The three o'clock meeting just ended. Fifty minutes, six people on Meet, three important decisions, and a task list nobody wrote down in full. The next day the classic begins: who said what, who owns which task, and that number someone mentioned in passing that nobody quite remembers. The recording exists, sure, but it is fifty minutes of video. Nobody is going to watch it again. That is where knowing how to transcribe a meeting stops being a luxury and becomes the difference between an hour well spent and an hour lost.
Transcribing with AI is not just turning voice into letters. Any phone dictation does that. We are after something else: an ordered text, with the speakers separated, the topics grouped, and the decisions in plain sight, ready to read in two minutes instead of reliving the whole meeting. Let us go step by step, with two real cases, and at the end see how Qirava Transcribe handles it without overselling what it does.
01 · the materialGet the meeting's audio or video
It all starts with the source. There are two paths depending on how the meeting happened. If it was over Zoom or Google Meet and you turned on recording, you already have a video or audio file waiting in the cloud or your downloads folder: an .mp4, an .m4a, sometimes a link. If it was a phone call or someone recorded on their cell, you will have a voice note, maybe from WhatsApp, in .ogg or .mp3 format. Either one works. The AI does not need the video to transcribe; the audio is enough, though the video does no harm.
The recording is not the transcript. A fifty minute video nobody reopens is worth the same as a meeting nobody remembers.
One tip that saves pain: mind the audio at the source. Microphones near whoever is speaking, ask people not to talk over each other, avoid the cafe background. The AI transcribes clean voices very well and struggles with noise, just like you. You do not need a studio, but decent audio comes through better than one where everyone talks at once over the fan.
| Case | How it was recorded | What file you have | Where to find it |
|---|---|---|---|
| Zoom or Meet meeting | Recording turned on during the call | Video .mp4 or audio .m4a | Service cloud or downloads |
| Phone call | Phone or app recorder | Audio .mp3 or .m4a | Phone recordings |
| Voice note | Someone recorded and sent it by chat | Audio .ogg (WhatsApp) or .mp3 | The chat it arrived in |
| No prior recording | You record live as they speak | Microphone audio | The app you use to capture |
02 · to textUpload the file and get the transcript
With the file ready, the next step is running it through a transcription engine. This is where most people meet the easy part: you upload the audio, wait a few minutes, and text comes out. That raw text already has value, but it usually arrives as a brick: one huge paragraph with no clear stops, no idea who spoke, no separation of topics. It is like getting the meeting minutes written by someone who jotted it all down without breathing. Readable, but awkward.
What makes the difference is diarization: the system telling voices apart and labeling them, Speaker 1, Speaker 2, or better yet with names if it knows them. Suddenly the brick becomes a dialogue. You see it was the finance person who raised the objection and the operations person who took the task. That detail, which sounds minor, is what turns a transcript into something that truly reconstructs the meeting.
Raw text is a strip of words: all run together, no paragraphs, no idea who said what. Structured text has Markdown formatting: headings per topic, bullets for the decisions, the speakers separated. The first one you have to work on yourself. The second is already worked. Always ask for the second, because the time you save reading it is the reason you transcribed at all.
03 · to MarkdownStructure the text into topics, speakers and decisions
Here is the step almost everyone skips, and the one that separates a useful transcript from a dead file. Plain text, even with the speakers, is still long. Nobody is going to read fifty minutes of transcribed dialogue. What you need is for that text to be ordered into structured Markdown: a simple format of headings and lists that any person reads at a glance and any program understands.
The structure that almost always works for a meeting has three layers. First, the topics: each block of conversation grouped under a heading, budget, hiring, launch. Second, the decisions: a list of choices made, each on a bullet, no fluff. Third, the tasks: who owns what and by when. With those three layers, the fifty minute meeting reads in two, and nobody argues later about who said what because it is written down.
| Layer | What it holds | What it is for |
|---|---|---|
| Header | Date, participants, duration | Place the meeting without opening the video |
| Topics | Each block under its title | Jump straight to what you care about |
| Decisions | List of choices made | Close debates with evidence |
| Tasks | Owner, action and date | So nothing is left hanging |
| Dialogue | The transcript by speaker | Return to the detail when needed |
A good transcript does not force you to read all of it. It puts the decisions up top and the detail below, just in case.
04 · what to doThe result is not the end, it is the start
You have the structured Markdown. Now what? This is where the transcript proves whether it was worth it. Copying the decisions into the follow up email takes seconds because they are already a list. Moving the tasks to your board is a matter of dragging, because they already have owner and date. And if weeks later someone asks what was decided about the budget, you search the word and jump straight to the topic, no rewinding.
There is a use people discover late: structured text is the best fuel for asking the AI for more. With the transcript in Markdown you can ask for an executive summary, draft the formal minutes, or pull out just one person's commitments. The model works infinitely better on ordered text than on a brick. That is why structuring is not decoration: it is what opens up everything that comes after.
05 · at QiravaHow Qirava Transcribe does it
It is worth being honest about what we do and what we do not. Qirava does not sell a transcription app. What we build are services, chains that weave layers of AI together with human hands wherever each performs better, and the value is in joining many pieces at once, not in a loose feature. Transcribe is one of those pieces, and its job is to turn anything with voice or text into clean raw material for everything else.
Concretely, Transcribe takes any file or link and returns it as text and structured Markdown, not as a brick of words. A YouTube, TikTok or Vimeo video or audio by its link; a recorded meeting or a WhatsApp voice note; and documents you upload, Word, PDF, Excel, PowerPoint, images, CSV. It all comes in through the same door and comes out ordered. The voice engine runs on our own infrastructure, without sending your content to third party APIs, which for a work meeting is no small detail.
Two things matter about the plan. On the free plan the transcriptions are not stored: the text goes to your device and stays there, under your control. The premium plan processes in batch, handy when you have ten meetings piled up, and keeps what you convert for three months from its creation, with a warning before it expires, delivering it to a knowledge vault where it becomes searchable. Nothing more, nothing less than that.
The transcript is the first link. On its own it is a file; chained to what follows, it is finished work.
So if you keep one idea, let it be this: transcribing a meeting well is not capturing words, it is leaving the text so ordered that the next day's work is almost done already. Record or upload, ask for structure and not just text, and treat the result as the start of the chain, not the end. That is where a meeting stops evaporating and becomes something you can read, search and use.
Sources
- OpenAI (2022). Introducing Whisper. Documentation of the open source speech recognition model, a reference on automatic audio to text transcription. openai.com/research/whisper.
- Google Cloud. Speech-to-Text: Speaker diarization. Official documentation on diarization, the technique that tells apart and labels different speakers in a recording. cloud.google.com/speech-to-text/docs/multiple-voices.
- Matt Cone. Markdown Guide. Reference on Markdown as a lightweight structured text format with headings and lists, readable by humans and machines. markdownguide.org.