diff --git a/formants.py b/formants.py
index 020ec2c..bdccbbf 100644
--- a/formants.py
+++ b/formants.py
@@ -5,14 +5,14 @@ from helpers.utilities import *
 from load_estimation_model import load_estimation_model
 
 
-def predict_from_times(wav_filename, preds_filename, begin, end):
+def predict_from_times(wav_filename, preds_filename, begin, end, csv_export=True):
     tmp_features_filename = "temp/" + next(tempfile._get_candidate_names()) + ".txt"
     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)
-        load_estimation_model(tmp_features_filename, preds_filename, begin, end)
+        load_estimation_model(tmp_features_filename, preds_filename, begin, end, csv_export=csv_export)
         #easy_call("luajit load_estimation_model.lua " + tmp_features_filename + ' ' + preds_filename)
     else:
         features.create_features(wav_filename, tmp_features_filename)
diff --git a/load_estimation_model.py b/load_estimation_model.py
index 5c04657..6a18f52 100644
--- a/load_estimation_model.py
+++ b/load_estimation_model.py
@@ -26,7 +26,7 @@ class LambdaReduce(LambdaBase):
         return reduce(self.lambda_func,self.forward_prepare(input))
 
 
-def load_estimation_model(inputfilename, outputfilename, begin, end):
+def load_estimation_model(inputfilename, outputfilename, begin, end, csv_export=True):
 	with open(inputfilename, "r") as rf:
 		contents = rf.read()
 		contents = contents.split(",")
@@ -53,8 +53,11 @@ def load_estimation_model(inputfilename, outputfilename, begin, end):
 	model.load_state_dict(torch.load("em.pth"))
 	my_prediction = model.forward(data)
 
-	with open(outputfilename, "w") as wf:
-		wf.write("NAME,begin,end,F1,F2,F3,F4\n")
-		wf.write(name + "," + str(begin) + "," + str(end) + "," +  \
-			str(1000 * float(my_prediction[0][0])) + "," + str(1000 * float(my_prediction[0][1])) + "," + \
-				str(1000 * float(my_prediction[0][2])) + "," + str(1000 * float(my_prediction[0][3])) + "\n")
+	if csv_export:
+		with open(outputfilename, "w") as wf:
+			wf.write("NAME,begin,end,F1,F2,F3,F4\n")
+			wf.write(name + "," + str(begin) + "," + str(end) + "," +  \
+				str(1000 * float(my_prediction[0][0])) + "," + str(1000 * float(my_prediction[0][1])) + "," + \
+					str(1000 * float(my_prediction[0][2])) + "," + str(1000 * float(my_prediction[0][3])) + "\n")
+	
+	return my_prediction