From 4137f5cf26e7c4b40853959cd2574edfde03aa60 Mon Sep 17 00:00:00 2001
From: 志浩 <neo.dzh@alibaba-inc.com>
Date: 星期五, 07 四月 2023 21:03:34 +0800
Subject: [PATCH] Merge branch 'main' of github.com:alibaba-damo-academy/FunASR into dev_dzh
---
egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1/README.md | 38 +++++++++++++++++++++++++++++++++-----
1 files changed, 33 insertions(+), 5 deletions(-)
diff --git a/egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1/README.md b/egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1/README.md
index 1587d3d..8bf63e5 100644
--- a/egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1/README.md
+++ b/egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1/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
@@ -20,11 +21,38 @@
Or you can use the finetuned model for inference directly.
-- Setting parameters in `infer.py`
- - <strong>data_dir:</strong> # the dataset dir
+- Setting parameters in `infer.sh`
+ - <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>batch_size:</strong> # batchsize of inference
+ - <strong>gpu_inference:</strong> # whether to perform gpu decoding, set false for cpu decoding
+ - <strong>gpuid_list:</strong> # set gpus, e.g., gpuid_list="0,1"
+ - <strong>njob:</strong> # the number of jobs for CPU decoding, if `gpu_inference`=false, use CPU decoding, please set `njob`
- Then you can run the pipeline to infer with:
```python
- python infer.py
+ sh infer.sh
```
+
+- Results
+
+The decoding results can be found in `$output_dir/1best_recog/text.cer`, which includes recognition results of each sample and the CER metric of the whole test set.
+
+### Inference using local finetuned model
+
+- Modify inference related parameters in `infer_after_finetune.py`
+ - <strong>modelscope_model_name: </strong> # model name on ModelScope
+ - <strong>output_dir:</strong> # result dir
+ - <strong>data_dir:</strong> # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed
+ - <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pb`
+ - <strong>batch_size:</strong> # batchsize of inference
+
+- Then you can run the pipeline to finetune with:
+```python
+ python infer_after_finetune.py
+```
+
+- Results
+
+The decoding results can be found in `$output_dir/decoding_results/text.cer`, which includes recognition results of each sample and the CER metric of the whole test set.
--
Gitblit v1.9.1