metadata

tags:
  - audio
  - automatic-speech-recognition
  - en-atc
  - en
dataset_info:
  features:
    - name: id
      dtype: string
    - name: audio
      dtype:
        audio:
          sampling_rate: 16000
    - name: text
      dtype: string
    - name: segment_start_time
      dtype: float32
    - name: segment_end_time
      dtype: float32
    - name: duration
      dtype: float32
  splits:
    - name: test
      num_bytes: 113872168
      num_examples: 871
  download_size: 113467762
  dataset_size: 113872168

Dataset Card for ATCO2 test set corpus (1hr set)

Dataset Description
Dataset Structure
- Data Fields
Additional Information
- Licensing Information
- Citation Information

Dataset Description

Homepage: ATCO2 project homepage
Repository: ATCO2 corpus
Paper: ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications

Dataset Summary

ATCO2 project aims at developing a unique platform allowing to collect, organize and pre-process air-traffic control (voice communication) data from air space. This project has received funding from the Clean Sky 2 Joint Undertaking (JU) under grant agreement No 864702. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the Clean Sky 2 JU members other than the Union.

The project collected the real-time voice communication between air-traffic controllers and pilots available either directly through publicly accessible radio frequency channels or indirectly from air-navigation service providers (ANSPs). In addition to the voice communication data, contextual information is available in a form of metadata (i.e. surveillance data). The dataset consists of two distinct packages:

A corpus of 5000+ hours (pseudo-transcribed) of air-traffic control speech collected across different airports (Sion, Bern, Zurich, etc.) in .wav format for speech recognition. Speaker distribution is 90/10% between males and females and the group contains native and non-native speakers of English.
A corpus of 4 hours (transcribed) of air-traffic control speech collected across different airports (Sion, Bern, Zurich, etc.) in .wav format for speech recognition. Speaker distribution is 90/10% between males and females and the group contains native and non-native speakers of English. This corpus has been transcribed with orthographic information in XML format with speaker noise information, SNR values and others. Read Less
A free sample of the 4 hours transcribed data is in ATCO2 project homepage

Supported Tasks and Leaderboards

automatic-speech-recognition. Already adapted/fine-tuned models are available here --> Wav2Vec 2.0 LARGE mdel.

Languages and other details

The text and the recordings are in English. For more information see Table 3 and Table 4 of ATCO2 corpus paper

Dataset Structure

Data Fields

id (string): a string of recording identifier for each example, corresponding to its.
audio (audio): audio data for the given ID
text (string): transcript of the file already normalized. Follow these repositories for more details w2v2-air-traffic and bert-text-diarization-atc
segment_start_time (float32): segment start time (normally 0)
`segment_end_time (float32): segment end time
duration (float32): duration of the recording, compute as segment_end_time - segment_start_time

Additional Information

Licensing Information

The licensing status of the ATCO2-test-set-1h corpus is in the file ATCO2-ASRdataset-v1_beta - End-User Data Agreement in the data folder. Download the data in ATCO2 project homepage

Citation Information

Contributors who prepared, processed, normalized and uploaded the dataset in HuggingFace:

@article{zuluaga2022how,
    title={How Does Pre-trained Wav2Vec2. 0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications},
    author={Zuluaga-Gomez, Juan and Prasad, Amrutha and Nigmatulina, Iuliia and Sarfjoo, Saeed and others},
    journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar},
    year={2022}
  }
@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 others},
  journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar},
  year={2022}
  }
@article{zuluaga2022atco2,
  title={ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications},
  author={Zuluaga-Gomez, Juan and Vesel{\`y}, Karel and Sz{\"o}ke, Igor and Motlicek, Petr and others},
  journal={arXiv preprint arXiv:2211.04054},
  year={2022}
}

Datasets:

Jzuluaga
/

atco2_corpus_1h