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 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 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)}
