Medix-AI/coqui-tts
Medix-AI
/
coqui-tts
mirror of https://github.com/coqui-ai/TTS.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

load pwgan models

This commit is contained in:
erogol 2020-07-17 11:37:33 +02:00
parent 320bc29496
commit 69f525f17d
1 changed files with 50 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

53
TTS/vocoder/utils/generic_utils.py
View File

@ -67,14 +67,34 @@ def setup_generator(c):
upsample_factors=c.generator_model_params['upsample_factors'],
res_kernel=3,
num_res_blocks=c.generator_model_params['num_res_blocks'])
if c.generator_model 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'],
aux_context_window=c['conv_pad'],
dropout=0.0,
bias=True,
use_weight_norm=True,
upsample_conditional_features=True,
upsample_factors=c.generator_model_params['upsample_factors'])
return model
def setup_discriminator(c):
print(" > Discriminator Model: {}".format(c.discriminator_model))
MyModel = importlib.import_module('TTS.vocoder.models.' +
c.discriminator_model.lower())
MyModel = getattr(MyModel, to_camel(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 'random_window_discriminator':
model = MyModel(
cond_channels=c.audio['num_mels'],
@ -95,6 +115,33 @@ def setup_discriminator(c):
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
)
return model