Aug 5, 2024 · The aim of this paper is to optimize the inference latency of the DIART pipeline. Different inference optimization methods such as knowledge distilation, ...
Aug 5, 2024 · The aim of this work is to optimize the latency of the DIART pipeline. The focus is on the embedding model, as this is the largest factor in the overall ...
Aug 5, 2024 · 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 ...
Aug 5, 2024 · This paper explores several techniques to optimize the inference of the DIART pipeline, with the goal of improving its performance and efficiency for practical ...
Aug 6, 2024 · ``An approach to optimize inference of the DIART speaker diarization pipeline,'' Roman Aperdannier, Sigurd Schacht, Alexander Piazza, ...
Speaker diarization answers the question "who spoke when" for an audio file. In some diarization scenarios, low latency is required for transcription.
This paper provides an overview. First the history of online speaker diarization is briefly presented. Next a taxonomy and datasets for training and evaluation ...
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 ...
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Different inference optimization methods such as knowledge distilation, pruning, quantization and layer fusion are applied to the embedding model of the ...
Aug 26, 2024 · We propose a novel framework applying EEND both locally and globally for long-form audio without separate speaker embeddings.