MooER: LLM-based Speech Recognition and Translation Models from Moore
  Threads
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.
66b9c47299e022a257ed4009
Junhao Xu
66b9c47299e022a257ed400a
Zhenlin Liang
66b9c47299e022a257ed400b
Yi Liu
66b9c47299e022a257ed400c
Yichao Hu
admin_assigned
2024-08-14T13:04:40.482Z
617a54187ced0304d1b75b78
/avatars/cb43f2079ddded2fb3eb280f926abb8e.svg
Yichao hu
user
Jeffrey1hu
66b9c47299e022a257ed400d
Jian Li
66b9c47299e022a257ed400e
Yajun Zheng
66b9c47299e022a257ed400f
Meng Cai
admin_assigned
2024-08-14T13:04:55.605Z
65f50a2d61a19b8f17f79432
/avatars/b61deae87349a2698a03b53fe8aca27b.svg
Meng Cai
user
mcai0076
66b9c47299e022a257ed4010
Hua Wang
MooER: LLM-based Speech Recognition and Translation Models from Moore
  Threads
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.
MooER, a LLM-based ASR/AST model, achieves competitive performance with less labeled data and outperforms other open-source models on speech translation benchmarks.
LLM-based automatic speech recognition automatic speech translation pseudo labeled dataset BLEU score encoders LLMs speech related tasks