et al., 2022), and Maestro (Chen et al., 2022b) in a zero-shot
setting. We caution that we do use a simple text standardizer
for this result which prevents direct comparison or claims
of SOTA performance. On VoxPopuli, however, Whisper
signiﬁcantly underperforms prior work and only beats the
VP-10K+FT baseline from the original paper. We suspect
the underperformance of Whisper models on VoxPopuli
could be due to other models including this distribution as
a major source for their unsupervised pre-training data and
the dataset having signiﬁcantly more supervised data, which
beneﬁts ﬁne-tuning. While MLS has 10 hours of training
data per language, the average amount of training data per
language is roughly 10× higher for VoxPopuli.
These two benchmarks are somewhat narrow since they
only include 15 unique languages, almost all of which are in
RobustSpeechRecognitionviaLarge-ScaleWeakSupervision