File size: 6,474 Bytes
8c70653 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 |
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
|