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Data preparation scripts

Table of Contents

Downloading Data

For the experiments carried out in this paper, you need to download the data folder one by one as follows:

ATCO2 test set corpus

Dataset is in HuggingFace:https://huggingface.co/datasets/Jzuluaga/atco2 | GitHub

UWB-ATCC corpus

The Air Traffic Control Communication corpus, or UWB-ATCC corpus, contains recordings of communication between air traffic controllers and pilots. The speech is manually transcribed and labeled with the information about the speaker (pilot/controller, not the full identity of the person). The audio data format is: 8kHz, 16bit PCM, mono.

You can download for free in: https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0001-CCA1-0

This item is Publicly Available and licensed under: Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

Dataset is in HuggingFace: https://huggingface.co/datasets/Jzuluaga/uwb_atcc | GitHub

LDC-ATCC corpus

The Air Traffic Control Complete (LDC94S14A) or LDC-ATCC corpus comprises recorded speech for use in supporting research and development activities in the area of robust speech recognition in domains similar to air traffic control (several speakers, noisy channels, relatively small vocabulary, constrained languaged, etc.) The audio data is composed of voice communication traffic between various controllers and pilots. The audio files are 8 KHz, 16-bit linear sampled data, representing continuous monitoring, without squelch or silence elimination, of a single FAA frequency for one to two hours.

You can purchase it and download here: https://catalog.ldc.upenn.edu/LDC94S14A

Preparing the Data

The folder containing the preparation scripts to format and prepare each dataset looks like:

data/databases/
├── atco2_corpus
│   ├── data_prepare_atco2_corpus.sh
│   └── exp_prepare_atco2_corpus.sh
├── ldc_atcc
│   ├── data_prepare_ldc_atcc_corpus.sh
│   ├── exp_prepare_ldc_atcc_corpus.sh
│   ├── link_acronyms.sh
│   └── parse_lisp_array.sh
└── uwb_atcc
    ├── data_prepare_uwb_atcc_corpus.sh
    ├── exp_prepare_uwb_atcc_corpus.sh
    └── spk_id_tagger.py

You can format only one database and do all the experiments with it! For instance, UWB-ATCC corpus is completly free to download and use! You can start right away with it!

Prepare only one database: UWB-ATCC use case

For preparing one database, you can simply go to root directory and run:

conda activate diarization
bash data/databases/data_prepare_uwb_atcc.sh
bash data/databases/exp_prepare_uwb_atcc.sh

That will generate the files required for all the experiments in experiments/data/uwb_atcc.

Prepare all 3 databases

Don't worry! We have prepared a bash script/wrapper to format and prepare the 3 databases simultaneously. Go to the project root directory (one level up) and run:

conda activate diarization
bash data/PREPARE_AND_FORMAT_DATASETS.sh

This will prepare each dataset in KALDI format (you DO NOT need to install KALDI). The outputs are generated in the experiments/data/* folder (around 203 MB of space for the 3 databases), and its structure should be:

experiments/data/
├── atco2_corpus
│   ├── prep
│   └── test
│       ├── diarization
│       └── diarization_acoustic
│           └── wav_path
├── ldc_atcc
│   ├── prep
│   ├── test
│   │   ├── diarization
│   │   └── diarization_acoustic
│   │       └── wav_path
│   └── train
│       ├── diarization
│       └── diarization_augmented
└── uwb_atcc
    ├── prep
    ├── test
    │   ├── diarization
    │   └── diarization_acoustic
    │       ├── prep
    │       └── wav_path
    └── train
        ├── diarization
        └── diarization_augmented

Where atco2_corpus, ldc_atcc, and uwb_atcc are the 3 public datasets used in the BERT-based diarization paper.

Each folder contains a diarization folder. The diarization folder contains the file required (utt2text_tags) for training the BERT model in NER style.

!!! LDC-ATC and UWB-ATCC are used for training and testing, while ATCO2-test-set corpus only for testing.

Description of folder naming

You can see in the tree above that there are 3 diarization folders:

  • diarization: default folder to perform text-based diarization with a BERT module
  • diarization_augmented: folder to perform text-based diarization with a BERT module, but using DATA AUGMENTATION. See the script in data_augmentation_diarization.py
  • diarization_acoustic: folder to perform acoustic-based diarization with VBx system module. In this case, we created a script to merge segments from the original test sets. See the script in generate_mixed_rttm_kaldi.py

How to cite us

If you use this code for your research, please cite our paper with:

Zuluaga-Gomez, J., Sarfjoo, S. S., Prasad, A., Nigmatulina, I., Motlicek, P., Ondrej, K., Ohneiser, O., & Helmke, H. (2021). BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications. 2022 IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar.

Or use the bibtex item:

@article{zuluaga2022bertraffic,
  title={BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications},
  author={Zuluaga-Gomez, Juan and Sarfjoo, Seyyed Saeed and Prasad, Amrutha and Nigmatulina, Iuliia and Motlicek, Petr and Ondre, Karel and Ohneiser, Oliver and Helmke, Hartmut},
  journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar},
  year={2022}
  }