Nemotron-3-Diarization-preview

Model · Voice activity detection

segmentation-3.0

Pyannote

Using this open-source model in production? Consider switching to pyannoteAI for better and faster options. This model ingests 10 seconds of mono audio sampled at 16kHz and outputs speaker diarization as a (numframes, numclasses) matrix where the 7 classes are non-speech, speaker #1, speaker #2, speaker #3, speakers #1 and #2, speakers #1 and #3, and speakers #2 and #3. The various concepts behind this model are described in details in this paper. It has been trained by Séverin Baroudi with pyannote.audio 3.0.0 using the combination of the training sets of AISHELL, AliMeeting, AMI, AVA-AVD, DIHARD, Ego4D, MSDWild, REPERE, and VoxConverse. This companion repository by Alexis Plaquet also…

Model · Voice activity detection

segmentation

Pyannote

Using this open-source model in production? Consider switching to pyannoteAI for better and faster options. In order to reproduce the results of the paper "End-to-end speaker segmentation for overlap-aware resegmentation ", use pyannote/segmentation@Interspeech2021 with the following hyper-parameters: Expected outputs (and VBx baseline) are also provided in the /reproducibleresearch sub-directories.