ASR Models
Collection
Automatic Speech Recognition (ASR) models fine-tuned and optimized from pre-trained architectures for enhanced perfomance on specialized datasets.
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Fine-Tuned Model
fjmgAI/whisper-large-v3-ATC
Base Model
unsloth/whisper-large-v3
Fine-Tuning Method
Fine-tuning was performed using unsloth, an efficient fine-tuning framework optimized for low-resource environments.
Dataset
Description
This dataset contains 14,830 examples transcriptions and corresponding audio files from two main sources: ATCO2 and the UWB-ATCC corpus, specifically selected for aviation-related communications.
Fine-Tuning Details
- The model was trained using the Seq2SeqTrainer.
- The Word Error Rate (WER) was employed as the loss metric to evaluate and optimize the model's performance during the fine-tuning process.
Purpose
This fine-tuned model is designed for Speech-to-Text (STT) applications in Air Traffic Control (ATC) environments, leveraging a specialized ATC dataset to enhance robustness and precision in transcribing ATC recordings. The model aims to deliver accurate and reliable transcription while maintaining efficient performance.
- Developed by: fjmgAI
- License: apache-2.0
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