Category Archives: tutorial

Nkululeko: generate a latex/pdf report

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

How to fix different sampling rates in a dataset with Nkululeko

With nkululeko since version 0.62.0 you can automatically adjust the sampling rate to the standard of 16 kHz, which is required by most models that might need to process your data.

A special module can be configured in the configuration file like this:

[RESAMPLE]
# which of the data splits to re-sample: train, test or all (both)
sample_selection = all
replace = True
target = data_resampled.csv

and then you call it like this

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

WARNING: if replace = True, this changes (overwrites) ALL files in the splits, directly on your hard disk. Make sure to make a safety copy of your database before, in case the results are undesired, or you still need the data in other sample rates.

The default value though, is replace = False . Then, the target value will be used as filename for the new dataframe with filenames that indicate that the sampling rate has been changed.

As stated above, only files in the test and train splits are affected. This means that you can use all filtering, e.g. limit samples per speaker to 20 samples to pre-select samples.

Nkululeko: how to predict labels for your data from existing models and check them

With nkululeko since version 0.58.0, you can predict labels automatically for a given database, and then perhaps use these predictions to check on bias within your data.
One example:
You have a database labeled with smokers/non-smokers. You evaluate a machine learning model, check on the features and find to your astonishment, that the mean pitch is the most important feature to distinguish between smokers and non-smokers, with a very high accuracy.
You suspect foul-play and auto-label the data with a public model predicting biological sex (called gender in Nkululeko).
After a data exploration you see that most of the smokers are female and most of the non-smokers are male.
The machine learning model detected biological sex and not smoking behaviour.

How do you do this?
Firstly, you need to predict labels. In a configuration file, state the annotations you'd like to be added to your data like this:

[DATA]
databases = ['mydata']
mydata = ... # location of the data
mydata.split_strategy = random # not important for this 
...
[PREDICT]
# the label names that should be predicted: possible are: 'gender', 'age', 'snr', 'valence', 'arousal', 'dominance', 'pesq', 'mos'
targets = ['gender']
# the split selection, use "all" for all samples in the database
sample_selection = all

You can then call the predict module with python:

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

The resulting new database file in CSV format will appear in the experiment folder.
The newly predicted values will be named with a trailing _pred, e.g. "gender_pred" for "gender"
You can than configure the explore module to visualize the the correlation between the new labels and the original target:

[DATA]
databases = ['predicted']
predicted = ./my_exp/mydata_predicted.csv
predicted.type = csv
predicted.absolute_path = True
predicted.split_strategy = random
...
[EXPL]
# which labels to investigate in context with target label
value_counts = [['gender_pred']]
# the split selection
sample_selection = all

and then call the explore module:

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

The resulting visualizations are in the image folder of the experiment folder.
Here is an example of the correlation between emotion and estimated PESQ (Perceptual Evaluation of Speech Quality)

The effect size is stated as Cohen's d, for categories that have the largest value, in this case the difference of estimated speech quality is largest between the categories neutral and angry.

Nkululeko: segmenting a database

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

Nkululeko: check your dataset

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

Nkululeko: how to visualize your data distribution

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):

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 method and name of the output list of file

  • augment: is the classic augmentation, e.g. by cropping data or adding a bit of noise.
  • random-splice: is a special method introduced in this paper that randomly splices and re-connects the audio samples
[DATA]
# select the samples to augment: either train, test, or all
augment = train
# file name to store the augmented data (can then be added to training)
augment_result = augment.csv

or, if you want to use random-splicing:

[DATA]
# random_splice: select the samples to be random spliced: either train, test, or all
random_splice = train
# random_splice_result: file name to store the random spliced data (can then be added to training)
random_splice_result = random_spliced.csv

and then call the interface:

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

Currently, apart from random-splicing, 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:

[DATA]
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

[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

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:

[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:

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

    [EXP]
    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:

    [DATA]
    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