What PiPedal Is (edits)

README.md

@@ -35,7 +35,7 @@ If your USB audio adapter has MIDI connectors, you can use MIDI devices (keyboar
 ----
  
 
-#### [What PiPedal Is](https://rerdavies.github.io/pipedal/AboutPiPedal.md)
+### [What PiPedal Is](https://rerdavies.github.io/pipedal/AboutPiPedal.html)
 ### [System Requirements](https://rerdavies.github.io/pipedal/SystemRequirements.html)
 ### [Installing PiPedal](https://rerdavies.github.io/pipedal/Installing.html)
 ### [Configuring PiPedal After Installation](https://rerdavies.github.io/pipedal/Configuring.html)

docs/AboutPiPedal.md

@@ -12,35 +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, surprisingly, 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, 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.
 
-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 substantial gains to be made by doubling the size of the Neural Networks that he used in the original versions of the ML library, most current 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 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.
 
 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.
 
-Steven D. Atkinson has since released a 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 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. 
 
-Subsequently, a large open-source minded community has sprung up around both of these libaries, who have devoted themselves to training new Neural Net models. 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 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. 
 
-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. The major reason why I dislike Helix is that the models sort of sound like what they're supposed to sound like but don't play or feel at all like the amps they're supposed to sound like. 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 those sorts of things about Helix emulations either).
+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).
 
-Now, people are even building guitar stop boxes and amps that are based entirely around using uploadable ML and NAM models.
-
-So 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 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?
 
 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 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. Which, on Pipedal, thanks to Jatin Chowdhury's escaped monster, sound amazing.  
+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.
 
-So. A little more about what Pipedal is. 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). 
+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. 
 
-You can access all of those plugins and configure them using PiPedal's remote web 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. Just Moving my mouse when I'm connected to my Raspberry Pi causes overruns. 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.
+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.
 
-And 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.
+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.
 
-And 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 laptiap, and you're all ready to go.
+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.
 
 But most importantly PiPedal sounds great because it leverages the work of Jatin Chodhury, and Steven D. Atkinson. And in the end, whether it sounds great is all that really matters. So please do spend some serious time with the TooB ML and TooB Neural Amp Modeler plugins.