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How To Build a Multilingual Text-to-Audio Converter With Python

In this article, we will build a simple text-to-audio converter app using Python, googletrans API and gTTS for text-to-speech conversion.
The app will enable seamless conversation in the local language of a new country, making it an ideal tool for tourists.
This project has three main components — Translation (googletrans), Text-to-speech (gTTS), and Audio playback (pygame).
The translate_text() function uses the googletrans library for text translation, and takes in text and the target language code.
text_to_audio() function helps convert the text to audio using gTTS and pygame.
This application has many uses, such as helping people explore foreign countries, aiding in language learning, multi-tasking content consumption, and multinational communication.
Pygame can play the audio files, and builds game-related Python applications and software.
Googletrans is a Python binding for the Google Translate API and allows python scripts to integrate a text translation feature into custom apps.
gTTS comes with a pre-defined set of language templates which can be used to convert text to speech easily.
One can add features to the application like an interactive GUI, options for changing pronunciation and pace of the audio.

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Scientificworldinfo

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Big Data vs. Small Data: What’s the Difference? Explained

Big Data refers to datasets that are so large, complex, or fast-moving that traditional data management tools cannot process them efficiently. Distributed Computing, Data Warehousing and Storage Solutions, Machine Learning, Artificial Intelligence, and Data Integration Tools are key technologies for Big Data.
Small Data refers to datasets that are manageable in size and complexity, making them easier to analyze and interpret using traditional tools like spreadsheets and databases. Customer Feedback Surveys, Sales Data Analysis and Website Analytics are some of the examples of Small Data.
Big Data is targeted for unlocking macro-level insights for industries like healthcare, finance, and retail, while Small Data shines in localized scenarios such as improving customer satisfaction through survey feedback.
Big Data provides comprehensive insights, supports scalability, while Small Data is easy to use, quick for decision-making and cost-effective.
While Big Data and Small Data differ, integrating big data and small data creates a powerful synergy, enabling businesses to align their data strategies with specific business needs, balancing the depth and breadth of insights derived.
Emerging trends such as Edge Computing, Data Democratization, and AI-Powered Insights are set to shape this dynamic landscape, paving the way for a future where data is not just a byproduct of digital interactions but a central driver of progress.
By mastering the balance between Big Data and Small Data, organizations can create data strategies that are both effective and efficient, staying competitive in an increasingly complex world.

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Medium

224

The Scope of Data Science: Unlocking Opportunities Across Industries

Data science helps in improving diagnostics and treatment and predicting potential health risks in healthcare.
Data science provides financial institutions with tools to enhance their decision-making process in the finance industry.
Retail and e-commerce businesses rely heavily on data science to forecast demand and manage inventory efficiently and personalize customer experiences.
Telecommunication and networking companies leverage data science to improve customer experience, optimize service delivery, and ensure secure and reliable services.
The entertainment industry relies on data science to personalize content recommendations for users in streaming platforms like Netflix and Spotify.
The agricultural industry uses machine learning algorithms, satellite imagery, and IoT sensors to monitor crop health, predict weather patterns, and optimize irrigation schedules.
Data science is used to reduce downtime, improve production processes, and optimize maintenance schedules in the manufacturing industry.
Automotive companies are improving vehicle performance and enhancing safety features by analyzing data from sensors and telematics systems and relying heavily on data science for navigation, obstacle detection, and route optimization.
Staying up to date with the latest trends, including AI and machine learning, big data and IoT integration, and NLP can take data scientists career to new heights.
By enrolling in Pumo Technovation’s data science course in Coimbatore, you can equip yourself with the expertise needed to thrive in this exciting field and gain placement support.

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Pymnts

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GenAI’s Retail Revolution Expands to B2B Marketplaces

Artificial intelligence is making a significant impact in the online retail sector, particularly in the realm of marketplace platforms.
AI technologies are transforming marketplace operations, improving user experiences, and generating new revenue streams.
Mirakl, a pioneer in enterprise marketplace software, enables B2B and B2C businesses to expand their eCommerce capabilities by integrating third-party sellers.
AI applications within the Mirakl ecosystem include Catalog Transformer and Mirakl Ads, which streamline product catalog management and automate ad campaigns.

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Designveloper

241

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Data Engineer Job Description: Key Duties, Skills & Wages

A data engineer is responsible for building, managing, and maintaining the systems that collect, store, and transform big data.
Key responsibilities usually include developing data architecture, creating data pipelines, optimizing data storage, maintaining data quality and security, tracking system performance, and working with other teams.
The core technical skills required for the job include expertise in programming languages (Python, SQL, Java, or Scala), familiarity with big data technologies (Hadoop, Spark, or Kafka), experience with cloud platforms (AWS, Google Cloud, Azure, etc.), database management skill, and knowledge of data warehouses.
Soft skills such as problem-solving and communication abilities are also crucial for data engineers.
A bachelor's degree in computer science, engineering, applied math, or any data-related field is sufficient to get into the field, but data engineers may require advanced certification for more senior positions.
The demand for data engineers is high because big data and real-time data processing are necessary for companies' success.
Average data engineer salaries in the United States are $125,345 a year, but can vary depending on experience level, industry and location, role's complexity level, and skills.
Coding knowledge is essential for data engineering as the majority of tasks involve coding. Limited coding skills can limit a candidate's ability to enter the field, but transitioning through data analyst roles is possible.
To become a data engineer, candidates need to understand the job responsibilities, develop essential skills through courses and practical projects, continuously learn from industry leaders, and look for mentorship or internship opportunities.
Data engineering position is an in-demand role with job security and a promising salary. Designveloper is recruiting data engineers for their Lumin project.

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Medium

154

How to Load a JSON File in Python

To load a JSON file in Python, you can use the json.load() method if you have a file, or the json.loads() method if you have a string.
Use json.load() to load a JSON file and convert it to a Python object.
If the file contains multiple JSON objects, you can iterate through each line and load them separately using json.loads().
If the JSON objects are not separated by newlines, you can use regex to identify and load each object one by one.

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Pymnts

339

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Small Banks and Credit Unions Turn to Direct Issuing for Top-of-Wallet

For banks and credit unions looking to modernize, i2c Senior Vice President of Global Product Management Dan Hanks has said self-issued credit cards are no longer a luxury but a necessity.
Self-issued credit cards give community banks and credit unions the tools to meet digital-first, user-centric financial environment and modern consumers' expectations.
Banks want to offer more competitive products, increase profitability and claim ownership over the customer experience, particularly credit unions that focus on member satisfaction.
For decades, community banks and credit unions partnered with third-party issuers to offer credit cards, often ceding control over fees, rewards structures, and cardholder engagement.
Self-issued cards allow community banks and credit unions to enhance profitability, create digital-first offerings and own the cardholder experience by taking control of their credit card programs.
Smaller banks, some of whom thought they were too small before, now have a clear path to self-issuance.
Self-issued credit cards for mid-sized banks and credit unions can generate recurring and predictable revenue streams as interest rate uncertainty rises and margins are squeezed.
The key is finding a partner that delivers the services end-to-end as credit cards are largely a scale business.
Ultimately, self-issued credit cards are more than just a new product for banks and credit unions. They’re a strategy to stay relevant in a competitive, digital-first market that's largely led by personalization and meeting customers where they are.

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Designveloper

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Data Analyst vs Data Scientist vs Data Engineer: A Detailed Comparison

Data analysts gather, process, and analyze data from different sources, and examine historical data for patterns to convey actionable insights to the stakeholders in visual forms.
Data scientists employ advanced analytics, statistical methods, and machine learning to build and train ML models that predict future demands or automate decision-making.
Data engineers develop programs to collect data reliably and efficiently for usage and analysis, and are responsible for designing custom solutions to integrate data from various sources.
Data analysts require skills in analytical techniques and tools, statistical knowledge, programming languages, critical thinking, and communication and earn an average annual salary of $80,898 in the US.
Data scientists need programming languages like Python, SQL, and R, big data technologies, deep expertise in machine learning and advanced mathematics, data mining, conditioning & optimization skills, API development, critical thinking & problem-solving, and earn an average annual salary of $123,141 in the US.
Data engineers must have skills in data manipulation, experience with tools like databases, data warehouses, data frameworks, programming, and algorithms, proficiency in SQL, and problem-solving and creative thinking skills, and earn an average annual salary of $125,345 in the US.
The choice between the three positions depends on an individual's interests, background, and career aspirations.
The job openings for data scientists are predicted to increase by 36% from 2023 to 2033, as per the US Bureau of Labor Statistics.
Lumin is a document editor that allows users to edit, send, and sign PDFs easily and effectively and is opening lucrative positions for data engineers.
Designveloper, which has more than a million users globally, is hiring data engineers for their Lumin team.

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Medium

100

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How AI Tools Can 10x Your Capacity to Synthesize Data: A Theoretical Case Study

AI tools can enhance data analysis and decision-making in cybersecurity.
The challenge lies in transforming scattered data into actionable insights.
Using AI tools, data can be integrated from various sources and analyzed.
Theoretical findings include identifying silos, uneven expertise distribution, and uncovering collaboration opportunities.

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Hackernoon

361

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Discover Funnel Bottlenecks: Step-by-Step Analysis with BigQuery

Funnel analysis is the process of analyzing the customer journey and identifying drop-off points.
Using BigQuery, funnel analysis can be performed by querying funnel steps and tracking user transitions.
Insights from funnel analysis can be used to optimize product pages, simplify checkout process, and improve conversion rates.
Comparing transitions over time can help evaluate the effectiveness of changes made in the e-commerce funnel.

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Pymnts

370

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FIS: Payments Sector Needs New Way of Thinking

FIS FinTech platform Atelio arose from innovative thinking around embedded finance.
FIS is fostering a culture of curiosity, collaboration, and a bias toward action by encouraging a 'yes, and...' approach.
The company is focused on driving innovation and building solutions for embedded finance and fraud prevention.
FIS believes that the payments and banking industry needs a new way of thinking to see possibilities instead of roadblocks.

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Semiengineering

336

Semiconductor Manufacturing’s Transformational Challenges

Semiconductor manufacturing is facing transformational challenges due to strong demand for advanced computing.
The slowdown of Moore's Law is pushing semiconductor design in new directions, such as 3D and chiplets.
Global supply chain business models are being impacted, requiring new capabilities and complex analytics.
AI is playing a crucial role in revolutionizing semiconductor design and manufacturing, enhancing productivity and efficiency.

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Pymnts

124

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3 Reasons Now Is the Golden Age of B2B Procurement

Procurement is now being positioned as an enabler of broader organizational goals.
Procurement teams are tasked with enabling agility, ensuring supply chain resilience, and contributing to long-term value creation.
Technologies like blockchain, predictive analytics, and AI are being deployed to enhance procurement processes and create transparent supply chains.
Procurement's insights into market trends and supplier dynamics can contribute to broader business strategies.

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Medium

215

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Making the Invisible Visible: Bringing Backend Operations to Life

Backend activity is often difficult to explain or visualize, but visualizing it can provide a better understanding of how different functions work together to handle user requests.
By using real customer data and creating animations, backend processes can be transformed into something engaging and relatable.
The project aims to show that creating unique animations to visualize backend activity can be done using tools like Google Cloud and p5.js.
The animation represents backend functions visually, with each line reflecting the traffic and load handled by each function over the past two weeks.

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TheStartupMag

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3 Startup Ideas for 2025

The pest control industry is expected to grow at a steady pace, providing opportunities for startups to offer environmentally friendly and innovative pest management solutions.
The auto repair industry offers room for innovation, particularly in advanced diagnostic tools and technologies, niche markets like electric vehicle maintenance, and emphasizing customer service.
The luxury restroom trailer market presents a lucrative opportunity for startups to provide upscale and hygienic solutions for outdoor events, with customization options and partnerships with event planners and venues.
Entrepreneurs can tap into these markets by focusing on innovative solutions, environmental consciousness, and customer satisfaction to create successful startups in these industries.

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