Merge branch 'main' of https://github.com/MicrosoftDocs/azure-docs-pr into gen2

id#

Author: normesta

.openpublishing.redirection.json

@@ -5079,6 +5079,11 @@
       "source_path_from_root": "/articles/xplat-cli-install.md",
       "redirect_url": "/cli/azure/install-azure-cli",
       "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/virtual-network/template-samples.md",
+      "redirect_url": "/samples/browse/?expanded=azure&products=azure-resource-manager&terms=virtual%20network",
+      "redirect_document_id": false
     }
   ]
 }

articles/ai-services/computer-vision/how-to/identity-access-token.md

@@ -112,61 +112,51 @@ curl -X POST 'https://<client-endpoint>/face/v1.0/identify' \
 
 #### [C#](#tab/csharp)
 
-The following code snippets show you how to use an access token with the [Face SDK for C#](https://www.nuget.org/packages/Microsoft.Azure.CognitiveServices.Vision.Face).
+The following code snippets show you how to use an access token with the [Face SDK for C#](https://aka.ms/azsdk-csharp-face-pkg).
 
-The following class uses an access token to create a **ServiceClientCredentials** object that can be used to authenticate a Face API client object. It automatically adds the access token as a header in every request that the Face client will make.
+The following class uses an access token to create a **HttpPipelineSynchronousPolicy** object that can be used to authenticate a Face API client object. It automatically adds the access token as a header in every request that the Face client will make.
 
 ```csharp
-public class LimitedAccessTokenWithApiKeyClientCredential : ServiceClientCredentials 
+public class LimitedAccessTokenPolicy : HttpPipelineSynchronousPolicy
 {
-    /// <summary> 
-    /// Creates a new instance of the LimitedAccessTokenWithApiKeyClientCredential class 
-    /// </summary> 
-    /// <param name="apiKey">API Key for the Face API or CognitiveService endpoint</param> 
-    /// <param name="limitedAccessToken">LimitedAccessToken to bypass the limited access program, requires ISV sponsership.</param> 
-
-    public LimitedAccessTokenWithApiKeyClientCredential(string apiKey, string limitedAccessToken) 
-    { 
-        this.ApiKey = apiKey; 
-        this.LimitedAccessToken = limitedAccessToken; 
+    /// <summary>
+    /// Creates a new instance of the LimitedAccessTokenPolicy class
+    /// </summary>
+    /// <param name="limitedAccessToken">LimitedAccessToken to bypass the limited access program, requires ISV sponsership.</param>
+    public LimitedAccessTokenPolicy(string limitedAccessToken)
+    {
+        _limitedAccessToken = limitedAccessToken;
     }
 
-    private readonly string ApiKey; 
-    private readonly string LimitedAccesToken; 
-
-    /// <summary> 
-    /// Add the Basic Authentication Header to each outgoing request 
-    /// </summary> 
-    /// <param name="request">The outgoing request</param>
-    /// <param name="cancellationToken">A token to cancel the operation</param> 
-    public override Task ProcessHttpRequestAsync(HttpRequestMessage request, CancellationToken cancellationToken) 
-    { 
-        if (request == null) 
-            throw new ArgumentNullException("request"); 
-        request.Headers.Add("Ocp-Apim-Subscription-Key", ApiKey); 
-        request.Headers.Add("LimitedAccessToken", $"Bearer {LimitedAccesToken}");
-
-        return Task.FromResult<object>(null); 
-    } 
-} 
+    private readonly string _limitedAccessToken;
+
+    /// <summary>
+    /// Add the authentication header to each outgoing request
+    /// </summary>
+    /// <param name="message">The outgoing message</param>
+    public override void OnSendingRequest(HttpMessage message)
+    {
+        message.Request.Headers.Add("LimitedAccessToken", $"Bearer {_limitedAccessToken}");
+    }
+}
 ```
 
 In the client-side application, the helper class can be used like in this example:
 
 ```csharp
-static void Main(string[] args) 
-{ 
+static void Main(string[] args)
+{
     // create Face client object
-    var faceClient = new FaceClient(new LimitedAccessTokenWithApiKeyClientCredential(apiKey: "<client-face-key>", limitedAccessToken: "<token>")); 
-
-    faceClient.Endpoint = "https://mytest-eastus2.cognitiveservices.azure.com"; 
+    var clientOptions = new AzureAIVisionFaceClientOptions();
+    clientOptions.AddPolicy(new LimitedAccessTokenPolicy("<token>"), HttpPipelinePosition.PerCall);
+    FaceClient faceClient = new FaceClient(new Uri("<client-endpoint>"), new AzureKeyCredential("<client-face-key>"), clientOptions);
 
     // use Face client in an API call
-    using (var stream = File.OpenRead("photo.jpg")) 
+    using (var stream = File.OpenRead("photo.jpg"))
     {
-        var result = faceClient.Face.DetectWithStreamAsync(stream, detectionModel: "Detection_03", recognitionModel: "Recognition_04", returnFaceId: true).Result; 
+        var response = faceClient.Detect(BinaryData.FromStream(stream), FaceDetectionModel.Detection03, FaceRecognitionModel.Recognition04, returnFaceId: true);
 
-        Console.WriteLine(JsonConvert.SerializeObject(result)); 
+        Console.WriteLine(JsonConvert.SerializeObject(response.Value));
     }
 }
 ```

articles/ai-services/computer-vision/how-to/specify-detection-model.md

@@ -73,7 +73,7 @@ var faces = response.Value;
 
 The Face service can extract face data from an image and associate it with a **Person** object through the [Add Person Group Person Face] API. In this API call, you can specify the detection model in the same way as in [Detect].
 
-See the following code example for the .NET client library.
+See the following .NET code example.
 
 ```csharp
 // Create a PersonGroup and add a person with face detected by "detection_03" model
@@ -110,7 +110,7 @@ This code creates a **PersonGroup** with ID `mypersongroupid` and adds a **Perso
 
 ## Add face to FaceList with specified model
 
-You can also specify a detection model when you add a face to an existing **FaceList** object. See the following code example for the .NET client library.
+You can also specify a detection model when you add a face to an existing **FaceList** object. See the following .NET code example.
 
 ```csharp
 using (var content = new ByteArrayContent(Encoding.UTF8.GetBytes(JsonConvert.SerializeObject(new Dictionary<string, object> { ["name"] = "My face collection", ["recognitionModel"] = "recognition_04" }))))
@@ -139,6 +139,7 @@ In this article, you learned how to specify the detection model to use with diff
 
 * [Face .NET SDK](../quickstarts-sdk/identity-client-library.md?pivots=programming-language-csharp%253fpivots%253dprogramming-language-csharp)
 * [Face Python SDK](../quickstarts-sdk/identity-client-library.md?pivots=programming-language-python%253fpivots%253dprogramming-language-python)
+* [Face Java SDK](../quickstarts-sdk/identity-client-library.md?pivots=programming-language-java%253fpivots%253dprogramming-language-java)
 * [Face JavaScript SDK](../quickstarts-sdk/identity-client-library.md?pivots=programming-language-javascript%253fpivots%253dprogramming-language-javascript)
 
 [Detect]: /rest/api/face/face-detection-operations/detect

articles/ai-services/computer-vision/how-to/specify-recognition-model.md

@@ -132,6 +132,7 @@ In this article, you learned how to specify the recognition model to use with di
 
 * [Face .NET SDK](../quickstarts-sdk/identity-client-library.md?pivots=programming-language-csharp%253fpivots%253dprogramming-language-csharp)
 * [Face Python SDK](../quickstarts-sdk/identity-client-library.md?pivots=programming-language-python%253fpivots%253dprogramming-language-python)
+* [Face Java SDK](../quickstarts-sdk/identity-client-library.md?pivots=programming-language-java%253fpivots%253dprogramming-language-java)
 * [Face JavaScript SDK](../quickstarts-sdk/identity-client-library.md?pivots=programming-language-javascript%253fpivots%253dprogramming-language-javascript)
 
 [Detect]: /rest/api/face/face-detection-operations/detect

articles/ai-services/speech-service/batch-transcription-create.md

@@ -108,6 +108,10 @@ You can query the status of your transcriptions with the [Transcriptions_Get](/r
 Call [Transcriptions_Delete](/rest/api/speechtotext/transcriptions/delete)
 regularly from the service, after you retrieve the results. Alternatively, set the `timeToLive` property to ensure the eventual deletion of the results.
 
+> [!TIP]
+> You can also try the Batch Transcription API using Python on [GitHub](https://github.com/Azure-Samples/cognitive-services-speech-sdk/blob/master/samples/batch/python/python-client/main.py).
+
+
 ::: zone-end
 
 ::: zone pivot="speech-cli"
@@ -168,7 +172,7 @@ spx help batch transcription
 
 ::: zone pivot="rest-api"
 
-Here are some property options that you can use to configure a transcription when you call the [Transcriptions_Create](/rest/api/speechtotext/transcriptions/create) operation.
+Here are some property options to configure a transcription when you call the [Transcriptions_Create](/rest/api/speechtotext/transcriptions/create) operation. You can find more examples on the same page, such as [creating a transcription with language identification](/rest/api/speechtotext/transcriptions/create/#create-a-transcription-with-language-identification).
 
 | Property | Description |
 |----------|-------------|

articles/ai-services/speech-service/embedded-speech.md

@@ -182,12 +182,12 @@ var embeddedSpeechConfig = EmbeddedSpeechConfig.FromPaths(paths.ToArray());
 // For speech to text
 embeddedSpeechConfig.SetSpeechRecognitionModel(
     "Microsoft Speech Recognizer en-US FP Model V8",
-    Environment.GetEnvironmentVariable("MODEL_KEY"));
+    Environment.GetEnvironmentVariable("EMBEDDED_SPEECH_MODEL_LICENSE"));
 
 // For text to speech
 embeddedSpeechConfig.SetSpeechSynthesisVoice(
     "Microsoft Server Speech Text to Speech Voice (en-US, JennyNeural)",
-    Environment.GetEnvironmentVariable("VOICE_KEY"));
+    Environment.GetEnvironmentVariable("EMBEDDED_SPEECH_MODEL_LICENSE"));
 embeddedSpeechConfig.SetSpeechSynthesisOutputFormat(SpeechSynthesisOutputFormat.Riff24Khz16BitMonoPcm);
 ```
 ::: zone-end
@@ -207,12 +207,12 @@ auto embeddedSpeechConfig = EmbeddedSpeechConfig::FromPaths(paths);
 // For speech to text
 embeddedSpeechConfig->SetSpeechRecognitionModel((
     "Microsoft Speech Recognizer en-US FP Model V8",
-    GetEnvironmentVariable("MODEL_KEY"));
+    GetEnvironmentVariable("EMBEDDED_SPEECH_MODEL_LICENSE"));
 
 // For text to speech
 embeddedSpeechConfig->SetSpeechSynthesisVoice(
     "Microsoft Server Speech Text to Speech Voice (en-US, JennyNeural)",
-    GetEnvironmentVariable("VOICE_KEY"));
+    GetEnvironmentVariable("EMBEDDED_SPEECH_MODEL_LICENSE"));
 embeddedSpeechConfig->SetSpeechSynthesisOutputFormat(SpeechSynthesisOutputFormat::Riff24Khz16BitMonoPcm);
 ```
 
@@ -230,12 +230,12 @@ var embeddedSpeechConfig = EmbeddedSpeechConfig.fromPaths(paths);
 // For speech to text
 embeddedSpeechConfig.setSpeechRecognitionModel(
     "Microsoft Speech Recognizer en-US FP Model V8",
-    System.getenv("MODEL_KEY"));
+    System.getenv("EMBEDDED_SPEECH_MODEL_LICENSE"));
 
 // For text to speech
 embeddedSpeechConfig.setSpeechSynthesisVoice(
     "Microsoft Server Speech Text to Speech Voice (en-US, JennyNeural)",
-    System.getenv("VOICE_KEY"));
+    System.getenv("EMBEDDED_SPEECH_MODEL_LICENSE"));
 embeddedSpeechConfig.setSpeechSynthesisOutputFormat(SpeechSynthesisOutputFormat.Riff24Khz16BitMonoPcm);
 ```
 

articles/ai-services/speech-service/includes/how-to/speech-synthesis/python.md

@@ -36,6 +36,15 @@ The voice that speaks is determined in order of priority as follows:
 - If both `SpeechSynthesisVoiceName` and `SpeechSynthesisLanguage` are set, the `SpeechSynthesisLanguage` setting is ignored. The voice that you specify by using `SpeechSynthesisVoiceName` speaks.
 - If the voice element is set by using [Speech Synthesis Markup Language (SSML)](../../../speech-synthesis-markup.md), the `SpeechSynthesisVoiceName` and `SpeechSynthesisLanguage` settings are ignored.
 
+In summary, the order of priority can be described as:
+
+| `SpeechSynthesisVoiceName` | `SpeechSynthesisLanguage` | SSML | Outcome |
+|:----------------------------:|:----------------------------:|:--------:|---------------------------------------------------------|
+| ✗ | ✗ | ✗ | Default voice for `en-US` speaks |
+| ✗ | ✔ | ✗ | Default voice for specified locale speaks. |
+| ✔ | ✔ | ✗ | The voice that you specify by using `SpeechSynthesisVoiceName` speaks. |
+| ✔| ✔ | ✔ | The voice that you specify by using SSML speaks. |
+
 ## Synthesize speech to a file
 
 Create a [SpeechSynthesizer](/python/api/azure-cognitiveservices-speech/azure.cognitiveservices.speech.speechsynthesizer) object. This object runs text to speech conversions and outputs to speakers, files, or other output streams. `SpeechSynthesizer` accepts as parameters:
@@ -53,7 +62,8 @@ Create a [SpeechSynthesizer](/python/api/azure-cognitiveservices-speech/azure.co
 
    ```python
    speech_synthesizer = speechsdk.SpeechSynthesizer(speech_config=speech_config, audio_config=audio_config)
-   speech_synthesizer.speak_text_async("I'm excited to try text to speech")
+   speech_synthesis_result = speech_synthesizer.speak_text_async("I'm excited to try text to speech").get()
+    
    ```
 
 When you run the program, it creates a synthesized *.wav* file, which is written to the location that you specify. This result is a good example of the most basic usage. Next, you can customize output and handle the output response as an in-memory stream for working with custom scenarios.
@@ -85,8 +95,8 @@ In this example, use the `AudioDataStream` constructor to get a stream from the
 
 ```python
 speech_synthesizer = speechsdk.SpeechSynthesizer(speech_config=speech_config, audio_config=None)
-result = speech_synthesizer.speak_text_async("I'm excited to try text to speech").get()
-stream = speechsdk.AudioDataStream(result)
+speech_synthesis_result = speech_synthesizer.speak_text_async("I'm excited to try text to speech").get()
+stream = speechsdk.AudioDataStream(speech_synthesis_result)
 ```
 
 At this point, you can implement any custom behavior by using the resulting `stream` object.
@@ -112,8 +122,8 @@ This example specifies the high-fidelity RIFF format `Riff24Khz16BitMonoPcm` by
 speech_config.set_speech_synthesis_output_format(speechsdk.SpeechSynthesisOutputFormat.Riff24Khz16BitMonoPcm)
 speech_synthesizer = speechsdk.SpeechSynthesizer(speech_config=speech_config, audio_config=None)
 
-result = speech_synthesizer.speak_text_async("I'm excited to try text to speech").get()
-stream = speechsdk.AudioDataStream(result)
+speech_synthesis_result = speech_synthesizer.speak_text_async("I'm excited to try text to speech").get()
+stream = speechsdk.AudioDataStream(speech_synthesis_result)
 stream.save_to_wav_file("path/to/write/file.wav")
 ```
 
@@ -146,9 +156,9 @@ To start using SSML for customization, make a minor change that switches the voi
    speech_synthesizer = speechsdk.SpeechSynthesizer(speech_config=speech_config, audio_config=None)
 
    ssml_string = open("ssml.xml", "r").read()
-   result = speech_synthesizer.speak_ssml_async(ssml_string).get()
+   speech_synthesis_result = speech_synthesizer.speak_ssml_async(ssml_string).get()
 
-   stream = speechsdk.AudioDataStream(result)
+   stream = speechsdk.AudioDataStream(speech_synthesis_result)
    stream.save_to_wav_file("path/to/write/file.wav")
    ```
 

articles/app-service/environment/index.yml

@@ -8,7 +8,7 @@ metadata:
   description: Host fully isolated websites in a secure and scalable environment. Find quickstarts, tutorials, and read about concepts to help you succeed with App Service Environment.
   ms.topic: landing-page
   ms.author: mijacobs
-  ms.service: app-service
+  ms.service: azure-app-service
   ms.date: 11/15/2021
 
 landingContent:

articles/app-service/environment/side-by-side-migrate.md

@@ -223,6 +223,8 @@ Once you're ready to redirect traffic, you can complete the final step of the mi
 > [!NOTE]
 > It's important to complete this step as soon as possible. When your App Service Environment is in the hybrid state, it's unable to receive platform upgrades and security patches, which makes it more vulnerable to instability and security threats.
 >
+> **You have 14 days to complete this step. After 14 days, the platform will automatically complete the migration and delete your old environment. If you need more time, you can open a support case to discuss your options**.
+>
 
 If you discover any issues with your new App Service Environment v3, don't run the command to redirect customer traffic. This command also initiates the deletion of your App Service Environment v2. If you find an issue, contact support.
 
@@ -458,7 +460,11 @@ az rest --method get --uri "${ASE_ID}?api-version=2022-03-01" --query properties
 
 ### 11. Redirect customer traffic, validate your App Service Environment v3, and complete migration
 
-This step is your opportunity to test and validate your new App Service Environment v3.
+This step is your opportunity to test and validate your new App Service Environment v3. 
+
+> [!IMPORTANT]
+> You have 14 days to complete this step. After 14 days, the platform will automatically complete the migration and delete your old environment. If you need more time, you can open a support case to discuss your options.
+>
 
 Once you confirm your apps are working as expected, you can finalize the migration by running the following command. This command also deletes your old environment.
 

articles/app-service/includes/configure-azure-storage/azure-storage-linux-container-pivot.md

@@ -1,6 +1,6 @@
 ---
 author: msangapu-msft
-ms.service: app-service
+ms.service: azure-app-service
 ms.custom: linux-related-content
 ms.topic: include
 ms.date: 01/05/2024