Volume Normalizer — Normalize Audio Loudness Online
Analyze the loudness of any audio file and normalize it to a consistent target level. Perfect for leveling podcasts, music, and voice recordings. 100% client-side — nothing leaves your browser.
How to Use the Volume Normalizer
- Upload audio — click the upload area or drag an audio file onto it.
- Choose a target — select how loud you want the output (peak or RMS normalization).
- Normalize — click Normalize to adjust the loudness in your browser.
- Preview — use the player to compare before and after levels.
- Download — save the normalized audio as a WAV file.
Why Use This Volume Normalizer
Inconsistent audio levels are a common problem when mixing podcasts, playlists, or voice recordings from different sources. This tool measures the peak or average loudness of your file and applies a uniform gain so the output sits at a consistent level.
Everything runs in your browser using the Web Audio API. Your audio is decoded, analyzed, and re-encoded locally — it is never uploaded to any server.
Frequently Asked Questions
Peak normalization scales the file so its loudest sample hits the target, which is safe and simple but ignores perceived loudness. RMS normalization scales based on the average power of the signal, giving a result that sounds more consistently "loud" across different recordings.
No. The tool applies linear gain to the decoded samples, so no data is lost or re-compressed. The output is saved as uncompressed WAV at the same sample rate as your source.
No. All processing runs locally in your browser. Your audio file never leaves your device.
Yes. Once normalized, you can use the BPM Detector to find the tempo, or the MP3 to MIDI Converter to transcribe the melody.
Use Cases
Podcast Leveling
Even out the loudness of podcast episodes recorded on different setups.
Playlist Consistency
Normalize tracks so a playlist plays at a consistent listening volume.
Voice Messages
Boost quiet voice recordings before sharing or archiving them.
Mastering Preparation
Level tracks before export to get a consistent baseline for further processing.