100% local — your file never leaves your browser
Transcribe Audio to Text
Free audio to text converter that runs entirely in your browser — your file never leaves your device.
- Free
- No sign-up
- No watermark
Good to know
A free audio to text converter with real privacy
Most transcription tools upload your recording to a server. This one does not: the speech model runs inside your browser using WebAssembly, so the audio stays on your machine. That makes it safe for interviews, medical notes, legal recordings and anything else you would rather not hand to a third party.
Works offline once loaded
The model is cached after the first run, so you can transcribe audio to text again later with no connection at all. There is no per-minute cost and no upload limit. It is also the simplest way to prove the tool is genuinely local — disconnect from the network and try again. If it still works, nothing was ever being uploaded.
Supported formats and what to feed it
MP3, WAV, M4A, AAC, OGG and FLAC all work, as do common video containers like MP4, MOV and WebM — the audio track is extracted for you. Quality of input matters more than format: a 128 kbps recording of someone speaking clearly into a nearby microphone beats a lossless file recorded across a room. If you can choose, record mono at 16 kHz or above and keep the speaker close to the mic.
How long files are handled
Speech models work on short windows, so long recordings are split into overlapping chunks and stitched back together. The overlap is what stops sentences from being cut in half at the boundaries — if you have ever seen a transcript where words vanish at suspiciously regular intervals, that is a tool that skipped this step. Very long files are limited by your device memory rather than by any upload cap, so an hour of audio is comfortable on a laptop and can be heavy on a phone.
Who this is built for
The privacy angle is not abstract. Uploading identifiable patient audio to a service without an agreement in place is a compliance problem in healthcare. Client recordings carry privilege in legal work. Source recordings are the whole job in journalism, and a third-party processor is a real attack path. Unreleased campaign material under NDA usually cannot be sent to unapproved vendors at all. In each of those cases, processing locally does not just reduce the risk — it removes the disclosure entirely, because there is no third party involved.
Editing the output efficiently
Read the transcript against the audio once at speed rather than correcting word by word. Names, product names and technical terms are where the errors cluster, so a find-and-replace pass over the handful of proper nouns in the recording usually fixes most of what is wrong. Punctuation is inferred rather than heard, so sentence boundaries in fast speech are worth a second look. Everything else is generally close enough to leave alone.
Frequently asked questions
- Is this audio to text converter really free?
- Yes, and unlimited. Because transcription happens on your own device there is no server cost to pass on.
- Is my audio uploaded anywhere?
- No. The file is read locally and processed in your browser. Nothing is sent to a server.
- Which formats are supported?
- MP3, WAV, M4A, AAC, OGG, FLAC and common video files such as MP4 and MOV.
- How accurate is it?
- It uses OpenAI Whisper, the same family of models used by most paid transcription services. Clean speech typically comes out very close to verbatim.
- Why is the first run slower?
- The model is downloaded once, then cached. Subsequent transcriptions start immediately.
- Can I verify that nothing is uploaded?
- Yes, and you should. Open your browser’s network panel before transcribing: you will see the model files download on the first run and nothing else. Or disconnect from the network after the model has cached and transcribe anyway — that is a claim no tool can fake.
- Does it work on mobile?
- Yes, though a phone is meaningfully slower than a laptop and long recordings can exhaust its memory. Short clips are fine.
- What does it not do well?
- Overlapping speakers, proper nouns and heavily accented speech over background noise are the weak spots. It also does not label who is speaking.