[User] Please transcribe this text

[Assistant] Violinists, violists, and cellists sit in pairs, sharing one music stand between them, which is referred to as a desk. A 16-member first violin section would consist of eight desks. Sometimes, these desks are assigned different parts in a score.


[User] Please transcribe this text

[Assistant] Usually, the second player of each desk has the responsibility to turn the pages of parts during a performance, which can keep the music moving without any interruption if the page turns don't have sufficient rests.


[User] Please transcribe this text

[Assistant] There are a number of different seating plans, often dictated by the preference of the conductor or the players themselves. The old school usually places the first violins on the left and the seconds on the right, with the cellos and basses skewed to the left or right behind them, and the violas filling up the remaining space.


[User] Please transcribe this text

[Assistant] The advantage of this was that it kept the higher sound of the strings spread out across the stage. The disadvantage was that the seconds had their violins tilted away from the audience, making them a little less clear. A new seating arrangement has gradually replaced this setup.


[User] Please transcribe this text

[Assistant] Both sections of violin sit to the conductor's left, with the violas center-right and the cellos along the right side of the stage. The double basses loom in the back on the right.


[User] Please transcribe this text

[Assistant] The beauty of this system is that it concentrates the lower pitched instruments in one corner, including timpani, tuba, bassoons, and trombones, allowing these tones to combine with more power and confidence.


[User] Please transcribe this text

[Assistant] Despite the popularity of this seating plan, conductors may change things around, even during the same concert, if there are certain relationships that need encouragement within the orchestra. Say, the horns with the cellos in a piece by Mahler or Bruckner.


[User] Please transcribe this text

[Assistant] Sometimes you'll even see the violas sitting at the front on the right, though admittedly this is more to help balance an underpowered section than anything else.


[User] Please transcribe this text

[Assistant] The final issue with string seating is how many per instrument in a standard symphony orchestra.


[User] Please transcribe this text

[Assistant] This adds up to 60 musicians, or 30 desks. The largest professional orchestras often have one additional desk per section for a total of 70 players. In some orchestras, there will be equal numbers of violas and cellos, ideally around 10 to 12 a piece.


[User] Please transcribe this text

[Assistant] But it's important to recognize, in the case of amateur and semi-pro orchestras, all calculations depend on the availability of players. A more realistic estimate in these cases has to take into account that violists are quite hard to find, and are often violinists filling in on viola so that there will be some kind of section.


[User] Please transcribe this text

[Assistant] I've commonly seen small orchestras with ten firsts and seconds a piece, eight cellos, four basses, and only four to five violists.


[User] Please transcribe this text

[Assistant] How these instruments fit together is one of the fundamental pieces of knowledge that's often passed over by beginning orchestrators, but is basic to understanding strings as a section.


[User] Please transcribe this text

[Assistant] Just as important, and just as often ignored, is why the string section sounds the way it does, which we'll cover in the next video.


[User] Please transcribe this text

[Assistant] I have to drive slowly. And I'll get dirty.


[User] Please transcribe this text

[Assistant] Tire Town School, Episode 9, Fast Eddie.


[User] Please transcribe this text

[Assistant] Someday, I'll be a race car, he said.


[User] Please transcribe this text

[Assistant] No you won't, said Zippy. You're too slow.


[User] Please transcribe this text

[Assistant] Good morning, said the teacher. It's time for safety class.


[User] Please transcribe this text

[Assistant] He saw stop signs. He saw traffic lights.


[User] Please transcribe this text

[Assistant] We'll practice here, said Miss Fender. Everyone will drive on the track. Don't go too fast. Zippy laughed. This will be easy for Eddie.


[User] Please transcribe this text

[Assistant] Hi, and welcome to this fourth and final week of this course on convolutional neural networks. By now, you've learned a lot about ConfNet. What I want to do this week is show you a couple important special applications of ConfNet. We'll start to face recognition.


[User] Please transcribe this text

[Assistant] And then go on later this week to Neural Style Transfer, which you get to implement in the programming exercise as well to create your own art work.


[User] Please transcribe this text

[Assistant] But first, let's start with face recognition. And just for fun, I want to show you a demo. When I was leading Baidu's AI group, one of the teams I worked with, led by Yuanqing Lin, had built a face recognition system that I thought was really cool. Let's take a look.


[User] Please transcribe this text

[Assistant] So I'm going to play this video here, but, uh, I can also get whoever's editing this, the raw video config relative is better to, uh, splicing the raw video or take the one I'm playing here.


[User] Please transcribe this text

[Assistant] Pretty cool, right? The video you just saw demoed both face recognition as well as liveness detection, the latter meaning making sure that you are a live human.


[User] Please transcribe this text

[Assistant] It turns out live dense detection can be implemented using supervised learning as well to predict live human versus not live human, but I want to spend less time on that. Instead, I want to focus our time on talking about how to build the face recognition portion of the system.


[User] Please transcribe this text

[Assistant] First let's start by going over some of the terminology used in face recognition. In the face recognition literature, people often talk about face verification and face recognition.


[User] Please transcribe this text

[Assistant] This is the face verification problem, which is if you're given an input image as well as a name or ID of a person,


[User] Please transcribe this text

[Assistant] And the job of the system is to verify whether or not the input image is that of the claimed person. So sometimes this is also called a one-to-one problem where you just want to know if the person is the person they claim to be. So the recognition problem is much harder than the verification problem.


[User] Please transcribe this text

[Assistant] With a hundred people in your database, you now have a hundred times of chance of making a mistake. And the chance of making a mistake on each person is just one percent. So if you have a database of a hundred persons, uhm,


[User] Please transcribe this text

[Assistant] And if you want an acceptable recognition error, you might actually need a verification system with maybe 99.9 or even higher accuracy before you can run it on a database of 100 persons and have a


[User] Please transcribe this text

[Assistant] But what we'll do in the next few videos is focus on building a face verification system as a building block. And then if the accuracy is high enough, then you'll be able to use that in a recognition system as well.


[User] Please transcribe this text

[Assistant] So in the next video we'll start describing how you can build a face verification system. It turns out one of the reasons that is a difficult problem is you need to solve a one-shot learning problem. Let's see in the next video what that means.


[User] Please transcribe this text

[Assistant] Tire Town School. Episode 17. Tag.


[User] Please transcribe this text

[Assistant] Jeb sighed. Oh, I'm it again. You can't catch me! Eddie shouted. He zoomed away from Jeb.


[User] Please transcribe this text

[Assistant] Jeb tagged her. You're it, said Jeb.


[User] Please transcribe this text

[Assistant] Poppy chased Dipper. Dipper got away. Poppy chased Eddie. Eddie got away. Then Eddie saw something.


[User] Please transcribe this text

[Assistant] In this video, we'll discuss how to train this U-net neural network and get it to predict noise.


[User] Please transcribe this text

[Assistant] So the goal of the neural network is for it to predict noise, and really it learns the distribution of what is noise on the image, but also what is not noise, what is sprite likeness, right?


[User] Please transcribe this text

[Assistant] And so how we do that is that we take a sprite from our training data and we actually add noise to it. We add noise to it and we give it to the neural network and we ask the neural network to predict that noise. And then we compare the predicted noise against the actual noise that was added to that image.


[User] Please transcribe this text

[Assistant] And that's how we compute the loss. Uhm, and that backprops through the neural network so that the neural network learns to predict that noise.


[User] Please transcribe this text

[Assistant] So how do you determine what this noise here is? You could just go through time and sampling and give it different noise levels. But realistically in training, we don't want the neural network to be looking at the same sprite all the time. It helps it to be more stable if it looks at different sprites across an epoch.


[User] Please transcribe this text

[Assistant] We add it to this image and then we have the neural network predict it. We take the next sprite image in our training data. We again sample a random time step. Could be totally different like you see here. And then we add it to this sprite image. And again, we have the neural network predicted the noise that was added. And this results in a much more stable training scheme.


[User] Please transcribe this text

[Assistant] So what does training actually look like? Here is a wizard hat sprite. And here is what a noise input would look like.


[User] Please transcribe this text

[Assistant] But by the time you get to epoch 31, the neural network has a better understanding of what this sprite looks like. So then it predicts noise that is then subtracted from this input to produce something that does look like this wizard hat sprite.


[User] Please transcribe this text

[Assistant] Cool, so that was for one sample. This is for multiple different samples, multiple different sprites across many epochs, and what that looks like. As you can see in this first epoch, it is quite far from sprites, but by the time you get to epoch 32 here, it looks quite like little video game characters. And even before that.


[User] Please transcribe this text

[Assistant] So first you want to sample a training image. So here we are loading up all the data into the data loader. We're putting it into a progress bar so that we can visualize it. But essentially imagine all the data here. We're then iterating through all of the data samples. So X here is a training image. So just looking at X now.


[User] Please transcribe this text

[Assistant] Within this loop, we are sampling a time step t, and this determines the level of noise. We're not going through all the time steps, just sampling a time step t.


[User] Please transcribe this text

[Assistant] We sample a noise, we add that noise to the image based on that time step.


[User] Please transcribe this text

[Assistant] And then we input that noise image into the neural network.


[User] Please transcribe this text

[Assistant] Uh, we also put in the time step because we also care about adding that time embedding in and the neural network predicts the noise as output.


[User] Please transcribe this text

[Assistant] Comparing that predicted noise with the noise that we actually added, we can compute the loss using mean squared error, MSE.


[User] Please transcribe this text

[Assistant] Cool, so onto our training notebook here. So this is all the same as before, just hitting shift enter, uhm, setting things up.


[User] Please transcribe this text

[Assistant] With the UNet, uh, here what's interesting is, you know, our training hyperparameters, our batch size of 100, and we are going through 32 different epochs and our learning rate. I'm just gonna shift enter there to run it.


[User] Please transcribe this text

[Assistant] Again, similar things here of setting up the model and the noise schedule for those scaling parameters.


[User] Please transcribe this text

[Assistant] Now here's where you get into training. So you can load your data set and we're loading it into that data loader here. It's this 16 by 16 sprites data set. And we're also loading up our optimizer.


[User] Please transcribe this text

[Assistant] And here's the function that perturbs our input, meaning that it takes the image, it adds the right noise level for that specific time step to that image and returns it. So I can hit shift enter here.


[User] Please transcribe this text

[Assistant] So here we won't actually step through training because it takes many hours on CPU and that's where these notebooks are hosted. But I really recommend that you do go through this. This is the exact code that we had just looked at together.


[User] Please transcribe this text

[Assistant] But what we can do is that we did train this model and save the model at different epochs such that you can then run sampling and be able to see how it does at each epoch.


[User] Please transcribe this text

[Assistant] So this is again the same sampling code you saw before. I'm just gonna breeze through that.


[User] Please transcribe this text

[Assistant] And here is where you would load the model for epoch zero. So this is the path to that model checkpoint, model underscore zero for epoch zero. I'm going to load that model. And then here you can just visualize the samples.


[User] Please transcribe this text

[Assistant] And again, this is running the same sampling method as before called DDPM that you learned in the previous video. This takes a couple minutes and we'll speed this up in the video. Great. So we can hit play here.


[User] Please transcribe this text

[Assistant] A bit amorphous still, but you know, starting to understand the general outlines of sprites. It's not pure noise.


[User] Please transcribe this text

[Assistant] So we also have Epoch 4 here for you to see. So you can see the model improving. These look a little bit more like sprites. And Epoch 8. Little bit more. See some books in there actually.


[User] Please transcribe this text

[Assistant] Uhm, or this might actually be 32 when we index from zero.


[User] Please transcribe this text

[Assistant] Uhm, so these look a lot more like sprites. You can see a sword here. This is probably the wizard hat. See a potion here.


[User] Please transcribe this text

[Assistant] But of course there are still some blobs here and there, some people here, uhm, so it's not perfect and it could keep going.


[User] Please transcribe this text

[Assistant] So in the next video, you'll get to control what you generate, meaning you can tell it to generate objects or these people, for example.


[User] Please transcribe this text

[Assistant] In this video, you'll learn how to control the model and what it generates.


[User] Please transcribe this text

[Assistant] For many, this is the most exciting piece because you get to tell the model what you want and it gets to imagine it for you. When it comes to controlling these models,


[User] Please transcribe this text

[Assistant] Uh, there are vectors, there are numbers that are able to capture meaning. And here it's capturing the meaning of this sentence, or this joke perhaps, or diffusion models. Brownians often bump into each other.


[User] Please transcribe this text

[Assistant] And what's special about embeddings is because they can capture the semantic meeting, text with similar content will have similar vectors. And one of the kind of magical things about embeddings is you can almost do this vector arithmetic with it. So Paris minus France plus England equals the London embedding, for example.


[User] Please transcribe this text

[Assistant] Okay, so how did these embeddings actually become context to the model during training? Well here you have an avocado image, which you want the neural network to understand.


[User] Please transcribe this text

[Assistant] And then compute the loss and do the same thing as before.


[User] Please transcribe this text

[Assistant] And you can do this across a lot of different images with captions. So here is a comfy armchair. You can pass it through an embedding, pass it into the model and have that be part of training.


[User] Please transcribe this text

[Assistant] Now the magic of this section is that while you were able to scrape these images of avocados and armchairs off the internet with those captions, you're able to, at sample time,


[User] Please transcribe this text

[Assistant] Be able to generate things that the model has never seen before. And that could be an avocado armchair.


[User] Please transcribe this text

[Assistant] And the magic of this is because you can embed the words avocado armchair into this embedding that has, you know, a bit of avocado in there, a bit of armchair in there, put that through the neural network, have it predict noise, subtract that noise out and get, lo and behold, an avocado armchair.


[User] Please transcribe this text

[Assistant] So, more broadly, context is a vector that can control generation. Context can be, just as we have seen now, the text embeddings of that avocado armchair that's very long.


[User] Please transcribe this text

[Assistant] But context doesn't have to be that long. Context can also be different categories that are five in length, you know, five different dimensions, such as having a hero or being a non-hero, like these objects of a fireball and a mushroom. It could be food items, you know, apple, orange, watermelon.


[User] Please transcribe this text

[Assistant] It could be spells and weapons like this bow and arrow or this candle. And finally, it could be whether these sprites are side-facing or not.


[User] Please transcribe this text

[Assistant] So now let's take a look at adding context to your model in the next lab.


[User] Please transcribe this text

[Assistant] And again, we're not training, but I'm going to call out a few places where we do add the context. So when we do load the data here, we now iterate through both the data point and the context vector associated with it.


[User] Please transcribe this text

[Assistant] And the context that we do have are these one-hot encoded vectors of hero, non-hero, food, spells, and weapons, and side-facing.


[User] Please transcribe this text

[Assistant] We created context mask and what's important here is that actually with some randomness we completely mask out the context so that the model is able to learn generally what a sprite is as well.


[User] Please transcribe this text

[Assistant] It's pretty common for diffusion models. And then we add context when we call the neural network right here.


[User] Please transcribe this text

[Assistant] So let's load a checkpoint where we did train the model with context. So just loading that model here, running that.


[User] Please transcribe this text

[Assistant] And here you can see that when you run this, this is actually choosing completely random context right here, completely randomly. You can see the different types of outputs of, you can see the different types of outputs of objects and people. And now controlling it a bit, you can actually define it here.


[User] Please transcribe this text

[Assistant] So here I just defined, you know, oh hero, a couple heroes, you know, first two, so these two are heroes. The next two are side-facing, so it's one-hot with this last value here for side-facing.


[User] Please transcribe this text

[Assistant] And the last two are food items. So this kind of looks like an apple and this kind of looks like a pear.


[User] Please transcribe this text

[Assistant] And now getting into the avocado armchair vibe, we can actually mix and match these a bit. So while we trained it on one hot encoded vectors, we can also provide it with these float numbers between zero and one to get a mix of things. So here,


[User] Please transcribe this text

[Assistant] For, uh, the second one here, it is a hero and partially food, and so now it looks like a potato man.


[User] Please transcribe this text

[Assistant] The third one's also a little bit quirky, it is part food and part spells, so it kinda looks like this potion. But yea, you can play with these yourself, you can try to put in something contradictory, like it's supposed to be a hero, but also side-facing, like both front-facing and side-facing, so uhm, this is, this is good fun.


[User] Please transcribe this text

[Assistant] Go. You are not going in the water. You need to see a doctor.


[User] Please transcribe this text

[Assistant] We quit. We all did. You gave us the idea.


[User] Please transcribe this text

[Assistant] Precious, don't feel bad. You did your best, but Sasha will never be


[User] Please transcribe this text

[Assistant] I don't know. A fancy restaurant somewhere on Madison Avenue.


[User] Please transcribe this text

[Assistant] Okay, water with bubbles.


[User] Please transcribe this text

[Assistant] Reach in control.


[User] Please transcribe this text

[Assistant] They're not, uhm, giving me the service I want.


[User] Please transcribe this text

[Assistant] I'm sure you have a very good bank. Which bank do you use?


[User] Please transcribe this text

[Assistant] You're right. This bank is better than the other two we saw.


[User] Please transcribe this text

[Assistant] I like what the woman here said about saving money for the future.


[User] Please transcribe this text

[Assistant] Are you sure you wouldn't prefer that? I thought you went to that fancy restaurant all the time.


[User] Please transcribe this text

[Assistant] My friend at work helped me open a bank account too.


[User] Please transcribe this text

[Assistant] It just arrived from Ecuador yesterday. I promised her I would wear it.


[User] Please transcribe this text

[Assistant] So, how long have you been in New York? Two years. Oh, how nice. It's my great pleasure to meet you. Oh.


[User] Please transcribe this text

[Assistant] Thank you. Goodbye. Goodbye. Thank you. Goodbye.


[User] Please transcribe this text

[Assistant] Are we finished? Done. We did it.


[User] Please transcribe this text

[Assistant] At first, I wasn't even thinking about art.


[User] Please transcribe this text

[Assistant] So, how was your visit with the family?


[User] Please transcribe this text

[Assistant] Oh, I see you have an artist in your family.


[User] Please transcribe this text

[Assistant] Soon you'll be back in that picture at home with your family.


[User] Please transcribe this text

[Assistant] It looks like your grandson left these.


[User] Please transcribe this text

[Assistant] Oh, no, no, not me. My father planted them. One for each child, one for my brother, one for my sister, and one for me.


[User] Please transcribe this text

[Assistant] But I'm sure you have beautiful memories. You must remember what it looked like.


[User] Please transcribe this text

[Assistant] Ah, that's why your grandson is so good at drawing. He takes after his grandmother.


[User] Please transcribe this text

[Assistant] Drawing is like riding a bicycle.


[User] Please transcribe this text

[Assistant] Once you learn it, you never forget. I bet you can still draw.


[User] Please transcribe this text

[Assistant] I never imagined that drawing a picture would help her so much. But it did. She started to get much better.


[User] Please transcribe this text

[Assistant] Oh my goodness. That's Dr. Patel. She's going to love this.


[User] Please transcribe this text

[Assistant] Yes, hang them on the wall, Dr. Collins.


[User] Please transcribe this text

[Assistant] But wait a minute. Some of the people who work here are artists too.


[User] Please transcribe this text

[Assistant] I think we need to speak to Dr. Collins again.


[User] Please transcribe this text

[Assistant] With people from all over the world.


[User] Please transcribe this text

[Assistant] Life isn't about finding yourself. Life is about creating yourself.


[User] Please transcribe this text

[Assistant] You see things, you say, why? But I dream of things that never were, and I say, why not?


[User] Please transcribe this text

[Assistant] The unreasonable one persists in trying to adapt the world to himself. Therefore, all progress depends on the unreasonable man.


[User] Please transcribe this text

[Assistant] Those who cannot change their minds cannot change anything.


[User] Please transcribe this text

[Assistant] Success does not consist in never making mistakes.


[User] Please transcribe this text

[Assistant] But in never making the same one a second time.


[User] Please transcribe this text

[Assistant] He knows nothing. And he thinks he knows everything.


[User] Please transcribe this text

[Assistant] Dancing is a perpendicular expression of a horizontal desire.


[User] Please transcribe this text

[Assistant] You don't stop laughing when you grow old. You grow old when you stop laughing.


[User] Please transcribe this text

[Assistant] Liquor is the chloroform which enables the poor man to endure the painful operation of living.


[User] Please transcribe this text

[Assistant] There are two tragedies in life. One is to lose your heart's desire. The other is to gain it.


[User] Please transcribe this text

[Assistant] We learn from experience that men never learn anything from experience.


[User] Please transcribe this text

[Assistant] Never wrestle with pigs. You both get dirty, and the pig likes it.


[User] Please transcribe this text

[Assistant] When a stupid man is doing something he is ashamed of, he always declares that it is his duty.


[User] Please transcribe this text

[Assistant] Why aren't you in school? School will take forever. My family needs the money now


[User] Please transcribe this text

[Assistant] I uncooled Ron. School?


[User] Please transcribe this text

[Assistant] With my English, I'll be in school forever.


[User] Please transcribe this text

[Assistant] I want to make my mother proud.


[User] Please transcribe this text

[Assistant] So what happened next? Well, my family got busy. We had to ask questions, find out who to talk to, figure things out.


[User] Please transcribe this text

[Assistant] In the last video, you saw some of the wide range of applications to which you can apply sequence models. Let's start by defining a notation that we'll use to build up these sequence models. As a motivating example, let's say you want to build a sequence model to input a sentence like this, Harry Potter and Hermione Granger invented a new spell.


[User] Please transcribe this text

[Assistant] Uhm, and these are characters, by the way, from the Harry Potter sequence of novels by J.K. Rowling.


[User] Please transcribe this text

[Assistant] And let's say you want a sequence model to automatically tell you where are the people's names in this sentence.


[User] Please transcribe this text

[Assistant] So this is a problem called named entity recognition and this is used by search engines, for example, to index all of, uh, say the last 24 hours news of all the people mentioned in the news articles so that they can index them appropriately.


[User] Please transcribe this text

[Assistant] And name entity recognition systems can be used to find people's names, companies names, uh, times, locations, countries names, currency names, and so on in different types of text. Now, given this input x, let's say that you want a model to output y that has one output per input word.


[User] Please transcribe this text

[Assistant] The target output to decide why tells you for each of the input words is that part of a person's name.


[User] Please transcribe this text

[Assistant] And technically, this maybe isn't the best output representation. Uhm, there are some more sophisticated output representations that tells you not just is a word part of a person's name, but tells you where the start and ends of people's names in a sentence. You might want to know, Harry Potter starts here and ends here, starts here and ends here.


[User] Please transcribe this text

[Assistant] For this motivating example, I'm just going to stick with this simpler output representation.


[User] Please transcribe this text

[Assistant] Now, the input is a sequence of nine words, so eventually we're going to have nine


[User] Please transcribe this text

[Assistant] Set of, uh, features to represent these nine words and index into the positions in the sequence, I'm going to use x and then super strip angle brackets, one, two, three, and so on up to x.


[User] Please transcribe this text

[Assistant] Angle racket 9 to index into the different positions. I'm going to use


[User] Please transcribe this text

[Assistant] I'm going to use the index t to index into positions in the middle of the sequence. And t implies that these are temporal sequences, although whether the sequence is a temporal one or not, I'm going to use the index t to


[User] Please transcribe this text

[Assistant] index into the positions in the sequence. And similarly for the outputs, we're going to refer to these outputs as y angle bracket. One, two, three, and so on up to y


[User] Please transcribe this text

[Assistant] Let's also use t subscript x to denote the length of the input sequence. So in this case, there are nine words. So t x is equal to nine.


[User] Please transcribe this text

[Assistant] And we'll use ty to denote the length of the output sequence. In this example, tx is equal to ty, but as shown in the last video, tx and ty can be different.


[User] Please transcribe this text

[Assistant] You will remember that in the notation we've been using, we've been writing x round brackets i to denote the i-th training example. So to refer to the t-th


[User] Please transcribe this text

[Assistant] The teeth element, or the teeth element in the sequence of training example i will use this notation, and uhm,


[User] Please transcribe this text

[Assistant] If Tx is the length of a sequence, then different examples in your training set can have different lengths. And so Txi will be the input sequence length for training example i.


[User] Please transcribe this text

[Assistant] And similarly, y i t means the t-th element in the output sequence of the i-th training example, and t y i will be the length of the output sequence in the i-th training example.


[User] Please transcribe this text

[Assistant] So in this particular example, you know, TXI is equal to 9, but if you had a different training example with a sentence of 15 words, then TXI would be equal to 15 for that different training example.


[User] Please transcribe this text

[Assistant] Now this is our first serious foray into NLP or Natural Language Processing.


[User] Please transcribe this text

[Assistant] Let's next talk about how we would represent individual words in a sentence.


[User] Please transcribe this text

[Assistant] So to represent the words in the sentence, the first thing you do is come up with a vocabulary, uhm, sometimes also called a dictionary, and that means making a list of


[User] Please transcribe this text

[Assistant] The words that you will use in your representation. So the first word in the vocabulary is A. That's the first word in the dictionary. The second word is, uh, Aaron. And then a little bit further down is the word and. And then eventually you get to the word Harry. Then eventually the word Potter. And then all the way down to maybe the last word in the dictionary is, uh, Zulu.


[User] Please transcribe this text

[Assistant] And so, a would be word one, Aaron is word two.


[User] Please transcribe this text

[Assistant] And uhm, in my dictionary, the word ant appears in position or index 367, uhm,


[User] Please transcribe this text

[Assistant] And Zulu is the last word of the dictionary that is maybe worth 10,000.


[User] Please transcribe this text

[Assistant] So in this example, I'm going to use a dictionary with size.


[User] Please transcribe this text

[Assistant] Ten thousand words. Uhm, this is quite small by modern NLP applications.


[User] Please transcribe this text

[Assistant] For commercial applications, for model, for reasonable sized commercial applications, dictionary sizes of 30 to 50 thousand are more common and, uh, a hundred thousand is not uncommon. And then some of the large internet companies will use dictionary sizes that are maybe a million words or even bigger than that.


[User] Please transcribe this text

[Assistant] But you see a lot of commercial applications use dictionary sizes of maybe 30,000 or maybe 50,000 words.


[User] Please transcribe this text

[Assistant] But I'm gonna use 10,000 for illustration, uh, since it's a nice round number.


[User] Please transcribe this text

[Assistant] So if you have chosen a dictionary of 10,000 words and one way to build this dictionary would be to


[User] Please transcribe this text

[Assistant] Look through your training sets and find the top 10,000 occurring words or to look through, uhm, some of the online dictionaries that tells you what are the most common 10,000 words in the English language say.


[User] Please transcribe this text

[Assistant] What you can do is then use one-hot representations to represent each of these words. For example, x1


[User] Please transcribe this text

[Assistant] Which represents the word Harry would be a vector with all zeros, uhm, except for a 1 in position 4075, because that was the position of Harry in the dictionary. And then x2,


[User] Please transcribe this text

[Assistant] Will be again similarly a vector of all zeros except for one in position six eight three zero and then zeros everywhere else.


[User] Please transcribe this text

[Assistant] And then zeros everywhere else, and each of these would be a 10,000 dimensional vector if your vocabulary has 10,000 words. Uhm, and this one, a, I guess because a is the first word of the dictionary, then x7, which corresponds to the word a, that would be the vector 1.


[User] Please transcribe this text

[Assistant] You know, this is the first element of the dictionary, and then zero everywhere else. So in this representation, x


[User] Please transcribe this text

[Assistant] T for each of the values of t in your sentence will be a one-hot vector. One-hot because there's exactly one one that's on and zeroes everywhere else and you would have nine of them to represent the nine words in this sentence. And the goal is, given this representation for x,


[User] Please transcribe this text

[Assistant] To learn a mapping using a sequence model to the target output y.


[User] Please transcribe this text

[Assistant] And we'll do this as a supervised learning problem where we should give in labelled data with both x and y.


[User] Please transcribe this text

[Assistant] And just one last detail, which we'll talk more about in a later video, is what if you encounter a word that is not in your vocabulary? Well, the answer is you create a new token or a new fake word called unknown word, which I'm denote as follows, angle brackets, u and k.


[User] Please transcribe this text

[Assistant] To represent words not in your vocabulary, but come what to talk more about this later.


[User] Please transcribe this text

[Assistant] So to summarize, in this video we described a notation for describing your training set for both x and y for sequence data. In the next video, let's start to describe a recurrent neural network for learning the mapping from x to y.


[User] Please transcribe this text

[Assistant] Do you have any empty containers? Pots or something?


[User] Please transcribe this text

[Assistant] Yes, we are all in the same boat together.


[User] Please transcribe this text

[Assistant] Dad, you're not going to ask your son if he's beaten his wife? You don't know that for sure.


[User] Please transcribe this text

[Assistant] What an awful situation. What can she do?


[User] Please transcribe this text

[Assistant] Or goes to the police. The family will have more trouble.


[User] Please transcribe this text

[Assistant] Where do you get information about things like this?


[User] Please transcribe this text

[Assistant] You have to do something before it gets worse.


[User] Please transcribe this text

[Assistant] Not exactly. We have a friend, and we think her husband is abusing her. You have come to the right place. It is my job to help women like your friend. Can you tell me what's going on? He doesn't let her see her friends.


[User] Please transcribe this text

[Assistant] Doesn't have to worry. She does not need to have papers. Here in New York we help everyone.


[User] Please transcribe this text

[Assistant] You can tell your friend to come see me. I'll be here for her.


[User] Please transcribe this text

[Assistant] Tire Town School, Episode 22, The Racetrack.


[User] Please transcribe this text

[Assistant] The sky grew dark. Raindrops flashed everywhere.


[User] Please transcribe this text

[Assistant] Eddie's tires slipped in a puddle. Eddie slowed down. But Zippy went faster. Water sprayed off Zippy's tires.


[User] Please transcribe this text

[Assistant] Lightning struck a big tree. The tree fell onto the racetrack.


[User] Please transcribe this text

[Assistant] Language modeling is one of the most basic and important tasks in natural language processing.


[User] Please transcribe this text

[Assistant] There's also one that RNNs do very well. In this video, you'll learn about how to build a language model using an RNN, and this will lead up toward a fun programming exercise at the end of this week, where you build a language model and use it to generate Shakespeare-like text and other types of text.


[User] Please transcribe this text

[Assistant] Let's get started. So what is a language model? Let's say you're building a speech recognition system and you hear the sentence, the apple and pear salad was delicious. So what did you just hear me say? Did I say the apple and pear salad?


[User] Please transcribe this text

[Assistant] You probably think the second sentence is much more likely, and in fact, that's what a good speech recognition system would output, even though these two sentences sound exactly the same.


[User] Please transcribe this text

[Assistant] And the way a speech recognition system picks the second sentence is by using a language model which tells it what is the probability of either of these two sentences. For example, a language model might say that the chance of the first sentence is 3.2 by 10 to the negative 13.


[User] Please transcribe this text

[Assistant] And the chance of the second sentence is, say, 5.7 by 10 to the negative 10. And so,


[User] Please transcribe this text

[Assistant] With these probabilities, the second sentence is much more likely by over a factor of 10 to the 3 compared to the first sentence, and that's why a speech management system would pick the second choice.


[User] Please transcribe this text

[Assistant] So what the language model does is, given any sentence, as job as it tells you,


[User] Please transcribe this text

[Assistant] What is the probability of a sentence, of that particular sentence? And uhm, by probability of sentence, I mean, if you were to pick up a random newspaper, open a random email, or pick a random webpage, or listen to the next thing someone says, a friend of you says,


[User] Please transcribe this text

[Assistant] What is the chance that the next sentence you read somewhere out there in the world will be a particular sentence, like the apple and pear salad?


[User] Please transcribe this text

[Assistant] And this is a fundamental component for both speech recognition systems as you've just seen, as well as for machine translation systems, where translation systems want to output only sentences that are likely.


[User] Please transcribe this text

[Assistant] And so the basic job of a language model is to input a sentence, which I'm going to write as a sequence, y1, y2, up to y1.


[User] Please transcribe this text

[Assistant] Estimates the probability of that particular sequence of words.


[User] Please transcribe this text

[Assistant] To build such a model using a RNN, you would first need a training set comprising a large corpus of English text.


[User] Please transcribe this text

[Assistant] Or text from whatever language you want to build a language model of.


[User] Please transcribe this text

[Assistant] And the word corpus is an NLP terminology that just means a large body or a very large set of English texts, of English sentences.


[User] Please transcribe this text

[Assistant] So let's say you get a sentence in your training set as follows, cat averaged 15 hours of sleep a day. The first thing you would do is, uhm,


[User] Please transcribe this text

[Assistant] Tokenize the sentence, and that means you would form a vocabulary, as we saw in an earlier video, and then map each of these words to, uhm, say, one-hot vectors or to indices in your vocabulary.


[User] Please transcribe this text

[Assistant] One thing you might also want to do is model when sentences end. So another common thing to do is to add an extra token called EOS that stands for End of Sentence that can help you figure out.


[User] Please transcribe this text

[Assistant] When a sentence ends, we'll talk more about this later, but the EOS token can be appended to the end of every sentence in your training set if you want your model to explicitly capture when sentences end.


[User] Please transcribe this text

[Assistant] We won't use the end of sentence token for the program exercise at the end of this week, but for some applications you might want to use this.


[User] Please transcribe this text

[Assistant] I will say later where this comes in handy. So in this example we have y1, y2, y3, 4, 5, 6, 7, 8, 9.


[User] Please transcribe this text

[Assistant] Nine inputs in this example if you append the end of sentence token to the end.


[User] Please transcribe this text

[Assistant] And during the tokenization step, you can decide whether or not the period should be a token as well. In this example, I am just ignoring punctuation. So I'm just using day as another token and omitting the period, but you want to treat the period or other punctuation as explicit token.


[User] Please transcribe this text

[Assistant] Then you can add the period to your vocabulary as well.


[User] Please transcribe this text

[Assistant] Now, one other detail would be uhm, what if some of the words in your training set are not in your vocabulary? So if your vocabulary uses 10,000 words, maybe the 10,000 most common words in English,


[User] Please transcribe this text

[Assistant] The term Mao as in the Egyptian Mao is a breed of cat that might not be in one of your top 10,000 tokens. So in that case, you could take the word Mao and replace it with a unique token called


[User] Please transcribe this text

[Assistant] Unk, which stands for unknown word, and we just model.


[User] Please transcribe this text

[Assistant] The chance of the unknown word instead of the specific word of my own.


[User] Please transcribe this text

[Assistant] Having carried out the tokenization step, which basically means taking the input sentence and mapping it to the individual tokens or the individual words in your vocabulary, next let's build an RNN to model the chance of these different sequences.


[User] Please transcribe this text

[Assistant] And one of the things we'll see on the next slide is that you end up setting the inputs xt to be equal to y of t minus 1. But you'll see that in a little bit.


[User] Please transcribe this text

[Assistant] So let's go on to build the RNN model, and I'm going to continue to use this sentence as the running example.


[User] Please transcribe this text

[Assistant] This will be the RNN architecture. At time zero,


[User] Please transcribe this text

[Assistant] You're going to end up computing some activation A1.


[User] Please transcribe this text

[Assistant] As a function of some input x1, and x1 would just be, uh, set it to the set of all zeros, to, to, to zero vector. And, uh, the previous a0 by convention also set that to vector zeros. But what a1 does is it will make a softmax prediction to try to figure out what is the probability of the first word y. And so that's going to be y1.


[User] Please transcribe this text

[Assistant] So what this step does is really, uhm, it has a softmax that's trying to predict what is the probability of any word in your dictionary. What's the chance that, you know, the first word is A, what's the chance that the first word is Aaron, and then what's the chance that the first word is, uhm, cats.


[User] Please transcribe this text

[Assistant] All the way up to what's the chance the first word is zulu, or what's the chance that the first word is an unknown word, or what's the first chance that, you know, the first word is the end of sentence though, or shouldn't happen really. Right, but so, uh,


[User] Please transcribe this text

[Assistant] Y hat 1 is output according to a softmax, it just predicts what's the chance of the first word being whatever it ends up being. And in our example, it wanted to be the word cats.


[User] Please transcribe this text

[Assistant] So this would be a 10,000 way softmax output if you have a 10,000 word vocabulary or 10,002 I guess econ unknown wording and the sentence has two additional tokens. Then the RNN steps forward to the next step.


[User] Please transcribe this text

[Assistant] And has some activation A2, next step.


[User] Please transcribe this text

[Assistant] And at this step, his job is to try to figure out what is the second word. But now, we will also give it the


[User] Please transcribe this text

[Assistant] Correct first word. So we'll tell it that G in reality, the first word was actually cats, so that's Y1.


[User] Please transcribe this text

[Assistant] Two. And so at the second step, the output is again predicted by our softmax.


[User] Please transcribe this text

[Assistant] The RNN's job is to predict what's the chance of it being whatever word it is. Is it A or Aaron or Cat or Zulu or unknown word or EOS or whatever, uhm, given what had come previously. So in this case I guess the right answer was average since the sentence starts with Cat's average. And then you go on to the next step of the RNN where you now compute A3


[User] Please transcribe this text

[Assistant] But to predict what is the third word, which is fifteen, we can now give it the first two words. We're going to tell it cats.


[User] Please transcribe this text

[Assistant] Average are the first two words, so this next input here, x3, will be equal to


[User] Please transcribe this text

[Assistant] So the word average is input, and this job is to figure out what is the next word in the sequence. So in other words, trying to figure out what is the probability of any word in the dictionary given that what just came before was cat's average. And in this case, the right answer is 15, and so on. Until at the end, uhm,


[User] Please transcribe this text

[Assistant] This happens to be the EOS token. So what's the chance of whatever it is given, you know, everything that's come before and hopefully a predict that there's a high chance of an EOS in the sentence token.


[User] Please transcribe this text

[Assistant] So each step in the RNN will look at some set of preceding words, such as, given the first three words, what is the distribution over the next word?


[User] Please transcribe this text

[Assistant] And so this RNN learns to predict one word at a time going from left to right. Next, to train this neural network, we're going to define


[User] Please transcribe this text

[Assistant] If the true word was yt and the neural network softmax predicted some y hat t, then this is the softmax loss function that you should really be familiar with.


[User] Please transcribe this text

[Assistant] And then the overall loss is just the sum over all time steps of the losses associated with the individual predictions. And if you train this RNN on a loss training set, what it will be able to do is


[User] Please transcribe this text

[Assistant] Given any initial set of words, such as cat's average fifteen or cat's average fifteen hours of, it can predict what is the chance of the next word. And given a new sentence, say y1, y2, y3,


[User] Please transcribe this text

[Assistant] Y3 with just, uh, three words for simplicity. The way you can figure out what is the chance of this entire sentence would be. Well, the first softmax tells you what's the chance of Y1.


[User] Please transcribe this text

[Assistant] That would be this first output. And then the second one can tell you what's the chance of P of y2 given y1. And then the third one tells you what's the chance of y3 given y1 and y2.


[User] Please transcribe this text

[Assistant] And so it's by multiplying out these three probabilities, and you see much more of the details of this in the programming exercise, because by multiplying out these three that you end up with the probability of this three sentence, of this three word sentence.


[User] Please transcribe this text

[Assistant] So that's the basic structure of how you can train a language model using an RNN. If some of these ideas still seem a little bit abstract, don't worry about it. You get to practice all of these ideas in the programming exercise. But next it turns out one of the most fun things you can do with a language model is to sample sequences from the model. Let's take a look at that in the next video.


[User] Please transcribe this text

[Assistant] I don't need your smart mouth. It's serious business. Serious business.


[User] Please transcribe this text

[Assistant] Christian, like, knowing all these villains that you've had, what is, uh, what does Christian bring to the villain into the Thor universe?


[User] Please transcribe this text

[Assistant] The operating room is a quiet place, peaceful. It has to be in order for us to stay alert, anticipate complications.


[User] Please transcribe this text

[Assistant] What is it? With you guys and your need to dirty everything up.


[User] Please transcribe this text

[Assistant] Maybe. You might want to see a doctor about that too.


[User] Please transcribe this text

[Assistant] What is it? With you guys and your need to dirty everything up.


[User] Please transcribe this text

[Assistant] Maybe. You might want to see a doctor about that too.


[User] Please transcribe this text

[Assistant] Very important news, I now have an Instagram, along with my Facebook, and obviously my YouTube channel, which is right here. And for my Chinese followers, I've created a Weibo account as well, and you can communicate with me on there.


[User] Please transcribe this text

[Assistant] This is or these are. Listen and repeat.


[User] Please transcribe this text

[Assistant] These are my arms. This is my elbow. This is my hand.


[User] Please transcribe this text

[Assistant] For singing about your body to everybody. Thank you very much.


[User] Please transcribe this text

[Assistant] Magic Animals 2 by Elf Learning. Good job!


[User] Please transcribe this text

[Assistant] Lucifer Morningstar. Is that, uh, a stage name or something?


[User] Please transcribe this text

[Assistant] Yeah, five minutes ago, and I'm asking the questions. Talk to me about your relationship with the victim.


[User] Please transcribe this text

[Assistant] Lucifer Morningstar. Is that, uh, a stage name or something?


[User] Please transcribe this text

[Assistant] Yeah, five minutes ago, and I'm asking the questions. Talk to me about your relationship with the victim.


[User] Please transcribe this text

[Assistant] Yes. Yes, we do. And, uh, what planet are you from? London?


[User] Please transcribe this text

[Assistant] Yes. Yes, we do. And, uh, what planet are you from? London?


[User] Please transcribe this text

[Assistant] Yeah, five minutes ago, and I'm asking the questions. Talk to me about your relationship with the victim.


[User] Please transcribe this text

[Assistant] Yes. Yes, we do. And, uh, what planet are you from? London?


[User] Please transcribe this text

[Assistant] Bugs with Blackmail Grayson! Then I, sir, am no bugs.


[User] Please transcribe this text

[Assistant] Once, traveling at night, I fell asleep in the passenger seat of a moving car. Then the driver took a bridge too fast.


[User] Please transcribe this text

[Assistant] You're not alright. Didn't you tell her badly you were bleeding? You're lucky you're not dead. Lift him up.


[User] Please transcribe this text

[Assistant] Quite the appetite. You'd think you'd eat grass if there was nothing else.


[User] Please transcribe this text

[Assistant] Raiding cattle and living the life of a thief instead of tending to your own farm.


[User] Please transcribe this text

[Assistant] Yes, it must be cleaned of dirt, protected from germs.


[User] Please transcribe this text

[Assistant] There's word from the village. The midwife was called away to tend to a relative who's taken ill.


[User] Please transcribe this text

[Assistant] Winter. Europe are lost. They're living rough, you know. In the woods we are. A group of lads raiding cattle. And we had poor luck for a week and more and no food amongst us left.


[User] Please transcribe this text

[Assistant] We had poor luck for a week and more and no food amongst us left.


[User] Please transcribe this text

[Assistant] I was living rough, you know. In the woods we are. A group of lads raiding cattle.


[User] Please transcribe this text

[Assistant] Body Phrases 2 by L Learning


[User] Please transcribe this text

[Assistant] Listen and repeat. Shoulders, shoulders. Shrug my shoulders. Shoulders, shoulders. Shrug my shoulders. Belly, belly. Tickle my belly. Belly, belly. Tickle my belly. Back, back. Scratch my back. Back, back.


[User] Please transcribe this text

[Assistant] Scratch my back. Arms, arms. Wave my arms. Arms, arms. Wave my arms. Elbow, elbow.


[User] Please transcribe this text

[Assistant] Bend my elbow. Elbow. Elbow! Bend my elbow. Hand. Hand!


[User] Please transcribe this text

[Assistant] Clap my hands, fingers, fingers. Wiggle my fingers, fingers, fingers. Wiggle my fingers, legs, legs.


[User] Please transcribe this text

[Assistant] Pat my legs. Legs. LEGS!


[User] Please transcribe this text

[Assistant] Pat my legs. Knees. Knees!


[User] Please transcribe this text

[Assistant] Bend my knees, knees, knees. Bend my knees, feet, feet. Stomp my feet, feet, feet. Stomp my feet.


[User] Please transcribe this text

[Assistant] Toes, toes. Wiggle my toes. Toes, toes. Wiggle my toes. Butt, butt.


[User] Please transcribe this text

[Assistant] Potter, you're underage. Which means you've still got the trace on you. What's the trace?


[User] Please transcribe this text

[Assistant] If you, the minister, know who wipes your nose, the point is we have to use those means of transport. The trace can't detect brooms, testers and the like. We go in pairs.


[User] Please transcribe this text

[Assistant] Potter, you're underage. Which means you've still got the trace on you. What's the trace?


[User] Please transcribe this text

[Assistant] If you sneeze, the Ministry will know who wipes your nose. The point is, we have to use those means of transport. The trace can't detect rooms, testers and the like. We go in pairs.


[User] Please transcribe this text

[Assistant] If you have no confidence in self, you are twice defeated in the race of life. With confidence, you have won even before you have started.


[User] Please transcribe this text

[Assistant] It is our own evil thoughts which make us.


[User] Please transcribe this text

[Assistant] No one can give you better advice than yourself.


[User] Please transcribe this text

[Assistant] Thrift is an important source of wealth.


[User] Please transcribe this text

[Assistant] The greatest virtue of a speaker is not only to say what is necessary.


[User] Please transcribe this text

[Assistant] When were you planning on getting married? Just get on with it. On with what?


[User] Please transcribe this text

[Assistant] Your majesty, I am Prince Ali Ababla. Just let her meet me. I will win your daughter.


[User] Please transcribe this text

[Assistant] So the estimates have been that 18 million Americans lack broadband access, 14 million are in rural America. Now having said that, the broadband, I'm thinking, trying to remember what the name of the organization is, did a study and they actually say the number is more like 42 million.


[User] Please transcribe this text

[Assistant] It would show up on the map as though that community largely has broadband, and it does not.


[User] Please transcribe this text

[Assistant] And so it's a significant issue. Think about the education of your children. I know you have three. I have three. And there were, you know, many of them were doing remote schooling. Well, what's happening on the farm? This is legitimately what's occurring is that the teacher from the town is driving paper homework out to the farms.


[User] Please transcribe this text

[Assistant] To give them the homework so that they can continue to go to school.


[User] Please transcribe this text

[Assistant] And, and that's just one example of many. And so this is a major challenge because if we don't have basic wiring, we, I can say we need like a 1930s rural electric initiative where we go across the country and we make this, this should be a right


[User] Please transcribe this text

[Assistant] This should be something that is a basic like mail delivery and electricity. This should not be just for those who have.


[User] Please transcribe this text

[Assistant] And the scale of it is, the estimate is to be 80 to 100 billion dollars to close this gap.


[User] Please transcribe this text

[Assistant] But the challenge of that is that uhm, you get the funding and then how is it implemented?


[User] Please transcribe this text

[Assistant] Now we all know, you can't just say, I say, well I don't go in as CEO, go to the board and say I need $100 million for this tech platform. And then, uh, approved, here's the return.


[User] Please transcribe this text

[Assistant] And then I go in and I throw it to the business unit heads and I'm like, you guys decide who's in charge of that, right? And there's a little bit about it becomes a jump ball between, you know, the FCC and the USDA and then the governors and, and suddenly we're in the, we don't have enough and we're in the couches looking for quarters and nickels to pay the pizza guy so that we can finish this off as though it's not a priority.


[User] Please transcribe this text

[Assistant] As though it's not a priority. As though it's somebody else's problem. And it is all of our problem. This is a national security issue. I cannot say it, you know, more directly. So it's a major issue because I'm concerned about speed here.


[User] Please transcribe this text

[Assistant] In addition to the funding, and the good news is, this has been bipartisan support whenever I speak, I speak to the National Governors Association, the, you know, State Departments of Ag, I mean, name it. Name the administration official, I've done it.


[User] Please transcribe this text

[Assistant] This is a bipartisan issue. It has to be a prioritization issue. It has to be something that we decide as a country is a priority. And it means in every state.


[User] Please transcribe this text

[Assistant] There is, you know, so I, often times I'm at an event and they're talking about all this cool new tech. And listen, I just said, we use satellite technology, we have big data, data and analytics, um, John Deere's tractors are auto steer. And I'm like, how can you use, are you using that? No. Why? Well, because I lose connection.


[User] Please transcribe this text

[Assistant] Uhm, and so I can't, I can't utilize it. You know, there's so many exciting investments and innovation occurring in the sector that will make farmers more efficient.


[User] Please transcribe this text

[Assistant] Uhm, we'll be better for climate and the environment. I'll talk a little bit about that in a minute, but, uhm, but none of that can be used if we don't have the appropriate


[User] Please transcribe this text

[Assistant] Uhm, technology access, if we don't have broadband. These are data intensive models and they require bandwidth to do so and to utilize them effectively. So, it's a, it's a major issue. So that from a business perspective, it's an efficiency, it's a sustainable production issue, it's a,


[User] Please transcribe this text

[Assistant] And then back to the fa- the community. Again, stable operating environment. You know, I constantly have my mother in my ear like, you're only as happy as your least happy child, right?


[User] Please transcribe this text

[Assistant] Right? And why is that? Because, you know, if you can't feel confident, your child


[User] Please transcribe this text

[Assistant] has the best education, has access to a doctor. Pretty hard to be focused on your business. And farming is a business. And so we have to recognize if there's a number of hospitals that local hospitals have shut down


[User] Please transcribe this text

[Assistant] Uhm, there's no banking, there's this great unbanked, no housing.


[User] Please transcribe this text

[Assistant] Uh, it, it doesn't work to have a stable operating environment in these farming communities. And yes, directly, you cannot use the new innovation and technology, uhm, the high, the data utilization if you do not have broadband.


[User] Please transcribe this text

[Assistant] Sure, well, you know, first of all, what you're helping them do is make improved decisions that make their farms more resilient and that they can take, make a more sustainable business practices. So what we've started, we have a business called True Terra.


[User] Please transcribe this text

[Assistant] Uhm, it's run by Jason Weller who used to run NRCS for the, under the Obama administration.


[User] Please transcribe this text

[Assistant] And uhm, and what this d- this business does, it works in multiple ways. We work through our local retailer, the agronomist. They get insights from our True Terra Insights engine. It literally is at the farm level, acre by acre.


[User] Please transcribe this text

[Assistant] Where they can understand what the soil type is, what the water situation is, all those things. You can also talk, what kind of tractor do you use? How many passes at the field do you make?


[User] Please transcribe this text

[Assistant] So we can make improved sustainable production decisions. More recently, probably just last week, we announced our True Carbon platform, which is a systemic way that we can work with farmers, data-based, this is partnered with the Soil Health Institute, with many environmental groups, to create a carbon credit.


[User] Please transcribe this text

[Assistant] So where the farmer is making a decision that is improving, you know, carbon capture and at the same time is improving their own profitability.


[User] Please transcribe this text

[Assistant] And then is able to monetize that. Our first customer, uhm, was Microsoft.


[User] Please transcribe this text

[Assistant] Uhm, agriculture is a great way, uhm, for us to, to address this issue using basic photosynthesis. So if we, if we agree that, then we have to say what is that pricing environment? One of the fundamental things we started with is the farmer.


[User] Please transcribe this text

[Assistant] In fact, Secretary Vilsack more recently said in his confirmation hearing, we've got to start with a farmer. And that farmer has to be profitable when making that decision. It's a virtuous circle.


[User] Please transcribe this text

[Assistant] We're working with this inside engine with our agronomic advice with soil health and soil testing with any number of


[User] Please transcribe this text

[Assistant] Uhm, their Farm Beats program, their AirBand, uhm, they're working with us on closing this digital divide in different communities so we can take advantage of these opportunities. Uhm, and at the same time, as I said, that we're our first customer for our carbon credits.


[User] Please transcribe this text

[Assistant] The statistic I gave you about the hobby farmer, about the fact that they're working off the, uhm, off the farm to retain their, their farms.


[User] Please transcribe this text

[Assistant] It will be in some ways driven by the electrification of the transportation sector. That means biofuel utilization and a significant portion of the corn crop goes into biofuels, ethanol.


[User] Please transcribe this text

[Assistant] And if that is no longer needed because we've gone to electric vehicles over the next 10 years, what happens to corn price, to land values? Uhm, and at the same time, soybeans, some of that is used for, uhm, biodiesel.


[User] Please transcribe this text

[Assistant] So that will be a major disruptor to row crop farming. So we've got to point farmers that are pretty smart, uhm, to the right levels of investment they can make for other sources of potential revenue for their farms. And I think that this is a great way to think about it.


[User] Please transcribe this text

[Assistant] Uhm, you know, improve a variable rate application of your fertilizer where you don't put everything on at the same time and then have it seep into the soil. Instead, you look and you use, you know, your satellite technology and everything and you say, oh, I'm going to put partial on now and maybe I don't need as much later.


[User] Please transcribe this text

[Assistant] Every piece, every, uhm, acre on a farm is not the same.


[User] Please transcribe this text

[Assistant] And with that, then, where do you make the right investments? And a model like this helps you understand where to make those investments. We also have applied research plots, answer plots, where we help with our agronomists.


[User] Please transcribe this text

[Assistant] So there's any number of variables, and I say it makes a farm more resilient. And let's agree, we're going to get into a situation where the financial markets and the bankers are going to say you need to be making investments that make your farm more resilient given the amount of disruption we're seeing already, right? Climate.


[User] Please transcribe this text

[Assistant] Think of the fires in the west, the freeze in the south, you know, in Texas. And we're going to see these events over the next number of years. We have to make the farm more resilient. That makes it more profitable for the farmer. It helps their ability to make an investment that we all know is necessary.


[User] Please transcribe this text

[Assistant] Well, there's two things. One, I was just mentioning more directly this carbon, this true carbon, where you're generating a carbon credit because you're able to prove the new practices you're putting in place.


[User] Please transcribe this text

[Assistant] Uhm, in those products as you're putting on, uh, fertilizer or, uhm, or planting different crops.


[User] Please transcribe this text

[Assistant] But then more directly, that credit that you develop, because this market is evolving, can be sold. So what we're doing with the Microsoft...


[User] Please transcribe this text

[Assistant] Uhm, partnership here in, in, uh, their purchases. If you are able to prove this tonnage reduction, uhm, it's like $20 a met ton, uhm, for a carbon credit. So it's a more direct line payment.


[User] Please transcribe this text

[Assistant] Chapter 1. Hawk in Trouble.


[User] Please transcribe this text

[Assistant] In that book, robbers stole some money and hid it.


[User] Please transcribe this text

[Assistant] In a very secret place in the woods. But Tom Sawyer and I found it. After that, we were rich.


[User] Please transcribe this text

[Assistant] We got six thousand dollars each. All gold.


[User] Please transcribe this text

[Assistant] In those days, I never had a home.


[User] Please transcribe this text

[Assistant] Or went to school like Tom and all the other boys in St. Petersburg. Pop was always drunk and he moved around a lot.


[User] Please transcribe this text

[Assistant] So, he wasn't a very good father, but it didn't matter to me.


[User] Please transcribe this text

[Assistant] I slept in the streets or in the woods.


[User] Please transcribe this text

[Assistant] And I could do what I wanted when I wanted. It was a fine life.


[User] Please transcribe this text

[Assistant] When we got all that money, Tom and I were famous for a while. Judge Thatcher, who was an important man in our town, kept my money in the bank for me.


[User] Please transcribe this text

[Assistant] And the widow Douglas took me to live in her house and said I could be her son. She was very nice and kind, but it was a hard life.


[User] Please transcribe this text

[Assistant] Because I had to wear new clothes and be good all the time. In the end,


[User] Please transcribe this text

[Assistant] And the widow cried, and I had to put on those new clothes again. I didn't like it at all. Her sister, Miss Watson, lived there too. She was always saying


[User] Please transcribe this text

[Assistant] Don't put your feet there, Huckleberry. And don't do that, Huckleberry. It was terrible.


[User] Please transcribe this text

[Assistant] Very softly, I answered,


[User] Please transcribe this text

[Assistant] Quietly, I put out the light and got out through the window. In the trees, Tom Sawyer was waiting for me.


[User] Please transcribe this text

[Assistant] We went through the trees to the end of the widow's garden.


[User] Please transcribe this text

[Assistant] Soon we were on top of a hill on the other side of the house.


[User] Please transcribe this text

[Assistant] Below us, we could see the river and the town. One or two lights were still on, but everything was quiet.


[User] Please transcribe this text

[Assistant] Then Tom took us down the river by boat to a secret place, which was a cave deep in the side of a hill. When we got there, Tom told us all his plan. Now,


[User] Please transcribe this text

[Assistant] We stop people on the road, and we kill them, and take their money and things. But we can keep a few of the people.


[User] Please transcribe this text

[Assistant] That's what they do in the stories in books. But Ben wasn't happy. What about women? He asked. Do we kill them too? Oh no, Tom answered.


[User] Please transcribe this text

[Assistant] We're very nice to them, and they all love us, and they don't want to go home.


[User] Please transcribe this text

[Assistant] Then the cave will be full of women and people waiting and we'll have to watch them all night. We'll all go home now, Tom said, and we'll meet next week.


[User] Please transcribe this text

[Assistant] And we'll kill somebody and rob somebody. Ben wanted to begin on Sunday, but the other said no.


[User] Please transcribe this text

[Assistant] It was bad to kill and rob on a Sunday. My clothes were very dirty.


[User] Please transcribe this text

[Assistant] And I was very tired when I got back. Of course, the next morning, Miss Watson was angry with me because of my dirty clothes. But the widow just looked unhappy.


[User] Please transcribe this text

[Assistant] Soon after that, we stopped playing robbers, because we never rob people, and we never kill them.


[User] Please transcribe this text

[Assistant] Time went on, and winter came.


[User] Please transcribe this text

[Assistant] I went to school most of the time, and I was learning to read and write a little.


[User] Please transcribe this text

[Assistant] But I still didn't like living in a house and sleeping in a bed.


[User] Please transcribe this text

[Assistant] Then, one morning, there was some new snow on the ground, and outside the back garden, I could see footprints in the snow. I went out to look at them more carefully. They were Pops' footprints.


[User] Please transcribe this text

[Assistant] A minute later, I was running down the hill to Judge Thatcher's house. When he opened the door, I cried, Sir?


[User] Please transcribe this text

[Assistant] I want you to take all my money. I want to give it to you. He looked surprised. Why? What's the matter? Please sir, take it. Don't ask me why. In the end he said, Well, you can sell it to me then.


[User] Please transcribe this text

[Assistant] And he gave me a dollar, and I wrote my name on a piece of paper for him. That night, when I went up to my room, Hop was sitting there, waiting for me. I saw that the window was open, so that was how he got in. He was almost fifty, and he looked old.


[User] Please transcribe this text

[Assistant] His hair was long and dirty.


[User] Please transcribe this text

[Assistant] He looked at me all over for a long time. And then he said, well,


[User] Please transcribe this text

[Assistant] They say you can read and write now. Who said you could go to school? The widow, I began.


[User] Please transcribe this text

[Assistant] Ask Judge Thatcher. He'll tell you. I haven't got any money. Well.


[User] Please transcribe this text

[Assistant] And he went to Judge Thatcher to get my money. The judge wouldn't give it to him.


[User] Please transcribe this text

[Assistant] But Pop didn't stop trying and every few days I got two or three dollars from the judge to stop Pop from hitting me. But when Pop had money, he got drunk again and made trouble in town.


[User] Please transcribe this text

[Assistant] He was always coming to the widow's house and she got angry and told him to stay away. Then Pop got really angry.


[User] Please transcribe this text

[Assistant] I had to stay with him in a hut in the woods and I couldn't go out by myself. He watched all the time.


[User] Please transcribe this text

[Assistant] The widow sent a man to find me and bring me home, but Pop went after him with a gun and the man ran away. Chapter 2. Huck escapes and finds a friend.


[User] Please transcribe this text

[Assistant] Mostly, it was a lazy, comfortable kind of life.


[User] Please transcribe this text

[Assistant] But after about two months, Pop began to hit me too much with his stick. He often went away into town, too.


[User] Please transcribe this text

[Assistant] And then, he always locked me in the hut. Once, he was away for three days.


[User] Please transcribe this text

[Assistant] When he came back that time, he was drunk and angry. He wanted my money, but Judge Thatcher wouldn't give it to him.


[User] Please transcribe this text

[Assistant] The judge wanted to send me to live with the widow again, Pop told me. I wasn't very pleased about that. I didn't want to go back there. So I decided to escape.


[User] Please transcribe this text

[Assistant] Go down the river and live in the woods somewhere. When Pop was out.


[User] Please transcribe this text

[Assistant] Pop sent me down to the river to catch some fish for breakfast. To my surprise, there was a canoe in the water and there was no one in it. Immediately, I jumped into the river and brought the canoe to the side. It was lucky that Pop didn't see me.


[User] Please transcribe this text

[Assistant] And I decided to hide the canoe under some trees and use it when I escape. That afternoon, Hop locked me in and went off to town. He won't be back tonight, I thought.


[User] Please transcribe this text

[Assistant] Then I put back the wood to hide the hole, took the gun and went into the woods. There I shot a wild pig and took it back to the hut with me.


[User] Please transcribe this text

[Assistant] I carried the pig into the hut and put some of its blood on the ground.


[User] Please transcribe this text

[Assistant] They won't know it's only a pig in the river, I said to myself. They'll think it's me.


[User] Please transcribe this text

[Assistant] Then I took the canoe and went down the river to Jackson's Island. By then it was nearly dark. So I hid the canoe under some trees and went to sleep. It was after eight o'clock when I woke up the next day.


[User] Please transcribe this text

[Assistant] And the sun was high in the sky. I was warm and comfortable and I didn't want to get up. Suddenly, I heard a noise up the river. Carefully, I looked through the trees and I saw a boat full of people. There was Pop, Judge Thatcher,


[User] Please transcribe this text

[Assistant] Tom Sawyer and his Aunt Polly and his brother Sid and lots of others.


[User] Please transcribe this text

[Assistant] They were looking for my body in the river. I watched them, but they didn't see me. And in the end, they went away.


[User] Please transcribe this text

[Assistant] I knew that nobody was going to come and look for me again.


[User] Please transcribe this text

[Assistant] I found a good place under the trees to sleep and to put my things. Then I caught a fish and cooked it over a fire.


[User] Please transcribe this text

[Assistant] And then I decided to have a look around the island. So I went into the woods. This is my island, I thought. I'm the only person on it. Suddenly, just in front of me, I saw a fire and it was still smoking.


[User] Please transcribe this text

[Assistant] There was somebody on my island. I didn't wait. I turned and went straight back. But I couldn't sleep. After a time, I said to myself, I can't live like this.


[User] Please transcribe this text

[Assistant] Silently, I moved along the river in my canoe, under the darkness of the trees. Then, I stopped. Through the trees, I could see the light of a fire. Afraid, I left my canoe and went nearer. There was a man lying by the fire.


[User] Please transcribe this text

[Assistant] When it was dark, I got onto a big boat and hid. We came near this island.


[User] Please transcribe this text

[Assistant] And then we both carried all our things into a cave and hid the canoe under some trees. We were just in time because then the rains came. It rained for days.


[User] Please transcribe this text

[Assistant] And the river got higher and higher.


[User] Please transcribe this text

[Assistant] All kinds of things came down the river, and one night there was a little wooden house lying half on its side.


[User] Please transcribe this text

[Assistant] We got the canoe out and went to take a look. Through the window, we could see a bed, two old chairs, and some old clothes. There was something lying in the corner, and we thought it looked like a man. Jim went in to see, but he said, He's dead. Someone shot him in the back.


[User] Please transcribe this text

[Assistant] I didn't want to see the dead man's face. So I didn't look.


[User] Please transcribe this text

[Assistant] We just took the old clothes and a few other things and went back to our cave on the island.


[User] Please transcribe this text

[Assistant] This could be useful, I said Jim.


[User] Please transcribe this text

[Assistant] So we pulled it back to the island behind the canoe and tied it up under the trees. Chapter 3 Huck and Jim Travel South


[User] Please transcribe this text

[Assistant] For some days, everything went along quietly. But we were getting bored.


[User] Please transcribe this text

[Assistant] We wanted to know what was happening in town.


[User] Please transcribe this text

[Assistant] And so I decided to go and find out. Jim said,


[User] Please transcribe this text

[Assistant] Why don't you wear the old dress and the hat that we found in the house? People won't know you then. They'll think you're a girl. And so I did. Just after it was dark, I got into the canoe and went up the river to the bottom of the town. There, I left the canoe


[User] Please transcribe this text

[Assistant] And went on foot. Before long, I came to a little house, which was always empty. Now, there was a light on. And when I looked through the window, I saw a woman of about forty. She was a stranger, and that was good, because she didn't know me. So I knocked on the door.


[User] Please transcribe this text

[Assistant] She looked at me with her little bright eyes. What's your name? She asked. Sarah Williams. I replied.


[User] Please transcribe this text

[Assistant] My husband will be home in about an hour. Wait for him, and he'll walk with you.


[User] Please transcribe this text

[Assistant] And then she began to tell me about all her troubles. I was getting bored with all this.


[User] Please transcribe this text

[Assistant] Until she said something about Pop and my murder. Who did it? I asked. Well, she replied.


[User] Please transcribe this text

[Assistant] Some people say old Finn did it himself. Other people think it was a slave who ran away that night. His name was Jim.


[User] Please transcribe this text

[Assistant] He got drunk and left town with two strangers.


[User] Please transcribe this text

[Assistant] A lot of people think he killed his boy and he's going to come back one day and get all Huck Finn's money.


[User] Please transcribe this text

[Assistant] What about the slave? I asked. Oh, they'll soon catch him. People want the three hundred dollars.


[User] Please transcribe this text

[Assistant] I think he's on Jackson's Island, you know? I've seen smoke there.


[User] Please transcribe this text

[Assistant] My husband's gone to get two of his friends and they're going over there with a gun later tonight. When I heard this, my hands began to shake. The woman looked at me strangely.


[User] Please transcribe this text

[Assistant] I know you're not really a girl. So then I had to tell her another story with a different name and I said I was running away. She said she wouldn't tell anybody and gave me some food before I left. I hurried back to the island and Jim.


[User] Please transcribe this text

[Assistant] And we decided to go on down to Cairo in Illinois, sell the raft there, and get a boat to Ohio. There are no slaves in Ohio.


[User] Please transcribe this text

[Assistant] And we began our journey again when it was dark. After some time.


[User] Please transcribe this text

[Assistant] We saw lights on the Illinois side of the river and Jim got very excited. He thought it was Cairo. Jim got the canoe ready and I went off in it to take a look at those lights. But it wasn't Cairo. After that, we went on down the river. It was very dark that night.


[User] Please transcribe this text

[Assistant] And it wasn't easy to see where we were going. Suddenly.


[User] Please transcribe this text

[Assistant] A big steamboat came at us. Very fast.


[User] Please transcribe this text

[Assistant] I saw that I was near a big old wooden house.


[User] Please transcribe this text

[Assistant] Suddenly, a lot of very angry dogs jumped out at me.


[User] Please transcribe this text

[Assistant] And they took me in and gave me some new clothes and a good meal. I told them that my family were all dead.


[User] Please transcribe this text

[Assistant] So they said I could stay with them as long as I wanted. It was a beautiful house. And the food was good there. So I stayed. A few days later, one of the slaves in the house came to me and said, Come with me. Together, we went down to some trees by the river.


[User] Please transcribe this text

[Assistant] I was really pleased to see him. When the steamboat hit the raft, Jim told me, the raft didn't break up.


[User] Please transcribe this text

[Assistant] Jim swam after it and caught it. Then he began to look for me. We decided to leave at once.


[User] Please transcribe this text

[Assistant] It's alright living in a house for a little while.


[User] Please transcribe this text

[Assistant] But you feel more free and easy and comfortable on a raft. Chapter 4 The Duke and the King


[User] Please transcribe this text

[Assistant] Two or three days and nights went by and nothing much happened.


[User] Please transcribe this text

[Assistant] We traveled at night when it was dark and everybody was asleep. We didn't want anyone to see Jim and ask questions about him. Then, one morning, just after it was light, I found a little canoe.


[User] Please transcribe this text

[Assistant] So I got into it and went over to the side of the river. I was looking round when suddenly two men ran through the trees. Help! They cried.


[User] Please transcribe this text

[Assistant] One of the men was about 70 years old. The other was about 30.


[User] Please transcribe this text

[Assistant] And they both had very old dirty clothes. I said they could come with me, and we ran quickly back to the canoe. Back on the raft, we talked for a time, and then the young man said, My friends,


[User] Please transcribe this text

[Assistant] I think I can tell you my secret now. I'm really a duke.


[User] Please transcribe this text

[Assistant] My grandfather was the son of the Duke of Bridgewater, but he left England and came to America.


[User] Please transcribe this text

[Assistant] Very unhappy for our friend the dude, but he said, I'll be happier if you do things for me. Bring me my dinner. So we did things for him and he liked it.


[User] Please transcribe this text

[Assistant] But the old man spoke very little, and he looked unhappy too. After a time, he said, you know, Bridgewater, I too have a secret. And he began to cry. What do you mean? The Duke asked. What's your secret?


[User] Please transcribe this text

[Assistant] And then the old man told us that he was really the first son of the King of France.


[User] Please transcribe this text

[Assistant] He asked us all to go down on one knee when we spoke to.


[User] Please transcribe this text

[Assistant] We could call him your majesty too. So that was what we did. And they were both happy. Of course.


[User] Please transcribe this text

[Assistant] I knew that they weren't really a duke and a king, but I didn't tell Jim. It's best if everybody is happy when you're living together on a raft. The king and the duke were very interested in Jim. Is he a slave? They wanted to know. Is he running away?


[User] Please transcribe this text

[Assistant] So I said that Jim belonged to my uncle.


[User] Please transcribe this text

[Assistant] And was taking me to my family in New Orleans. Where?


[User] Please transcribe this text

[Assistant] So the four of us went on down the river.


[User] Please transcribe this text

[Assistant] But Jim and I didn't like those two men. They were always getting drunk.


[User] Please transcribe this text

[Assistant] It's alright to take a chicken or something if you're hungry. But these men were really bad. Jim and I decided to get away from them as soon as we could. It wasn't easy.


[User] Please transcribe this text

[Assistant] Because they wanted to be with us all the time. Then one morning


[User] Please transcribe this text

[Assistant] The king went off into a town and told us to wait for him. We waited all morning and he didn't come back.


[User] Please transcribe this text

[Assistant] And in the end, we found him in a bar, drunk.


[User] Please transcribe this text

[Assistant] And then he and the Duke began to fight about some money. Now we can get away from them, I thought. I turned and ran back to the river. Quit, Jim! I shouted. It's time to go! But there was no answer. Jim wasn't there.


[User] Please transcribe this text

[Assistant] The Phelpses are going to take him back to his owner.


[User] Please transcribe this text

[Assistant] I began to make plans to get Jim back. First, I took the raft and went down to a little island.


[User] Please transcribe this text

[Assistant] I hid the raft under the trees and lay down to sleep. Before it was light, I went off down the river in the canoe.


[User] Please transcribe this text

[Assistant] When I thought I was near the Phelps' place, I stopped, got out of the canoe, and went up to the house. Suddenly, a lot of dogs ran out. They came from everywhere, and they made a terrible noise.


[User] Please transcribe this text

[Assistant] A woman about 50 years old ran out of the house with some little children behind her.


[User] Please transcribe this text

[Assistant] She was smiling all over her face and she took me by the hands and cried,


[User] Please transcribe this text

[Assistant] Chapter 5 The Plan to Free Gym


[User] Please transcribe this text

[Assistant] He and his brother Sid lived with their Aunt Polly up in St. Petersburg. And I knew all about them. Now I learned.


[User] Please transcribe this text

[Assistant] She and her husband were Tom's Aunt Sally and Uncle Silas.


[User] Please transcribe this text

[Assistant] And Tom was coming down south by boat to stay with them for a bit.


[User] Please transcribe this text

[Assistant] We all sat there talking, and I could answer all their questions about the Sawyer family.


[User] Please transcribe this text

[Assistant] And call out my name before I can stop him. I've got to go and meet him.


[User] Please transcribe this text

[Assistant] So I told the Phelpses that I would go into town to get my bags, which were at the boat station.


[User] Please transcribe this text

[Assistant] There was Tom Sawyer coming along. When he saw me, his mouth fell open.


[User] Please transcribe this text

[Assistant] Then I told him about the Phelpses and that they thought I was Tom Sawyer. What shall we do? I asked him. Tom thought for a bit and then he said, I know.


[User] Please transcribe this text

[Assistant] So I went back to the house with the bags. And Tom came along half an hour later.


[User] Please transcribe this text

[Assistant] She and Uncle Silas were really nice people. When we were alone later, Tom and I talked about Jim's escape. I said I had a plan, and Tom listened to it. It's a good plan, he said when I finished. But it's too easy. It's got to be a real escape.


[User] Please transcribe this text

[Assistant] Because Tom's plans are always crazy and exciting.


[User] Please transcribe this text

[Assistant] We knew that Jim was locked up in a hut outside the house. Every night we got out through our bedroom window.


[User] Please transcribe this text

[Assistant] Doug a hole right under the wall of the hut. It took us a week and it was hard work. We talked to Jim secretly and told him about the plan and he was really pleased.


[User] Please transcribe this text

[Assistant] Tom said that people always do this in books.


[User] Please transcribe this text

[Assistant] We wrote that there was a gang of slave thieves coming up from the South. They wanted to steal Jim.


[User] Please transcribe this text

[Assistant] And get the $300 from his owner. Well...


[User] Please transcribe this text

[Assistant] And there was a crowd of men in there, all with guns. I ran and told Tom, and he said that this was really good.


[User] Please transcribe this text

[Assistant] Well, there wasn't time to think about it, because it all happened so quickly.


[User] Please transcribe this text

[Assistant] We got in it and went over to Spanish Island.


[User] Please transcribe this text

[Assistant] My raft was there, and our plan was to escape on that and go on down river. Now Jim, I cried, you're a free man. We were all very happy, but Tom was the happiest of all.


[User] Please transcribe this text

[Assistant] Because he had a bullet in his leg. When Jim and I heard that, we weren't so happy. Tom wanted the adventure to go on.


[User] Please transcribe this text

[Assistant] You listen to me, Tom Sawyer.


[User] Please transcribe this text

[Assistant] So I'm staying right here until a doctor come. I knew Jim say that. He was a good, true friend. Can't say that about many people. Well, that was the end of the adventure really. I went and found a doctor in the town. He was a kind old man.


[User] Please transcribe this text

[Assistant] And he said he would go over to the island. But Tom's leg got very bad.


[User] Please transcribe this text

[Assistant] And the next day, the doctor and some other men carried Tom home to the Phelps's house. They brought Jim too. They locked him up in the hut again. But the doctor said, Be kind to him, because he didn't run away.


[User] Please transcribe this text

[Assistant] He stayed to help me with the boy. They took Tom up to bed, cause his leg was really bad.


[User] Please transcribe this text

[Assistant] And Aunt Sally sat with him while he slept. I didn't want to answer any questions, so I kept out of everybody's way. When Tom woke up the next day, he felt better. I was in the room and he said to me, Jim's all right, isn't he? I didn't know what to say, because Aunt Sally was listening.


[User] Please transcribe this text

[Assistant] Aunt Sally's mouth was opening and closing like a fish.


[User] Please transcribe this text

[Assistant] Then she got really angry, Tom.


[User] Please transcribe this text

[Assistant] That slave is locked up again and he's going to stay there. And if I catch you again. Tom suddenly sat up in bed. You can't do that. He cried.


[User] Please transcribe this text

[Assistant] Jim was old Miss Watson's slave, but she died two months ago.


[User] Please transcribe this text

[Assistant] That was a surprise to me and Aunt Sally. She thought Tom was crazy. But Sid...


[User] Please transcribe this text

[Assistant] Aunt Sally was really pleased to see her sister.


[User] Please transcribe this text

[Assistant] There was trouble coming for me and Tom. That was for sure.


[User] Please transcribe this text

[Assistant] So, Tom and I had to explain everything.


[User] Please transcribe this text

[Assistant] Aunt Polly said that Aunt Sally wrote and told her that Tom and Sid were there. She knew that it wasn't true.


[User] Please transcribe this text

[Assistant] So she decided to come and find out what was happening.


[User] Please transcribe this text

[Assistant] But she said that it was true about Miss Watson.


[User] Please transcribe this text

[Assistant] And Aunt Sally and Uncle Silas were really nice too. Later.


[User] Please transcribe this text

[Assistant] Tom, Jim and I had a long talk by ourselves. Tom talked and talked and then he said,


[User] Please transcribe this text

[Assistant] And go and have adventures in the wild country, down south. It sounded like a good plan to me. The only thing is, I said,


[User] Please transcribe this text

[Assistant] All my money back in St. Petersburg will be in Pop's pockets by now. Oh, said Tom, your money's all there. Your Pop never came back. Oh, and he won't come back, Huck, Jim said.


[User] Please transcribe this text

[Assistant] Cuz Aunt Sally wants me to live with her. I'll have to sleep in a bed, and wear clean clothes, and learn to be good. And I can't do that again. I've done it once already. The end. Yours truly, Huck Finn.


[User] Please transcribe this text

[Assistant] So uhm,


[User] Please transcribe this text

[Assistant] Yeah.


[User] Please transcribe this text

[Assistant] Okay, let's practice another T sound that is very American.


[User] Please transcribe this text

[Assistant] I'm going to say one word in two different ways and I want you to ask yourself which one sounds American. Listen carefully. City. City. Ability. Ability. Better. Better. Water. Water. It is. It is. The second one is an American accent.


[User] Please transcribe this text

[Assistant] All of those words that I said had a t in it, but the t didn't really sound like t, t.


[User] Please transcribe this text

[Assistant] No, it didn't. It sounded more like a D sound. Duh. Let me explain.


[User] Please transcribe this text

[Assistant] Is between two vowels, the T sounds more like a D sound or what we call a very fast D. So for example, when we say the word


[User] Please transcribe this text

[Assistant] Better. We have a vowel. T.


[User] Please transcribe this text

[Assistant] And another vowel. This T changes and it becomes this fast D sound. So we don't say better, we say better, better. So it's not a really strong D, it's not better, it's a real quick R, R, R. Better, better. Look again. We don't say city, we say city, city.


[User] Please transcribe this text

[Assistant] That's a big city. That's a better city.


[User] Please transcribe this text

[Assistant] Alright, this also happens when the T is between two vowels even when the words are separate. For example, it is. We link this together.


[User] Please transcribe this text

[Assistant] And therefore the T is between two vowels. So we don't say, yes it is. We say, yes it is.


[User] Please transcribe this text

[Assistant] Let's practice some more words with the T between two vowels. Repeat after me. Water. Water. We don't say water. We say water. Auto. Auto. Automatic. We don't say automatic. We say automatic. Metal. We don't say metal.


[User] Please transcribe this text

[Assistant] We say metal. Therefore, this word, metal, sounds exactly like this word. Metal, metal.


[User] Please transcribe this text

[Assistant] Changes into a D when it's between two vowels. Let's practice some sentences now. I got up at 11 o'clock. Don't say, I got up at 11 o'clock. Say, I got up at 11 o'clock.


[User] Please transcribe this text

[Assistant] Do you want to get in or get out? Don't say, get in and get out. Say, do you want to get in or get out?


[User] Please transcribe this text

[Assistant] I bought it at a computer store. Don't say I bought it at a computer store. Say I bought it at a computer store.


[User] Please transcribe this text

[Assistant] What a nice day it is. Don't say, what a nice day it is. Say, what a nice day it is.


[User] Please transcribe this text

[Assistant] And by the way, this is not considered lazy or sloppy or casual speech. This is a standard American accent.


[User] Please transcribe this text

[Assistant] Then there's only a 1 in 64 chance that you have the disease. So if it comes back negative, it's a pretty good indication that you're okay.


[User] Please transcribe this text

[Assistant] For a thousand years, it's been the symbol of Britain's royal dynasties. For the Queen, this is a palace like no other.


[User] Please transcribe this text

[Assistant] Billions of pounds worth of paintings and fine furnishings. Five thousand acres of parkland.