def test_melgan_trainable_with_melgan_discriminator(dict_g, dict_d, dict_loss):
# setup
batch_size = 4
batch_length = 4096
args_g = make_melgan_generator_args(**dict_g)
args_d = make_melgan_discriminator_args(**dict_d)
args_loss = make_mutli_reso_stft_loss_args(**dict_loss)
y = torch.randn(batch_size, 1, batch_length)
c = torch.randn(batch_size, args_g["in_channels"],
batch_length // np.prod(
args_g["upsample_scales"]))
model_g = MelGANGenerator(**args_g)
model_d = MelGANMultiScaleDiscriminator(**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(c)
p_hat = model_d(y_hat)
y, y_hat = y.squeeze(1), y_hat.squeeze(1)
sc_loss, mag_loss = aux_criterion(y_hat, y)
aux_loss = sc_loss + mag_loss
adv_loss = 0.0
for i in range(len(p_hat)):
adv_loss += F.mse_loss(
p_hat[i][-1], p_hat[i][-1].new_ones(p_hat[i][-1].size()))
adv_loss /= (i + 1)
with torch.no_grad():
p = model_d(y.unsqueeze(1))
fm_loss = 0.0
for i in range(len(p_hat)):
for j in range(len(p_hat[i]) - 1):
fm_loss += F.l1_loss(p_hat[i][j], p[i][j].detach())
fm_loss /= (i + 1) * j
loss_g = adv_loss + aux_loss + fm_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)
real_loss = 0.0
fake_loss = 0.0
for i in range(len(p)):
real_loss += F.mse_loss(
p[i][-1], p[i][-1].new_ones(p[i][-1].size()))
fake_loss += F.mse_loss(
p_hat[i][-1], p_hat[i][-1].new_zeros(p_hat[i][-1].size()))
real_loss /= (i + 1)
fake_loss /= (i + 1)
loss_d = real_loss + fake_loss
optimizer_d.zero_grad()
loss_d.backward()
optimizer_d.step()
def test_melgan_trainable_with_melgan_discriminator(dict_g, dict_d, dict_loss):
# setup
batch_size = 4
batch_length = 4096
args_g = make_melgan_generator_args(**dict_g)
args_d = make_melgan_discriminator_args(**dict_d)
args_loss = make_mutli_reso_stft_loss_args(**dict_loss)
y = torch.randn(batch_size, 1, batch_length)
c = torch.randn(
batch_size,
args_g["in_channels"],
batch_length // np.prod(args_g["upsample_scales"]),
)
model_g = MelGANGenerator(**args_g)
model_d = MelGANMultiScaleDiscriminator(**args_d)
aux_criterion = MultiResolutionSTFTLoss(**args_loss)
feat_match_criterion = FeatureMatchLoss()
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(c)
p_hat = model_d(y_hat)
sc_loss, mag_loss = aux_criterion(y_hat, y)
aux_loss = sc_loss + mag_loss
adv_loss = gen_adv_criterion(p_hat)
with torch.no_grad():
p = model_d(y)
fm_loss = feat_match_criterion(p_hat, p)
loss_g = adv_loss + aux_loss + fm_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_with_residual_discriminator_trainable(
dict_g, dict_d, dict_loss):
# setup
batch_size = 4
batch_length = 4096
args_g = make_generator_args(**dict_g)
args_d = make_residual_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 = ResidualParallelWaveGANDiscriminator(**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 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"].get("aux_context_window", 0)
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"],
# keep compatibility
aux_context_window=config["generator_params"].get(
"aux_context_window", 0),
# keep compatibility
use_noise_input=config.get(
"generator_type", "ParallelWaveGANGenerator") != "MelGANGenerator",
)
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
generator_class = getattr(
parallel_wavegan.models,
# keep compatibility
config.get("generator_type", "ParallelWaveGANGenerator"),
)
discriminator_class = getattr(
parallel_wavegan.models,
# keep compatibility
config.get("discriminator_type", "ParallelWaveGANDiscriminator"),
)
model = {
"generator":
generator_class(**config["generator_params"]).to(device),
"discriminator":
discriminator_class(**config["discriminator_params"]).to(device),
}
criterion = {
"stft":
MultiResolutionSTFTLoss(**config["stft_loss_params"]).to(device),
"mse": torch.nn.MSELoss().to(device),
}
if config.get("use_feat_match_loss", False): # keep compatibility
criterion["l1"] = torch.nn.L1Loss().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.")
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():
"""The main function that runs training process."""
# initialize the argument parser
parser = argparse.ArgumentParser(description="Train Parallel WaveGAN.") # just a description of the job that the parser is used to support.
# Add arguments to the parser
#first is name of the argument
#default: The value produced if the argument is absent from the command line
#type: The type to which the command-line argument should be converted
#help: hint that appears when the user doesnot know what is this argument [-h]
#required: Whether or not the command-line option may be omitted (optionals only)
#nargs:The number of command-line arguments that should be consumed
# "?" One argument will be consumed from the command line if possible, and produced as a single item. If no command-line argument is present,
# the value from default will be produced.
# Note that for optional arguments, there is an additional case - the option string is present but not followed by a command-line argument. In this case the value from const will be produced.
parser.add_argument("--train-wav-scp", default=None, type=str,
help="kaldi-style wav.scp file for training. "
"you need to specify either train-*-scp or train-dumpdir.")
parser.add_argument("--train-feats-scp", default=None, type=str,
help="kaldi-style feats.scp file for training. "
"you need to specify either train-*-scp or train-dumpdir.")
parser.add_argument("--train-segments", default=None, type=str,
help="kaldi-style segments file for training.")
parser.add_argument("--train-dumpdir", default=None, type=str,
help="directory including training data. "
"you need to specify either train-*-scp or train-dumpdir.")
parser.add_argument("--dev-wav-scp", default=None, type=str,
help="kaldi-style wav.scp file for validation. "
"you need to specify either dev-*-scp or dev-dumpdir.")
parser.add_argument("--dev-feats-scp", default=None, type=str,
help="kaldi-style feats.scp file for vaidation. "
"you need to specify either dev-*-scp or dev-dumpdir.")
parser.add_argument("--dev-segments", default=None, type=str,
help="kaldi-style segments file for validation.")
parser.add_argument("--dev-dumpdir", default=None, type=str,
help="directory including development data. "
"you need to specify either dev-*-scp or dev-dumpdir.")
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("--pretrain", default="", type=str, nargs="?",
help="checkpoint file path to load pretrained params. (default=\"\")")
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.")
# parse all the input arguments
args = parser.parse_args()
args.distributed = False
if not torch.cuda.is_available(): #if gpu is not available
device = torch.device("cpu") #train on cpu
else: #GPU
device = torch.device("cuda")#train on gpu
torch.backends.cudnn.benchmark = True # effective when using fixed size inputs (no conditional layers or layers inside loops),benchmark mode in cudnn,faster runtime
torch.cuda.set_device(args.rank) # sets the default GPU for distributed training
if "WORLD_SIZE" in os.environ:#determine max number of parallel processes (distributed)
args.world_size = int(os.environ["WORLD_SIZE"]) #get the world size from the os
args.distributed = args.world_size > 1 #set distributed if woldsize > 1
if args.distributed:
torch.distributed.init_process_group(backend="nccl", init_method="env://") #Use the NCCL backend for distributed GPU training (Rule of thumb)
#NCCL:since it currently provides the best distributed GPU training performance, especially for multiprocess single-node or multi-node distributed training
# suppress logging for distributed training
if args.rank != 0: #if process is not p0
sys.stdout = open(os.devnull, "w")#DEVNULL is Special value that can be used as the stdin, stdout or stderr argument to
# set logger
if args.verbose > 1: #if level of logging is heigher then 1
logging.basicConfig( #configure the logging
level=logging.DEBUG, stream=sys.stdout, #heigh logging level,detailed information, typically of interest only when diagnosing problems.
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s") #format includes Time,module,line#,level,and message.
elif args.verbose > 0:#if level of logging is between 0,1
logging.basicConfig(#configure the logging
level=logging.INFO, stream=sys.stdout,#moderate logging level,Confirmation that things are working as expected.
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s")#format includes Time,module,line#,level,and message.
else:#if level of logging is 0
logging.basicConfig(#configure the logging
level=logging.WARN, stream=sys.stdout,#low logging level,An indication that something unexpected happened, or indicative of some problem in the near future (e.g. ‘disk space low’).
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s")#format includes Time,module,line#,level,and message.
logging.warning("Skip DEBUG/INFO messages")#tell the user that he will skip logging DEBUG/INFO messages by choosing this level.
# check directory existence
if not os.path.exists(args.outdir): #directory to save checkpoints
os.makedirs(args.outdir)
# check arguments
if (args.train_feats_scp is not None and args.train_dumpdir is not None) or \
(args.train_feats_scp is None and args.train_dumpdir is None):
# if the user chooses both training data files (examples) or
# the user doesnot choose any training data file
raise ValueError("Please specify either --train-dumpdir or --train-*-scp.") #raise an error to tell the user to choose one training file
if (args.dev_feats_scp is not None and args.dev_dumpdir is not None) or \
(args.dev_feats_scp is None and args.dev_dumpdir is None):
# if the user chooses both validatation data files (examples) or
# the user doesnot choose any validatation data file
raise ValueError("Please specify either --dev-dumpdir or --dev-*-scp.") #raise an error to tell the user to choose one validation data file
# load config
with open(args.config) as f:#open configuration file (yaml format)
config = yaml.load(f, Loader=yaml.Loader) #load configuration file (yaml format to python object)
# update config
config.update(vars(args))#update arguments in configuration file
config["version"] = parallel_wavegan.__version__ # add parallel wavegan version info
# save config
with open(os.path.join(args.outdir, "config.yml"), "w") as f:#open outdir/config.yml
yaml.dump(config, f, Dumper=yaml.Dumper) #dump function accepts a Python object and produces a YAML document.
# add config info to the high level logger.
for key, value in config.items():
logging.info(f"{key} = {value}")
# get dataset
if config["remove_short_samples"]:#if configuration tells to remove short samples from training.
mel_length_threshold = config["batch_max_steps"] // config["hop_size"] + \
2 * config["generator_params"].get("aux_context_window", 0)#th of length = floor(batch_max_steps/hop_size) + 2 * (generator_params.aux_context_window)
else:
mel_length_threshold = None # No th.
if args.train_wav_scp is None or args.dev_wav_scp is None: #if at least one of training or evaluating datasets = None
if config["format"] == "hdf5":# format of data = hdf5
audio_query, mel_query = "*.h5", "*.h5" # audio and text queries = "...".h5
#lambda example:
#x = lambda a, b: a * b
#x(5, 6)-->x(a=5,b=6)=a*b=5*6=30
audio_load_fn = lambda x: read_hdf5(x, "wave") # The function to load data,NOQA
mel_load_fn = lambda x: read_hdf5(x, "feats") # The function to load data,NOQA
elif config["format"] == "npy":# format of data = npy
audio_query, mel_query = "*-wave.npy", "*-feats.npy" #audio query = "..."-wave.npy and text query = "..."-feats.h5
audio_load_fn = np.load#The function to load data.
mel_load_fn = np.load#The function to load data.
else:#if any other data format
raise ValueError("support only hdf5 or npy format.") #raise error to tell the user the data format is not supported.
if args.train_dumpdir is not None: # if training ds is not None
train_dataset = AudioMelDataset( # define the training dataset
root_dir=args.train_dumpdir,#the directory of ds.
audio_query=audio_query,#audio query according to format above.
mel_query=mel_query,#mel query according to format above.
audio_load_fn=audio_load_fn,#load the function that loads the audio data according to format above.
mel_load_fn=mel_load_fn,#load the function that loads the mel data according to format above.
mel_length_threshold=mel_length_threshold,#th to remove short samples -calculated above-.
allow_cache=config.get("allow_cache", False), # keep compatibility.
)
else:# if training ds is None
train_dataset = AudioMelSCPDataset(# define the training dataset
wav_scp=args.train_wav_scp,
feats_scp=args.train_feats_scp,
segments=args.train_segments, #segments of dataset
mel_length_threshold=mel_length_threshold,#th to remove short samples -calculated above-.
allow_cache=config.get("allow_cache", False), # keep compatibility
)
logging.info(f"The number of training files = {len(train_dataset)}.") # add length of trainning data set to the logger.
if args.dev_dumpdir is not None: #if evaluating ds is not None
dev_dataset = AudioMelDataset( # define the evaluating dataset
root_dir=args.dev_dumpdir,#the directory of ds.
audio_query=audio_query,#audio query according to format above.
mel_query=mel_query,#mel query according to format above.
audio_load_fn=audio_load_fn,#load the function that loads the audio data according to format above.
mel_load_fn=mel_load_fn,#load the function that loads the mel data according to format above.
mel_length_threshold=mel_length_threshold,#th to remove short samples -calculated above-.
allow_cache=config.get("allow_cache", False), # keep compatibility
)
else:# if evaluating ds is None
dev_dataset = AudioMelSCPDataset(
wav_scp=args.dev_wav_scp,
feats_scp=args.dev_feats_scp,
segments=args.dev_segments,#segments of dataset
mel_length_threshold=mel_length_threshold,#th to remove short samples -calculated above-.
allow_cache=config.get("allow_cache", False), # keep compatibility
)
logging.info(f"The number of development files = {len(dev_dataset)}.") # add length of evaluating data set to the logger.
dataset = {
"train": train_dataset,
"dev": dev_dataset,
} #define the whole dataset used which is divided into training and evaluating datasets
# get data loader
collater = Collater(
batch_max_steps=config["batch_max_steps"],
hop_size=config["hop_size"],
# keep compatibility
aux_context_window=config["generator_params"].get("aux_context_window", 0),
# keep compatibility
use_noise_input=config.get(
"generator_type", "ParallelWaveGANGenerator") != "MelGANGenerator",
)
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
generator_class = getattr(
parallel_wavegan.models,
# keep compatibility
config.get("generator_type", "ParallelWaveGANGenerator"),
)
discriminator_class = getattr(
parallel_wavegan.models,
# keep compatibility
config.get("discriminator_type", "ParallelWaveGANDiscriminator"),
)
model = {
"generator": generator_class(
**config["generator_params"]).to(device),
"discriminator": discriminator_class(
**config["discriminator_params"]).to(device),
}
criterion = {
"stft": MultiResolutionSTFTLoss(
**config["stft_loss_params"]).to(device),
"mse": torch.nn.MSELoss().to(device),
}
if config.get("use_feat_match_loss", False): # keep compatibility
criterion["l1"] = torch.nn.L1Loss().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,
)
# load pretrained parameters from checkpoint
if len(args.pretrain) != 0:
trainer.load_checkpoint(args.pretrain, load_only_params=True)
logging.info(f"Successfully load parameters from {args.pretrain}.")
# 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.")