def test(self,
gaussians=1,
iter=8,
mmi=False,
diag=False,
xword_id='',
output_dir=None):
## Copy config file to the experiment dir
config_output = '%s/config' % self.exp
self.config.write(open(config_output, 'w'))
log(self.logfh, 'TESTING with config [%s]' % config_output)
if self.test_pipeline['coding']:
import coding
coding_dir = '%s/Coding' % self.exp
util.create_new_dir(coding_dir)
count = coding.wav_to_mfc(self, coding_dir, self.mfc_list)
log(self.logfh, 'CODING finished [%d files]' % count)
if self.test_pipeline['test']:
import dict_and_lm
start_time = time.time()
num_utts, words = dict_and_lm.make_mlf_from_transcripts(
model,
self.dict,
self.setup,
self.data,
self.word_mlf,
self.mfc_list,
skip_oov=True)
log(self.logfh,
'wrote word mlf [%d utts] [%s]' % (num_utts, self.word_mlf))
self.decode(model, self.mfc_list, self.word_mlf, self.lm,
gaussians, iter, mmi, diag, xword_id, output_dir)
total_time = time.time() - start_time
log(self.logfh,
'TESTING finished; secs elapsed [%1.2f]' % total_time)
def test(self, gaussians=1, iter=8, mmi=False, diag=False, xword_id='', output_dir=None):
## Copy config file to the experiment dir
config_output = '%s/config' %self.exp
self.config.write(open(config_output, 'w'))
log(self.logfh, 'TESTING with config [%s]' %config_output)
if self.test_pipeline['coding']:
import coding
coding_dir = '%s/Coding' %self.exp
util.create_new_dir(coding_dir)
count = coding.wav_to_mfc(self, coding_dir, self.mfc_list)
log(self.logfh, 'CODING finished [%d files]' %count)
if self.test_pipeline['test']:
import dict_and_lm
start_time = time.time()
num_utts, words = dict_and_lm.make_mlf_from_transcripts(model, self.dict, self.setup, self.data, self.word_mlf, self.mfc_list, skip_oov=True)
log(self.logfh, 'wrote word mlf [%d utts] [%s]' %(num_utts, self.word_mlf))
self.decode(model, self.mfc_list, self.word_mlf, self.lm, gaussians, iter, mmi, diag, xword_id, output_dir)
total_time = time.time() - start_time
log(self.logfh, 'TESTING finished; secs elapsed [%1.2f]' %total_time)
def train(self):
## Copy config file to the experiment dir
config_output = '%s/config' %self.exp
self.config.write(open(config_output, 'w'))
log(self.logfh, 'TRAINING with config [%s]' %config_output)
if self.train_pipeline['coding']:
log(self.logfh, 'CODING started')
import coding
util.create_new_dir(self.coding_root)
coding.create_config(self)
count = coding.wav_to_mfc(self, self.coding_root, self.mfc_list)
os.system('cp %s %s/mfc.list.original' %(self.mfc_list, self.misc))
log(self.logfh, 'wrote mfc files [%d]' %count)
log(self.logfh, 'CODING finished')
if self.train_pipeline['lm']:
log(self.logfh, 'MLF/LM/DICT started')
import dict_and_lm
phone_set = dict_and_lm.fix_cmu_dict(self.orig_dict, self.htk_dict)
num_utts, words = dict_and_lm.make_mlf_from_transcripts(self, self.htk_dict, self.setup, self.data, self.word_mlf, self.mfc_list)
log(self.logfh, 'wrote word mlf [%d utts] [%s]' %(num_utts, self.word_mlf))
os.system('cp %s %s/mfc.list.filtered.by.dict' %(self.mfc_list, self.misc))
num_entries = dict_and_lm.make_train_dict(self.htk_dict, self.train_dict, words)
dict_and_lm.make_decode_dict(self.htk_dict, self.decode_dict, words)
log(self.logfh, 'wrote training dictionary [%d entries] [%s]' %(num_entries, self.train_dict))
util.create_new_dir(self.lm_dir)
train_vocab = '%s/vocab' %self.lm_dir
ppl = dict_and_lm.build_lm_from_mlf(self, self.word_mlf, self.train_dict, train_vocab, self.lm_dir, self.lm, self.lm_order)
log(self.logfh, 'wrote lm [%s] training ppl [%1.2f]' %(self.lm, ppl))
log(self.logfh, 'MLF/LM/DICT finished')
if self.train_pipeline['flat_start']:
log(self.logfh, 'FLAT START started')
import init_hmm
init_hmm.word_to_phone_mlf(self, self.train_dict, self.word_mlf, self.phone_mlf, self.phone_list)
log(self.logfh, 'wrote phone mlf [%s]' %self.phone_mlf)
os.system('cp %s %s/phone.mlf.from.dict' %(self.phone_mlf, self.misc))
os.system('bzip2 -f %s/phone.mlf.from.dict' %self.misc)
init_hmm.make_proto_hmm(self, self.mfc_list, self.proto_hmm)
hmm_dir, num_mfcs = init_hmm.initialize_hmms(self, self.mono_root, self.mfc_list, self.phone_list, self.proto_hmm)
log(self.logfh, 'initialized an HMM for each phone in [%s]' %hmm_dir)
log(self.logfh, 'used [%d] mfc files to compute variance floor' %num_mfcs)
import train_hmm
for iter in range(1, self.initial_mono_iters+1):
hmm_dir, k, L = train_hmm.run_iter(self, self.mono_root, hmm_dir, self.phone_mlf, self.phone_list, 1, iter, '')
log(self.logfh, 'ran an iteration of BW in [%s] lik/fr [%1.4f]' %(hmm_dir, L))
align_config = '%s/config.align' %self.mono_root
fh = open(align_config, 'w')
fh.write('HPARM: TARGETKIND = MFCC_0_D_A_Z\n')
fh.close()
align_dir = train_hmm.align(self, self.mono_root, self.mfc_list, hmm_dir, self.word_mlf, self.phone_mlf, self.phone_list, self.train_dict, align_config)
log(self.logfh, 'aligned with model in [%s], wrote phone mlf [%s]' %(hmm_dir, self.phone_mlf))
os.system('cp %s %s/mfc.list.filtered.by.mono.align' %(self.mfc_list, self.misc))
os.system('cp %s %s/phone.mlf.from.mono.align' %(self.phone_mlf, self.misc))
os.system('bzip2 -f %s/phone.mlf.from.mono.align' %self.misc)
for iter in range(self.initial_mono_iters+1, self.initial_mono_iters+1+self.mono_iters):
hmm_dir, k, L = train_hmm.run_iter(self, self.mono_root, hmm_dir, self.phone_mlf, self.phone_list, 1, iter, '')
log(self.logfh, 'ran an iteration of BW in [%s] lik/fr [%1.4f]' %(hmm_dir, L))
log(self.logfh, 'FLAT START finished')
if self.train_pipeline['mixup_mono']:
log(self.logfh, 'MIXUP MONO started')
import train_hmm
hmm_dir = '%s/HMM-%d-%d' %(self.mono_root, 1, self.initial_mono_iters+self.mono_iters)
## mixup everything
for mix_size in self.mono_mixup_schedule:
hmm_dir = train_hmm.mixup(self, self.mixup_mono_root, hmm_dir, self.phone_list, mix_size)
log(self.logfh, 'mixed up to [%d] in [%s]' %(mix_size, hmm_dir))
for iter in range(1, self.mono_iters+1):
hmm_dir, k, L = train_hmm.run_iter(self, self.mixup_mono_root, hmm_dir, self.phone_mlf, self.phone_list, mix_size, iter, '')
log(self.logfh, 'ran an iteration of BW in [%s] lik/fr [%1.4f]' %(hmm_dir, L))
log(self.logfh, 'MIXUP MONO finished')
if self.train_pipeline['mixdown_mono']:
log(self.logfh, 'MIXDOWN MONO started')
import train_hmm
num_gaussians = self.mono_mixup_schedule[-1]
hmm_dir = '%s/HMM-%d-%d' %(self.mixup_mono_root, num_gaussians, self.mono_iters)
train_hmm.mixdown_mono(self, self.mixdown_mono_root, hmm_dir, self.phone_list)
log(self.logfh, 'MIXDOWN MONO finished')
if self.train_pipeline['mono_to_tri']:
log(self.logfh, 'MONO TO TRI started')
import train_hmm
if self.train_pipeline['mixdown_mono']:
mono_final_dir = '%s/HMM-1-0' %self.mixdown_mono_root
else:
mono_final_dir = '%s/HMM-%d-%d' %(self.mono_root, 1, self.initial_mono_iters+self.mono_iters)
hmm_dir = train_hmm.mono_to_tri(self, self.xword_root, mono_final_dir, self.phone_mlf, self.tri_mlf, self.phone_list, self.tri_list)
log(self.logfh, 'initialized triphone models in [%s]' %hmm_dir)
log(self.logfh, 'created triphone mlf [%s]' %self.tri_mlf)
os.system('cp %s %s/tri.mlf.from.mono.align' %(self.tri_mlf, self.misc))
os.system('bzip2 -f %s/tri.mlf.from.mono.align' %self.misc)
os.system('cp %s %s/tri.list.from.mono.align' %(self.tri_list, self.misc))
for iter in range(1, self.initial_tri_iters+1):
hmm_dir, k, L = train_hmm.run_iter(self, self.xword_root, hmm_dir, self.tri_mlf, self.tri_list, 1, iter, '')
log(self.logfh, 'ran an iteration of BW in [%s] lik/fr [%1.4f]' %(hmm_dir, L))
xword_tie_dir = '%s/HMM-%d-%d' %(self.xword_root, 1, self.initial_tri_iters+1)
hmm_dir = train_hmm.tie_states_search(self, xword_tie_dir, hmm_dir, self.phone_list, self.tri_list, self.tied_list)
log(self.logfh, 'tied states in [%s]' %hmm_dir)
os.system('cp %s %s/tied.list.initial' %(self.tied_list, self.misc))
hmm_dir = '%s/HMM-%d-%d' %(self.xword_root, 1, self.initial_tri_iters+1)
for iter in range(self.initial_tri_iters+2, self.initial_tri_iters+1+self.tri_iters+1):
hmm_dir, k, L = train_hmm.run_iter(self, self.xword_root, hmm_dir, self.tri_mlf, self.tied_list, 1, iter, '')
log(self.logfh, 'ran an iteration of BW in [%s] lik/fr [%1.4f]' %(hmm_dir, L))
log(self.logfh, 'MONO TO TRI finished')
if self.train_pipeline['mixup_tri']:
log(self.logfh, 'MIXUP TRI started')
import train_hmm
## mixup everything
start_gaussians = 1
start_iter = self.initial_tri_iters+self.tri_iters+1
hmm_dir = '%s/HMM-%d-%d' %(self.xword_root, start_gaussians, start_iter)
for mix_size in self.tri_mixup_schedule:
if mix_size==2:
hmm_dir = train_hmm.mixup(self, self.xword_root, hmm_dir, self.tied_list, mix_size, estimateVarFloor=1)
else:
hmm_dir = train_hmm.mixup(self, self.xword_root, hmm_dir, self.tied_list, mix_size)
log(self.logfh, 'mixed up to [%d] in [%s]' %(mix_size, hmm_dir))
for iter in range(1, self.tri_iters_per_split+1):
hmm_dir, k, L = train_hmm.run_iter(self, self.xword_root, hmm_dir, self.tri_mlf, self.tied_list, mix_size, iter, '')
log(self.logfh, 'ran an iteration of BW in [%s] lik/fr [%1.4f]' %(hmm_dir, L))
log(self.logfh, 'MIXUP TRI finished')
if self.train_pipeline['align_with_xword']:
log(self.logfh, 'XWORD ALIGN started')
import train_hmm
align_config = '%s/config.align' %self.xword_root
train_hmm.make_hvite_xword_config(self, align_config, 'MFCC_0_D_A_Z')
num_gaussians = self.tri_mixup_schedule[-1]
iter_num = self.tri_iters_per_split
hmm_dir = '%s/HMM-%d-%d' %(self.xword_root, num_gaussians, iter_num)
realigned_mlf = '%s/raw_tri_xword_realigned.mlf' %self.misc
# Use the original, mfc list that has prons for every word
os.system('cp %s/mfc.list.filtered.by.dict %s' %(self.misc, self.mfc_list))
align_dir = train_hmm.align(self, self.xword_root, self.mfc_list, hmm_dir, self.word_mlf, realigned_mlf, self.tied_list, self.train_dict, align_config)
log(self.logfh, 'aligned with model in [%s], tri mlf [%s]' %(hmm_dir, realigned_mlf))
# Because of state tying, the triphones in the mlf will only be
# valid for this state tying. Strip down to monophones, the
# correct triphones will be created later in mono_to_tri
train_hmm.map_tri_to_mono(self, align_dir, realigned_mlf, self.phone_mlf)
os.system('cp %s %s/phone.mlf.from.xword.align' %(self.phone_mlf, self.misc))
os.system('bzip2 -f %s/phone.mlf.from.xword.align' %self.misc)
os.system('bzip2 -f %s' %realigned_mlf)
log(self.logfh, 'XWORD ALIGN finished')
if self.train_pipeline['mono_to_tri_from_xword']:
log(self.logfh, 'MONO TO TRI FROM XWORD started')
import train_hmm
#Assume that midown mono happened?
mono_final_dir = '%s/HMM-1-0' %self.mixdown_mono_root
hmm_dir = train_hmm.mono_to_tri(self, self.xword_1_root, mono_final_dir, self.phone_mlf, self.tri_mlf, self.phone_list, self.tri_list)
log(self.logfh, 'initialized triphone models in [%s]' %hmm_dir)
os.system('cp %s %s/tri.mlf.from.xword.align' %(self.tri_mlf, self.misc))
os.system('bzip2 -f %s/tri.mlf.from.xword.align' %self.misc)
os.system('cp %s %s/tri.list.from.xword.align' %(self.tri_list, self.misc))
two_model_config = '%s/config.two_model' %self.xword_1_root
fh = open(two_model_config, 'w')
fh.write('ALIGNMODELMMF = %s/HMM-%d-%d/MMF\n' %(self.xword_root, self.tri_mixup_schedule[-1], self.tri_iters_per_split))
fh.write('ALIGNHMMLIST = %s\n' %self.tied_list)
fh.close()
# Do one pass of two-model re-estimation
extra = ' -C %s' %two_model_config
hmm_dir, k, L = train_hmm.run_iter(self, self.xword_1_root, hmm_dir, self.tri_mlf, self.tri_list, 1, 1, extra)
log(self.logfh, 'ran an iteration of BW in [%s] lik/fr [%1.4f]' %(hmm_dir, L))
xword_tie_dir = '%s/HMM-1-2' %self.xword_1_root
hmm_dir = train_hmm.tie_states_search(self, xword_tie_dir, hmm_dir, self.phone_list, self.tri_list, self.tied_list)
log(self.logfh, 'tied states in [%s]' %hmm_dir)
os.system('cp %s %s/tied.list.second' %(self.tied_list, self.misc))
hmm_dir = '%s/HMM-1-2' %self.xword_1_root
for iter in range(3, self.tri_iters+3):
hmm_dir, k, L = train_hmm.run_iter(self, self.xword_1_root, hmm_dir, self.tri_mlf, self.tied_list, 1, iter, '')
log(self.logfh, 'ran an iteration of BW in [%s] lik/fr [%1.4f]' %(hmm_dir, L))
log(self.logfh, 'MONO TO TRI FROM XWORD finished')
if self.train_pipeline['mixup_tri_2']:
log(self.logfh, 'MIXUP TRI 2 started')
import train_hmm
## mixup everything
start_gaussians = 1
start_iter = self.tri_iters+2
hmm_dir = '%s/HMM-%d-%d' %(self.xword_1_root, start_gaussians, start_iter)
for mix_size in self.tri_mixup_schedule:
if mix_size==2:
hmm_dir = train_hmm.mixup(self, self.xword_1_root, hmm_dir, self.tied_list, mix_size, estimateVarFloor=1)
else:
hmm_dir = train_hmm.mixup(self, self.xword_1_root, hmm_dir, self.tied_list, mix_size)
log(self.logfh, 'mixed up to [%d] in [%s]' %(mix_size, hmm_dir))
for iter in range(1, self.tri_iters_per_split+1):
hmm_dir, k, L = train_hmm.run_iter(self, self.xword_1_root, hmm_dir, self.tri_mlf, self.tied_list, mix_size, iter, '')
log(self.logfh, 'ran an iteration of BW in [%s] lik/fr [%1.4f]' %(hmm_dir, L))
log(self.logfh, 'MIXUP TRI 2 finished')
if self.train_pipeline['diag']:
log(self.logfh, 'DIAG started')
import train_hmm
num_gaussians = self.tri_mixup_schedule[-1]
iter_num = self.tri_iters_per_split
if self.train_pipeline['mixup_tri_2']:
seed_dir = '%s/HMM-%d-%d' %(self.xword_1_root, num_gaussians, iter_num)
else:
seed_dir = '%s/HMM-%d-%d' %(self.xword_root, num_gaussians, iter_num)
hmm_dir, L = train_hmm.diagonalize(self, self.diag_root, seed_dir, self.tied_list, self.tri_mlf, num_gaussians)
log(self.logfh, 'ran diag in [%s] lik/fr [%1.4f]' %(hmm_dir, L))
for iter in range(1, self.tri_iters_per_split+1):
hmm_dir, k, L = train_hmm.run_iter(self, self.diag_root, hmm_dir, self.tri_mlf, self.tied_list, num_gaussians, iter, '')
log(self.logfh, 'ran an iteration of BW in [%s] lik/fr [%1.4f]' %(hmm_dir, L))
log(self.logfh, 'DIAG finished')
if self.train_pipeline['mmi']:
log(self.logfh, 'DISCRIM started')
## Common items
import mmi
mmi_dir = '%s/MMI' %self.exp
util.create_new_dir(mmi_dir)
mfc_list_mmi = '%s/mfc.list' %mmi_dir
os.system('cp %s %s' %(self.mfc_list, mfc_list_mmi))
## Create weak LM
import dict_and_lm
train_vocab = '%s/vocab' %self.lm_dir
lm_order = 2
target_ppl_ratio = 8
ppl = dict_and_lm.build_lm_from_mlf(self, self.word_mlf, self.train_dict, train_vocab, self.lm_dir, self.mmi_lm, lm_order, target_ppl_ratio)
log(self.logfh, 'wrote lm for mmi [%s] training ppl [%1.2f]' %(self.mmi_lm, ppl))
## Create decoding lattices for every utterance
lattice_dir = '%s/Denom/Lat_word' %mmi_dir
util.create_new_dir(lattice_dir)
num_gaussians = self.tri_mixup_schedule[-1]
iter_num = self.tri_iters_per_split
if self.train_pipeline['diag']:
model_dir = '%s/HMM-%d-%d' %(self.diag_root, num_gaussians, iter_num)
elif self.train_pipeline['mixup_tri_2']:
model_dir = '%s/HMM-%d-%d' %(self.xword_1_root, num_gaussians, iter_num)
else:
model_dir = '%s/HMM-%d-%d' %(self.xword_root, num_gaussians, iter_num)
mmi.decode_to_lattices(model, lattice_dir, model_dir, mfc_list_mmi, self.mmi_lm, self.decode_dict,
self.tied_list, self.word_mlf)
log(self.logfh, 'generated training lattices in [%s]' %lattice_dir)
## Prune and determinize lattices
pruned_lattice_dir = '%s/Denom/Lat_prune' %mmi_dir
util.create_new_dir(pruned_lattice_dir)
mmi.prune_lattices(model, lattice_dir, pruned_lattice_dir, self.decode_dict)
log(self.logfh, 'pruned lattices in [%s]' %pruned_lattice_dir)
## Phone-mark lattices
phone_lattice_dir = '%s/Denom/Lat_phone' %mmi_dir
util.create_new_dir(phone_lattice_dir)
mmi.phonemark_lattices(model, pruned_lattice_dir, phone_lattice_dir, model_dir, mfc_list_mmi,
self.mmi_lm, self.decode_dict, self.tied_list)
log(self.logfh, 'phone-marked lattices in [%s]' %phone_lattice_dir)
## Create numerator word lattices
num_lattice_dir = '%s/Num/Lat_word' %mmi_dir
util.create_new_dir(num_lattice_dir)
mmi.create_num_lattices(model, num_lattice_dir, self.mmi_lm, self.decode_dict, self.word_mlf)
log(self.logfh, 'generated numerator lattices in [%s]' %num_lattice_dir)
## Phone-mark numerator lattices
num_phone_lattice_dir = '%s/Num/Lat_phone' %mmi_dir
util.create_new_dir(num_phone_lattice_dir)
mmi.phonemark_lattices(model, num_lattice_dir, num_phone_lattice_dir, model_dir, mfc_list_mmi,
self.mmi_lm, self.decode_dict, self.tied_list)
log(self.logfh, 'phone-marked numerator lattices in [%s]' %num_phone_lattice_dir)
## Add LM scores to numerator phone lattices
num_phone_lm_lattice_dir = '%s/Num/Lat_phone_lm' %mmi_dir
util.create_new_dir(num_phone_lm_lattice_dir)
mmi.add_lm_lattices(model, num_phone_lattice_dir, num_phone_lm_lattice_dir, self.decode_dict, self.mmi_lm)
log(self.logfh, 'added LM scores to numerator lattices in [%s]' %num_phone_lm_lattice_dir)
## Modified Baum-Welch estimation
root_dir = '%s/Models' %mmi_dir
util.create_new_dir(root_dir)
mmi_iters = 12
mix_size = num_gaussians
for iter in range(1, mmi_iters+1):
model_dir = mmi.run_iter(model, model_dir, num_phone_lm_lattice_dir, phone_lattice_dir, root_dir,
self.tied_list, mfc_list_mmi, mix_size, iter)
log(self.logfh, 'ran an iteration of Modified BW in [%s]' %model_dir)
log(self.logfh, 'DISCRIM finished')
def train(self):
## Copy config file to the experiment dir
config_output = '%s/config' % self.exp
self.config.write(open(config_output, 'w'))
log(self.logfh, 'TRAINING with config [%s]' % config_output)
if self.train_pipeline['coding']:
log(self.logfh, 'CODING started')
import coding
util.create_new_dir(self.coding_root)
coding.create_config(self)
count = coding.wav_to_mfc(self, self.coding_root, self.mfc_list)
os.system('cp %s %s/mfc.list.original' %
(self.mfc_list, self.misc))
log(self.logfh, 'wrote mfc files [%d]' % count)
log(self.logfh, 'CODING finished')
if self.train_pipeline['lm']:
log(self.logfh, 'MLF/LM/DICT started')
import dict_and_lm
phone_set = dict_and_lm.fix_cmu_dict(self.orig_dict, self.htk_dict)
num_utts, words = dict_and_lm.make_mlf_from_transcripts(
self, self.htk_dict, self.setup, self.data, self.word_mlf,
self.mfc_list)
log(self.logfh,
'wrote word mlf [%d utts] [%s]' % (num_utts, self.word_mlf))
os.system('cp %s %s/mfc.list.filtered.by.dict' %
(self.mfc_list, self.misc))
num_entries = dict_and_lm.make_train_dict(self.htk_dict,
self.train_dict, words)
dict_and_lm.make_decode_dict(self.htk_dict, self.decode_dict,
words)
log(
self.logfh, 'wrote training dictionary [%d entries] [%s]' %
(num_entries, self.train_dict))
util.create_new_dir(self.lm_dir)
train_vocab = '%s/vocab' % self.lm_dir
ppl = dict_and_lm.build_lm_from_mlf(self, self.word_mlf,
self.train_dict, train_vocab,
self.lm_dir, self.lm,
self.lm_order)
log(self.logfh,
'wrote lm [%s] training ppl [%1.2f]' % (self.lm, ppl))
log(self.logfh, 'MLF/LM/DICT finished')
if self.train_pipeline['flat_start']:
log(self.logfh, 'FLAT START started')
import init_hmm
init_hmm.word_to_phone_mlf(self, self.train_dict, self.word_mlf,
self.phone_mlf, self.phone_list)
log(self.logfh, 'wrote phone mlf [%s]' % self.phone_mlf)
os.system('cp %s %s/phone.mlf.from.dict' %
(self.phone_mlf, self.misc))
os.system('bzip2 -f %s/phone.mlf.from.dict' % self.misc)
init_hmm.make_proto_hmm(self, self.mfc_list, self.proto_hmm)
hmm_dir, num_mfcs = init_hmm.initialize_hmms(
self, self.mono_root, self.mfc_list, self.phone_list,
self.proto_hmm)
log(self.logfh,
'initialized an HMM for each phone in [%s]' % hmm_dir)
log(self.logfh,
'used [%d] mfc files to compute variance floor' % num_mfcs)
import train_hmm
for iter in range(1, self.initial_mono_iters + 1):
hmm_dir, k, L = train_hmm.run_iter(self, self.mono_root,
hmm_dir, self.phone_mlf,
self.phone_list, 1, iter,
'')
log(
self.logfh,
'ran an iteration of BW in [%s] lik/fr [%1.4f]' %
(hmm_dir, L))
align_config = '%s/config.align' % self.mono_root
fh = open(align_config, 'w')
fh.write('HPARM: TARGETKIND = MFCC_0_D_A_Z\n')
fh.close()
align_dir = train_hmm.align(self, self.mono_root, self.mfc_list,
hmm_dir, self.word_mlf, self.phone_mlf,
self.phone_list, self.train_dict,
align_config)
log(
self.logfh,
'aligned with model in [%s], wrote phone mlf [%s]' %
(hmm_dir, self.phone_mlf))
os.system('cp %s %s/mfc.list.filtered.by.mono.align' %
(self.mfc_list, self.misc))
os.system('cp %s %s/phone.mlf.from.mono.align' %
(self.phone_mlf, self.misc))
os.system('bzip2 -f %s/phone.mlf.from.mono.align' % self.misc)
for iter in range(self.initial_mono_iters + 1,
self.initial_mono_iters + 1 + self.mono_iters):
hmm_dir, k, L = train_hmm.run_iter(self, self.mono_root,
hmm_dir, self.phone_mlf,
self.phone_list, 1, iter,
'')
log(
self.logfh,
'ran an iteration of BW in [%s] lik/fr [%1.4f]' %
(hmm_dir, L))
log(self.logfh, 'FLAT START finished')
if self.train_pipeline['mixup_mono']:
log(self.logfh, 'MIXUP MONO started')
import train_hmm
hmm_dir = '%s/HMM-%d-%d' % (
self.mono_root, 1, self.initial_mono_iters + self.mono_iters)
## mixup everything
for mix_size in self.mono_mixup_schedule:
hmm_dir = train_hmm.mixup(self, self.mixup_mono_root, hmm_dir,
self.phone_list, mix_size)
log(self.logfh,
'mixed up to [%d] in [%s]' % (mix_size, hmm_dir))
for iter in range(1, self.mono_iters + 1):
hmm_dir, k, L = train_hmm.run_iter(self,
self.mixup_mono_root,
hmm_dir, self.phone_mlf,
self.phone_list,
mix_size, iter, '')
log(
self.logfh,
'ran an iteration of BW in [%s] lik/fr [%1.4f]' %
(hmm_dir, L))
log(self.logfh, 'MIXUP MONO finished')
if self.train_pipeline['mixdown_mono']:
log(self.logfh, 'MIXDOWN MONO started')
import train_hmm
num_gaussians = self.mono_mixup_schedule[-1]
hmm_dir = '%s/HMM-%d-%d' % (self.mixup_mono_root, num_gaussians,
self.mono_iters)
train_hmm.mixdown_mono(self, self.mixdown_mono_root, hmm_dir,
self.phone_list)
log(self.logfh, 'MIXDOWN MONO finished')
if self.train_pipeline['mono_to_tri']:
log(self.logfh, 'MONO TO TRI started')
import train_hmm
if self.train_pipeline['mixdown_mono']:
mono_final_dir = '%s/HMM-1-0' % self.mixdown_mono_root
else:
mono_final_dir = '%s/HMM-%d-%d' % (self.mono_root, 1,
self.initial_mono_iters +
self.mono_iters)
hmm_dir = train_hmm.mono_to_tri(self, self.xword_root,
mono_final_dir, self.phone_mlf,
self.tri_mlf, self.phone_list,
self.tri_list)
log(self.logfh, 'initialized triphone models in [%s]' % hmm_dir)
log(self.logfh, 'created triphone mlf [%s]' % self.tri_mlf)
os.system('cp %s %s/tri.mlf.from.mono.align' %
(self.tri_mlf, self.misc))
os.system('bzip2 -f %s/tri.mlf.from.mono.align' % self.misc)
os.system('cp %s %s/tri.list.from.mono.align' %
(self.tri_list, self.misc))
for iter in range(1, self.initial_tri_iters + 1):
hmm_dir, k, L = train_hmm.run_iter(self, self.xword_root,
hmm_dir, self.tri_mlf,
self.tri_list, 1, iter, '')
log(
self.logfh,
'ran an iteration of BW in [%s] lik/fr [%1.4f]' %
(hmm_dir, L))
xword_tie_dir = '%s/HMM-%d-%d' % (self.xword_root, 1,
self.initial_tri_iters + 1)
hmm_dir = train_hmm.tie_states_search(self, xword_tie_dir, hmm_dir,
self.phone_list,
self.tri_list,
self.tied_list)
log(self.logfh, 'tied states in [%s]' % hmm_dir)
os.system('cp %s %s/tied.list.initial' %
(self.tied_list, self.misc))
hmm_dir = '%s/HMM-%d-%d' % (self.xword_root, 1,
self.initial_tri_iters + 1)
for iter in range(self.initial_tri_iters + 2,
self.initial_tri_iters + 1 + self.tri_iters + 1):
hmm_dir, k, L = train_hmm.run_iter(self, self.xword_root,
hmm_dir, self.tri_mlf,
self.tied_list, 1, iter, '')
log(
self.logfh,
'ran an iteration of BW in [%s] lik/fr [%1.4f]' %
(hmm_dir, L))
log(self.logfh, 'MONO TO TRI finished')
if self.train_pipeline['mixup_tri']:
log(self.logfh, 'MIXUP TRI started')
import train_hmm
## mixup everything
start_gaussians = 1
start_iter = self.initial_tri_iters + self.tri_iters + 1
hmm_dir = '%s/HMM-%d-%d' % (self.xword_root, start_gaussians,
start_iter)
for mix_size in self.tri_mixup_schedule:
if mix_size == 2:
hmm_dir = train_hmm.mixup(self,
self.xword_root,
hmm_dir,
self.tied_list,
mix_size,
estimateVarFloor=1)
else:
hmm_dir = train_hmm.mixup(self, self.xword_root, hmm_dir,
self.tied_list, mix_size)
log(self.logfh,
'mixed up to [%d] in [%s]' % (mix_size, hmm_dir))
for iter in range(1, self.tri_iters_per_split + 1):
hmm_dir, k, L = train_hmm.run_iter(self, self.xword_root,
hmm_dir, self.tri_mlf,
self.tied_list,
mix_size, iter, '')
log(
self.logfh,
'ran an iteration of BW in [%s] lik/fr [%1.4f]' %
(hmm_dir, L))
log(self.logfh, 'MIXUP TRI finished')
if self.train_pipeline['align_with_xword']:
log(self.logfh, 'XWORD ALIGN started')
import train_hmm
align_config = '%s/config.align' % self.xword_root
train_hmm.make_hvite_xword_config(self, align_config,
'MFCC_0_D_A_Z')
num_gaussians = self.tri_mixup_schedule[-1]
iter_num = self.tri_iters_per_split
hmm_dir = '%s/HMM-%d-%d' % (self.xword_root, num_gaussians,
iter_num)
realigned_mlf = '%s/raw_tri_xword_realigned.mlf' % self.misc
# Use the original, mfc list that has prons for every word
os.system('cp %s/mfc.list.filtered.by.dict %s' %
(self.misc, self.mfc_list))
align_dir = train_hmm.align(self, self.xword_root, self.mfc_list,
hmm_dir, self.word_mlf, realigned_mlf,
self.tied_list, self.train_dict,
align_config)
log(
self.logfh, 'aligned with model in [%s], tri mlf [%s]' %
(hmm_dir, realigned_mlf))
# Because of state tying, the triphones in the mlf will only be
# valid for this state tying. Strip down to monophones, the
# correct triphones will be created later in mono_to_tri
train_hmm.map_tri_to_mono(self, align_dir, realigned_mlf,
self.phone_mlf)
os.system('cp %s %s/phone.mlf.from.xword.align' %
(self.phone_mlf, self.misc))
os.system('bzip2 -f %s/phone.mlf.from.xword.align' % self.misc)
os.system('bzip2 -f %s' % realigned_mlf)
log(self.logfh, 'XWORD ALIGN finished')
if self.train_pipeline['mono_to_tri_from_xword']:
log(self.logfh, 'MONO TO TRI FROM XWORD started')
import train_hmm
#Assume that midown mono happened?
mono_final_dir = '%s/HMM-1-0' % self.mixdown_mono_root
hmm_dir = train_hmm.mono_to_tri(self, self.xword_1_root,
mono_final_dir, self.phone_mlf,
self.tri_mlf, self.phone_list,
self.tri_list)
log(self.logfh, 'initialized triphone models in [%s]' % hmm_dir)
os.system('cp %s %s/tri.mlf.from.xword.align' %
(self.tri_mlf, self.misc))
os.system('bzip2 -f %s/tri.mlf.from.xword.align' % self.misc)
os.system('cp %s %s/tri.list.from.xword.align' %
(self.tri_list, self.misc))
two_model_config = '%s/config.two_model' % self.xword_1_root
fh = open(two_model_config, 'w')
fh.write('ALIGNMODELMMF = %s/HMM-%d-%d/MMF\n' %
(self.xword_root, self.tri_mixup_schedule[-1],
self.tri_iters_per_split))
fh.write('ALIGNHMMLIST = %s\n' % self.tied_list)
fh.close()
# Do one pass of two-model re-estimation
extra = ' -C %s' % two_model_config
hmm_dir, k, L = train_hmm.run_iter(self, self.xword_1_root,
hmm_dir, self.tri_mlf,
self.tri_list, 1, 1, extra)
log(self.logfh,
'ran an iteration of BW in [%s] lik/fr [%1.4f]' % (hmm_dir, L))
xword_tie_dir = '%s/HMM-1-2' % self.xword_1_root
hmm_dir = train_hmm.tie_states_search(self, xword_tie_dir, hmm_dir,
self.phone_list,
self.tri_list,
self.tied_list)
log(self.logfh, 'tied states in [%s]' % hmm_dir)
os.system('cp %s %s/tied.list.second' %
(self.tied_list, self.misc))
hmm_dir = '%s/HMM-1-2' % self.xword_1_root
for iter in range(3, self.tri_iters + 3):
hmm_dir, k, L = train_hmm.run_iter(self, self.xword_1_root,
hmm_dir, self.tri_mlf,
self.tied_list, 1, iter, '')
log(
self.logfh,
'ran an iteration of BW in [%s] lik/fr [%1.4f]' %
(hmm_dir, L))
log(self.logfh, 'MONO TO TRI FROM XWORD finished')
if self.train_pipeline['mixup_tri_2']:
log(self.logfh, 'MIXUP TRI 2 started')
import train_hmm
## mixup everything
start_gaussians = 1
start_iter = self.tri_iters + 2
hmm_dir = '%s/HMM-%d-%d' % (self.xword_1_root, start_gaussians,
start_iter)
for mix_size in self.tri_mixup_schedule:
if mix_size == 2:
hmm_dir = train_hmm.mixup(self,
self.xword_1_root,
hmm_dir,
self.tied_list,
mix_size,
estimateVarFloor=1)
else:
hmm_dir = train_hmm.mixup(self, self.xword_1_root, hmm_dir,
self.tied_list, mix_size)
log(self.logfh,
'mixed up to [%d] in [%s]' % (mix_size, hmm_dir))
for iter in range(1, self.tri_iters_per_split + 1):
hmm_dir, k, L = train_hmm.run_iter(self, self.xword_1_root,
hmm_dir, self.tri_mlf,
self.tied_list,
mix_size, iter, '')
log(
self.logfh,
'ran an iteration of BW in [%s] lik/fr [%1.4f]' %
(hmm_dir, L))
log(self.logfh, 'MIXUP TRI 2 finished')
if self.train_pipeline['diag']:
log(self.logfh, 'DIAG started')
import train_hmm
num_gaussians = self.tri_mixup_schedule[-1]
iter_num = self.tri_iters_per_split
if self.train_pipeline['mixup_tri_2']:
seed_dir = '%s/HMM-%d-%d' % (self.xword_1_root, num_gaussians,
iter_num)
else:
seed_dir = '%s/HMM-%d-%d' % (self.xword_root, num_gaussians,
iter_num)
hmm_dir, L = train_hmm.diagonalize(self, self.diag_root, seed_dir,
self.tied_list, self.tri_mlf,
num_gaussians)
log(self.logfh, 'ran diag in [%s] lik/fr [%1.4f]' % (hmm_dir, L))
for iter in range(1, self.tri_iters_per_split + 1):
hmm_dir, k, L = train_hmm.run_iter(self, self.diag_root,
hmm_dir, self.tri_mlf,
self.tied_list,
num_gaussians, iter, '')
log(
self.logfh,
'ran an iteration of BW in [%s] lik/fr [%1.4f]' %
(hmm_dir, L))
log(self.logfh, 'DIAG finished')
if self.train_pipeline['mmi']:
log(self.logfh, 'DISCRIM started')
## Common items
import mmi
mmi_dir = '%s/MMI' % self.exp
util.create_new_dir(mmi_dir)
mfc_list_mmi = '%s/mfc.list' % mmi_dir
os.system('cp %s %s' % (self.mfc_list, mfc_list_mmi))
## Create weak LM
import dict_and_lm
train_vocab = '%s/vocab' % self.lm_dir
lm_order = 2
target_ppl_ratio = 8
ppl = dict_and_lm.build_lm_from_mlf(self, self.word_mlf,
self.train_dict, train_vocab,
self.lm_dir, self.mmi_lm,
lm_order, target_ppl_ratio)
log(
self.logfh, 'wrote lm for mmi [%s] training ppl [%1.2f]' %
(self.mmi_lm, ppl))
## Create decoding lattices for every utterance
lattice_dir = '%s/Denom/Lat_word' % mmi_dir
util.create_new_dir(lattice_dir)
num_gaussians = self.tri_mixup_schedule[-1]
iter_num = self.tri_iters_per_split
if self.train_pipeline['diag']:
model_dir = '%s/HMM-%d-%d' % (self.diag_root, num_gaussians,
iter_num)
elif self.train_pipeline['mixup_tri_2']:
model_dir = '%s/HMM-%d-%d' % (self.xword_1_root, num_gaussians,
iter_num)
else:
model_dir = '%s/HMM-%d-%d' % (self.xword_root, num_gaussians,
iter_num)
mmi.decode_to_lattices(model, lattice_dir, model_dir, mfc_list_mmi,
self.mmi_lm, self.decode_dict,
self.tied_list, self.word_mlf)
log(self.logfh,
'generated training lattices in [%s]' % lattice_dir)
## Prune and determinize lattices
pruned_lattice_dir = '%s/Denom/Lat_prune' % mmi_dir
util.create_new_dir(pruned_lattice_dir)
mmi.prune_lattices(model, lattice_dir, pruned_lattice_dir,
self.decode_dict)
log(self.logfh, 'pruned lattices in [%s]' % pruned_lattice_dir)
## Phone-mark lattices
phone_lattice_dir = '%s/Denom/Lat_phone' % mmi_dir
util.create_new_dir(phone_lattice_dir)
mmi.phonemark_lattices(model, pruned_lattice_dir,
phone_lattice_dir, model_dir, mfc_list_mmi,
self.mmi_lm, self.decode_dict,
self.tied_list)
log(self.logfh,
'phone-marked lattices in [%s]' % phone_lattice_dir)
## Create numerator word lattices
num_lattice_dir = '%s/Num/Lat_word' % mmi_dir
util.create_new_dir(num_lattice_dir)
mmi.create_num_lattices(model, num_lattice_dir, self.mmi_lm,
self.decode_dict, self.word_mlf)
log(self.logfh,
'generated numerator lattices in [%s]' % num_lattice_dir)
## Phone-mark numerator lattices
num_phone_lattice_dir = '%s/Num/Lat_phone' % mmi_dir
util.create_new_dir(num_phone_lattice_dir)
mmi.phonemark_lattices(model, num_lattice_dir,
num_phone_lattice_dir, model_dir,
mfc_list_mmi, self.mmi_lm, self.decode_dict,
self.tied_list)
log(
self.logfh, 'phone-marked numerator lattices in [%s]' %
num_phone_lattice_dir)
## Add LM scores to numerator phone lattices
num_phone_lm_lattice_dir = '%s/Num/Lat_phone_lm' % mmi_dir
util.create_new_dir(num_phone_lm_lattice_dir)
mmi.add_lm_lattices(model, num_phone_lattice_dir,
num_phone_lm_lattice_dir, self.decode_dict,
self.mmi_lm)
log(
self.logfh, 'added LM scores to numerator lattices in [%s]' %
num_phone_lm_lattice_dir)
## Modified Baum-Welch estimation
root_dir = '%s/Models' % mmi_dir
util.create_new_dir(root_dir)
mmi_iters = 12
mix_size = num_gaussians
for iter in range(1, mmi_iters + 1):
model_dir = mmi.run_iter(model, model_dir,
num_phone_lm_lattice_dir,
phone_lattice_dir, root_dir,
self.tied_list, mfc_list_mmi,
mix_size, iter)
log(self.logfh,
'ran an iteration of Modified BW in [%s]' % model_dir)
log(self.logfh, 'DISCRIM finished')
