When Live Content Requires a Different Dubbing Architecture
If you manage localization for a broadcaster or an events business, you already have a workflow for prerecorded content: ingest the asset, transcribe, translate, generate or record voices, review, deliver. That workflow assumes one thing above all else, time. Even a fast AI pipeline that turns a video around in hours still has hours.
A live news feed, a match commentary, or a keynote gives you none. There is no file to upload, no review pass before air, and no chance to rerun a segment. If the target-language audio arrives noticeably behind the source, the product fails: viewers hear the crowd react before the commentator explains why, or conference attendees watch a speaker's slides advance while the interpretation lags a sentence behind.
This is why live AI dubbing is not a faster version of file-based dubbing. It is a different architecture: continuous audio capture instead of file ingestion, streaming inference instead of batch processing, and return audio paths instead of downloadable deliverables. Ollang, which runs a file-based AI dubbing platform for prerecorded content, addresses the live case with a separate product, LiveVoice, built around exactly that architecture. Its documented flow: capture a microphone or broadcast feed via WebRTC or RTMP, stream the speech through Ollang's edge network, dub it into a target language in real time, and send the dubbed audio back to viewers.
The rest of this article walks through each stage of that flow, what it means for your existing systems, and, critically, the difference between what Ollang claims about latency and language coverage and what you should treat as verified before you commit a live event to it.
Capture Microphone and Broadcast Feeds
The first architectural difference from prerecorded localization is the input. Ollang's standard AI dubbing product accepts uploaded media files or URLs. LiveVoice instead documents two live ingestion paths: WebRTC and RTMP.
The distinction matters because it maps to two different types of live production:
- WebRTC is the browser-native, low-latency streaming protocol. It fits scenarios where the source is a microphone or a web-based capture point, a conference speaker on stage, a panel discussion, a remote contributor joining through a browser. If your event AV setup can route a microphone feed to a WebRTC endpoint, you have an ingestion path without broadcast infrastructure.
- RTMP is the workhorse contribution protocol in broadcast and streaming workflows. Encoders, production switchers, and streaming platforms speak it already. If you are localizing a news channel, a sports feed, or a livestream that already pushes RTMP to a CDN or platform, sending a parallel RTMP feed to a dubbing endpoint is an incremental change rather than a re-architecture.
For a localization manager, the practical takeaway is that the integration conversation starts with your broadcast engineers or event AV vendor, not with your LSP. The question to bring them is concrete: can we split or duplicate the program audio feed as WebRTC or RTMP, and where does it originate, the venue, the master control room, or the encoder?
Generate and Return Target-Language Audio in Real Time
Once the feed is captured, Ollang states that speech streams through its edge network and is dubbed into the target language in real time, with the dubbed audio sent back to viewers. Two claims sit at the center of the product's positioning, and both deserve careful handling.
The latency claim. Ollang claims sub-second end-to-end latency for LiveVoice. That figure comes from Ollang's own product materials; it has not been independently benchmarked, and no binding service-level agreement guaranteeing that latency was found in public documentation. This distinction is the single most important thing to internalize before procurement. "Claimed sub-second" and "guaranteed sub-second under contract, measured at your venue, on your network, in your language pair" are different statements. Real-world latency in a live dubbing chain depends on factors outside the vendor's model: your contribution encoder settings, network path to the edge, the return path to the audience, and the playout mechanism (in-venue audio channels, a second audio track on a stream, or a companion app). Any of these can add delay the vendor's marketing figure does not account for.
The language claim. Ollang markets LiveVoice with 30+ language pairs, plus custom-language support. Note that this is a separate and smaller figure than the 240+ languages Ollang claims across its broader localization platform, the platform-wide number should not be read as live dubbing coverage. Before planning a multilingual event, get the specific pairs you need confirmed in writing, in the direction you need them. A pair that works source-to-target may not be documented in reverse.
What real-time generation changes operationally: there is no human review gate before the audience hears the output. Ollang's prerecorded workflow supports segment-level editing, human review levels, and reruns. None of that applies mid-broadcast. Your quality assurance for live content shifts entirely upstream, to pilot testing, terminology preparation where the product supports it, and monitoring during the event, rather than downstream correction.