Fluid, natural voice translation with Gemini 3.5 Live Translate
Gemini 3.5 Live Translate brings near real-time, natural speech translation to Google AI Studio, Google Translate and Google Meet.
That experiment has come a long way with over a trillion words being translated for billions of users across our products every month. Today, we’re taking our next step with the release of Gemini 3.5 Live Translate, our latest audio model for live speech-to-speech translation. The model automatically detects 70+ languages and generates smooth, natural-sounding translated speech that preserves the speakers’ intonation, pacing and pitch. Unlike turn by turn systems that wait for the speaker to finish speaking before responding, 3.5 Live Translate generates speech continuously, balancing the trade-off between waiting for context to improve quality and translating immediately to stay in sync with the speaker. It delivers fluid audio without awkward pauses and stays just a few seconds behind the speaker throughout the session. Gemini 3.5 Live Translate is rolling out starting today across Google products: Gemini 3.5 Live Translate processes speech as it’s streamed, enabling a more seamless connection across languages. The model handles multilingual inputs without the need to manually configure settings. At the same time, its noise robustness ensures applications can handle loud, unpredictable environments. You can use its capabilities to help facilitate live interpretation for multilingual calls, meetings, lessons, broadcasts and more. Watch the Gemini Live API in action, enabling dubbing and simultaneous multi-language translation. Dive into the demo or more example code in the Gemini Cookbook. By utilizing the Gemini Live API, developer platforms like Agora, Fishjam, LiveKit, Pipecat, and Vision Agents enable developers to build and deploy voice translation apps with ease. These integrations handle the complex real-time media streaming infrastructure, so developers can focus on the user experience. Our partners at Grab are testing the model to enable multilingual communication in near real-time between drivers and travelers at pickups. These users make over 10 million voice calls per month through Grab.
