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Moore Threads has announced the open-source release of its audio understanding model, MooER, making it the first large-scale open-source speech model based on domestically produced full-feature GPUs. MooER supports Chinese and English speech recognition and translation, utilizing a three-part model structure that demonstrates robust multilingual processing capabilities. The inference code and a model trained on 5000 hours of data have been released as open source, with plans to further open-source training code and an enhanced version trained on 80,000 hours of data. In comparative testing, MooER-5K has shown outstanding performance, achieving a Chinese CER of 4.21% and an English WER of 17.98%, particularly.

MoE Technology has announced the open-source release of its independently developed audio understanding large model, MooER, which is the first large-scale open-source speech model trained and inferred on domestically produced full-featured GPUs. MooER was trained on a large-scale audio dataset in just 38 hours on the MoE KuaE Intelligence Computing Platform, demonstrating excellent performance in Chinese and English speech recognition, as well as Chinese-to-English speech translation, achieving an impressive BLEU score of 25.2 in the Covost2 Chinese-to-English test set, nearing industrial-grade effectiveness. MoE Technology plans to further open-source the training code.