Most localization managers discover the deliverables problem late. The dub sounds acceptable in the review player, the client approves the sample, and then the postproduction team asks a question nobody scoped: "Can we get the vocals separate from the bed?" Or the broadcaster's spec sheet demands an M&E track, and the vendor only produced a fully mixed file. Reworking audio deliverables after synthesis is expensive and slow, and it usually means regenerating or remixing work that was already approved.
Planning AI dubbing audio deliverables up front, which stems you need, where the M&E comes from, who does the final mix, and what each downstream team receives, is what separates a dub that ships from a dub that stalls in postproduction. This article walks through how Ollang structures those deliverables and how to plan them against your own audio pipeline.
Why the Audio Bed Determines Dubbing Usability
A dubbed program is not one audio asset. It is at minimum two: the localized dialogue and the music-and-effects (M&E) bed underneath it. If the M&E is clean, music, effects, ambience, and room tone with no source-language dialogue, the localized vocals can sit on top and the result sounds like the program was made in the target language. If the M&E is dirty, the original dialogue bleeds through under the dub, and no amount of vocal quality fixes it.
This is why the audio bed, not the voice synthesis, is often the first thing an experienced mixer evaluates. It also determines what you can do later. With separated stems, you can remix for a different loudness spec, swap a single line after a legal note, or hand assets to a broadcast partner that mixes in-house. With only a flattened mixdown, every change means going back to the start.
Ollang's documented AI dubbing workflow treats the audio bed as a first-class asset rather than a byproduct. Projects can carry an M&E track alongside the source video, subtitles, glossaries, and voice instructions, and the platform's deliverables include the separated stems as well as the finished mix. That structure is what makes the rest of this article possible to plan around.
Choose Between Uploaded and Extracted M&E
Ollang supports two paths for the audio bed, and the choice should be made at project setup, not at delivery.
Uploaded clean M&E. If your content owner can supply a true M&E track, common for theatrical, broadcast, and premium series content, upload it as a supporting asset when you create the project. A production M&E was mixed deliberately: effects are intact, music is at full fidelity, and dialogue was never printed into it. Whenever this asset exists, use it. Ollang's documentation confirms that a clean Music & Effects track can be attached to a project and used in the dubbing workflow, and its bulk folder-upload structure can associate M&E files with the right projects automatically, useful when you are onboarding an episodic series or a library rather than a single title.
Extracted M&E. For content where no M&E exists, creator video, older archive material, interviews, corporate content, Ollang can extract and create an M&E track from the source media, removing the source dialogue while retaining music, effects, room tone, and environmental sound. Extraction is what makes large back catalogs dubbable at all, since chasing original session files for hundreds of titles is rarely realistic.
The operational rule for localization managers: make M&E availability a field in your intake checklist. Titles with delivered M&E and titles requiring extraction should be flagged differently, because extraction quality depends on the source mix and you will want reviewers listening for dialogue residue on extracted titles specifically.
Generate and Review Localized Vocals
Once the bed is settled, Ollang's workflow handles the dialogue: transcription, translation, and synthesis of localized speech through configurable providers, with optional lip sync where the project calls for it. The output that matters for postproduction planning is the localized vocals-only track, the AI-dubbed dialogue isolated from music, effects, and ambience.
This deliverable is easy to overlook if you only think in terms of final mixes, but it changes how review works. A vocals-only track lets linguists and voice reviewers evaluate pronunciation, pacing, and speaker consistency without music masking problems. It lets your mixer set dialogue levels independently rather than accepting whatever balance the automated mix produced. And it means a single flawed line can be fixed and re-dropped without touching the bed.
Ollang's review tooling supports this loop directly: orders can stay AI-only but editable, or route through assigned linguists and editors who adjust translations and pacing, manage speakers, and trigger resynthesis at the segment level. When a reviewer flags a segment, the fix regenerates that segment's audio, and because vocals exist as their own deliverable, the correction propagates cleanly into whatever mix comes next.
If your organization uses external reviewers, note that Ollang's role system includes separate environments for project managers and for editors or LSPs, with restricted visibility on assigned orders. You can hand a reviewer the vocals track and the review interface without exposing the rest of the project.