Transcribe Audio to Text — Free, Local Whisper AI
Transcribe interviews, meetings, and voice notes to text with Whisper AI running locally in your browser. Export text or SRT subtitles. No upload, no API key.
Cloud transcription charges by the minute and sends your recordings — client calls, medical notes, unreleased strategy meetings — to someone else's servers. This tool runs OpenAI's Whisper model locally in your browser: drop in an audio or video file and get an accurate transcript with timestamps.
Export plain text for articles and notes, or SRT subtitles for video editing. It is free with no account and no API key, and nothing is uploaded — the model weights download to your browser once, then everything runs on your machine.
100% in-browser · no upload · no signup · nothing you paste or drop here leaves your device
Whisper Audio Transcription
Transcribe audio and video files to text locally. Export SRT subtitles or plain text. 100% browser-based AI — no upload.
Audio transcription uses OpenAI Whisper models (tiny ~144MB or small ~278MB, fp32) from Xenova, running locally via Transformers.js / ONNX Runtime Web in your browser. Multi-CDN fallback ensures reliable downloads worldwide. Optional GTCRN speech enhancement (WASM SIMD) pre-processes audio to reduce background noise. All models download on first use and are cached afterward. All processing is 100% local — your audio never leaves your device.
How local audio transcription works — illustrated
This tool runs OpenAI's Whisper model in your browser to turn speech into text. If you enable noise reduction, the audio is first passed through the GTCRN speech-enhancement network (WASM SIMD) to suppress background noise. The cleaned audio is then decoded into mel spectrogram frames and processed by Whisper in ~30-second chunks with a 5-second stride, so each chunk overlaps the last and no words are dropped at boundaries. Whisper's encoder turns each chunk into a representation of the spoken audio, and its decoder emits the text tokens plus start and end timestamps. The output chunks are normalized into readable sentences and can be exported as plain text, CSV, or SRT subtitles. Your recording, the denoiser, and the model all stay on your device.
Audio is optionally denoised with GTCRN, split into overlapping 30-second windows, transcribed by the local Whisper encoder–decoder, and exported as text or SRT.
Priya records a 12-minute interview with a guest in a noisy café. She uploads the M4A, leaves noise reduction on, picks the Small model for accuracy, and starts transcription.
- Denoise:GTCRN processes the clip first, suppressing the café background so the voice is clearer before Whisper sees it.
- Chunk:Whisper splits the 12 minutes into 30-second windows with 5-second overlap, so words spoken across a boundary are still transcribed once.
- Transcribe:The encoder turns each window into mel spectrogram features and the decoder emits text with timestamps — 18 segments in total.
- Export:Priya downloads subtitles.srt and drops it into her podcast editor, using the per-segment timestamps to line up captions automatically.
About this transcribe audio to text
This page covers audio to text converter free, speech to text online no upload, whisper transcription online, meeting transcription free — all the same underlying task as transcribe audio to text. The tool above is FreeToolHub's audio transcribe embedded in full: every feature works right here, and nothing you process is uploaded to any server.
Frequently asked questions
How accurate is browser-based transcription?
Whisper is the same model family behind many paid transcription services; on clear audio it typically lands in the low-to-mid nineties word-accuracy range. Heavy accents, crosstalk, and background noise reduce accuracy — the local small model trades some accuracy for speed on modest hardware.
Can I get subtitles (SRT) instead of plain text?
Yes. The transcript includes segment-level timestamps, and the SRT export turns them into ready-to-use subtitle files for video editors and YouTube. Plain-text export strips the timestamps for articles, notes, and documentation.