---
datasets:
- Homie0609/MatchTime
language:
- en
license: cc-by-sa-4.0
tags:
- sports
- soccer
pipeline_tag: video-text-to-text
library_name: transformers
---
# Commentary Generation for Soccer Highlights
This repository contains the code and model for **Commentary Generation for Soccer Highlights**, as presented in our paper:
**[Commentary Generation for Soccer Highlights](https://huggingface.co/papers/2508.07543)**
## Abstract
Automated soccer commentary generation has evolved from template-based systems to advanced neural architectures, aiming to produce real-time descriptions of sports events. While frameworks like SoccerNet-Caption laid foundational work, their inability to achieve fine-grained alignment between video content and commentary remains a significant challenge. Recent efforts such as MatchTime, with its MatchVoice model, address this issue through coarse and fine-grained alignment techniques, achieving improved temporal synchronization. In this paper, we extend MatchVoice to commentary generation for soccer highlights using the GOAL dataset, which emphasizes short clips over entire games. We conduct extensive experiments to reproduce the original MatchTime results and evaluate our setup, highlighting the impact of different training configurations and hardware limitations. Furthermore, we explore the effect of varying window sizes on zero-shot performance. While MatchVoice exhibits promising generalization capabilities, our findings suggest the need for integrating techniques from broader video-language domains to further enhance performance.
[\u25b6\ufe0fDemo Video (YouTube)](https://www.youtube.com/watch?v=E3RxHR-M6y0) [\u25b6\ufe0fDemo Video (bilibili)](https://www.bilibili.com/video/BV1L4421U76m) \u00b7 [\ud83c\udfe0Project Page](https://haoningwu3639.github.io/MatchTime/) \u00b7 [\ud83d\udcbbCode](https://github.com/Homie0609/MatchTime) \u00b7 [\ud83d\udcddOriginal Paper (MatchTime)](https://arxiv.org/abs/2406.18530/) \u00b7 [\ud83d\udccaDataset](https://drive.google.com/drive/folders/14tb6lV2nlTxn3VygwAPdmtKm7v0Ss8wG) \u00b7 [\ud83d\udce5Checkpoint](https://huggingface.co/Homie0609/MatchVoice)
## Requirements
- Python >= 3.8 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html))
- [PyTorch >= 2.0.0](https://pytorch.org/) (If use A100)
- transformers >= 4.42.3
- pycocoevalcap >= 1.2
A suitable [conda](https://conda.io/) environment named `matchtime` can be created and activated with:
```
cd MatchTime
conda env create -f environment.yaml
conda activate matchtime
```
## Training
Before training, make sure you have prepared [features](https://pypi.org/project/SoccerNet/) and caption [data]((https://drive.google.com/drive/folders/14tb6lV2nlTxn3VygwAPdmtKm7v0Ss8wG)), and put them into according folders. The structure after collating should be like:
``````
└─ MatchTime
├─ dataset
│ ├─ MatchTime
│ │ ├─ valid
│ │ └─ train
│ │ ├─ england_epl_2014-2015
│ │ ... ├─ 2015-02-21 - 18-00 Chelsea 1 - 1 Burnley
│ │ ... └─ Labels-caption.json
│ │
│ ├─ SN-Caption
│ └─ SN-Caption-test-align
│ ├─ england_epl_2015-2016
│ ... ├─ 2015-08-16 - 18-00 Manchester City 3 - 0 Chelsea
│ ... └─ Labels-caption_with_gt.json
│
├─ features
│ ├─ baidu_soccer_embeddings
│ │ ├─ england_epl_2014-2015
... │ ... ├─ 2015-02-21 - 18-00 Chelsea 1 - 1 Burnley
│ ... ├─ 1_baidu_soccer_embeddings.npy
│ └─ 2_baidu_soccer_embeddings.npy
├─ C3D_PCA512
...
``````
with the format of features is adjusted by
```
python ./features/preprocess.py directory_path_of_feature
```
Above example gives the format of Baidu feature, in our experiments we also used ResNET_PCA_512, C3D_PCA_512 from official website. If you want to use [CLIP](https://github.com/openai/CLIP)(2 FPS) or [InternVideo](https://github.com/OpenGVLab/InternVideo/tree/main/InternVideo1)(1FPS) feature. You can follow their official website to extract feature or contact us for features.
After preparing the data and features, you can pre-train (or finetune) with the following terminal command (Check hyper-parameters at the bottom of *train.py*):
```
python train.py
```
## Inference
We provide two types of inference:
#### For all test set
You can generate a *.csv* file with the following code to test the ***MatchVoice*** model with the following code (Check hyper-parameters at the bottom of *inference.py*)
```
python inference.py
```
There is a sample of this type of inference in *./inference_result/sample.csv*.
#### For Single Video
We also provide a version for predict the commentary single video (for our checkpoints, use 30s video)
```
python inference_single_video_CLIP.py single_video_path
```
Here we only provide the version of CLIP feature (using VIT/B-32), for crop the CLIP feature, please check [here](https://github.com/openai/CLIP). CLIP features are not the one with best performance but are the most friendly for new new videos.
## Alignment
Before doing alignment, you should download videos from [here](https://www.soccer-net.org/data) (224p is enough) and make it in the following format:
``````
└─ MatchTime
├─ videos_224p
... ├─ england_epl_2014-2015
... ├─ 2015-02-21 - 18-00 Chelsea 1 - 1 Burnley
... ├─ 1_224.mkv
└─ 2_224p.mkv
``````
### Pre-process (Coarse Align)
We need to use [WhisperX](https://github.com/m-bain/whisperX) and [LLaMA3](https://huggingface.co/docs/transformers/model_doc/llama3)(as agent) to finish coarse alignment with following steps:
*WhisperX ASR:*
```
python ./alignment/soccer_whisperx.py --process_directory video_folder(eg. ./videos_224p/england_epl_2014-2015) --output_directory output_folder(eg. ./ASR_results/england_epl_2014-2015)
```
*Transform to Events:*
```
python ./alignment/soccer_asr2events.py --base_path ASR_results_folder(eg. ./ASR_results/england_epl_2014-2015) --output_dir envent_results_folder(eg. ./event_results/england_epl_2014-2015)
```
*Align from Events:*
```
python ./alignment/soccer_align_from_event.py --event_path envent_results_folder(eg. ./event_results/england_epl_2014-2015) --output_dir output_directory(eg. ./pre-processed/england_epl_2014-2015)
```
More details could be checked in paper.
### Contrastive Learning (Fine-grained Align)
After downloading checkpoints from [here](https://huggingface.co/Homie0609/MatchTime/tree/main). Use the following code to finish alignment with contrastive learning:
```
python ./alignment/do_alignment.py
```
By changing the hyper-parameter ***finding_words***, you can freely align from ASR, enent, or original SN-Caption.
Also, you can directly use alignment model by
```
from alignment.matchtime_model import ContrastiveLearningModel
```
## Evaluation
We provide codes for evaluate the prediction results:
```
# for single csv file
python ./evaluation/scoer_single.py --csv_path ./inference_result/sample.csv
# for many csv files to record scores in a new csv file
python ./evaluation/scoer_group.py
# for gpt score (need OpenAI API Key)
python ./evaluation/scoer_gpt.py ./inference_result/sample.csv
```
## Citation
If you use this code for your research or project, please cite:
```bibtex
@article{rao2024matchtimeautomaticsoccergame,
title={MatchTime: Towards Automatic Soccer Game Commentary Generation},
author={Jiayuan Rao and Haoning Wu and Chang Liu and Yanfeng Wang and Weidi Xie},
year={2024},
journal={arXiv preprint arXiv:2406.18530},
}
@article{rao2024commentary,
title={Commentary Generation for Soccer Highlights},
author={Rao, Jiayuan and Wu, Haoning and Liu, Chang and Wang, Yanfeng and Xie, Weidi},
journal={arXiv preprint arXiv:2508.07543},
year={2024},
}
```
## Acknowledgements
Many thanks to the code bases from [Video-LLaMA](https://github.com/DAMO-NLP-SG/Video-LLaMA) and source data from [SoccerNet-Caption](https://arxiv.org/abs/2304.04565).
## Contact
If you have any questions, please feel free to contact jy_rao@sjtu.edu.cn or haoningwu3639@gmail.com. 
