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acoustic_model
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triphone training is added.

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yemaozi88 3 years ago
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fdd165ce6a
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bf586fcde5
3 changed files with 109 additions and 48 deletions
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  1. BIN
      .vs/acoustic_model/v15/.suo
  2. 1
      acoustic_model/fame_functions.py
  3. 156
      acoustic_model/fame_hmm.py

BIN
.vs/acoustic_model/v15/.suo
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1
acoustic_model/fame_functions.py
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@ -345,6 +345,7 @@ def fix_lexicon(lexicon_file):
for i in lex[lex['word'].str.startswith('\'')].index.values:
lex.iat[i, 0] = lex.iat[i, 0].replace('\'', '\\\'')
# to_csv does not work with space seperator. therefore all tabs should manually be replaced.
#lex.to_csv(lexicon_file, index=False, header=False, encoding="utf-8", sep=' ', quoting=csv.QUOTE_NONE, escapechar='\\')
lex.to_csv(lexicon_file, index=False, header=False, sep='\t', encoding='utf-8')

156
acoustic_model/fame_hmm.py
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@ -25,11 +25,11 @@ make_label = 0 # it takes roughly 4800 sec on Surface pro 2.
make_mlf = 0
extract_features = 0
flat_start = 0
train_model_without_sp = 0
train_monophone_without_sp = 0
add_sp = 0
train_model_with_re_aligned_mlf = 0
train_triphone = 1
train_monophone_with_re_aligned_mlf = 0
train_triphone = 0
train_triphone_tied = 1
# pre-defined values.
@ -46,16 +46,18 @@ lexicon_oov = os.path.join(default.fame_dir, 'lexicon', 'lex.oov')
config_dir = os.path.join(default.htk_dir, 'config')
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')
model1sp2_dir = os.path.join(model_dir, 'hmm1sp2')
model_mono0_dir = os.path.join(model_dir, 'mono0')
model_mono1_dir = os.path.join(model_dir, 'mono1')
model_mono1sp_dir = os.path.join(model_dir, 'mono1sp')
model_mono1sp2_dir = os.path.join(model_dir, 'mono1sp2')
model_tri1_dir = os.path.join(model_dir, 'tri1')
# directories / files to be made.
lexicon_dir = os.path.join(default.htk_dir, 'lexicon')
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')
#lexicon_htk_with_sp = os.path.join(lexicon_dir, 'lex_with_sp.htk')
feature_dir = os.path.join(default.htk_dir, 'mfc')
fh.make_new_directory(feature_dir, existing_dir='leave')
@ -68,6 +70,7 @@ 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_with_sp = os.path.join(label_dir, 'train_phone_with_sp.mlf')
mlf_file_train_aligned = os.path.join(label_dir, 'train_phone_aligned.mlf')
hcompv_scp_train_updated = hcompv_scp_train.replace('.scp', '_updated.scp')
@ -97,8 +100,17 @@ if make_lexicon:
# http://electroblaze.blogspot.nl/2013/03/understanding-htk-error-messages.html
print('>>> fixing the lexicon...')
fame_functions.fix_lexicon(lexicon_htk)
print("elapsed time: {}".format(time.time() - timer_start))
## add sp to the end of each line.
#print('>>> adding sp...')
#with open(lexicon_htk) as f:
# lines = f.read().split('\n')
#lines = [line + ' sp' for line in lines]
#with open(lexicon_htk_with_sp, 'wb') as f:
# f.write(bytes('\n'.join(lines), 'ascii'))
print("elapsed time: {}".format(time.time() - timer_start))
## intialize the instance for HTK.
chtk = pyhtk.HTK(config_dir, fame_asr.phoneset_htk, lexicon_htk, feature_size)
@ -164,12 +176,15 @@ if make_mlf:
label_dir_ = os.path.join(label_dir, dataset)
mlf_word = os.path.join(label_dir, dataset + '_word.mlf')
mlf_phone = os.path.join(label_dir, dataset + '_phone.mlf')
mlf_phone_with_sp = os.path.join(label_dir, dataset + '_phone_with_sp.mlf')
print(">>> generating a word level mlf file for {}...".format(dataset))
chtk.label2mlf(label_dir_, mlf_word)
print(">>> generating a phone level mlf file for {}...".format(dataset))
chtk.mlf_word2phone(mlf_phone, mlf_word)
chtk.mlf_word2phone(mlf_phone, mlf_word, with_sp=False)
chtk.mlf_word2phone(mlf_phone_with_sp, mlf_word, with_sp=True)
print("elapsed time: {}".format(time.time() - timer_start))
@ -224,33 +239,33 @@ if extract_features:
if flat_start:
timer_start = time.time()
print('==== flat start ====')
fh.make_new_directory(model0_dir, existing_dir='leave')
fh.make_new_directory(model_mono0_dir, existing_dir='leave')
chtk.flat_start(hcompv_scp_train, model0_dir)
chtk.flat_start(hcompv_scp_train, model_mono0_dir)
# create macros.
vFloors = os.path.join(model0_dir, 'vFloors')
vFloors = os.path.join(model_mono0_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...')
chtk.create_hmmdefs(
os.path.join(model0_dir, proto_name),
os.path.join(model0_dir, 'hmmdefs')
os.path.join(model_mono0_dir, proto_name),
os.path.join(model_mono0_dir, 'hmmdefs')
)
print("elapsed time: {}".format(time.time() - timer_start))
## ======================= train model without short pause =======================
if train_model_without_sp:
print('==== train model without sp ====')
if train_monophone_without_sp:
print('==== train monophone without sp ====')
timer_start = time.time()
niter = chtk.re_estimation_until_saturated(
model1_dir,
model0_dir, improvement_threshold, hcompv_scp_train,
model_mono1_dir,
model_mono0_dir, improvement_threshold, hcompv_scp_train,
os.path.join(htk_stimmen_dir, 'mfc'),
'mfc',
os.path.join(htk_stimmen_dir, 'word_lattice.ltc'),
@ -270,32 +285,34 @@ if add_sp:
# make model with sp.
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')
fh.make_new_directory(model_mono1sp_dir, existing_dir='leave')
niter = chtk.get_niter_max(model_mono1_dir)
modeln_dir_pre = os.path.join(model_mono1_dir, 'iter'+str(niter))
modeln_dir = os.path.join(model_mono1sp_dir, 'iter0')
#hmmdefs_pre = os.path.join(modeln_dir_pre, 'hmmdefs')
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,
model_mono1sp_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,
mlf_file=mlf_file_train_with_sp,
lexicon_file=os.path.join(htk_stimmen_dir, 'lexicon_recognition.dic'),
model_type='monophone_with_sp'
)
## ======================= train model with re-aligned mlf =======================
if train_model_with_re_aligned_mlf:
print('==== traina model with re-aligned mlf ====')
if train_monophone_with_re_aligned_mlf:
print('==== traina monophone with re-aligned mlf ====')
print('>>> re-aligning the training data... ')
timer_start = time.time()
niter = chtk.get_niter_max(model1sp_dir)
modeln_dir = os.path.join(model1sp_dir, 'iter'+str(niter))
niter = chtk.get_niter_max(model_mono1sp_dir)
modeln_dir = os.path.join(model_mono1sp_dir, 'iter'+str(niter))
chtk.make_aligned_label(
os.path.join(modeln_dir, 'macros'),
os.path.join(modeln_dir, 'hmmdefs'),
@ -306,18 +323,18 @@ if train_model_with_re_aligned_mlf:
print('>>> updating the script file... ')
chtk.update_script_file(
mlf_file_train_aligned,
mlf_file_train,
mlf_file_train_with_sp,
hcompv_scp_train,
hcompv_scp_train_updated)
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)
fh.make_new_directory(model_mono1sp2_dir, existing_dir='leave')
niter = chtk.get_niter_max(model_mono1sp_dir)
niter = chtk.re_estimation_until_saturated(
model1sp2_dir,
os.path.join(model1sp_dir, 'iter'+str(niter)),
model_mono1sp2_dir,
os.path.join(model_mono1sp_dir, 'iter'+str(niter)),
improvement_threshold,
hcompv_scp_train_updated,
os.path.join(htk_stimmen_dir, 'mfc'),
@ -332,25 +349,68 @@ if train_model_with_re_aligned_mlf:
## ======================= train triphone =======================
if train_triphone:
model_out_dir = os.path.join(model_dir, 'hmm1_tri', 'iter1')
print('==== traina triphone model ====')
#model_out_dir = os.path.join(model_dir, 'hmm1_tri', 'iter1')
triphonelist_txt = os.path.join(config_dir, 'triphonelist_txt')
triphonelist_txt = os.path.join(config_dir, 'triphonelist.txt')
triphone_mlf = os.path.join(default.htk_dir, 'label', 'train_triphone.mlf')
print('>>> making triphone list... ')
chtk.make_triphonelist(
triphonelist_txt,
triphone_mlf,
mlf_file_train_aligned)
#run_command([
# 'HERest', '-B',
# '-C', config_train,
# '-I', triphone_mlf,
# '-t', '250.0', '150.0', '1000.0',
# '-s', 'stats'
# '-S', hcompv_scp_train,
# '-H', macros,
# '-H', hmmdefs,
# '-M', model_out_dir,
# os.path.join(config_dir, 'triphonelist.txt')
#])
print('>>> making triphone header... ')
chtk.make_tri_hed(
os.path.join(config_dir, 'mktri.hed')
)
print('>>> init triphone model... ')
niter = chtk.get_niter_max(model_mono1sp2_dir)
fh.make_new_directory(os.path.join(model_tri1_dir, 'iter0'), existing_dir='leave')
chtk.init_triphone(
os.path.join(model_mono1sp2_dir, 'iter'+str(niter)),
os.path.join(model_tri1_dir, 'iter0')
)
print('>>> re-estimation... ')
# I wanted to train until satulated:
# #niter = chtk.re_estimation_until_saturated(
# model_tri1_dir,
# os.path.join(model_tri1_dir, 'iter0'),
# improvement_threshold,
# hcompv_scp_train_updated,
# os.path.join(htk_stimmen_dir, 'mfc'),
# 'mfc',
# os.path.join(htk_stimmen_dir, 'word_lattice.ltc'),
# mlf_file=triphone_mlf,
# lexicon_file=os.path.join(htk_stimmen_dir, 'lexicon_recognition.dic'),
# model_type='triphone'
# )
#
# but because the data size is limited, some triphone cannot be trained and received the error:
# ERROR [+8231] GetHCIModel: Cannot find hmm [i:-]r[+???]
# therefore only two times re-estimation is performed.
output_dir = model_tri1_dir
for niter in range(1, 4):
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)
fh.make_new_directory(_modeln_dir, 'leave')
chtk.re_estimation(
os.path.join(_modeln_dir_pre, 'hmmdefs'),
_modeln_dir,
hcompv_scp_train_updated,
mlf_file=triphone_mlf,
macros=os.path.join(_modeln_dir_pre, 'macros'),
model_type='triphone')
## ======================= train triphone =======================
if train_triphone_tied:
print('==== traina tied-state triphone ====')
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