freshness and editorial pass

Author: eric-urban

articles/ai-services/speech-service/speech-container-cstt.md

@@ -30,7 +30,7 @@ The fully qualified container image name is, `mcr.microsoft.com/azure-cognitive-
 | Version | Path |
 |-----------|------------|
 | Latest | `mcr.microsoft.com/azure-cognitive-services/speechservices/custom-speech-to-text:latest` |
-| 3.12.0 | `mcr.microsoft.com/azure-cognitive-services/speechservices/custom-speech-to-text:3.12.0-amd64` |
+| 4.5.0 | `mcr.microsoft.com/azure-cognitive-services/speechservices/custom-speech-to-text:4.5.0-amd64` |
 
 All tags, except for `latest`, are in the following format and are case sensitive:
 

articles/ai-services/speech-service/speech-container-howto-on-premises.md

@@ -1,18 +1,18 @@
 ---
 title: Use Speech service containers with Kubernetes and Helm
 titleSuffix: Azure AI services
-description: Using Kubernetes and Helm to define the speech to text and text to speech container images, we'll create a Kubernetes package. This package will be deployed to a Kubernetes cluster on-premises.
+description: Using Kubernetes and Helm to define the speech to text and text to speech container images, we create a Kubernetes package. This package is deployed to a Kubernetes cluster on-premises.
 author: eric-urban
 manager: nitinme
 ms.service: azure-ai-speech
 ms.topic: how-to
-ms.date: 07/22/2021
+ms.date: 1/22/2024
 ms.author: eur
 ---
 
 # Use Speech service containers with Kubernetes and Helm
 
-One option to manage your Speech containers on-premises is to use Kubernetes and Helm. Using Kubernetes and Helm to define the speech to text and text to speech container images, we'll create a Kubernetes package. This package will be deployed to a Kubernetes cluster on-premises. Finally, we'll explore how to test the deployed services and various configuration options. For more information about running Docker containers without Kubernetes orchestration, see [install and run Speech service containers](speech-container-howto.md).
+One option to manage your Speech containers on-premises is to use Kubernetes and Helm. Using Kubernetes and Helm to define the speech to text and text to speech container images, we create a Kubernetes package. This package is deployed to a Kubernetes cluster on-premises. Finally, we explore how to test the deployed services and various configuration options. For more information about running Docker containers without Kubernetes orchestration, see [install and run Speech service containers](speech-container-howto.md).
 
 ## Prerequisites
 
@@ -21,14 +21,14 @@ The following prerequisites before using Speech containers on-premises:
 | Required | Purpose |
 |----------|---------|
 | Azure Account | If you don't have an Azure subscription, create a [free account][free-azure-account] before you begin. |
-| Container Registry access | In order for Kubernetes to pull the docker images into the cluster, it will need access to the container registry. |
+| Container Registry access | In order for Kubernetes to pull the docker images into the cluster, it needs access to the container registry. |
 | Kubernetes CLI | The [Kubernetes CLI][kubernetes-cli] is required for managing the shared credentials from the container registry. Kubernetes is also needed before Helm, which is the Kubernetes package manager. |
 | Helm CLI | Install the [Helm CLI][helm-install], which is used to install a helm chart (container package definition). |
 |Speech resource |In order to use these containers, you must have:<br><br>A _Speech_ Azure resource to get the associated billing key and billing endpoint URI. Both values are available on the Azure portal's **Speech** Overview and Keys pages and are required to start the container.<br><br>**{API_KEY}**: resource key<br><br>**{ENDPOINT_URI}**: endpoint URI example is: `https://eastus.api.cognitive.microsoft.com/sts/v1.0`|
 
 ## The recommended host computer configuration
 
-Refer to the [Speech service container host computer][speech-container-host-computer] details as a reference. This *helm chart* automatically calculates CPU and memory requirements based on how many decodes (concurrent requests) that the user specifies. Additionally, it will adjust based on whether optimizations for audio/text input are configured as `enabled`. The helm chart defaults to, two concurrent requests and disabling optimization.
+Refer to the [Speech service container host computer][speech-container-host-computer] details as a reference. This *helm chart* automatically calculates CPU and memory requirements based on how many decodes (concurrent requests) that the user specifies. Additionally, it adjusts based on whether optimizations for audio/text input are configured as `enabled`. The helm chart defaults to, two concurrent requests and disabling optimization.
 
 | Service | CPU / Container | Memory / Container |
 |--|--|--|
@@ -41,13 +41,13 @@ The host computer is expected to have an available Kubernetes cluster. See this
 
 ## Configure Helm chart values for deployment
 
-Visit the [Microsoft Helm Hub][ms-helm-hub] for all the publicly available helm charts offered by Microsoft. From the Microsoft Helm Hub, you'll find the **Azure AI Speech On-Premises Chart**. The **Azure AI Speech On-Premises** is the chart we'll install, but we must first create an `config-values.yaml` file with explicit configurations. Let's start by adding the Microsoft repository to our Helm instance.
+Visit the [Microsoft Helm Hub][ms-helm-hub] for all the publicly available helm charts offered by Microsoft. From the Microsoft Helm Hub, you find the **Azure AI Speech On-Premises Chart**. The **Azure AI Speech On-Premises** is the chart we install, but we must first create an `config-values.yaml` file with explicit configurations. Let's start by adding the Microsoft repository to our Helm instance.
 
 ```console
 helm repo add microsoft https://microsoft.github.io/charts/repo
 ```
 
-Next, we'll configure our Helm chart values. Copy and paste the following YAML into a file named `config-values.yaml`. For more information on customizing the **Azure AI Speech On-Premises Helm Chart**, see [customize helm charts](#customize-helm-charts). Replace the `# {ENDPOINT_URI}` and `# {API_KEY}` comments with your own values.
+Next, we configure our Helm chart values. Copy and paste the following YAML into a file named `config-values.yaml`. For more information on customizing the **Azure AI Speech On-Premises Helm Chart**, see [customize helm charts](#customize-helm-charts). Replace the `# {ENDPOINT_URI}` and `# {API_KEY}` comments with your own values.
 
 ```yaml
 # These settings are deployment specific and users can provide customizations
@@ -97,15 +97,15 @@ The provided *Helm charts* pull the docker images of the Speech service, both te
 
 ## Install the Helm chart on the Kubernetes cluster
 
-To install the *helm chart* we'll need to execute the [`helm install`][helm-install-cmd] command, replacing the `<config-values.yaml>` with the appropriate path and file name argument. The `microsoft/cognitive-services-speech-onpremise` Helm chart referenced below is available on the [Microsoft Helm Hub here][ms-helm-hub-speech-chart].
+Run the [`helm install`][helm-install-cmd] command to install the helm chart, replacing the `<config-values.yaml>` with the appropriate path and file name argument. The `microsoft/cognitive-services-speech-onpremise` Helm chart is available on the [Microsoft Helm Hub][ms-helm-hub-speech-chart].
 
 ```console
 helm install onprem-speech microsoft/cognitive-services-speech-onpremise \
     --version 0.1.1 \
     --values <config-values.yaml> 
 ```
 
-Here is an example output you might expect to see from a successful install execution:
+Here's an example output you might expect to see from a successful install execution:
 
 ```console
 NAME:   onprem-speech
@@ -182,7 +182,7 @@ horizontalpodautoscaler.autoscaling/text-to-speech-autoscaler   Deployment/text-
 
 ### Verify Helm deployment with Helm tests
 
-The installed Helm charts define *Helm tests*, which serve as a convenience for verification. These tests validate service readiness. To verify both **speech to text** and **text to speech** services, we'll execute the [Helm test][helm-test] command.
+The installed Helm charts define *Helm tests*, which serve as a convenience for verification. These tests validate service readiness. To verify both **speech to text** and **text to speech** services, we execute the [Helm test][helm-test] command.
 
 ```console
 helm test onprem-speech
@@ -191,7 +191,7 @@ helm test onprem-speech
 > [!IMPORTANT]
 > These tests will fail if the POD status is not `Running` or if the deployment is not listed under the `AVAILABLE` column. Be patient as this can take over ten minutes to complete.
 
-These tests will output various status results:
+These tests output various status results:
 
 ```console
 RUNNING: speech to text-readiness-test

articles/ai-services/speech-service/speech-container-howto.md

@@ -6,14 +6,14 @@ author: eric-urban
 manager: nitinme
 ms.service: azure-ai-speech
 ms.topic: how-to
-ms.date: 04/18/2023
+ms.date: 1/22/2024
 ms.author: eur
 keywords: on-premises, Docker, container
 ---
 
 # Install and run Speech containers with Docker
 
-By using containers, you can use a subset of the Speech service features in your own environment. In this article, you'll learn how to download, install, and run a Speech container.
+By using containers, you can use a subset of the Speech service features in your own environment. In this article, you learn how to download, install, and run a Speech container.
 
 > [!NOTE]
 > Disconnected container pricing and commitment tiers vary from standard containers. For more information, see [Speech service pricing](https://azure.microsoft.com/pricing/details/cognitive-services/speech-services/).
@@ -29,13 +29,13 @@ You must meet the following prerequisites before you use Speech service containe
 
 ### Billing arguments
 
-Speech containers aren't licensed to run without being connected to Azure for metering. You must configure your container to communicate billing information with the metering service at all times. 
+Speech containers aren't licensed to run without being connected to Azure for metering. You must configure your container to communicate billing information with the metering service always. 
 
 Three primary parameters for all Azure AI containers are required. The Microsoft Software License Terms must be present with a value of **accept**. An Endpoint URI and API key are also needed.
 
 Queries to the container are billed at the pricing tier of the Azure resource that's used for the `ApiKey` parameter.
 
-The <a href="https://docs.docker.com/engine/reference/commandline/run/" target="_blank">`docker run` <span class="docon docon-navigate-external x-hidden-focus"></span></a> command will start the container when all three of the following options are provided with valid values:
+The <a href="https://docs.docker.com/engine/reference/commandline/run/" target="_blank">`docker run` <span class="docon docon-navigate-external x-hidden-focus"></span></a> command starts the container when all three of the following options are provided with valid values:
 
 | Option | Description |
 |--------|-------------|
@@ -67,7 +67,7 @@ Core and memory correspond to the `--cpus` and `--memory` settings, which are us
 
 > [!NOTE]
 > The minimum and recommended allocations are based on Docker limits, *not* the host machine resources.
-> For example, speech to text containers memory map portions of a large language model. We recommend that the entire file should fit in memory. You need to add an additional 4 to 8 GB to load the speech models (see above table).
+> For example, speech to text containers memory map portions of a large language model. We recommend that the entire file should fit in memory. You need to add an additional 4 to 8 GB to load the speech models (see the previous table).
 > Also, the first run of either container might take longer because models are being paged into memory.
 
 ## Host computer requirements and recommendations
@@ -99,8 +99,8 @@ Use the [docker run](https://docs.docker.com/engine/reference/commandline/run/)
 
 Take note the following best practices with the `docker run` command:
 
-- **Line-continuation character**: The Docker commands in the following sections use the back slash, `\`, as a line continuation character. Replace or remove this based on your host operating system's requirements.
-- **Argument order**: Do not change the order of the arguments unless you are familiar with Docker containers.
+- **Line-continuation character**: The Docker commands in the following sections use the back slash, `\`, as a line continuation character. Replace or remove this character based on your host operating system's requirements.
+- **Argument order**: Don't change the order of the arguments unless you're familiar with Docker containers.
 
 You can use the [docker images](https://docs.docker.com/engine/reference/commandline/images/) command to list your downloaded container images. The following command lists the ID, repository, and tag of each downloaded container image, formatted as a table:
 
@@ -191,7 +191,7 @@ Billing={ENDPOINT_URI} \
 ApiKey={API_KEY}
 ```
 
-The container will test for network connectivity to the billing endpoint.
+The container tests for network connectivity to the billing endpoint.
 
 ## Run disconnected containers
 

articles/ai-services/speech-service/speech-container-lid.md

@@ -7,15 +7,15 @@ manager: nitinme
 ms.service: azure-ai-speech
 ms.custom: devx-track-extended-java, devx-track-go, devx-track-js, devx-track-python
 ms.topic: how-to
-ms.date: 08/28/2023
+ms.date: 1/22/2024
 ms.author: eur
 zone_pivot_groups: programming-languages-speech-sdk-cli
 keywords: on-premises, Docker, container
 ---
 
 # Language identification containers with Docker
 
-The Speech language identification container detects the language spoken in audio files. You can get real-time speech or batch audio recordings with intermediate results. In this article, you'll learn how to download, install, and run a language identification container.
+The Speech language identification container detects the language spoken in audio files. You can get real-time speech or batch audio recordings with intermediate results. In this article, you learn how to download, install, and run a language identification container.
 
 > [!NOTE]
 > The Speech language identification container is available in public preview. Containers in preview are still under development and don't meet Microsoft's stability and support requirements.
@@ -64,7 +64,7 @@ The tags are also available [in JSON format](https://mcr.microsoft.com/v2/azure-
 
 ## Get the container image with docker pull
 
-You need the [prerequisites](speech-container-howto.md#prerequisites) including required hardware. Please also see the [recommended allocation of resources](speech-container-howto.md#container-requirements-and-recommendations) for each Speech container. 
+You need the [prerequisites](speech-container-howto.md#prerequisites) including required hardware. Also see the [recommended allocation of resources](speech-container-howto.md#container-requirements-and-recommendations) for each Speech container. 
 
 Use the [docker pull](https://docs.docker.com/engine/reference/commandline/pull/) command to download a container image from Microsoft Container Registry:
 
@@ -107,7 +107,7 @@ For more information about `docker run` with Speech containers, see [Install and
 
 ## Run with the speech to text container
 
-If you want to run the language identification container with the [speech to text](speech-container-stt.md) container, you can use this [docker image](https://hub.docker.com/r/antsu/on-prem-client). After both containers have been started, use this `docker run` command to execute `speech-to-text-with-languagedetection-client`:
+If you want to run the language identification container with the [speech to text](speech-container-stt.md) container, you can use this [docker image](https://hub.docker.com/r/antsu/on-prem-client). After both containers are started, use this `docker run` command to execute `speech-to-text-with-languagedetection-client`:
 
 ```bash
 docker run --rm -v ${HOME}:/root -ti antsu/on-prem-client:latest ./speech-to-text-with-languagedetection-client ./audio/LanguageDetection_en-us.wav --host localhost --lport 5003 --sport 5000