  The evolution of speech technology has been spurred by the rapid increase in
dataset sizes. Traditional speech models generally depend on a large amount of
labeled training data, which is scarce for low-resource languages. This paper
presents GigaSpeech 2, a large-scale, multi-domain, multilingual speech
recognition corpus. It is designed for low-resource languages and does not rely
on paired speech and text data. GigaSpeech 2 comprises about 30,000 hours of
automatically transcribed speech, including Thai, Indonesian, and Vietnamese,
gathered from unlabeled YouTube videos. We also introduce an automated pipeline
for data crawling, transcription, and label refinement. Specifically, this
pipeline uses Whisper for initial transcription and TorchAudio for forced
alignment, combined with multi-dimensional filtering for data quality
assurance. A modified Noisy Student Training is developed to further refine
flawed pseudo labels iteratively, thus enhancing model performance.
Experimental results on our manually transcribed evaluation set and two public
test sets from Common Voice and FLEURS confirm our corpus's high quality and
broad applicability. Notably, ASR models trained on GigaSpeech 2 can reduce the
word error rate for Thai, Indonesian, and Vietnamese on our challenging and
realistic YouTube test set by 25% to 40% compared to the Whisper large-v3
model, with merely 10% model parameters. Furthermore, our ASR models trained on
Gigaspeech 2 yield superior performance compared to commercial services. We
believe that our newly introduced corpus and pipeline will open a new avenue
for low-resource speech recognition and significantly facilitate research in
this area.
