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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 Dataset - Fine-Tuning Whisper
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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.
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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).
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## Dataset Overview
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- **Dataset Name**: ATC Dataset
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- **Total Samples**: 11.9k (Training), 2.93k (Test)
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- **Data Sources**:
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- **[ATCO2 Corpus (1-hour test subset)](https://huggingface.co/datasets/Jzuluaga/atco2_corpus_1h)**
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- **[UWB-ATCC Corpus](https://huggingface.co/datasets/Jzuluaga/uwb_atcc)**
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- **Format**: Audio files (WAV format) with corresponding transcriptions.
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- **The Transciption Ground Truth** is within the `text` column.
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- **The Audio Ground Truth** is within the `audio` column.
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- **License**: MIT
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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.
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### Key Features
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- **Domain-specific**: Tailored to ATC communications with specialized phraseology and terms.
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- **Diverse accents**: Contains multiple accent variations to reflect real-world international aviation communication.
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- **Cleaned Data**: Includes only high-quality samples after filtering erroneous or incomplete transcriptions.
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## Usage
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1. **Install Dependencies**:
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Use Hugging Face's `datasets` library to load the dataset:
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```
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from datasets import load_dataset
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dataset = load_dataset("jacktol/atc-dataset")
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```
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2. **Training**:
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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).
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## License
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This dataset is shared under the **MIT License**. You are free to use, modify, and distribute it as long as you provide proper attribution.
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