将高保真吉他音色模型压缩90%后，实现在iPhone上实时运行。
WaveNet-Style Guitar Amplifier Model Pruning for Real-Time iOS Deployment
- 通过迭代权重剪枝去除90%参数，保持音质无感知损失。
- 自研稀疏引擎在仅用CPU的iPhone上实现低延迟实时处理。
- 适合音乐人、音频开发与移动端模型部署研究者。
WaveNet风格卷积网络能以高保真度模拟电子管放大器和失真踏板，但计算开销使其仅限于桌面或专用数字信号处理器硬件。本文提出一种面向iOS的稀疏化支持型WaveNet推理引擎，可在iPhone上实时运行经过重度剪枝的神经吉他放大器模型。采用激进的迭代幅度剪枝方法，移除90%网络权重，音质无感知损失。定制的稀疏C++引擎直接将稀疏性转化为计算效率提升，在仅使用CPU的iPhone上维持低延迟实时运行，而密集模型无法实现。设备端输出与训练模型的误差在int16量化范围内。演示中，用户可通过该应用在真实iPhone硬件上演奏吉他，并对比设备端剪枝模型与所模拟的真实踏板效果。源代码与音频示例见https://github.com/ryos17/wavenet-imp。
原文摘要 · Abstract (English)
WaveNet-style convolutional networks emulate tube amplifiers and distortion pedals with high fidelity, but their computational cost has confined them to desktops or dedicated DSP hardware. We present a sparse-enabled WaveNet inference engine for iOS that runs heavily pruned neural guitar amplifier models in real time on iPhones. Aggressive iterative magnitude pruning removes 90% of the network weights with no perceptible loss in quality. A custom sparse C++ engine turns this sparsity directly into compute savings, sustaining low-latency real-time operation on a CPU-only iPhone implementation where the dense model cannot. On-device output matches the trained model to within int16 quantization error. At the demonstration, visitors will play a guitar through the app on iPhone hardware and A/B the on-device pruned model against the physical pedal it emulates. Source code and audio examples are available at https://github.com/ryos17/wavenet-imp.
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