README.md

metadata

license: openrail
language:
  - en
  - es
  - fr
  - pl
  - el
  - id
  - zh
  - ja
  - ru
  - tr
  - da
  - et
  - hr
  - pt
  - vi
  - sl
  - cs
  - it
  - uk
  - ko
  - sv
  - lv
  - hu
  - fi
  - ar
  - nl
  - sk
  - bg
  - hi
  - de
  - lt
base_model:
  - Supertone/supertonic-3
pipeline_tag: text-to-speech

Supertonic 3 TTS

This repository hosts the Supertonic 3 model for the React Native ExecuTorch library. It performs text-to-speech synthesis supporting 30+ languages, with a single voice style per language.

The model is composed of four sub-models that run sequentially:

1. Duration predictor — estimates speech duration from text
2. Text encoder — encodes text into a style-conditioned representation
3. Vector estimator — flow-matching denoiser that generates the audio latent
4. Vocoder — decodes the latent into a 44.1 kHz waveform

Compatibility

These models were exported using v1.3.1 of ExecuTorch and no forward compatibility is guaranteed. Older versions of the runtime may not work with these files.

The models are intended to be used within the React Native ExecuTorch package. If you want to use them outside the package, make sure your runtime is compatible with the ExecuTorch version used to export the .pte files and follow the example scripts to run the models.

Backends

Backend	Description	RTF (Apple Silicon)
xnnpack	CPU-optimized via XNNPACK delegate	~0.07 (14× faster than real-time)
mlx	Apple Silicon GPU via MLX delegate	~0.026 (38× faster than real-time)

Repository Structure

.
├── config.json              # Backend-agnostic model manifest
├── unicode_indexer.json     # Character-to-id mapping for text preprocessing
├── voices/                  # Pre-computed speaker embeddings
│   ├── M1.json
│   ├── M2.json
│   ├── F1.json
│   └── ...
├── xnnpack/                 # XNNPACK-exported .pte files
└── mlx/                     # MLX-exported .pte files

Each .pte file exposes two methods:

• forward — the sub-model inference
• get_dynamic_dims_forward — returns per-input [rank, 3] shape constraints ([min, max, step]) for runtime input validation