Upgrade waveflow api to 1.8.2

This commit is contained in:
Yibing Liu 2020-06-12 08:45:55 +00:00
parent b7c584e2f7
commit 33ed693ccf
3 changed files with 6 additions and 16 deletions

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@ -40,7 +40,7 @@ sudo apt-get install libsndfile1
### Install PaddlePaddle ### Install PaddlePaddle
See [install](https://www.paddlepaddle.org.cn/install/quick) for more details. This repo requires PaddlePaddle **1.8.0** or above. See [install](https://www.paddlepaddle.org.cn/install/quick) for more details. This repo requires PaddlePaddle **1.8.2** or above.
### Install Parakeet ### Install Parakeet

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@ -79,7 +79,7 @@ class Conditioner(dg.Layer):
stride=(1, s), stride=(1, s),
param_attr=param_attr, param_attr=param_attr,
bias_attr=bias_attr, bias_attr=bias_attr,
dtype="float32") dtype=dtype)
self.upsample_conv2d.append(conv_trans2d) self.upsample_conv2d.append(conv_trans2d)
for i, layer in enumerate(self.upsample_conv2d): for i, layer in enumerate(self.upsample_conv2d):
@ -88,12 +88,7 @@ class Conditioner(dg.Layer):
def forward(self, x): def forward(self, x):
x = fluid.layers.unsqueeze(x, 1) x = fluid.layers.unsqueeze(x, 1)
for layer in self.upsample_conv2d: for layer in self.upsample_conv2d:
in_dtype = x.dtype
if in_dtype == fluid.core.VarDesc.VarType.FP16:
x = fluid.layers.cast(x, "float32")
x = layer(x) x = layer(x)
if in_dtype == fluid.core.VarDesc.VarType.FP16:
x = fluid.layers.cast(x, "float16")
x = fluid.layers.leaky_relu(x, alpha=0.4) x = fluid.layers.leaky_relu(x, alpha=0.4)
return fluid.layers.squeeze(x, [1]) return fluid.layers.squeeze(x, [1])
@ -101,12 +96,7 @@ class Conditioner(dg.Layer):
def infer(self, x): def infer(self, x):
x = fluid.layers.unsqueeze(x, 1) x = fluid.layers.unsqueeze(x, 1)
for layer in self.upsample_conv2d: for layer in self.upsample_conv2d:
in_dtype = x.dtype
if in_dtype == fluid.core.VarDesc.VarType.FP16:
x = fluid.layers.cast(x, "float32")
x = layer(x) x = layer(x)
if in_dtype == fluid.core.VarDesc.VarType.FP16:
x = fluid.layers.cast(x, "float16")
# Trim conv artifacts. # Trim conv artifacts.
time_cutoff = layer._filter_size[1] - layer._stride[1] time_cutoff = layer._filter_size[1] - layer._stride[1]
x = fluid.layers.leaky_relu(x[:, :, :, :-time_cutoff], alpha=0.4) x = fluid.layers.leaky_relu(x[:, :, :, :-time_cutoff], alpha=0.4)

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@ -55,7 +55,7 @@ setup_info = dict(
'inflect', 'inflect',
'librosa', 'librosa',
'unidecode', 'unidecode',
'numba==0.48.0', 'numba==0.47.0',
'tqdm==4.19.8', 'tqdm==4.19.8',
'matplotlib', 'matplotlib',
'tensorboardX', 'tensorboardX',