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

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 capability.

News

• [06.10] NOTE: we have NOT updated the HF demo yet because the whole framework (with 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 and ModelScope!!
• [05.22] ⭐️ Release Video-LLaMA v2 built with Vicuna-7B
• [05.18] 🚀🚀 Support video-grounded chat in Chinese
  • Video-LLaMA-BiLLA: we introduce BiLLa-7B as language decoder and fine-tune the video-language aligned model (i.e., stage 1 model) with machine-translated VideoChat instructions.
  • Video-LLaMA-Ziya: same with Video-LLaMA-BiLLA but the language decoder is changed to Ziya-13B.
• [05.18] ⭐️ Create a Hugging Face repo to store the model weights of all the variants of our Video-LLaMA.
• [05.15] ⭐️ Release Video-LLaMA v2: we use the training data provided by VideoChat 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.

Introduction

• Video-LLaMA is built on top of BLIP-2 and 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) 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, LLaVA and VideoChat.
  • AL Branch (Audio encoder: ImageBind-Huge)
    • A two-layer audio Q-Former and a 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 instrucaption data only, just to connect the output of ImageBind to language decoder.
• Note that only the Video/Audio Q-Former, positional embedding layers and the 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.

Vision-Language Branch

Audio-Language Branch

Usage

Enviroment 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:

Pre-trained Language Decoder

• Get the original LLaMA weights in the Hugging Face format by following the instructions here.
• Download Vicuna delta weights 👉 [7B][13B].
• Use the following command to add delta weights to the original LLaMA weights to obtain the Vicuna weights:

python apply_delta.py \
    --base /path/to/llama-13b \
    --target /output/path/to/vicuna-13b --delta /path/to/vicuna-13b-delta

Pre-trained Visual Encoder in Vision-Language Branch

• Download the MiniGPT-4 model (trained linear layer) from this link.

Pre-trained Audio Encoder in Audio-Language Branch

• Download the weight of ImageBind from this link.

Download Learnable Weights

Use git-lfs to download the learnable weights of our Video-LLaMA (i.e., positional embedding layer + Q-Former + linear projection layer):

git lfs install
git clone https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-Series

The above commands will download the model weights of all the Video-LLaMA variants. For sure, you can choose to download the weights on demand. For example, if you want to run Video-LLaMA with Vicuna-7B as language decoder locally, then:

wget https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-Series/resolve/main/finetune-vicuna7b-v2.pth
wget https://huggingface.co/DAMO-NLP-SG/Video-LLaMA-Series/resolve/main/finetune_vicuna7b_audiobranch.pth

should meet the requirement.

How to Run Demo Locally

Firstly, set the llama_model, imagebind_ckpt_path, ckpt and ckpt_2 in eval_configs/video_llama_eval_withaudio.yaml. Then run the script:

python demo_audiovideo.py \
    --cfg-path eval_configs/video_llama_eval_withaudio.yaml  --gpu-id 0

Training

The training of Video-LLaMA consists of two stages,

1. Pre-training on the Webvid-2.5M video caption dataset and LLaVA-CC3M image caption dataset.

2. Fine-tuning using the image-based instruction-tuning data from MiniGPT-4.

1. Vision-Language Pre-training

Data Preparation

Download the metadata and video following the instruction from the official Github repo of 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 the checkpoint and dataset paths in video_llama_stage1_pretrain.yaml Run the script:

conda activate videollama
torchrun --nproc_per_node=8 train.py --cfg-path  ./train_configs/video_llama_stage1_pretrain.yaml

2. Instruction Fine-tuning

Data

For now, the fine-tuning dataset consists of:

• 150K image-based instructions from LLaVA [link]
• 3K image-based instructions from MiniGPT-4 [link]
• 11K video-based instructions from VideoChat [link]

Script

Config the the checkpoint and dataset paths in video_llama_stage2_finetune.yaml

conda activate videollama
torchrun --nproc_per_node=8 train.py --cfg-path  ./train_configs/video_llama_stage2_finetune.yaml

Acknowledgement

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

• MiniGPT-4: Enhancing Vision-language Understanding with Advanced Large Language Models
• FastChat: An Open Platform for Training, Serving, and Evaluating Large Language Model based Chatbots
• BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
• EVA-CLIP: Improved Training Techniques for CLIP at Scale
• ImageBind: One Embedding Space To Bind Them All
• LLaMA: Open and Efficient Foundation Language Models
• VideoChat: Chat-Centric Video Understanding
• LLaVA: Large Language and Vision Assistant
• WebVid: A Large-scale Video-Text dataset
• mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality

The logo of Video-LLaMA is generated by Midjourney.

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}
}