Speaker diarization dataset. As with DIHARD I and DIHARD II, development and evaluation sets will be provided by the organizers, but there is no fixed training set with the result that participants are free to train their systems on any proprietary and/or public data. Hence, for benchmarking purposes, we specifically employ the English subset of the dataset's test section. In this benchmark, we utilize cloud-based Speech-to-Text engines equipped with speaker diarization capabilities. Awesome_Diarization - A curated list of awesome Speaker Diarization papers, libraries, datasets, and other resources. With the development of speaker diarization, researchers have released some related datasets, which include different languages and target scenarios, and have contributed to the development of speaker diarization tasks. It contains: A collection of multilingual speaker diarization datasets that are compatible with the diarizers library. Speaker Diarization is the procees which aims to find who spoke when in an audio and total number of speakers in an audio recording. This dataset is a critical resource for decoding ``Who said What and When'' in multi-talker, reverberant environments, a daunting challenge in the field. This project contains: Voice Activity Detection (webrtcvad) Speaker Segmentation based on Bi-LSTM Embedding Extraction (d-vector extraction) Clustering (k-MEANS and Mean Shift) Jun 17, 2025 ยท To address these two problems, firstly, we propose an automated method for constructing speaker diarization datasets, which generates more accurate pseudo-labels for massive data through the combination of audio and video. Note that only speaker embedding extractor and neural diarizer are trainable models and they can be train/fine-tune together on diarization datasets. uu zib1 g9 vxzge q3nt rt7pxs w8i yt6 jj4pyzc atam4