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OpenVINO AI effects for Audacity
- sorenjan 2y agoThis only works with Intel GPUs, and CPUs and NPUs. No Nvidia support for instance. https://docs.openvino.ai/2024/about-openvino/release-notes-openvino/system-requirements.html#gpu https://docs.openvino.ai/2024/about-openvino/release-notes-o...
- idunnoman1222 2y agoSo use your cpu
- htsh 2y agoA lot of us have ryzen / nvidia combos... hopefully, soon, though.
- bigbones 2y agoOpenvino runs fine on AMD last I checked
- _carbyau_ 2y agoMaybe it does, however the system requirements page makes it looks like it supports everything BUT AMD. https://docs.openvino.ai/2024/about-openvino/release-notes-openvino/system-requirements.html https://docs.openvino.ai/2024/about-openvino/release-notes-o...
- 7speter 2y agoIt supports AMD cpus because, if I understand correctly, AMD licenses x86 from Intel, so it shares the same bits needed to run openVINO as Intel’s cpus. Go look at CPUs benchmarks on Phoronix; AMD Ryzen cpus regularly trounce Intel cpus using openVINO inference.
- dragonwriter 2y agoOr use the underlying open-source models directly; this is just several existing open models packaged by an Intel-specific deployment framework and wrapped as Audacity plugins.
- 7speter 2y agoThis is a great suggestion and all, but don’t you need a frontend/pipeline to run data through these models?
- dragonwriter 2y agoThere are existing frontends for these models that aren't tied to Intel hardware. It may be somewhat less convenient than having them packaged as audacity plugins, but they certainly exist, for people who would want to use them but do not want to be limited to Intel hardware.
- VTimofeenko 2y agoNvidia has Broadcast which is Windows-only: https://www.nvidia.com/en-us/geforce/broadcasting/broadcast-app/ https://www.nvidia.com/en-us/geforce/broadcasting/broadcast-...
- sorenjan 2y agoDoesn't work on files so can't be used in Audacity, only on live mic audio.
- VTimofeenko 2y agoFair point! I don't suppose Windows allows pulse audio style routing of audio streams to use denoising on a fake microphone.
- 7speter 2y agoIt might also work with AMD CPUs too
- jogu 2y agoI've used this plugin on an AMD CPU, it definitely works.
- Aromasin 2y agoI used to work at Intel doing OpenVINO stuff. Should work on AMD too; it's just not validated for it so there might be quirks.
- vient 2y agoThey have some NVIDIA support in the form of external project: https://github.com/openvinotoolkit/openvino_contrib/tree/master/modules/nvidia_plugin https://github.com/openvinotoolkit/openvino_contrib/tree/mas...
- BizarroLand 2y agoI ran it on my amd 5950x with an rtx3090. It crashed when I attempted GPU process but ran fine on CPU. YMMV
- smusamashah 2y agoIs there a tool that can remove very noisy audio recording of a song using actual song as a reference? I found a very old audio cassette from my childhood with me and some other kids talking while a song is playing in background. I tried subtracting the song using Audacity but for that to work reference song and recording must align "perfectly" which is very very hard. Not just the timing (which i found can be a problem with cassettes) loudness/frequency distribution must also align perfectly. Found Smartsubtract https://oxfordwaveresearch.com/products/smartsubtract/ https://oxfordwaveresearch.com/products/smartsubtract/ which seems to do exactly the same but it's not available for download. Is there any (AI even?) tool that might do that? I tried an online AI tool which claimed it can extract voices but it returned back silence. I want to try OpenVino but not sure it will be useful with faint spoken words in a noisy environment with a song.
- oDot 2y agoTry iZotope RX
- smusamashah 2y agoThanks for suggestion. Some quick lookup suggests it should be able to do it. Will give this a try.
- regularfry 2y agoI don't know if there's an available tool to do it, but "assuming these sources are aligned in time, remove this reference B from that recording A" would be quite a nice undergrad problem. You'd do something like cross-correlate A with B, multiply B by the correlation coefficient and then subtract the result from A. In the frequency domain, because that makes things a little easier. The next question on the problem would be "Give at least three reasons why this doesn't perfectly remove the reference sound," of course.
- BizarroLand 2y agoI would try openvino, it might get you part of the way there. Other things to do would be to fix any tape warble or flutter, normalize the volume, do simple things like high pass filtering anything below 75hz (as most voices don't make audible volume at those frequencies, especially children's voices. Then I would get a spectrum analyzer plugin and see if there are any spots that are clearly music vs children speaking and zap them out. (Audition is pretty good software for this, you might still be able to find the download and mass unlock serial key that Adobe released for version 3.0 somewhere on the internet, of course, this is only for people who bought it as a perpetual license back in the day and need to activate it now that the activation servers have gone offline, so no pirating it!) I'm not gonna say it will be perfect but you might do well enough to be able to hear what everyone is saying and it not sound too bizarre.
- kmfrk 2y agoI'm a big fan of RTX Voice, but it seems like the kind of feature you can only use in real-time as virtual audio and not as postprocessing. Anyone if Nvidia makes this possible?
- mmastrac 2y agoI tried the Audacity noise-removal plugin recently and it's complete crap. I fed a high-quality audio stream from a Rode mic into a few different options to see which could remove the noise of my server rack. iMovie made the voice sound like a robot and Audacity barely did anything. The only thing that worked was DeepFilterNet and it's free, open-source and cargo installable. There's no reason to lock yourself into an intel-only solution. Just use DeepFilterNet. The results of this on my noisy server room were insanely good. Almost no voice dropout with 100% fan noise removal. https://github.com/Rikorose/DeepFilterNet https://github.com/Rikorose/DeepFilterNet EDIT: Even more interesting, it looks like OpenVino is just DeepFilterNet glued to Whisper.cpp and tied to Intel hardware. https://github.com/intel/openvino-plugins-ai-audacity/tree/main/mod-openvino/noise_suppression/deepfilternet https://github.com/intel/openvino-plugins-ai-audacity/tree/m...
- zeckalpha 2y agoThe Noise Removal plugin takes a bit getting used to, but I have had great results from it. I don't mean to point to operator error... it's got too many options for someone new to it to tune.
- dragonwriter 2y agoOpenVINO is an Intel toolkit for deploying AI models. This particular project is an Intel project using the OpenVINO toolkit to package several existing models as audacity plugins.
- refulgentis 2y ago> an intel-only solution. > OpenVino is just DeepFilterNet glued to Whisper.cpp and tied to Intel hardware. Well, no. When you want to run a model on a truly wide set of devices, you end up sort of wedged into either ONNX, OpenVINO, TensorFlow Lite, and a few other frameworks. They're all FOSS, and they're software libraries. YMMV on which is best, of course, but broadly and widely: where are your users, mostly? Desktop? OpenVINO. Web? TensorFlow. Mobile and desktop? ONNX. This isnt entirely accurate because ex. I reach for ONNX every time because that is what I'm familiar with. All of them make effort to reach every platform, ex. OpenVINO goes supports ARM, and not in a trivial manner. That all being said, TL;DR: It is "not even wrong", in the Pauli sense, to imply OpenVINO is Intel-only, and to describe OpenVINO as "just glu[ing a model to inference code]" You're describing 3 different components (a hardware acceleration library, and inference library, and a model) and suggesting the hardware accelerated inference library just glues together a model-specific inference library and a model. The mastroyshka doll is inverted: whisper.cpp uses openvino to acclerate its model-specific inference code.
- pabs3 2y agoWonder if these are open models like RNNoise now is. https://github.com/xiph/rnnoise https://github.com/xiph/rnnoise