With nkululeko since version 0.85.0 you can finetune a transformer model with huggingface (and even publish it there if you like).

If you like to have your model published, set:

[MODEL]
push_to_hub = True

Finetuning in this context means to train the (pre-trained) transformer layers with your new training data labels, as opposed to only using the last layer as embeddings.

The only thing you need to do is to set your MODEL type to finetune:

[FEATS]
type = []
[MODEL]
type = finetune

The acoustic features can/should be empty, because the transformer model starts with CNN layers to model the acoustics frame-wise. The frames are then getting pooled by the model for the whole utterance (max. duration the first 8 seconds, the rest is ignored).

The default base model is the one from facebook, but you can specify a different one like this:

[MODEL]
type = finetune
pretrained_model = microsoft/wavlm-base

duration = 10.5

The parameter max_duration is also optional (default=8) and means the maximum duration of your samples / segments (in seconds) that will be used, starting from 0. The rest is disregarded.

You can use the usual deep learning parameters:

[MODEL]
learning_rate = .001
batch_size = 16
device = cuda:3
measure = mse
loss = mse

but all of them have defaults.

The loss function is fixed to

• weighted cross entropy for classification
• concordance correlation coefficient for regression

The resulting best model and the huggingface logs (which can be read by tensorboard) are stored in the project folder.

Sometimes you want to compare two different databases that share a similar target variable, say, related to likability, but in a different scaling, say the one asked on a scale from 1 to 10 and the other used likert-scale from 1-7.

With nkululeko you can re-name labels, normalize the target values, and even inverse the polarity, for each databases.

In the following example there are two databases

[DATA]
databases = ['db1', 'db2']
db1.split_strategy = test
db1.scale = True
db2.colnames = {'non-attractive':'likability'}
db2.split_strategy = train
db2.scale = True
db2.reverse = True
db2.reverse.max = 10
target = likability
bins = [-1000, .2, 1000]
labels = ['less likable', 'more likable']

The one database db1 already has a likability label and just needs to be standard-normalized, the second one db2 has a related label non-attractive which needs to be renamed, inverted (based on a hypothetical maximum value of 10) and normalized.
Then, db1 can be used as test data and db2 as training.

Sometimes, with categorically labeled data, the number of samples per class is very unevenly distributed, misleading the model to think that the overwhelming majority class is more important than the others.
In this case, two techniques might help: class weighting assigns a higher weight to samples from minority classes, and oversampling "invents" new samples for the minority classes.
With nkululeko since version 0.70.0, you can oversample the trainig set with different algorithms implemented by the imb_learn package.
You simply state the method in the FEATS section like so:

[FEATS]
...
balancing = adasyn # either ros, smote or adasyn

Three methods are available:

• ros: simply repeat random samples from the minority classes
• smote: "invent" new minority samples by little changes from the existing ones
• adasyn: similar to smote, but resulting in uneven class distributions

With nkululeko since version 0.68.1, you can re-name data fields (columns in your data table) by setting the following in your ini-file:

[DATA]
databases = ['mydata']
mydata.colnames = {'Participant ID':'speaker', 'sex':'gender', 'Age': 'age'}

which means, that, before further processing, the Participant ID field in your database mydata will be treated as speaker label and so on.

With nkululeko since version 0.68.0, the selection of test/dev vs. train samples can be done automatically in a stratified manner, i.e. trying to find splits that are age or gender balanced.
An example for such a configuration is this:

[DATA]
# the name of the database
databases = ['emodb']
# the location of the data
emodb = ./data/emodb/emodb
# set the split strategy to "balanced"
emodb.split_strategy = balanced
# set a percentage value for your test split
emodb.test_size = 20
# stratify variables with weights for importance
balance = {'emotion':2, 'age':1, 'gender':1}
# all stratification variables need to be categorical, 
# so we need to state the number of bins for "age" 
age_bins = 2
# a value for how much importance to give for the ideal group sizes
size_diff_weight = 1
# the target value of the experiment
target = emotion

Nkululeko will always keep the speaker variable disjunct, i.e. resulting splits will contain different speakers.
With the example above, the algorithm will try to balance emotion, gender and (binned) age distributions across the splits.

With nkululeko since version 0.67.0, the spotlight software is directly integrated as part of the EXPLORE module.

You can simply run your data filters, augmentations, machine learning experiments, segmentations and model predictions as usual, and then call the spotlight software by adding to your configuration file:

[EXPL]
sample_selection = all # or train or test 
spotlight = True 

and running the EXPORE module

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

Note that you might require to install an extra package:

pip install renumics-spotlight

A new web browser window should open as an interface to spotlight:

With nkululeko since version 0.64.0, some statistics are printed as part of the plot's titles.
With the explore module, you can plot correlations between the target (e.g. emotion or age) and other variables that are in the database, e.g. gender or duration, or everything you might have predicted with the predict module.
You need to differentiate if any of your variables is categorical/nominal (strings) or real valued (numbers).

If you plot the distribution of two categories, the Chi2 statistics is used to estimate if the correlation is significant. A p-value is given in the title, like e.g. in this plot:

If you plot the distribution of two real-valued variables, the correlation will be estimated by the Pearson's correlation coefficient:

If the target is categorical and the variable real-valued, we use Cohen's d to print out the maximal effect size of a category combination and the variable:

If the target is also real-valued, it will be binned (made categorical) per default:

If you want to prevent that, you can set a value in the configuration:

[EXPL]
bin_reals = False

and you get a different plot: