  This paper introduces GigaSpeech, an evolving, multi-domain English speech
recognition corpus with 10,000 hours of high quality labeled audio suitable for
supervised training, and 40,000 hours of total audio suitable for
semi-supervised and unsupervised training. Around 40,000 hours of transcribed
audio is first collected from audiobooks, podcasts and YouTube, covering both
read and spontaneous speaking styles, and a variety of topics, such as arts,
science, sports, etc. A new forced alignment and segmentation pipeline is
proposed to create sentence segments suitable for speech recognition training,
and to filter out segments with low-quality transcription. For system training,
GigaSpeech provides five subsets of different sizes, 10h, 250h, 1000h, 2500h,
and 10000h. For our 10,000-hour XL training subset, we cap the word error rate
at 4% during the filtering/validation stage, and for all our other smaller
training subsets, we cap it at 0%. The DEV and TEST evaluation sets, on the
other hand, are re-processed by professional human transcribers to ensure high
transcription quality. Baseline systems are provided for popular speech
recognition toolkits, namely Athena, ESPnet, Kaldi and Pika.
