title: MooER: LLM-based Speech Recognition and Translation Models from Moore
    Threads
summary: 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.
paper_authors._id: 66b9c47299e022a257ed4009
paper_authors.name: Junhao Xu
paper_authors._id: 66b9c47299e022a257ed400a
paper_authors.name: Zhenlin Liang
paper_authors._id: 66b9c47299e022a257ed400b
paper_authors.name: Yi Liu
paper_authors._id: 66b9c47299e022a257ed400c
paper_authors.name: Yichao Hu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-14T13:04:40.482Z
paper_authors._id: 617a54187ced0304d1b75b78
paper_authors.avatarUrl: /avatars/cb43f2079ddded2fb3eb280f926abb8e.svg
paper_authors.fullname: Yichao hu
paper_authors.type: user
paper_authors.user: Jeffrey1hu
paper_authors._id: 66b9c47299e022a257ed400d
paper_authors.name: Jian Li
paper_authors._id: 66b9c47299e022a257ed400e
paper_authors.name: Yajun Zheng
paper_authors._id: 66b9c47299e022a257ed400f
paper_authors.name: Meng Cai
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-14T13:04:55.605Z
paper_authors._id: 65f50a2d61a19b8f17f79432
paper_authors.avatarUrl: /avatars/b61deae87349a2698a03b53fe8aca27b.svg
paper_authors.fullname: Meng Cai
paper_authors.type: user
paper_authors.user: mcai0076
paper_authors._id: 66b9c47299e022a257ed4010
paper_authors.name: Hua Wang
paper_title: MooER: LLM-based Speech Recognition and Translation Models from Moore
    Threads
paper_summary: 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.
paper_ai_keywords: LLM-based large-scale automatic speech recognition (ASR) automatic speech translation (AST) pseudo labeled dataset training strategy encoders speech related tasks BLEU score