supertonic/csharp

TTS ONNX Inference Examples

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

📰 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

Prerequisites

• .NET 9.0 SDK or later
• Download .NET SDK

Install dependencies

dotnet restore

Basic Usage

Example 1: Default Inference

Run inference with default settings:

dotnet run

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:

dotnet run -- \
  --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." \
  --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 text
• Use female voice style (F1.json) for the second text
• Process both samples in a single batch (automatic text chunking disabled)

Example 3: High Quality Inference

Increase denoising steps for better quality:

dotnet run -- \
  --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:

dotnet run -- \
  --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
--use-gpu	flag	False	Use GPU for inference (not supported yet)
--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) (comma-separated)
--text	str+	(long default text)	Text(s) to synthesize (pipe-separated: `
--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
• 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

Building the Project

Build for Release

dotnet build -c Release

Run the compiled executable

./bin/Release/net9.0/Supertonic

Project Structure

csharp/
├── ExampleONNX.cs        # Main inference script
├── Helper.cs             # Helper functions and classes
├── Supertonic.csproj     # Project configuration
├── README.md             # This file
└── results/              # Output directory (created automatically)