java

TTS ONNX Inference Examples

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

📰 Update News

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.

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="--batch --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

Example 4: Long-Form Inference

The system automatically chunks long texts into manageable segments, synthesizes each segment separately, and concatenates them with natural pauses (0.3 seconds by default) into a single audio file. This happens by default when you don't use the --batch flag:

mvn exec:java -Dexec.args="--voice-style assets/voice_styles/M1.json --text 'This is a very long text that will be automatically split into multiple chunks. The system will process each chunk separately and then concatenate them together with natural pauses between segments. This ensures that even very long texts can be processed efficiently while maintaining natural speech flow and avoiding memory issues.'"

This will:

• Automatically split the text into chunks based on paragraph and sentence boundaries
• Synthesize each chunk separately
• Add 0.3 seconds of silence between chunks for natural pauses
• Concatenate all chunks into a single audio file

Note: Automatic text chunking is disabled when using --batch mode. In batch mode, each text is processed as-is without chunking.

Tip: 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
--batch	flag	False	Enable batch mode (multiple text-style pairs, disables automatic chunking)

Notes

• Batch Processing: When using --batch, the number of --voice-style files must match the number of --text entries
• Automatic Chunking: Without --batch, long texts are automatically split and concatenated with 0.3s pauses
• 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