def main():
parser = argparse.ArgumentParser()
parser.add_argument('--checkpoint',
default='',
help='Path to model checkpoint')
parser.add_argument(
'--name',
default='test',
help='Name of the run. Used for logging. Defaults to model name.')
parser.add_argument(
'--hp',
default='',
help=
'Hyperparameter overrides as a comma-separated list of name=value pairs'
)
parser.add_argument('--model', default='SED_MDD')
parser.add_argument('--tf_log_level',
type=int,
default=1,
help='Tensorflow C++ log level.')
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_log_level)
run_name = args.name
os.makedirs(hp.logdir, exist_ok=True)
infolog.init(os.path.join(hp.logdir, 'eval_new.log'), run_name)
hp.parse(args.hp)
eval(args)
def main():
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 1.0
session = tf.Session(config=config)
parser = argparse.ArgumentParser()
parser.add_argument('--base_dir', default='E:\\SRP\\TS')
parser.add_argument('--input', default='training\\train.txt')
parser.add_argument('--model', default='tacotron')
parser.add_argument('--name')
parser.add_argument('--hparams', default='')
parser.add_argument('--restore_step', type=bool, default=True)
parser.add_argument('--summary_interval',
type=int,
default=1000,
help='每隔多少步进行一次总结')
parser.add_argument('--checkpoint_interval',
type=int,
default=5000,
help='每隔多少步生成检查点')
parser.add_argument('--slack_url')
parser.add_argument('--tf_log_level',
type=int,
default=1,
help='Tensorflow日志等级')
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_log_level)
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
run_name = args.name or args.model
log_dir = os.path.join(args.base_dir, 'logs-%s' % run_name)
os.makedirs(log_dir, exist_ok=True)
infolog.init(os.path.join(log_dir, 'train.log'), run_name, args.slack_url)
hparams.parse(args.hparams)
train(log_dir, args)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--base_dir', default=os.path.expanduser('~/tacotron'))
parser.add_argument('--input', default='training/train.txt')
parser.add_argument('--model', default='tacotron')
parser.add_argument(
'--name',
help='Name of the run. Used for logging. Defaults to model name.')
parser.add_argument(
'--hparams',
default='',
help=
'Hyperparameter overrides as a comma-separated list of name=value pairs'
)
parser.add_argument('--summary_interval',
type=int,
default=100,
help='Steps between running summary ops.')
parser.add_argument('--checkpoint_interval',
type=int,
default=100,
help='Steps between writing checkpoints.')
parser.add_argument('--tf_log_level',
type=int,
default=1,
help='Tensorflow C++ log level.')
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_log_level)
run_name = args.name or args.model
log_dir = os.path.join('./Ckpts_amused', 'logs-%s' % run_name)
os.makedirs(log_dir, exist_ok=True)
infolog.init(os.path.join(log_dir, 'train.log'), run_name)
hparams.parse(args.hparams)
train(log_dir, args)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--base_dir', default=os.getcwd())
parser.add_argument('--input', default='training_data/train.txt')
parser.add_argument('--model', default='first_ctc')
parser.add_argument('--hparams', default='',
help='Hyperparameter overrides as a comma-separated list of name=value pairs')
parser.add_argument('--restore_step', type=int, help='Global step to restore from checkpoint.')
parser.add_argument('--summary_interval', type=int, default=1, help='Steps between running summary ops.')
parser.add_argument('--checkpoint_interval', type=int, default=1, help='Steps between writing checkpoints.')
parser.add_argument('--tf_log_level', type=int, default=1, help='Tensorflow C++ log level.')
parser.add_argument('--sample_file', type=str)
parser.add_argument('--label_file',type=str)
parser.add_argument('--seg_file',type=str)
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_log_level)
run_name = args.model
log_dir = os.path.join(args.base_dir, 'predictors/logs_%s' % run_name)
os.makedirs(log_dir, exist_ok=True)
infolog.init(os.path.join(log_dir, 'inference.log'), run_name)
#hparams.parse(args.hparams) #FIXME
run_ctc(log_dir, args)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--base_dir', default=os.path.expanduser('~/tacotron/Tacotron2/'))
parser.add_argument('--input', default='training/train.txt')
parser.add_argument('--model', default='tacotron')
parser.add_argument('--name', help='Name of the run. Used for logging. Defaults to model name.')
parser.add_argument('--hparams', default='',
help='Hyperparameter overrides as a comma-separated list of name=value pairs')
parser.add_argument('--restore_step', type=int, help='Global step to restore from checkpoint.')
parser.add_argument('--summary_interval', type=int, default=100,
help='Steps between running summary ops.')
parser.add_argument('--checkpoint_interval', type=int, default=1000,
help='Steps between writing checkpoints.')
parser.add_argument('--slack_url', help='Slack webhook URL to get periodic reports.')
parser.add_argument('--tf_log_level', type=int, default=1, help='Tensorflow C++ log level.')
parser.add_argument('--git', action='store_true', help='If set, verify that the client is clean.')
parser.add_argument('--gpu', default='1')
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_log_level)
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
run_name = args.name or args.model
hparams.parse(args.hparams)
attention_name = hparams.attention_type
print(attention_name)
log_dir = os.path.join(args.base_dir, 'logs-%s-%s' % (run_name, attention_name))
os.makedirs(log_dir, exist_ok=True)
infolog.init(os.path.join(log_dir, 'train.log'), run_name, args.slack_url)
train(log_dir, args)
def prepare_run(args):
modified_hp = hparams.parse(args.hparams)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_log_level)
log_dir = os.path.join(args.base_dir, 'logs-{}'.format(args.name))
os.makedirs(log_dir, exist_ok=True)
infolog.init(os.path.join(log_dir, 'Terminal_train_log'), args.name)
return log_dir, modified_hp
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--base_dir', default=os.path.expanduser('~/tacotron'))
parser.add_argument('--input', default='training/train.txt') # metadata
parser.add_argument('--model', default='tacotron')
parser.add_argument(
'--name',
help='Name of the run. Used for logging. Defaults to model name.')
parser.add_argument(
'--hparams',
default='',
help=
'Hyperparameter overrides as a comma-separated list of name=value pairs'
)
parser.add_argument('--restore_step',
type=int,
help='Global step to restore from checkpoint.')
parser.add_argument('--summary_interval',
type=int,
default=100,
help='Steps between running summary ops.')
parser.add_argument('--checkpoint_interval',
type=int,
default=1000,
help='Steps between writing checkpoints.')
parser.add_argument('--slack_url',
help='Slack webhook URL to get periodic reports.')
parser.add_argument('--git',
action='store_true',
help='If set, verify that the client is clean.')
# 追加
parser.add_argument('--gpu',
'-g',
type=int,
default=-1,
help='GPU ID (negative value indicates CPU)')
parser.add_argument('--epoch',
'-e',
type=int,
default=20,
help='Number of sweeps over the dataset to train')
parser.add_argument('--resume',
'-r',
default='',
help='Resume the training from snapshot')
parser.add_argument('--out',
'-o',
default='result',
help='Directory to output the result')
args = parser.parse_args()
run_name = args.name or args.model
log_dir = os.path.join(args.base_dir, 'logs-%s' % run_name)
os.makedirs(log_dir, exist_ok=True)
infolog.init(os.path.join(log_dir, 'train.log'), run_name, args.slack_url)
parse_hparams(args.hparams)
train(log_dir, args)
def main():
def _str_to_bool(s):
"""Convert string to bool (in argparse context)."""
if s.lower() not in ['true', 'false']:
raise ValueError('Argument needs to be a '
'boolean, got {}'.format(s))
return {'true': True, 'false': False}[s.lower()]
parser = argparse.ArgumentParser()
parser.add_argument('--base_dir', default='./logs/')
parser.add_argument('--model', default='tacotron')
parser.add_argument('--summary_interval',
type=int,
default=100,
help='Steps between running summary ops.')
parser.add_argument('--tf_log_level',
type=int,
default=1,
help='Tensorflow C++ log level.')
parser.add_argument(
'--hparams',
default='',
help=
'Hyperparameter overrides as a comma-separated list of name=value pairs'
)
parser.add_argument('--restore_step',
type=int,
help='Global step to restore from checkpoint.')
parser.add_argument('--checkpoint_interval',
type=int,
default=1000,
help='Steps between writing checkpoints.')
parser.add_argument(
'--GPUs_id',
default='[0]',
help='The GPUs\' id list that will be used. Default is 0')
parser.add_argument('--description',
default=None,
help='description of the model')
parser.add_argument('--train_data',
type=str,
default='THCHS',
help='training datas to be used, comma-separated')
parser.add_argument('--data_type',
type=str,
default='npy',
help='tfrecord or npy')
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_log_level)
log_dir = os.path.join(args.base_dir,
'logs-%s-%s' % (args.model, args.description))
os.makedirs(log_dir, exist_ok=True)
infolog.init(os.path.join(log_dir, 'train.log'))
hparams.parse(args.hparams)
train(log_dir, args)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--base_dir',
default=os.path.expanduser('/mnt1/tacotron'))
parser.add_argument('--input', default='training/train.txt')
parser.add_argument('--model', default='tacotron')
parser.add_argument(
'--name',
help='Name of the run. Used for logging. Defaults to model name.')
parser.add_argument(
'--hparams',
default='',
help=
'Hyperparameter overrides as a comma-separated list of name=value pairs'
)
parser.add_argument(
'--transfer_dir',
default='',
help='Directory from which to get checkpoint from transfer')
parser.add_argument('--transfer_run',
default='',
help='Name of the run from which to get checkpoint')
parser.add_argument('--restore_step',
type=int,
help='Global step to restore from checkpoint.')
parser.add_argument('--summary_interval',
type=int,
default=100,
help='Steps between running summary ops.')
parser.add_argument('--checkpoint_interval',
type=int,
default=1000,
help='Steps between writing checkpoints.')
parser.add_argument('--slack_url',
help='Slack webhook URL to get periodic reports.')
parser.add_argument('--tf_log_level',
type=int,
default=1,
help='Tensorflow C++ log level.')
parser.add_argument('--git',
action='store_true',
help='If set, verify that the client is clean.')
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_log_level)
run_name = args.name or args.model
log_dir = os.path.join(args.base_dir, 'logs-%s' % run_name)
if len(args.transfer_dir):
ckpt_dir = os.path.join(args.transfer_dir,
'logs-%s' % args.transfer_run)
os.makedirs(log_dir, exist_ok=True)
infolog.init(os.path.join(log_dir, 'train.log'), run_name, args.slack_url)
hparams.parse(args.hparams)
train(log_dir, args, trans_ckpt_dir=ckpt_dir)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--base_dir', default=os.path.expanduser('.'))
parser.add_argument('--dataset',
default='bznsyp',
choices=['bznsyp', 'ljspeech'])
parser.add_argument('--model', default='tacotron')
parser.add_argument(
'--name',
help='Name of the run. Used for logging. Defaults to model name.')
parser.add_argument(
'--hparams',
default='',
help=
'Hyperparameter overrides as a comma-separated list of name=value pairs'
)
parser.add_argument('--restore_step',
type=bool,
default=True,
help='Global step to restore from checkpoint.')
parser.add_argument('--restore_decoder',
type=bool,
default=False,
help='if set, restore the decoder weights')
parser.add_argument('--summary_interval',
type=int,
default=100,
help='Steps between running summary ops.')
parser.add_argument('--checkpoint_interval',
type=int,
default=1000,
help='Steps between writing checkpoints.')
parser.add_argument('--slack_url',
help='Slack webhook URL to get periodic reports.')
parser.add_argument('--tf_log_level',
type=int,
default=1,
help='Tensorflow C++ log level.')
parser.add_argument('--git',
action='store_true',
help='If set, verify that the client is clean.')
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_log_level)
run_name = args.name or args.model
log_dir = os.path.join(args.base_dir, 'logs-%s' % run_name)
os.makedirs(log_dir, exist_ok=True)
infolog.init(os.path.join(log_dir, 'train.log'), run_name, args.slack_url)
hparams.parse(args.hparams)
train(log_dir, args)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--base_dir', default='./')
parser.add_argument('--input', default='training/train.txt')
parser.add_argument('--model', default='tacotron')
parser.add_argument('--restore_step', type=int)
parser.add_argument('--summary_interval', type=int, default=100)
parser.add_argument('--checkpoint_interval', type=int, default=1000)
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
run_name = args.model
log_dir = os.path.join(args.base_dir, 'logs-%s' % run_name)
os.makedirs(log_dir, exist_ok=True)
infolog.init(os.path.join(log_dir, 'train.log'), run_name)
train(log_dir, args)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--base_dir', default=os.path.expanduser('~/Songbird'))
parser.add_argument('--input', default='training/train.txt')
parser.add_argument('--model', default='tacotron')
parser.add_argument('--vgg19_pretrained_model',
default='training/vgg19/vgg19.npy')
parser.add_argument(
'--name',
help='Name of the run. Used for logging. Defaults to model name.')
parser.add_argument(
'--hparams',
default='',
help=
'Hyperparameter overrides as a comma-separated list of name=value pairs'
)
parser.add_argument('--restore_step',
type=int,
help='Global step to restore from checkpoint.')
parser.add_argument('--summary_interval',
type=int,
default=100,
help='Steps between running summary ops.')
parser.add_argument('--checkpoint_interval',
type=int,
default=1000,
help='Steps between writing checkpoints.')
parser.add_argument(
'--slack_url',
help='Slack web-hook URL to get periodic reports.',
default=
'https://hooks.slack.com/services/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'
)
parser.add_argument('--tf_log_level',
type=int,
default=1,
help='Tensorflow C++ log level.')
parser.add_argument('--git',
action='store_true',
help='If set, verify that the client is clean.')
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_log_level)
run_name = args.name or args.model
log_dir = os.path.join(args.base_dir, 'logs-%s' % run_name)
os.makedirs(log_dir, exist_ok=True)
infolog.init(os.path.join(log_dir, 'train.log'), run_name, args.slack_url)
hparams.parse(args.hparams)
train(log_dir, args)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--base_dir', default=os.getcwd())
#### read both positive metadata and negative metadata
parser.add_argument('--input_pos', default='training/train-pos.txt')
parser.add_argument('--input_neg', default='training/train-neg.txt')
parser.add_argument('--model', default='ttsGAN')
parser.add_argument(
'--name',
help='Name of the run. Used for logging. Defaults to model name.')
parser.add_argument(
'--hparams',
default='',
help=
'Hyperparameter overrides as a comma-separated list of name=value pairs'
)
parser.add_argument('--restore_step',
type=int,
help='Global step to restore from checkpoint.')
parser.add_argument('--summary_interval',
type=int,
default=100,
help='Steps between running summary ops.')
parser.add_argument('--checkpoint_interval',
type=int,
default=1000,
help='Steps between writing checkpoints.')
parser.add_argument('--tf_log_level',
type=int,
default=1,
help='Tensorflow C++ log level.')
parser.add_argument('--slack_url', default=None)
parser.add_argument('--git', default=False)
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_log_level)
run_name = args.name or args.model
log_dir = os.path.join(args.base_dir, 'logs-%s' % run_name)
os.makedirs(log_dir, exist_ok=True)
infolog.init(os.path.join(log_dir, 'train.log'), run_name, args.slack_url)
hparams.parse(args.hparams)
train(log_dir, args)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--name', default='test', help='Name of the run. Used for logging. Defaults to model name.')
parser.add_argument('--hp', default='',
help='Hyperparameter overrides as a comma-separated list of name=value pairs')
parser.add_argument('--model', default='SED_MDD')
parser.add_argument('--restore_step', type=int, help='Global step to restore from checkpoint.')
parser.add_argument('--summary_interval', type=int, default=100, help='Steps between running summary ops.')
parser.add_argument('--checkpoint_interval', type=int, default=1000, help='Steps between writing checkpoints.')
parser.add_argument('--tf_log_level', type=int, default=1, help='Tensorflow C++ log level.')
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_log_level)
#os.environ["CUDA_VISIBLE_DEVICES"] = '0'
run_name = args.name
log_dir = hp.logdir
os.makedirs(log_dir, exist_ok=True)
infolog.init(os.path.join(log_dir, 'train.log'), run_name)
hp.parse(args.hp)
train(log_dir, args)
def main():
parser = argparse.ArgumentParser()
# runtime
parser.add_argument('--base_dir', default=os.path.expanduser('~/Work/Projects/keithito-tacotron'))
parser.add_argument('--input', default='training/train.txt')
parser.add_argument('--model', default='tacotron')
parser.add_argument('--name', help='Name of the run. Used for logging. Defaults to model name.')
parser.add_argument('--hparams', default='',
help='Hyperparameter overrides as a comma-separated list of name=value pairs')
parser.add_argument('--restore_step', type=int, help='Global step to restore from checkpoint.')
parser.add_argument('--summary_interval', type=int, default=100,
help='Steps between running summary ops.')
parser.add_argument('--checkpoint_interval', type=int, default=1000,
help='Steps between writing checkpoints.')
parser.add_argument('--slack_url', help='Slack webhook URL to get periodic reports.')
parser.add_argument('--tf_log_level', type=int, default=1, help='Tensorflow C++ log level.')
parser.add_argument('--git', action='store_true', help='If set, verify that the client is clean.')
args = parser.parse_args()
# # debug
# # args = parser.parse_args()
# args = Parser()
# args.base_dir = os.path.expanduser('~/Work/Projects/keithito-tacotron')
# args.input = 'training/train.txt'
# args.model = 'tacotron'
# args.name = 'run2'
# args.hparams = ''
# args.restore_step = 0
# args.summary_interval = 100
# args.checkpoint_interval = 100
# args.slack_url = ''
# args.tf_log_level = 1
# args.git = False
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_log_level)
run_name = args.name or args.model
log_dir = os.path.join(args.base_dir, 'logs-%s' % run_name)
os.makedirs(log_dir, exist_ok=True)
infolog.init(os.path.join(log_dir, 'train.log'), run_name, args.slack_url)
hparams.parse(args.hparams)
train(log_dir, args)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--base_dir', default=os.path.expanduser('~/tacotron'))
parser.add_argument('--input', default='training/train.txt')
parser.add_argument('--model', default='tacotron')
parser.add_argument('--name', help='Name of the run. Used for logging. Defaults to model name.')
parser.add_argument('--hparams', default='',
help='Hyperparameter overrides as a comma-separated list of name=value pairs')
parser.add_argument('--restore_step', type=int, help='Global step to restore from checkpoint.')
parser.add_argument('--summary_interval', type=int, default=100,
help='Steps between running summary ops.')
parser.add_argument('--checkpoint_interval', type=int, default=1000,
help='Steps between writing checkpoints.')
parser.add_argument('--slack_url', help='Slack webhook URL to get periodic reports.')
parser.add_argument('--tf_log_level', type=int, default=1, help='Tensorflow C++ log level.')
parser.add_argument('--git', action='store_true', help='If set, verify that the client is clean.')
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_log_level)
run_name = args.name or args.model
log_dir = os.path.join(args.base_dir, 'logs-%s' % run_name)
os.makedirs(log_dir, exist_ok=True)
infolog.init(os.path.join(log_dir, 'train.log'), run_name, args.slack_url)
hparams.parse(args.hparams)
train(log_dir, args)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--base_dir', default=os.path.expanduser('~/tacotron'))
parser.add_argument('--input', default='training/train.txt')
parser.add_argument('--model', default='tacotron')
parser.add_argument(
'--name',
help='Name of the run. Used for logging. Defaults to model name.')
parser.add_argument(
'--hparams',
default='',
help=
'Hyperparameter overrides as a comma-separated list of name=value pairs'
)
parser.add_argument('--restore_step',
type=int,
help='Global step to restore from checkpoint.')
parser.add_argument('--summary_interval',
type=int,
default=100,
help='Steps between running summary ops.')
parser.add_argument('--checkpoint_interval',
type=int,
default=1000,
help='Steps between writing checkpoints.')
parser.add_argument('--slack_url',
help='Slack webhook URL to get periodic reports.')
parser.add_argument('--tf_log_level',
type=int,
default=1,
help='Tensorflow C++ log level.')
parser.add_argument('--git',
action='store_true',
help='If set, verify that the client is clean.')
parser.register("type", "bool", lambda v: v.lower() == "true")
# Flags for defining the tf.train.ClusterSpec
parser.add_argument("--ps_hosts",
type=str,
default="",
help="Comma-separated list of hostname:port pairs")
parser.add_argument("--worker_hosts",
type=str,
default="",
help="Comma-separated list of hostname:port pairs")
parser.add_argument("--job_name",
type=str,
default="",
help="One of 'ps', 'worker'")
# Flags for defining the tf.train.Server
parser.add_argument("--task_index",
type=int,
default=0,
help="Index of task within the job")
args, unparsed = parser.parse_known_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_log_level)
run_name = args.name or args.model
log_dir = os.path.join(args.base_dir, 'logs-%s' % run_name)
os.makedirs(log_dir, exist_ok=True)
infolog.init(os.path.join(log_dir, 'train.log'), run_name, args.slack_url)
hparams.parse(args.hparams)
train(log_dir, args)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--base_dir', default=os.path.expanduser('./'))
parser.add_argument('--input', default='training')
parser.add_argument('--model', default='tacotron')
parser.add_argument(
'--name',
help='Name of the run. Used for logging. Defaults to model name.')
parser.add_argument(
'--hparams',
default='',
help=
'Hyperparameter overrides as a comma-separated list of name=value pairs'
)
parser.add_argument('--restore_step',
type=int,
help='Global step to restore from checkpoint.')
parser.add_argument('--summary_interval',
type=int,
default=100,
help='Steps between running summary ops.')
parser.add_argument('--checkpoint_interval',
type=int,
default=1000,
help='Steps between writing checkpoints.')
parser.add_argument('--slack_url',
help='Slack webhook URL to get periodic reports.')
parser.add_argument('--tf_log_level',
type=int,
default=1,
help='Tensorflow C++ log level.')
parser.add_argument('--git',
action='store_true',
help='If set, verify that the client is clean.')
parser.add_argument(
'--GPUs_id',
default='[0]',
help='The GPUs\' id list that will be used. Default is 0')
parser.add_argument('--description',
default=None,
help='description of the model')
parser.add_argument('--datasets',
default="['npy_ljspeech','npy_vctk']",
help='the datasets used for training'
) # "['npy_vctk', 'npy_ljspeech']"
parser.add_argument('--batch_size',
default=None,
type=int,
help='batch_size') #
parser.add_argument('--prenet_layer1',
default=256,
type=int,
help='prenet_layer1') #
parser.add_argument('--prenet_layer2',
default=128,
type=int,
help='prenet_layer2') #
parser.add_argument('--gru_size', default=256, type=int,
help='gru_size') #
parser.add_argument('--attention_size',
default=256,
type=int,
help='attention_size') #
parser.add_argument('--rnn_size', default=256, type=int,
help='rnn_size') #
parser.add_argument('--weight_decay',
default=0,
type=float,
help='weight_decay') #
parser.add_argument('--enable_fv1',
default=True,
type=bool,
help='enable_fv1') #
parser.add_argument('--enable_fv2',
default=True,
type=bool,
help='enable_fv2') #
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_log_level)
run_name = args.name or args.model
log_dir = os.path.join(args.base_dir,
'logs-%s-%s' % (run_name, args.description))
os.makedirs(log_dir, exist_ok=True)
infolog.init(os.path.join(log_dir, 'train.log'), run_name, args.slack_url)
hparams.parse(args.hparams)
train(log_dir, args)
def train(log_dir, args):
run_name = args.name or args.model
log_dir = os.path.join(args.base_dir, 'logs-%s' % run_name)
os.makedirs(log_dir, exist_ok=True)
infolog.init(os.path.join(log_dir, 'train.log'), run_name, args.slack_url)
checkpoint_path = os.path.join(log_dir, 'model.ckpt')
with open(args.input, encoding='utf-8') as f:
metadata = [row.strip().split('|') for row in f]
metadata = sorted(metadata, key=lambda x: x[2])
data_element = get_dataset(metadata, args.data_dir, hparams)
global_step = tf.Variable(0, name='global_step', trainable=False)
with tf.variable_scope('model') as scope:
model = create_model(args.model, hparams)
model.initialize(data_element['input'], data_element['input_lengths'],
data_element['mel_targets'],
data_element['linear_targets'])
model.add_loss()
model.add_optimizer(global_step)
saver = tf.train.Saver(max_to_keep=5, keep_checkpoint_every_n_hours=2)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
for _ in range(int(args.max_iter)):
start_time = time.time()
step, mel_loss, lin_loss, loss, opt = sess.run([
global_step, model.mel_loss, model.linear_loss, model.loss,
model.optimize
])
end_time = time.time()
message = 'Step %7d [%.03f sec/step, loss = %.05f (mel : %.05f + lin : %.05f)]' % (
step, end_time - start_time, loss, mel_loss, lin_loss)
log(message)
if loss > 100 or math.isnan(loss):
log('Loss exploded to %.05f at step %d!' % (loss, step))
raise Exception('Loss Exploded')
if step % args.checkpoint_interval == 0:
log('Saving checkpoint to: %s-%d' % (checkpoint_path, step))
saver.save(sess, checkpoint_path, global_step=step)
log('Saving audio and alignment...')
input_seq, spectrogram, alignment = sess.run([
model.inputs[0], model.linear_outputs[0], model.alignments[0]
])
waveform = audio.inv_spectrogram(spectrogram.T)
audio.save_wav(waveform,
os.path.join(log_dir, 'step-%d-audio.wav' % step))
plot.plot_alignment(alignment,
os.path.join(log_dir,
'step-%d-align.png' % step),
info='%s, %s, step=%d, loss=%.5f' %
(args.model, time_string(), step, loss))
log('Input: %s' % sequence_to_text(input_seq))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--base_dir',
default=os.path.expanduser(r'E:\data\logs'))
parser.add_argument('--input',
default=r'E:\data\biaobei\specs\train-blank.txt')
parser.add_argument('--model', default='tacotron')
parser.add_argument(
'--name',
default='biaobei-blank',
help='Name of the run. Used for logging. Defaults to model name.')
parser.add_argument(
'--hparams',
default='',
help=
'Hyperparameter overrides as a comma-separated list of name=value pairs'
)
parser.add_argument('--restore_step',
default=6000,
type=int,
help='Global step to restore from checkpoint.')
parser.add_argument(
'--summary_interval',
type=int,
default=200, # 100,
help='Steps between running summary ops.')
parser.add_argument(
'--checkpoint_interval',
type=int,
default=2000, # 1000,
help='Steps between writing checkpoints.')
parser.add_argument('--slack_url',
help='Slack webhook URL to get periodic reports.')
parser.add_argument('--tf_log_level',
type=int,
default=1,
help='Tensorflow C++ log level.')
parser.add_argument('--git',
action='store_true',
help='If set, verify that the client is clean.')
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_log_level)
run_name = args.name or args.model
log_dir = os.path.join(args.base_dir, 'logs-%s' % run_name)
os.makedirs(log_dir, exist_ok=True)
infolog.init(os.path.join(log_dir, 'train.log'), run_name, args.slack_url)
hparams.parse(args.hparams)
json.dump(hparams.values(),
open(os.path.join(log_dir, 'hparams.json'),
'wt',
encoding='utf8'),
indent=4)
json.dump(args.__dict__,
open(os.path.join(log_dir, 'args.json'), 'wt', encoding='utf8'),
indent=4)
json.dump(symbols,
open(os.path.join(log_dir, 'symbols.json'),
'wt',
encoding='utf8'),
indent=4)
train(log_dir, args)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--base_dir',
default=os.path.dirname(os.path.abspath(__file__)),
help="tacotron install-dir (Def: %(default)s)")
parser.add_argument('--input', default='training/train.txt')
parser.add_argument('--model', default='tacotron')
parser.add_argument(
'--name',
help='Name of the run. Used for logging. Defaults to model name.')
parser.add_argument(
'--hparams',
default='',
help=
'Hyperparameter overrides as a comma-separated list of name=value pairs'
)
parser.add_argument('--restore_step',
type=int,
help='Global step to restore from checkpoint.')
parser.add_argument('--summary_interval',
type=int,
default=100,
help='Steps between running summary ops.')
parser.add_argument('--checkpoint_interval',
type=int,
default=1000,
help='Steps between writing checkpoints.')
parser.add_argument('--slack_url',
help='Slack webhook URL to get periodic reports.')
parser.add_argument('--tf_log_level',
type=int,
default=1,
help='Tensorflow C++ log level.')
parser.add_argument('--git',
action='store_true',
help='If set, verify that the client is clean.')
device_arg = parser.add_mutually_exclusive_group()
device_arg.add_argument(
"--cpu",
action="store_true",
help=
'use cpu for calculations, this is the default on sytems without available gpu card (Def: %(default)s)'
)
device_arg.add_argument(
'-d',
"--gpu_device",
default=None,
nargs="?",
const=-1,
type=int,
help=
'use gpu device, use without argument for arbitrary gpu, this is the default for systems with gpu (Def: %(default)s)'
)
parser.add_argument(
"--soft_lock",
action="store_true",
help='only request a soft lock on the GPU (Def: %(default)s)')
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_log_level)
comp_device = None
gpu_device_id = None
if not args.cpu:
# gpu_ids will be None on systems without gpu nvidia card
gpu_ids = gpl.board_ids()
if gpu_ids is not None:
if args.gpu_device is None or args.gpu_device == -1:
gpu_device_id = -1
elif args.gpu_device in gpu_ids:
gpu_device_id = args.gpu_device
else:
raise RuntimeError(
"train_onsets::error:: selected gpu device if {} is not free, select an id from {}"
.format(args.gpu_device, gpu_ids))
elif args.gpu_device is not None:
raise RuntimeError(
"train_onsets::error:: no gpu devices available on thsi system, you cannot select a gpu"
)
print("gpu_device_id", gpu_device_id)
try:
# now we lock a GPU because we will need one
if gpu_device_id is not None:
gpu_id_locked = get_gpu_lock(gpu_device_id=gpu_device_id,
soft=args.soft_lock)
# obtainlock positions CUDA_VISIBLE_DEVICES such that only the selected GPU is visibale,
# therefore we need now select /GPU:0
comp_device = "/GPU:0"
else:
gpu_id_locked = -1
comp_device = "/cpu:0"
os.environ['CUDA_VISIBLE_DEVICES'] = ""
run_name = args.name or args.model
log_dir = os.path.join(args.base_dir, 'logs-%s' % run_name)
os.makedirs(log_dir, exist_ok=True)
infolog.init(os.path.join(log_dir, 'train.log'), run_name,
args.slack_url)
hparams.parse(args.hparams)
train(log_dir, args)
except Exception as ex:
import traceback
tb = traceback.format_exc()
print("{0} received exception::".format(sys.argv[0]),
str(ex),
tb,
file=sys.stderr)
finally:
# terminate input pipeline
if ("GPU" in comp_device) and (gpu_id_locked >= 0):
gpl.free_lock(gpu_id_locked)