Papers › OxfordVGG Submission to the EGO4D AV Transcription Challenge
OxfordVGG Submission to the EGO4D AV Transcription Challenge
Jaesung Huh, Max Bain, Andrew Zisserman
This report presents the technical details of our submission on the EGO4D Audio-Visual (AV) Automatic Speech Recognition Challenge 2023 from the OxfordVGG team. We present WhisperX, a system for efficient speech transcription of long-form audio with word-level time alignment, along with two text normalisers which are publicly available. Our final submission obtained 56.0% of the Word Error Rate (WER) on the challenge test set, ranked 1st on the leaderboard. All baseline codes and models are available on https://github.com/m-bain/whisperX.
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