NN76 v1.1.2 WiN

Team R2R | 08 April 2026 | 29.4MB

Neural Network FET Compressor

Level-up your mixes with NN76, the first plugin to harness machine learning and neural networks to emulate the iconic FET compressor. Next-Generation Technology powering authentic analog sound. NN76 leverages a groundbreaking neural network engine that captures unparalleled levels of detail. We started by capturing hundreds of hours of high-resolution audio running through the legendary “Blackface” hardware. Then, over weeks, our neural network was trained to replicate the hardware’s unique response using cutting-edge machine learning techniques. The result? A breathtaking recreation that captures every non-linearity and subtle distortion that mix engineers adore. Using NN76 offers an uncanny experience—it feels as if you have the real hardware sitting right there in front of you.

TF-72a v2.2.2 WiN

Team R2R | 18 September 2024 | 51.5 MB

Introducing the “TF-72a” – a cutting-edge audio plugin meticulously crafted through the revolutionary power of Neural Networks and Deep Learning. This plugin is a faithful emulation of the legendary Vintage German tube preamp, an iconic piece of German engineering that graced some of the most renowned recording studios of yesteryears. Whether you’re tracking vocals, guitars, drums, or any other instrument, this plugin brings a touch of history and a dose of analog charm to your digital audio workstation. Unlock the secrets of the past and add a timeless sheen to your modern productions with the TF-72a – where the art of yesterday meets the science of tomorrow.

Audio Classification Neural Net

P2P | 29 August 2024 | 4.44 GB

This course is designed to provide a real understanding of handling audio files in machine learning. This course will give you a complete track record of processing audio files from A to Z using Python. This course will explain how to use Convolutional Neural Networks to generate an H5 AI model for audio classification purposes. This course gives you a complete understanding of Raspberry Pi 5 assembly, programming, AI Model deployment, and prediction of audio files. We will learn how to identify audio environments for machine-learning purposes. We will learn how to record audio files and slice them into clips of positive and negative types. How to process the raw audio clips and inject the “keyword” to be detected by the neural network. Apply clip labeling, clip slicing, and clip batching for the preparation of feeding audio clips to the Neural Net. Apply the required stages (load, time domain, frequency domain, spectrogram, and resize) to process raw audio clips for prediction use. Use Python programming to generate an H5 AI model for audio prediction purposes. Deploy and run the H5 AI model inside the Raspberry Pi 5 to control the movement of the servo motor with audio order. Testing the model with a real-time audio prediction process.

Space Networks Mastering WiN

Team R2R | 15 August 2022 | 10.7MB

A.I. and Neural Networks Based.

The Space Networks Mastering is a plugin that enhances the mastering process. It has a powerful analog transformer inside that provides tones with balance and space. Moreover, it has a knob to provide more depth and space to the sound. Space Networks is an all-in-one audio plugin with a lot of highly addictive features that will give any sound more depth and space. It is a powerful yet easy to use mastering plugin, featuring analog transformer based effects and one-knob processing – all with the simplicity of a plugin at your fingertips. The Space Networks Mastering is a plugin that enhances the mastering process. It has a powerful analog transformer inside that provides tones with balance and space. Moreover, it has a knob to provide more depth and space to the sound.