AI video transcriber

Transcribe video and audio into text, captions, and clips

Point a pipeline at any video or audio file and get an accurate transcript with timestamps back. Turn it into burned-in captions, an SRT file, social clips, or titles and descriptions, in the same run.

Transcribe a video->See pricing

Word-level timestamps with Whisper

Whisper transcription returns word-level timing, which is what makes everything downstream possible: captions that land on the beat, clips cut on sentence boundaries, and quotes you can find by timecode.

SRT out of the box

Every timed transcription also produces an SRT file, so you can carry subtitles to YouTube, your editor, or a client deliverable without reformatting anything.

Built for long recordings

Long masters are transcribed in chunks automatically, so a two-hour podcast or webinar recording processes as reliably as a 30-second clip. Uploads support files up to 30GB with resumable multipart upload.

Fast text-only models when timing is not needed

When you just want the words, GPT-4o Transcribe and its mini variant return clean text quickly. Use Whisper when you need timestamps, the text-only models when you need a document.

Captions burned in, not bolted on

Wire the transcript into a captions node and the words are rendered into the video itself, with three styles, five positions, and karaoke-style word grouping tuned for vertical formats.

A transcript a language model can work with

The timestamped transcript feeds straight into a language model node that can find the best 30 to 60 second moments, write titles and descriptions, or summarize the recording, because it knows what was said and when.

How it works

From idea to finished video

Step 01
Bring in the recording
Upload a file, paste a direct media URL, or wire the output of another node. Video and audio both work, and big masters upload with resumable multipart.
Step 02
Pick the transcription model
Whisper for timed transcripts, captions, and clipping. GPT-4o Transcribe when you only need the text and want it fast.
Step 03
Run it
Long recordings chunk automatically and come back as one continuous transcript with segments, timestamps, and an SRT.
Step 04
Do something with it
Burn in captions, extract clips, generate metadata, or just copy the text. The transcript is a node output, so anything on the canvas can consume it.

Use cases

What people build with it

Podcasts into clip libraries

Transcribe an episode, let a language model mark the strongest moments, and cut them into captioned vertical clips in the same pipeline.

Captioning shorts and reels

Most short-form video plays muted. Transcribe the audio and burn in word-timed captions so the video still lands with the sound off.

Webinars and meetings into documents

Turn a long recording into a searchable transcript, then have a language model produce the summary, the action items, or the blog post.

SRT subtitle files for delivery

Client work and YouTube uploads often want a subtitle file rather than burned-in text. The timed transcript exports as SRT with no extra step.

Titles, descriptions, and tags from the transcript

Metadata written from what was actually said performs better than metadata guessed from a filename. The transcript feeds the metadata node directly.

Repurposing an archive

Old talks, streams, and tutorials become transcripts, and transcripts become clips, posts, and captions. One pipeline, run per file.

FAQ

AI video transcriber, answered

How do I transcribe a video with Treza?

Add a transcription node to a pipeline, wire in a video or audio source, and run it. The source can be an uploaded file, a direct media URL, or the output of another node like a Sequence or a text to speech node. Whisper models return text plus timestamped segments and an SRT; GPT-4o Transcribe models return text only.

How accurate is the transcription?

It runs on Whisper and GPT-4o class models, which are the current standard for speech to text, and clean audio transcribes very accurately. Heavy crosstalk, noise, or niche jargon will show up as errors, which is why the transcript stays editable before anything downstream consumes it.

How long can the video or audio be?

Long recordings are handled by design: files are transcribed in chunks and stitched back into one continuous transcript, so multi-hour podcasts and webinar masters work. Uploads support files up to 30GB with resumable multipart upload, so a full-resolution master does not need re-encoding first.

Can it transcribe YouTube videos by URL?

No. The pipeline takes your own uploaded files or direct media URLs, and it does not download from YouTube or other platforms. Point it at your own recordings: the export from your webinar tool, the master from your editor, or a direct MP4 link you host.

Does it produce subtitle files?

Yes. Timed transcriptions produce an SRT alongside the plain text and the segment list. Use the SRT as a deliverable, or skip the file entirely and burn the captions into the video with the captions node.

What languages can it transcribe?

Whisper is a multilingual model and handles major languages well, with English strongest. If you need the output in a different language than the recording, wire the transcript through a language model node to translate it before captions or delivery.

What can I do with the transcript besides read it?

This is where a pipeline beats a transcription website. The transcript is a node output, so in the same run it can become burned-in captions, an extracted highlight clip, a title and description for the upload, or a summary document. You configure that chain once and every future recording gets the same treatment.

How much does it cost to make ai video transcriber with Treza?

Treza runs on prepaid credits with no subscription. Each generation is charged at the model's own rate from your balance, and only successful runs are charged, so a failed generation costs nothing. A typical video generation settles around $1.06, and credit packs start at $5 and never expire.

Can I call this as an API instead of using the canvas?

Yes. Every pipeline can be published as a versioned HTTP endpoint. Call the typed /invoke endpoint with JSON in and JSON out, or point any OpenAI SDK at the OpenAI-compatible endpoint. Swap a model on a node later and the API your product calls does not change.

Do I need to know how to edit video?

No. Start from a template, change the topic, and run it. If you do want frame-level control, the timeline editor is there with multi-track video and audio, per-clip trims, fades, and volume, but nothing about the automated path requires opening it.

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