|
--- |
|
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: 113872168.0 |
|
num_examples: 871 |
|
download_size: 113467762 |
|
dataset_size: 113872168.0 |
|
--- |
|
|
|
# Dataset Card for ATCO2 test set corpus (1hr set) |
|
|
|
## Table of Contents |
|
- [Dataset Description](#dataset-description) |
|
- [Dataset Summary](#dataset-summary) |
|
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
|
- [Languages and Other Details](#languages-and-other-details) |
|
- [Dataset Structure](#dataset-structure) |
|
- [Data Fields](#data-fields) |
|
- [Additional Information](#additional-information) |
|
- [Licensing Information](#licensing-information) |
|
- [Citation Information](#citation-information) |
|
|
|
|
|
## Dataset Description |
|
- **Homepage:** [ATCO2 project homepage](https://www.atco2.org/) |
|
- **Repository:** [ATCO2 corpus](https://github.com/idiap/atco2-corpus) |
|
- **Paper:** [ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications](https://arxiv.org/abs/2211.04054) |
|
|
|
### 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](https://www.atco2.org/data) |
|
|
|
### Supported Tasks and Leaderboards |
|
|
|
- `automatic-speech-recognition`. Already adapted/fine-tuned models are available here --> [Wav2Vec 2.0 LARGE mdel](https://huggingface.co/Jzuluaga/wav2vec2-large-960h-lv60-self-en-atc-uwb-atcc-and-atcosim). |
|
|
|
### 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](https://arxiv.org/abs/2211.04054) |
|
|
|
## 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](https://github.com/idiap/w2v2-air-traffic) and [bert-text-diarization-atc](https://github.com/idiap/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](https://www.atco2.org/data) |
|
|
|
### 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} |
|
} |
|
``` |
|
|
|
|
