Tag Archives: machine learning

How to set up your first nkululeko project

Nkululeko is a framework build machine learning models that recognize speaker characteristics.

This post is meant to help you with setting up your first experiment, based on the Berlin Emodb.

1) Set up python

It's written in python so first you have to set up a Python environment

2) Get a database

Load the Berlin emodb database to some location on you harddrive, as discussed in this post. I will refer to the location as "emodb root" from now on.

3) Download nkululeko

Navigate with a browser to the nkululeko github page and click on the "code" button, download the zip or (better) clone with your git software (step 1).

Unpack (if zip file) to some location on your hard disk that I will call "nkululeko root" from now on.

4) Install the required python packages

Inside the virtual environment that you created!

Navigate with a shell to the nkululeko root and install the python packages needed by nkululeko with

pip install -r requirements.txt

5) Adapt the ini file

Use your favourite editor, e.g. visual studio code and open the nkululeko root. If you use visual studio code, set the path to the environment as python interpreter path and store this (nkululeko root and python envirnment path) as a workspace configuration, so next time you can simply open the wprkspace and you're set up.

Open the exp_emodb.ini file and put your nkululeko root as the root value, for me this looks like this:

root = /home/felix/data/research/nkululeko/

Put the emodb root folder as the emodb value, for me this looks like this

emodb = /home/felix/data/audb/emodb

An overview on all nkululeko options should be here

6) Run the experiment

Inside a shell type (or use VSC) and start the process with

python exp_emodb.py exp_emodb.ini

7) Inspect the results

If all goes well, the program should start by extracting opensmile features, and, if you're done, you should be able to inspect the results in the folder named like the experiment: exp_emodb.
There should be a subfolder with a confusion matrix named images` and a subfolder for the textual results named `results.

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Machine learning experiment framework

Currently i'm working on (yet another) framework for machine learning, i.e. a python coded set of classes that can be used to run machine learning experiments in a flexible but reusable way.

I'm not sure where this is heading yet, but a first runnable version exists, if interested check it out at my github account, I'll update news there.

The general idea looks something like this: