  In this paper, we introduce the Temporal Audio Source Counting Network
(TaCNet), an innovative architecture that addresses limitations in audio source
counting tasks. TaCNet operates directly on raw audio inputs, eliminating
complex preprocessing steps and simplifying the workflow. Notably, it excels in
real-time speaker counting, even with truncated input windows. Our extensive
evaluation, conducted using the LibriCount dataset, underscores TaCNet's
exceptional performance, positioning it as a state-of-the-art solution for
audio source counting tasks. With an average accuracy of 74.18 percentage over
11 classes, TaCNet demonstrates its effectiveness across diverse scenarios,
including applications involving Chinese and Persian languages. This
cross-lingual adaptability highlights its versatility and potential impact.
