From 23f53a2fcb2cd1a012f1143c3d875664cb38e83d Mon Sep 17 00:00:00 2001 From: speech_asr <wangjiaming.wjm@alibaba-inc.com> Date: 星期二, 14 二月 2023 19:29:38 +0800 Subject: [PATCH] update docs --- docs/get_started.md | 12 ++++++------ 1 files changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/get_started.md b/docs/get_started.md index 5a23e61..c62a610 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` ``` ... @@ -53,7 +53,7 @@ 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: -- Gitblit v1.9.1