tacotrongst

config_tacotron_gst.json

@@ -1,6 +1,6 @@
 {
         "run_name": "mozilla-tacotron-gst",
-        "run_description": "",
+        "run_description": "GST with single speaker",
     
         "audio":{
             // Audio processing parameters

models/tacotrongst.py

@@ -0,0 +1,67 @@
+# coding: utf-8
+import torch
+from torch import nn
+from math import sqrt
+from layers.tacotron import Prenet, Encoder, Decoder, PostCBHG
+from layers.gst_layers import GST
+from utils.generic_utils import sequence_mask
+
+
+class TacotronGST(nn.Module):
+    def __init__(self,
+                 num_chars,
+                 r=5,
+                 linear_dim=1025,
+                 mel_dim=80,
+                 memory_size=5,
+                 attn_win=False,
+                 attn_norm="sigmoid",
+                 prenet_type="original",
+                 prenet_dropout=True,
+                 forward_attn=False,
+                 trans_agent=False,
+                 forward_attn_mask=False,
+                 location_attn=True,
+                 separate_stopnet=True):
+        super(TacotronGST, self).__init__()
+        self.r = r
+        self.mel_dim = mel_dim
+        self.linear_dim = linear_dim
+        self.embedding = nn.Embedding(num_chars, 256)
+        self.embedding.weight.data.normal_(0, 0.3)
+        self.encoder = Encoder(256)
+        self.gst = GST(num_mel=80, num_heads=4, num_style_tokens=10, embedding_dim=256)
+        self.decoder = Decoder(256, mel_dim, r, memory_size, attn_win,
+                               attn_norm, prenet_type, prenet_dropout,
+                               forward_attn, trans_agent, forward_attn_mask,
+                               location_attn, separate_stopnet)
+        self.postnet = PostCBHG(mel_dim)
+        self.last_linear = nn.Sequential(
+            nn.Linear(self.postnet.cbhg.gru_features * 2, linear_dim),
+            nn.Sigmoid())
+
+    def forward(self, characters, text_lengths, mel_specs):
+        B = characters.size(0)
+        mask = sequence_mask(text_lengths).to(characters.device)
+        inputs = self.embedding(characters)
+        encoder_outputs = self.encoder(inputs)
+        gst_outputs = self.gst(mel_specs)
+        gst_outputs = gst_outputs.expand(-1, encoder_outputs.size(1), -1)
+        encoder_outputs = encoder_outputs + gst_outputs
+        mel_outputs, alignments, stop_tokens = self.decoder(
+            encoder_outputs, mel_specs, mask)
+        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, stop_tokens
+
+    def inference(self, characters):
+        B = characters.size(0)
+        inputs = self.embedding(characters)
+        encoder_outputs = self.encoder(inputs)
+        mel_outputs, alignments, stop_tokens = self.decoder.inference(
+            encoder_outputs)
+        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, stop_tokens