Summary of Llama-omni: Seamless Speech Interaction with Large Language Models, by Qingkai Fang et al.
LLaMA-Omni: Seamless Speech Interaction with Large Language Models
by Qingkai Fang, Shoutao Guo, Yan Zhou, Zhengrui Ma, Shaolei Zhang, Yang Feng
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Models that let people talk to large language models (LLMs) in real-time are cool! But building these models is still a challenge. To make it easier, we created LLaMA-Omni, a new model that lets you interact with LLMs using speech. This model is fast and good at responding to what you say. It can even generate text and speech responses from just your voice. We built our model based on the latest Llama-3.1-8B-Instruct model and tested it with a huge dataset of 200K speech instructions and responses. |
Keywords
» Artificial intelligence » Decoder » Encoder » Gpt » Llama
