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

acoustic_model.sln

@@ -9,14 +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
-		..\forced_alignment\forced_alignment\defaultfiles.py = ..\forced_alignment\forced_alignment\defaultfiles.py
+		..\toolbox\evaluation.py = ..\toolbox\evaluation.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
-		..\accent_classification\accent_classification\output_confusion_matrix.py = ..\accent_classification\accent_classification\output_confusion_matrix.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.pyproj

@@ -4,7 +4,7 @@
     <SchemaVersion>2.0</SchemaVersion>
     <ProjectGuid>4d8c8573-32f0-4a62-9e62-3ce5cc680390</ProjectGuid>
     <ProjectHome>.</ProjectHome>
-    <StartupFile>phone_conversion_check.py</StartupFile>
+    <StartupFile>performance_check.py</StartupFile>
     <SearchPath>
     </SearchPath>
     <WorkingDirectory>.</WorkingDirectory>
@@ -28,10 +28,13 @@
     <Compile Include="convert_xsampa2ipa.py">
       <SubType>Code</SubType>
     </Compile>
-    <Compile Include="performance_check.py">
+    <Compile Include="defaultfiles.py">
+      <SubType>Code</SubType>
+    </Compile>
+    <Compile Include="fa_test.py">
       <SubType>Code</SubType>
     </Compile>
-    <Compile Include="phone_conversion_check.py">
+    <Compile Include="performance_check.py">
       <SubType>Code</SubType>
     </Compile>
   </ItemGroup>

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 =======================
-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)
+sys.path.append(default.forced_alignment_module_dir)
 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. """
 
@@ -99,4 +130,22 @@ def combine_lexicon(lexicon_file1, lexicon_file2, lexicon_out):
 	lex2 = pd.read_table(lexicon_file2, names=['word', 'pronunciation'])
 	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')
+	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/convert_xsampa2ipa.py

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

@@ -1,255 +1,176 @@
 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
-
-
-## ======================= functions =======================
-
-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)
-
-
-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.')
-        
-    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
+#from sklearn.metrics import confusion_matrix
 
-                    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))
+import acoustic_model_functions as am_func
+import convert_xsampa2ipa
+import defaultfiles as default
 
 
 ## ======================= 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'
-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'
-accent_classification_dir   = r'C:\Users\Aki\source\repos\accent_classification\accent_classification'
+#curr_dir   = r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model'
+#config_ini = 'config.ini'
+#repo_dir   = r'C:\Users\Aki\source\repos'
+#forced_alignment_module     = repo_dir + '\\forced_alignment'
+#forced_alignment_module_old = repo_dir + '\\aki_tools'
+#ipa_xsampa_converter_dir    = repo_dir + '\\ipa-xsama-converter'
+#accent_classification_dir   = repo_dir + '\\accent_classification\accent_classification'
+excel_file = os.path.join(default.experiments_dir, 'stimmen', 'data', 'Frisian Variants Picture Task Stimmen.xlsx')
 
 
-experiments_dir = r'C:\OneDrive\Research\rug\experiments'
-data_dir        = experiments_dir + '\\stimmen\\data'
-csvfile         = data_dir + '\\Frisian Variants Picture Task Stimmen.csv'
+#experiments_dir = r'C:\OneDrive\Research\rug\experiments'
+data_dir        = os.path.join(default.experiments_dir, 'stimmen', 'data')
+#csvfile         = data_dir + '\\Frisian Variants Picture Task Stimmen.csv'
+wav_dir	= os.path.join(default.experiments_dir, 'stimmen', 'wav')
+acoustic_model_dir = os.path.join(default.experiments_dir, 'friesian', 'acoustic_model', 'model')
+htk_dict_dir = os.path.join(default.experiments_dir, 'stimmen', 'dic_short')
+fa_dir  = os.path.join(default.experiments_dir, 'stimmen', 'FA')
+
+#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')
 
-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 = 0
+do_forced_alignment_htk = 1
 make_kaldi_data_files = 0
 make_kaldi_lexicon_txt = 0
-load_forced_alignment_kaldi = 1
+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(curr_dir)
-import convert_xsampa2ipa
-import acoustic_model_functions as am_func
-
-# for forced-alignment
-sys.path.append(forced_alignment_module_old)
-import pyHTK
-
-# to output confusion matrix
-sys.path.append(accent_classification_dir)
-from output_confusion_matrix import plot_confusion_matrix
-
-
-## ======================= 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'
+sys.path.append(os.path.join(default.repo_dir, 'toolbox'))
+#import pyHTK
+from evaluation import plot_confusion_matrix
 
 
 ## ======================= convert phones ======================
-if convert_phones:
-    mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', ipa_xsampa_converter_dir)
 
-    ## check phones included in FAME!
-    # the phones used in the lexicon.
-    #phonelist = am_func.get_phonelist(lex_htk)
+mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', default.ipa_xsampa_converter_dir)
 
-    # the lines which include a specific phone.
-    #lines = am_func.find_phone(lex_asr, 'x')
+xls = pd.ExcelFile(excel_file)
 
-    with open(csvfile, encoding="utf-8") as fin:
-        lines = csv.reader(fin, delimiter=';', lineterminator="\n", skipinitialspace=True)
-        next(lines, None) # skip the headers
+## check conversion
+#df = pd.read_excel(xls, 'frequency')
+#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.
-                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)
 
-    # check if all phones are in the phonelist of the acoustic model.
-    #phonelist = ' '.join(pronunciations)
-    #np.unique(phonelist.split(' '))
-    #phonelist.find(':')
+## check phones included in FAME!
+# the phones used in the lexicon.
+#phonelist = am_func.get_phonelist(lex_asr)
 
-    filenames	   = np.array(filenames)
-    words		   = np.array(words)
-    pronunciations = np.array(pronunciations)
+# the lines which include a specific phone.
+#lines = am_func.find_phone(lex_asr, 'x')
 
-    del line, lines
-    del pron_xsampa, pron_ipa, pron_famehtk
 
-    np.save(data_dir + '\\filenames.npy', filenames)
-    np.save(data_dir + '\\words.npy', words)
-    np.save(data_dir + '\\pronunciations.npy', pronunciations)
-else:
-    filenames	   = np.load(data_dir + '\\filenames.npy')
-    words		   = np.load(data_dir + '\\words.npy')
+# Filename, Word, Self Xsampa
+df = pd.read_excel(xls, 'original')
     
-    pronunciations = np.load(data_dir + '\\pronunciations.npy')
-word_list = np.unique(words)
+ipas     = []
+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(';', ':')
+
+        ipa = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
+        ipa = ipa.replace('ː', ':')
+        ipa = ipa.replace(' ', '')
+        ipas.append(ipa)
+        famehtk = convert_phone_set.ipa2famehtk(ipa)
+        famehtks.append(famehtk)
+    else:
+        ipas.append('')
+        famehtks.append('')
+        
+# extract interesting cols.
+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_type = 2
-    output_dir = experiments_dir + r'\stimmen\dic_short'
+    word_list = np.unique(df['word'])
+
+    output_type = 3
     
     for word in word_list:
-        WORD	= word.upper()
-        fileDic = output_dir + '\\' + word + '.dic'
+        htk_dict_file = htk_dict_dir + '\\' + word + '.dic'
 
         # pronunciation variant of the target word.
-        pronvar_ = pronunciations[words == word]
-        # remove ''
-        pronvar_ = np.delete(pronvar_, np.where(pronvar_==''))
+        pronvar_ = df['famehtk'][df['word'].str.match(word)]
 
         # make dic file.
-        make_dic(word, pronvar_, fileDic, output_type)
+        am_func.make_dic(word, pronvar_, htk_dict_file, output_type)
         
 
 ## ======================= forced alignment using HTK =======================
 if do_forced_alignment_htk:
-    configHVite   = cygwin_dir + r'\config\config.HVite'
-    filePhoneList = experiments_dir + r'\friesian\acoustic_model\config\phonelist_friesian.txt'
-    wav_dir	= experiments_dir + r'\stimmen\wav'
 
-    #hmm_num = 128
-    for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256]:
+    #hmm_num = 2
+    for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]:
+
         hmm_num_str = str(hmm_num)
-        AcousticModel = experiments_dir + r'\friesian\acoustic_model\model\hmm' + hmm_num_str + r'-2\hmmdefs'
+        acoustic_model = os.path.join(acoustic_model_dir, 'hmm' + hmm_num_str + r'-2\hmmdefs')
 
         predictions = []
-        file_num_max = len(filenames)
-        for i in range(0, file_num_max):
-            #for i in range(500, 502):
-            print('=== {0}/{1} ==='.format(i, file_num_max))
-            filename = filenames[i]
-            fileWav = wav_dir + '\\' + filename
+        for i, filename in enumerate(df['filename']):
+            print('=== {0}/{1} ==='.format(i, len(df)))
+            wav_file = os.path.join(wav_dir, filename)
     
-            if os.path.exists(fileWav):
-                word = words[i]
+            if os.path.exists(wav_file) and i in df['filename'].keys():
+                word = df['word'][i]
                 WORD = word.upper()
 
                 # make label file.
-                fileLab = wav_dir + '\\' + filename.replace('.wav', '.lab') 
-                with open(fileLab, 'w') as f:
+                label_file = os.path.join(wav_dir, filename.replace('.wav', '.lab'))
+                with open(label_file, 'w') as f:
                     lines = f.write(WORD)
 
-                fileDic = experiments_dir + r'\stimmen\dic_top3' + '\\' + word + '.dic'
-                fileFA  = experiments_dir + r'\stimmen\FA' + '\\' + filename.replace('.wav', '.txt') + hmm_num_str
+                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)
 
-                pyHTK.doHVite(fileWav, fileLab, fileDic, fileFA, configHVite, filePhoneList, AcousticModel)
-                prediction = read_fileFA(fileFA)
+                prediction = am_func.read_fileFA(fa_file)
                 predictions.append(prediction)
 
-                os.remove(fileLab)
-                print('{0}: {1} -> {2}'.format(WORD, pronunciations[i], 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(data_dir + '\\match_hmm' + hmm_num_str + '.npy', match)
+        #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 =======================
@@ -392,7 +313,7 @@ if make_kaldi_lexicon_txt:
                 pronvar_list = np.unique(pronvar_list_)
 
             for pronvar_ in pronvar_list:
-                split_ipa   = convert_phone_set.split_ipa_fame(pronvar_)
+                split_ipa   = convert_phone_set.split_fame_ipa(pronvar_)
                 pronvar_out = ' '.join(split_ipa) 
                 pronvar_list_all.append([word, pronvar_out])
     
@@ -456,13 +377,12 @@ if load_forced_alignment_kaldi:
             filename_ = filename
 
     # correct or not.
-    for filename, fa_pronunciation in zip(fa_filenames, fa_pronunciations):
+    #for filename, fa_pronunciation in zip(fa_filenames, fa_pronunciations):
         
 
 
 ## ======================= 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

acoustic_model/phone_conversion_check.py

@@ -1,54 +0,0 @@
-import os
-os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
-
-import sys
-
-import pandas as pd
-
-
-## ======================= user define =======================
-
-forced_alignment_module     = r'C:\Users\Aki\source\repos\forced_alignment'
-ipa_xsampa_converter_dir    = r'C:\Users\Aki\source\repos\ipa-xsama-converter'
-
-experiments_dir = r'c:\OneDrive\Research\rug\experiments'
-excel_file = experiments_dir + '\\stimmen\\data\\Frisian Variants Picture Task Stimmen.xlsx'
-
-
-## ======================= add paths =======================
-
-sys.path.append(forced_alignment_module)
-from forced_alignment import convert_phone_set
-
-import convert_xsampa2ipa
-
-
-xls = pd.ExcelFile(excel_file)
-df = pd.read_excel(xls, 'frequency')
-
-mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', ipa_xsampa_converter_dir)
-
-def xsampa2ipa(mapping, xsampa):
-    # 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 
-
-
-for xsampa, ipa in zip(df['X-SAMPA'], df['IPA']):
-    #ipa_converted = convert_xsampa2ipa.conversion('xsampa', 'ipa', mapping, xsampa_)
-    ipa_converted = xsampa2ipa(mapping, xsampa)
-    if not ipa_converted == ipa:
-        print('{0}: {1} - {2}'.format(xsampa_, ipa_converted, ipa))