Nkululeko: How to import a database

Nkululeko is a tool to ease machine learning on speech databases.
This tutorial should help you to import databases.
There are two formats upported:
1) csv (comma seperated values)
2) audformat

CSV format

The easiest is CSV, you simply create a table with the following informations:

  • file: the path to the audio file
  • speaker: a speaker identifier
  • sex: the biological sex (has quite an influence on the voice, so sometimes submodeling makes senss)
  • task: is the speaker characteristics value that you want to explore, e.g. age or emotion.

and then fill it with values of your database.
So a file for emotion might look like this

file, speaker, sex, emotion
<path to>/s12343.wav, s1, female, happy
...

You can then specify the data in your initialization file like this:

[DATA]
databases = ['my_db']
my_db.type = csv
my_db = <path to>/my_data_file.csv
...
target = emotion

You can not specify split tables with this format, but would have to simply split the file in several databases.

audformat

audformat allows for many usecases, so the specification might be more complex.
So in the easiest case you have a database with two tables, one called files that contains the speaker informations (id and sex) and one called like your task (aka target), so for example age or emotion.
That's the case for our demo example, the Berlin EmoDB, ando so you can include it simply with.

[DATA]
databases = ['emodb']
emodb = /<path to>/emodb/
target = emotion
...

But if there are more tables and they have special names, you can specifiy them like this:

[DATA]
databases = ['msp']
# path to data
msp = /<path to>/msppodcast/
# tables with speaker information
msp.files_tables =  ['files.test-1', 'files.train']
# tables with task labels
msp.target_tables =  ['emotion.test-1', 'emotion.train']
# train and evaluation splits will be provided
msp.split_strategy = specified
# here are the test/evaluatoin split tables
msp.test_tables = ['emotion.test-1']
# here are the training tables
msp.train_tables = ['emotion.train']
target = emotion

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