---
dataset_info:
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  - name: audio
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  - name: text
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  - name: validation
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  - name: test
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  dataset_size: 866521015.782
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
---

# ATC ASR Dataset

**ATC ASR Dataset** is a high-quality, fine-tuning-ready speech recognition dataset constructed from two real-world Air Traffic Control (ATC) corpora: the [UWB ATC Corpus](https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0001-CCA1-0) and the [ATCO2 1-Hour Test Subset](https://www.replaywell.com/atco2/download/ATCO2-ASRdataset-v1_beta.tgz).

The dataset consists of cleanly segmented `audio + transcript` pairs at the utterance level, specifically curated for Automatic Speech Recognition (ASR) training and fine-tuning in the ATC domain.

## Contents

This dataset includes:

- Audio files (`.wav`, 16kHz mono) of individual ATC utterances
- Transcripts (`.txt`) aligned with each audio file
- Training, validation, and test splits

## Use Cases

This dataset is ideal for:

- Training ASR models specialized in aviation communication
- Benchmarking domain-adapted speech recognition systems
- Studying accented and noisy English in operational ATC environments

## Source Datasets

This dataset combines data from:

- **[UWB ATC Corpus](https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0001-CCA1-0)** - ATC speech and corresponding transcripts recorded over Czech airspace, featuring heavily accented English.

- **[ATCO2 1-Hour Test Subset](https://www.replaywell.com/atco2/download/ATCO2-ASRdataset-v1_beta.tgz)** - A publicly released evaluation slice from the larger ATCO2 corpus, featuring diverse ATC environments, speaker accents, and acoustic conditions.

## Cleaning & Preprocessing

The raw corpora were normalized and cleaned using custom Python scripts. Key steps included:

- Segmenting long audio files into utterance-level clips using timestamps
- Uppercasing all transcripts for uniformity
- Converting digits to words (e.g., `3 5 0` → `THREE FIVE ZERO`)
- Expanding letters to phonetic alphabet equivalents (e.g., `N` → `NOVEMBER`)
- Removing non-English, unintelligible, or corrupted segments
- Normalizing diacritics and fixing broken Unicode characters
- Manual filtering of misaligned or low-quality samples

## Usage

1. **Install Dependencies**:  
   Use Hugging Face's `datasets` library to load the dataset:
   ```
   from datasets import load_dataset
   dataset = load_dataset("jacktol/ATC-ASR-Dataset")
   ```

2. **Training**:  
   The dataset is ready for speech recognition tasks such as fine-tuning Whisper models. It includes training and test splits to evaluate models based on Word Error Rate (WER).

## Reproducibility

All preprocessing scripts and data creation pipelines are publicly available in the companion GitHub repository:

[ATC ASR Dataset Preparation Toolkit (GitHub)](https://github.com/jack-tol/atc-asr-dataset-preparation-toolkit)

This includes:

- Scripts to process raw UWB, ATCO2, and ATCC datasets
- Tools for combining, splitting, and augmenting data
- Upload scripts for Hugging Face dataset integration

## References

- [ATC ASR_Dataset (Combined and Cleaned Dataset on Hugging Face)](https://huggingface.co/datasets/jacktol/ATC_ASR_Dataset)
- [ATC ASR Dataset Preparation Toolkit (GitHub Repository)](https://github.com/jack-tol/atc-asr-dataset-preparation-toolkit)
- [ATCC Corpus (LDC94S14A, Raw)](https://catalog.ldc.upenn.edu/LDC94S14A)
- [ATCO2 1-Hour Test Subset (Raw)](https://www.replaywell.com/atco2/download/ATCO2-ASRdataset-v1_beta.tgz)
- [Juan Pablo Zuluaga – UWB ATC Dataset on GitHub](https://github.com/idiap/atco2-corpus/tree/main/data/databases/uwb_atcc)
- [Juan Pablo Zuluaga – UWB ATC Dataset on Hugging Face](https://huggingface.co/datasets/Jzuluaga/uwb_atcc)
- [UWB ATC Corpus (Raw)](https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0001-CCA1-0)


## Citation

If you use this dataset, please cite the original UWB and ATCO2 corpora where appropriate.
For data processing methodology and code, reference the [ATC ASR Dataset Preparation Toolkit](https://github.com/jack-tol/atc-asr-dataset-preparation-toolkit).

Mentioning or linking to this Hugging Face dataset page helps support transparency and future development of open ATC ASR resources.