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arXiv · 2307.09006

OxfordVGG Submission to the EGO4D AV Transcription Challenge

Jaesung Huh·Max Bain·Andrew Zisserman
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Abstract

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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Jaesung Huh, Max Bain, Andrew Zisserman. 2023-07-18. OxfordVGG Submission to the EGO4D AV Transcription Challenge. https://arxiv.org/abs/2307.09006

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