Normalize audio loudness

Get consistent, broadcast-standard loudness — no more clips that are too quiet or too loud.

Podcast standard (Spotify, Apple Podcasts)

Drop a video or audio file to normalize

or click to browse

Processed on your device — never on a server.

How it works

1

Drop a video or audio file in and choose a loudness target.

2

WavyVid analyzes and adjusts the audio using the same EBU R128 loudness standard streaming platforms use.

3

Download the normalized file, ready for consistent playback everywhere.

When you'd use this

  • You have a podcast episode with one segment noticeably quieter than another and want consistent levels throughout.
  • You're publishing to YouTube or Spotify and want to hit their target loudness so your content doesn't sound quiet next to others.
  • You're combining clips from different sources (guest recordings, voice memos) that all came in at different volumes.

Frequently asked questions

What's the difference between -16 and -14 LUFS?

-16 LUFS is quieter and is the established podcast standard (with more headroom for dynamic speech). -14 LUFS is louder and matches what YouTube and most music/social streaming platforms target — use whichever matches where you're publishing.

What is LUFS, in plain terms?

A standardized measure of perceived loudness (unlike raw peak volume, it accounts for how loud audio actually sounds to human ears) — it's the measurement broadcast and streaming platforms use to keep volume consistent across different content.

Will this fix audio that's already clipping/distorted?

No — loudness normalization adjusts overall level, it doesn't repair distortion that's already baked into a clipped recording.

Does this work on video files, or just audio?

Both — drop in a video and only its audio track is processed; the video itself is passed through unchanged.

Should I normalize before or after removing background noise?

After — cleaning noise first means the loudness measurement reflects your actual speech, not the noise floor mixed in with it, so the normalization target lands more accurately.

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