Krotos Audio
The Industry-Standard Performative Sound Design Platform for AI-Enhanced Post-Production.
Professional-grade text-to-music generation via Meta's state-of-the-art transformer architecture.
MusicGen, developed by Meta AI's FAIR (Fundamental AI Research) team, represents a significant leap in controllable audio synthesis. Built on the AudioCraft framework, it utilizes a single-stage Auto-regressive Transformer model trained on over 20,000 hours of licensed music. Unlike previous diffusion-based approaches, MusicGen processes compressed audio tokens through Meta’s EnCodec neural audio compressor, allowing it to generate high-fidelity 32kHz mono or stereo audio. By 2026, MusicGen has established itself as the industry standard for locally-hosted generative audio, favored by developers and sound designers who require data privacy and fine-grained control over melodic conditioning. The architecture supports both text-only prompts and melody-guided generation, where an input audio file provides the structural backbone (pitch and rhythm) for the generated output. Its market position is unique as it bridges the gap between high-level creative direction and low-level signal processing, providing a scalable solution for everything from dynamic video game soundscapes to rapid prototyping in commercial music production environments.
Uses a convolutional autoencoder with a latent space compressed by Residual Vector Quantization (RVQ).
The Industry-Standard Performative Sound Design Platform for AI-Enhanced Post-Production.
Transform text prompts into broadcast-quality, full-length musical compositions in seconds.
Reactive, copyright-safe AI music tailored to your gameplay in real-time.
Professional-grade generative audio engine for non-destructive music production and sonic branding.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Extracts chromagrams from an input audio file to guide the transformer's pitch generation.
An efficient decoder-only transformer that predicts multiple streams of parallel codebooks.
Implements a sliding window approach with audio overlap for seamless continuation beyond 30 seconds.
Combines melody structure from source A with stylistic descriptors from text prompt B.
Support for FP16 and quantization for running the 'small' model (300M params) on consumer hardware.
Propagates spatial information through specialized stereo-head training.
Creating adaptive music that changes based on player actions without huge storage overhead.
Registry Updated:2/7/2026
Avoiding copyright strikes and high licensing fees for YouTube/Social Media background music.
Producers needing a specific melody line or rhythm to build a track around.