  Contrastive Language-Audio Pretraining (CLAP) became of crucial importance in
the field of audio and speech processing. Its employment ranges from sound
event detection to text-to-audio generation. However, one of the main
limitations is the considerable amount of data required in the training process
and the overall computational complexity during inference. This paper
investigates how we can reduce the complexity of contrastive language-audio
pre-trained models, yielding an efficient model that we call tinyCLAP. We
derive an unimodal distillation loss from first principles and explore how the
dimensionality of the shared, multimodal latent space can be reduced via
pruning. TinyCLAP uses only 6% of the original Microsoft CLAP parameters with a
minimal reduction (less than 5%) in zero-shot classification performance across
the three sound event detection datasets on which it was tested
