Remove unecessary semi-colons in Python script

README.md

@@ -1,21 +1,24 @@
 # Goodness of Pronunciation (GoP)
-This code reflects the work described in the **[INTERSPEECH 2019](https://www.interspeech2019.org/)** published paper on **["An improved goodness of pronunciation (GoP) measure for pronunciation evaluation with DNN-HMM system considering HMM transition probabilities"](https://www.isca-speech.org/archive/Interspeech_2019/pdfs/2363.pdf)**.
+This code reflects the work described in the **[INTERSPEECH 2019](https://www.interspeech2019.org/)** published paper on **["An improved goodness of pronunciation (GoP) measure for pronunciation evaluation with DNN-HMM system considering HMM transition probabilities"](https://www.isca-speech.org/archive/pdfs/interspeech_2019/sudhakara19_interspeech.pdf)**.
 
 # Requirements :
 * Python (tested with v.2.7.5 & v.3.5.7).
-* Kaldi ASR toolkit (for documentation checkout : http://kaldi-asr.org/) considering acoustic models trained with _nnet2_ (Dan's recipe) (tested with nnet2 & nnet3).
+* Kaldi ASR toolkit (for documentation checkout : http://kaldi-asr.org/) considering acoustic models trained with _nnet2_ (Dan's recipe) (tested with nnet2 & nnet3) on LibriSpeech.
 
 # How to run the code : 
-Run the below code (**_prop_gop_eqn.py_**) to compute the score using the proposed GoP formulation by passing **_alignment_infile.txt_** and **_posterior_infile.ark_** generated for a given learner's utterance. 
+Run the below code (**_prop_gop_eqn.py_**) to compute the score using the proposed GoP formulation by passing **_alignment_infile.txt_** and **_posterior_infile.ark_** generated for a single learner's utterance. 
 ```python
 python prop_gop_eqn.py posterior_infile.ark alignment_infile.txt gop_outfile.txt
 ```
 * The **_alignment_infile.txt_** file is the output of the forced-alignment of the learner's uttered speech (.wav file) and this is  obtained using **_align.sh_**.
 * The **_posterior_infile.ark_** file contains the frame level posterior-probabilities of the learner's uttered speech (.wav file) and this is obtained using **_nnet_am_compute.cc_**. 
 * The **_gop_outfile.txt_** file contains the score for each phoneme.
 
+How to generate the input files:
+
+
 **NOTE** :
-* The above python script requires a lookup table to generate the scores for an acoustic model as discussed in the paper, which can be generated using the following code :
+* The above Python script requires a lookup table to generate the scores for an acoustic model as discussed in the paper, which can be generated using the following code :
 ```shell
 ./gen_lookup_table.sh
 ```

prop_gop_eqn.py

@@ -9,72 +9,86 @@
 import pandas as pd
 import numpy as np
 
-path = os.getcwd(); 
-  
+path = os.getcwd();
+
 # Modifying posterior.ark to posterior.txt
-var1 = [path + '/reqd_files/' + sys.argv[1]];
-subprocess.call(['bash', 'modify_post.sh', str(var1[0])]);
-
-# Creating segment information list, aligned phones list & transition_id's list 
-var2 = [path + '/reqd_files/' + sys.argv[2]];
-subprocess.call(['bash', 'extract_from_alignments.sh', str(var2[0])]);
-
+#var1 = [path + '/reqd_files/'+ sys.argv[1]]
+
+var1 = [sys.argv[1]]
+subprocess.call(['bash', 'modify_post.sh', str(var1[0])])
+
+# Creating segment information list, aligned phones list & transition_id's list
+#var2 = [path +'/reqd_files/'+sys.argv[2]]
+var2 = [sys.argv[2]]
+subprocess.call(['bash', 'extract_from_alignments.sh', str(var2[0])])
+
 with open(path + '/reqd_files/tmp_segments.txt','r') as f:
-    x = f.readlines();
-number_of_segments = [int(tmp.split(' ')[0]) for tmp in x]; 
-                      
+    x = f.readlines()
+number_of_segments = [int(tmp.split(' ')[0]) for tmp in x]
+print(number_of_segments)
 with open(path + '/reqd_files/tmp_t_ids.txt','r') as f:
-    x = f.readlines();
-transition_id = [int(tmp.rstrip().split(' ')[0]) for tmp in x];
+    x = f.readlines()
+transition_id = [int(tmp.rstrip().split(' ')[0]) for tmp in x]
 
 with open(path + '/reqd_files/tmp_phones.txt','r') as f:
-    x = f.readlines();
-aligned_phones = [tmp.rstrip().split(' ')[0] for tmp in x];
+    x = f.readlines()
+aligned_phones = [tmp.rstrip().split(' ')[0] for tmp in x]
 
 with open(path + '/reqd_files/posterior.txt','r') as f:
-    x = f.readlines();
-posterior = [tmp.rstrip().split(' ') for tmp in x];
-num_of_senones = len(posterior[0]);
-total_num_frames = len(posterior);                    
-                      
+    x = f.readlines()
+posterior = [tmp.rstrip().split(' ') for tmp in x]
+num_of_senones = len(posterior[0])
+total_num_frames = len(posterior)
+#print(total_num_frames,num_of_senones)
 with open(path + '/reqd_files/lookup_table.txt','r') as f:
-    x = f.readlines();
-lookup_tab = [tmp.rstrip().split(' ') for tmp in x];
-
-series = pd.Series(number_of_segments);
-cum_number_of_segments_tmp = series.cumsum();
-cum_number_of_segments=cum_number_of_segments_tmp.tolist();
-cum_number_of_segments.insert(0,0);
-
-phone_score = [];
+    x = f.readlines()
+lookup_tab = [tmp.rstrip().split(' ') for tmp in x]
 
-# Code for Proposed GoP formulation :                             
+series = pd.Series(number_of_segments)
+cum_number_of_segments_tmp = series.cumsum()
+cum_number_of_segments=cum_number_of_segments_tmp.tolist()
+cum_number_of_segments.insert(0,0)
+print(cum_number_of_segments)
+phone_score = []
+phone_post=[]#<---
+# Code for Proposed GoP formulation :
 for x in range(len(number_of_segments)):
-    
+
     req_t_id=transition_id[cum_number_of_segments[x]:(cum_number_of_segments[x+1])];
-    req_t_id.sort();
-    score=0.0;
-
+    req_t_id.sort()
+    #print("Segment num",x,req_t_id)
+    score=0.0
+    tmp_phone_post=[] # <--
     for y in range(len(req_t_id)-1):
-
-        tmp_prob = float(lookup_tab[req_t_id[y]-1][2]);                                
-        tmp_pdf = int(lookup_tab[req_t_id[y]-1][1]);
-        tmp_post = float(posterior[cum_number_of_segments[x]+y][tmp_pdf]);
-        score = score + math.log(tmp_prob) + math.log(tmp_post);
-
-    tmp_pdf = int(lookup_tab[req_t_id[-1]-1][1]);
-    tmp_post = float(posterior[cum_number_of_segments[x+1]-1][tmp_pdf]);
-    score = (score + math.log(tmp_post) + float(number_of_segments[x]-1)*math.log(num_of_senones)) / float(number_of_segments[x]);
-    phone_score.append(score);
+
+        tmp_prob = float(lookup_tab[req_t_id[y]-1][2])
+        tmp_pdf = int(lookup_tab[req_t_id[y]-1][1])
+        tmp_post = float(posterior[cum_number_of_segments[x]+y][tmp_pdf])
+        #print("Senone_ID/Phone no.",x,"Transitions",cum_number_of_segments[x]+y,"PDF",tmp_pdf,tmp_post)
+        tmp_phone_post.append(tmp_post)# <-----
+        score = score + math.log(tmp_prob) + math.log(tmp_post)
+    tmp_pdf = int(lookup_tab[req_t_id[-1]-1][1])
+    tmp_post = float(posterior[cum_number_of_segments[x+1]-1][tmp_pdf])
+    tmp_phone_post.append(tmp_post) #<----------
+    #print("Senone_ID/Phone no.",x,"Transitions",cum_number_of_segments[x+1]-1,"PDF",tmp_pdf,tmp_post,end="\n")
+    score = (score + math.log(tmp_post) + float(number_of_segments[x]-1)*math.log(num_of_senones)) / float(number_of_segments[x])
+    phone_post.append(tmp_phone_post)
+    phone_score.append(score)
 
 # Displaying the scores :
-print('Forced aligned phonemes : ');                      
-print(aligned_phones);
-print('GOP formulated score of each phoneme : ');
-print(phone_score);   
-
-# The phoneme_list.txt file contains phoneme's in the 1st column and GoP formulated scores in the 2nd column     
+print('Forced aligned phonemes : ')
+print(aligned_phones)
+print('GOP formulated score of each phoneme : ')
+print(phone_score)
+#print('Posterior probability of each phoneme')
+#print(phone_post)
+
+# The phoneme_list.txt file contains phoneme's in the 1st column and GoP formulated scores in the 2nd column
 f = open(sys.argv[3], 'w')
 for i in range(len(phone_score)):
     f.write("%s   %f\n" % (aligned_phones[i], phone_score[i]))
 f.close()
+f=open(sys.argv[3].strip('.txt')+'_phone_posteriors.txt','w+')
+for i in range(len(phone_score)):
+    f.write("%s   %s\n" % (aligned_phones[i],str(phone_post[i]).rstrip(']').lstrip('[')))
+f.close()