supertonic / README.md

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	---
	license: openrail
	language:
	- en
	pipeline_tag: text-to-speech
	---

	# 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](https://huggingface.co/spaces/Supertone/supertonic#interactive-demo), or [Hugging Face app](https://huggingface.co/spaces/akhaliq/supertonic) or get started with pre-trained models from [Hugging Face Hub](https://huggingface.co/Supertone/supertonic)

	> 🛠 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?](#why-supertonic)
	- [Language Support](#language-support)
	- [Getting Started](#getting-started)
	- [Performance](#performance)
	- [Citation](#citation)
	- [License](#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:

	```bash
	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:

	```bash
	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](https://elevenlabs.io/docs/api-reference/text-to-speech/convert) \| 144 \| 209 \| 287 \|
	\| `API` [OpenAI TTS-1](https://platform.openai.com/docs/guides/text-to-speech) \| 37 \| 55 \| 82 \|
	\| `API` [Gemini 2.5 Flash TTS](https://ai.google.dev/gemini-api/docs/speech-generation) \| 12 \| 18 \| 24 \|
	\| `API` [Supertone Sona speech 1](https://docs.supertoneapi.com/en/api-reference/endpoints/text-to-speech) \| 38 \| 64 \| 92 \|
	\| `Open` [Kokoro](https://github.com/hexgrad/kokoro/) \| 104 \| 107 \| 117 \|
	\| `Open` [NeuTTS Air](https://github.com/neuphonic/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](https://elevenlabs.io/docs/api-reference/text-to-speech/convert) \| 0.133 \| 0.077 \| 0.057 \|
	\| `API` [OpenAI TTS-1](https://platform.openai.com/docs/guides/text-to-speech) \| 0.471 \| 0.302 \| 0.201 \|
	\| `API` [Gemini 2.5 Flash TTS](https://ai.google.dev/gemini-api/docs/speech-generation) \| 1.060 \| 0.673 \| 0.541 \|
	\| `API` [Supertone Sona speech 1](https://docs.supertoneapi.com/en/api-reference/endpoints/text-to-speech) \| 0.372 \| 0.206 \| 0.163 \|
	\| `Open` [Kokoro](https://github.com/hexgrad/kokoro/) \| 0.144 \| 0.124 \| 0.126 \|
	\| `Open` [NeuTTS Air](https://github.com/neuphonic/neutts-air) \| 0.390 \| 0.338 \| 0.343 \|

	<details>
	<summary><b>Additional Performance Data (5-step inference)</b></summary>

	<br>

	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 \|

	</details>

	## License

	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.

	Copyright (c) 2025 Supertone Inc.

	---
	license: openrail
	language:
	- en
	pipeline_tag: text-to-speech
	---

	# 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](https://huggingface.co/spaces/Supertone/supertonic#interactive-demo), or [Hugging Face app](https://huggingface.co/spaces/akhaliq/supertonic) or get started with pre-trained models from [Hugging Face Hub](https://huggingface.co/Supertone/supertonic)

	> 🛠 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?](#why-supertonic)
	- [Language Support](#language-support)
	- [Getting Started](#getting-started)
	- [Performance](#performance)
	- [Citation](#citation)
	- [License](#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:

	```bash
	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:

	```bash
	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](https://elevenlabs.io/docs/api-reference/text-to-speech/convert) \| 144 \| 209 \| 287 \|
	\| `API` [OpenAI TTS-1](https://platform.openai.com/docs/guides/text-to-speech) \| 37 \| 55 \| 82 \|
	\| `API` [Gemini 2.5 Flash TTS](https://ai.google.dev/gemini-api/docs/speech-generation) \| 12 \| 18 \| 24 \|
	\| `API` [Supertone Sona speech 1](https://docs.supertoneapi.com/en/api-reference/endpoints/text-to-speech) \| 38 \| 64 \| 92 \|
	\| `Open` [Kokoro](https://github.com/hexgrad/kokoro/) \| 104 \| 107 \| 117 \|
	\| `Open` [NeuTTS Air](https://github.com/neuphonic/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](https://elevenlabs.io/docs/api-reference/text-to-speech/convert) \| 0.133 \| 0.077 \| 0.057 \|
	\| `API` [OpenAI TTS-1](https://platform.openai.com/docs/guides/text-to-speech) \| 0.471 \| 0.302 \| 0.201 \|
	\| `API` [Gemini 2.5 Flash TTS](https://ai.google.dev/gemini-api/docs/speech-generation) \| 1.060 \| 0.673 \| 0.541 \|
	\| `API` [Supertone Sona speech 1](https://docs.supertoneapi.com/en/api-reference/endpoints/text-to-speech) \| 0.372 \| 0.206 \| 0.163 \|
	\| `Open` [Kokoro](https://github.com/hexgrad/kokoro/) \| 0.144 \| 0.124 \| 0.126 \|
	\| `Open` [NeuTTS Air](https://github.com/neuphonic/neutts-air) \| 0.390 \| 0.338 \| 0.343 \|

	<details>
	<summary><b>Additional Performance Data (5-step inference)</b></summary>

	<br>

	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 \|

	</details>

	## License

	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.

	Copyright (c) 2025 Supertone Inc.