diff --git a/formants.py b/formants.py
index db5e1ff..8a6272c 100644
--- a/formants.py
+++ b/formants.py
@@ -1,26 +1,26 @@
-
+from json import load
 import extract_features as features
 import argparse
 from helpers.textgrid import *
 from helpers.utilities import *
-import shutil
-
+from load_estimation_model import load_estimation_model
 
 
 def predict_from_times(wav_filename, preds_filename, begin, end):
     tmp_features_filename = tempfile._get_default_tempdir() + "/" + next(tempfile._get_candidate_names()) + ".txt"
-    print(tmp_features_filename)
+    print("Input Array Path: " +  tmp_features_filename)
 
     if begin > 0.0 or end > 0.0:
+        print(wav_filename + " interval " + str(begin) + "-" + str(end) + ":")
         features.create_features(wav_filename, tmp_features_filename, begin, end)
-        easy_call("luajit load_estimation_model.lua " + tmp_features_filename + ' ' + preds_filename)
+        load_estimation_model(tmp_features_filename, preds_filename)
+        #easy_call("luajit load_estimation_model.lua " + tmp_features_filename + ' ' + preds_filename)
     else:
         features.create_features(wav_filename, tmp_features_filename)
         easy_call("luajit load_tracking_model.lua " + tmp_features_filename + ' ' + preds_filename)
 
 
 def predict_from_textgrid(wav_filename, preds_filename, textgrid_filename, textgrid_tier):
-
     print(wav_filename)
 
     if os.path.exists(preds_filename):
@@ -42,7 +42,8 @@ def predict_from_textgrid(wav_filename, preds_filename, textgrid_filename, textg
                 tmp_features_filename = generate_tmp_filename("features")
                 tmp_preds = generate_tmp_filename("preds")
                 features.create_features(wav_filename, tmp_features_filename, interval.xmin(), interval.xmax())
-                easy_call("th load_estimation_model.lua " + tmp_features_filename + ' ' + tmp_preds)
+                load_estimation_model(tmp_features_filename, tmp_preds)
+                #easy_call("th load_estimation_model.lua " + tmp_features_filename + ' ' + tmp_preds)
                 csv_append_row(tmp_preds, preds_filename)
     else:  # process first tier
         for interval in textgrid[0]:
@@ -50,7 +51,8 @@ def predict_from_textgrid(wav_filename, preds_filename, textgrid_filename, textg
                 tmp_features_filename = generate_tmp_filename("features")
                 tmp_preds = generate_tmp_filename("preds")
                 features.create_features(wav_filename, tmp_features_filename, interval.xmin(), interval.xmax())
-                easy_call("th load_estimation_model.lua " + tmp_features_filename + ' ' + tmp_preds)
+                load_estimation_model(tmp_features_filename, tmp_preds)
+                #easy_call("th load_estimation_model.lua " + tmp_features_filename + ' ' + tmp_preds)
                 csv_append_row(tmp_preds, preds_filename)
 
 if __name__ == "__main__":
diff --git a/load_estimation_model.py b/load_estimation_model.py
index e0b31d7..f4bf11d 100644
--- a/load_estimation_model.py
+++ b/load_estimation_model.py
@@ -28,17 +28,17 @@ class LambdaReduce(LambdaBase):
 
 def load_estimation_model(inputfilename, outputfilename):
 	with open(inputfilename, "r") as rf:
-        	contents = rf.read()
-        	contents = contents.split(",")
+		contents = rf.read()
+		contents = contents.split(",")
 
 	data = torch.Tensor(1,350)
 	name = ""
 	for i in range(len(contents)):
-        	if i == 0:
-                	name = contents[i].strip()
-        	else:
-                	val = float(contents[i].strip())
-                	data[0][i-1] = val
+		if i == 0:
+			name = contents[i].strip()
+		else:
+			val = float(contents[i].strip())
+			data[0][i-1] = val
 
 	model = nn.Sequential( # Sequential,
         	nn.Sequential(Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x ),nn.Linear(350,1024)), # Linear,