  In the field of gravitational-wave (GW) interferometers, the most severe
limitation to the detection of transient signals from astrophysical sources
comes from transient noise artefacts, known as glitches, that happens at a rate
around $1$ per minute. Because glitches reduce the amount of scientific data
available, there is a need for better modelling and inclusion of glitches in
large-scale studies, such as stress testing the search pipelines and increasing
the confidence of detection. In this work, we employ a Generative Adversarial
Network (GAN) to produce a particular class of glitches ({\it blip}) in the
time domain. We share the trained network through a user-friendly open-source
software package called \texttt{gengli} and provide practical examples of its
usage.
