63 lines
2.9 KiB
Markdown
63 lines
2.9 KiB
Markdown
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---
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library_name: transformers
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license: mit
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datasets:
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- jacktol/ATC-ASR-Dataset
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language:
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- en
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metrics:
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- wer
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base_model:
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- openai/whisper-large-v3
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pipeline_tag: automatic-speech-recognition
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model-index:
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- name: Whisper Large v3 Fine-Tuned for Air Traffic Control (ATC)
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results:
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- task:
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type: automatic-speech-recognition
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dataset:
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name: ATC ASR Dataset
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type: jacktol/ATC-ASR-Dataset
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metrics:
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- name: Word Error Rate (WER)
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type: wer
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value: 6.5
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---
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## Model Overview
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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.
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- **Base Model**: OpenAI Large v3
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- **Fine-tuned Model WER**: 6.5%
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## Model Description
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This fine-tuned model is optimized to handle short, distinct transmissions between pilots and air traffic controllers. It is fine-tuned using data from:
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- **[ATC ASR Dataset](https://huggingface.co/datasets/jacktol/ATC-ASR-Dataset)**
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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.
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## Intended Use
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The fine-tuned Whisper model is designed for:
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- **Transcribing aviation communication**: Providing accurate transcriptions for ATC communications, including accents and variations in English phrasing.
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- **Air Traffic Control Systems**: Assisting in real-time transcription of pilot-ATC conversations, helping improve situational awareness.
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- **Research and training**: Useful for researchers, developers, or aviation professionals studying ATC communication or developing new tools for aviation safety.
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## Training Procedure
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- **Hardware**: Fine-tuning was conducted on two H100 SXM5 GPUs with 80GB VRAM.
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- **Epochs**: 3.25
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- **Learning Rate**: 1e-5
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- **Batch Size**: 10 with no gradient accumulation
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- **Augmentation**: Offline data augmentation techniques were utilized in the training set (Gaussian noise, pitch shifting, etc.).
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- **Evaluation Metric**: Word Error Rate (WER)
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## Limitations
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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.
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## References
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- [**ATC ASR Dataset**](https://huggingface.co/datasets/jacktol/ATC-ASR-Dataset)
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