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fa81b70b27
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b444b70af9
| Author | SHA1 | Date |
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yemaozi88
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b444b70af9 | |
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yemaozi88
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bf586fcde5 | |
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yemaozi88
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fdd165ce6a |
Binary file not shown.
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@ -51,6 +51,9 @@
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<Compile Include="fame_hmm.py" />
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<Compile Include="phoneset\fame_asr.py" />
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<Compile Include="phoneset\fame_ipa.py" />
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<Compile Include="phoneset\fame_phonetics.py">
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<SubType>Code</SubType>
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</Compile>
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<Compile Include="stimmen_functions.py" />
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<Compile Include="stimmen_test.py" />
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</ItemGroup>
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@ -345,6 +345,7 @@ def fix_lexicon(lexicon_file):
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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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#lex.to_csv(lexicon_file, index=False, header=False, encoding="utf-8", sep=' ', quoting=csv.QUOTE_NONE, escapechar='\\')
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lex.to_csv(lexicon_file, index=False, header=False, sep='\t', encoding='utf-8')
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@ -369,7 +370,8 @@ def ipa2asr(ipa):
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def ipa2htk(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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#translation_key_ipa2asr = np.load(r'c:\Users\Aki\source\repos\acoustic_model\acoustic_model\phoneset\fame_ipa2asr.npy').item(0)
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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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@ -11,7 +11,7 @@ import numpy as np
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import pandas as pd
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import fame_functions
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from phoneset import fame_ipa, fame_asr
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from phoneset import fame_ipa, fame_asr, fame_phonetics
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import defaultfiles as default
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sys.path.append(default.toolbox_dir)
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import file_handling as fh
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@ -25,11 +25,11 @@ make_label = 0 # it takes roughly 4800 sec on Surface pro 2.
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make_mlf = 0
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extract_features = 0
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flat_start = 0
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train_model_without_sp = 0
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train_monophone_without_sp = 0
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add_sp = 0
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train_model_with_re_aligned_mlf = 1
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train_monophone_with_re_aligned_mlf = 0
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train_triphone = 0
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train_triphone_tied = 1
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# pre-defined values.
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@ -44,21 +44,23 @@ lexicon_asr = os.path.join(default.fame_dir, 'lexicon', 'lex.asr')
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lexicon_oov = os.path.join(default.fame_dir, 'lexicon', 'lex.oov')
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config_dir = os.path.join(default.htk_dir, 'config')
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sil_hed = os.path.join(config_dir, 'sil.hed')
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prototype = os.path.join(config_dir, proto_name)
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phonelist_full_txt = os.path.join(config_dir, 'phonelist_full.txt')
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tree_hed = os.path.join(config_dir, 'tree.hed')
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quest_hed = os.path.join(config_dir, 'quests.hed')
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model_dir = os.path.join(default.htk_dir, 'model')
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model0_dir = os.path.join(model_dir, 'hmm0')
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model1_dir = os.path.join(model_dir, 'hmm1')
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model1sp_dir = os.path.join(model_dir, 'hmm1sp')
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model1sp2_dir = os.path.join(model_dir, 'hmm1sp2')
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model_mono0_dir = os.path.join(model_dir, 'mono0')
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model_mono1_dir = os.path.join(model_dir, 'mono1')
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model_mono1sp_dir = os.path.join(model_dir, 'mono1sp')
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model_mono1sp2_dir = os.path.join(model_dir, 'mono1sp2')
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model_tri1_dir = os.path.join(model_dir, 'tri1')
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# directories / files to be made.
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lexicon_dir = os.path.join(default.htk_dir, 'lexicon')
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lexicon_htk_asr = os.path.join(lexicon_dir, 'lex.htk_asr')
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lexicon_htk_oov = os.path.join(lexicon_dir, 'lex.htk_oov')
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lexicon_htk = os.path.join(lexicon_dir, 'lex.htk')
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lexicon_htk_triphone = os.path.join(lexicon_dir, 'lex_triphone.htk')
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feature_dir = os.path.join(default.htk_dir, 'mfc')
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fh.make_new_directory(feature_dir, existing_dir='leave')
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@ -71,7 +73,9 @@ fh.make_new_directory(label_dir, existing_dir='leave')
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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_with_sp = os.path.join(label_dir, 'train_phone_with_sp.mlf')
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mlf_file_train_aligned = os.path.join(label_dir, 'train_phone_aligned.mlf')
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hcompv_scp_train_updated = hcompv_scp_train.replace('.scp', '_updated.scp')
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## testing
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htk_stimmen_dir = os.path.join(default.htk_dir, 'stimmen')
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@ -99,8 +103,17 @@ if make_lexicon:
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# http://electroblaze.blogspot.nl/2013/03/understanding-htk-error-messages.html
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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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## add sp to the end of each line.
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#print('>>> adding sp...')
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#with open(lexicon_htk) as f:
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# lines = f.read().split('\n')
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#lines = [line + ' sp' for line in lines]
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#with open(lexicon_htk_with_sp, 'wb') as f:
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# f.write(bytes('\n'.join(lines), 'ascii'))
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print("elapsed time: {}".format(time.time() - timer_start))
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## intialize the instance for HTK.
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chtk = pyhtk.HTK(config_dir, fame_asr.phoneset_htk, lexicon_htk, feature_size)
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@ -166,12 +179,15 @@ if make_mlf:
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label_dir_ = os.path.join(label_dir, dataset)
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mlf_word = os.path.join(label_dir, dataset + '_word.mlf')
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mlf_phone = os.path.join(label_dir, dataset + '_phone.mlf')
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mlf_phone_with_sp = os.path.join(label_dir, dataset + '_phone_with_sp.mlf')
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print(">>> generating a word level mlf file for {}...".format(dataset))
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chtk.label2mlf(label_dir_, mlf_word)
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print(">>> generating a phone level mlf file for {}...".format(dataset))
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chtk.mlf_word2phone(mlf_phone, mlf_word)
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chtk.mlf_word2phone(mlf_phone, mlf_word, with_sp=False)
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chtk.mlf_word2phone(mlf_phone_with_sp, mlf_word, with_sp=True)
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print("elapsed time: {}".format(time.time() - timer_start))
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@ -226,38 +242,38 @@ if extract_features:
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if flat_start:
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timer_start = time.time()
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print('==== flat start ====')
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fh.make_new_directory(model0_dir, existing_dir='leave')
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fh.make_new_directory(model_mono0_dir, existing_dir='leave')
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chtk.flat_start(hcompv_scp_train, model0_dir)
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chtk.flat_start(hcompv_scp_train, model_mono0_dir)
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# create macros.
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vFloors = os.path.join(model0_dir, 'vFloors')
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vFloors = os.path.join(model_mono0_dir, 'vFloors')
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if os.path.exists(vFloors):
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chtk.create_macros(vFloors)
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# allocate mean & variance to all phones in the phone list
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print('>>> allocating mean & variance to all phones in the phone list...')
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chtk.create_hmmdefs(
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os.path.join(model0_dir, proto_name),
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os.path.join(model0_dir, 'hmmdefs')
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os.path.join(model_mono0_dir, proto_name),
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os.path.join(model_mono0_dir, 'hmmdefs')
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)
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print("elapsed time: {}".format(time.time() - timer_start))
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## ======================= train model without short pause =======================
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if train_model_without_sp:
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print('==== train model without sp ====')
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if train_monophone_without_sp:
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print('==== train monophone without sp ====')
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timer_start = time.time()
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niter = chtk.re_estimation_until_saturated(
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model1_dir,
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model0_dir, improvement_threshold, hcompv_scp_train,
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model_mono1_dir,
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model_mono0_dir, improvement_threshold, hcompv_scp_train,
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os.path.join(htk_stimmen_dir, 'mfc'),
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'mfc',
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os.path.join(htk_stimmen_dir, 'word_lattice.ltc'),
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mlf_file=mlf_file_train,
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lexicon_file=os.path.join(htk_stimmen_dir, 'lexicon_recognition.dic')
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lexicon=os.path.join(htk_stimmen_dir, 'lexicon_recognition.dic')
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)
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print("elapsed time: {}".format(time.time() - timer_start))
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@ -272,54 +288,62 @@ if add_sp:
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# make model with sp.
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print('>>> adding sp state to the last model in the previous step...')
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fh.make_new_directory(model1sp_dir, existing_dir='leave')
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niter = chtk.get_niter_max(model1_dir)
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modeln_dir_pre = os.path.join(model1_dir, 'iter'+str(niter))
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modeln_dir = os.path.join(model1sp_dir, 'iter0')
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chtk.add_sp(modeln_dir_pre, modeln_dir)
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print("elapsed time: {}".format(time.time() - timer_start))
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fh.make_new_directory(model_mono1sp_dir, existing_dir='leave')
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niter = chtk.get_niter_max(model_mono1_dir)
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modeln_dir_pre = os.path.join(model_mono1_dir, 'iter'+str(niter))
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modeln_dir = os.path.join(model_mono1sp_dir, 'iter0')
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chtk.add_sp(modeln_dir_pre, modeln_dir)
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print('>>> re-estimation...')
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niter = chtk.re_estimation_until_saturated(
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model1sp_dir, modeln_dir, improvement_threshold, hcompv_scp_train,
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model_mono1sp_dir, modeln_dir, improvement_threshold, hcompv_scp_train,
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os.path.join(htk_stimmen_dir, 'mfc'),
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'mfc',
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os.path.join(htk_stimmen_dir, 'word_lattice.ltc'),
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mlf_file=mlf_file_train,
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lexicon_file=os.path.join(htk_stimmen_dir, 'lexicon_recognition.dic'),
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mlf_file=mlf_file_train_with_sp,
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lexicon=os.path.join(htk_stimmen_dir, 'lexicon_recognition.dic'),
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model_type='monophone_with_sp'
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)
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print("elapsed time: {}".format(time.time() - timer_start))
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## ======================= train model with re-aligned mlf =======================
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if train_model_with_re_aligned_mlf:
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print('==== traina model with re-aligned mlf ====')
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if train_monophone_with_re_aligned_mlf:
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print('==== traina monophone with re-aligned mlf ====')
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timer_start = time.time()
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print('>>> re-aligning the training data... ')
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timer_start = time.time()
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niter = chtk.get_niter_max(model1sp_dir)
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modeln_dir = os.path.join(model1sp_dir, 'iter'+str(niter))
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niter = chtk.get_niter_max(model_mono1sp_dir)
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modeln_dir = os.path.join(model_mono1sp_dir, 'iter'+str(niter))
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chtk.make_aligned_label(
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os.path.join(modeln_dir, 'macros'),
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os.path.join(modeln_dir, 'hmmdefs'),
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mlf_file_train_aligned,
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os.path.join(label_dir, 'train_word.mlf'),
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hcompv_scp_train)
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print("elapsed time: {}".format(time.time() - timer_start))
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print('>>> updating the script file... ')
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chtk.update_script_file(
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mlf_file_train_aligned,
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mlf_file_train_with_sp,
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hcompv_scp_train,
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hcompv_scp_train_updated)
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print('>>> re-estimation... ')
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timer_start = time.time()
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fh.make_new_directory(model1sp2_dir, existing_dir='leave')
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niter = chtk.get_niter_max(model1sp_dir)
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fh.make_new_directory(model_mono1sp2_dir, existing_dir='leave')
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niter = chtk.get_niter_max(model_mono1sp_dir)
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niter = chtk.re_estimation_until_saturated(
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model1sp2_dir,
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os.path.join(model1sp_dir, 'iter'+str(niter)),
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model_mono1sp2_dir,
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os.path.join(model_mono1sp_dir, 'iter'+str(niter)),
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improvement_threshold,
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hcompv_scp_train,
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hcompv_scp_train_updated,
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os.path.join(htk_stimmen_dir, 'mfc'),
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'mfc',
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os.path.join(htk_stimmen_dir, 'word_lattice.ltc'),
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mlf_file=mlf_file_train,
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lexicon_file=os.path.join(htk_stimmen_dir, 'lexicon_recognition.dic'),
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mlf_file=mlf_file_train_aligned,
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lexicon=os.path.join(htk_stimmen_dir, 'lexicon_recognition.dic'),
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model_type='monophone_with_sp'
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)
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print("elapsed time: {}".format(time.time() - timer_start))
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@ -327,19 +351,81 @@ if train_model_with_re_aligned_mlf:
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## ======================= train triphone =======================
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if train_triphone:
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print('==== traina triphone model ====')
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timer_start = time.time()
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triphonelist_txt = os.path.join(config_dir, 'triphonelist.txt')
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triphone_mlf = os.path.join(default.htk_dir, 'label', 'train_triphone.mlf')
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macros = os.path.join(model_dir, 'hmm1_tri', 'iter0', 'macros')
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hmmdefs = os.path.join(model_dir, 'hmm1_tri', 'iter0', 'hmmdefs')
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model_out_dir = os.path.join(model_dir, 'hmm1_tri', 'iter1')
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run_command([
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'HERest', '-B',
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'-C', config_train,
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'-I', triphone_mlf,
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'-t', '250.0', '150.0', '1000.0',
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'-s', 'stats'
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'-S', hcompv_scp_train,
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'-H', macros,
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'-H', hmmdefs,
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'-M', model_out_dir,
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os.path.join(config_dir, 'triphonelist.txt')
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])
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print('>>> making triphone list... ')
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chtk.make_triphonelist(
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triphonelist_txt,
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triphone_mlf,
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mlf_file_train_aligned)
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print('>>> making triphone header... ')
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chtk.make_tri_hed(
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os.path.join(config_dir, 'mktri.hed')
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)
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print('>>> init triphone model... ')
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niter = chtk.get_niter_max(model_mono1sp2_dir)
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fh.make_new_directory(os.path.join(model_tri1_dir, 'iter0'), existing_dir='leave')
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chtk.init_triphone(
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os.path.join(model_mono1sp2_dir, 'iter'+str(niter)),
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os.path.join(model_tri1_dir, 'iter0')
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)
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print('>>> re-estimation... ')
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# I wanted to train until satulated:
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# #niter = chtk.re_estimation_until_saturated(
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# model_tri1_dir,
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# os.path.join(model_tri1_dir, 'iter0'),
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# improvement_threshold,
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# hcompv_scp_train_updated,
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# os.path.join(htk_stimmen_dir, 'mfc'),
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# 'mfc',
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# os.path.join(htk_stimmen_dir, 'word_lattice.ltc'),
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# mlf_file=triphone_mlf,
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# lexicon=os.path.join(htk_stimmen_dir, 'lexicon_recognition.dic'),
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# model_type='triphone'
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# )
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#
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# but because the data size is limited, some triphone cannot be trained and received the error:
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# ERROR [+8231] GetHCIModel: Cannot find hmm [i:-]r[+???]
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# therefore only two times re-estimation is performed.
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output_dir = model_tri1_dir
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for niter in range(1, 4):
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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(output_dir, hmm_n)
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_modeln_dir_pre = os.path.join(output_dir, hmm_n_pre)
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fh.make_new_directory(_modeln_dir, 'leave')
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chtk.re_estimation(
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os.path.join(_modeln_dir_pre, 'hmmdefs'),
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_modeln_dir,
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hcompv_scp_train_updated,
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mlf_file=triphone_mlf,
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macros=os.path.join(_modeln_dir_pre, 'macros'),
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model_type='triphone')
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print("elapsed time: {}".format(time.time() - timer_start))
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## ======================= train triphone =======================
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if train_triphone_tied:
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print('==== traina tied-state triphone ====')
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timer_start = time.time()
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print('>>> making lexicon for triphone... ')
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chtk.make_triphone_full(phonelist_full_txt, lexicon_htk_triphone)
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print('>>> making headers... ')
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chtk.make_tree_header(tree_hed)
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fame_phonetics.make_quests_hed(quest_hed)
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|
||||
print("elapsed time: {}".format(time.time() - timer_start))
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -109,30 +109,30 @@ np.save(os.path.join('phoneset', 'fame_ipa2asr.npy'), translation_key_ipa2asr)
|
|||
|
||||
|
||||
## check which letters are not coded in ascii.
|
||||
print('asr phones which cannot be coded in ascii:\n')
|
||||
for i in fame_asr.phoneset_short:
|
||||
try:
|
||||
i_encoded = i.encode("ascii")
|
||||
#print("{0} --> {1}".format(i, i.encode("ascii")))
|
||||
except UnicodeEncodeError:
|
||||
print(">>> {}".format(i))
|
||||
#print('asr phones which cannot be coded in ascii:\n')
|
||||
#for i in fame_asr.phoneset_short:
|
||||
# try:
|
||||
# i_encoded = i.encode("ascii")
|
||||
# #print("{0} --> {1}".format(i, i.encode("ascii")))
|
||||
# except UnicodeEncodeError:
|
||||
# print(">>> {}".format(i))
|
||||
|
||||
print("letters in the scripts which is not coded in ascii:\n")
|
||||
for dataset in ['train', 'devel', 'test']:
|
||||
timer_start = time.time()
|
||||
#print("letters in the scripts which is not coded in ascii:\n")
|
||||
#for dataset in ['train', 'devel', 'test']:
|
||||
# timer_start = time.time()
|
||||
|
||||
script_list = os.path.join(default.fame_dir, 'data', dataset, 'text')
|
||||
with open(script_list, "rt", encoding="utf-8") as fin:
|
||||
scripts = fin.read().split('\n')
|
||||
# script_list = os.path.join(default.fame_dir, 'data', dataset, 'text')
|
||||
# with open(script_list, "rt", encoding="utf-8") as fin:
|
||||
# scripts = fin.read().split('\n')
|
||||
|
||||
for line in scripts:
|
||||
sentence = ' '.join(line.split(' ')[1:])
|
||||
sentence_htk = fame_functions.word2htk(sentence)
|
||||
# for line in scripts:
|
||||
# sentence = ' '.join(line.split(' ')[1:])
|
||||
# sentence_htk = fame_functions.word2htk(sentence)
|
||||
|
||||
#if len(re.findall(r'[âêôûč\'àéèúćäëïöü]', sentence))==0:
|
||||
try:
|
||||
sentence_htk = bytes(sentence_htk, 'ascii')
|
||||
except UnicodeEncodeError:
|
||||
print(sentence)
|
||||
print(sentence_htk)
|
||||
# #if len(re.findall(r'[âêôûč\'àéèúćäëïöü]', sentence))==0:
|
||||
# try:
|
||||
# sentence_htk = bytes(sentence_htk, 'ascii')
|
||||
# except UnicodeEncodeError:
|
||||
# print(sentence)
|
||||
# print(sentence_htk)
|
||||
|
||||
|
|
|
|||
|
|
@ -80,8 +80,11 @@ def phone_reduction(phones):
|
|||
Args:
|
||||
phones (list): list of phones.
|
||||
"""
|
||||
if sum([phone in phones for phone in phones_to_be_removed]) != 0:
|
||||
print('input includes phone(s) which is not defined in fame_asr.')
|
||||
print('those phone(s) are removed.')
|
||||
return [reduction_key.get(i, i) for i in phones
|
||||
if not i in phones_to_be_removed]
|
||||
if i not in phones_to_be_removed]
|
||||
|
||||
phoneset_short = list(set(phone_reduction(phoneset)))
|
||||
phoneset_short.sort()
|
||||
|
|
@ -96,7 +99,7 @@ translation_key_asr2htk = {
|
|||
'ṷ': 'u_',
|
||||
|
||||
# on the analogy of German umlaut, 'e' is used.
|
||||
'ö': 'oe', 'ö:': 'oe:',
|
||||
'ö': 'oe', 'ö:': 'oe:', ''
|
||||
'ü': 'ue', 'ü:': 'ue:',
|
||||
|
||||
# on the analogy of Chinese...
|
||||
|
|
|
|||
|
|
@ -61,7 +61,7 @@ phoneset = [
|
|||
'ɔⁿ',
|
||||
'ɔ:',
|
||||
'ɔ:ⁿ',
|
||||
#'ɔ̈', # not included in lex.ipa
|
||||
'ɔ̈', # not included in lex.ipa
|
||||
'ɔ̈.',
|
||||
'ɔ̈:',
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,197 @@
|
|||
import sys
|
||||
import os
|
||||
os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')
|
||||
|
||||
import fame_functions
|
||||
from phoneset import fame_ipa, fame_asr
|
||||
import convert_phoneset
|
||||
|
||||
|
||||
## general
|
||||
stop = 'p, b, t, d, k, g'
|
||||
nasal = 'm, n, ŋ'
|
||||
fricative = 's, z, f, v, h, x, j'
|
||||
liquid = 'l, r'
|
||||
vowel = 'a, a:, e:, i, i:, i̯, o, o:, u, u:, ṷ, ö, ö:, ü, ü:, ɔ, ɔ:, ɔ̈, ə, ɛ, ɛ:, ɪ, ɪ:'
|
||||
|
||||
## consonant
|
||||
c_front = 'p, b, m, f, v'
|
||||
c_central = 't, d, n, s, z, l, r'
|
||||
c_back = 'k, g, ŋ, h, x, j'
|
||||
|
||||
fortis = 'p, t, k, f, s'
|
||||
lenis = 'b, d, g, v, z, j'
|
||||
neither_fortis_nor_lenis = 'm, n, ŋ, h, l, r, x'
|
||||
|
||||
coronal = 't, d, n, s, z, l, r, j'
|
||||
non_coronal = 'p, b, m, k, g, ŋ, f, v, h, x'
|
||||
|
||||
anterior = 'p, b, m, t, d, n, f, v, s, z, l'
|
||||
non_anterior = 'k, g, ŋ, h, x, j, r'
|
||||
|
||||
continuent = 'm, n, ŋ, f, v, s, z, h, l, r'
|
||||
non_continuent = 'p, b, t, d, k, g, x, j'
|
||||
|
||||
strident = 's, z, j'
|
||||
non_strident = 'f, v, h'
|
||||
unstrident = 'p, b, t, d, m, n, ŋ, k, g, r, x'
|
||||
|
||||
glide = 'h, l, r'
|
||||
syllabic = 'm, l, ŋ'
|
||||
|
||||
unvoiced = 'p, t, k, s, f, x, h'
|
||||
voiced = 'b, d, g, z, v, m, n, ŋ, l, r, j'
|
||||
|
||||
#affricate: ???
|
||||
non_affricate = 's, z, f, v'
|
||||
|
||||
voiced_stop = 'b, d, g'
|
||||
unvoiced_stop = 'p, t, k'
|
||||
front_stop = 'p, b'
|
||||
central_stop = 't, d'
|
||||
back_stop = 'k, g'
|
||||
|
||||
voiced_fricative = 'z, v'
|
||||
unvoiced_fricative = 's, f'
|
||||
front_fricative = 'f, v'
|
||||
central_fricative = 's, z'
|
||||
back_fricative = 'j'
|
||||
|
||||
|
||||
## vowel
|
||||
v_front = 'i, i:, i̯, ɪ, ɪ:, e:, ə, ɛ, ɛ:, a, a:'
|
||||
v_central = 'ə, ɛ, ɛ:, a, a:'
|
||||
v_back = 'u, u:, ü, ü:, ṷ, ɔ, ɔ:, ɔ̈, ö, ö:, o, o:'
|
||||
|
||||
long = 'a:, e:, i:, o:, u:, ö:, ü:, ɔ:, ɛ:, ɪ:'
|
||||
short = 'a, i, i̯, o, u, ṷ, ö, ü, ɔ, ɔ̈, ə, ɛ, ɪ'
|
||||
|
||||
#Dipthong: ???
|
||||
#Front-Start: ???
|
||||
#Fronting: ???
|
||||
|
||||
high = 'i, i:, i̯, ɪ, ɪ: u, u:, ṷ, ə, e:, o, o:, ö, ö:, ü, ü:'
|
||||
medium = 'e:, ə, ɛ, ɛ:, ɔ, ɔ:, ɔ̈, o, o:, ö, ö:'
|
||||
low = 'a, a:, ɛ, ɛ:, ɔ, ɔ:, ɔ̈'
|
||||
|
||||
rounded = 'a, a:, o, o:, u, u:, ṷ, ö, ö:, ü, ü:, ɔ, ɔ:, ɔ̈'
|
||||
unrounded = 'i, i:, i̯, e:, ə, ɛ, ɛ:, ɪ, ɪ:'
|
||||
|
||||
i_vowel = 'i, i:, i̯, ɪ, ɪ:'
|
||||
e_vowel = 'e:,ə, ɛ, ɛ:'
|
||||
a_vowel = 'a, a:'
|
||||
o_vowel = 'o, o:, ö, ö:, ɔ, ɔ:, ɔ̈'
|
||||
u_vowel = 'u, u:, ṷ, ü, ü:'
|
||||
|
||||
## htk phoneset
|
||||
phoneset = fame_asr.phoneset_htk
|
||||
|
||||
## convert ipa group to htk format for quests.hed.
|
||||
def _ipa2quest(R_or_L, ipa_text):
|
||||
assert R_or_L in ['R', 'L'], print('the first argument should be either R or L.')
|
||||
ipa_list = ipa_text.replace(' ', '').split(',')
|
||||
if R_or_L == 'R':
|
||||
quests_list = ['*+' + fame_functions.ipa2htk(ipa) for ipa in ipa_list]
|
||||
else:
|
||||
quests_list = [fame_functions.ipa2htk(ipa) + '-*' for ipa in ipa_list]
|
||||
return ','.join(quests_list)
|
||||
|
||||
|
||||
def make_quests_hed(quest_hed):
|
||||
def _add_quests_item(R_or_L, item_name_, ipa_text):
|
||||
assert R_or_L in ['R', 'L'], print('the first argument should be either R or L.')
|
||||
item_name = R_or_L + '_' + item_name_
|
||||
with open(quest_hed, 'ab') as f:
|
||||
f.write(bytes('QS "' + item_name + '"\t{ ' + _ipa2quest(R_or_L, ipa_text) + ' }\n', 'ascii'))
|
||||
|
||||
if os.path.exists(quest_hed):
|
||||
os.remove(quest_hed)
|
||||
|
||||
for R_or_L in ['R', 'L']:
|
||||
_add_quests_item(R_or_L, 'NonBoundary', '*')
|
||||
_add_quests_item(R_or_L, 'Silence', 'sil')
|
||||
|
||||
_add_quests_item(R_or_L, 'Stop', stop)
|
||||
_add_quests_item(R_or_L, 'Nasal', nasal)
|
||||
_add_quests_item(R_or_L, 'Fricative', fricative)
|
||||
_add_quests_item(R_or_L, 'Liquid', liquid)
|
||||
_add_quests_item(R_or_L, 'Vowel', vowel)
|
||||
|
||||
_add_quests_item(R_or_L, 'C-Front', c_front)
|
||||
_add_quests_item(R_or_L, 'C-Central', c_central)
|
||||
_add_quests_item(R_or_L, 'C-Back', c_back)
|
||||
|
||||
_add_quests_item(R_or_L, 'V-Front', v_front)
|
||||
_add_quests_item(R_or_L, 'V-Central', v_central)
|
||||
_add_quests_item(R_or_L, 'V-Back', v_back)
|
||||
|
||||
_add_quests_item(R_or_L, 'Front', c_front + v_front)
|
||||
_add_quests_item(R_or_L, 'Central', c_central + v_central)
|
||||
_add_quests_item(R_or_L, 'Back', c_front + v_back)
|
||||
|
||||
_add_quests_item(R_or_L, 'Fortis', fortis)
|
||||
_add_quests_item(R_or_L, 'Lenis', lenis)
|
||||
_add_quests_item(R_or_L, 'UnFortLenis', neither_fortis_nor_lenis)
|
||||
|
||||
_add_quests_item(R_or_L, 'Coronal', coronal)
|
||||
_add_quests_item(R_or_L, 'NonCoronal', non_coronal)
|
||||
|
||||
_add_quests_item(R_or_L, 'Anterior', anterior)
|
||||
_add_quests_item(R_or_L, 'NonAnterior', non_anterior)
|
||||
|
||||
_add_quests_item(R_or_L, 'Continuent', continuent)
|
||||
_add_quests_item(R_or_L, 'NonContinuent', non_continuent)
|
||||
|
||||
_add_quests_item(R_or_L, 'Strident', strident)
|
||||
_add_quests_item(R_or_L, 'NonStrident', non_strident)
|
||||
_add_quests_item(R_or_L, 'UnStrident', unstrident)
|
||||
|
||||
_add_quests_item(R_or_L, 'Glide', glide)
|
||||
_add_quests_item(R_or_L, 'Syllabic', syllabic)
|
||||
|
||||
_add_quests_item(R_or_L, 'Unvoiced-Cons', unvoiced)
|
||||
_add_quests_item(R_or_L, 'Voiced-Cons', voiced)
|
||||
_add_quests_item(R_or_L, 'Unvoiced-All', unvoiced + ', sil')
|
||||
|
||||
_add_quests_item(R_or_L, 'Long', long)
|
||||
_add_quests_item(R_or_L, 'Short', short)
|
||||
|
||||
#_add_quests_item(R_or_L, 'Dipthong', xxx)
|
||||
#_add_quests_item(R_or_L, 'Front-Start', xxx)
|
||||
#_add_quests_item(R_or_L, 'Fronting', xxx)
|
||||
|
||||
_add_quests_item(R_or_L, 'High', high)
|
||||
_add_quests_item(R_or_L, 'Medium', medium)
|
||||
_add_quests_item(R_or_L, 'Low', low)
|
||||
|
||||
_add_quests_item(R_or_L, 'Rounded', rounded)
|
||||
_add_quests_item(R_or_L, 'UnRounded', unrounded)
|
||||
|
||||
#_add_quests_item(R_or_L, 'Affricative', rounded)
|
||||
_add_quests_item(R_or_L, 'NonAffricative', non_affricate)
|
||||
|
||||
_add_quests_item(R_or_L, 'IVowel', i_vowel)
|
||||
_add_quests_item(R_or_L, 'EVowel', e_vowel)
|
||||
_add_quests_item(R_or_L, 'AVowel', a_vowel)
|
||||
_add_quests_item(R_or_L, 'OVowel', o_vowel)
|
||||
_add_quests_item(R_or_L, 'UVowel', u_vowel)
|
||||
|
||||
_add_quests_item(R_or_L, 'Voiced-Stop', voiced_stop)
|
||||
_add_quests_item(R_or_L, 'UnVoiced-Stop', unvoiced_stop)
|
||||
|
||||
_add_quests_item(R_or_L, 'Front-Stop', front_stop)
|
||||
_add_quests_item(R_or_L, 'Central-Stop', central_stop)
|
||||
_add_quests_item(R_or_L, 'Back-Stop', back_stop)
|
||||
|
||||
_add_quests_item(R_or_L, 'Voiced-Fric', voiced_fricative)
|
||||
_add_quests_item(R_or_L, 'UnVoiced-Fric', unvoiced_fricative)
|
||||
|
||||
_add_quests_item(R_or_L, 'Front-Fric', front_fricative)
|
||||
_add_quests_item(R_or_L, 'Central-Fric', central_fricative)
|
||||
_add_quests_item(R_or_L, 'Back-Fric', back_fricative)
|
||||
|
||||
for p in phoneset:
|
||||
_add_quests_item(R_or_L, p, p)
|
||||
|
||||
return
|
||||
|
||||
Loading…
Reference in New Issue