordEmitted = false;
            const checkDone = () => {
                if (errorEmitted && wakeWordEmitted) {
                    done();
                }
            };
            speechToText.on('error', (error) => {
                expect(error).toBeDefined();
                expect(error).toBeInstanceOf(TranscriptionError);
                expect(error.message).toContain('Failed to start Docker process');
                errorEmitted = true;
                checkDone();
            });
            speechToText.on('wake_word', () => {
                wakeWordEmitted = true;
                checkDone();
            });
            speechToText.startWakeWordDetection(testAudioDir);
            // Create a test audio file to trigger the event
            fs.writeFileSync(testFile, 'test audio content');
        }, 1000);
    });
    describe('transcribeAudio', () => {
        it('should handle Docker not being available for transcription', async () => {
            await expect(
                speechToText.transcribeAudio('/audio/test.wav')
            ).rejects.toThrow(TranscriptionError);
        });
        it('should emit progress events on error', (done) => {
            let progressEmitted = false;
            let errorThrown = false;
            const checkDone = () => {
                if (progressEmitted && errorThrown) {
                    done();
                }
            };
            speechToText.on('progress', (event: { type: string; data: string }) => {
                expect(event.type).toBe('stderr');
                expect(event.data).toBe('Failed to start Docker process');
                progressEmitted = true;
                checkDone();
            });
            speechToText.transcribeAudio('/audio/test.wav')
                .catch((error) => {
                    expect(error).toBeInstanceOf(TranscriptionError);
                    errorThrown = true;
                    checkDone();
                });
        }, 1000);
    });
 }); 
--- a/src/speech/index.ts
+++ b/src/speech/index.ts
@@ -0,0 +1,110 @@
 import { APP_CONFIG } from "../config/app.config.js";
 import { logger } from "../utils/logger.js";
 import type { IWakeWordDetector, ISpeechToText } from "./types.js";
 class SpeechService {
    private static instance: SpeechService | null = null;
    private isInitialized: boolean = false;
    private wakeWordDetector: IWakeWordDetector | null = null;
    private speechToText: ISpeechToText | null = null;
    private constructor() { }
    public static getInstance(): SpeechService {
        if (!SpeechService.instance) {
            SpeechService.instance = new SpeechService();
        }
        return SpeechService.instance;
    }
    public async initialize(): Promise<void> {
        if (this.isInitialized) {
            return;
        }
        if (!APP_CONFIG.SPEECH.ENABLED) {
            logger.info("Speech features are disabled. Skipping initialization.");
            return;
        }
        try {
            // Initialize components based on configuration
            if (APP_CONFIG.SPEECH.WAKE_WORD_ENABLED) {
                logger.info("Initializing wake word detection...");
                // Dynamic import to avoid loading the module if not needed
                const { WakeWordDetector } = await import("./wakeWordDetector.js");
                this.wakeWordDetector = new WakeWordDetector() as IWakeWordDetector;
                await this.wakeWordDetector.initialize();
            }
            if (APP_CONFIG.SPEECH.SPEECH_TO_TEXT_ENABLED) {
                logger.info("Initializing speech-to-text...");
                // Dynamic import to avoid loading the module if not needed
                const { SpeechToText } = await import("./speechToText.js");
                this.speechToText = new SpeechToText({
                    modelPath: APP_CONFIG.SPEECH.WHISPER_MODEL_PATH,
                    modelType: APP_CONFIG.SPEECH.WHISPER_MODEL_TYPE,
                }) as ISpeechToText;
                await this.speechToText.initialize();
            }
            this.isInitialized = true;
            logger.info("Speech service initialized successfully");
        } catch (error) {
            logger.error("Failed to initialize speech service:", error);
            throw error;
        }
    }
    public async shutdown(): Promise<void> {
        if (!this.isInitialized) {
            return;
        }
        try {
            if (this.wakeWordDetector) {
                await this.wakeWordDetector.shutdown();
                this.wakeWordDetector = null;
            }
            if (this.speechToText) {
                await this.speechToText.shutdown();
                this.speechToText = null;
            }
            this.isInitialized = false;
            logger.info("Speech service shut down successfully");
        } catch (error) {
            logger.error("Error during speech service shutdown:", error);
            throw error;
        }
    }
    public isEnabled(): boolean {
        return APP_CONFIG.SPEECH.ENABLED;
    }
    public isWakeWordEnabled(): boolean {
        return APP_CONFIG.SPEECH.WAKE_WORD_ENABLED;
    }
    public isSpeechToTextEnabled(): boolean {
        return APP_CONFIG.SPEECH.SPEECH_TO_TEXT_ENABLED;
    }
    public getWakeWordDetector(): IWakeWordDetector {
        if (!this.isInitialized || !this.wakeWordDetector) {
            throw new Error("Wake word detector is not initialized");
        }
        return this.wakeWordDetector;
    }
    public getSpeechToText(): ISpeechToText {
        if (!this.isInitialized || !this.speechToText) {
            throw new Error("Speech-to-text is not initialized");
        }
        return this.speechToText;
    }
 }
 export const speechService = SpeechService.getInstance(); 
--- a/src/speech/speechToText.ts
+++ b/src/speech/speechToText.ts
@@ -0,0 +1,247 @@
 import { spawn } from 'child_process';
 import { EventEmitter } from 'events';
 import { watch } from 'fs';
 import path from 'path';
 import { ISpeechToText, SpeechToTextConfig } from "./types.js";
 export interface TranscriptionOptions {
    model?: 'tiny.en' | 'base.en' | 'small.en' | 'medium.en' | 'large-v2';
    language?: string;
    temperature?: number;
    beamSize?: number;
    patience?: number;
    device?: 'cpu' | 'cuda';
 }
 export interface TranscriptionResult {
    text: string;
    segments: Array<{
        text: string;
        start: number;
        end: number;
        confidence: number;
    }>;
 }
 export interface WakeWordEvent {
    timestamp: string;
    audioFile: string;
    metadataFile: string;
 }
 export class TranscriptionError extends Error {
    constructor(message: string) {
        super(message);
        this.name = 'TranscriptionError';
    }
 }
 export class SpeechToText extends EventEmitter implements ISpeechToText {
    private containerName: string;
    private audioWatcher?: ReturnType<typeof watch>;
    private modelPath: string;
    private modelType: string;
    private isInitialized: boolean = false;
    constructor(config: SpeechToTextConfig) {
        super();
        this.containerName = config.containerName || 'fast-whisper';
        this.modelPath = config.modelPath;
        this.modelType = config.modelType;
    }
    public async initialize(): Promise<void> {
        if (this.isInitialized) {
            return;
        }
        try {
            // Initialization logic will be implemented here
            await this.setupContainer();
            this.isInitialized = true;
            this.emit('ready');
        } catch (error) {
            this.emit('error', error);
            throw error;
        }
    }
    public async shutdown(): Promise<void> {
        if (!this.isInitialized) {
            return;
        }
        try {
            // Cleanup logic will be implemented here
            await this.cleanupContainer();
            this.isInitialized = false;
            this.emit('shutdown');
        } catch (error) {
            this.emit('error', error);
            throw error;
        }
    }
    public async transcribe(audioData: Buffer): Promise<string> {
        if (!this.isInitialized) {
            throw new Error("Speech-to-text service is not initialized");
        }
        try {
            // Transcription logic will be implemented here
            this.emit('transcribing');
            const result = await this.processAudio(audioData);
            this.emit('transcribed', result);
            return result;
        } catch (error) {
            this.emit('error', error);
            throw error;
        }
    }
    private async setupContainer(): Promise<void> {
        // Container setup logic will be implemented here
        await new Promise(resolve => setTimeout(resolve, 100)); // Placeholder
    }
    private async cleanupContainer(): Promise<void> {
        // Container cleanup logic will be implemented here
        await new Promise(resolve => setTimeout(resolve, 100)); // Placeholder
    }
    private async processAudio(audioData: Buffer): Promise<string> {
        // Audio processing logic will be implemented here
        await new Promise(resolve => setTimeout(resolve, 100)); // Placeholder
        return "Transcription placeholder";
    }
    startWakeWordDetection(audioDir: string = './audio'): void {
        // Watch for new audio files from wake word detection
        this.audioWatcher = watch(audioDir, (eventType, filename) => {
            if (eventType === 'rename' && filename && filename.startsWith('wake_word_') && filename.endsWith('.wav')) {
                const audioFile = path.join(audioDir, filename);
                const metadataFile = `${audioFile}.json`;
                const parts = filename.split('_');
                const timestamp = parts[parts.length - 1].split('.')[0];
                // Emit wake word event
                this.emit('wake_word', {
                    timestamp,
                    audioFile,
                    metadataFile
                } as WakeWordEvent);
                // Automatically transcribe the wake word audio
                this.transcribeAudio(audioFile)
                    .then(result => {
                        this.emit('transcription', { audioFile, result });
                    })
                    .catch(error => {
                        this.emit('error', error);
                    });
            }
        });
    }
    stopWakeWordDetection(): void {
        if (this.audioWatcher) {
            this.audioWatcher.close();
            this.audioWatcher = undefined;
        }
    }
    async transcribeAudio(
        audioFilePath: string,
        options: TranscriptionOptions = {}
    ): Promise<TranscriptionResult> {
        const {
            model = 'base.en',
            language = 'en',
            temperature = 0,
            beamSize = 5,
            patience = 1,
            device = 'cpu'
        } = options;
        return new Promise((resolve, reject) => {
            const args = [
                'exec',
                this.containerName,
                'fast-whisper',
                '--model', model,
                '--language', language,
                '--temperature', temperature.toString(),
                '--beam-size', beamSize.toString(),
                '--patience', patience.toString(),
                '--device', device,
                '--output-json',
                audioFilePath
            ];
            let process;
            try {
                process = spawn('docker', args);
            } catch (error) {
                this.emit('progress', { type: 'stderr', data: 'Failed to start Docker process' });
                reject(new TranscriptionError('Failed to start Docker process'));
                return;
            }
            let stdout = '';
            let stderr = '';
            process.stdout?.on('data', (data: Buffer) => {
                stdout += data.toString();
                this.emit('progress', { type: 'stdout', data: data.toString() });
            });
            process.stderr?.on('data', (data: Buffer) => {
                stderr += data.toString();
                this.emit('progress', { type: 'stderr', data: data.toString() });
            });
            process.on('error', (error: Error) => {
                this.emit('progress', { type: 'stderr', data: error.message });
                reject(new TranscriptionError(`Failed to execute Docker command: ${error.message}`));
            });
            process.on('close', (code: number) => {
                if (code !== 0) {
                    reject(new TranscriptionError(`Transcription failed: ${stderr}`));
                    return;
                }
                try {
                    const result = JSON.parse(stdout) as TranscriptionResult;
                    resolve(result);
                } catch (error: unknown) {
                    if (error instanceof Error) {
                        reject(new TranscriptionError(`Failed to parse transcription result: ${error.message}`));
                    } else {
                        reject(new TranscriptionError('Failed to parse transcription result: Unknown error'));
                    }
                }
            });
        });
    }
    async checkHealth(): Promise<boolean> {
        try {
            const process = spawn('docker', ['ps', '--filter', `name=${this.containerName}`, '--format', '{{.Status}}']);
            return new Promise((resolve) => {
                let output = '';
                process.stdout?.on('data', (data: Buffer) => {
                    output += data.toString();
                });
                process.on('error', () => {
                    resolve(false);
                });
                process.on('close', (code: number) => {
                    resolve(code === 0 && output.toLowerCase().includes('up'));
                });
            });
        } catch (error) {
            return false;
        }
    }
 } 
--- a/src/speech/types.ts
+++ b/src/speech/types.ts
@@ -0,0 +1,20 @@
 import { EventEmitter } from "events";
 export interface IWakeWordDetector {
    initialize(): Promise<void>;
    shutdown(): Promise<void>;
    startListening(): Promise<void>;
    stopListening(): Promise<void>;
 }
 export interface ISpeechToText extends EventEmitter {
    initialize(): Promise<void>;
    shutdown(): Promise<void>;
    transcribe(audioData: Buffer): Promise<string>;
 }
 export interface SpeechToTextConfig {
    modelPath: string;
    modelType: string;
    containerName?: string;
 } 
--- a/src/speech/wakeWordDetector.ts
+++ b/src/speech/wakeWordDetector.ts
@@ -0,0 +1,64 @@
 import { IWakeWordDetector } from "./types.js";
 export class WakeWordDetector implements IWakeWordDetector {
    private isListening: boolean = false;
    private isInitialized: boolean = false;
    public async initialize(): Promise<void> {
        if (this.isInitialized) {
            return;
        }
        // Initialization logic will be implemented here
        await this.setupDetector();
        this.isInitialized = true;
    }
    public async shutdown(): Promise<void> {
        if (this.isListening) {
            await this.stopListening();
        }
        if (this.isInitialized) {
            await this.cleanupDetector();
            this.isInitialized = false;
        }
    }
    public async startListening(): Promise<void> {
        if (!this.isInitialized) {
            throw new Error("Wake word detector is not initialized");
        }
        if (this.isListening) {
            return;
        }
        await this.startDetection();
        this.isListening = true;
    }
    public async stopListening(): Promise<void> {
        if (!this.isListening) {
            return;
        }
        await this.stopDetection();
        this.isListening = false;
    }
    private async setupDetector(): Promise<void> {
        // Setup logic will be implemented here
        await new Promise(resolve => setTimeout(resolve, 100)); // Placeholder
    }
    private async cleanupDetector(): Promise<void> {
        // Cleanup logic will be implemented here
        await new Promise(resolve => setTimeout(resolve, 100)); // Placeholder
    }
    private async startDetection(): Promise<void> {
        // Start detection logic will be implemented here
        await new Promise(resolve => setTimeout(resolve, 100)); // Placeholder
    }
    private async stopDetection(): Promise<void> {
        // Stop detection logic will be implemented here
        await new Promise(resolve => setTimeout(resolve, 100)); // Placeholder
    }
 }
Author	SHA1	Message	Date
jango-blockchained	ddf9070a64	Merge commit 'f5c01ad83a43dd6495b7906bee63a0652c9d1100'	2025-02-04 22:51:11 +01:00
jango-blockchained	b9727981cc	feat(speech): enhance speech processing with advanced audio setup and detection - Add audio setup script for PulseAudio configuration - Improve wake word detection with advanced noise filtering - Implement continuous transcription and command processing - Update speech Dockerfile with additional audio dependencies - Enhance logging and error handling in wake word detector	2025-02-04 22:51:06 +01:00
jango-blockchained	e1db799b1d	chore(dependencies): update Bun lockfile and package configuration - Update bun.lock with latest package versions - Modify Dockerfile to improve dependency installation - Remove preinstall script from package.json - Add winston logging dependencies - Adjust Docker build process for cleaner dependency management	2025-02-04 21:42:50 +01:00
smithery-ai[bot]	f5c01ad83a	Update README	2025-02-04 20:29:52 +00:00
smithery-ai[bot]	190915214d	Add Smithery configuration	2025-02-04 20:29:51 +00:00
jango-blockchained	905339fb67	refactor(docker): switch to Node.js base image and optimize Bun installation - Replace Bun base image with Node.js slim image - Install Bun globally using npm in both builder and runner stages - Simplify Docker build process and dependency management - Remove unnecessary environment variables and build flags - Update docker-build.sh to use BuildKit and remove lockfile before build	2025-02-04 20:18:46 +01:00
jango-blockchained	849b080aba	chore: update project dependencies and build configuration - Remove bun.lockb from version control - Add comprehensive docker-build.sh script for optimized Docker builds - Update Dockerfile with multi-stage build and improved resource management - Add winston logging dependencies to package.json - Enhance Docker image build process with resource constraints and caching	2025-02-04 20:14:13 +01:00
jango-blockchained	f8bbe4af6f	refactor(docker): optimize Dockerfiles for multi-stage builds and production deployment - Implement multi-stage builds for main and speech Dockerfiles - Reduce image size by using slim base images - Improve dependency installation with frozen lockfile and production flags - Add resource constraints and healthcheck to speech service Dockerfile - Enhance build caching and separation of build/runtime dependencies	2025-02-04 19:41:23 +01:00
jango-blockchained	3a6f79c9a8	feat(speech): enhance speech configuration and example integration - Add comprehensive speech configuration in .env.example and app config - Update Docker speech Dockerfile for more flexible model handling - Create detailed README for speech-to-text examples - Implement example script demonstrating speech features - Improve speech service initialization and configuration management	2025-02-04 19:35:50 +01:00
jango-blockchained	60f18f8e71	feat(speech): add speech-to-text and wake word detection modules - Implement SpeechToText class with Docker-based transcription capabilities - Add wake word detection using OpenWakeWord and fast-whisper models - Create Dockerfile for speech processing container - Develop comprehensive test suite for speech recognition functionality - Include audio processing and event-driven transcription features	2025-02-04 19:08:01 +01:00
jango-blockchained	47f11b3d95	docs: update README.md	2025-02-04 18:35:17 +01:00