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End-to-end diarization presents an attractive alternative to standardcascaded diarization systems because a single system can handle all aspects ofthe task at once. Many flavors of end-to-end models have been proposed but allof them require (so far non-existing) large amounts of annotated data fortraining. The compromise solution consists in generating synthetic data and therecently proposed simulated conversations (SC) have shown remarkableimprovements over the original simulated mixtures (SM). In this work, we createSC with multiple speakers per conversation and show that they allow forsubstantially better performance than SM, also reducing the dependence on afine-tuning stage. We also create SC with wide-band public audio sources andpresent an analysis on several evaluation sets. Together with this publication,we release the recipes for generating such data and models trained on publicsets as well as the implementation to efficiently handle multiple speakers perconversation and an auxiliary voice activity detection loss.

Multi-Speaker and Wide-Band Simulated Conversations as Training Data for End-to-End Neural Diarization