label alignment using HVite is added.
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yemaozi88
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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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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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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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## 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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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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@ -85,392 +110,468 @@ 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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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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# 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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## ======================= 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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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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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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#fh.make_filelist(stimmen_test_dir, hvite_scp, file_type='wav')
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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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## ======================= 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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#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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# 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:
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f.write(bytes('sil\nspn\n', 'utf-8'))
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with open(optional_silence_txt, 'wb') as f:
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f.write(bytes('sil\n', 'utf-8'))
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with open(os.path.join(kaldi_data_dir, 'conf', 'decode.config'), 'wb') as f:
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f.write(bytes('first_beam=10.0\n', 'utf-8'))
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f.write(bytes('beam=13.0\n', 'utf-8'))
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f.write(bytes('lattice_beam=6.0\n', 'utf-8'))
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||||
|
||||
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('<UNK>\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('<UNK>\tSPN\n')
|
||||
for line in pronvar_list_all:
|
||||
f_lexicon_txt.write('{0}\t{1}\n'.format(line[0].lower(), line[1]))
|
||||
|
||||
f_lexicon_txt.close()
|
||||
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')
|
||||
|
||||
@ -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:',
|
||||
|
||||
|
||||
@ -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…
x
Reference in New Issue
Block a user