1Upload the audio files you want brought to a consistent level — a whole set at once is exactly the case this is for.
2Pick a target: about -14 LUFS for streaming platforms, -16 LUFS for podcasts, -23 LUFS for broadcast.
3Run the analysis; integrated loudness and true peak are measured before any gain is decided on.
4Download the normalized Audio file, with the dynamics inside each track left untouched.
Normalize Audio Loudness FAQ
Does normalisation compress the dynamics?
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No. It is a single gain adjustment for the whole file, so the relationship between the quiet and loud parts is untouched. Compression and limiting are separate operations and are not applied here.
How does Normalize Audio Loudness decide the right level?
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Concretely, the file is analysed for integrated loudness and true peak, then a single gain is applied to reach the target — no compression, no limiting, just the level. The file is measured first and the correction follows from the measurement, rather than a fixed gain being applied and hoped for.
Anything specific to Audio here?
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Yes — the codec is detected from the stream, so a mislabelled extension does not derail the job. Worth knowing when the goal is a consistent set rather than one file.
What loudness target should I aim for?
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About -14 LUFS is what the major streaming platforms normalise to, -16 LUFS is the usual podcast target, and -23 LUFS is the broadcast standard. Going louder than the platform target gains you nothing — the platform simply turns it back down.
Which audio formats does Normalize Audio Loudness accept?
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MP3, WAV, AAC, M4A, FLAC, OGG, Opus, WMA and AIFF. Codecs are detected from the stream rather than the extension, so a mislabelled file still works and a mixed batch is handled per file.
Is there a file size limit on Normalize Audio Loudness?
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Yes: free accounts process audio up to 25 MB per file whichever codec you brought; ffmpeg handles the decode and encode. In practice that limit only bites on uncompressed WAV; a compressed file of the same length is nowhere near it.
Will Normalize Audio Loudness lower the quality of my audio files?
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It depends on the source. Lossless input (WAV, FLAC, AIFF) is edited sample-exactly and loses nothing. Lossy input has already discarded detail once, so where the job can be done by copying the encoded stream rather than re-encoding it, that is the path taken.
Can I run Normalize Audio Loudness on several audio files at once?
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Yes. The set queues in parallel with identical settings, which is how you process an album or a podcast back catalogue without repeating yourself.
Why does a EPUB site host Normalize Audio Loudness?
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EPUB.to is built around the open eBook format — XHTML in a zip, reflowable by design, and readable on hardware nobody has thought about the layout for. A book is a zip full of markup, images and fonts, so almost every job people bring to an eBook site is really a job on one of those things. Normalize Audio Loudness runs on the same upload and the same account as the conversions because that is where it is needed.
What should I do with the result once Normalize Audio Loudness is finished?
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The converter on this site moves books between EPUB, MOBI, AZW3, PDF and DOCX, so a title ends up on whatever hardware is actually going to read it. Doing that after Normalize Audio Loudness means the conversion is made from the version you settled on, not from the one you were still fixing.
Is Normalize Audio Loudness here the same tool the sibling sites run?
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The engines are shared — the same toolchain, the same workers, the same limits. What a EPUB site adds is a view on reflow: which of these operations a reflowable book genuinely supports, and which ones only make sense on the fixed-layout media inside it. It also starts from one fact about the format this site is named after: the book is a zip of XHTML with a manifest, so the text costs almost nothing and every decision that matters is about the embedded images and font subsets.
Do I need an account, and does anything get kept?
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No account, and nothing is kept: uploads are deleted from the workers shortly after the job finishes, nothing is read and nothing is indexed. Free accounts exist for history and batch size, not for access.
