In this paper, we present MooER, a LLM-based large-scale automatic speech recognition (ASR) / automatic speech translation (AST) model of Moore Threads. A 5000h pseudo labeled dataset containing open source and self collected speech data is used for training. We achieve performance comparable to other open source models trained with up to hundreds of thousands of hours of labeled speech data. Meanwhile, experiments conducted on Covost2 Zh2en testset suggest that our model outperforms other open source Speech LLMs. A BLEU score of 25.2 can be obtained. The main contributions of this paper are summarized as follows. First, this paper presents a training strategy for encoders and LLMs on speech related tasks (including ASR and AST) using a small size of pseudo labeled data without any extra manual annotation and selection. Second, we release our ASR and AST models and plan to open-source our training code and strategy in the near future. Moreover, a model trained on 8wh scale training data is planned to be released later on.

本研究解决了大型语音识别和翻译模型训练数据不足的问题，提出了一种使用5000小时伪标签数据的新训练策略。MooER模型在评测中表现出色，BLEU得分达到25.2，显示出相较于其他开源模型的优势，具有广泛的应用潜力和研究价值。

MooER：基于大型语言模型的莫尔线程语音识别和翻译模型