diff --git a/.vs/acoustic_model/v15/.suo b/.vs/acoustic_model/v15/.suo
index 649e0b1..1238af1 100644
Binary files a/.vs/acoustic_model/v15/.suo and b/.vs/acoustic_model/v15/.suo differ
diff --git a/acoustic_model/__pycache__/defaultfiles.cpython-36.pyc b/acoustic_model/__pycache__/defaultfiles.cpython-36.pyc
index 545949d..7b3cfb7 100644
Binary files a/acoustic_model/__pycache__/defaultfiles.cpython-36.pyc and b/acoustic_model/__pycache__/defaultfiles.cpython-36.pyc differ
diff --git a/acoustic_model/acoustic_model.pyproj b/acoustic_model/acoustic_model.pyproj
index 715d6c0..ebdbadc 100644
--- a/acoustic_model/acoustic_model.pyproj
+++ b/acoustic_model/acoustic_model.pyproj
@@ -4,7 +4,7 @@
2.0
4d8c8573-32f0-4a62-9e62-3ce5cc680390
.
- fame_hmm.py
+ htk_vs_kaldi.py
.
diff --git a/acoustic_model/convert_phoneset.py b/acoustic_model/convert_phoneset.py
index 7bc39f7..d575c99 100644
--- a/acoustic_model/convert_phoneset.py
+++ b/acoustic_model/convert_phoneset.py
@@ -38,3 +38,9 @@ def convert_phoneset(word_list, translation_key):
translation_key (dict):
"""
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]
\ No newline at end of file
diff --git a/acoustic_model/defaultfiles.py b/acoustic_model/defaultfiles.py
index ef0dfd4..1e262f9 100644
--- a/acoustic_model/defaultfiles.py
+++ b/acoustic_model/defaultfiles.py
@@ -17,6 +17,7 @@ novo_api_dir = os.path.join(WSL_dir, 'python-novo-api', 'novoapi')
rug_dir = r'c:\OneDrive\Research\rug'
experiments_dir = os.path.join(rug_dir, 'experiments')
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')
# data
diff --git a/acoustic_model/fame_functions.py b/acoustic_model/fame_functions.py
index 10f16cd..9d3992b 100644
--- a/acoustic_model/fame_functions.py
+++ b/acoustic_model/fame_functions.py
@@ -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')
-def fix_single_quote(lexicon_file):
- """ add '\' before all single quote at the beginning of words.
- convert special characters to ascii compatible characters.
+def fix_lexicon(lexicon_file):
+ """ fix lexicon
+ - add '\' before all single quote at the beginning of words.
+ - convert special characters to ascii compatible characters.
+ - add silence.
Args:
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 = 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:
lex.iat[i, 0] = lex.iat[i, 0].replace('\'', '\\\'')
# 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):
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)
-
+ #ipa_ = fame_asr.phone_reduction(ipa)
ipa_splitted = convert_phoneset.split_word(ipa, fame_ipa.multi_character_phones)
ipa_splitted = fame_ipa.phone_reduction(ipa_splitted)
asr_splitted = convert_phoneset.convert_phoneset(ipa_splitted, translation_key_ipa2asr)
+ asr_splitted = fame_asr.phone_reduction(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 = fame_ipa.phone_reduction(ipa_splitted)
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)
return ''.join(htk_splitted)
\ No newline at end of file
diff --git a/acoustic_model/fame_hmm.py b/acoustic_model/fame_hmm.py
index b3d1070..7228c00 100644
--- a/acoustic_model/fame_hmm.py
+++ b/acoustic_model/fame_hmm.py
@@ -27,7 +27,8 @@ extract_features = 0
flat_start = 0
train_model_without_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
hcompv_scp_train = os.path.join(tmp_dir, 'train.scp')
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
niter_max = 10
@@ -102,7 +104,8 @@ if make_lexicon:
# (1) Replace all tabs with single space;
# (2) Put a '\' before any dictionary entry beginning with single quote
#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))
@@ -269,11 +272,11 @@ if train_model_without_sp:
fh.make_new_directory(modeln_dir)
pyhtk.re_estimation(
config_train,
- os.path.join(modeln_dir_pre, 'macros'),
os.path.join(modeln_dir_pre, hmmdefs_name),
modeln_dir,
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))
@@ -321,7 +324,6 @@ if add_sp:
## ======================= train model with short pause =======================
if 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):
timer_start = time.time()
hmm_n = 'iter' + str(niter)
@@ -333,9 +335,31 @@ if train_model_with_sp:
fh.make_new_directory(modeln_dir)
pyhtk.re_estimation(
config_train,
- os.path.join(modeln_dir_pre, 'macros'),
os.path.join(modeln_dir_pre, hmmdefs_name),
modeln_dir,
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))
\ No newline at end of file
diff --git a/acoustic_model/htk_vs_kaldi.py b/acoustic_model/htk_vs_kaldi.py
index 00c699c..c35a42f 100644
--- a/acoustic_model/htk_vs_kaldi.py
+++ b/acoustic_model/htk_vs_kaldi.py
@@ -11,6 +11,7 @@ import glob
import numpy as np
import pandas as pd
+from collections import Counter
#import matplotlib.pyplot as plt
#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_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
#do_forced_alignment_htk = 0
#eval_forced_alignment_htk = 0
-#make_kaldi_data_files = 0
+make_kaldi_files = 0
#make_kaldi_lexicon_txt = 0
#load_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'))
#from evaluation import plot_confusion_matrix
-config_dir = os.path.join(default.htk_dir, 'config')
-model_dir = os.path.join(default.htk_dir, 'model')
-lattice_file = os.path.join(config_dir, 'stimmen.ltc')
-#pyhtk.create_word_lattice_file(
-# os.path.join(config_dir, 'stimmen.net'),
-# lattice_file)
-hvite_scp = os.path.join(default.htk_dir, 'tmp', 'stimmen_test.scp')
+## HTK related files.
+config_dir = os.path.join(default.htk_dir, 'config')
+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')
+phonelist_txt = os.path.join(config_dir, 'phonelist.txt')
+stimmen_dic = os.path.join(default.htk_dir, 'lexicon', 'stimmen_recognition.dic')
+hcopy_scp = os.path.join(default.htk_dir, 'tmp', 'stimmen_test_hcopy.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 ======================
@@ -85,392 +110,468 @@ df = stimmen_functions.add_row_htk(df)
word_list = [i for i in list(set(df['word'])) if not pd.isnull(i)]
word_list = sorted(word_list)
-# pronunciation variants
+
+## ======================= 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]
+ 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'))
+
+ # 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(' 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'))
+
+
+## ======================= test data for recognition ======================
+# only target pronunciation variants.
+df_rec = pd.DataFrame(index=[], columns=list(df.keys()))
for word in word_list:
- df_ = df[df['word']==word]
- print('{0} has {1} variants'.format(word, len(np.unique(df_['htk'])))
+ 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)
-#fh.make_filelist(stimmen_test_dir, hvite_scp, file_type='wav')
-#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)
+## ======================= make files required for HTK ======================
+if make_HTK_files:
+ # make a word lattice file.
+ pyhtk.create_word_lattice_file(
+ os.path.join(config_dir, 'stimmen.net'),
+ lattice_file)
- #pyhtk.create_label_file(
- # row['word'],
- # os.path.join(stimmen_test_dir, filename.replace('.wav', '.lab')))
+ # 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']))
-## ======================= 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.
- #am_func.make_htk_dict(word, pronvar_, htk_dict_file, output_type)
-
## ======================= forced alignment using HTK =======================
if do_forced_alignment_htk:
-
- #for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]:
- for hmm_num in [256, 512, 1024]:
- hmm_num_str = str(hmm_num)
- acoustic_model = os.path.join(acoustic_model_dir, 'hmm' + hmm_num_str + r'-2\hmmdefs')
+
+ #for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]:
+ for hmm_num in [256, 512, 1024]:
+ hmm_num_str = str(hmm_num)
+ acoustic_model = os.path.join(acoustic_model_dir, 'hmm' + hmm_num_str + r'-2\hmmdefs')
- predictions = pd.DataFrame({'filename': [''],
- 'word': [''],
- 'xsampa': [''],
- 'ipa': [''],
- 'famehtk': [''],
- 'prediction': ['']})
- for i, filename in enumerate(df['filename']):
- print('=== {0}/{1} ==='.format(i, len(df)))
- 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):
- word = df['word'][i]
- WORD = word.upper()
- fa_file = os.path.join(fa_dir, filename.replace('.wav', '.txt') + hmm_num_str)
-
- #if not os.path.exists(fa_file):
- # make label file.
- label_file = os.path.join(wav_dir, filename.replace('.wav', '.lab'))
- with open(label_file, 'w') as f:
- lines = f.write(WORD)
+ predictions = pd.DataFrame({'filename': [''],
+ 'word': [''],
+ 'xsampa': [''],
+ 'ipa': [''],
+ 'famehtk': [''],
+ 'prediction': ['']})
+ for i, filename in enumerate(df['filename']):
+ print('=== {0}/{1} ==='.format(i, len(df)))
+ 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):
+ word = df['word'][i]
+ WORD = word.upper()
+ fa_file = os.path.join(fa_dir, filename.replace('.wav', '.txt') + hmm_num_str)
+
+ #if not os.path.exists(fa_file):
+ # make label file.
+ label_file = os.path.join(wav_dir, filename.replace('.wav', '.lab'))
+ with open(label_file, 'w') as f:
+ lines = f.write(WORD)
- htk_dict_file = os.path.join(htk_dict_dir, word + '.dic')
+ htk_dict_file = os.path.join(htk_dict_dir, word + '.dic')
- pyhtk.doHVite(wav_file, label_file, htk_dict_file, fa_file, default.config_hvite,
- default.phonelist, acoustic_model)
- os.remove(label_file)
+ pyhtk.doHVite(wav_file, label_file, htk_dict_file, fa_file, default.config_hvite,
+ default.phonelist, acoustic_model)
+ os.remove(label_file)
- prediction = am_func.read_fileFA(fa_file)
+ prediction = am_func.read_fileFA(fa_file)
- print('{0}: {1} -> {2}'.format(WORD, df['famehtk'][i], prediction))
- else:
- prediction = ''
- print('!!!!! file not found.')
+ print('{0}: {1} -> {2}'.format(WORD, df['famehtk'][i], prediction))
+ else:
+ prediction = ''
+ print('!!!!! file not found.')
- line = pd.Series([df['filename'][i], df['word'][i], df['xsampa'][i], df['ipa'][i], df['famehtk'][i], prediction], index=['filename', 'word', 'xsampa', 'ipa', 'famehtk', 'prediction'], name=i)
- predictions = predictions.append(line)
- else:
- prediction = ''
- print('!!!!! invalid entry.')
+ line = pd.Series([df['filename'][i], df['word'][i], df['xsampa'][i], df['ipa'][i], df['famehtk'][i], prediction], index=['filename', 'word', 'xsampa', 'ipa', 'famehtk', 'prediction'], name=i)
+ predictions = predictions.append(line)
+ else:
+ prediction = ''
+ print('!!!!! invalid entry.')
- 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 =======================
if make_kaldi_lexicon_txt:
- option_num = 6
+ option_num = 6
- # remove previous file.
- if os.path.exists(lexicon_txt):
- os.remove(lexicon_txt)
- lexiconp_txt = lexicon_txt.replace('lexicon.txt', 'lexiconp.txt')
- if os.path.exists(lexiconp_txt):
- os.remove(lexiconp_txt)
-
- # output lexicon.txt
- f_lexicon_txt = open(lexicon_txt, 'a', encoding="utf-8", newline='\n')
- pronvar_list_all = []
- for word in word_list:
+ # remove previous file.
+ if os.path.exists(lexicon_txt):
+ os.remove(lexicon_txt)
+ lexiconp_txt = lexicon_txt.replace('lexicon.txt', 'lexiconp.txt')
+ if os.path.exists(lexiconp_txt):
+ os.remove(lexiconp_txt)
+
+ # output lexicon.txt
+ f_lexicon_txt = open(lexicon_txt, 'a', encoding="utf-8", newline='\n')
+ pronvar_list_all = []
+ for word in word_list:
- # pronunciation variant of the target word.
- pronunciation_variants = df['ipa'][df['word'].str.match(word)]
+ # pronunciation variant of the target word.
+ pronunciation_variants = df['ipa'][df['word'].str.match(word)]
- c = Counter(pronunciation_variants)
- total_num = sum(c.values())
+ c = Counter(pronunciation_variants)
+ total_num = sum(c.values())
- #with open(result_dir + '\\' + word + '.csv', 'a', encoding="utf-8", newline='\n') as f:
- # for key in c.keys():
- # f.write("{0},{1}\n".format(key,c[key]))
+ #with open(result_dir + '\\' + word + '.csv', 'a', encoding="utf-8", newline='\n') as f:
+ # for key in c.keys():
+ # f.write("{0},{1}\n".format(key,c[key]))
- for key, value in c.most_common(option_num):
- # make possible pronunciation variant list.
- pronvar_list = am_func.fame_pronunciation_variant(key)
+ for key, value in c.most_common(option_num):
+ # make possible pronunciation variant list.
+ pronvar_list = am_func.fame_pronunciation_variant(key)
- for pronvar_ in pronvar_list:
- split_ipa = convert_phone_set.split_fame_ipa(pronvar_)
- pronvar_out = ' '.join(split_ipa)
- pronvar_list_all.append([word, pronvar_out])
+ for pronvar_ in pronvar_list:
+ split_ipa = convert_phone_set.split_fame_ipa(pronvar_)
+ pronvar_out = ' '.join(split_ipa)
+ pronvar_list_all.append([word, pronvar_out])
- pronvar_list_all = np.array(pronvar_list_all)
- pronvar_list_all = np.unique(pronvar_list_all, axis=0)
+ pronvar_list_all = np.array(pronvar_list_all)
+ pronvar_list_all = np.unique(pronvar_list_all, axis=0)
-
- # output
- f_lexicon_txt.write('\tSPN\n')
- for line in pronvar_list_all:
- f_lexicon_txt.write('{0}\t{1}\n'.format(line[0].lower(), line[1]))
+
+ # output
+ f_lexicon_txt.write('\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()
+ f_lexicon_txt.close()
## ======================= load kaldi forced alignment result =======================
if load_forced_alignment_kaldi:
- phones_txt = os.path.join(default.kaldi_dir, 'data', 'lang', 'phones.txt')
- merged_alignment_txt = os.path.join(default.kaldi_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)
+ phones_txt = os.path.join(default.kaldi_dir, 'data', 'lang', 'phones.txt')
+ merged_alignment_txt = os.path.join(default.kaldi_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:
- mapping_phone2id = f.read().split('\n')
+ # load the mapping between phones and ids.
+ with open(phones_txt, 'r', encoding="utf-8") as f:
+ mapping_phone2id = f.read().split('\n')
- phones = []
- phone_ids = [] # ID of phones
- for m in mapping_phone2id:
- m = m.split(' ')
- if len(m) > 1:
- phones.append(m[0])
- phone_ids.append(int(m[1]))
+ phones = []
+ phone_ids = [] # ID of phones
+ for m in mapping_phone2id:
+ m = m.split(' ')
+ if len(m) > 1:
+ phones.append(m[0])
+ phone_ids.append(int(m[1]))
- # load the result of FA.
- with open(merged_alignment_txt, 'r') as f:
- lines = f.read()
- lines = lines.split('\n')
+ # load the result of FA.
+ with open(merged_alignment_txt, 'r') as f:
+ lines = f.read()
+ lines = lines.split('\n')
- predictions = pd.DataFrame({'filename': [''],
- 'word': [''],
- 'xsampa': [''],
- 'ipa': [''],
- 'famehtk': [''],
- 'prediction': ['']})
- #fa_filenames = []
- #fa_pronunciations = []
- utterance_id_ = ''
- pronunciation = []
- for line in lines:
- line = line.split(' ')
- if len(line) == 5:
- utterance_id = line[0]
- if utterance_id == utterance_id_:
- phone_id = int(line[4])
- #if not phone_id == 1:
- phone_ = phones[phone_ids.index(phone_id)]
- phone = re.sub(r'_[A-Z]', '', phone_)
- if not phone == 'SIL':
- pronunciation.append(phone)
- else:
- filename = re.sub(r'speaker_[0-9]{4}-', '', utterance_id_)
- prediction = ''.join(pronunciation)
- df_ = df[df['filename'].str.match(filename)]
- df_idx = df_.index[0]
- prediction_ = pd.Series([#filename,
- #df_['word'][df_idx],
- #df_['xsampa'][df_idx],
- #df_['ipa'][df_idx],
- #df_['famehtk'][df_idx],
- df_.iloc[0,1],
- df_.iloc[0,3],
- df_.iloc[0,4],
- df_.iloc[0,2],
- df_.iloc[0,0],
- prediction],
- index=['filename', 'word', 'xsampa', 'ipa', 'famehtk', 'prediction'],
- name=df_idx)
- predictions = predictions.append(prediction_)
- #fa_filenames.append()
- #fa_pronunciations.append(' '.join(pronunciation))
- pronunciation = []
+ predictions = pd.DataFrame({'filename': [''],
+ 'word': [''],
+ 'xsampa': [''],
+ 'ipa': [''],
+ 'famehtk': [''],
+ 'prediction': ['']})
+ #fa_filenames = []
+ #fa_pronunciations = []
+ utterance_id_ = ''
+ pronunciation = []
+ for line in lines:
+ line = line.split(' ')
+ if len(line) == 5:
+ utterance_id = line[0]
+ if utterance_id == utterance_id_:
+ phone_id = int(line[4])
+ #if not phone_id == 1:
+ phone_ = phones[phone_ids.index(phone_id)]
+ phone = re.sub(r'_[A-Z]', '', phone_)
+ if not phone == 'SIL':
+ pronunciation.append(phone)
+ else:
+ filename = re.sub(r'speaker_[0-9]{4}-', '', utterance_id_)
+ prediction = ''.join(pronunciation)
+ df_ = df[df['filename'].str.match(filename)]
+ df_idx = df_.index[0]
+ prediction_ = pd.Series([#filename,
+ #df_['word'][df_idx],
+ #df_['xsampa'][df_idx],
+ #df_['ipa'][df_idx],
+ #df_['famehtk'][df_idx],
+ df_.iloc[0,1],
+ df_.iloc[0,3],
+ df_.iloc[0,4],
+ df_.iloc[0,2],
+ df_.iloc[0,0],
+ prediction],
+ index=['filename', 'word', 'xsampa', 'ipa', 'famehtk', 'prediction'],
+ name=df_idx)
+ predictions = predictions.append(prediction_)
+ #fa_filenames.append()
+ #fa_pronunciations.append(' '.join(pronunciation))
+ pronunciation = []
- utterance_id_ = utterance_id
- predictions.to_pickle(os.path.join(result_dir, 'kaldi', 'predictions.pkl'))
+ utterance_id_ = utterance_id
+ predictions.to_pickle(os.path.join(result_dir, 'kaldi', 'predictions.pkl'))
## ======================= evaluate the result of forced alignment =======================
if eval_forced_alignment_htk:
- htk_dict_dir = os.path.join(default.experiments_dir, 'stimmen', 'dic_short')
+ htk_dict_dir = os.path.join(default.experiments_dir, 'stimmen', 'dic_short')
- compare_hmm_num = 1
+ compare_hmm_num = 1
- if compare_hmm_num:
- f_result = open(os.path.join(result_dir, 'result.csv'), 'w')
- f_result.write("nmix,Oog,Oog,Oor,Oor,Pauw,Pauw,Reus,Reus,Reuzenrad,Reuzenrad,Roeiboot,Roeiboot,Rozen,Rozen\n")
+ if compare_hmm_num:
+ f_result = open(os.path.join(result_dir, 'result.csv'), 'w')
+ f_result.write("nmix,Oog,Oog,Oor,Oor,Pauw,Pauw,Reus,Reus,Reuzenrad,Reuzenrad,Roeiboot,Roeiboot,Rozen,Rozen\n")
- for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]:
- #for hmm_num in [256]:
- hmm_num_str = str(hmm_num)
- if compare_hmm_num:
- f_result.write("{},".format(hmm_num_str))
+ for hmm_num in [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]:
+ #for hmm_num in [256]:
+ hmm_num_str = str(hmm_num)
+ if compare_hmm_num:
+ f_result.write("{},".format(hmm_num_str))
- #match = np.load(data_dir + '\\match_hmm' + hmm_num_str + '.npy')
- #prediction = np.load(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.npy'))
- #prediction = pd.Series(prediction, index=df.index, name='prediction')
- #result = pd.concat([df, prediction], axis=1)
- result = pd.read_pickle(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.pkl'))
+ #match = np.load(data_dir + '\\match_hmm' + hmm_num_str + '.npy')
+ #prediction = np.load(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.npy'))
+ #prediction = pd.Series(prediction, index=df.index, name='prediction')
+ #result = pd.concat([df, prediction], axis=1)
+ result = pd.read_pickle(os.path.join(result_dir, 'htk', 'predictions_hmm' + hmm_num_str + '.pkl'))
- # load pronunciation variants
- for word in word_list:
- htk_dict_file = os.path.join(htk_dict_dir, word + '.dic')
- with open(htk_dict_file, 'r') as f:
- lines = f.read().split('\n')[:-1]
- pronunciation_variants = [line.split('\t')[1] for line in lines]
+ # load pronunciation variants
+ for word in word_list:
+ htk_dict_file = os.path.join(htk_dict_dir, word + '.dic')
+ with open(htk_dict_file, 'r') as f:
+ lines = f.read().split('\n')[:-1]
+ pronunciation_variants = [line.split('\t')[1] for line in lines]
- # see only words which appears in top 3.
- result_ = result[result['word'].str.match(word)]
- result_ = result_[result_['famehtk'].isin(pronunciation_variants)]
+ # see only words which appears in top 3.
+ result_ = result[result['word'].str.match(word)]
+ result_ = result_[result_['famehtk'].isin(pronunciation_variants)]
- match_num = sum(result_['famehtk'] == result_['prediction'])
- total_num = len(result_)
+ match_num = sum(result_['famehtk'] == result_['prediction'])
+ total_num = len(result_)
- print("word '{0}': {1}/{2} ({3:.2f} %)".format(word, match_num, total_num, match_num/total_num*100))
- if compare_hmm_num:
- f_result.write("{0},{1},".format(match_num, total_num))
- else:
- # output confusion matrix
- cm = confusion_matrix(result_['famehtk'], result_['prediction'])
+ print("word '{0}': {1}/{2} ({3:.2f} %)".format(word, match_num, total_num, match_num/total_num*100))
+ if compare_hmm_num:
+ f_result.write("{0},{1},".format(match_num, total_num))
+ else:
+ # output confusion matrix
+ cm = confusion_matrix(result_['famehtk'], result_['prediction'])
- plt.figure()
- plot_confusion_matrix(cm, classes=pronunciation_variants, normalize=False)
- plt.savefig(result_dir + '\\cm_' + word + '.png')
+ plt.figure()
+ plot_confusion_matrix(cm, classes=pronunciation_variants, normalize=False)
+ plt.savefig(result_dir + '\\cm_' + word + '.png')
- if compare_hmm_num:
- f_result.write('\n')
+ if compare_hmm_num:
+ f_result.write('\n')
- if compare_hmm_num:
- f_result.close()
+ if compare_hmm_num:
+ f_result.close()
## ======================= evaluate the result of forced alignment of kaldi =======================
if eval_forced_alignment_kaldi:
- result = pd.read_pickle(os.path.join(result_dir, 'kaldi', 'predictions.pkl'))
+ result = pd.read_pickle(os.path.join(result_dir, 'kaldi', 'predictions.pkl'))
- f_result = open(os.path.join(result_dir, 'result.csv'), 'w')
- f_result.write("word,total,valid,match,[%]\n")
+ f_result = open(os.path.join(result_dir, 'result.csv'), 'w')
+ f_result.write("word,total,valid,match,[%]\n")
- # load pronunciation variants
- with open(lexicon_txt, 'r', encoding="utf-8", newline='\n') as f:
- lines = f.read().split('\n')[:-1]
- pronunciation_variants_all = [line.split('\t') for line in lines]
+ # load pronunciation variants
+ with open(lexicon_txt, 'r', encoding="utf-8", newline='\n') as f:
+ lines = f.read().split('\n')[:-1]
+ pronunciation_variants_all = [line.split('\t') for line in lines]
- word_list = np.delete(word_list, [0], 0) # remove 'Oog'
- for word in word_list:
+ word_list = np.delete(word_list, [0], 0) # remove 'Oog'
+ for word in word_list:
- # load pronunciation variant of the word.
- pronunciation_variants = []
- for line in pronunciation_variants_all:
- if line[0] == word.lower():
- pronunciation_variants.append(line[1].replace(' ', ''))
+ # load pronunciation variant of the word.
+ pronunciation_variants = []
+ for line in pronunciation_variants_all:
+ if line[0] == word.lower():
+ pronunciation_variants.append(line[1].replace(' ', ''))
- # see only words which appears in top 3.
- result_ = result[result['word'].str.match(word)]
- result_tolerant = pd.DataFrame({
- 'filename': [''],
- 'word': [''],
- 'xsampa': [''],
- 'ipa': [''],
- 'prediction': [''],
- 'match': ['']})
+ # see only words which appears in top 3.
+ result_ = result[result['word'].str.match(word)]
+ result_tolerant = pd.DataFrame({
+ 'filename': [''],
+ 'word': [''],
+ 'xsampa': [''],
+ 'ipa': [''],
+ 'prediction': [''],
+ 'match': ['']})
- for i in range(0, len(result_)):
- line = result_.iloc[i]
+ for i in range(0, len(result_)):
+ line = result_.iloc[i]
- # make a list of all possible pronunciation variants of ipa description.
- # i.e. possible answers from forced alignment.
- ipa = line['ipa']
- pronvar_list = [ipa]
- pronvar_list_ = am_func.fame_pronunciation_variant(ipa)
- if not pronvar_list_ is None:
- pronvar_list += list(pronvar_list_)
+ # make a list of all possible pronunciation variants of ipa description.
+ # i.e. possible answers from forced alignment.
+ ipa = line['ipa']
+ pronvar_list = [ipa]
+ pronvar_list_ = am_func.fame_pronunciation_variant(ipa)
+ if not pronvar_list_ is None:
+ pronvar_list += list(pronvar_list_)
- # only focus on pronunciations which can be estimated from ipa.
- if len(set(pronvar_list) & set(pronunciation_variants)) > 0:
- if line['prediction'] in pronvar_list:
- ismatch = True
- else:
- ismatch = False
+ # only focus on pronunciations which can be estimated from ipa.
+ if len(set(pronvar_list) & set(pronunciation_variants)) > 0:
+ if line['prediction'] in pronvar_list:
+ ismatch = True
+ else:
+ ismatch = False
- line_df = pd.DataFrame(result_.iloc[i]).T
- df_idx = line_df.index[0]
- result_tolerant_ = pd.Series([line_df.loc[df_idx, 'filename'],
- line_df.loc[df_idx, 'word'],
- line_df.loc[df_idx, 'xsampa'],
- line_df.loc[df_idx, 'ipa'],
- line_df.loc[df_idx, 'prediction'],
- ismatch],
- index=['filename', 'word', 'xsampa', 'ipa', 'prediction', 'match'],
- name=df_idx)
- result_tolerant = result_tolerant.append(result_tolerant_)
- # remove the first entry (dummy)
- result_tolerant = result_tolerant.drop(0, axis=0)
+ line_df = pd.DataFrame(result_.iloc[i]).T
+ df_idx = line_df.index[0]
+ result_tolerant_ = pd.Series([line_df.loc[df_idx, 'filename'],
+ line_df.loc[df_idx, 'word'],
+ line_df.loc[df_idx, 'xsampa'],
+ line_df.loc[df_idx, 'ipa'],
+ line_df.loc[df_idx, 'prediction'],
+ ismatch],
+ index=['filename', 'word', 'xsampa', 'ipa', 'prediction', 'match'],
+ name=df_idx)
+ result_tolerant = result_tolerant.append(result_tolerant_)
+ # remove the first entry (dummy)
+ result_tolerant = result_tolerant.drop(0, axis=0)
- total_num = len(result_)
- valid_num = len(result_tolerant)
- match_num = np.sum(result_tolerant['match'])
+ total_num = len(result_)
+ valid_num = len(result_tolerant)
+ match_num = np.sum(result_tolerant['match'])
- print("word '{0}': {1}/{2} ({3:.2f} %) originally {4}".format(word, match_num, valid_num, match_num/valid_num*100, total_num))
- f_result.write("{0},{1},{2},{3},{4}\n".format(word, total_num, valid_num, match_num, match_num/valid_num*100))
+ print("word '{0}': {1}/{2} ({3:.2f} %) originally {4}".format(word, match_num, valid_num, match_num/valid_num*100, total_num))
+ f_result.write("{0},{1},{2},{3},{4}\n".format(word, total_num, valid_num, match_num, match_num/valid_num*100))
- f_result.close()
- ## output confusion matrix
- #cm = confusion_matrix(result_['ipa'], result_['prediction'])
+ f_result.close()
+ ## output confusion matrix
+ #cm = confusion_matrix(result_['ipa'], result_['prediction'])
- #plt.figure()
- #plot_confusion_matrix(cm, classes=pronunciation_variants, normalize=False)
- #plt.savefig(result_dir + '\\cm_' + word + '.png')
+ #plt.figure()
+ #plot_confusion_matrix(cm, classes=pronunciation_variants, normalize=False)
+ #plt.savefig(result_dir + '\\cm_' + word + '.png')
diff --git a/acoustic_model/phoneset/fame_asr.py b/acoustic_model/phoneset/fame_asr.py
index b11359b..6165d5c 100644
--- a/acoustic_model/phoneset/fame_asr.py
+++ b/acoustic_model/phoneset/fame_asr.py
@@ -68,14 +68,21 @@ phoneset = [
# the phones which seldom occur are replaced with another more popular phones.
# replacements are based on the advice from Martijn Wieling.
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.
-phones_to_be_removed = ['ú', 's:', 'ɔ̈:']
+phones_to_be_removed = ['ú', 's:']
def phone_reduction(phones):
+ """
+ Args:
+ phones (list): list of phones.
+ """
return [reduction_key.get(i, i) for i in phones
if not i in phones_to_be_removed]
+
phoneset_short = list(set(phone_reduction(phoneset)))
phoneset_short.sort()
@@ -96,7 +103,8 @@ translation_key_asr2htk = {
'ŋ': 'ng',
# 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:',
'ɪ': 'I', 'ɪ:': 'I:',
diff --git a/acoustic_model/stimmen_functions.py b/acoustic_model/stimmen_functions.py
index a272d42..cfdac62 100644
--- a/acoustic_model/stimmen_functions.py
+++ b/acoustic_model/stimmen_functions.py
@@ -81,3 +81,25 @@ def add_row_asr(df):
for index, row in df.iterrows():
asr.append(fame_functions.ipa2asr(row['ipa']))
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]
\ No newline at end of file
diff --git a/acoustic_model/stimmen_test.py b/acoustic_model/stimmen_test.py
index 60e96eb..93546ca 100644
--- a/acoustic_model/stimmen_test.py
+++ b/acoustic_model/stimmen_test.py
@@ -2,8 +2,9 @@ import os
os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
import sys
import shutil
+from collections import Counter
-#import numpy as np
+import numpy as np
import pandas as pd
import defaultfiles as default
@@ -62,3 +63,18 @@ for ipa in df['ipa']:
if ':' in 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)
\ No newline at end of file