Table 8 reports the number of repeated 5-gram word duplicates (5-Dup.) and insertion error rate
(IER) metrics averaged over the four long-form test sets. In addition to the Whisper and Distil-
Whisper models, we report the results for the official Wav2Vec 2.0 large model fine-tuned on 960
hours of LibriSpeech data. This checkpoint provides a comparison between the Whisper Seq2Seq
architecture and a CTC based one (Graves et al., 2006). A CTC model should be less prone to
hallucination errors given it is an encoder-only architecture with a linear head over the vocabulary.
The distil-large-v2 model has 1.3 times fewer repetition errors than Whisper large-v2. It also obtains
the lowest average IER, improving on Whisper by 1.2% absolute. This indicates that the amount of
hallucination is improved in Distil-Whisper compared to the original Whisper model. The average
deletion error rate (DER) is comparable for both large-v2 and distil-large-v2, performing to within
DISTIL-WHISPER