Merge pull request #51 from alibaba-damo-academy/dev

update paraformer-large model README

egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/README.md

@@ -6,8 +6,9 @@
 
 - Modify finetune training related parameters in `finetune.py`
     - <strong>output_dir:</strong> # result dir
-    - <strong>data_dir:</strong> # the dataset dir needs to include files: train/wav.scp, train/text; validation/wav.scp, validation/text.
-    - <strong>batch_bins:</strong> # batch size
+    - <strong>data_dir:</strong> # the dataset dir needs to include files: `train/wav.scp`, `train/text`; `validation/wav.scp`, `validation/text`
+    - <strong>dataset_type:</strong> # for dataset larger than 1000 hours, set as `large`, otherwise set as `small`
+    - <strong>batch_bins:</strong> # 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
     - <strong>max_epoch:</strong> # number of training epoch
     - <strong>lr:</strong> # learning rate
 
@@ -21,10 +22,32 @@
 Or you can use the finetuned model for inference directly.
 
 - Setting parameters in `infer.py`
-    - <strong>data_dir:</strong> # the dataset dir
+    - <strong>data_dir:</strong> # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed
     - <strong>output_dir:</strong> # result dir
+    - <strong>ngpu:</strong> # the number of GPUs for decoding
+    - <strong>njob:</strong> # 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 `$output_dir/1best_recog/text.cer`, which includes recognition results of each sample and the CER metric of the whole test set.
+
+### Inference using local finetuned model
+
+- Modify inference related parameters in `infer_after_finetune.py`
+    - <strong>output_dir:</strong> # result dir
+    - <strong>data_dir:</strong> # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed
+    - <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pth`
+
+- Then you can run the pipeline to finetune with:
+```python
+    python infer_after_finetune.py
+```
+
+- Results
+
+The decoding results can be found in `$output_dir/decoding_results/text.cer`, which includes recognition results of each sample and the CER metric of the whole test set.