From 38de2af5bf9976d2f14f087d9a0d31991daf6783 Mon Sep 17 00:00:00 2001
From: Zhihao Du <neo.dzh@alibaba-inc.com>
Date: 星期四, 16 三月 2023 19:41:34 +0800
Subject: [PATCH] Merge branch 'main' into dev_dzh
---
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/README.md | 10 +++++++---
1 files changed, 7 insertions(+), 3 deletions(-)
diff --git a/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/README.md b/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/README.md
index dfd509d..a044361 100644
--- a/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/README.md
+++ b/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/README.md
@@ -22,10 +22,12 @@
Or you can use the finetuned model for inference directly.
- Setting parameters in `infer.py`
+ - <strong>model:</strong> # model name on ModelScope
- <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
+ - <strong>ngpu:</strong> # the number of GPUs for decoding, if `ngpu` > 0, use GPU decoding
+ - <strong>njob:</strong> # the number of jobs for CPU decoding, if `ngpu` = 0, use CPU decoding, please set `njob`
+ - <strong>batch_size:</strong> # batchsize of inference
- Then you can run the pipeline to infer with:
```python
@@ -39,9 +41,11 @@
### Inference using local finetuned model
- Modify inference related parameters in `infer_after_finetune.py`
+ - <strong>modelscope_model_name: </strong> # model name on ModelScope
- <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`
+ - <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pb`
+ - <strong>batch_size:</strong> # batchsize of inference
- Then you can run the pipeline to finetune with:
```python
--
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