DOVER-Lap
Official implementation for DOVER-Lap: A method for combining overlap-aware diarization outputs.
Installation
pip install dover-lapHow to run
After installation, run
dover-lap [OPTIONS] OUTPUT_RTTM [INPUT_RTTMS]...Example:
dover-lap egs/ami/rttm_dl_test egs/ami/rttm_test_*Usage instructions
Usage: dover-lap [OPTIONS] OUTPUT_RTTM [INPUT_RTTMS]...
Apply the DOVER-Lap algorithm on the input RTTM files.
Options:
--custom-weight TEXT Weights for input RTTMs
--dover-weight FLOAT DOVER weighting factor [default: 0.1]
--weight-type [rank|custom] Specify whether to use rank weighting or
provide custom weights [default: rank]
--tie-breaking [uniform|all] Specify whether to assign tied regions to
all speakers or divide uniformly [default:
all]
--second-maximal If this flag is set, run a second iteration
of the maximal matching for greedy label
mapping [default: False]
--sort-first If this flag is set, sort inputs by DER
first before label mapping (only applicable
when label mapping type is hungarian)
[default: False]
--label-mapping [hungarian|greedy]
Choose label mapping algorithm to use
[default: greedy]
--random-seed INTEGER
-c, --channel INTEGER Use this value for output channel IDs
[default: 1]
-u, --uem-file PATH UEM file path
--help Show this message and exit.Note:
- If
--weight-type customis used, then--custom-weightmust be provided. For example:
dover-lap egs/ami/rttm_dl_test egs/ami/rttm_test_* --weight-type custom --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 in DOVER.
Results
We provide a sample result on the AMI mix-headset test set. The results can be
obtained using spyder, which is automatically
installed with dover-lap:
dover-lap egs/ami/rttm_dl_test egs/ami/rttm_test_*
spyder egs/ami/ref_rttm_test egs/ami/rttm_dl_testand similarly for the input hypothesis. The DER results are shown below.
| MS | FA | Conf. | DER | |
|---|---|---|---|---|
| Overlap-aware VB resegmentation | 9.84 | 2.06 | 9.60 | 21.50 |
| Overlap-aware spectral clustering | 11.48 | 2.27 | 9.81 | 23.56 |
| Region Proposal Network | 9.49 | 7.68 | 8.25 | 25.43 |
| DOVER-Lap (Hungarian mapping) | 9.81 | 2.76 | 8.17 | 20.73 |
| DOVER-Lap (Greedy mapping)* | 9.71 | 3.02 | 7.68 | 20.40 |
* The Greedy label mapping is exponential in number of inputs (see this paper).
Running time
The 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 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-Lap
DOVER-Lap is meant to be used to combine more 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 equally divided among the two 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},
}
Contact
For issues/bug reports, please raise an Issue in this repository, or reach out to me at [email protected].