Category Archives: nkululeko

Nkululeko: how to visualize your data distribution

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

In your config, you would specify like this:

# all samples, or only test or train split?
sample_selection = all 
# activate the plot
value_counts = True

and then, run this with the explore module:

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

The result looks similar to this:

Nkululeko: how to augment the training set

To do data augmentation with Nkululeko, you can use the augment interface.
In the DATA section of your configuration file, you specify the name of the output list of files like so

augment = my_augmentations.csv

and then call the interface:

python - nkululeko.augment --config myconfig.ini

Currently, Nkululeko simply uses the augmentations that are specified as a demo in the audiomentations documentation, i.e.:

self.audioment = Compose([
    AddGaussianNoise(min_amplitude=0.001, max_amplitude=0.015, p=0.5),
    TimeStretch(min_rate=0.8, max_rate=1.25, p=0.5),
    PitchShift(min_semitones=-4, max_semitones=4, p=0.5),
    Shift(min_fraction=-0.5, max_fraction=0.5, p=0.5),

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:

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

Nkululeko: show feature importance

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

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.

Nkululeko: how to plot distributions of feature values

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

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:

# 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:

Nkululeko: how to predict many samples

There are three ways to predict a number of samples:

  1. If you want to save the predictions of an experiment for later use, you can do so by stating in the EXP section

    save_test = ./my_saved_test_predictions.csv

    The output format is CSV, comma seperated values.

  2. Alternatively, you can test an existing database against the best model you trained before, by stating the databases as tests in the DATA section:

    tests = ['my_testdb']
    my_testdb = /mypath/my_testdb

    and then calling Nkululeko's test module

    python -m nkululeko.test --config mycoonfg.ini --outfile myresults.csv
  3. Run the demo module simply for a set of files:

    python -m nkululeko.demo --config mycoonfg.ini --list my_filelist.txt


This is the entry post for Nkululeko: a framework to do machine learning experiments on audio data based on configuration files.

Here's an overview on the tutorials:

How to import features from outside the Nkululeko software

Since version 0.29.1 there is the possibilty to directly import acoustic features into the Nkululeko framework.

You can specify a file to be imported in the FEATS section:

type = ['import']
import_file = /home/.../my_features.csv

Of course the features still can be combined with other feature sets and will be assigned to training and test splits accordingly.

The feature file must be in CSV format (comma separated values) in audformat with segmented index.
Here is an example:

file,start,end,voice segments,HNR Mean (dB),F1 Mean (Hz)
/home/.../a42_1.wav,0 days,0 days 00:00:07.815875,4.13,45,7.48,

Nkululeko: How to evaluate a test set with a given best model

Since version 0.27.0, Nululeko has a concept for a test set, despite train and dev set.

Let's recap the concept of train/dev/test splits:

  • train is used to train a supervised model
  • dev is a set to evaluate this model, i.e. know when it is a good model (that doesn't overfit)
  • test is a set to be used ONLY once: for the real use of the model. If you would use the test as a dev set, you can't be sure if you're not overfitting again (because you used the dev set to adjust the meta parameters of your model).

So, in order to evaluate a third dataset ( beneath train and dev) you set a tests entry in the configuration [DATA] section like so:

tests = ['my_testdb']
my_testdb = /mypath/my_testdb

and then call Nkululeko's test module

python -m nkululeko.test --config mycoonfg.ini --outfile myresults.csv

Nkululeko FAQ

This post is the start of a troubleshooting /FAQ list

The number of speakers in my test/train splits seems wrong

  • Did you check if perhaps the speakers from several datasets are the same? If so, you can add a dataname.rename_speakers = True to the configuration. This will prepend the dataset name to the speakers

How to combine feature sets with Nkululeko

If you want to use combine several acoustic parameter (feature) sets with nkululeko, you might state

type = ['mld', 'praat']
features = ['JitterPCA', 'meanF0Hz', 'hld_sylRate']

This would combine the

  • hld_sylRate feature from MLD
  • JitterPCA feature from Feinberg's Praat features and
  • meansF0Hz feature from Feinberg's Praat features

Of course you could omit the features entry and simply use all of them.

It's interesting to see how many emotions from Berlin Emodb can still be recognized with only these three parameters: