Build a Real-Time Interpreter Agent for Live Multilingual Meetings

Give every participant a live meeting in their own language, with no round-trip delay waiting for a full sentence.

Time Required

3 hours, one-time setup

Expected Result

A live meeting pipeline that streams transcription, translation, and spoken output in near real time for every language a participant needs.

Recommended Tools

Deepgram

DeepL

ElevenLabs

Stream the Audio Instead of Batching It

Connect to Deepgram's real-time streaming endpoint over WebSocket rather than its batch transcription API, with interim results turned on so you get partial transcripts as someone speaks, not just a finished transcript after they stop.

Deepgram

Translate on Rolling Chunks, Not Full Sentences

Send each finalized transcript chunk to DeepL as it lands rather than waiting to accumulate a full meeting transcript first, since translating everything at once reintroduces the exact delay a real-time pipeline is supposed to remove.

DeepL

Generate Spoken Output Per Language

Route each translated chunk to ElevenLabs' low-latency streaming model to synthesize speech in the target language, running one output stream per language a participant actually needs.

ElevenLabs

Set a Hard Latency Budget and Test Against It

Measure the real end-to-end delay from spoken word to translated audio output. Anything past 2 to 3 seconds starts to break the feel of a live conversation, so treat that number as a pass or fail gate on the whole pipeline, not an aspiration.

Know What Doesn't Transfer

This translates chunks as they land, not full sentences with complete intent, so idiom, tone, and context lag well behind what a human interpreter would catch. Overlapping speakers or fast back-and-forth can desync the pipeline, since each language stream processes independently. Do not treat this as a substitute for a certified human interpreter in a legal, medical, or diplomatic setting where a mistranslation has real consequences.