MSR-86K: An Evolving, Multilingual Corpus with 86,300 Hours of Transcribed Audio for Speech Recognition Research
<|reference_start|>MSR-86K: An Evolving, Multilingual Corpus with 86,300 Hours of Transcribed Audio for Speech Recognition Research: Recently, multilingual artificial intelligence assistants, exemplified by ChatGPT, have gained immense popularity. As a crucial gateway to human-computer interaction, multilingual automatic speech recognition (ASR) has also garnered significant attention, as evidenced by systems like Whisper. However, the proprietary nature of the training data has impeded researchers' efforts to study multilingual ASR. This paper introduces MSR-86K, an evolving, large-scale multilingual corpus for speech recognition research. The corpus is derived from publicly accessible videos on YouTube, comprising 15 languages and a total of 86,300 hours of transcribed ASR data. We also introduce how to use the MSR-86K corpus and other open-source corpora to train a robust multilingual ASR model that is competitive with Whisper. MSR-86K will be publicly released on HuggingFace, and we believe that such a large corpus will pave new avenues for research in multilingual ASR.<|reference_end|>
@article{li2024msr-86k:,
    title={MSR-86K: An Evolving, Multilingual Corpus with 86,300 Hours of
  Transcribed Audio for Speech Recognition Research},
    author={Song Li, Yongbin You, Xuezhi Wang, Zhengkun Tian, Ke Ding, Guanglu Wan},
    journal={arXiv preprint arXiv:2406.18301},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18301},
    primaryClass={eess.AS cs.CL cs.SD}
}