Updated and deleted code items.

.gitignore

@@ -1,6 +1,7 @@
 old/
 main.pdf
 .vscode/
+__pycache__/
 
 ## Core latex/pdflatex auxiliary files:
 *.aux

code/AudioPreprocessor/AudioPreprocessor.py

@@ -127,10 +127,3 @@ def load_preprocessed_frames(filepath=None, y=None, sr=None):
         windowed_frames = AudioPreprocessor.window_frames(frames=frames)
 
         return windowed_frames
-
-def main():
-    frames = AudioPreprocessor.load_preprocessed_frames("./audio.wav")
-    print(frames)
-
-if __name__ == '__main__':
-    main()

code/FeatureEvaluator/FeatureEvaluator.py

@@ -78,7 +78,7 @@ def create_dataset(self, speaker_ids, extraction_pattern, frames_per_chunk, chun
             print()
 
             dataset.extend(chunks[:chunks_per_speaker])
-            # create numpy array that has 200 times value 1
+            # create numpy array that has chunks_per_speaker times value speaker_id
             dataset_speaker_ids.extend(np.full((chunks_per_speaker), speaker_id))
 
         print("Dataset created.")

code/main.py

@@ -1,23 +1,20 @@
-from AudioPreprocessor.AudioPreprocessor import AudioPreprocessor
-from FeatureExtractor.FeatureExtractor import FeatureExtractor, Feature
+from FeatureExtractor.FeatureExtractor import Feature
 from FeatureEvaluator.FeatureEvaluator import FeatureEvaluator
 
-import librosa
 import numpy as np
 
 def main():
     evaluator = FeatureEvaluator("/home/henry/Downloads/archive/50_speakers_audio_data")
 
-    X, y = evaluator.create_dataset([0, 1, 2, 3, 4], [[Feature.LPC, 13, []]], 10, 200, 4410, 2205, np.hanning, start_at_file_index=0)
+    X, y = evaluator.create_dataset([0, 1, 2, 3, 4], [[Feature.LPC, 13, []]], 30, 1000, 500, 100, np.hanning, start_at_file_index=0)
     evaluator.set_model_dataset(X, y)
 
-    X, y = evaluator.create_dataset([0, 1, 2, 3, 4], [[Feature.LPC, 13, []]], 10, 200, 4410, 2205, np.hanning, start_at_file_index=12)
+    X, y = evaluator.create_dataset([0, 1, 2, 3, 4], [[Feature.LPC, 13, []]], 30, 1000, 500, 100, np.hanning, start_at_file_index=15)
     evaluator.set_evaluation_dataset(X, y)
 
     evaluator.create_nn_model(epochs=1000)
 
     evaluator.evaluate_model()
 
-
 if __name__ == "__main__":
     main()