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swimming987
swimming987

Posted on AI-assisted

How I Turn Audio and Video Files into Timestamped SRT Subtitles

Most transcription tools try to handle everything: meetings, YouTube links, podcasts, summaries, chat, collaboration, and a dozen export formats.

But in a lot of real workflows, the problem is much simpler:

You already have an audio or video file on your computer, and you want to turn it into clean, timestamped text.

That might be a podcast episode, a recorded interview, a class recording, a voice memo, a product demo, a webinar export, or a short video clip. The useful output is often not a fancy dashboard. It is a transcript you can search, edit, quote, or turn into subtitles.

While working on this workflow, I built a small web tool here:

https://videotranscriber.co/

The goal was to keep the process focused:

  1. Upload a local audio or video file
  2. Detect or choose the spoken language
  3. Generate timestamped transcript lines
  4. Review the transcript with playback
  5. Export TXT or SRT

Why timestamps matter

Plain text transcription is useful, but timestamps make it much easier to work with the original media.

If you are reviewing a long interview, you often need to jump back to the exact moment where someone said something. If you are creating subtitles, you need the transcript to preserve time information. If you are editing content, timestamps help you find the source section without scrubbing through the entire file.

That is why I focused on timestamped lines instead of only returning a block of text.

In the tool, each transcript line can be used as a reference point. You can read the transcript, jump through the media, and then export the result as either TXT or SRT.

Supported file formats

The tool works with common local audio and video formats:

  • MP3
  • MP4
  • MPEG
  • MPGA
  • M4A
  • WAV
  • WEBM

This is intentionally file-based. It does not currently support pasting a YouTube or external video URL.

That limitation is deliberate for now. A lot of people already have the file they need to process: a Zoom export, a phone recording, a podcast file, a downloaded clip, or a local video draft. For that workflow, uploading the file directly is simpler and more predictable.

Language support

Another important part of transcription is language handling.

The tool supports 99 spoken languages and can auto-detect speech, or you can choose the language before starting. Auto-detect is convenient when you are not sure, but manually choosing a language can be useful when accuracy matters.

For clear recordings, this works well for common use cases like:

  • meeting notes
  • interviews
  • podcasts
  • voice memos
  • lectures
  • saved videos
  • subtitle creation

As with any transcription tool, quality still depends on the source audio. Clear speech in a quiet room works much better than overlapping voices, heavy background noise, or low-quality recordings.

Exporting TXT and SRT

I wanted the exports to cover two common workflows.

TXT is useful when you want readable notes, searchable text, quotes, drafts, or content repurposing.

SRT is useful when you want subtitles. Many video editors and publishing platforms can work with SRT files, so this format is a practical bridge between transcription and editing.

That is why the first version focuses on TXT and SRT instead of trying to support every possible export type.

Billing by minutes

For pricing, I used a simple minute-based model.

One credit equals one billed minute, rounded up. So a 30-second file costs 1 credit, while a 30-minute and 1-second file costs 31 credits.

New accounts get 20 free minutes to try the workflow.

This makes the cost easier to understand than charging by file count, because audio and video files can vary a lot in length.

Current limitations

There are still several things I may improve later.

The tool does not support YouTube link import yet. It also does not have batch upload, folders, sharing links, speaker separation, or DOCX / VTT export right now.

For now, the focus is narrower: upload your own file, get a timestamped transcript, review it, and export TXT or SRT.

That smaller scope makes the workflow easier to reason about, especially for people who just need to process one recording at a time.

Try it

If you often work with audio or video files and need transcripts or subtitles, you can try it here:

https://videotranscriber.co/

I would be interested in feedback on the workflow, export formats, and whether TXT/SRT is enough for most use cases or if formats like VTT, DOCX, or Markdown should come next.

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