Buzz

Ubiquitous Computing Experiment 6

P5.js, Tensorflow.js & PoseNet

Alicia Blakey

Github

 

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Project

Utilizing the machine learning model PoseNet, an algorithm that locates specific points on the body then painting over a video feed with p5.js are the elements of this project. PoseNet operates by tracking certain elements of the face or body. Carrying these estimates PoseNet ranks a confidence value of a match. Tensorflow.js is a javascript library that operates within Ml5 teaching machine learning models to operate and initiate within the browser and node.js. Ml5.js is a library that produces a gateway to machine learning algorithms in the browser with an elucidated API.

 

screen-shot-2019-06-28-at-3-04-27-pmConcept

To amalgamate myself better with Ml5 I decided to integrate with PoseNet so I could understand this aspect of machine learning to coalesce with other functions of Ml5 later. Learning from the examples of existing code provided by Nick Puckett I decided to explore the interaction of hand movement tracked through PoseNet. The keypoint confidence score doesn’t relay that well with hands but it does track the wrists efficiently enough. The intention of this conceptualization was to create a simple interaction that instilled movement while on the computer. Now that it’s summer consistently seeing people swat away the little mosquitos that come out this year I thought what a fun way to get moving and to do this through a simple simulation of augmented reality with ml5.js and PoseNet.

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Process

After setting up a local server on my terminal my first step was to get the video working and call Ml5 and PoseNet with 2 arguments, video and modelReady. I was able to set this up using the examples from the ml5js. website. I then coded in the model ready function referred to as a callback. In this project I also started to define my variables a little differently and learned why using let is sometimes better. Utilizing let to define a global variable is better than using var as it let’s you re-assign the variable if needed. While using the examples from the Ml5 website I tried to imitate their style which is why I decided to change the way I defined variables in this instance.

video = createCapture(VIDEO);

video.size(width, height);

poseNet = ml5.poseNet(video, modelReady);

poseNet.on(‘pose’, function(results) {poses = results;

debugger;

 });

video.hide();

}

 

After getting PoseNet to work, learning from Daniel Schiffman’s Ml5 video I went to the p5.js website to look for pre-existing graphics and initially input the forces and particle system example to see how it worked with the code. Then I made a simple gif and searched for the preload function from p5.js so the image could be called before setup. After testing I wanted to see what the confidence values looked like so I could understand how the libraries were sharing data. By going into the sources panel of the browser I was able to see the data coming from the poses.

 

Conclusions

Thinking of future Iterations, now that I have a basis with Ml5 and PoseNet I think an interesting adaption would be to use PoseNet to maneuver graphics that interact with each other.  With a combination of interactive graphics and calling other images through poses on the browser, I could intricately add to the existing project. I could see a program like this used as a motivator to move from your desk to create interactions in front of your computer to not remain sedentary. It would be nice to be able to use it as a prompt to add to fitness tracking data when you have been in a solitary position for too long.

References

https://p5js.org/examples/

https://ml5js.org

https://www.youtube.com/watch?v=jmznx0Q1fPO

 

 

 

 

 

 

 

 

 

Sign Language with Ai

Code to this project: https://github.com/imaginere/UbiComp_Exp6

After looking through all the examples I felt a bit stumped as most of them seemed like they were already finished products and repurposing them in terms of code seemed beyond my current skills.

What appealed to me was using the Ai engines as an input device to make it learn different hand gestures, the idea was to make the sign alphabet shown below.

sign-chart
Copyright: Cavallini & Co. Sign Language Chart Poster

The KNNClissification example had a rock paper scissors example which was doing hand recognition and was the perfect start.

It took some tinkering with the source code to figure out what it was doing and how the html was rendering things from the .js file. This took a fair amount of time as most of the code was very alien looking and some very concise shortcut methods were used to optimize the code.

I had to stop and go back to Nick’s video to understand what tensorFlow and Ml5 were doing and how this all fits together as my code kept breaking and only the initial 3 values kept showing. A little mind mapping of the tensorflow and ml5 ecosystem cleared the fog and I could finally get the engine working with 5 values, it was understanding how the two things connected that helped me see what I was trying to do with the code, as most of it was copy-pasting code snippets.

mind map

The Idea for using ml5 and p5:
I had now the framework for the I wanted to use, I did not do the traditional chart as my idea was that I could use hand gestures instead. Like Stop, attention, Thumbs Up etc, this would allow me to make a game of it, where two players could train their own sets and ping-pong signs before the time ran out the winner would be the best at getting predictions precise and also switching back and forth in time before the timer ran out.

This was the charts I used to get the icons from01df

I mapped these in the window:

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The next part was training the different symbols to recognize the hand gestures, this was fairly straight forward but I could not get the Ai to load the data set I had saved, I tried replacing the permissions but it did not work when I clicked load, I kept having to retrain the data sets to get them to function.

The next thing I want to try is getting p5 to trigger an animation based on the gesture that was 100% I did this with a very simple example to a rotating cube which would turn based on the no that was 100%. This was testing the concept but the ultimate goal was to make a meter which would be like a gauge for where the most accurate recognition was.

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This was a proof of concept where the circle would be then replaced with a meter. I tried making this purely in p5 but it was way too much math for such a simple shape, so I made an image in illustrator and imported it.

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The final result does not have a working meter or a game but they are both edging towards that final outcome the where you would the final game where two users return the gesture they are given and then trigger a new one if they are unable to return the gesture in time they lose a point. When you return the gesture the timer gets faster on the next return, till its too hard to compete and one person loses a point. The game ends at 10 points.

The concept for the game:

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The above is mockUp of what I would like to create with the AI engine, I still have a long way to go to realize this but I have some of the pieces in place. It would use ml5->p5->PubNub->p5

 

References:

https://github.com/ml5js/ml5 examples/tree/release/p5js/KNNClassification/KNNClassification_Video

Special Thanks to Omid Ettehadi for help with understanding the code.
Icons Designed by Freepik

 

 

 

 

 

 

The new

ML5 – Self Identification with MobileNet

1.

The term ML5 and TensorFlow and PoseNet has been loosely tossed around in the DF studio nearing the end of this semester from my peers in Body-Centric Tech and perhaps I should be feeling lucky to finally dive into it, regardless of the stress from living deadline to deadline 🙂 But, snipes aside, this was a whole other realm of knowledge that was extremely new to me, and I decided that by getting a better sense of what ML5, etc. is, that I would find its true appeal. Below are key bullet points I made for myself:

  • ML5 – Machine Learning 5, in which it is symbiotic to P5.
  • ML5 doesn’t really need p5… but it makes things easier for you, in order for you to use Tensorflow, the mother to Tensorflow.js.
  • ML5 deals with a lot of pre-trained models.
  • ML5 library doesn’t know a lot about Humans.

After looking at all the juicy examples, I wanted to explore the capabilities of the Image Classification with MobileNet, since it is “trained to recognize the content of certain images.” I wondered to what extent is MobileNet been trained to, and how? In what context? How have its creators trained it, and Who were they? What kind of MobileNet am I tampering with here? Has it perhaps been… westernized? Or just based on completely obscure datasets?

With my burning questions, I played around with the ml5 index page’s example with the drag&drop/upload an image. Below are some of my discoveries when it came to images available on my desktop.

ubiqblog-01

Male Japanese Celebrity is a diaper/nappy, A Sim from the Sims 4 is a bathing cap apparently, and the Poliwag from Pokémon is a CD Player. Great! A lot of the ML confidence decreased, when it came to anything with human-like features. I continued to upload a lot of other screenshots I’ve taken from animated/drawn media and it came to an interesting result:

ubiq2-01

Game Fanart, Spongebob and A cover from the Case Closed manga were ALL considered Comic Book with varying degrees of confidence. I started to realize how even images that are completely different can all be classified under one type of image. From there, I decided to upload pictures of myself (Yes, I volunteer! As Tribute!) because I was entirely curious as to how MobileNet would classify by appearance.

ubiqme-01

Surprisingly, I am Abaya. The funny thing about MobileNet classifying this as Abaya is how completely specific this word is to the middle-eastern context, where culturally, women of the Arabian Gulf would wear a black dress/cloak called an Abaya in their daily lives. I would think that only the first image could be classified Abaya due to the black material I am wearing on my head, but then it did it for my lighter colored one. Regardless of its confidence in recognizing me, there are varying percentages of how sure it defines the head scarf as Abaya, instead of say, Hijab. I wanted to see if this was the same for random images on the internet, and it turned out to be true.

ubiq3-01

Random lady with headscarf is more of an Abaya than the model with the Nike Sports Hijab, but both still turned out as Abaya. How Fascinating! I decided to take this further by setting up a webpage to see the other probabilities it would consider images of majority “Abaya” would be.

2.

Following Daniel Shiffman’s examples, I checked various images and their probability percentages on a webpage I created called MobileNet vs Me.

ubiq5

Based on the Array results in the console, the results OTHER than abaya were significantly lower. Abaya was 66% while Cloak was at 29% and Sweatshirt was at 5%. I noticed that Cloak would be the next way in which I would be identified and then Sweatshirt. I tried this with a few other images and the results are below:

ubiqs-01

With different images, the third identifier in the array was Ski Mask and Bath Towel, which made Sweatshirt not a common third one. But yet, when I loaded the random images below:

ubiqs-02

Sweatshirt came out as a second result. So we have Abaya, Cloak and Sweatshirt to identify a woman wearing a headscarf, which is very interesting to me since none of the results were specific words such as the Hijab or Headscarf. MobileNet didn’t identify them as they were known as, which is to be worn on the head for religious reasons. Perhaps a good take away from this is that the machine knows no other concept than what objects/animals/types of things that the image might be similar to, which generalizes? the experience more than it specifies, regardless of how weirdly specific the identifier results were. In itself, perhaps the MobileNet dataset is Paradoxical in nature.

To step it up a notch, I decided to turn on my webcam to see if MobileNet can identify my appearance in real time, and how different the results would be as opposed to Abaya (or Cloak or Sweatshirt.)

It was interesting to investigate the capabilities of MobileNet in this manner to see if ML would elicit certain biases, of either political or sociological. What turned out was that it is quite innocent (for now) and that I may well be identified as an Abaya, a Bonnet or even a Bandaid or a Mosquito Net. These terms could even be what one would prefer to be identified with, rather than how mass media would choose to use certain terms to describe a woman with a headscarf (or anyone as near to how news outlets would describe someone who is of Islamic faith.)

To conclude, the varying probabilities that  MobileNet would give us in real time could very much be a reflection to how different even people within faith can be, from the extremes to people just simply living their lives, and should not be placed into just one universal definition.

References

Image Classifier Example
ML5 Homepage MobileNet Example
ml5.js: Image Classification with MobileNet by Daniel Schiffman

Intro to ML5_Exploring StyleTransfer Example

Process Journal #6

2

Learning: ML5&P5

Computer visions seems very interesting to me. I checked the 4 videos posted on canvas and started this assignment by going through all the examples available on GitHub and the ML5 website. I just want to use some tensorflow.js model to implement some functions in the web. Then I don’t need to learn tensorflow. js systematically, I found I just need to use a ready-made model packaged as an NPM package. Such as MobileNet (image classification), coco-ssd (object detection),PoseNet (human gesture recognition), roll commands (voice recognition). The NPM pages for these models have detailed code examples that you can copy. There are also a number of third-party developments of off-the-shelf model packages, such as ML5, which includes pix2pix, SketchRNN and other fun models. We were asked to build upon one of the existing ML5 examples by changing the graphic or hardware input or output. I found StyleTransfer example is quite interesting, so I decided to work on that example. I already had the chance to explore PoseNet in the Body-Centric course. For this project, I decided to explore something different. I still used the webcams and picture, I decided to experiment with the style transfer example in the ML5.

Concept:

It is an expansion on the style transfer example in ml5, where users select the paintings of their favorite artists as the materials within a limited range of choices, and the selected paintings will change the style of real-time images, thinking of a unique abstract painting video.

Process:

Firstly, the position detector detects the movement of the object. When the object moves to the visual center of the camera system, the detector immediately sends a signal to the image acquisition part to trigger the pulse.

Then, according to a predetermined program and delay, the image acquisition section sends pulses to the camera and lighting system, and both the camera machine and the light source are turned on.

The camera then starts a new scan. The camera opens the exposure mechanism before starting a new frame scan, and the exposure time can be pre-set. Turn on the lighting source at the same time. The lighting time should match the exposure time of the camera.

At this point, the screen scanning and output officially began. The image acquisition part obtains digital image or video through A/D mode conversion. At the same time, the obtained digital image/video is stored in the memory of the processor or computer, and then the processor processes, analyzes and recognizes the image.

StyleTransfer example:

Step1: allow styletransfer to use camera

screen-shot-2019-04-19-at-5-01-35-pm

Step 2: select the art work

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(Start and stop the transfer process)

Step 3: check new synthesized video

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In the example, there is only one painting, and the composition of video is too abstract. I wanted to give users more choices, so I tried several other paintings to see if they have different effects.

a

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Now I have a big problem, that is, there is no big difference between the color and composition of video between the chrysanthemum painting and the abstract painting. I don’t know what the problem is. Then, I tried an abstract painting in blue.

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The difference is so small that I don’t know what to do with the picture. The naked eye can only see very slight differences. But I’m going to make the framework so users can choose different images.

1

Users select the paintings of the artists they like as the materials, and the selected paintings will change the style of real-time images, as a unique abstract painting video.

2

References:

https://www.npmjs.com/package/@tensorflow-models/posenet

https://github.com/ml5js/ml5-examples/tree/release/p5js

https://canvas.ocadu.ca/courses/28083/pages/intro-to-ml5?module_item_id=226079

Exploring PoseNet & P5.js & ml5.js

For this workshop I chose to continue my explorations of PoseNet, which allows for real-time human pose estimation in the browser, having started learning the framework in the Body-Centric Technologies class. I wanted to try working with images and PoseNet but I couldn’t get the PoseNet model to track whenever I used images…my workaround for this was to group the images into a video. My idea was to explore how women posed on magazine covers by comparing poses from different fashion magazine covers. Below is a video showing the final results of poses that were captured when working with the PoseNet with webcam example.

I found that the body-tracking worked well only if I had the size of the video set to width (640 pixels) and height(480 pixels) which were the dimensions  used in the ml5 examples.

What is ml5.js? A wrapper for tensorflow.js that makes machine learning more approachable for creative coders and artists. It is built on top of tensorflow.js, accessed in the browser, and requires no dependencies installed apart from regular p5.js libraries.

NOTE: To use ml5.js you need to be running a local server. If you don’t have a localhost setup you can test your code in the p5.js web browser – you’ll need to create an account.

I also found that the multi-pose tracking seemed to tap off at 3 poses max tracked whenever there were more than 3 poses. Additionally, the model’s skin color affected the tracking so that at times some body parts were not tracked. I also found that the model’s clothes also affected whether some parts were tracked or not. At times the models limbs were ignored or the clothes were tracked as additional limbs. The keypoints seemed to be detected all the time but the lines for the skeleton were not always completed. What are keypoints? These are 17 datapoints that PoseNet returns and they reference different locations in the body/skeleton of a pose. They are returned in an array where the indices 0 to 16 reference a particular part of the body e.g in the array index 0 contains results about the nose such as x,y co-ordinates and percentage of detection accuracy.

Below are some of the images I tested with:

mag_covers

I’d like to continue working on this however I would like to explore using OpenPose which is a framework like PoseNet that provides more keypoints tracked as compared to PoseNet’s 17 keypoints. From my working with PoseNet so far, I find that it is more beneficial in areas where you aren’t tracking a skeleton but are doing something more with the data gotten back from keypoints e.g. right eye is at this x and y position so do certain action.

I tried some of the other ml5 examples however I wasn’t satisfied with the results. I was particularly interested in the style transfer and the interactive text generator. However, I found that in order for them to be useful to me, I would have to train my own custom models and I didn’t have the time and adequate dataset to do this.

I also tried out the video classification example where I was toying around with the idea of having the algorithm detect an image in a video and explored for a video classification. It quickly dawned on me that this was a case for a custom model as the pre-trained model seemed to only work best when generic objects were in view. e.g. At times it recognized my face as a basketball, my hand as a band-aid, my hair as an abaya etc. I also noticed that if I brought the objects closer to the screen, the detection was slightly better. Below are some of my findings using MobileNet Video Classification in p5.

mnet-768x1386

Pros & Cons of using a pre-trained model vs. a custom model? When using a pre-trained model like PoseNet & tensorflow.js a lot of the work has already been done for you. Creating a custom model is beneficial only if you are looking to capture a particular pose e.g. If you want to train the machine on your own body but in order to do this you will need tons of data. Think 1000s or even hundred of thousands of images, or 3D motion capture to get it right. You could crowdsource the images however you have to think of issues of copyright and your own bias of who is in the images and where they are in the world. It is imperative to be ethical in your thinking and choices.

Another issue to keep in mind is diversity of your source images as this may cause problems down the line when it comes to recognizing different genders or races. Pre-trained models too are not infallible and is recommended that you test out models before you commit to them.

Do you think you can “Sing”?

Code: https://webspace.ocad.ca/~3170557/UbiquitousComputing/Week6/CanYouSing.rar

untitled-1

I started this experiment by going through all the examples available on the ML5 website.  I found the webcam classification quite interesting, but also difficult to work with because of how sensitive it was to background objects. In addition, I already had the chance to explore PoseNet in the Body-Centric course. So for this project, I decided to explore something different. Moving away from webcams and picture, I decided to experiment with the pitch detection example in the ML5. Having already done work in digital signal processing (DSP), I found it quite fascinating how quickly and accurately the software was able to identify the musical note in the piano example. I wanted to modify the example, creating an user interaction with the algorithm using the existing data available to provide a tool for them to practice their musical skill.

“Can You Sing?” is an expansion on the Piano example, where the user can select the note they are trying to mimic to practice specific notes. They software indicates when they user has successfully mimic the sound by highlighting the key in green. Only then the user is allowed to select another key to repeat the experience.

To do that, I had to create a way for the user to select the note that they wanted to mimic. I divided the heights of the keys into two. The top half would look for black keys and the bottom half looks for the white keys. Every time the a mouse key is pressed, the Y location of the mouse is checked. if it is in the bottom half of the piano shape, then base on the X location of the mouse, a white key is selected. If the Y position is in the top half, base on the X position a black key is selected.untitled-2

After the key is selected, user’s voice is converted into a note and drawn on the screen. Only if the user’s input is the same as the selected note, the note will change color to green. After that it requests from the user to select another note.untitled-3

In  the other examples, I found the voice command also very interesting. So I added it to this program so that every time the user matches the selected key a voice will say “Nicely Done!”. This was purely so that I could also explore this feature of ML5.

untitled-4Setting up the example was very challenging. For some reason the device software would not always receive data from the microphone which I found irritating. I had to restart the browser each time that I made some changes to the code. I wasn’t able to figure out if the problem was because of the libraries or it was just the local server, but it took a few tries each time to run the application.

But the most challenging part of the program was to identify the location for each note on the screen so that the user could choose their desired note by clicking on it on the piano shape. After a few tries I was able to get a good understanding of how they were drawn and was able to use the same technique to identify which position is associated with which key. I did end of dividing the keys into half base on their Y location just to simply the separation between the black and the white keys.

What I found useful was how the algorithm could detect the note independent on the octave. Plus, the speed that it was able to process the data and its accuracy made it an ideal tool for musicians. This can simply be used as an online tuner for almost all musical instruments which I find quit useful.

References:

ML5 – https://ml5js.org/

ML5 Examples – https://github.com/ml5js/ml5-examples

ML5 Pitch Detection Documentation – https://ml5js.org/docs/PitchDetection

Tossing The Ball Around

doc4

GitHub

This is a fairly simple engagement with PoseNet. Tossing The Ball Around is a ball rendered in P5 that changes size based on the space between the user’s arms.

I started by messing around with some more ambitious ideas. I looked at trying to get PoseNet to create animated skeletons using .gif files in place of images. However, it seems that PoseNet doesn’t integrate with animated gifs easily.

After this I spent a fair bit of time trying to put together what I was thinking of as an “AR Costume”

docu1

I played with PoseNet and brought the code back to a place where I had access to all of the key points, and to a place where I was comfortable modifying things.

Then I went to the p5 Reference site for some tutorials on how to make particles. I imagined a series of animated streamers emanating from every key point, covering the user like a suit of grass.

docu2

I was able to create a particle system but I was not able to implement it in a way I was happy with, with multiple instances of it emanating from multiple points on the body.

I settled on experimenting with finding origins for drawings that were outside of the key points returned by PoseNet. I took the key points from the wrists, and drew an ellipse at the origin between them. I used the dist() function to measure the difference between the two user’s two wrists and return a circle that changes size based on the user’s movements. The effect is similar to that of holding a ball or a balloon that adjusts itself constantly.

docu3

I played around with coloration and making the image more complex, but I decided to quit while I was ahead.

Throughout the process, I tried to make use of coding techniques I had learned throughout the year. When I had first approached programming I knew nothing beyond the very basics. In coding this project I tried to use concepts like arrays, passing variables into functions, and object-oriented programming. I still have a lot more work to do to get comfortable, but this project demonstrated to me how far I’ve come.

Resources consulted:

https://ml5js.org/

https://ml5js.org/docs/PoseNet

https://github.com/tensorflow/tfjs-models/tree/master/posenet

https://p5js.org/reference/

LSTM Poetry with Text-to-Speech

For the final week’s project using ml5.js, I put together a LSTM text generator with a poetry model and text-to-speech generator.

Github: https://github.com/vulture-boy/lstmPoetry
(
there are a few extra models on the Github that you can access by modifying the code slightly; just change the model folder to load)

You can check it out here: https://vulture-boy.github.io/lstmPoetry/
[The text to speech seems to be giving the webhost some issues and only works some of the time. would recommend downloading it from the GitHub]

To accomplish this, I scraped poetry from a website and followed the tutorial listed on ml5: Training a LSTM

Scraping

webscraperchrome

I used Web Scraper in Chrome in order to get the text information I needed to train the machine learning process. I needed to create a text file containing the information I wanted the algorithm to learn from, but I didn’t want to go through the laborious process of manually collecting it from individual web pages or Google searches. Using a web scraper makes the task automated by the computer. The only information that is required is a ‘sitemap’ that you can put together using Web Scraper’s interface: pick out the html elements that designate which text, links and data of interest are located to describe to the scraper how to navigate the page and what to collect.

scrapepoems

After the process is complete (or if you decide to interrupt it), you can export a .csv containing the data collected by the Web Scraper process and copy the column(s) containing the desired data into a .txt file for the training process to use.

Training the Process

In order to prepare my computer for training, I had to install a few packages to my Windows 10 Powershell, namely Chocolatey, Python3, and a few python packages (pip, Tensorflow, virtual environment). It’s worth noting that in order to install these I needed to enable Remote Scripts: by default, Windows 10 prevents you from running scripts inside Powershell for safety purposes.

Installing Python3 (inc. Powershell setup)
Installing Tensorflow

capture

Once I had the packages installed, I ran the train.py file included in the training package repository on a .txt file collating all the text data I collected via web scraping. Each epoch denotes one full presentation of the data to the process and the time/batch section denotes how many seconds passed per process. The train_loss parameter indicates how accurate the process’ prediction was to the input data: the lower the value, the better the prediction. There are also several hyper-parameters that can be adjusted to improve the quality of the result and the time it takes to process (Google has a description of this here). I used the default settings for my first batch on the poetry:

  • with 15 minutes of scraped data (3500 iterations, poem paragraphs), it took about 15 minutes to process.
  • For a second batch, I collected about 30 minutes of data from a fanfiction website (227650 iterations, sentence and paragraph sizes) and I believe it took a little over 3 hours.
  • I adjusted the hyperparameters as recommended on the ml5 training instructions for 8mb of data on another 15 minute data set containing an entire novel (55000 iterations, 360 chapters) and instead chose to run the process on my laptop instead of my desktop computer. The average time/batch was ~7.5, larger than my desktop’s average of ~0.25 with default settings. This was also going to take approximately five days to complete, so I aborted the process. I tried again using default settings on my laptop: the iterations increased from 55000 to 178200 but the batch time was a respectable 0.115 on average.

scrape

The training file on completion creates a model folder, which can be substituted for any other LSTM model.

Text-to-Speech

One of the contributed libraries for p5.js is the p5.speech library. The library is easily integrated into existing p5.js projects and has comprehensive documentation on their website. For my LSTM generator, I created a voice object and a few extra sliders to control the voice’s pitch and playback speed as well as a playback button that read the output text. Now I can listen to beautiful machine-rendered poetry!

Here’s a sample output:

The sky was blue and horry The light a bold with a more in the garden, Who heard on the moon and song the down the rasson’t good the mind her beast be oft to smell on the doss of the must the place But the see though cold to the pain With sleep the got of the brown be brain. I was the men in the like the turned and so was the chinder from the soul the Beated and seen, Some in the dome they love me fall, to year that the more the mountent to smocties, A pet the seam me and dream of the sease ends of the bry sings.