孙豪泽-Baichuan-Omni-1.5：百川智能在端到端多模态大模型的实践探索.pdf

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1、ML-SummitML-Summitwww.cpp-www.ml-summit.orgwww.gosim.orgwww.pm-summit.orgML-SummitML-SummitML-SummitML-SummitML-SummitML-Summit孙孙豪豪泽泽 百百川川智智能能多多模模态态负负责责人人2017年毕业于北京大学，在NLP，搜索和推荐领域均有过行业一线的实践经历。加入百川智能以来先后从事文本预训练，SFT，code Agent以及多模态预训练相关工作，近期专注于全模态模型，特别是语音端到端模型的算法探索。目前开源的Baichuan-Omni-1.5全模态模型在文本能力，图像/

2、视频理解，语音理解和生成效果上取得了最佳的平衡。演演讲讲主主题题：B Ba ai ic ch hu ua an n-O Ommn ni i-1 1.5 5：百百川川智智能能在在端端到到端端多多模模态态大大模模型型的的实实践践探探索索ML-SummitML-Summit2 20 02 25 5 全球机器学习技术大会百百川川智智能能在在端端到到端端多多模模态态大大模模型型的的实实践践探探索索孙豪泽 百川多模态团队ML-SummitML-Summit目目录录Baichuan-Audio 端到端语音模型Baichuan-Omni-1.5 全模态模型实践未来展望ML-SummitML-Summit端到端

3、语音理解生成模型ML-SummitML-SummitMoshi:1.Full-duplex model capable of simultaneously generating audio tokens and text tokens through a multi-stream output mechanism.2.Balance semantic and acoustic features through distillation,similar to the approach of SpeechTokenizer.3.Streamable low-latency generation.M

4、ain Limitations:1.Dual-channel(text&speech)input requires extensive training from scratch,placing high demands on the LLMs parameter scale.2.Exhibits a noticeable intelligence degradation compared to text-only models.Compared to pure text-based dialogue models,their performance tends to degrade.End

5、to End Audio-LLM FrameworkMoshi:a speech-text foundation model for real-time dialogueML-SummitML-SummitVITA-1.5/MiniCPM-o/Freeze-Omni/etc.1.Pseudo end-to-end:three paradigms for text and speech input-output processing2.No intelligence degradation problem.3.Low training cost.Main Limitations:1.Inabil

6、ity to utilize pure audio data or reuse partial LLM parameters,limiting the scalability.2.Lack of paradigm unification between audio understanding,generation,and text processing,making it difficult to leverage advancements in the text domain for future improvements(e.g.RL).3.Potential deficiencies i