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
python
1from supertonic import TTS
23tts = TTS(auto_download=True)4style = tts.get_voice_style(voice_name="M1")56text ="A gentle breeze moved through the open window while everyone listened to the story."7wav, duration = tts.synthesize(text, voice_style=style, lang="en")89tts.save_audio(wav,"output.wav")10print(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
Supertonic 3 reading accuracy compared with measured model ranges and VoxCPM2
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
Supertonic 2 and Supertonic 3 comparison
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 CPU runtime compared with GPU baselines
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
Model size comparison
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.
