Speaker diarization based on Kaldi x-vectors using pretrained model from http://kaldi-asr.org/models/0003_sre16_v2_1a.tar.gz

Dependencies are listed in requirements.txt.

It is recommended to use anaconda environment https://www.anaconda.com/download/ because of mkl based implementation. Run python setup.py install Also, since we are using Kaldi, path to Kaldi root must be set in vbdiar/kaldi/__init__.py

Config file declares used models and paths to them. Example configuration file is configs/vbdiar.yml.

Pretrained models are stored in models/ directory.

Example script examples/diarization.py is able to run full diarization process. The code is designed in a way, that you have everything in same tree structure with relative paths in list and then you just specify directories - audio, VAD, output, etc. See example configuration.

'-l', '--input-list' - specifies relative path to files for testing, it is possible to specify number of speakers as the second column. Do not use file suffixes, path is always relative to input directory and suffix.

'-c', '--configuration' - specifies configuration file

'--audio-dir' - directory with audio files in .wav format - 8000Hz, 16bit-s, 1c.

'--vad-dir' - directory with lab files - Voice/Speech activity detection - format speech_start speech_end.

'--in-emb-dir' - input directory containing embeddings (if they were previously saved).

'--out-emb-dir' - output directory for storing embeddings.

'--norm-list' - input list with files for score normalization. When performing score normalization, it is necessary to use input ground truth .rttm files with unique speaker label. Speaker labels should not overlap, only in case, that there is same speaker in more audio files. All normalization utterances will be merged by speaker labels.

'--in-rttm-dir' - input directory with .rttm files (used primary for score normalization)

'--out-rttm-dir' - output directory for storing .rttm files

'--min-window-size' - minimal size of embedding window in miliseconds. Defines minimal size used for clustering algorithms.

'--max-window-size' - maximal size of embedding window in miliseconds.

'--vad-tolerance' - skip n frames of non-speech and merge them as speech.

'--max-num-speakers' - maximal number of speakers. Used in clustering algorithm.

It is important to note that these results are obtained using summed individual head-mounted microphones. Results are reporting when using oracle number of speakers, collar size 0.25s and without scoring overlapped speech.

Results can be obtained using similar command

python diarization.py -c ../configs/vbdiar.yml -l lists/AMI_dev-eval.scp --audio-dir wav/AMI/IHM_SUM --vad-dir vad/AMI --out-emb-dir emb/AMI/IHM_SUM --in-rttm-dir rttms/AMI -j 4