With nkululeko since version 0.88.0 you can combine experiment results and report on the outcome, by using the ensemble module.

For example, you would like to know if the combination of expert features and learned embeddings works better than one of those. You could then do

python -m nkululeko.ensemble \
--method max_class \
tests/exp_emodb_praat_xgb.ini \
tests/exp_emodb_ast_xgb.ini \
tests/exp_emodb_wav2vec_xgb.in

(all in one line)
and would then get the results for a majority voting of the three results for Praat, AST and Wav2vec2 features.

Other methods are mean, max, sum, max_class, uncertainty_threshold, uncertainty_weighted, confidence_weighted:

• majority_voting: The modality function for classification: predict the category that most classifiers agree on.
• mean: For classification: compute the arithmetic mean of probabilities from all predictors for each labels, use highest probability to infer the label.
• max: For classification: use the maximum value of probabilities from all predictors for each labels, use highest probability to infer the label.
• sum: For classification: use the sum of probabilities from all predictors for each labels, use highest probability to infer the label.
• max_class: For classification: compare the highest probabilities of all models across classes (instead of same class as in max_ensemble) and return the highest probability and the class
• uncertainty_threshold: For classification: predict the class with the lowest uncertainty if lower than a threshold (default to 1.0, meaning no threshold), else calculate the mean of uncertainties for all models per class and predict the lowest.
• uncertainty_weighted: For classification: weigh each class with the inverse of its uncertainty (1/uncertainty), normalize the weights per model, then multiply each class model probability with their normalized weights and use the maximum one to infer the label.
• confidence_weighted: Weighted ensemble based on confidence (1-uncertainty), normalized for all samples per model. Like before, but use confidence (instead of inverse of uncertainty) as weights.

With nkululeko since version 0.85.0 the acoustic features for the test and the train (aka dev) set are exported to the project store.

If you specify the store_format:

[FEATS]
store_format = csv

they will be exported to CSV (comma separated value) files, else PKL (readable by python pickle module).
I.e. you store should then after execution of any nkululeko module that computes features the two files:

• feats_test.csv
• feats_train.csv

If you specified scaling the features:

[FEATS]
scale = standard # or speaker

you will have two additional files with features:

• feats_test_scaled.csv
• feats_train_scaled..csv

In contrast to the other feature stores, these contain the exact features that are used for training or feature importance exploration, so they might be combined from different feature types and selected via the features value. An example:

[FEATS]
type = ['praat', 'os']
features = ['speechrate_nsyll_dur', 'F0semitoneFrom27.5Hz_sma3nz_amean']
scale = standard
store_format = csv

results in the following feats_test.csv:

file,start,end,speechrate_nsyll_dur,F0semitoneFrom27.5Hz_sma3nz_amean
./data/emodb/emodb/wav/11b03Wb.wav,0 days,0 days 00:00:05.213500,4.028004219813945,34.42206
./data/emodb/emodb/wav/16b10Td.wav,0 days,0 days 00:00:03.934187500,3.0501850763340586,31.227554

....

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.

With respect to the random_splicing method, you can adjust two parameters:

• p_reverse: probability of some samples to be in reverse order (default: 0.3)
• top_db: top dB level for silence to be recognized (default: 12)

This configuration, for example, would distort the samples much more than the default:

[AUGMENT]
augment = ['random_splice']
p_reverse = .8
top_db = 6

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