license: cc-by-sa-4.0
datasets:
- Homie0609/MatchTime
language:
- en
tags:
- sports
- soccer
Requirements
- Python >= 3.8 (Recommend to use Anaconda or Miniconda)
- PyTorch >= 2.0.0 (If use A100)
- transformers >= 4.42.3
- pycocoevalcap >= 1.2
A suitable conda 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 and caption data, 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
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. 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 (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 and 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. 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