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# -*- coding: utf-8 -*-
# Copyright 2020 Minh Nguyen (@dathudeptrai)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""MelGAN Config object."""
from tensorflow_tts.configs import BaseConfig
class MelGANGeneratorConfig(BaseConfig):
"""Initialize MelGAN Generator Config."""
def __init__(
self,
out_channels=1,
kernel_size=7,
filters=512,
use_bias=True,
upsample_scales=[8, 8, 2, 2],
stack_kernel_size=3,
stacks=3,
nonlinear_activation="LeakyReLU",
nonlinear_activation_params={"alpha": 0.2},
padding_type="REFLECT",
use_final_nolinear_activation=True,
is_weight_norm=True,
initializer_seed=42,
**kwargs
):
"""Init parameters for MelGAN Generator model."""
self.out_channels = out_channels
self.kernel_size = kernel_size
self.filters = filters
self.use_bias = use_bias
self.upsample_scales = upsample_scales
self.stack_kernel_size = stack_kernel_size
self.stacks = stacks
self.nonlinear_activation = nonlinear_activation
self.nonlinear_activation_params = nonlinear_activation_params
self.padding_type = padding_type
self.use_final_nolinear_activation = use_final_nolinear_activation
self.is_weight_norm = is_weight_norm
self.initializer_seed = initializer_seed
class MelGANDiscriminatorConfig(object):
"""Initialize MelGAN Discriminator Config."""
def __init__(
self,
out_channels=1,
scales=3,
downsample_pooling="AveragePooling1D",
downsample_pooling_params={"pool_size": 4, "strides": 2,},
kernel_sizes=[5, 3],
filters=16,
max_downsample_filters=1024,
use_bias=True,
downsample_scales=[4, 4, 4, 4],
nonlinear_activation="LeakyReLU",
nonlinear_activation_params={"alpha": 0.2},
padding_type="REFLECT",
is_weight_norm=True,
initializer_seed=42,
**kwargs
):
"""Init parameters for MelGAN Discriminator model."""
self.out_channels = out_channels
self.scales = scales
self.downsample_pooling = downsample_pooling
self.downsample_pooling_params = downsample_pooling_params
self.kernel_sizes = kernel_sizes
self.filters = filters
self.max_downsample_filters = max_downsample_filters
self.use_bias = use_bias
self.downsample_scales = downsample_scales
self.nonlinear_activation = nonlinear_activation
self.nonlinear_activation_params = nonlinear_activation_params
self.padding_type = padding_type
self.is_weight_norm = is_weight_norm
self.initializer_seed = initializer_seed
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