mirror of https://github.com/coqui-ai/TTS.git
wavegrad refactoring, fixing tests for glow-tts and wavegrad
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erogol
parent
946a0c0fb9
commit
39c71ee8a9
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@ -11,31 +11,8 @@ class Conv1d(nn.Conv1d):
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nn.init.zeros_(self.bias)
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# class PositionalEncoding(nn.Module):
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# def __init__(self, n_channels):
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# super().__init__()
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# self.n_channels = n_channels
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# self.length = n_channels // 2
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# assert n_channels % 2 == 0
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# def forward(self, x, noise_level):
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# """
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# Shapes:
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# x: B x C x T
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# noise_level: B
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# """
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# return (x + self.encoding(noise_level)[:, :, None])
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# def encoding(self, noise_level):
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# step = torch.arange(
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# self.length, dtype=noise_level.dtype, device=noise_level.device) / self.length
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# encoding = noise_level.unsqueeze(1) * torch.exp(
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# -ln(1e4) * step.unsqueeze(0))
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# encoding = torch.cat([torch.sin(encoding), torch.cos(encoding)], dim=-1)
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# return encoding
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class PositionalEncoding(nn.Module):
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"""Positional encoding with noise level conditioning"""
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def __init__(self, n_channels, max_len=10000):
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super().__init__()
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self.n_channels = n_channels
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@ -64,9 +41,7 @@ class FiLM(nn.Module):
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self.encoding = PositionalEncoding(input_size)
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self.input_conv = weight_norm(nn.Conv1d(input_size, input_size, 3, padding=1))
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self.output_conv = weight_norm(nn.Conv1d(input_size, output_size * 2, 3, padding=1))
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self.ini_parameters()
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def ini_parameters(self):
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nn.init.xavier_uniform_(self.input_conv.weight)
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nn.init.xavier_uniform_(self.output_conv.weight)
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nn.init.zeros_(self.input_conv.bias)
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@ -120,30 +95,28 @@ class UBlock(nn.Module):
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])
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def forward(self, x, shift, scale):
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block1 = F.interpolate(x, size=x.shape[-1] * self.factor)
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block1 = self.block1(block1)
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o1 = F.interpolate(x, size=x.shape[-1] * self.factor)
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o1 = self.block1(o1)
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block2 = F.leaky_relu(x, 0.2)
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block2 = F.interpolate(block2, size=x.shape[-1] * self.factor)
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block2 = self.block2[0](block2)
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# block2 = film_shift + film_scale * block2
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block2 = shif_and_scale(block2, scale, shift)
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block2 = F.leaky_relu(block2, 0.2)
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block2 = self.block2[1](block2)
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o2 = F.leaky_relu(x, 0.2)
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o2 = F.interpolate(o2, size=x.shape[-1] * self.factor)
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o2 = self.block2[0](o2)
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o2 = shif_and_scale(o2, scale, shift)
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o2 = F.leaky_relu(o2, 0.2)
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o2 = self.block2[1](o2)
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x = block1 + block2
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x = o1 + o2
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# block3 = film_shift + film_scale * x
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block3 = shif_and_scale(x, scale, shift)
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block3 = F.leaky_relu(block3, 0.2)
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block3 = self.block3[0](block3)
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# block3 = film_shift + film_scale * block3
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block3 = shif_and_scale(block3, scale, shift)
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block3 = F.leaky_relu(block3, 0.2)
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block3 = self.block3[1](block3)
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o3 = shif_and_scale(x, scale, shift)
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o3 = F.leaky_relu(o3, 0.2)
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o3 = self.block3[0](o3)
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x = x + block3
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return x
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o3 = shif_and_scale(o3, scale, shift)
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o3 = F.leaky_relu(o3, 0.2)
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o3 = self.block3[1](o3)
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o = x + o3
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return o
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class DBlock(nn.Module):
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@ -20,6 +20,15 @@ class Wavegrad(nn.Module):
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super().__init__()
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self.hop_len = np.prod(upsample_factors)
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self.noise_level = None
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self.num_steps = None
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self.beta = None
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self.alpha = None
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self.alpha_hat = None
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self.noise_level = None
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self.c1 = None
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self.c2 = None
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self.sigma = None
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# dblocks
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self.dblocks = nn.ModuleList([
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@ -75,6 +84,7 @@ class Wavegrad(nn.Module):
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def compute_y_n(self, y_0):
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"""Compute noisy audio based on noise schedule"""
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self.noise_level = self.noise_level.to(y_0)
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if len(y_0.shape) == 3:
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y_0 = y_0.squeeze(1)
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@ -87,6 +97,7 @@ class Wavegrad(nn.Module):
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return noise.unsqueeze(1), noisy_audio.unsqueeze(1), noise_scale[:, 0]
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def compute_noise_level(self, num_steps, min_val, max_val):
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"""Compute noise schedule parameters"""
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beta = np.linspace(min_val, max_val, num_steps)
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alpha = 1 - beta
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alpha_hat = np.cumprod(alpha)
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@ -101,5 +112,3 @@ class Wavegrad(nn.Module):
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self.c1 = 1 / self.alpha**0.5
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self.c2 = (1 - self.alpha) / (1 - self.alpha_hat)**0.5
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self.sigma = ((1.0 - self.alpha_hat[:-1]) / (1.0 - self.alpha_hat[1:]) * self.beta[1:])**0.5
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@ -1,12 +1,14 @@
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TF_CPP_MIN_LOG_LEVEL=3
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# tests
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nosetests tests -x &&\
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# nosetests tests -x &&\
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# runtime tests
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./tests/test_server_package.sh && \
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./tests/test_tts_train.sh && \
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./tests/test_vocoder_train.sh && \
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./tests/test_vocoder_gan_train.sh && \
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./tests/test_vocoder_wavernn_train.sh && \
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./tests/test_glow-tts_train.sh && \
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# linter check
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cardboardlinter --refspec master
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@ -62,7 +62,7 @@ class GE2ELossTests(unittest.TestCase):
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assert output.item() >= 0.0
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# check speaker loss with orthogonal d-vectors
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dummy_input = T.empty(3, 64)
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dummy_input = T.nn.init.orthogonal(dummy_input)
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dummy_input = T.nn.init.orthogonal_(dummy_input)
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dummy_input = T.cat(
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[
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dummy_input[0].repeat(5, 1, 1).transpose(0, 1),
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@ -91,7 +91,7 @@ class AngleProtoLossTests(unittest.TestCase):
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# check speaker loss with orthogonal d-vectors
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dummy_input = T.empty(3, 64)
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dummy_input = T.nn.init.orthogonal(dummy_input)
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dummy_input = T.nn.init.orthogonal_(dummy_input)
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dummy_input = T.cat(
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[
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dummy_input[0].repeat(5, 1, 1).transpose(0, 1),
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@ -6,12 +6,12 @@ if [[ ! -f tests/outputs/checkpoint_10.pth.tar ]]; then
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exit 1
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fi
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rm -f dist/*.whl
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python setup.py --quiet bdist_wheel --checkpoint tests/outputs/checkpoint_10.pth.tar --model_config tests/outputs/dummy_model_config.json
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python -m venv /tmp/venv
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source /tmp/venv/bin/activate
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pip install --quiet --upgrade pip setuptools wheel
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rm -f dist/*.whl
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python setup.py --quiet bdist_wheel --checkpoint tests/outputs/checkpoint_10.pth.tar --model_config tests/outputs/dummy_model_config.json
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pip install --quiet dist/TTS*.whl
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# this is related to https://github.com/librosa/librosa/issues/1160
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@ -30,9 +30,18 @@ class WavegradTrainTest(unittest.TestCase):
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upsample_dilations=[[1, 2, 1, 2], [1, 2, 1, 2],
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[1, 2, 4, 8], [1, 2, 4, 8],
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[1, 2, 4, 8]])
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model_ref = Wavegrad(in_channels=80,
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out_channels=1,
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upsample_factors=[5, 5, 3, 2, 2],
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upsample_dilations=[[1, 2, 1, 2], [1, 2, 1, 2],
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[1, 2, 4, 8], [1, 2, 4, 8],
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[1, 2, 4, 8]])
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model.train()
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model.to(device)
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model_ref = copy.deepcopy(model)
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model.compute_noise_level(1000, 1e-6, 1e-2)
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model_ref.load_state_dict(model.state_dict())
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model_ref.to(device)
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count = 0
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for param, param_ref in zip(model.parameters(),
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model_ref.parameters()):
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