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

master
yemaozi88 3 years ago
parent
commit
3a98e184fe
  1. BIN
      .vs/acoustic_model/v15/.suo
  2. 6
      acoustic_model.sln
  3. BIN
      acoustic_model/__pycache__/acoustic_model_functions.cpython-36.pyc
  4. BIN
      acoustic_model/__pycache__/convert_xsampa2ipa.cpython-36.pyc
  5. BIN
      acoustic_model/__pycache__/defaultfiles.cpython-36.pyc
  6. 9
      acoustic_model/acoustic_model.pyproj
  7. 67
      acoustic_model/acoustic_model_functions.py
  8. 259
      acoustic_model/convert_xsampa2ipa.py
  9. 35
      acoustic_model/defaultfiles.py
  10. 16
      acoustic_model/fa_test.py
  11. 282
      acoustic_model/performance_check.py
  12. 54
      acoustic_model/phone_conversion_check.py

BIN
.vs/acoustic_model/v15/.suo

6
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

BIN
acoustic_model/__pycache__/acoustic_model_functions.cpython-36.pyc

BIN
acoustic_model/__pycache__/convert_xsampa2ipa.cpython-36.pyc

BIN
acoustic_model/__pycache__/defaultfiles.cpython-36.pyc

9
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>

67
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)

259
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

35
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')

16
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'
)

282
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

54
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))
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