  Speaker diarization answers the question "who spoke when" for an audio file.
In some diarization scenarios, low latency is required for transcription.
Speaker diarization with low latency is referred to as online speaker
diarization. The DIART pipeline is an online speaker diarization system. It
consists of a segmentation and an embedding model. The embedding model has the
largest share of the overall latency. The aim of this paper is to optimize the
inference latency of the DIART pipeline. Different inference optimization
methods such as knowledge distilation, pruning, quantization and layer fusion
are applied to the embedding model of the pipeline. It turns out that knowledge
distillation optimizes the latency, but has a negative effect on the accuracy.
Quantization and layer fusion also have a positive influence on the latency
without worsening the accuracy. Pruning, on the other hand, does not improve
latency.
