Supertonic/py

TTS ONNX Inference Examples

This guide provides examples for running TTS inference using example_onnx.py.

📰 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 supertonic PyPI package! Install via pip install supertonic for a streamlined experience. This is a separate usage method from the ONNX examples in this directory. For more details, visit supertonic-py documentation and see example_pypi.py for usage.

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. Adjust the speed factor to make speech faster or slower while maintaining natural quality.

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

Installation

This project uses uv for fast package management.

Install uv (if not already installed)

curl -LsSf https://astral.sh/uv/install.sh | sh

Install dependencies

uv sync

Or if you prefer using traditional pip with requirements.txt:

pip install -r requirements.txt

Basic Usage

Example 1: Default Inference

Run inference with default settings:

uv run example_onnx.py

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:

uv run example_onnx.py \
  --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:

uv run example_onnx.py \
  --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:

uv run example_onnx.py \
  --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.

Example 5: Adjusting Speech Speed

Control the speed of speech synthesis:

# Faster speech (speed > 1.0)
uv run example_onnx.py \
  --voice-style assets/voice_styles/F2.json \
  --text "This text will be synthesized at a faster pace." \
  --speed 1.2

# Slower speech (speed < 1.0)
uv run example_onnx.py \
  --voice-style assets/voice_styles/M2.json \
  --text "This text will be synthesized at a slower, more deliberate pace." \
  --speed 0.9

This will:

• Use --speed 1.2 to generate faster speech
• Use --speed 0.9 to generate slower speech
• Default speed is 1.05 if not specified
• Recommended speed range is between 0.9 and 1.5 for natural-sounding results

Available Arguments

Argument	Type	Default	Description
--use-gpu	flag	False	Use GPU for inference (with CPU fallback)
--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)
--text	str+	(long default text)	Text(s) to synthesize
--lang	str+	en	Language(s) for text(s): en, ko, es, pt, fr
--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
• GPU Support: GPU mode is not supported yet