AI Audio Transcriber Free — Transcribe MP3, WAV, and More in Your Browser

What Makes This Transcription Tool Different

Most transcription tools work by uploading your audio to a cloud server, where it is processed by a speech recognition engine and the transcript is sent back. This means your audio — which may contain meetings, interviews, medical notes, or personal conversations — is transmitted over the internet and processed on someone else's infrastructure.

This tool works differently. It uses Transformers.js to run OpenAI's Whisper-tiny model entirely in your browser using WebAssembly. The audio is decoded using the Web Audio API and processed locally — no audio data ever leaves your device.

The model download (~75 MB) happens once, then is cached in browser storage. All subsequent transcriptions run instantly from the cached model without any network requests beyond the initial load.

How Whisper Works

Whisper is an automatic speech recognition (ASR) model released by OpenAI in 2022 and trained on 680,000 hours of multilingual audio. Unlike traditional ASR systems that were trained on clean, controlled speech, Whisper was trained on data from the web — which includes different accents, background noise, overlapping speech, and varying recording quality. This makes it significantly more robust than older speech recognition approaches.

Encoder-decoder architecture

Whisper uses a transformer encoder-decoder architecture. The encoder takes audio as input by first converting it to a log-Mel spectrogram — a visual representation of the audio frequency content over time. The spectrogram is processed by the encoder transformer, which produces a rich representation of the acoustic content. The decoder then generates text tokens autoregressively, attending to both the encoder output and the previously generated tokens to produce accurate transcriptions.

Whisper-tiny vs larger models

Whisper comes in five sizes: tiny, base, small, medium, and large. This tool uses Whisper-tiny because it is small enough to run in a browser (75 MB) while maintaining reasonable accuracy on clear speech. Larger models — particularly Whisper-large-v3 — produce significantly better accuracy on accented speech, noisy audio, and technical vocabulary, but at 1.5 GB they are not practical for browser-based use.

Supported Audio Formats

If your audio is in a video file (MP4, MOV, MKV), extract the audio track first using the MP4 to MP3 Converter, then upload the MP3 to the transcriber. This avoids browser codec compatibility issues and reduces file size significantly.

How to Transcribe an Audio File

1. Open the tool. Go to the AI Audio Transcriber. No login required.
2. Upload an audio file. Click the dropzone or drag a file onto it. Supported formats: MP3, WAV, M4A, WebM, OGG, FLAC.
3. Click Transcribe Audio. On first use, Whisper-tiny (~75 MB) downloads and caches in your browser. A progress bar shows the download status.
4. Review and edit the transcript. The text appears in an editable textarea. Correct errors, remove filler words, and format as needed.
5. Copy or download. Use the Copy button or Download .txt to save your final transcript.

Getting the Best Accuracy

Whisper-tiny produces its best results on clean, clear audio with minimal background noise and a single speaker. Here is how to maximise accuracy for different recording types.

Recording quality tips

Audio quality is the single biggest factor in transcription accuracy. For voice memos or planned recordings: record in a quiet room, speak at a consistent volume, stay within 20–30 cm of the microphone, and avoid rooms with heavy echo (tiled bathrooms, large empty halls). For existing recordings where you cannot control the source, noise reduction software applied before transcription can improve results.

Handling multiple speakers

Whisper transcribes all speech into a single continuous text without identifying who is speaking. In a two-person interview, both voices will appear as one block of text. The model does not diarise (assign speech to different speakers). For multi-speaker recordings, manually add speaker labels ("Interviewer:" / "Guest:") after transcription by listening alongside the transcript.

Technical vocabulary and names

Whisper handles common vocabulary well but may struggle with highly technical terms, brand names, proper nouns, and acronyms that were rare in its training data. Always review transcripts of technical content — medical terminology, legal terms, product names — with particular care. When a word is consistently wrong, the model has likely substituted a phonetically similar common word.

Accented speech

Whisper-tiny handles standard accents (American, British, Australian English) well. Strong regional accents, non-native English speakers, or fast speech may produce more errors. Slowing down your playback speed while reviewing the transcript alongside the audio is the most efficient way to catch and correct these.

Post-Transcription Editing Techniques

Cleaning up filler words

Natural speech contains filler words ("um", "uh", "like", "you know") and false starts that Whisper faithfully transcribes. For interview transcripts or content creation, do a text search for common fillers and delete them. Most text editors and word processors support find-and-replace with regex patterns if you want to batch-remove multiple filler variants.

Adding punctuation and paragraphs

Whisper adds punctuation automatically, but paragraph breaks depend on detected speech patterns. For long recordings, manually add paragraph breaks at logical topic transitions to make the transcript readable. In interviews, paragraph breaks at each question-answer pair are conventional.

Formatting for publication

If you are publishing a transcript as an article or blog post, convert the plain text to structured content: add a header with the speaker name and date, bold the question text in interviews, italicise emphasis, and add section headings at topic breaks. The word counter in the Word Counter can help you measure transcript length for publication planning.

Advanced Use Cases

Transcribing meeting recordings

Video conferencing tools (Zoom, Teams, Google Meet) export recordings in MP4 format. Extract the audio track first using the MP4 to MP3 Converter, then upload the MP3 here. Whisper handles multi-speaker audio but produces a single speaker transcript — manually mark speaker turns after generation if needed. For regular meeting transcription, exporting one audio file per meeting and transcribing immediately after the meeting produces the best results while the content is still fresh for editing.

Creating captions and subtitles

Generate the raw transcript here, then format it with timecodes manually in a text editor for use as SRT or VTT subtitle files. SRT format is straightforward — each subtitle entry has a sequence number, a start → end timecode in HH:MM:SS,ms format, and the text on the next line. Keep each subtitle to two lines maximum and no more than 42 characters per line for readable display on video.

For automated timecode generation with proper SRT output, a dedicated subtitle tool with Whisper's timestamp output would be needed. This tool produces plain text transcripts optimised for reading and editing.

Converting voice memos to notes

iPhone and Android voice memos export as M4A or MP3 files. Upload them here to convert spoken notes to text — useful for ideas captured while driving, exercising, or away from a keyboard. After transcription, clean up the text and copy it into your notes app, document, or task manager.

Transcribing interviews and podcasts

For interview transcription, clear audio with one speaker at a time produces the best results. Edit the transcript to attribute quotes before exporting. For podcasts with multiple hosts, Whisper merges all voices into one transcript without speaker differentiation — use the audio timeline and context cues to attribute sections manually.

Transcribing lectures and educational content

Lecture recordings are a common use case. For students, transcribing recorded lectures produces a searchable text document for revision. For educators, transcripts make video content accessible to deaf and hard-of-hearing students and satisfy many accessibility requirements for online learning platforms.

Legal and medical transcription

The privacy advantage of local processing makes this tool particularly suitable for legal depositions, client consultations, and medical dictation — audio you legally or ethically cannot send to a cloud service. Since no audio leaves the device, there is no third-party data exposure. Always review these transcripts carefully, as errors in legal or medical content have real consequences.

Privacy and Security

Is my audio private?

Yes. The Whisper model runs entirely in your browser. No audio is transmitted to any server. The only network request is the one-time download of the model file from Hugging Face CDN.

Where is the model stored?

The model weights are stored in your browser's IndexedDB (a local browser database). They persist across sessions — you only download once. To remove them, clear your browser's site storage via Settings → Privacy → Site Data for publicsofttools.com.

GDPR and data residency

Because no audio data is transmitted, there is no data controller relationship for the audio content — you are processing your own data on your own device. This makes the tool compatible with data residency requirements that prohibit sending audio to servers in other jurisdictions.

Comparing Transcription Approaches

Common Questions

How long does transcription take?

After the model is loaded, transcription speed depends on the audio length and your device. On a modern laptop, Whisper-tiny processes audio at roughly 2–4× real time (a 5-minute recording takes 1–2 minutes to transcribe). Older or lower-spec devices will be slower. Mobile devices, particularly older phones, may be significantly slower due to limited WebAssembly performance.

How is this different from the Speech to Text tool?

The Speech to Text tool captures live microphone input in real time using the browser's Web Speech API — it requires speaking in the moment and sends audio to a cloud backend. This Audio Transcriber processes uploaded files using a local Whisper model, making it suitable for recordings you already have.

What is the maximum file size?

The tool processes audio in browser memory. Very long recordings (over 30 minutes) or lossless formats like FLAC with high sample rates can be large files that challenge browser memory limits on low-RAM devices. For very long recordings, split the file into 15–20 minute segments using audio editing software before transcribing.

Does it support languages other than English?

Whisper was trained on multilingual data and can transcribe many languages. Whisper-tiny supports transcription in the source language for common languages including Spanish, French, German, Italian, Portuguese, Dutch, Russian, and Chinese. Accuracy varies by language and drops for less-represented languages. Check the Whisper documentation for the full language list and word error rates.

Can I transcribe phone call recordings?

Yes, as long as the call has been recorded to a file. Phone audio is typically 8 kHz mono, which is lower quality than modern recordings — Whisper handles this but accuracy will be lower than for higher-quality audio. Telephone-quality audio with background noise or compression artefacts will produce more transcription errors.