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README.md
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- split: test
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path: data/test-*
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path: data/test-*
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---
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# ATC_ASR_Dataset
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**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).
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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.
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## Contents
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This dataset includes:
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- Audio files (`.wav`, 16kHz mono) of individual ATC utterances
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- Transcripts (`.txt`) aligned with each audio file
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- Training, validation, and test splits
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- Augmented training set (50% of utterances expanded with synthetic noise, pitch shift, or bandpass filtering)
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## Use Cases
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This dataset is ideal for:
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- Training ASR models specialized in aviation communication
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- Benchmarking domain-adapted speech recognition systems
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- Studying accented and noisy English in operational ATC environments
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## Source Datasets
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This dataset combines data from:
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- **[UWB ATC Corpus](https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0001-CCA1-0)**
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~20 hours of ATC speech recorded over Czech airspace, featuring heavily accented English, transcription inconsistencies, and realistic code-switching artifacts.
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- **[ATCO2 1-Hour Test Subset](https://www.replaywell.com/atco2/download/ATCO2-ASRdataset-v1_beta.tgz)**
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A publicly released evaluation slice from the larger ATCO2 corpus, featuring diverse ATC environments, speaker accents, and acoustic conditions.
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## Cleaning & Preprocessing
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The raw corpora were normalized and cleaned using custom Python scripts. Key steps included:
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- Segmenting long audio files into utterance-level clips using timestamps
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- Uppercasing all transcripts for uniformity
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- Converting digits to words (e.g., `350` → `THREE FIVE ZERO`)
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- Expanding letters to phonetic alphabet equivalents (e.g., `N` → `NOVEMBER`)
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- Removing non-English, unintelligible, or corrupted segments
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- Normalizing diacritics and fixing broken Unicode characters
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- Manual filtering of misaligned or low-quality samples
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- Augmenting 50% of training data with offline audio transformations
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## Reproducibility
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All preprocessing scripts and data creation pipelines are publicly available in the companion GitHub repository:
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[ATC ASR Dataset Preparation Toolkit (GitHub)](https://github.com/jack-tol/atc-asr-dataset-preparation-toolkit)
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This includes:
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- Scripts to process raw UWB, ATCO2, and ATCC datasets
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- Tools for combining, splitting, and augmenting data
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- Upload scripts for Hugging Face dataset integration
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## References
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- [ATC_ASR_Dataset (Combined and Cleaned Dataset on Hugging Face)](https://huggingface.co/datasets/jacktol/ATC_ASR_Dataset)
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- [ATC ASR Dataset Preparation Toolkit (GitHub Repository)](https://github.com/jack-tol/atc-asr-dataset-preparation-toolkit)
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- [ATCC Corpus (LDC94S14A, Raw)](https://catalog.ldc.upenn.edu/LDC94S14A)
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- [ATCO2 1-Hour Test Subset (Raw)](https://www.replaywell.com/atco2/download/ATCO2-ASRdataset-v1_beta.tgz)
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- [Juan Pablo Zuluaga – UWB ATC Dataset on GitHub](https://github.com/idiap/atco2-corpus/tree/main/data/databases/uwb_atcc)
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- [Juan Pablo Zuluaga – UWB ATC Dataset on Hugging Face](https://huggingface.co/datasets/Jzuluaga/uwb_atcc)
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- [UWB ATC Corpus (Raw)](https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0001-CCA1-0)
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## Citation
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If you use this dataset, please cite the original UWB and ATCO2 corpora where appropriate.
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For data processing methodology and code, reference the [ATC ASR Dataset Preparation Toolkit](https://github.com/jack-tol/atc-asr-dataset-preparation-toolkit).
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Mentioning or linking to this Hugging Face dataset page helps support transparency and future development of open ATC ASR resources.
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