标题： TinyMusician：基于知识蒸馏和混合精度量化的设备端音乐生成 显示英文标题

标题： TinyMusician: On-Device Music Generation with Knowledge Distillation and Mixed Precision Quantization

摘要： 生成模型的成功在音乐生成领域引起了前所未有的关注。基于Transformer的架构为模型性能设定了新的基准。然而，由于参数数量庞大，它们的实际应用受到一些关键挑战的阻碍：对大量计算资源和推理时间的需求。这些障碍使得它们难以部署在计算资源有限的边缘设备上，如智能手机和可穿戴设备。在本工作中，我们提出了TinyMusician，这是一个从MusicGen（一个最先进的音乐生成模型）中提炼出的轻量级音乐生成模型。TinyMusician集成了两项创新：(i) 阶段混合的双向和偏斜KL散度以及(ii) 自适应混合精度量化。实验结果表明，TinyMusician在模型规模减少55%的情况下保留了93%的MusicGen-Small性能。TinyMusician是第一个可在移动设备上部署的音乐生成模型，在消除云依赖的同时保持了高音频保真度和高效的资源使用。 显示英文摘要

摘要： The success of the generative model has gained unprecedented attention in the music generation area. Transformer-based architectures have set new benchmarks for model performance. However, their practical adoption is hindered by some critical challenges: the demand for massive computational resources and inference time, due to their large number of parameters. These obstacles make them infeasible to deploy on edge devices, such as smartphones and wearables, with limited computational resources. In this work, we present TinyMusician, a lightweight music generation model distilled from MusicGen (a State-of-the-art music generation model). TinyMusician integrates two innovations: (i) Stage-mixed Bidirectional and Skewed KL-Divergence and (ii) Adaptive Mixed-Precision Quantization. The experimental results demonstrate that TinyMusician retains 93% of the MusicGen-Small performance with 55% less model size. TinyMusician is the first mobile-deployable music generation model that eliminates cloud dependency while maintaining high audio fidelity and efficient resource usage