What PiPedal is -- copyediting

docs/AboutPiPedal.md

@@ -12,31 +12,33 @@ To get the most out of PiPedal, you need to understand a little about what's goi
 
 Machine Learning (Artifical Intelligence) has changed everything. 
 
-In the world of guitar effect pedals, the revolution started in 2019, when Jatin Chowdhury published [a paper](https://arxiv.org/pdf/2106.03037), describing the results of using machine learning to simulate guitar amplifier effects in real time. To put things in perspective, the LLM AIs like ChatGPT have billions of parameters. Jatin was more interested in the question of how well AI techniques worked if you used small Neural Net models with a few thousand parameters -- models that were small enough that they could be run in realtime.  And the answer was: yes, suprisingly, you can use small models, and get really impressive results. He then proceeded to publish his source code, both for the realtime simulations, and the tools that were used to train his models under an open-source MIT license. And that has created an avalanche of inovation that has followed on from there.
+In the world of guitar effect pedals, the revolution started in 2019 when Jatin Chowdhury published [a paper](https://arxiv.org/pdf/2106.03037) describing the results of using machine learning to simulate guitar amplifier effects in real-time. To put things in perspective, LLM AIs like ChatGPT have billions of parameters. Jatin was more interested in how well AI techniques worked if you used small Neural Net models with a few thousand parameters—models small enough to run in real-time. The answer was surprisingly positive: you can use small models and get impressive results. He then proceeded to publish his source code, both for the real-time simulations and the tools used to train his models, under an open-source MIT license. This has created an avalanche of innovation.
 
-Jatin Chowdhury's ML library continues to exist, and can be freely downloaded and incorporated into guitar effect plugins that are now available in pretty much all plugin formats. The ML library, and the model training tools that came with it are substantially the same as the version that Jatin Chowdhury initially released. There are significant gains in quality if you double the size of the Neural Networks that he used in the original versions of the ML library. So most models for the ML library use a large model. And many of the AI models that are available now have gone through significantly more training than Chodhury's original modes. So now the models sound amazing! Really amazing! Amp simulations based on Jatin Chowdhury's ML library can run in realtime on an ordinary computer, and produce amp emulations that sound significantly better than previous-generation amp emulations like those used by commercial stomp boxes like Helix that cost over $1,000.
+Jatin Chowdhury's ML library continues to exist and can be freely downloaded and incorporated into guitar effect plugins available in most formats. The ML library and model training tools remain substantially the same as Chowdhury's initial release. There are significant gains in quality if you double the size of the Neural Networks he used in the original versions. Most models for the ML library now use a larger model, and many have gone through significantly more training than Chowdhury's originals. The result? The models sound amazing! Amp simulations based on Chowdhury's ML library can run in real-time on an ordinary computer and produce emulations that sound significantly better than previous-generation amp emulations used by commercial stomp boxes costing over $1,000.
 
-The ML library has since been incorporated, by community developers, into free open source guitar plugins that run on Windows and Mac and Linux, and are available in most plugin formats (VST2, VST3, AU, RTAS &c). And very recently as LV2 plugins that run well, in realtime, with low latency on on Raspberry Pi.
+Community developers have incorporated the ML library into free, open-source guitar plugins that run on Windows, Mac, and Linux, available in most plugin formats (VST2, VST3, AU, RTAS, etc.). Recently, they've also been made available as LV2 plugins that run well in real-time with low latency on Raspberry Pi.
 
-Steven D. Atkinson has, since then, released the Neural Amp Modeler library, which traces its heritage to Jatin Chowdhury's ML library, while providing support for a wider variety of Machine Learning algorithms that have been developed since Jatin's original paper. Amazingly, the Neural Amp Modeler libary has also been released  under an open-source MIT license as well. And Steve Atkinson's Neural Amp Modeler library has, like the original ML library, been incorporated into plugins for most plugin formats and for all major computing platforms. 
+Steven D. Atkinson has since released the Neural Amp Modeler library, which traces its heritage to Chowdhury's ML library while providing support for a wider variety of Machine Learning algorithms. Amazingly, the Neural Amp Modeler library has also been released under an open-source MIT license and incorporated into plugins for most formats and major computing platforms.
 
-Subsequently, a large open-source minded community have devoted themselves to training new Neural Net models for these libraries. The compute time required to train these models is substantial, and typically requires renting time on NVidia AI hardware in the cloud. And to train the models you have to have access to the equipment that you're trying to model. The compute time isn't particularly expensive, but it takes time and effort to record goood source material, and time and effort to train the models, Which is why you need a community. So there are now hundreds of high-quality models for both of these libraries which be easily downloaded from the internet, which are also free. Models that cover everything from the heaviest of heavy metal amps to the most sublime Tweed emulations. And distortion/overdrive/fuzz pedals. Even famous tube-based mixing board strips. 
+Subsequently, a large open-source-minded community has devoted itself to training new Neural Net models for these libraries. The compute time required is substantial, typically requiring rented time on NVIDIA AI hardware in the cloud. Training models also requires access to the equipment being modeled. While the compute time isn't particularly expensive, it takes time and effort to record good source material and train the models, which is why a community effort is necessary. There are now hundreds of high-quality, free models for both libraries, covering everything from heavy metal amps to sublime Tweed emulations, distortion/overdrive/fuzz pedals, and even famous tube-based mixing board strips.
 
-And quality is amazing, and readily apparent. This is not a subtle improvement in amp emulation technology. Amp simulations that not only sound exactly like what they are simulating, but also play like and feel like the amps they are simulating as well. We're talking about 5150 emulations that actually chug, and a Twin emulation that really has that sparkly chime that makes your ears itch, and an emulation of a 1962 Fender Bassmaster that has the kind of warmth and forgiveness that jazz players are looking for. (You can't say any of those sorts of things about previous-generation amp emulations).
+The quality is readily apparent and not a subtle improvement. These are amp simulations that not only sound exactly like what they're simulating but also play and feel like the amps they're emulating. We're talking about 5150 emulations that actually chug, Twin emulations with that sparkly chime that makes your ears itch, and 1962 Fender Bassmaster emulations with the warmth and forgiveness jazz players seek. (These qualities are not often found in previous-generation amp emulations).
 
 So let's just to put all of that in perspective, because the results of all of that have huge implications for the music industry going forward. Jatin Chowdhury's machine learning experiment escaped from the lab in 2019, and has since taken over the world. You can use his code (and derivatives thereof) for free, in guitar plugins that are availble on all major audio platforms and on all major hardware platforms, for free, and get access to a huge library of community-developed models for those plugins which are also free. All of which sound better than $1000+ stopboxes. 
 
-### So where does PiPedal come into all of this?
+### So Where Does PiPedal Fit In?
 
 And now you can plug in a USB audio adapter (not free, I'm afraid) into your Raspberry Pi (also not free, but very cheap), and run those incredible amp models in realtime with low latency using PiPedal (which is also free). That isn't entirely what PiPedal started off as. But at this particular moment in time, that's what PiPedal is. 
 
 And yes, all of the easy effects (reverb, delay, chorus, flangers, modulators, phasers, etc. etc. etc) are either included with PiPedal, or are available for free as LV2 plugins that can also be downloaded from the internet. And Machine Learning plugins also provide good emulations of overdrive, fuzz pedals and other distortion effects, so that's covered. And convolution reverb and cab IR effects aren't particularly easy, but once you've coverd that, you pretty much have it all. But the living heart and core of a guitar stomp box is the amp emulations, and how good they are. On Pipedal, thanks to Jatin Chowdhury's escaped monster, they are very good indeed.
 
-A little more about what Pipedal is... PiPedal is a guitar stomp box implmenentation that runs on a Raspberry Pi. It provides a basic set of plugin, just to get you started, among are which are TooB ML, which uses Jatin Chowdhury's ML library; and TooB Neural Amp Modeler, wich uses Steven Atkinson's Neural Amp Model library. PiPedal provides a basic set of LV2 plugins to get you started. Among those plugins are TooB ML (which uses the ML library), and TooB Neural Amp Modeler -+(which uses the Neural Amp Modeler library). PiPedal uses Linux-standard LV2 plugins. So you can download and install LV2 plugins, and use them within PiPedal. 
+### What PiPedal Is
 
-You access all of those plugins and configure them using PiPedal's web interface, which is important. GPUs and real-time audio effects do not get along well together. So if the user interface you use to control PiPedal is remote, it means that PiPedal can be configured to run with extraordinarily low latency, and use 80% of availabe CPU to run what really matters: guitar effects plugins. GPUs, by the way, are why you can't really ever get low latency on a laptop or PC. So PiPedal lets you  use your phone, or your tablet. or maybe even your laptop to run the user interface, and let your raspberry pi concentrate on processing low-latency realtime audio.
+PiPedal is a guitar stomp box implmenentation that runs on a Raspberry Pi. It provides a basic set of plugin to get you started, among are which are, notably,  TooB ML (using Jatin Chowdhury's ML library) and TooB Neural Amp Modeler (using Steven Atkinson's Neural Amp Model library). PiPedal provides a basic set of LV2 plugins to get you started. Among those plugins are TooB ML (which uses the ML library), and TooB Neural Amp Modeler (which uses the Neural Amp Modeler library).  PiPedal uses Linux-standard LV2 plugins, allowing you to download and install additional LV2 plugins as needed.
 
-Unlike most other audio host applications, PiPedal runs as a daemon, whether you're logged on or not. So all you have to do is plug in your Rasberry Pi, and play. No login required.
+You access all of those plugins and configure them using PiPedal's web interface, which is important. GPUs and real-time audio effects do not get along well together. So if the user interface you use to control PiPedal is remote, it means that PiPedal can be configured to run with extraordinarily low latency, and use 80% or more of availabe CPU to run what really matters: guitar effects plugins. GPUs, by the way, are why you can't really ever get low latency on a laptop or PC. PiPedal lets you  use your phone, or your tablet  or maybe even your laptop to run the user interface, and let your Raspberry Pi concentrate on processing low-latency realtime audio.
+
+Unlike most other audio host applications, PiPedal runs as a daemon, whether you're logged on or not. So all you have to do is plug in your Rasberry Pi, and play - no login required.
 
 When you're playing away from home, PiPedal provides an auto-hotspot feature, which automatically brings up a Wi-Fi hotspot on your Raspberry Pi whenever Pipedal can't see your home router (or an ethernet connection, if that's how you connect to your Pi at home). So all you have to do when you're playing away from home, is power on your Raspberry Pi, pull out your phone or tablet or laptop, and you're all ready to go.