Shaving Latency Off Real-Time Speech Translation: What Actually Worked in Our Flutter App
In a chat app, a slow reply is mildly annoying. In a spoken conversation, it breaks the exchange. Silence feels longer than a spinner does, and people judge the delay from the moment they stop speaking, not from when a request reaches a server. We build Owll Translator , a real-time interpretation app. You wear earbuds, speak normally, and the app translates live. It can also speak the…
The article discusses the challenges and solutions in reducing latency in a real-time speech translation app called Owll Translator. The app uses earbuds to translate speech in real-time, with the option to speak the translation in the user's own voice. The author explains that most of the latency work focused on hiding unavoidable waits, cutting down avoidable delays, and getting sound into the listener's ear as soon as possible.
The article details the two main backend paths used in the app - client-orchestrated and server-orchestrated - and outlines various techniques employed to minimize latency, such as starting the translation process before the user initiates it, prefetching authentication tokens and creating sessions in advance, and optimizing the audio pipeline.
The author also acknowledges the lack of reliable end-to-end latency measurements and explains the steps being taken to address this issue.
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