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sdatkinson avatar

sdatkinson/neural-amp-modeler

0
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2,460 stars·222 forks·Python·mit·22 views

Neural Amp Modeler

Neural Amp Modeler is an open-source project that captures the tonal character of analog audio gear by training a neural network on paired dry and reamped audio recordings. It provides a complete pipeline for learning how a guitar amplifier, effects pedal, or other audio device transforms a signal, then exports the trained model into a portable file format for use in other applications.

The project centers on a file-format-based approach to model distribution, where each trained neural network is saved as a single .nam file that can be shared and loaded by different host applications. A real-time inference engine processes live audio streams with low-latency neural network forward passes, enabling the emulated device to be used in performance or recording contexts.

The training workflow uses a supervised learning approach, taking a dry input recording and a reamped output recording as paired training data to learn the device's transfer function. Once trained, the model weights and architecture are serialized into a compact, portable format that simplifies model management and swapping across different software environments.

Features

  • Audio Gear Model Trainers - Trains neural networks from paired dry and reamped audio recordings to learn gear transformations.
  • Analog Gear Emulators - Trains neural networks to emulate the sound of guitar amplifiers and effects pedals.
  • Audio Transfer Function Learners - Provides a supervised training pipeline using paired dry and reamped audio recordings.
  • Audio Device Character Capturers - Trains neural networks on paired input-output audio samples to capture audio device sonic character.
  • Audio Gear Response Modelers - Trains neural networks on paired DI and reamp audio to model guitar amplifier and effect responses.
  • Neural Network Training - Trains neural networks on audio samples to replicate the tonal character of guitar amplifiers and effects.
  • Neural Amp Model Runtimes - Loads serialized models and processes live audio streams with low-latency neural network inference.
  • Guitar Amp Modelers - Trains neural networks to capture the tonal character of guitar amplifiers and effects pedals.
  • Neural Amp Model Files - Ships a dedicated .nam file format for packaging trained neural amp models.
  • Model Serialization Formats - Serializes trained neural network weights and architecture into a portable file format.
  • Model Export and Portability - Exports trained neural networks into portable files that other applications can load and use.
  • Model Exporting - Exports trained neural networks into file formats loadable by real-time playback plugins or standalone apps.
  • Functional Model Exports - Exports trained neural networks into a file format designed for real-time playback in other applications.
  • Model Export Formats - Saves trained neural networks into a portable file format that other applications can load and use.
  • Model Packaging - Packages each trained neural network into a single self-contained file for simplified model management.

Star history

Star history chart for sdatkinson/neural-amp-modelerStar history chart for sdatkinson/neural-amp-modeler

How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.

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Frequently asked questions

What does sdatkinson/neural-amp-modeler do?

Neural Amp Modeler is an open-source project that captures the tonal character of analog audio gear by training a neural network on paired dry and reamped audio recordings. It provides a complete pipeline for learning how a guitar amplifier, effects pedal, or other audio device transforms a signal, then exports the trained model into a portable file format for use in other applications.

What are the main features of sdatkinson/neural-amp-modeler?

The main features of sdatkinson/neural-amp-modeler are: Audio Gear Model Trainers, Analog Gear Emulators, Audio Transfer Function Learners, Audio Device Character Capturers, Audio Gear Response Modelers, Neural Network Training, Neural Amp Model Runtimes, Guitar Amp Modelers.

What are some open-source alternatives to sdatkinson/neural-amp-modeler?

Open-source alternatives to sdatkinson/neural-amp-modeler include: harthur/brain — Brain is a JavaScript library for building, training, and running feed-forward neural networks. It implements a… nvidia/digits — DIGITS is a GPU deep learning training platform and model manager used to train, fine-tune, and manage neural network… suragnair/alpha-zero-general — This project is a reinforcement learning framework and game AI engine designed for training adversarial agents in… dragen1860/tensorflow-2.x-tutorials — This project is a collection of TensorFlow 2.x machine learning tutorials and practical code examples. It serves as a… snowkylin/tensorflow-handbook — This project is a comprehensive educational resource and tutorial handbook for building, training, and deploying… morvanzhou/pytorch-tutorial — This project is a collection of PyTorch learning resources and educational guides designed to teach the construction…