With nkululeko since version 0.93.0 the pyannote segmentation package is interfaced (as an alternative to silero)

There are two modules that you can use for this:

• SEGMENT
• PREDICT

The (huge) difference is, that the SEGMENT module looks at each file in the input data and looks for speakers per file (can be only one large file), while the PREDICT module concatenates all input data and looks for different speakers in the whole database.

In any case best run it on a GPU, as CPU will be very slow (and there is no progress bar).

Segment module

If you specify the method in [SEGMENT] section and the hf_token (needed for the pyannote model) in the [MODEL] section

[SEGMENT]
method = pyannote
segment_target = _segmented
sample_selection = all
[MODEL]
hf_token = <my hugging face token>

your resulting segmentations will have predicted speaker id attachched.. Be aware that this is really slow on CPU, so best run on GPU and declare so in the [MODEL] section:

[MODEL]
hf_token = <my hugging face token>
device=gpu # or cuda:0

As a result a new plot would appear in the image folder: the distribution of speakers that were found, e.g. like this:

Predict module

Simply select speaker as the prediction target:

[PREDICT]
targets = ["speaker"]

Generally, the PREDICT module is described here