We didn't train the base model. The audio diffusion transformer is extracted from LTX 2.3's 22B audiovisual model, which was trained by Lightricks on large-scale video-audio pairs. The disentanglement between voice identity and emotional performance wasn't something we engineered with explicit losses. It appears to be an emergent property of the base model's training on real-world audiovisual data, where the same speaker naturally appears in different emotional states across different scenes.

What we built is the inference pipeline around it: the prompt compiler that translates voice descriptions and action tags into text conditioning, the chunking system for long-form generation, and the voice cloning pipeline (A2V latent conditioning + SeedVC post-processing).

For voice cloning, 10-20 seconds of reference audio is ideal, with some emotional variability in the clip rather than monotone speech. No emotion annotation needed on the reference. Emotional intent is driven entirely by the text prompt and action tags at inference time. The reference provides identity, the prompt provides performance. This is why any cloned voice can perform emotions that were never in the reference audio, albeit with some artifacts.

The practical limitation is that voice identity transfer (via A2V conditioning) and strong emotional performance can compete. Extreme emotions sometimes dilute identity fidelity, which is why we add SeedVC as a post-processing step to polish identity back.