azure speech to text online demo

AzureDevOps Guide

• Azure / Azure Speech services

Azure Speech to Text | Demo & Pricing

by Shan · August 13, 2022

Azure Speech to Text is an offering similar to that of Google Cloud’s Speech to Text and it can be easily integrated into websites and apps with minimal coding. This is offered under a service called “ Speech Services “ under Azure which includes Speech to Text as well as other related services like speech transcription, text-to-speech, speech translation, and speaker recognition. Let us see below how we can quickly get started with Azure Speech to Text by using CLI as well as with Javascript NPM or Python or any other language..

Creating Azure Speech Services Resource: The first step is to create Speech Services under your Azure Subscription and then get the keys which can be further used to access the Speech Service API (Cognitive Services API). The resource has 2 keys and you can use either one of them.There are different options available for translating Speech to Text such as Real Time translation as well as Batch translation.

Step 1: Go to Azure Portal and search for Speech services

Step 2 : Then enter the details as shown below. Enter a name and also select a region where it should be hosted along with a pricing tier. (For Demo purposes , Select Pricing tier as Free

Step 3: Once the resource is created and deployed a success message would be displayed as shown below

Step 4: Go to the resource and then click on Keys and Endpoint from where you will be able to copy the keys needed to access the speech services. This keys are needed to access the Speech to Text provided by Azure Speech Services.

Azure Speech to Text Demo using .NET CLI: This is quick demo that would allow you to get Azure Speech to Text up and running using CLI commands in your desktop provided you have Visual Studio installed already

Step 1: Open Visual Studio and then go to Tools → Command Line → Developer Command Prompt

Step 2: Then run the below command to install Speech Services CLI SDK

Step 3: Once it’s installed, the next step is to set the subscription key by running the below command which is used to access the speech services resource in Azure via APIs. This can be found under “Keys and Endpoints” in Azure speech services resource (In the below command, subscription-key should be replaced by your actual subscription key)

Once the command is executed as shown below the key would be saved at the particular location as shown below.

Step 4: Now we are all set to convert Speech into Text! All we need to do is run the below command as shown below. Instead of source as “en-US”, other languages codes such “en-IN”,”en-gb” etc.,

And once the command is executed, you can start speaking into your Microphone and it would translate the text as shown below. If there is a longer pause then the sentence would be completed, it would start as a newer sentence!

Tags: Azure Speech to Text Speech services Speech to Text Azure

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Quickstart: Convert text to speech

• 2 contributors

Reference documentation | Package (NuGet) | Additional Samples on GitHub

In this quickstart, you run an application that does text to speech synthesis.

You can try text to speech in the Speech Studio Voice Gallery without signing up or writing any code.

Prerequisites

• Azure subscription - Create one for free .
• Create a Speech resource in the Azure portal.
• Your Speech resource key and region. After your Speech resource is deployed, select Go to resource to view and manage keys. For more information about Azure AI services resources, see Get the keys for your resource .

Set up the environment

The Speech SDK is available as a NuGet package that implements .NET Standard 2.0. Install the Speech SDK later in this guide. For any requirements, see Install the Speech SDK .

Set environment variables

Your application must be authenticated to access Azure AI services resources. For production, use a secure way of storing and accessing your credentials. For example, after you get a key for your Speech resource, write it to a new environment variable on the local machine that runs the application.

Don't include the key directly in your code, and never post it publicly. See Azure AI services security for more authentication options such as Azure Key Vault .

To set the environment variable for your Speech resource key, open a console window, and follow the instructions for your operating system and development environment.

• To set the SPEECH_KEY environment variable, replace your-key with one of the keys for your resource.
• To set the SPEECH_REGION environment variable, replace your-region with one of the regions for your resource.

If you only need to access the environment variables in the current console, you can set the environment variable with set instead of setx .

After you add the environment variables, you might need to restart any programs that need to read the environment variable, including the console window. For example, if you're using Visual Studio as your editor, restart Visual Studio before you run the example.

Edit your .bashrc file, and add the environment variables:

After you add the environment variables, run source ~/.bashrc from your console window to make the changes effective.

Edit your .bash_profile file, and add the environment variables:

After you add the environment variables, run source ~/.bash_profile from your console window to make the changes effective.

For iOS and macOS development, you set the environment variables in Xcode. For example, follow these steps to set the environment variable in Xcode 13.4.1.

• Select Product > Scheme > Edit scheme .
• Select Arguments on the Run (Debug Run) page.
• Under Environment Variables select the plus (+) sign to add a new environment variable.
• Enter SPEECH_KEY for the Name and enter your Speech resource key for the Value .

To set the environment variable for your Speech resource region, follow the same steps. Set SPEECH_REGION to the region of your resource. For example, westus .

For more configuration options, see the Xcode documentation .

Synthesize to speaker output

Follow these steps to create a console application and install the Speech SDK.

Open a command prompt window in the folder where you want the new project. Run this command to create a console application with the .NET CLI.

The command creates a Program.cs file in the project directory.

Install the Speech SDK in your new project with the .NET CLI.

Replace the contents of Program.cs with the following code.

To change the speech synthesis language, replace en-US-AvaMultilingualNeural with another supported voice .

All neural voices are multilingual and fluent in their own language and English. For example, if the input text in English is "I'm excited to try text to speech" and you set es-ES-ElviraNeural , the text is spoken in English with a Spanish accent. If the voice doesn't speak the language of the input text, the Speech service doesn't output synthesized audio.

Run your new console application to start speech synthesis to the default speaker.

Make sure that you set the SPEECH_KEY and SPEECH_REGION environment variables . If you don't set these variables, the sample fails with an error message.

Enter some text that you want to speak. For example, type I'm excited to try text to speech . Select the Enter key to hear the synthesized speech.

More speech synthesis options

This quickstart uses the SpeakTextAsync operation to synthesize a short block of text that you enter. You can also use long-form text from a file and get finer control over voice styles, prosody, and other settings.

• See how to synthesize speech and Speech Synthesis Markup Language (SSML) overview for information about speech synthesis from a file and finer control over voice styles, prosody, and other settings.
• See batch synthesis API for text to speech for information about synthesizing long-form text to speech.

OpenAI text to speech voices in Azure AI Speech

OpenAI text to speech voices are also supported. See OpenAI text to speech voices in Azure AI Speech and multilingual voices . You can replace en-US-AvaMultilingualNeural with a supported OpenAI voice name such as en-US-FableMultilingualNeural .

Clean up resources

You can use the Azure portal or Azure Command Line Interface (CLI) to remove the Speech resource you created.

Create a C++ console project in Visual Studio Community named SpeechSynthesis .

Replace the contents of SpeechSynthesis.cpp with the following code:

Select Tools > Nuget Package Manager > Package Manager Console . In the Package Manager Console , run this command:

Build and run your new console application to start speech synthesis to the default speaker.

Reference documentation | Package (Go) | Additional Samples on GitHub

Install the Speech SDK for Go. For requirements and instructions, see Install the Speech SDK .

Follow these steps to create a Go module.

Open a command prompt window in the folder where you want the new project. Create a new file named speech-synthesis.go .

Copy the following code into speech-synthesis.go :

Run the following commands to create a go.mod file that links to components hosted on GitHub:

Now build and run the code:

Reference documentation | Additional Samples on GitHub

To set up your environment, install the Speech SDK . The sample in this quickstart works with the Java Runtime.

Install Apache Maven . Then run mvn -v to confirm successful installation.

Create a pom.xml file in the root of your project, and copy the following code into it:

Install the Speech SDK and dependencies.

Follow these steps to create a console application for speech recognition.

Create a file named SpeechSynthesis.java in the same project root directory.

Copy the following code into SpeechSynthesis.java :

Run your console application to start speech synthesis to the default speaker.

Reference documentation | Package (npm) | Additional Samples on GitHub | Library source code

To set up your environment, install the Speech SDK for JavaScript. If you just want the package name to install, run npm install microsoft-cognitiveservices-speech-sdk . For guided installation instructions, see the Install the Speech SDK .

Synthesize to file output

Follow these steps to create a Node.js console application for speech synthesis.

Open a console window where you want the new project, and create a file named SpeechSynthesis.js .

Install the Speech SDK for JavaScript:

Copy the following code into SpeechSynthesis.js :

In SpeechSynthesis.js , optionally you can rename YourAudioFile.wav to another output file name.

Run your console application to start speech synthesis to a file:

The provided text should be in an audio file:

Reference documentation | Package (Download) | Additional Samples on GitHub

The Speech SDK for Objective-C is distributed as a framework bundle. The framework supports both Objective-C and Swift on both iOS and macOS.

The Speech SDK can be used in Xcode projects as a CocoaPod , or downloaded directly and linked manually. This guide uses a CocoaPod. Install the CocoaPod dependency manager as described in its installation instructions .

Follow these steps to synthesize speech in a macOS application.

Clone the Azure-Samples/cognitive-services-speech-sdk repository to get the Synthesize audio in Objective-C on macOS using the Speech SDK sample project. The repository also has iOS samples.

Open the directory of the downloaded sample app ( helloworld ) in a terminal.

Run the command pod install . This command generates a helloworld.xcworkspace Xcode workspace that contains both the sample app and the Speech SDK as a dependency.

Open the helloworld.xcworkspace workspace in Xcode.

Open the file named AppDelegate.m and locate the buttonPressed method as shown here.

In AppDelegate.m , use the environment variables that you previously set for your Speech resource key and region.

Optionally in AppDelegate.m , include a speech synthesis voice name as shown here:

To make the debug output visible, select View > Debug Area > Activate Console .

To build and run the example code, select Product > Run from the menu or select the Play button.

After you input some text and select the button in the app, you should hear the synthesized audio played.

This quickstart uses the SpeakText operation to synthesize a short block of text that you enter. You can also use long-form text from a file and get finer control over voice styles, prosody, and other settings.

The Speech SDK for Swift is distributed as a framework bundle. The framework supports both Objective-C and Swift on both iOS and macOS.

Clone the Azure-Samples/cognitive-services-speech-sdk repository to get the Synthesize audio in Swift on macOS using the Speech SDK sample project. The repository also has iOS samples.

Navigate to the directory of the downloaded sample app ( helloworld ) in a terminal.

Open the file named AppDelegate.swift and locate the applicationDidFinishLaunching and synthesize methods as shown here.

Reference documentation | Package (PyPi) | Additional Samples on GitHub

The Speech SDK for Python is available as a Python Package Index (PyPI) module . The Speech SDK for Python is compatible with Windows, Linux, and macOS.

• On Windows, install the Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017, 2019, and 2022 for your platform. Installing this package might require a restart.
• On Linux, you must use the x64 target architecture.

Install a version of Python from 3.7 or later . For any requirements, see Install the Speech SDK .

Follow these steps to create a console application.

Open a command prompt window in the folder where you want the new project. Create a file named speech_synthesis.py .

Run this command to install the Speech SDK:

Copy the following code into speech_synthesis.py :

This quickstart uses the speak_text_async operation to synthesize a short block of text that you enter. You can also use long-form text from a file and get finer control over voice styles, prosody, and other settings.

Speech to text REST API reference | Speech to text REST API for short audio reference | Additional Samples on GitHub

Synthesize to a file

At a command prompt, run the following cURL command. Optionally, you can rename output.mp3 to another output file name.

The provided text should be output to an audio file named output.mp3 .

For more information, see Text to speech REST API .

Follow these steps and see the Speech CLI quickstart for other requirements for your platform.

Run the following .NET CLI command to install the Speech CLI:

Run the following commands to configure your Speech resource key and region. Replace SUBSCRIPTION-KEY with your Speech resource key and replace REGION with your Speech resource region.

Run the following command for speech synthesis to the default speaker output. You can modify the voice and the text to be synthesized.

If you don't set a voice name, the default voice for en-US speaks.

All neural voices are multilingual and fluent in their own language and English. For example, if the input text in English is "I'm excited to try text to speech" and you set --voice "es-ES-ElviraNeural" , the text is spoken in English with a Spanish accent. If the voice doesn't speak the language of the input text, the Speech service doesn't output synthesized audio.

Run this command for information about more speech synthesis options such as file input and output:

SSML support

You can have finer control over voice styles, prosody, and other settings by using Speech Synthesis Markup Language (SSML) .

Learn more about speech synthesis

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Additional resources

Anthony Chu

Web app developer.

When we do a live presentation — whether online or in person — there are often folks in the audience who are not comfortable with the language we're speaking or they have difficulty hearing us. Microsoft created Presentation Translator to solve this problem in PowerPoint by sending real-time translated captions to audience members' devices.

In this article, we'll look at how (with not too many lines of code) we can build a similar app that runs in the browser. It will transcribe and translate speech using the browser's microphone and broadcast the results to other browsers in real-time. And because we are using serverless and fully managed services, it can scale to support thousands of audience members. Best of all, these services all have generous free tiers so we can get started without paying for anything!

The app consists of two projects:

• A Vue.js app that is our main interface. It uses the Microsoft Azure Cognitive Services Speech SDK to listen to the device's microphone and perform real-time speech-to-text and translations.
• An Azure Function app providing serverless HTTP APIs that the user interface will call to broadcast translated captions to connected devices using Azure SignalR Service.

Speech SDK for Cognitive Services

Most of the heavy-lifting required to listen to the microphone from the browser and call Cognitive Speech Services to retrieve transcriptions and translations in real-time is done by the service's JavaScript SDK .

The SDK requires a Speech Services key. You can create a free account (up to 5 hours of speech-to-text and translation per month) and view its keys by running the following Azure CLI commands:

You can also create a free Speech Services account using the Azure portal using this link (select F0 for the free tier).

Azure SignalR Service

Azure SignalR Service is a fully managed real-time messaging platform that supports WebSockets. We'll use it in combination with Azure Functions to broadcast translated captions from the presenter's browser to each audience member's browser. SignalR Service can scale up to support hundreds of thousands of simultaneous connections.

SignalR Service has a free tier. To create an instance and obtain its connection string, use the following Azure CLI commands:

You can also use the Azure portal to create one by using this link .

Speech-to-text and translation in the browser

Cognitive Service's Speech SDK is really easy to use. To get started, we'll pull it into our Vue app:

Then we just need to initialize and start it:

And that's it! The recognizerCallback method will be invoked whenever text has been recognized. It is passed an event argument with a translations property that contains all the translations we asked for. For example, we can obtain the French translation with e.translations.get('fr') .

Broadcast captions to other clients

Now that we can obtain captions and translations thanks to the Cognitive Services Speech SDK, we need to broadcast that information to all viewers who are connected to SignalR Service via WebSocket so that they can display captions in real-time.

First, we'll create an Azure Function that our UI can call whenever new text is recognized. It's a basic HTTP function that uses an Azure SignalR Service output binding to send messages.

The output binding is configured in function.json. It takes a SignalR message object returned by the function and sends it to all clients connected to a SignalR Service hub named captions .

The function simply takes the incoming payload, which includes translations in all available languages, and relays it to clients using SignalR Service. (Sending every language to every client is quite inefficient; we'll improve on this later with SignalR groups.)

Back in our Vue app, we bring in the SignalR SDK:

Note that even though this package is under the @aspnet org on npm, it's the JavaScript client for SignalR. It may move to a different org later to make it easier to find.

When an audience member decides to join the captioning session and our Vue component is mounted, we'll start a connection to SignalR Service.

Whenever a newCaption event arrives, the onNewCaption callback function is invoked. We pick out the caption that matches the viewer's selected language and add it to the view model. Vue does the rest and updates the screen with the new caption.

We also add some code to disconnect from SignalR Service when the Vue component is destroyed (e.g, when the user navigates away from the view).

And that's pretty much the whole app! It captures speech from the microphone, translates it to multiple languages, and broadcasts the translations in real-time to thousands of people.

Increase efficiency with SignalR groups

There's a flaw in the app we've built so far: each viewer receives captions in every available language but they only need the one they've selected. Sometimes captions are sent multiple times per second, so sending every language to every client uses a lot of unnecessary bandwidth. We can see this by inspecting the WebSocket traffic:

To solve problems like this, SignalR Service has a concept called "groups". Groups allow the application to place users into arbitrary groups. Instead of broadcasting messages to everyone who is connected, we can target messages to a specific group. In our case, we'll treat each instance of the Vue app as a "user", and we will place each of them into a single group based on their selected language.

Instead of sending a single message containing every language to everyone, we will send smaller, targeted messages that each contains only a single language. Each message is sent to the group of users that have selected to receive captions in that language.

Add a unique client ID

We can generate a unique ID that represents the Vue instance when the app starts up. The first step to using groups is for the app to authenticate to SignalR Service using that identifier as the user ID. We achieve this by modifying our negotiate Azure Function. The SignalR client calls this function to retrieve an access token that it will use to connect to the service. So far, we've been using anonymous tokens.

We'll start by changing the route of the negotiate function to include the user ID. We then use the user ID passed in the route as the user ID in the SignalRConnectionInfo input binding. The binding generates a SignalR Service token that is authenticated to that user.

There are no changes required in the actual function itself.

Next, we need to change our Vue app to pass the ID in the route ( clientId is the unique ID generated by this instance of our app):

The SignalR client will append /negotiate to the end of the URL and call our function with the user ID.

Add the client to a group

Now that each client connects to SignalR Service with a unique user ID, we'll need a way to add a user ID to the group that represents the client's selected language.

We can do this by creating an Azure Function named selectLanguage that our app will call to add itself to a group. Like the function that sends messages to SignalR Service, this function also uses the SignalR output binding. Instead of passing SignalR messages to the output binding, we'll pass group action objects that are used to add and remove users to and from groups.

The function is invoked with a languageCode and a userId in the body. We'll output a SignalR group action for each language that our application supports — setting an action of add for the language we have chosen to subscribe to, and remove for all the remaining languages. This ensures that any existing subscriptions are deleted.

Lastly, we need to modify our Vue app to call the selectLanguage function when our component is created. We do this by creating a watch on the language code that will call the function whenever the user updates its value. In addition, we'll set the immediate property of the watch to true so that it will call the function immediately when the watch is initially created.

Send messages to groups

The last thing we have to do is modify our Azure Function that broadcasts the captions to split each message into one message per language and send each to its corresponding group. To send a message to a group of clients instead of broadcasting it to all clients, add a groupName property (set to the language code) to the SignalR message:

Now when we run the app, it still works the same as it did before, but if we inspect the SignalR traffic over the WebSocket connection, each caption only contains a single language.

• Check out the source code on GitHub
• Deploy the app — more details in the SignalR Service serverless programming guide
• Explore Azure Speech Services and the SignalR Service bindings for Azure Functions

Comments? Questions? Find me on Twitter .

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Command line tools utilising Azure Speech to Text Cognitive Services using the mlhub.ai framework

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Azure/azspeech2txt, folders and files.

Repository files navigation

Azure speech to text.

This MLHub package provides a quick introduction to the pre-built Speech to Text model provided through Azure's Cognitive Services. This service takes an audio signal and transcribes it to return the text.

In addition to the demonstration this package provides a collection of commands that turn the service into a useful command line tool for transcribing from the microphone or from an audio file.

A free Azure subscription allowing up to 5,000 transactions per month is available from https://azure.microsoft.com/free/ . After subscribing visit https://ms.portal.azure.com and Create a resource under AI and Machine Learning called Speech Services. Once created you can access the web API subscription key and endpoint from the portal. This will be prompted for in the demo.

This package is part of the Azure on MLHub repository. Please note that these Azure models, unlike the MLHub models in general, use closed source services which have no guarantee of ongoing availability and do not come with the freedom to modify and share.

Visit the github repository for more details: https://github.com/Azure/azspeech2txt

The Python code is based on the Azure Speech Services Quick Start for Python .

• To install mlhub (Ubuntu 18.04 LTS)
• To install and configure the demo:

Command Line Tools

In addition to the demo presented below, the azspeech2txt package provides a number of useful command line tools.

The listen command will listen for an utterance from the computer microphone for up to 15 seconds and then transcribe it to standard output.

The transcribe command takes an audio file and transcribes it to standard output. For large audio files this can take some time.

The audio file comes from Github: https://github.com/realpython/python-speech-recognition/raw/master/audio_files/harvard.wav

Demonstration

As you can see I read the first paragraph from the screen and the Azure Speech to Text service was quite accurate in its transcription. If the accuracy for the particular accent is good then it is quite suitable, for example, to be used as a dictation tool.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com .

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct . For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Legal Notices

Microsoft and any contributors grant you a license to the Microsoft documentation and other content in this repository under the Creative Commons Attribution 4.0 International Public License , see the LICENSE file, and grant you a license to any code in the repository under the MIT License , see the LICENSE-CODE file.

Microsoft, Windows, Microsoft Azure and/or other Microsoft products and services referenced in the documentation may be either trademarks or registered trademarks of Microsoft in the United States and/or other countries. The licenses for this project do not grant you rights to use any Microsoft names, logos, or trademarks. Microsoft's general trademark guidelines can be found at http://go.microsoft.com/fwlink/?LinkID=254653 .

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Microsoft and any contributors reserve all other rights, whether under their respective copyrights, patents, or trademarks, whether by implication, estoppel or otherwise.

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Streaming Speech to Text Solutions: A Comprehensive Guide

Step-by-Step Process

Media and broadcasting, krisp call center transcription, streaming speech-to-text faq.

Spread the word

Streaming speech-to-text technology has revolutionized the way enterprises handle communication, particularly in call centers. By converting spoken language into written text in real-time, businesses can significantly improve customer service, streamline operations, and enhance data management. This advanced technology leverages sophisticated algorithms and AI to ensure accuracy and efficiency, making it an indispensable tool for modern enterprises. In this guide, we provide a comprehensive overview of streaming speech-to-text solutions, their applications, industry trends, and the leading providers in 2024.

How Speech-to-Text Technology Works

Understanding the mechanics behind speech-to-text technology is crucial for appreciating its benefits. Here’s a detailed breakdown of the process:

• Microphone Specifications : High-quality microphones ensure clarity. Specifications like sensitivity, frequency response, and signal-to-noise ratio (SNR) are critical.
• Telephony Systems : Digital systems are preferred for their noise reduction capabilities and higher fidelity compared to analog systems.
• Noise Reduction Algorithms : Techniques like spectral subtraction, Wiener filtering, and deep learning-based denoising are employed.
• Echo Cancellation : Important in telephony, it removes echoes that can confuse the transcription algorithms.
• Acoustic Feature Extraction : Methods like Mel-frequency cepstral coefficients (MFCCs) and spectrogram analysis are used to capture important audio features.
• Temporal Features : Techniques like dynamic time warping (DTW) help in aligning sequences of varying speeds.
• Hidden Markov Models (HMMs) : Traditional models that segment and recognize patterns in the audio data.
• Deep Neural Networks (DNNs) : More advanced models that provide higher accuracy by learning complex patterns in large datasets.
• N-grams and Statistical Models : Used to predict the next word in a sequence based on the probability of word combinations.
• Recurrent Neural Networks (RNNs) and Transformers : Modern approaches that handle longer dependencies and context, leading to more accurate transcriptions.
• Real-time Text Rendering : Ensures minimal delay between speech and text output, crucial for live applications.
• Post-Processing : Includes tasks like punctuation addition, capitalization, and correcting common transcription errors.

Leading Use Cases of Streaming Speech-to-Text Technology

Streaming Speech-to-Text technology has a wide range of use cases across various industries and applications. This technology, which converts spoken language into written text in real-time, is proving to be invaluable for enhancing communication, accessibility, and productivity. Here are some key industries and how they are utilizing Streaming Speech-to-Text technology:

Call Centers

• Real-Time Assistance : Transcripts enable supervisors to provide real-time guidance to agents during calls.
• Customer History : Agents can quickly review previous transcripts to understand the customer’s history.
• Automated Workflows : Integration with CRM systems can automate task creation based on call transcripts.
• Resource Allocation : Transcripts help in analyzing call volumes and adjusting staffing levels accordingly.
• Sentiment Analysis : Textual data allows for sentiment analysis, helping to gauge customer satisfaction.
• Trend Analysis : Identifying common issues and trends from transcripts can inform product and service improvements.

Business Meetings

• Automated Summarization : Tools can summarize key points and actions from meeting transcripts.
• Follow-up Actions : Transcripts ensure that action items are clearly documented and followed up.
• Live Captions : Real-time transcription provides live captions for participants.
• Translatable Transcripts : Transcripts can be easily translated into other languages for non-native speakers.
• Keyword Search : Allows users to quickly find specific discussions or decisions in meeting transcripts.
• Knowledge Management : Integrates with knowledge management systems to archive and retrieve meeting content.
• Broadcast Delay Compensation : Ensures that subtitles are synchronized with live audio.
• Multilingual Support : Supports multiple languages for international broadcasts.
• Transcription for Editing : Editors can use transcripts to streamline the video and audio editing process.
• SEO Optimization : Transcripts can be used to generate searchable text content for SEO purposes.

Streaming Speech-to-Text Solutions in 2024

Here are some leading providers offering robust transcription services:

Picovoice Leopard

• On-Device Processing : Ensures privacy and reduces latency by processing audio locally.
• Low Latency : Provides near-instantaneous transcription suitable for real-time applications.
• Privacy-Preserving : No audio data leaves the device, ensuring maximum privacy.

Azure Speech-to-Text

• Customizable Models : Users can train custom models to improve accuracy for specific terminologies and accents.
• Real-Time and Batch Transcription : Supports both real-time and batch processing, allowing for flexible use cases.
• Multi-Language Support : Provides transcription in over 60 languages and dialects.

• Customizable Features: Users can fine-tune the noise cancellation and accent localization to better fit the specific needs of their call centers.
• On-Device Transcription: Supports on-device transcription, ensuring accurate representation of calls.
• Background Noise Cancellation: Utilizes advanced AI to filter out background noises, enhancing call clarity and customer experience.
• Accent Localization: Automatically adjusts to various accents, ensuring clear and accurate transcription regardless of the speaker’s accent.

Krisp’s Transcription Software: Leading the Way

Krisp Call Center Transcription employs noise-robust deep learning algorithms for on-device speech-to-text conversion. Specifically, the process consists of several stages :

• Processes and turns speech into  unformatted text.
• Adds  punctuation, capitalization,  and  numerical values.
• Removes  PII/PCI  and filler words on-device and in real time.
• Assigns text to  speakers  with  timestamps.
• Temporarily stores the  encrypted  transcript  locally.
• Safely transmits the transcript to a  private cloud.

Technical Advantages of Krisp for Enterprise Call Centers

Superior transcription accuracy.

• 96% Accuracy: Leveraging cutting-edge AI, Krisp ensures high-quality transcriptions even in noisy environments, boasting a Word Error Rate (WER) of only 4%.

On-Device Processing

• Enhanced Security: Krisp’s desktop app processes transcriptions and noise cancellation directly on your device, ensuring sensitive information remains secure and compliant with stringent security standards.

Unmatched Privacy

• Real-Time Redaction: Ensures the utmost privacy by redacting Personally Identifiable Information (PII) and Payment Card Information (PCI) in real-time.
• Private Cloud Storage: Stores transcripts in a private cloud owned by customers, with write-only access, ensuring complete control over data.

Centralized Solution Across All Platforms

• Cost Optimization: By centralizing call transcriptions across all platforms, Krisp CCT optimizes costs and simplifies data management.
• Streamlined Operations: Eliminates the need for multiple transcription services, making data handling more efficient.

No Additional Integrations Required

• Effortless Integration: Krisp’s plug-and-play setup integrates seamlessly with major Contact Center as a Service (CCaaS) and Unified Communications as a Service (UCaaS) platforms.
• Operational Efficiency: Requires no additional configurations, ensuring smooth and secure operations from the start.

Book a Demo

Wrapping up

Streaming speech-to-text technology is a game-changer for enterprises, particularly in call centers. It enhances customer service, operational efficiency, and data management. Krisp’s transcription software, with its superior noise cancellation and on-device transcription capabilities, is a standout choice for businesses looking to leverage this technology.

Related Articles

Redesign of Microsoft's Azure speech-to-text demo to showcase product ability

The Azure text-to-speech demo needed an update to improve functionality and drive delight for users

I was the sole user experience designer and supported the Microsoft cognitive service team to improve the capabilities of a speech-to-text demo that highlighted Microsoft speech services' capabilities. The prior demo was outdated and didn't highlight the technology's capabilities very well, and there are grammatical errors in the output.

User Experience Design Wireframe Consultant

Nina Lascano , UI Designer Oliver Scholz, Program Manager

Figma Pen Paper

https://azure.microsoft.com/en-us/services/cognitive-services/speech-to-text/#features

Previous state

Speech-to-text.

Before the redesign, the speech-to-text demo suffered from a myriad of problems. Languages did not translate with correct grammar, the response time of the demo was slow, the samples were nonsensical, and it didn't highlight all of the product's capabilities. 

The product team wanted to showcase all of the features that the technology had to drive traffic to use the API. They asked that I showcase:

• the full list of languages available
• inverse text normalization
• punctuation
• customized phrase list
• multiple input sources such as both live voice and file uploads.

Experience Design

First, I needed to understand who I was designing for and what their goals were. Through discussions with the PM, I discovered that this product had two main customers. Business decision-makers and developers. 

Developers​

• Has 2+ years experience
• Uses programs in a professional capacity at least 11 hours a week
• Uses cloud computing and AI

Business decision makers​

• Work in midsize to large companies
• They are business or project managers​
• They have worked on products that involve voice control​

To truly understand how the product was supposed to work, I created a user flow to nail down, with the team, the expected interaction with the demo.

Sketches and wire frames

After I understood what the expected behavior of the new demo and what the asks were I created some sketches. I used these sketches to explore multiple versions of the design. I used these sketches when I talked with the project manager. It helped me narrow down the most important functionalities that were needed in the design as well as the nice to haves.

Once I got the sign-off, I designed a wireflow to illustrate each step of how the demo should work. This was imperative to ensure that the demo's experience was fully understood by the UI designer finalizing the content and giving them context for how each piece of the design was expected to work. 

Moving into the final design and the hand-off​

During the share-out, I was informed that the file saving portion of the design was not feasible, and that would have to be removed. When I handed off the concept designs to the final designer, it was a smooth transition because we had ensured that she was a part of the conversation during the ideation phase. At the last minute, there was a decision to remove translation so that it could be highlighted in a replicated demo for the translation API page.

The final product was trimmed down even more to highlight the functionality and drive customers to the Azure Cognitive Services portal to test the service. This demo is an easy to interface way to drive customers to convert to Azure's speech-to-text service. The final product was tested with five unique users that provided feedback that is currently being implemented to improve the demo and feedback that confirmed our hypothesis.

• ​People loved the demo. They were surprised and delighted when using it and were impressed by how well the technology works.
• In two of the five tests, people did not know what kind of files they could upload into the demo. It was my recommendation to add a tooltip or descriptor text.
• One person had difficulty clearing the chat and tried multiple ways to do so, including deleting text in the box and changing the language.
• The microphone's timeout was longer than users expected, and they were thrown off when their speech would continually be caught. This confusion was short, but it was important to note. I recommended that an informational earcon and an animation would help the user identify the state change.

Email: [email protected]  |   Twitter: @magpies_seven

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Best Microsoft Azure Text to Speech (TTS) & AI Voiceover

Try the best Microsoft Azure text to speech app featuring the most conversational AI text to speech voices. Convert any text into natural-sounding Microsoft Azure speech, instantly.

Download your audio files as MP3 or WAV, or access our AI voices through our advanced TTS API .

Clone a voice

Trusted by individuals and teams of all sizes

Microsoft Azure Text to Speech Features

Enjoy flawless, natural-sounding Microsoft Azure AI text to speech that sound native and conversational. No dull, robotic voices here - only conversational voice overs indistinguishable from humans

Text to Speech Voice Library

The PlayHT voice library features over 900 premium AI TTS voices. From distinctive characters and even accents, each voice brings depth and authenticity to your projects.

Real-time TTS Generation

Convert Microsoft Azure text to speech with one of the fastest TTS apps. Create voice overs instantaneously without the need for voice actors. With real-time voice generation, quickly generate audio content on-the-fly.

Text to Speech in 142+ Languages

Read aloud in languages other than Microsoft Azure. Choose from over 143 other languages that are native sounding. Some of our supported languages include English, Spanish, Arabic, Chinese, French, Czech, Dutch, and more. Explore all our available languages & accents.

Microsoft Azure TTS Accents

PlayHT is one of the very few text to speech apps that offers Microsoft Azure TTS accents. Want to listen in English, but you need a Southern drawl? No problem. Want to convert text to Tamil? Switch to a natural sounding Tamil accent.

What is Microsoft Azure Text to Speech?

Text to speech (TTS) is a technology that reads aloud digital text—the words on computers, smartphones, and tablets. Using TTS, you can listen to books, articles, and websites without having to read them on a screen. It's like having someone read to you.

This technology is vital for accessibility, helping people with visual impairments access written content through audio. It's also integrated into navigation systems and virtual assistants for seamless human-machine interaction.

Improving naturalness in synthesized speech is an ongoing goal. Researchers use machine learning and neural networks to refine algorithms and enhance speech quality. The aim is to make synthesized speech sound more like human speech, reducing the gap between the two.

How Microsoft Azure Text to Speech Works

Here's a step-by-step tutorial for using Microsoft Azure text to speech on PlayHT:

Sign Up or Log In

Create a new account or log into your existing PlayHT dashboard to access the TTS studio.

Enter Your Microsoft Azure Text

Once logged in, you can type, paste, or upload your text directly into the text box.

Choose a Microsoft Azure Voice

Choose some of the best Microsoft Azure AI voices or 900+ conversational AI voices and 142 other languages. You’re sure to find the perfect voice for your project.

Custom Voices

Clone your voice to create a custom sounding Microsoft Azure text to speech voice. It only takes 30 seconds.

Move sliders to adjust tone, speed, and style to get the perfect speech output your project demands.

Generate & Download

Whenever you are ready, you can download a high quality MP3 audio file of your natural-sounding speech.

Microsoft Azure Text to Speech Use Cases

Unlock premium synthesized speech tailored to almost ever use case. With PlayHT's diverse array of AI text to speech voices and accents, ensuring your content is not just read aloud, but also enjoyable to listen.

Microsoft Azure AI Voice overs

Empower content creators with PlayHT's TTS for Microsoft Azure AI voice overs, allowing them to generate realistic and expressive voice overs for various applications such as animation, gaming, and storytelling, adding depth and authenticity to multimedia projects.

Audio Articles and Accessibility

Enhance accessibility and inclusivity by using PlayHT's TTS for Audio Articles and Accessibility, converting written Microsoft Azure content into audio formats such as podcasts and audiobooks, making it easier for individuals with dyslexia and other reading challenges to access and enjoy the content effortlessly.

Google Docs & Email

Listen to your Google Docs, email, or any Microsoft Azure website and automate your reading. Professionals and students that have to read a lot at school or work, can blaze through their reading and save minutes on every article. It all adds up.

Conversational AI

Create lifelike virtual Microsoft Azure assistants that engage users in natural dialogue, enhancing user experiences across platforms like chatbots, virtual agents, and smart speakers.

E-Learning and Training

Create Microsoft Azure E-Learning, training, or educational content that sounds conversational and engaging. Improve accessibility and engagement for students and employees of all abilities.

IVR Systems

Integrate PlayHT's Microsoft Azure TTS into IVR Systems to deliver clear and natural-sounding prompts and messages, enhancing the efficiency and effectiveness of customer interactions by providing easy-to-understand voice guidance.

YouTube and TikTok Videos

Create YouTube and TikTok Microsoft Azure TTS videos that sound native. Your listeners will not be distracted by a robotic voice or be able to distinguish it from human narration.

Benefits of Using Microsoft Azure Text-to-Speech

Eliminate the need for manual Microsoft Azure voiceovers, saving time and resources.

Consistency

Achieve a uniform voice quality across all your projects.

Global Reach

Cater to a worldwide audience with our multilingual voice options.

Cost-effective

Reduce expenses associated with traditional voice recording methods.

PlayHT AI Voice Capabilities for Enterprises

Ai voice cloning.

PlayHT's advanced AI Voice Cloning allows businesses to replicate any voice, ensuring brand consistency and personalization in voice interactions.

Listen to AI Voice performances created using PlayHT

Ultra Realistic AI Voices

PlayHT’s state-of-the-art technology captures the nuances of human speech, delivering voices that are indistinguishable from real human narrators, enhancing user engagement and trust.

Why Choose PlayHT Text to Speech?

PlayHT has pioneered Microsoft Azure conversational AI Text to Speech (TTS) and this makes the voices very human sounding. Convert your text into lifelike speech that is indistinguishable from humans.

Streamlined Content Production

Quickly convert extensive written Microsoft Azure content into audio, expanding your audience reach without the usual limitations of time and recording resources.

Cutting-edge API Integration

Seamlessly incorporate dynamic Microsoft Azure text to speech functionalities into your applications or services, unlocking new avenues for immersive auditory experiences.

Contextually-Aware TTS

Our AI dives deeper into the essence of your content, delivering Microsoft Azure speech synthesis that not only reflects the text but also captures its emotional core.

Authentic Language Representation

Immerse your audience in genuine speech across 29 languages, from subtle nuances to native expressions, ensuring an authentic, multilingual user experience regardless of language.

Extensive Support Resources

Get access to PlayHT’s dedicated support team and extensive knowledge base, empowering you with the tools and assistance needed to fully leverage our advanced technology.

Ethical AI Standards

Upholding the highest ethical standards in AI development and deployment, we prioritize user privacy and data protection, ensuring responsible and ethical use of our technology.

Who else can benefit from text to speech?

Elevate your content with playht's text-to-speech, choose from over 142 text to speech languages & accents.

Create the most natural-sounding conversational speech in not just various languages, but also accents.

About Marathi and India

Interesting facts about Indian Marathi

Marathi is spoken by about 83 million people, primarily in the state of Maharashtra in India. It belongs to the Indo-Aryan language family and uses the Devanagari script. Marathi has a rich literary history, with significant contributions to Indian literature and culture.

Maharashtra, located in western India, is the country's second-most populous state with about 124 million people. Its history includes the Maratha Empire and significant British colonial influence. Mumbai, the capital, is India's financial and entertainment hub. Maharashtrian culture includes traditional music, dance forms like Lavani, and festivals like Ganesh Chaturthi. Key landmarks include the Gateway of India, Ajanta and Ellora Caves, and the beaches of Goa. The official language is Marathi.

Customer Reviews

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The service team was exceptional and was very helpful in supporting my business needs. Would definitely use it again if needed!

The interface is clean, uncluttered, and super easy and intuitive to use. Having tried many others, PlayHT is my #1 favorite. Many natural sounding high quality voices to choose from...

I tried the bigger companies first and noting compare to this awesome website. The voices are so real that is amazing how AI is now. Don't waste your time in Polly, Azure, or Cloud; this is your text-to-voice software.

PlayHT was easy for me to use and add to my website. I am NOT computer savvy, so I appreciate the ease of this product. I believe this is going to help me stand out a bit from my peers.

How Can I Convert Text to Audio?

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EmoCtrl-TTS

Controlling Time-Varying Emotional States of Flow-Matching-Based Zero-Shot Text-to-Speech

EmoCtrl-TTS is an emotion-controllable zero-shot TTS that can generate highly emotional speech with non-verbal vocalizations such as laughter and crying for any speaker. EmoCtrl-TTS is purely a research project. Currently, we have no plans to incorporate EmoCtrl-TTS into a product or expand access to the public.

Controlling time-varying emotional states of zero-shot text-to-speech

EmoCtrl-TTS utilizes embeddings that represent emotion and non-verbal vocalizations to condition the flow-matching-based zero-shot TTS. In order to generate high-quality emotional speech, EmoCtrl-TTS is trained with over 27,000 hours of expressive data, curated using pseudo-labeling.

EmoCtrl-TTS can generate a voice of any speaker with non-verbal vocalizations like laughter and crying.

Generated speech samples

EmoCtrl-TTS is specifically designed to capture the time-varying emotional states found in the audio prompt.

Audio prompt (Angry → Calm)

Generate speech by Voicebox (prior work)

Generated speech by EmoCtrl-TTS (our work)

Audio samples

Below, we included audio samples demonstrating how EmoCtrl-TTS performs. The speech samples were taken from JVNV dataset, DiariST-AliMeeting dataset, and RAVDESS dataset. The speech samples below are provided for the sole purpose of illustrating EmoCtrl-TTS.

Capturing the time-varying emotional states

EmoCtrl-TTS can generate a speech that closely mimics the time-varying emotional states found in the audio prompt. In these demo samples, the audio prompt is created by concatenating two audio samples from RAVDESS data set. The text prompt is “dogs are sitting by the door dogs are sitting by the door” for all generated speech samples.

Generating non-verbal vocalization

EmoCtrl-TTS can generate non-verbal vocalizations, such as laughter and crying, that closely match the audio prompt.

Laughing speech generation

(Audio prompt from AliMeeting-DiariST dataset; real conversational speech in Chinese)

Crying speech generation

(Audio prompt from JVNV dataset; staged speech in Japanese)

Emotional speech-to-speech translation

EmoCtrl-TTS can be applied to speech-to-speech translation, transferring not only the voice characteristic but also the precise nuance of the source audio. The source audios were sampled from the JNVN dataset, which is a Japanese staged emotional speech corpus.

(*) We used Seamless Expressive for a pure research purpose. Seamless Expressive was used based on the Seamless Licensing Agreement. Copyright © Meta Platforms, Inc. All Rights Reserved. (**) We used Whisper to transcribe the speech, and then applied GPT-4 to translate the transcription to English.

Ethics statement

EmoCtrl-TTS is purely a research project. Currently, we have no plans to incorporate EmoCtrl-TTS into a product or expand access to the public. EmoCtrl-TTS could synthesize speech that maintains speaker identity and could be used for educational learning, entertainment, journalistic, self-authored content, accessibility features, interactive voice response systems, translation, chatbot, and so on. While EmoCtrl-TTS can speak in a voice like the voice talent, the similarity, and naturalness depend on the length and quality of the speech prompt, the background noise, as well as other factors. It may carry potential risks in the misuse of the model, such as spoofing voice identification or impersonating a specific speaker. We conducted the experiments under the assumption that the user agrees to be the target speaker in speech synthesis. If the model is generalized to unseen speakers in the real world, it should include a protocol to ensure that the speaker approves the use of their voice and a synthesized speech detection model. If you suspect that EmoCtrl-TTS is being used in a manner that is abusive or illegal or infringes on your rights or the rights of other people, you can report it at the Report Abuse Portal.

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