lzr265946 532c56b3c1 update inference config		пре 3 година
..
conf	c087854f71 create	пре 3 година
modelscope_utils	a9e857e452 update funasr 0.1.3	пре 3 година
utils	0b8348376a update FunASR version==0.1.4	пре 3 година
README.md	c087854f71 create	пре 3 година
modelscope_common_finetune.sh	532c56b3c1 update inference config	пре 3 година
modelscope_common_infer.sh	532c56b3c1 update inference config	пре 3 година
modelscope_common_infer_after_finetune.sh	532c56b3c1 update inference config	пре 3 година
path.sh	c087854f71 create	пре 3 година

ModelScope Model

How to finetune and infer using a pretrained ModelScope Model

Modify finetune training related parameters in conf/train_asr_paraformer_sanm_50e_16d_2048_512_lfr6.yaml
Setting parameters in modelscope_common_finetune.sh
- dataset: the dataset dir needs to include files: train/wav.scp, train/text; optional dev/wav.scp, dev/text, test/wav.scp test/text
- 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:
```
sh ./modelscope_common_finetune.sh
```

Or you can use the finetuned model for inference directly.

Setting parameters in modelscope_common_infer.sh
- data_dir: # wav list, ${data_dir}/wav.scp
- exp_dir: the result path
- model_name: speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch # pre-trained model, download from modelscope
Then you can run the pipeline to infer with:
```
sh ./modelscope_common_infer.sh
```