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README.md

Groq Speech Demo Solution - Documentation

⚠️ Disclaimer: This is a demonstrative solution for educational and prototyping purposes. It is not production-grade and should be used as a reference implementation for learning.

Welcome to the Groq Speech Demo Solution documentation! This directory contains comprehensive guides and references for this demonstrative speech processing solution.

📚 Documentation Index

🚀 Getting Started

📖 Core Documentation

🔧 Development

🎯 Quick Navigation

For Users

  1. Start with Quick Start Guide
  2. Configure your environment: Environment Setup
  3. Learn the Library: Library Reference

For Developers

  1. Understand the system: Architecture Guide
  2. Set up your dev environment: Quick Start Guide
  3. Follow contribution guidelines: Contributing Guide

For DevOps

  1. Review deployment options: Deployment Guide
  2. Understand the architecture: Architecture Guide
  3. Use automation scripts: Scripts Reference

📁 Project Structure

/
├── README.md                    # Main project overview
└── docs/                        # All documentation (here!)
    ├── README.md               # This file
    ├── QUICKSTART.md           # Quick start guide
    ├── ENVIRONMENT_SETUP.md    # Environment configuration
    ├── SCRIPTS.md              # Scripts reference
    ├── Library_REFERENCE.md        # Library API documentation
    ├── ARCHITECTURE.md         # System architecture
    ├── DEPLOYMENT.md           # Deployment guide
    ├── CONTRIBUTING.md         # Contributing guidelines
    ├── DEBUGGING_GUIDE.md      # Debugging guide
    └── CHANGELOG.md            # Version history

🏗️ System Overview

This Groq Speech Demo Solution is a demonstrative speech recognition and translation implementation with three main components:

┌─────────────────────────────────────────────────────────────┐
│                    Layer 3: User Interfaces                │
├─────────────────────────────────────────────────────────────┤
│  CLI Client (speech_demo.py)  │  Web UI (groq-speech-ui)   │
└─────────────────────────────────────────────────────────────┘
                                │
                                ▼
┌─────────────────────────────────────────────────────────────┐
│                    Layer 2: API Layer                      │
├─────────────────────────────────────────────────────────────┤
│                    FastAPI Server (api/)                   │
└─────────────────────────────────────────────────────────────┘
                                │
                                ▼
┌─────────────────────────────────────────────────────────────┐
│                    Layer 1: Core Library                       │
├─────────────────────────────────────────────────────────────┤
│              groq_speech/ (Python Library)                     │
└─────────────────────────────────────────────────────────────┘

🔄 Key Features

Speech Processing

  • ✅ File Transcription - Process audio files with high accuracy
  • ✅ File Translation - Translate audio to different languages
  • ✅ Speaker Diarization - Identify and separate multiple speakers
  • ✅ Microphone Processing - Real-time audio processing

Advanced Features

  • ✅ Voice Activity Detection - Client-side real-time VAD
  • ✅ Continuous Processing - Long-form audio with chunking
  • ✅ GPU Acceleration - CUDA support for diarization
  • ✅ Performance Monitoring - Built-in metrics and analytics

Deployment Options

  • ✅ Local Development - Docker Compose with hot reload
  • ✅ Production - Docker containers with GPU support
  • ✅ GCP Cloud Run - Serverless deployment with auto-scaling
  • ✅ GKE GPU - Kubernetes deployment with GPU acceleration

📊 Current Status

Working Features

  • CLI Interface: All 10 command types working perfectly
  • Web Interface: Complete feature parity with CLI
  • API Server: REST API with all endpoints functional
  • VAD Processing: Client-side real-time silence detection
  • Diarization: GPU-accelerated speaker diarization
  • Translation: Multi-language translation support
  • Production Deployment: GCP Cloud Run and GKE deployment options
  • Docker Support: Local development and production containers

Performance

  • CLI: Direct Library access, no network overhead
  • Web UI: Client-side VAD for real-time processing
  • API: REST API with efficient audio processing
  • GPU: Automatic CUDA detection and usage
  • Cloud: Auto-scaling and pay-per-use deployment

🔧 Technical Highlights

Architecture Decisions

  1. Client-Side VAD - Real-time processing without network latency
  2. Unified Components - Single classes handle multiple modes
  3. REST API Only - Simplified architecture, easier maintenance
  4. Library Factory Methods - Centralized configuration creation

Performance Optimizations

  1. Chunked Processing - Handles large files efficiently
  2. Memory Management - Optimized for both short and long audio
  3. GPU Support - Automatic detection and usage
  4. Client-Side VAD - 3-second silence detection with adaptive thresholds
  5. Intelligent Buffer Management - Prevents duplicate audio processing
  6. Audio Content Validation - Filters background noise (RMS threshold: 0.015)
  7. Adaptive Silence Mode - Different thresholds for active vs silence states

📈 Common Workflows

First-Time Setup

# Clone and run setup
git clone https://github.com/build-with-groq/groq-speech
cd groq-speech
./setup.sh

# Configure environment
nano .env.api  # Add your GROQ_API_KEY

# Activate virtual environment
source .venv/bin/activate

Daily Development

# Activate virtual environment
source .venv/bin/activate

# Run development servers
./run-dev.sh

Running CLI

# Basic transcription
python examples/speech_demo.py --file audio.wav

# Translation with diarization
python examples/speech_demo.py --file audio.wav --operation translation --diarize

Docker Deployment

# Standard deployment
docker-compose -f deployment/docker/docker-compose.yml up

# GPU-enabled deployment
docker-compose -f deployment/docker/docker-compose.gpu.yml up

Cloud Deployment

# Deploy to GCP Cloud Run
cd deployment/gcp && ./deploy.sh

# Deploy to GKE with GPU
cd deployment/gcp && ./deploy-simple-gke.sh

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Add tests if applicable
  5. Submit a pull request

Code Standards

  • Follow existing code patterns
  • Add comprehensive documentation
  • Include tests for new features
  • Update documentation as needed
  • Test with both CLI and web interfaces
  • Verify deployment options work correctly

📞 Support

For issues and questions:

  1. Check the Quick Start Guide for setup help
  2. Review Debugging Guide for common issues
  3. Check the Library Reference for API usage
  4. Review existing issues
  5. Create a new issue with detailed information

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

Built with ❤️ using Groq, Pyannote.audio, and modern web technologies.

Last Updated: October 2025
Maintained By: Build with Groq