Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming
Authors: name: Zhifei Xie, name: Changqiao Wu

Recent advances in language models have achieved significant progress.
GPT-4o, as a new milestone, has enabled real-time conversations with humans,
demonstrating near-human natural fluency. Such human-computer interaction
necessitates models with the capability to perform reasoning directly with the
audio modality and generate output in streaming. However, this remains beyond
the reach of current academic models, as they typically depend on extra TTS
systems for speech synthesis, resulting in undesirable latency. This paper
introduces the Mini-Omni, an audio-based end-to-end conversational model,
capable of real-time speech interaction. To achieve this capability, we propose
a text-instructed speech generation method, along with batch-parallel
strategies during inference to further boost the performance. Our method also
helps to retain the original model's language capabilities with minimal
degradation, enabling other works to establish real-time interaction
capabilities. We call this training method "Any Model Can Talk". We also
introduce the VoiceAssistant-400K dataset to fine-tune models optimized for
speech output. To our best knowledge, Mini-Omni is the first fully end-to-end,
open-source model for real-time speech interaction, offering valuable potential
for future research.