def initialize_new(self, scp_list, word_mlf, dict, remove_previous=False):
System.set_log_dir(self.name)
if remove_previous:
for f in glob.iglob(System.get_log_dir() + '/*'):
os.remove(f)
if not remove_previous and (
os.path.exists(self.train_files_dir) or
len(glob.glob(self.model_dir + '/' + self.name + '.*')) > 0):
raise ExistingFilesException
if os.path.exists(self.train_files_dir):
shutil.rmtree(self.train_files_dir)
for f in glob.iglob(self.model_dir + '/' + self.name + '.*'):
os.remove(f)
os.mkdir(self.train_files_dir)
# handle dictionary
dic = HTK_dictionary()
if isinstance(dict, basestring):
dic.read_dict(dict)
elif all(isinstance(d, basestring) for d in dict):
for d in dict:
dic.read_dict(d)
else:
raise TypeError
dic.write_dict(self.training_dict)
self.phones = dic.get_phones()
# handle transcription
trans = HTK_transcription()
# if isinstance(word_mlf,basestring):
# trans.read_mlf(word_mlf, HTK_transcription.WORD)
# elif all(isinstance(w,basestring) for w in word_mlf):
# for w in word_mlf:
# trans.read_mlf(w, HTK_transcription.WORD)
# else:
# raise TypeError
word_mlf = word_mlf.strip().split(',')
for w in word_mlf:
trans.read_mlf(w, HTK_transcription.WORD)
self.id = 1
phones_list = self._get_model_name_id() + '.hmmlist'
with open(phones_list, 'w') as phones_desc:
for p in self.phones:
print(p, file=phones_desc)
# handle scp files
scp_list = scp_list.strip().split(',')
# if isinstance(scp_list,basestring):
# scp_list = [scp_list]
real_trans = HTK_transcription()
real_trans.transcriptions[real_trans.WORD] = {}
with open(self.training_scp, 'w') as scp_desc:
for scp in scp_list:
for file in open(scp):
id = os.path.splitext(os.path.basename(file.strip()))[0]
if not file.startswith('/'):
file = os.path.join(os.path.dirname(scp), file.strip())
ok = True
for word in trans.transcriptions[
HTK_transcription.WORD][id]:
if not dic.word_in_dict(word):
print("%s skipped, because has missing word %s" %
(file.strip(), word))
ok = False
break
if ok:
print(file.strip(), file=scp_desc)
real_trans.transcriptions[real_trans.WORD][
id] = trans.transcriptions[real_trans.WORD][id]
real_trans.write_mlf(self.training_word_mlf,
target=HTK_transcription.WORD)
self.expand_word_transcription()
shuffle(t_files)
# if options.num_adaptation_files > 0:
# t_files = t_files[:options.num_adaptation_files]
for t in t_files:
f = splitext(basename(t))[0]
mlf.transcriptions[HTK_transcription.WORD][
"%s_%s" %
(sp, f)] = mlf.transcriptions[HTK_transcription.WORD][f]
new_f = join(file_dir, "%s_%s" % (sp, basename(t)))
symlink(t, new_f)
print >> transform_desc, new_f
mlf.write_mlf(transform_mlf, target=HTK_transcription.WORD)
recognizer.add_adaptation(transform_scp,
transform_mlf,
num_speaker_chars=options.transform_speaker_chars)
recognizer.add_adaptation(transform_scp,
transform_mlf,
num_speaker_chars=options.transform_speaker_chars,
num_nodes=options.tree_size)
recognizer.recognize(None, 'neighbour_transform')
if options.dostack:
recognizer.add_adaptation(scp,
recognizer.name + '.neighbour_transform.mlf',
num_speaker_chars=options.eval_speaker_chars,
for n in neighbors:
t_files.extend(transform_files[n])
shuffle(t_files)
# if options.num_adaptation_files > 0:
# t_files = t_files[:options.num_adaptation_files]
for t in t_files:
f = splitext(basename(t))[0]
mlf.transcriptions[HTK_transcription.WORD]["%s_%s"%(sp,f)] = mlf.transcriptions[HTK_transcription.WORD][f]
new_f = join(file_dir,"%s_%s"%(sp,basename(t)))
symlink(t,new_f)
print >> transform_desc, new_f
mlf.write_mlf(transform_mlf,target=HTK_transcription.WORD)
recognizer.add_adaptation(transform_scp,transform_mlf,num_speaker_chars=options.transform_speaker_chars)
recognizer.add_adaptation(transform_scp,transform_mlf,num_speaker_chars=options.transform_speaker_chars,num_nodes=options.tree_size)
recognizer.recognize(None,'neighbour_transform')
if options.dostack:
recognizer.add_adaptation(scp,recognizer.name+'.neighbour_transform.mlf',num_speaker_chars=options.eval_speaker_chars,files_per_speaker=options.num_adaptation_files)
recognizer.add_adaptation(scp,recognizer.name+'.neighbour_transform.mlf',num_speaker_chars=options.eval_speaker_chars,num_nodes=64,files_per_speaker=options.num_adaptation_files)
#
recognizer.recognize(None,'neighbour_transform_stack.%d'%options.num_adaptation_files)
