一、语音识别包

1、安装

  安装很简单，直接使用ubuntu命令即可，首先安装依赖库：
1
2
3
$ sudo apt-get install gstreamer0.10-pocketsphinx
$ sudo apt-get install ros-indigo-audio-common        //我安装的是indigo版本的
$ sudo apt-get install libasound2 
$ sudo apt-get install gstreamer0.10-gconf

   然后来安装ROS包：

sudo apt-get install ros-indigo-pocketsphinx

2.   安装完成后我们就可以运行测试了。
    首先，插入你的麦克风设备，然后在系统设置里测试麦克风是否有语音输入。
    然后，运行包中的测试程序：      

1
$ roslaunch pocketsphinx robocup.launch 

 我们也可以直接看ROS最后发布的结果消息：

1
$ rostopic echo /recognizer/output

 

 

二、语音库

1、查看语音库

        这个语音识别时一种离线识别的方法，将一些常用的词汇放到一个文件中，作为识别的文本库，然后分段识别语音信号，最后在库中搜索对应的文本信息。如果想看语音识别库中有哪些文本信息，可以通过下面的指令进行查询：
1
2
$ roscd rbx1_speech/config 
$ more nav_commands.txt

 

 这就是需要添加的文本，我们也可以修改其中的某些文本，改成自己需要的。
        然后我们要把这个文件在线生成语音信息和库文件，这一步需要登陆网站http://www.speech.cs.cmu.edu/tools/lmtool-new.html，根据网站的提示上传文件，然后在线编译生成库文件。

 上传nav_commands.txt文件

把下载的文件都解压放在rbx1_speech包的config文件夹下。我们可以给这些文件改个名字：

1
rename -f 's/593/nav_commands/' *

 在rbx1_speech/launch文件夹下看看voice_nav_commands.launch这个文件：

1
2
3
4
5
6
7
8
<launch>
 
   <node name= "recognizer" pkg= "pocketsphinx" type= "recognizer.py" output= "screen" >
     <param name= "lm" value= "$(find rbx1_speech)/config/nav_commands.lm" />
     <param name= "dict" value= "$(find rbx1_speech)/config/nav_commands.dic" />
   </node>
   
</launch>

 

可以看到，这个launch文件在运行recognizer.py节点的时候使用了我们生成的语音识别库和文件参数，这样就可以实用我们自己的语音库来进行语音识别了。
 通过之前的命令来测试一下效果如何吧：

    $ roslaunch rbx1_speech voice_nav_commands.launch  
    $ rostopic echo /recognizer/output

三、语音控制

        有了语音识别，我们就可以来做很多犀利的应用了，首先先来尝试一下用语音来控制机器人动作。

1、机器人控制节点

        前面说到的recognizer.py会将最后识别的文本信息通过消息发布，那么我们来编写一个机器人控制节点接收这个消息，进行相应的控制即可。
        在pocketsphinx包中本身有一个语音控制发布Twist消息的例程voice_cmd_vel.py，rbx1_speech包对其进行了一些简化修改，在nodes文件夹里可以查看voice_nav.py文件：
 
#!/usr/bin/env python

"""
  voice_nav.py - Version 1.1 2013-12-20
  
  Allows controlling a mobile base using simple speech commands.
  
  Based on the voice_cmd_vel.py script by Michael Ferguson in
  the pocketsphinx ROS package.
  
  See http://www.ros.org/wiki/pocketsphinx
"""

import rospy
from geometry_msgs.msg import Twist
from std_msgs.msg import String
from math import copysign

class VoiceNav:
    def __init__(self):
        rospy.init_node('voice_nav')
        
        rospy.on_shutdown(self.cleanup)
        
        # Set a number of parameters affecting the robot's speed
        self.max_speed = rospy.get_param("~max_speed", 0.4)
        self.max_angular_speed = rospy.get_param("~max_angular_speed", 1.5)
        self.speed = rospy.get_param("~start_speed", 0.1)
        self.angular_speed = rospy.get_param("~start_angular_speed", 0.5)
        self.linear_increment = rospy.get_param("~linear_increment", 0.05)
        self.angular_increment = rospy.get_param("~angular_increment", 0.4)
        
        # We don't have to run the script very fast
        self.rate = rospy.get_param("~rate", 5)
        r = rospy.Rate(self.rate)
        
        # A flag to determine whether or not voice control is paused
        self.paused = False
        
        # Initialize the Twist message we will publish.
        self.cmd_vel = Twist()

        # Publish the Twist message to the cmd_vel topic
        self.cmd_vel_pub = rospy.Publisher('cmd_vel', Twist, queue_size=5)
        
        # Subscribe to the /recognizer/output topic to receive voice commands.
        rospy.Subscriber('/recognizer/output', String, self.speech_callback)
        
        # A mapping from keywords or phrases to commands
        self.keywords_to_command = {'stop': ['stop', 'halt', 'abort', 'kill', 'panic', 'off', 'freeze', 'shut down', 'turn off', 'help', 'help me'],
                                    'slower': ['slow down', 'slower'],
                                    'faster': ['speed up', 'faster'],
                                    'forward': ['forward', 'ahead', 'straight'],
                                    'backward': ['back', 'backward', 'back up'],
                                    'rotate left': ['rotate left'],
                                    'rotate right': ['rotate right'],
                                    'turn left': ['turn left'],
                                    'turn right': ['turn right'],
                                    'quarter': ['quarter speed'],
                                    'half': ['half speed'],
                                    'full': ['full speed'],
                                    'pause': ['pause speech'],
                                    'continue': ['continue speech']}
        
        rospy.loginfo("Ready to receive voice commands")
        
        # We have to keep publishing the cmd_vel message if we want the robot to keep moving.
        while not rospy.is_shutdown():
            self.cmd_vel_pub.publish(self.cmd_vel)
            r.sleep()                       
            
    def get_command(self, data):
        # Attempt to match the recognized word or phrase to the 
        # keywords_to_command dictionary and return the appropriate
        # command
        for (command, keywords) in self.keywords_to_command.iteritems():
            for word in keywords:
                if data.find(word) > -1:
                    return command
        
    def speech_callback(self, msg):
        # Get the motion command from the recognized phrase
        command = self.get_command(msg.data)
        
        # Log the command to the screen
        rospy.loginfo("Command: " + str(command))
        
        # If the user has asked to pause/continue voice control,
        # set the flag accordingly 
        if command == 'pause':
            self.paused = True
        elif command == 'continue':
            self.paused = False
        
        # If voice control is paused, simply return without
        # performing any action
        if self.paused:
            return       
        
        # The list of if-then statements should be fairly
        # self-explanatory
        if command == 'forward':    
            self.cmd_vel.linear.x = self.speed
            self.cmd_vel.angular.z = 0
            
        elif command == 'rotate left':
            self.cmd_vel.linear.x = 0
            self.cmd_vel.angular.z = self.angular_speed
                
        elif command == 'rotate right':  
            self.cmd_vel.linear.x = 0      
            self.cmd_vel.angular.z = -self.angular_speed
            
        elif command == 'turn left':
            if self.cmd_vel.linear.x != 0:
                self.cmd_vel.angular.z += self.angular_increment
            else:        
                self.cmd_vel.angular.z = self.angular_speed
                
        elif command == 'turn right':    
            if self.cmd_vel.linear.x != 0:
                self.cmd_vel.angular.z -= self.angular_increment
            else:        
                self.cmd_vel.angular.z = -self.angular_speed
                
        elif command == 'backward':
            self.cmd_vel.linear.x = -self.speed
            self.cmd_vel.angular.z = 0
            
        elif command == 'stop': 
            # Stop the robot!  Publish a Twist message consisting of all zeros.         
            self.cmd_vel = Twist()
        
        elif command == 'faster':
            self.speed += self.linear_increment
            self.angular_speed += self.angular_increment
            if self.cmd_vel.linear.x != 0:
                self.cmd_vel.linear.x += copysign(self.linear_increment, self.cmd_vel.linear.x)
            if self.cmd_vel.angular.z != 0:
                self.cmd_vel.angular.z += copysign(self.angular_increment, self.cmd_vel.angular.z)
            
        elif command == 'slower':
            self.speed -= self.linear_increment
            self.angular_speed -= self.angular_increment
            if self.cmd_vel.linear.x != 0:
                self.cmd_vel.linear.x -= copysign(self.linear_increment, self.cmd_vel.linear.x)
            if self.cmd_vel.angular.z != 0:
                self.cmd_vel.angular.z -= copysign(self.angular_increment, self.cmd_vel.angular.z)
                
        elif command in ['quarter', 'half', 'full']:
            if command == 'quarter':
                self.speed = copysign(self.max_speed / 4, self.speed)
        
            elif command == 'half':
                self.speed = copysign(self.max_speed / 2, self.speed)
            
            elif command == 'full':
                self.speed = copysign(self.max_speed, self.speed)
            
            if self.cmd_vel.linear.x != 0:
                self.cmd_vel.linear.x = copysign(self.speed, self.cmd_vel.linear.x)

            if self.cmd_vel.angular.z != 0:
                self.cmd_vel.angular.z = copysign(self.angular_speed, self.cmd_vel.angular.z)
                
        else:
            return

        self.cmd_vel.linear.x = min(self.max_speed, max(-self.max_speed, self.cmd_vel.linear.x))
        self.cmd_vel.angular.z = min(self.max_angular_speed, max(-self.max_angular_speed, self.cmd_vel.angular.z))

    def cleanup(self):
        # When shutting down be sure to stop the robot!
        twist = Twist()
        self.cmd_vel_pub.publish(twist)
        rospy.sleep(1)

if __name__=="__main__":
    try:
        VoiceNav()
        rospy.spin()
    except rospy.ROSInterruptException:
        rospy.loginfo("Voice navigation terminated.")
        可以看到，代码中定义了接收到各种命令时的控制策略。

2、仿真测试

        和前面一样，我们在rviz中进行仿真测试。
        首先是运行一个机器人模型：

1
$ roslaunch rbx1_bringup fake_turtlebot.launch 

         然后打开rviz：

1
$ rosrun rviz rviz - d `rospack find rbx1_nav` / sim.rviz

     再打开语音识别的节点：

$ roslaunch rbx1_speech voice_nav_commands.laun

   最后就是机器人的控制节点了：

$ roslaunch rbx1_speech turtlebot_voice_nav.launch

    OK，打开上面这一堆的节点之后，就可以开始了。可以使用的命令如下：


 

可能是英语太差吧，很难识别；

 

 

四、播放语音

        现在机器人已经可以按照我们说的话行动了，要是机器人可以和我们对话就更好了。ROS中已经集成了这样的包，下面就来尝试一下。
        运行下面的命令：
1
2
$ rosrun sound_play soundplay_node.py 
$ rosrun sound_play say.py "Greetings Humans. Take me to your leader."

         有没有听见声音！ROS通过识别我们输入的文本，让机器人读了出来。发出这个声音的人叫做kal_diphone，如果不喜欢，我们也可以换一个人来读：

1
2
$ sudo apt - get install festvox - don 
$ rosrun sound_play say.py "Welcome to the future" voice_don_diphone 

  哈哈，这回换了一个人吧，好吧，这不也是我们的重点。
       在rbx1_speech/nodes文件夹中有一个让机器人说话的节点talkback.py:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
#!/usr/bin/env python
 
"""
     talkback.py - Version 1.1 2013-12-20
     
     Use the sound_play client to say back what is heard by the pocketsphinx recognizer.
     
     Created for the Pi Robot Project: http://www.pirobot.org
     Copyright (c) 2012 Patrick Goebel.  All rights reserved.
 
     This program is free software; you can redistribute it and/or modify
     it under the terms of the GNU General Public License as published by
     the Free Software Foundation; either version 2 of the License, or
     (at your option) any later version.5
     
     This program is distributed in the hope that it will be useful,
     but WITHOUT ANY WARRANTY; without even the implied warranty of
     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
     GNU General Public License for more details at:
     
     http://www.gnu.org/licenses/gpl.htmlPoint
"""
 
import rospy
from std_msgs.msg import String
from sound_play.libsoundplay import SoundClient
import sys
 
class TalkBack:
     def __init__( self , script_path):
         rospy.init_node( 'talkback' )
 
         rospy.on_shutdown( self .cleanup)
         
         # Set the default TTS voice to use
         self .voice = rospy.get_param( "~voice" , "voice_don_diphone" )
         
         # Set the wave file path if used
         self .wavepath = rospy.get_param( "~wavepath" , script_path + "/../sounds" )
         
         # Create the sound client object
         self .soundhandle = SoundClient()
         
         # Wait a moment to let the client connect to the
         # sound_play server
         rospy.sleep( 1 )
         
         # Make sure any lingering sound_play processes are stopped.
         self .soundhandle.stopAll()
         
         # Announce that we are ready for input
         self .soundhandle.playWave( self .wavepath + "/R2D2a.wav" )
         rospy.sleep( 1 )
         self .soundhandle.say( "Ready" , self .voice)
         
         rospy.loginfo( "Say one of the navigation commands..." )
 
         # Subscribe to the recognizer output and set the callback function
         rospy.Subscriber( '/recognizer/output' , String, self .talkback)
         
     def talkback( self , msg):
         # Print the recognized words on the screen
         rospy.loginfo(msg.data)
         
         # Speak the recognized words in the selected voice
         self .soundhandle.say(msg.data, self .voice)
         
         # Uncomment to play one of the built-in sounds
         #rospy.sleep(2)
         #self.soundhandle.play(5)
         
         # Uncomment to play a wave file
         #rospy.sleep(2)
         #self.soundhandle.playWave(self.wavepath + "/R2D2a.wav")
 
     def cleanup( self ):
         self .soundhandle.stopAll()
         rospy.loginfo( "Shutting down talkback node..." )
 
if __name__ = = "__main__" :
     try :
         TalkBack(sys.path[ 0 ])
         rospy.spin()
     except rospy.ROSInterruptException:
         rospy.loginfo( "Talkback node terminated." )

 

   我们来运行看一下效果：

1
$ roslaunch rbx1_speech talkback.launch 
Logo
CSDN学习社区

CSDN联合极客时间，共同打造面向开发者的精品内容学习社区，助力成长！

更多推荐

嵌入式作业（七）：基于Ardunio的STM32串口通信

嵌入式作业（七）0作业要求1Ardunio 完成STM32的串口通信（1）安装Ardunio IDE（2）stm32串口通信2关于 stduino IDE0作业要求安装 Ardunio IDE 和相关软件支持库，在Ardunio 完成STM32板子的串口通信程序：（1）持续向串口输出“Hello world！”；（2）当接收到“stop!”时，停止输出。网上有一个国人版的MCU集成开发平台， st

avatar CSDN学习社区

JDBC详解

JDBC文章目录JDBC什么是JDBC?JDBC驱动程序:Java使用JDBC访问数据库的步骤:设置classpath:Oracle连接字符串的书写格式:简单的例子:常用数据库的驱动程序及JDBC URL:Oracle数据库:SQL Server数据库MySQL数据库Access数据库PreparedStatement接口:JNDI-数据源（Data Source）与连接池（Connection

avatar CSDN学习社区

“模式识别与机器学习”学习笔记no2.再谈感知机

接**上篇：上篇主要进行了PLA，Pocket算法的理论过程分析和在给定数据集上利用pocket算法对数据集进行分类学习，得到错分数量最少的分类面。上篇中pocket算法的过程已经进行了编程和测试，框架已经建立了起来，这一篇主要上篇中没有提到或涉及不深的几个问题。1.数据集的构造。上篇是直接使用了题目给的向量，这次来根据正态分布来产生数据集。np.random.normal函数可以根据均值和方差生

avatar CSDN学习社区