---
library_name: transformers
license: mit
datasets:
- jacktol/ATC-ASR-Dataset
language:
- en
metrics:
- wer
base_model:
- openai/whisper-large-v3
pipeline_tag: automatic-speech-recognition
model-index:
  - name: Whisper Large v3 Fine-Tuned for Air Traffic Control (ATC)
    results:
      - task:
          type: automatic-speech-recognition
        dataset:
          name: ATC ASR Dataset
          type: jacktol/ATC-ASR-Dataset
        metrics:
          - name: Word Error Rate (WER)
            type: wer
            value: 6.5

---

## Model Overview

This model is a fine-tuned version of OpenAI's Whisper Large v3 model, specifically trained on **Air Traffic Control (ATC)** communication datasets. The fine-tuning process significantly improves transcription accuracy on domain-specific aviation communications, achieving a Word Error Rate (WER) of 6.5% on the test set. The model is particularly effective at handling accent variations and ambiguous phrasing often encountered in ATC communications.

- **Base Model**: OpenAI Large v3
- **Fine-tuned Model WER**: 6.5%

## Model Description

This fine-tuned model is optimized to handle short, distinct transmissions between pilots and air traffic controllers. It is fine-tuned using data from:
- **[ATC ASR Dataset](https://huggingface.co/datasets/jacktol/ATC-ASR-Dataset)**

The fine-tuned model demonstrates enhanced performance in interpreting various accents, recognizing non-standard phraseology, and processing noisy or distorted communications. It is highly suitable for aviation-related transcription tasks.

## Intended Use

The fine-tuned Whisper model is designed for:
- **Transcribing aviation communication**: Providing accurate transcriptions for ATC communications, including accents and variations in English phrasing.
- **Air Traffic Control Systems**: Assisting in real-time transcription of pilot-ATC conversations, helping improve situational awareness.
- **Research and training**: Useful for researchers, developers, or aviation professionals studying ATC communication or developing new tools for aviation safety.

## Training Procedure

- **Hardware**: Fine-tuning was conducted on two H100 SXM5 GPUs with 80GB VRAM.
- **Epochs**: 3.25
- **Learning Rate**: 1e-5
- **Batch Size**: 10 with no gradient accumulation
- **Augmentation**: Offline data augmentation techniques were utilized in the training set (Gaussian noise, pitch shifting, etc.).
- **Evaluation Metric**: Word Error Rate (WER)

## Limitations

While the fine-tuned model performs well in ATC-specific communications, it may not generalize as effectively to other domains of speech. Additionally, like most speech-to-text models, transcription accuracy can be affected by extremely poor-quality audio or heavily accented speech not encountered or properly represented during training.

## References
- [**ATC ASR Dataset**](https://huggingface.co/datasets/jacktol/ATC-ASR-Dataset)