Goodness-of-Pronunciation/prop_gop_eqn.py at c607d967731c0f21ca76825add0ff4ef429aa829 · sweekarsud/Goodness-of-Pronunciation · GitHub

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"""
Authors : Sweekar Sudhakara, Manoj Kumar Ramanathi, Chiranjeevi Yarra, Prasanta Kumar Ghosh
Paper Title : An improved goodness of pronunciation (GoP) measure for pronunciation evaluation with DNN-HMM system considering HMM transition probabilities
"""
import sys
import math
import os
import subprocess
import pandas as pd
import numpy as np

path = os.getcwd();

# Modifying posterior.ark to posterior.txt
#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]
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]

with open(path + '/reqd_files/tmp_phones.txt','r') as f:
    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)
#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)
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()
    #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])
        #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)
#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()