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CodeLobster – Professional IDE and Code Editor for PHP, HTML, CSS and JavaScript

CodeLobster – Professional IDE & Code Editor for PHP, HTML, CSS & JavaScript. Cross-platform IDE also supports a large number of modern frameworks & CMS.

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CodeLobster Professional IDE and Code Editor for PHP

Earlier, it was quite easy to enter Web development, but now you need a decent amount of knowledge. You will have to work efficiently and quickly if you want to be competitive and get profitable projects from customers.

To complete any project qualitatively and on time, we use CodeLobster IDE, a robust development environment that includes all the necessary features.

This cross-platform IDE also supports a large number of modern frameworks and CMS. Later in the article, we will talk about the key functions of the program.

1. Editor Features

The code editor in CodeLobster allows you to work comfortably using tooltips and quick autocomplete.

It easily handles large files and offers the developer various convenient features:

  • l Multiple preassigned hotkey combinations;
  • l Code collapsing, local bookmarks and searching;
  • l Instant navigation through source files of the project.

IDE and Code Editor

Having official documentation will make your work much easier and faster. The help system will pick up links for all supported technologies and programming languages on the fly.

Open the “Dynamic Help” tab and study the documentation in the browser; you do not need to be distracted by searching on Google.

Automatic code formatting is another important feature; it is especially useful when a group of programmers is working on source codes.

You can design your code by the company standards or use ready-made coding styles: Allman, PEAR, PSR2, Drupal, Symfony2, or Zend.

2. JavaScript Programming in CodeLobster

The rapid spread of JavaScript across all platforms and devices provides tremendous opportunities for every WEB programmer.

With CodeLobster, you can freely inspect browser JS and Node scripts using the built-in JavaScript debugger.

It is unlikely that you will only use your code when creating sites. For example, Bootstrap and popular JavaScript libraries can help you successfully develop UI:

  • l AngularJS, BackboneJS, EmberJS, MeteorJS and VueJS for building single-page applications;
  • l JQuery and Node for solving all routine tasks in the server and browser environment.

CodeLobster Professional IDE and Code Editor

3. PHP Development in CodeLobster

Frameworks provide a codebase for any type of project, even with unconventional functionality:

  • l Popular CMS support: Joomla, WordPress, Drupal, and Magento. Based on these systems, you can quickly create a forum, blog, online store or news site and easily find a specialist for such problems;
  • l PHP frameworks, such as Smarty, Symfony, CakePHP, Laravel, CodeIgniter, Phalcon, Twig, and Yii, allow programmers to apply MVC approach and use ORM technology.

Of course, not only ORM frameworks allow you to interact with the database. We have direct access to MySQL via the built-in Database Manager.

Use the editor with SQL syntax highlighting to create queries. You can select records, change data, and edit the structure of databases and tables.

The licensed version of the IDE has other important features:

  • l Integration with XDebug PHP extension to detect and correct logical errors in your code;
  • l Convenient FTP client for remote editing and management of files located on the hosting;
  • l Git support for teamwork, rapid implementation, and testing of new software product functionality.

4. Let’s Summarize

With the professional version of the IDE, you can confidently create a frontend and backend for your sites, and you will always have up-to-date frameworks at your disposal.

You can download CodeLobster IDE directly from the official site, quickly install the program, and test all available features. Then you can use all the basic editor functionality as long as you want.

Buying a license requires a small investment, which will be quickly repaid. Since your work will go smoothly, and you will significantly increase the productivity of each developer and the whole team.

We are an Instructor's, Modern Full Stack Web Application Developers, Freelancers, Tech Bloggers, and Technical SEO Experts. We deliver a rich set of software applications for your business needs.

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How do collect and train data for speech projects?

Data collection is the process of gathering, analyzing, and, measuring accurate data from diverse systems to use for business process decision-making, speech projects, and research.

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How do collect and train data for speech projects

With technology evolution, we are moving towards machine learning systems that can understand what we say. In our daily lives, we all have encountered many virtual assistants like Alexa, Siri, and others. These virtual assistants often help us in tuning the lights of our homes, finding information on the internet, and even starting a video conference. But do you know how it does that?

To produce results, these virtual assistants use natural language processing to understand the user’s intent. Natural Language Processing technology enables virtual assistants to understand user intent and produce outcomes. Basically, these virtual assistants are applications of automatic speech recognition and are also known as speech recognition software. This software uses machine learning and NLP to analyze and convert human speech data into text.

But, attaining maximum efficiency of these software requires the collection of substantial speech and audio datasets. The purpose of collecting these audio datasets is to have enough sample recordings that can be fed into automatic speech recognition (ASR) software.

Furthermore, these datasets can be used against the speakers using unspecified speech recognition models. And to make ASR software work as intended, speech data collection and audio datasets must be conducted for all target demographics, locations, languages, dialects, and accents.

Artificial Intelligence can be as intelligent as the data given to it. Hence, collecting data for feeding the machine learning model is a must to maximize the effect of ASR. Let’s discuss steps in speech data collection for effective automatic speech recognition training.

1. Create a Demographic Matrix

For creating a demographic matrix, the enterprise must consider the following information like language, locations, ages, genders, and accents. Along with these, it is a must to note down a variety of information related to environments like busy streets, waiting rooms, offices, and homes. Enterprises can also consider the devices people are using like mobile phones, headsets, and a desktop.

2. Collect and transcribe speech data

To train the speech recognition model, gather speech samples from real humans and take the help of a human transcriptionist to take notes of long and short utterances by following your key demographic matrix. In this way, human is a vital and essential part of building proper audio datasets and labeled speech and further development of applications.

6 Reasons to Transcribe Audio to Text

3. Build a separate test data

Once the text subscription is completed, it’s time to pair the transcribed test with the corresponding audio data and segment them to include one statement in each. Later on, take the segmented pairs and extract a random 20% of the data to form a set for testing.

4. Train the language model

To maximize the effectiveness of the speech recognition model, you can train the language model by adding general additional text that was not additionally recorded. For example in canceling a subscription, you recorded one statement that ‘I want to cancel my subscription, but you can also add texts like “Can I cancel my subscription” or “I want to unsubscribe”. To make it more effective and catchy you can also add expressions and relevant jargon.

5. Measure and Iterate

The last and important step is to evaluate the output of automatic speech recognition software to benchmark its performance. In the next step take the trained model and measure how well it predicts the test set. In case of any gaps and errors, engage your machine learning model in the loop to yield the desired output. 

Conclusion

From travel, transportation, media, and entertainment, the use of speech recognition software is evident. We all have been using voice assistants like Alexa and Siri to complete some of our routine tasks. To effectively use this speech recognition software requires proper training in the audio datasets and the use of relevant data for the machine learning model.

Proper execution and the right use of data make sure the speech recognition software going to work efficiently and enterprises can scale them for further upgrades and development. As data and speech recognition go hand in hand, make sure you are using data with the right approach.

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