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README.md

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
    • data_aishell: please set the aishell 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 ./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 aishell 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 ./paraformer_large_infer.sh