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import argparse
from typing import Any
import tensorflow as tf
class EasyDict(dict):
def __getattr__(self, name: str) -> Any:
try:
return self[name]
except KeyError:
raise AttributeError(name)
def __setattr__(self, name: str, value: Any) -> None:
self[name] = value
def __delattr__(self, name: str) -> None:
del self[name]
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("Boolean value expected.")
def params_args(args):
parser = argparse.ArgumentParser()
parser.add_argument(
"--hop",
type=int,
default=256,
help="Hop size (window size = 4*hop)",
)
parser.add_argument(
"--mel_bins",
type=int,
default=256,
help="Mel bins in mel-spectrograms",
)
parser.add_argument(
"--sr",
type=int,
default=44100,
help="Sampling Rate",
)
parser.add_argument(
"--small",
type=str2bool,
default=False,
help="If True, use model with shorter available context, useful for small datasets",
)
parser.add_argument(
"--latdepth",
type=int,
default=64,
help="Depth of generated latent vectors",
)
parser.add_argument(
"--coorddepth",
type=int,
default=64,
help="Dimension of latent coordinate and style random vectors",
)
parser.add_argument(
"--max_lat_len",
type=int,
default=512,
help="Length of .npy arrays used for training",
)
parser.add_argument(
"--base_channels",
type=int,
default=128,
help="Base channels for generator and discriminator architectures",
)
parser.add_argument(
"--shape",
type=int,
default=128,
help="Length of spectrograms time axis",
)
parser.add_argument(
"--window",
type=int,
default=64,
help="Generator spectrogram window (must divide shape)",
)
parser.add_argument(
"--bs",
type=int,
default=32,
help="Batch size",
)
parser.add_argument(
"--lr",
type=float,
default=0.0001,
help="Learning Rate",
)
parser.add_argument(
"--gp_max_weight",
type=float,
default=10.0,
help="Maximum allowed R1 gradient penalty loss weight. The weight will self-adapt if high values are not needed for stable training.",
)
parser.add_argument(
"--totsamples",
type=int,
default=300000,
help="Max samples chosen per epoch",
)
parser.add_argument(
"--epochs",
type=int,
default=250,
help="Number of epochs",
)
parser.add_argument(
"--save_every",
type=int,
default=1,
help="Save after x epochs",
)
parser.add_argument(
"--mu_rescale",
type=float,
default=-25.0,
help="Spectrogram mu used to normalize",
)
parser.add_argument(
"--sigma_rescale",
type=float,
default=75.0,
help="Spectrogram sigma used to normalize",
)
parser.add_argument(
"--save_path",
type=str,
default="checkpoints",
help="Path where to save checkpoints",
)
parser.add_argument(
"--train_path",
type=str,
default="training_samples",
help="Path of training samples",
)
parser.add_argument(
"--dec_path",
type=str,
default="checkpoints/ae",
help="Path of pretrained decoders weights",
)
parser.add_argument(
"--load_path",
type=str,
default="None",
help="If not None, load models weights from this path",
)
parser.add_argument(
"--base_path",
type=str,
default="checkpoints",
help="Path where pretrained models are downloaded",
)
parser.add_argument(
"--log_path",
type=str,
default="logs",
help="Path where to save tensorboard logs",
)
parser.add_argument(
"--testing",
type=str2bool,
default=False,
help="True if optimizers weight do not need to be loaded",
)
parser.add_argument(
"--cpu",
type=str2bool,
default=False,
help="True if you wish to use cpu",
)
parser.add_argument(
"--mixed_precision",
type=str2bool,
default=True,
help="True if your GPU supports mixed precision",
)
parser.add_argument(
"--xla",
type=str2bool,
default=True,
help="True if you wish to improve training speed with XLA",
)
parser.add_argument(
"--share_gradio",
type=str2bool,
default=False,
help="True if you wish to create a public URL for the Gradio interface",
)
tmp_args = parser.parse_args()
args.hop = tmp_args.hop
args.mel_bins = tmp_args.mel_bins
args.sr = tmp_args.sr
args.small = tmp_args.small
args.latdepth = tmp_args.latdepth
args.coorddepth = tmp_args.coorddepth
args.max_lat_len = tmp_args.max_lat_len
args.base_channels = tmp_args.base_channels
args.shape = tmp_args.shape
args.window = tmp_args.window
args.bs = tmp_args.bs
args.lr = tmp_args.lr
args.gp_max_weight = tmp_args.gp_max_weight
args.totsamples = tmp_args.totsamples
args.epochs = tmp_args.epochs
args.save_every = tmp_args.save_every
args.mu_rescale = tmp_args.mu_rescale
args.sigma_rescale = tmp_args.sigma_rescale
args.save_path = tmp_args.save_path
args.train_path = tmp_args.train_path
args.dec_path = tmp_args.dec_path
args.load_path = tmp_args.load_path
args.base_path = tmp_args.base_path
args.log_path = tmp_args.log_path
args.testing = tmp_args.testing
args.cpu = tmp_args.cpu
args.mixed_precision = tmp_args.mixed_precision
args.xla = tmp_args.xla
args.share_gradio = tmp_args.share_gradio
if args.small:
args.latlen = 128
else:
args.latlen = 256
args.coordlen = (args.latlen // 2) * 3
print()
args.datatype = tf.float32
gpuls = tf.config.list_physical_devices("GPU")
if len(gpuls) == 0 or args.cpu:
args.cpu = True
args.mixed_precision = False
tf.config.set_visible_devices([], "GPU")
print()
print("Using CPU...")
print()
if args.mixed_precision:
args.datatype = tf.float16
print()
print("Using GPU with mixed precision enabled...")
print()
if not args.mixed_precision and not args.cpu:
print()
print("Using GPU without mixed precision...")
print()
return args
def parse_args():
args = EasyDict()
return params_args(args) |