create fine-tune refresh in foundry portal

Author: eric-urban

articles/ai-services/speech-service/how-to-custom-speech-deploy-model.md

@@ -80,11 +80,11 @@ Select the endpoint link to view information specific to it, such as the endpoin
 
 To create an endpoint and deploy a model, use the `spx csr endpoint create` command. Construct the request parameters according to the following instructions:
 
-- Set the `project` parameter to the ID of an existing project. This is recommended so that you can also view and manage the endpoint in Speech Studio. You can run the `spx csr project list` command to get available projects.
-- Set the required `model` parameter to the ID of the model that you want deployed to the endpoint. 
-- Set the required `language` parameter. The endpoint locale must match the locale of the model. The locale can't be changed later. The Speech CLI `language` parameter corresponds to the `locale` property in the JSON request and response.
-- Set the required `name` parameter. This is the name that is displayed in the Speech Studio. The Speech CLI `name` parameter corresponds to the `displayName` property in the JSON request and response.
-- Optionally, you can set the `logging` parameter. Set this to `enabled` to enable audio and diagnostic [logging](#view-logging-data) of the endpoint's traffic. The default is `false`. 
+- Set the `project` property to the ID of an existing project. This property is recommended so that you can also view and manage the endpoint in the [Azure AI Foundry portal](https://ai.azure.com). You can run the `spx csr project list` command to get available projects.
+- Set the required `model` property to the ID of the model that you want deployed to the endpoint. 
+- Set the required `language` property. The endpoint locale must match the locale of the model. The locale can't be changed later. The Speech CLI `language` property corresponds to the `locale` property in the JSON request and response.
+- Set the required `name` property. This is the name that is displayed in the [Azure AI Foundry portal](https://ai.azure.com). The Speech CLI `name` property corresponds to the `displayName` property in the JSON request and response.
+- Optionally, you can set the `logging` property. Set this to `enabled` to enable audio and diagnostic [logging](#view-logging-data) of the endpoint's traffic. The default is `false`. 
 
 Here's an example Speech CLI command to create an endpoint and deploy a model:
 
@@ -138,10 +138,10 @@ spx help csr endpoint
 
 To create an endpoint and deploy a model, use the [Endpoints_Create](/rest/api/speechtotext/endpoints/create) operation of the [Speech to text REST API](rest-speech-to-text.md). Construct the request body according to the following instructions:
 
-- Set the `project` property to the URI of an existing project. This is recommended so that you can also view and manage the endpoint in Speech Studio. You can make a [Projects_List](/rest/api/speechtotext/projects/list) request to get available projects.
+- Set the `project` property to the URI of an existing project. This property is recommended so that you can also view and manage the endpoint in the [Azure AI Foundry portal](https://ai.azure.com). You can make a [Projects_List](/rest/api/speechtotext/projects/list) request to get available projects.
 - Set the required `model` property to the URI of the model that you want deployed to the endpoint. 
 - Set the required `locale` property. The endpoint locale must match the locale of the model. The locale can't be changed later.
-- Set the required `displayName` property. This is the name that is displayed in the Speech Studio.
+- Set the required `displayName` property. This is the name that is displayed in the [Azure AI Foundry portal](https://ai.azure.com).
 - Optionally, you can set the `loggingEnabled` property within `properties`. Set this to `true` to enable audio and diagnostic [logging](#view-logging-data) of the endpoint's traffic. The default is `false`. 
 
 Make an HTTP POST request using the URI as shown in the following [Endpoints_Create](/rest/api/speechtotext/endpoints/create) example. Replace `YourSubscriptionKey` with your Speech resource key, replace `YourServiceRegion` with your Speech resource region, and set the request body properties as previously described.
@@ -225,8 +225,8 @@ To use a new model and redeploy the custom endpoint:
 
 To redeploy the custom endpoint with a new model, use the `spx csr model update` command. Construct the request parameters according to the following instructions:
 
-- Set the required `endpoint` parameter to the ID of the endpoint that you want deployed.
-- Set the required `model` parameter to the ID of the model that you want deployed to the endpoint.
+- Set the required `endpoint` property to the ID of the endpoint that you want deployed.
+- Set the required `model` property to the ID of the model that you want deployed to the endpoint.
 
 Here's an example Speech CLI command that redeploys the custom endpoint with a new model:
 
@@ -351,7 +351,7 @@ To download the endpoint logs:
 
 To get logs for an endpoint, use the `spx csr endpoint list` command. Construct the request parameters according to the following instructions:
 
-- Set the required `endpoint` parameter to the ID of the endpoint that you want to get logs.
+- Set the required `endpoint` property to the ID of the endpoint that you want to get logs.
 
 Here's an example Speech CLI command that gets logs for an endpoint:
 

articles/ai-services/speech-service/how-to-custom-speech-evaluate-data.md

@@ -69,12 +69,12 @@ Follow these steps to create an accuracy test:
 
 To create a test, use the `spx csr evaluation create` command. Construct the request parameters according to the following instructions:
 
-- Set the `project` parameter to the ID of an existing project. This parameter is recommended so that you can also view the test in Speech Studio. You can run the `spx csr project list` command to get available projects.
-- Set the required `model1` parameter to the ID of a model that you want to test.
-- Set the required `model2` parameter to the ID of another model that you want to test. If you don't want to compare two models, use the same model for both `model1` and `model2`.
-- Set the required `dataset` parameter to the ID of a dataset that you want to use for the test.
-- Set the `language` parameter, otherwise the Speech CLI sets "en-US" by default. This parameter should be the locale of the dataset contents. The locale can't be changed later. The Speech CLI `language` parameter corresponds to the `locale` property in the JSON request and response.
-- Set the required `name` parameter. This parameter is the name that is displayed in the Speech Studio. The Speech CLI `name` parameter corresponds to the `displayName` property in the JSON request and response.
+- Set the `project` property to the ID of an existing project. This property is recommended so that you can also view the test in the [Azure AI Foundry portal](https://ai.azure.com). You can run the `spx csr project list` command to get available projects.
+- Set the required `model1` property to the ID of a model that you want to test.
+- Set the required `model2` property to the ID of another model that you want to test. If you don't want to compare two models, use the same model for both `model1` and `model2`.
+- Set the required `dataset` property to the ID of a dataset that you want to use for the test.
+- Set the `language` property, otherwise the Speech CLI sets "en-US" by default. This parameter should be the locale of the dataset contents. The locale can't be changed later. The Speech CLI `language` property corresponds to the `locale` property in the JSON request and response.
+- Set the required `name` property. This parameter is the name that is displayed in the [Azure AI Foundry portal](https://ai.azure.com). The Speech CLI `name` property corresponds to the `displayName` property in the JSON request and response.
 
 Here's an example Speech CLI command that creates a test:
 
@@ -152,13 +152,13 @@ spx help csr evaluation
 
 To create a test, use the [Evaluations_Create](/rest/api/speechtotext/evaluations/create) operation of the [Speech to text REST API](rest-speech-to-text.md). Construct the request body according to the following instructions:
 
-- Set the `project` property to the URI of an existing project. This property is recommended so that you can also view the test in Speech Studio. You can make a [Projects_List](/rest/api/speechtotext/projects/list) request to get available projects.
-- Set the `testingKind` property to `Evaluation` within `customProperties`. If you don't specify `Evaluation`, the test is treated as a quality inspection test. Whether the `testingKind` property is set to `Evaluation` or `Inspection`, or not set, you can access the accuracy scores via the API, but not in the Speech Studio.
+- Set the `project` property to the URI of an existing project. This property is recommended so that you can also view the test in the [Azure AI Foundry portal](https://ai.azure.com). You can make a [Projects_List](/rest/api/speechtotext/projects/list) request to get available projects.
+- Set the `testingKind` property to `Evaluation` within `customProperties`. If you don't specify `Evaluation`, the test is treated as a quality inspection test. Whether the `testingKind` property is set to `Evaluation` or `Inspection`, or not set, you can access the accuracy scores via the API, but not in the [Azure AI Foundry portal](https://ai.azure.com).
 - Set the required `model1` property to the URI of a model that you want to test.
 - Set the required `model2` property to the URI of another model that you want to test. If you don't want to compare two models, use the same model for both `model1` and `model2`.
 - Set the required `dataset` property to the URI of a dataset that you want to use for the test.
 - Set the required `locale` property. This property should be the locale of the dataset contents. The locale can't be changed later.
-- Set the required `displayName` property. This property is the name that is displayed in the Speech Studio.
+- Set the required `displayName` property. This property is the name that is displayed in the [Azure AI Foundry portal](https://ai.azure.com).
 
 Make an HTTP POST request using the URI as shown in the following example. Replace `YourSubscriptionKey` with your Speech resource key, replace `YourServiceRegion` with your Speech resource region, and set the request body properties as previously described.
 
@@ -272,7 +272,7 @@ This page lists all the utterances in your dataset and the recognition results,
 
 To get test results, use the `spx csr evaluation status` command. Construct the request parameters according to the following instructions:
 
-- Set the required `evaluation` parameter to the ID of the evaluation that you want to get test results.
+- Set the required `evaluation` property to the ID of the evaluation that you want to get test results.
 
 Here's an example Speech CLI command that gets test results:
 
@@ -539,7 +539,7 @@ Incorrectly identified words fall into three categories:
 * Deletion (D): Words that are undetected in the hypothesis transcript
 * Substitution (S): Words that were substituted between reference and hypothesis
 
-In the Speech Studio, the quotient is multiplied by 100 and shown as a percentage. The Speech CLI and REST API results aren't multiplied by 100.
+In the [Azure AI Foundry portal](https://ai.azure.com) and [Speech Studio](https://speech.microsoft.com), the quotient is multiplied by 100 and shown as a percentage. The Speech CLI and REST API results aren't multiplied by 100.
 
 $$
 WER = {{I+D+S}\over N} \times 100

articles/ai-services/speech-service/how-to-custom-speech-inspect-data.md

@@ -14,7 +14,7 @@ zone_pivot_groups: foundry-speech-studio-cli-rest
 
 # Test recognition quality of a custom speech model
 
-You can inspect the recognition quality of a custom speech model in the [Speech Studio](https://aka.ms/speechstudio/customspeech). You can play back uploaded audio and determine if the provided recognition result is correct. After a test is successfully created, you can see how a model transcribed the audio dataset, or compare results from two models side by side.
+You can inspect the recognition quality of a custom speech model. You can play back uploaded audio and determine if the provided recognition result is correct. After a test is successfully created, you can see how a model transcribed the audio dataset, or compare results from two models side by side.
 
 Side-by-side model testing is useful to validate which speech recognition model is best for an application. For an objective measure of accuracy, which requires transcription datasets input, see [Test model quantitatively](how-to-custom-speech-evaluate-data.md).
 
@@ -24,7 +24,6 @@ Side-by-side model testing is useful to validate which speech recognition model
 
 ::: zone pivot="ai-foundry-portal"
 
-
 1. After the model is trained, select **Test models** from the left menu. Then select **+ Create test**.
 
     :::image type="content" source="./media/ai-foundry/custom-speech/new-fine-tune-test-model.png" alt-text="Screenshot of the page with an option to test your custom speech model." lightbox="./media/ai-foundry/custom-speech/new-fine-tune-test-model.png":::
@@ -62,12 +61,12 @@ Follow these instructions to create a test:
 
 To create a test, use the `spx csr evaluation create` command. Construct the request parameters according to the following instructions:
 
-- Set the `project` parameter to the ID of an existing project. This parameter is recommended so that you can also view the test in Speech Studio. You can run the `spx csr project list` command to get available projects.
-- Set the required `model1` parameter to the ID of a model that you want to test.
-- Set the required `model2` parameter to the ID of another model that you want to test. If you don't want to compare two models, use the same model for both `model1` and `model2`.
-- Set the required `dataset` parameter to the ID of a dataset that you want to use for the test.
-- Set the `language` parameter, otherwise the Speech CLI sets "en-US" by default. This parameter should be the locale of the dataset contents. The locale can't be changed later. The Speech CLI `language` parameter corresponds to the `locale` property in the JSON request and response.
-- Set the required `name` parameter. This parameter is the name that is displayed in the Speech Studio. The Speech CLI `name` parameter corresponds to the `displayName` property in the JSON request and response.
+- Set the `project` property to the ID of an existing project. This property is recommended so that you can also view the test in the [Azure AI Foundry portal](https://ai.azure.com). You can run the `spx csr project list` command to get available projects.
+- Set the required `model1` property to the ID of a model that you want to test.
+- Set the required `model2` property to the ID of another model that you want to test. If you don't want to compare two models, use the same model for both `model1` and `model2`.
+- Set the required `dataset` property to the ID of a dataset that you want to use for the test.
+- Set the `language` property, otherwise the Speech CLI sets "en-US" by default. This parameter should be the locale of the dataset contents. The locale can't be changed later. The Speech CLI `language` property corresponds to the `locale` property in the JSON request and response.
+- Set the required `name` property. This parameter is the name that is displayed in the [Azure AI Foundry portal](https://ai.azure.com). The Speech CLI `name` property corresponds to the `displayName` property in the JSON request and response.
 
 Here's an example Speech CLI command that creates a test:
 
@@ -142,12 +141,12 @@ spx help csr evaluation
 
 To create a test, use the [Evaluations_Create](/rest/api/speechtotext/evaluations/create) operation of the [Speech to text REST API](rest-speech-to-text.md). Construct the request body according to the following instructions:
 
-- Set the `project` property to the URI of an existing project. This property is recommended so that you can also view the test in Speech Studio. You can make a [Projects_List](/rest/api/speechtotext/projects/list) request to get available projects.
+- Set the `project` property to the URI of an existing project. This property is recommended so that you can also view the test in the [Azure AI Foundry portal](https://ai.azure.com). You can make a [Projects_List](/rest/api/speechtotext/projects/list) request to get available projects.
 - Set the required `model1` property to the URI of a model that you want to test. 
 - Set the required `model2` property to the URI of another model that you want to test. If you don't want to compare two models, use the same model for both `model1` and `model2`.
 - Set the required `dataset` property to the URI of a dataset that you want to use for the test.
 - Set the required `locale` property. This property should be the locale of the dataset contents. The locale can't be changed later.
-- Set the required `displayName` property. This property is the name that is displayed in the Speech Studio.
+- Set the required `displayName` property. This property is the name that is displayed in the [Azure AI Foundry portal](https://ai.azure.com).
 
 Make an HTTP POST request using the URI as shown in the following example. Replace `YourSubscriptionKey` with your Speech resource key, replace `YourServiceRegion` with your Speech resource region, and set the request body properties as previously described.
 
@@ -256,7 +255,7 @@ This page lists all the utterances in your dataset and the recognition results,
 
 To get test results, use the `spx csr evaluation status` command. Construct the request parameters according to the following instructions:
 
-- Set the required `evaluation` parameter to the ID of the evaluation that you want to get test results.
+- Set the required `evaluation` property to the ID of the evaluation that you want to get test results.
 
 Here's an example Speech CLI command that gets test results:
 

articles/ai-services/speech-service/how-to-custom-speech-model-and-endpoint-lifecycle.md

@@ -73,7 +73,7 @@ Follow these instructions to get the transcription expiration date for a base mo
 
 To get the training and transcription expiration dates for a base model, use the `spx csr model status` command. Construct the request parameters according to the following instructions:
 
-- Set the `url` parameter to the URI of the base model that you want to get. You can run the `spx csr list --base` command to get available base models for all locales.
+- Set the `url` property to the URI of the base model that you want to get. You can run the `spx csr list --base` command to get available base models for all locales.
 
 Here's an example Speech CLI command to get the training and transcription expiration dates for a base model:
 
@@ -159,7 +159,6 @@ You should receive a response body in the following format:
 ::: zone pivot="ai-foundry-portal"
 
 
-
 ::: zone-end
 
 ::: zone pivot="speech-studio"
@@ -187,7 +186,7 @@ You can also follow these instructions to get the transcription expiration date
 
 To get the transcription expiration date for your custom model, use the `spx csr model status` command. Construct the request parameters according to the following instructions:
 
-- Set the `url` parameter to the URI of the model that you want to get. Replace `YourModelId` with your model ID and replace `YourServiceRegion` with your Speech resource region.
+- Set the `url` property to the URI of the model that you want to get. Replace `YourModelId` with your model ID and replace `YourServiceRegion` with your Speech resource region.
 
 Here's an example Speech CLI command to get the transcription expiration date for your custom model: