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Azure AI Speech To Text API

By Ashwin Venugopal -
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  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...
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Create Speech-Enabled Apps With Azure AI Services

By Ashwin Venugopal -
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  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...
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Custom Translations

By Ashwin Venugopal -
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  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...
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Specify Translation Options

By Ashwin Venugopal -
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  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...
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Translate Text With Azure AI Translator Service

By Ashwin Venugopal -
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  Introduction You may develop clever apps and services that can translate text between languages with the help of the Translator service. The capacity of speakers of various languages to communicate with one another is frequently a crucial prerequisite for global solutions, and there are numerous widely spoken languages in the globe. An API for translating text between 90 recognized languages is offered by the Azure AI Translator.   Provision An Azure AI Translator Resource Azure AI Translator provides a multilingual text translation API that you can use for: Language detection. One-to-many translation. Script transliteration (converting text from its native script to an alternative script).   Azure Resource For Azure AI Translator You need to allocate a resource in your Azure subscription in order to use the Azure AI Translator service. The Text Analytics API can be used in a multi-service Azure AI Services resource, or you can provision a single-s...
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Custom Named Entity Recognition (Part 3)

By Ashwin Venugopal -
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  Label Your Data Properly labeling or tagging your data is a crucial step in developing a custom entity extraction model. Labels serve to indicate instances of particular entities within the text that are used for training the model. Three things to focus on are: Consistency - Label your data the same way across all files for training. Consistency allows your model to learn without any conflicting inputs. Precision - Label your entities consistently, without unnecessary extra words. Precision ensures only the correct data is included in your extracted entity. Completeness - Label your data completely, and don't miss any entities. Completeness helps your model always recognize the entities present. How To Label Your Data ? Language Studio offers a straightforward approach for annotating your data. It enables you to view the file, mark the start and end of your entities, and specify their type. Each label you identify is saved in a file located in your storage account alongs...
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Custom Named Entity Recognition (Part 2)

By Ashwin Venugopal -
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  Azure AI Language Project Life Cycle Creating an entity extraction model typically follows a similar path to most Azure AI Language service features: Define entities : Understanding the data and entities you want to identify, and try to make them as clear as possible. For example, defining exactly which parts of a bank statement you want to extract. Tag data: Label, or tag, your existing data, describing what text in your dataset relates to which entity. This phase is vital to accomplish precisely and thoroughly, as any improper or missed labels will reduce the effectiveness of the trained model. A good variation of possible input documents is useful. For example, label bank name, customer name, customer address, specific loan or account terms, loan or account amount, and account number. Train model: Train your model once your entities are labeled. Training teaches your model how to recognize the entities you label. View model: After your model is trained, view the results ...
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