NeuralSeed VS PrincePedal

Compare NeuralSeed vs PrincePedal and see what are their differences.

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NeuralSeed PrincePedal
1 1
79 30
- -
6.9 10.0
about 1 year ago over 1 year ago
C++ C++
MIT License GNU General Public License v3.0 only
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

NeuralSeed

Posts with mentions or reviews of NeuralSeed. We have used some of these posts to build our list of alternatives and similar projects.

PrincePedal

Posts with mentions or reviews of PrincePedal. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing NeuralSeed and PrincePedal you can also consider the following projects:

SmartGuitarAmp - Guitar plugin made with JUCE that uses neural networks to emulate a tube amplifier.

Proteus - Guitar amp and pedal capture plugin using neural networks.

Nanoterm - A minimal implementation of an Arduino Framework terminal emulator

deepC - vendor independent TinyML deep learning library, compiler and inference framework microcomputers and micro-controllers

tracktion_engine - Tracktion Engine module

amp-rack - Amp Rack is a Guitar / Voice Audio Effects Processor for Android. Amp Rack is an Open Source LADSPA Plugins Host for Android. More than 150 high quality audio plugins are available which can be added in any order to the audio effect chain to create distinct high quality tones for your guitar!

pedalboard - 🎛 🔊 A Python library for audio.

DALI - A GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.