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Copy file name to clipboardExpand all lines: articles/azure-portal/supportability/quotas-overview.md
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## Next steps
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- Learn more about [viewing quotas in the Azure portal](view-quotas.md).
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- Learn how to increase Compute quotas.
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- Learn how to request increases for [VM-family vCPU quotas](per-vm-quota-requests.md), [vCPU quotas by region](regional-quota-requests.md), and [spot vCPU quotas](spot-quota.md).
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- Learn about [Azure subscription and service limits, quotas, and constraints](../../azure-resource-manager/management/azure-subscription-service-limits.md).
|**Deploy**| 1. Deploy the Resource Manager template. |
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## Generate deployment credentials
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# [Service principal](#tab/userlevel)
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You can create a [service principal](../../active-directory/develop/app-objects-and-service-principals.md#service-principal-object) with the [az ad sp create-for-rbac](/cli/azure/ad/sp#az-ad-sp-create-for-rbac) command in the [Azure CLI](/cli/azure/). Run this command with [Azure Cloud Shell](https://shell.azure.com/) in the Azure portal or by selecting the **Try it** button.
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Create a resource group if you do not already have one.
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> [!IMPORTANT]
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> It is always a good practice to grant minimum access. The scope in the previous example is limited to the resource group.
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# [OpenID Connect](#tab/openid)
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You need to provide your application's **Client ID**, **Tenant ID**, and **Subscription ID** to the login action. These values can either be provided directly in the workflow or can be stored in GitHub secrets and referenced in your workflow. Saving the values as GitHub secrets is the more secure option.
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1. Open your GitHub repository and go to **Settings**.
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1. Select **Settings > Secrets > New secret**.
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1. Create secrets for `AZURE_CLIENT_ID`, `AZURE_TENANT_ID`, and `AZURE_SUBSCRIPTION_ID`. Use these values from your Active Directory application for your GitHub secrets:
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|GitHub Secret | Active Directory Application |
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|---------|---------|
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|AZURE_CLIENT_ID | Application (client) ID |
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|AZURE_TENANT_ID | Directory (tenant) ID |
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|AZURE_SUBSCRIPTION_ID | Subscription ID |
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1. Save each secret by selecting **Add secret**.
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---
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## Configure the GitHub secrets
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# [Service principal](#tab/userlevel)
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You need to create secrets for your Azure credentials, resource group, and subscriptions.
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1. In [GitHub](https://github.com/), browse your repository.
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1. Create an additional secret named `AZURE_SUBSCRIPTION`. Add your subscription ID to the secret's value field (example: `90fd3f9d-4c61-432d-99ba-1273f236afa2`).
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# [OpenID Connect](#tab/openid)
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You need to provide your application's **Client ID**, **Tenant ID**, and **Subscription ID** to the login action. These values can either be provided directly in the workflow or can be stored in GitHub secrets and referenced in your workflow. Saving the values as GitHub secrets is the more secure option.
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1. Open your GitHub repository and go to **Settings**.
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1. Select **Settings > Secrets > New secret**.
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1. Create secrets for `AZURE_CLIENT_ID`, `AZURE_TENANT_ID`, and `AZURE_SUBSCRIPTION_ID`. Use these values from your Active Directory application for your GitHub secrets:
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|GitHub Secret | Active Directory Application |
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|---------|---------|
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|AZURE_CLIENT_ID | Application (client) ID |
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|AZURE_TENANT_ID | Directory (tenant) ID |
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|AZURE_SUBSCRIPTION_ID | Subscription ID |
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1. Save each secret by selecting **Add secret**.
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---
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## Add Resource Manager template
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Add a Resource Manager template to your GitHub repository. This template creates a storage account.
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1. Select **set up a workflow yourself**.
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1. Rename the workflow file if you prefer a different name other than **main.yml**. For example: **deployStorageAccount.yml**.
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1. Replace the content of the yml file with the following:
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# [Service principal](#tab/userlevel)
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```yml
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```yml
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on: [push]
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name: Azure ARM
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jobs:
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> You can specify a JSON format parameters file instead in the ARM Deploy action (example: `.azuredeploy.parameters.json`).
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The first section of the workflow file includes:
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- **name**: The name of the workflow.
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- **on**: The name of the GitHub events that triggers the workflow. The workflow is trigger when there is a push event on the main branch, which modifies at least one of the two files specified. The two files are the workflow file and the template file.
> You can specify a JSON format parameters file instead in the ARM Deploy action (example: `.azuredeploy.parameters.json`).
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> [!NOTE]
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> You can specify a JSON format parameters file instead in the ARM Deploy action (example: `.azuredeploy.parameters.json`).
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The first section of the workflow file includes:
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The first section of the workflow file includes:
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- **name**: The name of the workflow.
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- **on**: The name of the GitHub events that triggers the workflow. The workflow is trigger when there is a push event on the main branch, which modifies at least one of the two files specified. The two files are the workflow file and the template file.
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- **name**: The name of the workflow.
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- **on**: The name of the GitHub events that triggers the workflow. The workflow is trigger when there is a push event on the main branch, which modifies at least one of the two files specified. The two files are the workflow file and the template file.
Copy file name to clipboardExpand all lines: articles/azure-signalr/signalr-concept-authenticate-oauth.md
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| Setting Name | Suggested Value | Description |
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| ------------ | --------------- | ----------- |
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| Application name |*Azure SignalR Chat*| The GitHub user should be able to recognize and trust the app they are authenticating with. |
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| Homepage URL |`http://localhost:5000/home`||
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| Homepage URL |`http://localhost:5000`||
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| Application description |*A chat room sample using the Azure SignalR Service with GitHub authentication*| A useful description of the application that will help your application users understand the context of the authentication being used. |
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| Authorization callback URL |`http://localhost:5000/signin-github`| This setting is the most important setting for your OAuth application. It's the callback URL that GitHub returns the user to after successful authentication. In this tutorial, you must use the default callback URL for the *AspNet.Security.OAuth.GitHub* package, */signin-github*. |
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Copy file name to clipboardExpand all lines: articles/cognitive-services/Speech-Service/includes/how-to/recognize-speech-results/example-offset-duration.md
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### Example offset and duration
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The following table shows potential offset and duration in ticks when a speaker says "Welcome to Applied Mathematics course 201." For each utterance, the offset doesn't change throughout the `Recognizing` and `Recognized` events. The duration of speech recognized so far is calculated as an offset from the beginning of the utterance.
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The following table shows potential offset and duration in ticks when a speaker says "Welcome to Applied Mathematics course 201." In this example, the offset doesn't change throughout the `Recognizing` and `Recognized` events. However, don't rely on the offset to remain the same between the `Recognizing` and `Recognized` events, since the final result could be different.
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|Event |Text |Offset (in ticks) |Duration (in ticks) |
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|---------|---------|---------|---------|
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|RECOGNIZING |welcome to applied mathematics course 201 |17000000 |33400000 |
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|RECOGNIZED |Welcome to applied Mathematics course 201. |17000000 |34500000 |
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The total duration of the first utterance was 3.45 seconds. It was recognized at 1.7 to 5.15 seconds offset from the start of speech recognition (00:00:01.700 --> 00:00:05.150).
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The total duration of the first utterance was 3.45 seconds. It was recognized at 1.7 to 5.15 seconds offset from the start of the audio stream being recognized (00:00:01.700 --> 00:00:05.150).
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If the speaker continues to say "Let's get started," a new offset is calculated. Again, the offset is always calculated from the start of recognition to the start of an utterance. The following table shows potential offset and duration for an utterance that was recognized two seconds after the previous utterance ended.
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If the speaker continues then to say "Let's get started," a new offset is calculated from the start of the audio stream being recognized, to the start of the new utterance. The following table shows potential offset and duration for an utterance that started two seconds after the previous utterance ended.
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|Event |Text |Offset (in ticks) |Duration (in ticks) |
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|---------|---------|---------|---------|
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|RECOGNIZING |OK, now let's get started. |71500000 |18500000 |
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|RECOGNIZED |OK, now let's get started. |71500000 |20600000 |
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The total duration of the second utterance was 2.06 seconds. It was recognized at 7.15 to 9.21 seconds offset from the start of speech recognition (00:00:07.150 --> 00:00:09.210).
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The total duration of the second utterance was 2.06 seconds. It was recognized at 7.15 to 9.21 seconds offset from the start of the audio stream being recognized (00:00:07.150 --> 00:00:09.210).
Copy file name to clipboardExpand all lines: articles/cognitive-services/Speech-Service/speech-studio-overview.md
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ms.service: cognitive-services
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ms.subservice: speech-service
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ms.topic: overview
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ms.date: 01/24/2022
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ms.date: 05/13/2022
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ms.author: eur
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---
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# What is Speech Studio?
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[Speech Studio](https://speech.microsoft.com) is a set of UI-based tools for building and integrating features from Azure Cognitive Services Speech service in your applications. You create projects in Speech Studio by using a no-code approach, and then reference those assets in your applications by using the [Speech SDK](speech-sdk.md), the [Speech CLI](spx-overview.md), or the REST APIs.
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## Prerequisites
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Before you can begin using [Speech Studio](https://speech.microsoft.com), you need to have an Azure account and a Speech resource. Create a Speech resource on the [Azure portal](https://portal.azure.com). For more information, see [Get the keys for your resource](~/articles/cognitive-services/cognitive-services-apis-create-account.md#get-the-keys-for-your-resource).
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After you've created an Azure account and a Speech service resource, do the following:
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1. Sign in to [Speech Studio](https://speech.microsoft.com) with your Azure account.
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1. In your Speech Studio subscription, select a Speech resource. You can change the resource at any time by selecting **Settings** at the top of the pane.
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## Speech Studio features
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In Speech Studio, the following Speech service features are available as project types:
Copy file name to clipboardExpand all lines: articles/container-apps/samples.md
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| Name | Description |
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|--|--|
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|[Deploy an Orleans Cluster to Container Apps](https://github.com/Azure-Samples/Orleans-Cluster-on-Azure-Container-Apps)| An end-to-end sample and tutorial for getting a Microsoft Orleans cluster running on Azure Container Apps. Worker microservices rapidly transmit data to a back-end Orleans cluster for monitoring and storage, emulating thousands of physical devices in the field. |
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|[ASP.NET Core front-end with two back-end APIs on Azure Container Apps](https://github.com/Azure-Samples/dotNET-FrontEnd-to-BackEnd-on-Azure-Container-Apps)| This sample demonstrates ASP.NET Core 6.0 can be used to build a cloud-native application hosted in Azure Container Apps. |
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|[ASP.NET Core front-end with two back-end APIs on Azure Container Apps (with Dapr)](https://github.com/Azure-Samples/dotNET-FrontEnd-to-BackEnd-with-DAPR-on-Azure-Container-Apps)| Demonstrates how ASP.NET Core 6.0 is used to build a cloud-native application hosted in Azure Container Apps using Dapr. |
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|[A/B Testing your ASP.NET Core apps using Azure Container Apps](https://github.com/Azure-Samples/dotNET-Frontend-AB-Testing-on-Azure-Container-Apps)<br /> | Shows how to use Azure App Configuration, ASP.NET Core Feature Flags, and Azure Container Apps revisions together to gradually release features or perform A/B tests. <br /> |
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|[gRPC with ASP.NET Core on Azure Container Apps](https://github.com/Azure-Samples/dotNET-Workers-with-gRPC-messaging-on-Azure-Container-Apps)| This repository contains a simple scenario built to demonstrate how ASP.NET Core 6.0 can be used to build a cloud-native application hosted in Azure Container Apps that uses gRPC request/response transmission from Worker microservices. The gRPC service simultaneously streams sensor data to a Blazor server frontend, so you can watch the data be charted in real-time. <br /> |
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|[Deploy an Orleans Cluster to Container Apps](https://github.com/Azure-Samples/Orleans-Cluster-on-Azure-Container-Apps)| An end-to-end sample and tutorial for getting a Microsoft Orleans cluster running on Azure Container Apps. Worker microservices rapidly transmit data to a back-end Orleans cluster for monitoring and storage, emulating thousands of physical devices in the field.<br /> |
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|[ASP.NET Core front-end with two back-end APIs on Azure Container Apps](https://github.com/Azure-Samples/dotNET-FrontEnd-to-BackEnd-on-Azure-Container-Apps)<br /> | This sample demonstrates ASP.NET Core 6.0 can be used to build a cloud-native application hosted in Azure Container Apps. |
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|[ASP.NET Core front-end with two back-end APIs on Azure Container Apps (with Dapr)](https://github.com/Azure-Samples/dotNET-FrontEnd-to-BackEnd-with-DAPR-on-Azure-Container-Apps)<br /> | Demonstrates how ASP.NET Core 6.0 is used to build a cloud-native application hosted in Azure Container Apps using Dapr. |
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