AI fake news detector helps detect fake news through binary classification methods. It helps build experiences by controlling the flow of disinformation. It's built on top of various powerful machine learning libraries. The tool works by training a neural network to spot fake articles based on their text content. When you run your own data through the tool, it gives you back a list of articles that it thinks are likely to be fake. You can then train the model further or decide if those results are acceptable or not. In addition to identifying fake news, this model can also be trained to identify real news. This allows you to compare the model's performance across different domains (e.g., politics vs. sports). The following installer and deployment instructions will walk you through the steps of creating an AI fake news detector by using fakenews-detection, jupyter, vscode, and pandas. We will use fake news detection libraries (having fully modifiable source code) to customize and build a simple classifier that can detect fake news articles. kandi kit provides you with a fully deployable AI Fake News Detector. Source code included so that you can customize it for your requirement.

With this kit, you can

1. Use a pre-trained model for detecting fake news.

2. Train the model on your custom dataset.

3. Expose the fake news detection as an API

Add-on on examples are also included as given below

1. Use web scraper to automatically make your training dataset.

2. Visualise training and prediction data for useful insights.

Instructions to Run

Follow the below instructions to run the solution.

1. Locate and open the FakeNewsDetection-starter.ipynb notebook from the Jupyter Notebook browser window.

2. Execute cells in the notebook by selecting Cell --> Run All from Menu bar

3. Once all the cells of the notebook are executed, the prediction result will be written to the file 'fake_news_test_output.csv'

Training with your dataset:

1. Add news articles to a csv file under a column name 'news_text'.

2. Add corresponding labels as 'real' or 'fake' denoting whether a news article is real or not.

3. You can refer to the file 'fake_news_train.csv' for an example.

4. Set the variable for training file in the notebook under Variables section.

Testing with your dataset:

1. Add news articles to a csv file under a column name 'news_text'.

2. You can refer to the file 'fake_news_test.csv' for an example.

3. Set the variable for testing file in the notebook under Variables section.

You can execute the cells of notebook by selecting Cell from the menu bar.

For any support, you can reach us at FAQ & Support

Libraries useful for this solution

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

Exploratory Data Analysis

For extensive analysis and exploration of data, and to deal with arrays, these libraries are used. They are also used for performing scientific computation and data manipulation.

Text mining

Libraries in this group are used for analysis and processing of unstructured natural language. The data, as in its original form aren't used as it has to go through processing pipeline to become suitable for applying machine learning techniques and algorithms.

Machine Learning

Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning.

Data Visualization

The patterns and relationships are identified by representing data visually and below libraries are used for generating visual plots of the data.

Troubleshooting

1. If you encounter any error related to MS Visual C++, please install MS Visual Build tools

2.While running batch file, if you encounter Windows protection alert, select More info --> Run anyway.

3.During kit installer, if you encounter Windows security alert, click Allow.

4. If you encounter Memory Error, check if the available memory is sufficient and it is proportion to the size of the data being used. For our dataset, the minimum required memory is 8GB.

If your computer doesn't support standard commands from windows 10, you can follow the instructions below to finish the kit installation.

1. Click here to install python

2. Click here to download the repository

3. Extract the zip file and navigate to the directory 'fakenews-detection-main'

4. Open terminal in the extracted directory 'fakenews-detection-main'

5. Install dependencies by executing the command 'pip install -r requirements.txt'

6. Run the command ‘jupyter notebook’ and select the notebook ‘FakeNewsdetection-starter.ipynb’ on the browser window.

Support

For any support, you can reach us at FAQ & Support

Deepfake detection is identifying manipulated or synthetic media content using machine learning algorithms and computer vision techniques. It detects anomalies in facial and body movements, and other visual artifacts.

In this kit, we build a Deepfake Detection Engine using the popular Facenet_pytorch is a Python library that provides implementations of deep learning models for face recognition tasks. It includes pre-trained models such as

1. MTCNN (Multi-Task Cascaded Convolutional Networks) for face detection and alignment, and
2. InceptionResnetV1 for detecting whether an image is fake or real.

We use these two models to detect and recognize faces in images with high accuracy. The library is built on top of PyTorch, a popular open-source machine learning framework, and provides an easy-to-use API for face recognition tasks

Libraries used in this solution

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.

Jupyter Notebook is used for our development.

Machine Learning

Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning

Kit Solution Source

API Integration

Support

For any support, you can reach us at OpenWeaver Community Support

Generative artificial intelligence (AI) describes algorithms that help in creating/generating new content, including audio, code, images, text and videos. 

In this kit, we build a real-time Voice-to-Image Generator using the concept of Generative AI. It is carried out in two steps:

• Voice-to-text conversion - The speech is captured in real-time through the microphone and converted to text using state-of-the-art Opensource AI models from OpenAI and Whisper libraries.

• Text to Image Generation - The converted text is provided as input to the state-of-the-art Image Generation models like Dalle-2, and the image is thus generated.

Libraries used in this solution

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.

Jupyter Notebook is used for our development.

Machine Learning

Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning

Kit Solution Source

UI App Integration

Support

For any support, you can reach us at OpenWeaver Community Support

This Predictive Analytics kit provides an analytical view of students’ performance in mathematics and predicts grades to be scored in the final test.

The key features of this solution are:

• Analysis of grades of students
• Visualisation of patterns
• Prediction of grade in the final test

For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

Data Mining

Our solution integrates data from various sources, and we have used below libraries for exploring patterns in these data and understanding correlation between the features.

Data Visualisation

The patterns and relationships are identified by representing data visually and below libraries are used for that.

Machine learning

Below libraries and model collections helps to create the machine learning models for the core prediction of use case in our solution.

Support

If you need help using this kit, you may reach us at the OpenWeaver Community.

The use case of AI Course Recommender System is to provide personalized recommendation to the user based on their interest, course they can take and their current knowledge. This system will be able to recommend course based on user’s interest, current knowledge, analytical view of students’ performance in mathematics and recommends if a student can consider math subject for his/ her higher education. The recommended course will be based on the information of user’s profile, analysis of grades of students, visualization of patterns, prediction of grade in final test, and some rules that were set by their instructor. Using machine learning algorithms, we can train our model on a set of data and then predict the ratings for new items. This is all done in Python using numpy, pandas, matplotlib, scikit-learn and seaborn. kandi kit provides you with a fully deployable AI Course Recommender System. Source code included so that you can customize it for your requirement.

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.

Data Mining

Our solution integrates data from various sources, and we have used below libraries for exploring patterns in these data and understanding correlation between the features.

Data Visualisation

The patterns and relationships are identified by representing data visually and below libraries are used.

Machine learning

Below libraries and model collections helps to create the machine learning models for the core prediction of use case in our solution.

Federated Learning can train machine learning models on data from different hospitals, banks and autonomous vehicles without sharing sensitive data. But how do you create a Federated learning application? The answer is the kandi 1-click solution kit for Credit-risk-federated-learning.

Certainly, Federated Learning can be applied in the credit risk scenario to improve credit risk models' accuracy without compromising customer data privacy.

Banks collect and centralize customer data to train their credit risk models in the traditional approach. However, this approach can be challenging due to regulatory compliance, data privacy, and security concerns. Federated Learning addresses these challenges by allowing banks to train their credit risk models on customer data without transferring it to a centralized location.

This fully editable source code builds your Credit risk federated learning in minutes. The entire solution is available as a package to download from the source code repository.

Federated Learning in credit risk scenarios can have several benefits, including:

• Improved accuracy: Federated Learning allows banks to train models on a larger and more diverse dataset, leading to better accuracy.
• Data privacy: Federated Learning ensures that sensitive customer data is kept private and secure, which is critical in the context of credit risk.
• Regulatory compliance: Federated Learning can help banks comply with regulations around data privacy and security.

Troubleshooting

1. Install the Microsoft Visual C++ Redistributable for Visual Studio 2022 in case the kit doesn't successfully run on your windows system.
2. In case, step 1 doesn't solve your issue, set up Microsoft build Tools.

For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

Data Pre-processing

Numpy and Pandas are powerful tools for data preprocessing in machine learning. They provide tools for handling missing data, feature scaling, one-hot encoding, data normalization, and transformation.

These tools can help you to prepare your data for machine learning and improve the performance of your models.

Machine learning

Scikit-learn is a powerful and versatile machine learning library in Python that provides a wide range of tools and algorithms for building and training machine learning models. It is widely used in academia and industry for various machine learning applications.

Federated Learning Framework

Flower is an open-source framework for Federated Learning that provides tools and APIs to simplify the development and deployment of Federated Learning models. Flower is designed to make it easier for developers to implement Federated Learning in their applications by providing a flexible and scalable platform for building and training models.

Kit Solution Source

Support

If you need help using this kit, you may reach us at the OpenWeaver Community.

Large Language Models are foundation models that utilize deep learning in natural language processing and natural language generation tasks. Typically these models are trained on billions of parameters with a huge corpus of data.

GPT4all provides an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue. GPT4All is a 7B parameter LLM trained using a Low-Rank Adaptation (LoRA) method, yielding 430k post-processed instances, on a vast curated corpus of over 800k high-quality assistant interactions.

In this kit, we will use GPT4All to create a content generator, similar to ChatGPT, without the need for API keys and Internet to create content.

Libraries used in this solution

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.

Jupyter Notebook is used for our development.

Machine Learning

Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning

Kit Solution Source

API Integration

Support

For any support, you can reach us at OpenWeaver Community Support

The next word predictor is an exciting feature that helps you type faster on your mobile phone. It predicts the next word in the context you want to type. It is a very useful tool for people who type often and make mistakes while typing. It can be leveraged for auto-suggestion features in messenger and search engine apps.

The next word predictor makes it easy for readers to understand what exactly you are trying for them to read about.

• Next word predictor is a very useful feature as it increases the readability of your content as well as makes it more understandable for readers.
• Saves time by reducing the number of typos and grammatical errors in your content.
• Modify source code to customize as per your requirements.

Instructions to Run

Follow the below instructions to run the solution.

1. Locate and open the 'Next Word Predictor.ipynb' notebook from the Jupyter Notebook browser window.
2. Execute cells in the notebook by selecting Cell --> Run All from the Menu bar.
3. Once all the cells of the notebook are executed, the last interactive cell (Customisation) will be active, there we can give the input data or we can give the input text in the variable 'text_seq' under the variable section.

Input

text_seq = "I'm gonna make him an offer he can't"

Output

['refuse', 'resist', 'take', 'deny', 'get']

Troubleshooting

1. If you encounter any error related to MS Visual C++, please install MS Visual Build tools
2. While running batch file, if you encounter Windows protection alert, select More info --> Run anyway.
3. During kit installer, if you encounter Windows security alert, click Allow.
4. If you encounter Memory Error, check if the available memory is sufficient and it is proportion to the size of the data being used. For our dataset, the minimum required memory is 8GB.

If your computer doesn't support standard commands from windows 10, you can follow the instructions below to finish the kit installation.

1. Install python
2. Download the repository
3. Extract the zip file and navigate to the directory 'next-word-prediction-main'
4. Open terminal in the extracted directory 'next-word-prediction-main'
5. Install dependencies by executing the command 'pip install -r requirements.txt'
6. Run the command ‘jupyter notebook’ and select the notebook ‘Next Word Predictor.ipynb’ on the browser window.

For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web-based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

Exploratory Data Analysis

For extensive analysis and exploration of data, and to deal with arrays, these libraries are used. They are also used for performing scientific computation and data manipulation.

Text Mining

Libraries in this group are used for analysis and processing of unstructured natural language.

Machine Learning

The library offers state-of-the-art pre-trained models for Natural Language Processing (NLP).

Support

If you need help using this kit, you may reach us at the OpenWeaver Community.

AI-powered emoji detectors can help increase engagement with their customers. It will help them to build strong relationships with their customers. The emoji detector will help you in analyzing your audience and their preferences so that you can deliver the right content. You can also use the technology to provide customer support to your customers by providing customized answers.

One of the most important aspects of AI-Powered Emoji Detector is that it will help you in detecting any kind of emotions and expressions on your face OR hand gestures from a web camera. It will help in detecting whether you are happy, sad, or angry, and so on. This technology is also used for predicting different kinds of expressions like happiness, fear, sadness, etc.

For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

Image Preparation and Processing

These libraries help in preparing data by annotating and labelling images. Also processes images for running machine learning algorithm. We use opencv library for capturing frames from live streaming videocam.

Data Analysis/Manipulation

These libraries help in analyzing data and doing data manipulations.

Machine Learning

Below libraries and model collections helps to create the machine learning models for the core recognition use cases in our solution.

Utilities

The below utility library helps in storing huge amounts of numerical data and manipulate that data easily from NumPy.

Support

If you need help using this kit, you may reach us at the OpenWeaver Community.

AI Object Detection is used to build computer vision-based applications. It helps in face & vehicle detection, pedestrian counting, and security systems. 

Using this 1-click install kandi kit you can build an application that can (a) localize and classify objects, (b) detect objects in a video stream. You can download this pre-trained model and run it on any device. It is fast and very effective at identifying objects in images with high accuracy (99%). It also provides many advanced features like face detection, smile detection, etc. without any extra effort from your side!

For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

Image Preparation and Processing

These libraries help in preparing data by annotating and labelling images. Also processes images for running machine learning algorithm. We use opencv library for capturing frames from live streaming videocam.

Machine Learning

There are libraries and model collections that help to create machine learning models for the core recognition use cases in our solution. We will use pytorch to load pre-trained models of Object detection.

Support

If you need help using this kit, you may reach us at the OpenWeaver Community.

Real-time object tracking system is a technology used to track objects in real time. It can be used for security purposes or for commercial purposes. Tracking can be done for video formats and live streaming webcam.

The real-time object tracking system has many applications, such as in retail stores, airports, stadiums and other places where security is important. The system can be used to monitor customer activity in stores, track inventory and detect shoplifting. It can also be used to increase safety in public places by monitoring the movements of pedestrians or vehicles.

For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community

Development Environment

VSCode and Jupyter Notebook can be used for development and debugging. Jupyter Notebook is a web-based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.

Object Detection and Tracking

The following libraries have a set of pre-trained models which could be used to identify objects and track them from live streaming videos.

Machine Learning Libraries

The following libraries could be used to create machine learning models which focus on the vision, extraction of data, image processing, and more. Thus making it handy for the users.

Support

If you need help using this kit, you may reach us at the OpenWeaver Community.

Disease predictor is a way to recognize patient’s health by applying data mining and machine learning techniques on patient treatment history.

Symptoms, Diagnosis for Personalized Healthcare Services for a Predictive Analytic Perspective. Pandas library is used in this kandi kit to predict the probability of disease. The kit has used pandas to load datasets and visualize the data, NumPy to implement our algorithm, and sklearn-pandas to build our model.

In this project we will be using Pandas and Scikit-Learn to create a model that predicts whether or not a patient has a disease based on their demographics and lab results. We will also be using Jupyter Notebook to write code interactively so that we can see how our model performs when we change various parameters such as the number of features, amount of training data, etc.

kandi kit provides you with a fully deployable Disease Predictor. Source code included so that you can customize it for your requirement.

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

Exploratory Data Analysis

For extensive analysis and exploration of data, and to deal with arrays, these libraries are used. They are also used for performing scientific computation and data manipulation.

Data Visualization

The patterns and relationships are identified by representing data visually and below libraries are used for generating visual plots of the data.

Support

If you need help to use this kit, you can email us at kandi.support@openweaver.com or direct message us on Twitter Message @OpenWeaverInc .

Python Machine Learning libraries help develop supervised and unsupervised learning, data pre-processing, feature extraction tools, and deep learning. 

Following are the top use cases of these shortlisted libraries for Python Machine Learning,

• Pre-processing of data that includes data cleaning and feature engineering tasks such as normalization, imputation, missing value treatment, and outlier detection.
• Model selecting and optimizing, such as cross-validation, hyperparameter tuning, and model selection metrics.
• Visualizations to understand data and results. This includes visualizing data distributions, feature importance, and model performance.
• Developing algorithms, including supervised learning algorithms (e.g. linear regression, logistic regression, support vector machines, decision trees, random forests, and neural networks) as well as unsupervised learning algorithms (e.g. clustering, dimensionality reduction, and anomaly detection).
• Calculating performance metrics such as accuracy, precision, recall, and F1 score.

The following is a list of the 18 most popular open-source Python libraries for Machine Learning,

keras:  

• It provides a high-level API for building and training deep neural networks.   
• Keras allows you to define and incorporate custom layers and loss functions.    
• Configure Keras to run on top of deep learning frameworks like TensorFlow, etc.

Scikit-Learn:  

• It is an essential library in the field of machine learning and data science.  
• It provides tools for cross-validation, hyperparameter tuning, and model selection.    
• The library runs on top of other scientific Python libraries like NumPy and SciPy.   

Pandas:  

• It is a popular Python library for data manipulation and analysis.   
• It offers tools for data cleaning. This includes handling missing values, data alignment, and data type conversion.   
• It supports time series data, making it valuable for financial analysis and forecasting.   

YOLOv5:  

• "You Only Look Once version 5," is a popular computer vision model for object detection. 
• It is popular for its real-time object detection capabilities. 
• It has improved upon the accuracy of its predecessors while maintaining its speed. 

Ray:  

• It is an open-source distributed computing framework used in Python.   
• It enables you to parallelize and distribute Python applications.   
• It helps with low-latency, high-throughput computing tasks.   

ML-From-Scratch:  

• This helps you gain a deep understanding of the underlying algorithms and mathematics.   
• This allows you to customize it for your specific problem and data. This makes it more effective and efficient.   
• Building models from scratch provides insight into optimization techniques.   

examples:  

• It helps in AI, ML, DL, Pytorch, TensorFlow applications.   
• This library in PyTorch is essential for working with computer vision tasks.   
• You can access pre-trained models like ResNet, VGG, and AlexNet through "torchvision.models".   

Paddle:  

• It is an open-source deep learning platform developed by Baidu.  
• It is a powerful deep learning framework, like TensorFlow and PyTorch.   
• It focuses on simplicity and efficiency.  

rasa:  

• It is an open-source Python library designed for building conversational AI apps.   
• It provides tools for creating and managing conversational flows.  
• It supports many languages and can helps in a global context.  

horovod:  

• It is a popular library in Python used for distributed deep learning.   
• It enables you to scale your DL models to many GPUs and even across many machines.   
• It supports various deep learning frameworks like TensorFlow, PyTorch, and MXNet.   

mlflow:  

• It is an open-source platform for managing the end-to-end machine learning lifecycle.  
• It allows you to log and compare experiments.  
• It provides tools for packaging models in a standard format.

imgaug:  

• It is an important tool for image augmentation. It is especially used in machine learning and computer vision tasks.   
• It allows you to customize augmentation pipelines to suit your specific needs.    
• It works well with other popular libraries like OpenCV and NumPy.  

ChatterBot:  

• It provides a framework and pre-built components. That makes it easier to create chatbots.   
• This library often includes NLP capabilities. This allows chatbots to understand and generate human-like text responses.  
• These libraries offer options for customizing the behavior and responses of chatbots.

nni:  

• NNI handles distributed training, making it suitable for large-scale experiments.  
• NNI is important for streamlining and improving the machine learning model development process.  
• It automates and optimizes ML model selection and hyperparameter tuning.    

numpy-ml:  

• It is a fundamental library in the Python ecosystem. It is especially used in the context of machine learning and data science.  
• It is open-source and has a large and active community.  
• It is crucial for performing efficient numerical and array-based operations.  

tpot: 

• It is a Python library for automated machine learning (AutoML).  
• This includes feature selection, data preprocessing, and the choice of models.
• It employs techniques like cross-validation to reduce the risk of overfitting.    

autokeras:  

• It is an open-source library for automated machine learning (AutoML).  
• It simplifies the process of building and training machine learning models.  
• It is accessible to both beginners and experienced ML practitioners.  

pattern:  

• It is often referred to as a design pattern library.   
• It is a collection of reusable solutions to common software design problems.
• These patterns help developers create more efficient, maintainable, and scalable code.  

FAQ 

1. What is scikit-learn?  

It is an ML library for Python. That provides simple and efficient tools for data analysis and modeling. It offers a wide range of algorithms for classification, regression, clustering, and more.    

2. What is PyTorch?   

PyTorch is an open-source machine learning library. It is developed by Facebook's AI Research lab. It helps with deep learning and provides dynamic computation graphs. This makies it popular among researchers.    

3. What is Keras?   

Keras is an open-source deep learning API. That runs on top of other deep learning frameworks like TensorFlow and Theano. It's designed to be and allows for rapid prototyping of neural networks.   

4. How do I install these libraries?  

You can install these libraries using Python's package manager, pip. For example, you can install scikit-learn with pip install scikit-learn. Also, install TensorFlow with pip install tensorflow, and PyTorch with pip install torch.    

5. What is the difference between a tensor and an array in TensorFlow?  

In TensorFlow, a tensor is a multi-dimensional array. This array can be placed on GPU for accelerated computation. It is like NumPy arrays but optimized for deep learning tasks. 

Real-time speech recognition in Python refers to the ability of a computer program to transcribe spoken words into written text in real-time. You can use a library like SpeechRecognition to recognize speech in real time in Python. It supports several various engines and APIs, such as Microsoft Bing Voice Recognition and Google Speech Recognition. 

Real-time voice recognition in Python has a wide range of uses, including: 

• Voice-controlled assistants: These virtual assistants, like Siri or Alexa, can be operated via voice commands. 
• Speech-to-text transcription: This tool turns audible words into written text and is useful in professions including journalism, law, and medicine. 
• Voice biometrics: This application uses a person's distinctive voice patterns to authenticate and identify them. 
• Real-time language translation: This program helps people who speak various languages communicate more easily by translating spoken words from one language to another. 
• Speech-based accessibility: Applications that assist people with disabilities, such as text-to-speech or speech-to-text for the visually impaired. 

Here is how you can recognize speech in real-time in Python: 

Fig 1: Preview of the output that you will get on running this code from your IDE

Code

In this solution, we use the Recognizer function of the Speech Recognition library

1. Copy the code using the "Copy" button above, and paste it in a Python file in your IDE.
2. Run the file. You will be prompted to speak something through your microphone
3. The speech in real-time gets processed and displayed on screen

I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.

I found this code snippet by searching for "speech recognition in python" in kandi. You can try any such use case!

Dependent Libraries

If you do not have Speech Recognition that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the Speech Recognition page in kandi.

You can search for any dependent library on kandi like SpeechRecognition.

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

1. The solution is created in Python3.9.
2. The solution is tested on SpeechRecognition 3.8.1 and PyAudio 0.2.12 versions.

Using this solution, we are able to make blurred images using the OpenCV library in Python with simple steps. This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us to recognize speech in real-time in Python.

Support

1. For any support on kandi solution kits, please use the chat
2. For further learning resources, visit the Open Weaver Community learning page.

AI has been to build intelligent agents that can understand the vision and language inputs and communicate with humans through natural language. 

Vision and language, two of the most fundamental methods for humans to perceive the world, are also two key cornerstones of AI. A longstanding goal of AI has been to build intelligent agents that can understand the world through vision and language inputs, and communicate with humans through natural language.

In order to achieve this goal, vision-language pre-training has emerged as an effective approach, where deep neural network models are pre-trained on large scale image-text datasets to improve performance on downstream vision-language tasks, such as image-text retrieval, image captioning, and visual question answering.

Image Captioning and Visual Question and Answering involves the usage of Large Multimodal Models (LMMs). Multimodal Learning seeks to allow computers to represent real-world objects and concepts using multiple data streams. We make use of one such model - Saleforce's BLIP (Bootstrapping Language-Image Pre-training)

Libraries used in this solution

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.

Jupyter Notebook is used for our development.

Machine Learning

Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning

Kit Solution Source

App User Interface

Support

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Image Restoration is the process of converting a noisy/blur/low quality image to a better-quality image in terms of resolution, color, noise, and blur.

Image Restoration techniques involve Deblurring, colorization, super resolution enhancement and other popular techniques as well.

This kit provides the solution for

• Restoring old images to colored images
• Upscale and enhance image resolution

The Image colorization is done by a technique called NoGAN from an open source project called "deoldify". Deoldify is a shading rendering model for images and videos that enables the restoration of native color to black and white photos and videos.

The Image Upscaling and enhancement is done through a technique called GFPGAN which uses Generative Adversarial Networks comprising of Deep learning techniques for Face/Image restoration.

Libraries used in this solution

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.

Jupyter Notebook is used for our development.

Machine Learning

Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning

App User Interface

Kit Solution Source

Support

For any support, you can reach us at OpenWeaver Community Support

Emotion Detection and Recognition is related to Sentiment Analysis. Sentiment Analysis aims to detect positive, neutral, or negative feelings from text. 

Emotion Analysis aims to detect and recognize types of feelings through the expression of texts, such as joy, anger, fear, sadness.

In this kit, we build an AI based Speech Emotion Detector using open-source libraries. The concepts covered in the kit are:

• Voice-to-text transcription- The speech can be captured in real-time through the microphone or by uploading an audio file. It is then converted to text using state-of-the-art Opensource AI models from OpenAI Whisper library.
• Emotion detection- Emotion detection on the transcribed text is carried out using a finetuned XLM-RoBERTa model.

Whisper is a general-purpose speech recognition model released by OpenAI that can perform multilingual speech recognition as well as speech translation and language identification. Combined with an emotion detection model, this allows for detecting emotion directly from speech in multiple languages.

XLM-RoBERTa is a multilingual version of RoBERTa. It is pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It can be finetuned to perform any specific task such as emotion classification, text completion etc. Combining these, the emotion detection model could be used to transcribe and detect different emotions to enable a data-driven analysis.

Libraries used in this solution

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.

Jupyter Notebook is used for our development.

Machine Learning

Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning

Kit Solution Source

APP Interface

Support

For any support, you can reach us at OpenWeaver Community Support

Generative artificial intelligence (AI) describes algorithms that help in creating/generating new content, including audio, code, images, text and videos.

In this kit, we build a Music generator from Text and Audio prompts using the Meta's MusicGen model. Meta's Audiocraft research team has released MusicGen, an open source deep learning language model that can generate new music based on text prompts and even be aligned to an existing song.

MusicGen is a single stage auto-regressive Transformer model capable of generating high-quality music samples conditioned on text descriptions or audio prompts. The text descriptions are passed through a frozen text encoder model to obtain a sequence of hidden-state representations. MusicGen is then trained to predict discrete audio tokens, or audio codes, conditioned on these hidden states. These audio tokens are then decoded using an audio compression model, such as EnCodec, to recover the audio waveform.

MusicGen has been trained on 20000 hours of licensed music. MusicGen is exceptional in its capacity to handle both text and musical cues, in addition to the effectiveness of the design and the speed of creation. The text establishes the fundamental style, which the audio file’s music subsequently follows.

Libraries used in this solution

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.

Jupyter Notebook is used for our development.

Artificial Intelligence

AI libraries and frameworks here are helpful in providing state-of-the-art solutions using AI

Kit Solution Source

UI app Integration

Support

For any support, you can reach us at OpenWeaver Community Support

The Generative AI Kandi Kit for Image Generation is an exciting and innovative toolkit that enables users to explore the fascinating field of Generative Artificial Intelligence (AI) and unleash their creativity through the generation of unique and diverse images. This kit harnesses the power of open-source libraries, such as PyTorch and TorchVision, to create a fully functional Generative Adversarial Network (GAN) for generating high-quality images.

With this Kandi Kit, users can delve into the world of AI-driven image generation and witness the magic of AI creating realistic and novel images. The kit provides a user-friendly and customizable script that allows users to specify various hyperparameters, including batch size, number of epochs, latent dimension, and image size, providing full control over the image generation process.

The Kandi Kit comes with pre-defined Generator and Discriminator models, built using PyTorch's neural network module, and optimized using the Adam optimizer for efficient training. The Generator network cleverly generates images from random noise, while the Discriminator network efficiently distinguishes between real and fake images.

Additionally, users can leverage their own image datasets by specifying the path to the image folder, enabling them to train the GAN on custom datasets, leading to the creation of images tailored to their specific requirements.

The training loop provided in the script ensures the GAN iteratively learns to produce increasingly realistic and diverse images over a specified number of epochs. As training progresses, the Generator learns to create images that become almost indistinguishable from real images, making the process of generating images a truly magical and awe-inspiring experience.

The Kandi Kit also allows users to visualize the progress of image generation, with images being saved periodically during training. This feature enables users to observe the gradual improvement of the GAN over time and generate impressive images at different stages of the training process.

Overall, the Generative AI Kandi Kit for Image Generation offers an accessible and enjoyable way to explore the potential of AI in creating unique and visually captivating images. Whether for artistic endeavors, data augmentation, or creating realistic synthetic data, this kit empowers users to unlock the endless possibilities of Generative AI for image generation.

Screenshots

Test run

With Gradio GUI

You can build predictive analytic based applications with this ready to deploy template application. Fully modifiable source code modifies your needs. 

Use this kandi 1-Click Solution kit to build your own AI-based Breast Cancer Detection Engine in minutes.

✅ Using this application you can do early stage detection for breast cancer and help in identifying it as malignant(cancerous) or benign(non-cancerous).

✅ You can build predictive analytic based applications with this ready to deploy template application.

✅ Fully modifiable source code is provided to enable you to modify for your requirements.

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.

Jupyter Notebook is used for our development.

Machine Learning

Simple and efficient tools for predictive data analysis.

Scikit-learn is a free software machine learning library which features various classification, regression and clustering algorithms including support-vector machines,etc. Similar libraries for ML support in Java, Scala and R programming language

Support

If you need help using this kit, you may reach us at the OpenWeaver Community.