From 3c3754dcc7568e76fa7d4b2c4e14849f68cc6ee7 Mon Sep 17 00:00:00 2001
From: 嘉渊 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期日, 28 五月 2023 23:46:01 +0800
Subject: [PATCH] update repo
---
docs/academic_recipe/asr_recipe.md | 12 ++++++------
egs/librispeech_100h/conformer/run.sh | 2 +-
2 files changed, 7 insertions(+), 7 deletions(-)
diff --git a/docs/academic_recipe/asr_recipe.md b/docs/academic_recipe/asr_recipe.md
index 4e8f072..0b2dd17 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:
@@ -26,16 +26,16 @@
... 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
+```
+
The inference results are saved in `${exp_dir}/exp/${model_dir}/decode_asr_*/$dset`. The main two files are `text.cer` and `text.cer.txt`. `text.cer` saves the comparison between the recognized text and the reference text, like follows:
```text
...
diff --git a/egs/librispeech_100h/conformer/run.sh b/egs/librispeech_100h/conformer/run.sh
index c0a06a2..41df5a4 100755
--- a/egs/librispeech_100h/conformer/run.sh
+++ b/egs/librispeech_100h/conformer/run.sh
@@ -120,7 +120,7 @@
# ASR Training Stage
world_size=$gpu_num # run on one machine
-if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4; then
+if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
echo "stage 4: ASR Training"
mkdir -p ${exp_dir}/exp/${model_dir}
mkdir -p ${exp_dir}/exp/${model_dir}/log
--
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