OpenOmni: Large Language Models Pivot Zero-shot Omnimodal Alignment
  across Language with Real-time Self-Aware Emotional Speech Synthesis
Recent advancements in omnimodal learning have been achieved in understanding
and generation across images, text, and speech, though mainly within
proprietary models. Limited omnimodal datasets and the inherent challenges
associated with real-time emotional speech generation have hindered open-source
progress. To address these issues, we propose openomni, a two-stage training
method combining omnimodal alignment and speech generation to develop a
state-of-the-art omnimodal large language model. In the alignment phase, a
pre-trained speech model is further trained on text-image tasks to generalize
from vision to speech in a (near) zero-shot manner, outperforming models
trained on tri-modal datasets. In the speech generation phase, a lightweight
decoder facilitates real-time emotional speech through training on speech tasks
and preference learning. Experiments demonstrate that openomni consistently
improves across omnimodal, vision-language, and speech-language evaluations,
enabling natural, emotion-rich dialogues and real-time emotional speech
generation.
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Run Luo
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Ting-En Lin
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Haonan Zhang
claimed_verified
2025-01-09T20:01:38.484Z
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Haonan Zhang
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haonanzhang
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Yuchuan Wu
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Xiong Liu
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Min Yang
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Yongbin Li
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Longze Chen
claimed_verified
2025-06-04T09:05:51.650Z
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Longze Chen
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lzchen2001
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Jiaming Li
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2025-09-03T08:33:33.466Z
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Jiaming Li
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Geaming
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Lei Zhang
claimed_verified
2025-01-09T10:07:04.349Z
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Lei Zhang
user
Lemoncoke
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Yangyi Chen
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Hamid Alinejad-Rokny
677f302c7bd81737ce7a9411
Fei Huang
OpenOmni: Large Language Models Pivot Zero-shot Omnimodal Alignment
  across Language with Real-time Self-Aware Emotional Speech Synthesis
Recent advancements in omnimodal learning have been achieved in understanding
and generation across images, text, and speech, though mainly within
proprietary models. Limited omnimodal datasets and the inherent challenges
associated with real-time emotional speech generation have hindered open-source
progress. To address these issues, we propose openomni, a two-stage training
method combining omnimodal alignment and speech generation to develop a
state-of-the-art omnimodal large language model. In the alignment phase, a
pre-trained speech model is further trained on text-image tasks to generalize
from vision to speech in a (near) zero-shot manner, outperforming models
trained on tri-modal datasets. In the speech generation phase, a lightweight
decoder facilitates real-time emotional speech through training on speech tasks
and preference learning. Experiments demonstrate that openomni consistently
improves across omnimodal, vision-language, and speech-language evaluations,
enabling natural, emotion-rich dialogues and real-time emotional speech
generation.
Openomni, a two-stage training method combining omnimodal alignment and speech generation, achieves state-of-the-art performance in generating natural, emotion-rich dialogues and real-time emotional speech.
omnimodal learning pre-trained speech model text-image tasks zero-shot lightweight decoder preference learning omnimodal large language model