def mixdown_mono(model, root_dir, prev_dir, phone_list):
"""
Run HHEd to mixdown monophones
"""
output_dir = '%s/HMM-1-0' % root_dir
util.create_new_dir(output_dir)
## Create the full list of possible triphones
phones = open(phone_list).read().splitlines()
non_sil_phones = [p for p in phones if p not in ['sp', 'sil']]
## Make the hed script
mixdown_hed = '%s/mix_down.hed' % output_dir
fh = open(mixdown_hed, 'w')
fh.write('MD 12 {(sil,sp).state[2-%d].mix}\n' % (model.states - 1))
for phone in non_sil_phones:
fh.write('MD 1 {%s.state[2-%d].mix}\n' % (phone, model.states - 1))
fh.close()
hhed_log = '%s/hhed_mixdown.log' % output_dir
cmd = 'HHEd -A -D -T 1 -H %s/MMF -M %s' % (prev_dir, output_dir)
cmd += ' %s %s > %s' % (mixdown_hed, phone_list, hhed_log)
if model.local == 1: os.system(cmd)
else: util.run(cmd, output_dir)
return output_dir
def mixdown_mono(model, root_dir, prev_dir, phone_list):
"""
Run HHEd to mixdown monophones
"""
output_dir = '%s/HMM-1-0' %root_dir
util.create_new_dir(output_dir)
## Create the full list of possible triphones
phones = open(phone_list).read().splitlines()
non_sil_phones = [p for p in phones if p not in ['sp', 'sil']]
## Make the hed script
mixdown_hed = '%s/mix_down.hed' %output_dir
fh = open(mixdown_hed, 'w')
fh.write('MD 12 {(sil,sp).state[2-%d].mix}\n' %(model.states-1))
for phone in non_sil_phones:
fh.write('MD 1 {%s.state[2-%d].mix}\n' %(phone, model.states-1))
fh.close()
hhed_log = '%s/hhed_mixdown.log' %output_dir
cmd = 'HHEd -A -D -T 1 -H %s/MMF -M %s' %(prev_dir, output_dir)
cmd += ' %s %s > %s' %(mixdown_hed, phone_list, hhed_log)
if model.local == 1: os.system(cmd)
else: util.run(cmd, output_dir)
return output_dir
def init_tri_from_mono(model, root_dir, mono_dir, tri_mlf, mono_list,
tri_list):
"""
Convert a monophone model and triphone mlf to triphones
"""
## Create the xword directory and the current output directory
output_dir = '%s/HMM-0-0' % root_dir
util.create_new_dir(root_dir)
util.create_new_dir(output_dir)
mktri_hed = '%s/mktri.hed' % output_dir
hhed_log = '%s/hhed_clone_mono.log' % output_dir
## Create an HHEd script to clone monophones to triphones
fh = open(mktri_hed, 'w')
for line in open(mono_list):
mono = line.strip()
fh.write('TI T_%s {(%s).transP}\n' % (mono, mono))
fh.write('CL %s\n' % tri_list)
fh.close()
## Run HHEd to clone monophones and tie transition matricies
cmd = 'HHEd -A -T 1 -H %s/MMF' % mono_dir
cmd += ' -M %s' % output_dir
cmd += ' %s %s > %s' % (mktri_hed, mono_list, hhed_log)
if model.local: os.system(cmd)
else: util.run(cmd, output_dir)
return output_dir
def init_tri_from_mono(model, root_dir, mono_dir, tri_mlf, mono_list, tri_list):
"""
Convert a monophone model and triphone mlf to triphones
"""
## Create the xword directory and the current output directory
output_dir = '%s/HMM-0-0' %root_dir
util.create_new_dir(root_dir)
util.create_new_dir(output_dir)
mktri_hed = '%s/mktri.hed' %output_dir
hhed_log = '%s/hhed_clone_mono.log' %output_dir
## Create an HHEd script to clone monophones to triphones
fh = open(mktri_hed, 'w')
for line in open(mono_list):
mono = line.strip()
fh.write('TI T_%s {(%s).transP}\n' %(mono, mono))
fh.write('CL %s\n' %tri_list)
fh.close()
## Run HHEd to clone monophones and tie transition matricies
cmd = 'HHEd -A -T 1 -H %s/MMF' %mono_dir
cmd += ' -M %s' %output_dir
cmd += ' %s %s > %s' %(mktri_hed, mono_list, hhed_log)
if model.local: os.system(cmd)
else: util.run(cmd, output_dir)
return output_dir
def mixup(model, root_dir, prev_dir, model_list, mix_size, estimateVarFloor=0):
"""
Run HHEd to initialize a mixup to mix_size gaussians
"""
output_dir = '%s/HMM-%d-%d' % (root_dir, mix_size, 0)
util.create_new_dir(output_dir)
## Make the hed script
mix_hed = '%s/mix_%d.hed' % (output_dir, mix_size)
fh = open(mix_hed, 'w')
if estimateVarFloor:
fh.write('LS %s/stats\n' % prev_dir)
fh.write('FA 0.1\n')
fh.write('MU %d {(sil,sp).state[2-%d].mix}\n' %
(2 * mix_size, model.states - 1))
fh.write('MU %d {*.state[2-%d].mix}\n' % (mix_size, model.states - 1))
fh.close()
hhed_log = '%s/hhed_mix.log' % output_dir
cmd = 'HHEd -A -D -T 1 -H %s/MMF -M %s' % (prev_dir, output_dir)
cmd += ' %s %s > %s' % (mix_hed, model_list, hhed_log)
if model.local == 1: os.system(cmd)
else: util.run(cmd, output_dir)
return output_dir
def mixup(model, root_dir, prev_dir, model_list, mix_size, estimateVarFloor=0):
"""
Run HHEd to initialize a mixup to mix_size gaussians
"""
output_dir = '%s/HMM-%d-%d' %(root_dir, mix_size, 0)
util.create_new_dir(output_dir)
## Make the hed script
mix_hed = '%s/mix_%d.hed' %(output_dir, mix_size)
fh = open(mix_hed, 'w')
if estimateVarFloor:
fh.write('LS %s/stats\n' %prev_dir)
fh.write('FA 0.1\n')
fh.write('MU %d {(sil,sp).state[2-%d].mix}\n' %(2*mix_size,model.states-1))
fh.write('MU %d {*.state[2-%d].mix}\n' %(mix_size, model.states-1))
fh.close()
hhed_log = '%s/hhed_mix.log' %output_dir
cmd = 'HHEd -A -D -T 1 -H %s/MMF -M %s' %(prev_dir, output_dir)
cmd += ' %s %s > %s' %(mix_hed, model_list, hhed_log)
if model.local == 1: os.system(cmd)
else: util.run(cmd, output_dir)
return output_dir
def diagonalize(model, output_dir, model_dir, model_list, mlf_file, mix_size):
"""
Diagonalize output distributions
"""
util.create_new_dir(output_dir)
diag_config = '%s/config.diag' %output_dir
global_class = '%s/global' %output_dir
fh = open(diag_config, 'w')
fh.write('HADAPT:TRANSKIND = SEMIT\n')
fh.write('HADAPT:USEBIAS = FALSE\n')
fh.write('HADAPT:BASECLASS = global\n')
fh.write('HADAPT:SPLITTHRESH = 0.0\n')
fh.write('HADAPT:MAXXFORMITER = 100\n')
fh.write('HADAPT:MAXSEMITIEDITER = 20\n')
fh.write('HADAPT:TRACE = 61\n')
fh.write('HMODEL:TRACE = 512\n')
fh.write('HADAPT: SEMITIED2INPUTXFORM = TRUE\n')
fh.close()
max_mix = 2 * mix_size
fh = open(global_class, 'w')
fh.write('~b "global"\n')
fh.write('<MMFIDMASK> *\n')
fh.write('<PARAMETERS> MIXBASE\n')
fh.write('<NUMCLASSES> 1\n')
fh.write('<CLASS> 1 {*.state[2-4].mix[1-%d]}\n' %max_mix)
fh.close()
extra = ' -C %s -J %s -K %s/HMM-%d-0 -u stw' %(diag_config, output_dir, output_dir, mix_size)
hmm_dir, k, likelihood = run_iter(model, output_dir, model_dir, mlf_file, model_list, mix_size, 0, extra)
return hmm_dir, likelihood
def tie_states(model, output_dir, model_dir, mono_list, tri_list, tied_list):
"""
Tie HMM states using decision tree clustering
"""
util.create_new_dir(output_dir)
tree_hed = '%s/tree.hed' % output_dir
all_tri_list = '%s/all_tri.list' % model.exp
tree_output = '%s/trees' % output_dir
hhed_log = '%s/hhed_cluster.log' % output_dir
## Decision tree parameters
ro = 200
tb = 750
## Create the full list of possible triphones
phones = open(mono_list).read().splitlines()
non_sp_phones = [p for p in phones if p not in ['sp', 'sil']]
fh = open(all_tri_list, 'w')
fh.write('sp\n')
fh.write('sil\n')
for p1 in non_sp_phones:
fh.write('sil-%s+sil\n' % p1)
for p2 in non_sp_phones:
fh.write('sil-%s+%s\n' % (p1, p2))
fh.write('%s-%s+sil\n' % (p2, p1))
for p3 in non_sp_phones:
fh.write('%s-%s+%s\n' % (p2, p1, p3))
fh.close()
## Set up decision tree clustering
fh = open(tree_hed, 'w')
fh.write('RO %d %s/stats\n' % (ro, model_dir))
fh.write('TR 0\n')
fh.write('%s\n' % open(model.tree_questions).read())
fh.write('TR 12\n')
for p in non_sp_phones:
for s in range(1, model.states + 1)[1:-1]:
fh.write('TB %d "ST_%s_%d_" {(%s,*-%s+*,%s+*,*-%s).state[%d]}\n' %
(tb, p, s, p, p, p, p, s))
fh.write('TR 1\n')
fh.write('AU "%s"\n' % all_tri_list)
fh.write('CO "%s"\n' % tied_list)
fh.write('ST "%s"\n' % tree_output)
fh.close()
## Use HHEd to cluster
cmd = 'HHEd -A -T 1 -H %s/MMF' % model_dir
cmd += ' -M %s' % output_dir
cmd += ' %s %s > %s' % (tree_hed, tri_list, hhed_log)
if model.local == 1: os.system(cmd)
else: util.run(cmd, output_dir)
return output_dir
def tie_states(model, output_dir, model_dir, mono_list, tri_list, tied_list):
"""
Tie HMM states using decision tree clustering
"""
util.create_new_dir(output_dir)
tree_hed = '%s/tree.hed' %output_dir
all_tri_list = '%s/all_tri.list' %model.exp
tree_output = '%s/trees' %output_dir
hhed_log = '%s/hhed_cluster.log' %output_dir
## Decision tree parameters
ro = 200
tb = 750
## Create the full list of possible triphones
phones = open(mono_list).read().splitlines()
non_sp_phones = [p for p in phones if p not in ['sp', 'sil']]
fh = open(all_tri_list, 'w')
fh.write('sp\n')
fh.write('sil\n')
for p1 in non_sp_phones:
fh.write('sil-%s+sil\n' %p1)
for p2 in non_sp_phones:
fh.write('sil-%s+%s\n' %(p1, p2))
fh.write('%s-%s+sil\n' %(p2, p1))
for p3 in non_sp_phones:
fh.write('%s-%s+%s\n' %(p2, p1, p3))
fh.close()
## Set up decision tree clustering
fh = open(tree_hed, 'w')
fh.write('RO %d %s/stats\n' %(ro, model_dir))
fh.write('TR 0\n')
fh.write('%s\n' %open(model.tree_questions).read())
fh.write('TR 12\n')
for p in non_sp_phones:
for s in range(1, model.states+1)[1:-1]:
fh.write('TB %d "ST_%s_%d_" {(%s,*-%s+*,%s+*,*-%s).state[%d]}\n' %(tb,p,s,p,p,p,p,s))
fh.write('TR 1\n')
fh.write('AU "%s"\n' %all_tri_list)
fh.write('CO "%s"\n' %tied_list)
fh.write('ST "%s"\n' %tree_output)
fh.close()
## Use HHEd to cluster
cmd = 'HHEd -A -T 1 -H %s/MMF' %model_dir
cmd += ' -M %s' %output_dir
cmd += ' %s %s > %s' %(tree_hed, tri_list, hhed_log)
if model.local == 1: os.system(cmd)
else: util.run(cmd, output_dir)
return output_dir
def mono_to_tri(model, root_dir, mono_dir, phone_mlf, tri_mlf, mono_list,
tri_list):
"""
Convert a monophone model and phone mlf to triphones
"""
## Create the xword directory and the current output directory
output_dir = '%s/HMM-0-0' % root_dir
util.create_new_dir(root_dir)
util.create_new_dir(output_dir)
mktri_led = '%s/mktri_cross.led' % output_dir
mktri_hed = '%s/mktri.hed' % output_dir
hled_log = '%s/hled_make_tri.log' % output_dir
hhed_log = '%s/hhed_clone_mono.log' % output_dir
## Create an HLEd script
fh = open(mktri_led, 'w')
fh.write('NB sp\n')
fh.write('TC\n')
fh.write('IT\n')
fh.write('CH sil * sil *\n')
fh.write('CH sp * sp *\n')
fh.write('ME sil sil sil sil\n')
fh.write('ME sil sil sil\n')
fh.write('ME sil sp sil\n')
fh.close()
## Create a new alignment in tri_mlf and output used triphones to tri_list
cmd = 'HLEd -A -n %s' % tri_list
cmd += ' -i %s' % tri_mlf
cmd += ' %s %s > %s' % (mktri_led, phone_mlf, hled_log)
if model.local: os.system(cmd)
else: util.run(cmd, output_dir)
## Create an HHEd script to clone monophones to triphones
fh = open(mktri_hed, 'w')
for line in open(mono_list):
mono = line.strip()
fh.write('TI T_%s {(%s).transP}\n' % (mono, mono))
fh.write('CL %s\n' % tri_list)
fh.close()
## Run HHEd to clone monophones and tie transition matricies
cmd = 'HHEd -A -T 1 -H %s/MMF' % mono_dir
cmd += ' -M %s' % output_dir
cmd += ' %s %s > %s' % (mktri_hed, mono_list, hhed_log)
if model.local: os.system(cmd)
else: util.run(cmd, output_dir)
return output_dir
def mono_to_tri(model, root_dir, mono_dir, phone_mlf, tri_mlf, mono_list, tri_list):
"""
Convert a monophone model and phone mlf to triphones
"""
## Create the xword directory and the current output directory
output_dir = '%s/HMM-0-0' %root_dir
util.create_new_dir(root_dir)
util.create_new_dir(output_dir)
mktri_led = '%s/mktri_cross.led' %output_dir
mktri_hed = '%s/mktri.hed' %output_dir
hled_log = '%s/hled_make_tri.log' %output_dir
hhed_log = '%s/hhed_clone_mono.log' %output_dir
## Create an HLEd script
fh = open(mktri_led, 'w')
fh.write('NB sp\n')
fh.write('TC\n')
fh.write('IT\n')
fh.write('CH sil * sil *\n')
fh.write('CH sp * sp *\n')
fh.write('ME sil sil sil sil\n')
fh.write('ME sil sil sil\n')
fh.write('ME sil sp sil\n')
fh.close()
## Create a new alignment in tri_mlf and output used triphones to tri_list
cmd = 'HLEd -A -n %s' %tri_list
cmd += ' -i %s' %tri_mlf
cmd += ' %s %s > %s' %(mktri_led, phone_mlf, hled_log)
if model.local: os.system(cmd)
else: util.run(cmd, output_dir)
## Create an HHEd script to clone monophones to triphones
fh = open(mktri_hed, 'w')
for line in open(mono_list):
mono = line.strip()
fh.write('TI T_%s {(%s).transP}\n' %(mono, mono))
fh.write('CL %s\n' %tri_list)
fh.close()
## Run HHEd to clone monophones and tie transition matricies
cmd = 'HHEd -A -T 1 -H %s/MMF' %mono_dir
cmd += ' -M %s' %output_dir
cmd += ' %s %s > %s' %(mktri_hed, mono_list, hhed_log)
if model.local: os.system(cmd)
else: util.run(cmd, output_dir)
return output_dir
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 diagonalize(model, output_dir, model_dir, model_list, mlf_file, mix_size):
"""
Diagonalize output distributions
"""
util.create_new_dir(output_dir)
diag_config = '%s/config.diag' % output_dir
global_class = '%s/global' % output_dir
fh = open(diag_config, 'w')
fh.write('HADAPT:TRANSKIND = SEMIT\n')
fh.write('HADAPT:USEBIAS = FALSE\n')
fh.write('HADAPT:BASECLASS = global\n')
fh.write('HADAPT:SPLITTHRESH = 0.0\n')
fh.write('HADAPT:MAXXFORMITER = 100\n')
fh.write('HADAPT:MAXSEMITIEDITER = 20\n')
fh.write('HADAPT:TRACE = 61\n')
fh.write('HMODEL:TRACE = 512\n')
fh.write('HADAPT: SEMITIED2INPUTXFORM = TRUE\n')
fh.close()
max_mix = 2 * mix_size
fh = open(global_class, 'w')
fh.write('~b "global"\n')
fh.write('<MMFIDMASK> *\n')
fh.write('<PARAMETERS> MIXBASE\n')
fh.write('<NUMCLASSES> 1\n')
fh.write('<CLASS> 1 {*.state[2-4].mix[1-%d]}\n' % max_mix)
fh.close()
extra = ' -C %s -J %s -K %s/HMM-%d-0 -u stw' % (diag_config, output_dir,
output_dir, mix_size)
hmm_dir, k, likelihood = run_iter(model, output_dir, model_dir, mlf_file,
model_list, mix_size, 0, extra)
return hmm_dir, likelihood
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 tie_states_search(model, output_dir, model_dir, mono_list, tri_list, tied_list):
"""
Tie HMM states using decision tree clustering
"""
util.create_new_dir(output_dir)
tree_hed = '%s/tree.hed' %output_dir
tree_output = '%s/trees' %output_dir
hhed_log = '%s/hhed_cluster.log' %output_dir
all_tri_list = '%s/all_tri.list' %model.exp
## Decision tree parameters
ro = model.dt_ro
tb = model.dt_tb
tb_min = 100.0
tb_max = 10000.0
## Create the full list of possible triphones
phones = open(mono_list).read().splitlines()
non_sp_phones = [p for p in phones if p not in ['sp', 'sil']]
fh = open(all_tri_list, 'w')
fh.write('sp\n')
fh.write('sil\n')
for p1 in non_sp_phones:
fh.write('sil-%s+sil\n' %p1)
for p2 in non_sp_phones:
fh.write('sil-%s+%s\n' %(p1, p2))
fh.write('%s-%s+sil\n' %(p2, p1))
for p3 in non_sp_phones:
fh.write('%s-%s+%s\n' %(p2, p1, p3))
fh.close()
## Search over tb arguments to get the right number states
num_states = 0
attempts = 0
prev_tb = 0
while True:
os.system('rm -f %s %s %s' %(tree_hed, tree_output, hhed_log))
## Set up decision tree clustering
fh = open(tree_hed, 'w')
fh.write('RO %d %s/stats\n' %(ro, model_dir))
fh.write('TR 0\n')
fh.write('%s\n' %open(model.tree_questions).read())
fh.write('TR 12\n')
for p in non_sp_phones:
for s in range(1, model.states+1)[1:-1]:
fh.write('TB %d "ST_%s_%d_" {(%s,*-%s+*,%s+*,*-%s).state[%d]}\n' %(tb,p,s,p,p,p,p,s))
fh.write('TR 1\n')
fh.write('AU "%s"\n' %all_tri_list)
fh.write('CO "%s"\n' %tied_list)
fh.write('ST "%s"\n' %tree_output)
fh.close()
## Use HHEd to cluster
cmd = 'HHEd -A -T 1 -H %s/MMF' %model_dir
cmd += ' -M %s' %output_dir
cmd += ' %s %s > %s' %(tree_hed, tri_list, hhed_log)
if model.local == 1: os.system(cmd)
else: util.run(cmd, output_dir)
num_states = int(os.popen('grep -c "<MEAN>" %s/MMF' %output_dir).read().strip())
if abs(float(num_states - model.triphone_states)/model.triphone_states) <= 0.01:
util.log_write(model.logfh, ' current states [%d] tb [%1.2f]' %(num_states, tb))
break
if abs(prev_tb - tb) <= 0.01:
util.log_write(model.logfh, ' Could not converge. Stopping. Current states [%d] tb [%1.2f]' %(num_states,tb))
break
attempts += 1
prev_tb = tb
if num_states < model.triphone_states:
tb = (tb_min + tb) / 2
tb_max = prev_tb
else:
tb = (tb_max + tb) / 2
tb_min = prev_tb
util.log_write(model.logfh, ' [%d] goal [%d] current states [%d] tb [%1.2f] -> [%1.2f] [%1.1f %1.1f]' %(attempts, model.triphone_states, num_states, prev_tb, tb, tb_min, tb_max))
if attempts > 50:
util.log_write(model.logfh, ' Goal not reached after 50 tries. Exiting.')
sys.exit()
return output_dir
def initialize_hmms(model, root_dir, mfc_list, mono_list, proto_hmm):
"""
Compute mean and variance of each feature across all utterances and
set all Gaussians in the prototype HMM to have the same mean and variance
"""
output_dir = '%s/HMM-0-0' % root_dir
util.create_new_dir(root_dir)
util.create_new_dir(output_dir)
cmd_log = '%s/hcompv.log' % output_dir
## Sample from the full mfc list to reduce computation
sampled_mfc_list = '%s/mfc_sample.list' % output_dir
fh = open(sampled_mfc_list, 'w')
mfcs = open(mfc_list).read().splitlines()
random.shuffle(mfcs)
mfc_frac = model.var_floor_fraction
num_mfcs_for_hcompv = int(mfc_frac * len(mfcs))
for mfc in mfcs[:num_mfcs_for_hcompv]:
fh.write('%s\n' % mfc)
fh.close()
cmd = 'HCompV -A -T 1 -m'
cmd += ' -C %s' % model.mfc_config
cmd += ' -f 0.01'
cmd += ' -S %s' % sampled_mfc_list
cmd += ' -M %s' % output_dir
cmd += ' %s > %s' % (proto_hmm, cmd_log)
if model.local == 1: os.system(cmd)
else: util.run(cmd, output_dir)
## Copy the initial HMM for each monophone
proto_hmm = '%s/proto_hmm' % output_dir
hmm_defs_init = '%s/init.mmf' % output_dir
hmm = re.search(re.compile('<BEGINHMM>.*<ENDHMM>', re.DOTALL),
open(proto_hmm).read()).group()
fh = open(hmm_defs_init, 'w')
for line in open(mono_list):
phone = line.strip()
fh.write('~h "%s"\n' % phone)
fh.write('%s\n' % hmm)
fh.close()
## Create mmf header file
## TODO: get rid of this?
mmf_header = '%s/header.mmf' % output_dir
cmd = 'head -3 %s | cat - %s/vFloors > %s' % (proto_hmm, output_dir,
mmf_header)
os.system(cmd)
## Fix sp and silence models
cleanup_config = '%s/cleanup_init.hed' % output_dir
fh = open(cleanup_config, 'w')
fh.write('AT 4 2 0.2 {sil.transP}\n')
fh.write('AT 2 4 0.2 {sil.transP}\n')
fh.write('AT 1 5 0.3 {sp.transP}\n')
fh.write('TI silsp_2 {sil.state[2],sp.state[2]}\n')
fh.write('TI silsp_3 {sil.state[3],sp.state[3]}\n')
fh.write('TI silsp_4 {sil.state[4],sp.state[4]}\n')
fh.close()
hmm_defs_final = '%s/MMF' % output_dir
cmd_log = '%s/hhed_sil.log' % output_dir
cmd = 'HHEd -A -D -T 1 -d %s' % output_dir
cmd += ' -H %s -H %s' % (mmf_header, hmm_defs_init)
cmd += ' -M %s' % output_dir
cmd += ' -w %s' % hmm_defs_final
cmd += ' %s %s > %s' % (cleanup_config, mono_list, cmd_log)
os.system(cmd)
return output_dir, num_mfcs_for_hcompv
def run_iter(model, root_dir, prev_dir, mlf_file, model_list, mix_size, iter,
extra):
"""
Run an iteration of Baum-Welch training using HERest
"""
output_dir = '%s/HMM-%d-%d' % (root_dir, mix_size, iter)
util.create_new_dir(output_dir)
mfc_list = '%s/mfc.list' % model.exp
utts_per_split = max(250, (1 + (model.setup_length / 200)))
## HERest parameters
min_train_examples = 0
prune_thresh = 250
prune_inc = 150
prune_limit = 2000
def herest(input, split_num, extra):
try:
log_id = os.path.basename(input).split('.')[2]
except:
log_id = 'acc'
cmd = '%s -D -A -T 1 -m %d' % (HEREST_CMD, min_train_examples)
cmd += ' -t %d %d %d' % (prune_thresh, prune_inc, prune_limit)
cmd += ' -s %s/stats' % output_dir
cmd += ' -C %s%s' % (model.mfc_config, extra)
cmd += ' -I %s' % mlf_file
cmd += ' -H %s/MMF' % prev_dir
cmd += ' -p %d' % split_num
cmd += ' -S %s' % input
#cmd += ' -M %s %s' %(output_dir, model_list)
cmd += ' -M %s %s >> %s/herest.%s.log' % (output_dir, model_list,
output_dir, log_id)
return cmd
## Split up MFC list with unix split
cmd = 'split -a 4 -d -l %d %s %s/%s' % (utts_per_split, mfc_list,
output_dir, 'mfc.list.')
os.system(cmd)
## Create the HERest commands
cmds = []
inputs = os.popen('ls %s/mfc.list.*' % output_dir).read().splitlines()
split_num = 0
for input in inputs:
split_num += 1
cmds.append(herest(input, split_num, extra))
## Non-parallel case
if model.local == 1:
for cmd in cmds:
print cmd
print os.popen(cmd)
## Parallel case: one command per line in cmds_file
else:
cmds_file = '%s/herest.commands' % output_dir
fh = open(cmds_file, 'w')
for cmd in cmds:
fh.write('%s\n' % cmd)
fh.close()
util.run_parallel(cmds_file, model.jobs, output_dir)
## Gather the created .acc files
acc_file = '%s/herest.list' % output_dir
os.system('ls %s/HER*.acc > %s' % (output_dir, acc_file))
## Combine acc files into a new HMM
cmd = herest(acc_file, 0, extra)
cmd = cmd.split('>>')[0]
cmd += ' >> %s/herest.log' % output_dir
if model.local == 1: os.system(cmd)
else: util.run(cmd, output_dir)
## Clean up
os.system('rm -f %s/mfc.list.* %s/HER*.acc' % (output_dir, output_dir))
os.system('bzip2 %s/herest.*.log %s/run-command*.log' %
(output_dir, output_dir))
## Get a few stats
num_models = int(
os.popen('grep "<MEAN>" %s/MMF -c' % output_dir).read().strip())
likelihood = float(
os.popen('cat %s/herest.log | grep aver' %
output_dir).read().strip().split()[-1])
return output_dir, num_models, likelihood
def align(model, root_dir, mfc_list, model_dir, word_mlf, new_mlf, model_list,
dict, align_config):
"""
Create a new alignment based on a model and the word alignment with HVite
"""
output_dir = '%s/Align' % root_dir
util.create_new_dir(output_dir)
utts_per_split = max(100, (1 + (model.setup_length / 200)))
## Copy old mfc list
os.system('cp %s %s/mfc_old.list' % (mfc_list, output_dir))
## HVite parameters
prune_thresh = 250
def hvite(input, output):
#-o SWT
cmd = 'HVite -D -A -T 1 -b silence -a -m -y lab '
cmd += '-t %d' % prune_thresh
cmd += ' -C %s' % align_config
cmd += ' -H %s/MMF' % model_dir
cmd += ' -i %s' % output
cmd += ' -I %s' % word_mlf
cmd += ' -S %s' % input
cmd += ' %s %s' % (dict, model_list)
cmd += ' >> %s.hvite.log' % output
return cmd
## Split up MFC list with unix split
cmd = 'split -a 4 -d -l %d %s %s/%s' % (utts_per_split, mfc_list,
output_dir, 'mfc.list.')
os.system(cmd)
## Create the HVite commands
cmds = []
outputs = []
inputs = os.popen('ls %s/mfc.list.*' % output_dir).read().splitlines()
for input in inputs:
output = input.replace('mfc.list', 'align.output')
outputs.append(output)
cmds.append(hvite(input, output))
if model.local == 1:
for cmd in cmds:
print cmd
print os.popen(cmd).read()
else:
cmds_file = '%s/hvite.commands' % output_dir
fh = open(cmds_file, 'w')
for cmd in cmds:
fh.write('%s\n' % cmd)
fh.close()
util.run_parallel(cmds_file, model.jobs, output_dir)
## Merge and fix silences
## TODO: -s file_list
merge_sil = '%s/merge_sp_sil.led' % output_dir
fh = open(merge_sil, 'w')
fh.write('ME sil sp sil\n')
fh.write('ME sil sil sil\n')
fh.write('ME sp sil sil\n')
fh.close()
cmd = 'HLEd -D -A -T 1 -i %s %s %s >> %s/hled.log' % (
new_mlf, merge_sil, ' '.join(outputs), output_dir)
if model.local == 1: os.system(cmd)
else: util.run(cmd, output_dir)
## Prune failed alignments from the mfc list
bad_count = 0
mlf_labels = os.popen('grep "\.lab" %s' % new_mlf).read().splitlines()
mlf_labels = set([os.path.basename(s).split('.')[0] for s in mlf_labels])
mfc_labels = open(mfc_list).read().splitlines()
fh = open(mfc_list, 'w')
for mfc in mfc_labels:
id = os.path.basename(mfc).split('.')[0]
## Check for missing transcriptions
if id not in mlf_labels:
if model.verbose > 0:
util.log_write(model.logfh, 'removed bad alignment [%s]' % id)
bad_count += 1
else:
fh.write(mfc + '\n')
fh.close()
util.log_write(model.logfh, 'removed alignments [%d]' % bad_count)
## Clean up
os.system('rm -f %s/mfc.list.* %s/align.output.*' %
(output_dir, output_dir))
return output_dir
def align(model, root_dir, mfc_list, model_dir, word_mlf, new_mlf, model_list, dict, align_config):
"""
Create a new alignment based on a model and the word alignment with HVite
"""
output_dir = '%s/Align' %root_dir
util.create_new_dir(output_dir)
utts_per_split = max(100, (1 + (model.setup_length / 200)))
## Copy old mfc list
os.system('cp %s %s/mfc_old.list' %(mfc_list, output_dir))
## HVite parameters
prune_thresh = 250
def hvite(input, output):
#-o SWT
cmd = 'HVite -D -A -T 1 -b silence -a -m -y lab '
cmd += '-t %d' %prune_thresh
cmd += ' -C %s' %align_config
cmd += ' -H %s/MMF' %model_dir
cmd += ' -i %s' %output
cmd += ' -I %s' %word_mlf
cmd += ' -S %s' %input
cmd += ' %s %s' %(dict, model_list)
cmd += ' >> %s.hvite.log' %output
return cmd
## Split up MFC list with unix split
cmd = 'split -a 4 -d -l %d %s %s/%s' %(utts_per_split, mfc_list, output_dir, 'mfc.list.')
os.system(cmd)
## Create the HVite commands
cmds = []
outputs = []
inputs = os.popen('ls %s/mfc.list.*' %output_dir).read().splitlines()
for input in inputs:
output = input.replace('mfc.list', 'align.output')
outputs.append(output)
cmds.append(hvite(input, output))
if model.local == 1:
for cmd in cmds:
print cmd
print os.popen(cmd).read()
else:
cmds_file = '%s/hvite.commands' %output_dir
fh = open(cmds_file, 'w')
for cmd in cmds: fh.write('%s\n' %cmd)
fh.close()
util.run_parallel(cmds_file, model.jobs, output_dir)
## Merge and fix silences
## TODO: -s file_list
merge_sil = '%s/merge_sp_sil.led' %output_dir
fh = open(merge_sil, 'w')
fh.write('ME sil sp sil\n')
fh.write('ME sil sil sil\n')
fh.write('ME sp sil sil\n')
fh.close()
cmd = 'HLEd -D -A -T 1 -i %s %s %s >> %s/hled.log' %(new_mlf, merge_sil, ' '.join(outputs), output_dir)
if model.local == 1: os.system(cmd)
else: util.run(cmd, output_dir)
## Prune failed alignments from the mfc list
bad_count = 0
mlf_labels = os.popen('grep "\.lab" %s' %new_mlf).read().splitlines()
mlf_labels = set([os.path.basename(s).split('.')[0] for s in mlf_labels])
mfc_labels = open(mfc_list).read().splitlines()
fh = open(mfc_list, 'w')
for mfc in mfc_labels:
id = os.path.basename(mfc).split('.')[0]
## Check for missing transcriptions
if id not in mlf_labels:
if model.verbose > 0: util.log_write(model.logfh, 'removed bad alignment [%s]' %id)
bad_count += 1
else: fh.write(mfc + '\n')
fh.close()
util.log_write(model.logfh, 'removed alignments [%d]' %bad_count)
## Clean up
os.system('rm -f %s/mfc.list.* %s/align.output.*' %(output_dir, output_dir))
return output_dir
def tie_states_search(model, output_dir, model_dir, mono_list, tri_list,
tied_list):
"""
Tie HMM states using decision tree clustering
"""
util.create_new_dir(output_dir)
tree_hed = '%s/tree.hed' % output_dir
tree_output = '%s/trees' % output_dir
hhed_log = '%s/hhed_cluster.log' % output_dir
all_tri_list = '%s/all_tri.list' % model.exp
## Decision tree parameters
ro = model.dt_ro
tb = model.dt_tb
tb_min = 100.0
tb_max = 10000.0
## Create the full list of possible triphones
phones = open(mono_list).read().splitlines()
non_sp_phones = [p for p in phones if p not in ['sp', 'sil']]
fh = open(all_tri_list, 'w')
fh.write('sp\n')
fh.write('sil\n')
for p1 in non_sp_phones:
fh.write('sil-%s+sil\n' % p1)
for p2 in non_sp_phones:
fh.write('sil-%s+%s\n' % (p1, p2))
fh.write('%s-%s+sil\n' % (p2, p1))
for p3 in non_sp_phones:
fh.write('%s-%s+%s\n' % (p2, p1, p3))
fh.close()
## Search over tb arguments to get the right number states
num_states = 0
attempts = 0
prev_tb = 0
while True:
os.system('rm -f %s %s %s' % (tree_hed, tree_output, hhed_log))
## Set up decision tree clustering
fh = open(tree_hed, 'w')
fh.write('RO %d %s/stats\n' % (ro, model_dir))
fh.write('TR 0\n')
fh.write('%s\n' % open(model.tree_questions).read())
fh.write('TR 12\n')
for p in non_sp_phones:
for s in range(1, model.states + 1)[1:-1]:
fh.write(
'TB %d "ST_%s_%d_" {(%s,*-%s+*,%s+*,*-%s).state[%d]}\n' %
(tb, p, s, p, p, p, p, s))
fh.write('TR 1\n')
fh.write('AU "%s"\n' % all_tri_list)
fh.write('CO "%s"\n' % tied_list)
fh.write('ST "%s"\n' % tree_output)
fh.close()
## Use HHEd to cluster
cmd = 'HHEd -A -T 1 -H %s/MMF' % model_dir
cmd += ' -M %s' % output_dir
cmd += ' %s %s > %s' % (tree_hed, tri_list, hhed_log)
if model.local == 1: os.system(cmd)
else: util.run(cmd, output_dir)
num_states = int(
os.popen('grep -c "<MEAN>" %s/MMF' % output_dir).read().strip())
if abs(
float(num_states - model.triphone_states) /
model.triphone_states) <= 0.01:
util.log_write(
model.logfh,
' current states [%d] tb [%1.2f]' % (num_states, tb))
break
if abs(prev_tb - tb) <= 0.01:
util.log_write(
model.logfh,
' Could not converge. Stopping. Current states [%d] tb [%1.2f]'
% (num_states, tb))
break
attempts += 1
prev_tb = tb
if num_states < model.triphone_states:
tb = (tb_min + tb) / 2
tb_max = prev_tb
else:
tb = (tb_max + tb) / 2
tb_min = prev_tb
util.log_write(
model.logfh,
' [%d] goal [%d] current states [%d] tb [%1.2f] -> [%1.2f] [%1.1f %1.1f]'
% (attempts, model.triphone_states, num_states, prev_tb, tb,
tb_min, tb_max))
if attempts > 50:
util.log_write(model.logfh,
' Goal not reached after 50 tries. Exiting.')
sys.exit()
return output_dir
def run_iter(model, root_dir, prev_dir, mlf_file, model_list, mix_size, iter, extra):
"""
Run an iteration of Baum-Welch training using HERest
"""
output_dir = '%s/HMM-%d-%d' %(root_dir, mix_size, iter)
util.create_new_dir(output_dir)
mfc_list = '%s/mfc.list' %model.exp
utts_per_split = max(250, (1 + (model.setup_length / 200)))
## HERest parameters
min_train_examples = 0
prune_thresh = 250
prune_inc = 150
prune_limit = 2000
def herest(input, split_num, extra):
try: log_id = os.path.basename(input).split('.')[2]
except: log_id = 'acc'
cmd = '%s -D -A -T 1 -m %d' %(HEREST_CMD, min_train_examples)
cmd += ' -t %d %d %d' %(prune_thresh, prune_inc, prune_limit)
cmd += ' -s %s/stats' %output_dir
cmd += ' -C %s%s' %(model.mfc_config, extra)
cmd += ' -I %s' %mlf_file
cmd += ' -H %s/MMF' %prev_dir
cmd += ' -p %d' %split_num
cmd += ' -S %s' %input
#cmd += ' -M %s %s' %(output_dir, model_list)
cmd += ' -M %s %s >> %s/herest.%s.log' %(output_dir, model_list, output_dir, log_id)
return cmd
## Split up MFC list with unix split
cmd = 'split -a 4 -d -l %d %s %s/%s' %(utts_per_split, mfc_list, output_dir, 'mfc.list.')
os.system(cmd)
## Create the HERest commands
cmds = []
inputs = os.popen('ls %s/mfc.list.*' %output_dir).read().splitlines()
split_num = 0
for input in inputs:
split_num += 1
cmds.append(herest(input, split_num, extra))
## Non-parallel case
if model.local == 1:
for cmd in cmds:
print cmd
print os.popen(cmd)
## Parallel case: one command per line in cmds_file
else:
cmds_file = '%s/herest.commands' %output_dir
fh = open(cmds_file, 'w')
for cmd in cmds: fh.write('%s\n' %cmd)
fh.close()
util.run_parallel(cmds_file, model.jobs, output_dir)
## Gather the created .acc files
acc_file = '%s/herest.list' %output_dir
os.system('ls %s/HER*.acc > %s' %(output_dir, acc_file))
## Combine acc files into a new HMM
cmd = herest(acc_file, 0, extra)
cmd = cmd.split('>>')[0]
cmd += ' >> %s/herest.log' %output_dir
if model.local == 1: os.system(cmd)
else: util.run(cmd, output_dir)
## Clean up
os.system('rm -f %s/mfc.list.* %s/HER*.acc' %(output_dir, output_dir))
os.system('bzip2 %s/herest.*.log %s/run-command*.log' %(output_dir, output_dir))
## Get a few stats
num_models = int(os.popen('grep "<MEAN>" %s/MMF -c' %output_dir).read().strip())
likelihood = float(os.popen('cat %s/herest.log | grep aver' %output_dir).read().strip().split()[-1])
return output_dir, num_models, likelihood
def initialize_hmms(model, root_dir, mfc_list, mono_list, proto_hmm):
"""
Compute mean and variance of each feature across all utterances and
set all Gaussians in the prototype HMM to have the same mean and variance
"""
output_dir = '%s/HMM-0-0' %root_dir
util.create_new_dir(root_dir)
util.create_new_dir(output_dir)
cmd_log = '%s/hcompv.log' %output_dir
## Sample from the full mfc list to reduce computation
sampled_mfc_list = '%s/mfc_sample.list' %output_dir
fh = open(sampled_mfc_list, 'w')
mfcs = open(mfc_list).read().splitlines()
random.shuffle(mfcs)
mfc_frac = model.var_floor_fraction
num_mfcs_for_hcompv = int(mfc_frac * len(mfcs))
for mfc in mfcs[:num_mfcs_for_hcompv]:
fh.write('%s\n' %mfc)
fh.close()
cmd = 'HCompV -A -T 1 -m'
cmd += ' -C %s' %model.mfc_config
cmd += ' -f 0.01'
cmd += ' -S %s' %sampled_mfc_list
cmd += ' -M %s' %output_dir
cmd += ' %s > %s' %(proto_hmm, cmd_log)
if model.local == 1: os.system(cmd)
else: util.run(cmd, output_dir)
## Copy the initial HMM for each monophone
proto_hmm = '%s/proto_hmm' %output_dir
hmm_defs_init = '%s/init.mmf' %output_dir
hmm = re.search(re.compile('<BEGINHMM>.*<ENDHMM>', re.DOTALL), open(proto_hmm).read()).group()
fh = open(hmm_defs_init, 'w')
for line in open(mono_list):
phone = line.strip()
fh.write('~h "%s"\n' %phone)
fh.write('%s\n' %hmm)
fh.close()
## Create mmf header file
## TODO: get rid of this?
mmf_header = '%s/header.mmf' %output_dir
cmd = 'head -3 %s | cat - %s/vFloors > %s' %(proto_hmm, output_dir, mmf_header)
os.system(cmd)
## Fix sp and silence models
cleanup_config = '%s/cleanup_init.hed' %output_dir
fh = open(cleanup_config, 'w')
fh.write('AT 4 2 0.2 {sil.transP}\n')
fh.write('AT 2 4 0.2 {sil.transP}\n')
fh.write('AT 1 5 0.3 {sp.transP}\n')
fh.write('TI silsp_2 {sil.state[2],sp.state[2]}\n')
fh.write('TI silsp_3 {sil.state[3],sp.state[3]}\n')
fh.write('TI silsp_4 {sil.state[4],sp.state[4]}\n')
fh.close()
hmm_defs_final = '%s/MMF' %output_dir
cmd_log = '%s/hhed_sil.log' %output_dir
cmd = 'HHEd -A -D -T 1 -d %s' %output_dir
cmd += ' -H %s -H %s' %(mmf_header, hmm_defs_init)
cmd += ' -M %s' %output_dir
cmd += ' -w %s' %hmm_defs_final
cmd += ' %s %s > %s' %(cleanup_config, mono_list, cmd_log)
os.system(cmd)
return output_dir, num_mfcs_for_hcompv
def run_iter(model, model_dir, num_lattice_dir, den_lattice_dir, root_dir, model_list, mfc_list, mix_size, iter):
"""
Run an iteration of modified Baum-Welch training using HMMIRest
"""
output_dir = '%s/HMMI-%d-%d' %(root_dir, mix_size, iter)
util.create_new_dir(output_dir)
utts_per_split = max(250, (1 + (model.setup_length / 200)))
## Create a config file to use with HLRescore
hmmirest_config = '%s/hmmirest.config' %output_dir
fh = open(hmmirest_config, 'w')
#fh.write('HLANGMODFILTER = "gunzip -c $.gz"\n')
fh.write('HNETFILTER = "gunzip -c < $.gz"\n')
fh.write('HNETOFILTER = "gzip -c > $.gz"\n')
fh.write('RAWMITFORMAT = T\n')
fh.write('HPARM: TARGETKIND = MFCC_0_D_A_Z\n')
#fh.write('HMMDEFOFILTER = "gzip -c > $.gz"\n')
#fh.write('HMMDEFFILTER = "gunzip -c < $.gz"\n')
fh.write('HMMIREST: LATMASKNUM = */%%%?????.???\n')
fh.write('HMMIREST: LATMASKDEN = */%%%?????.???\n')
#fh.write('HMMIREST: LATMASKNUM = */%%%%%%%%/???????????????????.???\n')
#fh.write('HMMIREST: LATMASKDEN = */%%%%%%%%/???????????????????.???\n')
fh.write('HFBLAT: LATPROBSCALE = 0.06667\n')
fh.write('HMMIREST: E = 2.0\n')
fh.write('ISMOOTHTAU = 50\n')
fh.write('MPE = TRUE\n')
#fh.write('MWE = TRUE\n')
fh.close()
def hmmirest(input, split_num):
cmd = 'HMMIRest -A -D -T 1 -C %s' %hmmirest_config
cmd += ' -H %s/MMF' %model_dir
cmd += ' -q %s' %num_lattice_dir
cmd += ' -r %s' %den_lattice_dir
if split_num == 0:
cmd += ' -u mv'
cmd += ' -p %d' %split_num
cmd += ' -S %s' %input
cmd += ' -M %s %s' %(output_dir, model_list)
return cmd
## Split up MFC list with unix split
cmd = 'split -a 4 -d -l %d %s %s/%s' %(utts_per_split, mfc_list, output_dir, 'mfc.list.')
os.system(cmd)
## Create the HERest commands
cmds = []
inputs = os.popen('ls %s/mfc.list.*' %output_dir).read().splitlines()
split_num = 0
for input in inputs:
split_num += 1
cmds.append(hmmirest(input, split_num))
## Non-parallel case
if model.local == 1:
for cmd in cmds:
print cmd
print os.popen(cmd)
## Parallel case: one command per line in cmds_file
else:
cmds_file = '%s/hmmirest.commands' %output_dir
fh = open(cmds_file, 'w')
for cmd in cmds: fh.write('%s\n' %cmd)
fh.close()
util.run_parallel(cmds_file, model.jobs, output_dir)
## Gather the created .acc files
acc_file = '%s/hmmirest.list' %output_dir
os.system('ls %s/HDR*.acc* > %s' %(output_dir, acc_file))
## Combine acc files into a new HMM
cmd = hmmirest(acc_file, 0)
cmd += ' >> %s/hmmirest.log' %output_dir
if model.local == 1: os.system(cmd)
else: util.run(cmd, output_dir)
## Clean up
#os.system('rm -f %s/mfc.list.* %s/HER*.acc' %(output_dir, output_dir))
return output_dir
def run_iter(model, model_dir, num_lattice_dir, den_lattice_dir, root_dir,
model_list, mfc_list, mix_size, iter):
"""
Run an iteration of modified Baum-Welch training using HMMIRest
"""
output_dir = '%s/HMMI-%d-%d' % (root_dir, mix_size, iter)
util.create_new_dir(output_dir)
utts_per_split = max(250, (1 + (model.setup_length / 200)))
## Create a config file to use with HLRescore
hmmirest_config = '%s/hmmirest.config' % output_dir
fh = open(hmmirest_config, 'w')
#fh.write('HLANGMODFILTER = "gunzip -c $.gz"\n')
fh.write('HNETFILTER = "gunzip -c < $.gz"\n')
fh.write('HNETOFILTER = "gzip -c > $.gz"\n')
fh.write('RAWMITFORMAT = T\n')
fh.write('HPARM: TARGETKIND = MFCC_0_D_A_Z\n')
#fh.write('HMMDEFOFILTER = "gzip -c > $.gz"\n')
#fh.write('HMMDEFFILTER = "gunzip -c < $.gz"\n')
fh.write('HMMIREST: LATMASKNUM = */%%%?????.???\n')
fh.write('HMMIREST: LATMASKDEN = */%%%?????.???\n')
#fh.write('HMMIREST: LATMASKNUM = */%%%%%%%%/???????????????????.???\n')
#fh.write('HMMIREST: LATMASKDEN = */%%%%%%%%/???????????????????.???\n')
fh.write('HFBLAT: LATPROBSCALE = 0.06667\n')
fh.write('HMMIREST: E = 2.0\n')
fh.write('ISMOOTHTAU = 50\n')
fh.write('MPE = TRUE\n')
#fh.write('MWE = TRUE\n')
fh.close()
def hmmirest(input, split_num):
cmd = 'HMMIRest -A -D -T 1 -C %s' % hmmirest_config
cmd += ' -H %s/MMF' % model_dir
cmd += ' -q %s' % num_lattice_dir
cmd += ' -r %s' % den_lattice_dir
if split_num == 0:
cmd += ' -u mv'
cmd += ' -p %d' % split_num
cmd += ' -S %s' % input
cmd += ' -M %s %s' % (output_dir, model_list)
return cmd
## Split up MFC list with unix split
cmd = 'split -a 4 -d -l %d %s %s/%s' % (utts_per_split, mfc_list,
output_dir, 'mfc.list.')
os.system(cmd)
## Create the HERest commands
cmds = []
inputs = os.popen('ls %s/mfc.list.*' % output_dir).read().splitlines()
split_num = 0
for input in inputs:
split_num += 1
cmds.append(hmmirest(input, split_num))
## Non-parallel case
if model.local == 1:
for cmd in cmds:
print cmd
print os.popen(cmd)
## Parallel case: one command per line in cmds_file
else:
cmds_file = '%s/hmmirest.commands' % output_dir
fh = open(cmds_file, 'w')
for cmd in cmds:
fh.write('%s\n' % cmd)
fh.close()
util.run_parallel(cmds_file, model.jobs, output_dir)
## Gather the created .acc files
acc_file = '%s/hmmirest.list' % output_dir
os.system('ls %s/HDR*.acc* > %s' % (output_dir, acc_file))
## Combine acc files into a new HMM
cmd = hmmirest(acc_file, 0)
cmd += ' >> %s/hmmirest.log' % output_dir
if model.local == 1: os.system(cmd)
else: util.run(cmd, output_dir)
## Clean up
#os.system('rm -f %s/mfc.list.* %s/HER*.acc' %(output_dir, output_dir))
return output_dir
def decode(self,
model,
mfc_list,
gold_mlf,
lm_file,
gaussians,
iter,
mmi=False,
diag=False,
xword_id='',
output_dir=None):
if mmi:
model_file = '%s/MMI/Models/HMMI-%d-%d/MMF' % (model.exp,
gaussians, iter)
elif diag:
model_file = '%s/Diag/HMM-%d-%d/MMF' % (model.exp, gaussians, iter)
else:
model_file = '%s/Xword%s/HMM-%d-%d/MMF' % (model.exp, xword_id,
gaussians, iter)
model_list = '%s/tied.list' % model.exp
if not output_dir: output_dir = '%s/decode' % self.exp
output_dir = '%s/decode' % output_dir
util.create_new_dir(output_dir)
results_log = '%s/hresults.log' % output_dir
output_mlf = '%s/decoded.mlf' % output_dir
def hvite(input, output):
cmd = 'HVite -D -A -T 1 -l "*" '
cmd += '-t %f ' % self.beam
cmd += '-C %s ' % self.decode_config
cmd += '-H %s ' % model_file
cmd += '-S %s ' % input
cmd += '-i %s ' % output
cmd += '-w %s ' % lm_file
cmd += '-p %f ' % self.insertion_penalty
cmd += '-s %f ' % self.lm_scale
cmd += '%s %s' % (self.dict, model_list)
return cmd
## HDecode parameters
utts_per_split = 5
block_size = 1
word_end_beam = 150.0
max_model = 0
def hdecode(input, output):
cmd = 'HDecode -D -A -V -T 9 -o M -C %s' % self.decode_config
cmd += ' -H %s' % model_file
cmd += ' -k %d' % block_size
cmd += ' -t %f 100.0' % self.beam
cmd += ' -v %f 115.0' % word_end_beam
cmd += ' -u %d' % max_model
cmd += ' -s %f' % self.lm_scale
cmd += ' -p %f' % self.insertion_penalty
cmd += ' -w %s' % lm_file
cmd += ' -S %s' % input
cmd += ' -i %s' % output
cmd += ' %s %s' % (self.dict, model_list)
if model.verbose > 0:
cmd += ' >%s/%s.log' % (output_dir, os.path.basename(input))
return cmd
## Split up MFC list with unix split
cmd = 'split -a 4 -d -l %d %s %s/%s' % (utts_per_split, mfc_list,
output_dir, 'mfc.list.')
os.system(cmd)
## Create appropriate config file
self.decode_config = '%s/%s.config' % (output_dir, self.decode_func)
fh = open(self.decode_config, 'w')
if self.decode_func == 'hvite':
fh.write('FORCECXTEXP = T\n')
fh.write('ALLOWXWRDEXP = T\n')
elif self.decode_func == 'hdecode':
#fh.write('HLANGMODFILTER = "gunzip -c $.gz"\n')
fh.write('HNETFILTER = "gunzip -c < $.gz"\n')
fh.write('HNETOFILTER = "gzip -c > $.gz"\n')
fh.write('RAWMITFORMAT = T\n')
fh.write('HPARM: TARGETKIND = MFCC_0_D_A_Z\n')
fh.write('STARTWORD = <s>\n')
fh.write('ENDWORD = </s>\n')
fh.close()
## Create the HVite/HDecode commands
cmds = []
outputs = []
inputs = os.popen('ls %s/mfc.list.*' % output_dir).read().splitlines()
for input in inputs:
output = input.replace('mfc.list', 'align.output')
outputs.append(output)
if self.decode_func == 'hvite':
cmds.append(hvite(input, output))
else:
cmds.append(hdecode(input, output))
if self.local == 1:
for cmd in cmds:
print cmd
print os.popen(cmd).read()
else:
cmds_file = '%s/hvite.commands' % output_dir
fh = open(cmds_file, 'w')
for cmd in cmds:
fh.write('%s\n' % cmd)
fh.close()
util.run_parallel(cmds_file, self.jobs, output_dir)
#os.system('rm -f %s' %cmds_file)
## Merge outputs
os.popen('rm -f %s' % output_mlf)
os.popen('cat %s | grep -v "<" - > %s' %
(' '.join(outputs), output_mlf))
## Evaluate
cmd = 'HResults -h -n -A -T 1 -c'
cmd += ' -I %s' % gold_mlf
cmd += ' %s %s > %s' % (model_list, output_mlf, results_log)
os.system(cmd)
print os.popen('cat ' + results_log).read()
cmd = open(results_log).read().splitlines()[0]
raw_wer = 100 - float(
re.findall(r'Acc=([0-9.]*)',
os.popen(cmd.replace('-h ',
'')).read())[0].split('=')[-1])
return raw_wer
os.system('rm -f %s/mfc.list.* %s/align.output.*' %
(output_dir, output_dir))
def decode(self, model, mfc_list, gold_mlf, lm_file, gaussians, iter, mmi=False, diag=False, xword_id='', output_dir=None):
if mmi:
model_file = '%s/MMI/Models/HMMI-%d-%d/MMF' %(model.exp, gaussians, iter)
elif diag:
model_file = '%s/Diag/HMM-%d-%d/MMF' %(model.exp, gaussians, iter)
else:
model_file = '%s/Xword%s/HMM-%d-%d/MMF' %(model.exp, xword_id, gaussians, iter)
model_list = '%s/tied.list' %model.exp
if not output_dir: output_dir = '%s/decode' %self.exp
output_dir = '%s/decode' %output_dir
util.create_new_dir(output_dir)
results_log = '%s/hresults.log' %output_dir
output_mlf = '%s/decoded.mlf' %output_dir
def hvite(input, output):
cmd = 'HVite -D -A -T 1 -l "*" '
cmd += '-t %f ' %self.beam
cmd += '-C %s ' %self.decode_config
cmd += '-H %s ' %model_file
cmd += '-S %s ' %input
cmd += '-i %s ' %output
cmd += '-w %s ' %lm_file
cmd += '-p %f ' %self.insertion_penalty
cmd += '-s %f ' %self.lm_scale
cmd += '%s %s' %(self.dict, model_list)
return cmd
## HDecode parameters
utts_per_split = 5
block_size = 1
word_end_beam = 150.0
max_model = 0
def hdecode(input, output):
cmd = 'HDecode -D -A -V -T 9 -o M -C %s' %self.decode_config
cmd += ' -H %s' %model_file
cmd += ' -k %d' %block_size
cmd += ' -t %f 100.0' %self.beam
cmd += ' -v %f 115.0' %word_end_beam
cmd += ' -u %d' %max_model
cmd += ' -s %f' %self.lm_scale
cmd += ' -p %f' %self.insertion_penalty
cmd += ' -w %s' %lm_file
cmd += ' -S %s' %input
cmd += ' -i %s' %output
cmd += ' %s %s' %(self.dict, model_list)
if model.verbose > 0: cmd += ' >%s/%s.log' %(output_dir, os.path.basename(input))
return cmd
## Split up MFC list with unix split
cmd = 'split -a 4 -d -l %d %s %s/%s' %(utts_per_split, mfc_list, output_dir, 'mfc.list.')
os.system(cmd)
## Create appropriate config file
self.decode_config = '%s/%s.config' %(output_dir, self.decode_func)
fh = open(self.decode_config, 'w')
if self.decode_func == 'hvite':
fh.write('FORCECXTEXP = T\n')
fh.write('ALLOWXWRDEXP = T\n')
elif self.decode_func == 'hdecode':
#fh.write('HLANGMODFILTER = "gunzip -c $.gz"\n')
fh.write('HNETFILTER = "gunzip -c < $.gz"\n')
fh.write('HNETOFILTER = "gzip -c > $.gz"\n')
fh.write('RAWMITFORMAT = T\n')
fh.write('HPARM: TARGETKIND = MFCC_0_D_A_Z\n')
fh.write('STARTWORD = <s>\n')
fh.write('ENDWORD = </s>\n')
fh.close()
## Create the HVite/HDecode commands
cmds = []
outputs = []
inputs = os.popen('ls %s/mfc.list.*' %output_dir).read().splitlines()
for input in inputs:
output = input.replace('mfc.list', 'align.output')
outputs.append(output)
if self.decode_func == 'hvite':
cmds.append(hvite(input, output))
else:
cmds.append(hdecode(input, output))
if self.local == 1:
for cmd in cmds:
print cmd
print os.popen(cmd).read()
else:
cmds_file = '%s/hvite.commands' %output_dir
fh = open(cmds_file, 'w')
for cmd in cmds: fh.write('%s\n' %cmd)
fh.close()
util.run_parallel(cmds_file, self.jobs, output_dir)
#os.system('rm -f %s' %cmds_file)
## Merge outputs
os.popen('rm -f %s' %output_mlf)
os.popen('cat %s | grep -v "<" - > %s' %(' '.join(outputs), output_mlf))
## Evaluate
cmd = 'HResults -h -n -A -T 1 -c'
cmd += ' -I %s' %gold_mlf
cmd += ' %s %s > %s' %(model_list, output_mlf, results_log)
os.system(cmd)
print os.popen('cat ' + results_log).read()
cmd = open(results_log).read().splitlines()[0]
raw_wer = 100 - float(re.findall(r'Acc=([0-9.]*)', os.popen(cmd.replace('-h ', '')).read())[0].split('=')[-1])
return raw_wer
os.system('rm -f %s/mfc.list.* %s/align.output.*' %(output_dir, output_dir))
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')
