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import importlib
import re
from coqpit import Coqpit
def to_camel(text):
text = text.capitalize()
return re.sub(r"(?!^)_([a-zA-Z])", lambda m: m.group(1).upper(), text)
def setup_model(config: Coqpit):
"""Load models directly from configuration."""
if "discriminator_model" in config and "generator_model" in config:
MyModel = importlib.import_module("TTS.vocoder.models.gan")
MyModel = getattr(MyModel, "GAN")
else:
MyModel = importlib.import_module("TTS.vocoder.models." + config.model.lower())
if config.model.lower() == "wavernn":
MyModel = getattr(MyModel, "Wavernn")
elif config.model.lower() == "gan":
MyModel = getattr(MyModel, "GAN")
elif config.model.lower() == "wavegrad":
MyModel = getattr(MyModel, "Wavegrad")
else:
try:
MyModel = getattr(MyModel, to_camel(config.model))
except ModuleNotFoundError as e:
raise ValueError(f"Model {config.model} not exist!") from e
print(" > Vocoder Model: {}".format(config.model))
return MyModel.init_from_config(config)
def setup_generator(c):
"""TODO: use config object as arguments"""
print(" > Generator Model: {}".format(c.generator_model))
MyModel = importlib.import_module("TTS.vocoder.models." + c.generator_model.lower())
MyModel = getattr(MyModel, to_camel(c.generator_model))
# this is to preserve the Wavernn class name (instead of Wavernn)
if c.generator_model.lower() in "hifigan_generator":
model = MyModel(in_channels=c.audio["num_mels"], out_channels=1, **c.generator_model_params)
elif c.generator_model.lower() in "melgan_generator":
model = MyModel(
in_channels=c.audio["num_mels"],
out_channels=1,
proj_kernel=7,
base_channels=512,
upsample_factors=c.generator_model_params["upsample_factors"],
res_kernel=3,
num_res_blocks=c.generator_model_params["num_res_blocks"],
)
elif c.generator_model in "melgan_fb_generator":
raise ValueError("melgan_fb_generator is now fullband_melgan_generator")
elif c.generator_model.lower() in "multiband_melgan_generator":
model = MyModel(
in_channels=c.audio["num_mels"],
out_channels=4,
proj_kernel=7,
base_channels=384,
upsample_factors=c.generator_model_params["upsample_factors"],
res_kernel=3,
num_res_blocks=c.generator_model_params["num_res_blocks"],
)
elif c.generator_model.lower() in "fullband_melgan_generator":
model = MyModel(
in_channels=c.audio["num_mels"],
out_channels=1,
proj_kernel=7,
base_channels=512,
upsample_factors=c.generator_model_params["upsample_factors"],
res_kernel=3,
num_res_blocks=c.generator_model_params["num_res_blocks"],
)
elif c.generator_model.lower() in "parallel_wavegan_generator":
model = MyModel(
in_channels=1,
out_channels=1,
kernel_size=3,
num_res_blocks=c.generator_model_params["num_res_blocks"],
stacks=c.generator_model_params["stacks"],
res_channels=64,
gate_channels=128,
skip_channels=64,
aux_channels=c.audio["num_mels"],
dropout=0.0,
bias=True,
use_weight_norm=True,
upsample_factors=c.generator_model_params["upsample_factors"],
)
elif c.generator_model.lower() in "univnet_generator":
model = MyModel(**c.generator_model_params)
else:
raise NotImplementedError(f"Model {c.generator_model} not implemented!")
return model
def setup_discriminator(c):
"""TODO: use config objekt as arguments"""
print(" > Discriminator Model: {}".format(c.discriminator_model))
if "parallel_wavegan" in c.discriminator_model:
MyModel = importlib.import_module("TTS.vocoder.models.parallel_wavegan_discriminator")
else:
MyModel = importlib.import_module("TTS.vocoder.models." + c.discriminator_model.lower())
MyModel = getattr(MyModel, to_camel(c.discriminator_model.lower()))
if c.discriminator_model in "hifigan_discriminator":
model = MyModel()
if c.discriminator_model in "random_window_discriminator":
model = MyModel(
cond_channels=c.audio["num_mels"],
hop_length=c.audio["hop_length"],
uncond_disc_donwsample_factors=c.discriminator_model_params["uncond_disc_donwsample_factors"],
cond_disc_downsample_factors=c.discriminator_model_params["cond_disc_downsample_factors"],
cond_disc_out_channels=c.discriminator_model_params["cond_disc_out_channels"],
window_sizes=c.discriminator_model_params["window_sizes"],
)
if c.discriminator_model in "melgan_multiscale_discriminator":
model = MyModel(
in_channels=1,
out_channels=1,
kernel_sizes=(5, 3),
base_channels=c.discriminator_model_params["base_channels"],
max_channels=c.discriminator_model_params["max_channels"],
downsample_factors=c.discriminator_model_params["downsample_factors"],
)
if c.discriminator_model == "residual_parallel_wavegan_discriminator":
model = MyModel(
in_channels=1,
out_channels=1,
kernel_size=3,
num_layers=c.discriminator_model_params["num_layers"],
stacks=c.discriminator_model_params["stacks"],
res_channels=64,
gate_channels=128,
skip_channels=64,
dropout=0.0,
bias=True,
nonlinear_activation="LeakyReLU",
nonlinear_activation_params={"negative_slope": 0.2},
)
if c.discriminator_model == "parallel_wavegan_discriminator":
model = MyModel(
in_channels=1,
out_channels=1,
kernel_size=3,
num_layers=c.discriminator_model_params["num_layers"],
conv_channels=64,
dilation_factor=1,
nonlinear_activation="LeakyReLU",
nonlinear_activation_params={"negative_slope": 0.2},
bias=True,
)
if c.discriminator_model == "univnet_discriminator":
model = MyModel()
return model
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