# TTS ONNX Node.js Implementation

Node.js implementation for TTS inference. Uses ONNX Runtime to generate speech from text.

## Requirements

- Node.js v16 or higher
- npm or yarn

## Installation

```bash
cd nodejs
npm install
```

## Basic Usage

### Example 1: Default Inference
Run inference with default settings:
```bash
npm start
```

Or:
```bash
node example_onnx.js
```

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
node example_onnx.js \
  --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 style (M1.json) for the first text
- Use female voice style (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
node example_onnx.js \
  --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 (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). Separate multiple files with commas |
| `--text` | str+ | (long default text) | Text(s) to synthesize. Separate multiple texts with pipes |
| `--save-dir` | str | `results` | Output directory |

## Notes

- **Batch Processing**: The number of voice style files must match the number of texts. Use commas to separate files and pipes to separate texts
- **Quality vs Speed**: Higher `--total-step` values produce better quality but take longer
- **GPU Support**: GPU mode is not supported yet

## Architecture

- `helper.js`: Node.js port of Python's `helper.py`
  - `Preprocessor`: Audio preprocessing (STFT, Mel Spectrogram)
  - `UnicodeProcessor`: Text preprocessing
  - Utility functions (mask generation, tensor conversion, etc.)

- `example_onnx.js`: Main inference script
  - ONNX model loading
  - TTS inference pipeline execution
  - WAV file saving

- `package.json`: Node.js project configuration and dependencies

## Implementation Notes

1. **Pure Node.js WAV Processing**: Writes WAV files without external native libraries. Outputs 16-bit PCM format.

2. **Memory Efficiency**: Note that Node.js may consume significant memory when processing large arrays.

3. **Performance**: The mel spectrogram extraction (Step 1-1) is currently slower than Python's Librosa, which uses highly optimized C extensions. This bottleneck could be further improved with additional optimizations such as WASM-based FFT libraries or native addons.