How to Add Audio to Text Conversion to Your App (Copy-Paste Prompts Included)

Web Speech API costs nothing — it's built into Chrome, Edge, and desktop Safari. The catch: it's English-heavy, not available on iOS Safari, and gives no transcript history or file upload. For a production app that needs cross-browser support and multi-language, OpenAI Whisper at $0.006/minute is the most cost-effective paid option. At 10 minutes of audio per user per month, 100 users costs roughly $6/mo in API fees — significantly less than most SaaS subscription tiers.

Yes. Whisper supports 99 languages and auto-detects the language by default. To specify a language explicitly — which improves accuracy and skips the detection step — add a language parameter to the API request with the ISO 639-1 code ('es' for Spanish, 'fr' for French, 'de' for German). For multi-language meeting recordings where speakers switch languages mid-conversation, let Whisper auto-detect; the model handles code-switching reasonably well.

Whisper enforces a hard 25 MB limit per request. For longer files, use ffmpeg.wasm in the browser to split the audio into 3-minute chunks before sending to your Edge Function. Each chunk goes to Whisper independently. Concatenate the results by offsetting the timestamps from each chunk by the cumulative start time of previous chunks. Store the chunk array in your transcript_json field so you can reconstruct the original timeline if needed.

Web Speech API runs in the browser, is free, and gives you words in real time as the user speaks — but only works in Chrome and Edge (not iOS Safari or Firefox), is trained primarily on English, and produces no permanent transcript you can retrieve later. Whisper runs on OpenAI's servers, costs $0.006/minute, works in 99 languages, accepts uploaded audio files, and returns a permanent transcript with optional word timestamps. Use Web Speech API as a real-time 'live preview' layer on supported browsers, and send the final audio Blob to Whisper for the authoritative, cross-browser-compatible result.

For Chrome/Edge users: yes, use Web Speech API's onresult event which fires on interim and final results as speech is recognized. Words appear in real time. For iOS Safari and Firefox users: no native real-time option exists — the best you can do is show a waveform visualizer during recording and display the full Whisper transcript when they stop. A common pattern is to use Web Speech API for the live preview on supported browsers and always send the final audio to Whisper for the saved transcript, so the history is always Whisper-quality regardless of browser.

Whisper does not natively diarize speakers (it transcribes what was said, not who said it). For speaker labels, either use Deepgram Nova-2 with diarize: true in the request parameters — it returns speaker numbers with each word — or run pyannote-audio as a post-processing step on your server to add speaker segmentation to a Whisper transcript. Deepgram is the simpler option at $0.0043/minute; it adds speaker labels without changing your frontend at all, just the API response schema.

For English in a quiet environment, Whisper achieves roughly 5% word error rate — good enough for meeting notes where humans will review and correct. Accuracy drops in meetings with crosstalk, strong accents, or heavy technical jargon. For critical workflows (legal depositions, medical dictation), pair Whisper with post-processing to flag low-confidence segments using the avg_logprob values in verbose_json, and always build in an editing step before the transcript is finalized.

Use the docx.js library (^8.x) to generate a .docx file client-side. Create a Document with a Paragraph for each transcript segment or speaker block, then call Packer.toBlob(doc) and trigger a download with URL.createObjectURL(). This runs entirely in the browser — no server required for export. For PDF export, jsPDF is the equivalent client-side library. Both add roughly 50 lines of code and no additional API cost.