Questions this page answers
Is mp3 to text a file conversion?
No. An MP3 is a compressed container holding an audio stream, and turning it into text is speech recognition of that audio, not a format conversion. The browser decodes the MP3 to raw samples and a speech model reads them. Our container matrix holds the same two seconds of audio at 16,526 bytes as MP3 and 176,478 bytes as WAV PCM — an 11× size difference with the same words. The container changes how much disk the file takes, not what comes out as text.
What actually determines how accurate the text is?
The audio, not the file type. On our measured set the default tier scores 6.1% word error on clean synthetic speech, 10.7% on real speech with no noise, 8.8% with light noise, 34.8% with heavy noise, and 22.7% on a telephone-band clip. Same model, same 229 reference words; only the acoustic condition changes. The question of mp3 versus wav never enters the equation, because the recognizer hears the decoded audio either way.
Do these sites keep my MP3 private?
Of the seven readable results, 2 (Earscribe, Uniscribe) say the file is processed in your browser and never leaves your device. The other 5 say nothing about where the file goes, and four of those are explicit upload-and-process services. Saying it runs locally is not the same as being able to see it run locally. Our tool loads an open model into the tab and the file is never sent anywhere.
What does mp3 to text cost on a private tool?
Running it in the browser, the first visit downloads about 78.4 MiB for the model and the job uses roughly a gigabyte of memory; the audio file itself is not uploaded. The caps are 30 minutes of audio and 512 MiB per file, and for any standard audio it is the 30 minutes that binds.
Does aac to text need the file converted first?
No. A .aac file is usually the AAC audio on its own, with no wrapper around it, and this page reads it as it stands: a bare .aac and the same audio in an .m4a came back from the live page as byte-identical text. The two files carry the same 189 AAC frames (101,314 bytes of audio in both), and the .aac differs only by a 7-byte ADTS header in front of each frame, 1,323 bytes across the whole file. What the bare stream cannot carry is the bookkeeping: the box records how long the audio is and which samples at the front are the encoder's own priming, and ADTS has no field for either, so the browser hands back 12.096 s against 12.000 s, with the speech starting 64 ms later. The words are the same; the subtitle clock is not: same five cues, the last one ending at 12.100 instead of 12.000. On the first page for aac to text (ten results, nine readable, read 2026-10-26) one page says a .aac is a raw ADTS stream, and none of the nine puts a byte size on the file.
The container is the easy part
An MP3 is a compressed audio container. Turning it into text is speech recognition of the audio stream inside it, not a format conversion in the sense that Zamzar converts file types. The browser decodes the MP3 to raw samples and a model reads them. We read the first page of results for mp3 to text on 2026-10-05: ten organic results, ten distinct domains, the same ten from Bing and DuckDuckGo (DuckDuckGo draws on Bing’s index, so one count). 7 of 10 returned readable text; three answered HTTP 403 or were JavaScript shells. In those seven, the words convert and converter appear between 79 and 242 times per page. None of them tells you that the result depends on the audio, not the label.
- The MP3 is small and the browser reads it natively. In our container matrix the same two seconds of audio is 16,526 bytes as MP3 and 176,478 bytes as WAV PCM — an 11× size difference for the same words. That works out to about 8,000 bytes per second at 64 kbps, so a twenty-minute interview is only about 9.6 MB. Every one of the eight common audio formats (MP3, WAV, OGG, M4A, MP4, MOV, WebM, AAC) decodes in a real Chrome tab. An .ogg is a wrapper, and the extension does not say which codec is inside it: Vorbis and Opus both travel in one. The .ogg in our matrix carries Vorbis — 6,913 bytes for the same two seconds the MP3 spends 16,526 on — and it decodes to 2.00 s, 1 channel, 44,100 Hz, the identical row to the MP3, so an .ogg and an .mp3 of one recording reach the model as the same samples. What we have not done is put Opus-in-Ogg through the page; the Opus file we did test sat inside an .mp4 and was accepted in 24 seconds. If the file in front of you is an .ogg, the extension is not the question — the codec inside it is.
- So the format is the cheap step. Whatever the wrapper, the decoder turns it into the same PCM samples, and the recognizer hears those. The container decides how much disk the file takes and whether your player opens it. It does not decide what the words are.
- Transcribe mp3 is the same job. The phrase swaps the word order and changes nothing about the work; this page covers both, because the model does not know which verb you typed.
Counted on 2026-10-05 from our saved copies of the results page for mp3 to text. The 8,000 bytes per second is the definition of 64 kbps; the 16,526-byte MP3 and 176,478-byte WAV are read from container-codec-matrix.csv in the public repository, measured 2026-09-18 with a real Chrome window. The decode claim is from decode-format-support.csv. The ogg to text first page was read the same way on 2026-10-21: ten results, 9 of 10 readable, and unlike the mp3 to transcript set below, all 9 of 9 print the phrase. Two of the nine do tell the container story — Sonix on .ogg / .oga / .ogv sharing one Ogg box, Speak AI on Opus and Vorbis and on where OGG files come from — but 0 of 9 puts a measured size, a sample rate, or a channel count on an .ogg; the only byte figures on those pages are upload caps (5 MB at two of them, 500 MB at a third). The 6,913-byte .ogg and its decode row are from the same two files named above.
What the text actually depends on
If the container does not matter, something does. On our benchmark the variable is the acoustic condition of the audio — how clean it is, what noise sits under the voice, whether it came through a telephone band. The set is eight clips and 229 reference words, scored against the default tier with the same normalized word-error scorer the site publishes.
| acoustic condition | clips | reference words | default-tier WER |
|---|---|---|---|
| clean synthetic speech | 1 | 33 | 6.1% |
| real speech, no added noise | 2 | 28 | 10.7% |
| light background noise | 2 | 57 | 8.8% |
| heavy background noise | 2 | 89 | 34.8% |
| telephone band + echo | 1 | 22 | 22.7% |
| all eight clips | 8 | 229 | 20.1% |
| clean + light real speech only | 4 | 85 | 9.4% |
Read across the rows and the point is the spread, not any single number. The same model, the same scoring, a 6.1% to 34.8% range — and the column headed “file type” does not exist, because we never varied it. A page that headlines an accuracy percentage without telling you which of these rows it is measuring is not telling you what you would get on your own recording.
- A second tier is offered if you want lower error. The optional higher-precision model scores 9.6% across all eight clips and 8.2% on the clean-plus-light grouping, against 20.1% and 9.4% for the default. It is the same pattern — the audio dominates — at a larger download. We are not going to call one “better”; both are in the repository so you can score them on your own audio.
- 0 of 7 of the top pages state what their number was measured on. Across all seven saved copies we searched for a test set, a corpus, a word-error rate, a sample size, or the words measured on, and found none. They print “accuracy” constantly and a denominator never.
All percentages are from wer-by-condition.csv in the public benchmark repository. WER is total errors divided by total reference words (weighted), not an average of per-clip percentages. The 229-word total is the scorer count after apostrophe splitting, documented in the repository README.
Mp3 to transcript is the same job
Mp3 to transcript is the same request as mp3 to text: the recognizer reads the decoded audio, and the noun you typed never reaches it. The two phrases return the same set of pages whether those pages mean them to or not. We read the first page for mp3 to transcript on 2026-10-16 — ten results, ten distinct domains — and 0 of the 9 readable pages contains the phrase “mp3 to transcript”, while 9 of 9 carry “mp3 to text”, and on every one of them the word text appears more often than transcript. The engines answer the noun with the converter set.
The word transcript does point at something text does not: a document, and a document has a shape. That shape is the one thing the ten pages do disclose, but only inside a download menu. Any2Text lists four formats (.docx, .xlsx, .srt, .txt), Uniscribe and ElevenLabs six each (adding .pdf, .vtt, .csv, .json), VEED three (.txt, .vtt, .srt). What turns those files into a transcript rather than a wall of words is thinner on the page: 5 of the 9 offer timestamps and 4 of 9 offer speaker labels, and one of the nine answers both questions in the negative — no timestamps, no speaker separation, plain text. Not one of them says which of these the recognition was measured to support, because the wrapper is a formatting decision and the words are not.
What our tool hands back is the words the model heard, and the number that says what it will cost you to use them is not the error rate but the edit load. On the same 229 reference words as the table above, the default tier leaves 4 dropped words and 6 spots to fix by hand; across our three long files (7,385 reference words) it leaves 82 dropped words and 133 spots. A spot is a run of consecutive edits, so a garbled sentence counts once — which is why the same run reads as 20.1% word error and as six places to touch. Pick whichever export format you like; the fixing is the same either way.
The first-page counts were read on 2026-10-16 from our saved copies of the ten results for mp3 to transcript; nine returned readable text and one answered HTTP 403, so those counts are out of the nine. The format lists and the timestamp and speaker-label mentions are read from the same copies. The edit-load figures are from edit-load-8-clips.csv and edit-load-long-files.csv in the public repository, scored on the same clips with the same alignment as the word-error table above.
Where the file goes
The second thing the seven pages do not line up on is privacy, and here the set has split since the last time we looked at this market.
- 2 of 7 say the file stays on your device. Earscribe answers its own FAQ “Can I convert MP3 to text without uploading it?” in the affirmative, and Uniscribe writes “browser-based conversion — files never leave your device” and “all processing happens locally on your device.” That is a real change from a market where every page sent the file to a server.
- The other 5 of 7 are silent or explicit uploads. None of the remaining five says where the file goes, and four of them are upload-and-process web services whose entire model is that your audio reaches their machines. A page that never mentions your file after you click Convert has decided for you where it goes.
- Says local is not is local. A claim in a marketing sentence is not the same as an open model running in the tab you can watch. Our tool loads the recognizer into the browser, reads the audio there, and sends nothing to a server — there is no account step that would require it.
The local-processing claims were read from our saved copies of the Earscribe and Uniscribe pages on 2026-10-05. We have not uploaded a file to any of the seven and make no claim about whether their limits are enforced as written — only that the privacy story on the first page for this phrase is now two-sided instead of one.
Where the raw data is
The container sizes, the per-condition error rates, and the decode support on this page come from the same published measurement run as the rest of this site.
- Raw data and the measurement scripts — github.com/supersophia8888-cloud/clipquill-asr-benchmark, including the raw model text behind every number, so the scoring can be redone under different rules.
- Archived copy with a DOI — 10.5281/zenodo.22826968, the concept DOI: that link always resolves to the newest version of this dataset, all versions.
- The same data as a dataset — huggingface.co/datasets/sophia8888/clipquill-asr-benchmark
- The per-condition error rates — wer-by-condition.csv in the repository, the same model on the same 229 words across five acoustic conditions.
- Why the container barely matters — the extension is not what it converts
- What an accuracy number means when it has no denominator — five pages make an accuracy claim, and not one says what it was measured on
- The wall on the other side of the file — five pages ask where your recording goes, and all five answer “to us”
Run it on your own file
The transcriber is on the front page of this site. It reads the audio in the tab and writes the words from it, so the file does not leave your machine. Expect the first run to spend about 78.4 MiB on the download and roughly a gigabyte of RAM, then the recognition runs on your own audio. Accuracy is the 20.1% figure with the conditions named — and if your recording is clean, read speech, the honest comparison is the 9.4% grouping, not the headline. The caps are 30 minutes of audio and 512 MiB per file, checked before any work starts, and for any standard audio file it is the 30 minutes that will stop you.