Convert Audio to Text, Accurately

Free auto-caption tools are built to overlay video in real time, not to produce a document. The output is lowercase, unpunctuated, and treats every speaker as one continuous stream — fine for a few seconds of captions, unusable for a document you'll actually read or share.

Manually cleaning that output — adding punctuation, labeling speakers, fixing misheard names — can take longer than the recording itself.

There's also a second, quieter problem: most auto-caption engines throw away the audio the moment captions are generated, so if the wording turns out wrong later, there's nothing left to check it against. A transcript that isn't linked back to the original recording is a transcript you can't verify, which matters a lot more once it's being quoted in an article, cited in research, or used as a record of what someone actually agreed to on a call.

And manual transcription, the fallback for anyone who's tried the free tools and given up, has its own cost: a typical typist manages roughly a 3:1 to 4:1 ratio of listening time to typing time, so a one-hour interview can eat three or four hours of someone's afternoon before it's even proofread.

Every audio file is really two things stacked together: a codec, which is the method used to compress or store the sound data, and a container, which is the file wrapper around it. MP3 and AAC (the codec inside most M4A files) are lossy — they throw away audio detail a listener is unlikely to notice, which is exactly why they compress so well and stay small. WAV and FLAC are the opposite approach: WAV stores audio uncompressed, and FLAC compresses it losslessly, so both preserve every bit of the original recording at the cost of a much bigger file.

None of this changes how well a recording transcribes, because speech recognition cares about clarity, not file size. A well-recorded voice memo saved as a small M4A file will usually transcribe more accurately than a poorly recorded WAV file at ten times the size. What matters is the recording itself — how close the microphone was, how much background noise crept in, whether people talked over each other — not the three-letter extension at the end of the filename.

Transkio normalizes every upload internally before transcribing it, so you never have to think about codecs or containers at all. Drop in an MP3 from a podcast host, a WAV from a field recorder, an M4A from an iPhone, or an OPUS file exported from a video call, and the pipeline treats them the same way. If your file is video rather than audio-only — a Zoom recording, a screen capture, a downloaded webinar — the video to text page covers that case in more detail, including container formats like MP4 and MOV.

Microphone distance matters more than microphone price. A built-in laptop mic six inches from someone's mouth will usually out-transcribe an expensive microphone sitting three feet away across a conference table, because distance introduces room echo and drops the ratio of voice to background noise. If you have a choice, get the mic closer rather than better.

Background noise is the single biggest accuracy killer that's actually avoidable: a fan, an open window near traffic, a coffee shop, a second conversation in the same room. None of these are impossible to transcribe around, but they all reduce accuracy, and none of them are fixable after the fact — the recording is the recording. If you're capturing a phone call, a landline or a wired headset connection tends to transcribe more cleanly than a speakerphone in a noisy room.

Multiple speakers and overlapping speech are the other two big variables. A recording where people talk over each other is genuinely harder for any transcription system, human or AI, because the audio at that moment literally contains two signals at once. Where possible, encourage speakers to avoid interrupting, and if the conversation involves more than two or three people, a recording setup with each person closer to their own microphone (a conference room mic array, or separate phone lines dialed into the same call) will transcribe noticeably better than one shared mic in the middle of the table.

Accents and regional speech patterns are handled reasonably well by modern speech recognition, but heavy accents combined with poor audio quality compound each other. If you know a recording will have both — a phone interview with someone on a bad connection, say — it's worth budgeting a little extra editing time rather than assuming the first pass will be perfect.

Each export format exists for a different downstream use, and picking the right one saves a reformatting step later. TXT is the plainest option: just the words, good for pasting into a document, an email, or a search index. SRT and VTT are subtitle formats — timestamped blocks of text meant to be loaded into a video player or editing software so the words appear synced to the footage; SRT is the more universally supported of the two, while VTT is the web-native standard used by HTML5 video players. Both are available on every Transkio plan, including free.

DOCX is a formatted Word document, useful when a transcript needs to go to someone who'll read it in Word, add comments, or drop it into a larger report. JSON is the developer-facing option: the full transcript structure — words, timestamps, speaker labels — as structured data, meant for feeding into another tool or script rather than reading directly. DOCX and JSON exports, along with timestamps and speaker labels inside them, are available starting on paid plans.

A practical way to decide: if a human is going to read the transcript as a document, export TXT or DOCX. If the words need to appear on top of video, export SRT or VTT. If another piece of software needs to consume the transcript programmatically, export JSON.

The most common one is uploading a recording where the microphone is far from the speaker and expecting studio-quality output — accuracy tracks recording quality closely, and no amount of AI processing fully recovers a signal that was muddy to begin with. A close second is skipping the editing pass entirely: even a 95%-accurate transcript has one wrong word in every twenty, which adds up quickly in a document meant to be quoted or shared, so treat the first pass as a strong draft, not a finished one.

Another frequent mistake is not renaming speaker labels before exporting — "Speaker 1" and "Speaker 2" mean nothing to someone reading the transcript later, and the rename takes seconds once you know who's who. And for recordings with unusual proper nouns — company names, technical jargon, uncommon names — it's worth doing one deliberate pass specifically checking those terms, since they're the words a general-purpose speech model is most likely to guess wrong.

Transkio runs entirely in the browser — there's no app to download on desktop or mobile, and no plugin or extension required. Upload from a laptop, a tablet, or a phone, using whichever browser you already have open; the upload flow, the transcript editor, and the export buttons all work the same way regardless of device, though a larger screen makes line-by-line editing more comfortable for long transcripts.

The in-browser recorder works the same way: it uses your device's microphone directly through the browser, so recording a quick memo from a phone works without installing a separate recording app first. This is particularly useful for capturing something on the spot — a thought while walking, a quick note before a meeting — without needing to remember which app you last used to record something.

Because everything runs server-side once a file is uploaded, the processing itself doesn't depend on your device's power at all — a five-year-old laptop and a brand-new one transcribe an hour of audio in roughly the same time, since the actual transcription work happens on Transkio's infrastructure, not your machine.

Beyond uploading an existing file, Transkio can record straight from your microphone in the browser — no separate recording app, no exporting a file and re-uploading it. This matters for anything you want captured the moment it happens: a quick thought, a live conversation, a lecture you want transcribed as it's delivered rather than after the fact.

Recording sessions are chunked in the background as they happen, which is what makes the process resilient to a flaky connection — a brief network drop doesn't lose the whole recording, since already-captured audio has already been sent. Sessions are capped at 30 minutes on the free plan and longer on paid plans, a limit set to bound how long any single recording session runs rather than to cap how much you can record in total; starting a new session picks up where the last one left off.

Once a recording session ends, it moves into the same transcription and editing pipeline as an uploaded file — there's no separate 'recorded' track that behaves differently from an uploaded one. The transcript comes back the same way, with timestamps and, on plans with speaker detection, labeled turns.

For anyone who transcribes the same type of recording regularly — weekly team meetings, a recurring interview series, daily voice journal entries — a little bit of consistency in file naming and speaker renaming pays off quickly. Naming files with a consistent date format, and renaming speaker labels to the same names each time rather than starting from Speaker 1 and Speaker 2 fresh every session, turns a folder of one-off transcripts into something closer to a searchable archive.

It's also worth deciding upfront which export format you'll standardize on for a recurring workflow — TXT for a simple archive, DOCX if transcripts get shared with people who expect a Word document, or JSON if they feed into another tool — rather than switching formats each time and ending up with an inconsistent collection.