Supertonic — Lightning Fast, On-Device TTS

Supertonic is a lightning-fast, on-device text-to-speech system designed for extreme performance with minimal computational overhead. Powered by ONNX Runtime, it runs entirely on your device—no cloud, no API calls, no privacy concerns.

🎧 Try it now: Experience Supertonic in your browser with our Interactive Demo, or Hugging Face app or get started with pre-trained models from Hugging Face Hub

🛠 GitHub Repository
To use Supertonic most easily, visit the official GitHub repository:
https://github.com/supertone-inc/supertonic
You’ll find multi-language example codes.

Table of Contents

• Why Supertonic?
• Language Support
• Getting Started
• Performance
• Citation
• License

Why Supertonic?

• ⚡ Blazingly Fast: Generates speech up to 167× faster than real-time on consumer hardware (M4 Pro)—unmatched by any other TTS system
• 🪶 Ultra Lightweight: Only 66M parameters, optimized for efficient on-device performance with minimal footprint
• 📱 On-Device Capable: Complete privacy and zero latency—all processing happens locally on your device
• 🎨 Natural Text Handling: Seamlessly processes numbers, dates, currency, abbreviations, and complex expressions without pre-processing
• ⚙️ Highly Configurable: Adjust inference steps, batch processing, and other parameters to match your specific needs
• 🧩 Flexible Deployment: Deploy seamlessly across servers, browsers, and edge devices with multiple runtime backends.

Language Support

We provide ready-to-use TTS inference examples across multiple ecosystems:

Language/Platform	Path	Description
[Python]	py/	ONNX Runtime inference
[Node.js]	nodejs/	Server-side JavaScript
[Browser]	web/	WebGPU/WASM inference
[Java]	java/	Cross-platform JVM
[C++]	cpp/	High-performance C++
[C#]	csharp/	.NET ecosystem
[Go]	go/	Go implementation
[Swift]	swift/	macOS applications
[iOS]	ios/	Native iOS apps
[Rust]	rust/	Memory-safe systems

For detailed usage instructions, please refer to the README.md in each language directory.

Getting Started

First, clone the repository:

git clone https://github.com/supertone-inc/supertonic.git
cd supertonic

Prerequisites

Before running the examples, download the ONNX models and preset voices, and place them in the assets directory:

git clone https://huggingface.co/Supertone/supertonic assets

Note: The Hugging Face repository uses Git LFS. Please ensure Git LFS is installed and initialized before cloning or pulling large model files.

• macOS: brew install git-lfs && git lfs install
• Generic: see https://git-lfs.com for installers

Technical Details

• Runtime: ONNX Runtime for cross-platform inference (CPU-optimized; GPU mode is not tested)
• Browser Support: onnxruntime-web for client-side inference
• Batch Processing: Supports batch inference for improved throughput
• Audio Output: Outputs 16-bit WAV files

Performance

We evaluated Supertonic's performance (with 2 inference steps) using two key metrics across input texts of varying lengths: Short (59 chars), Mid (152 chars), and Long (266 chars).

Metrics:

• Characters per Second: Measures throughput by dividing the number of input characters by the time required to generate audio. Higher is better.
• Real-time Factor (RTF): Measures the time taken to synthesize audio relative to its duration. Lower is better (e.g., RTF of 0.1 means it takes 0.1 seconds to generate one second of audio).

Characters per Second

System	Short (59 chars)	Mid (152 chars)	Long (266 chars)
Supertonic (M4 pro - CPU)	912	1048	1263
Supertonic (M4 pro - WebGPU)	996	1801	2509
Supertonic (RTX4090)	2615	6548	12164
API ElevenLabs Flash v2.5	144	209	287
API OpenAI TTS-1	37	55	82
API Gemini 2.5 Flash TTS	12	18	24
API Supertone Sona speech 1	38	64	92
Open Kokoro	104	107	117
Open NeuTTS Air	37	42	47

Notes:
API = Cloud-based API services (measured from Seoul)
Open = Open-source models
Supertonic (M4 pro - CPU) and (M4 pro - WebGPU): Tested with ONNX
Supertonic (RTX4090): Tested with PyTorch model
Kokoro: Tested on M4 Pro CPU with ONNX
NeuTTS Air: Tested on M4 Pro CPU with Q8-GGUF

Real-time Factor

System	Short (59 chars)	Mid (152 chars)	Long (266 chars)
Supertonic (M4 pro - CPU)	0.015	0.013	0.012
Supertonic (M4 pro - WebGPU)	0.014	0.007	0.006
Supertonic (RTX4090)	0.005	0.002	0.001
API ElevenLabs Flash v2.5	0.133	0.077	0.057
API OpenAI TTS-1	0.471	0.302	0.201
API Gemini 2.5 Flash TTS	1.060	0.673	0.541
API Supertone Sona speech 1	0.372	0.206	0.163
Open Kokoro	0.144	0.124	0.126
Open NeuTTS Air	0.390	0.338	0.343

Additional Performance Data (5-step inference)

Characters per Second (5-step)

System	Short (59 chars)	Mid (152 chars)	Long (266 chars)
Supertonic (M4 pro - CPU)	596	691	850
Supertonic (M4 pro - WebGPU)	570	1118	1546
Supertonic (RTX4090)	1286	3757	6242

Real-time Factor (5-step)

System	Short (59 chars)	Mid (152 chars)	Long (266 chars)
Supertonic (M4 pro - CPU)	0.023	0.019	0.018
Supertonic (M4 pro - WebGPU)	0.024	0.012	0.010
Supertonic (RTX4090)	0.011	0.004	0.002

License

This project’s sample code is released under the MIT License. - see the LICENSE for details.

The accompanying model is released under the OpenRAIL-M License. - see the LICENSE file for details.

This model was trained using PyTorch, which is licensed under the BSD 3-Clause License but is not redistributed with this project. - see the LICENSE for details.

Copyright (c) 2025 Supertone Inc.