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README.md
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- speech-language models
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- speech interaction
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- speech-to-speech
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- speech-language models
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- speech interaction
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- speech-to-speech
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---
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# π§ LLaMA-Omni: Seamless Speech Interaction with Large Language Models
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> **Authors: [Qingkai Fang](https://fangqingkai.github.io/), [Shoutao Guo](https://scholar.google.com/citations?hl=en&user=XwHtPyAAAAAJ), [Yan Zhou](https://zhouyan19.github.io/zhouyan/), [Zhengrui Ma](https://scholar.google.com.hk/citations?user=dUgq6tEAAAAJ), [Shaolei Zhang](https://zhangshaolei1998.github.io/), [Yang Feng*](https://people.ucas.edu.cn/~yangfeng?language=en)**
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[![arXiv](https://img.shields.io/badge/arXiv-xxxx.xxxxx-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/xxxx.xxxxx)
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[![model](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging_Face-Model-blue.svg)](https://huggingface.co/ICTNLP/Llama-3.1-8B-Omni)
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[![code](https://img.shields.io/badge/Github-Code-keygen.svg?logo=github)](https://github.com/ictnlp/LLaMA-Omni)
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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.
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![](images/model.png)
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## π‘ Highlights
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πͺ **Built on Llama-3.1-8B-Instruct, ensuring high-quality responses.**
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π **Low-latency speech interaction with a latency as low as 226ms.**
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π§ **Simultaneous generation of both text and speech responses.**
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β»οΈ **Trained in less than 3 days using just 4 GPUs.**
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## Install
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1. Clone this repository.
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```shell
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git clone https://github.com/ictnlp/LLaMA-Omni
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cd LLaMA-Omni
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```
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2. Install packages.
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```shell
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conda create -n llama-omni python=3.10
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conda activate llama-omni
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pip install pip==24.0
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pip install -e .
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```
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3. Install `fairseq`.
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```shell
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git clone https://github.com/pytorch/fairseq
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cd fairseq
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pip install -e . --no-build-isolation
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```
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4. Install `flash-attention`.
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```shell
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pip install flash-attn --no-build-isolation
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```
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## Quick Start
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1. Download the `Llama-3.1-8B-Omni` model from π€[Huggingface](https://huggingface.co/ICTNLP/Llama-3.1-8B-Omni).
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2. Download the `Whisper-large-v3` model.
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```shell
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import whisper
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model = whisper.load_model("large-v3", download_root="models/speech_encoder/")
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```
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3. Download the unit-based HiFi-GAN vocoder.
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```shell
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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/
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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/
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```
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## Gradio Demo
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1. Launch a controller.
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```shell
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python -m omni_speech.serve.controller --host 0.0.0.0 --port 10000
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```
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2. Launch a gradio web server.
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```shell
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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
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```
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3. Launch a model worker.
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```shell
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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
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```
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4. Visit [http://localhost:8000/](http://localhost:8000/) and interact with LLaMA-3.1-8B-Omni!
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## Local Inference
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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.
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```shell
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bash omni_speech/infer/run.sh omni_speech/infer/examples
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```
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## Acknowledgements
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- [LLaVA](https://github.com/haotian-liu/LLaVA): The codebase we built upon.
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- [SLAM-LLM](https://github.com/X-LANCE/SLAM-LLM): We borrow some code about speech encoder and speech adaptor.
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## Citation
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If you have any questions, please feel free to submit an issue or contact `[email protected]`.
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If our work is useful for you, please cite as:
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```
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@article{fang-etal-2024-llama-omni,
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title={LLaMA-Omni: Seamless Speech Interaction with Large Language Models},
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author={Fang, Qingkai and Guo, Shoutao and Zhou, Yan and Ma, Zhengrui and Zhang, Shaolei and Feng, Yang},
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journal={arXiv preprint arXiv:xxxx.xxxxx},
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year={2024}
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}
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```
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