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MinMo: A Multimodal Large Language Model for Seamless Voice Interaction

10 January 2025
Qian Chen
Yafeng Chen
Yanni Chen
Mengzhe Chen
Yuxiao Chen
Chong Deng
Zhihao Du
Ruize Gao
Changfeng Gao
Zhifu Gao
Yabin Li
Xiang Lv
Jiaqing Liu
Haoneng Luo
B. Ma
Chongjia Ni
Xian Shi
Jialong Tang
Hui Wang
Hao Wang
Wen Wang
Yansen Wang
Yunlan Xu
Fan Yu
Zhijie Yan
Yexin Yang
Baosong Yang
Xian Yang
Guanrou Yang
Tianyu Zhao
Qinglin Zhang
Shiliang Zhang
Nan Zhao
Pei Zhang
Chuxu Zhang
Jinren Zhou
    AuLLMMLLM
ArXiv (abs)PDFHTML
Abstract

Recent advancements in large language models (LLMs) and multimodal speech-text models have laid the groundwork for seamless voice interactions, enabling real-time, natural, and human-like conversations. Previous models for voice interactions are categorized as native and aligned. Native models integrate speech and text processing in one framework but struggle with issues like differing sequence lengths and insufficient pre-training. Aligned models maintain text LLM capabilities but are often limited by small datasets and a narrow focus on speech tasks. In this work, we introduce MinMo, a Multimodal Large Language Model with approximately 8B parameters for seamless voice interaction. We address the main limitations of prior aligned multimodal models. We train MinMo through multiple stages of speech-to-text alignment, text-to-speech alignment, speech-to-speech alignment, and duplex interaction alignment, on 1.4 million hours of diverse speech data and a broad range of speech tasks. After the multi-stage training, MinMo achieves state-of-the-art performance across various benchmarks for voice comprehension and generation while maintaining the capabilities of text LLMs, and also facilitates full-duplex conversation, that is, simultaneous two-way communication between the user and the system. Moreover, we propose a novel and simple voice decoder that outperforms prior models in voice generation. The enhanced instruction-following capabilities of MinMo supports controlling speech generation based on user instructions, with various nuances including emotions, dialects, and speaking rates, and mimicking specific voices. For MinMo, the speech-to-text latency is approximately 100ms, full-duplex latency is approximately 600ms in theory and 800ms in practice. The MinMo project web page isthis https URL, and the code and models will be released soon.

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