Merge branch 'main' of https://github.com/GuitarML/SmartAmpPro into main

README.md

@@ -3,9 +3,9 @@
 Guitar plugin made with JUCE that uses neural network models to emulate real world hardware.
 This plugin uses a LSTM model to recreate the sound of real amps and pedals. You can record
 samples and train models from the plugin. Tone models are saved in .json format. Model training
-is accomplished using Tensorflow/Keras. The main improvement from the original SmartAmp is
-training takes less than five minutes on CPU (vs. GPU) for comparable sound quality. Training has
-also been integrated into the plugin.
+is accomplished using Tensorflow/Keras. The main improvement from the original SmartAmp is that
+training takes less than five minutes on CPU (vs. 8 hours on GPU) for comparable sound quality.
+Training has also been integrated into the plugin.
 
 ![app](https://github.com/GuitarML/SmartAmpPro/blob/main/resources/plugin.jpg)
 
@@ -66,7 +66,7 @@ directory to the chosen location.
 Note: The original SmartAmp/PedalNetRT .json files are not compatible with this plugin because it is a
 different machine learning model. Only use tone files trained from SmartAmpPro.
 
-### Capturing .wav samples for model training:
+### "Start Capture" button:
 
 1. Click the "Start Capture" button and enter a model name to save. Ensure this name contains no spaces. 
 2. After saving, the plugin will begin a 10 second coundown followed by a 3 minute recording session.
@@ -78,7 +78,7 @@ different machine learning model. Only use tone files trained from SmartAmpPro.
    Note: The playing prompts such as "Play loud!" that appear while training are suggestions, and don't need to be
           followed exactly to successfully train a model.
 
-### Training .json models from recorded .wav samples.
+### "Train Tone" button:
 
 The Python dependencies from the ```requirements.txt``` file must be installed on your system (not a virtual env)
 for the tone training to work.
@@ -98,6 +98,13 @@ for the tone training to work.
 Note: You can modify the train.py script to test different parameters, but it may produce undesired results in the plugin. Recommended to only
       modify the number of epochs, learning rate, or the number of hidden units of the LSTM layer. 
 
+See the GuitarLSTM repo for more information on how training works, and tips for creating sample recordings:
+https://github.com/GuitarML/GuitarLSTM<br>
+
+**Colab Notebook**:
+Use Google Colab notebook (train_colab.ipynb) for training 
+SmartAmpPro models in the cloud. See notebook comments for instructions.
+
 
 ## Build Instructions
 
@@ -121,6 +128,7 @@ Dev Note: The above dependencies were chosen to facilitate rapid prototyping. It
 
 Note: Make sure to build in Release mode unless actually debugging. Debug mode will not keep up with real time playing.
 
+
 ## Future Work
 
 This plugin is designed to showcase the speed at which the guitar models can be trained.  Using models with larger