%0 Journal Article
%T 基于全局通道数剪枝的Wav2Lip模型轻量化的研究
Research on Lightweight Wav2Lip Model Based on Global Channel Number Pruning
%A 徐康杰
%A 陈云翔
%A 张龙
%A 唐帅
%A 周庆华
%J Computer Science and Application
%P 606-614
%@ 2161-881X
%D 2025
%I Hans Publishing
%R 10.12677/csa.2025.155133
%X 针对Wav2Lip模型计算量大，推理速度慢，在一些对实时性要求较高或算力较为有限的应用场景中可能难以满足预期效果等问题，论文提出了基于全局通道数剪枝的方法，选用了三种不同剪枝比例，对Wav2Lip模型进行了全局通道数剪枝并对比。实验结果表明，论文提出的全局通道数剪枝方案成功地：1) 提升了推理速度；2) 减小了模型体积；3) 保持或提升了所生成图像的效果。该方案在降低计算成本的同时，能够实现高效且稳定的推理性能。
In response to the issues of high computational complexity, slow inference speed, and potential difficulty in achieving expected results in some application scenarios that require high real-time performance or limited computing power for the Wav2Lip model, the paper proposes a method based on global channel pruning, using three different pruning ratios to perform global channel pruning on the Wav2Lip model and compare them, the experimental results show that the global channel pruning scheme proposed in the paper successfully: 1) improves inference speed; 2) Reduced the size of the model; 3) Maintained or improved the effect of the generated image. This solution can achieve efficient and stable inference performance while reducing computational costs.
%K Wav2Lip，
%K 深度学习，
%K 模型轻量化，
%K 全局通道数剪枝
Wav2Lip
%K Deep Learning
%K Model Lightweighting
%K Global Channel Pruning
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=115022