# Spectral Forcing

WaveGen pulls zero-shot speech synthesis back from multi-stage pipelines into a single diffusion model: text produces raw waveform directly, without a neural audio codec, Mel vocoder, VAE latent or duration predictor. Spectral Forcing makes the same model learn a low-resolution spectral trajectory alongside the target waveform trajectory, giving text-speech alignment a structured guide in high-resolution waveform space. Trained in one stage on open Emilia data, the 0.6B-parameter WaveGen reaches WER 1.47 and speaker similarity SIM 0.75 on the Seed-en benchmark.

WaveGen pulls zero-shot speech synthesis back from multi-stage pipelines into a single diffusion model: text produces raw waveform directly, without a neural audio codec, Mel vocoder, VAE latent or duration predictor. Spectral Forcing makes the same model learn a low-resolution spectral trajectory alongside the target waveform trajectory, giving text-speech alignment a structured guide in high-resolution waveform space. Trained in one stage on open Emilia data, the 0.6B-parameter WaveGen reaches WER 1.47 and speaker similarity SIM 0.75 on the Seed-en benchmark.