BYTE the Cloud

Chris 0:00
Hey, cloud engineers, welcome back for another deep dive.

Kelly 0:02
Glad to be here

Chris 0:03
today. We're gonna get our hands dirty with Amazon Transcribe. You know, really understand it inside and out. Yeah,

Kelly 0:09
more than just clicking around on the console Exactly.

Chris 0:11
Plus we'll hit on those exam questions you might see, so you'll be totally prepped for that AWS certification. So

Kelly 0:17
let's start with what Amazon Transcribe actually is okay, break it down for us. Well, imagine having a super powered stenographer that's always on, ready to transcribe all your audio and video content. So pretty handy. It's more than just basic voice to text, though, right? Like what's on our phones, right? We're talking highly accurate, automatic speech Rekognition, Asr, Asr, exactly, and it's powered by deep learning models.

Chris 0:45
So these models are trained on tons of data, massive data

Kelly 0:49
sets of audio and text, yeah, which is why they're so good at understanding different accents and even background noise. So it's

Chris 0:55
not just turning sound into words, it's understanding what's being said. But how important is that really? I mean, we've got voice assistants, but they're not running our businesses.

Kelly 1:04
Think of all the audio and video data generated every day, customer service, calls, meetings, lectures, even podcasts like this one. Oh yeah, good point. It's a gold mine of information. It's just kind of locked away until it's transcribed

Chris 1:16
and Amazon Transcribe unlocks that data, unlocks it,

Kelly 1:20
turns it into searchable text that you can analyze and use for all kinds of things. Okay,

Chris 1:24
now I'm seeing the potential. Give me some real world examples.

Kelly 1:29
Well, take contact centers. Imagine analyzing 1000s of customer calls,

Chris 1:34
not just for the words, but like the emotion behind them exactly. You can identify

Kelly 1:37
trends, pain points. Measure how effective your agents are, huge for customer satisfaction. Wow,

Chris 1:43
that's pretty powerful. What about media companies? Are they using this for captions and subtitles all

Kelly 1:49
the time? It makes their content accessible to a wider audience, and it's not just captions. Think about transcribing interviews for documentaries or generating transcripts for podcasts, makes the content searchable and much easier to work with. Makes

Chris 2:02
me think differently about all those zoom meetings I've been recording. So Amazon Transcribe is powerful, versatile. What are the features that make it

Kelly 2:12
tick? Well, one that stands out is its support for multiple languages. How many are we talking dozens of languages and dialects, essential for global applications, plus you can create custom vocabularies to improve accuracy for specific industries, so you can customize it. Yeah, like, let's say you're working on a healthcare app. You can train transcribe to recognize medical terminology perfectly

Chris 2:34
makes sense. How about from a cloud engineer's perspective, how does it fit within AWS?

Kelly 2:38
Great question. Amazon Transcribe integrates seamlessly with other AWS services, like what think storing your audio files in S3 triggering transcription jobs with Lambda functions, or even streaming audio in real time with Kinesis. It's all about automation. Okay,

Chris 2:54
so we've got this powerful transcription service that integrates with AWS. But what about the nitty gritty details. What are the limitations?

Kelly 3:02
Well, like any technology, there are nuances. Audio quality is a big one, background noise, Speaker clarity, even the recording format can impact the accuracy. So you gotta have good audio quality. You got it, and it needs to be in a supported format. So

Chris 3:15
preparation is key, absolutely. What about cost? As a cloud engineer, I'm always budget conscious, and you should

Kelly 3:22
be. Luckily, Amazon Transcribe uses a pay as you go pricing model so you only pay for what you use. Okay, that makes it cost effective. Very cost effective compared to building your own solution. And the pricing is based on how much audio you transcribe. Easy to predict your costs and scale your usage as needed.

Chris 3:40
Okay, that'll make sense. But let's not forget, you're here to prep for the AWS certification exam too. What are some questions you might see related to Amazon Transcribe?

Kelly 3:49
A common one is, when would you choose Amazon Transcribe over building a custom speech Rekognition solution? This is about understanding the trade offs

Chris 3:58
between a managed service and a custom build, right? You have to think about

Kelly 4:02
things like cost development time, how accurate you need it to be, and if there are pre built solutions available, building a custom solution from scratch is a lot of work. It is requires expertise and machine learning, audio processing, all that,

Chris 4:16
yeah. Plus, it's expensive upfront, and you need to constantly maintain and update it exactly.

Kelly 4:20
Amazon Transcribe is ready to use highly accurate, scalable and cost effective. If you need a fast, reliable and affordable solution, it's usually the way to go.

Chris 4:32
Makes sense. What other tricky questions might they ask? Another

Kelly 4:35
one is, how can you improve the accuracy of Amazon Transcribe? I bet audio quality is a big part of that, definitely, but there's more to it. You can also

Speaker 1 4:44
use custom vocabularies to help it recognize specific terms

Kelly 4:48
exactly, like if you're transcribing medical dictations. You can make a custom vocabulary with all the medical terms

Chris 4:54
so it transcribes them correctly right

Kelly 4:56
and you can choose the right language model for your use case. Uh, because there are different ones. Amazon Transcribe offers models optimized for different accents, dialects, even certain industries. So it's

Chris 5:07
about fine tuning the engine got it. What else might they ask about? Let's

Kelly 5:11
talk about multi speaker conversations. How does Amazon Transcribe handle multiple people talking? That seems tricky. It uses something called channel identification. Channel

Chris 5:22
identification,

Kelly 5:24
it lets transcribe distinguish between different speakers in an audio file. It analyzes the audio and figures out the different voices, then it assigns each speaker to a different channel. That's

Chris 5:36
cool. So it can tell who said what, yeah, so that's great for meetings and conference calls. Exactly really helps with transcribing and analyzing complex audio for sure. Okay, let's wrap up this first part with one final question. Okay, hit me. How does Amazon Transcribe fit into a serverless architecture? That seems to be a hot topic, it

Kelly 5:54
is, and Amazon Transcribe fits right in. Think about integrating it with AWS Lambda for event driven processing, right? You could have an S3 bucket that triggers a Lambda function whenever a new audio file is uploaded. That function could use Amazon Transcribe to transcribe the file and then store the output, maybe another S3 bucket or send it somewhere else for more processing. So it's all automated, totally serverless. You don't have to manage any infrastructure. It's scalable, cost effective, perfect for building those automated workflows. This has

Chris 6:26
been a great start to our deep dive on Amazon Transcribe. We've covered the basics of ASR, some pretty advanced features, and tackled some of those tough exam questions. Yeah, we've covered a lot, but there's still more to come.

Kelly 6:37
So let's dive even deeper into Amazon Transcribe, you know, explore some of those advanced features and use cases that can really up your cloud game. Sounds

Chris 6:45
good. I'm really curious about how we can use Amazon Transcribe to go beyond just transcribing, like to actually get valuable insights from the audio. Ah,

Kelly 6:54
you're thinking like a true cloud innovator. Amazon Transcribe has a feature called Content redaction.

Chris 6:59
Content redaction was that it

Kelly 7:02
lets you automatically redact sensitive information from your transcript. Oh, that's important for privacy, absolutely crucial for compliance, especially when you're dealing with things like personal data. So like in healthcare or finance, exactly, imagine you're working with patient recordings. Content redaction can automatically remove names, addresses, social security numbers,

Chris 7:24
all that. Wow. And it still keeps the important medical info

Kelly 7:27
it does. It's a game changer for protecting patient confidentiality while still being able to use the data.

Chris 7:33
That's amazing. So content redaction is a must have for sensitive data. What other features should we know about?

Kelly 7:40
Let's talk about custom language models. This lets you train transcribe on your specific vocabulary and audio characteristics. So it's like teaching it our lingo, yeah, like giving it a crash course in your industry. Imagine you're working with technical jargon, really specific terminology. You can train a custom language model on a data set with that vocabulary, so it transcribes everything accurately, exactly. It's like giving transcribe a custom dictionary just for your use case. That's really cool, and it gets even better. Custom language models can be trained to recognize different accents, dialects, or even audio with lots of background noise. This

Chris 8:16
is making me realize Amazon Transcribe is more than just a transcription service. It's a tool for unlocking the potential of all this audio data.

Kelly 8:26
Exactly. It's all about getting insights and turning that data into something usable, and it keeps getting more powerful. AWS is always adding new features. If you're a cloud engineer, you got to stay on top of this stuff. All

Chris 8:37
right, back to the exam for a sec. Yeah. What kind of question might they ask about content redaction?

Kelly 8:42
They might give you a scenario where you're dealing with customer calls that have sensitive info, like credit card numbers, okay, and they'll ask you how you would use Amazon Transcribe to redact that info but still keep the rest of the transcript sounds

Chris 8:56
like a real world situation. What would be the best way to answer that

Kelly 8:59
you want to talk about using content redaction to automatically find and remove those credit card numbers. You could mention that it uses machine learning to identify sensitive data. You can even configure it to redact specific patterns or entities.

Chris 9:14
So it's all about automating the redaction process and

Kelly 9:17
protecting that sensitive data.

Chris 9:18
Okay, makes sense. What about custom language models? What might they ask about that?

Kelly 9:23
They might ask you to explain how custom language models improve the accuracy of transcriptions for a specific industry.

Chris 9:29
Okay, I think I'm ready for this one. You'd say the custom language models are trained on audio and transcripts that are specific to that industry. No. So the model learns the vocabulary and the way people talk in that field,

Kelly 9:42
exactly. And you could even mention that custom language models can help with difficult audio environments, like with background noise or multiple speakers.

Chris 9:50
So it's all about tailoring the model to the specific situation. You got it Okay, feeling pretty confident about those exam questions, let's switch gears and. It and talk about some real world challenges that cloud engineers might run into when they're using Amazon Transcribe.

Kelly 10:07
One common challenge is audio quality. You know, files that are poorly recorded or have a ton of background noise. Yeah, I could see that being a problem. Amazon Transcribe is powerful, but it still relies on good audio quality. So

Chris 10:20
garbage in, garbage out, right? What can you do about that? A

Kelly 10:24
few things. First, try to clean up the audio before you send it to transcribe. So like use some audio editing software, yeah, reduce noise, enhance the clarity. Or you can use Amazon Transcribes pre processing features. I didn't know it had those. It can automatically adjust the levels and reduce noise. That's good

Chris 10:42
to know, so it's worth spending a little time to make sure the audio is as good as possible. Absolutely.

Kelly 10:46
Another challenge is dealing with multiple speakers. Yeah,

Chris 10:50
transcribing a conversation with a bunch of people sounds like a nightmare.

Kelly 10:53
It can be, but remember that channel identification feature we talked about? Oh, right, that's your best friend in these situations. Tell transcribe to separate the speakers and it'll put them on different channels, makes the transcript way easier to read and analyze. Those

Chris 11:08
Advanced Features can really make a difference. They totally can.

Kelly 11:11
And another challenge you might face is integrating Amazon Transcribe into your workflows and applications. This might mean writing some custom code or using services like Lambda or step functions to automate the transcription. So it's not always just plug and play. Sometimes you gotta get your hands a little dirty, but that's where AWS really shines. It gives you all the tools you need to build custom solutions that fit your needs. Okay,

Chris 11:35
this has been a great deep dive into Amazon Transcribe. We've covered the features, the use cases, exam, prep, even real world challenges. But before we wrap up, let's take a step back and think big picture for a minute. What are some of the trends we're seeing in speech Rekognition and transcription? What's coming next?

Kelly 11:52
That's a great question. One trend is the rise of real time transcription, real time, yeah, like live captioning for meetings or events or even real time translation. Wow. The possibilities are huge. They are and with all the advancements in machine learning and AI, this technology is just going to get better and better. It's

Chris 12:10
pretty exciting to think about. It

Kelly 12:11
really is. It is it's amazing to think about how far we've come. You know, I know, from basic voice to text to like, sophisticated AI doing the transcription and analysis.

Chris 12:23
It really is changing how we use audio and video data. Totally. Okay, but let's get back to our main goal here. We've covered a ton about Amazon Transcribe, but you know, there's always more to learn, especially with those exam questions in mind, what are some other situations where a cloud engineer might find transcribe useful.

Kelly 12:43
Let's say you're working on a project where you need to analyze customer feedback. Okay? And that feedback is coming from all over the place, phone calls, video testimonials, even social media posts, lots of different formats, right? And transcribe can help you transcribe all of that, turn it into text data you can actually analyze, so you can look for things like sentiment, yeah, keywords, trends, all that good stuff.

Chris 13:04
So it's not just about the transcripts. It's about turning them into something useful Exactly.

Kelly 13:08
It's about finding the insights. And don't forget about accessibility. Oh, right. Transcribe can automatically create captions and subtitles for videos, making the content accessible to more people, exactly, including people who are deaf or hard of hearing. It's about making things more inclusive, too. I love

Chris 13:25
that. Okay, let's get back to the exam for a minute. What about questions on integrating transcribe with other AWS services? What might we see there?

Kelly 13:35
They might give you a scenario where you have to build a serverless workflow, okay? And this workflow has to transcribe audio files from an S3 bucket, then analyze the sentiment of the text and finally store the results in a database. Wow, that's complex. They might ask you to explain how you would design and build that whole workflow using different AWS services. So what would we use? Well, you'd need Lambda for those serverless functions Amazon comprehend for the sentiment analysis and maybe DynamoDB to store the results so

Chris 14:05
many different pieces, right?

Kelly 14:08
But the key is choosing the right service for each step and making sure they all work together smoothly.

Chris 14:12
It's like building a pipeline exactly starting to see how all the AWS services can work together is pretty cool. It is. It's like a giant toolbox, yet. AWS gives you tons of documentation and examples to help you figure it out. Oh

Kelly 14:24
yeah, for sure. Don't be afraid to experiment. You know,

Chris 14:26
good advice. Okay, before we wrap up completely, let's give our listeners one last challenge, a real brain teaser. Imagine you have to transcribe audio from a live event, okay, like a conference, yeah, or webinar, and you need to make those transcripts available to people in real time as it's happening. How would you do that with AWS? Ooh,

Kelly 14:49
that's a tough one. We're talking real time processing here, and scalability and super low latency. We'd have to use Amazon Transcribe streaming capabilities.

Chris 14:58
So if. Transcribing the audio as it comes in. Yep. Then we could use

Kelly 15:02
Amazon Kinesis to stream the data, Lambda to process and format the transcripts, and API gateway so people can actually access that's a whole system. It is a real time transcription pipeline. And to make sure it can handle lots of users and stay fast, we'd need to use multiple availability zones and caching. That's some advanced stuff it is, but it shows what you can do with AWS. This has been an awesome

Chris 15:25
deep dive into Amazon Transcribe. We went deep on the features, looked at how it's used, tackled some Exam questions, and even talked about real world problems and solutions. I think we covered it all we really did. Hopefully everyone listening has a better understanding of this service and how it can help them unlock the power of audio data.

Kelly 15:44
It's been great sharing what I know about Amazon Transcribe. The most important thing is to never stop learning and exploring. You know, I agree,

Chris 15:51
stay curious, try new things and see what you can build. Thanks for joining us on this deep dive. We'll catch you next time for another adventure in the cloud.