diff --git a/ml/data_proc.py b/ml/data_proc.py
index 3956e35..bb74a98 100644
--- a/ml/data_proc.py
+++ b/ml/data_proc.py
@@ -2,44 +2,30 @@
 Testing file for data_pipeline.py.
 Run this file to generate all output data folders on disk and load them into dataloaders in memory.
 """
+# DON"T PUSH THIS PART TO GITHUB!! The original code works for everyone else
+import sys
+import os
+
+print(sys.path)  # Debugging: Check where Python is looking for modules
+
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+#####################################
+
 from data_processing.audio_processor import AudioProcessor
 from data_processing.spectrogram_processor import SpectrogramProcessor
 from data_processing.extracted_features_processor import ExtractedFeaturesProcessor
-from data_processing.audio_augment import DataAugmentProcessor
-#from data_processing.data_pipeline import DataPipeline
+from data_processing.data_pipeline import DataPipeline
 
-
-# GENERATE OUTPUT DATA FOLDERS
+# Generate output folder of all processed audio
 audio_proc = AudioProcessor()
-#audio_proc.process_all_audio()
+audio_proc.process_all_audio()
 
-spectroproc = SpectrogramProcessor(stft=False)
-#spectroproc.process_all_images()
+# spectroproc = SpectrogramProcessor(stft=True)
+# spectroproc.process_all_images()
 
-extractproc = ExtractedFeaturesProcessor(feature_type="fbank")
+# extractproc = ExtractedFeaturesProcessor(feature_type="fbank")
 #extractproc.process_all_images()
 
-augment_proc = DataAugmentProcessor(audio_path=None)
-
-# Parameters
-percent = 0.5
-vol_shift = 0
-time_shift = 0.4 # between 0.1-0.5 seconds since clip is only 5 secs long
-pitch_shift = 3 # between 1 to 4 semitones
-freq_mask = 10 # between 10-50
-time_mask = 30 # between 10-50
-
-augment_proc.augment_all_audio(
-    ["TS", "PS", "FM", "TM"], # Augmentations to apply, apply only 3 or less at once
-    ["positive"], # Labels to augment, can be ["positive"], ["negative"], or ["positive", "negative"]
-    percent,
-    vol_shift,
-    time_shift,
-    pitch_shift,
-    freq_mask,
-    time_mask,
-)
-
 #datapipe = DataPipeline(test_size=0.2, val_size=0.3, audio_processor=audio_proc, image_processor=spectroproc)
 #datapipe.process_all()