supertonic/go

TTS ONNX Inference Examples

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

Installation

This project uses Go modules for dependency management.

Prerequisites

1. Install Go 1.21 or later from https://golang.org/dl/
2. Install ONNX Runtime C library:

macOS (via Homebrew):

brew install onnxruntime

Linux:

# Download ONNX Runtime from GitHub releases
wget https://github.com/microsoft/onnxruntime/releases/download/v1.16.0/onnxruntime-linux-x64-1.16.0.tgz
tar -xzf onnxruntime-linux-x64-1.16.0.tgz
sudo cp onnxruntime-linux-x64-1.16.0/lib/* /usr/local/lib/
sudo cp -r onnxruntime-linux-x64-1.16.0/include/* /usr/local/include/
sudo ldconfig

Install Go dependencies

go mod download

Configure ONNX Runtime Library Path (Optional)

If the ONNX Runtime library is not in a standard location, set the environment variable:

Automatic Detection (Recommended):

# macOS
export ONNXRUNTIME_LIB_PATH=$(brew --prefix onnxruntime 2>/dev/null)/lib/libonnxruntime.dylib

# Linux
export ONNXRUNTIME_LIB_PATH=$(find /usr/local/lib /usr/lib -name "libonnxruntime.so*" 2>/dev/null | head -n 1)

Manual Configuration:

export ONNXRUNTIME_LIB_PATH=/path/to/libonnxruntime.so  # Linux
# or
export ONNXRUNTIME_LIB_PATH=/path/to/libonnxruntime.dylib  # macOS

Basic Usage

Example 1: Default Inference

Run inference with default settings:

go run example_onnx.go helper.go

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:

go run example_onnx.go helper.go \
  -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:

go run example_onnx.go helper.go \
  -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), comma-separated
-text	str	(long default text)	Text(s) to synthesize, pipe-separated
-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

Building a Binary

To build a standalone executable:

go build -o tts_example example_onnx.go helper.go

Then run it:

./tts_example -voice-style "../assets/voice_styles/M1.json" -text "Hello world"