@@ -44,12 +44,12 @@ We use LoRA, or low rank approximation, to fine-tune models in a way that reduce
 
 ::: zone-end
 
-## Global Standard
+## Global Standard (preview)
 
 Azure OpenAI fine-tuning supports [global standard deployments](./deployment-types.md#global-standard) in East US2, North Central US, and Sweden Central for:
 
-- `gpt-4o-2024-08-06`
 - `gpt-4o-mini-2024-07-18`
+- `gpt-4o-2024-08-06` (New deployments aren't available until January 2025)
 
 Global standard fine-tuned deployments offer [cost savings](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/), but custom model weights may temporarily be stored outside the geography of your Azure OpenAI resource.
 

@@ -0,0 +1,18 @@
+---
+author: eric-urban 
+ms.author: eur 
+ms.service: azure-ai-openai
+ms.topic: include
+ms.date: 12/27/2024
+---
+
+|Variable name | Value |
+|--------------------------|-------------|
+| `AZURE_OPENAI_ENDPOINT`               | This value can be found in the **Keys and Endpoint** section when examining your resource from the Azure portal. |
+| `AZURE_OPENAI_API_KEY` | This value can be found in the **Keys and Endpoint** section when examining your resource from the Azure portal. You can use either `KEY1` or `KEY2`.|
+| `AZURE_OPENAI_DEPLOYMENT_NAME` | This value will correspond to the custom name you chose for your deployment when you deployed a model. This value can be found under **Resource Management** > **Model Deployments** in the Azure portal.|
+| `OPENAI_API_VERSION`|Learn more about [API Versions](/azure/ai-services/openai/api-version-deprecation).|
+
+Learn more about [finding API keys](/azure/ai-services/cognitive-services-environment-variables) and [setting environment variables](/azure/ai-services/cognitive-services-environment-variables).
+
+[!INCLUDE [Azure key vault](~/reusable-content/ce-skilling/azure/includes/ai-services/security/azure-key-vault.md)]
\ No newline at end of file

@@ -0,0 +1,15 @@
+---
+author: eric-urban 
+ms.author: eur 
+ms.service: azure-ai-openai
+ms.topic: include
+ms.date: 12/27/2024
+---
+
+|Variable name | Value |
+|--------------------------|-------------|
+| `AZURE_OPENAI_ENDPOINT`               | This value can be found in the **Keys and Endpoint** section when examining your resource from the Azure portal. |
+| `AZURE_OPENAI_DEPLOYMENT_NAME` | This value will correspond to the custom name you chose for your deployment when you deployed a model. This value can be found under **Resource Management** > **Model Deployments** in the Azure portal.|
+| `OPENAI_API_VERSION`|Learn more about [API Versions](/azure/ai-services/openai/api-version-deprecation).|
+
+Learn more about [keyless authentication](/azure/ai-services/authentication) and [setting environment variables](/azure/ai-services/cognitive-services-environment-variables).
\ No newline at end of file

@@ -0,0 +1,18 @@
+---
+manager: nitinme
+author: eric-urban
+ms.author: eur
+ms.service: azure-ai-openai
+ms.topic: include
+ms.date: 12/26/2024
+---
+
+To deploy the `gpt-4o-realtime-preview` model in the Azure AI Foundry portal:
+1. Go to the [Azure AI Foundry portal](https://ai.azure.com) and make sure you're signed in with the Azure subscription that has your Azure OpenAI Service resource (with or without model deployments.)
+1. Select the **Real-time audio** playground from under **Playgrounds** in the left pane.
+1. Select **Create new deployment** to open the deployment window. 
+1. Search for and select the `gpt-4o-realtime-preview` model and then select **Confirm**.
+1. In the deployment wizard, make sure to select the `2024-10-01` model version.
+1. Follow the wizard to finish deploying the model.
+
+Now that you have a deployment of the `gpt-4o-realtime-preview` model, you can interact with it in real time in the Azure AI Foundry portal **Real-time audio** playground or Realtime API.

@@ -0,0 +1,278 @@
+---
+manager: nitinme
+author: eric-urban
+ms.author: eur
+ms.service: azure-ai-openai
+ms.topic: include
+ms.date: 12/26/2024
+---
+
+## Prerequisites
+
+- An Azure subscription - <a href="https://azure.microsoft.com/free/cognitive-services" target="_blank">Create one for free</a>
+- <a href="https://nodejs.org/" target="_blank">Node.js LTS or ESM support.</a>
+- An Azure OpenAI resource created in the East US 2 or Sweden Central regions. See [Region availability](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability).
+- Then, you need to deploy a `gpt-4o-realtime-preview` model with your Azure OpenAI resource. For more information, see [Create a resource and deploy a model with Azure OpenAI](../how-to/create-resource.md). 
+
+## Microsoft Entra ID prerequisites
+
+For the recommended keyless authentication with Microsoft Entra ID, you need to:
+- Install the [Azure CLI](/cli/azure/install-azure-cli) used for keyless authentication with Microsoft Entra ID.
+- Assign the `Cognitive Services User` role to your user account. You can assign roles in the Azure portal under **Access control (IAM)** > **Add role assignment**.
+
+## Deploy a model for real-time audio
+
+[!INCLUDE [Deploy model](realtime-deploy-model.md)]
+
+## Set up
+
+1. Create a new folder `realtime-audio-quickstart` to contain the application and open Visual Studio Code in that folder with the following command:
+
+    ```shell
+    mkdir realtime-audio-quickstart && code realtime-audio-quickstart
+    ```
+    
+1. Create the `package.json` with the following command:
+
+    ```shell
+    npm init -y
+    ```
+
+1. Update the `package.json` to ECMAScript with the following command: 
+
+    ```shell
+    npm pkg set type=module
+    ```
+    
+
+1. Install the real-time audio client library for JavaScript with:
+
+    ```console
+    npm install https://github.com/Azure-Samples/aoai-realtime-audio-sdk/releases/download/js/v0.5.2/rt-client-0.5.2.tgz
+    ```
+
+1. For the **recommended** keyless authentication with Microsoft Entra ID, install the `@azure/identity` package with:
+
+    ```console
+    npm install @azure/identity
+    ```
+
+## Retrieve resource information
+
+#### [Microsoft Entra ID](#tab/javascript-keyless)
+
+[!INCLUDE [keyless-environment-variables](env-var-without-key.md)]
+
+#### [API key](#tab/javascript-key)
+
+[!INCLUDE [key-environment-variables](env-var-key.md)]
+
+---
+
+> [!CAUTION]
+> To use the recommended keyless authentication with the SDK, make sure that the `AZURE_OPENAI_API_KEY` environment variable isn't set. 
+
+## Text in audio out
+
+#### [Microsoft Entra ID](#tab/javascript-keyless)
+
+1. Create the `text-in-audio-out.js` file with the following code:
+
+    ```javascript 
+    import { DefaultAzureCredential } from "@azure/identity";
+    import { LowLevelRTClient } from "rt-client";
+    import dotenv from "dotenv";
+    dotenv.config();
+    async function text_in_audio_out() {
+        // Set environment variables or edit the corresponding values here.
+        const endpoint = process.env["AZURE_OPENAI_ENDPOINT"] || "yourEndpoint";
+        const deployment = "gpt-4o-realtime-preview";
+        if (!endpoint || !deployment) {
+            throw new Error("You didn't set the environment variables.");
+        }
+        const client = new LowLevelRTClient(new URL(endpoint), new DefaultAzureCredential(), { deployment: deployment });
+        try {
+            await client.send({
+                type: "response.create",
+                response: {
+                    modalities: ["audio", "text"],
+                    instructions: "Please assist the user."
+                }
+            });
+            for await (const message of client.messages()) {
+                switch (message.type) {
+                    case "response.done": {
+                        break;
+                    }
+                    case "error": {
+                        console.error(message.error);
+                        break;
+                    }
+                    case "response.audio_transcript.delta": {
+                        console.log(`Received text delta: ${message.delta}`);
+                        break;
+                    }
+                    case "response.audio.delta": {
+                        const buffer = Buffer.from(message.delta, "base64");
+                        console.log(`Received ${buffer.length} bytes of audio data.`);
+                        break;
+                    }
+                }
+                if (message.type === "response.done" || message.type === "error") {
+                    break;
+                }
+            }
+        }
+        finally {
+            client.close();
+        }
+    }
+    await text_in_audio_out();
+    ```
+
+1. Sign in to Azure with the following command:
+
+    ```shell
+    az login
+    ```
+
+1. Run the JavaScript file.
+
+    ```shell
+    node text-in-audio-out.js
+    ```
+
+
+#### [API key](#tab/javascript-key)
+
+1. Create the `text-in-audio-out.js` file with the following code:
+
+    ```javascript 
+    import { AzureKeyCredential } from "@azure/core-auth";
+    import { LowLevelRTClient } from "rt-client";
+    import dotenv from "dotenv";
+    dotenv.config();
+    async function text_in_audio_out() {
+        // Set environment variables or edit the corresponding values here.
+        const apiKey = process.env["AZURE_OPENAI_API_KEY"] || "yourKey";
+        const endpoint = process.env["AZURE_OPENAI_ENDPOINT"] || "yourEndpoint";
+        const deployment = "gpt-4o-realtime-preview";
+        if (!endpoint || !deployment) {
+            throw new Error("You didn't set the environment variables.");
+        }
+        const client = new LowLevelRTClient(new URL(endpoint), new AzureKeyCredential(apiKey), { deployment: deployment });
+        try {
+            await client.send({
+                type: "response.create",
+                response: {
+                    modalities: ["audio", "text"],
+                    instructions: "Please assist the user."
+                }
+            });
+            for await (const message of client.messages()) {
+                switch (message.type) {
+                    case "response.done": {
+                        break;
+                    }
+                    case "error": {
+                        console.error(message.error);
+                        break;
+                    }
+                    case "response.audio_transcript.delta": {
+                        console.log(`Received text delta: ${message.delta}`);
+                        break;
+                    }
+                    case "response.audio.delta": {
+                        const buffer = Buffer.from(message.delta, "base64");
+                        console.log(`Received ${buffer.length} bytes of audio data.`);
+                        break;
+                    }
+                }
+                if (message.type === "response.done" || message.type === "error") {
+                    break;
+                }
+            }
+        }
+        finally {
+            client.close();
+        }
+    }
+    await text_in_audio_out();
+    ```
+
+1. Run the JavaScript file.
+
+    ```shell
+    node text-in-audio-out.js
+    ```
+
+---
+
+Wait a few moments to get the response.
+
+## Output
+
+The script gets a response from the model and prints the transcript and audio data received.
+
+The output will look similar to the following:
+
+```console
+Received text delta: Hello
+Received text delta: !
+Received text delta:  How
+Received text delta:  can
+Received text delta:  I
+Received 4800 bytes of audio data.
+Received 7200 bytes of audio data.
+Received text delta:  help
+Received 12000 bytes of audio data.
+Received text delta:  you
+Received text delta:  today
+Received text delta: ?
+Received 12000 bytes of audio data.
+Received 12000 bytes of audio data.
+Received 12000 bytes of audio data.
+Received 24000 bytes of audio data.
+```
+
+## Web application sample
+
+Our JavaScript web sample [on GitHub](https://github.com/azure-samples/aoai-realtime-audio-sdk) demonstrates how to use the GPT-4o Realtime API to interact with the model in real time. The sample code includes a simple web interface that captures audio from the user's microphone and sends it to the model for processing. The model responds with text and audio, which the sample code renders in the web interface.
+
+You can run the sample code locally on your machine by following these steps. Refer to the [repository on GitHub](https://github.com/azure-samples/aoai-realtime-audio-sdk) for the most up-to-date instructions.
+1. If you don't have Node.js installed, download and install the [LTS version of Node.js](https://nodejs.org/).
+
+1. Clone the repository to your local machine:
+    
+    ```bash
+    git clone https://github.com/Azure-Samples/aoai-realtime-audio-sdk.git
+    ```
+
+1. Go to the `javascript/samples/web` folder in your preferred code editor.
+
+    ```bash
+    cd ./javascript/samples
+    ```
+
+1. Run `download-pkg.ps1` or `download-pkg.sh` to download the required packages. 
+
+1. Go to the `web` folder from the `./javascript/samples` folder.
+
+    ```bash
+    cd ./web
+    ```
+
+1. Run `npm install` to install package dependencies.
+
+1. Run `npm run dev` to start the web server, navigating any firewall permissions prompts as needed.
+1. Go to any of the provided URIs from the console output (such as `http://localhost:5173/`) in a browser.
+1. Enter the following information in the web interface:
+    - **Endpoint**: The resource endpoint of an Azure OpenAI resource. You don't need to append the `/realtime` path. An example structure might be `https://my-azure-openai-resource-from-portal.openai.azure.com`.
+    - **API Key**: A corresponding API key for the Azure OpenAI resource.
+    - **Deployment**: The name of the `gpt-4o-realtime-preview` model that [you deployed in the previous section](#deploy-a-model-for-real-time-audio).
+    - **System Message**: Optionally, you can provide a system message such as "You always talk like a friendly pirate."
+    - **Temperature**: Optionally, you can provide a custom temperature.
+    - **Voice**: Optionally, you can select a voice.
+1. Select the **Record** button to start the session. Accept permissions to use your microphone if prompted.
+1. You should see a `<< Session Started >>` message in the main output. Then you can speak into the microphone to start a chat.
+1. You can interrupt the chat at any time by speaking. You can end the chat by selecting the **Stop** button.

@@ -0,0 +1,31 @@
+---
+manager: nitinme
+author: eric-urban
+ms.author: eur
+ms.service: azure-ai-openai
+ms.topic: include
+ms.date: 12/26/2024
+---
+
+## Deploy a model for real-time audio
+
+[!INCLUDE [Deploy model](realtime-deploy-model.md)]
+
+## Use the GPT-4o real-time audio
+
+To chat with your deployed `gpt-4o-realtime-preview` model in the [Azure AI Foundry](https://ai.azure.com) **Real-time audio** playground, follow these steps:
+
+1. Go to the [Azure OpenAI Service page](https://ai.azure.com/resource/overview) in Azure AI Foundry portal. Make sure you're signed in with the Azure subscription that has your Azure OpenAI Service resource and the deployed `gpt-4o-realtime-preview` model.
+1. Select the **Real-time audio** playground from under **Playgrounds** in the left pane.
+1. Select your deployed `gpt-4o-realtime-preview` model from the **Deployment** dropdown. 
+1. Select **Enable microphone** to allow the browser to access your microphone. If you already granted permission, you can skip this step.
+
+    :::image type="content" source="../media/how-to/real-time/real-time-playground.png" alt-text="Screenshot of the real-time audio playground with the deployed model selected." lightbox="../media/how-to/real-time/real-time-playground.png":::
+
+1. Optionally, you can edit contents in the **Give the model instructions and context** text box. Give the model instructions about how it should behave and any context it should reference when generating a response. You can describe the assistant's personality, tell it what it should and shouldn't answer, and tell it how to format responses.
+1. Optionally, change settings such as threshold, prefix padding, and silence duration.
+1. Select **Start listening** to start the session. You can speak into the microphone to start a chat.
+
+    :::image type="content" source="../media/how-to/real-time/real-time-playground-start-listening.png" alt-text="Screenshot of the real-time audio playground with the start listening button and microphone access enabled." lightbox="../media/how-to/real-time/real-time-playground-start-listening.png":::
+
+1. You can interrupt the chat at any time by speaking. You can end the chat by selecting the **Stop listening** button.

@@ -0,0 +1,247 @@
+---
+manager: nitinme
+author: eric-urban
+ms.author: eur
+ms.service: azure-ai-openai
+ms.topic: include
+ms.date: 12/26/2024
+---
+
+## Prerequisites
+
+- An Azure subscription. <a href="https://azure.microsoft.com/free/ai-services" target="_blank">Create one for free</a>.
+- <a href="https://www.python.org/" target="_blank">Python 3.8 or later version</a>. We recommend using Python 3.10 or later, but having at least Python 3.8 is required. If you don't have a suitable version of Python installed, you can follow the instructions in the [VS Code Python Tutorial](https://code.visualstudio.com/docs/python/python-tutorial#_install-a-python-interpreter) for the easiest way of installing Python on your operating system.
+- An Azure OpenAI resource created in the East US 2 or Sweden Central regions. See [Region availability](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability).
+- Then, you need to deploy a `gpt-4o-realtime-preview` model with your Azure OpenAI resource. For more information, see [Create a resource and deploy a model with Azure OpenAI](../how-to/create-resource.md).
+
+## Microsoft Entra ID prerequisites
+
+For the recommended keyless authentication with Microsoft Entra ID, you need to:
+- Install the [Azure CLI](/cli/azure/install-azure-cli) used for keyless authentication with Microsoft Entra ID.
+- Assign the `Cognitive Services User` role to your user account. You can assign roles in the Azure portal under **Access control (IAM)** > **Add role assignment**.
+
+## Deploy a model for real-time audio
+
+[!INCLUDE [Deploy model](realtime-deploy-model.md)]
+
+## Set up
+
+1. Create a new folder `realtime-audio-quickstart` to contain the application and open Visual Studio Code in that folder with the following command:
+
+    ```shell
+    mkdir realtime-audio-quickstart && code realtime-audio-quickstart
+    ```
+    
+1. Create a virtual environment. If you already have Python 3.10 or higher installed, you can create a virtual environment using the following commands:
+    
+    # [Windows](#tab/windows)
+    
+    ```bash
+    py -3 -m venv .venv
+    .venv\scripts\activate
+    ```
+    
+    # [Linux](#tab/linux)
+    
+    ```bash
+    python3 -m venv .venv
+    source .venv/bin/activate
+    ```
+    
+    # [macOS](#tab/macos)
+    
+    ```bash
+    python3 -m venv .venv
+    source .venv/bin/activate
+    ```
+    
+    ---
+    
+    Activating the Python environment means that when you run ```python``` or ```pip``` from the command line, you then use the Python interpreter contained in the ```.venv``` folder of your application. You can use the ```deactivate``` command to exit the python virtual environment, and can later reactivate it when needed.
+
+    > [!TIP]
+    > We recommend that you create and activate a new Python environment to use to install the packages you need for this tutorial. Don't install packages into your global python installation. You should always use a virtual or conda environment when installing python packages, otherwise you can break your global installation of Python.
+
+1. Install the real-time audio client library for Python with:
+
+    ```console
+    pip install "https://github.com/Azure-Samples/aoai-realtime-audio-sdk/releases/download/py%2Fv0.5.3/rtclient-0.5.3.tar.gz"
+    ```
+
+1. For the **recommended** keyless authentication with Microsoft Entra ID, install the `azure-identity` package with:
+
+    ```console
+    pip install azure-identity
+    ```
+
+## Retrieve resource information
+
+#### [Microsoft Entra ID](#tab/javascript-keyless)
+
+[!INCLUDE [keyless-environment-variables](env-var-without-key.md)]
+
+#### [API key](#tab/javascript-key)
+
+[!INCLUDE [key-environment-variables](env-var-key.md)]
+
+---
+
+## Text in audio out
+
+## [Microsoft Entra ID](#tab/javascript-keyless)
+
+1. Create the `text-in-audio-out.py` file with the following code:
+
+    ```python
+    import base64
+    import asyncio
+    from azure.identity.aio import DefaultAzureCredential
+    from rtclient import (
+        ResponseCreateMessage,
+        RTLowLevelClient,
+        ResponseCreateParams
+    )
+    
+    # Set environment variables or edit the corresponding values here.
+    endpoint = os.environ["AZURE_OPENAI_ENDPOINT"]
+    deployment = "gpt-4o-realtime-preview"
+    
+    async def text_in_audio_out():
+        async with RTLowLevelClient(
+            url=endpoint,
+            azure_deployment=deployment,
+            token_credential=DefaultAzureCredential(),
+        ) as client:
+            await client.send(
+                ResponseCreateMessage(
+                    response=ResponseCreateParams(
+                        modalities={"audio", "text"}, 
+                        instructions="Please assist the user."
+                    )
+                )
+            )
+            done = False
+            while not done:
+                message = await client.recv()
+                match message.type:
+                    case "response.done":
+                        done = True
+                    case "error":
+                        done = True
+                        print(message.error)
+                    case "response.audio_transcript.delta":
+                        print(f"Received text delta: {message.delta}")
+                    case "response.audio.delta":
+                        buffer = base64.b64decode(message.delta)
+                        print(f"Received {len(buffer)} bytes of audio data.")
+                    case _:
+                        pass
+    
+    async def main():
+        await text_in_audio_out()
+    
+    asyncio.run(main())
+    ```
+
+1. Run the Python file.
+
+    ```shell
+    python text-in-audio-out.py
+    ```
+
+## [API key](#tab/javascript-key)
+
+1. Create the `text-in-audio-out.py` file with the following code:
+
+    ```python
+    import base64
+    import asyncio
+    from azure.core.credentials import AzureKeyCredential
+    from rtclient import (
+        ResponseCreateMessage,
+        RTLowLevelClient,
+        ResponseCreateParams
+    )
+    
+    # Set environment variables or edit the corresponding values here.
+    api_key = os.environ["AZURE_OPENAI_API_KEY"]    
+    endpoint = os.environ["AZURE_OPENAI_ENDPOINT"]
+    deployment = "gpt-4o-realtime-preview"
+    
+    async def text_in_audio_out():
+        async with RTLowLevelClient(
+            url=endpoint,
+            azure_deployment=deployment,
+            key_credential=AzureKeyCredential(api_key) 
+        ) as client:
+            await client.send(
+                ResponseCreateMessage(
+                    response=ResponseCreateParams(
+                        modalities={"audio", "text"}, 
+                        instructions="Please assist the user."
+                    )
+                )
+            )
+            done = False
+            while not done:
+                message = await client.recv()
+                match message.type:
+                    case "response.done":
+                        done = True
+                    case "error":
+                        done = True
+                        print(message.error)
+                    case "response.audio_transcript.delta":
+                        print(f"Received text delta: {message.delta}")
+                    case "response.audio.delta":
+                        buffer = base64.b64decode(message.delta)
+                        print(f"Received {len(buffer)} bytes of audio data.")
+                    case _:
+                        pass
+    
+    async def main():
+        await text_in_audio_out()
+    
+    asyncio.run(main())
+    ```
+
+1. Run the Python file.
+
+    ```shell
+    python text-in-audio-out.py
+    ```
+
+---
+
+Wait a few moments to get the response.
+
+## Output
+
+The script gets a response from the model and prints the transcript and audio data received.
+
+The output will look similar to the following:
+
+```console
+Received text delta: Hello
+Received text delta: !
+Received text delta:  How
+Received 4800 bytes of audio data.
+Received 7200 bytes of audio data.
+Received text delta:  can
+Received 12000 bytes of audio data.
+Received text delta:  I
+Received text delta:  assist
+Received text delta:  you
+Received 12000 bytes of audio data.
+Received 12000 bytes of audio data.
+Received text delta:  today
+Received text delta: ?
+Received 12000 bytes of audio data.
+Received 12000 bytes of audio data.
+Received 12000 bytes of audio data.
+Received 12000 bytes of audio data.
+Received 28800 bytes of audio data.
+```
+
+
+

@@ -0,0 +1,290 @@
+---
+manager: nitinme
+author: eric-urban
+ms.author: eur
+ms.service: azure-ai-openai
+ms.topic: include
+ms.date: 12/26/2024
+---
+
+## Prerequisites
+
+- An Azure subscription - <a href="https://azure.microsoft.com/free/cognitive-services" target="_blank">Create one for free</a>
+- <a href="https://nodejs.org/" target="_blank">Node.js LTS or ESM support.</a>
+- [TypeScript](https://www.typescriptlang.org/download/) installed globally.
+- An Azure OpenAI resource created in the East US 2 or Sweden Central regions. See [Region availability](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability).
+- Then, you need to deploy a `gpt-4o-realtime-preview` model with your Azure OpenAI resource. For more information, see [Create a resource and deploy a model with Azure OpenAI](../how-to/create-resource.md). 
+
+## Microsoft Entra ID prerequisites
+
+For the recommended keyless authentication with Microsoft Entra ID, you need to:
+- Install the [Azure CLI](/cli/azure/install-azure-cli) used for keyless authentication with Microsoft Entra ID.
+- Assign the `Cognitive Services User` role to your user account. You can assign roles in the Azure portal under **Access control (IAM)** > **Add role assignment**.
+
+## Deploy a model for real-time audio
+
+[!INCLUDE [Deploy model](realtime-deploy-model.md)]
+
+## Set up
+
+1. Create a new folder `realtime-audio-quickstart` to contain the application and open Visual Studio Code in that folder with the following command:
+
+    ```shell
+    mkdir realtime-audio-quickstart && code realtime-audio-quickstart
+    ```
+    
+
+1. Create the `package.json` with the following command:
+
+    ```shell
+    npm init -y
+    ```
+
+1. Update the `package.json` to ECMAScript with the following command: 
+
+    ```shell
+    npm pkg set type=module
+    ```
+    
+1. Install the real-time audio client library for JavaScript with:
+
+    ```console
+    npm install https://github.com/Azure-Samples/aoai-realtime-audio-sdk/releases/download/js/v0.5.2/rt-client-0.5.2.tgz
+    ```
+
+1. For the **recommended** keyless authentication with Microsoft Entra ID, install the `@azure/identity` package with:
+
+    ```console
+    npm install @azure/identity
+    ```
+
+## Retrieve resource information
+
+#### [Microsoft Entra ID](#tab/javascript-keyless)
+
+[!INCLUDE [keyless-environment-variables](env-var-without-key.md)]
+
+#### [API key](#tab/javascript-key)
+
+[!INCLUDE [key-environment-variables](env-var-key.md)]
+
+---
+
+> [!CAUTION]
+> To use the recommended keyless authentication with the SDK, make sure that the `AZURE_OPENAI_API_KEY` environment variable isn't set. 
+    
+## Text in audio out
+
+#### [Microsoft Entra ID](#tab/typescript-keyless)
+
+1. Create the `text-in-audio-out.ts` file with the following code:
+
+    ```typescript
+    import { DefaultAzureCredential } from "@azure/identity";
+    import { LowLevelRTClient } from "rt-client";
+    import dotenv from "dotenv";
+    dotenv.config();
+    
+    async function text_in_audio_out() {
+        // Set environment variables or edit the corresponding values here.
+        const endpoint: string = process.env["AZURE_OPENAI_ENDPOINT"] || "yourEndpoint";
+        const deployment = "gpt-4o-realtime-preview";
+        if (!endpoint || !deployment) {
+            throw new Error("You didn't set the environment variables.");
+        }
+        const client = new LowLevelRTClient(
+            new URL(endpoint), 
+            new DefaultAzureCredential(), 
+            {deployment: deployment}
+        );
+        try {
+            await client.send({
+                type: "response.create",
+                response: {
+                    modalities: ["audio", "text"],
+                    instructions: "Please assist the user."
+                }
+            });
+    
+            for await (const message of client.messages()) {
+                switch (message.type) {
+                    case "response.done": {
+                        break;
+                    }
+                    case "error": {
+                        console.error(message.error);
+                        break;
+                    }
+                    case "response.audio_transcript.delta": {
+                        console.log(`Received text delta: ${message.delta}`);
+                        break;
+                    }
+                    case "response.audio.delta": {
+                        const buffer = Buffer.from(message.delta, "base64");
+                        console.log(`Received ${buffer.length} bytes of audio data.`);
+                        break;
+                    }
+                }
+                if (message.type === "response.done" || message.type === "error") {
+                    break;
+                }
+            }
+        } finally {
+            client.close();
+        }
+    }
+    
+    await text_in_audio_out();
+    ```
+
+1. Create the `tsconfig.json` file to transpile the TypeScript code and copy the following code for ECMAScript.
+
+    ```json
+    {
+        "compilerOptions": {
+          "module": "NodeNext",
+          "target": "ES2022", // Supports top-level await
+          "moduleResolution": "NodeNext",
+          "skipLibCheck": true, // Avoid type errors from node_modules
+          "strict": true // Enable strict type-checking options
+        },
+        "include": ["*.ts"]
+    }
+    ```
+
+1. Transpile from TypeScript to JavaScript.
+
+    ```shell
+    tsc
+    ```
+    
+1. Sign in to Azure with the following command:
+
+    ```shell
+    az login
+    ```
+
+1. Run the code with the following command:
+
+    ```shell
+    node text-in-audio-out.js
+    ```
+
+#### [API key](#tab/typescript-key)
+
+1. Create the `text-in-audio-out.ts` file with the following code:
+
+    ```typescript
+    import { AzureKeyCredential } from "@azure/core-auth";
+    import { LowLevelRTClient } from "rt-client";
+    import dotenv from "dotenv";
+    dotenv.config();
+    
+    async function text_in_audio_out() {
+        // Set environment variables or edit the corresponding values here.
+        const apiKey: string = process.env["AZURE_OPENAI_API_KEY"] || "yourKey";
+        const endpoint: string = process.env["AZURE_OPENAI_ENDPOINT"] || "yourEndpoint";
+        const deployment = "gpt-4o-realtime-preview";
+        if (!endpoint || !deployment) {
+            throw new Error("You didn't set the environment variables.");
+        }
+        const client = new LowLevelRTClient(
+            new URL(endpoint), 
+            new AzureKeyCredential(apiKey),
+            {deployment: deployment}
+        );
+        try {
+            await client.send({
+                type: "response.create",
+                response: {
+                    modalities: ["audio", "text"],
+                    instructions: "Please assist the user."
+                }
+            });
+    
+            for await (const message of client.messages()) {
+                switch (message.type) {
+                    case "response.done": {
+                        break;
+                    }
+                    case "error": {
+                        console.error(message.error);
+                        break;
+                    }
+                    case "response.audio_transcript.delta": {
+                        console.log(`Received text delta: ${message.delta}`);
+                        break;
+                    }
+                    case "response.audio.delta": {
+                        const buffer = Buffer.from(message.delta, "base64");
+                        console.log(`Received ${buffer.length} bytes of audio data.`);
+                        break;
+                    }
+                }
+                if (message.type === "response.done" || message.type === "error") {
+                    break;
+                }
+            }
+        } finally {
+            client.close();
+        }
+    }
+    
+    await text_in_audio_out();
+    ```
+
+1. Create the `tsconfig.json` file to transpile the TypeScript code and copy the following code for ECMAScript.
+
+    ```json
+    {
+        "compilerOptions": {
+          "module": "NodeNext",
+          "target": "ES2022", // Supports top-level await
+          "moduleResolution": "NodeNext",
+          "skipLibCheck": true, // Avoid type errors from node_modules
+          "strict": true // Enable strict type-checking options
+        },
+        "include": ["*.ts"]
+    }
+    ```
+
+1. Transpile from TypeScript to JavaScript.
+
+    ```shell
+    tsc
+    ```
+
+1. Run the code with the following command:
+
+    ```shell
+    node text-in-audio-out.js
+    ```
+
+---
+
+Wait a few moments to get the response.
+
+## Output
+
+The script gets a response from the model and prints the transcript and audio data received.
+
+The output will look similar to the following:
+
+```console
+Received text delta: Hello
+Received text delta: !
+Received text delta:  How
+Received text delta:  can
+Received text delta:  I
+Received 4800 bytes of audio data.
+Received 7200 bytes of audio data.
+Received text delta:  help
+Received 12000 bytes of audio data.
+Received text delta:  you
+Received text delta:  today
+Received text delta: ?
+Received 12000 bytes of audio data.
+Received 12000 bytes of audio data.
+Received 12000 bytes of audio data.
+Received 24000 bytes of audio data.
+```

@@ -5,11 +5,11 @@ description: Learn how to use GPT-4o Realtime API for speech and audio with Azur
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: how-to
-ms.date: 12/11/2024
+ms.date: 12/26/2024
 author: eric-urban
 ms.author: eur
 ms.custom: references_regions, ignite-2024
-zone_pivot_groups: openai-portal-js
+zone_pivot_groups: openai-portal-js-python-ts
 recommendations: false
 ---
 
@@ -37,91 +37,27 @@ Support for the Realtime API was first added in API version `2024-10-01-preview`
 > [!NOTE]
 > For more information about the API and architecture, see the [Azure OpenAI GPT-4o real-time audio repository on GitHub](https://github.com/azure-samples/aoai-realtime-audio-sdk).
 
-## Prerequisites
-
-- An Azure subscription - <a href="https://azure.microsoft.com/free/cognitive-services" target="_blank">Create one for free</a>.
-- An Azure OpenAI resource created in a [supported region](#supported-models). For more information, see [Create a resource and deploy a model with Azure OpenAI](./how-to/create-resource.md).
-
-## Deploy a model for real-time audio
-
-Before you can use GPT-4o real-time audio, you need a deployment of the `gpt-4o-realtime-preview` model in a supported region as described in the [supported models](#supported-models) section.
-
-1. Go to the [Azure AI Foundry home page](https://ai.azure.com) and make sure you're signed in with the Azure subscription that has your Azure OpenAI Service resource (with or without model deployments.)
-1. Select the **Real-time audio** playground from under **Resource playground** in the left pane.
-1. Select **+ Create a deployment** to open the deployment window. 
-1. Search for and select the `gpt-4o-realtime-preview` model and then select **Confirm**.
-1. In the deployment wizard, make sure to select the `2024-10-01` model version.
-1. Follow the wizard to deploy the model.
-
-Now that you have a deployment of the `gpt-4o-realtime-preview` model, you can interact with it in real time in the Azure AI Foundry portal **Real-time audio** playground or Realtime API.
-
-## Use the GPT-4o real-time audio
-
-> [!TIP]
-> Right now, the fastest way to get started development with the GPT-4o Realtime API is to download the sample code from the [Azure OpenAI GPT-4o real-time audio repository on GitHub](https://github.com/azure-samples/aoai-realtime-audio-sdk).
-
 ::: zone pivot="ai-foundry-portal"
 
-To chat with your deployed `gpt-4o-realtime-preview` model in the [Azure AI Foundry](https://ai.azure.com) **Real-time audio** playground, follow these steps:
-
-1. the [Azure OpenAI Service page](https://ai.azure.com/resource/overview) in Azure AI Foundry portal. Make sure you're signed in with the Azure subscription that has your Azure OpenAI Service resource and the deployed `gpt-4o-realtime-preview` model.
-1. Select the **Real-time audio** playground from under **Resource playground** in the left pane.
-1. Select your deployed `gpt-4o-realtime-preview` model from the **Deployment** dropdown. 
-1. Select **Enable microphone** to allow the browser to access your microphone. If you already granted permission, you can skip this step.
-
-    :::image type="content" source="./media/how-to/real-time/real-time-playground.png" alt-text="Screenshot of the real-time audio playground with the deployed model selected." lightbox="./media/how-to/real-time/real-time-playground.png":::
-
-1. Optionally you can edit contents in the **Give the model instructions and context** text box. Give the model instructions about how it should behave and any context it should reference when generating a response. You can describe the assistant's personality, tell it what it should and shouldn't answer, and tell it how to format responses.
-1. Optionally, change settings such as threshold, prefix padding, and silence duration.
-1. Select **Start listening** to start the session. You can speak into the microphone to start a chat.
-
-    :::image type="content" source="./media/how-to/real-time/real-time-playground-start-listening.png" alt-text="Screenshot of the real-time audio playground with the start listening button and microphone access enabled." lightbox="./media/how-to/real-time/real-time-playground-start-listening.png":::
-
-1. You can interrupt the chat at any time by speaking. You can end the chat by selecting the **Stop listening** button.
+[!INCLUDE [Azure AI Foundry portal quickstart](includes/realtime-portal.md)]
 
 ::: zone-end
 
 ::: zone pivot="programming-language-javascript"
 
-The JavaScript web sample demonstrates how to use the GPT-4o Realtime API to interact with the model in real time. The sample code includes a simple web interface that captures audio from the user's microphone and sends it to the model for processing. The model responds with text and audio, which the sample code renders in the web interface.
-
-You can run the sample code locally on your machine by following these steps. Refer to the [repository on GitHub](https://github.com/azure-samples/aoai-realtime-audio-sdk) for the most up-to-date instructions.
-1. If you don't have Node.js installed, download and install the [LTS version of Node.js](https://nodejs.org/).
+[!INCLUDE [JavaScript quickstart](includes/realtime-javascript.md)]
 
-1. Clone the repository to your local machine:
-    
-    ```bash
-    git clone https://github.com/Azure-Samples/aoai-realtime-audio-sdk.git
-    ```
-
-1. Go to the `javascript/samples/web` folder in your preferred code editor.
-
-    ```bash
-    cd ./javascript/samples
-    ```
+::: zone-end
 
-1. Run `download-pkg.ps1` or `download-pkg.sh` to download the required packages. 
+::: zone pivot="programming-language-python"
 
-1. Go to the `web` folder from the `./javascript/samples` folder.
+[!INCLUDE [Python quickstart](includes/realtime-python.md)]
 
-    ```bash
-    cd ./web
-    ```
+::: zone-end
 
-1. Run `npm install` to install package dependencies.
+::: zone pivot="programming-language-typescript"
 
-1. Run `npm run dev` to start the web server, navigating any firewall permissions prompts as needed.
-1. Go to any of the provided URIs from the console output (such as `http://localhost:5173/`) in a browser.
-1. Enter the following information in the web interface:
-    - **Endpoint**: The resource endpoint of an Azure OpenAI resource. You don't need to append the `/realtime` path. An example structure might be `https://my-azure-openai-resource-from-portal.openai.azure.com`.
-    - **API Key**: A corresponding API key for the Azure OpenAI resource.
-    - **Deployment**: The name of the `gpt-4o-realtime-preview` model that [you deployed in the previous section](#deploy-a-model-for-real-time-audio).
-    - **System Message**: Optionally, you can provide a system message such as "You always talk like a friendly pirate."
-    - **Temperature**: Optionally, you can provide a custom temperature.
-    - **Voice**: Optionally, you can select a voice.
-1. Select the **Record** button to start the session. Accept permissions to use your microphone if prompted.
-1. You should see a `<< Session Started >>` message in the main output. Then you can speak into the microphone to start a chat.
-1. You can interrupt the chat at any time by speaking. You can end the chat by selecting the **Stop** button.
+[!INCLUDE [TypeScript quickstart](includes/realtime-typescript.md)]
 
 ::: zone-end