add unit testings back
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chenfeiyu
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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import torch
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from parallel_wavegan.layers import upsample, residual_block
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from parallel_wavegan.models import parallel_wavegan as pwgan
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from parakeet.utils.layer_tools import summary
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from parakeet.models.parallel_wavegan import ConvInUpsampleNet, ResidualBlock
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from parakeet.models.parallel_wavegan import PWGGenerator, PWGDiscriminator, ResidualPWGDiscriminator
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def test_convin_upsample_net():
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net = ConvInUpsampleNet(
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[4, 4, 4, 4],
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"LeakyReLU", {"negative_slope": 0.2},
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freq_axis_kernel_size=3,
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aux_context_window=0)
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net2 = upsample.ConvInUpsampleNetwork(
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[4, 4, 4, 4],
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nonlinear_activation="LeakyReLU",
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nonlinear_activation_params={"negative_slope": 0.2},
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freq_axis_kernel_size=3,
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aux_context_window=0)
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summary(net)
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for k, v in net2.named_parameters():
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print(k, v.shape)
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net.state_dict()[k].set_value(v.data.cpu().numpy())
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c = paddle.randn([4, 80, 180])
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out = net(c)
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out2 = net2(torch.as_tensor(c.numpy()))
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print(out.numpy()[0])
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print(out2.data.cpu().numpy()[0])
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def test_residual_block():
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net = ResidualBlock(dilation=9)
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net2 = residual_block.ResidualBlock(dilation=9)
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summary(net)
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summary(net2)
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for k, v in net2.named_parameters():
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net.state_dict()[k].set_value(v.data.cpu().numpy())
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x = paddle.randn([4, 64, 180])
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c = paddle.randn([4, 80, 180])
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res, skip = net(x, c)
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res2, skip2 = net2(torch.as_tensor(x.numpy()), torch.as_tensor(c.numpy()))
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print(res.numpy()[0])
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print(res2.data.cpu().numpy()[0])
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print(skip.numpy()[0])
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print(skip2.data.cpu().numpy()[0])
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def test_pwg_generator():
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net = PWGGenerator(
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nonlinear_activation="LeakyReLU",
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nonlinear_activation_params={"negative_slope": 0.2})
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net2 = pwgan.ParallelWaveGANGenerator(upsample_params={
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"upsample_scales": [4, 4, 4, 4],
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"nonlinear_activation": "LeakyReLU",
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"nonlinear_activation_params": {
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"negative_slope": 0.2
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}
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})
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summary(net)
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summary(net2)
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for k, v in net2.named_parameters():
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p = net.state_dict()[k]
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if k.endswith("_g"):
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p.set_value(v.data.cpu().numpy().reshape([-1]))
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else:
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p.set_value(v.data.cpu().numpy())
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x = paddle.randn([4, 1, 180 * 256])
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c = paddle.randn([4, 80, 180 + 4])
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out = net(x, c)
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out2 = net2(torch.as_tensor(x.numpy()), torch.as_tensor(c.numpy()))
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print(out.numpy()[0])
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print(out2.data.cpu().numpy()[0])
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# print(out.shape)
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def test_pwg_discriminator():
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net = PWGDiscriminator()
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net2 = pwgan.ParallelWaveGANDiscriminator()
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summary(net)
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summary(net2)
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for k, v in net2.named_parameters():
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p = net.state_dict()[k]
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if k.endswith("_g"):
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p.set_value(v.data.cpu().numpy().reshape([-1]))
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else:
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p.set_value(v.data.cpu().numpy())
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x = paddle.randn([4, 1, 180 * 256])
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y = net(x)
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y2 = net2(torch.as_tensor(x.numpy()))
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print(y.numpy()[0])
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print(y2.data.cpu().numpy()[0])
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print(y.shape)
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def test_residual_pwg_discriminator():
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net = ResidualPWGDiscriminator()
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net2 = pwgan.ResidualParallelWaveGANDiscriminator()
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summary(net)
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summary(net2)
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for k, v in net2.named_parameters():
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p = net.state_dict()[k]
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if k.endswith("_g"):
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p.set_value(v.data.cpu().numpy().reshape([-1]))
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else:
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p.set_value(v.data.cpu().numpy())
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x = paddle.randn([4, 1, 180 * 256])
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y = net(x)
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y2 = net2(torch.as_tensor(x.numpy()))
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print(y.numpy()[0])
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print(y2.data.cpu().numpy()[0])
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print(y.shape)
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@ -0,0 +1,73 @@
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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import torch
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import librosa
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import numpy as np
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from parakeet.modules.stft_loss import STFT, MultiResolutionSTFTLoss
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from parallel_wavegan.losses import stft_loss as sl
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from scipy import signal
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def test_stft():
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stft = STFT(n_fft=1024, hop_length=256, win_length=1024)
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x = paddle.uniform([4, 46080])
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S = stft.magnitude(x)
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window = signal.get_window('hann', 1024, fftbins=True)
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D2 = torch.stft(
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torch.as_tensor(x.numpy()),
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n_fft=1024,
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hop_length=256,
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win_length=1024,
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window=torch.as_tensor(window))
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S2 = (D2**2).sum(-1).sqrt()
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S3 = np.abs(
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librosa.stft(
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x.numpy()[0], n_fft=1024, hop_length=256, win_length=1024))
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print(S2.shape)
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print(S.numpy()[0])
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print(S2.data.cpu().numpy()[0])
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print(S3)
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def test_torch_stft():
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# NOTE: torch.stft use no window by default
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x = np.random.uniform(-1.0, 1.0, size=(46080, ))
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window = signal.get_window('hann', 1024, fftbins=True)
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D2 = torch.stft(
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torch.as_tensor(x),
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n_fft=1024,
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hop_length=256,
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win_length=1024,
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window=torch.as_tensor(window))
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D3 = librosa.stft(
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x, n_fft=1024, hop_length=256, win_length=1024, window='hann')
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print(D2[:, :, 0].data.cpu().numpy()[:, 30:60])
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print(D3.real[:, 30:60])
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# print(D3.imag[:, 30:60])
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def test_multi_resolution_stft_loss():
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net = MultiResolutionSTFTLoss()
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net2 = sl.MultiResolutionSTFTLoss()
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x = paddle.uniform([4, 46080])
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y = paddle.uniform([4, 46080])
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sc, m = net(x, y)
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sc2, m2 = net2(torch.as_tensor(x.numpy()), torch.as_tensor(y.numpy()))
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print(sc.numpy())
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print(sc2.data.cpu().numpy())
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print(m.numpy())
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print(m2.data.cpu().numpy())
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