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https://github.com/DAMO-NLP-SG/Video-LLaMA

[EMNLP 2023 Demo] Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
https://github.com/DAMO-NLP-SG/Video-LLaMA

blip2 cross-modal-pretraining large-language-models llama minigpt4 multi-modal-chatgpt video-language-pretraining vision-language-pretraining

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[EMNLP 2023 Demo] Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding

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Video-LLaMA




# Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding



This is the repo for the Video-LLaMA project, which is working on empowering large language models with video and audio understanding capabilities. 











 






## News

- 
 [2024.06.03] 🚀🚀 We officially launch VideoLLaMA2 with stronger performances and easier-to-use codebase, come to try it out!


- [11.14] ⭐️ The current README file is for **Video-LLaMA-2** (LLaMA-2-Chat as language decoder) only, instructions for using the previous version of Video-LLaMA (Vicuna as language decoder) can be found at [here](https://github.com/DAMO-NLP-SG/Video-LLaMA/blob/main/README_Vicuna.md).

- [08.03] 🚀🚀 Release **Video-LLaMA-2** with [Llama-2-7B/13B-Chat](https://huggingface.co/meta-llama) as language decoder

    - **NO** delta weights and separate Q-former weights anymore, full weights to run Video-LLaMA are all here :point_right: [[7B](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-2-7B-Finetuned)][[13B](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-2-13B-Finetuned)] 

    - Allow further customization starting from our pre-trained checkpoints [[7B-Pretrained](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-2-7B-Pretrained)] [[13B-Pretrained](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-2-13B-Pretrained)]

- [06.14]  **NOTE**: The current online interactive demo is primarily for English chatting and it may **NOT** be a good option to ask Chinese questions since Vicuna/LLaMA does not represent Chinese texts very well. 

- [06.13]  **NOTE**: The audio support is **ONLY** for Vicuna-7B by now although we have several VL checkpoints available for other decoders.

- [06.10]  **NOTE**: We have NOT updated the HF demo yet because the whole framework (with the audio branch) cannot run normally on A10-24G. The current running demo is still the previous version of Video-LLaMA. We will fix this issue soon.

- [06.08] 🚀🚀 Release the checkpoints of the audio-supported Video-LLaMA. Documentation and example outputs are also updated.    

- [05.22] 🚀🚀 Interactive demo online, try our Video-LLaMA (with **Vicuna-7B** as language decoder) at [Hugging Face](https://huggingface.co/spaces/DAMO-NLP-SG/Video-LLaMA) and [ModelScope](https://pre.modelscope.cn/studios/damo/video-llama/summary)!!

- [05.22] ⭐️ Release **Video-LLaMA v2** built with Vicuna-7B

- [05.18] 🚀🚀 Support video-grounded chat in Chinese 

    - [**Video-LLaMA-BiLLA**](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-Series/resolve/main/finetune-billa7b-zh.pth): we introduce [BiLLa-7B-SFT](https://huggingface.co/Neutralzz/BiLLa-7B-SFT) as language decoder and fine-tune the video-language aligned model (i.e., stage 1 model) with machine-translated [VideoChat](https://github.com/OpenGVLab/InternVideo/tree/main/Data/instruction_data) instructions.   

    - [**Video-LLaMA-Ziya**](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-Series/resolve/main/finetune-ziya13b-zh.pth): same with Video-LLaMA-BiLLA but the language decoder is changed to [Ziya-13B](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1).    

- [05.18] ⭐️ Create a Hugging Face [repo](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-Series) to store the model weights of all the variants of our Video-LLaMA.

- [05.15] ⭐️ Release [**Video-LLaMA v2**](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-Series/resolve/main/finetune-vicuna13b-v2.pth): we use the training data provided by [VideoChat](https://github.com/OpenGVLab/InternVideo/tree/main/Data/instruction_data) to further enhance the instruction-following capability of Video-LLaMA.

- [05.07] Release the initial version of **Video-LLaMA**, including its pre-trained and instruction-tuned checkpoints.





Video-LLaMA




## Introduction



- Video-LLaMA is built on top of [BLIP-2](https://github.com/salesforce/LAVIS/tree/main/projects/blip2) and [MiniGPT-4](https://github.com/Vision-CAIR/MiniGPT-4). It is composed of two core components: (1) Vision-Language (VL) Branch and (2) Audio-Language (AL) Branch.

  - **VL Branch** (Visual encoder: ViT-G/14 + BLIP-2 Q-Former)

    - A two-layer video Q-Former and a frame embedding layer (applied to the embeddings of each frame) are introduced to compute video representations. 

    - We train VL Branch on the Webvid-2M video caption dataset with a video-to-text generation task. We also add image-text pairs (~595K image captions from [LLaVA](https://github.com/haotian-liu/LLaVA)) into the pre-training dataset to enhance the understanding of static visual concepts.

    - After pre-training, we further fine-tune our VL Branch using the instruction-tuning data from [MiniGPT-4](https://github.com/Vision-CAIR/MiniGPT-4), [LLaVA](https://github.com/haotian-liu/LLaVA) and [VideoChat](https://github.com/OpenGVLab/Ask-Anything). 

  - **AL Branch** (Audio encoder: ImageBind-Huge) 

    - A two-layer audio Q-Former and an audio segment embedding layer (applied to the embedding of each audio segment) are introduced to compute audio representations.

    - As the used audio encoder (i.e., ImageBind) is already aligned across multiple modalities, we train AL Branch on video/image instruction data only, just to connect the output of ImageBind to the language decoder.    

- Only the Video/Audio Q-Former, positional embedding layers, and linear layers are trainable during cross-modal training.



## Example Outputs



- **Video with background sound**





    

    




- **Video without sound effects**



    

    




- **Static image**



    

    




## Pre-trained & Fine-tuned Checkpoints



~~The following checkpoints store learnable parameters (positional embedding layers, Video/Audio Q-former, and linear projection layers) only.~~



The following checkpoints are the full weights (visual encoder + audio encoder + Q-Formers + language decoder) to launch Video-LLaMA:



| Checkpoint       | Link | Note |

|:------------------|-------------|-------------|

| Video-LLaMA-2-7B-Pretrained    | [link](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-2-13B-Finetuned/tree/main)       | Pre-trained on WebVid (2.5M video-caption pairs) and LLaVA-CC3M (595k image-caption pairs) |

| Video-LLaMA-2-7B-Finetuned | [link](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-2-7B-Finetuned/tree/main) | Fine-tuned on the instruction-tuning data from [MiniGPT-4](https://github.com/Vision-CAIR/MiniGPT-4), [LLaVA](https://github.com/haotian-liu/LLaVA) and [VideoChat](https://github.com/OpenGVLab/Ask-Anything)|

| Video-LLaMA-2-13B-Pretrained    | [link](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-2-13B-Pretrained/tree/main)       | Pre-trained on WebVid (2.5M video-caption pairs) and LLaVA-CC3M (595k image-caption pairs) |

| Video-LLaMA-2-13B-Finetuned | [link](https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-2-13B-Finetuned/tree/main) | Fine-tuned on the instruction-tuning data from [MiniGPT-4](https://github.com/Vision-CAIR/MiniGPT-4), [LLaVA](https://github.com/haotian-liu/LLaVA) and [VideoChat](https://github.com/OpenGVLab/Ask-Anything)|



## Usage

#### Environment Preparation 



First, install ffmpeg.

```

apt update

apt install ffmpeg

```

Then, create a conda environment:

```

conda env create -f environment.yml

conda activate videollama

```



## Prerequisites



~~Before using the repository, make sure you have obtained the following checkpoints:~~



DON'T have to do anything now!!



## How to Run Demo Locally



Firstly, set the `llama_model` (for the path to the language decoder), `imagebind_ckpt_path` (for the path to the audio encoder), `ckpt` (for the path to VL branch) and `ckpt_2` (for the path to AL branch) in [eval_configs/video_llama_eval_withaudio.yaml](./eval_configs/video_llama_eval_withaudio.yaml) accordingly.

Then run the script:

```

python demo_audiovideo.py \

    --cfg-path eval_configs/video_llama_eval_withaudio.yaml \

    --model_type llama_v2 \ # or vicuna

    --gpu-id 0

```



## Training



The training of each cross-modal branch (i.e., VL branch or AL branch) in Video-LLaMA consists of two stages,



1. Pre-training on the [Webvid-2.5M](https://github.com/m-bain/webvid) video caption dataset and [LLaVA-CC3M]((https://github.com/haotian-liu/LLaVA)) image caption dataset.



2. Fine-tuning using the image-based instruction-tuning data from [MiniGPT-4](https://github.com/Vision-CAIR/MiniGPT-4)/[LLaVA](https://github.com/haotian-liu/LLaVA) and the video-based instruction-tuning data from [VideoChat](https://github.com/OpenGVLab/Ask-Anything).



### 1. Pre-training

#### Data Preparation

Download the metadata and video following the instructions from the official Github repo of [Webvid](https://github.com/m-bain/webvid).

The folder structure of the dataset is shown below:

```

|webvid_train_data

|──filter_annotation

|────0.tsv

|──videos

|────000001_000050

|──────1066674784.mp4

```

```

|cc3m

|──filter_cap.json

|──image

|────GCC_train_000000000.jpg

|────...

```

#### Script

Config the checkpoint and dataset paths in [visionbranch_stage1_pretrain.yaml](./train_configs/visionbranch_stage1_pretrain.yaml) and [audiobranch_stage1_pretrain.yaml](audiobranch_stage1_pretrain.yaml) respectively. Then, run the script:

```

conda activate videollama

# for pre-training VL branch

torchrun --nproc_per_node=8 train.py --cfg-path  ./train_configs/audiobranch_stage1_pretrain.yaml



# for pre-training AL branch

torchrun --nproc_per_node=8 train.py --cfg-path  ./train_configs/audiobranch_stage1_pretrain.yaml

```



### 2. Instruction Fine-tuning

#### Data

For now, the fine-tuning dataset consists of:

* 150K image-based instructions from LLaVA [[link](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/raw/main/llava_instruct_150k.json)]

* 3K image-based instructions from MiniGPT-4 [[link](https://github.com/Vision-CAIR/MiniGPT-4/blob/main/dataset/README_2_STAGE.md)]

* 11K video-based instructions from VideoChat [[link](https://github.com/OpenGVLab/InternVideo/tree/main/Data/instruction_data)]



#### Script

Config the checkpoint and dataset paths in [visionbranch_stage2_pretrain.yaml](./train_configs/visionbranch_stage2_pretrain.yaml) and [audiobranch_stage2_pretrain.yaml](audiobranch_stage2_pretrain.yaml) respectively. Then, run the following script:

```

conda activate videollama

# for fine-tuning VL branch

torchrun --nproc_per_node=8 train.py --cfg-path  ./train_configs/visionbranch_stage2_finetune.yaml



# for fine-tuning AL branch

torchrun --nproc_per_node=8 train.py --cfg-path  ./train_configs/audiobranch_stage2_finetune.yaml

```



## Recommended GPUs

* Pre-training: 8xA100 (80G)

* Instruction-tuning: 8xA100 (80G)

* Inference: 1xA100 (40G/80G) or 1xA6000



## Acknowledgement

We are grateful for the following awesome projects our Video-LLaMA arising from:

* [MiniGPT-4](https://github.com/Vision-CAIR/MiniGPT-4): Enhancing Vision-language Understanding with Advanced Large Language Models

* [FastChat](https://github.com/lm-sys/FastChat): An Open Platform for Training, Serving, and Evaluating Large Language Model based Chatbots

* [BLIP-2](https://github.com/salesforce/LAVIS/tree/main/projects/blip2): Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models 

* [EVA-CLIP](https://github.com/baaivision/EVA/tree/master/EVA-CLIP): Improved Training Techniques for CLIP at Scale

* [ImageBind](https://github.com/facebookresearch/ImageBind): One Embedding Space To Bind Them All

* [LLaMA](https://github.com/facebookresearch/llama): Open and Efficient Foundation Language Models

* [VideoChat](https://github.com/OpenGVLab/Ask-Anything): Chat-Centric Video Understanding

* [LLaVA](https://github.com/haotian-liu/LLaVA): Large Language and Vision Assistant

* [WebVid](https://github.com/m-bain/webvid): A Large-scale Video-Text dataset

* [mPLUG-Owl](https://github.com/X-PLUG/mPLUG-Owl/tree/main): Modularization Empowers Large Language Models with Multimodality



The logo of Video-LLaMA is generated by [Midjourney](https://www.midjourney.com/).



## Term of Use

Our Video-LLaMA is just a research preview intended for non-commercial use only. You must **NOT** use our Video-LLaMA for any illegal, harmful, violent, racist, or sexual purposes. You are strictly prohibited from engaging in any activity that will potentially violate these guidelines. 



## Citation

If you find our project useful, hope you can star our repo and cite our paper as follows:

```

@article{damonlpsg2023videollama,

  author = {Zhang, Hang and Li, Xin and Bing, Lidong},

  title = {Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding},

  year = 2023,

  journal = {arXiv preprint arXiv:2306.02858},

  url = {https://arxiv.org/abs/2306.02858}

}

```