Transcripts
1. Welcome: Hello and welcome to this
video course on how to create an AI-generated podcast from
news headlines using Python, the ChatGPT API, and the ElevenLabs API,
which are all free. My name is Daniel and I have over 20 years of programming and web
development experience. By the end of this
course, you'll be able to
automatically generate an MP3 audio file
from a news RSS feed, which you can then use to
create your own podcast. You'll also have learned
how to integrate AI and a text-to-speech
service into your code. Some of the specific
skills that you'll learn include… Using Python to get
content from an RSS feed. Generating human-like text
using the ChatGPT API. Converting text to speech
using the ElevenLabs API. Creating an MP3 audio file from the text-to-speech output. The course is designed
to be at a beginner or intermediate level and no Python or AI
experience is required. We'll go through each step with clear explanations so that you can then understand how to customize the code if you wish. By the end of the course,
you'll have worked on a real-world project
that you could show to potential employers or use as
a foundation for a podcast, or you'll have an
understanding of how to incorporate AI into
other projects of yours. The ideal student
for this course is someone who is interested
in learning about Python, about AI and other
third-party APIs, and who wants to
know how to automate content creation such as
with a podcast. It's for you if you're a blogger,
a journalist, a content creator, or a hobbyist who just wants to play with
the latest technologies. So, thank you for
considering this course. I hope to see you inside.
2. Setting Up: Okay, Let's get into it. But before we actually
start coding, there are three things
we need to get set up. Python and the various
packages that we need. An OpenAI account and API key, and an ElevenLabs
account and API key. So Python first. You hopefully
have it already installed and to double-check that, you can type "python" at the terminal. And it should show you
what you have installed. Here it's saying I've
got to 2.7 installed and actually we
want Python 3. But I'm on a Mac and it actually has two versions
of Python installed. So to specify Python 3, I need to type in "python3". I think for Windows just "python" will be OK if you have
Python 3 installed. Anyway, let's exit
the Python prompt and try again with
Python 3. OK. And it says I have Python 3.10
installed, which is fine. So whichever of
those works for you, "python" or "python3", use that as the command for
the rest of the course. I'm exiting the
Python prompt again. And now we need to
make sure we've got the various modules and
packages that we need. I've tried to make this easy by creating a
"requirements.txt" file, and it should be in the
resources for this course. So put it in the directory in which
you're currently working. Now I've made a project folder
called "ai-podcast-creator". Let's have a look at
what's inside that. There we go. Just "requirements.txt". That's all that's in
there at the moment, so that's fine. We're going
to use that with Pip, which is a Python installer, to automatically
grab those packages. The way we do that is
with the command "pip". If you need to type in
"python3" on your system, then here you should
type in "pip3", and then "install -r" and the name
of the requirements file, so "requirements.txt".
Press "Enter". And it will read that
"requirements.txt" file and automatically download
and install those packages. I already have them
installed. For you it might take a few
seconds to download and install them if
there's any missing, but then you should
be ready to go. So the next stage is
the OpenAI and ElevenLabs accounts and API keys. And for that, we need
to move to the browser. OK, here in the browser, if you search for" OpenAI API", you might get various guides
and explanations and stuff, but what we need is the
actual platform itself. So "platform.openai.com". If you don't have
an account already, please create one and
the free plan is fine. If you do have an
account, then this is where you need to sign in. Once you've signed in, you're
given a bunch of tokens, bunch of credits to use, and that'll be plenty
for our program here. We don't need to worry
about paying anything, but we do need to
grab the API key. So go over to the
account menu and look for the "View API keys" item. I've already created one,
yours might be empty here, in which case, click the
"Create new secret key" button, and that will show your API key one time only. This is the only time it's
going to be on the screen so you need to make a copy now, otherwise you'll lose it. But if you lose it, you can
also regenerate another one So it's not really
a big deal. Anyway, we want to copy this and we're going to put this in an environment variables file, so we need to switch back to
the terminal to create that. Back in the terminal
here we have the OpenAI API key
in our clipboard, but we need to create
a file to store it. And we're going to
create a special kind of file called an environment
variables file, otherwise known as
a ".env". So we want to create
that basic text file. I'm going to use the
Nano text editor here, but you can use any
text editor you like, So "nano" and it's ".env".
Please use that naming. Press "Enter". And it's created
this empty file and here, I'm going to put a variable name and
then paste the API key. So the variable name, I'll use "OPENAI_API_KEY" then
"=" with no space. And then here I will paste
the API key from OpenAI. Good. I'm going to leave this open because we're going to
come back to this in a minute after we've got
the ElevenLabs API key, which is again back
in the browser. Similar to before, at "beta.elevenlabs.io". Please create an account if
you don't already have one. If you do have an account,
then sign in here. Once you've signed
in, we need to go up to the account menu, and this time it's
"Profile" that we click on. Now we can't see
the API key here, so we need to click the eye button and that makes it visible. We can see this anytime,
you can regenerate a new one anytime. As before, let's copy and paste that and go back over
to the terminal. This ".env" file is still
open, which is good. So we need to create a new line, and this time we're going
to use the same format, but we'll call it "ELEVENLABS_API_KEY" "=", no spaces, and again, we paste
the API key there. That's all we need
for this file. So we can save and
exit this file. If you're using Nano here, then you do CTRL+X and that will ask
you to save it. Yes. And we close the file and
go back to the terminal. So we have our API keys ready and stored
in a ".env" file. We're going to access
that with Python. We have all the Python
modules installed. We're ready to
start programming.
3. Environment Variables: Now for the fun stuff,
the programming, and I've got an empty
Python file here, which I've called
"create_podcast.py". I've made sure this is
in the same folder as the ".env" file in which we
saved the API keys earlier. So they're in the same
folder. Within this program, I'm going to break
it up into chunks. And this first chunk is going to be a bit of code which will grab those API keys and just print them out to the screen
to make sure that worked. And then we're going to use
them in the other sections. So let's concentrate
on that first. To do that, we need to first
of all, import some modules. And we need three
for this section. So the first one is
a module that is specifically made for handling
the environment variables, the API keys. So "from dotenv", that's the name of the module, and we're going to import
just one part of it, which is called "load_dotenv". We're going to
use that method. Next thing we need to import
is the built-in "os" module. And then finally, it should all be installed already for you, it's called "openai".
OpenAI module. Once we have those
modules imported, the first thing
we'll do is just run the "load_dotenv" function and that'll just sort
of prepare it for grabbing the API keys. That's super easy. It's just "load_dotenv()" with brackets. Next, we need to prepare
somewhere to store the API keys temporarily
within the program, and the OpenAI module has a property
specifically for this so we'll use that with "openai". And then the "api_key" property. And we assign the API key
that's in our variable file. And here is where we can use the "os" module to grab that.
"os.getenv()" And try and remember the name of the variable that
we used earlier, and I'm pretty sure it
was "OPENAI_API_KEY". OK, now we can do the
same thing for ElevenLabs. This time we don't have
an ElevenLabs module, so we'll just create
our own variable, call it "elevenlabs_api_key". And again that equals "os." get environment variable. And it's called
"ELEVENLABS_API_KEY". That should be all we need
just to grab the API keys. The reason why we're
doing this rather than hard-coding them
into the program, is that if we share
this program, this code on GitHub or wherever, obviously people will be able to see our API keys if they're in here, and we don't want that, they're
supposed to be private, they're supposed to be secret. So we store them in a local
environment variable file which will stay on our machine, but then we can freely share this code and the API
keys stay secret. Let's make sure this
has worked by printing out the API keys
to the terminal. So "print(openai.api_key)" and "print(elevenlabs_api_key)". A slight difference between
the two there. Right, I'm going to save this and let's go over to the
terminal and run it. Here in the terminal,
we're going to run the program by typing "python". "python" might be enough for you. In my case, I need
to type "python3" and then the
name of the file. So it's "create_podcast.py".
Press "Enter". And it works. It prints the API keys that
we've stored in the environment variables file. Perfect, that means we're ready to move on
to the next part, which is actually
grabbing content and then getting AI
to play with it.
4. Grabbing RSS Content: Let's build up our program a bit more then, but first of all I want to remove these lines
that print out the API keys. We don't need those. So we'll get rid of that. What I do need though,
is an RSS feed, because I'm going to take the news headlines from an
RSS feed and use that as the input for ChatGPT.
So, over to the browser and I've got an RSS
feed already prepared. I live in Japan, so
I'm using one from the Japan Times,
the news headlines. And I just want to point
out the RSS feed consists of multiple items and each
one has got a title, it's got a description, it's got various other
elements as well, but I'm just going to
stick with the title and the description and combine those as the summary of the
news item for ChatGPT. So I just need the
URL of this feed and pretty much any
RSS feed should do. And I'm going to go
back to our program, and let's make a
new variable for this. So "news_feed =" that URL. I like to keep these
variables at the top of the program so that anybody can come in
and change the RSS feed easily later without having to scroll
down through all the code. Next, I want to work on
actually grabbing the content. And before I do that, I'm just going to print out something to the
terminal here that says that we are now
processing the RSS feed. And then I'll print
out stages for the different parts as we write the program so that when
we run the program, we know which bit is being
worked on at any one time. We can also see when it fails. When there are errors, we can see which bit those errors in. So let's just, for our
own sake, print out "Processing RSS feed". Right, and to do that we need a really good
module called FeedParser, which is going to make
it super easy for us. So we import it
as we did before. "import feedparser" Good. And this means in just
a very few lines, we can grab in that
content from the RSS feed. Now, we're going to
assign the output of the FeedParser processing to
a new variable called "feed". And so we use "feedparser" here, and we use one of
its methods called "parse". And the argument for that is
the URL of the RSS feed so that's just "news_feed"
that we prepared earlier. When we grab the feed, we can't give multiple bits of information easily to ChatGPT, what we really want is
one bit of content. So I'm going to have
one empty variable, I'm going to make it in
a minute, and then with each news item I'm going to append
the news summary to it. So in the end we'll have
one variable which contains all of the news summaries
joined together. News stories they are, so I'm going to call
the variable "stories" and that's just an empty
one to start off with. The RSS feed I'm using, I think it has about 30 items
and I don't want that many. I want the podcast
to be pretty short, so I'm going to limit it to 10, and I'll create another
variable for that to use later. So "stories…", let's call it
"stories_limit" equals 10. OK, now we're ready to loop
through the "feed" object which should contain
all that RSS feed data. To loop through, we're going to
create a for loop and let's call each item in the RSS feed "item",
because that's what it is. "for item in feed:" And there is a property
called "entries". Now that on its own is enough
to grab all of the items but as I mentioned, I want to
limit it to the first 10. So we can use a clever
bit of Python shorthand here and we'll use
square brackets, and then we'll use
that "stories_limit" here after a colon. So colon and then 10
or "stories_limit" and then a colon at the end
because it's a for-loop. And that will automatically truncate all the items
down to the first 10. Within each iteration
of the loop we're going to build up that
empty "stories" variable. So "stories = stories +" because we're appending
data to it each time. And because we're appending all of these news stories
into one string, I want to make it
absolutely clear to ChatGPT when a new story starts. So let's have "New Story: " for the beginning of each one. And then the items
in the RSS feed, they have a "title" element and they have a
"description" element. So let's grab them by
doing "item.title" Put a period after that, and then we'll grab
the "item.description". And that should be it for
the RSS feed parsing. I want to make sure it works so I'm just going to print
out the "stories" variable once everything has
been added into it, Let's make sure it's
worked by going over to the terminal and
running the program. So I'm saving this file.
Going over to the terminal. And as before, "python"
or "python3" and the name of the file.
Press "Enter" and cross your fingers… OK, that was quick. There we go, it grabbed the RSS feed and it's combined everything
into one long string. This is perfect as the input for ChatGPT,
which we'll do next.
5. Using ChatGPT: Right, we're doing well. We've got the content
of the RSS feeds, so let's turn to ChatGPT. I'm going to delete
this print line there. We don't need to print out
the "stories" variable anymore, but I am going to add
a line that prints out the next stage of processing. So we'll say
"Processing ChatGPT". With this one,
we're going to use the OpenAI module which
we've already imported. So there's no new
modules to import. To use that we need to assign the result or the output of
ChatGPT into a variable. So let's call it "chat_output". This will be from the
"openai" module and there is a service
called "ChatCompletion". They have other services such as image generation,
things like that, but we're using a
text chat here. And within that there
is a "create" method. The "create" method takes
various arguments, but it needs at least two, and they are "model" and – oops, forgot the comma –
"messages". And "messages" is
actually an array. First of all, with "model", this can specify the AI
model that we want to use. There'll probably be many things to choose from in future but at the moment
you've pretty much got GPT 3.5 currently. So "gpt-3.5" and there is
one called "turbo". And this is the most advanced publicly available AI model. In future this will
probably change so please keep an eye on
their API documentation to find out what the
latest and greatest is. So for now, the "model"
is "gpt-3.5-turbo". And "messages" is an
array of objects. Each object has a
"role" and "content", and we're just going to use
one object to keep it simple. So we want a "role" because
it's an object here, I'm going to use
"role" and "content". The "role" can be one
of three things. "user", which is the
role that actually sends the command or the requests
to ChatGPT. "system", which is sort of informing
ChatGPT what it is. So for example, "you are a helpful, polite assistant",
something like that. And then "assistant"
is the third role, and that could be some kind of background information,
for example if you're sending
it a bunch of data, you could say "this
data was collected in March 2023", something like that. We don't need that
kind of stuff. We're just going to
stick with the one role of the basic command, and that means the
"role" will be "user". The "content" is the
actual request that we send to ChatGPT. I've done various
trial and error here and I've come up with
quite a long command, so I'm going to paste
that in to save time. And there it is. "Please rewrite the
following news headlines and summaries in
a discussion way…", like a chatty kind of way, as though someone is talking
about them on a podcast. As you can see,
I've added various sort of extra bits here because I had to keep tweaking it to get
what I really wanted. And obviously you
can tweak that as well listening to the output. So that's the request but obviously we need to include the news
headlines themselves, so at the end of this request I'm going to just append
the "stories" variable. Good. That is going to grab
the output from ChatGPT but it's also going to give us a few other things as well, and we just want the core
content, the core response. So let's extract that by creating a new
variable "chat_content". And we're going to grab it from the "chat_output" that
we just created. And within that, there
will be a "choices" array, and we just want the first item, zero item from the
"choices" array, and then "message"
and then "content". So that's kind of
the fixed sort of way that we drill down to get just the content that
ChatGPT returns. That should be it so let's see if it's worked by printing it out
to the terminal as before: "print(chat_content)" And I'm saving that and now I'll go over to the terminal
and give it a try. I'm going to run
the program here but first of all, you may or may not know this, but if you press the up (↑) arrow on your keyboard
in the terminal, it will go through
commands that you previously typed in so you
don't need to type them again. So I've just pressed
up a couple of times and this is the one I want,
"create_podcast.py", press "Enter", and it's
running the program. And no errors so far, that's good. So now we just wait for ChatGPT
to return something. And there we go, took a few seconds, but that looks pretty
good, no errors. And just reading through this, it looks nice, sort of good, kind of podcast sort of content. And I'm always impressed
that it's grabbed this from news headlines and then
been able to convert it into something that
looks pretty natural. So what we need to do
next is convert this into audio using the ElevenLabs API.
6. Converting to Audio: Well this is going well so far so let's move on
to the next part which is to actually
generate the audio output. And as before, I'm going to just print out a line to
say what's happening. So "Processing audio",
something like that. OK, now there isn't an
ElevenLabs module for Python. Instead, we're going to be
using a couple of things. We're going to be using a "json" module because we're going to be
handling JSON format data. And we're going to be importing a "requests"
module which will deal with actually sending POST requests over the internet. So to import those, we go back
to the top of the program, "import json", and then
"import requests". Now when we deal with
the ElevenLabs API, there are actually various
voices that we can use, and so we have to specify
the voice that we want by specifying the voice ID. That's all listed in the
ElevenLabs API documentation so I won't go through it here, but I have one already ready so I'm going to add that first
of all to a new variable, and I'll call it "voice_id". And that's the ID,
difficult to remember so I pasted it in. It's actually for a female
American voice called Rachel and I think it
sounds very natural so that's why I'm
going with that one. Next, we want to use the "requests" module to actually send this request and
grab the response. To do that, let's
have a new variable to store the output in,
so "audio_output". We're going to use the
"requests" module and we actually have
to send POST data. So "post" is the method and then there are
three arguments that we're going to use
in the "post" method. The first argument
for the "post" method is just the URL of the API. That's just a string, and I've got it in the clipboard,
so I paste that in. But we also need to add the voice ID of the
voice that we want, so append "voice_id". The next two arguments
are "data" and "headers". So let's deal with "data" first. And the data will be
in a JSON format. So "data =" and here we use the "json" module, and it has a method
called "dumps" which will accept
a JSON object. In this object, we'll
put two things, "text" and "voice_settings". "text" is quite easy because we've
already got it ready, and that is the
output from ChatGPT. So it's "chat_content". And then the "voice_settings"… Let me just move this up
up the page a bit. "voice_settings" is
another object with, in the case of ElevenLabs, just two settings for
how we can control the tone or the
expressiveness of the voice. The two settings are "stability"
and "similarity_boost". First of all "stability". These are both values between
zero and one. And "stability" is
what it sounds like. The voice sounds quite
stable when you have a high value and much more expressive when
you have a low value. I'm going to set it at 0.2. I've tried a few things
and I quite like 0.2. In the following video, I'll explain what
these mean and I'll demonstrate a bit how you
can change these settings. But first of all
let's stick with 0.2. And then "similarity_boost". I've not found this
makes much difference so I'm just going to stick
with zero for that one. Then we move on to the
"headers" argument of this "post" method. So "headers", the third argument,
is also an object and this time with
three properties. The first one is "Content-Type" which will be "application/json", because we're sending
a JSON object. We're also sending an API key. That's the next property. And so we need, in this case, "xi-api-key". And we don't want to type it directly in the program here, but that's OK because
we've already got it as a variable that we created
right at the beginning. So we can use "elevenlabs_api_key"
that we prepared. And the third property is the format of the data
that we want to receive. And we actually
want audio format. So we can "accept" and this will be "audio/mpeg". OK, that's all we need. And so we have the "post"
method ready to be sent. And what we need to
do next is deal with the response with the
output that we get back. I'm going to just move
the code up a little bit. And within the
output from ElevenLabs there will be a thing
called a status code which will tell us whether
it's worked or not, so I think it's a good idea
to grab that and check first of all. So "if audio_output", which is the object,
and then within that the "status_code" property, if this equals 200, then it means
everything has worked. So let's just put a
placeholder there. "Do something." Otherwise, we want to print out something to tell us
that it hasn't worked. So we're going to print out to the screen a message
from the audio output. So ElevenLabs will include an error message
if there was a problem. That's what we
want to print out. So "audio_output", and that
is a "text" property there. So if something goes
wrong, we'll be able to understand whether it's because we run out of credits
or something else. I have found that ElevenLabs
does use up quite a lot of credits pretty quickly because the audio generation
is quite intensive. Anyway, that's in case
something goes wrong but hopefully it'll
all go right, in which case we can now,
in this "do something" line, convert that audio
that we've received into an audio file, an MP3 file. So we're going to use
the "with" command. So "with" and then we're going to "open" an audio file and we'll
call it "test.mp3". You can call it whatever you like and if it doesn't exist, it will automatically create it. But we need to add a
couple of flags here, "w" and "b". "w" means write, so it's in write mode, we can write content
to the MP3 file, and "b" means it's binary
data, a binary file. OK, and then we need to just assign a name to this output file so we'll call it "output_file". With this file,
we're going to write the content of the audio that
we received from ElevenLabs. So with this file, "output_file.write()" – what are
we going to write to it? We're going to write the
content of the audio output. So "audio_output", and that
has a "content" property. That should be
everything that we need to create the audio file. So to finish off, let's just have one more line to print out that everything's
finished. "Processing complete"
or something like that. I'm going to save this file, and now if we run it and cross our fingers, it should work. So over here in the
terminal one more time, I'm going to press the
up arrow on my keyboard. "create_podcast.py" – let's go. So as before, It's going and it's grabbed the stuff in the RSS
feed already. That was quick. It's now using ChatGPT
to convert that into a more kind of chatty
discussion kind of style. OK, it has got some text.
It's probably not identical to the text
that we had before, but it still looks
pretty good. And so this is now going to
be recorded, if you like, by the AI voice and hopefully saved as a file called "test.mp3". OK, that just finished and didn't show any error
messages, which is great. So now let's go over to the folder in which
we've been working. Right, there is a file
called "test.mp3" It's 1.2 Mb, which looks good. Let's play it.
"Hello and welcome to today's podcast where we will be discussing some of the latest
news stories out of Japan." "First up…"
It works! It works. I hope you can hear that, and it is amazingly realistic to me. I'm very impressed.
We can go and look at the content in the terminal and see that she is actually reading that. "Japan and South Korea are
eyeing a package to…" Yep, sounds really good. Wow. So we've created our program, you should feel very proud
of yourself, and it works. We've got an audio file,
so that's it really, but I do want to go
over in the next video just a couple of ways you can
tweak the settings here to change the feeling or the
tone or the expressiveness. So, see you in the next video.
7. Tweaking Settings: So everything works. That's great, but I just want to show you
a couple of ways where you can control what comes out of both ChatGPT and audio.
We'll do ChatGPT first and for that, I actually want to delete the
audio section just temporarily because I
don't want to waste credits generating
audio when it's the ChatGPT bit that
I'm focusing on. So that'll do for now. The "create" function that
we saw earlier for ChatGPT needs at least
two arguments. There are several
others, and you can see the API documentation to understand all the different arguments that are
available to you. But the one that has the most impact that I've
found, is "temperature". And you can think
of this as like creativity. It goes from 0 to 2. I think the default is 1. And 0, relatively no creativity, means you're pretty much going
to get back the same or very nearly the
same response every time you run that same command. A high number, the maximum 2, will give us a
lot of creativity and that's what I
want to show here. So let's use a temperature
of 2. Save this, and we'll go over and
run the command again. There we go. Oh, this
is very creative! So it kind of starts off OK. "Welcome to our podast today, where we are discussing…"
blah, blah, blah. But as you can see, as we
go further and further down, it's just rubbish! So this is really creative. So you probably never want
to use the "temperature" as a value of 2.
The lowest one, 0, as I mentioned, it'll
give you probably a much more stable output and pretty much the
same thing every time. It depends what you
want. You can stick with that predictable output. The default, I find,
is absolutely fine. I generally tend
to leave it blank. I don't use the
"temperature" argument unless I want to
control its creativity. But this is not very helpful! Anyway, that's up to you, you can play about with that. Let's look at the
audio controls now. For the audio settings, there's pretty much just two. Two voice settings: "stability" and "similarity_boost"
that we saw earlier. First of all, "similarity_boost". This is most effective when you have a custom voice
and you can do this… I think you might
have to be on a paid plan with ElevenLabs, but you can provide voice
samples and it will try to create an AI voice
very similar to that, and so you can use a
"similarity_boost" to make it very similar or a
little bit less similar. If you have it very high, there can be artefacts I
think that start to appear. I've found it doesn't
have much effect when we're using the
built-in voices. A higher number sounds a
little bit clearer maybe, but there is some
risk of artefacts coming in according to
the API documentation. So I've just been leaving it
at 0 and it's been fine. The "stability" setting has
more of a difference, and it's a little bit like the "creativity"
setting of ChatGPT. It controls the
expressiveness of the voice. And so a higher number
makes it more stable, a little bit
boring actually. And a low number is… the voice is very expressive. Now rather than generating
and using up my credits, I've actually created
two test files so it's much faster
for us to listen to the difference between
those two settings. I have a couple of sample
files that I've prepared here and let's listen to
the first one with the maximum possible
stability of 1. "Hello. This is a sample of
the voice called Rachel from ElevenLabs with a
stability setting of one and a similarity
boost setting of zero." OK, It sounds pretty good, pretty natural, but
also kind of boring. So let's try with a stability setting of 0
which is the minimum allowed value. "Hello. This is a sample of
the voice called Rachel from ElevenLabs with the
stability setting of zero and a similarity
boost setting of zero." Wow, she's really
excited about that! That is possibly too expressive. I've tried it with a few samples
and going down to 0… let's go back to the
program for a second. Yeah, the "stability" setting of 0 really is too much for me. I like 0.2. I've
seen other people in discussion forums recommend
anything between 0.2 to 0.4. So go with those and
a "similarity_boost", You can try playing
around with that, but generally 0 or even
1 is gonna be fine. There's not a huge difference. So those are the ways you
can control the output from ChatGPT and the ElevenLabs audio. Just be careful because the audio processing is
much more intensive and therefore you're
going to use up your ElevenLabs credits much
faster than ChatGPT ones. What I recommend is each time you generate
a new audio file, maybe make a copy of it and keep a record of the "stability" and the "similarity_boost"
settings that you used so that if it
gets overwritten, you've still got that copy. You can refer to as a
sample file in the future. Anyway, I think we're done.
All that's left now is just to recap
on what we've learned.
8. Wrapping Up: So there we go. You now know how
to easily create an AI-powered podcast
from just news headlines. And we've hopefully learned
a lot over the course so let's quickly recap over the specific skills
that we've covered. Using Python to get
content from an RSS feed. Generating human-like text
using the ChatGPT API. Converting text to speech
using the ElevenLabs API. Creating an MP3 audio file from the text-to-speech output. I hope you've enjoyed the
course and found it useful. Now that you've completed it, the skills you've learned
could be used for a variety of different
projects in future. You could obviously
automatically generate audio files on a daily
basis or a weekly basis, and then upload them to your
preferred podcast host. Some examples
include BuzzSprout, SimpleCast, or TransistorFM. You could alternatively use the code that we've developed to use other online
services such as image generation or
even video generation. It should be pretty easy
to tweak the code to use a variety of online APIs. Finally, I want to thank you for joining me in this course. It's been a pleasure
teaching you and please remember,
practice makes perfect, so keep honing
those coding skills and create interesting projects, and very importantly,
have fun along the way. If you have any
feedback or questions, then feel free to leave them
in the discussion section. And if you haven't already, please consider leaving
a review to help other learners find and
benefit from this course. So that's it from me and I wish you all the best
in your coding adventures.
Daniel Davis, Making tech & photography easy
