With nkululeko since version 0.77.8 you have the possibility to convert all feature values into the discreet classes low, mid and high

Simply state

[FEATS]
type = ['praat']
scale = bins
store_format = csv

in your config to use Praat features.
With the store format stated as csv you will be able to look at the train and test features in the store folder.

The binning will be done based on the 33 and 66 percent of the training feature values.

With nkululeko since version 0.66.3, a report document formatted in Latex and compiled as a PDF file can automatically be generated, basically as a compilation of the images that are generated.
There is a dedicated REPORT section in the config file for this, here is an example:

[REPORT]
# should the report be shown in the terminal at the end?
show = False 
# should a latex/pdf file be printed? if so, state the filename
latex = emodb_report
# name of the experiment author (default "anon")
author = Felix
# title of the report (default "report")
title = EmoDB

NOTE:
with each run of a nkululeko module in the same experiment environment, the details of the report will be added.
So a typical use would be, to first run the general module and than more specialized ones:

# first run a segmentation 
python -m nkululeko.segment --config myconf.ini 
# then rename the data-file in the config.ini and
# run some data exploration
python -m nkululeko.explore --config myconf.ini 
# then run a machine learning experiment
python -m nkululeko.nkululeko --config myconf.ini 

Each run will add some contents to the report

Segmenting a database means to split the audio samples of a database into smaller segments or chunks. With speech data this is usually done on the basis of VAD, aka voice activity detection, meaning that the pauses between speech in the audio samples are used as segment borders.

The reason for segmenting could be to label the data with something that would not last over the whole sample, e.g. emotional state.
Another motivation to segment audio data might be that the acoustic features are targeted at a specific stretch of audio, e.g. 3-5 seconds long.

Within nkululeko this would be done with the segment module, which is currently based on the silero software.

You simply call your experiment configuration with the segment module, and the train, test set or both will be segmented.
The advantage is, that you can use all filters on your data that might make sense beforehand, for example with the android corpus, only the reading task samples are not segmented.
You can select them like so:

[DATA]
filter = [['task', 'reading']]

and then call the segment module:

python -m nkululeko.segment --config my_conf.ini

The output is a new database file in CSV format.

If you want, you can specify if only the training, or test split, or both should be segmented, as well as the string that is added to the name of the resulting csv file (the name per default consists of the database names):

[SEGMENT]
# name postfix
target = _segmented
# which model to use
method = silero
# which split: train, test or all (both)
sample_selection = all
# the minimum lenght of rest-samples (in seconds)
min_length = 2
# the maximum length of segments, longer ones are cut here.  (in seconds)
max_length = 10

Within nkululeko, since version 0.53.0, you can perform automatic data checks, which means that some of your data might be filtered out if it doesn't fulfill certain requirements.

Currently two checks are implemented:

[DATA]
# check the filesize of all samples in train and test splits, in bytes
 check_size = 1000
# check if the files contain speech with voice activity detection (VAD)
 check_vad = True

VAD is using silero VAD

If you just want to see how your data distributes on the target with nkululeko, you can do a value_counts plot with the explore module

In your config, you would specify like this:

[EXPL]
# all samples, or only test or train split?
sample_selection = all 
# activate the plot
value_counts = [['age'], ['gender'], ['duration'], ['duration', 'age']] 

and then, run this with the explore module:

python -m nkululeko.explore --config myconfig.ini

The results, for a data set with target=depression, looks similar to this for all samples:

and this for the speakers (if there is a speaker annotation)

If you prefer a kernel density estimation over a histogram, you can select this with

[EXPL]
dist_type = kde

which would result for duration to:

Nkululeko distinguishes between categorical and continuous properties, this would be the output for gender

You can show the distribution of two sample properties at once, by using a scatter plot:

In addition, this module will automatically plot the distribution of samples per speaker, per gender (if annotated):

To do data augmentation with Nkululeko, you can use the augment or the aug_train interface.
The difference is that the former only augments samples, whereas the latter augments the training set of a configuration and then immediately performs the training, including the augmented files.

In the AUGMENT section of your configuration file, you specify the method and name of the output list of file

• traditional: is the classic augmentation, e.g. by cropping data or adding a bit of noise. We use the audiomentations package for this
• random-splice: is a special method introduced in this paper that randomly splices and re-connects the audio samples

[AUGMENT]
# select the samples to augment: either train, test, or all
sample_selection = train
# select the method(s)
augment = ['traditional', 'random_splice']
# file name to store the augmented data (can then be added to training)
result = augmented.csv

and then call the interface:

python -m nkululeko.augment --config myconfig.ini

or

python -m nkululeko.aug_train--config myconfig.ini

if you want to run a training in the same run.

Currently, apart from random-splicing, Nkululeko simply uses the audiomentations module, i.e.:

[AUGMENT]
augment = ['traditional']
augmentations = Compose([
AddGaussianNoise(min_amplitude=0.001, max_amplitude=0.05),
Shift(p=0.5),
BandPassFilter(min_center_freq=100.0, max_center_freq=6000),])

These manipulations are applied randomly to your training set.

You should find the augmented files in the storage folder of the result folder of your experiment and could listen to them there.

Once you augmentations have been processed, you can add them to the training in a new experiment:

[DATA]
databases = ['original data', 'augment']
augment = my_augmentations.csv
augment.type = csv
augment.split_strategy = train

Since version 0.40, Nkululeko can now show the best performing X acoustic features according to some model.

There is a new section call EXPL (short for exploration), and you could state

[EXPL]
model = tree
sample_num = 15

in your config file, and then run the exploration module like this:

python -m nkululeko.explore --config my_config.ini

The resulting list will then appear in the result folder and a barplot image in the image folder.

Afterwards you could inspect single features as described here

As shown in this post, with Nkululeko you can select only specific features from your features sets by specifying them in the [FEAT] section:

[FEATS]
features = ['JitterPCA', 'meanF0Hz', 'hld_sylRate']

What you can also do, is plotting them per category (only for classification), by specifying in the PLOT section if you would like that for all samples or only test or train samples:

[EXPL]
# turn it on
feature_distributions = True 
# use only training samples
sample_selection = train 
# only plot the 5 most important features 
max_feats = 5  

You would have to call nkululeko with the explore interface:

python -m nkululeko.explore --config <myConfig.ini>

The image file is in the image folder and should look similar to this: