  This paper describes the winning systems developed by the BUT team for the
four tracks of the Second DIHARD Speech Diarization Challenge. For tracks 1 and
2 the systems were mainly based on performing agglomerative hierarchical
clustering (AHC) of x-vectors, followed by another x-vector clustering based on
Bayes hidden Markov model and variational Bayes inference. We provide a
comparison of the improvement given by each step and share the implementation
of the core of the system. For tracks 3 and 4 with recordings from the Fifth
CHiME Challenge, we explored different approaches for doing multi-channel
diarization and our best performance was obtained when applying AHC on the
fusion of per channel probabilistic linear discriminant analysis scores.
