def get_model(conf, spkr_size=0, device="cuda"):
models = {"G": VQVAE2(conf, spkr_size=spkr_size).to(device)}
logging.info(models["G"])
# discriminator
if conf["gan_type"] == "lsgan":
output_channels = 1
if conf["acgan_flag"]:
output_channels += spkr_size
if conf["trainer_type"] in ["lsgan", "cyclegan"]:
if conf["discriminator_type"] == "pwg":
D = ParallelWaveGANDiscriminator(
in_channels=conf["input_size"],
out_channels=output_channels,
kernel_size=conf["discriminator_kernel_size"],
layers=conf["n_discriminator_layers"],
conv_channels=64,
dilation_factor=1,
nonlinear_activation="LeakyReLU",
nonlinear_activation_params={"negative_slope": 0.2},
bias=True,
use_weight_norm=True,
)
else:
raise NotImplementedError()
models.update({"D": D.to(device)})
logging.info(models["D"])
if conf["speaker_adversarial"]:
SPKRADV = SpeakerAdversarialNetwork(conf, spkr_size)
models.update({"SPKRADV": SPKRADV.to(device)})
logging.info(models["SPKRADV"])
return models
def _construct_net(self):
self.grl = GradientReversalLayer(scale=self.conf["spkradv_lambda"])
# TODO(k2kobayashi): investigate peformance of residual network
# if self.conf["use_residual_network"]:
# self.classifier = ResidualParallelWaveGANDiscriminator(
# in_channels=sum(
# self.conf["emb_dim"][:self.conf["n_vq_stacks"]]),
# out_channels=self.spkr_size,
# kernel_size=self.conf["spkradv_kernel_size"],
# layers=self.conf["n_spkradv_layers"],
# stacks=self.conf["n_spkradv_layers"] // 2,
# )
# else:
self.classifier = ParallelWaveGANDiscriminator(
in_channels=sum(self.conf["emb_dim"][:self.conf["n_vq_stacks"]]),
out_channels=self.spkr_size,
kernel_size=self.conf["spkradv_kernel_size"],
layers=self.conf["n_spkradv_layers"],
conv_channels=64,
dilation_factor=1,
nonlinear_activation="LeakyReLU",
nonlinear_activation_params={"negative_slope": 0.2},
bias=True,
use_weight_norm=True,
)
def test_parallel_wavegan_trainable(dict_g, dict_d, dict_loss):
# setup
batch_size = 4
batch_length = 4096
args_g = make_generator_args(**dict_g)
args_d = make_discriminator_args(**dict_d)
args_loss = make_mutli_reso_stft_loss_args(**dict_loss)
z = torch.randn(batch_size, 1, batch_length)
y = torch.randn(batch_size, 1, batch_length)
c = torch.randn(
batch_size,
args_g["aux_channels"],
batch_length // np.prod(args_g["upsample_params"]["upsample_scales"]) +
2 * args_g["aux_context_window"],
)
model_g = ParallelWaveGANGenerator(**args_g)
model_d = ParallelWaveGANDiscriminator(**args_d)
aux_criterion = MultiResolutionSTFTLoss(**args_loss)
gen_adv_criterion = GeneratorAdversarialLoss()
dis_adv_criterion = DiscriminatorAdversarialLoss()
optimizer_g = RAdam(model_g.parameters())
optimizer_d = RAdam(model_d.parameters())
# check generator trainable
y_hat = model_g(z, c)
p_hat = model_d(y_hat)
adv_loss = gen_adv_criterion(p_hat)
sc_loss, mag_loss = aux_criterion(y_hat, y)
aux_loss = sc_loss + mag_loss
loss_g = adv_loss + aux_loss
optimizer_g.zero_grad()
loss_g.backward()
optimizer_g.step()
# check discriminator trainable
p = model_d(y)
p_hat = model_d(y_hat.detach())
real_loss, fake_loss = dis_adv_criterion(p_hat, p)
loss_d = real_loss + fake_loss
optimizer_d.zero_grad()
loss_d.backward()
optimizer_d.step()
def test_parallel_wavegan_trainable(dict_g, dict_d, dict_loss):
# setup
batch_size = 4
batch_length = 4096
args_g = make_generator_args(**dict_g)
args_d = make_discriminator_args(**dict_d)
args_loss = make_mutli_reso_stft_loss_args(**dict_loss)
z = torch.randn(batch_size, 1, batch_length)
y = torch.randn(batch_size, 1, batch_length)
c = torch.randn(
batch_size, args_g["aux_channels"],
batch_length // np.prod(args_g["upsample_params"]["upsample_scales"]) +
2 * args_g["aux_context_window"])
model_g = ParallelWaveGANGenerator(**args_g)
model_d = ParallelWaveGANDiscriminator(**args_d)
aux_criterion = MultiResolutionSTFTLoss(**args_loss)
optimizer_g = RAdam(model_g.parameters())
optimizer_d = RAdam(model_d.parameters())
# check generator trainable
y_hat = model_g(z, c)
p_hat = model_d(y_hat)
y, y_hat, p_hat = y.squeeze(1), y_hat.squeeze(1), p_hat.squeeze(1)
adv_loss = F.mse_loss(p_hat, p_hat.new_ones(p_hat.size()))
sc_loss, mag_loss = aux_criterion(y_hat, y)
aux_loss = sc_loss + mag_loss
loss_g = adv_loss + aux_loss
optimizer_g.zero_grad()
loss_g.backward()
optimizer_g.step()
# check discriminator trainable
y, y_hat = y.unsqueeze(1), y_hat.unsqueeze(1).detach()
p = model_d(y)
p_hat = model_d(y_hat)
p, p_hat = p.squeeze(1), p_hat.squeeze(1)
loss_d = F.mse_loss(p, p.new_ones(p.size())) + F.mse_loss(
p_hat, p_hat.new_zeros(p_hat.size()))
optimizer_d.zero_grad()
loss_d.backward()
optimizer_d.step()
def _construct_net(self):
self.grl = GradientReversalLayer(scale=self.conf["spkradv_lambda"])
self.classifier = ParallelWaveGANDiscriminator(
in_channels=sum(self.conf["emb_dim"][:self.conf["n_vq_stacks"]]),
out_channels=self.spkr_size,
kernel_size=self.conf["spkradv_kernel_size"],
layers=self.conf["n_spkradv_layers"],
conv_channels=64,
dilation_factor=1,
nonlinear_activation="LeakyReLU",
nonlinear_activation_params={"negative_slope": 0.2},
bias=True,
use_weight_norm=True,
)
def get_model(conf, spkr_size=0, device="cuda"):
G = VQVAE2(conf, spkr_size=spkr_size).to(device)
# discriminator
if conf["gan_type"] == "lsgan":
output_channels = 1
if conf["acgan_flag"]:
output_channels += spkr_size
if conf["discriminator_type"] == "pwg":
D = ParallelWaveGANDiscriminator(
in_channels=conf["input_size"],
out_channels=output_channels,
kernel_size=conf["kernel_size"][0],
layers=conf["n_discriminator_layers"],
conv_channels=64,
dilation_factor=1,
nonlinear_activation="LeakyReLU",
nonlinear_activation_params={"negative_slope": 0.2},
bias=True,
use_weight_norm=True,
)
return {"G": G.to(device), "D": D.to(device)}
def get_model(conf, spkr_size=0, device="cuda"):
models = {"G": VQVAE2(conf, spkr_size=spkr_size).to(device)}
logging.info(models["G"])
# speaker adversarial network
if conf["use_spkradv_training"]:
SPKRADV = SpeakerAdversarialNetwork(conf, spkr_size)
models.update({"SPKRADV": SPKRADV.to(device)})
logging.info(models["SPKRADV"])
# spkr classifier network
if conf["use_spkr_classifier"]:
C = ParallelWaveGANDiscriminator(
in_channels=conf["input_size"],
out_channels=spkr_size,
kernel_size=conf["spkr_classifier_kernel_size"],
layers=conf["n_spkr_classifier_layers"],
conv_channels=64,
dilation_factor=1,
nonlinear_activation="LeakyReLU",
nonlinear_activation_params={"negative_slope": 0.2},
bias=True,
use_weight_norm=True,
)
models.update({"C": C.to(device)})
logging.info(models["C"])
# discriminator
if conf["trainer_type"] in ["lsgan", "cyclegan", "stargan"]:
input_channels = conf["input_size"]
if conf["use_D_uv"]:
input_channels += 1 # for uv flag
if conf["use_D_spkrcode"]:
if not conf["use_spkr_embedding"]:
input_channels += spkr_size
else:
input_channels += conf["spkr_embedding_size"]
if conf["gan_type"] == "lsgan":
output_channels = 1
if conf["acgan_flag"]:
output_channels += spkr_size
D = ParallelWaveGANDiscriminator(
in_channels=input_channels,
out_channels=output_channels,
kernel_size=conf["discriminator_kernel_size"],
layers=conf["n_discriminator_layers"],
conv_channels=64,
dilation_factor=1,
nonlinear_activation="LeakyReLU",
nonlinear_activation_params={"negative_slope": 0.2},
bias=True,
use_weight_norm=True,
)
models.update({"D": D.to(device)})
logging.info(models["D"])
return models
def main():
"""Run training process."""
parser = argparse.ArgumentParser(
description=
"Train Parallel WaveGAN (See detail in parallel_wavegan/bin/train.py)."
)
parser.add_argument("--train-dumpdir",
type=str,
required=True,
help="directory including trainning data.")
parser.add_argument("--dev-dumpdir",
type=str,
required=True,
help="directory including development data.")
parser.add_argument("--outdir",
type=str,
required=True,
help="directory to save checkpoints.")
parser.add_argument("--config",
type=str,
required=True,
help="yaml format configuration file.")
parser.add_argument(
"--resume",
default="",
type=str,
nargs="?",
help="checkpoint file path to resume training. (default=\"\")")
parser.add_argument(
"--verbose",
type=int,
default=1,
help="logging level. higher is more logging. (default=1)")
args = parser.parse_args()
# set logger
if args.verbose > 1:
logging.basicConfig(
level=logging.DEBUG,
format=
"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s")
elif args.verbose > 0:
logging.basicConfig(
level=logging.INFO,
format=
"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s")
else:
logging.basicConfig(
level=logging.WARN,
format=
"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s")
logging.warning('skip DEBUG/INFO messages')
# check directory existence
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
# load and save config
with open(args.config) as f:
config = yaml.load(f, Loader=yaml.Loader)
config.update(vars(args))
with open(os.path.join(args.outdir, "config.yml"), "w") as f:
yaml.dump(config, f, Dumper=yaml.Dumper)
for key, value in config.items():
logging.info(f"{key} = {value}")
# get dataset
if config["remove_short_samples"]:
mel_length_threshold = config["batch_max_steps"] // config["hop_size"] + \
2 * config["generator_params"]["aux_context_window"]
else:
mel_length_threshold = None
if config["format"] == "hdf5":
audio_query, mel_query = "*.h5", "*.h5"
audio_load_fn = lambda x: read_hdf5(x, "wave") # NOQA
mel_load_fn = lambda x: read_hdf5(x, "feats") # NOQA
elif config["format"] == "npy":
audio_query, mel_query = "*-wave.npy", "*-feats.npy"
audio_load_fn = np.load
mel_load_fn = np.load
else:
raise ValueError("support only hdf5 or npy format.")
dataset = {
"train":
AudioMelDataset(
root_dir=args.train_dumpdir,
audio_query=audio_query,
mel_query=mel_query,
audio_load_fn=audio_load_fn,
mel_load_fn=mel_load_fn,
mel_length_threshold=mel_length_threshold,
allow_cache=config.get("allow_cache", False), # keep compatibilty
),
"dev":
AudioMelDataset(
root_dir=args.dev_dumpdir,
audio_query=audio_query,
mel_query=mel_query,
audio_load_fn=audio_load_fn,
mel_load_fn=mel_load_fn,
mel_length_threshold=mel_length_threshold,
allow_cache=config.get("allow_cache", False), # keep compatibilty
),
}
# get data loader
if torch.cuda.is_available():
device = torch.device("cuda")
else:
device = torch.device("cpu")
collater = Collater(
batch_max_steps=config["batch_max_steps"],
hop_size=config["hop_size"],
aux_context_window=config["generator_params"]["aux_context_window"],
)
data_loader = {
"train":
DataLoader(dataset=dataset["train"],
shuffle=True,
collate_fn=collater,
batch_size=config["batch_size"],
num_workers=config["num_workers"],
pin_memory=config["pin_memory"]),
"dev":
DataLoader(dataset=dataset["dev"],
shuffle=True,
collate_fn=collater,
batch_size=config["batch_size"],
num_workers=config["num_workers"],
pin_memory=config["pin_memory"]),
}
# define models and optimizers
model = {
"generator":
ParallelWaveGANGenerator(**config["generator_params"]).to(device),
"discriminator":
ParallelWaveGANDiscriminator(
**config["discriminator_params"]).to(device),
}
criterion = {
"stft":
MultiResolutionSTFTLoss(**config["stft_loss_params"]).to(device),
"mse": torch.nn.MSELoss().to(device),
}
optimizer = {
"generator":
RAdam(model["generator"].parameters(),
**config["generator_optimizer_params"]),
"discriminator":
RAdam(model["discriminator"].parameters(),
**config["discriminator_optimizer_params"]),
}
scheduler = {
"generator":
torch.optim.lr_scheduler.StepLR(
optimizer=optimizer["generator"],
**config["generator_scheduler_params"]),
"discriminator":
torch.optim.lr_scheduler.StepLR(
optimizer=optimizer["discriminator"],
**config["discriminator_scheduler_params"]),
}
logging.info(model["generator"])
logging.info(model["discriminator"])
# define trainer
trainer = Trainer(
steps=0,
epochs=0,
data_loader=data_loader,
model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
config=config,
device=device,
)
# resume from checkpoint
if len(args.resume) != 0:
trainer.load_checkpoint(args.resume)
logging.info(f"resumed from {args.resume}.")
# run training loop
try:
trainer.run()
finally:
trainer.save_checkpoint(
os.path.join(config["outdir"],
f"checkpoint-{trainer.steps}steps.pkl"))
logging.info(f"successfully saved checkpoint @ {trainer.steps}steps.")
def main():
"""Run training process."""
parser = argparse.ArgumentParser(
description=
"Train Parallel WaveGAN (See detail in parallel_wavegan/bin/train.py)."
)
parser.add_argument("--train-dumpdir",
type=str,
required=True,
help="directory including training data.")
parser.add_argument("--dev-dumpdir",
type=str,
required=True,
help="directory including development data.")
parser.add_argument("--outdir",
type=str,
required=True,
help="directory to save checkpoints.")
parser.add_argument("--config",
type=str,
required=True,
help="yaml format configuration file.")
parser.add_argument(
"--resume",
default="",
type=str,
nargs="?",
help="checkpoint file path to resume training. (default=\"\")")
parser.add_argument(
"--verbose",
type=int,
default=1,
help="logging level. higher is more logging. (default=1)")
parser.add_argument(
"--rank",
"--local_rank",
default=0,
type=int,
help="rank for distributed training. no need to explictly specify.")
args = parser.parse_args()
args.distributed = False
if not torch.cuda.is_available():
device = torch.device("cpu")
else:
device = torch.device("cuda")
# effective when using fixed size inputs
# see https://discuss.pytorch.org/t/what-does-torch-backends-cudnn-benchmark-do/5936
torch.backends.cudnn.benchmark = True
torch.cuda.set_device(args.rank)
# setup for distributed training
# see example: https://github.com/NVIDIA/apex/tree/master/examples/simple/distributed
if "WORLD_SIZE" in os.environ:
args.world_size = int(os.environ["WORLD_SIZE"])
args.distributed = args.world_size > 1
if args.distributed:
torch.distributed.init_process_group(backend="nccl",
init_method="env://")
# suppress logging for distributed training
if args.rank != 0:
sys.stdout = open(os.devnull, "w")
# set logger
if args.verbose > 1:
logging.basicConfig(
level=logging.DEBUG,
stream=sys.stdout,
format=
"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s")
elif args.verbose > 0:
logging.basicConfig(
level=logging.INFO,
stream=sys.stdout,
format=
"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s")
else:
logging.basicConfig(
level=logging.WARN,
stream=sys.stdout,
format=
"%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s")
logging.warning("skip DEBUG/INFO messages")
# check directory existence
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
# load and save config
with open(args.config) as f:
config = yaml.load(f, Loader=yaml.Loader)
config.update(vars(args))
config["version"] = parallel_wavegan.__version__ # add version info
with open(os.path.join(args.outdir, "config.yml"), "w") as f:
yaml.dump(config, f, Dumper=yaml.Dumper)
for key, value in config.items():
logging.info(f"{key} = {value}")
# get dataset
if config["remove_short_samples"]:
mel_length_threshold = config["batch_max_steps"] // config["hop_size"] + \
2 * config["generator_params"]["aux_context_window"]
else:
mel_length_threshold = None
if config["format"] == "hdf5":
audio_query, mel_query = "*.h5", "*.h5"
audio_load_fn = lambda x: read_hdf5(x, "wave") # NOQA
mel_load_fn = lambda x: read_hdf5(x, "feats") # NOQA
elif config["format"] == "npy":
audio_query, mel_query = "*-wave.npy", "*-feats.npy"
audio_load_fn = np.load
mel_load_fn = np.load
else:
raise ValueError("support only hdf5 or npy format.")
dataset = {
"train":
AudioMelDataset(root_dir=args.train_dumpdir,
audio_query=audio_query,
mel_query=mel_query,
audio_load_fn=audio_load_fn,
mel_load_fn=mel_load_fn,
mel_length_threshold=mel_length_threshold,
allow_cache=config.get("allow_cache",
False)), # keep compatibility
"dev":
AudioMelDataset(root_dir=args.dev_dumpdir,
audio_query=audio_query,
mel_query=mel_query,
audio_load_fn=audio_load_fn,
mel_load_fn=mel_load_fn,
mel_length_threshold=mel_length_threshold,
allow_cache=config.get("allow_cache",
False)), # keep compatibility
}
# get data loader
collater = Collater(
batch_max_steps=config["batch_max_steps"],
hop_size=config["hop_size"],
aux_context_window=config["generator_params"]["aux_context_window"],
)
train_sampler, dev_sampler = None, None
if args.distributed:
# setup sampler for distributed training
from torch.utils.data.distributed import DistributedSampler
train_sampler = DistributedSampler(dataset=dataset["train"],
num_replicas=args.world_size,
rank=args.rank,
shuffle=True)
dev_sampler = DistributedSampler(dataset=dataset["dev"],
num_replicas=args.world_size,
rank=args.rank,
shuffle=False)
data_loader = {
"train":
DataLoader(dataset=dataset["train"],
shuffle=False if args.distributed else True,
collate_fn=collater,
batch_size=config["batch_size"],
num_workers=config["num_workers"],
sampler=train_sampler,
pin_memory=config["pin_memory"]),
"dev":
DataLoader(dataset=dataset["dev"],
shuffle=False if args.distributed else True,
collate_fn=collater,
batch_size=config["batch_size"],
num_workers=config["num_workers"],
sampler=dev_sampler,
pin_memory=config["pin_memory"]),
}
# define models and optimizers
model = {
"generator":
ParallelWaveGANGenerator(**config["generator_params"]).to(device),
"discriminator":
ParallelWaveGANDiscriminator(
**config["discriminator_params"]).to(device),
}
criterion = {
"stft":
MultiResolutionSTFTLoss(**config["stft_loss_params"]).to(device),
"mse": torch.nn.MSELoss().to(device),
}
optimizer = {
"generator":
RAdam(model["generator"].parameters(),
**config["generator_optimizer_params"]),
"discriminator":
RAdam(model["discriminator"].parameters(),
**config["discriminator_optimizer_params"]),
}
scheduler = {
"generator":
torch.optim.lr_scheduler.StepLR(
optimizer=optimizer["generator"],
**config["generator_scheduler_params"]),
"discriminator":
torch.optim.lr_scheduler.StepLR(
optimizer=optimizer["discriminator"],
**config["discriminator_scheduler_params"]),
}
if args.distributed:
# wrap model for distributed training
try:
from apex.parallel import DistributedDataParallel
except ImportError:
raise ImportError(
"apex is not installed. please check https://github.com/NVIDIA/apex."
)
model["generator"] = DistributedDataParallel(model["generator"])
model["discriminator"] = DistributedDataParallel(
model["discriminator"])
logging.info(model["generator"])
logging.info(model["discriminator"])
# define trainer
trainer = Trainer(
steps=0,
epochs=0,
data_loader=data_loader,
model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
config=config,
device=device,
)
# resume from checkpoint
if len(args.resume) != 0:
trainer.load_checkpoint(args.resume)
logging.info(f"successfully resumed from {args.resume}.")
# run training loop
try:
trainer.run()
except KeyboardInterrupt:
trainer.save_checkpoint(
os.path.join(config["outdir"],
f"checkpoint-{trainer.steps}steps.pkl"))
logging.info(f"successfully saved checkpoint @ {trainer.steps}steps.")
