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nodejs/README.md

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+# 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.