Performance Bottleneck Analysis
TTS systems are prone to latency on devices with limited CPU resources. kokoro-ONNX achieves performance optimization through the following design:
Specific optimization measures
- Model quantification: Use of 8-bit integer quantized version (80MB) reduces memory footprint by 75% compared to floating point model (300MB)
- Batch Disable: Modification
hello.pyhit the nail on the headstreaming=TrueParameters to enable streaming - Thread control: The program is available through the ONNX Runtime's
session_optionsLimit the number of threads to the number of physical CPU cores - Cache Optimization: Use local wav caching mechanism for duplicate text to reduce the pressure of real-time computation.
advanced skill
For ARM devices such as the Raspberry Pi, you can 1) Compile an ARM-optimized version of the ONNX Runtime 2) Use theonnxruntime.transformersPerform layer fusion 3) EnableORT_ENABLE_EXTENDEDInstruction Set Optimization
This answer comes from the articleKokoro-ONNX: Efficient Text-to-Speech Tool with Multi-Language and Multi-Voice SupportThe
May not be reproduced without permission:AI productivity tools " How to optimize Kokoro-ONNX's real-time speech synthesis performance on low-configuration devices?
