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README.md

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 title: ATC Transcription Assistant
 emoji: ✈️
-colorFrom: yellow
-colorTo: blue
+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 [[email protected]](mailto:[email protected]).
+
+## License
+
+This project is licensed under the [MIT License](LICENSE).