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models/tacotron.py

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+# coding: utf-8
+import torch
+from torch.autograd import Variable
+from torch import nn
+from utils.text.symbols import symbols
+from Tacotron.layers.tacotron import Prenet, Encoder, Decoder, CBHG
+
+class Tacotron(nn.Module):
+    def __init__(self, embedding_dim=256, linear_dim=1025, mel_dim=80,
+                 freq_dim=1025, r=5, padding_idx=None,
+                 use_memory_mask=False):
+        super(Tacotron, self).__init__()
+        self.mel_dim = mel_dim
+        self.linear_dim = linear_dim
+        self.use_memory_mask = use_memory_mask
+        self.embedding = nn.Embedding(len(symbols), embedding_dim,
+                                      padding_idx=padding_idx)
+        # Trying smaller std
+        self.embedding.weight.data.normal_(0, 0.3)
+        self.encoder = Encoder(embedding_dim)
+        self.decoder = Decoder(mel_dim, r)
+
+        self.postnet = CBHG(mel_dim, K=8, projections=[256, mel_dim])
+        self.last_linear = nn.Linear(mel_dim * 2, freq_dim)
+
+    def forward(self, characters, mel_specs=None, input_lengths=None):
+        B = characters.size(0)
+
+        inputs = self.embedding(characters)
+        # (B, T', in_dim)
+        encoder_outputs = self.encoder(inputs, input_lengths)
+
+        if self.use_memory_mask:
+            memory_lengths = input_lengths
+        else:
+            memory_lengths = None
+        # (B, T', mel_dim*r)
+        mel_outputs, alignments = self.decoder(
+            encoder_outputs, mel_specs, memory_lengths=memory_lengths)
+
+        # Post net processing below
+
+        # Reshape
+        # (B, T, mel_dim)
+        mel_outputs = mel_outputs.view(B, -1, self.mel_dim)
+
+        linear_outputs = self.postnet(mel_outputs)
+        linear_outputs = self.last_linear(linear_outputs)
+
+        return mel_outputs, linear_outputs, alignments