photophobic martin's repositories
GuitarAmpModellingGUI
GUI for easier installation and training of neural network models for guitar amplifiers and pedals, based on the GuitarML Proteus models. These are usable for Proteus, Chowdhury-DSP BYOD and even NeuralPi VST plugins, on all platforms incl. Linux and RaspberryPi.
NAM-Runner
A Windows 10 batch file, that installs and runs the NAM model trainer (neural-amp-modeler) right into the GUI application. Fully automated. Custom one-time installation, no Conda required. Runs as a launcher afterwards. Portable install. New pyTorch. Enjoy!
neural-amp-central
Windows desktop application to manage/train neural network amplifier models for audio use. NAM and more to come.
AIDA-X
AIDA-X, an Amp Model Player leveraging AI
AudioSlicer
Audio Slicer that uses silence detection to split .wav audio files into several .wav samples.
BYOD
Build-your-own guitar distortion!
chowdsp_utils
JUCE module with utilities for ChowDSP
Geo3D
DirectX Stereoscopic 3D
google-research
Google Research
JUCEPluginTemplate
JUCE/CMake template for ChowDSP plugins
llama
Inference code for LLaMA models
mod-live-usb
MOD platform as bootable Live-USB image
mod-plugin-builder
MOD Plugin Builder
nam-batch
A fork of NAM for batch processing
NAM_models
A repository collecting model files for Neural Amp Modeler (NAM) all in one place
neural-amp-modeler
Neural network emulator for guitar amplifiers.
neural-amp-modeler-lv2
Neural Amp Modeler LV2 plugin implementation
NeuralAmpModelerCore
Core DSP library for NAM plugins
NeuralAmpModelerPlugin
Plugin for Neural Amp Modeler
Proteus
Guitar amp and pedal capture plugin using neural networks.
releases
Release pipeline for ChowDSP plugins
RTNeural
Real-time neural network inferencing
so-vits-svc-fork
so-vits-svc fork with realtime support, improved interface and more features.
spleeter
Deezer source separation library including pretrained models.
versatile_audio_super_resolution
Versatile audio super resolution (any -> 48kHz) with AudioSR.
vits
VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech