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# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# 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 pytest
import torch
from einops import rearrange
from omegaconf import DictConfig
from nemo.collections.tts.models import SpectrogramEnhancerModel
from nemo.collections.tts.parts.utils.helpers import mask_sequence_tensor
@pytest.fixture
def enhancer_config():
n_bands = 80
latent_dim = 192
style_depth = 4
network_capacity = 16
fmap_max = 192
config = {
"model": {
"n_bands": n_bands,
"latent_dim": latent_dim,
"style_depth": style_depth,
"network_capacity": network_capacity,
"mixed_prob": 0.9,
"fmap_max": fmap_max,
"generator": {
"_target_": "nemo.collections.tts.modules.spectrogram_enhancer.Generator",
"n_bands": n_bands,
"latent_dim": latent_dim,
"network_capacity": network_capacity,
"style_depth": style_depth,
"fmap_max": fmap_max,
},
"discriminator": {
"_target_": "nemo.collections.tts.modules.spectrogram_enhancer.Discriminator",
"n_bands": n_bands,
"network_capacity": network_capacity,
"fmap_max": fmap_max,
},
"spectrogram_min_value": -13.18,
"spectrogram_max_value": 4.78,
"consistency_loss_weight": 10.0,
"gradient_penalty_loss_weight": 10.0,
"gradient_penalty_loss_every_n_steps": 4,
"spectrogram_predictor_path": None,
},
"generator_opt": {"_target_": "torch.optim.Adam", "lr": 2e-4, "betas": [0.5, 0.9]},
"discriminator_opt": {"_target_": "torch.optim.Adam", "lr": 2e-4, "betas": [0.5, 0.9]},
}
return DictConfig(config)
@pytest.fixture
def enhancer(enhancer_config):
return SpectrogramEnhancerModel(cfg=enhancer_config.model)
@pytest.fixture
def enhancer_with_fastpitch(enhancer_config_with_fastpitch):
return SpectrogramEnhancerModel(cfg=enhancer_config_with_fastpitch.model)
@pytest.fixture
def sample_input(batch_size=15, max_length=1000):
generator = torch.Generator()
generator.manual_seed(0)
lengths = torch.randint(max_length // 4, max_length - 7, (batch_size,), generator=generator)
input_spectrograms = torch.randn((batch_size, 80, 1000), generator=generator)
input_spectrograms = mask_sequence_tensor(input_spectrograms, lengths)
return input_spectrograms, lengths
@pytest.mark.unit
def test_pad_spectrograms(enhancer: SpectrogramEnhancerModel, sample_input):
input_spectrograms, lengths = sample_input
output = enhancer.pad_spectrograms(input_spectrograms)
assert output.size(-1) >= input_spectrograms.size(-1)
@pytest.mark.unit
def test_spectrogram_norm_unnorm(enhancer: SpectrogramEnhancerModel, sample_input):
input_spectrograms, lengths = sample_input
same_input_spectrograms = enhancer.unnormalize_spectrograms(
enhancer.normalize_spectrograms(input_spectrograms, lengths), lengths
)
assert torch.allclose(input_spectrograms, same_input_spectrograms, atol=1e-5)
@pytest.mark.unit
def test_spectrogram_unnorm_norm(enhancer: SpectrogramEnhancerModel, sample_input):
input_spectrograms, lengths = sample_input
same_input_spectrograms = enhancer.normalize_spectrograms(
enhancer.unnormalize_spectrograms(input_spectrograms, lengths), lengths
)
assert torch.allclose(input_spectrograms, same_input_spectrograms, atol=1e-5)
@pytest.mark.unit
def test_spectrogram_norm_unnorm_dont_look_at_padding(enhancer: SpectrogramEnhancerModel, sample_input):
input_spectrograms, lengths = sample_input
same_input_spectrograms = enhancer.unnormalize_spectrograms(
enhancer.normalize_spectrograms(input_spectrograms, lengths), lengths
)
for i, length in enumerate(lengths.tolist()):
assert torch.allclose(input_spectrograms[i, :, :length], same_input_spectrograms[i, :, :length], atol=1e-5)
@pytest.mark.unit
def test_spectrogram_unnorm_norm_dont_look_at_padding(enhancer: SpectrogramEnhancerModel, sample_input):
input_spectrograms, lengths = sample_input
same_input_spectrograms = enhancer.normalize_spectrograms(
enhancer.unnormalize_spectrograms(input_spectrograms, lengths), lengths
)
for i, length in enumerate(lengths.tolist()):
assert torch.allclose(input_spectrograms[i, :, :length], same_input_spectrograms[i, :, :length], atol=1e-5)
@pytest.mark.unit
def test_generator_pass_keeps_size(enhancer: SpectrogramEnhancerModel, sample_input):
input_spectrograms, lengths = sample_input
output = enhancer.forward(input_spectrograms=input_spectrograms, lengths=lengths)
assert output.shape == input_spectrograms.shape
@pytest.mark.unit
def test_discriminator_pass(enhancer: SpectrogramEnhancerModel, sample_input):
input_spectrograms, lengths = sample_input
input_spectrograms = rearrange(input_spectrograms, "b c l -> b 1 c l")
logits = enhancer.discriminator(x=input_spectrograms, condition=input_spectrograms, lengths=lengths)
assert logits.shape == lengths.shape
@pytest.mark.unit
def test_nemo_save_load(enhancer: SpectrogramEnhancerModel, tmp_path):
path = tmp_path / "test-enhancer-save-load.nemo"
enhancer.save_to(path)
SpectrogramEnhancerModel.restore_from(path)
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