@ -4,52 +4,92 @@ 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 ' )
"""
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 ] )
phones = [ ]
for line in lines :
line_split = line . split ( )
if len ( line_split ) > 1 :
phones . append ( line_split [ 2 ] )
return ' ' . join ( phones )
return ' ' . join ( phones )
#####################
## USER DEFINE ##
#####################
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 % e ntries 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
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 ) )
## ======================= 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 '
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 '
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 '
data_dir = experiments_dir + ' \\ stimmen \\ data '
csvfile = data _dir + ' \\ Frisian Variants Picture Task Stimmen.csv '
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 = 0
eval_forced_alignment = 1
do_forced_alignment_htk = 0
make_kaldi_data_files = 0
make_kaldi_lexicon_txt = 0
load_forced_alignment_kaldi = 1
eval_forced_alignment = 0
@ -67,6 +107,10 @@ import acoustic_model_functions as am_func
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 ( )
@ -81,177 +125,393 @@ lex_asr_htk = FAME_dir + '\\lexicon\\lex.asr_htk'
## ======================= convert phones ======================
if convert_phones :
mapping = convert_xsampa2ipa . load_converter ( ' xsampa ' , ' ipa ' , ipa_xsampa_converter_dir )
mapping = convert_xsampa2ipa . load_converter ( ' xsampa ' , ' ipa ' , ipa_xsampa_converter_dir )
## check phones included in FAME!
# the phones used in the lexicon.
#phonelist = am_func.get_phonelist(lex_htk)
## check phones included in FAME!
# the phones used in the lexicon.
#phonelist = am_func.get_phonelist(lex_htk)
# the lines which include a specific phone.
#lines = am_func.find_phone(lex_asr, 'x')
# the lines which include a specific phone.
#lines = am_func.find_phone(lex_asr, 'x')
with open ( csvfile , encoding = " utf-8 " ) as fin :
lines = csv . reader ( fin , delimiter = ' ; ' , lineterminator = " \n " , skipinitialspace = True )
next ( lines , None ) # skip the headers
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 = [ ]
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 ' )
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 )
#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 if all phones are in the phonelist of the acoustic model.
#phonelist = ' '.join(pronunciations)
#np.unique(phonelist.split(' '))
#phonelist.find(':')
filenames = np . array ( filenames )
words = np . array ( words )
pronunciations = np . array ( pronunciations )
filenames = np . array ( filenames )
words = np . array ( words )
pronunciations = np . array ( pronunciations )
del line , lines
del pron_xsampa , pron_ipa , pron_famehtk
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 )
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 ' )
pronunciations = np . load ( data_dir + ' \\ pronunciations.npy ' )
filenames = np . load ( data_dir + ' \\ filenames.npy ' )
words = np . load ( data_dir + ' \\ words.npy ' )
pronunciations = np . load ( data_dir + ' \\ pronunciations.npy ' )
word_list = np . unique ( words )
## ======================= make dict files used for HTK. ======================
if make_dic_files :
output_dir = experiments_dir + r ' \ stimmen \ dic '
output_type = 2
output_dir = experiments_dir + r ' \ stimmen \ dic_short '
for word in word_list :
WORD = word . upper ( )
fileDic = output_dir + ' \\ ' + word + ' .dic '
for word in word_list :
WORD = word . upper ( )
fileDic = output_dir + ' \\ ' + word + ' .dic
# pronunciation variant of the target word.
pronvar_ = pronunciations [ words == word ]
# remove ' '
pronvar_ = np . delete ( pronvar_ , np . where ( pronvar_ == ' ' ) )
# make dic file.
pronvar_ = pronunciations [ words == word ]
pronvar = np . unique ( pronvar_ )
# make dic file.
make_dic ( word , pronvar_ , fileDic , output_type )
with open ( fileDic , ' w ' ) as f :
for pvar in pronvar :
f . write ( ' {0} \t {1} \n ' . format ( WORD , pvar ) )
## ======================= 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_str = str ( hmm_num )
AcousticModel = experiments_dir + r ' \ friesian \ acoustic_model \ model \ 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
if os . path . exists ( fileWav ) :
word = words [ i ]
WORD = word . upper ( )
# make label file.
fileLab = wav_dir + ' \\ ' + filename . replace ( ' .wav ' , ' .lab ' )
with open ( fileLab , ' 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
pyHTK . doHVite ( fileWav , fileLab , fileDic , fileFA , configHVite , filePhoneList , AcousticModel )
prediction = read_fileFA ( fileFA )
predictions . append ( prediction )
os . remove ( fileLab )
print ( ' {0} : {1} -> {2} ' . format ( WORD , pronunciations [ 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 )
## ======================= make dict files for most popular words. ======================
if make_dic_files_short :
output _dir = experiments_dir + r ' \ stimmen \ dic '
## ======================= make files which is used for forced alignment by Kaldi =======================
if make_kaldi_data_files :
wav _dir = r ' c: \ OneDrive \ WSL \ kaldi-trunk \ egs \ fame \ s5 \ corpus \ stimmen '
kaldi_work_dir = r ' C: \ OneDrive \ WSL \ kaldi-trunk \ egs \ fame \ s5 '
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 ' )
#word = word_list[3]
for word in word_list :
WORD = word . upper ( )
fileStat = output_dir + ' \\ ' + word + ' _stat.csv '
pronvar = pronunciations [ words == word ]
c = Counter ( pronvar )
total_num = sum ( c . values ( ) )
wav_scp = os . path . join ( kaldi_data_dir , ' wav.scp ' )
text_file = os . path . join ( kaldi_data_dir , ' text ' )
utt2spk = os . path . join ( kaldi_data_dir , ' utt2spk ' )
with open ( fileStat , ' w ' ) as f :
for key , value in c . items ( ) :
f . write ( ' {0} \t {1:.2f} \t {2} \t {3} \n ' . format ( value , value / total_num * 100 , WORD , key ) )
lexicon_txt = os . path . join ( kaldi_dict_dir , ' lexicon.txt ' )
predictions = [ ]
file_num_max = len ( filenames )
# remove previous files.
if os . path . exists ( wav_scp ) :
os . remove ( wav_scp )
if os . path . exists ( text_file ) :
os . remove ( text_file )
if os . path . exists ( utt2spk ) :
os . remove ( utt2spk )
f_wav_scp = open ( wav_scp , ' a ' , encoding = " utf-8 " , newline = ' \n ' )
f_text_file = open ( text_file , ' a ' , encoding = " utf-8 " , newline = ' \n ' )
f_utt2spk = open ( utt2spk , ' a ' , encoding = " utf-8 " , newline = ' \n ' )
# make wav.scp, text, and utt2spk files.
for i in range ( 0 , file_num_max ) :
#for i in range(400, 410):
print ( ' === {0} / {1} === ' . format ( i + 1 , file_num_max ) )
filename = filenames [ i ]
wav_file = wav_dir + ' \\ ' + filename
if os . path . exists ( wav_file ) :
speaker_id = ' speaker_ ' + str ( i ) . zfill ( 4 )
utterance_id = filename . replace ( ' .wav ' , ' ' )
utterance_id = utterance_id . replace ( ' ' , ' _ ' )
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 ( )
## ======================= forced alignment =======================
if do_forced_alignmen t:
configHVite = cygwin _dir + r ' \ config \ config.HVite '
filePhoneList = experiments _dir + r ' \ friesian \ acoustic_model \ config \ phonelist_friesian.tx t'
wav_dir = experiments _dir + r ' \ stimmen \ wav '
## ======================= make lexicon txt which is used by Kaldi =======================
if make_kaldi_lexicon_tx t:
kaldi_work _dir = r ' C: \ OneDrive \ WSL \ kaldi-trunk \ egs \ fame \ s5 '
kaldi_dict_dir = os . path . join ( kaldi_work _dir , ' data ' , ' local ' , ' dic t' )
lexicon_txt = os . path . join ( kaldi_dict _dir , ' lexicon.txt ' )
option_num = 5
#for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128]:
for hmm_num in [ 64 ] :
hmm_num_str = str ( hmm_num )
AcousticModel = experiments_dir + r ' \ friesian \ acoustic_model \ model \ hmm ' + hmm_num_str + r ' -3 \ hmmdefs '
# remove previous file.
if os . path . exists ( lexicon_txt ) :
os . remove ( lexicon_txt )
predictions = [ ]
file_num_max = l en( 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 ) :
word = words [ i ]
WORD = word . upper ( )
mapping = convert_xsampa2ipa . load_converter ( ' xsampa ' , ' ipa ' , ipa_xsampa_converter_dir )
with op en( csv file, encoding = " utf-8 " ) as fin :
lines = csv . reader ( fin , delimiter = ' ; ' , lineterminator = " \n " , skipinitialspace = True )
next ( lines , None ) # skip the headers
# make label file.
fileLab = wav_dir + ' \\ ' + filename . replace ( ' .wav ' , ' .lab ' )
with open ( fileLab , ' w ' ) as f :
lines = f . write ( WORD )
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 )
fileDic = experiments_dir + r ' \ stimmen \ dic_short ' + ' \\ ' + word + ' .dic '
fileFA = experiments_dir + r ' \ stimmen \ FA_short ' + ' \\ ' + filename . replace ( ' .wav ' , ' .txt ' ) + hmm_num_str
# 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 ɪ ɑ ɪ
pyHTK . doHVite ( fileWav , fileLab , fileDic , fileFA , configHVite , filePhoneList , AcousticModel )
prediction = read_fileFA ( fileFA )
predictions . append ( prediction )
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 :
os . remove ( fileLab )
print ( ' {0} : {1} -> {2} ' . format ( WORD , pronunciations [ i ] , prediction ) )
else :
predictions . append ( ' ' )
print ( ' !!!!! file not found. ' )
# pronunciation variant of the target word.
pronvar_ = pronunciations [ words == word ]
# remove ''
pronvar_ = np . delete ( pronvar_ , np . where ( pronvar_ == ' ' ) )
predictions = np . array ( predictions )
match = np . c_ [ words [ predictions != ' ' ] , pronunciations [ predictions ! =' ' ] , predictions [ predictions != ' ' ] ]
np . save ( data_dir + ' \\ match_hmm ' + hmm_num_str + ' .npy ' , match )
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_ipa_fame ( 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 ) :
## ======================= evaluate the result of forced alignment =======================
if eval_forced_alignment :
#for hmm_num in [1, 2, 4, 8, 16, 32, 64]:
hmm_num = 64
_str = str ( hmm_num )
match = np . load ( data_dir + ' \\ match_hmm ' + hmm_num_str + ' .npy ' )
# use dic_short?
if 1 :
pronunciation_variants = np . array ( [ ' WORD ' , ' pronunciation ' ] ) . reshape ( 1 , 2 )
for word in word_list :
fileDic = experiments_dir + r ' \ stimmen \ dic_short ' + ' \\ ' + word + ' .dic '
pronunciation_variants = np . r_ [ pronunciation_variants , pyHTK . loadHTKdic ( fileDic ) ]
match_num = [ ]
for hmm_num in [ 1 , 2 , 4 , 8 , 16 , 32 , 64 , 128 , 256 ] :
# hmm_num = 256
hmm_num_str = str ( hmm_num )
match = np . load ( data_dir + ' \\ match_hmm ' + hmm_num_str + ' .npy ' )
# use dic_short?
if 1 :
pronunciation_variants = np . array ( [ ' WORD ' , ' pronunciation ' ] ) . reshape ( 1 , 2 )
for word in word_list :
fileDic = experiments_dir + r ' \ stimmen \ dic_top3 ' + ' \\ ' + word + ' .dic '
pronunciation_variants = np . r_ [ pronunciation_variants , pyHTK . loadHTKdic ( fileDic ) ]
match_short = [ ]
for line in match :
word = line [ 0 ]
WORD = word . upper ( )
pronvar = pronunciation_variants [ pronunciation_variants [ : , 0 ] == word . upper ( ) , 1 ]
if line [ 1 ] in pronvar :
match_short . append ( line )
# see only words which appears in top 3.
match_short = [ ]
for line in match :
word = line [ 0 ]
WORD = word . upper ( )
pronvar = pronunciation_variants [ pronunciation_variants [ : , 0 ] == word . upper ( ) , 1 ]
if line [ 1 ] in pronvar :
match_short . append ( line )
match_short = np . array ( match_short )
match = np . copy ( match_short )
match_short = np . array ( match_short )
match = np . copy ( match_short )
# number of match
total_match = sum ( match [ : , 1 ] == match [ : , 2 ] )
print ( " {} : {} / {} " . format ( hmm_num_str , 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 ] ) )
match_num . append ( [ hmm_num , 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')
yemaozi88