  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 improves the quality of the evaluation and reduces
their computation time by 65 to 95%, depending on the metric. Our code is fully
open source.
