Fine-tuning Whisper on ATC Data

Welcome to the repository for the Fine-tuning Whisper on ATC Data project. This project involves fine-tuning OpenAI’s Whisper model on domain-specific ATC datasets to significantly improve transcription accuracy for aviation communication, reducing the Word Error Rate (WER) by 84% compared to the pretrained model.

The repository includes scripts for dataset preparation, training, evaluation, and deployment of the fine-tuned model, which is available for use via Hugging Face.

Project Overview

Fine-tuning Whisper on ATC-specific data significantly improves the transcription of critical communication between pilots and air traffic controllers. The fine-tuned model reduces WER to 15.08% (compared to 94.59% for the pretrained model), addressing many common communication issues in aviation, such as accent variations and ambiguous phrasing.

You can explore the open-source models on Hugging Face:

• Whisper Medium EN Fine-Tuned for ATC
• Whisper Medium EN Fine-Tuned for ATC (Faster Whisper)

Additionally, the ATC Transcription Assistant is an application hosted on Hugging Face Spaces that allows you to upload audio (MP3 or WAV) to transcribe ATC communications. Try it out here.

The dataset used for fine-tuning can also be found on Hugging Face: ATC Dataset.

For further context, read the full blog post covering the fine-tuning process on my website: Fine-Tuning Whisper for ATC: 84% Improvement in Transcription Accuracy.

Repository Structure

• custom_dataset_processing/
  • create_and_upload_dataset.py: Script to combine and preprocess ATC datasets for training and testing.
• evaluation_scripts/
  • evaluate_fine-tuned.py: Evaluate the fine-tuned Whisper model on test data.
  • evaluate_pretrained.py: Evaluate the pretrained Whisper model for comparison.
  • interactive_model_comparison_fine-tuned_vs_pretrained_whisper_on_atc_data.ipynb: Interactive Jupyter Notebook to compare the fine-tuned model with the pretrained one, allowing you to listen to audio samples and see WER differences.
• removed_samples/: Contains data for samples that were removed from the dataset during evaluation due to incorrect ground truth.
• training_script/
  • train.py: The main training script that fine-tunes the Whisper model on the ATC dataset, with dynamic data augmentation.
• utils/
  • export_model_for_inference.py: Script to export the fine-tuned model for inference.
  • upload_local_models_to_huggingface.py: Script to upload models to the Hugging Face model hub.
  • whisper_to_optimized_bin.bash: Bash script to convert the Whisper model to an optimized format using transformers.
• requirements.txt: Contains the dependencies required to run the project.

Key Files

• create_and_upload_dataset.py: Combines and preprocesses the ATCO2 and UWB-ATCC datasets, applies filtering, and uploads the processed dataset to Hugging Face.
• train.py: Defines the fine-tuning process, including dynamic data augmentation and training configuration.
• evaluate_fine-tuned.py & evaluate_pretrained.py: Evaluate the fine-tuned and pretrained Whisper models on the ATC dataset to compare WER.

How to Run

1. Environment Setup: Install the required dependencies:

   pip install -r requirements.txt
   

   Dataset Preparation: Use the create_and_upload_dataset.py script to process the ATC datasets and upload them to Hugging Face.

Model Training: Fine-tune the Whisper model using the train.py script. This script dynamically augments data and trains the model over 10 epochs with early stopping.

Evaluation: Use the evaluation scripts to measure the WER of the fine-tuned model compared to the pretrained model. The Jupyter Notebook in evaluation_scripts/ offers an interactive way to explore the results.

Model Export & Upload: Use the export_model_for_inference.py script to prepare the model for inference, and the upload_local_models_to_huggingface.py script to upload your model to Hugging Face.

Model Usage

The fine-tuned model is readily available on Hugging Face. You have two options to access and use the models:

1. Download Locally: You can download the models directly to your local system via these links:

   • Whisper Medium EN Fine-Tuned for ATC
   • Whisper Medium EN Fine-Tuned for ATC (Faster Whisper)

2. Use Online: Alternatively, you can use the models online via the ATC Transcription Assistant on Hugging Face. Simply upload your ATC communication audio file (MP3 or WAV), and the model will generate transcriptions for you directly on the screen.

License

This project is licensed under the MIT License. See the LICENSE file for details.

For further details or inquiries, contact me at [email protected].