GPU TTS Toolkit

Text-to-speech tools using GPU acceleration. Useful for converting papers and documents to audio.

Overview

Text-to-speech scripts optimized for GPU use. Designed for processing scientific papers, documentation, and other text on local hardware.

Key Features

• GPU-First Architecture: Native CUDA acceleration for faster synthesis
• Neural TTS Models: FastSpeech2 implementation with optimization framework
• Batch Processing: Efficient processing of large text datasets
• HPC Ready: Designed for SLURM clusters and multi-GPU systems
• Local Deployment: No cloud dependencies, runs entirely on your hardware
• Research Friendly: Modular design for experimenting with new architectures

Performance Goals

Metric	Target	Notes
GPU Utilization	>80%	During batch synthesis
RTF (Real-Time Factor)	<0.1	Lower is better
Memory Efficiency	Optimized	For large batch processing
HPC Scaling	Linear	Multi-GPU support planned

Performance will vary based on GPU model, batch size, and model architecture

Repository Structure

gpu-tts-toolkit/
├── deep_voice_tts.py           # Main TTS script
├── improved_tts_pipeline.py    # Enhanced pipeline
├── core-engines/
│   └── synthesis/              # TTS synthesis scripts
├── deployment/
│   └── hpc/                    # SLURM job scripts
├── integrations/
│   └── mcp/                    # Model Context Protocol
└── examples/                   # Usage examples

Quick Start

Prerequisites

# System requirements
- Linux (Ubuntu 20.04+ or similar)
- NVIDIA GPU (GTX 1060 or better recommended)
- CUDA 11.0+ (check with: nvidia-smi)
- Python 3.8+
- 8GB+ GPU memory for batch processing

# Python dependencies
pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt

Installation

# Clone the repository
git clone https://github.com/olympus-terminal/gpu-tts-toolkit.git
cd gpu-tts-toolkit

# Install dependencies
pip install -r requirements.txt

Basic Usage

# Convert text file to audio
python deep_voice_tts.py input.txt output.wav

# Use improved pipeline with preprocessing
python improved_tts_pipeline.py paper.pdf paper_audio.wav

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Areas of interest:

• New model architectures
• Language support expansion
• Performance optimizations
• Cloud integrations
• Voice quality improvements

Citations

If you use this toolkit in research, please cite:

@software{gpu_tts_toolkit,
  author = {olympus-terminal},
  title = {GPU-Accelerated TTS Toolkit},
  url = {https://github.com/olympus-terminal/gpu-tts-toolkit},
  year = {2024}
}

License

MIT License - see LICENSE file for details.

Acknowledgments

• NVIDIA for CUDA and TensorRT
• Mozilla TTS contributors
• Tacotron2, FastSpeech2, and VITS authors
• Open source TTS community

Contact

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Author: @olympus-terminal