**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.
- **2025.12.10** - Added `supertonic` PyPI package! Install via `pip install supertonic`. For details, visit [supertonic-py documentation](https://supertone-inc.github.io/supertonic-py)
- **2025.12.10** - Added [6 new voice styles](https://huggingface.co/Supertone/supertonic/tree/b10dbaf18b316159be75b34d24f740008fddd381) (M3, M4, M5, F3, F4, F5). See [Voices](https://supertone-inc.github.io/supertonic-py/voices/) for details
- **2025.12.08** - Optimized ONNX models via [OnnxSlim](https://github.com/inisis/OnnxSlim) now available on [Hugging Face Models](https://huggingface.co/Supertone/supertonic)
- **2025.11.24** - Added Flutter SDK support with macOS compatibility
> 🎧 **Try it now**: Experience Supertonic in your browser with our [**Interactive Demo**](https://huggingface.co/spaces/Supertone/supertonic#interactive-demo), or get started with pre-trained models from [**Hugging Face Hub**](https://huggingface.co/Supertone/supertonic)
## 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:
- In Xcode: Targets → ExampleiOSApp → Signing: select your Team
- Choose your iPhone as run destination → Build & Run
### 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 is designed to handle complex, real-world text inputs that contain numbers, currency symbols, abbreviations, dates, and proper nouns.
> 🎧 **View audio samples more easily**: Check out our [**Interactive Demo**](https://huggingface.co/spaces/Supertone/supertonic#text-handling) for a better viewing experience of all audio examples
> **Note:** These samples demonstrate how each system handles text normalization and pronunciation of complex expressions **without requiring pre-processing or phonetic annotations**.
The following papers describe the core technologies used in Supertonic. If you use this system in your research or find these techniques useful, please consider citing the relevant papers:
### SupertonicTTS: Main Architecture
This paper introduces the overall architecture of SupertonicTTS, including the speech autoencoder, flow-matching based text-to-latent module, and efficient design choices.
```bibtex
@article{kim2025supertonic,
title={SupertonicTTS: Towards Highly Efficient and Streamlined Text-to-Speech System},
author={Kim, Hyeongju and Yang, Jinhyeok and Yu, Yechan and Ji, Seunghun and Morton, Jacob and Bous, Frederik and Byun, Joon and Lee, Juheon},
journal={arXiv preprint arXiv:2503.23108},
year={2025},
url={https://arxiv.org/abs/2503.23108}
}
```
### Length-Aware RoPE: Text-Speech Alignment
This paper presents Length-Aware Rotary Position Embedding (LARoPE), which improves text-speech alignment in cross-attention mechanisms.
```bibtex
@article{kim2025larope,
title={Length-Aware Rotary Position Embedding for Text-Speech Alignment},
author={Kim, Hyeongju and Lee, Juheon and Yang, Jinhyeok and Morton, Jacob},
journal={arXiv preprint arXiv:2509.11084},
year={2025},
url={https://arxiv.org/abs/2509.11084}
}
```
### Self-Purifying Flow Matching: Training with Noisy Labels
This paper describes the self-purification technique for training flow matching models robustly with noisy or unreliable labels.
```bibtex
@article{kim2025spfm,
title={Training Flow Matching Models with Reliable Labels via Self-Purification},
author={Kim, Hyeongju and Yu, Yechan and Yi, June Young and Lee, Juheon},
This project's sample code is released under the MIT License. - see the [LICENSE](https://github.com/supertone-inc/supertonic?tab=MIT-1-ov-file) for details.
The accompanying model is released under the OpenRAIL-M License. - see the [LICENSE](https://huggingface.co/Supertone/supertonic/blob/main/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](https://docs.pytorch.org/FBGEMM/general/License.html) for details.
