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Introduction to Text to Speech in Python

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Syntax:

Object_name = SpeechRecogonition.Recognizer()

The above code is the key syntax position to be assed. It explains the process of object creation through the Recognizer class of speech recognition objects.

How to Convert Text to Speech in Python?

The method of speech recognition in python happens in the below ways. The ways are the steps or the technical algorithm which could be involved for speech recognition conversion. Moreover, these are the step-by-step process of speech recognition. These step by steps helps to set the speech recognition process.

The process of importing the corresponding libraries is a very key aspect. Here the speech recognition libraries are imported. This speech recognition is imported is useful in setting the corresponding methods associated to the speech recognition process. Some of the famous speech recognition libraries in the market are SpeechRecogoition library from the pyspace library. These libraries set the remaining tone of operations for setting the speech recognition to happen in python code.

Next is the most important step. This step is responsible for setting the python object for helping to make the recognition process happen. This step is named as object-level initialization process. The class used here is the recognizer class which comes under the speech recognition process. So the process is to initialize the recognizer class to pick up the recolonize process to happen. The speech recognition library used by us here is google speech recognition.

Let’s look at the various file formats supported by the speech recognition process. So the google library supports various input formats of speech. These formats are mentioned below. Wav format a lossless audio format, AIFF, AIFF-C ,FLAG. These are among the key types supported for this process of speech recognition briefly.

The audio clip has to be verified to determine the type of word used in the speech to confirm whether the conversion happens exactly as needed.

The default recognition language of speech recognition software is English. With English being the default language used it supports various other languages of speech recognition too. The below-listed table below mentions some of the most famous languages supported by speech recognition software support. The below table mentions only some languages in it but googles search recognition software support several other languages.

Example of Text to Speech in Python

Given below is the example mentioned:

Code:

#import library import speech_recognition as Speech_item # The recogonizer class is initialized at the below code. recogonizer_class = Speech_item.Recognizer() #the audio file is mentioned here in the below location with Speech_item.AudioFile('input.wav') as input_source: retrived_audio = recogonizer_class.listen(input_source) # The method of recogonize will involve an error item when the expected value in the audio file is not found # using google speech recognition Extracted_text_value = recogonizer_class.recognize_google(retrived_audio) print('Audi converion') print('Extracted_text_value') except: print('Exception occured')

Explanation:

The first item in the above-given code is the process of declaring the corresponding libraries. This is the most important step. In the case of this problem the speech recognition library of google is been declared. This is the foremost and the critical step. Next, an object is declared for this item using the recognized method. In our above given example, the recognized class is declared by the name recogonizer_class. The next thing to be noted is the audio sample is gathered into a variable. The audio sample is gathered by the means of listening to the method in the recognizer class.

The listen method is useful in converting the voice item into a python understandable item into a variable. In our example, the values are stored in the retrieved audio variable. So the retrieved audio variable holds the expected value. This variable is then passed to the recognized google class.

This is the most important section. The recognizer google is again a method of speech recognition class. It can be again retrieved from the class of speech recognition by means of the object item declared. The object item, in this case, is the recognizer object named as recognizer class. As a result of this operation, the out text values get filled up in the extracted text value variable. So this variable holds the output now. The last process now remaining is the process of printing the extracted output. This is done next. This is the last process where the extracted output will be printed onto the console. We can notice the output in the output section screenshot.

Conclusion

The above-given article clearly explains the various ways through which the speech recognition can be performed in the google recognition system. A suitable example is also shared for the same with the output snapshots attached.

Which Industries Can Benefit From Text To Speech Technology?

In this post, I will highlight some of the best uses with text to speech technology and how it really is causing breakthroughs in certain industries. Keep in mind that these are not certainly the only industries, because there are a lot of industries that are benefitting from this technology.

I have included some of the major impacts that text to speech is having on some of the biggest industries in the world. It will obviously continue to have a huge positive impact on these industries and others that are not listed.

Learning disabled children struggle to learn this skill right away, and there are practical uses for a text to speech converter to step in and aid them with their reading. Not only can text to speech help learning disabled people, but it can help other conditions and disorders like dyslexia.

Learning and education are one of the biggest beneficiaries of this technology and it will likely continue to adapt and improve for the better of the industry.

Have you ever been to a large gathering of people or busy places and an automated message is coming from the main speakers? Well, there is a good chance that text to speech technology is at work.

It makes sense for people to use this for public announcements instead of having a person say a message over and over. It is also probably cheaper for the establishment because they do not need to pay a person to sit there and say things into the microphone.

An automated message that uses text to speech conversion technology is beneficial to both the company and the people inside.

The last industry that I will mention is the automotive industry. If you have an old car and have not been in a new one, then be ready for your mind to be blown. A lot of newer cars in the last 5 years or so have adapted to use text to speech.

Their technology is incredible in many ways, and it has made the user experience something to remember. A car uses text to speech conversion tools to let the driver know about something and keep the driver’s eye on the road.

Instead of a bunch of words on a screen that could be distracting, the text to speech converters works to reduce the risk of distractions and still let the driver know about anything that is necessary. It is pretty cool to think about how text to speech is saving lives and improving them at the same time.

If you found this post helpful, then please share it with anyone that is interested in the main industries that are affected by text to speech converter

Even the travelers use the text to speech technology while traveling in a country where people do not speak english. Text to speech helps the traveler to type and explain what he or she is trying to say.

Moreover, this feature is very accurate and helps the travelers in various ways. Google and Apple have trained their TTS technology a lot and the accent, pronunciation matches with that of the locals of the specific country, however this may vary in some languages.

Have you seen a small audio bar icon in some top news sites? Well, some sites use a pre recorded audio in a human’s voice while some sites make use of the TTS technology to convert text into audio.

One can use this feature to listen to news while doing his or her work or sipping a hot cup of coffee.

Moreover, media industries prefer TTS over a voice over as TTS is a free to use technology whereas hiring a voiceover artist is quite expensive. Also, there are no chances of error.

Audiobooks are a new way of consuming content in the form of audios. One can complete a book without having to read it and do it while traveling, playing, or doing household chores.

Some audiobook companies use the TTS technology to convert text novels into audio form.

However, the only con of this is that the audio tone is flat, which is not as pleasing as a human narrating the novel.

The TTS technology is definitely a better way to convey our messages which are in textual form. This technology has been adapted by various industries however, everything has its own pros and cons, TTS technology too has some flaws such as lack of emotions, flat tone and inaccuracy in pronunciation.

Fuzzywuzzy Python Library: Interesting Tool For Nlp And Text Analytics

This article was published as a part of the Data Science Blogathon

Introduction

There are many ways to compare text in python. But, often we search for an easy way to compare text. Comparing text is needed for various text analytics and Natural Language Processing purposes.

One of the easiest ways of comparing text in python is using the fuzzy-wuzzy library. Here, we get a score out of 100, based on the similarity of the strings. Basically, we are given the similarity index. The library uses Levenshtein distance to calculate the difference between two strings.

Levenshtein Distance

The Levenshtein distance is a string metric to calculate the difference between two different strings. Soviet mathematician Vladimir Levenshtein formulated this method and it is named after him.

where the tail of some string x is a string of all but the first character of x, and x[n] is the nth character of the string x starting with character 0.

FuzzyWuzzy

Fuzzy Wuzzy is an open-source library developed and released by SeatGeek. You can read their original blog here. The simple implementation and the unique score (out of 100) metic makes it interesting to use FuzzyWuzzy for text comparison and it has numerous applications.

Installation:

pip install fuzzywuzzy pip install python-Levenshtein

These are the requirements that must be installed.

Let us now get started with the code by importing the necessary libraries.

Python Code:

Here, in this case, even though the two different strings had different cases, conversion of both to the lower case was done and the score was 100.

Substring Matching

Now, often various cases in text-matching might arise where we need to compare two different strings where one might be a substring of the other. For example, we are testing a text summarizer and we have to check how well is the summarizer performing. So, the summarized text will be a substring of the original string. FuzzyWuzzy has powerful functions to deal with such cases.

#fuzzywuzzy functions to work with substring matching b1 = "The Samsung Group is a South Korean multinational conglomerate headquartered in Samsung Town, Seoul." b2 = "Samsung Group is a South Korean company based in Seoul" print("Ratio:",Ratio) print("Partial Ratio:",Partial_Ratio)

Output:

Ratio: 64 Partial Ratio: 74

Here, we can see that the score for the Partial Ratio function is more. This indicates that it is able to recognize the fact that the string b2 has words from b1.

Token Sort Ratio

But, the above method of substring matching is not foolproof. Often the words are jumbled up and do not follow an order. Similarly, in the case of similar sentences, the order of words is different or mixed up. In this case, we use a different function.

Output:

Ratio: 56 Partial Ratio: 60 Token Sort Ratio: 100

So, here, in this case, we can see that the strings are just jumbled up versions of each other. And the two strings show the same sentiment and also mention the same entity. The standard fuzz function shows the score between them to be 56. And the Token Sort Ratio function shows the similarity to be 100.

So, it becomes clear that in some situations or applications, the Token Sort Ratio will be more useful.

Token Set Ratio

But, now if the two strings have different lengths. Token sort ratio functions might not be able to perform well in this situation. For this purpose, we have the Token Set Ratio function.

Output:

Ratio: 41 Partial Ratio: 65 Token Sort Ratio: 59 Token Set Ratio: 100

Ah! The score of 100. Well, the reason is that the string d2 components are entirely present in string d1.

Now, let us slightly modify string d2.

By, slightly modifying the text d2 we can see that the score is reduced to 92. This is because the text “10” is not present in string d1.

WRatio()

This function helps to manage the upper case, lower case, and some other parameters.

#fuzz.WRatio()

Output:

Slightly change of cases: 100

Let us try removing a space.

#fuzz.WRatio()

Output:

Slightly change of cases and a space removed: 97

Let us try some punctuation.

#handling some random punctuations g1='Microsoft Windows is good, but takes up lof of ram!!!' g2='Microsoft Windows is good but takes up lof of ram?'

Output: 99

Thus, we can see that FuzzyWuzzy has a lot of interesting functions which can be used to do interesting text comparison tasks.

Some Suitable Applications:

FuzzyWuzzy can have some interesting applications.

It can be used to assess summaries of larger texts and judge their similarity. This can be used to measure the performance of text summarizers.

Based on the similarity of texts, it can also be used to identify the authenticity of a text, article, news, book etc. Often, we come across various incorrect text/ data. Often cross-checking each and every text data is not possible. Using text similarity, cross-checking of various texts can be done.

FuzzyWuzzy can also come in handy in selecting the best similar text out of a number of texts. So, the applications of FuzzyWuzzy are numerous.

Text similarity is an important metric that can be used for various NLP and Text Analytics purposes. The interesting thing about FuzzyWuzzy is that similarities are given as a score out of 100. This allows relative scoring and also generates a new feature /data that can be used for analytics/ ML purposes.

Summary Similarity:

#uses of fuzzy wuzzy #summary similarity

The above is the original text.

output_text="Text Analytics involves the use of unstructured text data, processing them into usable structured data. Text Analytics is an interesting application of Natural Language Processing. Text Analytics has various processes including cleaning of text, removing stopwords, word frequency calculation, and much more. Text Analytics is used to understand patterns and trends in text data. Keywords, topics, and important features of Text are found using Text Analytics. There are many more interesting aspects of Text Analytics, now let us proceed with our resume dataset. The dataset contains text from various resume types and can be used to understand what people mainly use in resumes."

Output:

Ratio: 54 Partial Ratio: 79 Token Sort Ratio: 54 Token Set Ratio: 100

We can see the various scores. The partial ratio does show that they are quite similar, which should be the case. Also, the token set ratio is 100, which is evident as the summary is completely taken from the original text.

Best possible String match:

Let us use the process library to find the best possible string match among a list of strings.

#choosing the possible string match #using process library query = 'Stack Overflow' choices = ['Stock Overhead', 'Stack Overflowing', 'S. Overflow',"Stoack Overflow"] print("List of ratios: ")

Output:

List of ratios: [('Stoack Overflow', 97), ('Stack Overflowing', 90), ('S. Overflow', 85), ('Stock Overhead', 64)] Best choice: ('Stoack Overflow', 97)

Hence, the similarity scores and the best match are given.

Final Words

FuzzyWuzzy library is created on top of the difflib library. And python-Levenshtein used for optimizing the speed. So we can understand that FuzzyWuzzy is one of the best ways for string comparison in Python.

Do check out the code on Kaggle here.

About me:

Prateek Majumder

Connect with me on Linkedin.

My other articles on Analytics Vidhya: Link.

Thank You.

The media shown in this article are not owned by Analytics Vidhya and are used at the Author’s discretion.

How To Strikethrough Text In Discord Chats

Discord is an extremely popular platform that many gamers use to communicate with other players while roaming distant lands and taking on fierce enemies or battling monsters. There are thousands of channels that you can join, but no matter which one wins your favor, there is usually no shortage of snark or sarcasm. It’s not always mean spirited, as most gamers just want to blow off steam while having fun with friends. But the tone of voice is often lost in all the typing. That’s when strikethrough can be the only way to get your message across. This quick tip shows how to strikethrough text on Discord.

Good to know: when taking a break, you can even add some music bots to rally the troops.

How to Strikethrough Text in Discord Chats

In traditional typographical presentations, the strikethrough means that there’s a correction or deleted information. However, when used in the context of Discord and other chat apps, it indicates a deliberate change of thought that would have otherwise been seen as a Freudian slip.

To inject some humor into your Discord posts, you can add a strikethrough to any word. The actions can be somewhat laborious, especially if you need to get your message across quickly.

Go to the channel where you would like to chat.

Type your message as usual. Add two tildes (~) before and after a word when you want to strikethrough it.

Tip: learn how to change your status on Discord and let everyone know what you’re doing.

The word will appear with a strikethrough preview.

Hit Enter when you want to send the message, and you’ll see that the word has a proper strikethrough.

Use either method described in the steps above to add a strikethrough over your text.

FYI: not happy with your original Discord username or nickname? Learn how to change it.

On the mobile apps, you can strikethrough a word/phrase in similar fashion. Once you are in the text block, tap on “~” twice before and after the word you want to strikethrough. You won’t see a preview like you would in the desktop version, but when you post the message, it appears correctly.

To edit a message on mobile, long-press on it, then select “Edit” from the pop-up menu that appears from the bottom.

There are several text styles in Discord, each with its own shortcut. However, those aren’t the only ones. There are Discord shortcuts for everything from scrolling up the list of servers to toggling the GIF picker.

Image credit: Freepik. All screenshots by Charlie Fripp.

Charlie Fripp

Charlie Fripp is a technology writer with a strong focus on consumer gadgets, video games, and cyber security. He holds an undergraduate degree in professional journalism and has worked as a journalist for over 15 years. In his spare time, he enjoys playing various musical instruments and gardening.

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Wikipedia Censorship Sparks Free Speech Debate

The banning of a Wikipedia page by a U.K. Internet watchdog is raising tough questions over how far online censorship should go — and the decisions made in the coming days could prove crucial to how we balance free speech with content regulation in the future.

“Indecent” Decision

The Internet Watch Foundation — a nonprofit, nongovernment-affiliated organization — added the Wikipedia page for the Scorpions’ 1976 album “Virgin Killer” onto its blacklist Friday. The IWF’s concern comes over the image on the album’s original cover, which shows a young girl completely nude. (A cracked glass effect obscures a direct view of her genital area.) Someone had reported the image as inappropriate through the IWF’s online submission tool, the organization says, and its internal assessment found the photo to be “a potentially illegal indecent image of a child under the age of 18.”

The IWF’s blacklist is used by the vast majority of British Internet service providers to maintain decency standards for their subscribers. As a result of the ban, affected U.K. Internet users are unable to view the page or access Wikipedia’s article editing function.

Ethical Quandary

Here’s where things get tricky: The image, by all accounts, has never been flagged as illegal. The FBI did reportedly launch an investigation this past May, but no resulting decision has been announced. If you read over the legal definition of “child pornography,” you can see where this image might fall outside of its lines.

That’s the main complaint of those who oppose the IWF’s ban — the idea that this image may be deemed “distasteful” by many people, but as long as it’s not illegal, a self-governing group has no right to impose its own moral assessment onto millions of others. The image is also printed in books accessible in libraries, a spokesperson for Wikipedia’s U.K.-based volunteers pointed out to the BBC.

The IWF ultimately acts as the morality police for about 95 percent of the U.K.’s Internet users, and the fact that one nongovernment company has so much control over what’s decent and what isn’t is a bit alarming. Where does the U.K. government stand on all of this? Should its opinion count?

Broader Implications

The questions reach further than this single image on this specific Wikipedia page. If an independent group such as the IWF can make its own assessments as to the appropriateness of content, many are asking, where do we draw the line? A complaint has already been filed with the IWF against Amazon for hosting the album’s image on its store pages. Should Internet users in the U.K. be banned from accessing Amazon, too? Does a group of self-appointed moral judges have the right to make that call? And how far do we take it — should we block other sites like, say, the Internet Archive’s Wayback Machine, since one could pull up the image there as well?

Don’t get caught in the “not my problem” line of thinking, either — this model of private censorship could easily be exported to the U.S. In fact, we’ve already seen a taste of it. Clear Channel faced claims of banning “offensive” songs shortly after 9/11, and Verizon blocked an activist group from sending text messages over its network late last year. Verizon said the content, which focused on the issue of abortion, could be considered “controversial or unsavory.”

Slippery Slope

It’s a potentially slippery slope, and one reminiscent of other battles as to the appropriateness of various content. Just this month, a representative from The Family Foundation — a nonprofit group from Virginia — put out a statement suggesting “porn has no place in civil society.”

Regardless of your feelings about the image on the Scorpions’ album cover, is this group’s stance any different than the IWF’s on its most basic operating level? Each organization is asserting its own right, outside of the law, to determine what legally acceptable content you should or should not be allowed to see. The IWF just presently has the power to enact its decisions, while The Family Foundation does not.

To be clear, I’m by no means suggesting an image of a young girl nude is comparable to adult pornography. I’m not even saying that the image of the young girl should necessarily be legal. I’m just saying that I’m in no position to make that determination — and, so long as the image is legal, I’m in no position to keep you from looking at a Web site about it. And I’m not sure if a group like the IWF should be, either.

These are tough questions, and there may not be any definitively correct answers. I sure don’t have them. But there’s no doubt an important debate brewing here that’s far bigger than this one case — and everyone who uses the Internet has reason to be invested in its outcome.

How To Display Text In Different Fonts Using Java

How to display text in different fonts using Java

Problem Description

How to display text in different fonts?

Solution

Following example demonstrates how to display text in different fonts using setFont() method of Font class.

import java.awt.*; import java.awt.event.*; import javax.swing.*; public class Main extends JPanel { String[] type = { "Serif","SansSerif"}; int[] styles = { Font.PLAIN, Font.ITALIC, Font.BOLD, Font.ITALIC + chúng tôi }; String[] stylenames = { "Plain", "Italic", "Bold", "Bold & Italic" }; public void paint(Graphics g) { for (int f = 0; f < type.length; f++) { for (int s = 0; s < styles.length; s++) { Font font = new Font(type[f], styles[s], 18); g.setFont(font); String name = type[f] + " " + stylenames[s]; g.drawString(name, 20, (f * 4 + s + 1) * 20); } } } public static void main(String[] a) { JFrame f = new JFrame(); f.addWindowListener(new WindowAdapter() { public void windowClosing(WindowEvent e) { System.exit(0); } }); f.setContentPane(new Main()); f.setSize(400,400); f.setVisible(true); } } Result

The above code sample will produce the following result.

Different font names are displayed in a frame.

The following is an another sample example to display text in different fonts

import java.awt.*; import javax.swing.*; public class Main extends JComponent { String[] dfonts; Font[] font; static final int IN = 15; public Main() { dfonts = GraphicsEnvironment.getLocalGraphicsEnvironment().getAvailableFontFamilyNames(); font = new Font[dfonts.length]; } public void paintComponent(Graphics g) { for (int j = 0; j < dfonts.length; j += 1) { if (font[j] == null) { font[j] = new Font(dfonts[j], Font.PLAIN, 16); } g.setFont(font[j]); int p = 15; int q = 15+ (IN * j); g.drawString(dfonts[j],p,q); } } public static void main(String[] args) { JFrame frame = new JFrame("Different Fonts"); frame.getContentPane().add(new JScrollPane(new Main())); frame.setSize(350, 650); frame.setVisible(true); } }

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