Diarization recipe for CALLHOME, AMI and DIHARD II by Brno University of Technology.
The recipe consists of
- computing x-vectors
- doing agglomerative hierarchical clustering on x-vectors as a first step to produce an initialization
- apply variational Bayes HMM over x-vectors to produce the diarization output
- score the diarization output
More details about the full recipe in
F. Landini, J. Profant, M. Diez, L. Burget: Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: theory, implementation and analysis on standard tasks
If you are interested in the original version of VBx (prepared for the Second DIHARD Challenge), please refer to the corresponding branch.
If you are interested in the VBx recipe prepared for the track 4 of VoxSRC-20 Challenge (on VoxConverse), please refer to the corresponding branch.
To run the recipe, execute the run scripts for the different datasets with the corresponding parameters. Please refer to the scripts for more details. The CALLHOME and DIHARD II recipes require the corresponding datasets and the paths need to be provided. For AMI, the recordings need to be downloaded (for free) but the VAD segments and reference rttms are obtained from our proposed setup.
This repository has x-vector extractors already trained to function as a standalone recipe. However, the recipes for training the extractors can be found here.
We recommend to create anaconda environment
conda create -n VBx python=3.6
conda activate VBxClone the repository
git clone https://github.com/BUTSpeechFIT/VBx.gitInstall the package
pip install -e .Initialize submodule dscore:
git submodule init
git submodule updateRun the example
./run_example.shThe output (last few lines) should look like this
File DER JER B3-Precision B3-Recall B3-F1 GKT(ref, sys) GKT(sys, ref) H(ref|sys) H(sys|ref) MI NMI
--------------- ----- ----- -------------- ----------- ------- --------------- --------------- ------------ ------------ ---- -----
ES2005a 7.06 29.99 0.65 0.78 0.71 0.71 0.56 1.14 0.59 1.72 0.67
*** OVERALL *** 7.06 29.99 0.65 0.78 0.71 0.71 0.56 1.14 0.59 1.72 0.67
In case of using the software please cite:
F. Landini, J. Profant, M. Diez, L. Burget: Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: theory, implementation and analysis on standard tasks
We present here the results of our systems for the different datasets and different evaluation protocols. A more thorough discussion on the protocols and results can be found in the paper. All results are obtained using oracle VAD.
CALLHOME DIHARD II
----------------- -------------------------
Protocol DER Protocol DER
Forgiving 4.42 dev eval
Fair 14.21 Full 18.19 18.55
Full 21.77 Fair 12.23 12.29
AMI beamformed AMI Mix-Headset
------------------------- -------------------------
Protocol DER Protocol DER
dev eval dev eval
Forgiving 2.80 3.90 Forgiving 1.56 2.10
Fair 10.81 14.23 Fair 9.68 12.53
Full 17.66 20.84 Full 16.33 18.99
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
If you have any comment or question, please contact landini@fit.vutbr.cz or mireia@fit.vutbr.cz