Azure Custom Speech Service Lifecycle Management

Azure Custom Speech Service

Azure Custom Speech Service Lifecycle Management

Understanding the Model Lifecycle

The Azure Custom Speech Service offers a model lifecycle management system that ensures optimal performance and accuracy. When deploying a custom speech model, it is essential to understand the key terms like training, transcription, and endpoints. Training involves customizing a base model to your specific domain using text and/or audio data. Transcription is the process of converting speech into text using a model, and endpoints are specific deployments of models that only you can access.

Expiration Timeline

Models in the Azure Custom Speech Service have specific expiration timelines. Training with a base model is available for one year after Microsoft creates the model, while transcription with a base model is available for two years. Transcription using a custom model is also available for two years after creation. It's important to note the quarterly cycle that determines model expiration dates.

What to Do When a Model Expires

When a custom or base model expires, it affects transcription capabilities. For custom endpoint users, speech recognition requests may fall back to the most recent base model, leading to potential accuracy issues. Batch transcription requests for expired models will fail unless the model property is set to a non-expired model. Updating endpoints or setting appropriate model properties is crucial to avoid transcription failures.

Checking Model Expiration Dates

To ensure seamless operation, users should regularly check model expiration dates. The Azure AI Foundry portal provides a straightforward way to access expiration dates for base and custom models. By following simple instructions under the 'Fine-tuning' section, users can identify the expiration date of their desired model. This information is crucial for planning model updates and maintaining transcription capabilities.

Related Resources

The Azure Custom Speech Service offers extensive resources for users looking to enhance their speech models. From training models to deploying custom models, the service provides detailed documentation to guide users through the process. Additionally, users can access resources that help in quantitatively measuring and improving the quality of speech to text models. Leveraging these resources can significantly enhance the overall performance of custom speech models.

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