label alignment using HVite is added.
This commit is contained in:
yemaozi88
parent
8f89f60538
commit
c185072d5b
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@ -4,7 +4,7 @@
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<SchemaVersion>2.0</SchemaVersion>
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<ProjectGuid>4d8c8573-32f0-4a62-9e62-3ce5cc680390</ProjectGuid>
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<ProjectHome>.</ProjectHome>
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<StartupFile>fame_hmm.py</StartupFile>
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<StartupFile>htk_vs_kaldi.py</StartupFile>
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<SearchPath>
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</SearchPath>
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<WorkingDirectory>.</WorkingDirectory>
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@ -38,3 +38,9 @@ def convert_phoneset(word_list, translation_key):
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translation_key (dict):
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"""
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return [translation_key.get(phone, phone) for phone in word_list]
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def phone_reduction(phones, reduction_key):
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multi_character_tokenize(wo.strip(), multi_character_phones)
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return [reduction_key.get(i, i) for i in phones
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if not i in phones_to_be_removed]
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@ -17,6 +17,7 @@ novo_api_dir = os.path.join(WSL_dir, 'python-novo-api', 'novoapi')
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rug_dir = r'c:\OneDrive\Research\rug'
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experiments_dir = os.path.join(rug_dir, 'experiments')
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htk_dir = os.path.join(experiments_dir, 'acoustic_model', 'fame', 'htk')
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kaldi_dir = os.path.join(WSL_dir, 'kaldi-trunk', 'egs', '_stimmen')
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stimmen_dir = os.path.join(experiments_dir, 'stimmen')
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# data
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@ -321,9 +321,11 @@ def combine_lexicon(lexicon_file1, lexicon_file2, lexicon_out):
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lex.to_csv(lexicon_out, index=False, header=False, sep='\t', encoding='utf-8')
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def fix_single_quote(lexicon_file):
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""" add '\' before all single quote at the beginning of words.
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convert special characters to ascii compatible characters.
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def fix_lexicon(lexicon_file):
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""" fix lexicon
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- add '\' before all single quote at the beginning of words.
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- convert special characters to ascii compatible characters.
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- add silence.
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Args:
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lexicon_file (path): lexicon file, which will be overwitten.
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@ -331,6 +333,12 @@ def fix_single_quote(lexicon_file):
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"""
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lex = load_lexicon(lexicon_file)
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lex = lex.dropna() # remove N/A.
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# add 'sil'
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row = pd.Series(['SILENCE', 'sil'], index=lex.columns)
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lex = lex.append(row, ignore_index=True)
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lex = lex.sort_values(by='word', ascending=True)
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for i in lex[lex['word'].str.startswith('\'')].index.values:
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lex.iat[i, 0] = lex.iat[i, 0].replace('\'', '\\\'')
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# to_csv does not work with space seperator. therefore all tabs should manually be replaced.
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@ -346,10 +354,11 @@ def word2htk(word):
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def ipa2asr(ipa):
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curr_dir = os.path.dirname(os.path.abspath(__file__))
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translation_key_ipa2asr = np.load(os.path.join(curr_dir, 'phoneset', 'fame_ipa2asr.npy')).item(0)
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#ipa_ = fame_asr.phone_reduction(ipa)
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ipa_splitted = convert_phoneset.split_word(ipa, fame_ipa.multi_character_phones)
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ipa_splitted = fame_ipa.phone_reduction(ipa_splitted)
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asr_splitted = convert_phoneset.convert_phoneset(ipa_splitted, translation_key_ipa2asr)
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asr_splitted = fame_asr.phone_reduction(asr_splitted)
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return ''.join(asr_splitted)
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@ -360,5 +369,6 @@ def ipa2htk(ipa):
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ipa_splitted = convert_phoneset.split_word(ipa, fame_ipa.multi_character_phones)
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ipa_splitted = fame_ipa.phone_reduction(ipa_splitted)
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asr_splitted = convert_phoneset.convert_phoneset(ipa_splitted, translation_key_ipa2asr)
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asr_splitted = fame_asr.phone_reduction(asr_splitted)
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htk_splitted = convert_phoneset.convert_phoneset(asr_splitted, fame_asr.translation_key_asr2htk)
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return ''.join(htk_splitted)
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@ -27,7 +27,8 @@ extract_features = 0
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flat_start = 0
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train_model_without_sp = 0
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add_sp = 0
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train_model_with_sp = 1
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train_model_with_sp = 0
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train_model_with_sp_align_mlf = 1
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@ -75,6 +76,7 @@ if not os.path.exists(label_dir):
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## training
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hcompv_scp_train = os.path.join(tmp_dir, 'train.scp')
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mlf_file_train = os.path.join(label_dir, 'train_phone.mlf')
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mlf_file_train_aligned = os.path.join(label_dir, 'train_phone_aligned.mlf')
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## train without sp
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niter_max = 10
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@ -102,7 +104,8 @@ if make_lexicon:
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# (1) Replace all tabs with single space;
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# (2) Put a '\' before any dictionary entry beginning with single quote
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#http://electroblaze.blogspot.nl/2013/03/understanding-htk-error-messages.html
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fame_functions.fix_single_quote(lexicon_htk)
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print('>>> fixing the lexicon...')
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fame_functions.fix_lexicon(lexicon_htk)
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print("elapsed time: {}".format(time.time() - timer_start))
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@ -269,11 +272,11 @@ if train_model_without_sp:
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fh.make_new_directory(modeln_dir)
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pyhtk.re_estimation(
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config_train,
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os.path.join(modeln_dir_pre, 'macros'),
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os.path.join(modeln_dir_pre, hmmdefs_name),
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modeln_dir,
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hcompv_scp_train, phonelist_txt,
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mlf_file=mlf_file_train)
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mlf_file=mlf_file_train,
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macros=os.path.join(modeln_dir_pre, 'macros'))
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print("elapsed time: {}".format(time.time() - timer_start))
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@ -321,7 +324,6 @@ if add_sp:
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## ======================= train model with short pause =======================
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if train_model_with_sp:
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print('==== train model with sp ====')
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#for niter in range(niter_max+1, niter_max*2+1):
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for niter in range(20, 50):
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timer_start = time.time()
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hmm_n = 'iter' + str(niter)
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@ -333,9 +335,31 @@ if train_model_with_sp:
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fh.make_new_directory(modeln_dir)
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pyhtk.re_estimation(
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config_train,
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os.path.join(modeln_dir_pre, 'macros'),
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os.path.join(modeln_dir_pre, hmmdefs_name),
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modeln_dir,
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hcompv_scp_train, phonelist_txt,
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mlf_file=mlf_file_train)
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mlf_file=mlf_file_train,
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macros=os.path.join(modeln_dir_pre, 'macros'))
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print("elapsed time: {}".format(time.time() - timer_start))
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## ======================= train model with short pause =======================
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if train_model_with_sp_align_mlf:
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print('==== train model with sp with align.mlf ====')
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for niter in range(50, 60):
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timer_start = time.time()
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hmm_n = 'iter' + str(niter)
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hmm_n_pre = 'iter' + str(niter-1)
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modeln_dir = os.path.join(model1_dir, hmm_n)
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modeln_dir_pre = os.path.join(model1_dir, hmm_n_pre)
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# re-estimation
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fh.make_new_directory(modeln_dir)
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pyhtk.re_estimation(
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config_train,
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os.path.join(modeln_dir_pre, hmmdefs_name),
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modeln_dir,
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hcompv_scp_train, phonelist_txt,
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mlf_file=mlf_file_train_aligned,
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macros=os.path.join(modeln_dir_pre, 'macros'))
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print("elapsed time: {}".format(time.time() - timer_start))
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@ -11,6 +11,7 @@ import glob
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import numpy as np
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import pandas as pd
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from collections import Counter
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#import matplotlib.pyplot as plt
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#from sklearn.metrics import confusion_matrix
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@ -50,11 +51,14 @@ from htk import pyhtk
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#lex_asr = os.path.join(default.fame_dir, 'lexicon', 'lex.asr')
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#lex_asr_htk = os.path.join(default.fame_dir, 'lexicon', 'lex.asr_htk')
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## procedure
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# procedure
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make_dic_file = 0
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make_HTK_files = 1
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extract_features = 0
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#make_htk_dict_files = 0
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#do_forced_alignment_htk = 0
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#eval_forced_alignment_htk = 0
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#make_kaldi_data_files = 0
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make_kaldi_files = 0
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#make_kaldi_lexicon_txt = 0
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#load_forced_alignment_kaldi = 1
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#eval_forced_alignment_kaldi = 1
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@ -66,13 +70,34 @@ from htk import pyhtk
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#sys.path.append(os.path.join(default.repo_dir, 'toolbox'))
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#from evaluation import plot_confusion_matrix
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## HTK related files.
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config_dir = os.path.join(default.htk_dir, 'config')
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model_dir = os.path.join(default.htk_dir, 'model')
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feature_dir = os.path.join(default.htk_dir, 'mfc', 'stimmen')
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config_hcopy = os.path.join(config_dir, 'config.HCopy')
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# files to be made.
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lattice_file = os.path.join(config_dir, 'stimmen.ltc')
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#pyhtk.create_word_lattice_file(
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# os.path.join(config_dir, 'stimmen.net'),
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# lattice_file)
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hvite_scp = os.path.join(default.htk_dir, 'tmp', 'stimmen_test.scp')
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phonelist_txt = os.path.join(config_dir, 'phonelist.txt')
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stimmen_dic = os.path.join(default.htk_dir, 'lexicon', 'stimmen_recognition.dic')
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hcopy_scp = os.path.join(default.htk_dir, 'tmp', 'stimmen_test_hcopy.scp')
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hvite_scp = os.path.join(default.htk_dir, 'tmp', 'stimmen_test_hvite.scp')
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hresult_scp = os.path.join(default.htk_dir, 'tmp', 'stimmen_test_result.scp')
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## Kaldi related files.
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kaldi_data_dir = os.path.join(default.kaldi_dir, 'data')
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# files to be made.
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wav_scp = os.path.join(kaldi_data_dir, 'test', 'wav.scp')
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text_file = os.path.join(kaldi_data_dir, 'test', 'text')
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utt2spk = os.path.join(kaldi_data_dir, 'test', 'utt2spk')
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corpus_txt = os.path.join(kaldi_data_dir, 'local', 'corpus.txt')
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lexicon_txt = os.path.join(kaldi_data_dir, 'local', 'dict', 'lexicon.txt')
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nonsilence_phones_txt = os.path.join(kaldi_data_dir, 'local', 'dict', 'nonsilence_phones.txt')
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silence_phones_txt = os.path.join(kaldi_data_dir, 'local', 'dict', 'silence_phones.txt')
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optional_silence_txt = os.path.join(kaldi_data_dir, 'local', 'dict', 'optional_silence.txt')
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## ======================= load test data ======================
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@ -85,36 +110,159 @@ df = stimmen_functions.add_row_htk(df)
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word_list = [i for i in list(set(df['word'])) if not pd.isnull(i)]
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word_list = sorted(word_list)
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# pronunciation variants
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## ======================= make dic file to check pronunciation variants ======================
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# dic file should be manually modified depends on the task - recognition / forced-alignemnt.
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if make_dic_file:
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# for HTK.
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with open(stimmen_dic, mode='wb') as f:
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for word in word_list:
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df_ = df[df['word']==word]
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print('{0} has {1} variants'.format(word, len(np.unique(df_['htk'])))
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pronunciations = list(np.unique(df_['htk']))
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pronunciations_ = [word.upper() + ' sil ' + ' '.join(convert_phoneset.split_word(
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htk, fame_asr.multi_character_phones_htk)) + ' sil'
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for htk in pronunciations]
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f.write(bytes('\n'.join(pronunciations_) + '\n', 'ascii'))
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f.write(bytes('SILENCE sil\n', 'ascii'))
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#fh.make_filelist(stimmen_test_dir, hvite_scp, file_type='wav')
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# for Kaldi.
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fh.make_new_directory(os.path.join(kaldi_data_dir, 'local', 'dict'))
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with open(lexicon_txt, mode='wb') as f:
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f.write(bytes('!SIL sil\n', 'utf-8'))
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f.write(bytes('<UNK> spn\n', 'utf-8'))
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for word in word_list:
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df_ = df[df['word']==word]
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pronunciations = list(np.unique(df_['asr']))
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pronunciations_ = [word.lower() + ' ' + ' '.join(convert_phoneset.split_word(
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asr, fame_asr.multi_character_phones))
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for asr in pronunciations]
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f.write(bytes('\n'.join(pronunciations_) + '\n', 'utf-8'))
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#output = pyhtk.recognition(
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# os.path.join(default.htk_dir, 'config', 'config.rec',
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# lattice_file,
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# os.path.join(model_dir, 'hmm1', 'iter13'),
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# dictionary_file,
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# os.path.join(config_dir, 'phonelist.txt'),
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# hvite_scp)
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#pyhtk.create_label_file(
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# row['word'],
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# os.path.join(stimmen_test_dir, filename.replace('.wav', '.lab')))
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## ======================= test data for recognition ======================
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# only target pronunciation variants.
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df_rec = pd.DataFrame(index=[], columns=list(df.keys()))
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for word in word_list:
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variants = [htk.replace(' ', '')
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for htk in stimmen_functions.load_pronunciations(word.upper(), stimmen_dic)]
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df_ = df[df['word'] == word]
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for index, row in df_.iterrows():
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if row['htk'] in variants:
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df_rec = df_rec.append(row, ignore_index=True)
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## ======================= make a HTK dic file ======================
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#if make_htk_dic_file:
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# output_type = 3
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dictionary_txt = os.path.join(default.htk_dir, 'lexicon', 'stimmen.dic')
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#for word in word_list:
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word = word_list[2]
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# pronunciation variant of the target word.
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pronunciations = df_test['asr'][df_test['word'].str.match(word)]
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# make dic file.
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#am_func.make_htk_dict(word, pronvar_, htk_dict_file, output_type)
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## ======================= make files required for HTK ======================
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if make_HTK_files:
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# make a word lattice file.
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pyhtk.create_word_lattice_file(
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os.path.join(config_dir, 'stimmen.net'),
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lattice_file)
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# extract features.
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with open(hcopy_scp, 'wb') as f:
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filelist = [os.path.join(stimmen_test_dir, filename) + '\t'
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+ os.path.join(feature_dir, os.path.basename(filename).replace('.wav', '.mfc'))
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for filename in df['filename']]
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f.write(bytes('\n'.join(filelist), 'ascii'))
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pyhtk.wav2mfc(config_hcopy, hcopy_scp)
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# make label files.
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for index, row in df.iterrows():
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filename = row['filename'].replace('.wav', '.lab')
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label_file = os.path.join(feature_dir, filename)
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with open(label_file, 'wb') as f:
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label_string = 'START\n' + row['word'].upper() + '\nEND\n'
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f.write(bytes(label_string, 'ascii'))
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## ======================= make files required for Kaldi =======================
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if make_kaldi_files:
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fh.make_new_directory(os.path.join(kaldi_data_dir, 'test'))
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fh.make_new_directory(os.path.join(kaldi_data_dir, 'test', 'local'))
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fh.make_new_directory(os.path.join(kaldi_data_dir, 'conf'))
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# remove previous files.
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if os.path.exists(wav_scp):
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os.remove(wav_scp)
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if os.path.exists(text_file):
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os.remove(text_file)
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if os.path.exists(utt2spk):
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os.remove(utt2spk)
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f_wav_scp = open(wav_scp, 'a', encoding="utf-8", newline='\n')
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f_text_file = open(text_file, 'a', encoding="utf-8", newline='\n')
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f_utt2spk = open(utt2spk, 'a', encoding="utf-8", newline='\n')
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# make wav.scp, text, and utt2spk files.
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for i, row in df_rec.iterrows():
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filename = row['filename']
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print('=== {0}: {1} ==='.format(i, filename))
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wav_file = os.path.join(stimmen_test_dir, filename)
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#if os.path.exists(wav_file):
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speaker_id = 'speaker_' + str(i).zfill(4)
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utterance_id = filename.replace('.wav', '')
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utterance_id = utterance_id.replace(' ', '_')
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utterance_id = speaker_id + '-' + utterance_id
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# output
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f_wav_scp.write('{0} {1}\n'.format(
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utterance_id,
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wav_file.replace('c:/', '/mnt/c/').replace('\\', '/'))) # convert path to unix format.
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f_text_file.write('{0}\t{1}\n'.format(utterance_id, df_rec['word'][i].lower()))
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f_utt2spk.write('{0} {1}\n'.format(utterance_id, speaker_id))
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f_wav_scp.close()
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f_text_file.close()
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f_utt2spk.close()
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with open(corpus_txt, 'wb') as f:
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f.write(bytes('\n'.join([word.lower() for word in word_list]) + '\n', 'utf-8'))
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with open(nonsilence_phones_txt, 'wb') as f:
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f.write(bytes('\n'.join(fame_asr.phoneset_short) + '\n', 'utf-8'))
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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 =======================
|
||||
|
|
@ -168,54 +316,7 @@ if do_forced_alignment_htk:
|
|||
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 =======================
|
||||
|
|
|
|||
|
|
@ -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()
|
||||
|
||||
|
|
@ -97,6 +104,7 @@ translation_key_asr2htk = {
|
|||
|
||||
# refer to Xsampa.
|
||||
'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:',
|
||||
|
||||
|
|
|
|||
|
|
@ -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]
|
||||
|
|
@ -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)
|
||||
Loading…
Reference in New Issue