def transcribe_corpus(args):
command = 'transcribe'
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)
if args.config_path:
transcribe_config = transcribe_yaml_to_config(args.config_path)
else:
transcribe_config = load_basic_transcribe()
data_directory = os.path.join(temp_dir, corpus_name)
if getattr(args, 'clean', False) and os.path.exists(data_directory):
print('Cleaning old directory!')
shutil.rmtree(data_directory, ignore_errors=True)
logger = setup_logger(command, data_directory)
os.makedirs(data_directory, exist_ok=True)
os.makedirs(args.output_directory, exist_ok=True)
os.makedirs(data_directory, exist_ok=True)
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': 'transcribe',
'corpus_directory': args.corpus_directory,
'dictionary_path': args.dictionary_path,
'acoustic_model_path': args.acoustic_model_path,
'language_model_path': args.language_model_path,
}
if conf['dirty'] or conf['type'] != command \
or conf['corpus_directory'] != args.corpus_directory \
or conf['version'] != __version__ \
or conf['dictionary_path'] != args.dictionary_path \
or conf['language_model_path'] != args.language_model_path \
or conf['acoustic_model_path'] != args.acoustic_model_path:
logger.warning(
'WARNING: Using old temp directory, this might not be ideal for you, use the --clean flag to ensure no '
'weird behavior for previous versions of the temporary directory.')
if conf['dirty']:
logger.debug('Previous run ended in an error (maybe ctrl-c?)')
if conf['type'] != command:
logger.debug(
'Previous run was a different subcommand than {} (was {})'.
format(command, conf['type']))
if conf['corpus_directory'] != args.corpus_directory:
logger.debug('Previous run used source directory '
'path {} (new run: {})'.format(
conf['corpus_directory'], args.corpus_directory))
if conf['version'] != __version__:
logger.debug('Previous run was on {} version (new run: {})'.format(
conf['version'], __version__))
if conf['dictionary_path'] != args.dictionary_path:
logger.debug('Previous run used dictionary path {} '
'(new run: {})'.format(conf['dictionary_path'],
args.dictionary_path))
if conf['acoustic_model_path'] != args.acoustic_model_path:
logger.debug('Previous run used acoustic model path {} '
'(new run: {})'.format(conf['acoustic_model_path'],
args.acoustic_model_path))
if conf['language_model_path'] != args.language_model_path:
logger.debug('Previous run used language model path {} '
'(new run: {})'.format(conf['language_model_path'],
args.language_model_path))
try:
if args.evaluate:
corpus = AlignableCorpus(
args.corpus_directory,
data_directory,
speaker_characters=args.speaker_characters,
num_jobs=args.num_jobs,
use_mp=transcribe_config.use_mp)
else:
corpus = TranscribeCorpus(
args.corpus_directory,
data_directory,
speaker_characters=args.speaker_characters,
num_jobs=args.num_jobs,
use_mp=transcribe_config.use_mp)
print(corpus.speaker_utterance_info())
acoustic_model = AcousticModel(args.acoustic_model_path,
root_directory=data_directory)
language_model = LanguageModel(args.language_model_path,
root_directory=data_directory)
dictionary = Dictionary(args.dictionary_path, data_directory)
acoustic_model.validate(dictionary)
begin = time.time()
t = Transcriber(corpus,
dictionary,
acoustic_model,
language_model,
transcribe_config,
temp_directory=data_directory,
debug=getattr(args, 'debug', False),
evaluation_mode=args.evaluate)
if args.debug:
print('Setup pretrained aligner in {} seconds'.format(time.time() -
begin))
begin = time.time()
t.transcribe()
if args.debug:
print('Performed transcribing in {} seconds'.format(time.time() -
begin))
if args.evaluate:
t.evaluate(args.output_directory)
best_config_path = os.path.join(args.output_directory,
'best_transcribe_config.yaml')
save_config(t.transcribe_config, best_config_path)
t.export_transcriptions(args.output_directory)
else:
begin = time.time()
t.export_transcriptions(args.output_directory)
if args.debug:
print('Exported transcriptions in {} seconds'.format(
time.time() - begin))
print('Done! Everything took {} seconds'.format(time.time() -
all_begin))
except Exception as _:
conf['dirty'] = True
raise
finally:
handlers = logger.handlers[:]
for handler in handlers:
handler.close()
logger.removeHandler(handler)
if os.path.exists(data_directory):
with open(conf_path, 'w') as f:
yaml.dump(conf, f)
def transcribe_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)
print(data_directory, os.path.exists(data_directory))
os.makedirs(data_directory, exist_ok=True)
os.makedirs(args.output_directory, exist_ok=True)
os.makedirs(data_directory, exist_ok=True)
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:
pass # FIXME
# shutil.rmtree(data_directory, ignore_errors=True)
try:
if args.evaluate:
corpus = AlignableCorpus(
args.corpus_directory,
data_directory,
speaker_characters=args.speaker_characters,
num_jobs=args.num_jobs)
else:
corpus = TranscribeCorpus(
args.corpus_directory,
data_directory,
speaker_characters=args.speaker_characters,
num_jobs=args.num_jobs)
print(corpus.speaker_utterance_info())
acoustic_model = AcousticModel(args.acoustic_model_path)
language_model = LanguageModel(args.language_model_path)
dictionary = Dictionary(args.dictionary_path, data_directory)
acoustic_model.validate(dictionary)
if args.config_path:
transcribe_config = transcribe_yaml_to_config(args.config_path)
else:
transcribe_config = load_basic_transcribe()
begin = time.time()
t = Transcriber(corpus,
dictionary,
acoustic_model,
language_model,
transcribe_config,
temp_directory=data_directory,
debug=getattr(args, 'debug', False),
evaluation_mode=args.evaluate)
if args.debug:
print('Setup pretrained aligner in {} seconds'.format(time.time() -
begin))
begin = time.time()
t.transcribe()
if args.debug:
print('Performed transcribing in {} seconds'.format(time.time() -
begin))
if args.evaluate:
t.evaluate(args.output_directory)
best_config_path = os.path.join(args.output_directory,
'best_transcribe_config.yaml')
save_config(t.transcribe_config, best_config_path)
t.export_transcriptions(args.output_directory)
else:
begin = time.time()
t.export_transcriptions(args.output_directory)
if args.debug:
print('Exported transcriptions in {} seconds'.format(
time.time() - begin))
print('Done! Everything took {} seconds'.format(time.time() -
all_begin))
except Exception as _:
conf['dirty'] = True
raise
finally:
if os.path.exists(data_directory):
with open(conf_path, 'w') as f:
yaml.dump(conf, f)