Speaker anonymization is the task of modifying a speech recording such that the original speaker cannot be identified anymore. Since the first Voice Privacy Challenge in 2020, along with the release of a framework, the popularity of this research topic is continually increasing. However, the comparison and combination of different anonymization approaches remains challenging due to the complexity of evaluation and the absence of user-friendly research frameworks. We therefore propose an efficient speaker anonymization and evaluation framework based on a modular and easily extendable structure, almost fully in Python. The framework facilitates the orchestration of several anonymization approaches in parallel and allows for interfacing between different techniques. Furthermore, we propose modifications to common evaluation methods which make the evaluation more powerful and reduces their computation time by 65 to 95\%, depending on the metric. Our code is fully open source.

翻译：摘要：说话人匿名化是指对语音录音进行修改，使得原始说话人无法被识别的任务。自2020年首届语音隐私挑战赛及相关框架发布以来，该研究主题的受欢迎程度持续上升。然而，由于评估的复杂性以及缺乏用户友好的研究框架，不同匿名化方法的比较与组合仍面临挑战。为此，我们提出了一种基于模块化且易于扩展结构的说话人匿名化与评估框架，该框架几乎完全使用Python实现。该框架支持并行协调多种匿名化方法，并允许不同技术之间的接口交互。此外，我们改进了常见评估方法，使得评估更具效力，并根据指标的不同将计算时间缩减了65%至95%。我们的代码完全开源。