The text discusses the release of real-time translation in a SaaS offering, which provides translation of speech to and from English for 34 languages through its high-accuracy transcription system. The translation builds on top of the state-of-the-art speech-to-text system and benefits from the Ursa generation models, but it cannot recover from breakdowns in transcription, as seen in examples where small mistakes can have a large impact on the resulting translation. The text also compares the performance of different systems using metrics such as Word Error Rates (WER) and BLEU scores, which are limited in their ability to measure translation quality. It highlights the importance of capitalization and punctuation in real-time translation and notes that delivering high-quality real-time translation poses several challenges beyond translation quality, including minimizing delay between word recognition and translation, and striking a balance between gathering enough context for high-quality translation and minimizing delay. The system is expected to improve in line with its transcription accuracy, and more APIs are planned to be rolled out in the coming months.