1. Clone this repository: git clone https://github.com/lukewys/dcase_2020_T6.git
2. Install pytorch >=1.4.0
3. Use pip to install dependencies: pip install -r requirements.txt

• Download the required dataset of DCASE2020 Automated Audio Captioning task

• Enter the create_dataset directory，Put Clotho data set into data folder structured like this:

  data
  ├── clotho_csv_files
  │   ├── clotho_captions_development.csv
  │   ├── clotho_captions_evaluation.csv
  │   ├── clotho_metadata_development.csv
  │   └── clotho_metadata_evaluation.csv
  ├── clotho_audio_files
  │   ├── development
  │   └── evaluation
  └── clotho_test_audio
  │   ├── test_0001.wav
  │   ├── test_0002.wav
  │   ├── ...
  │   ├── test_1043.wav
  
  

• Enter the create_dataset directory.

• Run main.py to create dataset and extract features under current directory. Result folders would be data_splits, data_splits_audio and pickles. (The progress bar is not behaving correctly, please wait until the program to finish)

• Then, run create_test_set.py to create test set under current directory. Result folder would be test_data.

Please setup coco caption and download the file needed according to https://github.com/audio-captioning/caption-evaluation-tools. (~300MB of files will be downloaded)

• Enter the audio_tag directory.
• First, rungenerate_word_list.py to generate word list word_list_pretrain_rules.p, then run generate_tag.py to generate the tag label: audioTagNum_development_fin.pickle and audioTagNum_evaluation_fin.pickle.
• Back to the project directory, run Tag_train.py to train the model. The model is saved in ./models/tag_models.

• Enter the word_embedding_pretrain directory, run word2vec.py

• The file hparams.py holds the default arguments for our project. You can modify these parameters according to your own needs.

• You can use our pretrained models in models/best.pt to reproduce our results.

Set mode=train in hparams.py. Then you can run the project using the set script:

#You can modify the running script as needed
python run.py

The trained models and logs will be saved in models/{hparams.name}.

set mode=eval in hparams.py. Then choose the model you want to evaluate,run

python train.py. The system will score the model based on the evaluation dataset.

set mode=test in hparams.py. Then choose the model you want to test,run python train.py. The system will generate the corresponding captions according to the test dataset.

If you use our code, pleas kindly cite following:

@techreport{wuyusong2020_t6,
    Author = "Wu, Yusong and Chen, Kun and Wang, Ziyue and Zhang, Xuan and Nian, Fudong and Li, Shengchen and Shao, Xi",
    title = "Audio Captioning Based on Transformer and Pre-Training for 2020 {DCASE} Audio Captioning Challenge",
    institution = "DCASE2020 Challenge",
    year = "2020",
    month = "June",
    abstract = "This report proposes an automated audio captioning model for the 2020 DCASE audio captioning challenge. In this challenge, a model is required to be trained from scratch to generate natural language descriptions of a given audio signal. However, as limited data available and restrictions on using pre-trained models trained by external data, training directly from scratch can result in poor performance where acoustic events and language are poorly modeled. For better acoustic event and language modeling, a sequence-to-sequence model is proposed which consists of a CNN encoder and a Transformer decoder. In the proposed model, the encoder and word embedding are firstly pre-trained. Regulations and data augmentations are applied during training, while fine-tuning is applied after training. Experiments show that the proposed model can achieve a SPIDEr score of 0.227 on audio captioning performance."
}

how to get the results and score

how to load trained weight and reproduce exact result submitted in challenge

how to get all the scores in ablation test