Why We Moved from Transformers to Latent Diffusion for Audio

The industry standard (used by OpenAI and old ElevenLabs models) relies on Auto-Regressive Transformers. These predict the next audio token based on the previous one. This is great for text, but audio is continuous, not discrete. This often leads to 'robotic artifacts' or metallic sounds when the model gets confused.

The Diffusion Paradigm Shift

Morvoice utilizes a Latent Diffusion Model (LDM). Instead of predicting the next step, we start with pure noise and iteratively 'denoise' it guided by the text input. This allows for a holistic generation process. The model 'hears' the entire sentence structure before it commits to a sound.

Handling Breath and Pauses

Because Diffusion considers the whole context, it naturally inserts breaths *before* long sentences and pauses *after* commas, mimicking human physiology without explicit rules. This is the secret behind our high 'Naturalness MOS' (Mean Opinion Score).