What Embeddings does with a Markdown file, and where an audio recording fits
Embeddings takes Markdown through a chunk of text and nothing else, since that is the entire input, and that is the door this page is about. The question worth asking is what an audio recording looks like by the time it arrives, because Embeddings never sees the recording itself. It sees whatever the conversion handed over, and nothing else.
Embeddings averages meaning across whatever it is handed, furniture included. What comes out of an audio recording here is the speech as punctuated paragraphs, cut where the speaker actually stopped, so the structure Embeddings is looking for is actually present instead of merely implied. That is the entire trick, and there is nothing clever underneath it.
What survives from an audio recording into Embeddings
The conversion drops silence, room tone, and the stretches where nobody says anything at all before Embeddings ever sees them, because none of it carries meaning into Embeddings and all of it costs something. What it keeps is the part you would otherwise have retyped by hand.
- The words come through punctuated, which is most of the work, and Embeddings receives it as structure rather than as something to infer.
- The breaks between turns of speech are kept, so a handover is visible, which is the part Embeddings would otherwise have to reconstruct on its own.
- Figures said out loud are written as digits rather than spelled out, so nothing downstream of Embeddings has to guess at it.
What Embeddings never gets back from an audio recording
Every conversion costs something, and a page listing only the gains is a sales page. Here is what an audio recording gives up on the way to Embeddings, stated plainly enough that you can decide against it.
- Tone, emphasis and hesitation are gone, and they often carried the meaning, and Embeddings will not get it back from anywhere.
- Speaker names are not attached, because nothing in the audio declares them, so nothing you build in Embeddings should depend on it.
- The recording itself is not kept, so the transcript is all there is afterwards, which is a real cost inside Embeddings and not a rounding error.
The Audio to Markdown step behind this Embeddings page was measured
Groq does the conversion, and it was proven end to end on 30 August 2026: a recording made for the occasion, never transcribed anywhere before, came back with its witness word and its figure, split into two turns of speech. That is one run on one day and not an average, which is exactly why the date travels with the claim instead of being left out of it. The same output is what Embeddings would have received.
A crowded room and a cheap microphone cost accuracy, and nothing in the output marks which words were guessed. Embeddings has no way of telling you any of that from the inside, so it gets said here instead, on the page that sent you there.
Once the recording is Markdown, what to do inside Embeddings
With the Markdown in hand, check a few nearest neighbours by hand before embedding an entire corpus. That is a Embeddings habit rather than a conversion setting, and it is where most of the value of converting an audio recording in the first place actually lands.
The constraint worth knowing before you start: the vector has no idea which words were content and which were layout. It bites the same way whether the Markdown came out of an audio recording or out of something else entirely, but it bites hardest on long documents, which is what an audio recording usually is.
What we will not pretend about Audio in Embeddings
- Embeddings will not let you inspect a vector, only compare it to another one, and converting an audio recording first does not change that in the slightest.
- page furniture repeated across chunks pulls unrelated passages toward each other, which is a Embeddings behaviour rather than anything the recording conversion did.
- The conversion costs half a credit and the price is shown before the recording is sent, whether the Markdown ends up in Embeddings or nowhere at all.