README.md

metadata

license: apache-2.0
language:
  - en
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
tags:
  - large language models
  - speech-language models
  - speech interaction
  - speech-to-speech

🎧 LLaMA-Omni: Seamless Speech Interaction with Large Language Models

Authors: Qingkai Fang, Shoutao Guo, Yan Zhou, Zhengrui Ma, Shaolei Zhang, Yang Feng*

[Paper] [Model] [Code]

LLaMA-Omni is a speech-language model built upon Llama-3.1-8B-Instruct. It supports low-latency and high-quality speech interactions, simultaneously generating both text and speech responses based on speech instructions.

💡 Highlights

💪 Built on Llama-3.1-8B-Instruct, ensuring high-quality responses.

🚀 Low-latency speech interaction with a latency as low as 226ms.

🎧 Simultaneous generation of both text and speech responses.

♻️ Trained in less than 3 days using just 4 GPUs.

Install

1. Clone this repository.

git clone https://github.com/ictnlp/LLaMA-Omni
cd LLaMA-Omni

2. Install packages.

conda create -n llama-omni python=3.10
conda activate llama-omni
pip install pip==24.0
pip install -e .

3. Install fairseq.

git clone https://github.com/pytorch/fairseq
cd fairseq
pip install -e . --no-build-isolation

4. Install flash-attention.

pip install flash-attn --no-build-isolation

Quick Start

1. Download the Llama-3.1-8B-Omni model from 🤗Huggingface.

2. Download the Whisper-large-v3 model.

import whisper
model = whisper.load_model("large-v3", download_root="models/speech_encoder/")

3. Download the unit-based HiFi-GAN vocoder.

wget https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/vocoder/code_hifigan/mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj/g_00500000 -P vocoder/
wget https://dl.fbaipublicfiles.com/fairseq/speech_to_speech/vocoder/code_hifigan/mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj/config.json -P vocoder/

Gradio Demo

1. Launch a controller.

python -m omni_speech.serve.controller --host 0.0.0.0 --port 10000

2. Launch a gradio web server.

python -m omni_speech.serve.gradio_web_server --controller http://localhost:10000 --port 8000 --model-list-mode reload --vocoder vocoder/g_00500000 --vocoder-cfg vocoder/config.json

3. Launch a model worker.

python -m omni_speech.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path Llama-3.1-8B-Omni --model-name Llama-3.1-8B-Omni --s2s

4. Visit http://localhost:8000/ and interact with LLaMA-3.1-8B-Omni!

Note: Due to the instability of streaming audio playback in Gradio, we have only implemented streaming audio synthesis without enabling autoplay. If you have a good solution, feel free to submit a PR. Thanks!

Local Inference

To run inference locally, please organize the speech instruction files according to the format in the omni_speech/infer/examples directory, then refer to the following script.

bash omni_speech/infer/run.sh omni_speech/infer/examples

Acknowledgements

• LLaVA: The codebase we built upon.
• SLAM-LLM: We borrow some code about speech encoder and speech adaptor.

Citation

If you have any questions, please feel free to submit an issue or contact [email protected].

If our work is useful for you, please cite as:

@article{fang-etal-2024-llama-omni,
  title={LLaMA-Omni: Seamless Speech Interaction with Large Language Models},
  author={Fang, Qingkai and Guo, Shoutao and Zhou, Yan and Ma, Zhengrui and Zhang, Shaolei and Feng, Yang},
  journal={arXiv preprint arXiv:xxxx.xxxxx},
  year={2024}
}