Audio to text: how to transcribe a recording in the browser
The 'Audio to text' tool transcribes an uploaded audio recording using a local speech recognition model running directly in the browser. Sound is not sent to external servers for recognition — processing takes place on your device. This affects privacy and speed, but does not cancel limitations: quality depends on recording purity, background noise, accent and terminology. In this guide we will go through how to prepare audio, select a language, formulate an instruction, check the result and export the text. Lecture and interview scenarios are fictional examples, but the steps apply to real tasks: transcribing dictaphone notes, voice messages or meeting fragments.
Open tool: Audio transcription ↗What exactly the tool does and how it is built
'Audio to text' is a web form with text fields. You upload an audio file (up to 20 MB and up to 5 minutes), optionally select a speech recognition language, enter an instruction in a text field and press the Generate button. The result appears in a separate panel as plain text, which can be copied or downloaded as TXT. No sliders, style presets or change levels here — just a textual instruction and language.
Important to understand: the model works locally in the browser, so audio is not sent to an external AI service. This reduces the risk of leakage, but does not cancel limitations: quality depends on recording purity, presence of background noise, accent and terminology. The model may make mistakes, and queue and availability may vary.
The tool does not perform batch processing, does not automatically save history and does not integrate with other services. Export is available only in TXT, also you can get SRT subtitles. This is worth considering when planning work.
Example instruction: 'Transcribe audio to text, preserving punctuation and paragraph breaks. Proper nouns write with a capital letter'.
How to change criteria to get different variants
The same audio file can be processed several times, each time changing the instruction. For example, the first option — verbatim transcription preserving filler words and repeats. Second — literary processing: remove repeats, set punctuation, split into paragraphs. Third — notes: only key ideas and conclusions. This way you get three different texts from one source.
You can also change the speech recognition language. If the recording is in Russian but contains English terms, try specifying Russian and adding to the instruction: 'Leave English terms in English'. Or vice versa — choose English if the majority of speech is in English. Compare the results.
Another criterion is the output format. Request the text formatted as a dialogue with speakers indicated, if the recording has multiple voices. Or as a list of theses. Or with timestamps (if the model supports — check if it outputs). Each option will differ in structure.
Three instructions for one audio: 1) 'Verbatim transcription preserving all words'. 2) 'Literary processing: remove repeats, set punctuation'. 3) 'Brief notes: only main ideas'.
Step-by-step example: transcribing a lecture
Suppose you have a 4-minute recording of a lecture on climate (a fictional example). You upload the file, select the language 'Russian' and enter the first instruction: 'Transcribe audio to text, preserving pauses as paragraphs'. Press Generate, you get option 1 — raw text with remarks and repeats.
Then change the instruction to 'Literary processing: fix punctuation, remove filler words, split into meaningful paragraphs'. You get option 2 — more readable. Third time enter: 'Make a summary of 5 points with key theses'. You get option 3 — a condensed summary.
Now compare the three texts. Option 1 is useful for exact quotations, option 2 — for publication, option 3 — for quick familiarization. Choose the one that matches your task and if necessary refine manually: correct names, terms, numbers.
Option 3 result may look like this: '1. Climate is changing faster than predicted. 2. Main factor — CO2 emissions. 3. Urgent measures needed...'
How to check transcription result
Checking starts with listening to audio fragments and comparing with the text. Pay attention to proper names, numbers, dates and special terms — this is where the model often makes mistakes. If the recording has background noise or multiple speakers at the same time, there will be more errors.
Compare the three variants among themselves. If in one variant a term is written correctly and in another — it is not, this is a signal that the model is not sure. Check by context. Also useful to check logic: are whole sentences skipped, are there repeats you did not ask for.
For SRT subtitles check the timestamps: they may shift on long pauses. If accuracy is critical, use option 1 (verbatim) as a basis and edit manually. Do not rely on automatic punctuation setting without checking.
Check: play audio at 1:23 and compare with text. If in text 'climate' instead of 'climate', correct it.
Limitations and typical errors
The tool does not guarantee perfect accuracy. Local model may confuse words sounding similar, skip endings, incorrectly determine sentence boundaries. Length limitation — up to 5 minutes and 20 MB — means long recordings must be split into parts and processed separately.
No batch processing, automatic saving and integrations. Export only in TXT and SRT. Cannot upload a document for OCR or get PDF. This narrows use scenarios, but for one-off transcription it fits.
Do not expect the tool to recognize speech with strong accent or rare language as well as common ones. Also it does not clone voice and does not generate audio — this is only text from audio.
If the recording is longer than 5 minutes, split it into parts of 4 minutes and process each separately, then join the texts.
How to refine the selected variant
After choosing the best variant edit it manually: correct errors, add missing fragments, bring to a uniform style. If needed, split into chapters or add headers. For subtitles check synchronization and if necessary adjust timestamps in a text editor.
If you plan to use the text for publication, make sure it does not contain personal data not intended for distribution. Local processing reduces the risk of leakage, but responsibility for the content remains with you.
For recurring tasks create an instruction template that gives the best result for your type of audio. For example, for interviews — 'divide into questions and answers', for lectures — 'highlight definitions and examples'. This will save time.
Interview template: 'Transcribe audio, designate speakers as Q and A, preserve order of replies'.
The 'Audio to text' tool gives you the option to get several transcription variants from one audio by changing instructions and language. This is useful when you need different formats: verbatim text, literary processing or notes. However the result requires checking, especially as regards names, numbers and terms. Take limitations into account: length up to 5 minutes, export only TXT and SRT, no batch processing. Use local processing as a draft, and do final editing manually. Such an approach saves time and improves the quality of the final text.
Open tool: Audio transcription ↗Interface translations are machine-assisted and may need refinement.