Can run from external directory. Outputs CSV. Works with TextGrids.

formants.py

@@ -1,25 +1,11 @@
 
 import extract_features as features
-from subprocess import call
-import sys
 import argparse
-import tempfile
+from helpers.textgrid import *
+from helpers.utilities import *
+import shutil
 
-
-def easy_call(command, debug_mode=True):
-    try:
-        if debug_mode:
-            print >>sys.stderr, command
-        call(command, shell=True)
-    except Exception as exception:
-        print "Error: could not execute the following"
-        print ">>", command
-        print type(exception)     # the exception instance
-        print exception.args      # arguments stored in .args
-        exit(-1)
-
-
-def main(wav_filename, preds_filename, begin, end):
+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
 
@@ -31,14 +17,53 @@ def main(wav_filename, preds_filename, begin, end):
         easy_call("th 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):
+        os.remove(preds_filename)
+
+    textgrid = TextGrid()
+
+    # read TextGrid
+    textgrid.read(textgrid_filename)
+
+    # extract tier names
+    tier_names = textgrid.tierNames()
+
+    if textgrid_tier in tier_names:
+        tier_index = tier_names.index(textgrid_tier)
+        # run over all intervals in the tier
+        for interval in textgrid[tier_index]:
+            if re.search(r'\S', interval.mark()):
+                tmp_features_filename = generate_tmp_filename()
+                tmp_preds = generate_tmp_filename()
+                features.create_features(wav_filename, tmp_features_filename, interval.xmin(), interval.xmax())
+                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]:
+            if re.search(r'\S', interval.mark()):
+                tmp_features_filename = generate_tmp_filename()
+                tmp_preds = generate_tmp_filename()
+                features.create_features(wav_filename, tmp_features_filename, interval.xmin(), interval.xmax())
+                easy_call("th load_estimation_model.lua " + tmp_features_filename + ' ' + tmp_preds)
+                csv_append_row(tmp_preds, preds_filename)
+
 if __name__ == "__main__":
     # parse arguments
-    parser = argparse.ArgumentParser(description='Extract features for formants estimation.')
+    parser = argparse.ArgumentParser(description='Estimation and tracking of formants.')
     parser.add_argument('wav_file', default='', help="WAV audio filename (single vowel or an whole utternace)")
-    parser.add_argument('formants_file', default='', help="output formant text file")
+    parser.add_argument('formants_file', default='', help="output formant CSV file")
+    parser.add_argument('--textgrid_filename', default='', help="get beginning and end times from a TextGrid file")
+    parser.add_argument('--textgrid_tier', default='', help="a tier name with portion to process (default first tier)")
     parser.add_argument('--begin', help="beginning time in the WAV file", default=0.0, type=float)
     parser.add_argument('--end', help="end time in the WAV file", default=-1.0, type=float)
     args = parser.parse_args()
 
-    main(args.wav_file, args.formants_file, args.begin, args.end)
+    if args.textgrid_filename:
+        predict_from_textgrid(args.wav_file, args.formants_file, args.textgrid_filename, args.textgrid_tier)
+    else:
+        predict_from_times(args.wav_file, args.formants_file, args.begin, args.end)