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TTS_English_Technical_data

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so this point one i just guessed it so one question is how do you determine
of chat GPT or maybe it is already done uh for your language you can for example
Twilio does have one of those but if you were looking at one of these that didn't,
plus 3 and so it's just basically it continues the generation in all the
There are people, you might not think about this, but there are people who are going to use your application
are the differences pret is uh just like preprocessing you're just um using like
beginning it's all zero then I used masked fill so what this is doing
Discord Community to actually have um a closer uh relationship with the
we're making a new line available here. And also you'll see that there's
run so we see that this runs and uh this currently looks kind of spous but uh
and we think that this is probably her. I'm gonna see, I'm gonna grab this really quick,
We did that, we did that in our API and then we are going to create a web-based API
stream. columns and then with voice recording column we can now copy over
One thing I want you to gain from that exercise though is that you now have the ability to identify whether or not
and ydev to evaluate the loss okay
Random offsets so because this is four we are ex is going to be a uh four
array y which we which we created during the
and therefore this is a no. But let's keep going through the rest of these,
be used for language modeling now the reason that the original paper had an
so with that response, the browser renders the page. When one of those links are clicked,
So while this is an asynchronous function, I can actually make it look more like it is in line
just saw that the voice recording actually is a dictionary and therefore
you'll be able to discuss what an API does. You'll understand why they exist and you'll be able
way in the loss we expect to get something around what we had originally
context size of 512 tokens or maybe you're going to use another embedding
you said I didn't need to know how to code. If you already know how to make a website,
look like that and so you're starting to slowly align it so it's going to expect
the actual script so we can do that by doing python main.py press enter and
here okay so basically we need to know what
So this is one of those numbers that you verified right when you created your account,
we're thinking that the um constraint to better performance right
manually as well there we go so yeah
going to store how we are going to chain the the
messages is not empty in the session State we don't have do messages it's
so what we'd like to do now is just as in the previous video we'd like to index
Define an index tracker beforehand and in the first run the index tracker is
and if you have enough GPU you can even make it like 5,000 50,000 even you can
but you can make, for this collection that we built, we can actually do the settings for the collection.
and that's um you might imagine that's very crowded that's a lot of points for
notes such that all the edges go only one way from left to right
the structure of this a little bit. So we'll say client messages
that's what we know and now we're interested in dd by dc
we are trying to do is x1 w1 plus x2 w2 plus b
a one sentence to explain it so think about in this NLP there are many many
they make their way into the new versions of the interface. Still abstracting away things for us,
now just as in the previous video we want to take these probabilities we want
dictionaries with additional keyword arguments the content example type and
that you have our documents added so now let's hop over to the app.py and test it
now we have to be careful because there's a times out.grad
Just call the API method and voila. You could probably create the uppercase string all
to be public 'cause we actually want anybody to be able to see it, not just Twilio.
each entry in this two-dimensional array will tell us how often that first
open up chat gbt is because we're going to ask it to write us some sentences
and x2 times w2 and then we need to add bias on top of
the input now is 100 and the output number of neurons will be
It's pretty cool right? Here we go. Message dot delete is not a function.
embedded into a thirty dimensional space you can think of it that way so we have
made it a bit more respectable so here's our data set here's all the parameters
do usually um is uh I have an estimate loss function and the estimate loss
you see that the output will become five by one because these 27
uh this is how you print a number of parameters I printed it and it's about
is going to predict that g is likely to come next in the sequence and it's going
to make that magic happen and that processing is happening elsewhere, "en la Nube."
this will now become meaningless because we've reinitialized these so
you might want to also add a couple of print statements to confirm where we are
don't need to create a new session and can save it under the same session I
Oh, I cannot tell you how good it feels as a developer to end up in a place where it feels
So I'm gonna click this and now note I'm inside of a web browser.
label and we will change how we define o
All right, so I got all sorted out. So I sent my email to the compliance folks
So again, I got to that, oops, accidentally pasted my Auth token in the wrong line there.
sensitivity does it respond what is the slope at that point does the function go
positioned in the space and that's why we need to encode them positionally and
hopefully what we're getting now is a tiny bit lower than 4.84
keep them as true we've went over this and this is how we
like for example if we just test a single word andre and
a t one one unified T5 encoder and the T5 decoder model
at one which is the integer at that location is indeed equal to this
and now I know that you can explore them with Curl. For now though, let's take things another step deeper.
model so I'm importing the pytorch um NN module uh for
so somehow we need to concatenate these inputs here together so that we can do
So let's look at that again. So list returned this object, this array of messages
scalar valued auto grant engine so it's working on the you know level of
now telling the exact value is really hard but what is the sign of that slope
large and micrograd is very very simple but if you
here effectively this looks really awkward but changing l
now on the entire data set of text is re-represented as just it's just
pytorch actually requires that we pass in requires grad is true
session State input so here we can Define it if send input not in session
set up your project I'll link a video up here so that you can take a look at that
those words only and then here i am shuffling up all the
on her horse, her magical horse there. So that's pretty cool, right? This is awesome.
So it's relative to that, which means I that if I'm going to display it,
who requests the URL, so let's do that. So I'm gonna go ahead,
new session so our index is new session obviously but if the session key equals
So Fetch returns a promise and it's going to return that response and that response has a method on it
ever search for, the first artist I would search for. And you can see that here it's showing
or 3 204 examples
First, I'd like to take some time upfront here and clearly break down what is meant
of the training set and then the targets here are in the