Supertone/supertonic-3

Supertonic 3 | Lightning Fast, On-Device, Accurate TTS

Supertonic is a lightweight text-to-speech system for local inference. It runs with ONNX Runtime entirely on your device, with no cloud call required for synthesis.

Supertonic 3 expands the open-weight release from 5 to 31 languages, improves reading stability, and reduces repeat/skip failures.

Quick Start

Install the Python SDK and generate speech immediately. On first run, the SDK downloads the model assets from Hugging Face.

pip install supertonic

from supertonic import TTS

tts = TTS(auto_download=True)
style = tts.get_voice_style(voice_name="M1")

text = "A gentle breeze moved through the open window while everyone listened to the story."
wav, duration = tts.synthesize(text, voice_style=style, lang="en")

tts.save_audio(wav, "output.wav")
print(f"Generated {duration:.2f}s of audio")

What's New in Supertonic 3

• 31 languages: expanded from the 5-language Supertonic 2 release.
• More stable reading: fewer repeat and skip failures, especially on short and long utterances.
• Higher speaker similarity: improved similarity across the shared-language set compared with Supertonic 2.
• Expression tags: supports simple tags such as <laugh>, <breath>, and <sigh>.

Performance Highlights

Supertonic 3 is designed for practical on-device inference: compact enough to run locally, while staying competitive with much larger open TTS systems.

Reading Accuracy

Across measured languages, Supertonic 3 stays within a competitive WER/CER range against much larger open TTS models such as VoxCPM2, while preserving a lightweight on-device deployment path. Asterisked languages use CER; the others use WER.

Supertonic 2 to Supertonic 3

Compared with Supertonic 2, Supertonic 3 reduces repeat and skip failures, improves speaker similarity across the shared-language set, and expands language coverage from 5 to 31 languages.

Runtime Footprint

Supertonic 3 runs fast on CPU, even compared with larger baselines measured on A100 GPU, and uses substantially less memory. It does not require a GPU, which makes local, browser, and edge deployment much easier.

Model Size

At about 99M parameters across the public ONNX assets, Supertonic 3 is much smaller than 0.7B to 2B class open TTS systems. The smaller model size is a practical advantage for download size, startup time, and on-device inference.

Supported Languages

Code	Language	Code	Language	Code	Language	Code	Language
en	English	ko	Korean	ja	Japanese	ar	Arabic
bg	Bulgarian	cs	Czech	da	Danish	de	German
el	Greek	es	Spanish	et	Estonian	fi	Finnish
fr	French	hi	Hindi	hr	Croatian	hu	Hungarian
id	Indonesian	it	Italian	lt	Lithuanian	lv	Latvian
nl	Dutch	pl	Polish	pt	Portuguese	ro	Romanian
ru	Russian	sk	Slovak	sl	Slovenian	sv	Swedish
tr	Turkish	uk	Ukrainian	vi	Vietnamese

License

This project's sample code is released under the MIT License. See the GitHub repository for details.

The accompanying model is released under the OpenRAIL-M License. See the LICENSE file in this repository 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 PyTorch license for details.

Copyright (c) 2026 Supertone Inc.