label alignment using HVite is added.
This commit is contained in:
parent
8f89f60538
commit
c185072d5b
Binary file not shown.
Binary file not shown.
@ -4,7 +4,7 @@
|
|||||||
<SchemaVersion>2.0</SchemaVersion>
|
<SchemaVersion>2.0</SchemaVersion>
|
||||||
<ProjectGuid>4d8c8573-32f0-4a62-9e62-3ce5cc680390</ProjectGuid>
|
<ProjectGuid>4d8c8573-32f0-4a62-9e62-3ce5cc680390</ProjectGuid>
|
||||||
<ProjectHome>.</ProjectHome>
|
<ProjectHome>.</ProjectHome>
|
||||||
<StartupFile>fame_hmm.py</StartupFile>
|
<StartupFile>htk_vs_kaldi.py</StartupFile>
|
||||||
<SearchPath>
|
<SearchPath>
|
||||||
</SearchPath>
|
</SearchPath>
|
||||||
<WorkingDirectory>.</WorkingDirectory>
|
<WorkingDirectory>.</WorkingDirectory>
|
||||||
|
@ -38,3 +38,9 @@ def convert_phoneset(word_list, translation_key):
|
|||||||
translation_key (dict):
|
translation_key (dict):
|
||||||
"""
|
"""
|
||||||
return [translation_key.get(phone, phone) for phone in word_list]
|
return [translation_key.get(phone, phone) for phone in word_list]
|
||||||
|
|
||||||
|
|
||||||
|
def phone_reduction(phones, reduction_key):
|
||||||
|
multi_character_tokenize(wo.strip(), multi_character_phones)
|
||||||
|
return [reduction_key.get(i, i) for i in phones
|
||||||
|
if not i in phones_to_be_removed]
|
@ -17,6 +17,7 @@ novo_api_dir = os.path.join(WSL_dir, 'python-novo-api', 'novoapi')
|
|||||||
rug_dir = r'c:\OneDrive\Research\rug'
|
rug_dir = r'c:\OneDrive\Research\rug'
|
||||||
experiments_dir = os.path.join(rug_dir, 'experiments')
|
experiments_dir = os.path.join(rug_dir, 'experiments')
|
||||||
htk_dir = os.path.join(experiments_dir, 'acoustic_model', 'fame', 'htk')
|
htk_dir = os.path.join(experiments_dir, 'acoustic_model', 'fame', 'htk')
|
||||||
|
kaldi_dir = os.path.join(WSL_dir, 'kaldi-trunk', 'egs', '_stimmen')
|
||||||
stimmen_dir = os.path.join(experiments_dir, 'stimmen')
|
stimmen_dir = os.path.join(experiments_dir, 'stimmen')
|
||||||
|
|
||||||
# data
|
# data
|
||||||
|
@ -321,9 +321,11 @@ def combine_lexicon(lexicon_file1, lexicon_file2, lexicon_out):
|
|||||||
lex.to_csv(lexicon_out, index=False, header=False, sep='\t', encoding='utf-8')
|
lex.to_csv(lexicon_out, index=False, header=False, sep='\t', encoding='utf-8')
|
||||||
|
|
||||||
|
|
||||||
def fix_single_quote(lexicon_file):
|
def fix_lexicon(lexicon_file):
|
||||||
""" add '\' before all single quote at the beginning of words.
|
""" fix lexicon
|
||||||
convert special characters to ascii compatible characters.
|
- add '\' before all single quote at the beginning of words.
|
||||||
|
- convert special characters to ascii compatible characters.
|
||||||
|
- add silence.
|
||||||
|
|
||||||
Args:
|
Args:
|
||||||
lexicon_file (path): lexicon file, which will be overwitten.
|
lexicon_file (path): lexicon file, which will be overwitten.
|
||||||
@ -331,6 +333,12 @@ def fix_single_quote(lexicon_file):
|
|||||||
"""
|
"""
|
||||||
lex = load_lexicon(lexicon_file)
|
lex = load_lexicon(lexicon_file)
|
||||||
lex = lex.dropna() # remove N/A.
|
lex = lex.dropna() # remove N/A.
|
||||||
|
|
||||||
|
# add 'sil'
|
||||||
|
row = pd.Series(['SILENCE', 'sil'], index=lex.columns)
|
||||||
|
lex = lex.append(row, ignore_index=True)
|
||||||
|
lex = lex.sort_values(by='word', ascending=True)
|
||||||
|
|
||||||
for i in lex[lex['word'].str.startswith('\'')].index.values:
|
for i in lex[lex['word'].str.startswith('\'')].index.values:
|
||||||
lex.iat[i, 0] = lex.iat[i, 0].replace('\'', '\\\'')
|
lex.iat[i, 0] = lex.iat[i, 0].replace('\'', '\\\'')
|
||||||
# to_csv does not work with space seperator. therefore all tabs should manually be replaced.
|
# to_csv does not work with space seperator. therefore all tabs should manually be replaced.
|
||||||
@ -346,10 +354,11 @@ def word2htk(word):
|
|||||||
def ipa2asr(ipa):
|
def ipa2asr(ipa):
|
||||||
curr_dir = os.path.dirname(os.path.abspath(__file__))
|
curr_dir = os.path.dirname(os.path.abspath(__file__))
|
||||||
translation_key_ipa2asr = np.load(os.path.join(curr_dir, 'phoneset', 'fame_ipa2asr.npy')).item(0)
|
translation_key_ipa2asr = np.load(os.path.join(curr_dir, 'phoneset', 'fame_ipa2asr.npy')).item(0)
|
||||||
|
#ipa_ = fame_asr.phone_reduction(ipa)
|
||||||
ipa_splitted = convert_phoneset.split_word(ipa, fame_ipa.multi_character_phones)
|
ipa_splitted = convert_phoneset.split_word(ipa, fame_ipa.multi_character_phones)
|
||||||
ipa_splitted = fame_ipa.phone_reduction(ipa_splitted)
|
ipa_splitted = fame_ipa.phone_reduction(ipa_splitted)
|
||||||
asr_splitted = convert_phoneset.convert_phoneset(ipa_splitted, translation_key_ipa2asr)
|
asr_splitted = convert_phoneset.convert_phoneset(ipa_splitted, translation_key_ipa2asr)
|
||||||
|
asr_splitted = fame_asr.phone_reduction(asr_splitted)
|
||||||
return ''.join(asr_splitted)
|
return ''.join(asr_splitted)
|
||||||
|
|
||||||
|
|
||||||
@ -360,5 +369,6 @@ def ipa2htk(ipa):
|
|||||||
ipa_splitted = convert_phoneset.split_word(ipa, fame_ipa.multi_character_phones)
|
ipa_splitted = convert_phoneset.split_word(ipa, fame_ipa.multi_character_phones)
|
||||||
ipa_splitted = fame_ipa.phone_reduction(ipa_splitted)
|
ipa_splitted = fame_ipa.phone_reduction(ipa_splitted)
|
||||||
asr_splitted = convert_phoneset.convert_phoneset(ipa_splitted, translation_key_ipa2asr)
|
asr_splitted = convert_phoneset.convert_phoneset(ipa_splitted, translation_key_ipa2asr)
|
||||||
|
asr_splitted = fame_asr.phone_reduction(asr_splitted)
|
||||||
htk_splitted = convert_phoneset.convert_phoneset(asr_splitted, fame_asr.translation_key_asr2htk)
|
htk_splitted = convert_phoneset.convert_phoneset(asr_splitted, fame_asr.translation_key_asr2htk)
|
||||||
return ''.join(htk_splitted)
|
return ''.join(htk_splitted)
|
@ -27,7 +27,8 @@ extract_features = 0
|
|||||||
flat_start = 0
|
flat_start = 0
|
||||||
train_model_without_sp = 0
|
train_model_without_sp = 0
|
||||||
add_sp = 0
|
add_sp = 0
|
||||||
train_model_with_sp = 1
|
train_model_with_sp = 0
|
||||||
|
train_model_with_sp_align_mlf = 1
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
@ -75,6 +76,7 @@ if not os.path.exists(label_dir):
|
|||||||
## training
|
## training
|
||||||
hcompv_scp_train = os.path.join(tmp_dir, 'train.scp')
|
hcompv_scp_train = os.path.join(tmp_dir, 'train.scp')
|
||||||
mlf_file_train = os.path.join(label_dir, 'train_phone.mlf')
|
mlf_file_train = os.path.join(label_dir, 'train_phone.mlf')
|
||||||
|
mlf_file_train_aligned = os.path.join(label_dir, 'train_phone_aligned.mlf')
|
||||||
|
|
||||||
## train without sp
|
## train without sp
|
||||||
niter_max = 10
|
niter_max = 10
|
||||||
@ -102,7 +104,8 @@ if make_lexicon:
|
|||||||
# (1) Replace all tabs with single space;
|
# (1) Replace all tabs with single space;
|
||||||
# (2) Put a '\' before any dictionary entry beginning with single quote
|
# (2) Put a '\' before any dictionary entry beginning with single quote
|
||||||
#http://electroblaze.blogspot.nl/2013/03/understanding-htk-error-messages.html
|
#http://electroblaze.blogspot.nl/2013/03/understanding-htk-error-messages.html
|
||||||
fame_functions.fix_single_quote(lexicon_htk)
|
print('>>> fixing the lexicon...')
|
||||||
|
fame_functions.fix_lexicon(lexicon_htk)
|
||||||
print("elapsed time: {}".format(time.time() - timer_start))
|
print("elapsed time: {}".format(time.time() - timer_start))
|
||||||
|
|
||||||
|
|
||||||
@ -269,11 +272,11 @@ if train_model_without_sp:
|
|||||||
fh.make_new_directory(modeln_dir)
|
fh.make_new_directory(modeln_dir)
|
||||||
pyhtk.re_estimation(
|
pyhtk.re_estimation(
|
||||||
config_train,
|
config_train,
|
||||||
os.path.join(modeln_dir_pre, 'macros'),
|
|
||||||
os.path.join(modeln_dir_pre, hmmdefs_name),
|
os.path.join(modeln_dir_pre, hmmdefs_name),
|
||||||
modeln_dir,
|
modeln_dir,
|
||||||
hcompv_scp_train, phonelist_txt,
|
hcompv_scp_train, phonelist_txt,
|
||||||
mlf_file=mlf_file_train)
|
mlf_file=mlf_file_train,
|
||||||
|
macros=os.path.join(modeln_dir_pre, 'macros'))
|
||||||
print("elapsed time: {}".format(time.time() - timer_start))
|
print("elapsed time: {}".format(time.time() - timer_start))
|
||||||
|
|
||||||
|
|
||||||
@ -321,7 +324,6 @@ if add_sp:
|
|||||||
## ======================= train model with short pause =======================
|
## ======================= train model with short pause =======================
|
||||||
if train_model_with_sp:
|
if train_model_with_sp:
|
||||||
print('==== train model with sp ====')
|
print('==== train model with sp ====')
|
||||||
#for niter in range(niter_max+1, niter_max*2+1):
|
|
||||||
for niter in range(20, 50):
|
for niter in range(20, 50):
|
||||||
timer_start = time.time()
|
timer_start = time.time()
|
||||||
hmm_n = 'iter' + str(niter)
|
hmm_n = 'iter' + str(niter)
|
||||||
@ -333,9 +335,31 @@ if train_model_with_sp:
|
|||||||
fh.make_new_directory(modeln_dir)
|
fh.make_new_directory(modeln_dir)
|
||||||
pyhtk.re_estimation(
|
pyhtk.re_estimation(
|
||||||
config_train,
|
config_train,
|
||||||
os.path.join(modeln_dir_pre, 'macros'),
|
|
||||||
os.path.join(modeln_dir_pre, hmmdefs_name),
|
os.path.join(modeln_dir_pre, hmmdefs_name),
|
||||||
modeln_dir,
|
modeln_dir,
|
||||||
hcompv_scp_train, phonelist_txt,
|
hcompv_scp_train, phonelist_txt,
|
||||||
mlf_file=mlf_file_train)
|
mlf_file=mlf_file_train,
|
||||||
|
macros=os.path.join(modeln_dir_pre, 'macros'))
|
||||||
|
print("elapsed time: {}".format(time.time() - timer_start))
|
||||||
|
|
||||||
|
|
||||||
|
## ======================= train model with short pause =======================
|
||||||
|
if train_model_with_sp_align_mlf:
|
||||||
|
print('==== train model with sp with align.mlf ====')
|
||||||
|
for niter in range(50, 60):
|
||||||
|
timer_start = time.time()
|
||||||
|
hmm_n = 'iter' + str(niter)
|
||||||
|
hmm_n_pre = 'iter' + str(niter-1)
|
||||||
|
modeln_dir = os.path.join(model1_dir, hmm_n)
|
||||||
|
modeln_dir_pre = os.path.join(model1_dir, hmm_n_pre)
|
||||||
|
|
||||||
|
# re-estimation
|
||||||
|
fh.make_new_directory(modeln_dir)
|
||||||
|
pyhtk.re_estimation(
|
||||||
|
config_train,
|
||||||
|
os.path.join(modeln_dir_pre, hmmdefs_name),
|
||||||
|
modeln_dir,
|
||||||
|
hcompv_scp_train, phonelist_txt,
|
||||||
|
mlf_file=mlf_file_train_aligned,
|
||||||
|
macros=os.path.join(modeln_dir_pre, 'macros'))
|
||||||
print("elapsed time: {}".format(time.time() - timer_start))
|
print("elapsed time: {}".format(time.time() - timer_start))
|
@ -11,6 +11,7 @@ import glob
|
|||||||
|
|
||||||
import numpy as np
|
import numpy as np
|
||||||
import pandas as pd
|
import pandas as pd
|
||||||
|
from collections import Counter
|
||||||
#import matplotlib.pyplot as plt
|
#import matplotlib.pyplot as plt
|
||||||
#from sklearn.metrics import confusion_matrix
|
#from sklearn.metrics import confusion_matrix
|
||||||
|
|
||||||
@ -50,11 +51,14 @@ from htk import pyhtk
|
|||||||
#lex_asr = os.path.join(default.fame_dir, 'lexicon', 'lex.asr')
|
#lex_asr = os.path.join(default.fame_dir, 'lexicon', 'lex.asr')
|
||||||
#lex_asr_htk = os.path.join(default.fame_dir, 'lexicon', 'lex.asr_htk')
|
#lex_asr_htk = os.path.join(default.fame_dir, 'lexicon', 'lex.asr_htk')
|
||||||
|
|
||||||
## procedure
|
# procedure
|
||||||
|
make_dic_file = 0
|
||||||
|
make_HTK_files = 1
|
||||||
|
extract_features = 0
|
||||||
#make_htk_dict_files = 0
|
#make_htk_dict_files = 0
|
||||||
#do_forced_alignment_htk = 0
|
#do_forced_alignment_htk = 0
|
||||||
#eval_forced_alignment_htk = 0
|
#eval_forced_alignment_htk = 0
|
||||||
#make_kaldi_data_files = 0
|
make_kaldi_files = 0
|
||||||
#make_kaldi_lexicon_txt = 0
|
#make_kaldi_lexicon_txt = 0
|
||||||
#load_forced_alignment_kaldi = 1
|
#load_forced_alignment_kaldi = 1
|
||||||
#eval_forced_alignment_kaldi = 1
|
#eval_forced_alignment_kaldi = 1
|
||||||
@ -66,13 +70,34 @@ from htk import pyhtk
|
|||||||
#sys.path.append(os.path.join(default.repo_dir, 'toolbox'))
|
#sys.path.append(os.path.join(default.repo_dir, 'toolbox'))
|
||||||
#from evaluation import plot_confusion_matrix
|
#from evaluation import plot_confusion_matrix
|
||||||
|
|
||||||
|
## HTK related files.
|
||||||
config_dir = os.path.join(default.htk_dir, 'config')
|
config_dir = os.path.join(default.htk_dir, 'config')
|
||||||
model_dir = os.path.join(default.htk_dir, 'model')
|
model_dir = os.path.join(default.htk_dir, 'model')
|
||||||
|
feature_dir = os.path.join(default.htk_dir, 'mfc', 'stimmen')
|
||||||
|
|
||||||
|
config_hcopy = os.path.join(config_dir, 'config.HCopy')
|
||||||
|
|
||||||
|
# files to be made.
|
||||||
lattice_file = os.path.join(config_dir, 'stimmen.ltc')
|
lattice_file = os.path.join(config_dir, 'stimmen.ltc')
|
||||||
#pyhtk.create_word_lattice_file(
|
phonelist_txt = os.path.join(config_dir, 'phonelist.txt')
|
||||||
# os.path.join(config_dir, 'stimmen.net'),
|
stimmen_dic = os.path.join(default.htk_dir, 'lexicon', 'stimmen_recognition.dic')
|
||||||
# lattice_file)
|
hcopy_scp = os.path.join(default.htk_dir, 'tmp', 'stimmen_test_hcopy.scp')
|
||||||
hvite_scp = os.path.join(default.htk_dir, 'tmp', 'stimmen_test.scp')
|
hvite_scp = os.path.join(default.htk_dir, 'tmp', 'stimmen_test_hvite.scp')
|
||||||
|
hresult_scp = os.path.join(default.htk_dir, 'tmp', 'stimmen_test_result.scp')
|
||||||
|
|
||||||
|
|
||||||
|
## Kaldi related files.
|
||||||
|
kaldi_data_dir = os.path.join(default.kaldi_dir, 'data')
|
||||||
|
|
||||||
|
# files to be made.
|
||||||
|
wav_scp = os.path.join(kaldi_data_dir, 'test', 'wav.scp')
|
||||||
|
text_file = os.path.join(kaldi_data_dir, 'test', 'text')
|
||||||
|
utt2spk = os.path.join(kaldi_data_dir, 'test', 'utt2spk')
|
||||||
|
corpus_txt = os.path.join(kaldi_data_dir, 'local', 'corpus.txt')
|
||||||
|
lexicon_txt = os.path.join(kaldi_data_dir, 'local', 'dict', 'lexicon.txt')
|
||||||
|
nonsilence_phones_txt = os.path.join(kaldi_data_dir, 'local', 'dict', 'nonsilence_phones.txt')
|
||||||
|
silence_phones_txt = os.path.join(kaldi_data_dir, 'local', 'dict', 'silence_phones.txt')
|
||||||
|
optional_silence_txt = os.path.join(kaldi_data_dir, 'local', 'dict', 'optional_silence.txt')
|
||||||
|
|
||||||
|
|
||||||
## ======================= load test data ======================
|
## ======================= load test data ======================
|
||||||
@ -85,36 +110,159 @@ df = stimmen_functions.add_row_htk(df)
|
|||||||
word_list = [i for i in list(set(df['word'])) if not pd.isnull(i)]
|
word_list = [i for i in list(set(df['word'])) if not pd.isnull(i)]
|
||||||
word_list = sorted(word_list)
|
word_list = sorted(word_list)
|
||||||
|
|
||||||
# pronunciation variants
|
|
||||||
for word in word_list:
|
## ======================= make dic file to check pronunciation variants ======================
|
||||||
|
# dic file should be manually modified depends on the task - recognition / forced-alignemnt.
|
||||||
|
if make_dic_file:
|
||||||
|
# for HTK.
|
||||||
|
with open(stimmen_dic, mode='wb') as f:
|
||||||
|
for word in word_list:
|
||||||
df_ = df[df['word']==word]
|
df_ = df[df['word']==word]
|
||||||
print('{0} has {1} variants'.format(word, len(np.unique(df_['htk'])))
|
pronunciations = list(np.unique(df_['htk']))
|
||||||
|
pronunciations_ = [word.upper() + ' sil ' + ' '.join(convert_phoneset.split_word(
|
||||||
|
htk, fame_asr.multi_character_phones_htk)) + ' sil'
|
||||||
|
for htk in pronunciations]
|
||||||
|
f.write(bytes('\n'.join(pronunciations_) + '\n', 'ascii'))
|
||||||
|
f.write(bytes('SILENCE sil\n', 'ascii'))
|
||||||
|
|
||||||
#fh.make_filelist(stimmen_test_dir, hvite_scp, file_type='wav')
|
# for Kaldi.
|
||||||
|
fh.make_new_directory(os.path.join(kaldi_data_dir, 'local', 'dict'))
|
||||||
|
with open(lexicon_txt, mode='wb') as f:
|
||||||
|
f.write(bytes('!SIL sil\n', 'utf-8'))
|
||||||
|
f.write(bytes('<UNK> spn\n', 'utf-8'))
|
||||||
|
for word in word_list:
|
||||||
|
df_ = df[df['word']==word]
|
||||||
|
pronunciations = list(np.unique(df_['asr']))
|
||||||
|
pronunciations_ = [word.lower() + ' ' + ' '.join(convert_phoneset.split_word(
|
||||||
|
asr, fame_asr.multi_character_phones))
|
||||||
|
for asr in pronunciations]
|
||||||
|
f.write(bytes('\n'.join(pronunciations_) + '\n', 'utf-8'))
|
||||||
|
|
||||||
#output = pyhtk.recognition(
|
|
||||||
# os.path.join(default.htk_dir, 'config', 'config.rec',
|
|
||||||
# lattice_file,
|
|
||||||
# os.path.join(model_dir, 'hmm1', 'iter13'),
|
|
||||||
# dictionary_file,
|
|
||||||
# os.path.join(config_dir, 'phonelist.txt'),
|
|
||||||
# hvite_scp)
|
|
||||||
|
|
||||||
#pyhtk.create_label_file(
|
## ======================= test data for recognition ======================
|
||||||
# row['word'],
|
# only target pronunciation variants.
|
||||||
# os.path.join(stimmen_test_dir, filename.replace('.wav', '.lab')))
|
df_rec = pd.DataFrame(index=[], columns=list(df.keys()))
|
||||||
|
for word in word_list:
|
||||||
|
variants = [htk.replace(' ', '')
|
||||||
|
for htk in stimmen_functions.load_pronunciations(word.upper(), stimmen_dic)]
|
||||||
|
df_ = df[df['word'] == word]
|
||||||
|
for index, row in df_.iterrows():
|
||||||
|
if row['htk'] in variants:
|
||||||
|
df_rec = df_rec.append(row, ignore_index=True)
|
||||||
|
|
||||||
## ======================= make a HTK dic file ======================
|
|
||||||
#if make_htk_dic_file:
|
|
||||||
# output_type = 3
|
|
||||||
dictionary_txt = os.path.join(default.htk_dir, 'lexicon', 'stimmen.dic')
|
|
||||||
#for word in word_list:
|
|
||||||
word = word_list[2]
|
|
||||||
# pronunciation variant of the target word.
|
|
||||||
pronunciations = df_test['asr'][df_test['word'].str.match(word)]
|
|
||||||
|
|
||||||
# make dic file.
|
## ======================= make files required for HTK ======================
|
||||||
#am_func.make_htk_dict(word, pronvar_, htk_dict_file, output_type)
|
if make_HTK_files:
|
||||||
|
# make a word lattice file.
|
||||||
|
pyhtk.create_word_lattice_file(
|
||||||
|
os.path.join(config_dir, 'stimmen.net'),
|
||||||
|
lattice_file)
|
||||||
|
|
||||||
|
# extract features.
|
||||||
|
with open(hcopy_scp, 'wb') as f:
|
||||||
|
filelist = [os.path.join(stimmen_test_dir, filename) + '\t'
|
||||||
|
+ os.path.join(feature_dir, os.path.basename(filename).replace('.wav', '.mfc'))
|
||||||
|
for filename in df['filename']]
|
||||||
|
f.write(bytes('\n'.join(filelist), 'ascii'))
|
||||||
|
pyhtk.wav2mfc(config_hcopy, hcopy_scp)
|
||||||
|
|
||||||
|
# make label files.
|
||||||
|
for index, row in df.iterrows():
|
||||||
|
filename = row['filename'].replace('.wav', '.lab')
|
||||||
|
label_file = os.path.join(feature_dir, filename)
|
||||||
|
with open(label_file, 'wb') as f:
|
||||||
|
label_string = 'START\n' + row['word'].upper() + '\nEND\n'
|
||||||
|
f.write(bytes(label_string, 'ascii'))
|
||||||
|
|
||||||
|
|
||||||
|
## ======================= make files required for Kaldi =======================
|
||||||
|
if make_kaldi_files:
|
||||||
|
fh.make_new_directory(os.path.join(kaldi_data_dir, 'test'))
|
||||||
|
fh.make_new_directory(os.path.join(kaldi_data_dir, 'test', 'local'))
|
||||||
|
fh.make_new_directory(os.path.join(kaldi_data_dir, 'conf'))
|
||||||
|
|
||||||
|
# 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, row in df_rec.iterrows():
|
||||||
|
filename = row['filename']
|
||||||
|
print('=== {0}: {1} ==='.format(i, filename))
|
||||||
|
|
||||||
|
wav_file = os.path.join(stimmen_test_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
|
||||||
|
|
||||||
|
# output
|
||||||
|
f_wav_scp.write('{0} {1}\n'.format(
|
||||||
|
utterance_id,
|
||||||
|
wav_file.replace('c:/', '/mnt/c/').replace('\\', '/'))) # convert path to unix format.
|
||||||
|
f_text_file.write('{0}\t{1}\n'.format(utterance_id, df_rec['word'][i].lower()))
|
||||||
|
f_utt2spk.write('{0} {1}\n'.format(utterance_id, speaker_id))
|
||||||
|
|
||||||
|
f_wav_scp.close()
|
||||||
|
f_text_file.close()
|
||||||
|
f_utt2spk.close()
|
||||||
|
|
||||||
|
with open(corpus_txt, 'wb') as f:
|
||||||
|
f.write(bytes('\n'.join([word.lower() for word in word_list]) + '\n', 'utf-8'))
|
||||||
|
|
||||||
|
with open(nonsilence_phones_txt, 'wb') as f:
|
||||||
|
f.write(bytes('\n'.join(fame_asr.phoneset_short) + '\n', 'utf-8'))
|
||||||
|
|
||||||
|
with open(silence_phones_txt, 'wb') as f:
|
||||||
|
f.write(bytes('sil\nspn\n', 'utf-8'))
|
||||||
|
|
||||||
|
with open(optional_silence_txt, 'wb') as f:
|
||||||
|
f.write(bytes('sil\n', 'utf-8'))
|
||||||
|
|
||||||
|
with open(os.path.join(kaldi_data_dir, 'conf', 'decode.config'), 'wb') as f:
|
||||||
|
f.write(bytes('first_beam=10.0\n', 'utf-8'))
|
||||||
|
f.write(bytes('beam=13.0\n', 'utf-8'))
|
||||||
|
f.write(bytes('lattice_beam=6.0\n', 'utf-8'))
|
||||||
|
|
||||||
|
with open(os.path.join(kaldi_data_dir, 'conf', 'mfcc.conf'), 'wb') as f:
|
||||||
|
f.write(bytes('--use-energy=false', 'utf-8'))
|
||||||
|
|
||||||
|
|
||||||
|
## ======================= recognition ======================
|
||||||
|
|
||||||
|
listdir = glob.glob(os.path.join(feature_dir, '*.mfc'))
|
||||||
|
with open(hvite_scp, 'wb') as f:
|
||||||
|
f.write(bytes('\n'.join(listdir), 'ascii'))
|
||||||
|
|
||||||
|
with open(hresult_scp, 'wb') as f:
|
||||||
|
f.write(bytes('\n'.join(listdir).replace('.mfc', '.rec'), 'ascii'))
|
||||||
|
|
||||||
|
|
||||||
|
# calculate result
|
||||||
|
performance = np.zeros((1, 2))
|
||||||
|
for niter in range(1, 50):
|
||||||
|
output = pyhtk.recognition(
|
||||||
|
os.path.join(config_dir, 'config.rec'),
|
||||||
|
lattice_file,
|
||||||
|
os.path.join(default.htk_dir, 'model', 'hmm1', 'iter' + str(niter), 'hmmdefs'),
|
||||||
|
stimmen_dic, phonelist_txt, hvite_scp)
|
||||||
|
|
||||||
|
output = pyhtk.calc_recognition_performance(
|
||||||
|
stimmen_dic, hresult_scp)
|
||||||
|
per_sentence, per_word = pyhtk.load_recognition_output_all(output)
|
||||||
|
performance_ = np.array([niter, per_sentence['accuracy']]).reshape(1, 2)
|
||||||
|
performance = np.r_[performance, performance_]
|
||||||
|
print('{0}: {1}[%]'.format(niter, per_sentence['accuracy']))
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
## ======================= forced alignment using HTK =======================
|
## ======================= forced alignment using HTK =======================
|
||||||
@ -168,54 +316,7 @@ if do_forced_alignment_htk:
|
|||||||
predictions.to_pickle(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.pkl'))
|
predictions.to_pickle(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.pkl'))
|
||||||
|
|
||||||
|
|
||||||
## ======================= make files which is used for forced alignment by Kaldi =======================
|
|
||||||
if make_kaldi_data_files:
|
|
||||||
|
|
||||||
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')
|
|
||||||
|
|
||||||
# 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 df.index:
|
|
||||||
filename = df['filename'][i]
|
|
||||||
print('=== {0}: {1} ==='.format(i, filename))
|
|
||||||
|
|
||||||
#if (i in df['filename'].keys()) and (isinstance(df['filename'][i], str)):
|
|
||||||
wav_file = os.path.join(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 = df['word'][i].lower()
|
|
||||||
f_text_file.write('{0}\t{1}\n'.format(utterance_id, word))
|
|
||||||
|
|
||||||
# utt2spk
|
|
||||||
f_utt2spk.write('{0} {1}\n'.format(utterance_id, speaker_id))
|
|
||||||
|
|
||||||
f_wav_scp.close()
|
|
||||||
f_text_file.close()
|
|
||||||
f_utt2spk.close()
|
|
||||||
|
|
||||||
|
|
||||||
## ======================= make lexicon txt which is used by Kaldi =======================
|
## ======================= make lexicon txt which is used by Kaldi =======================
|
||||||
|
@ -68,14 +68,21 @@ phoneset = [
|
|||||||
# the phones which seldom occur are replaced with another more popular phones.
|
# the phones which seldom occur are replaced with another more popular phones.
|
||||||
# replacements are based on the advice from Martijn Wieling.
|
# replacements are based on the advice from Martijn Wieling.
|
||||||
reduction_key = {
|
reduction_key = {
|
||||||
'y':'i:', 'e':'e:', 'ə:':'ɛ:', 'r:':'r', 'ɡ':'g'
|
'y':'i:', 'e':'e:', 'ə:':'ɛ:', 'r:':'r', 'ɡ':'g',
|
||||||
|
# aki added because this is used in stimmen_project.
|
||||||
|
'ɔ̈:':'ɔ:'
|
||||||
}
|
}
|
||||||
# already removed beforehand in phoneset. Just to be sure.
|
# already removed beforehand in phoneset. Just to be sure.
|
||||||
phones_to_be_removed = ['ú', 's:', 'ɔ̈:']
|
phones_to_be_removed = ['ú', 's:']
|
||||||
|
|
||||||
def phone_reduction(phones):
|
def phone_reduction(phones):
|
||||||
|
"""
|
||||||
|
Args:
|
||||||
|
phones (list): list of phones.
|
||||||
|
"""
|
||||||
return [reduction_key.get(i, i) for i in phones
|
return [reduction_key.get(i, i) for i in phones
|
||||||
if not i in phones_to_be_removed]
|
if not i in phones_to_be_removed]
|
||||||
|
|
||||||
phoneset_short = list(set(phone_reduction(phoneset)))
|
phoneset_short = list(set(phone_reduction(phoneset)))
|
||||||
phoneset_short.sort()
|
phoneset_short.sort()
|
||||||
|
|
||||||
@ -97,6 +104,7 @@ translation_key_asr2htk = {
|
|||||||
|
|
||||||
# refer to Xsampa.
|
# refer to Xsampa.
|
||||||
'ɔ': 'O', 'ɔ:': 'O:', 'ɔ̈': 'Oe',
|
'ɔ': 'O', 'ɔ:': 'O:', 'ɔ̈': 'Oe',
|
||||||
|
#'ɔ̈:': 'O:', # does not appear in FAME, but used in stimmen.
|
||||||
'ɛ': 'E', 'ɛ:': 'E:',
|
'ɛ': 'E', 'ɛ:': 'E:',
|
||||||
'ɪ': 'I', 'ɪ:': 'I:',
|
'ɪ': 'I', 'ɪ:': 'I:',
|
||||||
|
|
||||||
|
@ -81,3 +81,25 @@ def add_row_asr(df):
|
|||||||
for index, row in df.iterrows():
|
for index, row in df.iterrows():
|
||||||
asr.append(fame_functions.ipa2asr(row['ipa']))
|
asr.append(fame_functions.ipa2asr(row['ipa']))
|
||||||
return df.assign(asr=asr)
|
return df.assign(asr=asr)
|
||||||
|
|
||||||
|
|
||||||
|
def load_pronunciations(WORD, htk_dic):
|
||||||
|
""" load pronunciation variants from HTK dic file.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
WORD (str): word in capital letters.
|
||||||
|
htk_dic (path): HTK dict file.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
(pronunciations) (list): pronunciation variants of WORD.
|
||||||
|
|
||||||
|
Notes:
|
||||||
|
Because this function loads all contents from htk_dic file,
|
||||||
|
it is not recommended to use for large lexicon.
|
||||||
|
|
||||||
|
"""
|
||||||
|
with open(htk_dic) as f:
|
||||||
|
lines = f.read().replace(' sil', '')
|
||||||
|
lines = lines.split('\n')
|
||||||
|
return [' '.join(line.split(' ')[1:])
|
||||||
|
for line in lines if line.split(' ')[0]==WORD]
|
@ -2,8 +2,9 @@ import os
|
|||||||
os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
|
os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
|
||||||
import sys
|
import sys
|
||||||
import shutil
|
import shutil
|
||||||
|
from collections import Counter
|
||||||
|
|
||||||
#import numpy as np
|
import numpy as np
|
||||||
import pandas as pd
|
import pandas as pd
|
||||||
|
|
||||||
import defaultfiles as default
|
import defaultfiles as default
|
||||||
@ -62,3 +63,18 @@ for ipa in df['ipa']:
|
|||||||
if ':' in ipa_splitted:
|
if ':' in ipa_splitted:
|
||||||
print(ipa_splitted)
|
print(ipa_splitted)
|
||||||
|
|
||||||
|
|
||||||
|
## check pronunciation variants
|
||||||
|
df_clean = stimmen_functions.load_transcriptions_clean(stimmen_test_dir)
|
||||||
|
df_clean = stimmen_functions.add_row_asr(df_clean)
|
||||||
|
df_clean = stimmen_functions.add_row_htk(df_clean)
|
||||||
|
|
||||||
|
for word in word_list:
|
||||||
|
#word = word_list[1]
|
||||||
|
df_ = df_clean[df_clean['word']==word]
|
||||||
|
c = Counter(df_['htk'])
|
||||||
|
pronunciations = dict()
|
||||||
|
for key, value in zip(c.keys(), c.values()):
|
||||||
|
if value > 3:
|
||||||
|
pronunciations[key] = value
|
||||||
|
print(pronunciations)
|
Loading…
Reference in New Issue
Block a user