---
title: ATC Transcription Assistant
emoji: ✈️
colorFrom: purple
colorTo: red
sdk: docker
pinned: false
---

# ATC Transcription Assistant

## Overview

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.

This project is a part of a broader research initiative aimed at enhancing Automatic Speech Recognition (ASR) accuracy in high-stakes aviation environments.

## Features

- **Transcription Model**: The app uses a fine-tuned version of the **Whisper medium.en** model.
- **Audio Formats**: Supports **MP3** and **WAV** files containing ATC audio.
- **Transcription Output**: Converts uploaded audio into text and displays it in an easily readable format.
- **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.

## Performance

- **Fine-tuned Whisper medium.en WER**: 15.08%
- **Non fine-tuned Whisper medium.en WER**: 94.59%
- **Relative Improvement**: 84.06%

> While the fine-tuned model provides substantial improvements, please note that transcription accuracy is not guaranteed.

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).

## How It Works

1. **Upload ATC Audio**: Upload an audio file containing ATC communications in **MP3** or **WAV** format.
2. **View Transcription**: The app will transcribe the audio and display the text on the screen.
3. **Transcribe More Audio**: To transcribe another file, click **New Chat** in the top-right corner of the app.

## Fine-Tuning Process

The Whisper model was fine-tuned on a custom ATC dataset created from publicly available resources, such as:

- The **ATCO2 test subset** (871 audio-transcription pairs).
- The **UWB-ATCC corpus** (11.3k rows in the training set and 2.82k rows in the test set).

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%**.

## Limitations

- **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.
- **Transcription Accuracy**: In real-world applications, minor errors may occur, but these often don't significantly impact communication.

## Get in Touch

If you have any questions or suggestions, feel free to contact me at [contact@jacktol.net](mailto:contact@jacktol.net).

## License

This project is licensed under the [MIT License](LICENSE).