NEURAL SPEECH-TO-TEXT · WHISPER · WAVE 24
Audio Transcription & Whisper AI Studio
Run genuine Whisper automatic speech recognition on imported audio recordings directly in your browser. Create editable transcripts, time-coded captions and downloadable TXT, JSON, SRT, VTT or batch ZIP packages.
Convert spoken audio to searchable text with AI
The transcription engine decodes each audio file locally, resamples it to 16,000 Hz mono using browser audio APIs, and passes the samples to a real pretrained Whisper automatic speech recognition model. Returned transcripts can be reviewed and edited before TXT download. Optional model timestamps generate approximate SRT and VTT subtitle cues.
Performance and accuracy
Initial inference requires downloading a large pretrained neural network and a supported WebAssembly/WebGPU environment. Mobile and low-memory machines may struggle with long recordings; this studio limits file count and duration to keep processing bounded. Whisper Tiny English is not designed for every language or professional verbatim accuracy.
Audio from video
For a video clip, use the existing Video to Audio Converter, then bring the extracted audio here. Direct upload of MP4/MOV is intentionally not claimed as supported in this version.
Related tools
Prepare recordings with Audio Restoration and edit final captions with Video Subtitle Burner. Official learning: Whisper in Transformers.js.
FAQ
Is transcription done using a real trained AI model?
Yes. The app runs Whisper Tiny English inference on locally decoded audio samples.
Can I download subtitles?
Yes, when the model returns time-stamped speech segments, the tool exports estimated SRT/VTT cues.
Can I upload video directly?
No. Direct video demux is not implemented; first extract audio using the video-to-audio tool.
Whisper AI audio transcription and subtitles
This browser-based speech-to-text workspace is designed for supported short English audio clips rather than long movie uploads. It decodes the recording, resamples the signal to 16 kHz and uses a pretrained Whisper recognition model. The resulting transcript can be edited and downloaded as TXT or JSON. If timestamp data was requested and produced, SRT/VTT subtitles can also be exported. Speaker diarization, guaranteed verbatim accuracy and direct MP4/MOV demultiplexing are not currently included.
Better transcription results
Start from the clearest recording you have, lower background music and split long recordings into smaller clips. Check names, numbers, technical terms and cue timing before publishing. Because running neural recognition locally consumes memory, first-run model download and inference speed depend on your device; some browsers may fail. Use Audio Restoration to prepare speech, and see our Whisper audio-to-SRT guide for subtitle format examples and a YouTube workflow. Model reference: Whisper Tiny English.
Whisper transcription model selection and subtitle exports
For quick spoken-English clips, start with English Tiny. For a potentially higher-quality transcription on a sufficiently powerful device, try English Base; it requires a larger model download and extra memory. For other supported spoken languages, choose Multilingual Tiny, which is designed for multilingual speech but may still misrecognize accents and technical names. Recordings are decoded and resampled locally, then estimated time-coded segments can be exported as SRT or WebVTT captions.
Audio quality matters. Remove loud noise, avoid overlapping speakers and review uncommon names. Turn on timestamps before running if you need caption files. The current workflow accepts short audio clips rather than directly decoding every MP4 container; extract source audio using MP4 to MP3. For an end-to-end tutorial, read How to Convert Audio to SRT Subtitles. An AI transcript is a draft, not verified testimony or accessibility-grade captioning until reviewed.