WAV to Text Converter — AI Transcription

From raw audio to working text

WAV is a recording at its most honest: uncompressed PCM, exactly what the microphone captured, with no codec artifacts standing between the engine and the waveform. Hand a WAV to a transcription service and there is no decode step to misfire and no compression choice for the acoustic model to second-guess. Sound goes in, words come out, and nothing in between has had a chance to mangle them. It is no accident that recording apps reach for WAV by default — no decision was ever made on your behalf before transcription starts. Speech occupies a far narrower band of frequencies than music, and an uncompressed file keeps all of it.

What you get back is more than a wall of text. Alongside a plain TXT holding the full transcription, the pipeline can produce an SRT subtitle file with timestamps and a dialogue view that arranges the text into segments. Those three artifacts map neatly onto the three common destinations for a transcript: reading it, captioning with it, and digging through it afterwards.

Three files, when the audio supports them

The TXT file is the core artifact: the complete transcription as plain text, ready to edit, search or paste anywhere. It is what every successful job produces, and it asks nothing more of you than any text editor.

The SRT and dialogue files are best-effort extras, not guarantees. They are derived from what the engine can infer about timing and segmentation, and some recordings — sparse speech, heavy overlap, long silences — give it too little to work with, in which case one or both may be absent. A job that returns only the TXT has not failed; the page refuses to promise three files every time. When they do appear, SRT timestamps stay aligned with the audio, and the dialogue view groups utterances into readable blocks.

If a subtitle file matters to your workflow, check the result page before closing the tab. Every artifact that was produced is listed there, and a missing SRT is a statement about your audio, not about the job.

Where it excels, where it struggles

The engine is the FunASR pipeline built on paraformer-zh, a model optimized for Chinese. Mandarin speech transcribes best, and Chinese-English mixed speech — the default register of standups and product meetings in tech — is a genuine strength: the model switches languages mid-sentence without losing the thread.

Purely foreign-language audio is the honest weak spot. English-only recordings and other languages work with limited quality, and heavy accents or noisy rooms drag results down further. We put that on the page because a confidently wrong transcript is worse than a candid warning.

Two more boundaries apply. Audio longer than 30 minutes is not accepted — split long recordings before uploading. And speaker information in the results is whatever the engine happens to produce: it may be absent, and no strong promise of speaker separation is made.

Timeline, cost and privacy

Most jobs finish within a few minutes. The ceiling for a transcription task is about 40 minutes, which covers the slowest accepted recordings, and progress stays visible while the engine works. The 40-minute figure is a timeout for the task, not a promise of delay — short recordings are the common case and return quickly.

The tool is free, no account required, and both your uploaded WAV and every generated file are deleted automatically within 24 hours. Uploads travel over HTTPS, processing happens in memory on the conversion cluster, and no third-party ad network runs on the site. No email address is requested at any point. A transcript of an internal meeting deserves nothing less.