Audio to text: how to improve the recognition result
The 'Audio to text' tool allows transcribing an uploaded audio recording using a local speech recognition model running directly in the browser. This is convenient for confidential recordings, but the first result often contains errors, extra pauses and missing punctuation. In this guide we will look at how to improve the rough draft into ready-to-use material, using only the tool's real capabilities: uploading audio, choosing a language, generating text and exporting to TXT. All examples are illustrative and are not a guarantee of a specific result.
Open tool: Audio transcription ↗Understanding the tool's capabilities and limitations
The 'Audio to text' tool works entirely in the browser: the audio file is not sent to a remote server, but processed by a local speech recognition model. This is important for privacy, but imposes limitations: maximum file size - 20 MB, duration - up to 5 minutes. If the recording is longer, it must be split into parts. There is also no batch processing, automatic saving or integrations.
After pressing the 'Generate' button you get one result in a text field. It can be copied or downloaded in TXT format. Other formats such as DOCX or PDF are not supported. There is also no change highlighting or version comparison function. Therefore all edits must be made manually in an external editor.
The model may make errors: confusing sound-alike words, skipping endings, incorrectly determining sentence boundaries. Quality depends on the clarity of speech, presence of background noise and accent. Do not expect a perfect result on the first try. The purpose of this guide is to show how to systematically improve the draft.
Load the file interview.mp3 (up to 20 MB), select language 'Russian', press 'Generate'. Get text in the right panel.
First pass: quick evaluation and segmentation
Having received the rough draft, do not start editing every word immediately. First read it through to understand the general meaning and structure. Pay attention to obvious omissions or nonsensical fragments - they may indicate audio problems. If the recording contains multiple speakers, mark where the speech changes so you can later assign replies.
Divide the text into paragraphs by meaning. The local model often gives a continuous stream without paragraphs. Highlight the introduction, main part and conclusion. This will help during subsequent editing. If there were pauses in the audio, they may be marked with dots or commas in the wrong places. Fix the sentence segmentation.
At this stage it is useful to save the draft in a separate file. So you will have the original version for comparison. Do not delete it until you finish editing. If the audio is long, split it into meaning blocks and work sequentially.
Draft: 'privet segodnya my obysnems new metody obucheniya kak vy znayete obucheniya eto process'. After segmentation: 'Privet! Segodnya my obysnems new methods of learning. Kak vy znayete, obucheniya - eto process.'
Correcting recognition errors
Main types of errors: incorrect endings, confusion with homophones (e.g., 'priti' and 'pridti'), missing prepositions, incorrect proper names. Reading the text aloud makes it easier to spot inconsistencies. If a word sounds strange in context, check it against the audio. For this it is convenient to use timecodes, if available, or just re-listen to the fragment.
Pay special attention to terms and professional vocabulary. The local model may not know specialized words. Make a list of recurring errors and replace them automatically in the text editor. But be careful: automatic replacement may ruin correct words. Better check each occurrence.
If the audio has names, company names or abbreviations, verify their spelling with a reliable source. Do not rely on guesses. In case of doubt leave a note and return to it later. Remember that the model does not have contextual knowledge and cannot fix semantic errors.
Draft: 'my ispolzuyem metot gradientnogo spuska dlya optimizatsii funktsii poteri'. Error: 'gradientnogo spuska' may be recognized as 'gradientnogo spuska'. Check against the audio.
Punctuation and formatting
Local transcription often does not set punctuation or does it inconsistently. You need to manually add periods, commas, question and exclamation marks. Rely on the intonation in the audio. If the speaker pauses, there is likely a comma or period there. But do not overdo it: excess punctuation makes reading harder.
Bring the text to a uniform style: if it is an interview, highlight speakers' replies with a new line and add a dash. If it is a monologue, split into paragraphs by topics. Remove filler words if they do not carry meaning, but only if this does not distort the speech. In official documents it is better to keep everything as is.
For subtitles (SRT) the tool does not create a file, but you can manually break the text into short lines. However this requires separate work. Within this guide we focus on plain text. Do not forget about capital letters at the beginning of sentences and in proper names.
Draft: 'kak dela u tebe vse khoroshe'. After editing: 'Kak dela? U tebe vse khoroshe?'
Checking meaning and logic
After fixing errors and setting punctuation read the text again, but now watch for logic. Make sure all pronouns are consistent, verb tenses do not jump, and causal links are not broken. Sometimes the model omits whole phrases, and the text becomes incoherent. In such a case go back to the audio and restore the missing.
If there were numerals in the recording, check them. The model may write 'five' as '5' or vice versa. Bring to a uniform format. The same applies to dates and units of measurement. In scientific or business texts this is critical.
Ask a colleague to read the ready text. A fresh look will help notice what you missed. If this is not possible, set the text aside for a while, then read it again. This will allow you to evaluate it more objectively.
Draft: 'v 2023 godu my grew na 20 protsentov'. Check whether it was '20 percent' or 'twenty percent' in the audio. Bring to a uniform style.
Export and further use
When the text is ready, copy it from the result panel or download it in TXT format. This file can be opened in any text editor. If you need another format, convert it manually. Remember that the tool does not support export to DOCX or PDF, and does not save history.
For further work you can use text processors that support spelling and grammar checking. But do not rely on them blindly: they may not know the context. Better to read the text yourself.
If you plan to publish the text, make sure you have permission from the speakers. Local processing does not remove copyright questions. Also note that the model may make mistakes, and you are responsible for the accuracy of the final material.
Download file result.txt, open in Word, apply heading styles and save as DOCX.
Editing text after local transcription is a sequential process: evaluation, segmentation, error correction, punctuation, meaning check and export. The 'Audio to text' tool gives a draft, but does not guarantee an ideal result. Use it as a first step, and do the final edit manually. Remember the limitations: file size, duration, no batch processing. Observe confidentiality and copyright. Following this guide, you will be able to turn raw transcription into neat and useful text.
Open tool: Audio transcription ↗Interface translations are machine-assisted and may need refinement.