This is a implementation of LP-MusicCaps: LLM-Based Pseudo Music Captioning. This project aims to generate captions for music. 1) Tag-to-Caption: Using existing tags, We leverage the power of OpenAI's GPT-3.5 Turbo API to generate high-quality and contextually relevant captions based on music tag. 2) Audio-to-Caption: Using music-audio and pseudo caption pairs, we train a cross-model encoder-decoder model for end-to-end music captioning
SeungHeon Doh, Keunwoo Choi, Jongpil Lee, Juhan Nam
To appear ISMIR 2023
- 23.12.12 Our paper has been invited to the TISMIR journal. Stay tuned for the extended version
- 23.11.10 Our paper has been nominated for the ISMIR Best Paper Award (5/104)
- Step 1.Tag-to-Caption: LLM Captioning: Generate caption from given tag input.
- Step 2.Pretrain Music Captioning Model: Generate pseudo caption from given audio.
- Step 3.Transfer Music Captioning Model: Generate human level caption from given audio.
are available online for future research. example of dataset in notebook
To run this project locally, follow the steps below:
-
Install python and PyTorch:
- python==3.10
- torch==1.13.1 (Please install it according to your CUDA version.)
-
Other requirements:
- pip install -e .
cd lpmc/llm_captioning
python run.py --prompt {writing, summary, paraphrase, attribute_prediction} --tags <music_tags>Replace <music_tags> with the tags you want to generate captions for. Separate multiple tags with commas, such as beatbox, finger snipping, male voice, amateur recording, medium tempo.
tag_to_caption generation writing results:
query:
write a song description sentence including the following attributes
beatbox, finger snipping, male voice, amateur recording, medium tempo
----------
results:
"Experience the raw and authentic energy of an amateur recording as mesmerizing beatbox rhythms intertwine with catchy finger snipping, while a soulful male voice delivers heartfelt lyrics on a medium tempo track."
cd demo
python app.py
# or
cd lpmc/music_captioning
wget https://huggingface.co/seungheondoh/lp-music-caps/resolve/main/transfer.pth -O exp/transfer/lp_music_caps
python captioning.py --audio_path ../../dataset/samples/orchestra.wav{'text': "This is a symphonic orchestra playing a piece that's riveting, thrilling and exciting.
The peace would be suitable in a movie when something grand and impressive happens.
There are clarinets, tubas, trumpets and french horns being played. The brass instruments help create that sense of a momentous occasion.",
'time': '0:00-10:00'}
{'text': 'This is a classical music piece from a movie soundtrack.
There is a clarinet playing the main melody while a brass section and a flute are playing the melody.
The rhythmic background is provided by the acoustic drums. The atmosphere is epic and victorious.
This piece could be used in the soundtrack of a historical drama movie during the scenes of an army marching towards the end.',
'time': '10:00-20:00'}
{'text': 'This is a live performance of a classical music piece. There is a harp playing the melody while a horn is playing the bass line in the background.
The atmosphere is epic. This piece could be used in the soundtrack of a historical drama movie during the scenes of an adventure video game.',
'time': '20:00-30:00'}
Checking lpmc/llm_captioning and lpmc/music_captioning
This project is under the CC-BY-NC 4.0 license. See LICENSE for details.
We would like to thank the WavCaps for audio-captioning training code and deezer-playntell for contents based captioning evaluation protocol. We would like to thank OpenAI for providing the GPT-3.5 Turbo API, which powers this project.
Please consider citing our paper in your publications if the project helps your research. BibTeX reference is as follow.
@article{doh2023lp,
title={LP-MusicCaps: LLM-Based Pseudo Music Captioning},
author={Doh, SeungHeon and Choi, Keunwoo and Lee, Jongpil and Nam, Juhan},
journal={arXiv preprint arXiv:2307.16372},
year={2023}
}
