4 ms·

I 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

I 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 ago
The 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 ago
OpenVINO 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.

zeckalpha 2y ago

The 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 ago

OpenVINO 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.