Deployment challenges
Edge devices have problems such as limited arithmetic power and tight memory, and need to be targeted to optimize the model deployment scheme.
optimization strategy
- Model LightweightOptional
CosyVoice-300MVersion, reduced memory footprint compared to version 0.5B 60% - quantitative compression: Implementation
torch.quantization.quantize_dynamicRealization of INT8 quantization - hardware acceleration: Using ONNX Runtime or TensorRT-Lite on devices such as the Raspberry Pi
concrete step
1. Convert the model format:
torchscript_model = torch.jit.trace(model, example_inputs)
2. Memory-mapped loading:
model = cosyvoice.load_mmap('model.bin')
3. Setting CPU affinity: Binding large-core runs
Performance indicators
It is optimized to run on 4GB memory devices with an RTF (Real-Time Factor) of 0.3 to meet real-time requirements.
This answer comes from the articleCosyVoice: Ali open source multilingual cloning and generation toolsThe
May not be reproduced without permission:AI productivity tools " How to optimize the efficiency of CosyVoice deployment on edge devices?
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