Top 3 ways to enhance AI video editing tools with Voice AI

Video editors have spent a decade automating the parts of editing that involve pixels — cutting, colour, stabilisation, background removal. The audio track mostly sat there, treated as something you level and move on from.

That's backwards. The audio track is the only part of a video that tells you what it's about.

Once you can read speech accurately, a video file stops being an opaque blob and becomes structured, searchable, editable data. That's the shift behind most of the AI features shipping in video platforms right now: captions that write themselves, timelines you can search by phrase, edits you make by deleting words. All of it runs on transcription underneath.

Veed, which builds a collaborative browser-based video editor, put the build-versus-buy question plainly:

"Assembly allowed our team to focus on what they are best at: Building a collaborative, browser-based video editor and distributing that product at speed and at velocity to our user base."

— Sabba Keynejad and Tim Mamedov, Veed

This article covers the three ways Voice AI models make an AI video editing tool meaningfully better, and what to look for in the API underneath them.

What is AI video editing?

AI video editing is any editing workflow where a model does work an editor would otherwise do by hand — generating captions, finding the interesting moments, removing silences, splitting a recording into segments, or turning a two-hour session into a set of clips.

The distinction that matters isn't "AI-powered" versus "manual." It's whether the tool understands the content of the video or only its pixels. A model that removes a green screen doesn't know what anyone said. A model that reads the transcript can find the moment someone mentioned the pricing objection.

What are the benefits of AI video editing tools?

Three, mostly:

• They delete the tedious review pass. Nobody wants to scrub a 90-minute recording to find four usable minutes.
• They make long video navigable. Timestamps, chapters, and searchable transcripts turn a linear file into something you can jump around inside.
• They make video accessible by default rather than as a post-production chore that gets skipped when the deadline is tight.

The three Voice AI building blocks

Voice AI, as it applies to video tooling, breaks into three layers:

Speech-to-text converts the audio track into text with timestamps. Everything else depends on it, which is why accuracy here is not a nice-to-have — an error at this layer propagates into every downstream feature.

Speech Understanding models run over the transcript to extract structure: entities, topics, sentiment, summaries, speaker identity, key phrases. These are priced à la carte, so you turn on what your product needs and skip the rest.

Large language models handle the open-ended work that doesn't fit a fixed schema — rewriting a summary in your product's voice, generating clip titles, answering a question about what happened in a call. Our LLM Gateway puts Anthropic, OpenAI, Google and Qwen models behind the same API key you already use for transcription.

Now let's look at what you build with them.

1. Add captions automatically

Captions are the highest-leverage Voice AI feature in a video editor, and it isn't close.

They're an accessibility requirement. They're a discoverability asset, since caption text is indexable and audio isn't. And they matter for plain viewing behaviour — in a 2019 Verizon Media study reported by Forbes, 69% of consumers said they watch video without sound in public places, and more than a quarter said they do it at home too. That study is now several years old, and if anything the sound-off default has hardened since.

Use case: AI subtitle generators

AI subtitle generators automatically transcribe speech from audio and video files using speech-to-text models. The workflow inside a video platform typically looks like this:

1. Upload a video file.
2. Generate subtitles automatically from the audio track.
3. Edit the subtitles where needed.
4. Style them — font, colour, position, animation.
5. Export with the subtitles burned in, or as a sidecar caption file.

Steps 3 through 5 are your product. Step 2 is a transcription call.

Subtitles in five lines of code

Here's the whole thing. Transcribe a video, export SRT.

Python

import assemblyai as aai

aai.settings.api_key = "<YOUR_API_KEY>"

config = aai.TranscriptionConfig(speech_models=["universal-3-5-pro", "universal-2"])
transcript = aai.Transcriber().transcribe("https://example.com/your-video.mp4", config)

print(transcript.export_subtitles_srt())

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Swap export_subtitles_srt() for export_subtitles_vtt() if you're rendering into an HTML5 <track> element. Caption export costs nothing extra — you're billed for the transcription, and the SRT and VTT files come out of the same completed transcript.

One detail worth copying: speech_models is set explicitly. It's a plural array on pre-recorded audio, and pinning it means your output doesn't quietly change when account defaults do. Pass chars_per_caption=32 to either export method if you need to cap caption width for a mobile player, or add it as a query parameter when calling the subtitles endpoint directly.

2. Support video searchability and indexing

An accurate transcript makes a video searchable. Speech Understanding models make a video library navigable, which is a much harder and much more valuable problem.

Three models do most of the work here:

Summarization is what you want for chapters. If you built against the old auto_chapters parameter, note that it is deprecated. You now pass a summarization object under speech_understanding.request and read sections from speech_understanding.response.summarization.summary. The migration guide maps every field, and the Summarization docs cover the summary_type and effort parameters.

Put those together and a video platform can auto-tag content by the people and companies mentioned, search across an entire library by phrase and jump to the exact second, generate chapter markers without anyone watching the file, and let a user filter a two-hour multi-speaker recording down to one person's contributions. That's the difference between storing video and having a searchable video archive.

Transcripts can also be translated into other languages, widening the reach of the same source file without a second recording session.

3. Unlock insights for smarter collaboration

Most video work is collaborative, and most collaboration friction is people watching things they didn't need to watch.

Summarization plus LLM Gateway covers this well. A reviewer gets the summary and the chapter list, skims to the two sections that concern them, and leaves a comment — instead of scrubbing the whole cut. A producer generates a shot list from a raw interview. An editor asks a question about a three-hour session and gets an answer with a timestamp attached.

The pattern is consistent: Speech Understanding gives you reliable structure with timestamps you can trust, and the LLM does the open-ended judgment on top. Keeping those responsibilities separate is what makes the output safe to show a user, because the timestamps never pass through a model that could nudge them.

What actually separates speech-to-text APIs for video

If you're evaluating an API for a video product, accuracy on a clean read-aloud benchmark tells you almost nothing. Here's what does.

Per-word timestamps

This is the one people underrate, and it's the one that determines whether you can build text-based editing at all. If your API returns word-level start and end times with confidence scores, a user can delete a word in a transcript and you can cut the corresponding frames. If it returns timestamps only at the sentence or utterance level, you can build captions and not much else. Every serious video editing feature — filler-word removal, text-based trimming, precise clip extraction, caption timing you can nudge — is downstream of word-level timing.

Streaming

Live captioning, live event subtitling, and real-time collaborative review all need results before the file ends. Universal-3.5 Pro Streaming returns a complete transcript in a median of 285 ms, with p95 at 374 ms and p99 at 443 ms. Formatting is always on, so what arrives is already punctuated and ready to render as a caption rather than a wall of lowercase.

Multilingual coverage

Video crosses borders faster than any other content type, and the useful question isn't the headline language count — it's what happens when a speaker switches mid-sentence. Universal-3.5 Pro handles 18 languages with native code-switching, so an English sentence with a French clause in the middle comes back as what was actually said rather than forced into one language. Anything outside those 18 falls back to Universal-2 automatically, for 99 languages total. Our approved figure for code-switching is a 22% relative reduction in word error rate, rising another 4% with contextual prompts.

Speaker diarization quality

Diarization is easy to demo and hard to do. Two people taking clean turns is a solved problem. A panel where three people talk over each other, someone joins late, and one speaker is on a bad laptop mic is not — and that's what real footage looks like. Universal-3.5 Pro ships our most accurate diarization yet, and the streaming variant adds revision: it labels speakers live, then re-clusters at the end of the stream and issues a correction within about half a second, handling up to 10 speakers. If you want the mechanics, we wrote up how diarization works and the cases where it breaks.

Notice what isn't on this list: a single headline accuracy number. For video tooling, the errors that hurt are almost never function words. They're names, product names, companies, and places — the exact strings a user searches for. That's why we increasingly lead with entity accuracy rather than word error rate, and why WER alone is a poor predictor of whether a product feels accurate to the person using it.

Final words

The video platforms winning on AI features right now aren't the ones with the best models. They're the ones that decided early which layer of the stack was theirs.

Veed's team said it directly: buying the speech layer let them spend their engineering on the collaborative editor and on shipping it fast. That's the trade. Transcription accuracy is a research problem with a moving frontier, funded by companies whose entire business is that frontier. The browser-based timeline, the collaboration model, the export pipeline, the thing your users actually chose you for — nobody else is building that.

So the question worth asking isn't "should we add AI features to our video editor." It's which layer you want to own, and whether the API underneath gives you word-level timing precise enough to build on. Everything else in this article follows from that one field in the response.

Pricing runs $0.21/hr on Universal-3.5 Pro and $0.15/hr on Universal-2, billed per second with no minimums and unlimited concurrency, with Speech Understanding features priced individually on top. Full rates are on the pricing page.

Frequently asked questions

AssemblyAI vs deepgram: what segments are they ideal for and why?

AssemblyAI is built for products where the accuracy of specific words — names, companies, products, identifiers — determines whether the feature works, which covers video tooling, meeting and sales intelligence, contact centers, and healthcare documentation. On Pipecat's open benchmark of real voice agent conversations, Universal-3.5 Pro Realtime posted a 6.99% pooled word error rate against 15.58% for Deepgram Flux, with entity error at 15.31% and phone numbers at 3.55%. For a video product the practical difference shows up in search: the transcript that gets the speaker's surname right is the one a user can find later. Full methodology is on our benchmarks page.

What is the best API for speaker diarization?

Judge diarization on hard audio, not clean turn-taking — overlapping speech, late joiners, and mismatched mic quality are where models separate. Universal-3.5 Pro ships our most accurate speaker diarization to date on pre-recorded audio at a $0.02/hr add-on, and Universal-3.5 Pro Realtime adds diarization with revision at $0.12/hr: it labels speakers live, re-clusters when the stream ends, and issues a correction within roughly half a second, supporting up to 10 speakers. Implementation details are in the speaker labelling docs, and we published how to measure diarization accuracy yourself with cpWER so you can run the comparison on your own footage.

Best API for video transcription and subtitles at scale

At scale the deciding factors are per-second billing, unlimited concurrency, and no upfront commitment, because video libraries arrive in bursts rather than at a steady rate. AssemblyAI runs $0.21/hr on Universal-3.5 Pro and $0.15/hr on Universal-2, billed per second with no minimums, and SRT and VTT export costs nothing extra on a completed transcript. Caption width is controllable with the chars_per_caption parameter, and an EU endpoint is available at api.eu.assemblyai.com at the same price if data residency is a requirement.

Best speech-to-text API: AssemblyAI vs Deepgram vs Google vs AWS

Run all four on your own audio before believing anyone's benchmark, including ours — the right answer depends heavily on your domain. That said, the comparison worth making is on entity accuracy rather than raw word error rate, since a model can score well on function words while missing the names and numbers your users search for. Our published async results put English mean WER at 5.6% and median at 4.9% across 26 datasets and more than 80,000 files, and on medical audio Medical Mode records the lowest missed entity rate of the providers we benchmarked, including Deepgram, Speechmatics, AWS and Google. Methodology and datasets are on the benchmarks page.

Does AssemblyAI provide customizable summary types?

Yes — Speech Understanding Summarization takes a summary_type parameter of either paragraph or bullets. Paragraph returns prose summaries per topic section and is the better fit for chapter descriptions; bullets returns terse points, which works better in a compact UI. There's also an effort parameter, low by default, that you can raise to medium for harder content — long files past roughly 90 minutes, multilingual audio, or recordings where a missed detail is expensive. Either way, every section comes back with its own headline, start and end. Summarization is $0.03 per hour of audio.

Can I generate video chapters automatically now that Auto Chapters is deprecated?

Yes — Speech Understanding Summarization replaced the auto_chapters parameter, which is deprecated, and it returns exactly what chapters returned: the transcript split into ordered topic sections with a headline and timestamps. Pass a summarization object under speech_understanding.request instead of the top-level boolean, and read sections from speech_understanding.response.summarization.summary rather than the top-level chapters array. Per section, the old summary field is now text and gist is gone — use headline for a short label. The migration guide has a field-by-field mapping.