Package: dover-lap
DOVER-LapOfficial implementation forDOVER-Lap: A method for combining overlap-aware diarization outputs.Installationpipinstalldover-lapHow to runAfter installation, rundover-lap[OPTIONS]OUTPUT_RTTM[INPUT_RTTMS]...Example:dover-lapegs/ami/rttm_dl_testegs/ami/rttm_test_*Usage instructionsUsage:dover-lap[OPTIONS]OUTPUT_RTTM[INPUT_RTTMS]...ApplytheDOVER-LapalgorithmontheinputRTTMfiles.

Options:--gaussian-filter-stdFLOATStandarddeviationforGaussianfilterappliedbeforevoting.ThiscanhelpreducetheeffectofoutliersintheinputRTTMs.Forquickturn-taking,setthistoasmallvalue(e.g.0.1).0.5isagoodvalueformostcases.Setthistoaverysmallvalue,e.g.0.01,toremovefiltering.[default:0.5]--custom-weightTEXTWeightsforinputRTTMs--dover-weightFLOATDOVERweightingfactor[default:0.1]--weight-type[rank|custom|norm]Specifywhethertouserankweightingorprovidecustomweights[default:rank]--voting-method[average]Choosevotingmethodtouse:average:useweightedaveragetocombineinputRTTMs[default:average]--second-maximalIfthisflagisset,runaseconditerationofthemaximalmatchingforgreedylabelmapping[default:False]--label-mapping[hungarian|greedy]Chooselabelmappingalgorithmtouse[default:greedy]--random-seedINTEGER-c,--channelINTEGERUsethisvalueforoutputchannelIDs[default:1]-u,--uem-filePATHUEMfilepath--helpShowthismessageandexit.Note:If--weight-type customis used, then--custom-weightmust be provided. For example:dover-lapegs/ami/rttm_dl_testegs/ami/rttm_test_*--weight-typecustom--custom-weight'[0.4,0.3,0.3]'label-mappingcan be set togreedy(default) orhungarian, which is a modified version of the mapping
technique originally proposed inDOVER.ResultsWe provide a sample result on the AMI mix-headset test set. The results can be
obtained usingspyder, which is automatically
installed withdover-lap:dover-lapegs/ami/rttm_dl_testegs/ami/rttm_test_*
spyderegs/ami/ref_rttm_testegs/ami/rttm_dl_testand similarly for the input hypothesis. The DER results are shown below.MSFAConf.DEROverlap-aware VB resegmentation9.842.069.6021.50Overlap-aware spectral clustering11.482.279.8123.56Region Proposal Network9.497.688.2525.43DOVER-Lap (Hungarian mapping)9.982.138.2520.35DOVER-Lap (Greedy mapping)*9.962.167.7519.86* The Greedy label mapping is exponential in number of inputs (seethis paper).Running timeThe algorithm is implemented in pure Python with NumPy for tensor computations.
The time complexity is expected to increase exponentially with the number of
inputs, but it should be reasonable for combining up to 10 input hypotheses. For
combining more than 10 inputs, we recommend setting--label-mapping hungarian.For smaller number of inputs (up to 5), the algorithm should take only a few seconds
to run on a laptop.Combining 2 systems with DOVER-LapDOVER-Lap is meant to be used to combinemore than 2 systems, since
black-box voting between 2 systems does not make much sense. Still, if 2 systems
are provided as input, we fall back on the Hungarian algorithm for label mapping,
since it is provably optimal for this case. Both the systems are assigned equal
weights, and in case of voting conflicts, the region is assigned to both
labels. This is not the intended use case and will almost certainly lead
to performance degradation.Citation@article{Raj2021Doverlap,
  title={{DOVER-Lap}: A Method for Combining Overlap-aware Diarization Outputs},
  author={D.Raj and P.Garcia and Z.Huang and S.Watanabe and D.Povey and A.Stolcke and S.Khudanpur},
  journal={2021 IEEE Spoken Language Technology Workshop (SLT)},
  year={2021}
}

@article{Raj2021ReformulatingDL,
  title={Reformulating {DOVER-Lap} Label Mapping as a Graph Partitioning Problem},
  author={Desh Raj and S. Khudanpur},
  journal={INTERSPEECH},
  year={2021},
}ContactFor issues/bug reports, please raise an Issue in this repository, or reach out to me atdraj@cs.jhu.edu.