Python Data Science

Python: Beginners Guide to NumPy Arrays


In this tutorial, you will learn the basics of NumPy Arrays from creating and working with NumPy Arrays to Indexing, Slicing, performing operations, joining and splitting of arrays. But first up we have a question that

What is NumPy?

NumPy is an open-source fundamental library for data science with Python. It stands for ‘Numerical Python’. NumPy was developed by Travis Oliphant in 2005. It is what you can say is a sequel to Numeric and Numarray. It’s a fast and powerful library for working with multidimensional arrays and matrices. As it provides a large number of functions to work with those arrays.

It is generally used in Data Science along with Python Pandas and Matplotlib.

NumPy is a really fast library and it is easy and fun to use as compared to Lists or Tuples. We’ll get into those details later in this tutorial. If you are new to NumPy then make sure to read the whole article as we are going to cover all important functions of NumPy extensively. We’ll try to be as concise as possible and this article will be everything you will ever need for NumPy.


You should have a basic understanding of python 3. You must have a basic understanding of working with lists and slicing.

The Audience

Beginners: This tutorial is made for beginners so that they can learn NumPy Arrays from scratch up to a standard level. We have only covered the handy and the necessary functions in this tutorial. We tried to avoid any advanced stuff but if you do encounter something that you do not understand please do not hesitate to ask in the comment section. There are some sections that you might find confusing if you are just starting up but you will get the hang of it with some practice.

TIP: Open your Python IDE and try the in-between code examples that are in this tutorial as you read the topic. And, instead of copy-pasting and checking the output, type the code yourself for better understanding. It will help you a lot, I mean a LOT!

People with Basic NumPy Experience: This tutorial can be a solid recap of all the necessary things you need to know or already know about NumPy. Do not read all the stuff, just take a quick look at the table of content and read whatever you might find interesting.

Setting Up

Numpy doesn’t come preinstalled with the default python install. Now there are some specifically designed programs for data science with python that make the processing faster so you might want to consider them as well.

Choosing the IDE(Integrated Development Environment)

  • Anaconda
  • Jupyter Notebook
  • PyCharm
  • Default IDLE

People tend to use Anaconda with Jupyter Notebook(It comes pre-installed with Anaconda) because it’s way faster than the default IDLE and it’s simple to use. I would not recommend the use of Jupyter Notebook alone, so if you are going for Jupyter Notebook then install Anaconda or Miniconda at least. But for the sake of simplicity, we will stick to idle. You can use any ide as the procedure will be similar in all of them.

My Recommendations: Sublime Text

Sublime Text is a fast and lightweight text editor plus its color schemes make it a much better choice than using any of the other IDE’s. Besides Sublime Text, I also recommend Visual Studio Code. Visual Studio Code is lighter than the original Visual Studio and it has all the functions which you’ll be needing.

Installing Numpy in Preferred IDE

To install Numpy on idle just open pip and type in the following command.

pip install numpy

That’s pretty much it. This will install the latest stable version of numpy available on your default python install.

Numpy comes pre-installed with Anaconda.

But if you are planning to work in a virtual environment then you have to install NumPy separately for that environment. Installing in a virtual environment is also the same as installing in a new python install. You have to use pip.

Working with Numpy Arrays

Arrays are the arrangement of data in tabular form i.e., in the form of rows and columns.

The Syntax(For Beginners)

As per the recommended sign convention, numpy package is imported as

import numpy as np

That’s just the basic sign convention that you’ll see almost everywhere where NumPy is used.

Now the most common function of the numpy package is array. Array takes a number of parameters as an input.

np.array(object, dtype = None, copy = True, order = None, subok = False, ndmin = 0)

It can seem overwhelming at first because of all those parameters but let me tell you one thing that not all of them are necessary for working with arrays. In fact, except for the object parameter, everything else is optional. We’ll talk briefly about each of them now:

  • Object: It is the sequence you want to pass into an array.
  • dtype: The data type of the resultant array.
  • copy: By default it is true. It returns an array copy of the given object.
  • order: C (row-major) or F (column-major) or A (any) (default). For better understanding check out this answer on StackOverflow
  • subok: It is used to make a subclass of the base array. By default it is turned off, so, the output array is the base array.
  • ndmin: Specifies the minimum dimensions of the final array

Now, for example, if you want to use arrays, you can use it like

>>>import numpy as np

The Output will be


In this example, we passed a list as an object into the array function and the output will be an array because the copy function is set to True by default.

You can also pass a nested list,tuple or dictionary into an array

Also if you want to import just a single function of that package you can do that as:

>>>from numpy import array

This way you only import a particular function of that package and not the whole package.
This is generally avoided because it makes the code confusing as you may not be able to find from what package or module is it imported from. Also if you have already made a function with this name it then it makes the code even more confusing.

Creating NumPy Arrays

There are many functions that are specifically used to create arrays. We will discuss all the important ones in this article


This is the standard function to create array in numpy. You pass a list or tuple as an object and the array is ready. We have already discussed the syntax above.


It is similar to the range() function of python. It runs through particular values one by one and appends to make an array.


start: the starting number is optional to enter as it is 0 by default.

end: this is the ending number to which an array will run. Remember that array will run till end-1 element.

stride: it is the number of steps you want to skip.

>>>np.arange(1, 10, 3)

The Output will be

array([1, 4, 7]) 

In the above example, the starting position is 1, ending is 10 and the stride is 3. Therefore, it will run till 9 and prints every third element.


This helps to create a quick array of zeros of specified order.


The Output will be

array([[0., 0., 0.], 
       [0., 0., 0.],
       [0., 0., 0.]])

This is used if you want to create the array to be used later for number storing purposes.

Like for example, if you are making a game then you’d want starting attributes of a character to be zero and increase as it further progresses in the game.


It is same as np.zeros(), it just replaces zeros one ones


The Output will be

array([[1., 1., 1.], 
       [1., 1., 1.],
       [1., 1., 1.]])


It creates an array of garbage content. Its values are random.


The Output will be

array([[0.65670626, 0.52097334, 0.99831087],
       [0.07280136, 0.4416958 , 0.06185705]])

This is only used because it is faster than np.zeros and np.ones. This is due to the reason that all the values are random and not specified.


The linspace() function returns an array of evenly spaced numbers. For example


The Output will be

array([3., 6., 9.])

In the above example, the resultant array contains 3,6 and 9. This is because we made the starting point as 3 and end point as 9 and we want 3 evenly spaced numbers between them. This is simple math, what are 3 evenly spaced numbers between 3 and 9(including both)? They are 3,6 and 9.

More functions of creating arrays

Though we have covered all the important functions which are used to create arrays there are even more of them. You probably won’t be needing any other function for creating arrays but if you are curious about other functions then you can take a look at this page on official SciPy documentation.

NumPy Array Attributes

Numpy arrays have various attributes that can make working with them easier. They help in organizing data in fast and convenient ways. We will be discussing only the most important attributes of the array.

np.array().shape and np.array().reshape()

These attributes helps to determine the order of the array and allow changes in them.

For example,

>>>import numpy as np
>>>cg = np.array([[1,2,3],[1,2,3]])

The Output will be


np.array.shape returns the tuple of the order of the array. You can change its order as

>>>cg.shape = (3,2)

The Output will be

array([[1, 2],
       [3, 1],
       [2, 3]])

Notice that the order of the array changed from 2 rows and 3 columns to 3 rows and 2 columns.

You can also do the same thing using the reshape function. For example:

>>>cg = np.array([[1,2,3],[1,2,3]])

The Output will be

array([[1, 2],
       [3, 1],
       [2, 3]])

Note: We do not use parenthesis with .shape but we do with .reshape(). This is because .shape is an attribute of the array while .reshape() is a function of array.

Below is another example of reshape() function

>>> np.arange(30).reshape(5,6)

The Output will be

array([[ 0,  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]])

You can also create a 3 dimensional array or greater. Here’s an example:


The Output will be

array([[[ 0,  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]]])

Tip: Make sure that the multiplication of the order of an array is equal to the number of elements. Else it will not work. For example, in our previous example, we have 30 elements and the order we defined was (5,2,3) i.e, 5*2*3 = 30.


This shows the dimension of the data in the array. For example

>>>a = np.array([[1,2,3],[1,2,3]])

The Output will be



This attribute tells the size of the datatype of the data stored in the array.

>>>a = np.array([[1,2,3],[1,2,3]])

The Output will be


Indexing and Slicing Arrays

Array Slicing is no different than list or string slicing in particular. So, if you are familiar with slicing then you’ll understand it in no time.

1D Arrays

Working with 1D Arrays is very simple. You just have to select the index number and you are done.

cg = np.arange(1,6)

This creates an array of 5 elements from 0 to 4

[1 2 3 4 5]

Now to select an element from it just type cg[element-index]. For example, if you want to select element 3 from it then all you have to do is


The output will be 3. This is because index starts from 0.

Suppose you want to select every element starting from 2 in this array. Then


The Output will be

[2 3 4 5]

2D Arrays

a = np.array([[1,2,3,4,5],[6,7,8,9,10]])

We have created a two dimension array. Look at the image below for better understanding of how it works

Indexing in 2D array is slightly different than 1D Array. It is written like

cg = np.array([[1,2,3,4,5],[6,7,8,9,10]])

So you first have to select the row of the element and then you select its column.

Suppose if you want to select 8 from the array then you first select its row which is 1 since it is in the second. Row and column which is 2 since it is third column

cg = np.arange(1,11).reshape(2,5)

Now if you want to print all the elements of the first row then you can do this by

a = np.arange(1,11).reshape(2,5)

The Output will be

[1 2 3 4 5]

This is because you selected 1st row which is at index 0 and empty:empty which represents that you select from 0 to end with stride 1. These are default values.

These are the basics of slicing and indexing. There’s a lot to slicing but it’s all practice. You will only understand slicing when you experiment with it.

Joining Arrays

You can also join two or more arrays into a single new array. Let us consider two arrays

a1 = np.array([1,2,3])
a2 = np.array([4,5,6])

In NumPy there’s a function named concatenate() which allows us to join the arrays both horizontally and vertically. Though it must satisfy the condition.

It takes 3 parameters


Sequence: The list or tuple of arrays that you want to concatenate.

Axis(Optional): How do you want to join? Along rows or columns? By default, the axis is 0 which is rows. To join along with columns, you can change it to axis=1.

Out(Optional): If provided, the destination to place the result. The shape must be correct, matching that of what concatenate would have returned if no out argument were specified.


The Output will be

[1 2 3 4 5 6]

Below is an image which can help you visualize axis

source: stackoverflow

Since it is a 1D Array, you can only join it in one way. If we create a 2D Array, then you will be able to join it along with both rows and columns provided it has the same number of rows or columns.

Joining along axis=0 or vstack()

For example, you cannot join an array which has 1 row with an array which has 2 rows.

 a1 = np.arange(1,11).reshape(2,5)
 a2 = np.arange(11,21).reshape(2,5)

The Output will be

[[ 1,  2,  3,  4,  5],
       [ 6,  7,  8,  9, 10],
       [11, 12, 13, 14, 15],
       [16, 17, 18, 19, 20]]

You can also achieve this by using the vstack() function.


Joining along axis=1 or hstack()

Further, if you want to join them along with columns then you can change the axis to 1. Since both arrays have the same number of rows, then you can join them along columns


The Output will be

[[ 1  2  3  4  5 11 12 13 14 15]
 [ 6  7  8  9 10 16 17 18 19 20]]

You can also achieve this using the hstack() function.


In the example, we took only 2 arrays and concatenated them but you can join as many arrays as you want. If you are starting out as a beginner, it will be more than enough for you for now. I’ll be making in-depth tutorials on each of these topics soon.

Splitting Arrays

You can also split an array into two or more arrays and store them differently. There are several functions for splitting arrays too.

The most common of them is the split () function.

split(array, indices_or_sections, axis=0)

Array: The array you want to split

Indices or Sections: Index numbers from which you want to split the array or the number of sections you want your array to split. I would recommend using sections unless it is necessary to use indices.

Axis: By which axis you’d want to split(by default it is 0).

In the case of a 1D array, you don’t really have a choice as to how you want to split it. You can only choose from which element you want to split. Let us consider a 1D array

a = np.arange(4)

Now we have an array of 4 elements. If we want to split it into two then


The Output will be

[array([0, 1]), array([2, 3])]

But this function isn’t really much helpful when you are not sure about how much elements you have in array. Because it splits the elements evenly into new arrays.

For example, if you try to split this array into 3 parts then it will throw an error. To prevent that, we have another function named array_split(). Just replace split() with array_split() and it will work fine.

b = np.array_split(a,3)

The Output will be

[array([0, 1]), array([2]), array([3])]

You can also store each array into a new variable for example

c = b[0]
d = b[1]
e = b[2]

The Output will be

[0 1]

Split() is only used because it is a little faster in comparison to array_split()but it doesn’t make much of a difference in time. I would recommend using array_split() as it reduces the chances of errors.

Splitting along axis=0 or vsplit()

Let us consider a two dimensional array. We will be splitting this array along axis 0 i.e., splitting along rows.

 a1 = np.arange(1,13).reshape(2,6)
b = np.array_split(a1,2)

The Output will be

[array([[1, 2, 3, 4, 5, 6]]), array([[ 7,  8,  9, 10, 11, 12]])]

You can also achieve this by using the vsplit() function.


Splitting along axis=1 or hsplit()

If you want to split them along with columns then you can change the axis to 1.

b = np.array_split(a1,2,axis=1)

The Output will be

[array([[1, 2, 3],
       [7, 8, 9]]), array([[ 4,  5,  6],
       [10, 11, 12]])]

You can also achieve this using the hsplit() function.


In the above example, we only split a 2D array but you can also split arrays of higher dimensions. Since it will increase the complexity to an intermediate level, we are not going to include that in this tutorial.

Comparison: Arrays, Lists and Tuples

1. Vectorized Operations: One of the main differences between Arrays, Lists, and Tuples is vectorized operations. Only Arrays allow vectorized operations i.e. when you apply a function it gets applied to each element of an array and not to array itself.

For example,

>>import numpy as np
>>cg = [1,2,3,4]
>>cg_array = np.array(cg)
>>cg_array += 2
>>array([3, 4, 5, 6])

If you try the same with list or tuple it will throw a traceback error, for example:

>>>cgt = (1,2,3,4)
>>>cgt += 1
Traceback (most recent call last):
  File "<pyshell#11>", line 1, in <module>
    cgt += 1
TypeError: can only concatenate tuple (not "int") to tuple

2. Data Type Declaration: Arrays need to be declared while list, tuples, and dictionaries, etc. do not need to be declared i.e., if you want to use arrays then you have to declare them using the .array() class while you do not have to do it with lists or tuples.

3. Mutability: Mutability means the ability to be changed. Data inside arrays can be changed while the data in a tuple cannot be changed or modified.

4. Heterogeneous Data: While arrays, lists, and tuples all are used to store data, arrays cannot store heterogeneous data.

If you observe carefully, the list I passed in array has each element of different data type but when printed as an array, it converted all the data as “string”. This is not the case with lists or tuples.

Vectorized OperationsYesNoNo
Pre-Defined Data TypeNoYesYes
Heterogeneous DataNoYesYes
Features of Arrays, Lists and Tuples

When and when not to use Arrays?

When to use Arrays:
  • You have to store a large amount of data.
  • The data you want to store is of the same type.
  • You may perform operations on each element.
When not to use Arrays:
  • The Data Type is different
  • The data is very small
  • You do not have to perform operations on each element.


First of all, congratulations on making it to the end. Now, this tutorial doesn’t really need a recap as it was kind of an overview so that you can get the hang of it. So I’ll just go through the most important of these functions which are a necessity for using NumPy.

For creating arrays, the most important functions are arange() function and the standard array() function. You may also need zeros() or ones(). I highly doubt that you will be needing any other function.

.shape is the single most important attribute of the array that you should know about. And, for adjusting shapes the .reshape() function may come in handy. It is easier to use than changing arrays shape using shape attribute.

With concatenate() function you will need to specify the axis whereas with hstack() and vstack() you do not need to type the axis. You can use either but using hstack() or vstack() may help in in future to remember the axis.

array_split() adjusts the array split according to the parameters whereas split() will throw an error if the parameters are not proper. So use array_split(). In most cases it’s a win-win.
With array_split() function you will need to specify the axis whereas with hsplit() and vsplit() you do not need to specify the axis.

I guess I have covered everything important. Know that there are some sections where you might get confused if you are a first-timer. Please don’t hesitate to ask in the comments section. I will try to answer each and every comment. So, that’s all.

Happy Coding!

Python Artificial Intelligence

Python: The Complete Guide To Speech Recognition

Speech Recognition is a hit in the market. It’s a flashy technology that is used mainly in voice assistants like Apple’s Siri, Amazon’s Alexa, Microsoft’s Cortana, Google’s Allo, etc. It can massively increase the user interaction in your project and will make it look amazing. Writing a program that uses speech recognition is far easier than you think. We will be covering everything from installing to implementation of speech recognition.
Though this program will also work with Python 2, we recommend using python 3.

There’s a lot that you can do with this package but I’ll try to keep it short and cover only the most important things needed to make a simple program or to implement vocal recognition in your own program.
NOTE: If you are looking just for the code then the final code is in the Conclusion section.
You can view the code for Working with microphone and Working with Audio Files directly. But if you want to know about the working of the code then you should read the article.

Introduction to Speech Recognition

Exactly What is speech recognition? In simple words, it is a technology that takes voice as an input and converts it into computer understandable language.

Setting Up

There are many modules that can be used for speech recognition like google cloud speech, apiai, SpeechRecognition, watson-developer-cloud, etc., but we will be using Speech Recognition Module for this tutorial because it is easy to use since you don’t have to code scripts for accessing audio devices also, it comes pre-packaged with many well-known API’s so you don’t have to signup for any kind of service which you may have to while using any other module. And, it gets the job done pretty well.


  • Python 3.3+
  • Speech Recognition
  • *PyAudio 0.2.11
  • *PocketSphinx (offline use)
  • FLAC encoder (required only if the system is not x86-based Windows/Linux/OS X)

We will be using SpeechRecognition and PyAudio Module.
*PyAudio: This module is only required if you want to take the user’s voice as an input and not use pre-recorded audio files.
*PocketSphinx: Only use PocketSphinx if you have to use your program offline. I don’t personally recommend using PocketSphinx because of low accuracy.

The Speech Recognition Module

The Speech Recognition engine has support for various APIs. The most common API is Google Speech Recognition because of its high accuracy. In this tutorial though, we will be making a program using both Google Speech Recognition and CMU Sphinx so that you will have a basic idea as to how offline version works as well. However, if you want to use any other API, its pretty easy to switch, you just have to change the recognizer method(we will discuss it later in this tutorial)


I’m assuming you have python 3 properly installed. Open command prompt and type

pip install speechrecognition
­pip install pyaudio
pip install pocketsphinx

NOTE: PyAudio is not available for python versions greater than 3.6.
If you are using python 3.7 or greater then download PyAudio wheel from here.

After downloading you can install wheel file as:

pip install C:/some-dir/some-file.whl

here some-dir and some-file.whl is the directory and the file name of that wheel respectively.
After installing, its always better to check the installation, open python terminal, and type

>>import speech_recognition as sr

If it returns ‘3.6.0’ or higher then you are good to go (3.6.0 is the version that I was using because some of the libraries were not compatible with 3.7+ that I needed in my other project)

Working with Speech Recognition

After installing all the packages we are finally ready to start writing our first voice-based program. We will first be discussing the working of the speech recognition package and then we will start coding the program. You can skip to the code directly but I recommend reading the working as well because that will give you the idea about the options available that you can experiment with.

How Does it Work?

Speech Recognition has an instance named recognizer and as the name suggests it recognizes the speech(whether from an audio file or microphone). You can create a recognizer instance as follows:

import speech_recognition as sr
r = sr.Recognizer()

Note that we imported the Speech Recognition package as speech_recognition whereas we installed it as SpeechRecognition. This is a common mistake that many users make while importing this package.

Now Each Recognizer instance has eight methods by which it can recognize speech those are:

Out of these if you want to make a trigger word for your program so that it only starts and stops listening speech at a particular word(like Hey Siri or Ok Google) then you can go for Snowboy Hotword Detection.
Now with speech recognition, you can do two things:

  1. Take voice as an input from the user or
  2. Use a pre-recorded audio file

Now for most of the people option two would be pretty useless but we will be discussing both of them in this tutorial. You can skip to the desired part of the article from the sidebar.

Using Microphone

The most awaited and most fun section finally. Writing the above part was kind of boring but it was a complete guide afterall so i had to do it.

Now for working with microphones, you need PyAudio as I mentioned above.
After installing everything, you just need to type in this code.

import speech_recognition as sr
r = sr.Recognizer()

with sr.Microphone() as source:
	audio = r.listen(source)

		data = r.recognize_google(audio)
		print("Please try again")

Note: If you are using microphones other than the default one then please refer to configuring microphone settings at the bottom.

Now what we did here is we made the microphone as a source using the Microphone() method. I print start so that I would know when to start speaking. Though a symbol of mic appears in the taskbar when the program is using microphones, I did it just out of convenience.

Then we declared new variable audio that records the input. Till now the audio is only recorded through the microphone and not converted into text.

Now here comes the recognize_google() method. The recognize google method sends the audio data to the google web speech server and retrieves response.

Please note that recognize_google() uses google web speech api which is free but it is provided for testing purposes only. So I advice you not to use it for real world project.

Now there might be a time when the audio recorded just couldn’t be converted to text because of noise or other problems. If that happens then you will encounter a traceback error like this.

Traceback (most recent call last):

To prevent this from happening we put a try and except block. This checks if there is some data in the audio variable that can be converted into text. If there is some data then it completes the try block and skips the except block. If there is no data or there is some problem in parsing the data then it runs the except block.

Also, you can raise multiple exception blocks for different errors but the detailed working of try and except blocks is beyond the scope of this tutorial, so we are going to skip it.

If everything is clear to this point then changing methods is simple like if you want to use CMU Sphinx instead of google then all you have to do is replace the recognize_google() with the recognize_sphinx() method. The code would be

import speech_recognition as sr
r = sr.Recognizer()

with sr.Microphone() as source:
	audio = r.listen(source)

		data = r.recognize_sphinx(audio)
		print("Please try again")

The main reason why I recommend google web speech instead of CMU sphinx is because of the accuracy

For the same sentence “test speech recognition” both outcomes are different and it is clear the google does it better.

Using Audio Files

Working with audio files is quite similar to working with microphones. The only difference is that instead of taking voice input from the users all you have to do is pass an audio file as a source.

We will be using the sample audio that you can download from here

After downloading, place the audio file in the same folder as your program file. After that, all you have to do is instead of sr.Microphone() use sr.AudioFile(‘filename.extension’) and change the r.listen() method to r.record() method.
That’s it, that’s all there is to it. You can check out the code below

import speech_recognition as sr
r = sr.Recognizer()

with sr.AudioFile('english.wav') as source:
	audio = r.record(source)
		data = r.recognize_google(audio)
		print("Please try again")


Congratulations on making it this far! This has been a long journey( at least for me haha :D)
Below is the quick recap of what we did so far( cause who’s got the time to read the freakin thing again and again)

If PyAudio doesn’t work properly, read the installation part again

The Speech Recognition has an instance named recognizer and as the name suggests it recognizes the speech(whether from an audio file or microphone). Depending on what method you use, it process audio data accordingly. There are both online and offline methods. In online methods like recognize_google() it sends data to Google Web Speech Server whereas in recognize_sphinx() method it process data within your device.

In order to take user’s voice as an input, you need PyAudio installed. Below is the complete code for processing audio data with microphone. Note that it uses sr.Microphone() and r.listen() methods

import speech_recognition as sr
r = sr.Recognizer()

with sr.Microphone() as source:
        r.adjust_for_ambient_noise(source, duration=0.5)­
	audio = r.listen(source)
		data = r.recognize_google(audio)
		print("Please try again")

Working with Audio files is similar to working with Microphones. Place the audio file that you want to process in the same directory as the program file.Below is the complete code for processing audio file. Note that it uses sr.AudioFile() and r.record() methods. Suppose you have a file name ‘english.wav’ then the code would be

import speech_recognition as sr
r = sr.Recognizer()
with sr.AudioFile('english.wav') as source:
    audio = r.record(source)
        data = r.recognize_google(audio)
        print("Please try again")

I tried to keep it as short and informative as possible but if I missed anything please tell me in the comments section below also if you have any doubts you can comment them down as well. I will try my best to answer you!


Configuring Microphone Settings

Note: If you are going to use your system’s default microphones then you can skip this section as there is no need to configure any microphone settings. If you want to use the microphone other than the default one then you have to select it manually.To get a list of microphones available type:

['Microsoft Sound Mapper - Input', 
'Microphone (2- Realtek Audio)',
 'Microsoft Sound Mapper - Output', 
'Speakers / Headphones (2- Realt', 'Primary Sound Capture Driver',
 'Microphone (2- Realtek Audio)', 
'Primary Sound Driver',
 'Speakers / Headphones (2- Realtek Audio)',
 'Realtek ASIO', 'Speakers / Headphones (2- Realtek Audio)',
 'Microphone (2- Realtek Audio)',
 'Stereo Mix (Realtek HD Audio Stereo input)',
 'Microphone (Realtek HD Audio Mic input)',
 'Speakers (Realtek HD Audio output)']

Note that the list of available microphones may vary. Now that you have a list of microphones all you have to do is select the index number of microphone. For example if you want to use Microsoft Sound Mapper which is at index number zero then all you have to do is

import speech_recognition as sr
r = sr.Recognizer()

with sr.Microphone(device_index=0) as source:
	audio = r.listen(source)

Reducing Noise Levels

Though you can not remove the noise completely from the background, there is adjust_for_ambient_noise() which can help you lower the noise levels in audio. For Example:

with sr.Microphone() as source: #You can also use this with audio files
	audio = r.listen(source)

adjust_for_ambient_noise() reads the audio file for one second and adjusts the recognizer for the noise of that level. You can reduce the time it takes to read the audio file but it is recommended to let it read file for at least one second. But if one second seems like too much time then you can lower it by using the duration keyword. Example:

r.adjust_for_ambient_noise(source, duration=0.5)

In the above example, I reduced the duration to 0.5 seconds. You can increase or decrease according to your needs. The more time you give it the better it does its work. But if you are reducing the time limit then keep it at least 0.5 seconds (the SpeechRecognition documentation recommends that)

Working with Multiple Languages

If you are using Google Web Speech then you don’t need to do anything else as it already works for almost all languages. But if you are using CMU Sphinx and need any other language pack installed then you can install addition language packs from here.
All instructions for installation are mentioned on that page as well.

Writing to an Audio File

If you want to record and store user’s voice for further use(for building neural networks or maybe some other purposes) then this option is very useful for you. And this step is really easy. All you have to do is add the write command after using the r.listen() method. For example:

import speech_recognition as sr
r = sr.Recognizer()

with sr.Microphone() as source:
	audio = r.listen(source)

with open("microphone-results.wav","wb") as f:

This will create a file named microphone-results.wav in the same directory as the program file. With some string formatting, you can save different audio files with different names but we will cover string formatting in some other articles.

So this sums it up. Thank you for reading it till here. This article took a lot of time and energy so if you like it please tell me in the comments below. I’ll be posting more awesome articles so in order to get notified sign up for our newsletter below.

Have a nice day!


Armstrong Numbers

 What are Armstrong Numbers?

The numbers in which the sum of the digits raised to the power of the number of digits is equal to the number itself are called Armstrong numbers. All the programs on Armstrong numbers that I’ve seen to date only work on the specific number of digits. Like they won’t work for an indefinite number of digits. So my approach is to write a program that works on any input. However, you can customize it according to your needs.

This Code is Written in Python 3!

A positive integer is called an Armstrong number of order n if

abcd….. = an + bn + c+ dn……. 

In the case of an Armstrong number of 3 digits, the sum of cubes of each digit is equal to the number itself.

There are four 3 digits Armstrong numbers 153, 370, 371, 407.

For example:  153 = 13 + 53 + 33

Similarly, 370 = 33 + 73 + 03

To Check if a number is Armstrong or not, we’ll first take input from user as

num = input("Enter a number: ")

*Note that we took input as string.

Now that we have input stored in variable num, we’ll create another variable to store the sum of numbers. Let’s name it sum. So,

sum = 0

Now if we modify our original variable num, we won’t have any real input from the user to check the output. So we will create another variable and store the value of  ‘num’ in it. We will perform operations on this temp variable.

temp = int(num)

Now we will initialize a while loop while loop which will work till the number becomes 0.

while temp > 0:

Now we’ll extract each digit in the digit variable as

digit = temp % 10

After extracting each digit we’ll raise it to power of number of digits and then adding it in sum variable.

sum += digit ** len(num)

*len() only works with string values.

After taking the sum, we’ll remove the last digit from the number.

temp = temp//10

All this will continue till the number becomes 0, after it becomes 0, while loop will break and the final result will be stored in ‘sum’.

We’ll use this sum to verify the user input using the if statement. If it returns True, we’ll print “Number is Armstrong”, if not, we will print “Number is not Armstrong”

Complete Code