And so me and Papertrae's talking. And next thing, you know, Papertrae said, man, they saying Pym C dead.  I did the same thing. Yeah. Bullshit, because you know, I talked to Pym. That's what I was gonna ask you, what was the time before that? I talked to him two days before he died. Me too. He said, man, he said. Two days before? Saturday night. So he was doing a show with two short days. Saturday night. Yeah, he was doing a show with two short days. He said, man, I'm having fun.  Two short guy to show. Yeah, we on boss talk one on one. You know, he like, man, don't cut me out, man. Don't cut me out, man. I want my million, right?  Now fucks with you. Because we did the best of both worlds together. You know what I'm saying? So while he in jail, you know, we get that call. Yeah. Pym dead. You know what I'm saying? I was about to ask you about that. I'm like, man, I'm tripping, right? Because I'm, I'm in Atlanta. And, you know, I had, I was trying to hide my cars. You know what I'm saying? So I had a lot of, you know, high-end cars. So it was just broad in College Park.  So I could put two cars there. You know what I'm saying? I had like about six cars. So I had to escalate in the compressor. I had the Benz here there. So I'm over her house. And you know, we, we put in the cars there, you know.  You know, and then I got some DVDs too. Yeah. Over by house. So we had like about 5,000 DVDs over there.  And so me and Piper Chase talking. And next thing, you know, Piper Chase said, man, they saying, Pym C, dad. I said, man, that shit, man. I did the same thing. Yeah. Because you know, I talked to Pym. That's what I'm going to ask you. What was the time before that? I talked to him two days before he died. Me too. He said, man. He said Saturday night. So, so he was, he was doing a show with Two Short. Saturday night. Yeah, he's doing a show with Two Short. He said, man, I'm having fun. I'm in LA.  Two Short got a show. He said, Ken, I'm sorry for being tardy. That was weird. You know what I'm saying? That was weird.  This nigga just called me right before he died.  And, you know, it was just, it was just strange. And then once, you know, we, you know, we was hearing about it. You know, Rick Fountain, right?  You know, so, so, so Rick calling me like, yeah, yeah, man, it's true, man. It's true. It's true. So, you know what I mean? I'm sitting there, you know, you got to remember, we got, we have every magazine together. We don't, we're doing all these songs together. And, you know, a lot of people, I know he probably know about it.  The nigga, seven. The nigga, 17. The nigga, 17. The nigga, bankroll. The nigga, IVP. These were, these were the niggas that was on the street having money. Yeah. So, how Pimp was gonna perform the record label was, he was gonna just take niggas with money. He said, y'all niggas buy your own jewelry, y'all buy your own thing, and I'm gonna let y'all use the UGK brand. Can I tell you that? He teach over the exact same thing. Didn't I tell you that? He was setting everybody up, and it was up by the...  Because Pimp had a vision that, Pimp and Boyle are the type of nigga that you better be helping yourself. Yeah, that's what he was doing. Don't wait on me, goddammit.  If you helping yourself, you got your own bread, yeah, we can put some with something to make something happen. Which makes sense. That was the move, and all the niggas became hot. Yeah, yeah.  You know, these was niggas that I knew personally that I just, I introduced Bank Road to Pimp, I introduced Ivy Peter Pimp.  So we was, all of us, he said, go find the niggas with the money, bring them, and that's how we're gonna form the UGK record.  Real street dudes. Real street niggas. And you know what I'm saying? That's how Pimp wanted to form it. He didn't want to do it. Doesn't make sense, because he's an underground king. He didn't want to make sense. He didn't want to go pick artists.  And then this brother, you know what I'm saying? He didn't understand the business, so you know what I mean? He didn't sign, but you know, Pimp only wanted to sign it because he seen me putting money in traction behind the nigga. You know what I'm saying? Mm-hmm.  James is funny, bro. Other than that? He didn't like, Chad didn't like rappers, and he didn't like athletes. And he, hey, tell him, he didn't like a lot of the big rappers, neither, man. Bro, he didn't like them. Man, hey, bro, bro. If somebody saw him, you hear him say stuff on him? I know the story behind what he's saying and who he talking to. What story is that? I can't just do that, bro. I can tell you, and all I can tell you, it made Rest in Peace, Maroy, you know what I'm saying? Yeah, Maroy. Maroy did the cross-country pivot. He had footage of Pimp talking bad about somebody, a lot of your favorite rappers. Pimp didn't like a lot of niggas that, today, y'all call moguls. What happened to that footage? He didn't put it out.  But you know, before he said, hey, he like, yeah, that nigga this bad, that nigga that bad, that nigga's a sucker, man.  That's all Pimp used to say. These niggas trying to steal the UGK brand, man.  The Trial too, like the word Trial. But a lot of niggas, you know what I'm saying? Me, like from New York and, you know, a lot of part of the country, you know what I'm saying? Me, they want to do Pimp songs.   What they were saying at the funeral, you remember the people from Jive Records, they said that they was telling, we was at the funeral, so this was Jive Records said. I didn't say this. I'm just livid. Yeah, so they was like, they said, yeah, man, so we telling Pimp to go through this, this is gonna be the biggest thing in his career.  He said, well, y'all ain't gonna help me down there with all the rich niggas, man. And I'm looking like a son, he said, I need a motherfucking brand new Benz, take part of a Benz. That's the Benz you're seeing on the Big Pimp in there.   He said, man, in order to make that, that Big Pimp in this the time and all that was going on. That was Pimp Benz Benz. To be in the video, Pimp made them of a buy him a Benz. Drove that motherfucker from Atlanta, was in Miami, Florida, somewhere in Florida. Yeah, smoking eyes, going up, keeping you. And he wasn't on the boat, I heard the story, he wasn't even on that, but he brought that Benz and drove it all the way down. Bro, he didn't mess with Jay-Z like that, bro. He didn't mess with Jay-Z like that. Well, I mean, Jay-Z here. He didn't mess with many people up. Well, see his name was, from personal talking to him, he was saying that they were trying to steal his style. So Pimp. The summer style.  Everybody know he get high. And he paranoid.  You know what I'm saying? We can try to see, the nigga trying to see was, I'm going to go on them nigga's side. So I'm telling Slick Puller, man, like man, ain't no tell Jay-Z, ain't no beef, man. We all family, you know what I'm saying? It's good. And Slick Pills, no man, Jay-Z ain't got no problem with Pimp see, you know what I'm saying?  Like, kid, you better not cross me, right? But you know, the nigga was crazy, man.


Chapter one of The Dream Coach. This is a LibriVox recording. All LibriVox recordings are in the public domain. For more information or to volunteer, please visit LibriVox.org. Recording by Lynette Calkins, Monument Colorado. The Dream Coach by Anne Parrish. If you have been unhappy all the day, wait patiently until the night. When in the sky, the gentle stars are bright, the Dream Coach comes to carry you away. Great Coach, great Coach, how fat and bright your sides, to please the child who rides. Painted with funny men, see that one's hose? How blue! How red and long is that one's nose? And under this one's arm, a flapping cock. Great dandelions, tell us what o'clock, with silver glow much bigger than the moon. Dream Coach, come soon, come soon! What pretty pictures! Angels at their play, and brown and lilac butterflies, and spray of stars and animals from far away. Great elephants, a bright pink waterbird, things lovely and absurd. As the wheels turn, they wake to lovely sound. Musical boxes, as the wheels go round, they play a little silver spray of notes. Swift runs the river, bluebells in the wood, the waterfall, the child who has been good, like splash of foam at keel of little boats.  His hat is as tall as a cypress tree, his hair is as white as snow, his cheeks and his nose are as red as can be. He sings, come along, come along with me, let us go, let us go. His coat is speckledy red and black, his boots are as green as a beetle's back, his beard has a fringe of silver bells and scarlet berries and small white shells, and as through the night the Dream Coach gleams, the song he sings like a banner streams, nothing is real in all the world, nothing is real but dreams. Through sound of rain the Dream Coach gallops fast, all those that we have loved are riding there. I hear their laughter on the misty air, I wait for you, I have been waiting long, far off I hear the driver's tiny song, O Dream Coach, come at last. CHAPTER II of the Dream Coach by Ann Parish. When the driver of the Dream Coach reached the last small star in the sky, he unharnessed his hundred misty horses and put them out to pasture in the great blue meadow of heaven. It was well he reached the end of his journey when he did, for in another moment a mounting wave of sunlight and wind rushing up from the world far below blew out the silver white flame of the star so that no one could follow the strange driver and his strange coach to their resting place. Resting place? What a mistake! The driver of the Dream Coach never rests. You see, there are so many things to do even when he is carrying no passengers. There are new dreams to invent, queer dreams, funny dreams, fairy dreams, goblin dreams, happy dreams, excited dreams, short dreams, long dreams, brightly colored dreams, and dreams made out of shadows and mist that vanish as soon as one opens one's eyes.  It is hard to keep all those dreams from getting mixed up, there are so many of them. Indeed, sometimes they do get mixed up, and a good child who was meant to have a dream as pretty as a pansy or as funny as a frog gets a nightmare by mistake. But the driver of the Dream Coach tries as hard as he possibly can never to let this happen. He has so very much to do that he never would catch up with his work no matter how quickly his beautiful horses galloped from star to star, from world to world, if there was not someone to help him. There are little angels who help the driver of the Dream Coach. In their gold and white book they keep a record of every one on earth. As soon as the driver of the Dream Coach had unharnessed his horses, he went to these angels and planned his next trip.  If I should use all the paper and all the pencils in the world, I could not begin to tell you about all the dreams he arranged to carry to the sleeping world. And yet there was one child who was nearly forgotten, a little princess whose name had been written at the top of a new page which the driver had neglected to turn in his hurry. Surely you are not going to forget the little princess on her birthday, pleaded the little angels turning the page. Oh dear, said the driver, that will never do, now will it? And yet I simply can't pack another dream onto the coach. I'm sorry but I'm afraid, oh dear, echoed the angels, perhaps just then one of the youngest angels who happened to be leaning over the parapet of Paradise saw the princess begin to cry and took in the situation instantly. So he hurried to the others and suggested that he himself should carry a dream to the little princess. The driver of the dream coach thought this was a splendid idea and thanked him again and again for his help. That is how the seven white dreams of the king's little daughter were carried to her by an angel, and as you know, or if you don't, I will tell you, the dreams carried in the moonbeam basket of the angels are the most beautiful of all. What did the princess dream? You shall hear. I cannot remember all the names of the king's little daughter and indeed few can. The archbishop who Christendor says that he can, but he is so great and so deaf, a dignitary that no one would think of asking him to prove it. They are all there, twelve pages of them, in the great book where there are recorded the baptisms of all the royal babies so that you can look for yourself if none of the ones I can remember. Angelica, Mary, Delphine, Violet, Candida, Pamelia, Petronella, Victor, Veronica, Monica, Anastasia, Yvonne, happen to please you. It was the fifth birthday of the little princess and there were to be great celebrations in her honor. Fireworks would blossom in the sky and in the gardens lanterns were hung like bubbles of colored light from white rose tree to red while the great fountains would turn from pink to mauve, from mauve to azure to amber and to green as they flung up slender stems and great spreading lacy fronds of water. Everyone from the king down to the smallest kitchen maid had new clothes for the occasion and the chief cook had created a birthday cake iced with fairy grottoes and gardens of sponge sugar so huge and so heavy that the princess's ten pages in their new sky blue and silver liveries staggered under the weight of it. The little princess had a new gown of white satin sewn so thickly with pearls that it was perfectly stiff and stood as well without her as when she was inside it. It was standing by her bedside when the bells of the city awoke her on her birthday morning together with her silver bath shaped like a great shell and her nine lace petticoats and her hoops to go over the petticoats and her little white slippers on a cushion of cloth of silver and her whalebone stays and her cobweb stockings and her ten ladies-in-waiting grand duchesses everyone. When she opened her blue eyes they all swept her the deepest curtsies their skirts of bright brocade billowing up around them and said together long life and happiness to your serene highness and then the first grand duchess popped her out of bed and into her bath where she got a great deal of soap in the princess's eyes while she conversed in a most respectful and edifying manner. The second grand duchess who was lady-in-waiting in charge of the imperial towel was even more respectful and nearly rubbed the princess's tiny button of a nose entirely off her face. The third grand duchess brushed and combed the little duck tails of yellow silk that covered the royal head and oh how she did pull. The fourth grand duchess was lady-in-waiting in charge of the imperial shift and as she was rather old and slow although extremely noble the princess grew cold indeed before the shift covered up her little pink body. The fifth grand duchess put on the rigid stays the sixth put on the stockings and slippers the seventh was very important and gave herself airs for the nine lace petticoats were her concern. The eighth grand duchess was lady-in-waiting in charge of the imperial hoops. The ninth put on the little princess the dress of satin and pearls that glowed softly like moonlit drops of water and the tenth grand duchess the oldest and ugliest and noblest and crossest and most respectful of them all placed on the yellow head the little frosty crown of diamonds. Then the princess's father confessor a very noble prince of the church dressed in violet from top to toe came in between two little boys in lace and said a long prayer in latin. It was so long that I am sorry to have to tell you right in the middle the princess yawned so of course another long prayer had to be said to ask heaven to overlook such shocking wickedness on the part of her highness. Then the chief steward in attendance on the princess brought her breakfast bread and milk in a silver pouringer. The little princess had hoped for strawberries as it was her birthday but the chief gardener was saving every strawberry in the royal gardens for the great birthday banquet that was to be held that evening. Then the little princess went to say good morning to her mother and father and this is the way she went. First came two heralds in forest green blowing on silver trumpets then came the father confessor and his little lace-covered boys. Then came the ladies in waiting in their bright brocades with feathers in their powdered hair and after each lady came a little black page to carry her handkerchief on a satin cushion. The ten pages of the princess were next and after them came the royal baby's own regiment of dragoons in white and scarlet and last came four gigantic blacks wearing white loincloths and enormous turbans of flamingo pink and carrying a great canopy of cloth of silver fringed with pearls and under this very tiny and looking in her spreading gown like a little white hollyhock out for a walk came the princess. After she had curtsied and kissed the hands of her royal parents her father gave her a rope of milk white pearls and her mother gave her a ruby as big as a pigeon's egg both of which were instantly locked up in the royal treasury. They then bestowed upon her in addition to her other titles that of grand duchess of pinch pincher wits which took so long to do that when she had said thank you it was time for lunch which was just the same as breakfast except that this time the pouringer was gold. After lunch the prime minister read the princess an illuminated birthday greeting from her loyal subjects which ran along so that the ladies in waiting nearly yawned their heads off behind their painted fans and the princess had a nice little nap and dreamed that there would be strawberries for supper but instead there was bread and milk in a pouringer covered with turquoise's and moonstones. Then as the younger ladies in waiting were thinking of the gentleman of the court who would be waiting for them among the rose trees and u-hedges to watch the colored water of the fountains and listen to the harps and flutes and as the older ladies in waiting were thinking of comfortable seats out of the draft in the state ballroom and having the choices morsels of roasted peacock and lark's tongue pie and frozen nectarines they popped the princess into bed pretty promptly indeed an hour earlier than usual and went off to celebrate her birthday. The room in which the princess lay was as big as a church and the great bed was as big as a chapel. Four carved posts as tall as palm trees in a tropic jungle held a canopy of needlework where hunters rode and hounds gave chase and deer fled through dark forests. Below this lay the broad smooth expanse of silk and sheet and counterpane and in the midst as little and alone as a bird in an empty sky lay the king's little daughter. One large tear rolled down her round pink cheek and then another. The long dull day had tired her and the great dim room frightened her and she wanted to see this fireworks she had heard her pages whispering about. She sat up among her lace pillows and her tears went splash splash on the embroidered flowers and leaves of her coverlet. One of the youngest angels happened to be leaning over the parapet of paradise when the princess began to cry and he took in the situation instantly and hurried off to his heavenly playmates to tell them about it. It's her birthday he said and no one has given her as much as a red apple or a white rose. Only silly old rubies and pearls that she wasn't even allowed to play marbles with and now they have left her to weep in the dark while they dance and feast. I shall go down to her and sit by her bed till her tears are dry and take her a white dream as a gift. Oh let me send a dream too cried another angel and let me and let me so that by the time the little angel was ready to start to earth there were seven white dreams to be taken as birthday gifts from heaven and he had to weave a basket of moon beams to carry them in. That night the princess dreamed that she was a daisy in a field dancing delicately in the wind among other daisies as thick as the stars in the Milky Way. Feathery grasses danced with them and yellow butterflies danced above and the larks in the sky flung down cascades of lovely notes that scattered like spray on the joyous wind. Some poor little girls were playing in the field their feet were bare and their faded frocks were torn but they danced and sang too. There came a rumbling like thunder and through a gap in the Hawthorne hedge the children and the daisies saw the king's little daughter driven past in her great scarlet coach drawn by eight doppled horses.  The next day the princess looked in vain for a daisy as she took her morning constitutional in the royal gardens. There were roses and lilies blue irises and striped red and yellow carnations tied to stakes all stiff and straight. Hold up your head serene highness snapped one of the ladies in waiting who had had too many cherry tarts at too late an hour the night before but daisies danced in the princess's heart. The next night the princess dreamed that she was a little white cloud float in the bright blue sky. She floated over the blue sea and the white sand and over black forests of whispering pines and over a land where fields of tulips bloomed for miles in squares of lovely colors delicate rose and mauve and purple, coppery pink and creamy yellow with canals running through them like strips of old dark looking glass. She floated over rye fields turning silver in the wind and over nuns at work in their walled gardens and finally over a great grim palace where a king's little daughter lived. I would rather be free and afloat in the sky thought the small white cloud. When she took her air the next day she looked up to see if any white clouds were in the sky. Her highness is growing very proud said the ladies in waiting. She holds her nose up in the air as a king's daughter should. On the third night the princess dreamed she was a little lamb skipping and nibbling the new green grass in a meadow where hundreds of lilies of the valley were in bloom. They were still wet and sparkling with rain but now the sun shone and a beautiful rainbow arched over the meadow and the lilies of the valley and the happy little lamb. Through the rest of her life the gentleness of the lamb lay in the heart of the princess. The next night she dreamed that she was a white butterfly drifting with other butterflies among the tree ferns and orchids of the jungle gentle and safe from harm although serpents lay among the branches of the trees and lions and tigers roamed through the green shadows. A white butterfly flew in at her window the next day. A moth! a moth! cried the ladies in waiting. Camphor and boughs of cedar must be procured instantly or the dreadful creature will eat up her highness's ermine robes but the little princess knew better than that. On the fifth night she dreamed that she was a tiny white egg lying in a nest that a hummingbird had hung to a spray of fern by a rope of twisted spider's web. The nest was softly and warmly lined with silky down and above her was the soft warmth of the mother bird's breast. On the sixth night she was a snowflake. It was Christmas night and the towns and villages were gay. Rosie light poured from every window blurred by the falling snow and the air was full of the sound of bells. High up on the mountain was a lonely wayside shrine with carved and painted wooden figures of the mother and her child whose birthday it was. There were no bells there nor yellow candlelight but only snow and dark evergreen trees. The snowflake whirling and dancing down from the sky a tiny frosty star gave its life as a birthday gift to the holy child lying for its little moment in his outstretched hand. The angel was distressed to find on the seventh night that the seventh dream had slipped through a hole in the moonbeam basket and was lost. Careless little angel but it really did not matter for instead of a dream he showed himself to the princess and she liked that the best of all for she had never had anyone to play with before and there is no playmate equal to an angel but the seventh dream is still drifting about the world. I wonder where perhaps it will be upon my pillow tonight perhaps upon yours who knows. End of section two recording by Lynette Colkin's Monument Colorado October 2019. Chapter three of The Dream Coach by Ann Parrish. This LibriVox recording is in the public domain. Recording by Lynette Colkin's Monument Colorado. The Dream Coach. Goran's Dream. Crack went the driver's whip but it did not hurt the galloping misty horses for it was only a ribbon of rainbow that he liked to use because both he and his horses thought it so pretty. And away went the great coach over the forests and over the seas over the cities and plains to a country where the sea thrusts long silver fingers into the land where mountains are white with snow at the same time that the meadows are bright with wild flowers and where in summer the sun never sets and in winter it never rises and here the Dream Coach drew up beside a cottage where a lonely little Norwegian boy was falling asleep. Come Goran called the driver come climb on to the coach and find the dream I have brought for you. Who was Goran? What dream did he find? That you shall hear. Little Goran and his grandmother lived in a tiny house in Norway high above the deep waters of a fjord. When Goran was a baby they used to tie one end of a rope around his waist and the other to the door so that if he toddled over the edge he could be hauled back like a fish on a line.  It was a lovely spot in summer when the waterfalls went pouring down milk white into the green fjord sending up so much spray that they looked as if they were steaming hot when rainbows hung in the sky, when the small steep meadows were bright with wild flowers and even the sod roof of the cottage was like a little wild garden of hair bells and pansies and strawberries that Goran gathered for breakfast sometimes. He was happy all day then fishing in the fjord making a little cart for Nana the goat to pull trying to teach Gustava the hen to sing putting on his fingers the pink and purple hats that he picked from the tall spires of wild fox glove and monk's hood and making them dance and bow and listening to the loud music of the waterfalls after rain and in the evening after supper Goran's grandmother would tell him splendid stories while they sat together in the doorway making straw bee hives sowing the rounds of straw together with split blackberry briars. The sun would shine on the straw and make it look so yellow and glistening that Goran would pretend he was making a golden beehive for the queen bee's palace for where Goran lived the sun never sets at all in the middle of summer and it is a bright daylight not only all day but all night as well you and I would never have known when to go to bed but Goran and his grandmother were used to it and even Gustava the hen knew enough to put her head under her wing and make her own dark night but with winter changes came the flowers slept under the earth until spring's call should wake them and yawning and stretching stretching they should stretch up into the air and sunlight the waterfalls no longer flung up clouds of spray like smoke but built roofs of ice over themselves and strangest of all the winter darkness came so that the days were like the nights and you and I would never have known when to get up. I must go to the village for our winter supplies before snow falls and cuts us off his grandmother said to Goran one day neighbor Skillstad has offered me a seat in his rowboat tomorrow and will bring me back the next day you won't be afraid to stay here alone will you Goran? no grandmother said Goran he pretended to be tremendously interested in poking his finger into the earth in a geranium pot so that his grandmother shouldn't see that his eyes were full of tears and his lower lip was trembling for to tell you the truth he was frightened the little house was so far from any other house and then Goran had never spent a night alone last year when their winter supplies were brought he had gone to the village with his grandfather and he had told Nana and Gustava and Mijel the cat all about what a wonderful place it was a thousand times over the warm shop with its great cheeses in wooden boxes painted with bright birds and flowers and its glowing stove as tall and slim as a proud lady in a black dress with a wreath of iron ferns upon her head the other children who had let him play with them while grandfather exchanged the socks and mittens knitted by grandmother for potatoes and candles and they had slept at the inn under a feather bed so heavy that you would have thought by morning they would have been pressed as flat as the flowers in grandmother's big bible but they weren't they got up just as round as ever and had a wonderful breakfast of dark grayish brown goat's milk cheese cold herring and stewed billberries grandfather had gone to heaven since then and Goran wondered if he could possibly be finding it as delightful as the village how he did want to go this time but of course he knew that someone must stay behind to feed Nana and Gustava and Mijel to tend to the fire and water the geraniums and wind of the clock so he said as bravely as he could i'll take care of everything grandmother soon after his grandmother left the snow began to fall how that frightened Goran sometimes it snowed so hard that she could never get back to him for when winter really began the little house was often up to its chimney in snow and they could get to no one and no one could get to them how poor little Goran's heart began to hammer at the thought he fell to work to make himself forget the snow first seizing a broom made of a bundle of twigs he swept the hard earth floor which in summer had so pretty a carpet of green leaves strewn fresh every day by Goran and his grandmother then he poured some water on the geraniums in the window only spilling a little on himself then he struck Mijel who was purring loudly in front of the fire and all this made him feel much better time for dinner Goran said the old clock on the wall at least it said ding ding ding ding ding ding ding ding ding ding ding which meant the same thing so Goran ate the goat's milk cheese and black bread that his grandmother had left for him and then and not before he summoned up enough courage to look out to see if the snow was still falling it was snowing harder than ever and already everything had a deep fluffy covering oh would his grandmother ever be able to get back to him but he must be brave and not cry for he was six years old he said a little prayer as his grandmother had taught him to do whenever he was frightened or unhappy and his heavy heart grew lighter i'll make a snowman Goran decided perhaps then the time would seem shorter grandfather and he had made a splendid snowman after the first snowman last winter it was not late enough in the year to have the day as dark as night it was only as dark as a deep winter twilight and the white snow seemed to give out a light of its own for Goran to work by first he found an old broomstick and thrust it into the snow so that it stood upright then he pushed the heavy wet snow around it patting on here scooping out there until there was a body to hold the big snowball he rolled for the head a bent twig pressed in made a pleasant smile and for eyes Goran ran indoors and took from the little box that held his treasures two marbles of sky blue glass that his grandfather had given him once for his birthday what a beautiful snowman with his sky blue eyes he gazed through the falling snow at little Goran called the old clock and that was the same as saying time for supper Goran the fire lit up the room with a warm glow painted the curtain's crimson and made wavering gigantic shadows on the walls the water bubbled in the pot and the boiling potatoes knocked against the lid said mijao blinking in front of the blaze and the old clock answered Goran had given their supper to Nana and Gustava and Mijao and had taken one good night look at his snowman now he put his bowl of boiled potatoes on the table in front of the fire and pulled up his chair lying on the floor where she had fallen from his box when he was getting his snowman's blue eyes was a playing card the queen of clubs his grandfather had found it lying in the road in the village and had brought it home as a present for Goran the little boy thought the queen was very splendid with her crown and her veil and her dress trimmed with bands of blue and leaves and stars and rising suns of yellow in one hand she held on high a little yellow flower now he picked her up and he put her on a chair beside him pretending the queen had come for supper to keep him from being lonely each mouthful potato he first offered her with great politeness but the delicate lady only gazed off into space Goran's supper made his insides feel as if a soft blanket had been tucked causally about them and he was warm and sleepy was there anything else grandmother told me to do before i went to bed he murmured tick tock yes there was the clock replied she told you to wind me up climb on a chair and do it carefully don't shake me i can't stand that for i'm not as young as i used to be and i want a drink cried the youngest geranium who was little and had been hidden by the bigger pots when Goran watered them knock knock knock what a knocking at the door Goran ran to open it and the firelight fell on nana the goat and gustiva the hen against a background of whirling snow nana was wearing grandmother's quilted jacket where in the world had she found that and Gustava had wrapped Goran's muffler about herself and the little basket she carried on her wing good evening began nana rather timidly for her hey gustava and i come in and sit by the fire we thought you might be lonely and then it is so cold in the shed i did have a muffler like gustavas but i absentmindedly ate it i'm growing very absentminded we've come with an important message for you but i can't remember what it is can you gustava clock clock no i can't but i've brought my beautiful child to call on you said gustava and she lifted her wing and shook Goran the brown egg in her basket shut the door shut the door several geraniums called indignantly we are very delicate and we shall catch our deaths of cold so in came nana and gustava and gustava's egg and Goran shut the door present my subjects commanded the queen of clubs and Goran saw that she was no longer a little card but a lady as big as his grandmother in front she still wore her blue and red and yellow dress but in back she was all blue every inch of her with a pattern of guilt stars and when she turned sideways she seemed to vanish for she was only as thick as cardboard but she was so proud and grand that Goran wished he had on his sunday suit with the long black trousers and the short black jacket with its big silver buttons the waistcoat all covered with needlework flowers and the raspberry pink neckerchief this is nana our goat your majesty he said goat you may kiss my hand said the queen i don't know whether i want to replied rude nana who had never been presented to a queen before and didn't know the proper way to behave mercy on us what matters cried the geraniums blushing deep red that the queen should be spoken to in that manner in what they thought of as their house but i wouldn't mind eating your yellow flower continued nana i like to eat flowers and she looked at the geraniums who nearly fainted your turn next said the queen to gustava she had her gentlemen say that so often when they were playing scat with her and her companions that she always repeated it when she could think of nothing else to say squawk clock cried gustava would your majesty like to see my beautiful child and she showed the queen her egg just look your majesty have you ever seen anything more lovely such a pale brown color such an innocent expression perhaps your majesty is also a mother tick talk don't forget to wind me said the old clock gustava hen talks too much the fat teapot in the corner cupboard told her daughters the tea cups when the queen speaks to you just say yes your majesty and know your majesty and i dare say she will take you all to court and find you handsome husbands among the royal coffee cups your majesty should see my beautiful home when angustava a nest of pure gold she thought it was gold but it was really yellow straw just like my throne replied the queen speaking of beautiful homes you should see my palace there are 53 rooms she said this because it was the highest number she knew for there are 53 cards in the pack counting the joker who keeps all the cards amused when they are shut up in their box and she had seen a room in the palace because she had been used in a game of scat there once in her early youth but that was long long ago my throne and the king's throne are pure gold just like your nest my good gustava and the walls are painted red and white in swirls like strawberries and cream the stove has such a tall slender figure and wears a golden crown and then just imagine all the lamps are dripping with icicles at the same time that the floor is covered with blooming roses for that is how she thought of the glass lusters on the lamps and the carpet on the floor icicles ice freezing that reminds me of our important message said nana your snowman goran he looks so dreadfully cold out there we were afraid he would perish oh yes how could we have forgotten for so long click click click he will certainly be frozen to death unless something is done quickly do you mean to tell me that anyone is out of doors on such a night as this questioned the queen have him brought in at once your turn next and she looked so severely at goran that he felt his ears getting red so goran and nana brought the snowman in while the queen gave orders from the doorway gustava sat on her darling egg to keep it warm mijao walked away with his tail as big as a bottle brush and the geraniums cried in chorus shut the door shut the door we shall all catch cold poor thing how pale he is exclaimed the queen and how dreadfully cold put him in a chair by the fire the snowman looked out of wandering sky blue glass eyes but never said a word for he was very shy and as he had only been born that afternoon everything in the world was new to him i want a drink cried the youngest geranium and tick tock tick don't forget to wind me the old clock repeated but no one paid any attention to them your turn next said the queen to nana make a blaze for this poor creature is nearly frozen so with a clatter of tiny hooves nana built up the fire only pausing to eat a twig or two until even mijao was nearly roasted but the poor snowman was worse instead of better his twig mouth still smiled bravely and his blue eyes remained wide open but tears seemed to pour down his cheeks and he was growing thinner before their very eyes if you please he said in a timid voice i give him a drink of something hot advised the fat teapot and that reminded the youngest geranium who began screaming i want a drink i want a drink i want a drink i'll be delighted to oblige with some nice warm milk nana offered so gore and milked a bowl full but the snowman could not drink it and the tears ran faster and faster down his face if you please he began again faintly we must put him to bed the queen interrupted with a stern look at gustava who was sitting on her darling egg in the center of grandmother's feather bed your turn next grandmother's bed was built into the wall like a cupboard it was all carved with hair bells and pinecones and cobalt and nixies the cobalt are the elves who live in the mountain forests and the nixies are water ferries who sit under the waterfalls playing upon their harps and making the sweetest music in the world there was a big white feather bed on grandmother's bed and a big red feather bed on top of that and two fat pillows stuffed with goose feathers and above all this was a little shelf with two smaller feather beds and two smaller pillows and that was goren's bed on dreadfully cold nights they pulled two little wooden doors shut and there they were quite warm and cozy even quite stuffy you and i might think the doors of the bed were painted with pink tulips and red hearts and grandmother said it made her feel quite young and warm to look at them and goren said it made him feel quite young and warm too and gustava the hen thought they were beautiful so there she sat on her darling egg and as she could never think of more than one thing at a time she had forgotten all about the snowman and was happily clucking this song to her egg make a wreath i beg for my darling egg flowers blue as cloudless sky when the summer sun is high hair bells little cups of blue holding drops of crystal dew rain wet pinks as sweet as spice lilies white as snow and ice lemon colored lilies too and the flex flowers lovely blue strawberries sweet and red and small and the purple monks head tall let the moon white daisies shine bring the coral columbine weave the shining buttercup bind the sweet wild roses up poppies red as coals of fire and the speckled fox club spire and the iris blue that gleams knee deep in the foaming streams bring the spruce cones brown and long thus ran on gustava's song make a wreath i beg for my darling egg make a wreath i beg for gustava's egg broken nan of the goat impatiently why leave the geraniums out add the teapots broken spout cheese and brown potatoes too anything at all will do feathers from the feather bed goran's mittens warm and red and the flower the queen holds up and the cracked blue china cup but the queen has said kindly leave that bed so gustava had to flop off the bed with a squawk while goran handed her her egg and then they put the poor snowman what was left of him into grandmother's bed and pulled the iderdown quilts over him if you please said the snowman in a feeble whisper oh if you please i'm i know this is the right thing to do because it is the way we always treat snowmen at the palace broken the queen to tell you the truth she had never seen a snowman in her life before but she would never admit that she didn't know all about everything the snowman looked at them with despairing sky blue eyes while his tears poured down soaking grandmother's pillow he had tried desperately to tell them something but they would none of them listen suddenly goran knew what it was i believe we're melting him said goran he needs air i need air said the snowman his face shining with hope air said the queen nonsense he's had too much air he needs a hot brick at his feet i need air faltered the snowman air air nonsense cried the fat teapot and all her teacup daughters hoping the queen would hear and take them back to the palace with her i need air sighed the snowman and now he looked discouraged air and mijao squeezed himself under the chest of drawers much annoyed with everyone i need air breathed the snowman looking at goran with imploring eyes air mercy on us that will mean opening the door again and the geraniums shivered in every leaf and petal but goran had helped the poor snowman now nearly melted away out of bed and was leading him to the door i need whispered the snowman and his voice was so faint that goran could hardly hear it and there because he was melting away so fast his mouth fell out and lay on the floor just a little bent twin poor snowman oh poor snowman he could not make a sound now he could only look at them so sadly so sadly but a little mouse peeping with bright eyes out of its hole saw what had happened and since mijao was nowhere in sight ventured to squeak so the queen picked it up and pressed it into place again but by mistake she put it on the wrong side so that instead of a pleasant smile the snowman had the cross's mouth in the world pulled far down at each corner and what a change it made in him before his voice had been a gentle whisper now it was an angry bellow that made the teacup shiver on the shelf and the geraniums turned quite pale and the little mouse dived back with a squeak into her hole thinking to herself well i never here you shouted the snowman get me out of here and get me out quick hop along my girl and open the door your turn next this was to the astonished queen now then carry me out tick tock i'm feeling dreadfully run down said the old clock tick tock wind the clock tick tock wind it and he stopped talking the astonished queen meekly threw open the door and gore and carried the snowman into the snowy darkness brrr it was bitter cold now bring some snow and built me up the snowman ordered leave the door open so that you can see don't dawdle the firelight from the open door shown on his blue glass eyes and made two angry red sparks gleam in them gore and the queen gusteva and nana scooped up handfuls and hooffuls and wingfuls of newly fallen snow and padded it on to the snowman until he stuck out his chest more proudly than he had done in the first place and he was so fat that he looked as if he were wearing six white fur coats one on top of another and all the time when he wasn't frightening the queen half out of her wits by shouting your turn next he kept muttering away to himself melt me over the fire smother me in a feather bed put a hot brick at my feet it was when gore was patting a little fresh snow on the snowman's nose that he accidentally knocked his twig mouth off again and this time it was put back right side up so that the snowman was as smiling as he had been in the beginning he stopped roaring he stopped muttering did the fire die down for the red sparks no longer gleamed in his gentle sky blue eyes oh thank you so much said the snowman you have been so kind to me and i know that you were trying to help me in the house forgive me for having been so cross will you please forgive me and the snowman looked so anxiously at gore and the queen and nana and gustava that they all answered yes yes of course we will and will you please forgive us for nearly melting you and now go in for this lovely air is cold for you i know oh it is bitter cold agreed the queen brr it is bitter cold brr it was bitter cold gore and rubbed his eyes only a few red embers glowed in the fireplace how stiff he was he must have slept in his chair all night but he could not tell how late it was for the clock had stopped he had forgotten to wind it he remembered now there sat the queen in her chair but she was just a little cardigan then he remembered the snowman he ran out of doors there the snowman stood as roly-poly as ever with his twig mouth smiling and his sky blue eyes wide open he said nothing but gore and felt that they too understood each other what a night it had been could it all have been a dream but now the night was over and the storm was over and best of all through the dim twilight he saw on the fjord far below him neighbor skillstead's robo and seated in it wrapped in her red shawl his own dear grandmother coming home to him end of chapter three recording by linette colkins monument colorado chapter four of the dream coach by ann perish this libra box recording is in the public domain a bird cage with tassels of purple and pearls three dreams of a little chinese emperor the driver of the dream coach paused as he turned over the pages of the great white and gold book in which are kept the names of all those who have written or are to be given rides in the brightly painted coach i see he said addressing the little angels who helped him keep these records i see the name of the little chinese emperor and there is a cross opposite his name has he been naughty he asked has he been picking the sacred lotus flowers of his honorable ancestors has he oh please interrupted one of the smallest angels i put that cross there to remind me to tell you something about the little emperor you see he hasn't been naughty not exactly but he's made a mistake he doesn't understand said the smallest angel with his eyes round and serious and can i help the little emperor understand asked the driver of the dream coach of course you can cried the smallest angel beaming brightly it's this way the little chinese emperor has a friend of mine fastened up in a cage where he is very sad an angel in a cage asked the driver i never heard of such a thing well not exactly an angel a but what it was and how the driver helped the little angel's friend that you shall hear the little emperor was dreadfully bored he yawned so that his round little face as round and yellow as a full moon grew quite long and his nose wrinkled up into soft yellow creases like cream that is being pushed back by the skimmer from the top of a bowl of milk his slanting black eyes shut up tight and when they opened they were so full of tears that they sparkled like blackthorn berries wet with rain oh dear oh dear cried his aunt princess autumn cloud the little emperor is bored what shall we do oh what shall we do to amuse him for when he is bored he very soon grows naughty and when he is naughty oh dear and she began to cry but then she was always crying when she was born her father and mother named her bright yellow butterfly floating in the sunshine but she cried so much that by the time she was five years old they saw that name would not do at all and changed it to autumn cloud pouring down rain upon the sad gray sea she cried about anything if her lady in waiting brought her a bowl of tea with honeysuckle blossoms in it she would cry because they weren't jasmine flowers if they were jasmine she would cry because they weren't honeysuckle when the peach trees bloomed she would cry because that meant that spring had come and that meant summer would soon follow and then autumn and then the cold winter and oh how cold the wind will be then and how fast the snow will fall sobbed princess autumn cloud looking through her tears at the bright pink peach blossoms she cried because her sea green jacket was embroidered with storks instead of bamboo trees she cried because they brought her shark fin soup in a bowl of green lacquer with a gold dragon twisting around it instead of a red lacquer bowl with a silver dragon she cried if the weather was hot she cried if the weather was cold and hardest of all she cried whenever the little emperor was naughty whenever she began to cry a lady in waiting knelt in front of her and caught her tears in a golden bowl for it never would have done to let them run down her cheeks like an ordinary person's tears they would have washed such deep roads through the thick white powder on her face every morning princess autumn cloud and indeed every lady in the court of the little emperor covered her face with honey in which white jasmine petals had been crushed to make it smell sweet then when she was all sticky she put on powder until her face was as white as an egg then she painted on very surprised looking black eyebrows and a little mouth as red and round as a blob of sealing wax it looked just as if her mouth were an important letter that had to be sealed up to keep all sorts of secrets from escaping princess autumn cloud and princess gentle breeze and lady gleaming dragonfly and lady moon seen through the mist and all the rest of them would have thought it as shocking to appear without paint and powder covering up their faces as they would have thought it to appear without any clothes so princess autumn cloud leaned over as if she were making a deep bow and let her tears fall into a golden bowl and then because they were royal tears they were poured into beautiful porcelain bottles that were sealed up and placed rows and rows and rows of them in a room all hung with silk curtains embroidered with weeping willows oh what shall be done to amuse the little emperor sobbed princess autumn cloud perhaps he would like some music and she clapped her hands with her long long fingernails covered with gold fingernail protectors so four fat musicians dressed in vermilion silk and wearing big horn rimmed spectacles to show how wise they were came and kowtowed to the little emperor that is they got down on their knees which was hard for them to do because they were so fat and then all together knocked their heads on the floor nine times apiece to show their deep respect then one beat on a drum boom boom and one clashed symbols of brass together crash bang and one rang little bells of green and milk white jade and the oldest and fattest beat with mallets up and down the back of a musical instrument carved and painted to look like a life-sized tiger with glaring eyes and sharp white teeth the little emperor sprawled back in his big dragon throne under the softly waving peacock feather fans stretched out his arms and legs and yawned harder than ever oh oh oh what shall be done to amuse him wailed princess autumn cloud bursting into tears afresh can no one suggest anything and although the mandarins and the court ladies thought to themselves that what they would really like to suggest for such a spoiled little boy would be to send him to bed without his supper they none of them dared say so but tried to look very solemn and sympathetic would the little old ancestor enjoy some sweetmeats suggested lady lotus blossom old ancestor is what you call the emperor if you are properly brought up and polite and chinese so gentlemen in waiting came and kowtowed and offered the little emperor lacquered boxes of crystallized ginger of sugared sunflower seeds and leaching nuts but do you think he was interested not at all he would not even look at them the wind is blowing hard would it amuse the little old ancestor to watch the kites fly asked old lord mighty swishing dragon's tail the little emperor didn't know whether it would or not however he couldn't be more bored than he was already so he climbed down from his throne and went out into the windy autumn garden first marched the musicians beating on drums to let everyone know that the emperor was coming then came the court ladies tottering along on their golden lilies which is what they call their tiny little feet that have been bound up tightly to keep them small ever since the ladies were babies then the mandarins with their long pigtails and their padded silk coats whose big sleeves held fans and tobacco and bags of betel nuts and sheets of pale green and vermilion writing paper then princess autumn cloud in a jade green gown embroidered with a hundred lilac butterflies a lilac jacket and pale rose colored trousers tied with lilac ribbons in her ears around her arms and on her fingers were jade and pearls and her rose colored shoes were trimmed with tassels of pearls and were so tiny that she could hardly hobble in her shiny black hair she wore on one side a big peony the petals made of mother of pearl and the leaves of jade each petal and leaf was on a fine wire so that when she moved her head they trembled as real flowers do when the wind blows over them on the other side were two jade butterflies that trembled too in front of her walking backward went her lady in waiting holding the golden tear bowl in case the princess should suddenly begin to cry and last of all surrounded by his gentleman in waiting came the little emperor dressed from head to foot in yellow the imperial color so that he looked like a little baby duckling and as he came everyone in the palace and in the garden had to stop whatever they were doing gossiping teasing the royal monkeys chewing betel nuts or sweeping up dead leaves and kneel down and knock their heads on the ground until he had passed how the wind was blowing it sent the willow branches streaming it wrinkled the lake water and turned the lotus leaves wrong side out it scattered the petals of the chrysanthemums it tossed the kites high in the sky how brightly their colors shown against the gray sky some were made to look like pink and yellow melons with trailing leaves some were like warriors in vermilion some were golden fish others were black bats and the biggest one of all was a great blue green dragon as for the little emperor he took one look at them and then yawned so hard that they were afraid he would dislocate his jaw a little brown bird the color of a dead leaf had been hopping about on the ground under the chrysanthemums looking for something for its supper and now suddenly flew up into a willow tree and began to sing the little emperor clapped his hands and all his servants dropped on their knees and began to kowtow catch me that little brown bird with the beautiful song he said he stopped yawning and his eyes grew bright with eagerness but little old ancestor that is such a plain little bird said his aunt timidly surely you would rather have a cockatoo as pink as a cloud at dawn or a pair of lovebirds as green as leaves in spring the rude little emperor paid not the slightest attention to her but stamped his foot and shouted catch me that little bird so his servants chased the poor little fluttering bird with butterfly nets the wind whipped their bright silk skirts and their pigtails streamed out behind and they puffed and panted for they were most of them very fat and at last the bird was caught and put in a cage trimmed with tassels of purple silk and pearls with drinking cup and seed cup made like the halves of plums from purple amethysts on brown amber twigs with green jade leaves for a time the little emperor was delighted with his new pet and every day he carried it in his cage when he went for a walk but it never sang only beat against the bars of its cage or huddled on his perch so presently he grew tired of it and it was hung up in its cage in a dark corner of one of the palace rooms where he soon forgot all about it how could the little bird sing it was sick for the wide blue roads of the air for wet green rice fields where the coolly stand with bare legs sky blue shirts and bamboo hats as big as umbrellas for the yellow rivers and for the mountains bright with red lilies how could it sing in a cage but sometimes it tried to cry to them what a sweet song everyone would say run and tell the little emperor that his bird is singing again after a while the little bird realized that they did not understand and it tried no longer but drooped dollied and silent in its cage one night the little emperor had a dream perhaps you won't wonder when i tell you what he had for supper first he had tea in a bowl of jade as round and white as the moon heaped up with honeysuckle flowers then in yellow lacquer boxes sugared seeds sunflower and lotus flower and watermelon seeds boiled walnuts and lotus buds then velvety golden peaches and purple plums with a bloom of silver on them pork cooked in 11 different ways chopped cold with red beans and with white beans with bamboo shoots with onions and with cherries with eggs with mushrooms with cabbage and with turnips ducks and chickens stuffed with pine needles and roasted smoked fish shrimps and crabs fried together shark fins boiled bird's nests porridge of tiny yellow seeds like birdseed cakes in the shapes of seashells fish dragons butterflies and flowers chrysanthemum soup steaming in a yellow bowl with a green dragon twisting around it not one other thing did that poor little emperor have for his supper when he was so full that he couldn't hold anything more not even one sugared watermelon they took off his silk napkin embroidered with the little brown monkeys eating pink and orange persimmons he was so sleepy that he did not even stamp his feet when they washed his face in hands then they took off his red silk gown embroidered with gold dragons and blue clouds and lined with soft gray fur his yellow silk shirt and his red satin shoes with their thick white soles but he went to bed in his pale yellow pantaloons tied around the ankles with rose colored ribbons i must tell you about his bed it was made of brick and inside of it a small fire was built to keep the little emperor warm on top of this three yellow silk mattresses were placed then silk sheets red yellow green blue and violet then a coverlet of yellow satin embroidered with stars under his head were pillows stuffed with tea leaves and above him was a canopy of yellow silk embroidered with a great ground moon whose golden rays streamed down the yellow silk curtains drawn around him he fell asleep and this is what he dreamed the long golden rays seemed to turn into the bars of a cage yes he was in a huge cage he tried frantically to get out he beat against the bars then he saw what looked like the roots of trees and brown tree trunks a grove all around the cage but the trees moved and stepped about and looking up the trunks instead of leaves he saw feathers and still farther sharp beaks and then bright eyes were looking at him they were birds what he had thought were the roots of trees were their claws and the trunks of the trees were their legs but what enormous birds they were as big as men while he was as small as a bird let me out he shouted don't you know i am the emperor and everyone must obey me let me out i say ah he is beginning to sing said one bird to another not a very musical song too shrill by far take my advice ring his neck and roast him he would make a tender juicy morsel for our supper oh let me out please please let me out cried the poor little emperor in terror he is singing more sweetly now remarked one of the birds too loud quite ear splitting said a lady bird fluffing out her breast feathers and lifting her wings to show how sensitive she was if he were mine i should pluck him his little yellow silk trousers would line my nest so softly oh please please set me free really his song is growing quite charming but one can't stand listening to it all day and with a great war and flap and rustle of wings the birds flew away and left him in his cage alone he called for help and threw himself against the bars until he was exhausted then bruised panting his heart nearly breaking out of his body he lay on the floor of the cage finally growing hungry and thirsty he looked in his seat in water cups drank a little lukewarm water and ate a dry breadcrumb now and then birds came and looked at him some of them tried to catch his pigtail with their beaks or claws next day the little emperor was thoughtful could it be he wondered that a little bird's nest was a deer to it as his own bed with its rainbow coverlets and its moon and stars was to him that a little bird liked ripe berries and cold brook water as much as he liked ripe peaches and tea with jasmine flowers that a little bird was as frightened when he tried to catch its tail in his fingers as he was when the birds tried to catch his pigtail and then he thought of how he had felt when the lady bird had wanted his pantaloons to line her nest and hot was shame he remembered his glistening jewel bright blue cloak made of thousands of kingfisher's feathers it had made him miserable to think of their taking his clothes but suppose his clothes grew on him as their feathers did on them how would he have felt then hearing the bird say i should pluck him his little silk trousers would line my nest so softly he went to bed thinking about his little brown bird and before he shut his eyes he made up his mind to set it free in the morning then he fell asleep and once again he dreamed that he was in the golden cage one of the great birds flew down by the cage door with his claw he unfastened it opened it oh how exciting the little emperor tore out so afraid he would be stopped and put back in the cage oh how he ran across the room and through the open door free he was free tears rushed to his eyes and his heart felt as if it would burst with happiness but it was winter the garden was deep in snow that was falling as if it would never stop the peaches and plums were gone and the lotus pond was frozen hard as stone the little emperor had never been out in the snow before except when he was dressed in his warm padded clothes with one gentleman in waiting carrying his porcelain stove and another bringing tea and a third with cakes in a box of yellow lacquer and a fourth holding between the snowflakes and the imperial head a great moss green umbrella so small and helpless and so big and cold a world what could a little boy find to eat or drink where could he warm himself he ran frantically through the snow the rose colored ribbons that tied his pantaloons came untied and trailed behind him and the cold snow went up his bare legs pausing to catch his sobbing breath he looked up to see the thick snow sliding from a pine tree branch and jumped aside just in time to keep from being buried beneath it then on he plunged again growing with each step more weak and cold and hungry stopping now and then to call for help in a quivering voice that grew febler every time blinking back the tears that froze on his lashes as he tried to remember that emperors must never cry then struggling on through the blinding snow a little boy lost and alone then as it began to grow dark he saw two great lanterns shining through the snow coming slowly nearer perhaps his aunt and his chief gentleman in waiting lord mighty swishing dragon's tail lord dragon tail for short had missed him and had come with lanterns to look for him he tried to go forward to them to call but he was too exhausted to move or make a sound and then imagine his terror when he realized that the glowing green lights were not lanterns at all but the eyes of a great crouching animal a cat gathering all his strength for one last desperate effort he tried to run but with a leap the cat was after him and with a paw now rolled into a velvet ball now unsheathing sharp curved claws tapped him first on one side then on the other nearly let him go caught him again with one bound and with a harder blow sent him spinning into stars and darkness someone was shaking him was it the cat the little emperor opened his eyes and saw the frightened face of princess autumn cloud bending over him as yellow as a lemon for she had jumped out of bed when she heard him cry out in his sleep and there hadn't been time to put on the honey and the powder to paint on the surprised black eyebrows or the round red mouth wake up wake up little old ancestor she was crying as she shook him you're having a bad dream aren't you the cat asked the little emperor who wasn't really awake yet certainly not little old ancestor replied his aunt rather offended the little emperor climbed out of his bed the room was full of the still white light that comes from snow and looking out of the window he saw that the plum trees and the cherry trees looked as if they had blossomed in the night the snow lay so white and light on every twig softly the snow fell deep deep it lay and the people who passed by his windows went as silently as though they were shod in white velvet the little emperor thought of his dream and decided that his little bird might suffer and die if he let it go free before winter was over but he explained to the bird and tried to make it happier when summer comes you shall fly away into the sky he told it he brought it fruit and green leaves to peck at talking to it gently and the little bird seemed to understand the dull eyes grew brighter and though it never sang it sometimes chirped as if it were trying to say thank you on the first night of summer when the moon lay like a great round pearl in the deep blue sea of the sky the little emperor slept and dreamed again that the cage door opened for him and let him go free but oh what happiness now happiness almost too great for a little boy to bear peonies were in bloom each petal like a big seashell and blue butterflies floated over them in the warm sunshine half hidden in the grass the little emperor found a great purple fruit a mulberry how good it was the dewy spider webs glistened like the great tensile bridge to heaven that they built for him on every birthday how happy he was how happy free and safe with the sun to warm him and the breeze to cool him with food tumbling down from heaven or the mulberry trees he wasn't sure which with a crystal clear dew drop to drink on every blade of grass how happy he was the lake was full of great rustling leaves and big pink lotus flowers venturing out on one of the leaves he paddled his feet over its edge in the very gently lapping water then climbing into one of the pink blossoms he lay so happy so happy looking up at the blue green dragonfly starting overhead and rocking gently in his rosy boat no it was not the lotus flower that rocked him on the water it was princess autumn cloud who was gently shaking him and saying wake up if you please dear little old ancestor and hard as it is to believe she was really smiling the little emperor had been so good lately and then it was such a beautiful day he could not wait until after breakfast to let his little brown bird go free as soon as he was dressed he ran as fast as he could to the room where the birdcage hung when his little feet in their blue satin shoes and thud thud puff puff his fat old gentleman and waiting lumbered along behind him i've come to set you free he whispered as he carried the cage with its tassels of purple and pearls out into the beautiful day for one minute he wanted to cry for he had grown to love the little bird but he remembered again the emperors must not cry he opened the door of the cage little old ancestors bird has flown away cried the mandarins it has flown so high in the sky that we can hardly see it the court ladies answered and they all wished that the little emperor would stop gazing up into the sky at the little dark speck so that they might go in and have their breakfasts but the little emperor the empty birdcage in his hand still looked up high high in the sky and now really he could no longer see it but a thread of song dropped down to him a silver thread of song a golden thread of love between the hearts of a little bird and a little boy thank you oh thank you my little emperor end of chapter four red bilinette caulkins monument colorado


My name is Vivek Singhal. I am a co-founder and data scientist at ShellStrat. We are AI startup. We do AI consulting, AI training, AI evangelism. So, anybody interested in a fight? Do you watch any kind of fight shows or anybody interested?  Oh, UFC. That is great. Anybody else? Oh, wow. I have to be careful with you guys around. So, okay. I am actually a peaceful guy, but I fight the neural networks. So, so we deal with GANs. So, let us come to our topic here. So, we will discuss generative modeling with GANs today. All right. So, this is just a plug on a startup, the AI startup based in Bangalore and Delhi. Just one minute intro on myself. I was in US for a long time, worked for AT&T, worked for IBM in Bangalore. Now, I run startup in AI space. Okay, so let us discuss about fighting neural networks, if you will. All right. So, GAN is an advanced type of neural network. And the theory is rather simple, but it has come around in last few years. Dr. Ion Goodfellow, who is a professor at University of Toronto, invented this concept along with couple of his fellow scientists. And the idea is we are dealing with two dueling neural networks, okay. So, is it part of deep learning, machine learning? Hard to say. Maybe you can say it is deep learning because we are dealing with neural networks, right? So, but it has become sort of a science of its own. And there is lot of progress and research happening. So, we are dealing with two neural networks. And all of you know what is a neural network? Raise of hands, please.  So, there are two neural networks. So, one neural network and let us talk about in context of images or computer vision, if you will. So, one neural network, which we call as the generator, okay. Also, the forger creates forged images from random noise, okay. And the discriminator, the second neural network is trying to, you know, detect which one is a forged image, which one is a real image, right? So, there are two dueling neural networks. The generator is constantly trying to produce images which look very authentic or just like real. Whereas, the discriminator is trying to detect which one is a forgery, which one is a real, right? So, we do need a data set. It is not unsupervised learning in that sense. We do need real images to compare it against, right? So, way this works is the generator starts with random noise, as you can see, starts creating forgeries, yeah. The discriminator is comparing forgeries and real images continuously. They are, you know, executing alternate fashion and then it is marking which one is real, which one is forged. So, in this process, this goes on for millions of iterations as you know, how you train a neural network. So, ultimately, the generator starts producing images which are very much like real images, all right? So, basically, we can use this for generative modeling. You can create new forms of art. You can create even new forms of text. So, you can combine images and create brand new images, all right? So, n number of applications. Yes, you can also create new data. Let us say you have less data.  You want to create new kinds of images. You can use a GAN to create new image forms, all right? When you will have to see if those created images satisfy the kind of data set you are looking for, yeah? Here, any questions on this? Yes, please. See, the generator did not have knowledge of the real, right? Generator is a neural network which is just using random noise as the input initially and it is creating images. So, initially, what it will create will be images which will be very different from real and the discriminator neural network will immediately tell you, oh, this is a forgery. This is not at all like real, right? So, this process goes on millions of times and eventually, generator starts producing images which discriminator is finding it harder and harder to distinguish from real images, all right? So, this is what is happening. There are two dueling neural networks and this was invented by Dr. Ion Goodfellow, Professor at University of Toronto. Now, with the open AI team belonging to Elon Musk and he is called the GAN father. So, this is the new kind of, it was a research paper on AXRIV website which you can Google, okay? Any other questions? Let us see. There is no feedback as apparently, but once we see the code, we will look at that, all right? That specific question, yes? Obviously, I mean some feedback is going back. That is why it is adjusting itself. Otherwise, how will generator adjust itself to answer that question? Sorry, trying to understand your question. Why will it generate a real image in the beginning? It started with random noise, yeah? See, eventually, it starts generating images which will look very close to real. So, how this happens programmatically? How is the loss working? I will explain you shortly, okay? So, then it will become clear how this is able to do it. So, when I started looking at this topic, initially I was thinking, how is this working? I knew neural networks very well. I know CNN, RNN, right? I was thinking, how is this really happening? But once I looked at the algorithm, then it was clear how this, and I will come to that, all right? So, in the interest of time, let me continue. So, I think basically what we are doing here is, so, you start getting a bit deeper now. So, discriminator is trying to output a zero for the forced image and a one for the real image, all right? So, discriminator is trying to output an output level of one for the real image, a zero for the forced image. So, this is A, yes, this is no, nay, yeah, A or nay. So, generator is actually always trying to produce a one. So, generator is trying to produce a one because the real is one. See, real is one, generator is also trying to produce a one, but discriminator's job is to mark the forced image as a zero, as a faith, all right? But generator is constantly trying to produce a one. So, if this is one, this is one, then generator has figured out how to produce real images, all right? So, the output level is one for both the neural networks, okay? Let me just keep going, it will become clearer. So, this is an example of this, it is from a research paper, okay? So, this is called a stacked, stacked gain because there are two gains which are stacked on top of each other, okay? So, the first gain and in this case, I am also using a text line caption to image processing, as you know from, you can do caption to image, right? Using RNN and CNN combination, you can do caption to image, right? If you know neural networks. So, this caption says this smaller brown bird has wide stripes on the coverse wingbars and secondaries. So, it, it describes to describe a bird, okay? A brown bird. So, stage one gain produces sort of blurred images. Stage two sharpens the images, okay? So, there are two gains stacked on each other. So, this is from a research paper, okay? Same thing we can do with this line, this flower is white, pink and yellow in color and has petals that are multicolored. Stage one gain produces images, but somewhat blurred. Stage two, stage two gain sharpens the images, alright? It uses the text line as well as the output produced by stage one, you know, images and is able to produce sharper images.  Anything you can think of an image, you can do with a gain, okay? And obviously, gains are working in conjunction with traditional neural networks and oftentimes convolutional neural networks to do stuff, right? So, this is one more application of gains. Apart from gains, which other algorithm you think can do generative modeling, anybody? Can it do generative modeling? Yeah, that is a classifier, right? Knife base is a classifier, probability of this or that happening, right? Sorry? Yes, variational autoencoder can do the same thing, exactly, right? Any others? I mean, I am not sure of that. Hidden Markov model, correct? By the way, gains use some of the theory from Hidden Markov model, HMM, okay? Yeah. So, see, so some of the maths behind gains is coming from RBMs and, you know, HMMs, right? They use the energy function concept, right? Minimizing that. So, some of the theory behind gain, by the way, come from this. If you have read the deep learning book from Professor Ion Goodfellow, he talks a lot about RBMs and, you know, Boltzmann machines and then he goes on to describe generative models, the mathematics of it, right? It is quite intricate. So, anyway, so coming back to this, so another example what we can do, I take a smiling woman, I subtract the woman part, but add the neutral man who is not smiling, which is normally the case, right? And then we produce a smiling man.  So, smiling woman minus neutral woman plus neutral man, you get a smiling man. So, you can guess I am married, right? So, I am smiling. So, a man with glasses minus, take out the man. So, you are left just with glasses at the woman. You get women with glasses, right? So, you can do all sorts of funky stuff with image processing. This is using, you know, it is manipulating the image vector to create new visual concepts. So, the mathematics can get quite intricate. So, another example, artistic style transfer. So, here you can see, I can take any kind of images and apply any kind of style on it. This is the image, there are couple of buildings. It looks like a European town and you apply a couple of a style, a certain style and you get a mixed image with similar buildings, but in this style now. This is called style transfer. It is a very interesting use case, can be used for if you are a criminal, you know. So, you can do all sorts of stuff with it. So, there are lot of interesting use cases, also crime detection on the other side. So, and art and craft and fashion and you can imagine. So, you know, because fashion industry is always interested in creating new art. So, you can imagine the possibilities, right? So, another application. So, here we are trying to do a video. So, there is a head rotating, right? So, it is trying to predict the next position of the head. So, this is a head of a person rotating, you know, on a pedestal and it is trying to predict the next, you know, the frame. So, this is the ground truth level, right? All of you understand ground truth level. This is the actual reality. MSC using normal mean square error, you know, reduction. I get a blurred image, but when I apply adversarial loss using again, I get a more sharper image. So, this is just expanded here. So, you can say regular MSC, you know, regression if you will, will give me a blurred image, but adversarial loss MSC gives me a more sharper image, even though there is a bit of blurriness here. So, you can train these models to do so many things, right? So, a lot of this, all of most of these examples are from some of the research papers, not like we invented them. So, a lot of good research has gone in and now folks like us are trying to create commercial, you know, POC is out of these research papers, if you will. So, one more example. So, this is very useful for image-based search. For example, anybody likes e-commerce shopping online? Okay, fair enough. So, you know, especially I have seen my wife. So, she is constantly on fashion websites looking for clothing, right? And shoes.  I pretty much know what book to buy and just go and get it done, right? But women want to look for lots of styles, right? So, what if we give them a paintbrush? So, they say, okay, I am looking for a church, but the user is given a brush stroke. Let's say Flipkart were to give them a brush stroke. So, the user starts creating edits and it will start creating images based on that edit. So, in this case, we are looking for a church, but the user said I want a triangular shape. So, it created an image of the church with a triangular shape, okay? And then the user said I also want a blue sky on top of it. So, the generated image has blue sky on top of it. The user said I want a green stretch in front of the church. It created a green stretch. So, these are generated images based on these user edits, right? And then it goes ahead and finds images from the real database, which match the generated images. So, this is a complex, pretty large, you know, image search application which can be used by the e-commerce industry where user can actually participate in the search process by suggesting what kind of shapes and colors they want, okay? So, this is a user carousel, a color carousel you have given them on which they can do edits, all right? So, in this case, the user is trying to suggest, show me structures of this shape. I am looking for a church again and then structures of this shape and then add the red line, red, you know, tanginess here. And so, it tries to find equivalent matches, okay? So, lots of interesting, you know, visual applications can be created, visually rich. So, yeah, another before I go to my demo. So, this is the last application I am showing you. So, here we are doing image to image translation. So, you know, we are trying to say that generate an image which looks something like this, okay? So, I feed this. So, this is in this case what is happening? I am feeding an additional image both to the generator and discriminator, okay? So, this is an aerial photo from a satellite. I feed that into my GAN model. It produces a kind of a map, map output, okay? Here, I am able to convert day to night. This is a photo of a daytime and it is able to convert to night. So, this is the input I give it, okay? And I tell it to apply to the generated model. So, this you can call it image to image translation.  And here, I am trying to create a first using this shape. So, by feeding this kind of input image, I can create output images which, you know, follow that input image. So, amazing use cases of different, you know, product generation or designers of different kinds, artists, you know? Any questions before we go to a programming demo? Any questions so far? Okay? So, it seems like, okay, we have 10 minutes. So, that is why we have to keep it a bit short. Yeah, please. Sorry. Yeah, actually, in the demo, I will show you. Yeah, yeah, I will show you in the demo. Yeah. Any other question? Yeah. Yeah. Yeah. See, if you could do with classic machine learning, so it is not able to produce very accurate models. So, that is the point we are trying to show with GAN by comparing with real images, we are able to create more accurate models. I mean, that is the whole point of GAN. It is able to generate more accurate model because it has been, sorry. Yeah, when I say adversarial training, I am using a GAN. Correct. When I say just MSC, I am not using a GAN. So, I could be using any of your other classical models. Yeah. Any other questions? Okay, let me go to the demo. Then you will see the programming mechanics of it. So, let us look at our traditional MNIST, which is mostly the most common data set. So, you can see that we are forging MNIST with fully connected GAN. So, we can use DC GANs, which is deep convolutional GANs. But in this case, just to keep it simple, we will show using two fully connected neural networks, discriminator and generator. Okay, all of you understand neural network programming. Okay. And so, both these, both G and D are fully connected neural networks. So, the neural network itself looks pretty simple if you are familiar with the concept. And you can see with increasing epochs or training groups, it is able to produce more accurate images. Okay. So, let me just run the, let me just explain the code first. Then actually I will run it for you in, you know, in pie chart. So, what is happening programmatically? There was a question before, how does it actually do it? So, what generator does, it takes random noise Z. Okay. This is the output of the generator, G. So, this is the fake image generated by the generator. Okay. What discriminator does, it runs two times. Okay. So, in one time, discriminator takes the real input X and outputs the logits and the output sigmoid. Okay. And then it takes the output of the generator and outputs the fake logits and the fake sigmoid. So, you can see generator run one time, the discriminator runs two times. Once on the real image, once on the fake image. Okay. So, then what happens? And they run in alternating cycle. First the generator runs, then the two cycles of discriminator, then again generator runs, then two cycles of discriminator. So, those of you who do not know, do not do programming, that is fine. Just, you know, logically we will try to understand. So, then in, you know how neural networks were, we try to minimize some kind of loss. This is a loss function. In this case, we are using cross entropy loss. Okay. And this is the generator loss. We are minimizing the loss function for the generator. So, this was the discriminator output. So, there was a reverse feedback loop. So, something from the discriminator goes back to the generator indeed. Okay. So, there is a feedback loop because D, this D fake logits is produced by the discriminator. So, I take the discriminator output and I compare against 1. So, I am trying to minimize the loss between the discriminator output and just 1. So, I am trying to say, bring it closer to 1. So, generator loss is trying to bring it closer to 1. Okay. And then discriminator is running two times. In first time, it is trying to reduce the loss between the real, you know, the real characters and 1. Okay. The discriminator, okay, this was, yeah. So, between real characters and 1 and between fake characters and 0s. So, discriminator is trying to bring the generated output to 0 in the real to 1. So, so, fake goes to 0, real goes to 1. Okay. For discriminator. But generator tries to take the output of the discriminator and tries to make it 1. So, if discriminator figures out how to make it to 1, it has started, it has learned how to produce real images. Okay. So, all of you are developers here. How many developers? Okay. Quite a few. So, we are good. And then what we are trying to do? We are minimize, trying to minimize the g loss and discriminator loss both. Right. So, the g loss is simply this. But discriminator loss is the sum of the loss on the fake images and the real images. Okay. So, it is a common sum. And I am trying to minimize. So, what happens? There is an equilibrium reach.  This guy is pushing this way. Right. They are all, they are both pushing each other. Finally, the discriminator finds enough strength to hold back the discriminator. The generator finds enough strength to hold back the discriminator. So, there is an equilibrium. All right. Clear. So, so, I am kind of out of time. So, I will just quickly show you the code just before I go to code quick take away. So, GANS can produce new images, other forms of content. There are two dueling neural networks involved generator and discriminator. The generator eventually learns how to produce real like images and they can be used in innovative applications. Okay. Let me quickly show you the program run. So, we will be done in just a minute or so once we see the program and we can take any questions. Okay. Fine. Yeah, no problem. So, you can see it is a regular training model. Those of you who are developers, you know, and here I am taking just the generator, right, taking the output G and passing it to discriminator, right. So, G is fake, fake output, right, from generator and I take the output of discriminator. I also take output of discriminator using real images X, right. And then I try to what is discriminator loss is the combination of the real images loss and fake images loss, all right. And what is generator loss? It is merely the loss on the output of the discriminator, which was produced earlier. So, discriminator is trying to reduce loss compared to once whereas generator is trying to sorry generator is trying to compare loss with respect to once and discriminator is trying to mark the fake images as 0. Okay. And we just run usual optimizer of the neural network and it is able to train. So, I am going to now actually run this, but we will have to see on timeline how it works because sometimes the computations are high. Fine. Fine. Okay. I guess I will I will just show you the output then we will be done. So, you can see in subsequent epochs is gets more and more accurate. So, it is producing more and more MNIST. This training and we are printing the output every 500 training loops and the images are getting they will get sharper. I have to run about 100,000 iterations to get almost real like images. This is based on just 1000 iterations. So, the final output is you know roughly same, but it is not too many iterations. So, it did not produce perfect images yet. Okay. So, that is it. Any final question. I mean conventionally you cannot produce images. So, you cannot even produce. So, with variational on data encoder it is much more inferior. This is better. Okay. Thank you.