Combine overlap-aware diarization output RTTMs
Project description
DOVER-Lap
Official implementation for DOVER-Lap: A method for combining overlap-aware diarization outputs.
Installation
To install, simply run:
pip install dover-lap
How 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 label mapping. It may
give better results sometimes. [default:
False]
-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 custom
is used, then --custom-weight
must 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]'
Results
We provide a sample result on the AMI mix-headset test set. The results can be obtained as follows:
dover-lap egs/ami/rttm_dl_test egs/ami/rttm_test_*
md-eval.pl -r egs/ami/ref_rttm_test -s egs/ami/rttm_dl_test
and 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 | 9.71 | 3.00 | 7.59 | 20.30 |
Note: A version of md-eval.pl can be found in dover_lap/libs
.
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}
}
Contact
For issues/bug reports, please raise an Issue in this repository, or reach out to me at draj@cs.jhu.edu
.
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