Writing is a gratifying activity, at least for people like me who love to share their thoughts. However, if it’s even better when you don’t actually have to type to write and rather may simply pull out your phone and start dictating your thoughts and let the magic happen. I mean who wouldn’t like to just lay-back on the couch with legs comfortable up and just dictate lengthy notes?

Well, thanks fully with the advancement of the google speech to text technology mean that now we can keep taking any amount of voice notes on the go through our android phones, using the speech to text android technology. Here in this blog, we will be briefly looking into the speech to text android technology; how you can enable it on your smartphone, and what are some of the best Ai speech to text android apps that you can start using right away.

So, let’s get started.

How to Send Speaking Text Messages Using Speech to Text Android Feature?

Well, obviously before we began looking at some of the speech to text android apps that you can use right away, you should know some basics of using the technology.

And while this feature may seem basic to many android users, still for new users that have just switched to the Android ecosystem or people who aren’t much tech-savvy, a quick guide on how to turn on speech to text on android or how to send a text message using the android speech to text is always helpful, right?

So, let’s see how to turn on speech to text on android and send text messages.

How To Text Message On Android Hands-Free

Step 1 – Go to your message app and start composing

Whichever messaging app you are using (the default or Google messages or any other third-party app) just open the app and start composing the message. Once you are in the composing message screen, you would see a “Microphone” icon at the bottom right of the keyboard.

Tap the icon and start to speak!

Just tap the button and you will see the “Speak Now” message; and you are done. Just start saying whatever you want to write and let the android speech to the text do the magic for you.

Now, for those of you who aren’t used to dictation through Google Speech to text, you may want to learn some dictation punctuation to get started (theses aren’t that difficult though). Nonetheless, you can easily find some of the most common punctuation like to place the “Period” mark you would have to literally say “Period”.

Step 3 – Confirm and Send

Once you are done dictating, just review the message to be sure it’s correctly transcribed and simply press the “Send” button to let it fly away to its destination.

How to Turn On Speech to Text on Android Devices?

Ok, now that you know how to send a text message using voice typing on android, let’s take a quick look at how to turn on speech to text on android devices.

Step 1: Checking the device settings

Before you turn on the voice typing option, you need to check the device settings.

Here’s are a few things you need to check and confirm:

Required:

Remember, voice typing only works with Android 5.0 or above, which means you should check out the Android version on your smartphone.

You would require the latest version of Google app, which is the default for the latest version of android or you can update it from Google Play.

Recommended:

Detect “Ok Google” from any screen.

For offline speech to text, you should download the language of your choice.

Note: Google's speech to text is available in multiple languages including; English, French, German, and others.

Step 2: Download Voice Access

Once you are done with checking with the prerequisites and recommendations, you would need to download the voice access from Google Play.

Step 3: Enjoy using Voice Access

Well, that’s all is required for you to get the magic started. Here some few simple steps you may follow to turn on Voice Access:

Go to your phone’s settings.

Click on the “Accessibility”.

Tap the “Voice Access”.

At the top right corner “Slide/Switch” On/Off the Voice Access.

Say Ok Google and speak a command like “Open Chrome” and you are on your way.

6 Best Free Android Speech to text Dictation Apps

Now that we are done with the basics of speech to text for android, let’s just quickly jump on some of the best speech to text app for android. We will start by looking at some of the free speech to text apps and then move up to some paid apps.

1. Converse Smartly®

Pros

Quite accurate.

Intelligible.

Adaptive.

Converse Smartly® has gained much appreciation and popularity as one of the best speech to text app for android. The app is developed by Folio3 and offers advanced speech to text features to assist you to perform all of the major tasks over your phone without having to actually touch the keyboard.

The best part about Converse Smartly® is its use of advanced technologies including artificial intelligence and machine learning that not only make it super accurate to transcribe the speech, but also give it the power to learn over time and further improve the accuracy. The developers at Folio3 have certainly done a commendable job using their expertise not just by making a super accurate and advanced speech to text software but also by keeping it intelligible and easy to comprehend for users. Once you are a few weeks into using the app, you will be producing 99% accurate write-ups using your voice. The Converse Smartly® platform is fully compatible with the latest android versions.

2. Google Keyboard

Pros

Free.

Easy to use.

Additional features.

Cons

No shortcut commands.

Well, Google Keyboard is certainly right at the top when it comes to the best speech to text apps for android. While the majority of the Android devices will already have the Google Keyboard accessible, if it’s not available you can always download it from Google Play. And while Google Keyboard is primarily meant as a physical input app (as can be taken by name) it also offers a speech input option directly. And when it comes to the power of Google behind it, you can very well comprehend the power and responsiveness of the tool.

And just if the accuracy and power of the tool weren’t enough, the app offers a plethora of other advanced features (you always expect something out of the box from Google) like triggering the images through voice commands. Furthermore, the app can support over 60 languages and is compatible with Google translate.

3. Speech notes

Pros

Powered by Google Voice recognition technology.

Recognize punctuations.

Easy to use.

Cons

No iOS app.

Next in line, we have a rather easy to use dictation app; Speechnotes. What I like the most about the app is that it doesn’t require you to make an account or anything to get started. Once you are done downloading, simply open the app, press the “Microphone Icon” and you are ready to go.

What makes it even better is that it used the Google Voice Recognition technology, which guarantees the utility and accuracy of the apps. To make life a little simpler for users, it enables easy dictation for punctuation marks, and even offers a keyboard for punctuation.

4.Voice Notes

Pros

Great for quick notes.

Reminder feature.

Organizational features.

Cons

Not great for lengthy notes.

Well, our next choice for the best speech to text apps for android is a smart app that’s perfectly developed to support speech to text conversion on the go. While some of the other apps like the Converse Smartly® or Speechnotes is great for lengthy dictation; the Voice Notes is definitely the go-to app when you are looking to send quick small notes on the go.

The app not only lets you transcribe the voice message quickly and accurately, but you can also save the audio message for later. Also, Voice Notes offer pretty impressive organizational features like reminder tools, which enable users to create an alert for anything or even set recurring reminders. Some of the other tools that come with this nitty tool include customizable categories and color tags for better management.

5.Speech Texter

Pros:

Uses Google’s database.

Online and offline features.

Custom library.

Last, in line, we have yet another powerful and robust android speech to text app that works impeccably well both online and offline. Again, what makes this app special is its use of Google’s database, which means you can certainly rely on its accuracy and powerful features. Also, if you want to use the app in offline mode, you would have to re-download the language library from Google Play. You can easily download the language library of your choice by heading towards the virtual keyboard following these steps:

Settings > system > languages and input > virtual keyboard

Once you are there, simply switch the Google Voice Typing “On” and choose “Offline Speech Recognition”. From there, you can choose the language of your choice.

Apart from the speech to text dictation, the app also lets you create and send messages, emails, or tweets using voice input.

5 Best Paid Speech to text App for Android

Now that we are done with the best free speech to text apps for android, let’s look at some of the best-paid android speech to text apps.

1. Dragon Anywhere

Pros:

Great accuracy.

easy sync with the desktop version.

Cons:

Limited dictation within the app.

Need internet connection.

Dragon Anywhere is undoubtedly one of the best Android speech to text apps available in the market. It’s a comprehensive app that offers robust dictation capabilities which are powered by the cloud. This means that you can are essentially able to get consistent experience and access to the notes from your mobile device or desktop software.

2. Otter

Pros:

Free tier.

Export options.

Team collaboration.

Live captioning.

Otter is another powerful cloud-based speech to text android app. What differentiates it from the Dragon Anywhere is that it is essentially meant to be used in mobile devices like smartphones or laptops. The app offers consistent, accurate, and real-time transcription services, as well as, enables users to edit and organize the notes, as and when required.

Perhaps the best part about the app is that it is meant to support meetings, lectures, or other team collaborations. This means that it’s the perfect business app that can be used for business meetings or during class lectures with multiple speakers.

3. Verbit

Pros

AI-powered.

Team working.

Enterprise service.

Cons

Not always live.

Verbit using AI capabilities to offer smart and reliable transcription and captioning services to users. While it may be used by individuals, it is primarily targeted towards the educational institutes and enterprises. By using advance and complex neural networks and algorithms, the app can suppress the background noise and concentrate on the speaker, thereby, greatly improving the accuracy and speed of the transcription. And while it does offer a speech to text capabilities, it is best described as a transcription app focused one educational institute and organizations.

4. Speechmatics

Pros:

Keyword triggers.

Media Captioning.

Support multiple accents.

Next in line, we have another powerful speech to text app – speechmatics. Like some of the other apps in the list, speechmatics use power technologies like machine learning to build the app accuracy over time and offer a reliable and precise speech to text services to users.

The best part about the app is its capability to comprehend multiple accents and keep up with its accuracy; which is great considering how many users suffer because of unintelligible accents.

6. Braina Pro

Pros

Robust digital assistant.

Great accuracy.

Cons:

Subscription model only.

Braina isn’t just a powerful speech to text application, rather it’s a robust and all-round digital assistance that helps you complete all major tasks on your mobile or PC. The app supports the third-party dictation in over 90 languages, which certainly take it to the top as the best android app for speech to text conversion.

And since it’s your virtual assistant, you can use it for a plethora of daily routine tasks like setting reminders, alerts, searching music, or any other tasks.

Folio3 is your best speech to text android custom app partner

Folio3 is known for its innovative and practical technology solutions that are meant to assist businesses to achieve their full potential. Converse Smartly® is the trademark speech to text software that has proven the deep expertise of the company in the application of futuristic technologies like machine learning, deep learning, and artificial intelligence to come up with precise and versatile technology solutions. Converse Smartly® is meant to offer businesses and individuals alike with a simple to use and interactive speech to text conversion platform which can help them to improve their workflow processes and systems and improve their operational efficiency.

How do I activate voice to text on android version 10?

To activate the voice to text on android version 10 follow these simple steps:

Install Gboard on your mobile device (can be downloaded from Google Play).

Open any typing app like message or Gmail.

Start composing the message.

Click on the “Microphone Icon” at the bottom right.

Start speaking once you see “Speak Now” message.

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at Contact@184.169.241.188

The simplest definition of audio transcription is the conversion from verbal to written form. However, this isn’t just limited to it, and once you start working your way through the audio transcription you will surely discover new things. The audio transcription enables you to carve out an entire experience for your audience through a video or audio file.

So, for a small investment, you make in speech to text software, you would be able to add life to your content. In this blog below, we will be going through everything you want to know about audio transcription briefly, while also discussing the experience of being an audio transcriptionist.

What is Audio Transcription?

One of the most common queries that I receive, whenever I advertise for the need of an audio transcriptionist, is the candidates asking “what is audio transcription?”

Well, to put it simply, audio transcription is the speech to text conversion. Yes, you can relate it to your good old childhood days when your teacher dictates you to improve your writing skills. Obviously, it’s no more limited to the scope of the teacher’s dictation, but I am sure it gives you a fair idea about what we are talking about.

Today, audio transcription is used in virtually every industry from public speeches to business meetings and courtroom proceedings. And if we move to the most recent definition of the work, it is about converting the digital recordings into text. This is to say that as an audio transcriptionist, you may be given digital proceedings of a webinar or an interview conducted over the phone recording app; to convert it into text.  

So, when you say “what is audio transcription?” it could be defined in several ways, however, at its core, it’s the written version of any verbal speech.

What is an Audio Hour in Transcription?

What is an audio hour in transcription is another frequent query asked by many aspiring transcriptionists. An audio hour in transcription refers to the time required to convert one hour of audio into written text. The standard time to convert one hour of clear audio into transcription is 4 hours (4:1) – or we can say that an hour of transcription is required for 15 minutes clear audio recording.  

Now, the above mentioned time is just standard and that the quality of the audio (clarity, speed), as well as, the skills of the transcriptionist will have the final say about the time required to transcript one hour of audio. In my experience, if the audio is clear with low noise, a slow speaker and an experienced transcriptionist may lower the transcription time required by as much as 3:1 or even 2:1.

Contrarily, if the audio quality is muffled or garbled, then the transcription time even with an experienced transcriptionist may sore to become 6 or even 10 hours of transcription time for one hour of audio.

So, when we talk about an audio hour in transcription, it simply represents the time required to transcript one hour of audio; which may take anywhere from two hours to 10 hours or more!

What is Converse Smartly Audio Transcription? 

Converse Smartly® is one of the most powerful and competent speeches to text software that enables businesses to transcribe any length of audio fast and precisely. Developed by Google speech to text service, the software is meant to assist businesses and individuals alike to improve their efficiency and accuracy of audio transcription requirements. The advanced features of the platform can also be used to analyze the speech with multiple voices (team meetings, interviews, or webinars).

Converse Smartly® is powered by robust cutting-edge technologies to deliver the most precise results possible in the speech to text industry today. To enable higher productivity and efficiency for businesses, the software also includes various built-in tools enabling users to leverage the full potential for the platform.

Powered by cognitive learning, the Converse Smartly® is developed using deep-learning neural network algorithms that offer unprecedented accuracy for speech to text conversion. The machine-learning algorithms powering the platform also enable the software to consistently improve the accuracy by learning the voices and words over time. 

Key Features of Converse Smartly® 

Apart from its unprecedented accuracy and speed for speech to text conversion, some of the advanced features of Converse Smartly® includes:

Speech Analysis

The platform comes with the capability to analyze speech patterns. The dedicated speech analysis capability of the software is meant to understand the speech content and recognize spoken words with more accuracy.

Text Analysis

The text analysis feature of the platform enables the advance representation of text as data which subsequently may be used to mined for insights and pattern analysis.

Multiple Speaker Detection

Another advanced feature of the Converse Smartly® is the multi-speaker detection which enables identification of speakers by partitioning the audio stream into smaller homogeneous segments.

Some of the other features of Converse Smartly® includes:

Automated summary generation

Real-time transcription for English language speeches

Sentiment analysis

Built on Google Speech and IBM Watson technologies

Generate word cloud

What is a Transcriber Definition? 

Just as there can be multiple definitions for transcription services, similarly there may be multiple definitions of the transcriber. In essence, the transcriber is the person/process that is used to convert audio into text. Going by this definition, a transcriber can be a human that has the understanding of the language and context; performing the speech to text conversion; whereas, the transcriber may also be an automated platform/software like Converse Smartly® that is used for voice recognition and speech to text conversion.

Advantages and Benefits of Audio Transcription?

Transcription services have various benefits and advantages depending on your goals and objections. For starter, businesses and individuals alike can leverage the accuracy and affordability of audio transcription services to add value to their audio/video content. Also, companies may leverage the benefits of audio transcription to record and generate text files for the webinars, group meetings, or client meetings.

Another benefit of audio transcription is that it enables businesses to reach a wider audience by creating text in different languages. Similarly, by creating an email marketing content out of a popular video is another beneficial use of the audio transcription services. Whereas, the service can also be used to generate an in-depth blog post through a popular podcast episode.

Folio3 is Your Best Audio Transcription Tech Partner

Thanks to the highly experienced and expert team of developers at Folio3, the company has established in the speech recognition technology with its powerful and advanced speech to text software; Converse Smartly®. The platform is already being used by some of the leading businesses across the world and has been recognized as a leader in Cognitive services by the “Cognitive Services – Global Market Outlook (2017 – 2026).

What is the best transcription software audio to text?

Honestly, the best transcription software for audio to text depends upon your individual needs and requirements. There are multiple great audios to text converter that can come handy for businesses and individuals to cater to their needs of audio to text transcriptions. Some of the best transcription software audio to text includes:

Google Speech

Converse Smartly®

Dragon Anywhere

What is it like to work in an audio transcription field?

As an audio transcriptionist, you would be listening to audio/video files of various length trying to hear the audio and convert it into text, while keeping up the context and integrity of the sentences and phrases. You may be working with single or multiple industries like legal, medical, and others; thereby, you would have to identify jargon and technical terminologies in each industry. As for the compensation, it will vary depending upon your skills and experience. Generally, you may be making from $20 to $45 per hour for the transcription services.

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at Contact@184.169.241.188

Writing isn’t an easy task, especially when you got to type thousands and thousands of words each day, it becomes increasingly difficult to keep up with the efficiency and quality of the writing.

As a content writer, I understand the pains experienced when you are required to type thousands of words. You write a few hundred words and then feel the need for stretching of changing the pose or even lie down for some time to get over the fatigue.

However, when I am exhausted typing, I switched to talking using speech to text software. This talking your way through the document isn’t just easy but it's more efficient and productive, while you also got the freedom to stretch or lay down or stretch without interrupting your daily productivity. In fact, I personally believe google speech to text is the better alternative to typing and assist you to come up with more creative ideas.

How Do You Use Speech to Text in Google Docs?

Now that you have the basic idea of the technology and understand the pros and cons of the system, let’s move to our main topic and see how do you use speech to text in google docs, and how do you use text to speech in google docs.

How Do You Use Speech to Text in Google Docs Using Android?

Step 1: Create a Google Document

The first step to use the speech to text in Google docs using android comprises creating a new Google document, which means you would need a Google account. Simply click on the “+” sign located at the bottom right corner of the application to create a new document. 

Once the document is created, you can start typing the document, however, with a small screen (compared to desktop or laptops) it isn’t the easiest task to type a long document over android devices. Thereby, you could switch to “Voice Typing”. You will find the option of “Voice Typing” on your Gboard.

Step 2: Voice Typing

Voice typing is just an easier, better, and faster alternative to writing using an android device. It’s a common practice and you must have seen many people talking to their phones for that sake. So, once you are in the voice typing mode, you can simply start dictating the software and let it do the rest for you.

A tip, in this case, is to use a headset for the purpose and be at a silent location to ensure the quality of the text.

Step 3: Enrich the text

Even better it’s not just the text that you can create using speech to text software, rather you can greatly enhance the richness of the content by inserting images, tables, and other elements. There are various formatting tools available to help you enrich the final product.

How to Enable Text to Speech Features with Google Docs on Chrome OS?

The speech recognition technology is also available for Chromebooks, where you can leverage the speech to text software as an alternative to typing using google docs. The technology also enables you to perform web searches through voice commands directly into your browser. The prerequisite here is to have a valid internet connection because your queries or commands would have to be sent to Google’s online servers for the conversion.

How Do You Use the Text to Speech in Google Docs on Chrome OS?

To be able to perform the text to speech conversion in Google docs on Chrome OS, follow the below-mentioned steps:

Web browsing through voice commands

Open your web browser and browse www.google.com 

Once you are on Google landing page, tap the “Microphone” icon adjacent to the tap box

As you tap the “Microphone” icon, you will see “Speak Now” prompt

Start to speak; you words will appear on-screen

Once you are done speaking, the command will automatically be used as a search query

Speech to text conversion

Now, if you are looking to use speech to text option in Google docs, online forms or elsewhere, you will have to tap the “Enable dictation” feature by below mentioned steps:

Open the “accessibility settings” on the Chromebooks

Choose Setting>Advanced >Manage accessibility features

Once you are in the accessibility feature, click the “keyboard and text input” section

Here, click the “enable dictation” feature (speak to type)

You will see a microphone icon located at the bottom right corner

You can click the microphone icon whenever you want to speak to enter text

Once you stop speaking, the dictation would automatically be over

Voice typing using the on-screen keyboard

If you wish to use the on-screen keyboard on your Chromebook for dictation, you can simply click on the microphone icon found on the keyboard. In this case, you won’t need to perform the above-mentioned steps to enable the option as it is enabled automatically.

How can I use speech to text in google docs using web add-on?

A third-party Add-on for Google Docs on the web also enables Speech Recognition (Figure C). Unlike the on-screen keyboard, the Add-on works only within a Google Doc; the Chrome OS on-screen keyboard works with all text fields.

To install the Speech Recognition Add-on, open a Google Doc, choose Add-ons, and then select Get add-ons. Next, search for Speech, then choose the + Free button to add it. Every time you want to start voice recognition, go to the Add-ons menu, choose Speech Recognition, and click Start. A sidebar will appear to the right of your document. Choose your language and dialect, select the blue Start button, and then start talking.

How do you use text to speech in google docs on apple and windows?

Apart from Android devices and Chromebooks, speech recognition technology is also offered by Windows and iOS devices. For iPhones or other Apple mobile devices, the speech recognition technology works much similar to the Google Voice Typing (as described for android devices). You can simply tap the microphone icon on the keyboard and start to speak. Alternatively, on Mac OS, you would need to enable the dictation much similar to the Chromebooks.

As for the Microsoft devices, you will need to change the system settings to enable the feature, while you would also have to spend some time to train the system for voice recognition before it’s ready to use.

Folio3 is your best tech partner for google speech to text

Folio3 is a leading technology partner offering businesses innovative and technologically backed solutions for cross-sector applications. The Converse Smartly® is a trademark speech to text software from Folio3 that comes with robust features and high accuracy to enable individuals and businesses alike, improve their efficiency, productivity, and quality. Converse Smartly® is ideal for organizations looking for an accurate and fast software to analyze speech or dialogues during seminars, group meetings, and interviews.

What is Speech to Text Software?

While I believe that most of you would be relatively familiar with the speech to text software, still for those who aren’t familiar, the speech to text software is a transcription technology that’s able to automatically transform spoken words into text. Using the software, you are able to transcribe, write speeches, blogs, articles, or even books by simply speaking to the software.

Don’t confuse the speech to text software technology with the virtual assistant apps, which are primarily meant to understand and respond to voice queries and complete the commands. In this case, technology is primarily meant to understand the spoken words and convert them into text.

In fact, these are few transcription software available that also comes with the assistive technology, giving you the best of both the worlds.

Today, the speech to text software is available for Windows, iOS, Android, and Mac operating systems, thereby, you don’t have to worry about the compatibility issues.

Here in this blog, we have come up with details of how do you use speech to text in google docs or how do you use text to speech in google docs. However, before we look into the how-to details; first let’s look at the technology, its benefits, and how to choose the best software.

What to look for in a Speech to Text software?

Before you invest in any speech to text software, it’s important to complete your background research and choose the best software that meets your needs and requirements.

How precise is the speech to text software?

Honestly, the technology has advanced greatly in the past decade and today the accuracy of the software would surprise most of the users using the technology for the first time. 

Having said that, not all speech to text software are developed equally efficiency and some of the software offered in the market do have inaccuracy and buggy issues, which makes them ineffective for professional use.

Thereby, when you are going for a speech to text software, the first consideration should be the accuracy and preciseness of the software; which most of the time will depend on the maturity of the software to be able to detect and identify the spoken words.

Compatibility

The next important aspect to consider is the compatibility of the software with multiple devices. As said before, the voice recognition technology has advanced greatly and today there is various highly accurate software that comes with full compatibility with the different devices and platforms. 

Remember when we said that there’s a difference between speech to text technology and the technology used in virtual assistant apps?

Fortunately, today many speech to text software includes the virtual assistant technology as well, which means that now you don’t only get to rejoice the freedom to get your spoken words typed automatically, but the software is also able to send you reminders or browse information for you.

So, basically, if you are looking for software that makes your world a lot easier, the speech to text software is the ideal investment you should make.

If Speech to Text Technology User-friendly?

If you choose a credible and reliable software, you can be sure of its user-friendliness, as well as, its various benefits for your daily work routine. 

Moreover, many reliable developer firms of the technology offer technical support for users to get them started with the software; either through online resources or even with dedicated training and customer support.

Budgetary Considerations

Last, in line, we have the budgetary considerations for choosing the speech to text software. Honestly, the technology doesn’t come cheap, and thereby, you shouldn’t be looking for the cheapest way out. That’s because going with the cheapest software may translate into the wrong choice of software with inaccuracy and buggy issues; leaving you frustrated for loss of money and time. Thereby, the best way to choose software is to compare the pricing of different software and choose the one that meets your technical requirements and budget.

Pros & Cons of Speech to text Software

Honestly, the speech to text technology has been a game-changer for writers, transcription service agents, and others who are required to type thousands of words on a daily basis. The technology not only improved the efficiency and productivity of writers and other professionals, but it also improved the quality of the work. However, like any other technology, the speech to text software comes with its own sets of limitations along with various benefits.

Let’s have a brief look at some of the pros and cons of the system:

Pros:

Fasten the writing/typing process and saves much time

A better alternative to writing in a conversational style, ideally suited for blogs, speeches, and other writing assignments

Enable you to multitask – keep doing your routine chores (cook, clean, etc.) while dictating to the software

Improve efficiency and productivity

Great technology for people who aren’t able to use their hands

Cons:

Users may need some time to get over the process and understand all the customization options

Few unreliable software may lead to inaccurate speech to text conversion

Some of the immature and unreliable software may have difficulty in identifying foreign words

The quality of the text may get compromised in case there is a background noise

You will have to edit and format the document

The resulting tone of the document compiled with speech to text software may be different from your typical writing style, which means that you may need some time to come up with a balanced new tone.

How do you use the chat in google docs online?

To use the chat option in Google docs online, open the Google docs. The number of participants collaborating on a Google doc doesn’t matter and that the system allows all the participants of a doc to send and receive the chat messages at once, as open group communication, which makes it a highly powerful feature.

To use the chat option, you would have to click on the name of the participant/s to start a new chat window. You can continue to chat with the participant/s using this window without having to leave the document. Thereby, it’s not just a great time saver feature but also reduces your dependence on third-party chat apps or messengers.

How do you download voice typing on google docs cloud?

You will need the latest version of Chrome and a microphone to download the voice typing on google docs cloud. Just follow the below-mentioned steps:

Visit docs.google.com and sign in to your Gmail account

Open a new Google doc in chrome

Go to tools>voice typing

Choose from 40+ languages and accents from the language option

Click the “microphone” icon and click “Allow” to use your microphone

Start to speak.

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at Contact@184.169.241.188

Machine learning (ML) is a subfield of the Artificial Intelligence (AI), which give machines the ability to learn and adopt from their experienced and enhance their ability to complete any specific tasks assigned to them. The most prevailing example of machine learning is the “people who bought XXX also saw XXXX”, which you must have seen across all ecommerce stores.

What is Machine Learning?

The term machine learning was coined by Arthur Samuel, in the year 1959. Arthur Samuel is also considered as the pioneer of Artificial Intelligence. According to him, machine learning is a field of study that enables computers to adapt and learn for themselves without any explicit need for programming. Obviously, this was the beginning of what we are seeing today, but machine learning as we know it today is pretty much what Arthur defined way back in the 1950s.

In this blog, we will be covering all aspects of machine learning including the working of machine learning, and machine learning process steps. We will also be looking at how does machine learning to work in today’s world, as well as, define some of the popular machine learning techniques used widely in different industries. last but not the least, we will also be looking at the best programming languages for machine learning, while finally rounding up our blog by summarizing the working of machine learning.

As said earlier, machine learning is a subfield of artificial intelligence. In the most basic terms, the machine learning algorithms are meant to create intelligent programs that are able to get trained for specific tasks by themselves and learn better ways to complete the tasks faster and with precision. It’s kind of similar like creating algorithms that replicate the human mind, with the ability to learn, adapt, and make intelligent decisions. Such algorithms are developed to minimize the need for human intervention. For the most part, the learning process of the machine is completed through supervised or unsupervised training with a large volume of the training dataset. The quality and volume of the data used to train machines are directly related to the preciseness of the machine learning models. Machine learning models can be developed for explicit tasks, where automation is desired.

How does Machine Learning work in today`s world?

Model, parameters, and the learners are three fundamental blocks of any machine learning model.

The model represents the system that is responsible to make forecasts/predictions. 

Parameters are the characteristics which are considered by the model to make forecasts/predictions.

The learner component of a machine learning model enables models to make learn and make necessary adjustments required to make accurate forecasts/predictions.

Now, to help you better understand how does machine learning work, we can consider an example. In this scenario, let’s consider that you have two different drinks; beer and wine. Now, you want machine learning algorithms to differentiate between the two drinks based on some fixed parameters which in this case would be the color and percentage of the alcohol found in each drink.

Here are the machine learning process steps that will complete the given task for you:

Learning from the training set

The first and foremost step for any machine learning model is to feed the model with a structured and large volume of data for training. In this case, you would require several (possible hundreds) samples of beer and wine with defined color and alcohol percentage. Now, you will feed the training data into the model and classify each of the samples as per their defined parameters. For instance, you would define the percentage of alcohol in samples of wine against the percentage of alcohol in samples of beers. Similarly, you will classify the other defined parameter that is ‘color’ for samples of wine and beet.

For easy representation, you may define the ‘color’ as parameter ‘X’ and alcohol percentage as parameter ‘Y’. Now in this case the (X, Y) will be the defined parameters of the training dataset for the model and will help the algorithm to adapt and learn differentiation between each of the drinks.

Measuring Error

The next logical step in our “how does machine learning work” is to measure the errors and discrepancies in the results of the model. For this step, you will input a fresh dataset (different from the training data) and the outcome of this step could be either one of these four:

- True Positive: this is the most ideal scenario where the machine learning model is able to predict each type of drink correctly

True Negative: in this scenario, the model misses out on classification of drinks when it is present.

False Positive: in this scenario, the model wrongly classify drinks when it isn’t present.

False Negative: in this scenario, the machine learning model isn’t able to classify drinks when it is present.

To estimate the total error from the model, you will use the sum of FP and FN

Manage Noise

Now, while to help you understand “how does the machine learning work” better, we have kept the set of defined parameters limited to only two. However, in real-world scenarios, there may be hundreds and thousands of parameters that have to be defined in the training data to enable machine learning models to classify the items precisely.

In such scenarios, there will be a large number of errors estimated in the second step. These errors will be sourced from the noise present in the training data. Noise in this case represents the unwanted anomalies that deviate the standards of the defined parameters; thus weakening the learning process of the model. The noise in training data may be present for various reasons including:

Large volume of the training data.

Unstructured training data.

Data labeling errors.

Overlooked attributes that influence the classification.

Testing and Generalization

In many cases, the machine learning algorithm fits perfectly with training data, however, it fails to produce results when a fresh dataset is an input to the model (other than the training data). This is why it is important to evaluate the fitness of the algorithm to the new/fresh dataset. This is achieved by giving new/fresh data as input and analyzing the results produced by the algorithm. The generalization in this case refers to the fitness of the model to make predictions for a fresh dataset.

How Supervised Machine Learning Works?

Supervised machine learning is one of the popular machine learning techniques. In this case, the model takes training data with known responses to the output to learn and build its capacity to make predictions for a new/fresh dataset. Supervised machine learning techniques should be used where users have a high-volume of structured data for training the model. In this case, the higher the volume and quality of the data, the better and more precise results can be expected from machine learning models. The supervised learning model makes prediction models based on regression and classification techniques.

Classification techniques 

The classification technique used by supervised machine learning models delivers discrete responses. For example, the model will simply inform if an email is a spam or genuine (you experience it in your email inbox). In classification techniques, the input data is classified into the defined categories. This technique is widely used in medical imaging, image processes, and speech recognition.

You can use classification technique if you have a structured, tagged, or categorized data which is divided into discrete classes or groups. For instance, the technique is used in handwriting recognition, where it is capable of classifying the handwriting based on the recognition of letters and numbers style. Unsupervised pattern recognition is widely used in image processes and computer vision to identify objects and image segmentation.

Some of the widely used supervised learning algorithms in the industry include Neural networks, support vector machine (SVM), K-nearest neighbor, logistical regression, and more.

Regression techniques 

The regression techniques forecast continuous responses. An example of these includes predicting the temperature changes or fluctuations in power demand. The regression techniques are used in algorithmic trading, energy load forecasting among others.

The regression techniques for classification can be used when the input data is in the form of a continuous range, or real numbers. Some of the regression algorithms include stepwise regression, linear regression models, non-linear regression models, adaptive neuro-fuzzy learning, and others.

How Unsupervised Machine Learning Works?

Unsupervised machine learning is used to identify hidden patterns and structures in data to draw inferences.

Clustering 

Clustering is one of the most widely used unsupervised learning techniques, which is used for exploratory data analysis to identify hidden patterns and structures in data. The technique is used in different applications including gene sequence analytics, object recognition, image processing, and others.

An example case study for the clustering technique is identifying the optimum location of building a cell phone tower for a telecommunication company. In this case, the unsupervised machine learning algorithm can be used to identify clusters of users in different areas who rely on cell phone towers. Since a cell phone may only be connected to a single tower at a time, the clustering algorithm can process the dataset and come up with the most suitable cell tower placement design to optimized signal reception for users.

Some of the common algorithms used to perform clustering include; fuzzy c-means clustering, K-means, Gaussian mixture model, and others.

Which Programming Language is best for Machine Learning?

Now that we are over the machine learning process steps and working of machine learning, let’s see which the best programming language for machine learning is.

This is rather a simple question, as Python is undoubted the most suited programming language that is also most widely used to develop machine learning applications. We will come to the advantages and strengths of Python as the best programming language for machine learning algorithms a little later. Some of the other popular languages that are used to develop machine learning applications include R, C++, Shell, Java, and others.

What makes Python the ideal choice for machine learning applications development is the simplicity and readability of the language, as compared to the other languages. This is important because developing machine learning algorithms is already inherently difficult with various complexities concepts like calculus and others involved in the process, which required a lot of time and effort to develop. The simplistic Python language is able to share some burden of the developers and ML engineers. There are various Python tutorials freely available online that can be checked to understand the basics of the language. Another great advantage of Python programming language for use in machine learning algorithms is its various pre-built libraries for machine learning. The language comes with various packages that can be used directly for various applications. Some of the Python packages widely used in machine learning applications development include:

Numpy, Scikit (used widely for image processing)

Numpy, Scikit, and NLTK (great when working with text)

Librosa (Audio applications)

Matplotlib, Scikit, and Seaborn (used for data representation)

TensorFlow, Pytorch (used for deep learning applications)

Pandas (used for high-level structured data analysis)

The language gives ML engineers and developers an option to choose between scripting or object-oriented programming. Moreover, the changes can be easily implemented without having to recompile the code.

Folio3 is Your Best Machine Learning Tech Partner

Folio3 is one of the leading machine learning application development companies in the world. With decades of experience in the development of high-tech and complex machine learning applications, Folio3 has partnered with some of the biggest companies from different industries and help them to initiate their digitalization process with robust customized solutions with advanced machine learning capabilities.

How Does Folio3 Machine Learning Solution Work?

Breast Cancer HER2 Subtype Identification

Folio3 partnered with one of the oldest public sector medical universities and hospital in Pakistan, Dow University of Health Sciences to develop a robust computer-aided assistance system with cell segmentation and spot counting capabilities to enable medical practitioners and doctors to perform faster and more precise breast cancer tests. The powerful computer-aided system was able to digitize and store the images for further analysis and processing.

Converse Smartly®- Speech to Text Software

Converse Smartly (CS) is a hugely successful in-house project of the company that was developed to establish the capabilities and expertise of Folio3 ML engineers in the fields of machine learning and Natural Language Processing.

Converse Smartly (CS) is an advance and robust speech to text application that uses state-of-the-art technologies including IBM Watson API, Amazon AWS, Microsoft Azure, Python’s Natural Language Toolkit, and Google Speech-to-Text API to deliver precise and outstanding results.

ATM Cash Forecasting

For this project, Folio3 partnered with one of the largest commercial banks in Pakistan, which is also a multination bank to develop an ATM cash forecasting system. The prediction model was able to forecast t4eh cash-flow management for over 2000 ATMs of the bank in Pakistan and globally. Based on the unique requirements of the bank, we developed a sophisticated and innovative predictive solution that increased the ATM management profits of the bank by up to 6%.

how does google assistant machine learning work?

Google Assistant is a personal assistant that leverage on the image recognition, NLP, and Google knowledge graph to converse with the users. It’s much like a personalized chatbot that using natural language processing to interact with the users and come up with the answers to users’ questions.

How does AI work with machine learning?

Machine learning is a subfield of a much broader Artificial Intelligence (AI) technology, which is meant to enable machines to execute tasks smartly. Machine Learning on its own is about developing intelligent algorithms for devices that can learn, adapt and execute tasks through their learned experiences.

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at Contact@184.169.241.188

In recent times, there has been an incline of various companies/businesses towards artificial intelligence. Many fields, such as the medical field, have benefitted a lot with artificial intelligence, such as with the advanced cancer diagnosis tools. Thus, it is safe to say that Machine Learning (ML) is a very crucial step for the advancement of various businesses/companies. To cater to this growing need, numerous deep learning libraries have been developed. Some libraries have been around for quite some time, and some have been launched very recently. Torch and Theano have been the oldest ones on the market, and TensorFlow and Caffe are considered to be the latest additions. However, TensorFlow and Theano are considered to be the most used and popular ones. TensorFlow vs. Theano- which one is right for you? Without any further ado, let's discuss these two, along with a few other frameworks.

What is Theano?

Theano, a deep learning library, was developed by Yoshua Bengio at Université de Montréal in 2007. It can be run on both CPU and GPU, hence, providing smooth and efficient operation, and is based and written in Python. Theano is pretty famous with academic researchers, due to it being a deep learning library. It is considered as the grandfather of deep learning libraries. Theano is mostly used in extensive research-based tasks, deep learning tasks, and also for defining, optimizing and evaluating different mathematical operations. Furthermore, Theano provides its users with extensive unit-testing and self-verification. This helps in minimizing many types of errors. It uses a dynamic C code generation, which means that Theano has the ability to evaluate expressions faster. 

Although Theano itself is dead now, the other open-source deep libraries which have been built on top of Theano are still functioning; these include Keras, Lasagne, and Blocks.

What is Tensorflow?

TensorFlow is a comprehensive, general deep learning library that was launched in late 2015. It is a Google open source project which replaced Theano. To date, it is considered to be one of the famous libraries. It is available on Linux, macOS, Windows, Android, and iOS platforms. Apart from being a deep learning library, it has various tools to help support reinforcement learning and numerous other algos. These include voice/sound recognition, text-based applications, image recognition, time series, and video detection. It is based on the languages of Python and C++ and is multi-GPU. TensorFlow comes with a sufficient amount of documentation for installation, along with tutorials, which makes running this framework hassle-free, even for beginners. Companies that have been using TensorFlow include Google, Twitter, Uber, Snapchat, GE Healthcare, PayPal, and Dropbox, just to name a few. Being run by Google means that it will stay around for a while, making it a safe investment.

Theano vs TensorFlow - Key Comparison Factors

The battle of the frameworks- Theano vs. TensorFlow. Which one is better? Let's discuss this.

-Theano has been around since 2007, and TensorFlow has been around since 2017. 

-Theano runs on cross-platform, whereas TensorFlow runs on Linux, macOS, Windows, and Android.

-Theano is written in Python, and TensorFlow is written in C++, Python, CUDA. However, they both provide CUDA support.

-Both the frameworks provide parallel execution.

-They both have trained models.

-TensorFlow has a faster compiling time than Theano, but the execution speed of TensorFlow is slower than Theano.

-Theano provides error messages, but those messages are cryptic.

-Theano is mostly used in carrying out Mathematical operations, whereas TensorFlow is used in voice/sound recognition, text-based applications, image recognition, time series, and video detection.

-The developer of Theano, Yoshua Bengio, had announced in 2017 that Theano would cease after its 1.0 version update, but the libraries built on top of Theano would still be available. On the other hand, TensorFlow is still available in the market. 

Theano vs. TensorFlow is a very vast topic with a lot of technical details attached to it. The basic and the most important salient features of both frameworks have been discussed.

9 Best Deep Learning Libraries/Machine Learning Frameworks in 2020

Apart from Tensor and Theano, there are quite a lot of other frameworks available in the market. Let's discuss a few of them.

1. PYTORCH AND TORCH

Torch is one of the oldest frameworks which provides a wide range of algorithms for deep machine learning. It provides multi-GPU and has Lua as its base language. It is considered to be easy to set up as it has numerous sample codes and tutorials. However, it can be a bit difficult to set it up in CentOS. The platform for Torch is Linux, macOS, Windows, iOS, and Android. Torch also provides the user with helpful error messages. It is used by Facebook, Google, and Twitter, just to name a few. The main uses of Torch are machine learning, signal processing, parallel processing, computer vision, video, audio, image, and networking. Pytorch is a Python version of Torch, which was developed by Facebook in 2017. It was quick to become the machine learning researchers' favorite. It is much more useful for small projects and prototyping. It also supports data parallelism and contains many pre-trained models. 

2. KERAS

This deep learning library, inspired by Torch, was created by Francois Chollet, a software engineering working at Google. Keras can be used as a high-level Application Programming Interface (API). This framework is considered a good option for beginners since it is ideal for learning and prototyping basic concepts. Furthermore, Keras generates easily readable and succinct codes. 

3. MXNET

It does not appear to be as widely used at TensorFlow, but this framework is considered to have the potential to have exponential growth in the near future. MXNet works on multiple GPUs with fast context switching. It is considered to have a clean and easily maintainable code. It comes with detailed documentation, which makes it good to use for both beginners and experienced users.

4. GLUON

This framework can be used to create both simple and complex models. Gluon is based on MXNet and is considered as a good alternative to Keras. It is known to provide flexibility without any compromise on performance.  

5. CHAINER

Chainer is based in Tokyo, with engineers mostly from the University of Tokyo. It is considered to be faster than other Python-based frameworks. The codes are written in Python on top of CUPY and Numpy libraries. It has better GPU performance than TensorFlow. 

6. BIGDL

A deep learning library from Apache Stark. It makes it easy to make Spark and BigDL applications. It is known to achieve high performance due to its usage of Intel MKL/Intel MKL-DNN and multi-threaded programming in every Spark task.

7. CAFFE AND CAFFE2

Caffe is a general deep learning framework that is based on C++. Yangqing Jia is the creator of Caffe2, who now works at Facebook. This framework is backed by Facebook after Torch. Caffe2 is considered to be lightweight. However, there is no commercial support for it.

8. CNTK

Computational Network Toolkit (CNTK) is an open-source deep learning framework made by Microsoft. It can be included in Python, C#, or C++ or used through its model language called BrainScript. It is supported by macOS, Android, Linux, and Windows operating systems. The CNTK has both low level and high-level API for building neural networks.

9. PADDLE

Parallel Distributed Deep Learning is a deep learning framework that was created and is supported by Baidu. Just like most of the deep learning frameworks, it offers a Python-based API.

TensorFlow vs Theano- Which is Better?

TensorFlow vs. Theano is a highly debatable topic. It all depends on the user's preferences and requirements. Although Theano itself is dead, the frameworks built on top of it are still functioning. It would be nearly impossible to get any support from the developers of Theano. Hence, we can easily say that TensorFlow is better than Theano. Due to an increase in the use of Artificial Intelligence by various businesses/companies, deep learning framework developers are striving hard to launch quality frameworks into the market to fulfil the needs of various users and their companies. For each and every user, there is something out there in the market; if not, then there are developers who are offering customized frameworks for your businesses. The efforts of these developers help in solving various business challenges. The market comprises of other frameworks too, apart from just TensorFlow and Theano.

FAQs

What is Torch machine learning?

Torch is one of the oldest scientific computing framework that puts Graphic Processing Units (GPUs) first, which was developed in Brazil, during the 1990s. It is a scripting language program which was initially written in and offered on the Lua programming language but has now been ported to various other languages, such as Python (Pytorch) and C/C++. Torch provides support and a wide range of algorithms for machine learning. It is efficient and user friendly due to the use of LuaJit, the scripting language, which provides maximum flexibility to the user. Torch uses macOS, Windows, Android, iOS, and Linux as its platform. Since it is one of the oldest open-source machine learning library, it is used by many leading labs, such as Facebook, Google, and Twitter, just to name a few. Furthermore, Torch provides the best packages in machine learning, signal processing, parallel processing, computer vision, video, audio, image, and networking. You can also build arbitrary graphs of neural networks and parallelize them over CPUs and GPUs in the most efficient way possible.

What is Theano used for in machine learning?

Theano was developed by Yoshua Bengio at Université de Montréal in 2007. It is a Python library based deep open-source library, and it can be run on CPU and GPU. It is mostly used in extensive research-based tasks, deep learning tasks, and also for defining, optimizing and evaluating different mathematical operations. Theano calculates the gradient when determining the backpropagation error by deriving an analytical expression. As Theano runs on CPU and GPU, it is much faster than Python itself, providing a much faster and efficient operation. Numerous other open-source deep libraries have been built on top of Theano, such as Keras, Lasagne, and Blocks.

What is better than Tensorflow and Theano?

With an increase in the trend of artificial intelligence, the market has been flooded with numerous deep learning frameworks. Hence, you can easily find a framework that caters the best to your needs and requirements. However, Tensorflow tends to be the most famous deep learning framework today. If you are a beginner and want to figure out how these frameworks work, you can opt for Keras; you can choose Pytorch for research purposes. 

Is Theano dead in 2020?

Theano itself is effectively dead, but the deep learning frameworks built on top of Theano, are still functioning. These include the more user-friendly frameworks- Keras, Lasagne, and Blocks. These three provide high-level frameworks for fast prototyping and model testing. In a statement released by its developer, Yoshua Bengio, on 28th September 2017, he stated that the development of Theano would cease after its 1.0 version update.

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at Contact@184.169.241.188

As per Project Lombok, “Lombok is a java library that automatically plugs into your editor and builds tools, spicing up your java. Never write another getter or equals method again, with one annotation your class has a fully-featured builder, Automate your logging variables, and much more.”

This blog focuses on Lombok installation into Eclipse IDE on Windows and additionally features the steps specific to the Gradle Apps.

Step 1

Download lombok.jar file from here: https://projectlombok.org/download

Step 2

Open terminal and change directory to the path where the downloaded file is located.

Step 3

In the terminal, run this command: java -jar lombok.jar

With the above command, an installer will open and would auto scan for the installation of Eclipse IDE. If you already know the IDE installation location, provide it by clicking the “Specify Location” button.

Step 4

Once the wizard has located the IDE installation location, click the “Install button”. Close the Installer when done.

Step 5

Restart the Eclipse IDE if already running.

Steps Specific to Gradle

Step 6

The Installer will auto-add the dependency to your build.gradle file. If it didn’t, open your build.gradle file and add the following dependencies.

repositories {

mavenCentral()

}

dependencies {

// other dependencies

compileOnly 'org.projectlombok:lombok:1.18.8'

annotationProcessor 'org.projectlombok:lombok:1.18.8'

// other dependencies

}

Sync the Gradle build.

Step 7

Run the App.

That’s all. Now you can use Lombok’s annotations to save your efforts and reduce the overall size of your code.

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at Contact@184.169.241.188

What is Deoldify?

Colorizing images has become more than an art technique and is now a real-world application, as more people continue to add extra details in their images. More costly, hectic and time-consuming methods of colorization have now been replaced with advanced technologies such as machine learning solutions. These may not be rendered as optimal solutions, but they offer impressive results. DeOldify is one such machine learning as a service which includes pre-trained weights, allowing you to colorize black and white images or video with minimal effort!

DeOldify Download Github Repository:

DeOldify is an open-source deep learning model, created by Jason Antic, to help colorize and restore old and grayscale images. This deep learning model is responsible for adding new life to dull and dark old images and film footage. Adding details into old images has become easy with DeOldify, which uses the machine learning technology. Use pre-trained weights to add color to grayscale images or videos with the most minimal effort.

You can download repo from below links:

https://github.com/alfagao/DeOldify

AI Colorization Process

The world has always been full of color but until recently we lacked the technology to capture its beautiful colors and the pictures were mostly black and white. But these can also be colorized to achieve realistic results, using AI. This method involves training a Convolutional Neural Network (CNN) on a large number of lab colorspace photos. The colospace method corresponds better with the human visual system than RGB. The images are made up of three channels and the predictive model is trained to predict a (red-green) or b (blue-yellow) depending on the lightness channel (input). By recombining into a colorized image, it can be converted back to RGB. 

This technique can offer good results on a number of materials, such as landscapes and countryside. However, results may not be the same for more complicated scenes. Even then it is less tedious than manual techniques and offers better results.

How to Use and Setup Deoldify (Step by Step Process)

For those who are looking to try it themselves; connect to the directory you want DeOldify to be placed in, then clone it via https://github.com/jantic/DeOldify.git DeOldify.

1. After doing this, connect to the freshly created DeOldify directory to create a new “models” directory for your pre-trained weights.
2. Now download the pre-trained weights via the command line: wget https://www.dropbox.com/s/zkehq1uwahhbc2o/ColorizeArtistic_gen.pth?dl=0 -O ./models/ColorizeArtistic_gen.pth
3. You can also manually download pre-trained weights in the DeOldify, in case you already do not have them.
4. Now install dependencies:
5. pip install -r requirements.txt
6. Once this is done, open the project in a Jupyter Notebook. 
7. You should now be able to open the ImageColorizer.ipynb file and colorize from there.

Deoldify Alternatives

Pigment

It is a unique application that allows you to explore colors utilizing lighting and pigments.

Colorize It

enables you to colorize black and white photos using deep learning algorithms. 

Pikka

This is an easy to use color picker for developers and designers, working on multiple screens.

Grayscale the Web

This application does the reverse of colorization, as it turns websites gray by removing color. 

NeuralPainting

It facilitates real-time conversion of videos and images into neural art. 

Colorize.cc

It utilizes powerful Machine Learning models to colorize black and white photos. 

Deep Style

Utilizes deep learning, enabling artistic style transfer. 

Colorado App

Offers great color schemes, shared by their creative community.

DeOldify Reddit Reviews 

DeOldify has been discussed by numerous members on Reddit. Most of the members have shared their positive reviews and experiences with DeOldify on Reddit. Some members have also shared their work with other members to show them the quality of work they can get with DeOldify. Furthermore, members also share any updates or information they receive regarding DeOldify. One such example of this is a discussion, posted about two months ago, about a tweet shared by the creator of DeOldify, that stated that the new versions of this software will not use GANs anymore, but something else related to the concept of perceptual loss applied in super-resolution. This discussion had comments from other users too, who gave in their two cents regarding this new update.

From the Reddit reviews, it is safe to say that people generally like the DeOldify platform for editing their old and grayscale pictures. However, there are numerous other options available on the market, as mentioned above. 

Free Film Colorization Software

Apart from DeOldify, there are numerous other options available on the market. A few of the other colourization software are mentioned below:

—Deep AI 

—Colourise.sg

—Algoritmia

—Pigment 

—Colorize It 

—Pikka 

—Grayscale the Web

—NeuralPainting 

—Colorize.cc

—Deep Style 

—Colorado App

FAQs:

How do you color old photos?

Colorizing black and white photos is not a new phenomenon, from being colored by hand in the past, these are being done with digital ai image processing solution or like Photoshop and other computer vision applications that produce more realistic and historically accurate results. Simple graphic tablets and a pen can also be utilized to colorize images. For this, the image has to be restored first, including the removal of stains and scratches. Then it just comes down to choosing the right tones and adding multiple layers to achieve higher accuracy and better feel. This process can take a couple of hours or even days. A good understanding of light can help produce meticulous results.

What is image colorization?

Image colorization is the process of converting a grayscale (black and white) image into a colorized image by adding colors and tones that can bring realistic accuracy.

How do you use DeOldify if you don't have any AI experience?

Since DeOldify is a pre-trained model, which means it has been fed large amounts of input already; it can be used by anyone, regardless of their level of AI exposure.

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at Contact@184.169.241.188

What are Celeb Deepfakes?

General public was made aware of the deepfake technology in 2017, when an anonymous Reddit user posted fake adult videos featuring famous celebrities like Scarlett Johansson. These were not real-life footage but celebrity faces and adult videos were combined using deepfake to make the videos appear real. Over the years the technology has picked up where the algorithm does not require ample video footage, which is only available for public figures to create fake videos.

Learn how Fraud detection solution will help out in detecting false patterns.

How are DeepFakes Celeb Made?

Creation of Face-swap videos involve a series of steps. It starts with running thousands of face shots of two people through the encoder (AI algorithm). The encoder finds similarities and differences between the two and compresses the images to combine the common features in the two faces. 

Then a decoder is trained to recover the faces from the compressed images. A different decoder is used for each face. The final step involves feeding encoded images to the “wrong” decoder, for instance feeding the images of person A to the decoder trained on person B. The decoder uses the expression and orientation of person A to  reconstruct the face of person B. In order to produce a convincing video, the process needs to be repeated for each frame.

Best Celeb DeepFakes Images

Check out this collage of Chris pine morph into some shape shifting alien 🙂 Lol.

Best Celeb DeepFakes Videos

Artist Bill Posters in response to Facebook’s refusal to remove Nancy Pelosi’s video, posted a fake video of Mark Zuckerberg in which he can be seen claiming to use Facebook users.

This video, Better Call Trump: Money Laundering 101 is based on a scene from Breaking Bad, whereby introducing Trump as James Gill.

A deepfake video was uploaded by Mystere Giraffe in which Will Smith’s face was plastered on Cardi’s body. It looked very real and was shared by both the celebrities on their social media accounts.

Best Celeb DeepFakes FAQs:

How are celeb DeepFakes made for free?

People download apps like FakeApp generally and look at how-to tutorials online to train their computers to pull off face-swaps. Users in China can use easy solutions like Zao, whereas Carica is another app that creates deepfakes in a matter of seconds.

What software is used for DeepFakes?

Deep learning is a set of intelligent algorithms that can learn and make decisions on their own. DeepFake employs AI and Machine Learning as a service to create content. The method also involves training generative neural network architectures.

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

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What is Fairseq?

Fairseq PyTorch is an opensource machine learning library based on a sequence modeling toolkit. It allows the researchers to train custom models for fairseq summarization transformer, language, translation, and other generation tasks. It supports distributed training across multiple GPUs and machines. GitHub hosts its repository.

Fairseq Features in 2020

Fairseq provides researchers with smooth implementation of sequence to sequence models. It supports various models. Some of them include:

Convolutional Neural Networks (CNN)

Convolutional Neural Networks are a form of deep neural networks commonly used for visual imagery. They are useful in areas such as object detection, image recognition and other computer vision stuff. Fairseq is handy with the following:

1) Fairseq Language Modelling with Gated CNN

2) Classical Structured Prediction Losses

3) Hierarchical Neural Story Generation

4) Unsupervised Learning for Speech Recognition using predictive analytics solution

LightConv and DynamicConv Models

This model contains some pre-trained dataset and as well as the instructions on training the new model. It includes models without graphics library hence making it faster. You can quickly get the fairseq-preprocess datasets for languages such as English, Chinese, German, and French with fairseq-train paper.

Long Short-Term Memory (LSTM) Networks

LSTM is an artificial recurrent neural network (RNN) that are well-suited for classification and making predictions on time series data. It is convenient to use for unsegmented handwriting recognition, speech recognition, and anomaly detection in network traffic. Fairseq provides a practical approach to solve Attention-based Neural Machine Translation.

Transformer (self-attention) Networks

In place of CNN and RNN, many researchers prefer to use transformer networks. They implement encoder and decoder as self-attention networks to draw global dependencies between input and output. It works well in:

1) Scaling Neural Machine Fairseq Translation

2) Understanding Back-Translation

3) Mixture Models for Diverse Machine Translation

4) Input Representations for Neural Language Modeling

Non-autoregressive Transformers

Non-autoregressive Transformers or NAT removes the dependencies from the inputs of the decoder on the previous target token with fairseq bart. It helps to achieve:

1) Non-autoregressive Neural Machine Translation

2) Neural Sequence Modeling Iterative Refinement

3) Flexible Sequence Generation by Fairseq Insertion Transformer Model

4) Mask-Predict: Conditional Masked Language Models Parallel Decoding.

Apart from all these supported models and techniques by Fairseq, it also has other advantages. You can do multi-GPU training either on one machine or multiple machines. One can quickly implement them on both CPU and GPU with search algorithms. With its mixed-precision training, you can train models while consuming lesser GPU memory. It is extensible and makes registering of new models, tasks, and optimizers convenient.

FairSeq GitHub

The GitHub repository of Fairseq is at this link. It has 1128 commits with eight branches and 11 releases. Over six thousand people have starred it while 1.7k forked it. It has about 132 contributors with an active community backing it up.

How to Use FairSeq - Installation Requirements and Prerequisite

1) As Fairseq is an ML library in python, so you need python with version 3.6 or onwards.

2) PyTorch is also necessary before proceeding with Fairseq. You will require version 1.2.0 or onwards.

3) For training models, you will need an NVIDIA GPU. For better and efficient results, use NCCL.

4) Install NVIDIA’s apex library for faster training with the following two commands. 

--cuda_ext 

--deprecated_fused_adam

5) After fulfilling all the requirements, install Fairseq. You can either clone it by ‘git clone https://github.com/pytorch/fairseq’ or use the command ‘pip install fairseq.’

After successfully installing the fairseq, you can view its documentation here to get started. You even get pre-trained models and datasets with which you can get familiarization with the new library. Each pre-trained model has its READMEs as well for your convenience.

How to Install Fairseq - Interactive Installation Guide

There are a few simple steps to get started with fairseq. Follow the sequence:

1) First, you need python installed on your machine. Make sure its version is either 3.6 or higher. You can get python for your computer here.

2) After getting python, you need PyTorch. The underlying technology behind fairseq is PyTorch. You need version 1.2.0 or higher. To get PyTorch, you can clone it by the command ‘git clone https://github.com/pytorch/pytorch.git.’ You can install it from Anaconda or Chocolatey based installed. Here is the documentation.

3) Get fairseq by typing the following commands on the terminal.
git clone https://github.com/pytorch/fairseq.git

cd fairseq

pip install -r requirements.txt

python setup.py build develop

Download pre-trained models and get acquainted with the syntax.

Start working on new projects and models.

Fairseq Machine Translation Youtube

FAQs

• fairseq.modules.AdaptiveInput (AdaptiveInput is the module name)
• fairseq.modules.AdaptiveSoftmax (AdaptiveSoftmax is the module name)

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at Contact@184.169.241.188

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at Contact@184.169.241.188