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Diff-BGM: A Diffusion Model for Video Background Music Generation

Official implementation for CVPR 2024 paper: Diff-BGM: A Diffusion Model for Video Background Music Generation

By Sizhe Li, Yiming Qin, Minghang Zheng, Xin Jin, Yang Liu.

Thanks for the code structure from Polyffusion

1. Installation

pip install -r requirements.txt
pip install -e diffbgm
pip isntall -e diffbgm/mir_eval

2. Training

Preparations

The extracted features of the dataset POP909 can be accessed here. Please put it under /data/ after extraction.
The extracted features of the dataset BGM909 can be accessed here. Please put them under /data/bgm909/ after extraction. We use VideoCLIP to extract the video feature, use BLIP to gain the video caption then use Bert-base-uncased as the language encoder and use TransNetV2 to capture the shot.
We also provide the original captions here.
The needed pre-trained models for training can be accessed here. Please put them under /pretrained/ after extraction. The split of the dataset can be find here.

Commands

python diffbgm/main.py --model ldm_chd8bar --output_dir [output_dir]

3. Inference

Please use the following message to generate music for videos in BGM909.

python diffbgm/inference_sdf.py --model_dir=[model_dir] --uncond_scale=5.

4. Test

To reproduce the metrics in our original paper, please refer to /diffbgm/test.ipynb.

Backbone	PCHE	GPS	SI	P@20	Weights
Diff-BGM (original)	2.840	0.601	0.521	44.10	weights
Diff-BGM (only visual)	2.835	0.514	0.396	43.20	weights
Diff-BGM (w/o SAC-Att)	2.721	0.789	0.523	38.47	weights

We provide our generation results here.

5. Make a Demo by yourself!

After generating a piece of music, you can use the following commands to generate a video.

sudo apt-get install ffmpeg fluidsynth
fluidsynth -i <SoundFont file> <midi file> -F <wav file>
ffmpeg -i <wav file> -b:a <bit rate> <mp3 file>
ffmpeg -i <video file> -i <mp3 file> -c:a aac -map 0:v:0 -map 1:a:0 <output file>

See our demo!