Modified sources

README.md

@@ -31,11 +31,10 @@ By the use of the aforementioned autoencoder, it allows the user to encode piano
 
 <!-- Provide the basic links for the model. -->
 
-[MAKE SOURCES MORE APA-STYLED]
-- **Code:** Some of the code of this repository includes modifications (not the entire code, due to the differences in the architecture) from the following sites:
-1. [Tensorflow tutorial where pretty-midi is used](https://www.tensorflow.org/tutorials/audio/music_generation)
-2. [VAE explanation and code](https://towardsdatascience.com/vae-with-tensorflow-6-ways-9c689cb76829)
-3. [Microsoft article on the KL training schedule which was applied in this model](https://www.microsoft.com/en-us/research/blog/less-pain-more-gain-a-simple-method-for-vae-training-with-less-of-that-kl-vanishing-agony/)
+**Code:** Some of the code of this repository includes modifications (not the entire code, due to the differences in the architecture) or implementations from the following sites:
+1. [TensorFlow. (n.d.). Generate music with an RNN | TensorFlow Core](https://www.tensorflow.org/tutorials/audio/music_generation) - Tensorflow tutorial where pretty-midi is used
+2. [Han, X. (2020, September 1). VAE with TensorFlow: 6 Ways](https://towardsdatascience.com/vae-with-tensorflow-6-ways-9c689cb76829) - VAE explanation and code
+3. [Li, C. (2019, April 15). Less pain, more gain: A simple method for VAE training with less of that KL-vanishing agony. Microsoft Research.](https://www.microsoft.com/en-us/research/blog/less-pain-more-gain-a-simple-method-for-vae-training-with-less-of-that-kl-vanishing-agony/)  - Microsoft article on the KL training schedule which was applied in this model
 
 There might be acknowledgments missing. If you find some other resemblance to a site's code, please notify me and I will make sure of including it.
 
@@ -102,6 +101,8 @@ The first one will clone the repository. Then, fluidsynth, a real-time MIDI synt
 
 ## Training Details
 
+[TODO: SONG MAP IMAGE]
+
 Pivaenist was trained on the [MAESTRO v2.0.0 dataset](https://magenta.tensorflow.org/datasets/maestro), which contains 1282 midi files [check it in colab]. Their preprocessing involves splitting each note in pitch, duration and step, which compose a column of a 3xN matrix (which we call song map), where N is the number of notes and a row represents sequentially the different pitches, durations and steps. The VAE's objective is to reconstruct these matrices, making it then possible to generate random maps by sampling from the distribution, and then convert them to a MIDI file.
 
 ### Training Data
@@ -225,4 +226,8 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ## Model Card Contact
 
-[More Information Needed]
+[More Information Needed]
+
+## Documentation
+
+###