An approach to optimize inference of the DIART speaker diarization pipeline
Roman Aperdannier; Sigurd Schacht; Alexander Piazza · 2024 · arXiv
WASTE classifies this as Failed Experiment Report · AI classification, approximate
An experimental approach did not work as intended — learn what to avoid before investing the same effort.
Abstract (excerpt)
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
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Metadata source: arXiv