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--- |
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license: llama3.1 |
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language: |
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- en |
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base_model: meta-llama/Meta-Llama-3.1-8B-Instruct |
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tags: |
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- large language models |
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- speech-language models |
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- speech interaction |
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- speech-to-speech |
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library_name: llama-omni |
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--- |
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# 🦙🎧 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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[[Paper]](https://arxiv.org/abs/2409.06666) [[Model]](https://huggingface.co./ICTNLP/Llama-3.1-8B-Omni) [[Code]](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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## 💡 Highlights |
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- 💪 **Built on Llama-3.1-8B-Instruct, ensuring high-quality responses.** |
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- 🚀 **Low-latency speech interaction with a latency as low as 226ms.** |
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- 🎧 **Simultaneous generation of both text and speech responses.** |
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- ♻️ **Trained in less than 3 days using just 4 GPUs.** |
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<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/65b7573482d384513443875e/dr4XWUxzuVQ52lBuzNBTt.mp4"></video> |
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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 🤗[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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**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!** |
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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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## LICENSE |
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Our code is released under the Apache-2.0 License. Our model, as it is built on Llama 3.1, is required to comply with the [Llama 3.1 License](https://llama.meta.com/llama3_1/license/). |
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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:2409.06666}, |
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year={2024} |
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} |
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``` |
