The AnyGPT model has been tested in various scenarios, demonstrating its ability to facilitate any-to-any multimodal conversation while achieving performance comparable to specialized models across all modalities. The model can handle tasks such as voice cloning, converting speech instructions into text, image, music, and speech responses, and translating emotions from images into music. The results prove that discrete representations can effectively and conveniently unify multiple modalities within a language model.
Key takeaways:
- The researchers introduce AnyGPT, a multimodal language model that can process various modalities like speech, text, images, and music using discrete representations.
- AnyGPT can be trained without any changes to the current large language model architecture or training paradigms, relying solely on data-level preprocessing.
- The team created a multimodal text-centric dataset for multimodal alignment pre-training and synthesized a large-scale any-to-any multimodal instruction dataset with 108k samples of multi-turn conversations.
- Experimental results show that AnyGPT can facilitate any-to-any multimodal conversation and achieve performance comparable to specialized models across all modalities.