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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
import logging
import numpy as np
import pytest
import torch
from parallel_wavegan.losses import DiscriminatorAdversarialLoss
from parallel_wavegan.losses import FeatureMatchLoss
from parallel_wavegan.losses import GeneratorAdversarialLoss
from parallel_wavegan.losses import MultiResolutionSTFTLoss
from parallel_wavegan.models import MelGANGenerator
from parallel_wavegan.models import MelGANMultiScaleDiscriminator
from parallel_wavegan.models import ParallelWaveGANDiscriminator
from parallel_wavegan.models import ResidualParallelWaveGANDiscriminator
from parallel_wavegan.optimizers import RAdam
from test_parallel_wavegan import make_discriminator_args
from test_parallel_wavegan import make_mutli_reso_stft_loss_args
from test_parallel_wavegan import make_residual_discriminator_args
logging.basicConfig(
level=logging.DEBUG,
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
def make_melgan_generator_args(**kwargs):
defaults = dict(
in_channels=80,
out_channels=1,
kernel_size=7,
channels=512,
bias=True,
upsample_scales=[8, 8, 2, 2],
stack_kernel_size=3,
stacks=3,
nonlinear_activation="LeakyReLU",
nonlinear_activation_params={"negative_slope": 0.2},
pad="ReflectionPad1d",
pad_params={},
use_final_nonlinear_activation=True,
use_weight_norm=True,
use_causal_conv=False,
)
defaults.update(kwargs)
return defaults
def make_melgan_discriminator_args(**kwargs):
defaults = dict(
in_channels=1,
out_channels=1,
scales=3,
downsample_pooling="AvgPool1d",
# follow the official implementation setting
downsample_pooling_params={
"kernel_size": 4,
"stride": 2,
"padding": 1,
"count_include_pad": False,
},
kernel_sizes=[5, 3],
channels=16,
max_downsample_channels=1024,
bias=True,
downsample_scales=[4, 4, 4, 4],
nonlinear_activation="LeakyReLU",
nonlinear_activation_params={"negative_slope": 0.2},
pad="ReflectionPad1d",
pad_params={},
use_weight_norm=True,
)
defaults.update(kwargs)
return defaults
@pytest.mark.parametrize(
"dict_g, dict_d, dict_loss",
[
({}, {}, {}),
({"kernel_size": 3}, {}, {}),
({"channels": 1024}, {}, {}),
({"stack_kernel_size": 5}, {}, {}),
({"stack_kernel_size": 5, "stacks": 2}, {}, {}),
({"upsample_scales": [4, 4, 4, 4]}, {}, {}),
({"upsample_scales": [8, 8, 2, 2, 2]}, {}, {}),
({"channels": 1024, "upsample_scales": [8, 8, 2, 2, 2, 2]}, {}, {}),
({"pad": "ConstantPad1d", "pad_params": {"value": 0.0}}, {}, {}),
({"nonlinear_activation": "ReLU", "nonlinear_activation_params": {}}, {}, {}),
({"bias": False}, {}, {}),
({"use_final_nonlinear_activation": False}, {}, {}),
({"use_weight_norm": False}, {}, {}),
({"use_causal_conv": True}, {}, {}),
],
)
def test_melgan_trainable(dict_g, dict_d, dict_loss):
# setup
batch_size = 4
batch_length = 4096
args_g = make_melgan_generator_args(**dict_g)
args_d = make_discriminator_args(**dict_d)
args_loss = make_mutli_reso_stft_loss_args(**dict_loss)
y = torch.randn(batch_size, 1, batch_length)
c = torch.randn(
batch_size,
args_g["in_channels"],
batch_length // np.prod(args_g["upsample_scales"]),
)
model_g = MelGANGenerator(**args_g)
model_d = ParallelWaveGANDiscriminator(**args_d)
aux_criterion = MultiResolutionSTFTLoss(**args_loss)
gen_adv_criterion = GeneratorAdversarialLoss()
dis_adv_criterion = DiscriminatorAdversarialLoss()
optimizer_g = RAdam(model_g.parameters())
optimizer_d = RAdam(model_d.parameters())
# check generator trainable
y_hat = model_g(c)
p_hat = model_d(y_hat)
adv_loss = gen_adv_criterion(p_hat)
sc_loss, mag_loss = aux_criterion(y_hat, y)
aux_loss = sc_loss + mag_loss
loss_g = adv_loss + aux_loss
optimizer_g.zero_grad()
loss_g.backward()
optimizer_g.step()
# check discriminator trainable
p = model_d(y)
p_hat = model_d(y_hat.detach())
real_loss, fake_loss = dis_adv_criterion(p_hat, p)
loss_d = real_loss + fake_loss
optimizer_d.zero_grad()
loss_d.backward()
optimizer_d.step()
@pytest.mark.parametrize(
"dict_g, dict_d, dict_loss",
[
({}, {}, {}),
({"kernel_size": 3}, {}, {}),
({"channels": 1024}, {}, {}),
({"stack_kernel_size": 5}, {}, {}),
({"stack_kernel_size": 5, "stacks": 2}, {}, {}),
({"upsample_scales": [4, 4, 4, 4]}, {}, {}),
({"upsample_scales": [8, 8, 2, 2, 2]}, {}, {}),
({"channels": 1024, "upsample_scales": [8, 8, 2, 2, 2, 2]}, {}, {}),
({"pad": "ConstantPad1d", "pad_params": {"value": 0.0}}, {}, {}),
({"nonlinear_activation": "ReLU", "nonlinear_activation_params": {}}, {}, {}),
({"bias": False}, {}, {}),
({"use_final_nonlinear_activation": False}, {}, {}),
({"use_weight_norm": False}, {}, {}),
],
)
def test_melgan_trainable_with_residual_discriminator(dict_g, dict_d, dict_loss):
# setup
batch_size = 4
batch_length = 4096
args_g = make_melgan_generator_args(**dict_g)
args_d = make_residual_discriminator_args(**dict_d)
args_loss = make_mutli_reso_stft_loss_args(**dict_loss)
y = torch.randn(batch_size, 1, batch_length)
c = torch.randn(
batch_size,
args_g["in_channels"],
batch_length // np.prod(args_g["upsample_scales"]),
)
model_g = MelGANGenerator(**args_g)
model_d = ResidualParallelWaveGANDiscriminator(**args_d)
aux_criterion = MultiResolutionSTFTLoss(**args_loss)
gen_adv_criterion = GeneratorAdversarialLoss()
dis_adv_criterion = DiscriminatorAdversarialLoss()
optimizer_g = RAdam(model_g.parameters())
optimizer_d = RAdam(model_d.parameters())
# check generator trainable
y_hat = model_g(c)
p_hat = model_d(y_hat)
adv_loss = gen_adv_criterion(p_hat)
sc_loss, mag_loss = aux_criterion(y_hat, y)
aux_loss = sc_loss + mag_loss
loss_g = adv_loss + aux_loss
optimizer_g.zero_grad()
loss_g.backward()
optimizer_g.step()
# check discriminator trainable
p = model_d(y)
p_hat = model_d(y_hat.detach())
real_loss, fake_loss = dis_adv_criterion(p_hat, p)
loss_d = real_loss + fake_loss
optimizer_d.zero_grad()
loss_d.backward()
optimizer_d.step()
@pytest.mark.parametrize(
"dict_g, dict_d, dict_loss",
[
({}, {}, {}),
({}, {"scales": 4}, {}),
({}, {"kernel_sizes": [7, 5]}, {}),
({}, {"max_downsample_channels": 128}, {}),
({}, {"downsample_scales": [4, 4]}, {}),
({}, {"pad": "ConstantPad1d", "pad_params": {"value": 0.0}}, {}),
({}, {"nonlinear_activation": "ReLU", "nonlinear_activation_params": {}}, {}),
],
)
def test_melgan_trainable_with_melgan_discriminator(dict_g, dict_d, dict_loss):
# setup
batch_size = 4
batch_length = 4096
args_g = make_melgan_generator_args(**dict_g)
args_d = make_melgan_discriminator_args(**dict_d)
args_loss = make_mutli_reso_stft_loss_args(**dict_loss)
y = torch.randn(batch_size, 1, batch_length)
c = torch.randn(
batch_size,
args_g["in_channels"],
batch_length // np.prod(args_g["upsample_scales"]),
)
model_g = MelGANGenerator(**args_g)
model_d = MelGANMultiScaleDiscriminator(**args_d)
aux_criterion = MultiResolutionSTFTLoss(**args_loss)
feat_match_criterion = FeatureMatchLoss()
gen_adv_criterion = GeneratorAdversarialLoss()
dis_adv_criterion = DiscriminatorAdversarialLoss()
optimizer_g = RAdam(model_g.parameters())
optimizer_d = RAdam(model_d.parameters())
# check generator trainable
y_hat = model_g(c)
p_hat = model_d(y_hat)
sc_loss, mag_loss = aux_criterion(y_hat, y)
aux_loss = sc_loss + mag_loss
adv_loss = gen_adv_criterion(p_hat)
with torch.no_grad():
p = model_d(y)
fm_loss = feat_match_criterion(p_hat, p)
loss_g = adv_loss + aux_loss + fm_loss
optimizer_g.zero_grad()
loss_g.backward()
optimizer_g.step()
# check discriminator trainable
p = model_d(y)
p_hat = model_d(y_hat.detach())
real_loss, fake_loss = dis_adv_criterion(p_hat, p)
loss_d = real_loss + fake_loss
optimizer_d.zero_grad()
loss_d.backward()
optimizer_d.step()
@pytest.mark.parametrize(
"dict_g",
[
({"use_causal_conv": True}),
({"use_causal_conv": True, "upsample_scales": [4, 4, 2, 2]}),
({"use_causal_conv": True, "upsample_scales": [4, 5, 4, 3]}),
],
)
def test_causal_melgan(dict_g):
batch_size = 4
batch_length = 4096
args_g = make_melgan_generator_args(**dict_g)
upsampling_factor = np.prod(args_g["upsample_scales"])
c = torch.randn(
batch_size, args_g["in_channels"], batch_length // upsampling_factor
)
model_g = MelGANGenerator(**args_g)
c_ = c.clone()
c_[..., c.size(-1) // 2 :] = torch.randn(c[..., c.size(-1) // 2 :].shape)
try:
# check not equal
np.testing.assert_array_equal(c.numpy(), c_.numpy())
except AssertionError:
pass
else:
raise AssertionError("Must be different.")
# check causality
y = model_g(c)
y_ = model_g(c_)
assert y.size(2) == c.size(2) * upsampling_factor
np.testing.assert_array_equal(
y[..., : c.size(-1) // 2 * upsampling_factor].detach().cpu().numpy(),
y_[..., : c_.size(-1) // 2 * upsampling_factor].detach().cpu().numpy(),
)
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