Merge pull request #271331 from eric-urban/eur/quick-refresh

quick docs refresh

Author: v-dirichards

articles/ai-services/openai/reference.md

@@ -1191,6 +1191,8 @@ The operation returns a `204` status code if successful. This API only succeeds
 
 ## Speech to text
 
+You can use a Whisper model in Azure OpenAI Service for speech to text transcription or speech translation. For more information about using a Whisper model, see the [quickstart](./whisper-quickstart.md) and [the Whisper model overview](../speech-service/whisper-overview.md). 
+
 ### Request a speech to text transcription
 
 Transcribes an audio file.
@@ -1220,7 +1222,7 @@ POST https://{your-resource-name}.openai.azure.com/openai/deployments/{deploymen
 
 | Parameter | Type | Required? | Default | Description |
 |--|--|--|--|--|
-| ```file```| file | Yes | N/A | The audio file object (not file name) to transcribe, in one of these formats: `flac`, `mp3`, `mp4`, `mpeg`, `mpga`, `m4a`, `ogg`, `wav`, or `webm`.<br/><br/>The file size limit for the Azure OpenAI Whisper model is 25 MB. If you need to transcribe a file larger than 25 MB, break it into chunks. Alternatively you can use the Azure AI Speech [batch transcription](../speech-service/batch-transcription-create.md#use-a-whisper-model) API.<br/><br/>You can get sample audio files from the [Azure AI Speech SDK repository at GitHub](https://github.com/Azure-Samples/cognitive-services-speech-sdk/tree/master/sampledata/audiofiles). |
+| ```file```| file | Yes | N/A | The audio file object (not file name) to transcribe, in one of these formats: `flac`, `mp3`, `mp4`, `mpeg`, `mpga`, `m4a`, `ogg`, `wav`, or `webm`.<br/><br/>The file size limit for the Whisper model in Azure OpenAI Service is 25 MB. If you need to transcribe a file larger than 25 MB, break it into chunks. Alternatively you can use the Azure AI Speech [batch transcription](../speech-service/batch-transcription-create.md#use-a-whisper-model) API.<br/><br/>You can get sample audio files from the [Azure AI Speech SDK repository at GitHub](https://github.com/Azure-Samples/cognitive-services-speech-sdk/tree/master/sampledata/audiofiles). |
 | ```language``` | string | No | Null | The language of the input audio such as `fr`. Supplying the input language in [ISO-639-1](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) format improves accuracy and latency.<br/><br/>For the list of supported languages, see the [OpenAI documentation](https://platform.openai.com/docs/guides/speech-to-text/supported-languages). |
 | ```prompt``` | string | No | Null | An optional text to guide the model's style or continue a previous audio segment. The prompt should match the audio language.<br/><br/>For more information about prompts including example use cases, see the [OpenAI documentation](https://platform.openai.com/docs/guides/speech-to-text/supported-languages). |
 | ```response_format``` | string | No | json | The format of the transcript output, in one of these options: json, text, srt, verbose_json, or vtt.<br/><br/>The default value is *json*. |

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

@@ -257,7 +257,7 @@ To use a Whisper model for batch transcription, you need to set the `model` prop
 > [!IMPORTANT]
 > For Whisper models, you should always use [version 3.2](./migrate-v3-1-to-v3-2.md) of the speech to text API.
 
-Whisper models by batch transcription are supported in the East US, Southeast Asia, and West Europe regions.
+Whisper models by batch transcription are supported in the Australia East, Central US, East US, North Central US, South Central US, Southeast Asia, and West Europe regions.
 
 ::: zone pivot="rest-api"
 You can make a [Models_ListBaseModels](https://westus.dev.cognitive.microsoft.com/docs/services/speech-to-text-api-v3-2-preview2/operations/Models_ListBaseModels) request to get available base models for all locales.

articles/ai-services/speech-service/whisper-overview.md

@@ -60,8 +60,8 @@ Whisper Model via Azure AI Speech might be best for:
 - Customization of the Whisper base model to improve accuracy for your scenario (coming soon)
 
 Regional support is another consideration. 
-- The Whisper model via Azure OpenAI Service is available in the following regions: North Central US and West Europe. 
-- The Whisper model via Azure AI Speech is available in the following regions: East US, Southeast Asia, and West Europe.
+- The Whisper model via Azure OpenAI Service is available in the following regions: EastUS 2, India South, North Central, Norway East, Sweden Central, and West Europe. 
+- The Whisper model via Azure AI Speech is available in the following regions: Australia East, Central US, East US, North Central US, South Central US, Southeast Asia, and West Europe.
 
 ## Next steps
 

articles/ai-studio/how-to/generate-data-qa.md

@@ -22,7 +22,7 @@ In this article, you learn how to get question and answer pairs from your source
 ## Install the Synthetics Package
 
 ```shell
-python --version  # ensure you've >=3.8
+python --version  # use version 3.8 or later
 pip3 install azure-identity azure-ai-generative
 pip3 install wikipedia langchain nltk unstructured
 ```

articles/ai-studio/toc.yml

@@ -178,18 +178,18 @@ items:
       displayName: endpoint
     - name: Deploy Azure OpenAI models
       href: how-to/deploy-models-openai.md
-    - name: Deploy Llama 2 family models
-      href: how-to/deploy-models-llama.md
-      displayName: maas, as a service
-    - name: Deploy Mistral family models
-      href: how-to/deploy-models-mistral.md
-      displayName: maas, as a service
     - name: Deploy Cohere models
       items:
       - name: Deploy Cohere Command models
         href: how-to/deploy-models-cohere-command.md
       - name: Deploy Cohere Embed models
         href: how-to/deploy-models-cohere-embed.md
+    - name: Deploy Llama 2 family models
+      href: how-to/deploy-models-llama.md
+      displayName: maas
+    - name: Deploy Mistral family models
+      href: how-to/deploy-models-mistral.md
+      displayName: maas
     - name: Deploy open models
       href: how-to/deploy-models-open.md
       displayName: oss, open source

articles/ai-studio/tutorials/deploy-copilot-ai-studio.md

@@ -413,8 +413,7 @@ Now that you have your evaluation dataset, you can evaluate your flow by followi
 1. Select a model to use for evaluation. In this example, select **gpt-35-turbo-16k**. Then select **Next**.
 
     > [!NOTE]
-    > Evaluation with AI-assisted metrics needs to call another GPT model to do the calculation. For best performance, use a GPT-4 or gpt-35-turbo-16k model. If you didn't previously deploy a GPT-4 or gpt-35-turbo-16k model, you can deploy another model by following the steps in [Deploy a chat model](#deploy-a-chat-model). Then return to this step and select the model you deployed.
-    > The evaluation process may take up lots of tokens, so it's recommended to use a model which can support >=16k tokens.
+    > Evaluation with AI-assisted metrics needs to call another GPT model to do the calculation. For best performance, use a model that supports at least 16k tokens such as gpt-4-32k or gpt-35-turbo-16k model. If you didn't previously deploy such a model, you can deploy another model by following the steps in [Deploy a chat model](#deploy-a-chat-model). Then return to this step and select the model you deployed.
 
 1. Select **Add new dataset**. Then select **Next**.