From 0acf868dbb02642348e3addfec8e56974facfc2f Mon Sep 17 00:00:00 2001
From: hnluo <haoneng.lhn@alibaba-inc.com>
Date: 星期一, 29 五月 2023 10:40:21 +0800
Subject: [PATCH] Merge pull request #558 from alibaba-damo-academy/dev_wjm2
---
docs/academic_recipe/asr_recipe.md | 17 ++++++++++-------
1 files changed, 10 insertions(+), 7 deletions(-)
diff --git a/docs/academic_recipe/asr_recipe.md b/docs/academic_recipe/asr_recipe.md
index 4e8f072..e5a7f02 100644
--- a/docs/academic_recipe/asr_recipe.md
+++ b/docs/academic_recipe/asr_recipe.md
@@ -12,7 +12,7 @@
Then you can directly start the recipe as follows:
```sh
conda activate funasr
-. ./run.sh
+. ./run.sh --CUDA_VISIBLE_DEVICES="0,1" --gpu_num=2
```
The training log files are saved in `${exp_dir}/exp/${model_dir}/log/train.log.*`锛� which can be viewed using the following command:
@@ -26,15 +26,18 @@
... 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 ...
```
+
+Also, users can use tensorboard to observe these training information by the following command:
+```sh
+tensorboard --logdir ${exp_dir}/exp/${model_dir}/tensorboard/train
+```
+Here is an example of loss:
+
+<img src="images/loss.png" width="200"/>
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
@@ -177,7 +180,7 @@
encoder: conformer
encoder_conf:
output_size: 256 # dimension of attention
- attention_heads: 4 # number of heads in multi-head attention
+ attention_heads: 4 # the number of heads in multi-head attention
linear_units: 2048 # the number of units of position-wise feed forward
num_blocks: 12 # the number of encoder blocks
dropout_rate: 0.1
--
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