diff --git a/.gitignore b/.gitignore
index 3a59d7509dfd3c8c5f62d45ba36e054e6fdf5ff4..1725bb66d4a9616fd86844b581cf480f53a5a3e3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,3 @@
 .idea/
 .venv/
+__pycache__/
\ No newline at end of file
diff --git a/main.py b/main.py
index c36e955485f06628858b39893b6aa7c341be0c90..27c3ed5d5db710c6d48e9c636f761197d4442eb7 100644
--- a/main.py
+++ b/main.py
@@ -1,2 +1,37 @@
+import sys
+import os.path
+import numpy as np
+from datetime import datetime
+
+import musicreader as mr
+import musicgenerator as mg
+
+# constants
+TRAINING_INPUT_FILENAME = "input.txt"
+TRAINING_INFOS_FILENAME = "training_infos.json"
+EMBEDDING_FILENAME = "embeddings.npy"
+MODEL_PARAMETER_FILENAME = "model_parameters.pt"
+
+
+# default parameters
+trainingFolder = os.path.abspath("./training_data/classical_music/test/")
+training = True
+outputPath = os.path.abspath("./output.mid")
+
+
+def isTrainingNeeded() -> bool:
+    # TODO
+    return training
+
+
 if __name__ == '__main__':
-    print("yo")
+
+    if training or isTrainingNeeded():
+        minBpm, maxBpm = mr.readTrainingFolder(trainingFolder, TRAINING_INPUT_FILENAME, TRAINING_INFOS_FILENAME)
+        mr.createEmbedding(os.path.join(trainingFolder, EMBEDDING_FILENAME), os.path.join(trainingFolder, TRAINING_INPUT_FILENAME))
+        mg.train(os.path.join(trainingFolder, TRAINING_INPUT_FILENAME), os.path.join(trainingFolder, EMBEDDING_FILENAME), os.path.join(trainingFolder, MODEL_PARAMETER_FILENAME))
+    else:
+        minBpm, maxBpm = mr.getMinMaxBpm(trainingFolder, TRAINING_INFOS_FILENAME)
+
+    bpm = np.random.randint(minBpm, maxBpm + 1)
+    mg.generate(outputPath, bpm,  os.path.join(trainingFolder, MODEL_PARAMETER_FILENAME))
diff --git a/musicgenerator.py b/musicgenerator.py
new file mode 100644
index 0000000000000000000000000000000000000000..a461b1d7e0734f3d57f85e919c7bb25e8b7303be
--- /dev/null
+++ b/musicgenerator.py
@@ -0,0 +1,11 @@
+def train(trainingInputFilePath: str, embeddingFilePath: str, modelParametersFilePath: str):
+    print(f"training from {trainingInputFilePath}")
+    print(f"read embedding from {embeddingFilePath}")
+    print(f"save training in {modelParametersFilePath}")
+    pass
+
+
+def generate(outputFilePath: str, bpm: int, modelParametersFilePath: str):
+    print(f"read parameters in {modelParametersFilePath}")
+    print(f"generate {outputFilePath}")
+    pass
diff --git a/musicreader.py b/musicreader.py
new file mode 100644
index 0000000000000000000000000000000000000000..6610c4fcd6fd124c08728b89fa08607897b3e6e5
--- /dev/null
+++ b/musicreader.py
@@ -0,0 +1,37 @@
+import json
+import os
+
+import music21
+
+
+def readTrainingFolder(folderPath: str, trainingTextFilename: str, trainingInfoJsonFilename: str) -> (int, int):
+    """
+    Read all midi files and save it into a text file that can be read for the training process.
+    Also save some info about the training data in trainingInfoJsonFilename.
+    :param folderPath: path to folder containing all training midi files to read
+    :param trainingInfoJsonFilename:
+    :param trainingTextFilename:
+    :return: min and max bpm of the training data
+    """
+    print(f"read all midi files in {folderPath}")
+    print(f"save content in {os.path.join(folderPath, trainingTextFilename)}")
+    print(f"save infos in {os.path.join(folderPath, trainingInfoJsonFilename)}")
+    return 0, 0
+
+
+def getMinMaxBpm(folderPath: str, trainingInfoJsonFilename: str) -> (int, int):
+    """
+    ASSUMING THAT THE TRAINING DATA HAS ALREADY BEEN READ ONCE.
+    Read the trainingInfoJsonFilename file and return the min/max bpm.
+    :param folderPath: path to folder containing all training midi files
+    :param trainingInfoJsonFilename:
+    :return: min and max bpm of the training data
+    """
+    print(f"read infos in {os.path.join(folderPath, trainingInfoJsonFilename)}")
+    return 0, 0
+
+
+def createEmbedding(embeddingFilePath: str, trainingTextFilePath: str):
+    print(f"read {trainingTextFilePath}")
+    print(f"create embedding {embeddingFilePath}")
+    pass
diff --git a/training_data/classical_music/.DS_Store b/training_data/classical_music/.DS_Store
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