supertonic/rust/README.md

TTS ONNX Inference Examples

This guide provides examples for running TTS inference using Rust.

Installation

This project uses Cargo for package management.

Install Rust (if not already installed)

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

Build the project

cargo build --release

Basic Usage

You can run the inference in two ways:

1. Using cargo run (builds if needed, then runs)
2. Direct binary execution (faster if already built)

Example 1: Default Inference

Run inference with default settings:

# Using cargo run
cargo run --release --bin example_onnx

# Or directly execute the built binary (faster)
./target/release/example_onnx

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:

# Using cargo run
cargo run --release --bin example_onnx -- \
  --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."

# Or using the binary directly
./target/release/example_onnx \
  --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:

# Using cargo run
cargo run --release --bin example_onnx -- \
  --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."

# Or using the binary directly
./target/release/example_onnx \
  --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 (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
• Known Issues: On some platforms (especially macOS), there might be a mutex cleanup warning during exit. This is a known ONNX Runtime issue and doesn't affect functionality. The implementation uses libc::_exit() and mem::forget() to bypass this issue.