supertonic/go/README.md

# TTS ONNX Inference Examples

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

## 📰 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 Go modules for dependency management.

### Prerequisites

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

**macOS (via Homebrew):**
```bash
brew install onnxruntime
```

**Linux:**
```bash
# 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

```bash
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):**

```bash
# 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:**

```bash
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:
```bash
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:
```bash
go run example_onnx.go helper.go \
  --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:
```bash
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

### 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:

```bash
go run example_onnx.go helper.go \
  -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.

## 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 |
| `--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

## Building a Binary

To build a standalone executable:
```bash
go build -o tts_example example_onnx.go helper.go
```

Then run it:
```bash
./tts_example -voice-style "../assets/voice_styles/M1.json" -text "Hello world"
```