# ModelScope: Paraformer-large Model ## Highlight ### ModelScope: Paraformer-Large Model - Fast: Non-autoregressive (NAR) model, the Paraformer can achieve comparable performance to the state-of-the-art AR transformer, with more than 10x speedup. - Accurate: SOTA in a lot of public ASR tasks, with a very significant relative improvement, capable of industrial implementation. - Convenient: Quickly and easily download Paraformer-large from Modelscope for finetuning and inference. - Support finetuning and inference on AISHELL-1 and AISHELL-2. - Support inference on AISHELL-1, AISHELL-2, Wenetspeech, SpeechIO and other audio. ## How to finetune and infer using a pretrained ModelScope Paraformer-large Model ### Finetune - Modify finetune training related parameters in `conf/train_asr_paraformer_sanm_50e_16d_2048_512_lfr6.yaml` - Setting parameters in `paraformer_large_finetune.sh` - tr_dir: please set the aishell2 train data path - dev_tst_dir: please set the aishell2 dev/test data path - tag: exp tag - init_model_name: speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch # pre-trained model, download from modelscope during fine-tuning - Then you can run the pipeline to finetune with our model download from modelscope and infer after finetune: ```sh sh ./paraformer_large_finetune.sh ``` ### Inference Or you can download the model from ModelScope for inference directly. - Setting parameters in `paraformer_large_infer.sh` - ori_data: please set the aishell2 dev/test raw data path - data_dir: data output dictionary - exp_dir: the result path - model_name: speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch # pre-trained model, download from modelscope - test_sets: please set the testsets name - Then you can run the pipeline to infer with: ```sh sh ./paraformer_large_infer.sh ```