monophone training is completed.

acoustic_model/fame_hmm.py

@@ -26,9 +26,8 @@ make_mlf		  = 0
 extract_features  = 0
 flat_start		  = 0
 train_model_without_sp = 0
-add_sp = 1
+add_sp = 0
-train_model_with_sp    = 0
+train_model_with_re_aligned_mlf = 1
-train_model_with_sp_align_mlf = 0
 train_triphone = 0
 
 
@@ -36,7 +35,7 @@ train_triphone = 0
 # pre-defined values.
 dataset_list = ['devel', 'test', 'train']
 feature_size = 39
-improvement_threshold = 0.5
+improvement_threshold = 0.3
 
 hmmdefs_name = 'hmmdefs'
 proto_name   = 'proto'
@@ -49,10 +48,11 @@ config_dir = os.path.join(default.htk_dir, 'config')
 sil_hed		 = os.path.join(config_dir, 'sil.hed')
 prototype    = os.path.join(config_dir, proto_name)
 
-model_dir    = os.path.join(default.htk_dir, 'model')
+model_dir     = os.path.join(default.htk_dir, 'model')
-model0_dir = os.path.join(model_dir, 'hmm0')
+model0_dir    = os.path.join(model_dir, 'hmm0')
-model1_dir = os.path.join(model_dir, 'hmm1')
+model1_dir    = os.path.join(model_dir, 'hmm1')
-model1sp_dir = os.path.join(model_dir, 'hmm1sp')
+model1sp_dir  = os.path.join(model_dir, 'hmm1sp')
+model1sp2_dir = os.path.join(model_dir, 'hmm1sp2')
 
 # directories / files to be made.
 lexicon_dir = os.path.join(default.htk_dir, 'lexicon') 
@@ -68,7 +68,6 @@ label_dir = os.path.join(default.htk_dir, 'label')
 fh.make_new_directory(label_dir, existing_dir='leave')
 
 
-
 ## training
 hcompv_scp_train = os.path.join(tmp_dir, 'train.scp')
 mlf_file_train   = os.path.join(label_dir, 'train_phone.mlf')
@@ -78,10 +77,6 @@ mlf_file_train_aligned = os.path.join(label_dir, 'train_phone_aligned.mlf')
 htk_stimmen_dir = os.path.join(default.htk_dir, 'stimmen')
 
 
-## train without sp
-niter_max = 10
-
-
 ## ======================= make lexicon for HTK =======================
 if make_lexicon:
 	timer_start = time.time()
@@ -273,64 +268,64 @@ if add_sp:
 	print('==== adding sp to the model ====')
 	# reference:
 	# http://www.f.waseda.jp/yusukekondo/htk.html#flat_start_estimation
+	timer_start = time.time()
 
 	# make model with sp.
-	niter = 7
 	print('>>> adding sp state to the last model in the previous step...')
 	fh.make_new_directory(model1sp_dir, existing_dir='leave')
+	niter = chtk.get_niter_max(model1_dir)
 	modeln_dir_pre = os.path.join(model1_dir, 'iter'+str(niter))
-
+	modeln_dir	   = os.path.join(model1sp_dir, 'iter0')
-	## update hmmdefs and macros.
-	print('>>> adding sp to the model...')
-	modeln_dir = os.path.join(model1sp_dir, 'iter0')
 	chtk.add_sp(modeln_dir_pre, modeln_dir)
+	print("elapsed time: {}".format(time.time() - timer_start))
+
+	niter = chtk.re_estimation_until_saturated(
+		model1sp_dir, modeln_dir, improvement_threshold, hcompv_scp_train, 
+		os.path.join(htk_stimmen_dir, 'mfc'), 
+		'mfc', 
+		os.path.join(htk_stimmen_dir, 'word_lattice.ltc'), 
+		mlf_file=mlf_file_train, 
+		lexicon_file=os.path.join(htk_stimmen_dir, 'lexicon_recognition.dic'), 
+		model_type='monophone_with_sp'
+		)
 		
 
-## ======================= train model with short pause =======================
+## ======================= train model with re-aligned mlf =======================
-if train_model_with_sp:
+if train_model_with_re_aligned_mlf:
-	print('==== train model with sp ====')
+	print('==== traina model with re-aligned mlf ====')
-	for niter in range(20, 50):
-		timer_start = time.time()
-		hmm_n = 'iter' + str(niter)
-		hmm_n_pre = 'iter' + str(niter-1)
-		modeln_dir	   = os.path.join(model1_dir, hmm_n)
-		modeln_dir_pre = os.path.join(model1_dir, hmm_n_pre) 
 
-		# re-estimation
+	print('>>> re-aligning the training data... ')
-		fh.make_new_directory(modeln_dir)
+	timer_start = time.time()
-		pyhtk.re_estimation(
+	niter = chtk.get_niter_max(model1sp_dir)
-			config_train,
+	modeln_dir = os.path.join(model1sp_dir, 'iter'+str(niter))
-			os.path.join(modeln_dir_pre, hmmdefs_name), 
+	chtk.make_aligned_label(
-			modeln_dir,
+		os.path.join(modeln_dir, 'macros'),
-			hcompv_scp_train, phonelist_txt,
+		os.path.join(modeln_dir, 'hmmdefs'), 
-			mlf_file=mlf_file_train, 
+		mlf_file_train_aligned, 		
-			macros=os.path.join(modeln_dir_pre, 'macros'))
+		os.path.join(label_dir,	'train_word.mlf'), 
-		print("elapsed time: {}".format(time.time() - timer_start))
+		hcompv_scp_train)
+	print("elapsed time: {}".format(time.time() - timer_start))
+
+	print('>>> re-estimation... ')
+	timer_start = time.time()
+	fh.make_new_directory(model1sp2_dir, existing_dir='leave')
+	niter = chtk.get_niter_max(model1sp_dir)
+	niter = chtk.re_estimation_until_saturated(
+		model1sp2_dir, 
+		os.path.join(model1sp_dir, 'iter'+str(niter)), 
+		improvement_threshold, 
+		hcompv_scp_train, 
+		os.path.join(htk_stimmen_dir, 'mfc'), 
+		'mfc', 
+		os.path.join(htk_stimmen_dir, 'word_lattice.ltc'), 
+		mlf_file=mlf_file_train, 
+		lexicon_file=os.path.join(htk_stimmen_dir, 'lexicon_recognition.dic'), 
+		model_type='monophone_with_sp'
+		)
+	print("elapsed time: {}".format(time.time() - timer_start))
 
 
-## ======================= train model with short pause =======================
+## ======================= train triphone =======================
-if train_model_with_sp_align_mlf:
-	print('==== train model with sp with align.mlf ====')
-	for niter in range(50, 60):
-		timer_start = time.time()
-		hmm_n = 'iter' + str(niter)
-		hmm_n_pre = 'iter' + str(niter-1)
-		modeln_dir	   = os.path.join(model1_dir, hmm_n)
-		modeln_dir_pre = os.path.join(model1_dir, hmm_n_pre) 
-		
-		# re-estimation
-		fh.make_new_directory(modeln_dir)
-		pyhtk.re_estimation(
-			config_train,
-			os.path.join(modeln_dir_pre, hmmdefs_name), 
-			modeln_dir,
-			hcompv_scp_train, phonelist_txt,
-			mlf_file=mlf_file_train_aligned, 
-			macros=os.path.join(modeln_dir_pre, 'macros'))
-		print("elapsed time: {}".format(time.time() - timer_start))
-
-
-# train triphone.
 if train_triphone:
 	triphone_mlf = os.path.join(default.htk_dir, 'label', 'train_triphone.mlf')
 	macros = os.path.join(model_dir, 'hmm1_tri', 'iter0', 'macros')