with bug-fixed xsampa->ipa conversion, FA is performed.

acoustic_model.sln

@@ -9,13 +9,12 @@ 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
-		..\ipa-xsama-converter\converter.py = ..\ipa-xsama-converter\converter.py
+		..\toolbox\evaluation.py = ..\toolbox\evaluation.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

@@ -22,12 +22,11 @@ dataset_list = ['devel', 'test', 'train']
 extract_features  = 0
 make_feature_list = 0
 conv_lexicon	  = 0
-check_lexicon	  = 1
+check_lexicon	  = 0
 make_mlf		  = 0
 combine_files	  = 0
 flat_start		  = 0
-train_model		  = 0
+train_model		  = 1
-forced_alignment  = 0
 
 
 sys.path.append(os.path.join(os.path.dirname(sys.path[0]), curr_dir))
@@ -288,7 +287,7 @@ if flat_start:
 ## ======================= estimate monophones =======================
 if train_model:
 	iter_num_max = 3
-	for mix_num in [16, 32, 64, 128]:
+	for mix_num in [128, 256, 512, 1024]:
 		for iter_num in range(1, iter_num_max+1):
 			print("===== mix{}, iter{} =====".format(mix_num, iter_num))
 			iter_num_pre = iter_num - 1
@@ -315,5 +314,6 @@ 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

@@ -28,6 +28,12 @@
     <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

@@ -1,17 +1,13 @@
 import os
 import sys
+from collections import Counter
 
+import numpy as np
 import pandas as pd
 
+import defaultfiles as default
 
-## ======================= user define =======================
+sys.path.append(default.forced_alignment_module_dir)
-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
 
 
@@ -42,6 +38,41 @@ 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. """
 
@@ -100,3 +131,21 @@ 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\Research\rug\experiments\friesian\corpus
+FAME_dir = C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\corpus

acoustic_model/convert_xsampa2ipa.py

@@ -7,122 +7,155 @@ import json
 import sys
 import os
 
-
+import defaultfiles as default
-#sys.path.append(ipa_xsampa_converter_dir)
+sys.path.append(os.path.join(default.repo_dir, 'forced_alignment'))
-#import converter
+from forced_alignment import convert_phone_set
 
 
 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

@@ -0,0 +1,35 @@
+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

@@ -0,0 +1,16 @@
+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

@@ -1,257 +1,437 @@
 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
 
 
-## ======================= functions =======================
+## ======================= user define =======================
-
+#curr_dir   = r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model'
-def read_fileFA(fileFA):
+#config_ini = 'config.ini'
-	"""
+#repo_dir   = r'C:\Users\Aki\source\repos'
-	read the result file of HTK forced alignment.
+#forced_alignment_module     = repo_dir + '\\forced_alignment'
-	this function only works when input is one word.
+#forced_alignment_module_old = repo_dir + '\\aki_tools'
-	"""
+#ipa_xsampa_converter_dir    = repo_dir + '\\ipa-xsama-converter'
-	with open(fileFA, 'r') as f:
+#accent_classification_dir   = repo_dir + '\\accent_classification\accent_classification'
-		lines = f.read()
+excel_file = os.path.join(default.experiments_dir, 'stimmen', 'data', 'Frisian Variants Picture Task Stimmen.xlsx')
-		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'
-##   USER DEFINE   ##
+data_dir        = os.path.join(default.experiments_dir, 'stimmen', 'data')
-#####################
+#csvfile         = data_dir + '\\Frisian Variants Picture Task Stimmen.csv'
-curr_dir   = r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model'
+wav_dir	= os.path.join(default.experiments_dir, 'stimmen', 'wav')
-config_ini = curr_dir + '\\config.ini'
+acoustic_model_dir = os.path.join(default.experiments_dir, 'friesian', 'acoustic_model', 'model')
-forced_alignment_module     = r'C:\Users\Aki\source\repos\forced_alignment'
+htk_dict_dir = os.path.join(default.experiments_dir, 'stimmen', 'dic_short')
-forced_alignment_module_old = r'C:\OneDrive\Research\rug\code\forced_alignment\forced_alignment'
+fa_dir  = os.path.join(default.experiments_dir, 'stimmen', 'FA')
-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
-make_dic_files_short = 0
+do_forced_alignment_htk = 1
-do_forced_alignment = 0
+make_kaldi_data_files = 0
-eval_forced_alignment = 1
+make_kaldi_lexicon_txt = 0
+load_forced_alignment_kaldi = 0
+eval_forced_alignment = 0
 
 
 
 ## ======================= add paths =======================
 
-sys.path.append(forced_alignment_module)
+sys.path.append(os.path.join(default.repo_dir, 'forced_alignment'))
 from forced_alignment import convert_phone_set
+from forced_alignment import pyhtk
 
-# for interactive window
+sys.path.append(os.path.join(default.repo_dir, 'toolbox'))
-sys.path.append(curr_dir)
+#import pyHTK
-import convert_xsampa2ipa
+from evaluation import plot_confusion_matrix
-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)
 
-	## check phones included in FAME!
+mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', default.ipa_xsampa_converter_dir)
-	# the phones used in the lexicon.
-	#phonelist = am_func.get_phonelist(lex_htk)
 
-	# the lines which include a specific phone.
+xls = pd.ExcelFile(excel_file)
-	#lines = am_func.find_phone(lex_asr, 'x')
 
-	with open(csvfile, encoding="utf-8") as fin:
+## check conversion
-		lines = csv.reader(fin, delimiter=';', lineterminator="\n", skipinitialspace=True)
+#df = pd.read_excel(xls, 'frequency')
-		next(lines, None) # skip the headers
+#for xsampa, ipa in zip(df['X-SAMPA'], df['IPA']):
+#    #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)
 
-				# adjust to phones used in the acoustic model.
+## check phones included in FAME!
-				pron_famehtk = pron_famehtk.replace('sp', 'sil')
+# the phones used in the lexicon.
-				pron_famehtk = pron_famehtk.replace('ce :', 'ce') # because ceh is ignored.
+#phonelist = am_func.get_phonelist(lex_asr)
-				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'}
+# the lines which include a specific phone.
-				#pron = []
+#lines = am_func.find_phone(lex_asr, 'x')
-				#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(':')
 
-	filenames	   = np.array(filenames)
+# Filename, Word, Self Xsampa
-	words		   = np.array(words)
+df = pd.read_excel(xls, 'original')
-	pronunciations = np.array(pronunciations)
     
-	del line, lines
+ipas     = []
-	del pron_xsampa, pron_ipa, pron_famehtk
+famehtks = []
+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(';', ':')
 
-	np.save(data_dir + '\\filenames.npy', filenames)
+        ipa = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
-	np.save(data_dir + '\\words.npy', words)
+        ipa = ipa.replace('ː', ':')
-	np.save(data_dir + '\\pronunciations.npy', pronunciations)
+        ipa = ipa.replace(' ', '')
-else:
+        ipas.append(ipa)
-	filenames	   = np.load(data_dir + '\\filenames.npy')
+        famehtk = convert_phone_set.ipa2famehtk(ipa)
-	words		   = np.load(data_dir + '\\words.npy')
+        famehtks.append(famehtk)
+    else:
+        ipas.append('')
+        famehtks.append('')
         
-	pronunciations = np.load(data_dir + '\\pronunciations.npy')
+# extract interesting cols.
-word_list = np.unique(words)
+df = pd.DataFrame({'filename': df['Filename'], 
+                    '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:
-	output_dir = experiments_dir + r'\stimmen\dic'
+    word_list = np.unique(df['word'])
 
-	for word in word_list:
+    output_type = 3
-		WORD	= word.upper()
-		fileDic = output_dir + '\\' + word + '.dic'
     
-		# make dic file.
+    for word in word_list:
-		pronvar_ = pronunciations[words == word]
+        htk_dict_file = htk_dict_dir + '\\' + word + '.dic'
-		pronvar  = np.unique(pronvar_)
 
-		with open(fileDic, 'w') as f:
+        # pronunciation variant of the target word.
-			for pvar in pronvar:
+        pronvar_ = df['famehtk'][df['word'].str.match(word)]
-				f.write('{0}\t{1}\n'.format(WORD, pvar))
+
+        # make dic file.
+        am_func.make_dic(word, pronvar_, htk_dict_file, output_type)
         
 
-## ======================= make dict files for most popular words. ======================	
+## ======================= forced alignment using HTK =======================
-if make_dic_files_short:
+if do_forced_alignment_htk:
-	output_dir = experiments_dir + r'\stimmen\dic'
 
-	#word = word_list[3]
+    #hmm_num = 2
-	for word in word_list:
+    for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]:
-		WORD	= word.upper()
-		fileStat = output_dir + '\\' + word + '_stat.csv'
 
-		pronvar = pronunciations[words == word]
+        hmm_num_str = str(hmm_num)
-		c = Counter(pronvar)
+        acoustic_model = os.path.join(acoustic_model_dir, 'hmm' + hmm_num_str + r'-2\hmmdefs')
-		total_num = sum(c.values())
 
-		with open(fileStat, 'w') as f:
+        predictions = []
-			for key, value in c.items():
+        for i, filename in enumerate(df['filename']):
-				f.write('{0}\t{1:.2f}\t{2}\t{3}\n'.format(value, value/total_num*100, WORD, key))
+            print('=== {0}/{1} ==='.format(i, len(df)))
+            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)
 
 
-## ======================= forced alignment =======================
+## ======================= make files which is used for forced alignment by Kaldi =======================
-if do_forced_alignment:
+if make_kaldi_data_files:
-	configHVite   = cygwin_dir + r'\config\config.HVite'
+    wav_dir	 = r'c:\OneDrive\WSL\kaldi-trunk\egs\fame\s5\corpus\stimmen'
-	filePhoneList = experiments_dir + r'\friesian\acoustic_model\config\phonelist_friesian.txt'
+    kaldi_work_dir = r'C:\OneDrive\WSL\kaldi-trunk\egs\fame\s5'
-	wav_dir	= experiments_dir + r'\stimmen\wav'
+    kaldi_data_dir = os.path.join(kaldi_work_dir, 'data', 'alignme') 
+    kaldi_dict_dir = os.path.join(kaldi_work_dir, 'data', 'local', 'dict')
+    htk_dict_dir   = os.path.join(experiments_dir, 'stimmen', 'dic_top3')
 
-	#for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128]:
+    wav_scp   = os.path.join(kaldi_data_dir, 'wav.scp')
-	for hmm_num in [64]:
+    text_file = os.path.join(kaldi_data_dir, 'text')
-		hmm_num_str = str(hmm_num)
+    utt2spk   = os.path.join(kaldi_data_dir, 'utt2spk')
-		AcousticModel = experiments_dir + r'\friesian\acoustic_model\model\hmm' + hmm_num_str + r'-3\hmmdefs'
 
-		predictions = []
+    lexicon_txt = os.path.join(kaldi_dict_dir, 'lexicon.txt')
-		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
 
-			if os.path.exists(fileWav):
+    predictions = []
-				word = words[i]
+    file_num_max = len(filenames)
-				WORD = word.upper()
 
-				# make label file.
+    # remove previous files.
-				fileLab = wav_dir + '\\' + filename.replace('.wav', '.lab') 
+    if os.path.exists(wav_scp):
-				with open(fileLab, 'w') as f:
+        os.remove(wav_scp)
-					lines = f.write(WORD)
+    if os.path.exists(text_file):
+        os.remove(text_file)
+    if os.path.exists(utt2spk):
+        os.remove(utt2spk)
 
-				fileDic = experiments_dir + r'\stimmen\dic_short' + '\\' + word + '.dic'
+    f_wav_scp   = open(wav_scp, 'a', encoding="utf-8", newline='\n')
-				fileFA  = experiments_dir + r'\stimmen\FA_short' + '\\' + filename.replace('.wav', '.txt') + hmm_num_str
+    f_text_file = open(text_file, 'a', encoding="utf-8", newline='\n')
+    f_utt2spk   = open(utt2spk, 'a', encoding="utf-8", newline='\n')
 
-				pyHTK.doHVite(fileWav, fileLab, fileDic, fileFA, configHVite, filePhoneList, AcousticModel)
+    # make wav.scp, text, and utt2spk files.
-				prediction = read_fileFA(fileFA)
+    for i in range(0, file_num_max):
-				predictions.append(prediction)
+    #for i in range(400, 410):
+        print('=== {0}/{1} ==='.format(i+1, file_num_max))
+        filename = filenames[i]
+        wav_file = wav_dir + '\\' + filename
 
-				os.remove(fileLab)
+        if os.path.exists(wav_file):
-				print('{0}: {1} -> {2}'.format(WORD, pronunciations[i], prediction))
+            speaker_id = 'speaker_' + str(i).zfill(4)
-			else:
+            utterance_id = filename.replace('.wav', '')
-				predictions.append('')
+            utterance_id = utterance_id.replace(' ', '_')
-				print('!!!!! file not found.')
+            utterance_id = speaker_id + '-' + utterance_id
+
+            # 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')
     
-	#for hmm_num in [1, 2, 4, 8, 16, 32, 64]:
+        # use dic_short?
-	hmm_num = 64
+        if 1:
-	hmm_num_str = str(hmm_num)
+            pronunciation_variants = np.array(['WORD', 'pronunciation']).reshape(1, 2)
-	match = np.load(data_dir + '\\match_hmm' + hmm_num_str + '.npy')
+            for word in word_list:
+                fileDic = experiments_dir + r'\stimmen\dic_top3' + '\\' + word + '.dic'
+                pronunciation_variants = np.r_[pronunciation_variants, pyHTK.loadHTKdic(fileDic)]
 
-	# use dic_short?
+            # see only words which appears in top 3.
-	if 1:
+            match_short = []
-		pronunciation_variants = np.array(['WORD', 'pronunciation']).reshape(1, 2)
+            for line in match:
-		for word in word_list:
+                word = line[0]
-			fileDic = experiments_dir + r'\stimmen\dic_short' + '\\' + word + '.dic'
+                WORD = word.upper()
-			pronunciation_variants = np.r_[pronunciation_variants, pyHTK.loadHTKdic(fileDic)]
+                pronvar = pronunciation_variants[pronunciation_variants[:, 0] == word.upper(), 1]
         
-		match_short = []
+                if line[1] in pronvar:
-		for line in match:
+                    match_short.append(line)
-			word = line[0]
-			WORD = word.upper()
-			pronvar = pronunciation_variants[pronunciation_variants[:, 0] == word.upper(), 1]
 
-			if line[1] in pronvar:
+            match_short = np.array(match_short)
-				match_short.append(line)
+            match = np.copy(match_short)
 
-		match_short = np.array(match_short)
+            # number of match
-		match = np.copy(match_short)
+            total_match = sum(match[:, 1] == match[:, 2])
+            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')