Diarization with unknown number of speakers
Hi there!I’m looking into audio diarization but with the caveat that the number of speakers is not known beforehand. This means ruling out models that need the number of speakers such asWav2Vec2ForAudioFrameClassification.My approach was to useWav2Vec2ForXVectoron each audio snippet and useagglomerative clusteringto cluster the vectors using cosine similarity and some value fordistance_threshold. Although the results on the training data were good (confusion matrix, etc.) the problem is that the results areverysensitive to the threshold value, to the point where the approach doesn’t generalize very well at all.Has anyone attempted this problem and obtained more stable results?Thanks!
2022-10-28T18:06:08Z
Hi,Can you try this model -GitHub - pyannote/pyannote-audio: Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding