Blogs by Ashwin

  Introduction Each of the several parts that make up an AI search solution is crucial to the extraction, enrichment, indexing, and querying of data. Data Source The majority of search solutions begin with a data source that has the information you are looking for. Azure AI Search is compatible with a variety of data sources, including: Unstructured files in Azure blob storage containers. Tables in Azure SQL Database. Documents in Cosmos DB. Azure AI Search can pull data from these data sources for indexing. Alternatively, applications can push JSON data directly into an index, without pulling it from an existing data store. Skillset You may index the data taken from the data source in a simple search solution. The data source determines the information that can be extracted. For example, while indexing data in a database, the fields in the database tables may be extracted. Similarly, when indexing a collection of documents, the text content of the document many be extracted along...

  Manage Capacity You must first build an Azure AI Search resource in your Azure subscription before you can develop an Azure AI Search solution. You might additionally require Azure resources for data storage and other application services, depending on the particular solution you want to develop. Service Tiers and Capacity Management When establishing an Azure AI Search resource, it is necessary to choose a pricing tier. The tier you choose dictates the capacity restrictions of your search service, the configuration options that are accessible to you, and the expense of the service. The available pricing tiers are: Free (F)- Use this tier to explore the service or try the tutorials in the product documentation. Basic (B)- Use this tier for small-scale search solutions that include a maximum of 15 indexes and 2 GB of index data. Standard (S)- Use this tier for enterprise-scale solutions. There are multiple variants of this tier, including S, S2, and S3; which offer increasing c...

  Introduction Information is necessary for all companies to make choices, respond to inquiries, and operate effectively. For the majority of businesses, the issue is not a lack of information but rather the difficulty of locating and retrieving it from the vast collection of documents, databases, and other sources that contain it. For example, that Margie's Travel is a travel company that specializes in planning vacations to cities all over the world. The business has accumulated a substantial amount of data over time in papers like brochures and customer-submitted hotel reviews.  Although the sheer amount of data can make it challenging to locate pertinent information to address a particular client query, this data is a significant source of insights for travel agents and customers as they plan trips. To address this challenge, Margie's Travel can implement a solution in which the documents are indexed and made easy to search. This solution enables agents and customers t...

  Introduction The TranslationRecognizer provides transcriptions of spoken input that have been translated, effectively converting spoken language into text. Additionally, you can produce the translation as speech, enabling speech-to-speech translation options. There are two methods available to achieve this. Event-based Synthesis When you aim to conduct a 1:1 translation (translating from one source language directly into a single target language), you can utilize event-driven synthesis to capture the translation as an audio stream. To accomplish this, you must indicate the preferred voice for the translated speech in the TranslationConfig. Develop an event handler for the Synthesizing event of the TranslationRecognizer object. Within the event handler, employ the GetAudio() method from the Result parameter to obtain the byte stream of the translated audio. The exact code for creating an event handler differs depending on the programming language you are using. Manual Synthesis Ma...

  Introduction By identifying and recording spoken input in a designated language and providing translations of the transcription in one or more other languages, translation of speech expands upon speech recognition. Provision an Azure Resource For Speech Translation The Azure AI Speech service offers comprehensive speech translation capabilities powered by machine learning and artificial intelligence, allowing developers to integrate real-time, end-to-end speech translation into their applications or services. You have the option to utilize either a dedicated Azure AI Speech resource or a multi-service Azure AI Services resource. Before you can use the service, you need to create an Azure AI Speech resource in your Azure subscription. After creating your Azure resource, you will need the following information to use it from a client application through one of the supported SDKs: The location in which the resource is deployed One of the keys assigned to your resource. You can view ...

  Configure Audio Format and Voices When generating speech, a SpeechConfig object can be utilized to tailor the audio provided by the Azure AI Speech service. Audio Format The Azure AI Speech service offers various output formats for the audio stream produced by speech synthesis. You can select a format that aligns with your specific requirements: Audio file type Sample-rate Bit-depth The supported formats are indicated in the SDK using the SpeechSynthesisOutputFormat enumeration. Voices The Azure AI Speech service offers a variety of voices that can be utilized to customize your speech-enabled applications. There are two kinds of voice that you can use: Standard voices - synthetic voices created from audio samples. Neural voices - more natural sounding voices created using deep neural networks. Voices are identified by names that indicate a locale and a person's name Use Speech Synthesis Markup Language While the Azure AI Speech SDK enables you to submit plain text to be synthes...

  Use the Text to Speech API The Azure AI Speech service provides additional REST APIs for speech synthesis, much like its Speech to Text APIs: The Text to speech API, which is the primary way to perform speech synthesis. The Batch synthesis API, which is designed to support batch operations that convert large volumes of text to audio, for example to generate an audio-book from the source text. Using the Azure AI Speech SDK Similar to voice recognition, the Azure AI voice SDK is actually used to create the majority of interactive speech-enabled applications. The pattern for implementing speech synthesis is similar to that of speech recognition: Use a SpeechConfig object to encapsulate the information required to connect to your Azure AI Speech resource. Specifically, its location and key. Optionally, use an AudioConfig to define the output device for the speech to be synthesized. By default, this is the default system speaker, but you can also specify an audio file, or by explic...

  Use the Azure AI Speech to Text API The Azure AI Speech service supports speech recognition through two REST APIs: The Speech to text API, which is the primary way to perform speech recognition. The Speech to text Short Audio API, which is optimized for short streams of audio (up to 60 seconds). Depending on how long the spoken input is expected to be, you can use either API for interactive voice recognition. Additionally, you can batch transcribe several audio recordings to text using the Speech to Text API. Using the Azure AI Speech SDK There is a standard procedure for utilizing the Speech to Text API, even though the specifics differ based on the SDK being used (Python, C#, etc.) : Use a SpeechConfig object to encapsulate the information required to connect to your Azure AI Speech resource. Specifically, its location and key. Optionally, use an AudioConfig to define the input source for the audio to be transcribed. By default, this is the default system microp...

  Introduction You may create speech-enabled apps with the Azure AI Speech service. You can create speech-enabled applications using the APIs provided by Azure AI Speech. This includes: Speech to text - An API that enables speech recognition in which your application can accept spoken input. Text to speech - An API that enables speech synthesis in which your application can provide spoken output. Speech Translation - An API that you can use to translate spoken input into multiple languages. Speaker Recognition - An API that enables your application to recognize individual speakers based on their voice. Intent Recognition - An API that uses conversational language understanding to determine the semantic meaning of spoken input. Provision An Azure Resource For Speech You must first build an Azure AI Speech resource in your Azure subscription in order to use Azure AI Speech. Either a multi-service Azure AI Services resource or a single Azure AI Speech resource are available. Afte...

  Define Custom Translations Although Azure AI Translator's default translation model works well for general translation, you might need to create a translation solution for companies or sectors with particular vocabularies of terms that call for specialized translation. To solve this problem, you can create a custom model that maps your own sets of source and target terms for translation. To create a custom model, use the Custom Translator portal to- Create a workspace linked to your Azure AI Translator resource. Create a project. Upload training data files and train a model Test your model and publish your model. Make translation calls to the API. You can use the category parameter in translate calls to your Azure AI Translator resource to specify the unique category Id that your custom model has been assigned. This will cause your custom model to execute translation rather than the default model. How To Call The API ? To initiate a translation, you send a POST request to the fol...

  The Translate function of the API supports numerous parameters that affect the output. Word Alignment Words in written English (written in Latin script) are separated by spaces. This isn't always the case, though, in several other languages, more especially, scripts. For example, translating "Smart Services" from en (English) to zh (Simplified Chinese) produces the result " 智能服 务 ", and it's difficult to understand the relationship between the characters in the source text and the corresponding characters in the translation. To resolve this problem, you can specify the includeAlignment parameter with a value of true in your call to produce the result. Sentence Length When deciding how best to display a translation in a user interface, for instance, it may be helpful to know the translation's length (i.e., character count). By making the includeSentenceLength argument true, you can obtain this data. Profanity Filtering Profanity can occasionally be foun...