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Video Captioning

Human labeling of videos is expensive and time-consuming. We adopt powerful image captioning models to generate captions for videos. Although GPT-4V achieves a better performance, its 20s/sample speed is too slow for us. LLaVA is the second best open-source model in MMMU and accepts any resolution. We find the quality of 34B model is comparable.

Caption

LLaVA Captioning

We extract three frames from the video for captioning. With batch inference, we can achieve 10 times speedup. With approximatly 720p resolution and 1 frames, the speed is 2~3 videos/s on 8 GPUs. If we resize the smaller side to 336, the speed can be 8 videos/s. In Open-Sora v1.1, to lower the cost, we use the 7B model.

Requirement

# create conda env
conda create -n llava python=3.10 -y
conda activate llava

# install torch
pip install torch torchvision

# clone llava
git clone https://github.com/haotian-liu/LLaVA.git
cd LLaVA
# CAUTION: This line is to remove torch dependency in pyproject.toml, which is:
# "torch==2.1.2", "torchvision==0.16.2",
# It is better manually remove it in your local pyproject.toml
sed -i '16d' pyproject.toml

# install llava
pip install --upgrade pip  # enable PEP 660 support
pip install -e .

# install flash attention
pip install flash-attn --no-build-isolation
# install colossalai and decord
pip install colossalai decord

Usage

Prepare a csv file for processing. The csv file can be generated by convert_dataset.py according to its documentation. Then, run the following command to generate captions for videos/images with Llava:

# caption with mistral-7B
torchrun --nproc_per_node 8 --standalone -m tools.caption.caption_llava DATA.csv --dp-size 8 --tp-size 1 --model-path liuhaotian/llava-v1.6-mistral-7b --prompt video

# caption with llava-34B
# NOTE: remember to enable flash attention for this model
torchrun --nproc_per_node 8 --standalone -m tools.caption.caption_llava DATA.csv --dp-size 4 --tp-size 2 --model-path liuhaotian/llava-v1.6-34b --prompt image-3ex --flash-attention

# we run this on 8xH800 GPUs
torchrun --nproc_per_node 8 --standalone -m tools.caption.caption_llava DATA.csv --tp-size 2 --dp-size 4 --bs 16

# at least two 80G GPUs are required
torchrun --nproc_per_node 2 --standalone -m tools.caption.caption_llava DATA.csv --tp-size 2 --dp-size 1 --bs 16

# can also caption images
torchrun --nproc_per_node 2 --standalone -m tools.caption.caption_llava DATA.csv --tp-size 2 --dp-size 1 --bs 16 --prompt image-3ex

Please note that you should add the --flash-attention flag when running with Llama-based Llava models as it provides speedup but do turn it off for mistral-based ones. Reasons can be found in this issue.

After running the script, with dp-size=N, you will get N parts of csv files. Run the following command to merge them:

python -m tools.datasets.datautil DATA_caption_part*.csv --output DATA_caption.csv

Resume

Sometimes the process may be interrupted. We can resume the process by running the following command:

# merge generated results
python -m tools.datasets.datautil DATA_caption_part*.csv --output DATA_caption.csv

# get the remaining videos
python -m tools.datasets.datautil DATA.csv --difference DATA_caption.csv --output DATA_remaining.csv

Then use the output csv file to resume the process.

GPT-4V Captioning

Run the following command to generate captions for videos with GPT-4V:

# output: DATA_caption.csv
python -m tools.caption.caption_gpt4 DATA.csv --key $OPENAI_API_KEY

The cost is approximately $0.01 per video (3 frames per video).