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sp is added to the model.

master
yemaozi88 3 years ago
parent
commit
41d4fa5ff9
  1. BIN
      .vs/acoustic_model/v15/.suo
  2. 13
      acoustic_model/fame_functions.py
  3. 108
      acoustic_model/fame_hmm.py

BIN
.vs/acoustic_model/v15/.suo

13
acoustic_model/fame_functions.py

@ -378,17 +378,22 @@ def ipa2htk(ipa):
return ''.join(htk_splitted)
def performance_on_stimmen(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'
def performance_on_stimmen(config_dir, stimmen_dir, hmmdefs):
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,
hmmdefs,

108
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
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,
macros=os.path.join(modeln_dir_pre, 'macros'))
print("elapsed time: {}".format(time.time() - timer_start))
timer_start = time.time()
niter = chtk.re_estimation_until_saturated(
model1_dir,
model0_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')
)
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...')
modeln_dir_pre = os.path.join(model1_dir, 'iter'+str(niter_max-1))
modeln_dir = modeln_dir_pre.replace('iter' + str(niter_max-1), 'iter' + str(niter_max))
fh.make_new_directory(modeln_dir)
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))
## =======================
## manually make changes to modeln_dir/hmmdefs
## =======================
# add states 'sil'.
# 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)
niter = 7
print('>>> adding sp state to the last model in the previous step...')
fh.make_new_directory(model1sp_dir, existing_dir='leave')
modeln_dir_pre = os.path.join(model1_dir, 'iter'+str(niter))
## 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)
## ======================= train model with short pause =======================

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