triphone training is added.
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yemaozi88
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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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@ -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')
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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')
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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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@ -97,6 +100,15 @@ 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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## 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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@ -164,11 +176,14 @@ 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:
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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:
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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:
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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:
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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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