A New Audio Transcription UI for Speed and Quality at Scale
HumanSignal Team
AI-powered transcription has come a long way and many workloads, it’s fast, inexpensive, and surprisingly accurate. But anyone who’s worked with real-world audio knows the story doesn’t end there.
Background noise, overlapping speakers, accents, dropped words—these issues still show up regularly. And if you’re using transcription data to train or evaluate models, “mostly correct” isn’t good enough. Human review is still essential, both to fix errors and to produce high-quality labeled data that actually improves downstream systems.
That raises a practical question: what’s the best way for humans to work with large volumes of audio transcription data efficiently and accurately?
Label Studio Enterprise already provides strong foundations for this kind of work: native support for multimodal data, flexible annotation schemas, and production-grade workflows for teams of annotators.
But when we started looking closely at long-form audio transcription tasks, it became clear that just the standard UI tags weren’t quite enough. They work—but they impose friction in places where annotators spend most of their time: navigating audio, aligning text with sound, and making fine-grained timing adjustments over hours of recordings.
So instead of trying to incrementally improve the existing UI, we asked a different question:
What would the interface look like if it were designed specifically for audio transcription, with no legacy constraints?
Designing with annotators, not just for them
We worked closely with experienced annotators who process hours of audio every day. They came in with strong opinions—and very concrete ideas—about what slows them down and what helps them stay focused.
The result is a new, dedicated audio transcription UI built directly into Label Studio Enterprise, optimized for speed, precision, and long sessions. It’s still recognizably Label Studio, but the interaction model is tuned for audio-first work. Watch a 2-minute video tour of the new UI in action.
The most fundamental decision was also the most important: audio on the left, transcript on the right, always in sync.
Playback, scrubbing, and editing are tightly coupled. Click anywhere in the waveform and you can immediately edit the corresponding text. Select text and the audio jumps to the right moment. There’s no mental mapping step, and no guessing where you are in the file.
This alone removed a surprising amount of friction from everyday annotation tasks.
Many real-world recordings aren’t single-channel. Interviews, call center data, and meeting recordings often have separate tracks per speaker or source.
Figure 1: playback and visualize mutl-channel audio
Timing edits are a constant part of transcription work, so we made segment editing visual and immediate.
Annotators can move, split, and merge segments directly in the timeline as they work. Segments can overlap—useful when background noise or crosstalk matters—and overlapping regions are automatically laid out into swim lanes to keep everything readable.
There’s no mode switching and no modal dialogs interrupting the flow.
Figure 2: adjust, merge, split, and overlap audio segments with precision
When someone spends their day inside a tool, every extra mouse movement adds up.
We added keyboard shortcuts for essentially every action in the interface. Annotators can play, pause, jump, split, merge, delete, and adjust segments without leaving the keyboard. For long sessions, this makes a dramatic difference in speed and fatigue.
Figure 3: zoom through annotation tasks with keyboard shortcuts
Different annotators work differently. Some want guardrails; others want maximum speed.
We added a dedicated settings panel where annotators can customize behavior—such as disabling confirmation dialogs for destructive actions—so the tool adapts to the user, not the other way around.
Figure 4: each annotator can customize the experience
What we ended up with is a UI purpose-built for long-form audio transcription: approachable enough for new annotators, but with the precision, feedback, and control you’d expect from professional audio software.
More importantly, it scales. Annotators move faster, make fewer mistakes, and stay in flow longer.
The most interesting part of this project isn’t just what we built—it’s how.
All of this was made possible by the new programmable UI engine in Label Studio Enterprise. It allows teams to build fully custom interfaces on top of Label Studio using standard React components, while still benefiting from the underlying data model, workflow engine, and permissions system.
Because the UI layer is just React, modern AI coding tools were surprisingly effective. With AI assistance, we built the first version of this dedicated interface in about a day.
Once annotators tried it, the feedback loop got even tighter. They immediately suggested improvements, which we could prototype and ship quickly. It was one of the fastest iterations we’ve seen between users and production software.
Reach out to us if you want to try the new audio transcription interface in your own projects.
If you have workflows that don’t fit neatly into generic annotation UIs—audio or otherwise—our team can now help you build custom, task-specific experiences on top of Label Studio that match how your team actually works.
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