region:us
GigaSpeech is 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.
@article{DBLP:journals/corr/abs-2106-06909,
  author    = {Guoguo Chen and
               Shuzhou Chai and
               Guanbo Wang and
               Jiayu Du and
               Wei{-}Qiang Zhang and
               Chao Weng and
               Dan Su and
               Daniel Povey and
               Jan Trmal and
               Junbo Zhang and
               Mingjie Jin and
               Sanjeev Khudanpur and
               Shinji Watanabe and
               Shuaijiang Zhao and
               Wei Zou and
               Xiangang Li and
               Xuchen Yao and
               Yongqing Wang and
               Yujun Wang and
               Zhao You and
               Zhiyong Yan},
  title     = {GigaSpeech: An Evolving, Multi-domain {ASR} Corpus with 10, 000 Hours
               of Transcribed Audio},
  journal   = {CoRR},
  volume    = {abs/2106.06909},
  year      = {2021},
  url       = {https://arxiv.org/abs/2106.06909},
  eprinttype = {arXiv},
  eprint    = {2106.06909},
  timestamp = {Wed, 29 Dec 2021 14:29:26 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2106-06909.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
# Unormalized AMI

```python
from datasets import load_dataset

ami = load_dataset("speech-seq2seq/ami", "ihm")
```

## TODO(PVP) - explain exactly what normalization was accepted what wasn't