xsampa to ipa conversion is added.

acoustic_model/acoustic_model.py

@@ -13,7 +13,7 @@ import pandas as pd
 repo_dir = 'C:\\Users\\Aki\\source\\repos\\acoustic_model'
 curr_dir = repo_dir + '\\acoustic_model'
 config_ini = curr_dir + '\\config.ini'
-output_dir = 'd:\\OneDrive\\Research\\rug\\experiments\\friesian\\acoustic_model'
+output_dir = 'C:\\OneDrive\\Research\\rug\\experiments\\friesian\\acoustic_model'
 forced_alignment_module = 'C:\\Users\\Aki\\source\\repos\\forced_alignment'
 
 dataset_list = ['devel', 'test', 'train']

acoustic_model/config.ini

@@ -2,4 +2,4 @@
 config_hcopy = c:\cygwin64\home\Aki\acoustic_model\config\config.HCopy
 config_train = c:\cygwin64\home\Aki\acoustic_model\config\config.train
 mkhmmdefs_pl = c:\cygwin64\home\Aki\acoustic_model\src\acoustic_model\mkhmmdefs.pl
-FAME_dir = d:\OneDrive\Research\rug\experiments\friesian\corpus
+FAME_dir = c:\OneDrive\Research\rug\experiments\friesian\corpus

acoustic_model/performance_check.py

@@ -2,25 +2,54 @@ import os
 import sys
 import csv
 import subprocess
+import configparser
 
 import numpy as np
-
-import convert_xsampa2ipa
+import pandas as pd
 
 
 ## ======================= user define =======================
+curr_dir   = r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model'
+config_ini = curr_dir + '\\config.ini'
 forced_alignment_module  = r'C:\Users\Aki\source\repos\forced_alignment'
 ipa_xsampa_converter_dir = r'C:\Users\Aki\source\repos\ipa-xsama-converter'
 csvfile = r"C:\OneDrive\Research\rug\stimmen\Frisian Variants Picture Task Stimmen.csv"
 
+# procedure
+
+
+## ======================= add paths =======================
 
 sys.path.append(forced_alignment_module)
 from forced_alignment import convert_phone_set
 
+# for interactive window
+sys.path.append(curr_dir)
+import convert_xsampa2ipa
+import acoustic_model_functions as am_func
+
+
+## ======================= load variables =======================
+config = configparser.ConfigParser()
+config.sections()
+config.read(config_ini)
+
+FAME_dir = config['Settings']['FAME_dir']
+
+lex_asr		= FAME_dir + '\\lexicon\\lex.asr'
+lex_asr_htk = FAME_dir + '\\lexicon\\lex.asr_htk'
 
+
+## ======================= check phones included in FAME! =======================
+# the phones used in the lexicon.
+#phonelist = am_func.get_phonelist(lex_htk)
+
+# the lines which include a specific phone.
+#lines = am_func.find_phone(lex_asr, 'x')
+
+
+## ======================= convert phones ======================
 mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', ipa_xsampa_converter_dir)
-#word_xsampa = 'e:j@X'
-#word_ipa = convert_xsampa2ipa.conversion('xsampa', 'ipa', mapping, word_xsampa)
 
 with open(csvfile, encoding="utf-8") as fin:
 	lines = csv.reader(fin, delimiter=';', lineterminator="\n", skipinitialspace=True)
@@ -33,14 +62,42 @@ with open(csvfile, encoding="utf-8") as fin:
 		if line[1] is not '' and len(line) > 5:
 			filenames.append(line[0])
 			words.append(line[1])
-			word_xsampa = line[3]
-			word_ipa = convert_xsampa2ipa.conversion('xsampa', 'ipa', mapping, word_xsampa)
-			word_ipa = word_ipa.replace('ː', ':')
-			word_famehtk = convert_phone_set.ipa2famehtk(word_ipa)
-			pronunciations.append(word_famehtk)
-phonelist = ' '.join(pronunciations)
-np.unique(phonelist.split(' '))
+			pron_xsampa = line[3]
+			pron_ipa = convert_xsampa2ipa.conversion('xsampa', 'ipa', mapping, pron_xsampa)
+			pron_ipa = pron_ipa.replace('ː', ':')
+			pron_famehtk = convert_phone_set.ipa2famehtk(pron_ipa)
+			
+			# adjust to phones used in the acoustic model.
+			pron_famehtk = pron_famehtk.replace('sp', 'sil')
+			pron_famehtk = pron_famehtk.replace('ce :', 'ce') # because ceh is ignored.
+			pron_famehtk = pron_famehtk.replace('w :', 'wh')
+			pron_famehtk = pron_famehtk.replace('e :', 'eh')
+			pron_famehtk = pron_famehtk.replace('eh :', 'eh')
+			pron_famehtk = pron_famehtk.replace('ih :', 'ih')
+
+			#translation_key = {'sp': 'sil', 'ce :': 'ceh', 'w :': 'wh'}
+			#pron = []
+			#for phoneme in pron_famehtk.split(' '):
+			#	pron.append(translation_key.get(phoneme, phoneme))
+			#pronunciations.append(' '.join(pron_famehtk))
+			pronunciations.append(pron_famehtk)
+
+filenames	   = np.array(filenames)
+words		   = np.array(words)
+pronunciations = np.array(pronunciations)
+
+del line, lines
+del pron_xsampa, pron_ipa, pron_famehtk
+
+# check if all phones are in the phonelist of the acoustic model.
+#phonelist = ' '.join(pronunciations)
+#np.unique(phonelist.split(' '))
+#phonelist.find(':')
 
+# make dict files.
+word_list = np.unique(words)
+word_id = 1
+word = word_list[word_id]
 
 
 ## ======================= forced alignment =======================