acoustic_model.sln

@@ -9,12 +9,13 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Solution Items", "Solution
 	ProjectSection(SolutionItems) = preProject
 		..\forced_alignment\forced_alignment\__init__.py = ..\forced_alignment\forced_alignment\__init__.py
 		..\forced_alignment\forced_alignment\convert_phone_set.py = ..\forced_alignment\forced_alignment\convert_phone_set.py
-		..\toolbox\evaluation.py = ..\toolbox\evaluation.py
+		..\ipa-xsama-converter\converter.py = ..\ipa-xsama-converter\converter.py
+		..\forced_alignment\forced_alignment\defaultfiles.py = ..\forced_alignment\forced_alignment\defaultfiles.py
 		..\forced_alignment\forced_alignment\forced_alignment.pyproj = ..\forced_alignment\forced_alignment\forced_alignment.pyproj
+		..\forced_alignment\forced_alignment\htk_dict.py = ..\forced_alignment\forced_alignment\htk_dict.py
 		..\forced_alignment\forced_alignment\lexicon.py = ..\forced_alignment\forced_alignment\lexicon.py
 		..\forced_alignment\forced_alignment\mlf.py = ..\forced_alignment\forced_alignment\mlf.py
 		..\forced_alignment\forced_alignment\pronunciations.py = ..\forced_alignment\forced_alignment\pronunciations.py
-		..\toolbox\pyHTK.py = ..\toolbox\pyHTK.py
 		..\forced_alignment\forced_alignment\pyhtk.py = ..\forced_alignment\forced_alignment\pyhtk.py
 		..\forced_alignment\forced_alignment\scripts.py = ..\forced_alignment\forced_alignment\scripts.py
 		..\forced_alignment\forced_alignment\tempfilename.py = ..\forced_alignment\forced_alignment\tempfilename.py

acoustic_model/acoustic_model.py

@@ -22,11 +22,12 @@ dataset_list = ['devel', 'test', 'train']
 extract_features  = 0
 make_feature_list = 0
 conv_lexicon	  = 0
-check_lexicon	  = 0
+check_lexicon	  = 1
 make_mlf		  = 0
 combine_files	  = 0
 flat_start		  = 0
-train_model		  = 1
+train_model		  = 0
+forced_alignment  = 0
 
 
 sys.path.append(os.path.join(os.path.dirname(sys.path[0]), curr_dir))
@@ -287,7 +288,7 @@ if flat_start:
 ## ======================= estimate monophones =======================
 if train_model:
 	iter_num_max = 3
-	for mix_num in [128, 256, 512, 1024]:
+	for mix_num in [16, 32, 64, 128]:
 		for iter_num in range(1, iter_num_max+1):
 			print("===== mix{}, iter{} =====".format(mix_num, iter_num))
 			iter_num_pre = iter_num - 1
@@ -314,6 +315,5 @@ if train_model:
 			fout.write("MU %d {*.state[2-4].mix}" % (mix_num_next))
 
 		subprocessStr =	'HHEd -T 1 -H ' + modelN_dir + '\\' + hmmdefs_name + ' -M ' + modelN_dir_next + ' ' + header_file + ' ' + phonelist
-		
 		subprocess.call(subprocessStr, shell=True)
 

acoustic_model/acoustic_model.pyproj

@@ -28,12 +28,6 @@
     <Compile Include="convert_xsampa2ipa.py">
       <SubType>Code</SubType>
     </Compile>
-    <Compile Include="defaultfiles.py">
-      <SubType>Code</SubType>
-    </Compile>
-    <Compile Include="fa_test.py">
-      <SubType>Code</SubType>
-    </Compile>
     <Compile Include="performance_check.py">
       <SubType>Code</SubType>
     </Compile>

acoustic_model/acoustic_model_functions.py

@@ -1,13 +1,17 @@
 import os
 import sys
-from collections import Counter
 
-import numpy as np
 import pandas as pd
 
-import defaultfiles as default
 
-sys.path.append(default.forced_alignment_module_dir)
+## ======================= user define =======================
+repo_dir = 'C:\\Users\\Aki\\source\\repos\\acoustic_model'
+curr_dir = repo_dir + '\\acoustic_model'
+forced_alignment_module = 'C:\\Users\\Aki\\source\\repos\\forced_alignment'
+
+
+sys.path.append(os.path.join(os.path.dirname(sys.path[0]), curr_dir))
+sys.path.append(forced_alignment_module)
 from forced_alignment import convert_phone_set
 
 
@@ -38,41 +42,6 @@ def make_filelist(input_dir, output_txt):
 			fout.write(input_dir + '\\' + filename + '\n')
 
 
-def make_dic(word, pronvar_, fileDic, output_type):
-    """
-    make dict files which can be used for HTK.
-    param word: target word.
-    param pronvar_: pronunciation variant. nx2 (WORD /t pronunciation) ndarray.
-    param fileDic: output dic file.
-    param output_type: 0:full, 1:statistics, 2:frequency <2% entries are removed. 3:top 3. 
-    """
-    #assert(output_type < 4 and output_type >= 0, 'output_type should be an integer between 0 and 3.')
-    WORD = word.upper()
-
-    if output_type == 0: # full
-        pronvar  = np.unique(pronvar_)
-
-        with open(fileDic, 'w') as f:
-            for pvar in pronvar:
-                f.write('{0}\t{1}\n'.format(WORD, pvar))
-    else:
-        c = Counter(pronvar_)
-        total_num = sum(c.values())
-        with open(fileDic, 'w') as f:
-            if output_type == 3:
-                for key, value in c.most_common(3):
-                    f.write('{0}\t{1}\n'.format(WORD, key))
-            else:
-                for key, value in c.items():
-                    percentage = value/total_num*100
-
-                    if output_type == 1: # all
-                        f.write('{0}\t{1:.2f}\t{2}\t{3}\n'.format(value, percentage, WORD, key))
-                    elif output_type == 2: # less than 2 percent
-                        if percentage < 2:
-                            f.write('{0}\t{1}\n'.format(WORD, key))
-
-
 def get_phonelist(lexicon_file):
 	""" Make a list of phones which appears in the lexicon. """
 
@@ -131,21 +100,3 @@ def combine_lexicon(lexicon_file1, lexicon_file2, lexicon_out):
 	lex  = pd.concat([lex1, lex2])
 	lex  = lex.sort_values(by='word', ascending=True)
 	lex.to_csv(lexicon_out, index=False, header=False, encoding="utf-8", sep='\t')
-
-
-def read_fileFA(fileFA):
-    """
-    read the result file of HTK forced alignment.
-    this function only works when input is one word.
-    """
-    with open(fileFA, 'r') as f:
-        lines = f.read()
-        lines = lines.split('\n')
-
-    phones = []
-    for line in lines:
-        line_split = line.split()
-        if len(line_split) > 1:
-            phones.append(line_split[2])
-
-    return ' '.join(phones)

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 = C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\corpus
+FAME_dir = c:\OneDrive\Research\rug\experiments\friesian\corpus

acoustic_model/convert_xsampa2ipa.py

@@ -7,155 +7,122 @@ import json
 import sys
 import os
 
-import defaultfiles as default
+
-sys.path.append(os.path.join(default.repo_dir, 'forced_alignment'))
+#sys.path.append(ipa_xsampa_converter_dir)
-from forced_alignment import convert_phone_set
+#import converter
 
 
 def load_converter(source, sink, ipa_xsampa_converter_dir):
-    """load the converter.
+	"""load the converter.
-    source and sink are either of "ipa", "xsampa" or "sassc".
+	source and sink are either of "ipa", "xsampa" or "sassc".
-    """
+	"""
-    choices = ["ipa", "xsampa", "sassc"]
+	choices = ["ipa", "xsampa", "sassc"]
 
-    # Validate params
+	# Validate params
-    try:
+	try:
-        choice1 = choices.index(source)
+		choice1 = choices.index(source)
-        choice2 = choices.index(sink)
+		choice2 = choices.index(sink)
-        if choice1 == choice2:
+		if choice1 == choice2:
-            print("source and destination format are the same.")
+			print("source and destination format are the same.")
-    except ValueError:
+	except ValueError:
-        print("source and destination should be one of [ipa xsampa sassc].")
+		print("source and destination should be one of [ipa xsampa sassc].")
-        exit(1)
+		exit(1)
 		
-    # Mappings from disk
+	# Mappings from disk
-    # some may not be used if source or sink is already IPA
+	# some may not be used if source or sink is already IPA
-    source_to_ipa = {}
+	source_to_ipa = {}
-    ipa_to_sink = {}
+	ipa_to_sink = {}
 
-    ipa_xsampa = []
+	ipa_xsampa = []
-    sassc_ipa = []
+	sassc_ipa = []
 
-    # The IPAs that actually occur within SASSC
+	# The IPAs that actually occur within SASSC
-    sassc_active_ipa = {}
+	sassc_active_ipa = {}
 
-    script_dir = os.path.dirname(os.path.realpath(__file__))
+	script_dir = os.path.dirname(os.path.realpath(__file__))
 
-    with open(os.path.join(ipa_xsampa_converter_dir, "ipa_xsampa_map.json"), encoding="utf-8") as f:
+	with open(os.path.join(ipa_xsampa_converter_dir, "ipa_xsampa_map.json"), encoding="utf-8") as f:
-        ipa_xsampa = json.load(f)
+		ipa_xsampa = json.load(f)
 
-    sassc_active = source == "sassc" or sink == "sassc"
+	sassc_active = source == "sassc" or sink == "sassc"
-    if sassc_active:
+	if sassc_active:
-        with open(os.path.join(script_dir, "./sassc_ipa.json")) as f:
+		with open(os.path.join(script_dir, "./sassc_ipa.json")) as f:
-            sassc_ipa = json.load(f)
+			sassc_ipa = json.load(f)
-        for pair in sassc_ipa:
+		for pair in sassc_ipa:
-            for char in pair[1]:
+			for char in pair[1]:
-                sassc_active_ipa[char] = 1
+				sassc_active_ipa[char] = 1
 
-    if source == "xsampa":
+	if source == "xsampa":
-        for pair in ipa_xsampa:
+		for pair in ipa_xsampa:
-            source_to_ipa[pair[1]] = pair[0]
+			source_to_ipa[pair[1]] = pair[0]
-    elif source == "sassc":
+	elif source == "sassc":
-        for pair in sassc_ipa:
+		for pair in sassc_ipa:
-            source_to_ipa[pair[0]] = pair[1]
+			source_to_ipa[pair[0]] = pair[1]
 
-    if sink == "xsampa":
+	if sink == "xsampa":
-        for pair in ipa_xsampa:
+		for pair in ipa_xsampa:
-            ipa_to_sink[pair[0]] = pair[1]
+			ipa_to_sink[pair[0]] = pair[1]
-    elif sink == "sassc":
+	elif sink == "sassc":
-        for pair in sassc_ipa:
+		for pair in sassc_ipa:
-            ipa_to_sink[pair[1]] = pair[0]
+			ipa_to_sink[pair[1]] = pair[0]
 
-    # Combine them into a single mapping
+	# Combine them into a single mapping
-    mapping = {}
+	mapping = {}
-    if source == "ipa":
+	if source == "ipa":
-        mapping = ipa_to_sink
+		mapping = ipa_to_sink
-    elif sink == "ipa":
+	elif sink == "ipa":
-        mapping = source_to_ipa
+		mapping = source_to_ipa
-    else:
+	else:
-        for k, ipas in source_to_ipa.iteritems():
+		for k, ipas in source_to_ipa.iteritems():
-            map_out = ""
+			map_out = ""
-            failed = False
+			failed = False
-            for ipa in ipas:
+			for ipa in ipas:
-                val = ipa_to_sink.get(ipa)
+				val = ipa_to_sink.get(ipa)
-                if not val:
+				if not val:
-                    failed = True
+					failed = True
-                    break
+					break
-                map_out += val
+				map_out += val
-            mapping[k] = map_out if not failed else None
+			mapping[k] = map_out if not failed else None
 
-    return mapping
+	return mapping
 
 
 def conversion(source, sink, mapping, line):
-    """
+	"""
-    conversion.
+	conversion.
-    Args:
+	Args:
-        mapping: can be obtained with load_converter().
+		mapping: can be obtained with load_converter().
-        line: must be seperated, by default the seperator is whitespace.
+		line: must be seperated, by default the seperator is whitespace.
-    """
+	"""
 
-    # Edit this to change the seperator
+	# Edit this to change the seperator
-    SEPERATOR = " "
+	SEPERATOR = " "
 
-    line = line.strip()
+	line = line.strip()
-    output = []
+	output = []
-    #if sassc_active:
+	#if sassc_active:
-    #	tokens = line.split(SEPERATOR)
+	#	tokens = line.split(SEPERATOR)
-    #else:
+	#else:
-    tokens = line
+	tokens = line
-    for token in tokens:
+	for token in tokens:
-        if token.isspace():
+		if token.isspace():
-            output.append(token)
+			output.append(token)
-            continue
+			continue
-        # Remove extraneous chars that IPA does not accept
+		# Remove extraneous chars that IPA does not accept
-        if sink == "sassc":
+		if sink == "sassc":
-            cleaned_token = u""
+			cleaned_token = u""
-            for char in token:
+			for char in token:
-                if sassc_active_ipa.get(char):
+				if sassc_active_ipa.get(char):
-                    cleaned_token += char
+					cleaned_token += char
-            token = cleaned_token
+			token = cleaned_token
-        mapped = mapping.get(token)
+		mapped = mapping.get(token)
-        if not mapped:
+		if not mapped:
-            print("WARNING: could not map token ", token, file=sys.stderr)
+			print("WARNING: could not map token ", token, file=sys.stderr)
-        else:
+		else:
-            output.append(mapped)
+			output.append(mapped)
-    #if sassc_active:
+	#if sassc_active:
-    #	output = SEPERATOR.join(output)
+	#	output = SEPERATOR.join(output)
-    #else:
+	#else:
-    output = "".join(output)
+	output = "".join(output)
 		
-    return output
+	return output
-
-
-def xsampa2ipa(mapping, xsampa):
-    """
-    conversion from xsampa to ipa.
-    
-    Args:
-        mapping: can be obtained with load_converter().
-        xsampa: a line written in xsampa.
-
-    Notes:
-        function conversion does not work when:
-        - the input is a word.
-        - when the line includes '\'.
-        - 'ɡ' and 'g' are considered to be different.
-
-    """
-    # make a multi_character_list to split 'xsampa'.
-    multi_character_list = []
-    for i in list(mapping):
-        if len(i) > 1:
-            multi_character_list.append(i)
-    
-    # conversion
-    ipa = []
-    for phone in convert_phone_set.multi_character_tokenize(xsampa, multi_character_list):
-        ipa.append(mapping.get(phone, phone))
-    ipa = ''.join(ipa)
-
-    # strange conversion.
-    ipa = ipa.replace('ɡ', 'g')
-    
-    return ipa 

acoustic_model/defaultfiles.py

@@ -1,35 +0,0 @@
-import os
-
-#default_hvite_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'data', 'htk', 'config.HVite')
-
-cygwin_dir   = r'C:\cygwin64\home\Aki\acoustic_model'
-#config_hcopy = os.path.join(cygwin_dir, 'config', 'config.HCopy')
-#config_train = os.path.join(cygwin_dir, 'config', 'config.train')
-config_hvite = os.path.join(cygwin_dir, 'config', 'config.HVite')
-#mkhmmdefs_pl = os.path.join(cygwin_dir, 'src', 'acoustic_model', 'mkhmmdefs.pl')
-
-#dbLexicon	  = C:\\Users\\Aki\\source\\repos\\rug_VS\\forced_alignment\\config\\lexicon.accdb
-#scriptBarbara = C:\\Users\\Aki\\source\\repos\\rug_VS\\forced_alignment\\config\\pronvars_barbara.perl
-#exeG2P		  = C:\\Users\\Aki\\source\\repos\\rug_VS\\forced_alignment\\config\\string2phon.exe
-
-#[pyHTK]
-#configHVite   = C:\\Users\\Aki\\source\\repos\\rug_VS\\forced_alignment\\config\\config.HVite
-#filePhoneList = C:\\Users\\Aki\\source\\repos\\rug_VS\\forced_alignment\\config\\phonelist_barbara.txt
-#AcousticModel = C:\\Users\\Aki\\source\\repos\\rug_VS\\forced_alignment\\config\\hmmdefs_16-2_barbara.compo
-
-#dbLexicon	  = config['cLexicon']['dbLexicon']
-#scriptBarbara = config['cLexicon']['scriptBarbara']
-#exeG2P		  = config['cLexicon']['exeG2P']
-
-#configHVite	  = config['pyHTK']['configHVite']
-#filePhoneList = config['pyHTK']['filePhoneList']
-#AcousticModel = config['pyHTK']['AcousticModel']
-
-repo_dir = r'C:\Users\Aki\source\repos'
-ipa_xsampa_converter_dir = os.path.join(repo_dir, 'ipa-xsama-converter')
-forced_alignment_module_dir = os.path.join(repo_dir, 'forced_alignment')
-
-fame_dir = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\corpus'
-experiments_dir = r'c:\OneDrive\Research\rug\experiments'
-
-phonelist = os.path.join(experiments_dir, 'friesian', 'acoustic_model', 'config', 'phonelist_friesian.txt')

acoustic_model/fa_test.py

@@ -1,16 +0,0 @@
-import os
-import sys
-os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
-
-import defaultfiles as default
-
-sys.path.append(os.path.join(default.repo_dir, 'forced_alignment'))
-from forced_alignment import forced_alignment
-
-wav_file = r'C:\Users\Aki\source\repos\forced_alignment\notebooks\sample\10147-1464186409-1917281.wav'
-forced_alignment(
-    wav_file,
-    #'Australië'
-    'BUFFETCOUPON COULISSEN DOUANE'
-    )
-

acoustic_model/performance_check.py

@@ -1,437 +1,257 @@
 import os
-os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
-
 import sys
 import csv
 import subprocess
+import configparser
 from collections import Counter
-import re
 
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
-#from sklearn.metrics import confusion_matrix
-
-import acoustic_model_functions as am_func
-import convert_xsampa2ipa
-import defaultfiles as default
 
 
-## ======================= user define =======================
+## ======================= functions =======================
-#curr_dir   = r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model'
+
-#config_ini = 'config.ini'
+def read_fileFA(fileFA):
-#repo_dir   = r'C:\Users\Aki\source\repos'
+	"""
-#forced_alignment_module     = repo_dir + '\\forced_alignment'
+	read the result file of HTK forced alignment.
-#forced_alignment_module_old = repo_dir + '\\aki_tools'
+	this function only works when input is one word.
-#ipa_xsampa_converter_dir    = repo_dir + '\\ipa-xsama-converter'
+	"""
-#accent_classification_dir   = repo_dir + '\\accent_classification\accent_classification'
+	with open(fileFA, 'r') as f:
-excel_file = os.path.join(default.experiments_dir, 'stimmen', 'data', 'Frisian Variants Picture Task Stimmen.xlsx')
+		lines = f.read()
+		lines = lines.split('\n')
+
+	phones = []
+	for line in lines:
+		line_split = line.split()
+		if len(line_split) > 1:
+			phones.append(line_split[2])
+
+	return ' '.join(phones)
 
 
-#experiments_dir = r'C:\OneDrive\Research\rug\experiments'
+#####################
-data_dir        = os.path.join(default.experiments_dir, 'stimmen', 'data')
+##   USER DEFINE   ##
-#csvfile         = data_dir + '\\Frisian Variants Picture Task Stimmen.csv'
+#####################
-wav_dir	= os.path.join(default.experiments_dir, 'stimmen', 'wav')
+curr_dir   = r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model'
-acoustic_model_dir = os.path.join(default.experiments_dir, 'friesian', 'acoustic_model', 'model')
+config_ini = curr_dir + '\\config.ini'
-htk_dict_dir = os.path.join(default.experiments_dir, 'stimmen', 'dic_short')
+forced_alignment_module     = r'C:\Users\Aki\source\repos\forced_alignment'
-fa_dir  = os.path.join(default.experiments_dir, 'stimmen', 'FA')
+forced_alignment_module_old = r'C:\OneDrive\Research\rug\code\forced_alignment\forced_alignment'
-
+ipa_xsampa_converter_dir = r'C:\Users\Aki\source\repos\ipa-xsama-converter'
-#cygwin_dir      = r'C:\cygwin64\home\Aki\acoustic_model'
-#lex_asr		= os.path.join(default.fame_dir, 'lexicon', 'lex.asr')
-#lex_asr_htk = os.path.join(default.fame_dir, 'lexicon', 'lex.asr_htk')
 
+csvfile = r"C:\OneDrive\Research\rug\stimmen\Frisian Variants Picture Task Stimmen.csv"
+experiments_dir = r'C:\OneDrive\Research\rug\experiments'
+data_dir = experiments_dir + '\\stimmen\\data'
+cygwin_dir = r'C:\cygwin64\home\Aki\acoustic_model'
 
 # procedure
+convert_phones = 0
 make_dic_files = 0
-do_forced_alignment_htk = 1
+make_dic_files_short = 0
-make_kaldi_data_files = 0
+do_forced_alignment = 0
-make_kaldi_lexicon_txt = 0
+eval_forced_alignment = 1
-load_forced_alignment_kaldi = 0
-eval_forced_alignment = 0
 
 
 
 ## ======================= add paths =======================
 
-sys.path.append(os.path.join(default.repo_dir, 'forced_alignment'))
+sys.path.append(forced_alignment_module)
 from forced_alignment import convert_phone_set
-from forced_alignment import pyhtk
 
-sys.path.append(os.path.join(default.repo_dir, 'toolbox'))
+# for interactive window
-#import pyHTK
+sys.path.append(curr_dir)
-from evaluation import plot_confusion_matrix
+import convert_xsampa2ipa
+import acoustic_model_functions as am_func
+
+# for forced-alignment
+sys.path.append(forced_alignment_module_old)
+import pyHTK
+
+
+## ======================= 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'
 
 
 ## ======================= convert phones ======================
+if convert_phones:
+	mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', ipa_xsampa_converter_dir)
 
-mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', default.ipa_xsampa_converter_dir)
+	## check phones included in FAME!
+	# the phones used in the lexicon.
+	#phonelist = am_func.get_phonelist(lex_htk)
 
-xls = pd.ExcelFile(excel_file)
+	# the lines which include a specific phone.
+	#lines = am_func.find_phone(lex_asr, 'x')
 
-## check conversion
+	with open(csvfile, encoding="utf-8") as fin:
-#df = pd.read_excel(xls, 'frequency')
+		lines = csv.reader(fin, delimiter=';', lineterminator="\n", skipinitialspace=True)
-#for xsampa, ipa in zip(df['X-SAMPA'], df['IPA']):
+		next(lines, None) # skip the headers
-#    #ipa_converted = convert_xsampa2ipa.conversion('xsampa', 'ipa', mapping, xsampa_)
-#    ipa_converted = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
-#    if not ipa_converted == ipa:
-#        print('{0}: {1} - {2}'.format(xsampa, ipa_converted, ipa))
 
+		filenames	   = []
+		words		   = []
+		pronunciations = []
+		for line in lines:
+			if line[1] is not '' and len(line) > 5:
+				filenames.append(line[0])
+				words.append(line[1])
+				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)
 			
-## check phones included in FAME!
+				# adjust to phones used in the acoustic model.
-# the phones used in the lexicon.
+				pron_famehtk = pron_famehtk.replace('sp', 'sil')
-#phonelist = am_func.get_phonelist(lex_asr)
+				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')
 
-# the lines which include a specific phone.
+				#translation_key = {'sp': 'sil', 'ce :': 'ceh', 'w :': 'wh'}
-#lines = am_func.find_phone(lex_asr, 'x')
+				#pron = []
+				#for phoneme in pron_famehtk.split(' '):
+				#	pron.append(translation_key.get(phoneme, phoneme))
+				#pronunciations.append(' '.join(pron_famehtk))
+				pronunciations.append(pron_famehtk)
 
+	# check if all phones are in the phonelist of the acoustic model.
+	#phonelist = ' '.join(pronunciations)
+	#np.unique(phonelist.split(' '))
+	#phonelist.find(':')
 
-# Filename, Word, Self Xsampa
+	filenames	   = np.array(filenames)
-df = pd.read_excel(xls, 'original')
+	words		   = np.array(words)
+	pronunciations = np.array(pronunciations)
 
-ipas     = []
+	del line, lines
-famehtks = []
+	del pron_xsampa, pron_ipa, pron_famehtk
-for xsampa in df['Self Xsampa']:
-    if not isinstance(xsampa, float): # 'NaN'
-        # typo?
-        xsampa = xsampa.replace('r2:z@rA:\\t', 'r2:z@rA:t')
-        xsampa = xsampa.replace(';', ':')
 
-        ipa = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
+	np.save(data_dir + '\\filenames.npy', filenames)
-        ipa = ipa.replace('ː', ':')
+	np.save(data_dir + '\\words.npy', words)
-        ipa = ipa.replace(' ', '')
+	np.save(data_dir + '\\pronunciations.npy', pronunciations)
-        ipas.append(ipa)
+else:
-        famehtk = convert_phone_set.ipa2famehtk(ipa)
+	filenames	   = np.load(data_dir + '\\filenames.npy')
-        famehtks.append(famehtk)
+	words		   = np.load(data_dir + '\\words.npy')
-    else:
-        ipas.append('')
-        famehtks.append('')
 	
-# extract interesting cols.
+	pronunciations = np.load(data_dir + '\\pronunciations.npy')
-df = pd.DataFrame({'filename': df['Filename'], 
+word_list = np.unique(words)
-                    'word': df['Word'], 
-                    'xsampa': df['Self Xsampa'],
-                    'ipa': pd.Series(ipas),
-                    'famehtk': pd.Series(famehtks)})
-# cleansing. 
-df = df[~df['famehtk'].isin(['/', ''])]
 
 
 ## ======================= make dict files used for HTK. ======================
 if make_dic_files:
-    word_list = np.unique(df['word'])
+	output_dir = experiments_dir + r'\stimmen\dic'
 
-    output_type = 3
+	for word in word_list:
+		WORD	= word.upper()
+		fileDic = output_dir + '\\' + word + '.dic'
 
-    for word in word_list:
+		# make dic file.
-        htk_dict_file = htk_dict_dir + '\\' + word + '.dic'
+		pronvar_ = pronunciations[words == word]
+		pronvar  = np.unique(pronvar_)
 
-        # pronunciation variant of the target word.
+		with open(fileDic, 'w') as f:
-        pronvar_ = df['famehtk'][df['word'].str.match(word)]
+			for pvar in pronvar:
-
+				f.write('{0}\t{1}\n'.format(WORD, pvar))
-        # make dic file.
-        am_func.make_dic(word, pronvar_, htk_dict_file, output_type)
 
 
-## ======================= forced alignment using HTK =======================
+## ======================= make dict files for most popular words. ======================	
-if do_forced_alignment_htk:
+if make_dic_files_short:
+	output_dir = experiments_dir + r'\stimmen\dic'
 
-    #hmm_num = 2
+	#word = word_list[3]
-    for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]:
+	for word in word_list:
+		WORD	= word.upper()
+		fileStat = output_dir + '\\' + word + '_stat.csv'
 		
-        hmm_num_str = str(hmm_num)
+		pronvar = pronunciations[words == word]
-        acoustic_model = os.path.join(acoustic_model_dir, 'hmm' + hmm_num_str + r'-2\hmmdefs')
+		c = Counter(pronvar)
+		total_num = sum(c.values())
 
-        predictions = []
+		with open(fileStat, 'w') as f:
-        for i, filename in enumerate(df['filename']):
+			for key, value in c.items():
-            print('=== {0}/{1} ==='.format(i, len(df)))
+				f.write('{0}\t{1:.2f}\t{2}\t{3}\n'.format(value, value/total_num*100, WORD, key))
-            wav_file = os.path.join(wav_dir, filename)
-    
-            if os.path.exists(wav_file) and i in df['filename'].keys():
-                word = df['word'][i]
-                WORD = word.upper()
-
-                # make label file.
-                label_file = os.path.join(wav_dir, filename.replace('.wav', '.lab'))
-                with open(label_file, 'w') as f:
-                    lines = f.write(WORD)
-
-                htk_dict_file = os.path.join(htk_dict_dir, word + '.dic')
-                fa_file       = os.path.join(fa_dir, filename.replace('.wav', '.txt') + hmm_num_str)
-                pyhtk.doHVite(wav_file, label_file, htk_dict_file, fa_file, default.config_hvite, default.phonelist, acoustic_model)
-
-                prediction = am_func.read_fileFA(fa_file)
-                predictions.append(prediction)
-
-                os.remove(label_file)
-                print('{0}: {1} -> {2}'.format(WORD, df['famehtk'][i], prediction))
-            else:
-                predictions.append('')
-                print('!!!!! file not found.')
-
-        predictions = np.array(predictions)
-        #match = np.c_[words[predictions != ''], pronunciations[predictions != ''], predictions[predictions != '']]
-        np.save(os.path.join(data_dir, 'predictions_hmm' + hmm_num_str + '.npy'), predictions)
 
 
-## ======================= make files which is used for forced alignment by Kaldi =======================
+## ======================= forced alignment =======================
-if make_kaldi_data_files:
+if do_forced_alignment:
-    wav_dir	 = r'c:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\corpus\stimmen'
+	configHVite   = cygwin_dir + r'\config\config.HVite'
-    kaldi_work_dir = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5'
+	filePhoneList = experiments_dir + r'\friesian\acoustic_model\config\phonelist_friesian.txt'
-    kaldi_data_dir = os.path.join(kaldi_work_dir, 'data', 'alignme') 
+	wav_dir	= experiments_dir + r'\stimmen\wav'
-    kaldi_dict_dir = os.path.join(kaldi_work_dir, 'data', 'local', 'dict')
-    htk_dict_dir   = os.path.join(experiments_dir, 'stimmen', 'dic_top3')
 
-    wav_scp   = os.path.join(kaldi_data_dir, 'wav.scp')
+	#for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128]:
-    text_file = os.path.join(kaldi_data_dir, 'text')
+	for hmm_num in [64]:
-    utt2spk   = os.path.join(kaldi_data_dir, 'utt2spk')
+		hmm_num_str = str(hmm_num)
+		AcousticModel = experiments_dir + r'\friesian\acoustic_model\model\hmm' + hmm_num_str + r'-3\hmmdefs'
 
-    lexicon_txt = os.path.join(kaldi_dict_dir, 'lexicon.txt')
+		predictions = []
+		file_num_max = len(filenames)
+		for i in range(0, file_num_max):
+			print('=== {0}/{1} ==='.format(i, file_num_max))
+			filename = filenames[i]
+			fileWav = wav_dir + '\\' + filename
 	
-    predictions = []
+			if os.path.exists(fileWav):
-    file_num_max = len(filenames)
+				word = words[i]
+				WORD = word.upper()
 
-    # remove previous files.
+				# make label file.
-    if os.path.exists(wav_scp):
+				fileLab = wav_dir + '\\' + filename.replace('.wav', '.lab') 
-        os.remove(wav_scp)
+				with open(fileLab, 'w') as f:
-    if os.path.exists(text_file):
+					lines = f.write(WORD)
-        os.remove(text_file)
-    if os.path.exists(utt2spk):
-        os.remove(utt2spk)
 
-    f_wav_scp   = open(wav_scp, 'a', encoding="utf-8", newline='\n')
+				fileDic = experiments_dir + r'\stimmen\dic_short' + '\\' + word + '.dic'
-    f_text_file = open(text_file, 'a', encoding="utf-8", newline='\n')
+				fileFA  = experiments_dir + r'\stimmen\FA_short' + '\\' + filename.replace('.wav', '.txt') + hmm_num_str
-    f_utt2spk   = open(utt2spk, 'a', encoding="utf-8", newline='\n')
 
-    # make wav.scp, text, and utt2spk files.
+				pyHTK.doHVite(fileWav, fileLab, fileDic, fileFA, configHVite, filePhoneList, AcousticModel)
-    for i in range(0, file_num_max):
+				prediction = read_fileFA(fileFA)
-    #for i in range(400, 410):
+				predictions.append(prediction)
-        print('=== {0}/{1} ==='.format(i+1, file_num_max))
-        filename = filenames[i]
-        wav_file = wav_dir + '\\' + filename
 
-        if os.path.exists(wav_file):
+				os.remove(fileLab)
-            speaker_id = 'speaker_' + str(i).zfill(4)
+				print('{0}: {1} -> {2}'.format(WORD, pronunciations[i], prediction))
-            utterance_id = filename.replace('.wav', '')
+			else:
-            utterance_id = utterance_id.replace(' ', '_')
+				predictions.append('')
-            utterance_id = speaker_id + '-' + utterance_id
+				print('!!!!! file not found.')
-
-            # wav.scp file
-            wav_file_unix = wav_file.replace('\\', '/')
-            wav_file_unix = wav_file_unix.replace('c:/', '/mnt/c/')
-
-            f_wav_scp.write('{0} {1}\n'.format(utterance_id, wav_file_unix))
-
-            # text file
-            word = words[i].lower()
-            f_text_file.write('{0}\t{1}\n'.format(utterance_id, word))
-
-            # utt2spk
-            f_utt2spk.write('{0} {1}\n'.format(utterance_id, speaker_id))
-
-    f_wav_scp.close()
-    f_text_file.close()
-    f_utt2spk.close()
-
-
-## ======================= make lexicon txt which is used by Kaldi =======================
-if make_kaldi_lexicon_txt:
-    kaldi_work_dir = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5'
-    kaldi_dict_dir = os.path.join(kaldi_work_dir, 'data', 'local', 'dict')
-    lexicon_txt = os.path.join(kaldi_dict_dir, 'lexicon.txt')
-    option_num = 5
-
-    # remove previous file.
-    if os.path.exists(lexicon_txt):
-        os.remove(lexicon_txt)
-
-    mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', ipa_xsampa_converter_dir)
-    with open(csvfile, encoding="utf-8") as fin:
-        lines = csv.reader(fin, delimiter=';', lineterminator="\n", skipinitialspace=True)
-        next(lines, None) # skip the headers
-
-        filenames	   = []
-        words		   = []
-        pronunciations = []
-        p = []
-        for line in lines:
-            if line[1] is not '' and len(line) > 5:
-                filenames.append(line[0])
-                words.append(line[1])
-                pron_xsampa = line[3]
-                pron_ipa = convert_xsampa2ipa.conversion('xsampa', 'ipa', mapping, pron_xsampa)
-                pron_ipa = pron_ipa.replace('ː', ':')
-            
-                # adjust to phones used in the acoustic model.
-                pronunciations.append(pron_ipa)
-
-    # check if all phones are in the phonelist of the acoustic model.
-    #'y', 'b', 'ɾ', 'u', 'ɔ:', 'ø', 't', 'œ', 'n', 'ɒ', 'ɐ', 'f', 'o', 'k', 'x', 'ɡ', 'v', 's', 'ɛ:', 'ɪ:', 'ɑ', 'ɛ', 'a', 'd', 'z', 'ɪ', 'ɔ', 'l', 'i:', 'm', 'p', 'a:', 'i', 'e', 'j', 'o:', 'ʁ', 'h', ':', 'e:', 'ə', 'æ', 'χ', 'w', 'r', 'ə:', 'sp', 'ʊ', 'u:', 'ŋ'
-
-    filenames	   = np.array(filenames)
-    words		   = np.array(words)
-    wordlist       = np.unique(words)
-    pronunciations = np.array(pronunciations)
-                
-    # output lexicon.txt
-    #f_lexicon_txt = open(lexicon_txt, 'a', encoding="utf-8", newline='\n')
-    pronvar_list_all = []
-    for word in word_list:
-
-        # pronunciation variant of the target word.
-        pronvar_ = pronunciations[words == word]
-        # remove ''
-        pronvar_ = np.delete(pronvar_, np.where(pronvar_==''))
-
-        c = Counter(pronvar_)
-        total_num = sum(c.values())
-
-        for key, value in c.most_common(option_num):
-            #print('{0}\t{1}\t{2}\t{3}'.format(word, key, value, total_num))
-            key = key.replace('æ', 'ɛ')
-            key = key.replace('ɐ', 'a')
-            key = key.replace('ɑ', 'a')
-            key = key.replace('ɾ', 'r')
-            key = key.replace('ʁ', 'r')
-            key = key.replace('ʊ', 'u')
-            key = key.replace('χ', 'x')
-            #print('-->{0}\t{1}\t{2}\t{3}\n'.format(word, key, value, total_num))
-            
-            # make possible pronounciation variant list.
-            pronvar_list = [key]
-            while 'ø:' in ' '.join(pronvar_list) or 'œ' in ' '.join(pronvar_list) or 'ɒ' in ' '.join(pronvar_list):
-                pronvar_list_ = []
-                for p in pronvar_list:
-                    if 'ø:' in p:
-                        pronvar_list_.append(p.replace('ø:', 'ö'))
-                        pronvar_list_.append(p.replace('ø:', 'ö:'))
-                    if 'œ' in p:
-                        pronvar_list_.append(p.replace('œ', 'ɔ̈'))
-                        pronvar_list_.append(p.replace('œ', 'ɔ̈:'))
-                    if 'ɒ' in p:
-                        pronvar_list_.append(p.replace('ɒ', 'ɔ̈'))
-                        pronvar_list_.append(p.replace('ɒ', 'ɔ̈:'))
-                pronvar_list = np.unique(pronvar_list_)
-
-            for pronvar_ in pronvar_list:
-                split_ipa   = convert_phone_set.split_fame_ipa(pronvar_)
-                pronvar_out = ' '.join(split_ipa) 
-                pronvar_list_all.append([word, pronvar_out])
-    
-    # output
-    pronvar_list_all = np.array(pronvar_list_all)
-    pronvar_list_all = np.unique(pronvar_list_all, axis=0)
-    #f_lexicon_txt.write('<UNK>\tSPN\n')
-    #for line in pronvar_list_all:
-    #    f_lexicon_txt.write('{0}\t{1}\n'.format(line[0].lower(), line[1]))
-
-    #f_lexicon_txt.close()
-
-## ======================= load kaldi forced alignment result =======================
-if load_forced_alignment_kaldi:
-    kaldi_work_dir = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5'
-    phones_txt = kaldi_work_dir + '\\data\\lang\\phones.txt'
-    merged_alignment_txt = kaldi_work_dir + '\\exp\\tri1_alignme\\merged_alignment.txt'
-    
-    filenames	   = np.load(data_dir + '\\filenames.npy')
-    words		   = np.load(data_dir + '\\words.npy')
-    pronunciations = np.load(data_dir + '\\pronunciations_ipa.npy')
-    pronvar_list_all = np.load(data_dir + '\\pronvar_list_all.npy')
-    word_list = np.unique(words)    
-
-    # load the mapping between phones and ids.
-    with open(phones_txt, 'r', encoding="utf-8") as f:
-        mappings = f.read().split('\n')
-
-    phones = []
-    phone_ids = []
-    for m in mappings:
-        m = m.split(' ')
-        if len(m) > 1:
-            phones.append(m[0])
-            phone_ids.append(int(m[1]))
-
-    with open(merged_alignment_txt, 'r') as f:
-        lines = f.read()
-        lines = lines.split('\n')
-
-    fa_filenames = []
-    fa_pronunciations = []
-    filename_ = ''
-    pron = []
-    for line in lines:
-        line = line.split(' ')
-        if len(line) == 5:
-            filename = line[0]
-            if filename == filename_:
-                phone_id = int(line[4])
-                #if not phone_id == 1:
-                phone    = phones[phone_ids.index(phone_id)]
-                pron_ = re.sub(r'_[A-Z]', '', phone)
-                if not pron_ == 'SIL':
-                    pron.append(pron_)
-            else:
-                fa_filenames.append(re.sub(r'speaker_[0-9]{4}-', '', filename))
-                fa_pronunciations.append(' '.join(pron))
-                pron = []
-
-            filename_ = filename
-
-    # correct or not.
-    #for filename, fa_pronunciation in zip(fa_filenames, fa_pronunciations):
 
+		predictions = np.array(predictions)
+		match = np.c_[words[predictions != ''], pronunciations[predictions != ''], predictions[predictions != '']]
+		np.save(data_dir + '\\match_hmm' + hmm_num_str + '.npy', match)
 
 
 ## ======================= evaluate the result of forced alignment =======================
 if eval_forced_alignment:
-    match_num = []
-    for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256]:
-    #hmm_num = 256
-        hmm_num_str = str(hmm_num)
-        match = np.load(data_dir + '\\match_hmm' + hmm_num_str + '.npy')
 
-        # use dic_short?
+	#for hmm_num in [1, 2, 4, 8, 16, 32, 64]:
-        if 1:
+	hmm_num = 64
-            pronunciation_variants = np.array(['WORD', 'pronunciation']).reshape(1, 2)
+	hmm_num_str = str(hmm_num)
-            for word in word_list:
+	match = np.load(data_dir + '\\match_hmm' + hmm_num_str + '.npy')
-                fileDic = experiments_dir + r'\stimmen\dic_top3' + '\\' + word + '.dic'
-                pronunciation_variants = np.r_[pronunciation_variants, pyHTK.loadHTKdic(fileDic)]
 	
-            # see only words which appears in top 3.
+	# use dic_short?
-            match_short = []
+	if 1:
-            for line in match:
+		pronunciation_variants = np.array(['WORD', 'pronunciation']).reshape(1, 2)
-                word = line[0]
+		for word in word_list:
-                WORD = word.upper()
+			fileDic = experiments_dir + r'\stimmen\dic_short' + '\\' + word + '.dic'
-                pronvar = pronunciation_variants[pronunciation_variants[:, 0] == word.upper(), 1]
+			pronunciation_variants = np.r_[pronunciation_variants, pyHTK.loadHTKdic(fileDic)]
 
-                if line[1] in pronvar:
+		match_short = []
-                    match_short.append(line)
+		for line in match:
+			word = line[0]
+			WORD = word.upper()
+			pronvar = pronunciation_variants[pronunciation_variants[:, 0] == word.upper(), 1]
 		
-            match_short = np.array(match_short)
+			if line[1] in pronvar:
-            match = np.copy(match_short)
+				match_short.append(line)
 
-            # number of match
+		match_short = np.array(match_short)
-            total_match = sum(match[:, 1] == match[:, 2])
+		match = np.copy(match_short)
-            print("{}: {}/{}".format(hmm_num_str, total_match, match.shape[0]))
-            match_num.append([hmm_num, total_match, match.shape[0]])
 
+	# number of match
+	total_match = sum(match[:, 1] == match[:, 2])
+	print("{}: {}/{}".format(hmm_num_str, total_match, match.shape[0]))
 
-    # number of mixtures vs accuracy
-    match_num = np.array(match_num)
-    plt.xscale("log")
-    plt.plot(match_num[:, 0], match_num[:, 1]/match_num[0, 2], 'o-')
-    plt.xlabel('number of mixtures', fontsize=14, fontweight='bold')
-    plt.ylabel('accuracy', fontsize=14, fontweight='bold')
-    plt.show()
-
-    # confusion matrix
-    #dir_out   = r'C:\OneDrive\Research\rug\experiments\stimmen\result'
-    #word_list = np.unique(match[:, 0])
-    
-    #for word in word_list:
-    #    match_ = match[match[:, 0] == word, :]
-    #    cm = confusion_matrix(match_[:, 1], match_[:, 2])
-    #    pronvar = pronunciation_variants[pronunciation_variants[:, 0] == word.upper(), 1]
-
-    #    plt.figure()
-    #    plot_confusion_matrix(cm, classes=pronvar, normalize=True)
-    #    plt.savefig(dir_out + '\\cm_' + word + '.png')