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---
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configs:
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path: data/train-*
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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. |