Bring your file
Drop in an MP3, WAV, M4A, or MP4. The browser checks the format, size, and duration before processing begins.
Local-first transcription
Free Audio to Text Converter
Turn voice recordings and spoken audio into searchable, editable text with private voice to text and speech to text processing in your browser.
No account required Multilingual Whisper model Works offline after setup
Private by default
Turn audio into editable text
Run Whisper locally in your browser. Nothing is uploaded and no account is needed.
Ready when you are
Drop an audio or video file hereor choose a file from your deviceMP3, WAV, M4A, MP4 · Up to 200 MB or 20 minutes
One private workspace
Voice to text, speech to text, and audio to text in one place
People search these phrases from different starting points. The workflow is the same: choose a recording, transcribe it locally, and review the words against the source before sharing.
Voice to text
Convert voice notes, personal recordings, and spoken ideas into text you can search, edit, and organize.
Explore voice to textSpeech to text
Use multilingual speech recognition for English, Chinese, Spanish, and other supported languages with auto detection or a manual choice.
Explore speech to textAudio to text
Upload a saved MP3, WAV, M4A, or MP4 file and create an editable transcript with timing information.
Explore audio to textFiles, languages, and limits
Convert common audio and video files in your browser
Use the format you already have. MP4 video is decoded for its audio track, while the transcript keeps timing information for review and subtitle export. The current local workflow is designed for one file at a time.
Supported formats
MP3, WAV, M4A, and MP4
Language workflow
Auto detect or choose a supported language before processing
Initial limits
Up to 200 MB or 20 minutes per file
Best browser setup
Desktop Chrome or Edge with JavaScript enabled
A focused workflow
From recording to a transcript you can check
Everything needed to prepare, review, and export a file stays in one browser workspace.
Guide the model
Select a language and add names, brands, or technical terms the model may otherwise mishear. These terms provide context and are not inserted automatically.
Review with context
Jump from timestamped segments to the audio, assign speaker labels when needed, edit the text, and export the version you trust.
Review before export
More useful than a raw block of text
A transcript should stay connected to the recording, especially when names, quotes, or technical terms matter.
Timestamped segments
Move from the text back to the relevant point in the audio instead of searching through the whole recording.
Manual speaker labels
Mark segments as Speaker 1 through Speaker 4 during review when a conversation has multiple participants. Automatic speaker diarization is not enabled in this local build.
Names and technical terms
Add people, brands, products, or specialist vocabulary before transcription and check glossary suggestions afterward.
Flexible exports
Download TXT for notes, JSON for structured data, or SRT and VTT for caption and subtitle workflows.
Example output
Timestamped text stays easy to verify
This is a sample format. Your exported transcript uses the words and timestamps returned by the local model.
[00:00:04.120] Speaker 1: Let us review the launch timeline. [00:00:08.640] Speaker 2: The first draft is ready for Tuesday.
Built for real recordings
Start with the type of recording you have
Each workflow page explains the relevant format, review steps, and limits before you start.
MP3 recordingsTurn voice notes, podcasts, and saved audio into editable text.→MP4 videosExtract spoken audio from a video and keep timestamps for captions.→MeetingsReview decisions, participant notes, and terminology from a meeting recording.→InterviewsKeep questions and answers close to the source audio while you edit.→
Practical guides
Get more reliable results from local transcription
Short guides explain the model setup, file preparation, and review decisions that matter when accuracy is important.
How local browser transcription worksUnderstand model caching, private processing, and the first-run setup.How to prepare an audio fileChoose a useful format, language setting, and terms list before processing.How to review an interview transcriptUse timestamps, labels, and terminology checks to verify important quotes.
Common questions
Private by design, clear about its limits
The local model runs in a browser worker. Your audio and transcript are not sent to us, and the FAQ explains what the first version can and cannot do.
What is the difference between voice to text, speech to text, and audio to text?
They describe closely related starting points. Voice to text usually begins with a voice note or personal recording, speech to text focuses on spoken language, and audio to text covers saved files such as MP3 or WAV. This browser tool supports all three workflows.
Does my audio leave my device?
No. The local workflow decodes your file and runs the Whisper model in your browser. Your audio and transcript are not sent to this website for processing.
Which files can I convert to text?
The first version accepts MP3, WAV, M4A, and MP4 files up to 200 MB or 20 minutes. MP4 video is read for its audio track and can be exported with timestamps for subtitle workflows.
Does the speech to text tool support multiple languages?
Yes. The multilingual Whisper model supports many languages, including English, Chinese, Spanish, French, German, Japanese, Korean, Portuguese, Italian, Hindi, Arabic, and Russian. Auto detection works best when one language is dominant in the recording.
Do I need an account or software installation?
No account is required and there is no desktop application to install. The first run downloads the local model into your browser cache; later runs can reuse it, including offline after the model has been cached.
Can I edit timestamps, speakers, and important terms?
You can edit transcript text, jump from timestamped segments to the audio, assign manual Speaker 1 through Speaker 4 labels, and add names, brands, or technical terms before transcription. Export is available as TXT, JSON, SRT, or VTT.
Does it automatically identify speakers?
The current local version does not promise automatic speaker diarization. It provides manual speaker labels for review, while overlapping speech, background noise, and distant microphones still need human checking.
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