| | --- |
| | dataset_info: |
| | features: |
| | - name: audio |
| | dtype: |
| | audio: |
| | sampling_rate: 16000 |
| | - name: text |
| | dtype: string |
| | splits: |
| | - name: train |
| | num_bytes: 689300787.16 |
| | num_examples: 11868 |
| | - name: test |
| | num_bytes: 168022373.81678608 |
| | num_examples: 2927 |
| | download_size: 821530354 |
| | dataset_size: 857323160.976786 |
| | configs: |
| | - config_name: default |
| | data_files: |
| | - split: train |
| | path: data/train-* |
| | - split: test |
| | path: data/test-* |
| | --- |
| | |
| | # Deprecation Notice (June 16, 2025) |
| |
|
| | This dataset is **deprecated**. A newer version is available on Hugging Face Datasets, featuring **higher data quality and reliability**, although with fewer overall samples. |
| |
|
| | [View the updated dataset on Hugging Face](https://huggingface.co/datasets/jacktol/ATC-ASR-Dataset) |
| |
|
| | # ATC Dataset - Fine-Tuning Whisper |
| |
|
| | This dataset was created to fine-tune OpenAI's Whisper model for improving transcription accuracy in **Air Traffic Control (ATC)** communications. The dataset contains transcriptions and corresponding audio files from two main sources: **ATCO2** and the **UWB-ATCC corpus**, specifically selected for aviation-related communications. The dataset is publicly available on Hugging Face for use in Automatic Speech Recognition (ASR) projects. |
| |
|
| | For more details on the fine-tuning process, check out the [blog post](https://jacktol.net/posts/fine-tuning_whisper_for_atc/) and the corresponding [GitHub repository](https://github.com/jack-tol/fine-tuning-whisper-on-atc-data/tree/main). |
| |
|
| | ## Dataset Overview |
| |
|
| | - **Dataset Name**: ATC Dataset |
| | - **Total Samples**: 11.9k (Training), 2.93k (Test) |
| | - **Data Sources**: |
| | - **[ATCO2 Corpus (1-hour test subset)](https://huggingface.co/datasets/Jzuluaga/atco2_corpus_1h)** |
| | - **[UWB-ATCC Corpus](https://huggingface.co/datasets/Jzuluaga/uwb_atcc)** |
| | - **Format**: Audio files (WAV format) with corresponding transcriptions. |
| | - **The Transciption Ground Truth** is within the `text` column. |
| | - **The Audio Ground Truth** is within the `audio` column. |
| | - **License**: MIT |
| |
|
| | This dataset is particularly useful for training speech recognition models like Whisper on short, domain-specific audio transmissions, such as those between pilots and air traffic controllers. |
| |
|
| | ### Key Features |
| | - **Domain-specific**: Tailored to ATC communications with specialized phraseology and terms. |
| | - **Diverse accents**: Contains multiple accent variations to reflect real-world international aviation communication. |
| | - **Cleaned Data**: Includes only high-quality samples after filtering erroneous or incomplete transcriptions. |
| |
|
| | ## Usage |
| |
|
| | 1. **Install Dependencies**: |
| | Use Hugging Face's `datasets` library to load the dataset: |
| | ``` |
| | from datasets import load_dataset |
| | dataset = load_dataset("jacktol/atc-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). |
| |
|
| | ## License |
| |
|
| | This dataset is shared under the **MIT License**. You are free to use, modify, and distribute it as long as you provide proper attribution. |