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acoustic_model
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sp is added to the model.

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yemaozi88 3 years ago
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f6e7c8eefa
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ab3887c6ca
2 changed files with 100 additions and 20 deletions
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  1. BIN
      .vs/acoustic_model/v15/.suo
  2. 120
      acoustic_model/fame_hmm.py

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.vs/acoustic_model/v15/.suo
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120
acoustic_model/fame_hmm.py
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@ -25,13 +25,17 @@ make_label = 0 # it takes roughly 4800 sec on Surface pro 2.
make_htk_files = 0
extract_features = 0
flat_start = 0
train_model_without_sp = 1
train_model_without_sp = 0
add_sp = 0
train_model_with_sp = 0
# pre-defined values.
dataset_list = ['devel', 'test', 'train']
hmmdefs_name = 'hmmdefs'
proto_name = 'proto39'
lexicon_asr = os.path.join(default.fame_dir, 'lexicon', 'lex.asr')
lexicon_oov = os.path.join(default.fame_dir, 'lexicon', 'lex.oov')
@ -41,7 +45,8 @@ config_hcopy = os.path.join(config_dir, 'config.HCopy')
config_train = os.path.join(config_dir, 'config.train')
global_ded = os.path.join(config_dir, 'global.ded')
mkphones_led = os.path.join(config_dir, 'mkphones.led')
prototype = os.path.join(config_dir, 'proto39')
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')
@ -55,6 +60,7 @@ lexicon_htk = os.path.join(lexicon_dir, 'lex.htk')
phonelist_txt = os.path.join(config_dir, 'phonelist.txt')
model0_dir = os.path.join(model_dir, 'hmm0')
model1_dir = os.path.join(model_dir, 'hmm1')
feature_dir = os.path.join(default.htk_dir, 'mfc')
if not os.path.exists(feature_dir):
@ -66,6 +72,13 @@ label_dir = os.path.join(default.htk_dir, 'label')
if not os.path.exists(label_dir):
os.makedirs(label_dir)
## training
hcompv_scp_train = os.path.join(tmp_dir, 'train.scp')
mlf_file_train = os.path.join(label_dir, 'train_phone.mlf')
## train without sp
niter_max = 10
## ======================= make lexicon for HTK =======================
if make_lexicon:
@ -95,6 +108,7 @@ if make_lexicon:
## ======================= make label files =======================
if make_label:
# train_2002_gongfansaken_10347.lab is empty. should be removed.
for dataset in dataset_list:
timer_start = time.time()
print("==== making label files on dataset {}".format(dataset))
@ -216,45 +230,111 @@ if extract_features:
## ======================= flat start monophones =======================
if flat_start:
hcompv_scp = os.path.join(tmp_dir, 'test.scp')
timer_start = time.time()
print('==== flat start ====')
pyhtk.flat_start(config_train, hcompv_scp, model0_dir, prototype)
pyhtk.flat_start(config_train, hcompv_scp_train, model0_dir, prototype)
# allocate mean & variance to all phones in the phone list
print('>>> allocating mean & variance to all phones in the phone list...')
pyhtk.create_hmmdefs(
os.path.join(model0_dir, 'proto39'),
os.path.join(model0_dir, proto_name),
os.path.join(model0_dir, 'hmmdefs'),
phonelist_txt)
# make macros
print('>>> making macros...')
with open(os.path.join(model0_dir, 'vFloors')) as f:
lines = f.read()
with open(os.path.join(model0_dir, 'macros'), 'wb') as f:
f.write(bytes('~o <MFCC_0_D_A> <VecSize> 39\n' + lines, 'ascii'))
print("elapsed time: {}".format(time.time() - timer_start))
## ======================= estimate monophones =======================
## ======================= train model without short pause =======================
if train_model_without_sp:
hcompv_scp = os.path.join(tmp_dir, 'test.scp')
mlf_file = os.path.join(label_dir, 'test_phone.mlf')
output_dir = os.path.join(model_dir, 'hmm1')
fh.make_new_directory(output_dir)
fh.make_new_directory(model1_dir)
print('==== train model without sp ====')
if not os.path.exists(os.path.join(output_dir, 'iter0')):
shutil.copytree(model0_dir, os.path.join(output_dir, 'iter0'))
niter = 1
for niter in range(1, 5):
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, 'macros'),
os.path.join(modeln_dir_pre, hmmdefs_name),
modeln_dir,
hcompv_scp_train, phonelist_txt,
mlf_file=mlf_file_train)
print("elapsed time: {}".format(time.time() - timer_start))
## ======================= adding sp to the model =======================
if add_sp:
print('==== adding sp to the model ====')
# 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)
## ======================= train model with short pause =======================
if train_model_with_sp:
print('==== train model with sp ====')
for niter in range(niter_max+1, niter_max*2+1):
timer_start = time.time()
hmm_n = 'iter' + str(niter)
hmm_n_pre = 'iter' + str(niter-1)
modeln_dir = os.path.join(output_dir, hmm_n)
modeln_dir_pre = os.path.join(output_dir, hmm_n_pre)
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, 'proto39'),
os.path.join(modeln_dir_pre, 'macros'),
os.path.join(modeln_dir_pre, hmmdefs_name),
modeln_dir,
hcompv_scp, phonelist_txt,
mlf_file=mlf_file)
hcompv_scp_train, phonelist_txt,
mlf_file=mlf_file_train)
print("elapsed time: {}".format(time.time() - timer_start))
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