In this comprehensive guide, we will be discussing the process of setting up a serverless architecture using AWS Lambda functions. The process involves setting up a system where every time a new audio file is uploaded to a specific S3 bucket, a Lambda function will be triggered. This function will then send the audio file to Amazon Transcribe which will transcribe the audio into text. Finally, the generated transcript will be summarized using Amazon Bedrock. This automation can be extremely beneficial for businesses that deal with large amounts of audio data that need quick and accurate transcription and summarization.
In the previous post of our series, we delved into the importance of logging in AI applications and how Amazon Bedrock, with its sophisticated AI capabilities, can aid in maintaining security compliances, identifying biases, and improving model performance. Now, in this post, we will take a step further by discussing the deployment process of a serverless architecture using AWS Lambda. This process involves setting up a system that automates the transcription and summarization of audio files uploaded to an S3 bucket. Let's get started!
The first step involves setting up your S3 bucket. Amazon S3, or Simple Storage Service, provides scalable storage in the cloud. This is where you will upload your audio files. You need to configure the bucket so that it triggers your Lambda function every time a new file is uploaded.
To create your first Amazon S3 bucket, you will first need to sign up for AWS, if you haven't already. Once you have an AWS account, you can proceed with creating a bucket in Amazon S3 through the AWS Management Console. Remember, every object in Amazon S3 is stored in a bucket, so before you can store data, a bucket must be created. Begin by signing into the AWS Management Console and opening the Amazon S3 console at https://console.aws.amazon.com/s3/. From there, navigate to the left pane and select 'Buckets'. Then, click on 'Create bucket'. This will take you to the 'Create bucket' page.
Next, you'll need to enter a name for your bucket in the 'Bucket name' field. Keep in mind that the bucket name must be unique within a partition, which is a grouping of Regions. The bucket name should be between 3 and 63 characters long and consist only of lowercase letters, numbers, dots (.), and hyphens (-). For best compatibility, it is recommended to avoid using dots (.) in bucket names, except for buckets that are used only for static website hosting. Also, ensure that the bucket name begins and ends with a letter or number. Please note that once the bucket is created, its name cannot be changed. For example, you can name it audio-to-text-transcription. For the 'Region' field, choose the AWS Region where you want the bucket to reside. Choose a Region close to you to minimize latency and costs and address any regulatory requirements. Objects stored in a Region never leave that Region unless you explicitly transfer them to another Region. Once these steps are done, hit 'Create bucket' to finalize the process. Congratulations, you've now created a bucket in Amazon S3!
Once your S3 bucket is ready, the next step is to set up your Lambda function. AWS Lambda is an event-driven computing service that can process files and run code in response to triggers such as changes to data in an S3 bucket. This function will be triggered each time an audio file is uploaded to your S3 bucket. The function should be designed to send the audio file to Amazon Transcribe.
Creating a Lambda function involves several steps, all of which can be completed using the AWS console. To start, navigate to the 'Functions' page of the Lambda console and click 'Create function'. Here, you're given the option to 'Author from scratch'. In the 'Basic information' pane, enter a function name - for instance, audioToTextTranscriptionFn. For 'Runtime', select 'Python 3.11'. Leave the architecture set to x86_64 and proceed by clicking 'Create function'.
At this point, AWS Lambda generates a basic 'Hello world' function and assigns it an execution role. An execution role is an AWS Identity and Access Management (IAM) role that grants a Lambda function the necessary permissions to access AWS services and resources. The role created by Lambda for your function has basic permissions to write to CloudWatch Logs.
