def align_corpus(corpus_dir, dict_path, output_directory, temp_dir,
output_model_path, args):
if temp_dir == '':
temp_dir = TEMP_DIR
else:
temp_dir = os.path.expanduser(temp_dir)
corpus_name = os.path.basename(corpus_dir)
if corpus_name == '':
corpus_dir = os.path.dirname(corpus_dir)
corpus_name = os.path.basename(corpus_dir)
data_directory = os.path.join(temp_dir, corpus_name)
if args.clean:
shutil.rmtree(data_directory, ignore_errors=True)
shutil.rmtree(output_directory, ignore_errors=True)
os.makedirs(data_directory, exist_ok=True)
os.makedirs(output_directory, exist_ok=True)
dictionary = Dictionary(dict_path, data_directory)
dictionary.write()
corpus = Corpus(corpus_dir,
data_directory,
args.speaker_characters,
num_jobs=args.num_jobs)
print(corpus.speaker_utterance_info())
corpus.write()
corpus.create_mfccs()
corpus.setup_splits(dictionary)
utt_oov_path = os.path.join(corpus.split_directory, 'utterance_oovs.txt')
if os.path.exists(utt_oov_path):
shutil.copy(utt_oov_path, output_directory)
oov_path = os.path.join(corpus.split_directory, 'oovs_found.txt')
if os.path.exists(oov_path):
shutil.copy(oov_path, 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,
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)
a.verbose = args.verbose
a.train_mono()
a.export_textgrids()
a.train_tri()
a.export_textgrids()
a.train_tri_fmllr()
a.export_textgrids()
if output_model_path is not None:
a.save(output_model_path)
def align_corpus(model_path, corpus_dir, output_directory, temp_dir, args, debug = False):
all_begin = time.time()
if temp_dir == '':
temp_dir = TEMP_DIR
else:
temp_dir = os.path.expanduser(temp_dir)
corpus_name = os.path.basename(corpus_dir)
if corpus_name == '':
corpus_dir = os.path.dirname(corpus_dir)
corpus_name = os.path.basename(corpus_dir)
data_directory = os.path.join(temp_dir, corpus_name)
if args.clean:
shutil.rmtree(data_directory, ignore_errors = True)
shutil.rmtree(output_directory, ignore_errors = True)
os.makedirs(data_directory, exist_ok = True)
os.makedirs(output_directory, exist_ok = True)
begin = time.time()
corpus = Corpus(corpus_dir, data_directory, args.speaker_characters, num_jobs = args.num_jobs)
print(corpus.speaker_utterance_info())
corpus.write()
if debug:
print('Wrote corpus information in {} seconds'.format(time.time() - begin))
begin = time.time()
corpus.create_mfccs()
if debug:
print('Calculated mfccs in {} seconds'.format(time.time() - begin))
archive = Archive(model_path)
begin = time.time()
a = PretrainedAligner(archive, corpus, output_directory,
temp_directory = data_directory, num_jobs = args.num_jobs, speaker_independent = args.no_speaker_adaptation)
if debug:
print('Setup pretrained aligner in {} seconds'.format(time.time() - begin))
a.verbose = args.verbose
begin = time.time()
corpus.setup_splits(a.dictionary)
if debug:
print('Setup splits in {} seconds'.format(time.time() - begin))
utt_oov_path = os.path.join(corpus.split_directory, 'utterance_oovs.txt')
if os.path.exists(utt_oov_path):
shutil.copy(utt_oov_path, output_directory)
oov_path = os.path.join(corpus.split_directory, 'oovs_found.txt')
if os.path.exists(oov_path):
shutil.copy(oov_path, output_directory)
begin = time.time()
a.do_align()
if debug:
print('Performed alignment in {} seconds'.format(time.time() - begin))
begin = time.time()
a.export_textgrids()
if debug:
print('Exported textgrids in {} seconds'.format(time.time() - begin))
print('Done! Everything took {} seconds'.format(time.time() - all_begin))
def align_corpus(corpus_dir, dict_path, output_directory, temp_dir,
output_model_path, args):
if temp_dir == '':
temp_dir = TEMP_DIR
else:
temp_dir = os.path.expanduser(temp_dir)
corpus_name = os.path.basename(corpus_dir)
if corpus_name == '':
corpus_dir = os.path.dirname(corpus_dir)
corpus_name = os.path.basename(corpus_dir)
data_directory = os.path.join(temp_dir, corpus_name)
if args.clean:
shutil.rmtree(data_directory, ignore_errors = True)
shutil.rmtree(output_directory, ignore_errors = True)
os.makedirs(data_directory, exist_ok = True)
os.makedirs(output_directory, exist_ok = True)
corpus = Corpus(corpus_dir, data_directory, args.speaker_characters, num_jobs = args.num_jobs)
print(corpus.speaker_utterance_info())
corpus.write()
corpus.create_mfccs()
dictionary = Dictionary(dict_path, data_directory, word_set=corpus.word_set)
dictionary.write()
corpus.setup_splits(dictionary)
utt_oov_path = os.path.join(corpus.split_directory, 'utterance_oovs.txt')
if os.path.exists(utt_oov_path):
shutil.copy(utt_oov_path, output_directory)
oov_path = os.path.join(corpus.split_directory, 'oovs_found.txt')
if os.path.exists(oov_path):
shutil.copy(oov_path, 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, 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)
a.verbose = args.verbose
a.train_mono()
a.export_textgrids()
a.train_tri()
a.export_textgrids()
a.train_tri_fmllr()
a.export_textgrids()
if output_model_path is not None:
a.save(output_model_path)
def align_corpus_no_dict(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)
data_directory = os.path.join(temp_dir, corpus_name)
if args.clean:
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)
corpus = Corpus(args.corpus_directory,
data_directory,
args.speaker_characters,
num_jobs=getattr(args, 'num_jobs', 3),
debug=getattr(args, 'debug', False),
ignore_exceptions=getattr(args, 'ignore_exceptions',
False))
print(corpus.speaker_utterance_info())
dictionary = no_dictionary(corpus, data_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,
debug=args.debug,
skip_input=getattr(args, 'quiet', False))
a.verbose = args.verbose
a.train_mono()
a.export_textgrids()
a.train_tri()
a.export_textgrids()
a.train_tri_fmllr()
a.export_textgrids()
if args.output_model_path is not None:
a.save(args.output_model_path)
def align_corpus(args, skip_input=False):
all_begin = time.time()
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': 'align',
'corpus_directory': args.corpus_directory,
'dictionary_path': args.dictionary_path}
if getattr(args, 'clean', False) \
or conf['dirty'] or conf['type'] != '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)
use_speaker_info = not args.no_speaker_adaptation
try:
corpus = Corpus(args.corpus_directory, data_directory,
speaker_characters=args.speaker_characters,
num_jobs=args.num_jobs,
use_speaker_information=use_speaker_info,
ignore_exceptions=getattr(args, 'ignore_exceptions', False))
print(corpus.speaker_utterance_info())
acoustic_model = AcousticModel(args.acoustic_model_path)
dictionary = Dictionary(args.dictionary_path, data_directory, word_set=corpus.word_set)
acoustic_model.validate(dictionary)
begin = time.time()
a = PretrainedAligner(corpus, dictionary, acoustic_model, args.output_directory, temp_directory=data_directory,
num_jobs=getattr(args, 'num_jobs', 3),
speaker_independent=getattr(args, 'no_speaker_adaptation', False),
debug=getattr(args, 'debug', False))
if getattr(args, 'errors', False):
check = a.test_utterance_transcriptions()
if not skip_input and not check:
user_input = input('Would you like to abort to fix transcription issues? (Y/N)')
if user_input.lower() == 'y':
return
if args.debug:
print('Setup pretrained aligner in {} seconds'.format(time.time() - begin))
a.verbose = args.verbose
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)
if not skip_input and a.dictionary.oovs_found:
user_input = input(
'There were words not found in the dictionary. Would you like to abort to fix them? (Y/N)')
if user_input.lower() == 'y':
return
begin = time.time()
a.do_align()
if args.debug:
print('Performed alignment in {} seconds'.format(time.time() - begin))
begin = time.time()
a.export_textgrids()
if args.debug:
print('Exported TextGrids in {} seconds'.format(time.time() - begin))
print('Done! Everything took {} seconds'.format(time.time() - all_begin))
except:
conf['dirty'] = True
raise
finally:
with open(conf_path, 'w') as f:
yaml.dump(conf, f)
def align_corpus(args):
all_begin = time.time()
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, Loader=yaml.SafeLoader)
else:
conf = {
'dirty': False,
'begin': time.time(),
'version': __version__,
'type': 'align',
'corpus_directory': args.corpus_directory,
'dictionary_path': args.dictionary_path
}
if getattr(args, 'clean', False) \
or conf['dirty'] or conf['type'] != '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)
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=args.num_jobs,
ignore_exceptions=getattr(args, 'ignore_exceptions',
False))
if corpus.issues_check:
print('WARNING: Some issues parsing the corpus were detected. '
'Please run the validator to get more information.')
print(corpus.speaker_utterance_info())
acoustic_model = AcousticModel(args.acoustic_model_path)
dictionary = Dictionary(args.dictionary_path,
data_directory,
word_set=corpus.word_set)
acoustic_model.validate(dictionary)
begin = time.time()
if args.config_path:
align_config = align_yaml_to_config(args.config_path)
else:
align_config = load_basic_align()
a = PretrainedAligner(corpus,
dictionary,
acoustic_model,
align_config,
args.output_directory,
temp_directory=data_directory,
debug=getattr(args, 'debug', False))
if args.debug:
print('Setup pretrained aligner in {} seconds'.format(time.time() -
begin))
a.verbose = args.verbose
begin = time.time()
a.align()
if args.debug:
print('Performed alignment in {} seconds'.format(time.time() -
begin))
begin = time.time()
a.export_textgrids()
if args.debug:
print('Exported TextGrids in {} seconds'.format(time.time() -
begin))
print('Done! Everything took {} seconds'.format(time.time() -
all_begin))
except:
conf['dirty'] = True
raise
finally:
with open(conf_path, 'w') as f:
yaml.dump(conf, f)
def align_corpus(args):
all_begin = time.time()
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': 'align',
'corpus_directory': args.corpus_directory,
'dictionary_path': args.dictionary_path}
if getattr(args, 'clean', False) \
or conf['dirty'] or conf['type'] != '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)
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=args.num_jobs,
ignore_exceptions=getattr(args, 'ignore_exceptions', False))
if corpus.issues_check:
print('WARNING: Some issues parsing the corpus were detected. '
'Please run the validator to get more information.')
print(corpus.speaker_utterance_info())
acoustic_model = AcousticModel(args.acoustic_model_path)
dictionary = Dictionary(args.dictionary_path, data_directory, word_set=corpus.word_set)
acoustic_model.validate(dictionary)
begin = time.time()
if args.config_path:
align_config = align_yaml_to_config(args.config_path)
else:
align_config = load_basic_align()
a = PretrainedAligner(corpus, dictionary, acoustic_model, align_config, args.output_directory,
temp_directory=data_directory,
debug=getattr(args, 'debug', False))
if getattr(args, 'errors', False):
check = a.test_utterance_transcriptions()
if not getattr(args, 'quiet', False) and not check:
user_input = input('Would you like to abort to fix transcription issues? (Y/N)')
if user_input.lower() == 'y':
return
if args.debug:
print('Setup pretrained aligner in {} seconds'.format(time.time() - begin))
a.verbose = args.verbose
begin = time.time()
a.align()
if args.debug:
print('Performed alignment in {} seconds'.format(time.time() - begin))
begin = time.time()
a.export_textgrids()
if args.debug:
print('Exported TextGrids in {} seconds'.format(time.time() - begin))
print('Done! Everything took {} seconds'.format(time.time() - all_begin))
except:
conf['dirty'] = True
raise
finally:
with open(conf_path, 'w') as f:
yaml.dump(conf, f)
def align_corpus(args):
all_begin = time.time()
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": "align",
"corpus_directory": args.corpus_directory,
"dictionary_path": args.dictionary_path,
}
if (
getattr(args, "clean", False)
or conf["dirty"]
or conf["type"] != "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)
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=args.num_jobs,
ignore_exceptions=getattr(args, "ignore_exceptions", False),
)
if corpus.issues_check:
print(
"WARNING: Some issues parsing the corpus were detected. "
"Please run the validator to get more information."
)
print(corpus.speaker_utterance_info())
acoustic_model = AcousticModel(args.acoustic_model_path)
dictionary = Dictionary(
args.dictionary_path, data_directory, word_set=corpus.word_set
)
acoustic_model.validate(dictionary)
begin = time.time()
if args.config_path:
align_config = align_yaml_to_config(args.config_path)
else:
align_config = load_basic_align()
a = PretrainedAligner(
corpus,
dictionary,
acoustic_model,
align_config,
args.output_directory,
temp_directory=data_directory,
debug=getattr(args, "debug", False),
)
if getattr(args, "errors", False):
check = a.test_utterance_transcriptions()
if not getattr(args, "quiet", False) and not check:
user_input = input(
"Would you like to abort to fix transcription issues? (Y/N)"
)
if user_input.lower() == "y":
return
if args.debug:
print("Setup pretrained aligner in {} seconds".format(time.time() - begin))
a.verbose = args.verbose
begin = time.time()
a.align()
if args.debug:
print("Performed alignment in {} seconds".format(time.time() - begin))
begin = time.time()
a.export_textgrids()
if args.debug:
print("Exported TextGrids in {} seconds".format(time.time() - begin))
print("Done! Everything took {} seconds".format(time.time() - all_begin))
except:
conf["dirty"] = True
raise
finally:
with open(conf_path, "w") as f:
yaml.dump(conf, f)
