The Browser AI

Audio to text

speech to text

Transcribe audio from meetings, interviews, and podcasts, then export it to TXT or SRT. Based on the local transcription of Whisper (Transformers.js / WASM) in the browser; the audio is not uploaded, and the model can be reused offline after downloading it for the first time.

Loading speech-to-text tool…

What is Audio to text suitable for?

Audio to text is suitable for organizing meetings, interviews, courses, voice memos and podcasts. The tool uses Transformers.js to run the Whisper speech recognition model in the browser, and can export the recognition results as TXT documents or SRT subtitles with time information.

Audio content is not uploaded to The Browser AI servers. The first transcription downloads model files from a CDN, which the browser can cache for later use. Recognition speed and accuracy depend on recording clarity, background noise, language, audio duration, and device performance; longer files use more memory and take longer.

Supported formats: MP3, WAV, M4A, WebM; single file limit: Within the browser’s available memory.

How to use Audio to text

  1. Step 1

    Upload or record audio

    Select common audio and video files, or record directly after allowing microphone permission.

  2. Step 2

    Run local transcription

    Select the recognition language and start transcribing; the Whisper model will be downloaded and cached on first use.

  3. Step 3

    Proofread and export

    Check the recognition text and timeline to export TXT or SRT depending on the usage.

FAQ

Do I need to upload the recording to convert audio to text?

No need. Both audio decoding and Whisper inference run locally in the browser; the network connection is mainly used to download the speech model and related running files for the first time.

Why is the first transcription slower?

The browser needs to download the model when first used, and then complete local inference based on device performance. Model caching reduces download waits when reusing later.

How to improve speech recognition accuracy?

Prioritize recordings with clear vocals, less background noise, and stable volume, and choose the correct language. Multiple people speaking overlappingly, strong noise, and professional terminology may increase recognition errors.

Technology and data sources

  • Transformers.js — Official documentation for running machine learning models in the browser.
  • Whisper — An open source project for the model architecture used in local speech recognition.