  Code-switching (CS), i.e. mixing different languages in a single sentence, is
a common phenomenon in communication and can be challenging in many Natural
Language Processing (NLP) settings. Previous studies on CS speech have shown
promising results for end-to-end speech translation (ST), but have been limited
to offline scenarios and to translation to one of the languages present in the
source (\textit{monolingual transcription}).
  In this paper, we focus on two essential yet unexplored areas for real-world
CS speech translation: streaming settings, and translation to a third language
(i.e., a language not included in the source). To this end, we extend the
Fisher and Miami test and validation datasets to include new targets in Spanish
and German. Using this data, we train a model for both offline and streaming ST
and we establish baseline results for the two settings mentioned earlier.
