开源框架让神经音频模型轻松接入DAW，支持实时与离线使用。
Neutone SDK: An Open Source Framework for Neural Audio Processing
- 封装缓冲区、采样率转换等难题，提供统一模型接口
- 支持实时处理，可在Python中直接开发神经音频插件
- 适合音频工程师、开发者及音乐创作者快速部署模型
神经音频处理带来了声音变换与合成的新方法，但将深度学习模型集成到数字音频工作站（DAWs）仍面临实时推理和插件开发复杂性的挑战。本文介绍Neutone SDK：一个开源框架，可简化基于PyTorch的神经音频模型在实时与离线场景下的部署。通过将变长缓冲区、采样率转换、延迟补偿及控制参数处理等常见问题封装为统一、模型无关的接口，该框架实现了神经模型与宿主插件间的无缝互操作性，并支持用户完全在Python中进行开发。我们提供了实现所需接口的技术概述及对应SDK实现。同时展示了该框架在音频效果仿真、音色迁移和样本生成等应用中的通用性，已被研究者、教育工作者、企业和艺术家广泛采用。Neutone SDK开源地址：https://github.com/Neutone/neutone_sdk
原文摘要 · Abstract (English)
Neural audio processing has unlocked novel methods of sound transformation and synthesis, yet integrating deep learning models into digital audio workstations (DAWs) remains challenging due to real-time / neural network inference constraints and the complexities of plugin development. In this paper, we introduce the Neutone SDK: an open source framework that streamlines the deployment of PyTorch-based neural audio models for both real-time and offline applications. By encapsulating common challenges such as variable buffer sizes, sample rate conversion, delay compensation, and control parameter handling within a unified, model-agnostic interface, our framework enables seamless interoperability between neural models and host plugins while allowing users to work entirely in Python. We provide a technical overview of the interfaces needed to accomplish this, as well as the corresponding SDK implementations. We also demonstrate the SDK's versatility across applications such as audio effect emulation, timbre transfer, and sample generation, as well as its adoption by researchers, educators, companies, and artists alike. The Neutone SDK is available at https://github.com/Neutone/neutone_sdk
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