madcat arabic scripts cleaning and adding higher order language model

egs/madcat_ar/v1/local/chain/run_cnn.sh

@@ -0,0 +1 @@
+tuning/run_cnn_1a.sh

egs/madcat_ar/v1/local/chain/run_cnn_chainali.sh

@@ -0,0 +1 @@
+tuning/run_cnn_chainali_1a.sh

egs/madcat_ar/v1/local/chain/run_cnn_e2eali.sh

@@ -0,0 +1 @@
+tuning/run_cnn_e2eali_1b.sh

egs/madcat_ar/v1/local/chain/run_e2e_cnn.sh

@@ -0,0 +1 @@
+tuning/run_e2e_cnn_1a.sh

egs/madcat_ar/v1/local/chain/run_cnn_e2eali_1b.sh → egs/madcat_ar/v1/local/chain/tuning/run_cnn_e2eali_1b.sh

@@ -193,7 +193,7 @@ if [ $stage -le 5 ]; then
  --trainer.srand=$srand \
  --trainer.max-param-change=2.0 \
  --trainer.num-epochs=4 \
- --trainer.frames-per-iter=1000000 \
+ --trainer.frames-per-iter=2000000 \
  --trainer.optimization.num-jobs-initial=3 \
  --trainer.optimization.num-jobs-final=16 \
  --trainer.optimization.initial-effective-lrate=0.001 \

egs/madcat_ar/v1/local/chain/run_flatstart_cnn1a.sh → egs/madcat_ar/v1/local/chain/tuning/run_e2e_cnn_1a.sh

@@ -5,16 +5,16 @@
 
 # local/chain/compare_wer.sh exp/chain/e2e_cnn_1a
 # System e2e_cnn_1a
-# WER 10.71
-# CER 2.85
-# Final train prob -0.0859
-# Final valid prob -0.1266
+# WER  7.81
+# CER 2.05
+# Final train prob -0.0812
+# Final valid prob -0.0708
 # Final train prob (xent)
 # Final valid prob (xent)
 # Parameters 2.94M
 
 # steps/info/chain_dir_info.pl exp/chain/e2e_cnn_1a/
-# exp/chain/e2e_cnn_1a/: num-iters=195 nj=6..16 num-params=2.9M dim=40->324 combine=-0.065->-0.064 (over 5) logprob:train/valid[129,194,final]=(-0.078,-0.077,-0.086/-0.129,-0.126,-0.127)
+# exp/chain/e2e_cnn_1a/: num-iters=98 nj=6..16 num-params=2.9M dim=40->330 combine=-0.073->-0.073 (over 2) logprob:train/valid[64,97,final]=(-0.084,-0.080,-0.081/-0.073,-0.070,-0.071)
 
 set -e
 
@@ -33,7 +33,7 @@ num_jobs_final=16
 minibatch_size=150=128,64/300=128,64/600=64,32/1200=32,16
 common_egs_dir=
 l2_regularize=0.00005
-frames_per_iter=1000000
+frames_per_iter=2000000
 cmvn_opts="--norm-means=true --norm-vars=true"
 train_set=train
 lang_test=lang_test
@@ -125,6 +125,7 @@ if [ $stage -le 3 ]; then
  --egs.opts "--num_egs_diagnostic 100 --num_utts_subset 400" \
  --chain.frame-subsampling-factor 4 \
  --chain.alignment-subsampling-factor 4 \
+ --chain.lm-opts="--ngram-order=2 --no-prune-ngram-order=1 --num-extra-lm-states=1000" \
  --trainer.add-option="--optimization.memory-compression-level=2" \
  --trainer.num-chunk-per-minibatch $minibatch_size \
  --trainer.frames-per-iter $frames_per_iter \

egs/madcat_ar/v1/local/create_line_image_from_page_image.py

@@ -60,6 +60,8 @@
  help='Path to the downloaded (and extracted) writing conditions file 3')
 parser.add_argument('--padding', type=int, default=400,
  help='padding across horizontal/verticle direction')
+parser.add_argument("--subset", type=lambda x: (str(x).lower()=='true'), default=False,
+ help="only processes subset of data based on writing condition")
 args = parser.parse_args()
 
 """
@@ -209,50 +211,6 @@ def get_orientation(origin, p1, p2):
  return difference
 
 
-def compute_hull(points):
- """
- Given input list of points, return a list of points that
- made up the convex hull.
- Returns
- -------
- [(float, float)]: convexhull points
- """
- hull_points = []
- start = points[0]
- min_x = start[0]
- for p in points[1:]:
- if p[0] < min_x:
- min_x = p[0]
- start = p
-
- point = start
- hull_points.append(start)
-
- far_point = None
- while far_point is not start:
- p1 = None
- for p in points:
- if p is point:
- continue
- else:
- p1 = p
- break
-
- far_point = p1
-
- for p2 in points:
- if p2 is point or p2 is p1:
- continue
- else:
- direction = get_orientation(point, far_point, p2)
- if direction > 0:
- far_point = p2
-
- hull_points.append(far_point)
- point = far_point
- return hull_points
-
-
 def minimum_bounding_box(points):
  """ Given a list of 2D points, it returns the minimum area rectangle bounding all
  the points in the point cloud.
@@ -272,7 +230,6 @@ def minimum_bounding_box(points):
 
  hull_ordered = [points[index] for index in ConvexHull(points).vertices]
  hull_ordered.append(hull_ordered[0])
- #hull_ordered = compute_hull(points)
  hull_ordered = tuple(hull_ordered)
 
  min_rectangle = bounding_area(0, hull_ordered)
@@ -535,16 +492,14 @@ def check_writing_condition(wc_dict, base_name):
  Returns
  (bool): True if writing condition matches.
  """
- return True
- writing_condition = wc_dict[base_name].strip()
- if writing_condition != 'IUC':
- return False
-
- return True
-
+ if args.subset:
+ writing_condition = wc_dict[base_name].strip()
+ if writing_condition != 'IUC':
+ return False
+ else:
+ return True
 
 ### main ###
-
 def main():
 
  wc_dict1 = parse_writing_conditions(args.writing_condition1)
@@ -564,8 +519,7 @@ def main():
  madcat_file_path, image_file_path, wc_dict = check_file_location(base_name, wc_dict1, wc_dict2, wc_dict3)
  if wc_dict is None or not check_writing_condition(wc_dict, base_name):
  continue
- if madcat_file_path is not None:
- get_line_images_from_page_image(image_file_path, madcat_file_path, image_fh)
+ get_line_images_from_page_image(image_file_path, madcat_file_path, image_fh)
 
 
 if __name__ == '__main__':

egs/madcat_ar/v1/local/extract_features.sh

@@ -1,7 +1,11 @@
 #!/bin/bash
+
 # Copyright 2017 Yiwen Shao
 # 2018 Ashish Arora
 
+# Apache 2.0
+# This script runs the make features script in parallel. 
+
 nj=4
 cmd=run.pl
 feat_dim=40

egs/madcat_ar/v1/local/process_data.py

@@ -42,6 +42,8 @@
  help='Path to the downloaded (and extracted) writing conditions file 2')
 parser.add_argument('writing_condition3', type=str,
  help='Path to the downloaded (and extracted) writing conditions file 3')
+parser.add_argument("--subset", type=lambda x: (str(x).lower()=='true'), default=False,
+ help="only processes subset of data based on writing condition")
 args = parser.parse_args()
 
 
@@ -97,50 +99,40 @@ def check_writing_condition(wc_dict):
  Returns:
  (bool): True if writing condition matches.
  """
- return True
- writing_condition = wc_dict[base_name].strip()
- if writing_condition != 'IUC':
- return False
+ if args.subset:
+ writing_condition = wc_dict[base_name].strip()
+ if writing_condition != 'IUC':
+ return False
+ else:
+ return True
 
- return True
 
-
-def get_word_line_mapping(madcat_file_path):
+def read_text(madcat_file_path):
  """ Maps every word in the page image to a corresponding line.
  Args:
-  madcat_file_path (string): complete path and name of the madcat xml file
+ madcat_file_path (string): complete path and name of the madcat xml file
  corresponding to the page image.
  Returns:
+ dict: Mapping every word in the page image to a corresponding line.
  """
+
+ word_line_dict = dict()
  doc = minidom.parse(madcat_file_path)
  zone = doc.getElementsByTagName('zone')
  for node in zone:
  line_id = node.getAttribute('id')
- line_word_dict[line_id] = list()
  word_image = node.getElementsByTagName('token-image')
  for tnode in word_image:
  word_id = tnode.getAttribute('id')
- line_word_dict[line_id].append(word_id)
  word_line_dict[word_id] = line_id
 
-
-def read_text(madcat_file_path):
- """ Maps every word in the page image to a corresponding line.
- Args:
- madcat_file_path (string): complete path and name of the madcat xml file
- corresponding to the page image.
- Returns:
- dict: Mapping every word in the page image to a corresponding line.
- """
  text_line_word_dict = dict()
- doc = minidom.parse(madcat_file_path)
  segment = doc.getElementsByTagName('segment')
  for node in segment:
  token = node.getElementsByTagName('token')
  for tnode in token:
  ref_word_id = tnode.getAttribute('ref_id')
  word = tnode.getElementsByTagName('source')[0].firstChild.nodeValue
- word = unicodedata.normalize('NFKC',word)
  ref_line_id = word_line_dict[ref_word_id]
  if ref_line_id not in text_line_word_dict:
  text_line_word_dict[ref_line_id] = list()
@@ -160,7 +152,6 @@ def get_line_image_location():
 
 
 ### main ###
-
 print("Processing '{}' data...".format(args.out_dir))
 
 text_file = os.path.join(args.out_dir, 'text')
@@ -188,24 +179,19 @@ def get_line_image_location():
  madcat_xml_path, image_file_path, wc_dict = check_file_location()
  if wc_dict is None or not check_writing_condition(wc_dict):
  continue
- if madcat_xml_path is not None:
- madcat_doc = minidom.parse(madcat_xml_path)
- writer = madcat_doc.getElementsByTagName('writer')
- writer_id = writer[0].getAttribute('id')
- line_word_dict = dict()
- word_line_dict = dict()
- get_word_line_mapping(madcat_xml_path)
- text_line_word_dict = read_text(madcat_xml_path)
- base_name = os.path.basename(image_file_path)
- base_name, b = base_name.split('.tif')
- for lineID in sorted(text_line_word_dict):
- updated_base_name = base_name + '_' + str(lineID).zfill(4) +'.png'
- location = image_loc_dict[updated_base_name]
- image_file_path = os.path.join(location, updated_base_name)
- line = text_line_word_dict[lineID]
- text = ' '.join(line)
- utt_id = writer_id + '_' + str(image_num).zfill(6) + '_' + base_name + '_' + str(lineID).zfill(4)
- text_fh.write(utt_id + ' ' + text + '\n')
- utt2spk_fh.write(utt_id + ' ' + writer_id + '\n')
- image_fh.write(utt_id + ' ' + image_file_path + '\n')
- image_num += 1
+ madcat_doc = minidom.parse(madcat_xml_path)
+ writer = madcat_doc.getElementsByTagName('writer')
+ writer_id = writer[0].getAttribute('id')
+ text_line_word_dict = read_text(madcat_xml_path)
+ base_name = os.path.basename(image_file_path).split('.tif')[0]
+ for lineID in sorted(text_line_word_dict):
+ updated_base_name = base_name + '_' + str(lineID).zfill(4) +'.png'
+ location = image_loc_dict[updated_base_name]
+ image_file_path = os.path.join(location, updated_base_name)
+ line = text_line_word_dict[lineID]
+ text = ' '.join(line)
+ utt_id = writer_id + '_' + str(image_num).zfill(6) + '_' + base_name + '_' + str(lineID).zfill(4)
+ text_fh.write(utt_id + ' ' + text + '\n')
+ utt2spk_fh.write(utt_id + ' ' + writer_id + '\n')
+ image_fh.write(utt_id + ' ' + image_file_path + '\n')
+ image_num += 1

egs/madcat_ar/v1/local/score.sh

@@ -1,5 +1,5 @@
 #!/bin/bash
 
 
-steps/scoring/score_kaldi_wer.sh --word_ins_penalty 0.0,0.5,1.0,1.5,2.0,2.5,3.0,3.5,4.0,4.5,5.0,5.5,6.0,6.5,7.0 "$@"
-steps/scoring/score_kaldi_cer.sh --stage 2 --word_ins_penalty 0.0,0.5,1.0,1.5,2.0,2.5,3.0,3.5,4.0,4.5,5.0,5.5,6.0,6.5,7.0 "$@"
+steps/scoring/score_kaldi_wer.sh "$@"
+steps/scoring/score_kaldi_cer.sh --stage 2 "$@"