Monthly Archives: August 2021

code, tutorial

How to set up your first nkululeko project

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Nkululeko is a framework to build machine learning models that recognize speaker characteristics on a very high level of abstraction (i.e. starting without programming experience).

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) Install nkululeko

Inside your virtual environment, run

pip install nkululeko

This should install nkululeko and all required modules.
It takes a long time and a lot of space, when done intially.

5) Adapt the ini file

Use your favourite editor, e.g. visual studio code and edit the file that defines your experiment. You might start with this demo sample.
You can find more templates to start here and an overview on all the options you can set here

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 -m nkululeko.nkululeko --config 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.

What to do next?

You might be interested in the hello world of nkululeko

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code, tutorial

Get all information from emodb

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When you load the Berlin emodb as has been done in numerous postings of this blog, you will get per default only information on file name, speaker id, text id and emotion.

But there is more information contained in the audformat file and this posts shows you how to access it.

If not already somewhere on your computer, start by downloading the emodb:

if not os.path.isdir('./emodb/'):
    !wget -c https://tubcloud.tu-berlin.de/s/LfkysdXJfiobiEG
    !mv download emodb_audformat.zip
    !unzip emodb_audformat.zip
    !rm emodb_audformat.zip

This code will then load the database, prepare a single dataframe with all information and store it to disk for later use:

# load the database to memory
root = './emodb/'
db = audformat.Database.load(root)
# map the file pathes to the audio
db.map_files(lambda x: os.path.join(root, x))   
# access speaker gender and age, and transcription, from the speaker dictionaries
df = db.tables['files'].get(map={'speaker': ['speaker', 'gender', 'age'], 'transcription': ['transcription']})
# copy the emotion label from the the emotion dataframe to the files dataframe
df['emotion'] = db.tables['emotion'].df['emotion']
# add a column with the word count
df['wordcount'] = df['transcription'].apply (lambda row: len(row.split()))
# store to disk for later use
df.to_pickle('store/emodb.pkl')

df.head(1)

code

Machine learning experiment framework

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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: