This repository show's how to use SageMaker's real-time inference endpoints to host OpenAI's Whisper model for audio-to-text transcription in real time. By using Amazon SageMaker's real-time model inference endpoints, this repository illustrates how to utilize the power and flexibility of SageMaker hosting in conjunction with open source generative models.
In order to run the example in this repo, navigate to the notebook. This notebook can be run end-to-end in Sagemaker Studio. We recommend using the Python 3 (Data Science 3.0) with Python 3.10, and a ml.m5.large instance inside of SageMaker Studio to run the notebook. Running through the notebook you will be able to...
- Save a serialized Whisper model to Amazon S3
- Create a SageMaker model object from this serialized model
- Deploy a SageMaker real time endpoint with a custom script for audio-to-text transcription
- Send in audio signals in real time for transcription
- Delete the SageMaker endpoint
This repository deploys a SageMaker endpoint with a custom inference script similar to this example in the SageMaker SDK documentation. The components required to deploy a pre-trained model to an endpoint in SageMaker are 1) a serialized model artifact (tar file) in Amazon S3 and 2) the code and requirements which runs inference. These components are then packaged into a SageMaker endpoint which serves the serialized model with custom code behind as an API. See the architecture below for a visual description.
Please see the CONTRIBUTING file for details on contributing.
This library is licensed under the MIT License. See the LICENSE file for specifics.
This guidance is for informational purposes only. You should still perform your own independent assessment, and take measures to ensure that you comply with your own specific quality control practices and standards, and the local rules, laws, regulations, licenses and terms of use that apply to you, your content, and the third-party generative AI service referenced in this guidance. AWS has no control or authority over the third-party generative AI service referenced in this guidance, and does not make any representations or warranties that the third-party generative AI service is secure, virus-free, operational, or compatible with your production environment and standards. AWS does not make any representations, warranties or guarantees that any information in this guidance will result in a particular outcome or result.
