Free Audio Noise Reducer

Yes, it is completely free with no signup, no account, and no usage limits. Because RNNoise runs on your own device through WebAssembly instead of a paid cloud service, there are no per-file costs to pass on. Many online noise removers and desktop apps such as Krisp or Adobe Podcast Enhance are subscription based or cap free usage, while this tool lets you clean as many files as you want for free.

No. The entire process, decoding the audio and running RNNoise, happens locally in your browser using WebAssembly. Your recording is processed on your own device with zero network requests carrying it. Nothing is uploaded, logged, or stored, which makes this safe for private interviews, calls, and voice memos. You can confirm this by opening the Network tab in your browser DevTools while you clean a file and seeing no upload.

RNNoise is an open-source noise-suppression library created by Jean-Marc Valin at Xiph.Org, the organization behind the Opus and Vorbis audio codecs. It combines traditional digital signal processing with a small recurrent neural network (a gated recurrent unit) trained on many hours of speech and noise. This hybrid design keeps the model tiny and fast enough to run in real time, which is why it can run in a browser tab, while still being effective at telling voice apart from background noise.

It is strongest on steady, continuous background noise such as fan and air-conditioner hum, computer and room tone, tape or electronic hiss, and general ambient noise behind a voice. It also reduces many everyday transient noises. It is less effective on very loud, sudden, or speech-like noises, and on music, since the model was trained specifically to preserve speech. For voice recordings it typically gives a clear, noticeable improvement.

No installation is needed. It is a pure web tool that runs in any modern browser. Unlike RNNoise itself, which is normally a C library you compile, here it is already compiled to WebAssembly and embedded in the page, so there is nothing to download or set up. There is no extension, no desktop app, and no command line involved.

Krisp and Adobe Podcast Enhance are cloud services: Krisp is a subscription app that processes your microphone in real time, and Adobe Podcast Enhance uploads your file to Adobe servers to clean it. Both can produce excellent results, but they involve accounts, limits, and sending your audio to a company. This tool trades a little of that polish for complete privacy and no cost: it uses the open-source RNNoise model, runs on your device, and never uploads anything.

RNNoise processes a single channel of 48kHz audio, so this tool downmixes a stereo file to mono before cleaning it, and the output is mono. For voice recordings, which are usually mono anyway, this is not a drawback. If you specifically need to keep stereo, you would need a tool that runs the model on each channel separately, which is outside the scope of this simple cleaner.

You can export the cleaned audio as a lossless WAV file, which preserves the exact processed samples, or as an MP3 at 128, 192, 256, or 320 kbps if you want a smaller file. WAV is the best choice if you plan to edit the audio further, while MP3 at 192 kbps or higher is a good balance of quality and size for sharing or listening.

There is no server-imposed limit, since everything runs in your browser. The practical limit is your device memory, because the audio is decoded and held in memory while it is processed. Short clips and typical podcast-length recordings work comfortably, while very long files use more memory and take longer, as each 10-millisecond frame is processed one at a time on your CPU.

Because the model is embedded in the page and everything runs on-device, once the page has loaded it can process audio without an active internet connection. It also works on mobile browsers, though phones are slower than a laptop or desktop, so long files will take more time. For the best experience with longer recordings, use a desktop browser.

Yes, and it is a great use case. Removing steady background noise first often improves the accuracy of speech-to-text and subtitle tools, because the recognizer has a cleaner voice signal to work with. You can clean a recording here, download the WAV, and then run it through a transcription or subtitle generator for better results.

No tool can fully recover audio that was badly recorded, but noise reduction can meaningfully improve clarity by pulling the voice out from under steady background noise. RNNoise is good at that specific job. For the best final result, combine a clean recording environment, this noise reducer, and light editing rather than relying on any single step to fix everything.