Summary of Mini-omni: Language Models Can Hear, Talk While Thinking in Streaming, by Zhifei Xie and Changqiao Wu
Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming
by Zhifei Xie, Changqiao Wu
First submitted to arxiv on: 29 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); 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 The abstract discusses recent advances in language models, specifically GPT-4o, which has achieved near-human natural fluency in real-time conversations with humans. However, current academic models rely on extra TTS systems for speech synthesis, resulting in undesirable latency. The paper introduces the Mini-Omni, an audio-based end-to-end conversational model capable of real-time speech interaction, by proposing a text-instructed speech generation method and batch-parallel strategies during inference. This enables the retention of the original model’s language capabilities with minimal degradation. The authors also introduce the VoiceAssistant-400K dataset to fine-tune models optimized for speech output. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to talk to computers using just your voice, like having a real conversation. Researchers have been working on making computers understand and respond to us more naturally, but it’s been hard because they need extra help from other systems to make the computer “talk”. The Mini-Omni model can do this all by itself, in real-time, without needing any extra help. This is important for things like voice assistants and chatbots that people use every day. |
Keywords
» Artificial intelligence » Gpt » Inference
