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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.
import logging
import os
import pytest
import tensorflow as tf
from tensorflow_tts.configs import MelGANDiscriminatorConfig, MelGANGeneratorConfig
from tensorflow_tts.models import TFMelGANGenerator, TFMelGANMultiScaleDiscriminator
os.environ["CUDA_VISIBLE_DEVICES"] = ""
logging.basicConfig(
level=logging.DEBUG,
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
def make_melgan_generator_args(**kwargs):
defaults = dict(
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",
)
defaults.update(kwargs)
return defaults
def make_melgan_discriminator_args(**kwargs):
defaults = dict(
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",
)
defaults.update(kwargs)
return defaults
@pytest.mark.parametrize(
"dict_g, dict_d, dict_loss",
[
({}, {}, {}),
({"kernel_size": 3}, {}, {}),
({"filters": 1024}, {}, {}),
({"stack_kernel_size": 5}, {}, {}),
({"stack_kernel_size": 5, "stacks": 2}, {}, {}),
({"upsample_scales": [4, 4, 4, 4]}, {}, {}),
({"upsample_scales": [8, 8, 2, 2]}, {}, {}),
({"filters": 1024, "upsample_scales": [8, 8, 2, 2]}, {}, {}),
],
)
def test_melgan_trainable(dict_g, dict_d, dict_loss):
batch_size = 4
batch_length = 4096
args_g = make_melgan_generator_args(**dict_g)
args_d = make_melgan_discriminator_args(**dict_d)
args_g = MelGANGeneratorConfig(**args_g)
args_d = MelGANDiscriminatorConfig(**args_d)
generator = TFMelGANGenerator(args_g)
discriminator = TFMelGANMultiScaleDiscriminator(args_d)