Create README.md

README.md

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+---
+license: mit
+datasets:
+- jacktol/atc-dataset
+language:
+- en
+metrics:
+- wer
+base_model:
+- openai/whisper-medium.en
+pipeline_tag: automatic-speech-recognition
+tags:
+- aviation
+- atc
+- aircraft
+- communication
+model-index:
+  - name: Whisper Medium EN Fine-Tuned for ATC (Faster-Whisper)
+    results:
+      - task:
+          type: automatic-speech-recognition
+        dataset:
+          name: ATC Dataset
+          type: jacktol/atc-dataset
+        metrics:
+          - name: Word Error Rate (WER)
+            type: wer
+            value: 15.08
+        source:
+          name: ATC Transcription Evaluation
+          url: https://huggingface.co/jacktol/whisper-medium.en-fine-tuned-for-ATC-faster-whisper
+---
+
+# Whisper Medium EN Fine-Tuned for Air Traffic Control (ATC) - Faster-Whisper Optimized
+
+## Model Overview
+
+This model is a fine-tuned version of OpenAI's Whisper Medium EN model, specifically trained on **Air Traffic Control (ATC)** communication datasets. The fine-tuning process significantly improves transcription accuracy on domain-specific aviation communications, reducing the **Word Error Rate (WER) by 84%**, compared to the original pretrained model. The model is particularly effective at handling accent variations and ambiguous phrasing often encountered in ATC communications.
+
+This model has been **converted to an optimized `.bin` format**, making it compatible with **Faster-Whisper** for faster and more efficient inference.
+
+- **Base Model**: OpenAI Whisper Medium EN
+- **Fine-tuned Model WER**: 15.08%
+- **Pretrained Model WER**: 94.59%
+- **Relative Improvement**: 84.06%
+- **Optimized Format**: Compatible with Faster-Whisper
+
+You can access the fine-tuned model on Hugging Face:
+- [Whisper Medium EN Fine-Tuned for ATC](https://huggingface.co/jacktol/whisper-medium.en-fine-tuned-for-ATC)
+- [Whisper Medium EN Fine-Tuned for ATC (Faster Whisper)](https://huggingface.co/jacktol/whisper-medium.en-fine-tuned-for-ATC-faster-whisper)
+
+## Model Description
+
+Whisper Medium EN fine-tuned for ATC is optimized to handle short, distinct transmissions between pilots and air traffic controllers. It is fine-tuned using data from the **[ATC Dataset](https://huggingface.co/datasets/jacktol/atc-dataset)**, a combined and cleaned dataset sourced from the following:
+
+- **[ATCO2 corpus](https://huggingface.co/datasets/Jzuluaga/atco2_corpus_1h)** (1-hour test subset)
+- **[UWB-ATCC corpus](https://huggingface.co/datasets/Jzuluaga/uwb_atcc)**
+
+The **ATC Dataset** merges these two original sources, filtering and refining the data to enhance transcription accuracy for domain-specific ATC communications. The model has been further **optimized to a `.bin` format for compatibility with Faster-Whisper**, ensuring faster and more efficient processing.
+
+## 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.
+
+You can test the model online using the [ATC Transcription Assistant](https://huggingface.co/spaces/jacktol/ATC-Transcription-Assistant), which lets you upload audio files and generate transcriptions.
+
+## Training Procedure
+
+- **Hardware**: Fine-tuning was conducted on two A100 GPUs with 80GB memory.
+- **Epochs**: 10
+- **Learning Rate**: 1e-5
+- **Batch Size**: 32 (effective batch size with gradient accumulation)
+- **Augmentation**: Dynamic data augmentation techniques (Gaussian noise, pitch shifting, etc.) were applied during training.
+- **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 during training.
+
+## References
+
+- **Blog Post**: [Fine-Tuning Whisper for ATC: 84% Improvement in Transcription Accuracy](https://jacktol.net/posts/fine-tuning_whisper_for_atc/)
+- **GitHub Repository**: [Fine-Tuning Whisper on ATC Data](https://github.com/jack-tol/fine-tuning-whisper-on-atc-data/tree/main)