from data.audio import Audio
np.random.seed(42)
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, default='config/session_paths.yaml')
parser.add_argument('--skip_phonemes', action='store_true')
parser.add_argument('--skip_mels', action='store_true')
parser.add_argument('--skip_speakers', action='store_true')
args = parser.parse_args()
for arg in vars(args):
print('{}: {}'.format(arg, getattr(args, arg)))
cm = Config(args.config, asr=True)
cm.create_remove_dirs()
metadatareader = DataReader.from_config(cm, kind='original')
summary_manager = SummaryManager(model=None,
log_dir=cm.log_dir / 'data_preprocessing',
config=cm.config,
default_writer='data_preprocessing')
print(f'\nFound {len(metadatareader.filenames)} audio files.')
audio = Audio(config=cm.config)
if not args.skip_mels:
def process_file(tuples):
len_dict = {}
spk_file_dict = {}
remove_files = []
for idx in trange(len(tuples), desc=''):
from ctc_segmentation import ctc_segmentation, determine_utterance_segments
from ctc_segmentation import CtcSegmentationParameters
from ctc_segmentation import prepare_token_list
import tgt
np.random.seed(42)
tf.random.set_seed(42)
dynamic_memory_allocation()
parser = basic_train_parser()
args = parser.parse_args()
config = Config(config_path=args.config, asr=True)
config_dict = config.config
config.create_remove_dirs(clear_dir=args.clear_dir,
clear_logs=args.clear_logs,
clear_weights=args.clear_weights)
config.dump_config()
config.print_config()
model = config.get_model()
config.compile_model(model)
data_handler = ASRDataset.from_config(config,
tokenizer=model.text_pipeline.tokenizer,
kind='valid')
dataset = data_handler.get_dataset(
bucket_batch_sizes=config_dict['bucket_batch_sizes'],
bucket_boundaries=config_dict['bucket_boundaries'],
shuffle=False)