Metadata-Version: 2.1
Name: dover-lap
Version: 0.1.2
Summary: Combine overlap-aware diarization output RTTMs
Home-page: https://github.com/desh2608/dover-lap
Author: Desh Raj
Author-email: r.desh26@gmail.com
License: Apache-2.0 License
Description: # DOVER-Lap
        Official implementation for [DOVER-Lap: A method for combining overlap-aware diarization outputs](https://arxiv.org/pdf/2011.01997.pdf).
        
        ## Installation
        
        DOVER-Lap can be simply installed using `pip`, which will also install the
        two dependencies: `numpy` and `intervaltree`, if not present.
        
        ```
        pip install dover-lap
        ```
        
        ## How to run
        
        After installation, run
        
        ```
        dover-lap -i <input-RTTMs> -o <output-RTTM>
        ```
        
        Example:
        
        ```
        dover-lap -i egs/ami/rttm_test_* -o egs/ami/rttm_dl_test
        ```
        
        ## Optional arguments
        
        ```
        -u, --uem 
        : UEM file indicating scoring regions
        
        -c, --channel
        : Channel ID for output RTTM (Default: 1)
        
        --second-maximal
        : Boolean argument to specify whether to apply an additional round of maximal
        matching in the label mapping stage. This may perform slightly better for larger
        number of inputs (Default: False)
        
        --dover-weight
        : Parameter for DOVER-style rank weighting applied to hypothesis for label
        voting, e.g. w_k = (1/k)^0.1, where k is the rank (Default: 0.1)
        ```
        
        ## Results
        
        We provide a sample result on the AMI mix-headset test set. The results can be 
        obtained as follows:
        
        ```
        dover-lap -i egs/ami/rttm_test_* -o egs/ami/rttm_dl_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                         | 10.66 | **2.03** |  **7.82** | **20.50** |
        
        **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.
        
        ## 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`.
        
Keywords: diarization dover
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Topic :: Utilities
Classifier: License :: OSI Approved :: BSD License
Description-Content-Type: text/markdown
