Solution: Breaking through performance limitations with WebGPU technology
When running TTS models in the browser, traditional WebAssembly computation may face performance bottlenecks. kokoro WebGPU provides two optimization schemes:
- WebGPU Acceleration Program: Use the device parameter set to 'webgpu' with dtype='fp32':
"`javascript
device: 'webgpu',
dtype: 'fp32'
"` This combination maximizes the benefits of GPU parallel computing - Quantitative modeling program: A quantized version can be used to reduce the amount of computation when the device does not support WebGPUs:
"`javascript
dtype: 'q8' // or a lighter version of 'q4'
“`
Extra suggestion: For long text synthesis, it is recommended to use segmentation processing strategy to control the text block size by split_pattern parameter to avoid excessive load of single computation.
This answer comes from the articleKokoro WebGPU: A Text-to-Speech Service for Offline Operation in BrowsersThe
May not be reproduced without permission:AI productivity tools " How to solve the performance bottleneck when realizing efficient text-to-speech in a browser environment?
