def get_runner_args():
parser = argparse.ArgumentParser(description='Argument Parser for the S3PLR project.')
# setting
parser.add_argument('--config', default='../config/deprecated_runner/tera_libri_fmllrBase_pretrain,yaml', type=str, help='Path to experiment config.', required=False)
parser.add_argument('--seed', default=1337, type=int, help='Random seed for reproducable results.', required=False)
# Logging
parser.add_argument('--logdir', default='../log/log_transformer/', type=str, help='Logging path.', required=False)
parser.add_argument('--name', default=None, type=str, help='Name for logging.', required=False)
# model ckpt
parser.add_argument('--load', action='store_true', help='Load pre-trained model to restore training, no need to specify this during testing.')
parser.add_argument('--ckpdir', default='../result/result_transformer/', type=str, help='path to store experiment result.', required=False)
parser.add_argument('--ckpt', default='fmllrBase960-F-N-K-libri/states-1000000.ckpt', type=str, help='path to transformer model checkpoint.', required=False)
parser.add_argument('--dckpt', default='baseline_sentiment_libri_sd1337/baseline_sentiment-500000.ckpt', type=str, help='path to downstream checkpoint.', required=False)
parser.add_argument('--apc_path', default='../result/result_apc/apc_libri_sd1337_standard/apc-500000.ckpt', type=str, help='path to the apc model checkpoint.', required=False)
# mockingjay
parser.add_argument('--train', action='store_true', help='Train the model.')
parser.add_argument('--run_transformer', action='store_true', help='train and test the downstream tasks using speech representations.')
parser.add_argument('--run_apc', action='store_true', help='train and test the downstream tasks using apc representations.')
parser.add_argument('--fine_tune', action='store_true', help='fine tune the transformer model with downstream task.')
parser.add_argument('--plot', action='store_true', help='Plot model generated results during testing.')
# phone task
parser.add_argument('--train_phone', action='store_true', help='Train the phone classifier on mel or speech representations.')
parser.add_argument('--test_phone', action='store_true', help='Test mel or speech representations using the trained phone classifier.')
# cpc phone task
parser.add_argument('--train_cpc_phone', action='store_true', help='Train the phone classifier on mel or speech representations with the alignments in CPC paper.')
parser.add_argument('--test_cpc_phone', action='store_true', help='Test mel or speech representations using the trained phone classifier with the alignments in CPC paper.')
# sentiment task
parser.add_argument('--train_sentiment', action='store_true', help='Train the sentiment classifier on mel or speech representations.')
parser.add_argument('--test_sentiment', action='store_true', help='Test mel or speech representations using the trained sentiment classifier.')
# speaker verification task
parser.add_argument('--train_speaker', action='store_true', help='Train the speaker classifier on mel or speech representations.')
parser.add_argument('--test_speaker', action='store_true', help='Test mel or speech representations using the trained speaker classifier.')
# Options
parser.add_argument('--with_head', action='store_true', help='inference with the spectrogram head, the model outputs spectrogram.')
parser.add_argument('--plot_attention', action='store_true', help='plot attention')
parser.add_argument('--load_ws', default='result/result_transformer_sentiment/10111754-10170300-weight_sum/best_val.ckpt', help='load weighted-sum weights from trained downstream model')
parser.add_argument('--cpu', action='store_true', help='Disable GPU training.')
parser.add_argument('--multi_gpu', action='store_true', help='Enable Multi-GPU training.')
parser.add_argument('--no_msg', action='store_true', help='Hide all messages.')
parser.add_argument('--test_reconstruct', action='store_true', help='Test reconstruction capability')
# parse
args = parser.parse_args()
setattr(args,'gpu', not args.cpu)
setattr(args,'verbose', not args.no_msg)
config = yaml.load(open(args.config,'r'), Loader=yaml.FullLoader)
parse_prune_heads(config)
return config, args
def get_upstream_args():
parser = argparse.ArgumentParser(
description='Argument Parser for Upstream Models of the S3PLR project.'
)
# required
parser.add_argument('--run',
choices=['transformer', 'apc'],
help='Select pre-training task. \
For the transformer models, which type of pre-training (mockingjay, tera, aalbert, etc) \
is determined by config file.',
required=True)
parser.add_argument('--config',
type=str,
help='Path to experiment config.',
required=True)
# ckpt and logging
parser.add_argument('--name',
default=None,
type=str,
help='Name for logging.',
required=False)
parser.add_argument(
'--ckpdir',
default='',
type=str,
help='Path to store checkpoint result, if empty then default is used.',
required=False)
parser.add_argument('--seed',
default=1337,
type=int,
help='Random seed for reproducable results.',
required=False)
# Options
parser.add_argument('--test',
default='',
type=str,
help='Input path to the saved model ckpt for testing.')
parser.add_argument('--cpu',
action='store_true',
help='Disable GPU training.')
parser.add_argument('--multi_gpu',
action='store_true',
help='Enable Multi-GPU training.')
parser.add_argument('--test_reconstruct',
action='store_true',
help='Test reconstruction capability')
# parse
args = parser.parse_args()
setattr(args, 'gpu', not args.cpu)
config = yaml.load(open(args.config, 'r'), Loader=yaml.FullLoader)
parse_prune_heads(config)
return args, config
def get_upstream_args():
parser = argparse.ArgumentParser(description='Argument Parser for Upstream Models of the S3PLR project.')
# required, set either (--run and --config) or (--resume)
parser.add_argument('--run', default=None, choices=['transformer', 'apc'], help='Select pre-training task. \
For the transformer models, which type of pre-training (mockingjay, tera, aalbert, etc) \
is determined by config file.')
parser.add_argument('--config', default=None, type=str, help='Path to experiment config.')
parser.add_argument('--resume', default=None, help='Specify the upstream checkpoint path to resume training')
# ckpt and logging
parser.add_argument('--name', default=None, type=str, help='Name for logging.')
parser.add_argument('--ckpdir', default='', type=str, help='Path to store checkpoint result, if empty then default is used.')
parser.add_argument('--seed', default=1337, type=int, help='Random seed for reproducable results.')
# Options
parser.add_argument('--test', default='', type=str, help='Input path to the saved model ckpt for testing.')
parser.add_argument('--cpu', action='store_true', help='Disable GPU training.')
parser.add_argument('--multi_gpu', action='store_true', help='Enable Multi-GPU training.')
parser.add_argument('--test_reconstruct', action='store_true', help='Test reconstruction capability')
parser.add_argument('--online_config', default=None, help='Explicitly specify the config of on-the-fly feature extraction')
parser.add_argument('--kaldi_data', action='store_true', help='Whether to use the Kaldi dataset')
# parse
args = parser.parse_args()
if args.resume is None:
assert args.run is not None and args.config is not None, '`--run` and `--config` must be given if `--resume` is not provided'
setattr(args, 'gpu', not args.cpu)
config = yaml.load(open(args.config, 'r'), Loader=yaml.FullLoader)
parse_prune_heads(config)
if args.online_config is not None:
online_config = yaml.load(open(args.online_config, 'r'), Loader=yaml.FullLoader)
config['online'] = online_config
else:
if os.path.isdir(args.resume):
ckpts = glob.glob(f'{args.resume}/*.ckpt')
assert len(ckpts) > 0
ckpts = sorted(ckpts, key=lambda pth: int(pth.split('-')[-1].split('.')[0]))
resume_ckpt = ckpts[-1]
else:
resume_ckpt = args.resume
def update_args(old, new):
old_dict = vars(old)
new_dict = vars(new)
old_dict.update(new_dict)
return Namespace(**old_dict)
ckpt = torch.load(resume_ckpt, map_location='cpu')
args = update_args(args, ckpt['Settings']['Paras'])
config = ckpt['Settings']['Config']
setattr(args, 'resume', resume_ckpt)
return args, config