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 |   35 +++++++++++++++++++----------------
 1 files changed, 19 insertions(+), 16 deletions(-)

diff --git a/docs/academic_recipe/asr_recipe.md b/docs/academic_recipe/asr_recipe.md
index 391d60b..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%
@@ -47,9 +56,12 @@
 - `CUDA_VISIBLE_DEVICES`: `0,1` (Default), visible gpu list
 - `gpu_num`: `2` (Default), the number of GPUs used for training
 - `gpu_inference`: `true` (Default), whether to use GPUs for decoding
-- `njob`: `1` (Default), for CPU decoding, indicating the total number of CPU jobs; for GPU decoding, indicating the number of jobs on each GPU
+- `njob`: `1`  (Default),for CPU decoding, indicating the total number of CPU jobs; for GPU decoding, indicating the number of jobs on each GPU
 - `raw_data`: the raw path of AISHELL-1 dataset
 - `feats_dir`: the path for saving processed data
+- `token_type`: `char` (Default), indicate how to process text
+- `type`: `sound` (Default), set the input type
+- `scp`: `wav.scp` (Default), set the input file
 - `nj`: `64` (Default), the number of jobs for data preparation
 - `speed_perturb`: `"0.9, 1.0 ,1.1"` (Default), the range of speech perturbed
 - `exp_dir`: the path for saving experimental results
@@ -80,7 +92,6 @@
 
 ### Stage 2: Dictionary Preparation
 This stage processes the dictionary, which is used as a mapping between label characters and integer indices during ASR training. The processed dictionary file is saved as `$feats_dir/data/$lang_toekn_list/$token_type/tokens.txt`. An example of `tokens.txt` is as follows:
-* `tokens.txt`
 ```
 <blank>
 <s>
@@ -92,10 +103,10 @@
 榫�
 <unk>
 ```
-* `<blank>`: indicates the blank token for CTC
-* `<s>`: indicates the start-of-sentence token
-* `</s>`: indicates the end-of-sentence token
-* `<unk>`: indicates the out-of-vocabulary token
+* `<blank>`: indicates the blank token for CTC, must be in the first line
+* `<s>`: indicates the start-of-sentence token, must be in the second line
+* `</s>`: indicates the end-of-sentence token, must be in the third line
+* `<unk>`: indicates the out-of-vocabulary token, must be in the last line
 
 ### Stage 3: LM Training
 
@@ -118,13 +129,6 @@
 
 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. 
 
@@ -143,7 +147,6 @@
 * Performance
 
 We adopt `CER` to verify the performance. The results are in `$exp_dir/exp/$model_dir/$decoding_yaml_name/$average_model_name/$dset`, namely `text.cer` and `text.cer.txt`. `text.cer` saves the comparison between the recognized text and the reference text while `text.cer.txt` saves the final `CER` results. The following is an example of `text.cer`:
-* `text.cer`
 ```
 ...
 BAC009S0764W0213(nwords=11,cor=11,ins=0,del=0,sub=0) corr=100.00%,cer=0.00%

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