Browser-side integration steps
To implement WebGPU-based speech synthesis, the following technical path needs to be followed:
- environmental preparation: Ensure you use a WebGPU-enabled browser such as Chrome 113+ or Edge 113+
- Installation of core libraries: get the latest version of kokoro-js via npm
npm install kokoro-js
Core Code Implementation
A typical realization process consists of three key stages:
- Model loading: Specify the use of a webgpu backend and optimized quantization parameters (e.g., q8)
const tts = await KokoroTTS.from_pretrained(model_id, {
dtype: 'fp32', device: 'webgpu'
}); - Voice customization: select different tones (e.g. af_heart) via tts.list_voices()
- Results processing: The generated WAV audio can be played instantly or saved via audio.save().
best practice
Recommended to use fp32 precision in WebGPU mode for best sound quality and note the 300MB+ model load time optimization
This answer comes from the articleKokoro WebGPU: A Text-to-Speech Service for Offline Operation in BrowsersThe
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