# Deploying Supertonic on Edge Devices Like Raspberry Pi: A Complete Guide

> Deploy Supertonic on Raspberry Pi for real-time text-to-speech synthesis. This powerful ONNX Runtime engine requires only 300MB storage and 500MB RAM, no GPU needed!

- Repository: [Supertone Inc./supertonic](https://github.com/supertone-inc/supertonic)
- Tags: how-to-guide
- Published: 2026-05-14

---

**Supertonic is an ONNX Runtime-based text-to-speech engine that deploys directly on Raspberry Pi without GPU acceleration, requiring only 300MB storage and 500MB RAM for real-time synthesis.**

Supertonic is a lightning-fast, on-device TTS system developed by Supertone Inc. The architecture is deliberately lightweight to enable local execution on modest hardware such as a Raspberry Pi without any network access. Deploying Supertonic on edge devices like Raspberry Pi requires only the pre-compiled ONNX Runtime wheels and the compressed model assets hosted on Hugging Face.

## Why Supertonic Works on Raspberry Pi

The repository is engineered specifically for edge compatibility. According to the [`README.md`](https://github.com/supertone-inc/supertonic/blob/main/README.md) Technical Details section, the system relies on ONNX Runtime for cross-platform, CPU-only execution with a minimal binary footprint.

### Optimized Model Size

The TTS model contains approximately 99 million parameters. After ONNX-Slim optimization, the binary footprint shrinks to roughly 300MB, making it small enough to fit comfortably on a Pi's SD card. This compression is documented in the repository's Update News section.

### CPU-Only Inference

Supertonic requires no GPU. ONNX Runtime's CPU execution path is highly optimized for ARM architectures, delivering real-time synthesis even on the modest Cortex-A72 cores found in Raspberry Pi 4. The runtime stays under 500MB RAM, well within the Pi's typical 1GB limit.

### Zero Network Dependency

Once the assets are cached in the `assets/` directory, all subsequent synthesis runs are completely offline. This preserves privacy and ensures functionality in air-gapped environments.

## Prerequisites for Edge Deployment

Before deploying Supertonic on your Raspberry Pi, ensure you meet the following requirements:

- A Raspberry Pi running recent Raspberry Pi OS (32-bit or 64-bit)
- Git LFS installed for downloading large model files
- Sufficient storage for the 300MB model assets
- No GPU required; CPU is sufficient for inference

## Step-by-Step Deployment Guide

Follow these steps to deploy Supertonic on your Raspberry Pi, based on the Quick Start instructions in the repository.

### 1. Install Git LFS and Clone the Repository

First, install Git LFS and clone the supertone-inc/supertonic repository:

```bash
sudo apt-get update && sudo apt-get install -y git-lfs
git lfs install
git clone https://github.com/supertone-inc/supertonic.git
cd supertonic

```

### 2. Download the ONNX Models

Pull the pre-trained ONNX assets from Hugging Face. These models support 31 languages and are compatible with ARM CPUs:

```bash
git clone https://huggingface.co/Supertone/supertonic-3 assets

```

This downloads approximately 300MB of compressed model files, including the voice style JSON files stored in `assets/voice_styles/`.

### 3. Set Up the Python Environment

Navigate to the Python SDK directory and install dependencies using `uv`, which resolves pre-compiled wheels for ARM architectures:

```bash
cd py
uv sync

```

The `py/uv.lock` file locks the exact Python dependencies, including `onnxruntime` builds for `arm64`/`armhf` architectures.

### 4. Run the Inference Example

Execute the provided example script to verify the installation:

```bash
uv run example_onnx.py
aplay outputs/output.wav

```

The [`py/example_onnx.py`](https://github.com/supertone-inc/supertonic/blob/main/py/example_onnx.py) script loads the model, synthesizes a short sentence, and writes the output to `outputs/output.wav`.

## Implementing TTS in Your Application

To integrate Supertonic into your own Python applications running on the Pi, use the following patterns from [`py/example_onnx.py`](https://github.com/supertone-inc/supertonic/blob/main/py/example_onnx.py) and [`py/helper.py`](https://github.com/supertone-inc/supertonic/blob/main/py/helper.py).

Initialize the TTS engine with automatic model downloading:

```python
from supertonic import TTS

tts = TTS(auto_download=True)

```

Select a voice style from the available assets:

```python
style = tts.get_voice_style(voice_name="M1")

```

Synthesize text to audio:

```python
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")

```

Save the resulting audio:

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

```

These four lines constitute the minimal implementation found in the repository's examples, utilizing the helper utilities in [`py/helper.py`](https://github.com/supertone-inc/supertonic/blob/main/py/helper.py) for text preprocessing and WAV output.

## Key Files and Architecture

Understanding these key files helps with troubleshooting and customization:

- **[`README.md`](https://github.com/supertone-inc/supertonic/blob/main/README.md)** - Central documentation containing the complete Raspberry Pi deployment guide and Technical Details section
- **[`py/example_onnx.py`](https://github.com/supertone-inc/supertonic/blob/main/py/example_onnx.py)** - Minimal runnable script demonstrating end-to-end synthesis on any platform, including the Pi
- **[`py/helper.py`](https://github.com/supertone-inc/supertonic/blob/main/py/helper.py)** - Utility functions for text cleaning, voice-style handling, and audio output
- **`assets/`** - Contains the ONNX model (`model.onnx`) and voice-style JSON files downloaded from Hugging Face
- **`py/uv.lock`** - Locks exact Python dependencies including ARM-compatible ONNX Runtime wheels

## Summary

Deploying Supertonic on edge devices like Raspberry Pi provides a fully offline TTS solution:

- **Lightweight footprint**: 300MB storage and under 500MB RAM usage
- **No GPU required**: Optimized CPU inference via ONNX Runtime on ARM architectures
- **Complete privacy**: Zero network dependency after initial model download
- **Multi-language support**: 31 languages available through the Hugging Face model repository
- **Simple deployment**: Four-step installation using standard Linux tools and `uv` for dependency management

## Frequently Asked Questions

### Can Supertonic run on Raspberry Pi Zero?

While the repository targets Raspberry Pi 4 with its Cortex-A72 cores, the CPU-only architecture and ARM-compatible wheels theoretically support Pi Zero. However, synthesis latency may increase significantly on the single-core Pi Zero due to the 99M parameter model requiring substantial compute. For production deployments, Raspberry Pi 4 or Pi 5 is recommended.

### Does Supertonic require an internet connection?

No. After the initial `git clone` of the model assets from Hugging Face, Supertonic operates completely offline. The `auto_download=True` parameter in the `TTS` class only triggers on first run if assets are missing, making it suitable for air-gapped edge deployments.

### How much storage space does Supertonic need?

The ONNX-Slim optimized models require approximately 300MB of storage. The Python environment with ONNX Runtime adds roughly 100-200MB depending on the architecture (arm64 vs armhf). A 16GB SD card is sufficient for the OS, Supertonic, and additional applications.

### Can I use Supertonic with other programming languages on the Pi?

Yes. The repository provides Language SDKs for Python, Node.js, Java, C++, Rust, Go, Swift, iOS, and C#. Each SDK invokes the same ONNX model, meaning you can deploy Supertonic in Go or Rust on the Raspberry Pi by following the build instructions in the respective SDK directories (e.g., `go/`, `rust/`).