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HTK related functions are moved to pyhtk project. fame acoustic models are made using fame_hmm.py. feature extraction is completed. A function is being made to get translation key from ipa to asr.

master
a.kunikoshi 3 years ago
parent
commit
7844a56281
  1. BIN
      .vs/acoustic_model/v15/.suo
  2. BIN
      _tmp/phone_to_be_searched.npy
  3. BIN
      _tmp/translation_key.npy
  4. 6
      acoustic_model.sln
  5. BIN
      acoustic_model/__pycache__/defaultfiles.cpython-36.pyc
  6. 13
      acoustic_model/acoustic_model.pyproj
  7. 202
      acoustic_model/acoustic_model_function.py
  8. 20
      acoustic_model/defaultfiles.py
  9. 252
      acoustic_model/fame_functions.py
  10. 156
      acoustic_model/fame_hmm.py

BIN
.vs/acoustic_model/v15/.suo

BIN
_tmp/phone_to_be_searched.npy

BIN
_tmp/translation_key.npy

6
acoustic_model.sln

@ -10,19 +10,21 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Solution Items", "Solution
..\forced_alignment\forced_alignment\__init__.py = ..\forced_alignment\forced_alignment\__init__.py
..\forced_alignment\forced_alignment\convert_phone_set.py = ..\forced_alignment\forced_alignment\convert_phone_set.py
..\toolbox\evaluation.py = ..\toolbox\evaluation.py
..\toolbox\toolbox\file_handling.py = ..\toolbox\toolbox\file_handling.py
..\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
reus-test\reus-test.py = reus-test\reus-test.py
..\forced_alignment\forced_alignment\scripts.py = ..\forced_alignment\forced_alignment\scripts.py
..\..\..\..\..\Python36-32\Lib\site-packages\novoapi\backend\session.py = ..\..\..\..\..\Python36-32\Lib\site-packages\novoapi\backend\session.py
..\forced_alignment\forced_alignment\tempfilename.py = ..\forced_alignment\forced_alignment\tempfilename.py
..\forced_alignment\forced_alignment\test_environment.py = ..\forced_alignment\forced_alignment\test_environment.py
EndProjectSection
EndProject
Project("{888888A0-9F3D-457C-B088-3A5042F75D52}") = "pyhtk", "..\pyhtk\pyhtk\pyhtk.pyproj", "{75FCEFAF-9397-43FC-8189-DE97ADB77AA5}"
EndProject
Global
GlobalSection(SolutionConfigurationPlatforms) = preSolution
Debug|Any CPU = Debug|Any CPU
@ -31,6 +33,8 @@ Global
GlobalSection(ProjectConfigurationPlatforms) = postSolution
{4D8C8573-32F0-4A62-9E62-3CE5CC680390}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
{4D8C8573-32F0-4A62-9E62-3CE5CC680390}.Release|Any CPU.ActiveCfg = Release|Any CPU
{75FCEFAF-9397-43FC-8189-DE97ADB77AA5}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
{75FCEFAF-9397-43FC-8189-DE97ADB77AA5}.Release|Any CPU.ActiveCfg = Release|Any CPU
EndGlobalSection
GlobalSection(SolutionProperties) = preSolution
HideSolutionNode = FALSE

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

13
acoustic_model/acoustic_model.pyproj

@ -4,7 +4,8 @@
<SchemaVersion>2.0</SchemaVersion>
<ProjectGuid>4d8c8573-32f0-4a62-9e62-3ce5cc680390</ProjectGuid>
<ProjectHome>.</ProjectHome>
<StartupFile>forced_aligner_comparison.py</StartupFile>
<StartupFile>
</StartupFile>
<SearchPath>
</SearchPath>
<WorkingDirectory>.</WorkingDirectory>
@ -21,10 +22,6 @@
<EnableUnmanagedDebugging>false</EnableUnmanagedDebugging>
</PropertyGroup>
<ItemGroup>
<Compile Include="acoustic_model.py" />
<Compile Include="acoustic_model_functions.py">
<SubType>Code</SubType>
</Compile>
<Compile Include="check_novoapi.py" />
<Compile Include="convert_xsampa2ipa.py">
<SubType>Code</SubType>
@ -35,9 +32,8 @@
<Compile Include="fa_test.py">
<SubType>Code</SubType>
</Compile>
<Compile Include="forced_aligner_comparison.py">
<SubType>Code</SubType>
</Compile>
<Compile Include="fame_functions.py" />
<Compile Include="forced_aligner_comparison.py" />
<Compile Include="novoapi_forced_alignment.py">
<SubType>Code</SubType>
</Compile>
@ -47,6 +43,7 @@
<Compile Include="novoapi_functions.py">
<SubType>Code</SubType>
</Compile>
<Compile Include="fame_hmm.py" />
</ItemGroup>
<ItemGroup>
<Content Include="config.ini" />

202
acoustic_model/acoustic_model_function.py

@ -1,202 +0,0 @@
import os
import sys
from collections import Counter
import numpy as np
import pandas as pd
import defaultfiles as default
sys.path.append(default.forced_alignment_module_dir)
from forced_alignment import convert_phone_set
def make_hcopy_scp_from_filelist_in_fame(FAME_dir, dataset, feature_dir, hcopy_scp):
""" Make a script file for HCopy using the filelist in FAME! corpus. """
filelist_txt = FAME_dir + '\\fame\\filelists\\' + dataset + 'list.txt'
with open(filelist_txt) as fin:
filelist = fin.read()
filelist = filelist.split('\n')
with open(hcopy_scp, 'w') as fout:
for filename_ in filelist:
filename = filename_.replace('.TextGrid', '')
if len(filename) > 3: # remove '.', '..' and ''
wav_file = FAME_dir + '\\fame\\wav\\' + dataset + '\\' + filename + '.wav'
mfc_file = feature_dir + '\\' + filename + '.mfc'
fout.write(wav_file + '\t' + mfc_file + '\n')
def make_filelist(input_dir, output_txt):
""" Make a list of files in the input_dir. """
filenames = os.listdir(input_dir)
with open(output_txt, 'w') as fout:
for filename in filenames:
fout.write(input_dir + '\\' + filename + '\n')
def make_htk_dict(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. """
with open(lexicon_file, "rt", encoding="utf-8") as fin:
lines = fin.read()
lines = lines.split('\n')
phonelist = set([])
for line in lines:
line = line.split('\t')
if len(line) > 1:
pronunciation = set(line[1].split())
phonelist = phonelist | pronunciation
return phonelist
def find_phone(lexicon_file, phone):
""" Search where the phone is used in the lexicon. """
with open(lexicon_file, "rt", encoding="utf-8") as fin:
lines = fin.read()
lines = lines.split('\n')
extracted = []
for line in lines:
line = line.split('\t')
if len(line) > 1:
pronunciation = line[1]
if phone in pronunciation:
extracted.append(line)
return extracted
def ipa2famehtk_lexicon(lexicon_file_in, lexicon_file_out):
""" Convert a lexicon file from IPA to HTK format for FAME! corpus. """
lexicon_in = pd.read_table(lexicon_file_in, names=['word', 'pronunciation'])
with open(lexicon_file_out, "w", encoding="utf-8") as fout:
for word, pronunciation in zip(lexicon_in['word'], lexicon_in['pronunciation']):
pronunciation_no_space = pronunciation.replace(' ', '')
pronunciation_famehtk = convert_phone_set.ipa2famehtk(pronunciation_no_space)
if 'ceh' not in pronunciation_famehtk and 'sh' not in pronunciation_famehtk:
fout.write("{0}\t{1}\n".format(word.upper(), pronunciation_famehtk))
def combine_lexicon(lexicon_file1, lexicon_file2, lexicon_out):
""" Combine two lexicon files and sort by words. """
with open(lexicon_file1, "rt", encoding="utf-8") as fin:
lines1 = fin.read()
lines1 = lines1.split('\n')
with open(lexicon_file2, "rt", encoding="utf-8") as fin:
lines2 = fin.read()
lines2 = lines2.split('\n')
lex1 = pd.read_table(lexicon_file1, names=['word', 'pronunciation'])
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')
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 fame_pronunciation_variant(ipa):
ipa = ipa.replace('æ', 'ɛ')
ipa = ipa.replace('ɐ', 'a')
ipa = ipa.replace('ɑ', 'a')
ipa = ipa.replace('ɾ', 'r')
ipa = ipa.replace('ɹ', 'r') # ???
ipa = ipa.replace('ʁ', 'r')
ipa = ipa.replace('ʀ', 'r') # ???
ipa = ipa.replace('ʊ', 'u')
ipa = ipa.replace('χ', 'x')
pronvar_list = [ipa]
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_)
return pronvar_list
def make_fame2ipa_variants(fame):
fame = 'rɛös'
ipa = [fame]
ipa.append(fame.replace('ɛ', 'æ'))
ipa.append(fame.replace('a', 'ɐ'))
ipa.append(fame.replace('a', 'ɑ'))
ipa.append(fame.replace('r', 'ɾ'))
ipa.append(fame.replace('r', 'ɹ'))
ipa.append(fame.replace('r', 'ʁ'))
ipa.append(fame.replace('r', 'ʀ'))
ipa.append(fame.replace('u', 'ʊ'))
ipa.append(fame.replace('x', 'χ'))
ipa.append(fame.replace('ö', 'ø:'))
ipa.append(fame.replace('ö:', 'ø:'))
ipa.append(fame.replace('ɔ̈', 'œ'))
ipa.append(fame.replace('ɔ̈:', 'œ'))
ipa.append(fame.replace('ɔ̈', 'ɒ'))
ipa.append(fame.replace('ɔ̈:', 'ɒ'))
return ipa

20
acoustic_model/defaultfiles.py

@ -2,11 +2,13 @@ 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'
#cygwin_dir = r'C:\cygwin64\home\Aki\acoustic_model'
#config_hcopy = os.path.join(cygwin_dir, 'config', 'config.HCopy')
htk_dir = r'C:\Aki\htk_fame'
config_hcopy = os.path.join(htk_dir, 'config', 'config.HCopy')
#config_train = os.path.join(cygwin_dir, 'config', 'config.train')
config_hvite = os.path.join(cygwin_dir, 'config', 'config.HVite')
#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
@ -26,19 +28,23 @@ config_hvite = os.path.join(cygwin_dir, 'config', 'config.HVite')
#filePhoneList = config['pyHTK']['filePhoneList']
#AcousticModel = config['pyHTK']['AcousticModel']
repo_dir = r'C:\Users\Aki\source\repos'
repo_dir = r'C:\Users\A.Kunikoshi\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')
accent_classification_dir = os.path.join(repo_dir, 'accent_classification', 'accent_classification')
pyhtk_dir = os.path.join(repo_dir, 'pyhtk', 'pyhtk')
toolbox_dir = os.path.join(repo_dir, 'toolbox', 'toolbox')
htk_config_dir = r'c:\Users\Aki\source\repos\forced_alignment\forced_alignment\data\htk\preset_models\aki_dutch_2017'
htk_config_dir = r'c:\Users\A.Kunikoshi\source\repos\forced_alignment\forced_alignment\data\htk\preset_models\aki_dutch_2017'
config_hvite = os.path.join(htk_config_dir, 'config.HVite')
#acoustic_model = os.path.join(htk_config_dir, 'hmmdefs.compo')
acoustic_model = r'c:\cygwin64\home\Aki\acoustic_model\model\barbara\hmm128-2\hmmdefs.compo'
acoustic_model = r'c:\cygwin64\home\A.Kunikoshi\acoustic_model\model\barbara\hmm128-2\hmmdefs.compo'
phonelist_txt = os.path.join(htk_config_dir, 'phonelist.txt')
WSL_dir = r'C:\OneDrive\WSL'
fame_dir = os.path.join(WSL_dir, 'kaldi-trunk', 'egs', 'fame')
#fame_dir = os.path.join(WSL_dir, 'kaldi-trunk', 'egs', 'fame')
fame_dir = r'f:\_corpus\fame'
fame_s5_dir = os.path.join(fame_dir, 's5')
fame_corpus_dir = os.path.join(fame_dir, 'corpus')

252
acoustic_model/fame_functions.py

@ -0,0 +1,252 @@
import os
os.chdir(r'C:\Users\A.Kunikoshi\source\repos\acoustic_model\acoustic_model')
import sys
from collections import Counter
import pickle
import numpy as np
import pandas as pd
import defaultfiles as default
#sys.path.append(default.forced_alignment_module_dir)
#from forced_alignment import convert_phone_set
#def find_phone(lexicon_file, phone):
# """ Search where the phone is used in the lexicon. """
# with open(lexicon_file, "rt", encoding="utf-8") as fin:
# lines = fin.read()
# lines = lines.split('\n')
# extracted = []
# for line in lines:
# line = line.split('\t')
# if len(line) > 1:
# pronunciation = line[1]
# if phone in pronunciation:
# extracted.append(line)
# return extracted
#def ipa2famehtk_lexicon(lexicon_file_in, lexicon_file_out):
# """ Convert a lexicon file from IPA to HTK format for FAME! corpus. """
# lexicon_in = pd.read_table(lexicon_file_in, names=['word', 'pronunciation'])
# with open(lexicon_file_out, "w", encoding="utf-8") as fout:
# for word, pronunciation in zip(lexicon_in['word'], lexicon_in['pronunciation']):
# pronunciation_no_space = pronunciation.replace(' ', '')
# pronunciation_famehtk = convert_phone_set.ipa2famehtk(pronunciation_no_space)
# if 'ceh' not in pronunciation_famehtk and 'sh' not in pronunciation_famehtk:
# fout.write("{0}\t{1}\n".format(word.upper(), pronunciation_famehtk))
#def combine_lexicon(lexicon_file1, lexicon_file2, lexicon_out):
# """ Combine two lexicon files and sort by words. """
# with open(lexicon_file1, "rt", encoding="utf-8") as fin:
# lines1 = fin.read()
# lines1 = lines1.split('\n')
# with open(lexicon_file2, "rt", encoding="utf-8") as fin:
# lines2 = fin.read()
# lines2 = lines2.split('\n')
# lex1 = pd.read_table(lexicon_file1, names=['word', 'pronunciation'])
# 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')
#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 fame_pronunciation_variant(ipa):
# ipa = ipa.replace('æ', 'ɛ')
# ipa = ipa.replace('ɐ', 'a')
# ipa = ipa.replace('ɑ', 'a')
# ipa = ipa.replace('ɾ', 'r')
# ipa = ipa.replace('ɹ', 'r') # ???
# ipa = ipa.replace('ʁ', 'r')
# ipa = ipa.replace('ʀ', 'r') # ???
# ipa = ipa.replace('ʊ', 'u')
# ipa = ipa.replace('χ', 'x')
# pronvar_list = [ipa]
# 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_)
# return pronvar_list
#def make_fame2ipa_variants(fame):
# fame = 'rɛös'
# ipa = [fame]
# ipa.append(fame.replace('ɛ', 'æ'))
# ipa.append(fame.replace('a', 'ɐ'))
# ipa.append(fame.replace('a', 'ɑ'))
# ipa.append(fame.replace('r', 'ɾ'))
# ipa.append(fame.replace('r', 'ɹ'))
# ipa.append(fame.replace('r', 'ʁ'))
# ipa.append(fame.replace('r', 'ʀ'))
# ipa.append(fame.replace('u', 'ʊ'))
# ipa.append(fame.replace('x', 'χ'))
# ipa.append(fame.replace('ö', 'ø:'))
# ipa.append(fame.replace('ö:', 'ø:'))
# ipa.append(fame.replace('ɔ̈', 'œ'))
# ipa.append(fame.replace('ɔ̈:', 'œ'))
# ipa.append(fame.replace('ɔ̈', 'ɒ'))
# ipa.append(fame.replace('ɔ̈:', 'ɒ'))
# return ipa
def make_hcopy_scp_from_filelist_in_fame(fame_dir, dataset, feature_dir, hcopy_scp):
""" Make a script file for HCopy using the filelist in FAME! corpus. """
filelist_txt = os.path.join(fame_dir, 'fame', 'filelists', dataset + 'list.txt')
with open(filelist_txt) as fin:
filelist = fin.read()
filelist = filelist.split('\n')
with open(hcopy_scp, 'w') as fout:
for filename_ in filelist:
filename = filename_.replace('.TextGrid', '')
if len(filename) > 3: # remove '.', '..' and ''
wav_file = os.path.join(fame_dir, 'fame', 'wav', dataset, filename + '.wav')
mfc_file = os.path.join(feature_dir, filename + '.mfc')
fout.write(wav_file + '\t' + mfc_file + '\n')
#def make_filelist(input_dir, output_txt):
# """ Make a list of files in the input_dir. """
# filenames = os.listdir(input_dir)
# with open(output_txt, 'w') as fout:
# for filename in filenames:
# fout.write(input_dir + '\\' + filename + '\n')
#def make_htk_dict(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 load_lexicon(lexicon_file):
lex = pd.read_csv(lexicon_file, delimiter='\t', header=None, encoding="utf-8")
lex.rename(columns={0: 'word', 1: 'pronunciation'}, inplace=True)
return lex
def get_phonelist(lexicon_asr):
""" Make a list of phones which appears in the lexicon. """
#with open(lexicon_file, "rt", encoding="utf-8") as fin:
# lines = fin.read()
# lines = lines.split('\n')
# phonelist = set([])
# for line in lines:
# line = line.split('\t')
# if len(line) > 1:
# pronunciation = set(line[1].split())
# phonelist = phonelist | pronunciation
lex = load_lexicon(lexicon_asr)
return set(' '.join(lex['pronunciation']).split(' '))
import time
timer_start = time.time()
#def get_translation_key():
dir_tmp = r'c:\Users\A.Kunikoshi\source\repos\acoustic_model\_tmp'
lexicon_ipa = r'f:\_corpus\FAME\lexicon\lex.ipa'
lexicon_asr = r'f:\_corpus\FAME\lexicon\lex.asr'
lex_ipa = load_lexicon(lexicon_ipa)
lex_asr = load_lexicon(lexicon_asr)
if 0:
phone_to_be_searched = get_phonelist(lexicon_asr)
translation_key = dict()
for word in lex_asr['word']:
if np.sum(lex_asr['word'] == word) == 1 and np.sum(lex_ipa['word'] == word) == 1:
asr = lex_asr[lex_asr['word'] == word].iat[0, 1]
ipa = lex_ipa[lex_ipa['word'] == word].iat[0, 1]
asr_list = asr.split(' ')
# if there are phones which is not in phone_to_be_searched
if len([True for i in asr_list if i in phone_to_be_searched]) > 0:
if(len(ipa) == len(asr_list)):
print("{0}: {1} --> {2}".format(word, ipa, asr))
for ipa_, asr_ in zip(ipa, asr_list):
if asr_ in phone_to_be_searched:
#if not translation_key[ipa_] == asr_:
translation_key[ipa_] = asr_
phone_to_be_searched.remove(asr_)
print("elapsed time: {}".format(time.time() - timer_start))
np.save(os.path.join(dir_tmp, 'translation_key.npy'), translation_key)
np.save(os.path.join(dir_tmp, 'phone_to_be_searched.npy'), phone_to_be_searched)
else:
translation_key = np.load(os.path.join(dir_tmp, 'translation_key.npy')).item()
phone_to_be_searched = np.load(os.path.join(dir_tmp, 'phone_to_be_searched.npy')).item()

156
acoustic_model/train_hmm_fame.py → acoustic_model/fame_hmm.py

@ -1,105 +1,127 @@
import os
import sys
import os
os.chdir(r'C:\Users\A.Kunikoshi\source\repos\acoustic_model\acoustic_model')
import tempfile
import configparser
import subprocess
from collections import Counter
#import configparser
#import subprocess
#from collections import Counter
#import numpy as np
#import pandas as pd
import numpy as np
import pandas as pd
import fame_functions
import defaultfiles as default
sys.path.append(default.pyhtk_dir)
import pyhtk
sys.path.append(default.toolbox_dir)
import file_handling
## ======================= user define =======================
repo_dir = 'C:\\Users\\Aki\\source\\repos\\acoustic_model'
curr_dir = repo_dir + '\\acoustic_model'
config_ini = curr_dir + '\\config.ini'
output_dir = 'C:\\OneDrive\\Research\\rug\\experiments\\friesian\\acoustic_model'
forced_alignment_module = 'C:\\Users\\Aki\\source\\repos\\forced_alignment'
#repo_dir = 'C:\\Users\\Aki\\source\\repos\\acoustic_model'
#curr_dir = repo_dir + '\\acoustic_model'
#config_ini = curr_dir + '\\config.ini'
#output_dir = 'C:\\OneDrive\\Research\\rug\\experiments\\friesian\\acoustic_model'
#forced_alignment_module = 'C:\\Users\\Aki\\source\\repos\\forced_alignment'
dataset_list = ['devel', 'test', 'train']
# procedure
extract_features = 0
make_feature_list = 0
conv_lexicon = 0
check_lexicon = 0
make_mlf = 0
combine_files = 0
flat_start = 0
train_model = 1
extract_features = 1
#conv_lexicon = 0
#check_lexicon = 0
#make_mlf = 0
#combine_files = 0
#flat_start = 0
#train_model = 1
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
#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
import acoustic_model_functions as am_func
## ======================= load variables =======================
config = configparser.ConfigParser()
config.sections()
config.read(config_ini)
config_hcopy = config['Settings']['config_hcopy']
config_train = config['Settings']['config_train']
mkhmmdefs_pl = config['Settings']['mkhmmdefs_pl']
FAME_dir = config['Settings']['FAME_dir']
lex_asr = FAME_dir + '\\lexicon\\lex.asr'
lex_asr_htk = FAME_dir + '\\lexicon\\lex.asr_htk'
lex_oov = FAME_dir + '\\lexicon\\lex.oov'
lex_oov_htk = FAME_dir + '\\lexicon\\lex.oov_htk'
#lex_ipa = FAME_dir + '\\lexicon\\lex.ipa'
#lex_ipa_ = FAME_dir + '\\lexicon\\lex.ipa_'
#lex_ipa_htk = FAME_dir + '\\lexicon\\lex.ipa_htk'
lex_htk = FAME_dir + '\\lexicon\\lex_original.htk'
lex_htk_ = FAME_dir + '\\lexicon\\lex.htk'
hcompv_scp = output_dir + '\\scp\\combined.scp'
combined_mlf = output_dir + '\\label\\combined.mlf'
model_dir = output_dir + '\\model'
model0_dir = model_dir + '\\hmm0'
proto_init = model_dir + '\\proto38'
proto_name = 'proto'
phonelist = output_dir + '\\config\\phonelist_friesian.txt'
hmmdefs_name = 'hmmdefs'
#config = configparser.ConfigParser()
#config.sections()
#config.read(config_ini)
#config_hcopy = config['Settings']['config_hcopy']
#config_train = config['Settings']['config_train']
#mkhmmdefs_pl = config['Settings']['mkhmmdefs_pl']
#FAME_dir = config['Settings']['FAME_dir']
#lex_asr = FAME_dir + '\\lexicon\\lex.asr'
#lex_asr_htk = FAME_dir + '\\lexicon\\lex.asr_htk'
#lex_oov = FAME_dir + '\\lexicon\\lex.oov'
#lex_oov_htk = FAME_dir + '\\lexicon\\lex.oov_htk'
##lex_ipa = FAME_dir + '\\lexicon\\lex.ipa'
##lex_ipa_ = FAME_dir + '\\lexicon\\lex.ipa_'
##lex_ipa_htk = FAME_dir + '\\lexicon\\lex.ipa_htk'
#lex_htk = FAME_dir + '\\lexicon\\lex_original.htk'
#lex_htk_ = FAME_dir + '\\lexicon\\lex.htk'
#hcompv_scp = output_dir + '\\scp\\combined.scp'
#combined_mlf = output_dir + '\\label\\combined.mlf'
#model_dir = output_dir + '\\model'
#model0_dir = model_dir + '\\hmm0'
#proto_init = model_dir + '\\proto38'
#proto_name = 'proto'
#phonelist = output_dir + '\\config\\phonelist_friesian.txt'
#hmmdefs_name = 'hmmdefs'
feature_dir = os.path.join(default.htk_dir, 'mfc')
if not os.path.exists(feature_dir):
os.makedirs(feature_dir)
tmp_dir = os.path.join(default.htk_dir, 'tmp')
if not os.path.exists(tmp_dir):
os.makedirs(tmp_dir)
## ======================= extract features =======================
if extract_features:
print("==== extract features ====\n")
for dataset in dataset_list:
print(dataset)
#for dataset in ['test']:
print('==== {} ===='.format(dataset))
# a script file for HCopy
print(">>> making a script file for HCopy... \n")
hcopy_scp = tempfile.NamedTemporaryFile(mode='w', delete=False)
hcopy_scp.close()
#hcopy_scp = os.path.join(default.htk_dir, 'tmp', 'HCopy.scp')
# get a list of features (hcopy.scp) from the filelist in FAME! corpus
feature_dir = output_dir + '\\mfc\\' + dataset
am_func.make_hcopy_scp_from_filelist_in_fame(FAME_dir, dataset, feature_dir, hcopy_scp.name)
feature_dir_ = os.path.join(feature_dir, dataset)
if not os.path.exists(feature_dir_):
os.makedirs(feature_dir_)
# extract features
subprocessStr = 'HCopy -C ' + config_hcopy + ' -S ' + hcopy_scp.name
subprocess.call(subprocessStr, shell=True)
print(">>> extracting features... \n")
fame_functions.make_hcopy_scp_from_filelist_in_fame(default.fame_dir, dataset, feature_dir_, hcopy_scp.name)
#subprocessStr = 'HCopy -C ' + config_hcopy + ' -S ' + hcopy_scp.name
#subprocess.call(subprocessStr, shell=True)
pyhtk.wav2mfc(default.config_hcopy, hcopy_scp.name)
# a script file for HCompV
print(">>> making a script file for HCompV... \n")
## ======================= make a list of features =======================
if make_feature_list:
print("==== make a list of features ====\n")
#if make_feature_list:
# print("==== make a list of features ====\n")
for dataset in dataset_list:
print(dataset)
# for dataset in dataset_list:
# print(dataset)
feature_dir = output_dir + '\\mfc\\' + dataset
hcompv_scp = output_dir + '\\scp\\' + dataset + '.scp'
#feature_dir = output_dir + '\\mfc\\' + dataset
hcompv_scp = os.path.join(tmp_dir, dataset + '.scp')
am_func.make_filelist(feature_dir, hcompv_scp)
#am_func.make_filelist(feature_dir, hcompv_scp)
file_handling.make_filelist(feature_dir_, hcompv_scp, '.mfc')
## ======================= convert lexicon from ipa to fame_htk =======================
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