sp is added to the model.

acoustic_model/fame_functions.py

@@ -378,16 +378,21 @@ def ipa2htk(ipa):
 	return ''.join(htk_splitted)
 
 
-def performance_on_stimmen(stimmen_dir, hmmdefs):
+def performance_on_stimmen(config_dir, stimmen_dir, hmmdefs):
-	#hmmdefs = r'c:\OneDrive\Research\rug\experiments\acoustic_model\fame\htk\model_\hmm1\iter20\hmmdefs'
-	#stimmen_dir = r'c:\OneDrive\Research\rug\experiments\acoustic_model\fame\htk\stimmen'
 	lattice_file = os.path.join(stimmen_dir, 'word_lattice.ltc')
 	hvite_scp	 = os.path.join(stimmen_dir, 'hvite.scp')
 	#fh.make_filelist(os.path.join(stimmen_dir, 'mfc'), hvite_scp, file_type='mfc')
 	hresult_scp  = os.path.join(stimmen_dir, 'hresult.scp')
 	#fh.make_filelist(os.path.join(stimmen_dir, 'mfc'), hresult_scp, file_type='rec')
 	lexicon_file = os.path.join(stimmen_dir, 'lexicon_recognition.dic')
-	chtk = pyhtk.HTK(config_dir, fame_asr.phoneset_htk, lexicon_file)
+	
+	# get feature_size from hmmdefs.
+	with open(hmmdefs) as f:
+		line = f.readline()
+		line = f.readline().strip()
+	feature_size = int(line.split(' ')[2])
+
+	chtk = pyhtk.HTK(config_dir, fame_asr.phoneset_htk, lexicon_file, feature_size)
 
 	result = chtk.recognition(
 		lattice_file,

acoustic_model/fame_hmm.py

@@ -26,7 +26,7 @@ make_mlf		  = 0
 extract_features  = 0
 flat_start		  = 0
 train_model_without_sp = 0
-add_sp = 0
+add_sp = 1
 train_model_with_sp    = 0
 train_model_with_sp_align_mlf = 0
 train_triphone = 0
@@ -35,6 +35,9 @@ train_triphone = 0
 
 # pre-defined values.
 dataset_list = ['devel', 'test', 'train']
+feature_size = 39
+improvement_threshold = 0.5
+
 hmmdefs_name = 'hmmdefs'
 proto_name   = 'proto'
 
@@ -47,7 +50,9 @@ 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')
-
+model0_dir = os.path.join(model_dir, 'hmm0')
+model1_dir = os.path.join(model_dir, 'hmm1')
+model1sp_dir = os.path.join(model_dir, 'hmm1sp')
 
 # directories / files to be made.
 lexicon_dir = os.path.join(default.htk_dir, 'lexicon') 
@@ -55,9 +60,6 @@ lexicon_htk_asr = os.path.join(lexicon_dir, 'lex.htk_asr')
 lexicon_htk_oov = os.path.join(lexicon_dir, 'lex.htk_oov')
 lexicon_htk     = os.path.join(lexicon_dir, 'lex.htk')
 
-
-#model1_dir	  = os.path.join(model_dir, 'hmm1')
-
 feature_dir = os.path.join(default.htk_dir, 'mfc')
 fh.make_new_directory(feature_dir, existing_dir='leave')
 tmp_dir = os.path.join(default.htk_dir, 'tmp')
@@ -65,11 +67,17 @@ fh.make_new_directory(tmp_dir, existing_dir='leave')
 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')
 mlf_file_train_aligned = os.path.join(label_dir, 'train_phone_aligned.mlf')
 
+## testing
+htk_stimmen_dir = os.path.join(default.htk_dir, 'stimmen')
+
+
 ## train without sp
 niter_max = 10
 
@@ -100,7 +108,7 @@ if make_lexicon:
 
 
 ## intialize the instance for HTK.
-chtk = pyhtk.HTK(config_dir, fame_asr.phoneset_htk, lexicon_htk)
+chtk = pyhtk.HTK(config_dir, fame_asr.phoneset_htk, lexicon_htk, feature_size)
 
 
 ## ======================= make label files =======================
@@ -223,11 +231,14 @@ if extract_features:
 if flat_start:
 	timer_start = time.time()
 	print('==== flat start ====')
-	feature_size = 39
-	model0_dir	  = os.path.join(model_dir, 'hmm0')
 	fh.make_new_directory(model0_dir, existing_dir='leave')
 
-	chtk.flat_start(hcompv_scp_train, model0_dir, feature_size)
+	chtk.flat_start(hcompv_scp_train, model0_dir)
+
+	# create macros.
+	vFloors = os.path.join(model0_dir, 'vFloors')
+	if os.path.exists(vFloors):
+		chtk.create_macros(vFloors)
 
 	# allocate mean & variance to all phones in the phone list
 	print('>>> allocating mean & variance to all phones in the phone list...')
@@ -241,69 +252,38 @@ if flat_start:
 
 ## ======================= train model without short pause =======================
 if train_model_without_sp:
-	fh.make_new_directory(model1_dir)
-
 	print('==== train model without sp ====')
-	if not os.path.exists(os.path.join(model1_dir, 'iter0')):
-		shutil.copytree(model0_dir, os.path.join(model1_dir, 'iter0'))
-	for niter in range(1, niter_max):
-		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
+	timer_start = time.time()
-		fh.make_new_directory(modeln_dir)
+	niter = chtk.re_estimation_until_saturated(
-		pyhtk.re_estimation(
+		model1_dir,
-			config_train,
+		model0_dir, improvement_threshold, hcompv_scp_train, 
-			os.path.join(modeln_dir_pre, hmmdefs_name), 
+		os.path.join(htk_stimmen_dir, 'mfc'), 
-			modeln_dir,
+		'mfc', 
-			hcompv_scp_train, phonelist_txt,
+		os.path.join(htk_stimmen_dir, 'word_lattice.ltc'), 
 		mlf_file=mlf_file_train, 
-			macros=os.path.join(modeln_dir_pre, 'macros'))
+		lexicon_file=os.path.join(htk_stimmen_dir, 'lexicon_recognition.dic')
+		)
+
 	print("elapsed time: {}".format(time.time() - timer_start))
 
 
 ## ======================= adding sp to the model =======================
 if add_sp:
 	print('==== adding sp to the model ====')
+	# reference:
+	# http://www.f.waseda.jp/yusukekondo/htk.html#flat_start_estimation
 
 	# make model with sp.
-	print('>>> modifying the last model in the previous step...')
+	niter = 7
-	modeln_dir_pre = os.path.join(model1_dir, 'iter'+str(niter_max-1))
+	print('>>> adding sp state to the last model in the previous step...')
-	modeln_dir = modeln_dir_pre.replace('iter' + str(niter_max-1), 'iter' + str(niter_max))
+	fh.make_new_directory(model1sp_dir, existing_dir='leave')
-	fh.make_new_directory(modeln_dir)
+	modeln_dir_pre = os.path.join(model1_dir, 'iter'+str(niter))
-	shutil.copy(
-		os.path.join(modeln_dir_pre, 'macros'), 
-		os.path.join(modeln_dir, 'macros'))
-	shutil.copy(
-		os.path.join(modeln_dir_pre, hmmdefs_name), 
-		os.path.join(modeln_dir, hmmdefs_name))
 
-	## ======================= 
+	## update hmmdefs and macros.
-	## manually make changes to modeln_dir/hmmdefs 
+	print('>>> adding sp to the model...')
-	## ======================= 
+	modeln_dir = os.path.join(model1sp_dir, 'iter0')
-	# add states 'sil'.
+	chtk.add_sp(modeln_dir_pre, modeln_dir)
-	# http://www.f.waseda.jp/yusukekondo/htk.html#flat_start_estimation
-	#shutil.copy(
-	#	os.path.join(model_dir, 'hmmdefs.txt'), 
-	#	os.path.join(modeln_dir, hmmdefs_name))
-
-	#hmmdefs_file_pre = os.path.join(modeln_dir_pre, hmmdefs_name)
-	hmmdefs_file = os.path.join(modeln_dir, hmmdefs_name)
-	macros_file  = os.path.join(modeln_dir, 'macros')
-	#with open(hmmdefs_file_pre) as f:
-	#	lines = f.read()
-	#lines_ = lines.split('~h ')
-	#sil_model = [line for line in lines_ if line.split('\n')[0].replace('"', '') == 'sil'][0]
-	
-	# update hmmdefs and macros.
-	print('>>> updating hmmdefs and macros...')
-	modeln_dir_pre = modeln_dir
-	modeln_dir = modeln_dir.replace('iter' + str(niter_max), 'iter' + str(niter_max+1))
-	fh.make_new_directory(modeln_dir)
-	pyhtk.include_sil_in_hmmdefs(macros_file, hmmdefs_file, modeln_dir, sil_hed, phonelist_txt)
 
 
 ## ======================= train model with short pause =======================