Merge branch 'MicrosoftDocs:main' into heidist-june12

Author: HeidiSteen

.openpublishing.redirection.json

@@ -4087,10 +4087,12 @@
             "source_path_from_root": "/articles/event-grid/move-domains-across-regions.md",
             "redirect_url": "/azure",
             "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/data-factory/continuous-integration-delivery-automate-github-actions.md",
+            "redirect_url": "/azure",
+            "redirect_document_id": false
         }
-
-
-
-        
+       
     ]
 }

articles/active-directory-b2c/TOC.yml

@@ -824,8 +824,6 @@
     href: user-flow-versions-legacy.md
 - name: Resources
   items:
-  - name: Azure Roadmap
-    href: https://azure.microsoft.com/updates/?status=nowavailable,inpreview,indevelopment&category=identity,security&query=b2c
   - name: Frequently asked questions
     href: ./faq.yml
     displayName: FAQ

articles/ai-services/computer-vision/includes/identity-curl-quickstart.md

@@ -24,8 +24,7 @@ Get started with facial recognition using the Face REST API. The Face service pr
     * You'll need the key and endpoint from the resource you create to connect your application to the Face API. You'll paste your key and endpoint into the code below later in the quickstart.
     * You can use the free pricing tier (`F0`) to try the service, and upgrade later to a paid tier for production.
 * [PowerShell version 6.0+](/powershell/scripting/install/installing-powershell-core-on-windows), or a similar command-line application.
-* [cURL](https://curl.haxx.se/) installed.
-
+* [cURL](https://curl.se/) installed.
 
 
 
@@ -34,58 +33,134 @@ Get started with facial recognition using the Face REST API. The Face service pr
 > [!NOTE]
 > If you haven't received access to the Face service using the [intake form](https://aka.ms/facerecognition), some of these functions won't work.
 
-1. First, call the Detect API on the source face. This is the face that we'll try to identify from the larger group. Copy the following command to a text editor, insert your own key, and then copy it into a shell window and run it.
+1. First, call the Detect API on the source face. This is the face that we'll try to identify from the larger group. Copy the following command to a text editor, insert your own key and endpoint, and then copy it into a shell window and run it.
+
+    #### [Windows](#tab/windows)
+
+    :::code source="~/cognitive-services-quickstart-code/curl/face/detect.ps1" ID="identify_detect":::
+
+    #### [Linux](#tab/linux)
 
     :::code source="~/cognitive-services-quickstart-code/curl/face/detect.sh" ID="identify_detect":::
 
+    ---
+
     Save the returned face ID string to a temporary location. You'll use it again at the end.
 
-1. Next you'll need to create a **LargePersonGroup**. This object will store the aggregated face data of several persons. Run the following command, inserting your own key. Optionally, change the group's name and metadata in the request body.
+1. Next you'll need to create a **LargePersonGroup** and give it an arbitrary ID that matches regex pattern `^[a-z0-9-_]+$`. This object will store the aggregated face data of several persons. Run the following command, inserting your own key. Optionally, change the group's name and metadata in the request body.
+
+    #### [Windows](#tab/windows)
+
+    :::code source="~/cognitive-services-quickstart-code/curl/face/detect.ps1" ID="identify_create_persongroup":::
+
+    #### [Linux](#tab/linux)
 
     :::code source="~/cognitive-services-quickstart-code/curl/face/detect.sh" ID="identify_create_persongroup":::
 
-    Save the returned ID of the created group to a temporary location.
+    ---
+
+    Save the specified ID of the created group to a temporary location.
 
 1. Next, you'll create **Person** objects that belong to the group. Run the following command, inserting your own key and the ID of the **LargePersonGroup** from the previous step. This command creates a **Person** named "Family1-Dad".
 
+    #### [Windows](#tab/windows)
+
+    :::code source="~/cognitive-services-quickstart-code/curl/face/detect.ps1" ID="identify_create_person":::
+
+    #### [Linux](#tab/linux)
+
     :::code source="~/cognitive-services-quickstart-code/curl/face/detect.sh" ID="identify_create_person":::
 
+    ---
+
     After you run this command, run it again with different input data to create more **Person** objects: "Family1-Mom", "Family1-Son", "Family1-Daughter", "Family2-Lady", and "Family2-Man".
 
     Save the IDs of each **Person** created; it's important to keep track of which person name has which ID.
 
 1. Next you'll need to detect new faces and associate them with the **Person** objects that exist. The following command detects a face from the image *Family1-Dad1.jpg* and adds it to the corresponding person. You need to specify the `personId` as the ID that was returned when you created the "Family1-Dad" **Person** object. The image name corresponds to the name of the created **Person**. Also enter the **LargePersonGroup** ID and your key in the appropriate fields.
 
+    #### [Windows](#tab/windows)
+
+    :::code source="~/cognitive-services-quickstart-code/curl/face/detect.ps1" ID="identify_add_face":::
+
+    #### [Linux](#tab/linux)
+
     :::code source="~/cognitive-services-quickstart-code/curl/face/detect.sh" ID="identify_add_face":::
 
+    ---
+
     Then, run the above command again with a different source image and target **Person**. The images available are: *Family1-Dad1.jpg*, *Family1-Dad2.jpg* *Family1-Mom1.jpg*, *Family1-Mom2.jpg*, *Family1-Son1.jpg*, *Family1-Son2.jpg*, *Family1-Daughter1.jpg*, *Family1-Daughter2.jpg*, *Family2-Lady1.jpg*, *Family2-Lady2.jpg*, *Family2-Man1.jpg*, and *Family2-Man2.jpg*. Be sure that the **Person** whose ID you specify in the API call matches the name of the image file in the request body.
 
     At the end of this step, you should have multiple **Person** objects that each have one or more corresponding faces, detected directly from the provided images.
 
 1. Next, train the **LargePersonGroup** with the current face data. The training operation teaches the model how to associate facial features, sometimes aggregated from multiple source images, to each single person. Insert the **LargePersonGroup** ID and your key before running the command.
 
+    #### [Windows](#tab/windows)
+
+    :::code source="~/cognitive-services-quickstart-code/curl/face/detect.ps1" ID="identify_train":::
+
+    #### [Linux](#tab/linux)
+
     :::code source="~/cognitive-services-quickstart-code/curl/face/detect.sh" ID="identify_train":::
+
+    ---
 
+1. Check whether the training status is succeeded. If not, wait for a while and query again.
+
+    #### [Windows](#tab/windows)
+
+    :::code source="~/cognitive-services-quickstart-code/curl/face/detect.ps1" ID="identify_check_status":::
+
+    #### [Linux](#tab/linux)
+
+    :::code source="~/cognitive-services-quickstart-code/curl/face/detect.sh" ID="identify_check_status":::
+
+    ---
+
 1. Now you're ready to call the Identify API, using the source face ID from the first step and the **LargePersonGroup** ID. Insert these values into the appropriate fields in the request body, and insert your key.
 
+    #### [Windows](#tab/windows)
+
+    :::code source="~/cognitive-services-quickstart-code/curl/face/detect.ps1" ID="identify_identify":::
+
+    #### [Linux](#tab/linux)
+
     :::code source="~/cognitive-services-quickstart-code/curl/face/detect.sh" ID="identify_identify":::
 
+    ---
+
     The response should give you a **Person** ID indicating the person identified with the source face. It should be the ID that corresponds to the "Family1-Dad" person, because the source face is of that person.
 
 1. To do face verification, you'll use the **Person** ID returned in the previous step, the **LargePersonGroup** ID, and also the source face ID. Insert these values into the fields in the request body, and insert your key.
 
+    #### [Windows](#tab/windows)
+
+    :::code source="~/cognitive-services-quickstart-code/curl/face/detect.ps1" ID="verify":::
+
+    #### [Linux](#tab/linux)
+
     :::code source="~/cognitive-services-quickstart-code/curl/face/detect.sh" ID="verify":::
 
+    ---
+
     The response should give you a boolean verification result along with a confidence value.
 
 
 
 ## Clean up resources
 
-To delete the **LargePersonGroup** you created in this exercise, run the LargePersonGroup - Delete call.
+To delete the **LargePersonGroup** you created in this exercise, run the [LargePersonGroup - Delete](/rest/api/face/person-group-operations/delete-large-person-group) call.
+
+#### [Windows](#tab/windows)
+
+:::code source="~/cognitive-services-quickstart-code/curl/face/detect.ps1" ID="identify_delete":::
+
+#### [Linux](#tab/linux)
 
 :::code source="~/cognitive-services-quickstart-code/curl/face/detect.sh" ID="identify_delete":::
 
+---
+
 If you want to clean up and remove an Azure AI services subscription, you can delete the resource or resource group. Deleting the resource group also deletes any other resources associated with it.
 
 * [Portal](../../multi-service-resource.md?pivots=azportal#clean-up-resources)

articles/ai-services/openai/assistants-reference.md

@@ -5,7 +5,7 @@ description: Learn how to use Azure OpenAI's Python & REST API with Assistants.
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: conceptual
-ms.date: 05/22/2024
+ms.date: 06/13/2024
 author: mrbullwinkle
 ms.author: mbullwin
 recommendations: false
@@ -35,7 +35,7 @@ Create an assistant with a model and instructions.
 | name | string or null | Optional | The name of the assistant. The maximum length is 256 characters.|
 | description| string or null | Optional | The description of the assistant. The maximum length is 512 characters.|
 | instructions | string or null | Optional | The system instructions that the assistant uses. The maximum length is 256,000 characters.|
-| tools | array | Optional | Defaults to []. A list of tools enabled on the assistant. There can be a maximum of 128 tools per assistant. Tools can currently be of types `code_interpreter`, or `function`.|
+| tools | array | Optional | Defaults to []. A list of tools enabled on the assistant. There can be a maximum of 128 tools per assistant. Tools can currently be of types `code_interpreter`, or `function`. A `function` description can be a maximum of 1,024 characters. |
 | file_ids | array | Optional | Defaults to []. A list of file IDs attached to this assistant. There can be a maximum of 20 files attached to the assistant. Files are ordered by their creation date in ascending order.|
 | metadata | map | Optional | Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format. Keys can be a maximum of 64 characters long and values can be a maximum of 512 characters long.|
 | temperature | number or null | Optional | Defaults to 1. Determines what sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. |
@@ -360,7 +360,7 @@ Modifies an assistant.
 | `name` | string or null | Optional | The name of the assistant. The maximum length is 256 characters. |
 | `description` | string or null | Optional | The description of the assistant. The maximum length is 512 characters. |
 | `instructions` | string or null | Optional | The system instructions that the assistant uses. The maximum length is 32768 characters. |
-| `tools` | array | Optional | Defaults to []. A list of tools enabled on the assistant. There can be a maximum of 128 tools per assistant. Tools can be of types code_interpreter, or function. |
+| `tools` | array | Optional | Defaults to []. A list of tools enabled on the assistant. There can be a maximum of 128 tools per assistant. Tools can be of types code_interpreter, or function. A `function` description can be a maximum of 1,024 characters. |
 | `file_ids` | array | Optional | Defaults to []. A list of File IDs attached to this assistant. There can be a maximum of 20 files attached to the assistant. Files are ordered by their creation date in ascending order. If a file was previously attached to the list but does not show up in the list, it will be deleted from the assistant. |
 | `metadata` | map | Optional | Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format. Keys can be a maximum of 64 characters long and values can be a maximum of 512 characters long. |
 
@@ -517,7 +517,7 @@ Assistants use the [same API for file upload as fine-tuning](/rest/api/azureopen
 | `description` | string or null | The description of the assistant. The maximum length is 512 characters.|
 | `model` | string | Name of the model deployment name to use.|
 | `instructions` | string or null | The system instructions that the assistant uses. The maximum length is 32768 characters.|
-| `tools` | array | A list of tool enabled on the assistant. There can be a maximum of 128 tools per assistant. Tools can be of types code_interpreter, or function.|
+| `tools` | array | A list of tool enabled on the assistant. There can be a maximum of 128 tools per assistant. Tools can be of types code_interpreter, or function. A `function` description can be a maximum of 1,024 characters.|
 | `file_ids` | array | A list of file IDs attached to this assistant. There can be a maximum of 20 files attached to the assistant. Files are ordered by their creation date in ascending order.|
 | `metadata` | map | Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format. Keys can be a maximum of 64 characters long and values can be a maximum of 512 characters long.|
 

articles/ai-services/openai/quotas-limits.md

@@ -27,6 +27,7 @@ The following sections provide you with a quick guide to the default quotas and
 | OpenAI resources per region per Azure subscription | 30 |
 | Default DALL-E 2 quota limits | 2 concurrent requests |
 | Default DALL-E 3 quota limits| 2 capacity units (6 requests per minute)|
+| Default Whisper quota limits | 3 requests per minute |
 | Maximum prompt tokens per request | Varies per model. For more information, see [Azure OpenAI Service models](./concepts/models.md)|
 | Max fine-tuned model deployments | 5 |
 | Total number of training jobs per resource | 100 |
@@ -48,6 +49,7 @@ The following sections provide you with a quick guide to the default quotas and
 | GPT-4o max images per request (# of images in the messages array/conversation history) | 10 |
 | GPT-4 `vision-preview` & GPT-4 `turbo-2024-04-09` default max tokens | 16 <br><br> Increase the `max_tokens` parameter value to avoid truncated responses. GPT-4o max tokens defaults to 4096. |
 
+
 ## Regional quota limits
 
 [!INCLUDE [Quota](./includes/model-matrix/quota.md)]
@@ -99,7 +101,7 @@ If your Azure subscription is linked to certain [offer types](https://azure.micr
 |Azure for Students, Free Trials | 1 K (all models)|
 | Monthly credit card based accounts <sup>1</sup> | GPT 3.5 Turbo Series: 30 K <br> GPT-4 series: 8 K  <br> |
 
-<sup>1</sup>This currently applies to [offer type 0003P](https://azure.microsoft.com/support/legal/offer-details/)
+<sup>1</sup> This currently applies to [offer type 0003P](https://azure.microsoft.com/support/legal/offer-details/)
 
 In the Azure portal you can view what offer type is associated with your subscription by navigating to your subscription and checking the subscriptions overview pane. Offer type corresponds to the plan field in the subscription overview.
 

articles/ai-services/openai/whats-new.md

@@ -10,7 +10,7 @@ ms.custom:
   - ignite-2023
   - references_regions
 ms.topic: whats-new
-ms.date: 06/11/2024
+ms.date: 06/13/2024
 recommendations: false
 ---
 
@@ -20,6 +20,10 @@ This article provides a summary of the latest releases and major documentation u
 
 ## June 2024
 
+### Token based billing for fine-tuning
+
+* Azure OpenAI fine-tuning billing is now based on the number of tokens in your training file – instead of the total elapsed training time. This can result in a significant cost reduction for some training runs, and makes estimating fine-tuning costs much easier. To learn more, you can consult the [official announcement](https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/pricing-update-token-based-billing-for-fine-tuning-training/ba-p/4164465).
+
 ### GPT-4o released in new regions
 
 * GPT-4o is now also available in:

articles/ai-services/speech-service/text-to-speech-avatar/content-credentials.md

@@ -0,0 +1,48 @@
+---
+title: Content Credentials in Azure Text to Speech Avatar
+titleSuffix: Azure Text to Speech Avatar
+description: Learn about the content credentials feature, which lets you verify that a video was generated by the Azure text to speech avatar system.
+author: sally-baolian
+ms.author: v-baolianzou
+ms.service: azure-ai-speech
+ms.topic: conceptual 
+ms.date: 6/11/2024
+manager: nitinme
+---
+
+# Content credentials
+
+The high-quality models in the Azure text to speech avatar feature generate realistic avatar videos from text input. To improve the transparency of the generated content, the Azure text to speech avatar provides content credentials, a tamper-evident way to disclose the origin and history of the content. Content credentials are based on an open technical specification from the [Coalition for Content Provenance and Authenticity (C2PA)](https://www.c2pa.org), a Joint Development Foundation project.
+
+## What are content credentials?
+
+Content credentials in the Azure text to speech avatar provide customers with information about the origin of an avatar video. This information is represented by a manifest attached to the video. The manifest is cryptographically signed by a certificate that traces back to Azure text to speech avatar.
+
+The manifest contains several key pieces of information:
+
+| Field name | Field content |
+| --- | --- |
+| `"generator"` | This field has a value of `"Microsoft Azure Text To Speech Avatar Service"` for all applicable videos, attesting to the AI-generated nature of the video. |
+| `"when"` | The timestamp of when the content credentials were created. |
+
+Content credentials in the Azure text to speech avatar can help people understand when video content is generated by the Azure text to speech avatar system. For more information on how to responsibly build solutions with text to speech avatar models, visit the [Text to speech transparency note](/legal/cognitive-services/speech-service/text-to-speech/transparency-note?context=/azure/ai-services/speech-service/context/context).
+
+## Limitations
+
+The content credentials are only supported in video files generated by batch synthesis of text to speech avatar, and only `mp4` file format is supported.  
+
+## How do I leverage content credentials in my solution today?
+
+You may leverage content credentials by:
+
+- Ensuring that your Azure text to speech avatar generated video files contain content credentials
+
+No additional set-up is necessary. Content credentials are automatically applied to all applicable videos generated by the Azure text to speech avatar.
+
+## Verifying that a video file has content credentials
+
+As for now, self-serve verification of content credentials for text to speech avatar video isn't yet available. You can contact [[email protected]](mailto:[email protected]) through email for verification of content credentials of Azure text to speech avatar generated videos.
+
+## Next steps
+
+* [Use batch synthesis for text to speech avatar](./batch-synthesis-avatar.md)

articles/ai-services/speech-service/toc.yml

@@ -200,6 +200,8 @@ items:
       - name: What is text to speech avatar?
         href: text-to-speech-avatar/what-is-text-to-speech-avatar.md
         displayName: avatar
+      - name: Content credentials
+        href: text-to-speech-avatar/content-credentials.md
       - name: How to synthesize text to speech avatar
         items:
         - name: Real-time synthesis