Merge pull request #1479 from MicrosoftDocs/main

Taojunshen · web-flow · commit 26cc3ac3a53f · 2024-11-12T02:05:07.000+08:00
11/11/2024 AM Publish
diff --git a/articles/ai-services/openai/concepts/provisioned-throughput.md b/articles/ai-services/openai/concepts/provisioned-throughput.md
@@ -164,6 +164,7 @@ For Provisioned-Managed and Global Provisioned-Managed, we use a variation of th
 3.	When a request finishes, we now know the actual compute cost for the call. To ensure an accurate accounting, we correct the utilization using the following logic:
 
     a.	If the actual > estimated, then the difference is added to the deployment's utilization
+
     b.	If the actual < estimated, then the difference is subtracted. 
 
 4.	The overall utilization is decremented down at a continuous rate based on the number of PTUs deployed. 
diff --git a/articles/ai-services/openai/concepts/system-message.md b/articles/ai-services/openai/concepts/system-message.md
@@ -165,5 +165,5 @@ Finally, remember that system messages, or metaprompts, are not "one size fits a
 
 - [Azure OpenAI Service](/azure/ai-services/openai/concepts/prompt-engineering)
 - [System message design with Azure OpenAI](/azure/ai-services/openai/concepts/advanced-prompt-engineering?pivots=programming-language-chat-completions) 
-- [Announcing Safety System Messages in Azure AI Studio and Azure OpenAI Studio](https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/announcing-safety-system-messages-in-azure-ai-studio-and-azure/ba-p/4146991) - Microsoft Community Hub 
+- [Announcing Safety System Messages in Azure AI Studio](https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/announcing-safety-system-messages-in-azure-ai-studio-and-azure/ba-p/4146991) - Microsoft Community Hub 
 - [Safety system message templates ](./safety-system-message-templates.md)
diff --git a/articles/ai-services/openai/how-to/monitor-openai.md b/articles/ai-services/openai/how-to/monitor-openai.md
@@ -71,8 +71,6 @@ After you configure the diagnostic settings, you can work with metrics and log d
 
 After you deploy an Azure OpenAI model, you can send some completions calls by using the **playground** environment in [Azure AI Studio](https://oai.azure.com/).
 
-:::image type="content" source="../media/monitoring/azure-openai-studio-playground.png" alt-text="Screenshot that shows how to generate completions for an Azure OpenAI resource in the Azure OpenAI Studio playground." lightbox="../media/monitoring/azure-openai-studio-playground.png" border="false":::
-
 Any text that you enter in the **Completions playground** or the **Chat completions playground** generates metrics and log data for your Azure OpenAI resource. In the Log Analytics workspace for your resource, you can query the monitoring data by using the [Kusto](/azure/data-explorer/kusto/query/) query language.
 
 > [!IMPORTANT]
diff --git a/articles/ai-services/openai/how-to/provisioned-throughput-onboarding.md b/articles/ai-services/openai/how-to/provisioned-throughput-onboarding.md
@@ -33,7 +33,7 @@ Determining the right amount of provisioned throughput, or PTUs, you require for
 
 ### Estimate provisioned throughput and cost
 
-To get a quick estimate for your workload, open the capacity planner in the [Azure OpenAI Studio](https://oai.azure.com). The capacity planner is under **Shared resources** > **Quota** > **Azure OpenAI Provisioned**.
+To get a quick estimate for your workload, open the capacity planner in the [Azure AI Studio](https://ai.azure.com). The capacity calculator is under **Shared resources** > **Model Quota** > **Azure OpenAI Provisioned**.
 
 The **Provisioned** option and the capacity planner are only available in certain regions within the Quota pane, if you don't see this option setting the quota region to *Sweden Central* will make this option available. Enter the following parameters based on your workload.
 
@@ -49,14 +49,14 @@ After you fill in the required details, select **Calculate** button in the outpu
 
 The values in the output column are the estimated value of PTU units required for the provided workload inputs. The first output value represents the estimated PTU units required for the workload, rounded to the nearest PTU scale increment. The second output value represents the raw estimated PTU units required for the workload. The token totals are calculated using the following equation: `Total = Peak calls per minute * (Tokens in prompt call + Tokens in model response)`.
 
-:::image type="content" source="../media/how-to/provisioned-onboarding/capacity-calculator.png" alt-text="Screenshot of the Azure OpenAI Studio landing page." lightbox="../media/how-to/provisioned-onboarding/capacity-calculator.png":::
+:::image type="content" source="../media/how-to/provisioned-onboarding/capacity-calculator.png" alt-text="Screenshot of the capacity calculator" lightbox="../media/how-to/provisioned-onboarding/capacity-calculator.png":::
 
 > [!NOTE]
 > The capacity calculator provides an estimate based on simple input criteria. The most accurate way to determine your capacity is to benchmark a deployment with a representational workload for your use case.
 
 ## Understanding the Provisioned Throughput Purchase Model 
 
-Azure OpenAI Provisioned and Global Provisiones are purchased on-demand at an hourly basis based on the number of deployed PTUs, with substantial term discount available via the purchase of Azure Reservations.   
+Azure OpenAI Provisioned and Global Provisioned are purchased on-demand at an hourly basis based on the number of deployed PTUs, with substantial term discount available via the purchase of Azure Reservations.   
 
 The hourly model is useful for short-term deployment needs, such as validating new models or acquiring capacity for a hackathon.  However, the discounts provided by the Azure Reservation for Azure OpenAI Provisioned and Global Provisioned are considerable and most customers with consistent long-term usage will find a reserved model to be a better value proposition. 
 
@@ -86,7 +86,7 @@ Customers that require long-term usage of provisioned and global provisioned dep
 
 Discounts on top of the hourly usage price can be obtained by purchasing an Azure Reservation for Azure OpenAI Provisioned and Global Provisioned. An Azure Reservation is a term-discounting mechanism shared by many Azure products. For example, Compute and Cosmos DB. For Azure OpenAI Provisioned and Global Provisioned, the reservation provides a discount for committing to payment for fixed number of PTUs for a one-month or one-year period.  
 
-* Azure Reservations are purchased via the Azure portal, not Azure OpenAI Studio Link to Azure reservation portal. 
+* Azure Reservations are purchased via the Azure portal, not the Azure AI Studio Link to Azure reservation portal.
 
 * Reservations are purchased regionally and can be flexibly scoped to cover usage from a group of deployments. Reservation scopes include: 
 
@@ -115,7 +115,7 @@ The PTU amounts in reservation purchases are independent of PTUs allocated in qu
  
 The best practice is to always purchase a reservation after deployments have been created.  This prevents purchasing a reservation and then finding out that the required capacity is not available for the desired region or model. 
  
-To assist customers with purchasing the correct reservation amounts. The total number of PTUs in a subscription and region that can be covered by a reservation are listed on the Quotas page of Azure OpenAI Studio. See the message "PTUs Available for reservation." 
+To assist customers with purchasing the correct reservation amounts. The total number of PTUs in a subscription and region that can be covered by a reservation are listed on the Quotas page of Azure AI Studio. See the message "PTUs Available for reservation." 
 
 :::image type="content" source="../media/provisioned/available-quota.png" alt-text="A screenshot showing available PTU quota." lightbox="../media/provisioned/available-quota.png":::
 
diff --git a/articles/ai-services/speech-service/batch-transcription-create.md b/articles/ai-services/speech-service/batch-transcription-create.md
@@ -180,7 +180,7 @@ Here are some property options to configure a transcription when you call the [T
 |`contentContainerUrl`| You can submit individual audio files or a whole storage container.<br/><br/>You must specify the audio data location by using either the `contentContainerUrl` or `contentUrls` property. For more information about Azure blob storage for batch transcription, see [Locate audio files for batch transcription](batch-transcription-audio-data.md).<br/><br/>This property isn't returned in the response.|
 |`contentUrls`| You can submit individual audio files or a whole storage container.<br/><br/>You must specify the audio data location by using either the `contentContainerUrl` or `contentUrls` property. For more information, see [Locate audio files for batch transcription](batch-transcription-audio-data.md).<br/><br/>This property isn't returned in the response.|
 |`destinationContainerUrl`|The result can be stored in an Azure container. If you don't specify a container, the Speech service stores the results in a container managed by Microsoft. When the transcription job is deleted, the transcription result data is also deleted. For more information, such as the supported security scenarios, see [Specify a destination container URL](#specify-a-destination-container-url).|
-|`diarization`|Indicates that the Speech service should attempt diarization analysis on the input, which is expected to be a mono channel that contains multiple voices. The feature isn't available with stereo recordings.<br/><br/>Diarization is the process of separating speakers in audio data. The batch pipeline can recognize and separate multiple speakers on mono channel recordings.<br/><br/>Specify the minimum and maximum number of people who might be speaking. You must also set the `diarizationEnabled` property to `true`. The [transcription file](batch-transcription-get.md#transcription-result-file) contains a `speaker` entry for each transcribed phrase.<br/><br/>You need to use this property when you expect three or more speakers. For two speakers, setting `diarizationEnabled` property to `true` is enough. For an example of the property usage, see [Transcriptions_Create](/rest/api/speechtotext/transcriptions/create).<br/><br/>The maximum number of speakers for diarization must be less than 36 and more or equal to the `minSpeakers` property. For an example, see [Transcriptions_Create](/rest/api/speechtotext/transcriptions/create).<br/><br/>When this property is selected, source audio length can't exceed 240 minutes per file.<br/><br/>**Note**: This property is only available with Speech to text REST API version 3.1 and later. If you set this property with any previous version, such as version 3.0, it's ignored and only two speakers are identified.|
+|`diarization`|Indicates that the Speech service should attempt diarization analysis on the input, which is expected to be a mono channel that contains multiple voices. The feature isn't available with stereo recordings.<br/><br/>Diarization is the process of separating speakers in audio data. The batch pipeline can recognize and separate multiple speakers on mono channel recordings.<br/><br/>Specify the minimum and maximum number of people who might be speaking. You must also set the `diarizationEnabled` property to `true`. The [transcription file](batch-transcription-get.md#transcription-result-file) contains a `speaker` entry for each transcribed phrase.<br/><br/>You need to use this property when you expect three or more speakers. For two speakers, setting `diarizationEnabled` property to `true` is enough. For an example of the property usage, see [Transcriptions_Create](/rest/api/speechtotext/transcriptions/create).<br/><br/>The maximum number of speakers for diarization must be less than 36 and more or equal to the `minCount` property. For an example, see [Transcriptions_Create](/rest/api/speechtotext/transcriptions/create).<br/><br/>When this property is selected, source audio length can't exceed 240 minutes per file.<br/><br/>**Note**: This property is only available with Speech to text REST API version 3.1 and later. If you set this property with any previous version, such as version 3.0, it's ignored and only two speakers are identified.|
 |`diarizationEnabled`|Specifies that the Speech service should attempt diarization analysis on the input, which is expected to be a mono channel that contains two voices. The default value is `false`.<br/><br/>For three or more voices you also need to use property `diarization`. Use only with Speech to text REST API version 3.1 and later.<br/><br/>When this property is selected, source audio length can't exceed 240 minutes per file.|
 |`displayName`|The name of the batch transcription. Choose a name that you can refer to later. The display name doesn't have to be unique.<br/><br/>This property is required.|
 |`displayFormWordLevelTimestampsEnabled`|Specifies whether to include word-level timestamps on the display form of the transcription results. The results are returned in the `displayWords` property of the transcription file. The default value is `false`.<br/><br/>**Note**: This property is only available with Speech to text REST API version 3.1 and later.|
diff --git a/articles/ai-services/speech-service/includes/release-notes/release-notes-tts.md b/articles/ai-services/speech-service/includes/release-notes/release-notes-tts.md
@@ -2,7 +2,7 @@
 author: eric-urban
 ms.service: azure-ai-speech
 ms.topic: include
-ms.date: 10/9/2024
+ms.date: 11/11/2024
 ms.author: eur
 ms.custom: references_regions
 ---
@@ -11,7 +11,35 @@ ms.custom: references_regions
 
 ##### Prebuilt neural voice
 
-- Introduce 4 turbo version of Azure OpenAI voices in public preview: `en-US-EchoTurboMultilingualNeural`, `en-US-FableTurboMultilingualNeural`, `en-US-OnyxTurboMultilingualNeural`, and `en-US-ShimmerTurboMultilingualNeural`. Turbo version of Azure OpenAI voices has the similar voice persona as Azure OpenAI voices but supports extra features. Turbo voices support the full set of SSML elements and more features like word boundary, just like other Azure AI Speech voices. See the [full language and voice list](../../language-support.md?tabs=tts) for more information.
+Introduced 4 turbo version of Azure OpenAI voices in public preview: `en-US-EchoTurboMultilingualNeural`, `en-US-FableTurboMultilingualNeural`, `en-US-OnyxTurboMultilingualNeural`, and `en-US-ShimmerTurboMultilingualNeural`. Turbo version of Azure OpenAI voices has the similar voice persona as Azure OpenAI voices but supports extra features. Turbo voices support the full set of SSML elements and more features like word boundary, just like other Azure AI Speech voices. See the [full language and voice list](../../language-support.md?tabs=tts) for more information.
+
+These voices are now generally available:
+
+| **Locale (BCP-47)** | **Voice name**              |
+|---------------------|---------------------------|
+| `de-DE` | `SeraphinaMultilingualNeural` |
+| `de-DE` | `FlorianMultilingualNeural` |
+| `en-GB` | `AdaMultilingualNeural` |
+| `en-GB` | `OllieMultilingualNeural` |
+| `en-US` | `LunaNeural` |
+| `en-US` | `KaiNeural` |
+| `en-US` | `CoraMultilingualNeural` |
+| `en-US` | `ChristopherMultilingualNeural` |
+| `en-US` | `BrandonMultilingualNeural` |
+| `es-ES` | `IsidoraMultilingualNeural` |
+| `es-ES` | `ArabellaMultilingualNeural` |
+| `es-ES` | `TristanMultilingualNeural` |
+| `es-ES` | `XimenaMultilingualNeural` |
+| `fr-FR` | `LucienMultilingualNeural` |
+| `fr-FR` | `VivienneMultilingualNeural` |
+| `fr-FR` | `RemyMultilingualNeural` |
+| `it-IT` | `IsabellaMultilingualNeural` |
+| `it-IT` | `MarcelloMultilingualNeural` |
+| `it-IT` | `AlessioMultilingualNeural` |
+| `it-IT` | `GiuseppeMultilingualNeural` |
+| `ko-KR` | `HyunsuMultilingualNeural` |
+| `pt-BR` | `ThalitaMultilingualNeural` |
+| `pt-BR` | `MacerioMultilingualNeural` |
 
 #### Prebuilt high definition (HD) neural voice
 
diff --git a/articles/ai-studio/how-to/access-on-premises-resources.md b/articles/ai-studio/how-to/access-on-premises-resources.md
@@ -64,7 +64,7 @@ Follow the [Quickstart: Direct web traffic using the portal](/azure/application-
         - Name: Provide a name for your private link configuration
         - Private link subnet: Select a subnet in your virtual network. 
         - Frontend IP Configuration: `appGwPrivateFrontendIpIPv4`
-    - To verify the Private link is set up correctly, navigate to the __Private endpoint connections__ tab and select __+ Private endpoint__. On the __Resource__ tab, the __Target sub-resource__ should be the name of your private Frontend IP configuration, `appGwPrivateFrontendIpIPv4`. If no value appears in the __Target sub-resource__, then the Application Gateway listener wasn't configured correctly. 
+    - To verify the Private link is set up correctly, navigate to the __Private endpoint connections__ tab and select __+ Private endpoint__. On the __Resource__ tab, the __Target sub-resource__ should be the name of your private Frontend IP configuration, `appGwPrivateFrontendIpIPv4`. If no value appears in the __Target sub-resource__, then the Application Gateway listener wasn't configured correctly. For more on setting up Private link in Application Gateway, see [Configure Azure Application Gateway Private Link](/azure/application-gateway/private-link-configure).
 
 ## Configure private link
 
diff --git a/articles/ai-studio/how-to/flow-develop.md b/articles/ai-studio/how-to/flow-develop.md
@@ -132,7 +132,7 @@ By selecting a span, you can see the detailed information on the right side. Thi
 :::image type="content" source="../media/prompt-flow/authoring-trace.png" alt-text=" Screenshot of trace detail." lightbox="../media/prompt-flow/authoring-trace.png":::
 
 > [!NOTE]
-> In prompt flow SDK, we defined serval span types, including **LLM**, **Function**, **Embedding**, **Retrieval**, and **Flow**. And the system automatically creates spans with execution information in designated attributes and events.
+> In prompt flow SDK, we defined several span types, including **LLM**, **Function**, **Embedding**, **Retrieval**, and **Flow**. And the system automatically creates spans with execution information in designated attributes and events.
 >
 > To learn more about span types, see  [Trace span](https://microsoft.github.io/promptflow/reference/trace-span-spec-reference.html).
 

Original file line number	Diff line number	Diff line change
`@@ -132,7 +132,7 @@ By selecting a span, you can see the detailed information on the right side. Thi`
`132`	`132`	`:::image type="content" source="../media/prompt-flow/authoring-trace.png" alt-text=" Screenshot of trace detail." lightbox="../media/prompt-flow/authoring-trace.png":::`
`133`	`133`
`134`	`134`	`> [!NOTE]`
`135`		`-> In prompt flow SDK, we defined serval span types, including LLM, Function, Embedding, Retrieval, and Flow. And the system automatically creates spans with execution information in designated attributes and events.`
	`135`	`+> In prompt flow SDK, we defined several span types, including LLM, Function, Embedding, Retrieval, and Flow. And the system automatically creates spans with execution information in designated attributes and events.`
`136`	`136`	`>`
`137`	`137`	`> To learn more about span types, see [Trace span](https://microsoft.github.io/promptflow/reference/trace-span-spec-reference.html).`
`138`	`138`