Trending October 2023 # Learn How To Use Lightroom Editing? # Suggested November 2023 # Top 12 Popular |

Trending October 2023 # Learn How To Use Lightroom Editing? # Suggested November 2023 # Top 12 Popular

You are reading the article Learn How To Use Lightroom Editing? updated in October 2023 on the website We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested November 2023 Learn How To Use Lightroom Editing?

Introduction to Lightroom editing

Lightroom Editing can understand as a process through which we can manipulate our raw photos in this software, and the best part is that it is a non-destructive process. Throughout the editing process in lightroom, we can do many adjustments such as color control, enhanced detail, and many more parametrical settings. Editing includes changing parameters of not the entire area of the image or photo, but it can work on a specific part of our photos by using some of the good features of this software. So let us understand different aspects of editing for having a good command of the editing process.

How to use Lightroom Editing?

There are many more things you have to know for understanding editing in lightroom, but today I will give you an overview of it so that you can have basic knowledge of it.

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On the left side, we have different parameters which we can change for having different parametrical changes in our photo.

At the top, we have a Histogram that shows different sections of the photo, such as Black, Shadow, Exposure, White, etc. When we make changes in parameters, you can see the change of value in the graph of the histogram.

Some tools help us for doing editing in a more précised way. You will learn about them by and by when you start working with this software.

In the left side section, we have Presets bar under which you can find a number of presets which you can use for having different settings in your photo in just choice one preset.

Such as if you go to Bright preset of the Color preset group, you can automatically see it applied brightness adjusted value on these photos.

During the editing process, if you want to switch to the previous setting, you can go to the History bar, and here you can see all the changed parameters and go with them again.

Lightroom editing Tips and Trick

Here I would like to tell you some of the tips and trick that helps you a lot during going through the editing process of your photos.

Local adjustment

The very first thing about which I want to tell you is that when we make changes in any parameter of our photo, then it applies to the entire area of the photo, but I will suggest you, rather than applying it to the entire area, localize the area then change parameters for having different variation. So let us understand it by an example.

This is an image.

And you can see when I make changes; in Contrast, it effects the entire image.

And I don’t want to do this, so I will take the Radial filter tool and select the region on which I want to make a change.

And now I have made some changes to have a little bit of exposure here in this mountain area, and you can see other areas of the image is unaffected.

Working with Detailing:

In some of the photos, we need to enhance the detailing, and for that, we work with the Clarity parameter.

You can see if I increase its clarity, then it is not looking so natural.

And decreasing will make it blur a little bit.

So not just play with clarity value; you should adjust the other values. For example, I will decrease the Clarity value up to -7.

Then adjust the Dehaze value a little bit.

After that, I will also adjust the black value of this image, and you can see now we have good detailing of leaves of this tree with snow.

Editing Individual Element

As we discussed, we can have an idea that lightroom is a very powerful software for photo editing, and editing any individual element of our photos is one of the effective features of it. So let me tell you how you can do this. I will take this raw photo for this purpose.

And you will have all its parameters.

For working with this brush, we have to adjust its Size, Feather (it can understand as to how smoothly you want to apply effect on selected area), and next is Flow (which you can understand as the intensity of brush).

After having brush setting adjust the value of parameters that you want to apply to the chosen area of the image. I will adjust the Exposure value to 1.06.

And start applying it on this front wall area, and you can see it only highlighting this wall area.

You can see I have made some changes in different parameters and created a sun light effect on the roof wall. In the same way, you can target individual elements in your image and manipulate them.

These are some important aspects of Lightroom editing.


Now you have knowledge about the editing of your photos in lightroom. I am sure you have been find it very interesting, and you are going to work with the editing process for having a different type of improvement in your raw photos. You can organize your photos also in this software for better handling of them.

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Learn How To Use Pytorch Opencl?

Introduction to PyTorch OpenCL

Web development, programming languages, Software testing & others

In this article, we will dive into the topic of PyTorch OpenCL. We will try to understand what PyTorch OpenCL is, how to use PyTorch OpenCL, porting code to OpenCL, PyTorch OpenCL backend in Progress, and a conclusion about the same.

What is PyTorch OpenCL?

OpenCL stands for open computing language and is licensed under Khronos and is used as a trademark in the company Apply Included. It is a completely loyalty-free and open-source standard platform that can be used for various Operating systems. It is specially used for parallel programming when we have multiple processors such as PCs, mobile phones, servers, and embedded platforms.

The main benefits we can reap from this are the increase in speed huge spectrum of domains where it can be used responsively, such as medical software, teaching, education, entertainment, scientific software, vision processing tools, neural network inferencing, and training and various other markets.

It is used as an alternative to CUDA. Its functionality, like AMD, is used inside the CPUs and GPUs for general-purpose in GPUs that are graphics processing units. OpenCL can be implemented in tensor as well as PyTorch.

How to use PyTorch OpenCL?

Torch 7 comes with the built-in support of OpenCL; when the base of PyTorch is the same as the Lua torch for backends, we can easily use it openly for working with integrated and modern graphics GPUs. You can find the official code on github from this link.

You can find it closed for the needed discussion, which can be found in labels though it has been open since 2023. The ticket says there is no planning regarding OpenCL work for now as AMD is moving from GPU open/ HIP with a CUDA transpiler. You can also go through this link, as there is a huge argument about whether CUDA is better or OpenCL.

Prepare the query for the devices and platforms of OpenCL that are available.

For one or more devices of OpenCL, we will need to prepare the context inside the platform.

In the created context, go for building and creating the programs of OpenCL.

To execute the program, select the required kernel.

To operate, create some of the objects of memory inside the kernel.

To execute the commands on the OpenCL device, create some command queues.

Whenever needed, you can go for the enqueuing process of data transfer commands to objects of memory.

For execution, enqueue the kernels into the queue of commands.

Whenever needed, you can go for enqueue commands creation that will transfer the required data back to the host device.

Porting code to OpenCL

There are various scenarios where we face this issue that it is necessary to port the existing software technology to OpenCL. For example, In the case of CUDA software, To make our application or software make runnable on the other architectures, including ARM Mali, Altera FPGA, AMD CPU, Imagination Power VR, Qualcomm Snapdragon, Xilinx FPGA, AMD GPU, Intel GPU, AMD APU, Intel CPU, and many other arising architectures.

OpenCL has the support of an extensive scale of vendors due to its Open standard nature. Moreover, it has that stability and security, which makes it capable of sustaining in the market other than any of the proprietary languages.

Until 2013, CUDA was a big hit in the market, and OpenCL was trying to come into the picture, but after that, OpenCL became a huge hit and had caught up in all the developers’ eyes. Hence, there arises a necessity of porting from CUDA to OpenCL.

There are various strategies that we can follow for porting to OpenCL. Let’s have a look at some of them –

We can make use of events for synchronizing here. We must create various kernels, queues, and copy calls for a buffer, all of which run in parallel. Unless and until you don’t synchronize the calls with the help of certain events, we will get undefined behavior when we go for sharing all the write and read operations on the buffer while managing multiple queues. The consistency of global memory is guaranteed only after the execution of the kernel is completed.

In each and every queue, we can accommodate any number of kernels. The execution will happen in order, and the kernels will be queued on the device as all the kernel’s execution calls are non-blocking.

We will convert the kernel into a complete C programming language code sequentially and compile all of them into a single kernel’s work item. Along with that, we will also remove all the optimizations that are based on GPU and are present inside our code.

Before doing so, it is necessary to know about Altera’s OpenCL and Khronos’s OpenCL standard docs, including their starting guide, best practices, and the guide for programming. Then, you can go through this case study for further reference for porting to OpenCL.

PyTorch OpenCL backend in Progress Mkdir new_build Cd new_build cmake -DCMAKE_PREFIX_PATH=$VIRTUAL_ENV/lib/python3.6/site-packages/torch/share/cmake/Torch


For running the test, execute the following command –

python chúng tôi --device OpenCL:0

Note that you must load the library before executing the code. For the complete backend reference, you can refer to this link.


PyTorch OpenCL is the Open Computing Language used for cross-platform and parallel programming when multiple processors are installed on our system device.

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How To Fix “Your Account Doesn’t Allow Editing On A Mac”

How To Fix “Your Account Doesn’t Allow Editing On A Mac”

Causes behind “Your account does not allow editing on a Mac”

If you are getting this error, there are possible two reasons behind this.

Microsoft is unable to recognize your purchased license for Office 365.

There is an availability of corrupted files in your Mac’s Library folder.

If you’re getting this error of reason 2, please know that Microsoft has identified the causes of the problem and suggests removing those corrupted files. Keep reading this blog to learn how to fix the “Your account does not allow editing on a Mac” error with removing corrupted files.

Related Read: Best MP3 Tag Editors For Mac 2023

Fixing the “Your account does not allow editing on a Mac” error

There are three methods or fixes that can be used for fixing the “Your account does not allow editing on a Mac” error:

1. Checking the license

Follow the below steps for checking the license:

Log in to Office 365 portal.

Under this section, check “latest desktop version”.

If you don’t have the rights to check, you will have to contact the admin for providing you with the correct license. If your license is correct, follow the below steps for fixing the error:

Check your internet connection.



4. Now, try to sign in again and open applications again.

I hope this works. If it does not work, let’s move to the other method.

2. Uninstalling and Reinstalling Office 365

Open Finder and go to Applications.

Now, press the Cmd key and Select all Office 365 applications like Word, Excel, etc.

3. Activating Office

If this does not work, you can contact the Microsoft Team and provide screenshots and your Subscription link. Follow the below steps to activate Office again on your Mac:

Now, wait for some time. Let the applications get activated completely.

Now, begin to use applications and check.

4. Fixing with Repair Disk Permissions

If you’re still facing the “Your account does not allow editing on a Mac” error in Office 365, it can happen due to broken or outdated permissions. This can be fixed with the help of CleanMyMac X which is a free tool.

Download CleanMyMac X

Download and launch the app on your Mac.

Moreover, you can also check the Junk section available in the sidebar. It will help you to clean your system. It cleans caches and other temporary problem-causing items that interfere when working with Microsoft Office.

Related Read: Office 365 vs. Office 2023: Which one is made for you?

I hope this blog helps you to fix the “Your account does not allow editing on a Mac” error. Comment down and let us know if you face any discrepancy during the process. For more such tech-related content, connect with us on social media platforms.

Thanks for reading!

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Aayushi Kapoor

Learn How To Install Staruml Download

Introduction to StarUML Download

Web development, programming languages, Software testing & others

Prerequisites of StarUML

Minimum requirements of the system to install StarUML is explained below

Intel® Pentium® 233MHz or higher versions of the processor should be used for the installation of StarUML. The processor is important for the installation of any software as it satisfies the requirements of the application. The application’s processing speed and the extensions depend on the processor. Higher versions help the software to update its higher versions automatically and integrate the tools with the software application.

Microsoft® Internet Explorer 5.0or higher versions of the browser is needed in the system so that the updates can be done through the browser. Only through the browsers in the system, the application can be updated and the templates can be downloaded easily.

The internal memory of the system should be a minimum of 128 MB RAM and 256 MB RAM is recommended for the same. The installation of the software is in the internal memory of the system and hence the RAM should be more for any systems to install the application.

To customize the application and to install the templates, 110 MB hard disc space (150MB space recommended) is required. However, as the application will not require much space in the internal and hard disc memory, it is easy to install the StarUML application. The hard disc space requirement is the basic one and acquiring this is very easy.

A higher resolution monitor is recommended for the system so that the diagrams drawn can be easily seen and modified up to the user’s preferences. 1024×768 is the recommended monitor size so that the diagrams can be seen with full precision. Also, a mouse or any pointing device is needed to draw the diagrams in the application. This pointing device is needed to modify and do the diagram alterations. It is not always needed to modify the size by changing the system requirements but can be resized with the help of a mouse.

CD-ROM drive is also needed as the basic requirement for the installation of the software.

How to install StarUML?

Run the .exe file.

After installation, you can see the StarUML icon on the desktop screen.

The screen appears like this for the evaluated version.

If you check the C Drive, you can see the StarUML folder in the program files location.

If you want to try and create a class in StarUML, a demo is explained below.

From the file option, select template and then template version of StarUML can be used in the system. We can create one to one, one to many or many to many in the application and store the diagram in the system.

There are some dependencies to be installed along with the StarUML package. Run the command sudo apt-get install -f in the CLI of the system which installs the packages.

The tools in StarUML helps to know the requirements in the system and to apply the design patterns so that proper analysis can be done to understand and modify the diagrams. These tools are open source and for more highly requirements, tools can be purchased from the software vendors of different applications.

No other tool will provide this customization to the user to draw diagrams. It provides the customizing variables in the application to modify the software’s development methodology or the platform being built or the language being used. These modifications help the user to be in comfort with the application while using it.

Platform independent models can be created easily within the application. Or if needed, platform-specific models can be created and the codes can be generated according to the user’s needs.

All the functions in StarUML is managed in XML format. This XML parser helps to change the codes efficiently with the easy-to-read structure. Since XML is used worldwide, anyone can use the parser and change the code format to the language which is known to them. This helps them to utilize the application based on their need and use the templates as well.

The diagrams being exported in the system do not support SVG format and hence the application can be used only within the supported formats.

All the key features including the download, installation requirements with figures is explained here. Though it appears complex for a beginner to start with StarUML, it helps the user to do the diagrams based on his preference and change the platforms if needed.

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Learn How To Build Your Own Speech


Learn how to build your very own speech-to-text model using Python in this article

The ability to weave deep learning skills with NLP is a coveted one in the industry; add this to your skillset today

We will use a real-world dataset and build this speech-to-text model so get ready to use your Python skills!


“Hey Google. What’s the weather like today?”

This will sound familiar to anyone who has owned a smartphone in the last decade. I can’t remember the last time I took the time to type out the entire query on Google Search. I simply ask the question – and Google lays out the entire weather pattern for me.

It saves me a ton of time and I can quickly glance at my screen and get back to work. A win-win for everyone! But how does Google understand what I’m saying? And how does Google’s system convert my query into text on my phone’s screen?

This is where the beauty of speech-to-text models comes in. Google uses a mix of deep learning and Natural Language Processing (NLP) techniques to parse through our query, retrieve the answer and present it in the form of both audio and text.

The same speech-to-text concept is used in all the other popular speech recognition technologies out there, such as Amazon’s Alexa, Apple’s Siri, and so on. The semantics might vary from company to company, but the overall idea remains the same.

I have personally researched quite a bit on this topic as I wanted to understand how I could build my own speech-to-text model using my Python and deep learning skills. It’s a fascinating concept and one I wanted to share with all of you.

So in this article, I will walk you through the basics of speech recognition systems (AKA an introduction to signal processing). We will then use this as the core when we implement our own speech-to-text model from scratch in Python.

Looking for a place to start your deep learning and/or NLP journey? We’ve got the perfect resources for you:

A Brief History of Speech Recognition through the Decades

Did you know that the exploration of speech recognition goes way back to the 1950s? That’s right – these systems have been around for over 50 years! We have prepared a neat illustrated timeline for you to quickly understand how Speech Recognition systems have evolved over the decades:

The first speech recognition system, Audrey, was developed back in 1952 by three Bell Labs researchers. Audrey was designed

to recognize only digits

Just after 10 years, IBM introduced its first speech recognition system

IBM Shoebox

, which was capable of recognizing 16 words including digits. It could identify commands like 

“Five plus three plus eight plus six plus four minus nine, total,” and would print out the correct answer, i.e., 17

The Defense Advanced Research Projects Agency (DARPA) contributed a lot to speech recognition technology during the 1970s. DARPA funded for around 5 years from 1971-76 to a program called

Speech Understanding Research

and finally,


was developed which was able to recognize 1011 words. It was quite a big achievement at that time.

In the 1980s, the Hidden Markov Model (HMM) was applied to the speech recognition system. HMM is a statistical model which is used to model the problems that involve sequential information. It has a pretty good track record in many real-world applications including speech recognition. 

In 2001, Google introduced the

Voice Search

application that allowed users to search for queries by speaking to the machine.  This was the first voice-enabled application which was very popular among the people. It made the conversation between the people and machines a lot easier. 

By 2011, Apple launched


that offered a real-time, faster, and easier way to interact with the Apple devices by just using your voice. As of now,

Amazon’s Alexa


Google’s Home

are the most popular voice command based virtual assistants that are being widely used by consumers across the globe. 

Wouldn’t it be great if we can also work on such great use cases using our machine learning skills? That’s exactly what we will be doing in this tutorial!

Introduction to Signal Processing

Before we dive into the practical aspect of speech-to-text systems, I strongly recommend reading up on the basics of signal processing first. This will enable you to understand how the Python code works and make you a better NLP and deep learning professional!

So, let us first understand some common terms and parameters of a signal.

What is an Audio Signal?

This is pretty intuitive – any object that vibrates produces sound waves. Have you ever thought of how we are able to hear someone’s voice? It is due to the audio waves. Let’s quickly understand the process behind it.

When an object vibrates, the air molecules oscillate to and fro from their rest position and transmits its energy to neighboring molecules. This results in the transmission of energy from one molecule to another which in turn produces a sound wave.

Parameters of an audio signal


Amplitude refers to the maximum displacement of the air molecules from the rest position

Crest and Trough:

The crest is the highest point in the wave whereas trough is the lowest point


The distance between 2 successive crests or troughs is known as a wavelength

Cycle: Every audio signal traverses in the form of cycles. One complete upward movement and downward movement of the signal form a cycle


Frequency refers to how fast a signal is changing over a period of time

The below GIF wonderfully depicts the difference between a high and low-frequency signal:

In the next section, I will discuss different types of signals that we encounter in our daily life.

Different types of signals

We come across broadly two different types of signals in our day-to-day life – Digital and Analog.

Digital signal

A digital signal is a discrete representation of a signal over a period of time. Here, the finite number of samples exists between any two-time intervals.

For example, the batting average of top and middle-order batsmen year-wise forms a digital signal since it results in a finite number of samples.

Analog signal

An analog signal is a continuous representation of a signal over a period of time. In an analog signal, an infinite number of samples exist between any two-time intervals.

For example, an audio signal is an analog one since it is a continuous representation of the signal.

Wondering how we are going to store the audio signal since it has an infinite number of samples? Sit back and relax! We will touch on that concept in the next section.

What is sampling the signal and why is it required?

An audio signal is a continuous representation of amplitude as it varies with time. Here, time can even be in picoseconds. That is why an audio signal is an analog signal.

Analog signals are memory hogging since they have an infinite number of samples and processing them is highly computationally demanding. Therefore, we need a technique to convert analog signals to digital signals so that we can work with them easily.

Sampling the signal is a process of converting an analog signal to a digital signal by selecting a certain number of samples per second from the analog signal. Can you see what we are doing here? We are converting an audio signal to a discrete signal through sampling so that it can be stored and processed efficiently in memory.

I really like the below illustration. It depicts how the analog audio signal is discretized and stored in the memory:

The key thing to take away from the above figure is that we are able to reconstruct an almost similar audio wave even after sampling the analog signal since I have chosen a high sampling rate. The sampling rate or sampling frequency is defined as the number of samples selected per second. 

Different Feature Extraction Techniques for an Audio Signal

The first step in speech recognition is to extract the features from an audio signal which we will input to our model later. So now, l will walk you through the different ways of extracting features from the audio signal.


Here, the audio signal is represented by the amplitude as a function of time. In simple words, it is a plot between amplitude and time. The features are the amplitudes which are recorded at different time intervals.

The limitation of the time-domain analysis is that it completely ignores the information about the rate of the signal which is addressed by the frequency domain analysis. So let’s discuss that in the next section.

Frequency domain

In the frequency domain, the audio signal is represented by amplitude as a function of frequency. Simply put – it is a plot between frequency and amplitude. The features are the amplitudes recorded at different frequencies.

The limitation of this frequency domain analysis is that it completely ignores the order or sequence of the signal which is addressed by time-domain analysis.


Time-domain analysis completely ignores the frequency component whereas frequency domain analysis pays no attention to the time component.

We can get the time-dependent frequencies with the help of a spectrogram.


Ever heard of a spectrogram? It’s a 2D plot between time and frequency where each point in the plot represents the amplitude of a particular frequency at a particular time in terms of intensity of color. In simple terms, the spectrogram is a spectrum (broad range of colors) of frequencies as it varies with time. 

The right features to extract from audio depends on the use case we are working with. It’s finally time to get our hands dirty and fire up our Jupyter Notebook!

Understanding the Problem Statement for our Speech-to-Text Project

Let’s understand the problem statement of our project before we move into the implementation part.

We might be on the verge of having too many screens around us. It seems like every day, new versions of common objects are “re-invented” with built-in wifi and bright touchscreens. A promising antidote to our screen addiction is voice interfaces. 

TensorFlow recently released the Speech Commands Datasets. It includes 65,000 one-second long utterances of 30 short words, by thousands of different people. We’ll build a speech recognition system that understands simple spoken commands.

You can download the dataset from here.

Implementing the Speech-to-Text Model in Python

The wait is over! It’s time to build our own Speech-to-Text model from scratch.

Import the libraries

First, import all the necessary libraries into our notebook. LibROSA and SciPy are the Python libraries used for processing audio signals.

Python Code:

Visualization of Audio signal in time series domain

Now, we’ll visualize the audio signal in the time series domain:

View the code on Gist.

Sampling rate

Let us now look at the sampling rate of the audio signals:

ipd.Audio(samples, rate=sample_rate)



From the above, we can understand that the sampling rate of the signal is 16,000 Hz. Let us re-sample it to 8000 Hz since most of the speech-related frequencies are present at 8000 Hz:

samples = librosa.resample(samples, sample_rate, 8000)

ipd.Audio(samples, rate=8000)

Now, let’s understand the number of recordings for each voice command:

View the code on Gist.

Duration of recordings

What’s next? A look at the distribution of the duration of recordings:

View the code on Gist.

Preprocessing the audio waves

In the data exploration part earlier, we have seen that the duration of a few recordings is less than 1 second and the sampling rate is too high. So, let us read the audio waves and use the below-preprocessing steps to deal with this.

Here are the two steps we’ll follow:


Removing shorter commands of less than 1 second

Let us define these preprocessing steps in the below code snippet:

View the code on Gist.

Convert the output labels to integer encoded:

View the code on Gist.

Now, convert the integer encoded labels to a one-hot vector since it is a multi-classification problem:

from keras.utils import np_utils y=np_utils.to_categorical(y, num_classes=len(labels))

Reshape the 2D array to 3D since the input to the conv1d must be a 3D array:

all_wave = np.array(all_wave).reshape(-1,8000,1) Split into train and validation set

Next, we will train the model on 80% of the data and validate on the remaining 20%:

from sklearn.model_selection import train_test_split x_tr, x_val, y_tr, y_val = train_test_split(np.array(all_wave),np.array(y),stratify=y,test_size = 0.2,random_state=777,shuffle=True) Model Architecture for this problem

We will build the speech-to-text model using conv1d. Conv1d is a convolutional neural network which performs the convolution along only one dimension. 

Here is the model architecture:

Model building

Let us implement the model using Keras functional API.

View the code on Gist.

Define the loss function to be categorical cross-entropy since it is a multi-classification problem:

Early stopping and model checkpoints are the callbacks to stop training the neural network at the right time and to save the best model after every epoch:

es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=10, min_delta=0.0001) mc = ModelCheckpoint('best_model.hdf5', monitor='val_acc', verbose=1, save_best_only=True, mode='max')

Let us train the model on a batch size of 32 and evaluate the performance on the holdout set:, y_tr ,epochs=100, callbacks=[es,mc], batch_size=32, validation_data=(x_val,y_val))

Diagnostic plot

I’m going to lean on visualization again to understand the performance of the model over a period of time:

View the code on Gist.

Loading the best model

from keras.models import load_model model=load_model('best_model.hdf5')

Define the function that predicts text for the given audio:

View the code on Gist.

Prediction time! Make predictions on the validation data:

View the code on Gist.

The best part is yet to come! Here is a script that prompts a user to record voice commands. Record your own voice commands and test it on the model:

View the code on Gist.

Let us now read the saved voice command and convert it to text:

View the code on Gist.

Here is an awesome video that I tested on one of my colleague’s voice commands:

Congratulations! You have just built your very own speech-to-text model!

Frequently Asked Questions

Q1. What is NLP model for speech-to-text?

A. One popular NLP model for speech-to-text is the Listen, Attend and Spell (LAS) model. It utilizes an attention mechanism to align acoustic features with corresponding output characters, allowing for accurate transcription of spoken language. LAS models typically consist of an encoder, an attention mechanism, and a decoder, and have been successful in various speech recognition tasks.

Q2. What are ASR models?

A. ASR (Automatic Speech Recognition) models are designed to convert spoken language into written text. They use techniques from both speech processing and natural language processing to transcribe audio recordings or real-time speech. ASR models can be based on various architectures such as Hidden Markov Models (HMM), Deep Neural Networks (DNN), or end-to-end models like Connectionist Temporal Classification (CTC) or Listen, Attend and Spell (LAS).


Find the notebook here

End Notes

Got to love the power of deep learning and NLP. This is a microcosm of the things we can do with deep learning. I encourage you to try it out and share the results with our community. 🙂

In this article, we covered all the concepts and implemented our own speech recognition system from scratch in Python.


How To Use Scrapy Xpath

Definition of Scrapy XPath

Web development, programming languages, Software testing & others

What is Scrapy XPath?

XPath is an XML-based language that may also be used with HTML to select nodes in XML documents. Scrapy xpath is very important in python.

Both XML and Scrapy Selectors use the libxml2 library, therefore their speed and parsing accuracy are extremely similar.

HTML is the language of web pages, and every web page’s beginning and closing html tags contain a wealth of information.

There are a variety of ways to do this, we can use Python’s Scrapy module and the Xpath selector. Scrapy is a strong web scraping library that is yet simple to use.

How to use Scrapy XPath?

XPath is an XML-based language that may also be used with HTML to select nodes in XML documents. It’s one of two ways to scan HTML text in web pages; the other is to utilize CSS selectors.

XPath has more functionality than basic CSS selectors, but it is more difficult to master. CSS selectors are, in fact, internally transformed to XPath. When compared to its CSS counterpart, XPath appears difficult, but once we understand how it works, it’s as simple as it gets.

It’s not a big deal, the more we know, the better choice we will make. However, before choosing CSS selectors, we need to check the Scrapy XPath.

Because its features contain function in syntax, Xpath is a very strong technique to parse html files, and it may be able to reduce the use of regular expressions.

Web automation selenium is an example of a library that allows Xpath parsing. When parsing HTML, Xpath provides a wealth of choices.

The below steps show how to use scrapy xpath are as follows.

1) When using text nodes in an XPath string function, use dot instead of dot/text since this produces a node-set, which is a collection of text elements. In this step, we are installing the scrapy by using the pip command. In the below example we have already installed a scrapy package in our system so, it will show that requirement is already satisfied then we have no need to do anything.

pip install scrapy

2) After installing the scrapy in this step we are login into the python shell by using the python command are as follows.


3) After login into the python shell in this step we are importing the selector module by using scrapy package.

from scrapy import selector

4) After importing the module in this step we are providing the XPath and creating the variable for the same. In the below example we can see that we have created the variable name as py_xpath, also we have called the module name a selector, in the selector module we have created the variable name as text. In py_text variable, we have provided the scrapy xpath.

5) After providing XPath and creating variables in this step we are converting the node set and using the extract method is as follows.

py_xpath.xpath ('//a//text()').extract() Scrapy XPath Firefox

The below steps shows scrapy xpath firefox are as follows. To use the scrapy xpath firefox first we need to install the firefox browser in our system.

2) After installing firefox we are installing firebug, it is a pre-requisites to install the path.

3) After installing the firebug plugin in this step we are installing the firepath are as follows. To install the firepath on our system first we need to download the required package.

Scrapy xpath URLs

When scraping a URL with xpath, we need to check two things while scraping xpath URL. The link text and the url portion, also known as href. The below example shows the scrapy xpath url is as follows.


def parse (self, response): for py_quote in response.xpath ('//a/py_text()'): yield { "py_text" : py_quote.get () }


def parse (self, response): for py_quote in response.xpath('//a/@href'): yield { "py_text" : py_quote.get() } Advanced Scrapy XPath

In most cases, a web page will have multiple elements. There could be URLs sets, for example, one for books and the other for photographs. So, what will we do now that we only scrap the books?

Fortunately, web developers typically allocate separate classes to such scenarios in order to maintain a method to distinguish between them.


def parse (self, response): for py_quote in response.xpath ('//div[@class = "path"]//a/@href'): yield { "py_text" : chúng tôi () }

Only URLs in divs with the class “path” are returned in the above code. This allows us to focus our search results. The / character is used to divide XPath statements. These characters, on the other hand, denote a set of instructions.


Both XML and Scrapy Selectors use the libxml2 library, therefore their speed and parsing accuracy are extremely similar. Scrapy XPath is the most typical activity we must accomplish while scraping web pages. XPath has more functionality than basic CSS selectors, but it is more difficult to master.

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