Advances in deep learning have enabled speech synthesis to rival human speech in realism. While many artists have experimented with these technologies, real-time applications have been limited. We define a new task, interactive artistic text-to-voice (IATV), in order to bridge this gap. We also present a novel IATV system which achieves low-latency synthesis, interactivity, and controllability while allowing for exploration of unconventional vocal expressions. It leverages a character-level text encoder, Tacotron2-based streaming alignment, and a RAVE streaming vocoder. Tungnaá is our open source Python package implementing IATV training and real-time inference, plus a graphical interface for experimental music performance with IATV models. We report on strategies for low-resource training on artist-created datasets, and on an artistic application of Tungnaá in collaboration with sound poet Jaap Blonk.