GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2021
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| _version_ | 1866912358848266240 |
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| author | Chen, Guoguo Chai, Shuzhou Wang, Guanbo Du, Jiayu Zhang, Wei-Qiang Weng, Chao Su, Dan Povey, Daniel Trmal, Jan Zhang, Junbo Jin, Mingjie Khudanpur, Sanjeev Watanabe, Shinji Zhao, Shuaijiang Zou, Wei Li, Xiangang Yao, Xuchen Wang, Yongqing Wang, Yujun You, Zhao Yan, Zhiyong |
| author_facet | Chen, Guoguo Chai, Shuzhou Wang, Guanbo Du, Jiayu Zhang, Wei-Qiang Weng, Chao Su, Dan Povey, Daniel Trmal, Jan Zhang, Junbo Jin, Mingjie Khudanpur, Sanjeev Watanabe, Shinji Zhao, Shuaijiang Zou, Wei Li, Xiangang Yao, Xuchen Wang, Yongqing Wang, Yujun You, Zhao Yan, Zhiyong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2106_06909 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio Chen, Guoguo Chai, Shuzhou Wang, Guanbo Du, Jiayu Zhang, Wei-Qiang Weng, Chao Su, Dan Povey, Daniel Trmal, Jan Zhang, Junbo Jin, Mingjie Khudanpur, Sanjeev Watanabe, Shinji Zhao, Shuaijiang Zou, Wei Li, Xiangang Yao, Xuchen Wang, Yongqing Wang, Yujun You, Zhao Yan, Zhiyong Sound Computation and Language Audio and Speech Processing 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. |
| title | GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio |
| topic | Sound Computation and Language Audio and Speech Processing |
| url | https://arxiv.org/abs/2106.06909 |