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.

该论文介绍了MSR-86K，这是一个逐渐增长的大规模多语言语音识别研究语料库，由YouTube上公开可访问的视频转录数据组成，包括15种语言和总共86300小时的ASR数据。同时，该论文还介绍了如何使用MSR-86K语料库和其他开源语料库来训练一个与Whisper相媲美的强大的多语言语音识别模型。我们将在HuggingFace上公开发布MSR-86K，相信这样一个大规模的语料库将为多语言语音识别的研究开辟新的道路。

MSR-86K：一个包含86,300小时语音转换文本的多语种演变语料库，用于语音识别研究