部署挑战
边缘设备存在算力受限、内存紧张等问题，需针对性优化模型部署方案。
optimization strategy
- 模型轻量化：选用
CosyVoice-300M版本，相较0.5B版内存占用减少60% - quantitative compression: Implementation
torch.quantization.quantize_dynamic实现INT8量化 - hardware acceleration：在树莓派等设备使用ONNX Runtime或TensorRT-Lite
具体步骤
1. 转换模型格式：
torchscript_model = torch.jit.trace(model, example_inputs)
2. 内存映射加载：
model = cosyvoice.load_mmap('model.bin')
3. 设置CPU亲和性：绑定大核运行
Performance indicators
经优化后可在4GB内存设备上运行，RTF(Real-Time Factor)达到0.3，满足实时性要求。
This answer comes from the articleCosyVoice: Ali open source multilingual cloning and generation toolsThe
May not be reproduced without permission:AI productivity tools " 如何优化CosyVoice在边缘设备上的部署效率？
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