#Web Speech API

[bhavanameti]

TOP 10 COMPANIES IN SPEECH-TO-TEXT API MARKET

The Speech-to-text API Market is projected to reach $10 billion by 2030, growing at a CAGR of 17.3% from 2023 to 2030. This market's expansion is fueled by the widespread use of voice-enabled devices, increasing applications of voice and speech technologies for transcription, technological advancements, and the rising adoption of connected devices. However, the market's growth is restrained by the lack of accuracy in recognizing regional accents and dialects in speech-to-text API solutions.

Innovations aimed at enhancing speech-to-text solutions for specially-abled individuals and developing API solutions for rare and local languages are expected to create growth opportunities in this market. Nonetheless, data security and privacy concerns pose significant challenges. Additionally, the increasing demand for voice authentication in mobile banking applications is a prominent trend in the speech-to-text API market.

Top 10 Companies in the Speech-to-text API Market

Google LLC

Founded in 1998 and headquartered in California, U.S., Google is a global leader in search engine technology, online advertising, cloud computing, and more. Google’s Speech-to-Text is a cloud-based transcription tool that leverages AI to provide real-time transcription in over 80 languages from both live and pre-recorded audio.

Microsoft Corporation

Established in 1975 and headquartered in Washington, U.S., Microsoft Corporation offers a range of technology services, including cloud computing and AI-driven solutions. Microsoft’s speech-to-text services enable accurate transcription across multiple languages, supporting applications like customer self-service and speech analytics.

Amazon Web Services, Inc.

Founded in 2006 and headquartered in Washington, U.S., Amazon Web Services (AWS) provides scalable cloud computing platforms. AWS’s speech-to-text software supports real-time transcription and translation, enhancing various business applications with its robust infrastructure.

IBM Corporation

Founded in 1911 and headquartered in New York, U.S., IBM Corporation focuses on digital transformation and data security. IBM’s speech-to-text service, part of its Watson Assistant, offers multilingual transcription capabilities for diverse use cases, including customer service and speech analytics.

Verint Systems Inc.

Established in 1994 and headquartered in New York, U.S., Verint Systems specializes in customer engagement management. Verint’s speech transcription solutions provide accurate data via an API, supporting call recording and speech analytics within their contact center solutions.

Download Sample Report Here @ https://www.meticulousresearch.com/download-sample-report/cp_id=5473

Rev.com, Inc.

Founded in 2010 and headquartered in Texas, U.S., Rev.com offers transcription, closed captioning, and subtitling services. Rev AI’s Speech-to-Text API delivers high-accuracy transcription services, enhancing accessibility and audience reach for various brands.

Twilio Inc.

Founded in 2008 and headquartered in California, U.S., Twilio provides communication APIs for voice, text, chat, and video. Twilio’s speech recognition solutions facilitate real-time transcription and intent analysis during voice calls, supporting comprehensive customer engagement.

Baidu, Inc.

Founded in 2000 and headquartered in Beijing, China, Baidu is a leading AI company offering a comprehensive AI stack. Baidu’s speech recognition capabilities are part of its diverse product portfolio, supporting applications across natural language processing and augmented reality.

Speechmatics

Founded in 1980 and headquartered in Cambridge, U.K., Speechmatics is a leader in deep learning and speech recognition. Their speech-to-text API delivers highly accurate transcription by training on vast amounts of data, minimizing AI bias and recognition errors.

VoiceCloud

Founded in 2007 and headquartered in California, U.S., VoiceCloud offers cloud-based voice-to-text transcription services. Their API provides high-quality transcription for applications such as voicemail, voice notes, and call recordings, supporting services in English and Spanish across 15 countries.

Top 10 companies: https://meticulousblog.org/top-10-companies-in-speech-to-text-api-market/

[beardedhandstoadshark]

wait what’s happening on reddit?

From what I’ve read here, Reddit‘s pulling a Twitter and planning to charge ( a LOT of) money for third-party applications to use their API - meaning a lot of things will be forced to go offline forever.

Those include ALL third-party apps, which is important because Reddit‘s own app seems to be an utter mess that makes tumblr‘s look like the best programmed thing in the world, so pretty much everyone uses Reddit over those instead. Like, someone did the math for one of the main 3rd-apps, Apollo, and it would’ve cost the single guy who’s programming it 20mil$. Per month. And unless they changed it since last time I tried to go on there, you can’t use web-Reddit on your phone because they won’t let you click a single thing or even look at most subreddits without blocking it behind a "use the app“! Popup. Ik Tumblr does that too, but at least it actually. Let’s you look at tumblr. Kinda ironic that their app is such trash then.

More importantly however, the Reddit App isn’t compatible with native text-to-speech help for blind/visibly impaired people, while all those 3rd party apps are/were- so they’re essentially fucking over all blind/visibly impaired people and making it impossible for them to use Reddit at all.

And also a lot of very important tools for MODERATION. Which mods are apparently really dependent on especially on bigger subreddits because otherwise the workload would be insane + a lot of moderation stuff a lot harder. So. Yknow. They’re basically forcing mods, who do this *for free* to pay money to keep their own site afloat. Or letting subs go haywire and then nuked for not following general Reddit guidelines.

Because of that A LOT of subreddits decided to go on strike for 48h and set to private, resulting in like7700/8somethingthousand of them to go black, which then resulted in the whole site crashing from the amount of change.

Why people migrated to tumblr of al places seems to be kind of a mystery, but my own guess is either because tumblr became the official refugee-site after the whole thing with Twitter before, or because r/196, one of the really big subreddits, closed indefinitely instead of just those 48h (just as a sidenote, is how strikes should work, because otherwise they’ll just wait out the hours instead of doing anything- which is apparently also exactly what happened now).

Anyways that subreddit is apparently Reddit’s version of tumblr anyways so the vibe seemed to fit. And now the 196 tag is trending and probably here to stay for a while lmao

[tripleatranscription]

Essential Traits of a Reliable Medical Transcription Partner

A reliable medical transcription partner ensures accurate patient records and efficient clinical workflows. Choosing the right provider reduces the risk of documentation errors and compliance breaches. Healthcare organizations benefit from partners who deliver precise transcripts on schedule. A structured vetting process reveals a partner’s dedication to quality control and data security.

This article outlines essential traits that define a dependable medical transcription ally, guiding decision-makers toward a service that enhances clinical documentation and supports patient care excellence.

Assessing Clinical Expertise

Providers must demonstrate deep familiarity with relevant medical specialties and terminology. A partner experienced in pathology, radiology, or surgical transcripts better captures nuanced language. Evaluating sample work exposes accuracy in capturing complex terms and abbreviations.

The organization should evaluate the track record of Australian medical transcription companies to ensure adherence to local clinical standards. Understanding a partner’s case studies and client feedback highlights subject matter mastery and commitment to ongoing training in evolving medical fields.

Ensuring Accuracy and Quality Control

Accuracy proves a partner’s value in clinical operations. Reliable teams apply multi-stage editing workflows involving transcriptionists and specialized editors. Automated speech recognition can aid speed, but human review catches contextual errors. Routine quality audits track metrics like word error rate and revision frequency. Transparent reporting on performance metrics supports continuous improvement.

Security and Compliance

Protecting patient information ranks among the top priorities. A partner must comply with regulations such as HIPAA, GDPR, and local privacy laws. Data residency policies determine where electronic records reside. Certifications and encryption protocols secure data in transit and at rest.

Key credentials include

HIPAA compliance

ISO 27001 certification

End-to-end data encryption

Technology and Turnaround Efficiency

Technology underpins efficient transcription delivery. Advanced platforms offer secure web portals and API integrations for seamless data exchange. Scalability ensures the ability to handle fluctuating volumes with consistent quality.

A robust online transcription service provides real-time job tracking and automated workflow triggers. Clear turnaround commitments outline expected delivery windows. High-volume demands benefit from batch processing and priority options that align with clinical schedules.

Communication and Client Support

Clear communication fosters strong partnerships. Dedicated account managers serve as primary contacts for updates and issue resolution. Service level agreements define response times and escalation paths. Training materials, onboarding sessions, and user guides accelerate team integration. Regular strategy reviews allow clients to provide feedback and request process refinements.

Transparent Pricing and Scalability

Transparent pricing fosters trust and budgeting accuracy. Partners should present tiered plans and volume discounts that reflect actual usage. Clarity on surcharges for rush orders or specialty formats prevents unexpected fees. Scalability to adjust service levels aligns costs with growth and seasonal demand. Contract flexibility, including short-term agreements and exit clauses, protects clients.

Choosing the Right Partner

Selecting a dependable medical transcription ally requires holistic evaluation. Organizations must weigh clinical expertise, quality assurance processes, and data security measures. Platform flexibility and support structures deserve close review. Pricing models should align with budget constraints without sacrificing quality. Engaging references and pilot projects offer real-world insight into service delivery.

Conclusion

Partner selection extends beyond cost and speed. Quality, security, and support define a reliable relationship. A partner that meets clinical, technical, and compliance criteria promotes efficient workflows and accurate records. Holistic vetting and trial engagements guide confident choices. A robust transcription partnership supports long-term patient care and organizational success.

[govindhtech]

Live API For The Development Of Real-Time Interactions

Live API allows real-time interaction. Developers may use the Live API to construct apps and intelligent agents that process text, video, and audio feeds with minimal latency. Creating really engaging experiences requires this speed, which will enable real-time monitoring, educational platforms, and customer support.

Also announced the Live API for Gemini models' preview launch, allowing developers to build scalable and dependable real-time apps. Test new features in Vertex AI and Google AI Studio using the Gemini API.

Updates to Live API

Since the beta debut in December, it has listened to your feedback and added functionality to prepare the Live API for production. Details are in the Live API documentation:

More reliable session control

Longer sessions and interactions are possible with context compression. Set context window compression using a sliding window approach to automatically regulate context duration to avoid context limit terminations.

Resuming sessions: Keep them after minor network cuts. Live API handles (session_resumption) allow you to rejoin and continue where you left off, and server-side session state storage is available for 24 hours.

Gentle disconnect: Get a GoAway server message when a connection is about to end to treat it nicely.

Adjustable turn coverage Choose whether the Live API processes audio and video input constantly or only records when the end-user speaks.

Configurable media resolution: Control input media resolution to optimise quality or token use.

Improved interaction dynamics control

Configurable VAD: Manually control turns using new client events (activityStart, activityEnd) and specify sensitivity levels or disable automated VAD.

Configurable interruption handling: Select if user input interrupts model response.

Flexible session settings: Change system instructions and other configuration options anytime throughout the session.

Enhanced output and features

Choose from 30 additional languages and two new voices for audio output. SpeechConfig now supports output language customisation.

Text streaming: Delivers text replies progressively, speeding up viewing.

Reporting token consumption: Compare token counts by modality and prompt/response stage in server message use information.

Real-world implementations of Live API

The Live API team is spotlighting developers who are using it in their apps to help you start your next project:

Daily.co

The Pipecat Open Source SDKs for Web, Android, iOS, and C++ enable Live API.

Pipecat Daily used Live API to create Word Wrangler, a voice-based word guessing game. Try your description skills in this AI-powered word game to build one for yourself!

Live Kit

LiveKit Agents support Live API. This voice AI agent framework provides an open-source server-side agentic application platform.

Bubba.ai

Hello Bubba is a voice-first, agentic AI software for truckers. The Live API allows seamless, multilingual speech communication for hands-free driving. Some key aspects are:

Find heaps of items and inform.

Calling shippers and brokers.

Market data helps negotiate freight prices.

Rate confirmations and load scheduling.

Finding and booking truck parking and calling hotel to confirm availability.

Setting up receiver-shipper meetings.

Live API powers Bubba's phone conversations for booking and negotiation and driver interaction (function calling and context caching for future pickups). This makes Hey Bubba a full AI tool for the US's largest and most diverse job sector.

[hats-off-solutions]

Google APIs: Powering Innovation Across the Web

In a world driven by data, seamless integrations, and intelligent services, Google APIs have become a go-to solution for developers. Whether you’re building a mobile app, a web tool, or an enterprise platform, Google’s APIs offer a reliable way to tap into the power of services like Maps, YouTube, Gmail, and Google Cloud.

What Are Google APIs?

Google APIs are tools and services offered by Google that allow developers to interact with Google’s platforms and use their functionalities within their own applications. These APIs cover everything from location tracking to machine learning and cloud services.

Popular Google APIs include:

Maps API — Embed maps and location features.

YouTube API — Manage videos and channels.

Drive API — Access and manage Google Drive files.

Translate API — Translate text between languages.

Cloud Vision API — Analyze image content.

Firebase APIs — Power real-time apps with backend services.

Why Use Google APIs?

Access Rich Data: Leverage real-time and historical data from Google.

Build Smarter Apps: Integrate AI, translation, and location features effortlessly.

Cross-Platform Support: Use on web, mobile, and desktop.

Scalable & Reliable: Backed by Google’s infrastructure.

Free Tiers Available: Many APIs offer generous free quotas for developers.

Common Categories of Google APIs

Maps & Location

Maps JavaScript API

Geocoding & Places API

Distance Matrix API

Media & YouTube

YouTube Data API

YouTube Analytics API

Productivity & Communication

Gmail API

Google Calendar API

Drive, Docs & Sheets APIs

Machine Learning

Vision API — Detect objects, faces, text.

Natural Language API — Understand text meaning.

Translation API — Instant language translation.

Speech APIs — Convert between speech and text.

Firebase APIs

Authentication, Firestore, Realtime Database, Cloud Messaging, and more.

How to Use a Google API

Create a Project in Google Cloud Console.

Enable the API you want (e.g., Maps, YouTube, etc.).

Generate Credentials (API key, OAuth client ID, or Service Account).

Install a Client Library or use direct REST calls.

Start Building your application using the API.

Discover the Full Guide Now

Authentication Methods

API Key: For simple apps that don’t access personal user data.

OAuth 2.0: Needed for accessing user-specific services like Gmail or Drive.

Service Account: For server-to-server interactions.

Real-World Use Cases

Ride-Sharing: Maps + Distance Matrix APIs.

E-commerce: Vision API for image recognition, Sheets API for inventory.

Education Apps: Drive & Classroom APIs for file management.

AI Chatbots: Natural Language + Speech APIs.

Costs & Quotas

Most Google APIs have free monthly usage quotas. Examples:

Maps API: 28,000 free map loads/month.

Vision API: 1,000 units/month free.

Translate API: 500K characters/month free.

Monitor usage in your Google Cloud Console and set billing alerts to avoid surprises.

Best Practices

Secure your API keys — don’t expose them in public code.

Use caching to reduce repeated API calls.

Read the official documentation thoroughly.

Handle errors and rate limits gracefully in your app.

Google APIs are powerful tools that help developers build feature-rich, scalable, and intelligent applications. Whether you’re building for web, mobile, or enterprise, there’s likely a Google API that can speed up development and improve user experience.

So if you’re planning to add maps, manage content, automate workflows, or introduce AI to your app — Google APIs have got you covered.

Helpful Links:

Google API Librar

Google API Doc

API Pricing

[sinchvoice]

The Benefits of Integrating Text-to-Speech Technology for Personalized Voice Service

Sinch is a fully managed service that generates voice-on-demand, converting text into an audio stream and using deep learning technologies to convert articles, web pages, PDF documents, and other text-to-speech (TTS). Sinch provides dozens of lifelike voices across a broad set of languages for you to build speech-activated applications that engage and convert. Meet diverse linguistic, accessibility, and learning needs of users across geographies and markets. Powerful neural networks and generative voice engines work in the background, synthesizing speech for you. Integrate the Sinch API into your existing applications to become voice-ready quickly.

Voice Service

Voice services, such as Voice over Internet Protocol (VoIP) or Voice as a Service (VaaS), are telecommunications technologies that convert Voice into a digital signal and route conversations through digital channels. Businesses use these technologies to place and receive reliable, high-quality calls through their internet connection instead of traditional telephones. We at Sinch provide the best voice service all over India.

Voice Messaging Service

A Voice Messaging Service or System, also known as Voice Broadcasting, is the process by which an individual or organization sends a pre-recorded message to a list of contacts without manually dialing each number. Automated Voice Message service makes communicating with customers and employees efficient and effective. With mobile marketing quickly becoming the fastest-growing advertising industry sector, the ability to send a voice broadcast via professional voice messaging software is now a crucial element of any marketing or communication initiative.

Voice Service Providers in India

Voice APIs, IVR, SIP Trunking, Number Masking, and Call Conferencing are all provided by Sinch, a cloud-based voice service provider in India. It collaborates with popular telecom companies like Tata Communications, Jio, Vodafone Idea, and Airtel. Voice services are utilized for automated calls, secure communication, and client involvement in banking, e-commerce, healthcare, and ride-hailing. Sinch is integrated by businesses through APIs to provide dependable, scalable voice solutions.

More Resources:

The future of outbound and inbound dialing services

The Best Cloud Communication Software which are Transforming Businesses in India

[eminence-technology]

Mastering .NET for Modern Application Development

Introduction to .NET Framework

.NET, developed by Microsoft, is a robust and versatile framework designed for building modern, scalable, and high-performance applications. From desktop solutions to web-based platforms, .NET has solidified its position as a developer’s go-to choice for application development in the tech-driven era.

Why Choose .NET for Application Development?

.NET offers a plethora of features that make it ideal for creating modern applications:

Cross-Platform Compatibility: With .NET Core, developers can build applications that run seamlessly across Windows, macOS, and Linux.

Language Flexibility: It supports multiple programmtuing languages, including C#, F#, and VB.NET, giving developers the freedom to choose.

Scalability and Performance: Optimized for high-speed execution, Custom .NET Solutions ensures your applications are fast and scalable.

Comprehensive Libraries: The extensive class library simplifies coding, reducing the need for writing everything from scratch.

Key Features of .NET Framework

Rich Development EnvironmentThe Visual Studio IDE provides powerful tools, including debugging, code completion, and cloud integration.

Security and ReliabilityBuilt-in authentication protocols and encryption mechanisms ensure application security.

Integration with Modern ToolsCompatibility with tools like Docker and Kubernetes enhances deployment efficiency.

Core Components of .NET

Common Language Runtime (CLR): Executes applications, providing services like memory management and exception handling.

Framework Class Library (FCL): Offers a standardized base for app development, including classes for file management, networking, and database connectivity.

ASP.NET Core: Specializes in building dynamic web applications and APIs.

How .NET Supports Modern Application Development

Building Scalable Web Applications

Modern web development often demands real-time, scalable, and efficient solutions. ASP.NET Core, a key component of the .NET ecosystem, empowers developers to create:

Interactive web applications.

Microservices using minimal resources.

APIs that integrate seamlessly with third-party tools.

Cloud-Native Development

With the integration of Microsoft Azure, .NET simplifies the development of cloud-native applications. Features like automated deployment, serverless computing, and global scalability make it indispensable.

Understanding .NET for Mobile Applications

Xamarin, a .NET-based framework, has become a popular choice for mobile application development. It enables developers to write code once and deploy it across Android, iOS, and Windows platforms. This approach significantly reduces development time and costs.

Comparing .NET with Other Frameworks

While frameworks like Java Spring and Node.js offer unique features, .NET stands out due to:

Unified Ecosystem: Provides a single platform for diverse app types.

Ease of Use: The learning curve is smoother, especially for developers familiar with Microsoft tools.

Cost-Effectiveness: Free tools and extensive documentation make it budget-friendly.

Diving Deeper into .NET Application Development

Cross-Platform Development Made Easy

With .NET Core, developers can write applications that run uniformly across multiple operating systems. This cross-platform capability is particularly beneficial for businesses targeting a broad audience.

Microservices Architecture

The modular nature of .NET makes it perfect for building microservices architectures, enabling efficient scaling and maintenance of applications.

Leveraging .NET for AI and Machine Learning

The integration of ML.NET offers developers the ability to create AI-powered applications directly within the .NET ecosystem. This includes:

Predictive analytics.

Image and speech recognition.

Natural language processing.

Best Practices for Mastering .NET

Stay UpdatedMicrosoft frequently updates .NET, introducing new features and optimizations. Regular learning ensures you stay ahead.

Focus on Code ReusabilityUse libraries and components to minimize repetitive coding tasks.

Leverage Debugging ToolsVisual Studio’s debugging capabilities help identify and resolve issues efficiently.

Embrace Cloud IntegrationCombining .NET with Azure ensures seamless scalability and deployment.

A Glance at Eminence Technology

Eminence Technology stands as a leading name in web development services. Specializing in .NET application development, the company delivers tailor-made solutions that cater to diverse industry needs. With a team of skilled developers, Eminence Technology excels in creating high-performance, secure, and scalable applications.

Why Choose Eminence Technology?

Proven expertise in the web development process.

Commitment to delivering cutting-edge solutions.

Exceptional customer support and post-development services.

[atplblog]

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[learning-code-ficusoft]

Building Chatbots with Amazon Lex and Polly

Building Chatbots with Amazon Lex and Polly

Amazon Lex and Polly are AWS services that help developers build conversational AI chatbots with natural language processing and text-to-speech capabilities.

Amazon Lex: A fully managed service for building voice and text-based conversational interfaces powered by automatic speech recognition (ASR) and natural language understanding (NLU).

Amazon Polly: A text-to-speech (TTS) service that converts text into lifelike speech using deep learning technologies.

Step 1: Setting Up Amazon Lex for Chatbot Development

1. Create a Bot in Amazon Lex

Go to the AWS Management Console → Open Amazon Lex.

Click Create Bot → Choose Start with an example or Create your own.

Name your bot (e.g., CustomerSupportBot).

Set IAM permissions (Lex needs permission to call Lambda functions if needed).

2. Define Intents and Utterances

Intent: Defines what the user wants (e.g., BookFlight, OrderPizza).

Utterances: Sample phrases the user might say (e.g., “I want to book a flight to New York.”).

Example of defining an intent for booking a flight:json{ "intentName": "BookFlight", "sampleUtterances": [ "I need to book a flight", "Can you help me find a flight?", "Book a ticket to {Destination}" ] }

3. Define Slots (User Inputs)

Slots capture user input for the intent. Example slots for a flight booking bot:

Slot Name Data Type Required ExampleDestination AMAZON.CityYes" New York"Date AMAZON.DateYes "Next Friday"NumTickets AMAZON.Number No"2"

4. Configure Responses

Add responses for the chatbot:json{ "messages": [ {"contentType": "PlainText", "content": "Where would you like to fly?"} ] }

5. Test the Bot

Use the built-in Test Chat Interface in Amazon Lex.

Deploy it to platforms like Slack, Facebook Messenger, or a website.

Step 2: Enhancing Conversational Experience with Amazon Polly

1. Convert Text to Speech

Amazon Polly provides natural-sounding voices. Example using Python & Boto3:pythonimport boto3polly = boto3.client("polly")response = polly.synthesize_speech( Text="Hello! How can I assist you today?", OutputFormat="mp3", VoiceId="Joanna" )# Save the audio response with open("speech.mp3", "wb") as file: file.write(response["AudioStream"].read())

2. Stream Speech Output in Real Time

Polly allows real-time streaming of responses, making interactions more human-like.

Step 3: Integrating Amazon Lex and Polly for Voice Chatbots

Capture User Speech Input (Lex processes user queries).

Generate Response in Text (Lex determines the response).

Convert Text Response to Speech (Polly speaks the response).

Example integration:pythondef text_to_speech(response_text): polly = boto3.client("polly") speech = polly.synthesize_speech(Text=response_text, OutputFormat="mp3", VoiceId="Matthew") return speech["AudioStream"].read()

Step 4: Deploying the Chatbot on Web & Mobile Apps

Amazon Connect: Integrate the chatbot for customer service.

AWS Lambda: Handle backend logic.

Amazon API Gateway: Expose chatbot as a REST API.

Amazon Lex SDK: Embed the bot into websites and mobile apps.

Conclusion

By combining Amazon Lex for NLP and Amazon Polly for speech synthesis, developers can create intelligent, voice-enabled chatbots for customer service, virtual assistants, and interactive applications.

WEBSITE: https://www.ficusoft.in/aws-training-in-chennai/

[hashmianasdigicult]

Meta Updates In 2025

Meta Updates In 2025

As we step into 2025,Key Changes in Facebook Advertising ,Paid Advertising & Advertising agency. Meta is ending a third party fact checking program and moving to a Community Notes model. Meta will allow more speech by lifting restrictions on some topics that are part of mainstream discourse & focusing our enforcement on illegal and high severity violations. Meta will take a more Personalized approach to Political contents, so that people who want to see more of it in their feeds can.

Facebook Advertising Updates

-Meta Business Suite: This platform will help you become a more effective marketer by allowing you to post across platforms, create ads, track insights, and access tools like Commerce Manager and Ads Manager . - Advantage+ Campaigns: Meta is bringing the power of video to its Advantage+ campaigns, including automatic optimization for Reels and videos, and the option to use branded videos or customer demonstration videos in catalog ads . - Shop Ads: Meta is expanding access to its integrations with Magento and Salesforce Commerce Cloud, making it easier for advertisers to drive sales through Shop ads . - Recurring Messenger Notifications: Meta released a recurring notification feature that lets you send personalized, automated messages to customers through Messenger to alert them of promotions, new product releases, sales, and major business updates .

Paid Advertising Updates

- Performance : Meta’s “Performance ” includes five changes to improve ad performance, including simplified ad sets, broad targeting, mobile-friendly video, ad testing, and Conversions API . - Billing Options: Meta now offers two billing options: billing threshold and net 30, giving advertisers more flexibility in managing their ad spend .

Advertising Agency Updates

- Top Meta Advertising Agencies: Some of the top Meta advertising agencies include inbeat, Web Tonic, Fixated, LYFE Marketing, and Brighter Click, each offering unique services and expertise ². - Agency Selection Tips: When selecting a Meta advertising agency, consider factors such as the agency’s track record and experience, understanding of your industry and target audience, and communication style

Why is Meta making these changes?

This change is part of Meta’s effort to prevent advertisers from sharing prohibited information under their terms of use. It aims to protect users of the Meta platforms and prevent sensitive information from being shared through the Meta pixel.

In WhatsApp Meta Announces New Option to Add Your WhatsApp to Accounts Center & Introducing New Ways to Chat on WhatsApp .They’re kicking off the new year with new features and design updates that make WhatsApp more fun and easier to use & They’re excited to announce that we’ll be rolling out a new option to add WhatsApp to Accounts Center over the next few months.

In conclusion

For advertising, agencies, and companies alike, the changing meta landscape of 2025 brings both new opportunities and problems. Businesses now have more options to maximize their advertising campaigns thanks to significant upgrades including the switch to Community Notes, improved customization in political material, and increased tools in Meta Business Suite. Paid advertising initiatives are further strengthened by the use of AI-driven Advantage+ campaigns, enhanced Shop Ads, and adaptable billing choices.

Working with a leading digital marketing agency in Abu Dhabi can be revolutionary for companies trying to optimize their digital marketing success. These agencies are experts in utilizing Meta's most recent developments, guaranteeing data-driven campaign management, strategic audience targeting, and enhanced ad performance. Furthermore, in the always changing world of digital marketing, choosing the best Meta advertising agency—one with experience, industry knowledge, and a solid grasp of the local market—remains essential for increasing engagement, increasing conversions, and attaining long-term growth.

[softwareknowledgesworld]

The Essential Tools and Frameworks for AI Integration in Apps

Artificial intelligence (AI) is no longer a futuristic concept; it's a transformative force reshaping how applications are built and used. Understanding the right tools and frameworks is essential if you're wondering how to integrate AI into an app. With many options, choosing the right ones can distinguish between a mediocre application and one that delivers a seamless, intelligent user experience. This guide will walk you through the most essential tools and frameworks for AI integration in app development.

1. Popular AI Frameworks

AI frameworks simplify the development and deployment of AI models, making them an essential part of the integration process. Below are some of the most widely used frameworks:

a) TensorFlow

Developed by Google, TensorFlow is an open-source framework widely used for machine learning and AI development. It supports a variety of tasks, including natural language processing (NLP), image recognition, and predictive analytics.

Key Features:

Robust library for neural network development.

TensorFlow Lite for on-device machine learning.

Pre-trained models are available in TensorFlow Hub.

b) PyTorch

Backed by Facebook, PyTorch has gained immense popularity due to its dynamic computation graph and user-friendly interface. It's particularly favoured by researchers and developers working on deep learning projects.

Key Features:

Seamless integration with Python.

TorchScript for transitioning models to production.

Strong community support.

c) Keras

Known for its simplicity and ease of use, Keras is a high-level API running on top of TensorFlow. It's ideal for quick prototyping and small-scale AI projects.

Key Features:

Modular and user-friendly design.

Extensive support for pre-trained models.

Multi-backend and multi-platform capabilities.

2. Tools for Data Preparation

AI models are only as good as the data they're trained on. Here are some tools to help prepare and manage your data effectively:

a) Pandas

Pandas is a powerful Python library for data manipulation and analysis. It provides data structures like DataFrames to manage structured data efficiently.

b) NumPy

Essential for numerical computing, NumPy supports large, multi-dimensional arrays and matrices and mathematical functions to operate on them.

c) DataRobot

DataRobot automates the data preparation process, including cleaning, feature engineering, and model selection, making it an excellent choice for non-technical users.

3. APIs and Services for AI Integration

For developers who want to incorporate AI without building models from scratch, APIs and cloud-based services provide an easy solution:

a) Google Cloud AI

Google Cloud offers pre-trained models and tools for various AI tasks, including Vision AI, Natural Language AI, and AutoML.

b) AWS AI Services

Amazon Web Services (AWS) provides AI services like SageMaker for building, training, and deploying machine learning models and tools for speech, text, and image processing.

c) Microsoft Azure AI

Azure AI provides cognitive services for vision, speech, language, and decision-making and tools for creating custom AI models.

d) IBM Watson

IBM Watson offers a range of AI services, including NLP, speech-to-text, and predictive analytics, designed to integrate seamlessly into apps.

4. Development Tools and IDEs

Efficient development environments are crucial for integrating AI into your app. Here are some recommended tools:

a) Jupyter Notebook

Jupyter Notebook is an open-source tool that allows developers to create and share live code, equations, and visualizations. It's widely used for exploratory data analysis and model testing.

b) Visual Studio Code

This lightweight yet powerful IDE supports Python and other languages commonly used in AI development. Extensions like Python and TensorFlow add specific capabilities for AI projects.

c) Google Colab

Google Colab is a cloud-based platform for running Jupyter Notebooks. It offers free GPU and TPU access, making it ideal for training AI models.

5. Version Control and Collaboration Tools

Managing code and collaboration effectively is essential for large-scale AI projects. Tools like GitHub and GitLab allow teams to collaborate, track changes, and manage repositories efficiently.

Key Features:

Branching and version control.

Integration with CI/CD pipelines for automated deployment.

Support for collaborative coding and reviews.

6. AI Deployment Platforms

Once your AI model is ready, deploying it efficiently is the next step. Here are some tools to consider:

a) Docker

Docker allows you to package your AI model and its dependencies into containers, ensuring consistent deployment across environments.

b) Kubernetes

Kubernetes is an orchestration tool for managing containerized applications. It's ideal for deploying large-scale AI models in distributed systems.

c) MLflow

MLflow is an open-source platform for managing the end-to-end machine learning lifecycle, including experimentation, reproducibility, and deployment.

Conclusion

Integrating AI into an app can be complex, but it becomes manageable and gratifying with the right tools and frameworks. Whether you're using TensorFlow for model building, Google Cloud AI for pre-trained APIs, or Docker for deployment, the key is to choose the solutions that align with your project's goals and technical requirements. You can create intelligent applications that deliver real value to users and businesses by leveraging these essential tools.

[govindhtech]

Amazon Nova Sonic: Human-like Voice Chats For Generative AI

Learn about Amazon Nova Sonic, a lifelike voice model for next-gen generative AI applications across sectors.

Interactive education, gaming, customer service call automation, and language acquisition all benefit from voice interfaces. However, voice-enabled app development is tricky.

Traditional voice-enabled software development requires complex coordination of text-to-speech, language models, and speech recognition models.

This disconnected strategy makes development harder and removes tone, prosody, and speaking style from genuine interactions. This may affect conversational AI applications that need low latency and excellent verbal and nonverbal cue understanding for seamless dialogue handling and natural turn-taking.

Amazon Nova Sonic, the latest foundation model (FM) in Amazon Bedrock, simplifies speech-enabled app deployment.

Amazon Nova Sonic integrates speech interpretation and generation into a single model allowing developers to build authentic, human-like conversational AI experiences with low latency and industry-leading cost. This integrated strategy streamlines conversational app development.

The unified model design allows real-time text transcription and expressive speech creation without a separate model. An adaptive speech response dynamically adapts to the input speech's prosody, including timbre and pace.

When using Amazon Nova Sonic, developers can use function calling, also known as tool use, and agentic workflows to interact with external services and APIs and perform tasks in the customer's environment, including knowledge grounding with enterprise data using Retrieval-Augmented Generation (RAG).

With additional languages coming, Amazon Nova Sonic delivers great voice recognition for American and British English at launch, regardless of speaking patterns or acoustics.

Amazon Nova Sonic was designed with ethical AI in mind, including watermarking and content screening.

Amazon Nova Sonic performs

The demo takes place at a telecom call centre. Amazon Nova Sonic fulfils a subscription plan upgrade request.

Tools let the model to interface with other systems and employ agentic RAG with Amazon Bedrock Knowledge Bases to acquire customer-specific data like price, subscription plans, and account details.

The demo transcribes streaming voice input and displays streaming speech answers as text. A pie chart depicts the conversation's broad distribution, while a time chart shows its evolution. Call centre agents can also get contextual assistance from AI insights. Another noteworthy web interface measure is the average response time and customer-agent chat time distribution. Looking at the analytics and listening to the voices can show how customer sentiment rises throughout support chats.

The video shows Amazon Nova Sonic pausing to listen before continuing the chat after a disturbance.

How to add speech to applications.

With Amazon Nova Sonic

Before utilising Amazon Nova Sonic, toggle model access in the Amazon Bedrock console, just like other FMs. Enable the Amazon Nova Sonic for your account in the Model access area of the navigation pane under Amazon models.

Amazon Bedrock's new bidirectional streaming API, Invoke Model With Bidirectional Stream, lets you develop HTTP/2-based, low-latency conversational experiences. To ensure authentic dialogue, use this API to send audio input to the model and receive audio output in real time.

Amazon Nova Sonic's new API may be accessed with model ID amazon.nova-sonic-v1:0.

After session initialisation, the model uses an event-driven architecture on input and output streams with inference parameters.

Three event kinds dominate the input stream:

To establish the conversation's overall system prompt

Processing streaming audio in real time

Tool result handling: The tool delivers tool usage results to the model after output events request tool use.

Three event sets are also in the output streams:

Automatic speech recognition (ASR) streaming: Real-time speech recognition produces a speech-to-text transcript.

Tool usage handling: Handle tool use events with this data and return the result as input events.

The Amazon Nova Sonic model creates audio faster than real-time playback, hence a buffer is needed to play output audio in real time.

Amazon Nova model cookbooks include Amazon Nova Sonic samples.

Prompt speech engineering

Create Amazon Nova Sonic prompts with conversational flow and intelligibility when heard rather than seen, and optimise the content for auditory understanding.

When assigning your assistant a position, prioritise conversational attributes like kindness, understanding, and succinctness over text-oriented ones like thoroughness, technique, and detail. The following system prompt may work:

You're pal. The user and you will vocally exchange real-time conversation transcripts. Keep conversational responses to two or three sentences.

Avoid asking for sound effects, voice characteristic changes (e.g., singing, age, or accent), or visual formatting when creating speech model prompts.

Know something

Amazon Nova Sonic is available in US East (N. Virginia). Amazon Bedrock pricing shows price models.

The Amazon Nova Sonic can understand and produce human-sounding feminine and male English voices in American and British accents. Coming soon: further language support.

Amazon Nova Sonic blocks background noise and handles user interruptions without losing context. The model allows lengthier talks with a 32K audio token context window and a rolling window. The default session limit is 8 minutes.

The following AWS SDKs support the new bidirectional streaming API:

C++ AWS SDK

Java AWS SDK

Amazon JavaScript SDK

Kotlin AWS SDK

AWS Ruby SDK

AWS Rust SDK

Swift AWS SDK

This experimental SDK lets Python developers leverage Amazon Nova Sonic's bidirectional streaming.

Amazon Nova Sonic enables natural, fascinating voice interactions for conversational experiences, language learning apps, and customer support solutions.