Supertonic/cpp/README.md

Supertonic C++ Implementation

High-performance text-to-speech inference using ONNX Runtime.

📰 Update News

2026.01.06 - 🎉 Supertonic 2 released with multilingual support! Now supports English (en), Korean (ko), Spanish (es), Portuguese (pt), and French (fr). Demo | Models

2025.12.10 - Added 6 new voice styles (M3, M4, M5, F3, F4, F5). See Voices for details

2025.12.08 - Optimized ONNX models via OnnxSlim now available on Hugging Face Models

2025.11.23 - Enhanced text preprocessing with comprehensive normalization, emoji removal, symbol replacement, and punctuation handling for improved synthesis quality.

2025.11.19 - Added --speed parameter to control speech synthesis speed (default: 1.05, recommended range: 0.9-1.5).

2025.11.19 - Added automatic text chunking for long-form inference. Long texts are split into chunks and synthesized with natural pauses.

Requirements

• C++17 compiler, CMake 3.15+
• Libraries: ONNX Runtime, nlohmann/json

Installation

Ubuntu/Debian:

⚠️ Note: Installation instructions not yet verified.

sudo apt-get install -y cmake g++ nlohmann-json3-dev
wget https://github.com/microsoft/onnxruntime/releases/download/v1.16.3/onnxruntime-linux-x64-1.16.3.tgz
tar -xzf onnxruntime-linux-x64-1.16.3.tgz
sudo cp -r onnxruntime-linux-x64-1.16.3/include/* /usr/local/include/
sudo cp -r onnxruntime-linux-x64-1.16.3/lib/* /usr/local/lib/
sudo ldconfig

macOS:

brew install cmake nlohmann-json onnxruntime

Windows (vcpkg):

⚠️ Note: Installation instructions not yet verified.

vcpkg install nlohmann-json:x64-windows onnxruntime:x64-windows
vcpkg integrate install

Building

cd cpp && mkdir build && cd build
cmake .. && cmake --build . --config Release
./example_onnx

Basic Usage

Example 1: Default Inference

Run inference with default settings:

./example_onnx

This will use:

• Voice style: ../assets/voice_styles/M1.json
• Text: "This morning, I took a walk in the park, and the sound of the birds and the breeze was so pleasant that I stopped for a long time just to listen."
• Output directory: results/
• Total steps: 5
• Number of generations: 4

Example 2: Batch Inference

Process multiple voice styles and texts at once:

./example_onnx \
  --voice-style ../assets/voice_styles/M1.json,../assets/voice_styles/F1.json \
  --text "The sun sets behind the mountains, painting the sky in shades of pink and orange.|오늘 아침에 공원을 산책했는데, 새소리와 바람 소리가 너무 좋아서 한참을 멈춰 서서 들었어요." \
  --lang en,ko \
  --batch

This will:

• Use --batch flag to enable batch processing mode
• Generate speech for 2 different voice-text pairs
• Use male voice style (M1.json) for the first English text
• Use female voice style (F1.json) for the second Korean text
• Process both samples in a single batch (automatic text chunking disabled)

Example 3: High Quality Inference

Increase denoising steps for better quality:

./example_onnx \
  --total-step 10 \
  --voice-style ../assets/voice_styles/M1.json \
  --text "Increasing the number of denoising steps improves the output's fidelity and overall quality."

This will:

• Use 10 denoising steps instead of the default 5
• Produce higher quality output at the cost of slower inference

Example 4: Long-Form Inference

For long texts, the system automatically chunks the text into manageable segments and generates a single audio file:

./example_onnx \
  --voice-style ../assets/voice_styles/M1.json \
  --text "Once upon a time, in a small village nestled between rolling hills, there lived a young artist named Clara. Every morning, she would wake up before dawn to capture the first light of day. The golden rays streaming through her window inspired countless paintings. Her work was known throughout the region for its vibrant colors and emotional depth. People from far and wide came to see her gallery, and many said her paintings could tell stories that words never could."

This will:

• Automatically split the long text into smaller chunks (max 300 characters by default)
• Process each chunk separately while maintaining natural speech flow
• Insert brief silences (0.3 seconds) between chunks for natural pacing
• Combine all chunks into a single output audio file

Note: When using batch mode (--batch), automatic text chunking is disabled. Use non-batch mode for long-form text synthesis.

Available Arguments

Argument	Type	Default	Description
--onnx-dir	str	../assets/onnx	Path to ONNX model directory
--total-step	int	5	Number of denoising steps (higher = better quality, slower)
--speed	float	1.05	Speech speed factor (higher = faster, lower = slower)
--n-test	int	4	Number of times to generate each sample
--voice-style	str	../assets/voice_styles/M1.json	Voice style file path(s) (comma-separated for batch)
--text	str	(long default text)	Text(s) to synthesize (pipe-separated for batch)
--lang	str	en	Language(s) for text(s): en, ko, es, pt, fr (comma-separated for batch)
--save-dir	str	results	Output directory
--batch	flag	False	Enable batch mode (disables automatic text chunking)

Notes

• Batch Processing: The number of --voice-style files must match the number of --text entries
• Multilingual Support: Use --lang to specify language(s). Available: en (English), ko (Korean), es (Spanish), pt (Portuguese), fr (French)
• Long-Form Inference: Without --batch flag, long texts are automatically chunked and combined into a single audio file with natural pauses
• Quality vs Speed: Higher --total-step values produce better quality but take longer