Summary of C3llm: Conditional Multimodal Content Generation Using Large Language Models, by Zixuan Wang et al.
C3LLM: Conditional Multimodal Content Generation Using Large Language Models
by Zixuan Wang, Qinkai Duan, Yu-Wing Tai, Chi-Keung Tang
First submitted to arxiv on: 25 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary C3LLM is a novel framework that combines three tasks – video-to-audio, audio-to-text, and text-to-audio – using a Large Language Model (LLM) structure. The framework adapts the LLM to align different modalities, synthesize conditional information, and generate multimodal outputs in a discrete manner. The contributions include adapting a hierarchical structure for audio generation tasks with pre-trained audio codebooks, training an LLM to generate audio semantic tokens from given conditions, and using a non-autoregressive transformer to generate acoustic tokens. Additionally, the framework compresses the semantic meanings of LLMs into acoustic tokens, similar to adding “acoustic vocabulary” to LLM. The combined tasks provide more versatility in an end-to-end fashion, achieving improved results through various automated evaluation metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary C3LLM is a new way for computers to understand and create different types of information – like pictures, sounds, and written words. This helps machines better understand us and how we communicate. The researchers used special computer models to make this happen, which lets them combine three tasks: turning videos into audio, understanding audio, and turning text into audio. They also came up with a new way to generate sounds that are more like real-life sounds. This is important because it makes machines better at understanding us and making things that sound more like real life. |
Keywords
» Artificial intelligence » Autoregressive » Large language model » Transformer
