WAV to Text Converter

What is a WAV file?

A WAV file (.wav) is a Microsoft RIFF container holding uncompressed PCM audio samples — co-developed by Microsoft and IBM in 1991, now the de facto format for studio masters, broadcast deliverables, and any workflow where preserving the original signal matters more than file size.

"Uncompressed" is the most common case but not the only one. The WAV container also supports compressed variants — ADPCM (a 4-bit Microsoft compression scheme used in older Windows voice recorders) and the telephony codecs μ-law and A-law (G.711, 8 kHz / 8-bit logarithmic). When people say "WAV file," they almost always mean linear PCM, which is what this page focuses on.

For audio engineers, WAV is what comes out of a DAW (Pro Tools, Logic, Reaper, Ableton) when you bounce a mix. For broadcast producers, 48 kHz 24-bit WAV is the BBC/EBU delivery standard. For forensic and legal work, WAV is the only format where the recording's admissibility-as-evidence isn't complicated by lossy compression. For everyone else uploading audio for transcription, the question is whether WAV's 10-15× file-size penalty actually buys you a better transcript — see the next section.

WAV vs MP3 for speech recognition: the honest answer

For typical podcast, Zoom, or voice-memo speech, WAV does not give meaningfully better transcripts than 128 kbps+ MP3. Whisper resamples every input to 16 kHz mono internally — so a 96 kHz / 24-bit / stereo WAV and a 128 kbps mono MP3 both produce the same 16 kHz mono samples by the time the recognizer sees them. WAV genuinely wins on three edge cases below.

Where WAV genuinely helps: noisy field recordings (the codec discards subtle frequency information the recognizer needs to separate speech from background), dense multi-speaker crosstalk (lossy compression smears spectral details that distinguish voices), and heavily accented speech (phonemes near codec compression boundaries get garbled).

Where WAV does not help: studio-clean single-speaker audio, headset Zoom calls, podcast interviews recorded in treated rooms, any audio originally captured at 16 kHz or lower (the codec savings happen above Whisper's 16 kHz internal sample rate). For these, MP3 at 128-192 kbps is functionally identical and uploads ~10× faster.

The Whisper paper's own ablations show robustness to lossy compression at common bitrates — the original Whisper paper (Radford et al., 2022) notes the model was deliberately trained on diverse compressed and uncompressed sources. For format-specific tradeoffs in the other direction, see our MP3 to text guide.

WAV file size: what to expect

Uncompressed PCM file size scales linearly: bytes = sample_rate × bit_depth × channels × duration_seconds / 8. A 1-hour 44.1 kHz stereo 16-bit WAV is ~606 MB; a 1-hour 96 kHz stereo 24-bit master is ~2 GB.

Practical implication: VexaScribe accepts files up to 5 GB, which covers everything except multi-hour 96/192 kHz 24-bit stereo masters. If you're close to the limit or your upload bandwidth is the bottleneck, downsample to 16 kHz mono before uploading — it's exactly the format Whisper consumes internally, so you lose nothing.

FFmpeg one-liner for 16 kHz mono PCM:

ffmpeg -i input.wav -ar 16000 -ac 1 -sample_fmt s16 output.wav

This produces a 110 MB/hour file with no Whisper-relevant information loss. For maximum upload speed convert to MP3 192 kbps mono instead (~85 MB/hour) — same transcript quality, dramatically smaller.

WAV technical variants

The WAV container supports a small zoo of sub-formats. VexaScribe accepts all of them and resamples internally. The dynamic range column shows the theoretical SNR — ~96 dB for 16-bit, ~144 dB for 24-bit, derived from the standard 6.02 × N + 1.76 dB formula for quantization noise.

For transcription, all variants produce identical output because Whisper resamples to 16 kHz mono regardless. Bit depth matters only when you also need the WAV for audio production downstream (mixing, mastering, sample manipulation). The narrowband formats — ADPCM, μ-law, A-law — start with telephony-quality source audio, so expect 80-88% accuracy similar to phone recordings, not the 95-97% you'd get from clean studio WAV.

How to convert WAV to text in 3 steps

Three steps. The upload usually takes longer than the transcription itself for large WAV files.

1. 1

   Upload your WAV file

   Drag-drop any PCM (8/16/24/32-bit), IEEE float, ADPCM, or μ-law WAV up to 5 GB. No conversion needed before upload — but if your file is multi-GB and your connection is slow, downsample to 16 kHz mono first.

2. 2

   AI transcribes the audio

   File is resampled to 16 kHz mono and run through Whisper Large-v3 at ~25-30× real-time. A 1-hour WAV finishes in ~2 minutes after upload completes.

3. 3

   Edit and export

   Review in the synced editor, label speakers if needed, then download as TXT, DOCX, SRT, VTT, or JSON. Word-level timestamps included.

For multi-speaker WAV files (interviews recorded in a DAW with each speaker on a separate channel), enable speaker diarization at upload — VexaScribe uses channel separation to improve speaker labels. To generate subtitle files from the same WAV, see the SRT generator.

When to use WAV (and when not to)

WAV is the right answer when you need to preserve the original signal — for editing, broadcast, or evidence. It's the wrong answer when upload speed or storage cost dominates and the transcript is the only output you care about.

Privacy and data handling

WAV uploads are encrypted in transit (TLS 1.2+) and at rest. We do not use customer audio to train Whisper or any other model.

• Encryption: TLS 1.2+ in transit; AES-256 at rest in AWS eu-west-2.
• No training: Customer audio is never used to train, fine-tune, or evaluate any model. Whisper runs in inference mode only.
• Self-serve deletion: Delete any file or your full account from the dashboard.

For forensic or legal workflows where the audio cannot leave your environment, hosted services are the wrong fit — use self-hosted Whisper instead. See our full privacy policy and editorial standards.

Export formats

Five export formats from your WAV transcript. JSON preserves word-level timestamps; SRT and VTT are ready for video captioning.