103 lines
3.5 KiB
Markdown
103 lines
3.5 KiB
Markdown
# TTS ONNX Node.js Implementation
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Node.js implementation for TTS inference. Uses ONNX Runtime to generate speech from text.
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## Requirements
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- Node.js v16 or higher
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- npm or yarn
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## Installation
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```bash
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cd nodejs
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npm install
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```
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## Basic Usage
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### Example 1: Default Inference
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Run inference with default settings:
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```bash
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npm start
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```
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Or:
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```bash
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node example_onnx.js
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```
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This will use:
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- Voice style: `assets/voice_styles/M1.json`
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- 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."
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- Output directory: `results/`
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- Total steps: 5
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- Number of generations: 4
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### Example 2: Batch Inference
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Process multiple voice styles and texts at once:
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```bash
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node example_onnx.js \
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--voice-style "assets/voice_styles/M1.json,assets/voice_styles/F1.json" \
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--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."
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```
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This will:
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- Generate speech for 2 different voice-text pairs
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- Use male voice style (M1.json) for the first text
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- Use female voice style (F1.json) for the second text
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- Process both samples in a single batch
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### Example 3: High Quality Inference
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Increase denoising steps for better quality:
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```bash
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node example_onnx.js \
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--total-step 10 \
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--voice-style "assets/voice_styles/M1.json" \
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--text "Increasing the number of denoising steps improves the output's fidelity and overall quality."
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```
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This will:
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- Use 10 denoising steps instead of the default 5
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- Produce higher quality output at the cost of slower inference
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## Available Arguments
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| Argument | Type | Default | Description |
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|----------|------|---------|-------------|
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| `--use-gpu` | flag | False | Use GPU for inference (not supported yet) |
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| `--onnx-dir` | str | `assets/onnx` | Path to ONNX model directory |
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| `--total-step` | int | 5 | Number of denoising steps (higher = better quality, slower) |
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| `--n-test` | int | 4 | Number of times to generate each sample |
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| `--voice-style` | str+ | `assets/voice_styles/M1.json` | Voice style file path(s). Separate multiple files with commas |
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| `--text` | str+ | (long default text) | Text(s) to synthesize. Separate multiple texts with pipes |
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| `--save-dir` | str | `results` | Output directory |
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## Notes
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- **Batch Processing**: The number of voice style files must match the number of texts. Use commas to separate files and pipes to separate texts
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- **Quality vs Speed**: Higher `--total-step` values produce better quality but take longer
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- **GPU Support**: GPU mode is not supported yet
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## Architecture
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- `helper.js`: Node.js port of Python's `helper.py`
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- `Preprocessor`: Audio preprocessing (STFT, Mel Spectrogram)
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- `UnicodeProcessor`: Text preprocessing
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- Utility functions (mask generation, tensor conversion, etc.)
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- `example_onnx.js`: Main inference script
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- ONNX model loading
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- TTS inference pipeline execution
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- WAV file saving
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- `package.json`: Node.js project configuration and dependencies
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## Implementation Notes
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1. **Pure Node.js WAV Processing**: Writes WAV files without external native libraries. Outputs 16-bit PCM format.
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2. **Memory Efficiency**: Note that Node.js may consume significant memory when processing large arrays.
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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.
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