MooER (摩耳): an LLM-based Speech Recognition and Translation Model from Moore Threads

Online Demo: https://mooer-speech.mthreads.com:10077/

🔥 Update

We release a new model MooER-80K-v2 using 80K hours of data. Click here to try the new model.

📖 Introduction

We introduce MooER (摩耳): an LLM-based speech recognition and translation model developed by Moore Threads. With the MooER framework, you can transcribe the speech into text (speech recognition or, ASR), and translate it into other languages (speech translation or, AST) in a end-to-end manner. The performance of MooER is demonstrated in the subsequent section, along with our insights into model configurations, training strategies, and more, provided in our technical report.

For the usage of the model files, please refer to our GitHub

🥊 Evaluation Results

We demonstrate the training data and the evaluation results below. For more comprehensive information, please refer to our report.

Training data

We utilize 5k hours of data (MT5K) to train our basic MooER-5K model. The data sources include:

Dataset	Duration
aishell2	137h
librispeech	131h
multi_cn	100h
wenetspeech	1361h
in-house data	3274h

Note that, data from the open-source datasets were randomly selected from the full training set. The in-house data, collected internally without text, were transcribed using a third-party ASR service.

Since all the above datasets were originally designed only for the speech recognition task, no translation results are available. To train our speech translation model, we used a third-party translation service to generate pseudo-labels. No data filtering techniques were applied.

At this moment, we are also developing a new model trained with 80K hours of data.

Speech Recognition

The performance of speech recognition is evaluated using WER/CER.

Language	Testset	Paraformer-large	SenseVoice-small	Qwen-audio	Whisper-large-v3	SeamlessM4T-v2	MooER-5K	MooER-80K	MooER-80K-v2
Chinese	aishell1	1.93	3.03	1.43	7.86	4.09	1.93	1.25	1.00
	aishell2_ios	2.85	3.79	3.57	5.38	4.81	3.17	2.67	2.62
	test_magicdata	3.66	3.81	5.31	8.36	9.69	3.48	2.52	2.17
	test_thchs	3.99	5.17	4.86	9.06	7.14	4.11	3.14	3.00
	fleurs cmn_dev	5.56	6.39	10.54	4.54	7.12	5.81	5.23	5.15
	fleurs cmn_test	6.92	7.36	11.07	5.24	7.66	6.77	6.18	6.14
	average	4.15	4.93	6.13	6.74	6.75	4.21	3.50	3.35
English	librispeech test_clean	14.15	4.07	2.15	3.42	2.77	7.78	4.11	3.57
	librispeech test_other	22.99	8.26	4.68	5.62	5.25	15.25	9.99	9.09
	fleurs eng_dev	24.93	12.92	22.53	11.63	11.36	18.89	13.32	13.12
	fleurs eng_test	26.81	13.41	22.51	12.57	11.82	20.41	14.97	14.74
	gigaspeech dev	24.23	19.44	12.96	19.18	28.01	23.46	16.92	17.34
	gigaspeech test	23.07	16.65	13.26	22.34	28.65	22.09	16.64	16.97
	average	22.70	12.46	13.02	12.46	14.64	17.98	12.66	12.47

Speech Translation (zh -> en)

For speech translation, the performanced is evaluated using BLEU score.

Testset	Speech-LLaMA	Whisper-large-v3	Qwen-audio	Qwen2-audio	SeamlessM4T-v2	MooER-5K	MooER-5K-MTL
CoVoST1 zh2en	-	13.5	13.5	-	25.3	-	30.2
CoVoST2 zh2en	12.3	12.2	15.7	24.4	22.2	23.4	25.2
CCMT2019 dev	-	15.9	12.0	-	14.8	-	19.6

🏁 Getting Started

Please visit our GitHub for the setup and usage.

🧾 License

Please see the LICENSE.

💖 Citation

If you find MooER useful for your research, please 🌟 this repo and cite our work using the following BibTeX:

@article{liang2024mooer,
  title   = {MooER: an LLM-based Speech Recognition and Translation Model from Moore Threads},
  author  = {Zhenlin Liang, Junhao Xu, Yi Liu, Yichao Hu, Jian Li, Yajun Zheng, Meng Cai, Hua Wang},
  journal = {arXiv preprint arXiv:2408.05101},
  url     = {https://arxiv.org/abs/2408.05101}, 
  year    = {2024}
}

📧 Contact

If you encouter any problems, feel free to create a discussion.

Moore Threads Website: https://www.mthreads.com/