AI Speech to Text: Transcribe Audio Step by Step (2026)

If you need to turn speech into text with AI (transcribe an hour-long interview, pull the decisions out of a call recording, turn a pile of voice messages into readable notes or put together subtitles for a video), in 2026 this is no longer an all-night manual job. A speech recognition model (STT, speech-to-text) listens to the audio and returns ready text in minutes. This article shows how to convert audio to text in Pixyn step by step and which tasks it handles well.

Pixyn is a content generation service: images, video, text and audio. Speech recognition is available in the web Studio, next to image generation and voice-over, so the whole work cycle stays in one window.

TL;DR

• Speech to text = upload audio or video, and the model returns a text transcript. No manual typing.
• Good for: interviews, podcasts, call recordings, lectures, voice messages, subtitle drafts.
• In Pixyn this is done in the Studio, next to text generation and voice-over.
• New accounts get welcome tokens to run your own real audio and judge the quality before paying. Plans are on the pricing page.
• The reverse task is to voice a text (text-to-speech): see how to voice a text with AI. STT and TTS are two sides of the same coin.

What AI speech recognition (STT) is

Speech recognition is a technology that turns sound into text: the model analyzes the audio stream, picks out phonemes, words and pauses and assembles them into a coherent transcript. It is called speech-to-text (STT) or ASR (automatic speech recognition). It is the exact reverse of speech synthesis (TTS), where text is turned into a voice-over.

Modern STT models are not the "dictaphone with hints" of ten years ago. They are trained on huge amounts of live speech, so they cope with conversational pace, "uhm" pauses and unfinished phrases. It is still a statistical model: it picks the most likely text for the sound rather than "understanding" the meaning, which is why the source recording matters so much.

What it is for in practice:

• Interview transcription: turn a conversation recording into text to quote and edit instead of listening again.
• Call and meeting recordings: pull agreements, tasks and decisions out of an hour-long meeting.
• Voice messages: when someone sent 12 minutes of audio and reading is faster than listening.
• Subtitles and timecodes: a draft text base for videos and Reels.
• Lectures, webinars, podcasts: notes and searchable text from a long recording.

How to turn speech into text with AI in Pixyn: step by step

Below is the basic "upload audio, get text" scenario. It is done in the web Studio, no separate program needed.

1. Sign up. Open the sign-up page and create an account. New accounts get welcome tokens, enough to run a real file and judge the transcription quality before any payment.
2. Go to the Studio. Open the Studio, the work window that brings together text, image, video and audio generation, including speech recognition.
3. Choose the speech recognition mode (audio to text). In the Studio pick the "speech to text" or audio transcription task. It is a separate mode from voice-over (text-to-speech), so do not mix up the direction: here sound goes in and text comes out.
4. Upload the file. Attach audio (an interview, a call, a voice message) or a video with a soundtrack. The cleaner the recording, the higher the accuracy; more on that in the preparation section.
5. Run the transcription and wait for the result. The model processes the audio and returns a text transcript. Long files take more time, which is normal.
6. Proofread and correct. Go through the text: check names, company names, terms and numbers, since these are what the model most often hears imprecisely. Add paragraphs by meaning if needed.
7. Use the result further. Copy the text into a document, cut it into quotes, build notes. Want a summary right away? Give the transcript to a text model in the same Studio and ask for a short summary or a task list.

Preparing audio: how to raise recognition accuracy

The quality of a transcript is about 80% determined by the quality of the source recording, not by the "magic" of the model. Before you convert audio to text, spend a minute on preparation; it will save hours of proofreading.

• Record close to the sound source. A lavalier mic or a headset on the speaker beats a recording "from the back of the room" many times over. Echo and reverberation are the main enemies of STT.
• Kill the background. An air conditioner, the street outside, music, cafe noise: the model tries to "hear" all of this as words. A quiet room means cleaner text.
• One voice at a time where possible. When two people talk at once or interrupt each other, STT gets confused about who said what and merges the lines. For interviews, a "no interrupting" culture is better.
• Normal volume. A recording that is too quiet loses endings and soft words. If you can barely hear it, the model will "underhear" it too.
• One language per file. If the recording keeps switching between languages, accuracy drops; it is steadier when there is one main language.

There is no perfection: even a clean recording leaves some proofreading. But the difference between "recorded on a laptop's built-in mic in a noisy open space" and "a lavalier in a quiet room" is the difference between "rewrite half of it" and "fix a couple of names".

Transcribing interviews, calls and voice messages: typical scenarios

Here is how AI speech recognition fits specific tasks.

Interviews and podcasts. Upload the recording and get a text base that is convenient for finding quotes and editing. Then you can give the transcript to a text model in the Studio and ask it to pick out the speaker's key points. The saving is hours of listening.

Call and meeting recordings. From an hour-long meeting you usually need three things: what was decided, who is responsible for what, and the deadlines. First the full transcript, then a short summary and a task list from the text. Double-check names and figures (amounts, dates): this is where STT errs most often.

Voice messages. When you received a long audio and need to answer to the point, a transcript turns 12 minutes of sound into text you read in a minute.

Subtitles and video content. STT gives a draft text track for subtitles. It is exactly a draft: timings and wording for video almost always need manual polishing, but you no longer type from scratch.

Batch processing with Workflow. If there are many files (dozens of voice messages or a batch of recordings for the week), uniform processing is convenient to build as a pipeline in Workflow in Pixyn: set up the chain once and run the files one by one without repeating manual steps. For a one-off transcription the Studio is enough; Workflow pays off on a stream. See the Workflow page.

What it costs in tokens

ElevenLabs Speech to Text: from 2 tokens; Whisper: from 6. Recognition is charged by the length of the recording.

The studio shows the exact charge before you run it, so check against it rather than the article: the catalog gets updated. Pack contents are on the pricing page.

FAQ: AI speech to text

Which formats can I upload for transcription? Audio recordings and videos with a soundtrack: interviews, calls, voice messages, lectures. The main thing is audible speech in the recording; the cleaner the sound, the more accurate the text.

Which languages are recognized? Whisper and ElevenLabs Speech to Text recognize speech in many languages, including English. Accuracy is higher on a clean recording without noise. Check names and terms after transcription.

How much does it cost? New accounts get welcome tokens, so you can run your own real audio and judge the quality before paying. Current plans are on the pricing page.

Can I get a short summary of a call recording right away? Yes. First make the transcript (speech to text), then give this text to a text model in the same Studio and ask for a summary, a list of decisions or tasks.

What should I do if the transcript has many errors? Most often the cause is the source audio: noise, echo, a quiet or "interrupting" recording. Re-record closer to the mic, in a quiet room, one voice at a time, and accuracy will rise noticeably. Always check names, brands and numbers by hand.

Is it suitable for subtitles? As a draft base, yes. STT gives the text that is then polished for the video (timings, wording). You no longer need to type subtitles from scratch.

Can I do the reverse and voice a text? Yes, that is the reverse task (text-to-speech). How to do it: the article how to voice a text with AI.

Try transcribing your own audio

The best way to see whether STT suits your recordings is to run a real file. Upload an interview, a call or a voice message and look at the result yourself.

1. Sign up on the sign-up page and get welcome tokens.
2. Open the Studio and choose the speech recognition mode (audio to text).
3. Upload your audio, run the transcription, proofread names and terms.
4. Plans for regular work are on the pricing page.

Questions about the service: support at @pixyn_support.

---

Read more on audio and AI:

• How to voice a text with AI in 2026: the reverse task, text to voice (TTS).