goobits-stt 1.0.0

GOOBITS STT - Pure speech-to-text engine with multiple operation modes

🎙️ Goobits STT

A pure speech-to-text engine with multiple operation modes and advanced text formatting. Features real-time transcription, WebSocket server capabilities, and comprehensive text processing with internationalization support. Built on Whisper models for accurate transcription across various languages and use cases.

🔗 Related Projects

• Matilda - AI assistant
• Goobits STT - Speech-to-Text engine (this project)
• Goobits TTS - Text-to-Speech engine
• Goobits TTT - Text-to-Text processing

📋 Table of Contents

• Installation
• Basic Usage
• Configuration
• Operation Modes
• Performance Optimization
• Text Formatting Features
• Server Deployment
• Testing & Development
• Model Comparison
• Audio Features
• Tech Stack

📦 Installation

# Install globally with pipx (recommended)
pipx install .                     # Install globally, isolated environment
pipx install .[dev]               # Install with development dependencies

# Or with pip for development
pip install -e .[dev]              # Install editable with dev dependencies
stt --version                      # Verify installation
stt --listen-once                  # Test basic functionality

🎯 Basic Usage

stt --listen-once                  # Single utterance with VAD
stt --conversation                 # Always listening mode
stt --tap-to-talk=f8              # Tap F8 to start/stop recording
stt --hold-to-talk=space          # Hold spacebar to record
stt --server --port=8769          # Run WebSocket server

⚙️ Configuration

# Edit main configuration
nano config.json

# Configure Whisper model
stt --model large-v3-turbo --language en

# Audio settings
stt --device "USB Audio" --sample-rate 16000

# Output formats
stt --format json | jq -r '.text'
stt --format text --no-formatting

🎤 Operation Modes

# Quick transcription
stt --listen-once | llm-process

# Interactive conversation
stt --conversation | tts-speak

# Hotkey control
stt --tap-to-talk=f8              # Toggle recording with F8
stt --hold-to-talk=ctrl+space     # Push-to-talk mode

# Server mode for remote clients
stt --server --host 0.0.0.0 --port 8769

🚀 Performance Optimization

# GPU acceleration (if available)
stt --model base --device cuda

# CPU optimization
stt --model tiny --device cpu

# Model selection by speed/quality
stt --model tiny      # Fastest, lower quality
stt --model base      # Balanced (default)
stt --model large-v3-turbo  # Best quality

🎭 Text Formatting Features

# Advanced entity detection
stt --listen-once  # "Call me at 555-123-4567" → "Call me at [phone]"
stt --listen-once  # "Go to github dot com" → "Go to github.com"
stt --listen-once  # "Three point one four" → "3.14"

# Multilingual support
stt --language es  # Spanish formatting rules
stt --language en  # English formatting (default)

# Disable formatting
stt --no-formatting  # Raw transcription output

🔧 Server Deployment

# Basic server
stt --server

# Production with SSL
stt --server --port 443 --host 0.0.0.0

# Docker deployment
docker run -p 8080:8080 -p 8769:8769 sttservice/transcribe

🎯 Testing & Development

# Run test suite
pytest                             # All tests
pytest tests/text_formatting/     # Specific module
pytest -v -n auto                 # Parallel with verbose output

# Code quality
ruff check src/ tests/             # Linting
black src/ tests/ stt.py          # Formatting
mypy src/ stt.py                  # Type checking

# Test with real audio
pytest tests/__fixtures__/audio/

🔧 Model Comparison

Model	Speed	Quality	Memory	Best For
tiny	⚡ Fastest	🌟 Basic	💾 39MB	Real-time, low resources
base	🔥 Fast	🌟🌟 Good	💾 74MB	General use (default)
small	⚡ Quick	🌟🌟🌟 Better	💾 244MB	Accuracy balance
medium	🔥 Moderate	🌟🌟🌟🌟 Great	💾 769MB	High accuracy
large-v3-turbo	🔥 Fast	🏆 Best	💾 1550MB	Production quality

Choose based on your speed/accuracy requirements and available system resources.

🎙️ Audio Features

• Real-time streaming: Opus audio encoding for efficient transmission
• Voice Activity Detection: Automatic speech detection and silence handling
• Multiple input devices: Support for various microphones and audio interfaces
• Hotkey integration: System-wide keyboard shortcuts for hands-free operation
• Background operation: Run as daemon with minimal resource usage

🛠️ Tech Stack

Core Technologies

• 🧠 AI/ML: OpenAI Whisper (faster-whisper), CTranslate2, PyTorch
• 🎙️ Audio: OpusLib, NumPy, custom pipe-based audio capture
• ⌨️ System: pynput for global hotkeys, cross-platform support

Text Processing

• 📝 NLP: spaCy, deepmultilingualpunctuation
• 🌍 i18n: Multi-language entity detection and formatting
• 🔧 Parsing: pyparsing for complex text transformations
• 📊 Output: JSON/text formatting with rich entity support

Development & Testing

• 🧪 Testing: pytest with asyncio, xdist, custom plugins
• 📊 Quality: ruff (linting), black (formatting), mypy (typing)
• 🔍 Security: bandit for security analysis
• 📦 Build: setuptools, pyproject.toml configuration

Deployment

• 🐳 Containerization: Docker with CUDA 12.1 support
• 🖥️ Interface: FastAPI admin dashboard (Docker), responsive web UI
• 🔒 Security: JWT authentication, RSA+AES encryption (Docker)
• 📈 Monitoring: Structured logging, health checks
• ☁️ Cloud: Ready for production deployment with SSL/TLS