README.md

---
license: apache-2.0
base_model: Supertone/supertonic-3
pipeline_tag: text-to-speech
library_name: onnx
tags:
  - text-to-speech
  - tts
  - supertonic
  - onnx
  - cpu
  - cuda
  - offline
language:
  - en
  - ru
  - de
  - fr
  - es
  - pt
  - it
  - nl
  - pl
  - uk
  - cs
  - sk
  - sl
  - hr
  - bg
  - ro
  - hu
  - el
  - tr
  - ar
  - hi
  - id
  - vi
  - ja
  - ko
  - sv
  - da
  - fi
  - et
  - lv
  - lt
---

# SuperClonica — a faster, cleaner engine for Supertonic 3

**SuperClonica** runs the [Supertonic 3](https://huggingface.co/Supertone/supertonic-3) text-to-speech model with the
same voices and the same seeds — and gets more out of it: **1.6–1.7× faster** on CPU and GPU, speech that starts after
**0.08 s instead of half a second**, no background noise in pauses, far fewer cut-off endings and numbers spelled out
correctly in 27 languages. It works offline on your own computer and reads any Supertonic 3 voice-style JSON file.

The original Supertonic repository was [archived](https://github.com/supertone-oss-archive/supertonic) in September 2026
and gets no more updates; SuperClonica runs the same Supertonic 3 model and is still developed.

🗣️ **Try it:** [online demo with free voices](https://huggingface.co/spaces/Superclonica/superclonica-tts) ·
⬇️ [free voices on GitHub](https://github.com/superclonica/supertonic-voices) ·
🎙️ [170+ voices in the marketplace](https://superclonica.com/en/explore?utm_source=huggingface&utm_medium=model)

| | |
|---|---|
| Model | Supertonic 3 (about 99M parameters, ONNX), unchanged |
| Languages | 31: en, ru, de, fr, es, pt, it, nl, pl, uk, cs, sk, sl, hr, bg, ro, hu, el, tr, ar, hi, id, vi, ja, ko, sv, da, fi, et, lv, lt |
| Voices | any Supertonic 3 voice-style JSON (`style_ttl` + `style_dp`), including Supertone's own M1–M5 and F1–F5 |
| Runs on | CPU (Windows, macOS, Linux) or NVIDIA GPU (Windows, Linux x86-64) |
| Install | `pip install superclonica-cpu` or `pip install superclonica-gpu-cuda`, or the Docker image |
| Output | 44.1 kHz mono WAV |

## Quick start

```bash
python -m pip install superclonica-cpu          # any CPU, Python 3.10+
# or, for an NVIDIA GPU (Python 3.11+; pip brings CUDA and cuDNN):
python -m pip install superclonica-gpu-cuda
```

Install only one of the two packages. The Supertonic 3 model (about 400 MB) downloads on the first run.

```python
from superclonica import Superclonica

tts = Superclonica()  # GPU if available, otherwise CPU; create it once and keep it
audio, sample_rate = tts.synthesize(
    "Hello! Your new voice is ready.", voice="ciri.json", lang="en", seed=7
)
tts.save("hello.wav", audio)
```

Command line:

```bash
superclonica --text "Hello! Your new voice is ready." --voice ciri.json --lang en --output hello.wav
```

HTTP server in Docker ([superclonica/superclonica](https://hub.docker.com/r/superclonica/superclonica)):

```bash
docker run -d -p 8000:8000 -v superclonica-models:/models superclonica/superclonica:cpu
docker run -d --gpus all -p 8000:8000 -v superclonica-models:/models superclonica/superclonica:cuda
```

A free voice to start with: [`ciri.json`](https://github.com/superclonica/supertonic-voices/raw/main/voices/ciri/ciri.json)
and seven more in [superclonica/supertonic-voices](https://github.com/superclonica/supertonic-voices).

## Speed

Same model, voice, text and seed for both engines, run in one process and taking turns on each phrase.

| | Supertonic 3 (stock) | SuperClonica |
|---|---:|---:|
| GPU (RTX 5070 Ti), median per phrase | 202 ms | **117 ms** |
| GPU, 10 phrases in a row | 2.03 s | **1.19 s** |
| CPU (Ryzen 7 5800X, 4 threads), median per phrase | 855 ms | **530 ms** |
| CPU, 24 phrases in 8 languages in a row | 22.03 s | **14.39 s** |

On the GPU the whole model runs as recorded CUDA graphs; on the CPU the models are rewritten for multithreaded
computation. SuperClonica was faster on all 10 GPU phrases and all 24 CPU phrases.

## Sound

The model is the same, so the voice and intonation match. The difference is around the words.

| On 10 sample phrases | Supertonic 3 | SuperClonica |
|---|---:|---:|
| Silence before the first word (median) | 0.50 s | **0.08 s** |
| Silence after the last word (median) | 0.63 s | **0.20 s** |
| Background in pauses (median, relative to peak) | −48 dB | **−92 dB** |

| Abrupt cut at the end of a phrase | Supertonic 3 | SuperClonica |
|---|---:|---:|
| Russian, 312 clips | 11.9% | **1.0%** |
| English, 300 clips | 17.7% | **2.0%** |
| German, 132 clips | 10.6% | **0.0%** |

From larger runs of our own checks:

- **Stray sounds after the last word** (clicks, breaths, extra syllables): 27 and 26 in runs of 1,800 and 1,392 phrases
  with stock output; 0 and 3 after SuperClonica's cleanup.
- **Words are never cut**: the cleanup is checked against the raw output with a phoneme recognizer — more than 25,000
  generations in 30+ languages with zero confirmed cuts of real speech.
- **Fewer stumbles on long text**: text is split at natural pauses into chunks of up to 160 characters (stock: 300).
- **Numbers**: spelled out as words in 27 languages, where the model alone misreads large numbers in some languages
  (`numbers=False` keeps stock behaviour).
- **No clipping**: a look-ahead limiter lowers only the peaks above full scale.

Every recording of the comparison, with its loudness envelope:
[Supertonic vs SuperClonica](https://superclonica.com/en/compare/supertonic?utm_source=huggingface&utm_medium=model).

## Where stock Supertonic 3 is better

- **Startup**: SuperClonica prepares CUDA graphs or CPU-optimized models on launch — 9.5 s vs 1.2 s on the GPU,
  4.0 s vs 0.9 s on the CPU. Create the engine once and keep it running.
- **Long paragraphs on a GPU** can come out slightly slower because of the shorter chunks (not seen in our measurement).
- **Numbers without grammatical case**: Russian ordinals and English years are read as plain numbers; use
  `numbers=False` to pass digits to the model as stock does.

## How it was measured

Supertonic 1.3.1 (supertonic-3) without processing against SuperClonica 0.6.23 with default settings, speed 1.05,
8 steps, the same voice and seed per phrase. GPU: RTX 5070 Ti, ONNX Runtime GPU 1.26 (10 rounds). CPU: Ryzen 7 5800X,
4 threads for both, 24 chat phrases in 8 languages (2 rounds). A working machine with applications open: absolute
times are lower on an idle machine, the ratio holds.

## Voices

Every Supertonic 3 voice is one JSON file of about 0.3 MB. Free ones are on
[GitHub](https://github.com/superclonica/supertonic-voices) and in the [online demo](https://huggingface.co/spaces/Superclonica/superclonica-tts);
character, narrator and custom voices are in the [SuperClonica marketplace](https://superclonica.com/en/explore?utm_source=huggingface&utm_medium=model),
where every voice page lets you voice your own phrase online. You can also
[order a voice](https://superclonica.com/en/requests?utm_source=huggingface&utm_medium=model) trained on your recording or
[train one for free](https://superclonica.com/en/studio?utm_source=huggingface&utm_medium=model) on your own computer.

## License

The SuperClonica engine is released under the Apache License 2.0 (`pip show superclonica-cpu`). It runs the
Supertonic 3 model, which is distributed by Supertone under its own license (OpenRAIL-M); its terms apply to the
model and to what you generate with it. SuperClonica is an independent derivative of Supertonic, not an official
Supertone release.

Use voices responsibly: never to deceive, defraud or impersonate anyone
([terms](https://superclonica.com/en/terms?utm_source=huggingface&utm_medium=model)).

## Links

[Website](https://superclonica.com/en/?utm_source=huggingface&utm_medium=model) ·
[Install guide](https://superclonica.com/en/superclonica?utm_source=huggingface&utm_medium=model) ·
[PyPI: superclonica-cpu](https://pypi.org/project/superclonica-cpu/) ·
[PyPI: superclonica-gpu-cuda](https://pypi.org/project/superclonica-gpu-cuda/) ·
[Docker Hub](https://hub.docker.com/r/superclonica/superclonica) ·
[Python guide](https://superclonica.com/en/guides/python-text-to-speech?utm_source=huggingface&utm_medium=model) ·
[Docker API guide](https://superclonica.com/en/guides/text-to-speech-api-docker?utm_source=huggingface&utm_medium=model)