Upgrade waveflow api to 1.8.2
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@ -40,7 +40,7 @@ sudo apt-get install libsndfile1
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### Install PaddlePaddle
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### Install PaddlePaddle
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See [install](https://www.paddlepaddle.org.cn/install/quick) for more details. This repo requires PaddlePaddle **1.8.0** or above.
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See [install](https://www.paddlepaddle.org.cn/install/quick) for more details. This repo requires PaddlePaddle **1.8.2** or above.
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### Install Parakeet
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### Install Parakeet
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@ -79,7 +79,7 @@ class Conditioner(dg.Layer):
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stride=(1, s),
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stride=(1, s),
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param_attr=param_attr,
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param_attr=param_attr,
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bias_attr=bias_attr,
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bias_attr=bias_attr,
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dtype="float32")
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dtype=dtype)
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self.upsample_conv2d.append(conv_trans2d)
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self.upsample_conv2d.append(conv_trans2d)
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for i, layer in enumerate(self.upsample_conv2d):
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for i, layer in enumerate(self.upsample_conv2d):
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@ -88,12 +88,7 @@ class Conditioner(dg.Layer):
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def forward(self, x):
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def forward(self, x):
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x = fluid.layers.unsqueeze(x, 1)
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x = fluid.layers.unsqueeze(x, 1)
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for layer in self.upsample_conv2d:
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for layer in self.upsample_conv2d:
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in_dtype = x.dtype
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if in_dtype == fluid.core.VarDesc.VarType.FP16:
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x = fluid.layers.cast(x, "float32")
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x = layer(x)
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x = layer(x)
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if in_dtype == fluid.core.VarDesc.VarType.FP16:
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x = fluid.layers.cast(x, "float16")
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x = fluid.layers.leaky_relu(x, alpha=0.4)
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x = fluid.layers.leaky_relu(x, alpha=0.4)
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return fluid.layers.squeeze(x, [1])
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return fluid.layers.squeeze(x, [1])
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@ -101,12 +96,7 @@ class Conditioner(dg.Layer):
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def infer(self, x):
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def infer(self, x):
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x = fluid.layers.unsqueeze(x, 1)
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x = fluid.layers.unsqueeze(x, 1)
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for layer in self.upsample_conv2d:
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for layer in self.upsample_conv2d:
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in_dtype = x.dtype
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if in_dtype == fluid.core.VarDesc.VarType.FP16:
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x = fluid.layers.cast(x, "float32")
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x = layer(x)
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x = layer(x)
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if in_dtype == fluid.core.VarDesc.VarType.FP16:
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x = fluid.layers.cast(x, "float16")
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# Trim conv artifacts.
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# Trim conv artifacts.
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time_cutoff = layer._filter_size[1] - layer._stride[1]
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time_cutoff = layer._filter_size[1] - layer._stride[1]
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x = fluid.layers.leaky_relu(x[:, :, :, :-time_cutoff], alpha=0.4)
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x = fluid.layers.leaky_relu(x[:, :, :, :-time_cutoff], alpha=0.4)
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