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MP3 to text free & accurate

By ShinobiTools Team · Last updated: July 2026

Turn any MP3 into accurate text for free: a downloaded podcast, a recorded lecture, a voice note. Upload it and get the transcript back in seconds, no account, no watermark, no per-minute fee.

Transcribe audio now →

Any MP3, straight to text

Most "free" transcribers cap you at a few minutes a day or stamp a watermark on the output until you pay. ScribeGrab runs Whisper large-v3 on our own GPU, so there's no cloud bill per minute and no daily cap: individual files up to 90 minutes and 2 GB, about 100 languages auto-detected, and your upload is deleted the moment it's done.

MP3 is the format most recordings end up in, so ScribeGrab reads it directly, no converting first. Got a different file? WAV, M4A, FLAC, OGG, AAC and common video files all work the same way. See the general audio to text workflow for the full list.

How to convert MP3 to text

1. Open ScribeGrab and click Open the tool.
2. Drag your MP3 in (up to 90 minutes / 2 GB).
3. Let Whisper large-v3 transcribe it on our GPU.
4. Download the transcript as TXT, plus SRT/VTT if you want subtitles.

How accurate is it

On clear speech the transcript is very close to word-perfect. Strong accents, overlapping voices or noisy recordings are harder for every tool, so give those a quick proofread. We wrote an honest breakdown of how accurate AI transcription really is.

Bitrate matters less than you think. The room matters more.

The first thing people ask about an MP3 is whether it's high enough quality to transcribe. Almost always, yes. Speech recognition doesn't listen the way you do: the audio is resampled down to 16 kHz mono before the model ever sees it, because that's where the information in human speech lives. A 320 kbps stereo file and a 128 kbps mono file end up in nearly the same place, and even 64 kbps voice recordings usually transcribe fine. Bitrate is the wrong thing to worry about.

What actually decides your accuracy is the recording itself, and in rough order of damage: distance from the microphone (a phone on the table two metres away loses more than any codec ever will), overlapping speech, hard reverberation from a bare room with parallel walls, and steady background noise like air conditioning, traffic or a laptop fan. A close-miked 96 kbps recording beats a room-miked lossless one every time.

Which means the useful improvements are things you do before recording, not after. Move the recorder closer to whoever is talking. If two people are speaking, put it between them rather than in front of one. Turn off the fan. And if you already have the file and it's rough, transcribe it anyway: it's free and takes minutes, and you'll learn more from reading the result than from guessing whether it's worth trying.

MP3, or one of the other formats you might have

MP3 is the format people mean when they say "audio file", but it's rarely the only copy you have, and sometimes another one is better. If you have both, use the list below.

• WAV or FLAC — uncompressed or losslessly compressed. Use these if you have them; they're bigger but nothing has been thrown away yet. FLAC is roughly half the size of WAV for identical audio.
• M4A / AAC — what iPhone voice memos and most modern recorders produce. Slightly more efficient than MP3 at the same bitrate. Upload directly, no conversion needed.
• MP3 — universal, and fine at any bitrate from 64 kbps upwards for speech.
• OGG / Opus — what WhatsApp voice notes and Discord recordings use. Excellent at low bitrates. Upload as-is.
• MP4, MOV, MKV, WebM — video files. There's no need to extract the audio first; the audio track is pulled out server-side.

Converting an MP3 to WAV before uploading does nothing except make the file bigger, because the information the MP3 discarded doesn't come back. Skip that step. Files up to 90 minutes each go through in one pass, and you get a plain text transcript plus SRT and VTT subtitle files from the same run, whether the source was audio or video.

What you get back, and which file to use

One MP3 in, three files out, all from the same pass — pick whichever fits what you're doing:

• transcript.txt — plain running text, one line per sentence. This is the one you want for reading, searching, quoting, or pasting into a document.
• subtitles.srt — the same words with start and end timecodes in the format nearly every video platform and editor accepts. Useful even for pure audio: it's how you find where in a two-hour recording something was said.
• subtitles.vtt — the web-native flavour of the same thing, for an HTML5 player or a browser-based editor.

When more than one person is talking, the transcript also gets speaker labels. They're worked out from the voices themselves — the tool measures the character of each spoken segment and groups the similar ones together — so they're an informed estimate rather than a certainty. Two people with similar voices can end up merged, and one person who moves closer to and further from the microphone can be split in two. Treat the labels as a first pass you'll correct in a minute, not as a transcript of who said what in court.

Clean up the text in the right order

A transcript is never publishable as-is, whichever tool made it. Doing the tidy-up in this order takes a fraction of the time:

1. Fix the proper nouns first, with find-and-replace. Names, companies, places and jargon are what speech models get wrong most, and they get them wrong consistently — the same name is misheard the same way every time. One replace-all per name fixes twenty instances.
2. Then read for sentence boundaries. Punctuation is inferred from pauses and intonation, so a speaker who trails off gets a full stop in an odd place. This is a quick read-through, not a rewrite.
3. Correct the speaker labels next, once you know the names — before you start cutting text around, while the structure still matches the audio.
4. Only then edit for readability: drop the filler words, tighten the repeats, cut the false starts. Do this last, because every earlier step is easier against a verbatim text.

Two things save more time than any of the above. Keep the SRT open next to the text — when a passage looks garbled, the timecode takes you straight to that second in the audio so you can listen instead of guess. And if you're recording the audio yourself, get the microphone close to the speaker: a phone lying on the table an arm's length away costs you more accuracy than any setting can give back, and it's the one variable entirely under your control.

Common questions