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rtneural-inference

Perform real-time neural network inference for guitar amp modeling with RTNeural.

1|Updated Nov 24, 2025
Search Tags:#real-time#audio#inference#lstm#rtneural#amp-modeling
One-click install
npx skills add https://github.com/SpiralCloudOmega/DevTeam6 --skill rtneural-inference
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: rtneural-inference
Source: https://github.com/SpiralCloudOmega/DevTeam6/tree/main/.github/skills/neural-audio/rtneural-inference
Command: npx skills add https://github.com/SpiralCloudOmega/DevTeam6 --skill rtneural-inference

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Perform real-time neural network inference for guitar amp/effect modeling.

Core Features & Use Cases

  • Real-time per-sample inference for guitar amp models.
  • Lightweight C++ integration with RTNeural for low-latency audio DSP.
  • Loading pretrained weights from JSON format and applying on the fly.

Quick Start

Load a pre-trained RTNeural model and instantiate it in your audio processing chain to run real-time inference.

Frequently Asked Questions about rtneural-inference

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run real-time neural network inference for guitar amp modeling?▼

Real-time neural network inference for guitar amp modeling is performed using RTNeural to execute deterministic per-sample forward passes in low-latency audio DSP chains. It processes audio on the fly by loading pretrained weights.

How do I load pretrained neural amp weights from JSON files in C++?▼

Pretrained neural amp weights are loaded directly from JSON format into the RTNeural::ModelT structure. This allows the C++ environment to instantiate the model and apply the weights on the fly for immediate audio processing.

Can I use RTNeural for per-sample audio processing without introducing latency?▼

RTNeural is designed specifically for per-sample audio processing workflows where low latency is essential. It achieves deterministic forward passes necessary for real-time NAM-style amp chains without introducing processing delays.

What is the best way to integrate neural network inference into an audio DSP chain?▼

The best way to integrate neural network inference into an audio DSP chain is using lightweight C++ integration with RTNeural. It enables real-time per-sample processing and supports optional SIMD backends configured via CMake for optimized performance.

Does RTNeural support SIMD backends for optimizing real-time audio inference?▼

RTNeural supports optional SIMD backends to optimize real-time audio inference performance. These backends are configured via CMake, enabling faster execution of neural network forward passes in C++ audio DSP applications.

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