AudioMelody
Professional-grade AI Harmonic Synthesis and Stem Reconstruction for Modern Sound Engineering.
Open-source generative audio research for high-fidelity music and sound design.
Harmonai is the specialized audio research laboratory within Stability AI, dedicated to developing open-source generative audio models. By 2026, Harmonai has cemented its position as the primary open-weights alternative to proprietary systems like Suno and Udio. Their architecture primarily leverages Latent Diffusion Models (LDM) and Variational Autoencoders (VAEs) to compress raw audio into manageable latent spaces, enabling the generation of 44.1kHz stereo audio. Unlike autoregressive models that generate audio token-by-token (leading to high latency), Harmonai's diffusion-based approach allows for rapid parallel sampling and superior temporal coherence in long-form compositions. The lab is best known for 'Dance Diffusion' and the underlying architecture powering 'Stable Audio'. For the 2026 market, Harmonai focus has shifted toward 'Audio-to-Audio' workflows, allowing producers to use their own recordings as structural scaffolds for AI-generated enhancements. Their commitment to ethical data sourcing, primarily through partnerships like AudioSparx, ensures that the generated outputs are commercially viable and free from copyright infringement concerns that plague other generative platforms.
Uses a VAE to compress 44.1kHz audio into a 1D latent space, reducing VRAM requirements for long-form generation.
Professional-grade AI Harmonic Synthesis and Stem Reconstruction for Modern Sound Engineering.
Transform static PDFs and long-form documents into immersive, studio-quality audiobooks using neural TTS.
The premier generative audio platform for lifelike speech synthesis and voice cloning.
Enterprise-grade AI music composition for instant, royalty-free creative workflows.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Injects noise into an existing audio latent and diffuses it back based on a text prompt.
Supports PyTorch Lightning for scaling model training across large GPU clusters.
Trained exclusively on licensed datasets from AudioSparx comprising over 800,000 tracks.
Uses CLAP embeddings to transfer aesthetic qualities from a prompt to an input audio file.
Dynamic positional embeddings allow the model to generate audio ranging from 1 second to 3 minutes.
Operates directly in the time domain via latent space rather than relying on lossy STFT spectrograms.
Sourcing specific sound effects like 'laser blast in a wet cave' is time-consuming and expensive.
Registry Updated:2/7/2026
Producers often struggle to find unique drum patterns that fit a specific tempo and mood.
Open-world games require hours of non-repetitive ambient audio.