add missing parallel_wavenet, and fix python2 compatability

parakeet/models/clarinet/parallel_wavenet.py

@@ -0,0 +1,69 @@
+# Copyright (c) 2020 PaddlePaddle Authors. 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 math
+import time
+import itertools
+import numpy as np
+
+import paddle.fluid.layers as F
+import paddle.fluid.dygraph as dg
+import paddle.fluid.initializer as I
+import paddle.fluid.layers.distributions as D
+
+from parakeet.modules.weight_norm import Linear, Conv1D, Conv1DCell, Conv2DTranspose
+from parakeet.models.wavenet import WaveNet
+
+
+class ParallelWaveNet(dg.Layer):
+    def __init__(self, n_loops, n_layers, residual_channels, condition_dim,
+                 filter_size):
+        super(ParallelWaveNet, self).__init__()
+        self.flows = dg.LayerList()
+        for n_loop, n_layer in zip(n_loops, n_layers):
+            # teacher's log_scale_min does not matter herem, -100 is a dummy value
+            self.flows.append(
+                WaveNet(n_loop, n_layer, residual_channels, 3, condition_dim,
+                        filter_size, "mog", -100.0))
+
+    def forward(self, z, condition=None):
+        """Inverse Autoregressive Flow. Several wavenets.
+        
+        Arguments:
+            z {Variable} -- shape(batch_size, time_steps), hidden variable, sampled from a standard normal distribution.
+        
+        Keyword Arguments:
+            condition {Variable} -- shape(batch_size, condition_dim, time_steps), condition, basically upsampled mel spectrogram. (default: {None})
+        
+        Returns:
+            Variable -- shape(batch_size, time_steps), transformed z.
+            Variable -- shape(batch_size, time_steps), output distribution's mu.
+            Variable -- shape(batch_size, time_steps), output distribution's log_std.
+        """
+
+        for i, flow in enumerate(self.flows):
+            theta = flow(z, condition)  # w, mu, log_std [0: T]
+            w, mu, log_std = F.split(theta, 3, dim=-1)  # (B, T, 1) for each
+            mu = F.squeeze(mu, [-1])  #[0: T]
+            log_std = F.squeeze(log_std, [-1])  #[0: T]
+            z = z * F.exp(log_std) + mu  #[0: T]
+
+            if i == 0:
+                out_mu = mu
+                out_log_std = log_std
+            else:
+                out_mu = out_mu * F.exp(log_std) + mu
+                out_log_std += log_std
+
+        return z, out_mu, out_log_std

parakeet/models/wavenet/net.py

@@ -57,7 +57,7 @@ class UpsampleNet(dg.Layer):
     """
 
     def __init__(self, upscale_factors=[16, 16]):
-        super().__init__()
+        super(UpsampleNet, self).__init__()
         self.upscale_factors = list(upscale_factors)
         self.upsample_convs = dg.LayerList()
         for i, factor in enumerate(upscale_factors):
@@ -92,7 +92,7 @@ class UpsampleNet(dg.Layer):
 # AutoRegressive Model
 class ConditionalWavenet(dg.Layer):
     def __init__(self, encoder: UpsampleNet, decoder: WaveNet):
-        super().__init__()
+        super(ConditionalWavenet, self).__init__()
         self.encoder = encoder
         self.decoder = decoder
 

parakeet/models/wavenet/wavenet.py

@@ -39,7 +39,7 @@ def dequantize(quantized, n_bands):
 class ResidualBlock(dg.Layer):
     def __init__(self, residual_channels, condition_dim, filter_size,
                  dilation):
-        super().__init__()
+        super(ResidualBlock, self).__init__()
         dilated_channels = 2 * residual_channels
         # following clarinet's implementation, we do not have parametric residual
         # & skip connection.
@@ -135,7 +135,7 @@ class ResidualBlock(dg.Layer):
 class ResidualNet(dg.Layer):
     def __init__(self, n_loop, n_layer, residual_channels, condition_dim,
                  filter_size):
-        super().__init__()
+        super(ResidualNet, self).__init__()
         # double the dilation at each layer in a loop(n_loop layers)
         dilations = [2**i for i in range(n_loop)] * n_layer
         self.context_size = 1 + sum(dilations)
@@ -198,7 +198,7 @@ class ResidualNet(dg.Layer):
 class WaveNet(dg.Layer):
     def __init__(self, n_loop, n_layer, residual_channels, output_dim,
                  condition_dim, filter_size, loss_type, log_scale_min):
-        super().__init__()
+        super(WaveNet, self).__init__()
         if loss_type not in ["softmax", "mog"]:
             raise ValueError("loss_type {} is not supported".format(loss_type))
         if loss_type == "softmax":