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 = csvin 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 0.77.7 there is a new interface named multidb which lets you compare several databases.
You can state their names in the [EXP] section and they will then be processed one after each other and against each other, the results are stored in a file called heatmap.png in the experiment folder.
!Mind YOU NEED TO OMIT THE PROJECT NAME!
Here is an example for such an ini.file:
[EXP]
root = ./experiments/emodbs/
# DON'T give it a name,
# this will be the combination
# of the two databases:
# traindb_vs_testdb
epochs = 1
databases = ['emodb', 'polish']
[DATA]
root_folders = ./experiments/emodbs/data_roots.ini
target = emotion
labels = ['neutral', 'happy', 'sad', 'angry']
[FEATS]
type = ['os']
[MODEL]
type = xgbyou can (but don't have to), state the specific dataset values in an external file like above.
data_roots.ini:
[DATA]
emodb = ./data/emodb/emodb
emodb.split_strategy = specified
emodb.test_tables = ['emotion.categories.test.gold_standard']
emodb.train_tables = ['emotion.categories.train.gold_standard']
emodb.mapping = {'anger':'angry', 'happiness':'happy', 'sadness':'sad', 'neutral':'neutral'}
polish = ./data/polish_emo
polish.mapping = {'anger':'angry', 'joy':'happy', 'sadness':'sad', 'neutral':'neutral'}
polish.split_strategy = speaker_split
polish.test_size = 30Call it with:
python -m nkululeko.multidb --config my_conf.iniHere's a result with two databases:
and this is the same experiment, but with augmentations:
In order to add augmentation, simply add an [AUGMENT] section:
[EXP]
root = ./experiments/emodbs/augmented/
epochs = 1
databases = ['emodb', 'polish']
[DATA]
--
[AUGMENT]
augment = ['traditional', 'random_splice']
[FEATS]
...In order to add an additional training database to all experiments, you can use:
[CROSSDB]
train_extra = [meta, emodb], to add two databases to all training data sets,
where meta and emodb should then be declared in the root_folders file
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 = EmoDBNOTE:
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
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.csvand then you call it like this
python -m nkululeko.resample --config my_config.iniWARNING: 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.
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 = allYou can then call the predict module with python:
python -m nkululeko.predict --config my_config.iniThe 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 = alland then call the explore module:
python -m nkululeko.explore --config my_config.iniThe 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.
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.iniThe 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 = 10Within 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 = TrueVAD 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.iniThe 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 = kdewhich 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
[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.csvand then call the interface:
python -m nkululeko.augment --config myconfig.inior
python -m nkululeko.aug_train--config myconfig.iniif 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 = trainSince 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 = 15in your config file, and then run the exploration module like this:
python -m nkululeko.explore --config my_config.iniThe 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