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--- |
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title: ATC Transcription Assistant |
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emoji: ✈️ |
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colorFrom: purple |
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colorTo: red |
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sdk: docker |
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pinned: false |
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--- |
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# ATC Transcription Assistant |
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## Overview |
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Welcome to the **ATC Transcription Assistant**, a tool designed to transcribe **Air Traffic Control (ATC)** audio. This app utilizes OpenAI’s **Whisper medium.en** model, fine-tuned specifically for ATC communications. The fine-tuned model significantly improves transcription accuracy for aviation communications, making it a useful tool for researchers, enthusiasts, and professionals interested in analyzing ATC communications. |
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This project is a part of a broader research initiative aimed at enhancing Automatic Speech Recognition (ASR) accuracy in high-stakes aviation environments. |
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## Features |
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- **Transcription Model**: The app uses a fine-tuned version of the **Whisper medium.en** model. |
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- **Audio Formats**: Supports **MP3** and **WAV** files containing ATC audio. |
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- **Transcription Output**: Converts uploaded audio into text and displays it in an easily readable format. |
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- **Enhanced Accuracy**: The fine-tuned model offers a **Word Error Rate (WER)** of **15.08%**, a significant improvement over the **94.59% WER** of the non-fine-tuned model. |
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## Performance |
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- **Fine-tuned Whisper medium.en WER**: 15.08% |
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- **Non fine-tuned Whisper medium.en WER**: 94.59% |
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- **Relative Improvement**: 84.06% |
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> While the fine-tuned model provides substantial improvements, please note that transcription accuracy is not guaranteed. |
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For more details on the fine-tuning process and model performance, see the [blog post](https://jacktol.net/posts/fine-tuning_whisper_on_atc_data), or check out the [project repository](https://github.com/jack-tol/fine-tuning-whisper-on-atc-data). |
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## How It Works |
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1. **Upload ATC Audio**: Upload an audio file containing ATC communications in **MP3** or **WAV** format. |
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2. **View Transcription**: The app will transcribe the audio and display the text on the screen. |
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3. **Transcribe More Audio**: To transcribe another file, click **New Chat** in the top-right corner of the app. |
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## Fine-Tuning Process |
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The Whisper model was fine-tuned on a custom ATC dataset created from publicly available resources, such as: |
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- The **ATCO2 test subset** (871 audio-transcription pairs). |
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- The **UWB-ATCC corpus** (11.3k rows in the training set and 2.82k rows in the test set). |
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After data preprocessing, dynamic data augmentation was applied to simulate challenging conditions during fine-tuning. The fine-tuned model was trained for 10 epochs on two A100 GPUs, achieving an average **WER of 15.08%**. |
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## Limitations |
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- **Word Error Rate (WER)**: While WER is a standard evaluation metric, it does not account for subtleties like meaning or word proximity, which can make the evaluation more rigid. |
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- **Transcription Accuracy**: In real-world applications, minor errors may occur, but these often don't significantly impact communication. |
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## Get in Touch |
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If you have any questions or suggestions, feel free to contact me at [[email protected]](mailto:[email protected]). |
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## License |
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This project is licensed under the [MIT License](LICENSE). |
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