supertonic/py/README.md

TTS ONNX Inference Examples

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

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." "The weather is beautiful and sunny outside. A gentle breeze makes the air feel fresh and pleasant."

This will:

• Generate speech for 2 different voice-text pairs
• Use male voice style (M1.json) for the first text
• Use female voice style (F1.json) for the second text
• Process both samples in a single batch

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

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)
--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
--save-dir	str	results	Output directory

Notes

• Batch Processing: The number of --voice-style files must match the number of --text entries
• Quality vs Speed: Higher --total-step values produce better quality but take longer
• GPU Support: GPU mode is not supported yet