Integrate a custom language model into Azure Video Indexer

Azure Video Indexer is a powerful tool that allows you to extract insights and metadata from your video content. It can automatically analyze videos, transcribe spoken words, detect people and emotions, and much more. While Azure Video Indexer already provides support for multiple languages, sometimes you might need to use a custom language model to cater to specific linguistic nuances. In this article, we will explore how to integrate a custom language model into Azure Video Indexer.

Step 1: Create and train a custom language model

First, you need to create a custom language model using Azure Cognitive Services. Follow these steps:

1. Create a new instance of the Language Understanding (LUIS) service in the Azure portal.
2. Go to the LUIS portal, create a new LUIS application, and train it using your own data or a pre-trained model.
3. Test your language model to ensure it provides accurate results for your target language.

Step 2: Retrieve the API key and endpoint

To integrate your custom language model into Azure Video Indexer, you need to retrieve the API key and endpoint. Here’s how:

1. In the Azure portal, navigate to your Language Understanding (LUIS) service instance.
2. Go to the Keys and Endpoint section and note down the Endpoint URL and either of the subscription keys.
3. These credentials will be used to authenticate and communicate with your language model.

Step 3: Accessing Azure Video Indexer

To access and interact with Azure Video Indexer, you need to have it provisioned in your Azure subscription. If you haven’t done this yet, follow these steps:

1. In the Azure portal, search for “Video Indexer” and create a new instance.
2. Once provisioned, open the Video Indexer portal and sign in with your Azure credentials.

Step 4: Integrate custom language model into Azure Video Indexer

To integrate your custom language model into Azure Video Indexer, you need to modify the code that interacts with the Video Indexer API. You can do this by using the Video Indexer SDK or directly making API calls.

Here’s an example of how to make an API call using your custom language model:

import requests

response = requests.get(api_endpoint + '/api/Method', headers=headers, params=params)


Replace ‘<your_video_indexer_subscription_key>’, ‘<your_video_indexer_endpoint>’, and ‘<your_custom_language_model_id>’ with the appropriate values.

Step 5: Analyze videos using the custom language model

Now that you have integrated your custom language model, you can use it to analyze videos in Azure Video Indexer. Simply make the appropriate API calls to Video Indexer, and it will leverage your custom language model to provide insights and metadata specific to your target language.

Conclusion

By integrating a custom language model into Azure Video Indexer, you can enhance the language processing capabilities and make it more suitable for your specific language requirements. Whether you need to analyze videos in a lesser-known language or account for unique linguistic nuances, Azure Video Indexer can be tailored to meet your needs. Follow the steps outlined in this article to seamlessly integrate your custom language model into Azure Video Indexer and unlock its full potential.