Trixy3/requirements-client.txt

# Trixy Voice Assistant - Client/Satellite Requirements
# ===================================================
# Minimal dependencies for client/satellite deployment mode
# Focused on wakeword detection, audio streaming, and server communication

# Core Python libraries
typing-extensions>=4.0.0  # Enhanced type hints for older Python versions

# Audio processing (minimal ML for wakeword detection)
torch>=1.11.0,<2.0.0      # PyTorch for wakeword model inference only
torchaudio>=0.11.0,<1.0.0  # Audio processing for wakeword detection
numpy>=1.21.0,<2.0.0       # Numerical computing foundation

# Audio I/O and real-time processing
soundfile>=0.10.3          # Audio file I/O support
pyaudio>=0.2.11            # Real-time audio input/output
sounddevice>=0.4.0         # Alternative audio I/O library

# Network communication
requests>=2.25.0           # HTTP requests for server communication
websockets>=10.0           # WebSocket client for real-time communication (if used)

# Configuration management
pyyaml>=6.0                # YAML configuration file support
pydantic>=1.8.0,<2.0.0     # Configuration validation

# Audio processing utilities (lightweight)
scipy>=1.7.0               # Signal processing for audio preprocessing
librosa>=0.9.0             # Basic audio feature extraction

# Voice Activity Detection
webrtcvad>=2.0.0           # WebRTC VAD for voice detection
silero-vad>=4.0.0          # Silero VAD models (lightweight)

# System monitoring (basic)
psutil>=5.8.0              # System resource monitoring

# Logging and error handling
structlog>=21.0.0          # Structured logging
rich>=12.0.0               # Rich console output for status display

# Cryptography for secure communication
cryptography>=3.4.8       # Secure model loading and communication

# File system monitoring for config changes
watchdog>=2.1.0            # Configuration file monitoring

# Date and time utilities
python-dateutil>=2.8.0    # Enhanced date/time parsing

# Audio format support
pydub>=0.25.0              # Audio format conversion (optional)

# Memory management
memory-profiler>=0.60.0    # Memory usage monitoring

# Threading and async support
asyncio-compat>=0.1.0      # Async compatibility utilities (if needed)

# Audio buffer management
collections-extended>=2.0.0  # Enhanced collections for audio buffering

# Error reporting
better-exceptions>=0.3.0   # Enhanced error reporting for debugging

# Platform-specific audio dependencies
# Note: These may require system-level installations
# Linux: python3-pyaudio, libasound2-dev, portaudio19-dev
# macOS: portaudio (via Homebrew)
# Windows: Usually included with PyAudio

# Minimal ML inference utilities
joblib>=1.1.0              # Model loading and persistence
scikit-learn>=1.0.0        # Basic ML utilities and preprocessing

# Audio codecs and format support
audioread>=2.1.0           # Audio file reading backend

# Optional: Advanced audio processing (only if needed)
# resampy>=0.2.2           # High-quality audio resampling
# pyroomacoustics>=0.6.0   # Room acoustics (for noise reduction)

# Client-specific utilities
click>=8.0.0               # Command line interface
typer>=0.4.0               # Modern CLI framework

# Configuration and environment
python-dotenv>=0.19.0      # Environment variable management

# Network utilities
urllib3>=1.26.0            # HTTP library utilities

# Data serialization
msgpack>=1.0.0             # Efficient data serialization for network

# Time synchronization
ntplib>=0.3.0              # NTP client for time synchronization

# Audio stream management
threading-timer>=1.0.0     # Timer utilities for audio streaming

# Signal processing for wakeword detection
scipy>=1.7.0               # Already included, signal processing

# Model format support (minimal)
onnx>=1.12.0               # ONNX model format (if models are in ONNX)
onnxruntime>=1.12.0        # ONNX runtime for inference

# Optional: Hardware-specific optimizations
# For Raspberry Pi and ARM devices:
# pip install torch torchaudio -f https://download.pytorch.org/whl/torch_stable.html

# System requirements notes:
# - Audio system: ALSA/PulseAudio (Linux), CoreAudio (macOS), DirectSound (Windows)
# - Microphone access permissions
# - Network access to server
# - Minimum 512MB RAM for basic operation
# - ARM64/x86_64 compatible PyTorch build

# Installation for different platforms:
# 
# Raspberry Pi 4 (ARM64):
# pip install torch torchaudio --index-url https://download.pytorch.org/whl/cpu
# sudo apt-get install portaudio19-dev python3-pyaudio
# 
# Ubuntu/Debian:
# sudo apt-get install portaudio19-dev python3-pyaudio libasound2-dev
# pip install -r requirements-client.txt
# 
# macOS:
# brew install portaudio
# pip install -r requirements-client.txt
# 
# Windows:
# pip install -r requirements-client.txt
# (PyAudio wheel usually includes PortAudio)

# Performance notes:
# - Keep PyTorch CPU-only for client devices to reduce size
# - Use quantized models for faster inference on edge devices
# - Consider ONNX Runtime for better performance on some hardware

# Approximate install size: ~500MB (much smaller than full ML environment)