def test_sick_lda_mllt(sick_dict, sick_corpus, generated_dir):
a = TrainableAligner(sick_corpus,
sick_dict,
os.path.join(generated_dir, 'sick_output'),
temp_directory=os.path.join(generated_dir,
'sickcorpus'))
a.train_lda_mllt()
a.export_textgrids()
def align_corpus(args):
if not args.temp_directory:
temp_dir = TEMP_DIR
else:
temp_dir = os.path.expanduser(args.temp_directory)
corpus_name = os.path.basename(args.corpus_directory)
if corpus_name == '':
args.corpus_directory = os.path.dirname(args.corpus_directory)
corpus_name = os.path.basename(args.corpus_directory)
data_directory = os.path.join(temp_dir, corpus_name)
conf_path = os.path.join(data_directory, 'config.yml')
if os.path.exists(conf_path):
with open(conf_path, 'r') as f:
conf = yaml.load(f)
else:
conf = {
'dirty': False,
'begin': time.time(),
'version': __version__,
'type': 'train_and_align',
'corpus_directory': args.corpus_directory,
'dictionary_path': args.dictionary_path
}
if getattr(args, 'clean', False) \
or conf['dirty'] or conf['type'] != 'train_and_align' \
or conf['corpus_directory'] != args.corpus_directory \
or conf['version'] != __version__ \
or conf['dictionary_path'] != args.dictionary_path:
shutil.rmtree(data_directory, ignore_errors=True)
shutil.rmtree(args.output_directory, ignore_errors=True)
os.makedirs(data_directory, exist_ok=True)
os.makedirs(args.output_directory, exist_ok=True)
try:
corpus = Corpus(args.corpus_directory,
data_directory,
speaker_characters=args.speaker_characters,
num_jobs=getattr(args, 'num_jobs', 3),
debug=getattr(args, 'debug', False),
ignore_exceptions=getattr(args, 'ignore_exceptions',
False))
dictionary = Dictionary(args.dictionary_path,
data_directory,
word_set=corpus.word_set)
utt_oov_path = os.path.join(corpus.split_directory,
'utterance_oovs.txt')
if os.path.exists(utt_oov_path):
shutil.copy(utt_oov_path, args.output_directory)
oov_path = os.path.join(corpus.split_directory, 'oovs_found.txt')
if os.path.exists(oov_path):
shutil.copy(oov_path, args.output_directory)
mono_params = {'align_often': not args.fast}
tri_params = {'align_often': not args.fast}
tri_fmllr_params = {'align_often': not args.fast}
a = TrainableAligner(corpus,
dictionary,
args.output_directory,
temp_directory=data_directory,
mono_params=mono_params,
tri_params=tri_params,
tri_fmllr_params=tri_fmllr_params,
num_jobs=args.num_jobs,
skip_input=getattr(args, 'quiet', False),
nnet=getattr(args, 'artificial_neural_net',
False))
a.verbose = args.verbose
# GMM training (looks like it needs to be done either way, as a starter for nnet)
a.train_mono()
a.export_textgrids()
a.train_tri()
a.export_textgrids()
a.train_tri_fmllr()
a.export_textgrids()
# nnet training
if args.artificial_neural_net:
# Do nnet training
a.train_lda_mllt()
#a.train_diag_ubm() # Uncomment to train i-vector extractor
#a.ivector_extractor() # Uncomment to train i-vector extractor (integrate with argument eventually)
a.train_nnet_basic()
a.export_textgrids()
if args.output_model_path is not None:
a.save(args.output_model_path)
except:
conf['dirty'] = True
raise
finally:
with open(conf_path, 'w') as f:
yaml.dump(conf, f)
