Go Toolstack

Speech to Text

Transcribe spoken audio and video to text, SRT, and VTT with OpenAI Whisper directly in your browser. 100% private, client-side speech recognition.

Speech to Text

Transcribe spoken audio and video to text, SRT, and VTT with OpenAI Whisper directly in your browser. 100% private, client-side speech recognition.

How It Works

1
Select Your File
Choose the file you want to process; your data is processed locally without uploads.
2
Configure Options
Adjust conversion, compression, or processing settings to suit your needs.
3
Process & Download
Run the task client-side in your browser and download the processed result.

Discover more client side utilities in this category

Audio & Video Hub • Workflows & Comparison Guide

Explore all audio & video tools, browser limits, and intent selection guide.

Explore Hub →
Client-Side Video Subtitle Editor & SRT Generator

Transcribe and edit timed subtitle captions with real-time video playback and SRT export.
Text to Speech (TTS) Voice Reader

Read text aloud with browser Web Speech synthesis. Local system voices run on-device; optional remote voices connect to speech providers.
Audio Noise & Low-Frequency Hum Reducer

Filter background hiss, electrical hums, and low-frequency rumble from voice recordings.
Browser Voice & Audio Recorder

Capture microphone audio in high-fidelity 16-bit PCM WAV & WebM with live visualizer waveform and pause/resume.
Browser Video Trimmer & Cutter

Interactive video player with timeline markers, live scrub preview, and frame-accurate cutting/export.

Related Practical Guides

Technical guides, troubleshooting, and best practice walkthroughs

Concept & Knowledge Hub

In-Browser Speech-to-Text & Subtitle Generation: OpenAI Whisper Architecture, WebGPU Acceleration & Privacy Mechanics

Audio and video transcription traditionally required streaming raw private voice recordings to cloud data centers, raising privacy, latency, and regulatory compliance concerns. GoToolstack Speech-to-Text brings modern deep-learning automatic speech recognition (ASR) directly inside your web browser using quantized OpenAI Whisper (whisper-tiny) executed via @huggingface/transformers and ONNX Runtime Web. Utilizing native WebGPU hardware acceleration with automatic CPU WebAssembly (WASM) fallback, entire audio and video files are transcribed 100% locally on your machine with zero server communication.

Concrete Implementation Scenario: A podcast producer or content creator records an interview containing confidential business discussions. Rather than uploading the 45-minute audio file to third-party transcription APIs, they drop the audio file into the local tool. The client-side preprocessor downmixes the audio to 16 kHz mono, slices it into 30-second windows with 1-second boundary overlap, and suppresses silent interludes using root-mean-square (RMS) energy filtering. The browser's GPU transcribes each segment into monotonic sentence cues. With one click on 'Open in Subtitle Studio', the generated cue list seamlessly transfers in-memory to Subtitle Studio for waveform fine-tuning and drift calibration without writing any raw transcripts to persistent disk or external networks.

The system provides verifiable monotonic timestamps, an in-place transcript editor, and instant exports in plain text (.txt), SubRip (.srt), and WebVTT (.vtt) formats.

Core Architecture & Mathematical Formula

Speech Recognition Pipeline = AudioContext Downmix(16kHz Mono) → RMS Silence Gate → 30s Window Slicing → Whisper Encoder/Decoder(WebGPU/WASM) → Monotonic Cue Formatting(SRT/VTT)

Sequential client-side ASR processing chain: multi-channel resampling to 16kHz float32 arrays, RMS energy threshold gating (< -50 dBFS), 30.0-second chunking, Transformer neural inference, and monotonic timestamp assembly.

Best Practices & Essential Guidelines

  • Confirm Hardware Acceleration: Check the hardware badge at the top of the tool. On modern browsers with WebGPU enabled (Chrome, Edge), transcription runs up to 5–10× faster than real-time playback.
  • Keep Audio Under 100 MB for Browser RAM Stability: Web browsers allocate memory inside strict sandboxes. For very long recordings (over 60 minutes), compress or trim the audio file or extract audio before transcribing to prevent tab memory exhaustion.
  • Utilize RMS Silence Suppression: Whisper models can occasionally hallucinate repetitive phantom phrases during long stretches of pure silence or background ambient hiss; keeping silence suppression enabled prevents spurious hallucinated cues.
  • Review and Edit Proper Nouns: Deep learning speech models predict phonetic probabilities. Verify proper nouns, specialized acronyms, technical terms, and speaker names in the editable transcript table before final export.

Frequently Asked Questions (FAQ)

Are my audio and video recordings uploaded to any cloud server?
No. The entire neural network inference, audio preprocessing, resampling, and text generation pipeline runs 100% locally inside your web browser. Neither your audio files nor the generated transcripts ever leave your device.
How does the in-browser Whisper model get downloaded?
Upon your explicit confirmation, the Whisper-Tiny model (~39 MB compressed, ~75 MB weights) is fetched once from verified content delivery networks and cached securely in your browser's Cache API. Subsequent visits load the model instantly from local cache without redownloading.
What happens if my browser does not support WebGPU?
The tool automatically detects hardware capabilities. If WebGPU is unavailable or fails to initialize, it immediately falls back to quantized 8-bit WebAssembly (WASM) running on your CPU, ensuring reliable transcription across all modern browsers.
Why does the tool generate segment-level timestamps instead of individual word timings?
Whisper-tiny outputs attention timestamps at the phrase and sentence segment level (roughly 2 to 7 seconds per subtitle line). Sub-word or character-level timings without secondary forced-alignment phoneme models are inherently inaccurate and prone to drift; segment-level timestamps provide authentic, monotonic, and standards-compliant cues for SRT and WebVTT.