# Text-to-Audio Generation

AutoSynth serializes MIDI performance events, fixed synthesizer parameters, and variable-length modulation routes into a unified sequence and learns their dependencies with an audio-conditioned autoregressive model, trained first by supervised learning on large-scale automatically constructed audio-program pairs and then by group-relative policy optimization with a mixed reward, so that it predicts a synthesizer program directly from reference audio and converts text into a program via a pretrained audio generation model, achieving competitive results in both synthesizer inversion and text-driven generation.

AutoSynth serializes MIDI performance events, fixed synthesizer parameters, and variable-length modulation routes into a unified sequence and learns their dependencies with an audio-conditioned autoregressive model, trained first by supervised learning on large-scale automatically constructed audio-program pairs and then by group-relative policy optimization with a mixed reward, so that it predicts a synthesizer program directly from reference audio and converts text into a program via a pretrained audio generation model, achieving competitive results in both synthesizer inversion and text-driven generation.

The work introduces Unite-Audio, which jointly learns continuous audio representations and latent flow matching for text-to-audio generation; by coupling reconstruction with self-supervised generative prediction it lets the generative objective directly shape the latent space, and it applies Flow-GRPO post-training to improve text-conditioned generation; experiments show competitive text-to-audio performance with a compact latent flow model, and ablation studies confirm the benefit of jointly learning the audio representation and the generative model.

The work introduces Unite-Audio, which jointly learns continuous audio representations and latent flow matching for text-to-audio generation; by coupling reconstruction with self-supervised generative prediction it lets the generative objective directly shape the latent space, and it applies Flow-GRPO post-training to improve text-conditioned generation; experiments show competitive text-to-audio performance with a compact latent flow model, and ablation studies confirm the benefit of jointly learning the audio representation and the generative model.