LLaMA-Omni: Seamless Speech Interaction with Large Language Models

TL;DR

LLaMA-Omni introduces a low-latency, open-source speech interaction model that directly generates text and speech responses from speech inputs, enhancing real-time user experience without transcription.

Contribution

The paper presents LLaMA-Omni, a novel architecture integrating speech encoding and streaming decoding for direct speech-to-response interaction using open-source LLMs.

Findings

Outperforms previous models in response quality and style.

Achieves response latency as low as 226ms.

Training completes in less than 3 days on 4 GPUs.

Abstract

Models like GPT-4o enable real-time interaction with large language models (LLMs) through speech, significantly enhancing user experience compared to traditional text-based interaction. However, there is still a lack of exploration on how to build speech interaction models based on open-source LLMs. To address this, we propose LLaMA-Omni, a novel model architecture designed for low-latency and high-quality speech interaction with LLMs. LLaMA-Omni integrates a pretrained speech encoder, a speech adaptor, an LLM, and a streaming speech decoder. It eliminates the need for speech transcription, and can simultaneously generate text and speech responses directly from speech instructions with extremely low latency. We build our model based on the latest Llama-3.1-8B-Instruct model. To align the model with speech interaction scenarios, we construct a dataset named InstructS2S-200K, which includes 200K speech instructions and corresponding speech responses. Experimental results show that compared to previous speech-language models, LLaMA-Omni provides better responses in both content and style, with a response latency as low as 226ms. Additionally, training LLaMA-Omni takes less than 3 days on just 4 GPUs, paving the way for the efficient development of speech-language models in the future.