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@ -25,11 +25,11 @@ make_label = 0 # it takes roughly 4800 sec on Surface pro 2.
@@ -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 = 0 |
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train_triphone = 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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@ -46,16 +46,18 @@ lexicon_oov = os.path.join(default.fame_dir, 'lexicon', 'lex.oov')
@@ -46,16 +46,18 @@ 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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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_with_sp = os.path.join(lexicon_dir, 'lex_with_sp.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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@ -68,6 +70,7 @@ fh.make_new_directory(label_dir, existing_dir='leave')
@@ -68,6 +70,7 @@ 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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@ -97,8 +100,17 @@ if make_lexicon:
@@ -97,8 +100,17 @@ 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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@ -164,12 +176,15 @@ if make_mlf:
@@ -164,12 +176,15 @@ 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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@ -224,33 +239,33 @@ if extract_features:
@@ -224,33 +239,33 @@ 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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@ -270,32 +285,34 @@ if add_sp:
@@ -270,32 +285,34 @@ 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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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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#hmmdefs_pre = os.path.join(modeln_dir_pre, 'hmmdefs') |
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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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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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mlf_file=mlf_file_train_with_sp, |
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lexicon_file=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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## ======================= 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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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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@ -306,18 +323,18 @@ if train_model_with_re_aligned_mlf:
@@ -306,18 +323,18 @@ if train_model_with_re_aligned_mlf:
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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, |
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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("elapsed time: {}".format(time.time() - timer_start)) |
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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_updated, |
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os.path.join(htk_stimmen_dir, 'mfc'), |
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@ -332,25 +349,68 @@ if train_model_with_re_aligned_mlf:
@@ -332,25 +349,68 @@ if train_model_with_re_aligned_mlf:
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## ======================= train triphone ======================= |
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if train_triphone: |
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model_out_dir = os.path.join(model_dir, 'hmm1_tri', 'iter1') |
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print('==== traina triphone model ====') |
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#model_out_dir = os.path.join(model_dir, 'hmm1_tri', 'iter1') |
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triphonelist_txt = os.path.join(config_dir, 'triphonelist_txt') |
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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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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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#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 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_file=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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## ======================= train triphone ======================= |
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if train_triphone_tied: |
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print('==== traina tied-state triphone ====') |
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