Convert Any Audio or Video File to Text

It's a reasonable assumption that a "better" audio format — WAV over MP3, FLAC over AAC — produces a meaningfully more accurate transcript. In practice, format matters far less than people expect, and the things that actually move accuracy are mostly invisible in a file's extension.

What the model actually sees

Whisper, like every modern speech recognition model, doesn't operate on the compressed file itself. Before transcription begins, any audio or video file — regardless of format — gets decoded into raw PCM samples and resampled to 16kHz mono. An MP3 at 320kbps and a WAV file of the same recording converge on nearly identical input once this conversion happens. The compression artifacts that distinguish formats live mostly in frequency ranges that matter for music fidelity, not for the speech-relevant frequency bands (roughly 300Hz–3400Hz for the core intelligibility range) that transcription models key on.

This is why the common advice to "always upload lossless audio for best accuracy" is mostly folklore. It's not wrong in principle — a lossless source genuinely has more information — but in practice the difference rarely shows up in transcript accuracy for any recording made under normal conditions (a decent microphone, a quiet-ish room, a standard bit rate). Where format does start to matter is at the extremes: very low bit rate MP3 (well under 64kbps) or aggressively compressed voice codecs designed for phone calls can introduce audible artifacts that do measurably hurt accuracy, because those codecs are specifically optimized to discard information the human ear tolerates but a model doesn't.

What actually moves the needle

**Source recording quality, not file format.** A WAV file of a recording made on a laptop's built-in mic from across a room will transcribe worse than an MP3 made with a lavalier mic six inches from someone's mouth. The format conversion happens after the recording; it can't add back information that was never captured.

**Background noise type matters more than noise level.** Steady-state noise — an air conditioner hum, a fan, highway traffic — is something Whisper handles reasonably well, because it's learned to treat consistent background signal as separable from speech. Intermittent noise — a door slamming, a dog barking, overlapping cross-talk — causes disproportionately more errors, because it looks structurally more like the kind of signal the model is trying to transcribe.

**Multiple speakers talking over each other is the single biggest accuracy killer**, independent of format or recording quality. Whisper (and every comparable model) attributes overlapping speech to a "best guess" single speaker's words, which is where most garbled or duplicated text in transcripts actually originates. This is a fundamentally different problem from audio quality — it's a limitation of how these models handle simultaneous audio streams, and no format or bit rate fixes it. Recording each speaker on a separate track (common in podcast production) and letting the transcription tool merge them afterward avoids this entirely.

**Video-specific note: audio track selection matters for multi-track files.** MKV and some MOV files can contain multiple audio tracks (e.g., a commentary track alongside the original audio). If a video file is transcribing content that doesn't match what you expected, this is the first thing worth checking — the file conversion step picks a default track that isn't always the one a viewer would expect.

Practical takeaway

If you're deciding whether to convert a file before uploading: don't bother, for any of the fourteen formats this tool accepts natively. If you're trying to improve a transcript's accuracy on a recording you haven't made yet, the return on investment goes: reduce speaker overlap first, then address intermittent background noise, then worry about microphone placement — and file format sits so far down the list it's rarely worth thinking about at all.