supertonic/java

TTS ONNX Inference Examples

This guide provides examples for running TTS inference using ExampleONNX.java.

Installation

This project uses Maven for dependency management.

Prerequisites

• Java 11 or higher
• Maven 3.6 or higher

Install dependencies

mvn clean install

Basic Usage

Example 1: Default Inference

Run inference with default settings:

mvn exec:java

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:

mvn exec:java -Dexec.args="--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 (M1.json) for the first text
• Use female voice (F1.json) for the second text
• Process both samples in a single batch

Example 3: High Quality Inference

Increase denoising steps for better quality:

mvn exec:java -Dexec.args="--total-step 10 --voice-style assets/voice_styles/M1.json --text 'Increasing the number of denoising steps improves the output 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

Note: If your text contains apostrophes, use escaping or run the JAR directly:

java -jar target/tts-example.jar --total-step 10 --text "Text with apostrophe's here"

Building a Fat JAR

To create a standalone JAR with all dependencies:

mvn clean package

Then run it directly:

java -jar target/tts-example.jar

Or with arguments:

java -jar target/tts-example.jar --total-step 10 --text "Your custom text here"

Available Arguments

Argument	Type	Default	Description
--use-gpu	flag	False	Use GPU for inference (default: CPU)
--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
• Voice Styles: Uses pre-extracted voice style JSON files for fast inference