Running Local AI Speech-to-Text in Browser CPU with WebAssembly: Zero Server Uploads

Beaming confidential client interviews, confidential board meetings, or unreleased podcast recordings to remote cloud speech-to-text APIs is a massive privacy risk.

Every major "AI Transcription" startup asks you to upload your audio files to their cloud S3 buckets. Once uploaded, your voice data sits on remote servers subject to data leaks or model retraining.

With modern WebAssembly SIMD and ONNX Runtime Web, we can execute quantized Whisper transformer models 100% locally inside your browser tab.

That's why we created SolveMyMedia Transcribe.

Audio is decoded into 16kHz float buffers in browser memory and passed directly to the local model runtime:

import { pipeline } from '@xenova/transformers';

export async function transcribeLocalAudio(audioBlob) {
  // Model weights cached in IndexedDB after initial load
  const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en', {
    device: 'webgpu'
  });

  const arrayBuffer = await audioBlob.arrayBuffer();
  const output = await transcriber(arrayBuffer, {
    chunk_length_s: 30,
    stride_length_s: 5
  });

  return output.text; // 100% private, 0 bytes leave your machine
}

Test it out:

👉 Local AI Transcribe: https://solvemymedia.com/transcribe

Have you experimented with client-side AI inference in production? Let's discuss in the comments!