From 6427c834dfd97b1f05c6659cdc7ccf010bf82fe1 Mon Sep 17 00:00:00 2001
From: 嘉渊 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期一, 24 四月 2023 19:50:07 +0800
Subject: [PATCH] update
---
docs/get_started.md | 29 ++++++++++++++---------------
1 files changed, 14 insertions(+), 15 deletions(-)
diff --git a/docs/get_started.md b/docs/get_started.md
index 5a23e61..4a7d86e 100644
--- a/docs/get_started.md
+++ b/docs/get_started.md
@@ -15,7 +15,7 @@
- `tag`: the suffix of experimental result directory
## Stage 0: Data preparation
-This stage processes raw AISHELL-1 data `$data_aishell` and generates the corresponding `wav.scp` and `text` in `$feats_dir/data/xxx` and `xxx` means `train/dev/test`. Here we assume you have already downloaded AISHELL-1 data. If not, you can download data [here](https://www.openslr.org/33/) and set the path for `$data_aishell`. Here we show examples for `wav.scp` and `text`, separately.
+This stage processes raw AISHELL-1 dataset `$data_aishell` and generates the corresponding `wav.scp` and `text` in `$feats_dir/data/xxx`. `xxx` means `train/dev/test`. Here we assume users have already downloaded AISHELL-1 dataset. If not, users can download data [here](https://www.openslr.org/33/) and set the path for `$data_aishell`. The examples of `wav.scp` and `text` are as follows:
* `wav.scp`
```
BAC009S0002W0122 /nfs/ASR_DATA/AISHELL-1/data_aishell/wav/train/S0002/BAC009S0002W0122.wav
@@ -30,17 +30,17 @@
BAC009S0002W0124 鑷� 鍏� 鏈� 搴� 鍛� 鍜� 娴� 鐗� 甯� 鐜� 鍏� 瀹� 甯� 鍙� 娑� 闄� 璐� 鍚�
...
```
-We can see that these two files both have two columns while the first column is the wav-id and the second column is the corresponding wav-path/label tokens.
+These two files both have two columns, while the first column is wav ids and the second column is the corresponding wav paths/label tokens.
## Stage 1: Feature Generation
-This stage extracts FBank feature from raw wav `wav.scp` and apply speed perturbation as data augmentation according to `speed_perturb`. You can set `nj` to control the number of jobs for feature generation. The output features are saved in `$feats_dir/dump/xxx/ark` and the corresponding `feats.scp` files are saved as `$feats_dir/dump/xxx/feats.scp`. An example of `feats.scp` can be seen as follows:
+This stage extracts FBank features from `wav.scp` and apply speed perturbation as data augmentation according to `speed_perturb`. Users can set `nj` to control the number of jobs for feature generation. The generated features are saved in `$feats_dir/dump/xxx/ark` and the corresponding `feats.scp` files are saved as `$feats_dir/dump/xxx/feats.scp`. An example of `feats.scp` can be seen as follows:
* `feats.scp`
```
...
-BAC009S0002W0122_sp0.9 /nfs/haoneng.lhn/funasr_data/aishell-1/dump/fbank/train/ark/feats.16.ark:592751055
+BAC009S0002W0122_sp0.9 /nfs/funasr_data/aishell-1/dump/fbank/train/ark/feats.16.ark:592751055
...
```
-Note that samples in this file have already been shuffled. This file contains two columns. The first column is the wav-id while the second column is the kaldi-ark feature path. Besides, `speech_shape` and `text_shape` are also generated in this stage, denoting the speech feature shape and text length of each sample. The examples are shown as follows:
+Note that samples in this file have already been shuffled randomly. This file contains two columns. The first column is wav ids while the second column is kaldi-ark feature paths. Besides, `speech_shape` and `text_shape` are also generated in this stage, denoting the speech feature shape and text length of each sample. The examples are shown as follows:
* `speech_shape`
```
...
@@ -53,10 +53,10 @@
BAC009S0002W0122_sp0.9 15
...
```
-These two files have two columns. The first column is the wav-id and the second column is the corresponding speech feature shape and text length.
+These two files have two columns. The first column is wav ids and the second column is the corresponding speech feature shape and text length.
## Stage 2: Dictionary Preparation
-This stage prepares a dictionary, which is used as a mapping between label characters and integer indices during ASR training. The output dictionary file is saved as `$feats_dir/data/$lang_toekn_list/$token_type/tokens.txt`. Here we show an example of `tokens.txt` as follows:
+This stage processes the dictionary, which is used as a mapping between label characters and integer indices during ASR training. The processed dictionary file is saved as `$feats_dir/data/$lang_toekn_list/$token_type/tokens.txt`. An example of `tokens.txt` is as follows:
* `tokens.txt`
```
<blank>
@@ -75,7 +75,7 @@
* `<unk>`: indicates the out-of-vocabulary token
## Stage 3: Training
-This stage achieves the training of the specified model. To start training, you should manually set `exp_dir`, `CUDA_VISIBLE_DEVICES` and `gpu_num`, which have already been explained above. By default, the best `$keep_nbest_models` checkpoints on validation dataset will be averaged to generate a better model and adopted for decoding.
+This stage achieves the training of the specified model. To start training, users should manually set `exp_dir`, `CUDA_VISIBLE_DEVICES` and `gpu_num`, which have already been explained above. By default, the best `$keep_nbest_models` checkpoints on validation dataset will be averaged to generate a better model and adopted for decoding.
* DDP Training
@@ -83,30 +83,29 @@
* DataLoader
-[comment]: <> (We support two types of DataLoaders for small and large datasets, respectively. By default, the small DataLoader is used and you can set `dataset_type=large` to enable large DataLoader. For small DataLoader, )
-We support an optional iterable-style DataLoader based on [Pytorch Iterable-style DataPipes](https://pytorch.org/data/beta/torchdata.datapipes.iter.html) for large dataset and you can set `dataset_type=large` to enable it.
+We support an optional iterable-style DataLoader based on [Pytorch Iterable-style DataPipes](https://pytorch.org/data/beta/torchdata.datapipes.iter.html) for large dataset and users can set `dataset_type=large` to enable it.
* Configuration
-The parameters of the training, including model, optimization, dataset, etc., are specified by a YAML file in `conf` directory. Also, you can directly specify the parameters in `run.sh` recipe. Please avoid to specify the same parameters in both the YAML file and the recipe.
+The parameters of the training, including model, optimization, dataset, etc., can be set by a YAML file in `conf` directory. Also, users can directly set the parameters in `run.sh` recipe. Please avoid to set the same parameters in both the YAML file and the recipe.
* 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 the two parameters, the training will be stopped.
+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
-You can use tensorboard to observe the loss, learning rate, etc. Please run the following command:
+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 4: Decoding
-This stage generates the recognition results with acoustic features as input and calculate the `CER` to verify the performance of the trained model.
+This stage generates the recognition results and calculates the `CER` to verify the performance of the trained model.
* Mode Selection
-As we support conformer, paraformer and uniasr in FunASR and they have different inference interfaces, a `mode` param is specified as `asr/paraformer/uniase` according to the trained model.
+As we support paraformer, uniasr, conformer and other models in FunASR, a `mode` parameter should be specified as `asr/paraformer/uniasr` according to the trained model.
* Configuration
--
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