Covo-Audio Technical Report
In this work, we present Covo-Audio, a 7B-parameter end-to-end LALM that directly processes continuous audio inputs and generates audio outputs within a single unified architecture. Through large-scale curated pretraining and targeted post-training, Covo-Audio achieves state-of-the-art or competitive performance among models of comparable scale across a broad spectrum of tasks, including speech-text modeling, spoken dialogue, speech understanding, audio understanding, and full-duplex voice interaction. Extensive evaluations demonstrate that the pretrained foundation model exhibits strong speech-text comprehension and semantic reasoning capabilities on multiple benchmarks, outperforming representative open-source models of comparable scale. Furthermore, Covo-Audio-Chat, the dialogue-oriented variant, demonstrates strong spoken conversational abilities, including understanding, contextual reasoning, instruction following, and generating contextually appropriate and empathetic responses, validating its applicability to real-world conversational assistant scenarios. Covo-Audio-Chat-FD, the evolved full-duplex model, achieves substantially superior performance on both spoken dialogue capabilities and full-duplex interaction behaviors, demonstrating its competence in practical robustness. To mitigate the high cost of deploying end-to-end LALMs for natural conversational systems, we propose an intelligence-speaker decoupling strategy that separates dialogue intelligence from voice rendering, enabling flexible voice customization with minimal text-to-speech (TTS) data while preserving dialogue performance. Overall, our results highlight the strong potential of 7B-scale models to integrate sophisticated audio intelligence with high-level semantic reasoning, and suggest a scalable path toward more capable and versatile LALMs.
698bf2d76052d3bed9630a6f
Wenfu Wang
698bf2d76052d3bed9630a70
Chenxing Li
698bf2d76052d3bed9630a71
Liqiang Zhang
admin_assigned
2026-03-16T13:21:23.051Z
6741591dac35394749d77a81
/avatars/7edfdc4f352e2335d1d74c960d3db81b.svg
Liqiang Zhang
Ckzhan9
user
Ckzhan9
698bf2d76052d3bed9630a72
Yiyang Zhao
698bf2d76052d3bed9630a73
Yuxiang Zou
admin_assigned
2026-03-16T13:21:39.355Z
644b7e4cc83645cbb7300223
/avatars/be8763e647266ca33653764325a4add9.svg
Yuxiang Zou
sysuzyx
user
sysuzyx
698bf2d76052d3bed9630a74
Hanzhao Li
admin_assigned
2026-03-16T13:21:46.512Z
6629fffe5e13d8145e4ec2bf
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/sfMur3rsdNP1pYrjzwkdG.jpeg
Hanzhao Li
kkksuper
user
kkksuper
698bf2d76052d3bed9630a75
Mingyu Cui
698bf2d76052d3bed9630a76
Hao Zhang
698bf2d76052d3bed9630a77
Kun Wei
admin_assigned
2026-03-16T13:21:59.896Z
6478495c5bf35e70ab5b6928
/avatars/de1661c8d2ed1a6fc26b808ed600f2cf.svg
kun wei
kunwei2005
user
kunwei2005
698bf2d76052d3bed9630a78
Le Xu
698bf2d76052d3bed9630a79
Zikang Huang
698bf2d76052d3bed9630a7a
Jiajun Xu
698bf2d76052d3bed9630a7b
Jiliang Hu
698bf2d76052d3bed9630a7c
Xiang He
698bf2d76052d3bed9630a7d
Zeyu Xie
admin_assigned
2026-03-16T13:22:33.058Z
64ca3251710645aa7bd1ccdc
https://cdn-avatars.huggingface.co/v1/production/uploads/64ca3251710645aa7bd1ccdc/iQV8f8gU220ECxIkTkea0.jpeg
Zeyu Xie
ZeyuXie
user
ZeyuXie
698bf2d76052d3bed9630a7e
Jiawen Kang
698bf2d76052d3bed9630a7f
Youjun Chen
admin_assigned
2026-03-16T13:23:01.098Z
669cb7b35bc23a0628ec163f
/avatars/1c2f0cd0d22cf40e2e2955fdef13b965.svg
Youjun Chen
James-cuhk
user
James-cuhk
698bf2d76052d3bed9630a80
Meng Yu
698bf2d76052d3bed9630a81
Dong Yu
698bf2d76052d3bed9630a82
Rilin Chen
698bf2d76052d3bed9630a83
Linlin Di
698bf2d76052d3bed9630a84
Shulin Feng
698bf2d76052d3bed9630a85
Na Hu
698bf2d76052d3bed9630a86
Yang Liu
698bf2d76052d3bed9630a87
Bang Wang
698bf2d76052d3bed9630a88
Shan Yang
Covo-Audio Technical Report
In this work, we present Covo-Audio, a 7B-parameter end-to-end LALM that directly processes continuous audio inputs and generates audio outputs within a single unified architecture. Through large-scale curated pretraining and targeted post-training, Covo-Audio achieves state-of-the-art or competitive performance among models of comparable scale across a broad spectrum of tasks, including speech-text modeling, spoken dialogue, speech understanding, audio understanding, and full-duplex voice interaction. Extensive evaluations demonstrate that the pretrained foundation model exhibits strong speech-text comprehension and semantic reasoning capabilities on multiple benchmarks, outperforming representative open-source models of comparable scale. Furthermore, Covo-Audio-Chat, the dialogue-oriented variant, demonstrates strong spoken conversational abilities, including understanding, contextual reasoning, instruction following, and generating contextually appropriate and empathetic responses, validating its applicability to real-world conversational assistant scenarios. Covo-Audio-Chat-FD, the evolved full-duplex model, achieves substantially superior performance on both spoken dialogue capabilities and full-duplex interaction behaviors, demonstrating its competence in practical robustness. To mitigate the high cost of deploying end-to-end LALMs for natural conversational systems, we propose an intelligence-speaker decoupling strategy that separates dialogue intelligence from voice rendering, enabling flexible voice customization with minimal text-to-speech (TTS) data while preserving dialogue performance. Overall, our results highlight the strong potential of 7B-scale models to integrate sophisticated audio intelligence with high-level semantic reasoning, and suggest a scalable path toward more capable and versatile LALMs.
Covo-Audio is a 7B-parameter end-to-end large audio language model that processes continuous audio inputs and generates audio outputs, achieving state-of-the-art performance across speech-text modeling, spoken dialogue, and full-duplex voice interaction tasks through large-scale pretraining and post-training techniques.
end-to-end LALM continuous audio inputs audio outputs large-scale curated pretraining targeted post-training speech-text modeling spoken dialogue speech understanding audio understanding full-duplex voice interaction speech-text comprehension semantic reasoning dialogue-oriented variant conversational abilities contextual reasoning instruction following empathetic responses full-duplex model intelligent-speaker decoupling strategy voice rendering text-to-speech