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Assembly notes are an important a part of collaboration, but they usually fall by means of the cracks. Between main discussions, listening intently, and typing notes, it’s straightforward for key info to slide away unrecorded. Even when notes are captured, they are often disorganized or illegible, rendering them ineffective.
On this put up, we discover the right way to use Amazon Transcribe and Amazon Bedrock to robotically generate clear, concise summaries of video or audio recordings. Whether or not it’s an inner group assembly, convention session, or earnings name, this strategy may help you distill hours of content material right down to salient factors.
We stroll by means of an answer to transcribe a undertaking group assembly and summarize the important thing takeaways with Amazon Bedrock. We additionally talk about how one can customise this answer for different frequent situations like course lectures, interviews, and gross sales calls. Learn on to simplify and automate your note-taking course of.
Answer overview
By combining Amazon Transcribe and Amazon Bedrock, it can save you time, seize insights, and improve collaboration. Amazon Transcribe is an automated speech recognition (ASR) service that makes it easy so as to add speech-to-text functionality to purposes. It makes use of superior deep studying applied sciences to precisely transcribe audio into textual content. Amazon Bedrock is a completely managed service that provides a selection of high-performing basis fashions (FMs) from main AI firms like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon with a single API, together with a broad set of capabilities you have to construct generative AI purposes. With Amazon Bedrock, you may simply experiment with a wide range of high FMs, and privately customise them along with your knowledge utilizing strategies reminiscent of fine-tuning and Retrieval Augmented Era (RAG).
The answer introduced on this put up is orchestrated utilizing an AWS Step Features state machine that’s triggered whenever you add a recording to the designated Amazon Easy Storage Service (Amazon S3) bucket. Step Features permits you to create serverless workflows to orchestrate and join elements throughout AWS providers. It handles the underlying complexity so you may deal with utility logic. It’s helpful for coordinating duties, distributed processing, ETL (extract, remodel, and cargo), and enterprise course of automation.
The next diagram illustrates the high-level answer structure.
The answer workflow consists of the next steps:
A person shops a recording within the S3 asset bucket.
This motion triggers the Step Features transcription and summarization state machine.
As a part of the state machine, an AWS Lambda operate is triggered, which transcribes the recording utilizing Amazon Transcribe and shops the transcription within the asset bucket.
A second Lambda operate retrieves the transcription and generates a abstract utilizing the Anthropic Claude mannequin in Amazon Bedrock.
Lastly, a remaining Lambda operate makes use of Amazon Easy Notification Service (Amazon SNS) to ship a abstract of the recording to the recipient.
This answer is supported in Areas the place Anthropic Claude on Amazon Bedrock is on the market.
The state machine orchestrates the steps to carry out the precise duties. The next diagram illustrates the detailed course of.
Conditions
Amazon Bedrock customers have to request entry to fashions earlier than they’re accessible to be used. This can be a one-time motion. For this answer, you’ll have to allow entry to the Anthropic Claude (not Anthropic Claude Prompt) mannequin in Amazon Bedrock. For extra info, confer with Mannequin entry.
Deploy answer assets
The answer is deployed utilizing an AWS CloudFormation template, discovered on the GitHub repo, to robotically provision the mandatory assets in your AWS account. The template requires the next parameters:
E mail tackle used to ship abstract – The abstract will likely be despatched to this tackle. You have to acknowledge the preliminary Amazon SNS affirmation e mail earlier than receiving extra notifications.
Abstract directions – These are the directions given to the Amazon Bedrock mannequin to generate the abstract.
Run the answer
After you deploy the answer utilizing AWS CloudFormation, full the next steps:
Acknowledge the Amazon SNS e mail affirmation that you must obtain just a few moments after creating the CloudFormation stack.
On the AWS CloudFormation console, navigate to stack you simply created.
On the stack’s Outputs tab, and search for the worth related to AssetBucketName; it’s going to look one thing like summary-generator-assetbucket-xxxxxxxxxxxxx.
On the Amazon S3 console, navigate to your asset bucket.
That is the place you’ll add your recordings. Legitimate file codecs are MP3, MP4, WAV, FLAC, AMR, OGG, and WebM.
Add your recording to the recordings folder.
Importing recordings will robotically set off the Step Features state machine. For this instance, we use a pattern group assembly recording within the sample-recording listing of the GitHub repository.
On the Step Features console, navigate to the summary-generator state machine.
Select the identify of the state machine run with the standing Operating.
Right here, you may watch the progress of the state machine because it processes the recording.
After it reaches its Success state, you must obtain an emailed abstract of the recording.
Alternatively, you may navigate to the S3 property bucket and consider the transcript there within the transcripts folder.
Overview the abstract
You’ll get the recording abstract emailed to the tackle you offered whenever you created the CloudFormation stack. In the event you don’t obtain the e-mail in just a few moments, just be sure you acknowledged the Amazon SNS affirmation e mail that you must have acquired after you created the stack after which add the recording once more, which can set off the abstract course of.
This answer features a mock group assembly recording that you should use to check the answer. The abstract will look just like the next instance. Due to the character of generative AI, nonetheless, your output will look a bit totally different, however the content material ought to be shut.
Listed below are the important thing factors from the standup:
Joe completed reviewing the present state for job EDU1 and created a brand new job to develop the long run state. That new job is within the backlog to be prioritized. He’s now beginning EDU2 however is blocked on useful resource choice.
Rob created a tagging technique for SLG1 primarily based on greatest practices, however might have to coordinate with different groups who’ve created their very own methods, to align on a uniform strategy. A brand new job was created to coordinate tagging methods.
Rob has made progress debugging for SLG2 however may have extra assist. This job will likely be moved to Dash 2 to permit time to get additional assets.Subsequent Steps:
Joe to proceed engaged on EDU2 as in a position till useful resource choice is determined
New job to be prioritized to coordinate tagging methods throughout groups
SLG2 moved to Dash 2
Standups transferring to Mondays beginning subsequent week
Develop the answer
Now that you’ve got a working answer, listed here are some potential concepts to customise the answer to your particular use circumstances:
Strive altering the method to suit your accessible supply content material and desired outputs:
For conditions the place transcripts can be found, create an alternate Step Features workflow to ingest present text-based or PDF-based transcriptions.
As an alternative of utilizing Amazon SNS to inform recipients through e mail, you should use it to ship the output to a special endpoint, reminiscent of a group collaboration website, or to the group’s chat channel.
Strive altering the abstract directions CloudFormation stack parameter offered to Amazon Bedrock to provide outputs particular to your use case (that is the generative AI immediate):
When summarizing an organization’s earnings name, you may have the mannequin deal with potential promising alternatives, areas of concern, and issues that you must proceed to watch.
In case you are utilizing this to summarize a course lecture, the mannequin might establish upcoming assignments, summarize key ideas, listing details, and filter out any small discuss from the recording.
For a similar recording, create totally different summaries for various audiences:
Engineers’ summaries deal with design selections, technical challenges, and upcoming deliverables.
Mission managers’ summaries deal with timelines, prices, deliverables, and motion gadgets.
Mission sponsors get a short replace on undertaking standing and escalations.
For longer recordings, attempt producing summaries for various ranges of curiosity and time dedication. For instance, create a single sentence, single paragraph, single web page, or in-depth abstract. Along with the immediate, it’s possible you’ll need to modify the max_tokens_to_sample parameter to accommodate totally different content material lengths.
Clear up
To wash up the answer, delete the CloudFormation stack that you simply created earlier. Word that deleting the stack won’t delete the asset bucket. In the event you now not want the recordings or transcripts, you may delete this bucket individually. Amazon Transcribe will robotically delete transcription jobs after 90 days, however you may delete these manually earlier than then.
Conclusion
On this put up, we explored the right way to use Amazon Transcribe and Amazon Bedrock to robotically generate clear, concise summaries of video or audio recordings. We encourage you to proceed evaluating Amazon Bedrock, Amazon Transcribe, and different AWS AI providers, like Amazon Textract, Amazon Translate, and Amazon Rekognition, to see how they may help meet what you are promoting goals.
In regards to the Authors
Rob Barnes is a principal advisor for AWS Skilled Providers. He works with our clients to deal with safety and compliance necessities at scale in advanced, multi-account AWS environments by means of automation.
Jason Stehle is a Senior Options Architect at AWS, primarily based within the New England space. He works with clients to align AWS capabilities with their biggest enterprise challenges. Exterior of labor, he spends his time constructing issues and watching comedian guide films together with his household.
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