add speaker verification demo and doc, test=doc

3 years ago · ad2caf2ccb
parent faf6b8daf8
commit ad2caf2ccb
9 changed files with 405 additions and 1 deletions
--- a/README.md
+++ b/README.md
@ -203,6 +203,11 @@ Developers can have a try of our models with [PaddleSpeech Command Line](./paddl
 paddlespeech cls --input input.wav
 ```
 **Speaker Verification**
 ```
 paddlespeech vector --task spk --input input_16k.wav
 ```
 **Automatic Speech Recognition**
 ```shell
 paddlespeech asr --lang zh --input input_16k.wav
@ -458,6 +463,29 @@ PaddleSpeech supports a series of most popular models. They are summarized in [r
  </tbody>
 </table>
 **Speaker Verification**
 <table style="width:100%">
  <thead>
    <tr>
      <th> Task </th>
      <th> Dataset </th>
      <th> Model Type </th>
      <th> Link </th>
    </tr>
  </thead>
  <tbody>
  <tr>
      <td>Speaker Verification</td>
      <td>VoxCeleb12</td>
      <td>ECAPA-TDNN</td>
      <td>
      <a href = "./examples/voxceleb/sv0">ecapa-tdnn-voxceleb12</a>
      </td>
    </tr>
  </tbody>
 </table>
 **Punctuation Restoration**
 <table style="width:100%">
@ -499,6 +527,7 @@ Normally, [Speech SoTA](https://paperswithcode.com/area/speech), [Audio SoTA](ht
    - [Chinese Rule Based Text Frontend](./docs/source/tts/zh_text_frontend.md)
    - [Test Audio Samples](https://paddlespeech.readthedocs.io/en/latest/tts/demo.html)
  - [Audio Classification](./demos/audio_tagging/README.md)
  - [Speaker Verification](./demos/speaker_verification/README.md)
  - [Speech Translation](./demos/speech_translation/README.md)
 - [Released Models](./docs/source/released_model.md)
 - [Community](#Community)
--- a/README_cn.md
+++ b/README_cn.md
@ -202,6 +202,10 @@ from https://github.com/18F/open-source-guide/blob/18f-pages/pages/making-readme
 ```shell
 paddlespeech cls --input input.wav
 ```
 **声纹识别**
 ```shell
 paddlespeech vector --task spk --input input_16k.wav
 ```
 **语音识别**
 ```shell
 paddlespeech asr --lang zh --input input_16k.wav
@ -453,6 +457,30 @@ PaddleSpeech 的 **语音合成** 主要包含三个模块：文本前端、声
  </tbody>
 </table>
 **声纹识别**
 <table style="width:100%">
  <thead>
    <tr>
      <th> Task </th>
      <th> Dataset </th>
      <th> Model Type </th>
      <th> Link </th>
    </tr>
  </thead>
  <tbody>
  <tr>
      <td>Speaker Verification</td>
      <td>VoxCeleb12</td>
      <td>ECAPA-TDNN</td>
      <td>
      <a href = "./examples/voxceleb/sv0">ecapa-tdnn-voxceleb12</a>
      </td>
    </tr>
  </tbody>
 </table>
 **标点恢复**
 <table style="width:100%">
@ -499,6 +527,7 @@ PaddleSpeech 的 **语音合成** 主要包含三个模块：文本前端、声
    - [中文文本前端](./docs/source/tts/zh_text_frontend.md)
    - [测试语音样本](https://paddlespeech.readthedocs.io/en/latest/tts/demo.html)
  - [声音分类](./demos/audio_tagging/README_cn.md)
  - [声纹识别](./demos/speaker_verification/README_cn.md)
  - [语音翻译](./demos/speech_translation/README_cn.md)
 - [模型列表](#模型列表)
  - [语音识别](#语音识别模型)
--- a/demos/speaker_verification/README.md
+++ b/demos/speaker_verification/README.md
@ -0,0 +1,158 @@
 ([简体中文](./README_cn.md)|English)
 # Speech Verification)
 ## Introduction
 Speaker Verification, refers to the problem of getting a speaker embedding from an audio. 
 This demo is an implementation to extract speaker embedding from a specific audio file. It can be done by a single command or a few lines in python using `PaddleSpeech`. 
 ## Usage
 ### 1. Installation
 see [installation](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/install.md).
 You can choose one way from easy, meduim and hard to install paddlespeech.
 ### 2. Prepare Input File
 The input of this demo should be a WAV file(`.wav`), and the sample rate must be the same as the model.
 Here are sample files for this demo that can be downloaded:
 ```bash
 wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
 ```
 ### 3. Usage
 - Command Line(Recommended)
  ```bash
  paddlespeech vector --task spk --input 85236145389.wav
  echo -e "demo1 85236145389.wav" > vec.job
  paddlespeech vector --task spk --input vec.job
  echo -e "demo2 85236145389.wav \n demo3 85236145389.wav" | paddlespeech vector --task spk
  ```
  Usage:
  ```bash
  paddlespeech asr --help
  ```
  Arguments:
  - `input`(required): Audio file to recognize.
  - `model`: Model type of asr task. Default: `conformer_wenetspeech`.
  - `sample_rate`: Sample rate of the model. Default: `16000`.
  - `config`: Config of asr task. Use pretrained model when it is None. Default: `None`.
  - `ckpt_path`: Model checkpoint. Use pretrained model when it is None. Default: `None`.
  - `device`: Choose device to execute model inference. Default: default device of paddlepaddle in current environment.
  Output:
 ```bash
  demo [ -5.749211     9.505463    -8.200284    -5.2075014    5.3940268
  -3.04878      1.611095    10.127234   -10.534177   -15.821609
   1.2032688   -0.35080156   1.2629458  -12.643498    -2.5758228
 -11.343508     2.3385992   -8.719341    14.213509    15.404744
  -0.39327756   6.338786     2.688887     8.7104025   17.469526
  -8.77959      7.0576906    4.648855    -1.3089896  -23.294737
   8.013747    13.891729    -9.926753     5.655307    -5.9422326
 -22.842539     0.6293588  -18.46266    -10.811862     9.8192625
   3.0070958    3.8072643   -2.3861165    3.0821571  -14.739942
   1.7594414   -0.6485091    4.485623     2.0207152    7.264915
  -6.40137     23.63524      2.9711294  -22.708025     9.93719
  20.354511   -10.324688    -0.700492    -8.783211    -5.27593
  15.999649     3.3004563   12.747926    15.429879     4.7849145
   5.6699696   -2.3826702   10.605882     3.9112158    3.1500628
  15.859915    -2.1832209  -23.908653    -6.4799504   -4.5365124
  -9.224193    14.568347   -10.568833     4.982321    -4.342062
   0.0914714   12.645902    -5.74285     -3.2141201   -2.7173362
  -6.680575     0.4757669   -5.035051    -6.7964664   16.865469
 -11.54324      7.681869     0.44475392   9.708182    -8.932846
   0.4123232   -4.361452     1.3948607    9.511665     0.11667654
   2.9079323    6.049952     9.275183   -18.078873     6.2983274
  -0.7500531   -2.725033    -7.6027865    3.3404543    2.990815
   4.010979    11.000591    -2.8873312    7.1352735  -16.79663
  18.495346   -14.293832     7.89578      2.2714825   22.976387
  -4.875734    -3.0836344   -2.9999814   13.751918     6.448228
 -11.924197     2.171869     2.0423572   -6.173772    10.778437
  25.77281     -4.9495463   14.57806      0.3044315    2.6132357
  -7.591999    -2.076944     9.025118     1.7834753   -3.1799617
  -4.9401326   23.465864     5.1685796   -9.018578     9.037825
  -4.4150195    6.859591   -12.274467    -0.88911164   5.186309
  -3.9988663  -13.638606    -9.925445    -0.06329413  -3.6709652
 -12.397416   -12.719869    -1.395601     2.1150916    5.7381287
  -4.4691963   -3.82819     -0.84233856  -1.1604277  -13.490127
   8.731719   -20.778936   -11.495662     5.8033476   -4.752041
  10.833007    -6.717991     4.504732    13.4244375    1.1306485
   7.3435574    1.400918    14.704036    -9.501399     7.2315617
  -6.417456     1.3333273   11.872697    -0.30664724   8.8845
   6.5569253    4.7948146    0.03662816  -8.704245     6.224871
  -3.2701402  -11.508579  ]
  ```
 - Python API
  ```python
  import paddle
  from paddlespeech.cli import VectorExecutor
  vector_executor = VectorExecutor()
  audio_emb = vector_executor(
      model='ecapatdnn_voxceleb12',
      sample_rate=16000,
      config=None, 
      ckpt_path=None,
      audio_file='./85236145389.wav',
      force_yes=False,
      device=paddle.get_device())
  print('Audio embedding Result: \n{}'.format(audio_emb))
  ```
  Output:
  ```bash
  # Vector Result:
  [ -5.749211     9.505463    -8.200284    -5.2075014    5.3940268
  -3.04878      1.611095    10.127234   -10.534177   -15.821609
   1.2032688   -0.35080156   1.2629458  -12.643498    -2.5758228
 -11.343508     2.3385992   -8.719341    14.213509    15.404744
  -0.39327756   6.338786     2.688887     8.7104025   17.469526
  -8.77959      7.0576906    4.648855    -1.3089896  -23.294737
   8.013747    13.891729    -9.926753     5.655307    -5.9422326
 -22.842539     0.6293588  -18.46266    -10.811862     9.8192625
   3.0070958    3.8072643   -2.3861165    3.0821571  -14.739942
   1.7594414   -0.6485091    4.485623     2.0207152    7.264915
  -6.40137     23.63524      2.9711294  -22.708025     9.93719
  20.354511   -10.324688    -0.700492    -8.783211    -5.27593
  15.999649     3.3004563   12.747926    15.429879     4.7849145
   5.6699696   -2.3826702   10.605882     3.9112158    3.1500628
  15.859915    -2.1832209  -23.908653    -6.4799504   -4.5365124
  -9.224193    14.568347   -10.568833     4.982321    -4.342062
   0.0914714   12.645902    -5.74285     -3.2141201   -2.7173362
  -6.680575     0.4757669   -5.035051    -6.7964664   16.865469
 -11.54324      7.681869     0.44475392   9.708182    -8.932846
   0.4123232   -4.361452     1.3948607    9.511665     0.11667654
   2.9079323    6.049952     9.275183   -18.078873     6.2983274
  -0.7500531   -2.725033    -7.6027865    3.3404543    2.990815
   4.010979    11.000591    -2.8873312    7.1352735  -16.79663
  18.495346   -14.293832     7.89578      2.2714825   22.976387
  -4.875734    -3.0836344   -2.9999814   13.751918     6.448228
 -11.924197     2.171869     2.0423572   -6.173772    10.778437
  25.77281     -4.9495463   14.57806      0.3044315    2.6132357
  -7.591999    -2.076944     9.025118     1.7834753   -3.1799617
  -4.9401326   23.465864     5.1685796   -9.018578     9.037825
  -4.4150195    6.859591   -12.274467    -0.88911164   5.186309
  -3.9988663  -13.638606    -9.925445    -0.06329413  -3.6709652
 -12.397416   -12.719869    -1.395601     2.1150916    5.7381287
  -4.4691963   -3.82819     -0.84233856  -1.1604277  -13.490127
   8.731719   -20.778936   -11.495662     5.8033476   -4.752041
  10.833007    -6.717991     4.504732    13.4244375    1.1306485
   7.3435574    1.400918    14.704036    -9.501399     7.2315617
  -6.417456     1.3333273   11.872697    -0.30664724   8.8845
   6.5569253    4.7948146    0.03662816  -8.704245     6.224871
  -3.2701402  -11.508579  ]
  ```
 ### 4.Pretrained Models
 Here is a list of pretrained models released by PaddleSpeech that can be used by command and python API:
 | Model | Sample Rate
 | :--- | :---: |
 | ecapatdnn_voxceleb12 | 16k
--- a/demos/speaker_verification/README_cn.md
+++ b/demos/speaker_verification/README_cn.md
@ -0,0 +1,156 @@
 (简体中文|[English](./README.md))
 # 声纹识别
 ## 介绍
 声纹识别是一项用计算机程序自动提取说话人特征的技术。
 这个 demo 是一个从给定音频文件提取说话人特征，它可以通过使用 `PaddleSpeech` 的单个命令或 python 中的几行代码来实现。
 ## 使用方法
 ### 1. 安装
 请看[安装文档](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/install_cn.md)。
 你可以从 easy，medium，hard 三中方式中选择一种方式安装。
 ### 2. 准备输入
 这个 demo 的输入应该是一个 WAV 文件（`.wav`），并且采样率必须与模型的采样率相同。
 可以下载此 demo 的示例音频：
 ```bash
 # 该音频的内容是数字串 85236145389
 wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
 ```
 ### 3. 使用方法
 - 命令行 (推荐使用)
  ```bash
  paddlespeech vector --task spk --input 85236145389.wav
  echo -e "demo1 85236145389.wav" > vec.job
  paddlespeech vector --task spk --input vec.job
  echo -e "demo2 85236145389.wav \n demo3 85236145389.wav" | paddlespeech vector --task spk
  ```
  使用方法：
  ```bash
  paddlespeech asr --help
  ```
  参数：
  - `input`(必须输入)：用于识别的音频文件。
  - `model`：声纹任务的模型，默认值：`ecapatdnn_voxceleb12`。
  - `sample_rate`：音频采样率，默认值：`16000`。
  - `config`：声纹任务的参数文件，若不设置则使用预训练模型中的默认配置，默认值：`None`。
  - `ckpt_path`：模型参数文件，若不设置则下载预训练模型使用，默认值：`None`。
  - `device`：执行预测的设备，默认值：当前系统下 paddlepaddle 的默认 device。
  输出：
  ```bash
  demo [ -5.749211     9.505463    -8.200284    -5.2075014    5.3940268
  -3.04878      1.611095    10.127234   -10.534177   -15.821609
   1.2032688   -0.35080156   1.2629458  -12.643498    -2.5758228
 -11.343508     2.3385992   -8.719341    14.213509    15.404744
  -0.39327756   6.338786     2.688887     8.7104025   17.469526
  -8.77959      7.0576906    4.648855    -1.3089896  -23.294737
   8.013747    13.891729    -9.926753     5.655307    -5.9422326
 -22.842539     0.6293588  -18.46266    -10.811862     9.8192625
   3.0070958    3.8072643   -2.3861165    3.0821571  -14.739942
   1.7594414   -0.6485091    4.485623     2.0207152    7.264915
  -6.40137     23.63524      2.9711294  -22.708025     9.93719
  20.354511   -10.324688    -0.700492    -8.783211    -5.27593
  15.999649     3.3004563   12.747926    15.429879     4.7849145
   5.6699696   -2.3826702   10.605882     3.9112158    3.1500628
  15.859915    -2.1832209  -23.908653    -6.4799504   -4.5365124
  -9.224193    14.568347   -10.568833     4.982321    -4.342062
   0.0914714   12.645902    -5.74285     -3.2141201   -2.7173362
  -6.680575     0.4757669   -5.035051    -6.7964664   16.865469
 -11.54324      7.681869     0.44475392   9.708182    -8.932846
   0.4123232   -4.361452     1.3948607    9.511665     0.11667654
   2.9079323    6.049952     9.275183   -18.078873     6.2983274
  -0.7500531   -2.725033    -7.6027865    3.3404543    2.990815
   4.010979    11.000591    -2.8873312    7.1352735  -16.79663
  18.495346   -14.293832     7.89578      2.2714825   22.976387
  -4.875734    -3.0836344   -2.9999814   13.751918     6.448228
 -11.924197     2.171869     2.0423572   -6.173772    10.778437
  25.77281     -4.9495463   14.57806      0.3044315    2.6132357
  -7.591999    -2.076944     9.025118     1.7834753   -3.1799617
  -4.9401326   23.465864     5.1685796   -9.018578     9.037825
  -4.4150195    6.859591   -12.274467    -0.88911164   5.186309
  -3.9988663  -13.638606    -9.925445    -0.06329413  -3.6709652
 -12.397416   -12.719869    -1.395601     2.1150916    5.7381287
  -4.4691963   -3.82819     -0.84233856  -1.1604277  -13.490127
   8.731719   -20.778936   -11.495662     5.8033476   -4.752041
  10.833007    -6.717991     4.504732    13.4244375    1.1306485
   7.3435574    1.400918    14.704036    -9.501399     7.2315617
  -6.417456     1.3333273   11.872697    -0.30664724   8.8845
   6.5569253    4.7948146    0.03662816  -8.704245     6.224871
  -3.2701402  -11.508579  ]
  ```
 - Python API
  ```python
  import paddle
  from paddlespeech.cli import VectorExecutor
  vector_executor = VectorExecutor()
  audio_emb = vector_executor(
      model='ecapatdnn_voxceleb12',
      sample_rate=16000,
      config=None,  # Set `config` and `ckpt_path` to None to use pretrained model.
      ckpt_path=None,
      audio_file='./zh.wav',
      force_yes=False,
      device=paddle.get_device())
  print('Audio embedding Result: \n{}'.format(audio_emb))
  ```
  输出：
  ```bash
  # Vector Result:
  [ -5.749211     9.505463    -8.200284    -5.2075014    5.3940268
  -3.04878      1.611095    10.127234   -10.534177   -15.821609
   1.2032688   -0.35080156   1.2629458  -12.643498    -2.5758228
 -11.343508     2.3385992   -8.719341    14.213509    15.404744
  -0.39327756   6.338786     2.688887     8.7104025   17.469526
  -8.77959      7.0576906    4.648855    -1.3089896  -23.294737
   8.013747    13.891729    -9.926753     5.655307    -5.9422326
 -22.842539     0.6293588  -18.46266    -10.811862     9.8192625
   3.0070958    3.8072643   -2.3861165    3.0821571  -14.739942
   1.7594414   -0.6485091    4.485623     2.0207152    7.264915
  -6.40137     23.63524      2.9711294  -22.708025     9.93719
  20.354511   -10.324688    -0.700492    -8.783211    -5.27593
  15.999649     3.3004563   12.747926    15.429879     4.7849145
   5.6699696   -2.3826702   10.605882     3.9112158    3.1500628
  15.859915    -2.1832209  -23.908653    -6.4799504   -4.5365124
  -9.224193    14.568347   -10.568833     4.982321    -4.342062
   0.0914714   12.645902    -5.74285     -3.2141201   -2.7173362
  -6.680575     0.4757669   -5.035051    -6.7964664   16.865469
 -11.54324      7.681869     0.44475392   9.708182    -8.932846
   0.4123232   -4.361452     1.3948607    9.511665     0.11667654
   2.9079323    6.049952     9.275183   -18.078873     6.2983274
  -0.7500531   -2.725033    -7.6027865    3.3404543    2.990815
   4.010979    11.000591    -2.8873312    7.1352735  -16.79663
  18.495346   -14.293832     7.89578      2.2714825   22.976387
  -4.875734    -3.0836344   -2.9999814   13.751918     6.448228
 -11.924197     2.171869     2.0423572   -6.173772    10.778437
  25.77281     -4.9495463   14.57806      0.3044315    2.6132357
  -7.591999    -2.076944     9.025118     1.7834753   -3.1799617
  -4.9401326   23.465864     5.1685796   -9.018578     9.037825
  -4.4150195    6.859591   -12.274467    -0.88911164   5.186309
  -3.9988663  -13.638606    -9.925445    -0.06329413  -3.6709652
 -12.397416   -12.719869    -1.395601     2.1150916    5.7381287
  -4.4691963   -3.82819     -0.84233856  -1.1604277  -13.490127
   8.731719   -20.778936   -11.495662     5.8033476   -4.752041
  10.833007    -6.717991     4.504732    13.4244375    1.1306485
   7.3435574    1.400918    14.704036    -9.501399     7.2315617
  -6.417456     1.3333273   11.872697    -0.30664724   8.8845
   6.5569253    4.7948146    0.03662816  -8.704245     6.224871
  -3.2701402  -11.508579  ]
  ```
 ### 4.预训练模型
 以下是 PaddleSpeech 提供的可以被命令行和 python API 使用的预训练模型列表：
 | 模型 | 采样率
 | :--- | :---: |
 | ecapatdnn_voxceleb12 | 16k
--- a/demos/speaker_verification/run.sh
+++ b/demos/speaker_verification/run.sh
@ -0,0 +1,6 @@
 #!/bin/bash
 wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
 # asr
 paddlespeech vector --task spk --input ./85236145389.wav
--- a/docs/source/released_model.md
+++ b/docs/source/released_model.md
@ -75,10 +75,16 @@ Model Type | Dataset| Example Link | Pretrained Models | Static Models
 PANN | Audioset| [audioset_tagging_cnn](https://github.com/qiuqiangkong/audioset_tagging_cnn) | [panns_cnn6.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn6.pdparams), [panns_cnn10.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn10.pdparams), [panns_cnn14.pdparams](https://bj.bcebos.com/paddleaudio/models/panns_cnn14.pdparams) | [panns_cnn6_static.tar.gz](https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn6_static.tar.gz)(18M), [panns_cnn10_static.tar.gz](https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn10_static.tar.gz)(19M), [panns_cnn14_static.tar.gz](https://paddlespeech.bj.bcebos.com/cls/inference_model/panns_cnn14_static.tar.gz)(289M) 
 PANN | ESC-50 |[pann-esc50](../../examples/esc50/cls0)|[esc50_cnn6.tar.gz](https://paddlespeech.bj.bcebos.com/cls/esc50/esc50_cnn6.tar.gz), [esc50_cnn10.tar.gz](https://paddlespeech.bj.bcebos.com/cls/esc50/esc50_cnn10.tar.gz), [esc50_cnn14.tar.gz](https://paddlespeech.bj.bcebos.com/cls/esc50/esc50_cnn14.tar.gz)
 ## Speaker Verification Models
 Model Type | Dataset| Example Link | Pretrained Models | Static Models 
 :-------------:| :------------:| :-----: | :-----: | :-----:
 PANN | VoxCeleb| [voxceleb_ecapatdnn](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/voxceleb/sv0) | [ecapatdnn.tar.gz](https://paddlespeech.bj.bcebos.com/vector/voxceleb/sv0_ecapa_tdnn_voxceleb12_ckpt_0_1_1.tar.gz) | -
 ## Punctuation Restoration Models
 Model Type | Dataset| Example Link | Pretrained Models
 :-------------:| :------------:| :-----: | :-----:
-Ernie Linear | IWLST2012_zh |[iwslt2012_punc0](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/iwslt2012/punc0)|[ernie_linear_p3_iwslt2012_zh_ckpt_0.1.1.zip](https://paddlespeech.bj.bcebos.com/text/ernie_linear_p3_iwslt2012_zh_ckpt_0.1.1.zip)
+Ernie Linear | IWLST2012_zh |[iwslt2012_punc0](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/iwslt2012/punc0)|[ernie_linear_p3_iwslt2012_zh_ckpt_0.1.1.zip](https://paddlespeech.bj.bcebos.com/vector/voxceleb/sv0_ecapa_tdnn_voxceleb12_ckpt_0_1_1.tar.gz)
 ## Speech Recognition Model  from paddle 1.8
--- a/examples/voxceleb/sv0/RESULT.md
+++ b/examples/voxceleb/sv0/RESULT.md
@ -0,0 +1,8 @@
 # VoxCeleb
 ## ECAPA-TDNN 
 | Model | Number of Params | Release | Config | Test set |  Cosine | Cosine + S-Norm | 
 | --- | --- | --- | --- | --- | --- | --- | 
 | ECAPA-TDNN | 85MM | 0.1.1 | conf/model.yaml | test | 1.15 |  1.06 | 
--- a/paddlespeech/cli/README.md
+++ b/paddlespeech/cli/README.md
@ -13,6 +13,12 @@
 paddlespeech cls --input input.wav
 ```
 ## Speaker Verification
 ```bash
 paddlespeech vector --task spk --input input_16k.wav
 ```
 ## Automatic Speech Recognition
 ```
 paddlespeech asr --lang zh --input input_16k.wav
--- a/paddlespeech/cli/README_cn.md
+++ b/paddlespeech/cli/README_cn.md
@ -12,6 +12,12 @@
 ## 声音分类
 ```bash
 paddlespeech cls --input input.wav
 ```
  ## 声纹识别
 ```bash
 paddlespeech vector --task spk --input input_16k.wav
 ```
 ## 语音识别