ModelScope Model

How to finetune and infer using a pretrained Paraformer-large Model

Finetune

• Modify finetune training related parameters in finetune.py

  • output_dir: # result dir
  • data_dir: # the dataset dir needs to include files: train/wav.scp, train/text; validation/wav.scp, validation/text
  • dataset_type: # for dataset larger than 1000 hours, set as large, otherwise set as small
  • batch_bins: # batch size. For dataset_type is small, batch_bins indicates the feature frames. For dataset_type is large, batch_bins indicates the duration in ms
  • max_epoch: # number of training epoch
  • lr: # learning rate

• Then you can run the pipeline to finetune with:
  python python finetune.py

Inference

Or you can use the finetuned model for inference directly.

• Setting parameters in infer.py

  • data_dir: # the dataset dir needs to include test/wav.scp. If test/text is also exists, CER will be computed
  • output_dir: # result dir
  • ngpu: # the number of GPUs for decoding
  • njob: # the number of jobs for each GPU

• Then you can run the pipeline to infer with:
  python python infer.py

• Results

The decoding results can be found in $output_dir/1best_recog/text.cer, which includes recognition results of each sample and the CER metric of the whole test set.

Inference using local finetuned model

• Modify inference related parameters in infer_after_finetune.py

  • output_dir: # result dir
  • data_dir: # the dataset dir needs to include test/wav.scp. If test/text is also exists, CER will be computed~~~~
  • decoding_model_name: # set the checkpoint name for decoding, e.g., valid.cer_ctc.ave.pb

• Then you can run the pipeline to finetune with:
  python python infer_after_finetune.py

• Results

The decoding results can be found in $output_dir/decoding_results/text.cer, which includes recognition results of each sample and the CER metric of the whole test set.