From e3401f22ae01a611cbc88fd226ec2da1e66924c5 Mon Sep 17 00:00:00 2001
From: 嘉渊 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期三, 24 五月 2023 14:57:13 +0800
Subject: [PATCH] update repo
---
docs/academic_recipe/asr_recipe.md | 20 +++++++++++---------
1 files changed, 11 insertions(+), 9 deletions(-)
diff --git a/docs/academic_recipe/asr_recipe.md b/docs/academic_recipe/asr_recipe.md
index 7f727e7..a1690c2 100644
--- a/docs/academic_recipe/asr_recipe.md
+++ b/docs/academic_recipe/asr_recipe.md
@@ -8,26 +8,35 @@
```sh
cd egs/aishell/paraformer
```
+
Then you can directly start the recipe as follows:
```sh
conda activate funasr
. ./run.sh
```
-The training log files are saved in `exp/*_train_*/log/train.log.*`锛� which can be viewed using the following command:
+
+The training log files are saved in `${exp_dir}/exp/${model_dir}/log/train.log.*`锛� which can be viewed using the following command:
```sh
vim exp/*_train_*/log/train.log.0
```
+
Users can observe the training loss, prediction accuracy and other training information, like follows:
```text
... 1epoch:train:751-800batch:800num_updates: ... loss_ctc=106.703, loss_att=86.877, acc=0.029, loss_pre=1.552 ...
... 1epoch:train:801-850batch:850num_updates: ... loss_ctc=107.890, loss_att=87.832, acc=0.029, loss_pre=1.702 ...
```
+
+Also, users can use tensorboard to observe these training information by the following command:
+```sh
+tensorboard --logdir ${exp_dir}/exp/${model_dir}/tensorboard/train
+```
+
At the end of each epoch, the evaluation metrics are calculated on the validation set, like follows:
```text
... [valid] loss_ctc=99.914, cer_ctc=1.000, loss_att=80.512, acc=0.029, cer=0.971, wer=1.000, loss_pre=1.952, loss=88.285 ...
```
-The inference results are saved in `exp/*_train_*/decode_asr_*/$dset`. The main two files are `text.cer` and `text.cer.txt`. `text.cer` saves the comparison between the recognized text and the reference text, like follows:
+The inference results are saved in `${exp_dir}/exp/${model_dir}/decode_asr_*/$dset`. The main two files are `text.cer` and `text.cer.txt`. `text.cer` saves the comparison between the recognized text and the reference text, like follows:
```text
...
BAC009S0764W0213(nwords=11,cor=11,ins=0,del=0,sub=0) corr=100.00%,cer=0.00%
@@ -119,13 +128,6 @@
* Training Steps
We support two parameters to specify the training steps, namely `max_epoch` and `max_update`. `max_epoch` indicates the total training epochs while `max_update` indicates the total training steps. If these two parameters are specified at the same time, once the training reaches any one of these two parameters, the training will be stopped.
-
-* Tensorboard
-
-Users can use tensorboard to observe the loss, learning rate, etc. Please run the following command:
-```
-tensorboard --logdir ${exp_dir}/exp/${model_dir}/tensorboard/train
-```
### Stage 5: Decoding
This stage generates the recognition results and calculates the `CER` to verify the performance of the trained model.
--
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