text grid integration; temp file management

.gitignore

@@ -5,3 +5,4 @@ data/*.*
 output/*.*
 tracking_model.dat
 tracking_model.dat.gz
+temp/*.*

extract_features.py

@@ -4,15 +4,12 @@ import argparse
 import numpy as np
 import wave
 import os
-from os import listdir
-from os.path import isfile, join
 import math
 from scipy.fftpack.realtransforms import dct
 from scipy.signal import lfilter, hamming
 from scipy.fftpack import fft, ifft
 #from scikits.talkbox.linpred import lpc  # obsolete
 from helpers.conch_lpc import lpc
-import shutil
 from helpers.utilities import *
 
 epsilon = 0.0000000001
@@ -274,6 +271,7 @@ def create_features(input_wav_filename, feature_filename, begin=None, end=None,
         arr = [input_wav_filename]
         arr.extend(build_single_feature_row(X, Atal))
         np.savetxt(feature_filename, np.asarray([arr]), delimiter=",", fmt="%s")
+        os.remove(tmp_wav16_filename)
         return arr
     arcep_mat = []
     for i in range(len(X)):
@@ -281,6 +279,9 @@ def create_features(input_wav_filename, feature_filename, begin=None, end=None,
         arr.extend(build_single_feature_row(X[i], Atal))
         arcep_mat.append(arr)
     np.savetxt(feature_filename, np.asarray(arcep_mat), delimiter=",", fmt="%s")
+    
+    os.remove(tmp_wav16_filename)
+
     return arcep_mat
 
 

formants.py

@@ -1,4 +1,3 @@
-from json import load
 import extract_features as features
 import argparse
 from helpers.textgrid import *
@@ -7,17 +6,19 @@ 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"
+    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)
+        load_estimation_model(tmp_features_filename, preds_filename, begin, end)
         #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)
+    
+    delete_temp_files()
 
 
 def predict_from_textgrid(wav_filename, preds_filename, textgrid_filename, textgrid_tier):
@@ -34,26 +35,27 @@ def predict_from_textgrid(wav_filename, preds_filename, textgrid_filename, textg
     # extract tier names
     tier_names = textgrid.tierNames()
 
-    if textgrid_tier in tier_names:
+    
+    if textgrid_tier in tier_names: # run over all intervals in the tier
         tier_index = tier_names.index(textgrid_tier)
-        # run over all intervals in the tier
+        textgrid_tier = textgrid[tier_index]
-        for interval in textgrid[tier_index]:
+    else: # process first tier
-            if re.search(r'\S', interval.mark()):
+        textgrid_tier = textgrid[0]
-                tmp_features_filename = generate_tmp_filename("features")
+    
-                tmp_preds = generate_tmp_filename("preds")
+    for interval in textgrid_tier:
-                features.create_features(wav_filename, tmp_features_filename, interval.xmin(), interval.xmax())
+        if re.search(r'\S', interval.mark()):
-                load_estimation_model(tmp_features_filename, tmp_preds)
+            tmp_features_filename = generate_tmp_filename("features")
-                #easy_call("th load_estimation_model.lua " + tmp_features_filename + ' ' + tmp_preds)
+            tmp_preds = generate_tmp_filename("preds")
-                csv_append_row(tmp_preds, preds_filename)
+            begin = interval.xmin()
-    else:  # process first tier
+            end = interval.xmax()
-        for interval in textgrid[0]:
+            features.create_features(wav_filename, tmp_features_filename, begin, end)
-            if re.search(r'\S', interval.mark()):
+            load_estimation_model(tmp_features_filename, tmp_preds, begin, end)
-                tmp_features_filename = generate_tmp_filename("features")
+            #easy_call("th load_estimation_model.lua " + tmp_features_filename + ' ' + tmp_preds)
-                tmp_preds = generate_tmp_filename("preds")
+            csv_append_row(tmp_preds, preds_filename)
-                features.create_features(wav_filename, tmp_features_filename, interval.xmin(), interval.xmax())
+            delete_temp_files()
-                load_estimation_model(tmp_features_filename, tmp_preds)
+
-                #easy_call("th load_estimation_model.lua " + tmp_features_filename + ' ' + tmp_preds)
+    delete_temp_files()
-                csv_append_row(tmp_preds, preds_filename)
+
 
 if __name__ == "__main__":
     # parse arguments

helpers/utilities.py

@@ -25,6 +25,8 @@ import wave
 import tempfile
 import os
 
+from isort import file
+
 
 def csv_append_row(tmp_preds, preds_filename, with_headers=True):
 
@@ -55,7 +57,7 @@ def csv_append_row(tmp_preds, preds_filename, with_headers=True):
 
 
 def generate_tmp_filename(extension):
-    return tempfile._get_default_tempdir() + "/" + next(tempfile._get_candidate_names()) + "." + extension
+    return "temp/" + next(tempfile._get_candidate_names()) + "." + extension
 
 
 def logging_defaults(logging_level="INFO"):
@@ -169,3 +171,8 @@ def is_valid_wav(filename):
         or wav_file.getcomptype() != 'NONE':
         return False
     return True
+
+def delete_temp_files():
+    print("Clearing temp files...")
+    for filename in os.listdir("temp"):
+        os.remove("temp/" + filename)

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):
+def load_estimation_model(inputfilename, outputfilename, begin, end):
 	with open(inputfilename, "r") as rf:
 		contents = rf.read()
 		contents = contents.split(",")
@@ -54,5 +54,7 @@ def load_estimation_model(inputfilename, outputfilename):
 	my_prediction = model.forward(data)
 
 	with open(outputfilename, "w") as wf:
-		wf.write("NAME,F1,F2,F3,F4\n")
+		wf.write("NAME,begin,end,F1,F2,F3,F4\n")
-		wf.write(name + "," + 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])))
+		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")