monophone training is completed.
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yemaozi88
parent
41d4fa5ff9
commit
fa81b70b27
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@ -26,9 +26,8 @@ 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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add_sp = 1
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train_model_with_sp = 0
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train_model_with_sp_align_mlf = 0
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add_sp = 0
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train_model_with_re_aligned_mlf = 1
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train_triphone = 0
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@ -36,7 +35,7 @@ train_triphone = 0
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# pre-defined values.
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dataset_list = ['devel', 'test', 'train']
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feature_size = 39
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improvement_threshold = 0.5
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improvement_threshold = 0.3
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hmmdefs_name = 'hmmdefs'
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proto_name = 'proto'
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@ -49,10 +48,11 @@ 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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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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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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# directories / files to be made.
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lexicon_dir = os.path.join(default.htk_dir, 'lexicon')
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@ -68,7 +68,6 @@ label_dir = os.path.join(default.htk_dir, 'label')
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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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@ -78,10 +77,6 @@ mlf_file_train_aligned = os.path.join(label_dir, 'train_phone_aligned.mlf')
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htk_stimmen_dir = os.path.join(default.htk_dir, 'stimmen')
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## train without sp
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niter_max = 10
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## ======================= make lexicon for HTK =======================
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if make_lexicon:
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timer_start = time.time()
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@ -273,64 +268,64 @@ if add_sp:
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print('==== adding sp to the model ====')
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# reference:
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# http://www.f.waseda.jp/yusukekondo/htk.html#flat_start_estimation
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timer_start = time.time()
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# make model with sp.
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niter = 7
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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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## update hmmdefs and macros.
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print('>>> adding sp to the model...')
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modeln_dir = os.path.join(model1sp_dir, 'iter0')
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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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## ======================= 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(20, 50):
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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,
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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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niter = chtk.re_estimation_until_saturated(
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model1sp_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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model_type='monophone_with_sp'
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)
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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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## ======================= 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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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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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('>>> 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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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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improvement_threshold,
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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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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 triphone.
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## ======================= train triphone =======================
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if train_triphone:
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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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