Significant advances have been achieved in Speech-to-Speech (S2S) translation systems in recent years. However, rapid configuration of S2S systems for low-resource language pairs and domains remains a challenging problem due to lack of human translated bilingual training data. In this paper, we report on an effort to port our existing English/Iraqi S2S system to the English/Farsi language pair in just 90 days, using only a small amount of training data. This effort included developing acoustic models for Farsi, domain-relevant language models for English and Farsi, and translation models for English-to-Farsi and Farsi-to-English. As part of this work, we developed two novel techniques for expanding the training data, including the reuse of data from different language pairs, and directed collection of new data. In an independent evaluation, the resulting system achieved the highest performance of all systems.