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#!/usr/bin/env python3
# Copyright 2021 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Test code for HiFi-GAN modules."""
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 HiFiGANGenerator
from parallel_wavegan.models import HiFiGANMultiScaleMultiPeriodDiscriminator
from test_parallel_wavegan import make_mutli_reso_stft_loss_args
logging.basicConfig(
level=logging.DEBUG,
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
def make_hifigan_generator_args(**kwargs):
defaults = dict(
in_channels=80,
out_channels=1,
channels=512,
kernel_size=7,
upsample_scales=(8, 8, 2, 2),
upsample_kernel_sizes=(16, 16, 4, 4),
resblock_kernel_sizes=(3, 7, 11),
resblock_dilations=[(1, 3, 5), (1, 3, 5), (1, 3, 5)],
use_additional_convs=True,
bias=True,
nonlinear_activation="LeakyReLU",
nonlinear_activation_params={"negative_slope": 0.1},
use_weight_norm=True,
)
defaults.update(kwargs)
return defaults
def make_hifigan_multi_scale_multi_period_discriminator_args(**kwargs):
defaults = dict(
scales=3,
scale_downsample_pooling="AvgPool1d",
scale_downsample_pooling_params={
"kernel_size": 4,
"stride": 2,
"padding": 2,
},
scale_discriminator_params={
"in_channels": 1,
"out_channels": 1,
"kernel_sizes": [15, 41, 5, 3],
"channels": 128,
"max_downsample_channels": 128,
"max_groups": 16,
"bias": True,
"downsample_scales": [2, 2, 4, 4, 1],
"nonlinear_activation": "LeakyReLU",
"nonlinear_activation_params": {"negative_slope": 0.1},
},
follow_official_norm=False,
periods=[2, 3, 5, 7, 11],
period_discriminator_params={
"in_channels": 1,
"out_channels": 1,
"kernel_sizes": [5, 3],
"channels": 32,
"downsample_scales": [3, 3, 3, 3, 1],
"max_downsample_channels": 128,
"bias": True,
"nonlinear_activation": "LeakyReLU",
"nonlinear_activation_params": {"negative_slope": 0.1},
"use_weight_norm": True,
"use_spectral_norm": False,
},
)
defaults.update(kwargs)
return defaults
@pytest.mark.parametrize(
"dict_g, dict_d, dict_loss",
[
({}, {}, {}),
({}, {"scales": 1}, {}),
({}, {"periods": [2]}, {}),
({}, {"scales": 1, "periods": [2]}, {}),
({}, {"follow_official_norm": True}, {}),
({"use_additional_convs": False}, {}, {}),
],
)
def test_hifigan_trainable(dict_g, dict_d, dict_loss):
# setup
batch_size = 4
batch_length = 2 ** 13
args_g = make_hifigan_generator_args(**dict_g)
args_d = make_hifigan_multi_scale_multi_period_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 = HiFiGANGenerator(**args_g)
model_d = HiFiGANMultiScaleMultiPeriodDiscriminator(**args_d)
aux_criterion = MultiResolutionSTFTLoss(**args_loss)
feat_match_criterion = FeatureMatchLoss(
average_by_layers=False,
average_by_discriminators=False,
include_final_outputs=True,
)
gen_adv_criterion = GeneratorAdversarialLoss(
average_by_discriminators=False,
)
dis_adv_criterion = DiscriminatorAdversarialLoss(
average_by_discriminators=False,
)
optimizer_g = torch.optim.AdamW(model_g.parameters())
optimizer_d = torch.optim.AdamW(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()
print(model_d)
print(model_g)
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