integrade concatinative speker embedding to tacotron

layers/tacotron.py

@@ -273,7 +273,7 @@ class Decoder(nn.Module):
     def __init__(self, in_features, memory_dim, r, memory_size, attn_windowing,
                  attn_norm, prenet_type, prenet_dropout, forward_attn,
                  trans_agent, forward_attn_mask, location_attn,
-                 separate_stopnet):
+                 separate_stopnet, speaker_embedding_dim):
         super(Decoder, self).__init__()
         self.r_init = r
         self.r = r
@@ -285,8 +285,9 @@ class Decoder(nn.Module):
         self.separate_stopnet = separate_stopnet
         self.query_dim = 256
         # memory -> |Prenet| -> processed_memory
+        prenet_dim = memory_dim * self.memory_size + speaker_embedding_dim if self.use_memory_queue else memory_dim + speaker_embedding_dim
         self.prenet = Prenet(
-            memory_dim * self.memory_size if self.use_memory_queue else memory_dim,
+            prenet_dim,
             prenet_type,
             prenet_dropout,
             out_features=[256, 128])
@@ -407,7 +408,7 @@ class Decoder(nn.Module):
             # use only the last frame prediction
             self.memory_input = new_memory[:, :self.memory_dim]
 
-    def forward(self, inputs, memory, mask):
+    def forward(self, inputs, memory, mask, speaker_embeddings=None):
         """
         Args:
             inputs: Encoder outputs.
@@ -432,6 +433,8 @@ class Decoder(nn.Module):
             if t > 0:
                 new_memory = memory[t - 1]
                 self._update_memory_input(new_memory)
+            if speaker_embeddings is not None:
+                self.memory_input = torch.cat([self.memory_input, speaker_embeddings], dim=-1)
             output, stop_token, attention = self.decode(inputs, mask)
             outputs += [output]
             attentions += [attention]
@@ -440,13 +443,15 @@ class Decoder(nn.Module):
 
         return self._parse_outputs(outputs, attentions, stop_tokens)
 
-    def inference(self, inputs):
+    def inference(self, inputs, speaker_embeddings=None):
         """
         Args:
-            inputs: Encoder outputs.
+            inputs: encoder outputs.
+            speaker_embeddings: speaker vectors.
 
         Shapes:
             - inputs: batch x time x encoder_out_dim
+            - speaker_embeddings: batch x embed_dim
         """
         outputs = []
         attentions = []
@@ -459,6 +464,8 @@ class Decoder(nn.Module):
             if t > 0:
                 new_memory = outputs[-1]
                 self._update_memory_input(new_memory)
+            if speaker_embeddings is not None:
+                self.memory_input = torch.cat([self.memory_input, speaker_embeddings], dim=-1)
             output, stop_token, attention = self.decode(inputs, None)
             stop_token = torch.sigmoid(stop_token.data)
             outputs += [output]

models/tacotron.py

@@ -1,4 +1,5 @@
 # coding: utf-8
+import torch
 from torch import nn
 from TTS.layers.tacotron import Encoder, Decoder, PostCBHG
 from TTS.utils.generic_utils import sequence_mask
@@ -25,28 +26,50 @@ class Tacotron(nn.Module):
         self.r = r
         self.mel_dim = mel_dim
         self.linear_dim = linear_dim
+        self.num_speakers = num_speakers
         self.embedding = nn.Embedding(num_chars, 256)
         self.embedding.weight.data.normal_(0, 0.3)
+        decoder_dim = 512 if num_speakers > 1 else 256
+        encoder_dim = 512 if num_speakers > 1 else 256
+        proj_speaker_dim = 80 if num_speakers > 1 else 0
         if num_speakers > 1:
             self.speaker_embedding = nn.Embedding(num_speakers, 256)
             self.speaker_embedding.weight.data.normal_(0, 0.3)
-        self.encoder = Encoder(256)
+            self.speaker_project_mel = nn.Sequential(nn.Linear(256, proj_speaker_dim), nn.Tanh())
-        self.decoder = Decoder(256, mel_dim, r, memory_size, attn_win,
+        self.encoder = Encoder(encoder_dim)
+        self.decoder = Decoder(decoder_dim, mel_dim, r, memory_size, attn_win,
                                attn_norm, prenet_type, prenet_dropout,
                                forward_attn, trans_agent, forward_attn_mask,
-                               location_attn, separate_stopnet)
+                               location_attn, separate_stopnet, proj_speaker_dim)
         self.postnet = PostCBHG(mel_dim)
         self.last_linear = nn.Linear(self.postnet.cbhg.gru_features * 2, linear_dim)
-        
+
+    def __init_states(self):
+        self.speaker_embeddings = None 
+        self.speaker_embeddings_projected = None
+
+    def compute_speaker_embedding(self, speaker_ids):
+        if hasattr(self, "speaker_embedding") and speaker_ids is None:
+            raise RuntimeError(" [!] Model has speaker embedding layer but speaker_id is not provided")
+        if hasattr(self, "speaker_embedding") and speaker_ids is not None:
+            self.speaker_embeddings = self._compute_speaker_embedding(speaker_ids)
+            self.speaker_embeddings_projected = self.speaker_project_mel(self.speaker_embeddings).squeeze(1)
+    
     def forward(self, characters, text_lengths, mel_specs, speaker_ids=None):
         B = characters.size(0)
         mask = sequence_mask(text_lengths).to(characters.device)
         inputs = self.embedding(characters)
+        self.__init_states()
+        self.compute_speaker_embedding(speaker_ids)
+        if self.num_speakers > 1:
+            inputs = self._concat_speaker_embedding(inputs,
+                                                self.speaker_embeddings)
         encoder_outputs = self.encoder(inputs)
-        encoder_outputs = self._add_speaker_embedding(encoder_outputs,
+        if self.num_speakers > 1:
-                                                      speaker_ids)
+            encoder_outputs = self._concat_speaker_embedding(encoder_outputs,
+                                                        self.speaker_embeddings)
         mel_outputs, alignments, stop_tokens = self.decoder(
-            encoder_outputs, mel_specs, mask)
+            encoder_outputs, mel_specs, mask, self.speaker_embeddings_projected)
         mel_outputs = mel_outputs.view(B, -1, self.mel_dim)
         linear_outputs = self.postnet(mel_outputs)
         linear_outputs = self.last_linear(linear_outputs)
@@ -55,25 +78,30 @@ class Tacotron(nn.Module):
     def inference(self, characters, speaker_ids=None):
         B = characters.size(0)
         inputs = self.embedding(characters)
+        self.__init_states()
+        self.compute_speaker_embedding(speaker_ids)
+        if self.num_speakers > 1:
+            inputs = self._concat_speaker_embedding(inputs,
+                                                self.speaker_embeddings)
         encoder_outputs = self.encoder(inputs)
-        encoder_outputs = self._add_speaker_embedding(encoder_outputs,
+        if self.num_speakers > 1:
-                                                      speaker_ids)
+            encoder_outputs = self._concat_speaker_embedding(encoder_outputs,
+                                                        self.speaker_embeddings)
         mel_outputs, alignments, stop_tokens = self.decoder.inference(
-            encoder_outputs)
+            encoder_outputs, self.speaker_embeddings_projected)
         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 _add_speaker_embedding(self, encoder_outputs, speaker_ids):
+    def _compute_speaker_embedding(self, speaker_ids):
-        if hasattr(self, "speaker_embedding") and speaker_ids is None:
+        speaker_embeddings = self.speaker_embedding(speaker_ids)
-            raise RuntimeError(" [!] Model has speaker embedding layer but speaker_id is not provided")
+        return speaker_embeddings.unsqueeze_(1)
-        if hasattr(self, "speaker_embedding") and speaker_ids is not None:
+
-            speaker_embeddings = self.speaker_embedding(speaker_ids)
+    def _concat_speaker_embedding(self, outputs, speaker_embeddings):
+        speaker_embeddings_ = speaker_embeddings.expand(outputs.size(0),
+                                                      outputs.size(1),
+                                                      -1)
+        outputs = torch.cat([outputs, speaker_embeddings_], dim=-1)
+        return outputs
 
-            speaker_embeddings.unsqueeze_(1)
-            speaker_embeddings = speaker_embeddings.expand(encoder_outputs.size(0),
-                                                           encoder_outputs.size(1),
-                                                           -1)
-            encoder_outputs = encoder_outputs + speaker_embeddings
-        return encoder_outputs

tests/test_layers.py

@@ -54,7 +54,8 @@ class DecoderTests(unittest.TestCase):
             trans_agent=True,
             forward_attn_mask=True,
             location_attn=True,
-            separate_stopnet=True)
+            separate_stopnet=True,
+            speaker_embedding_dim=0)
         dummy_input = T.rand(4, 8, 256)
         dummy_memory = T.rand(4, 2, 80)
 
@@ -66,6 +67,34 @@ class DecoderTests(unittest.TestCase):
         assert output.shape[2] == 80 * 2, "size not {}".format(output.shape[2])
         assert stop_tokens.shape[0] == 4
 
+    def test_in_out_multispeaker(self):
+        layer = Decoder(
+            in_features=256,
+            memory_dim=80,
+            r=2,
+            memory_size=4,
+            attn_windowing=False,
+            attn_norm="sigmoid",
+            prenet_type='original',
+            prenet_dropout=True,
+            forward_attn=True,
+            trans_agent=True,
+            forward_attn_mask=True,
+            location_attn=True,
+            separate_stopnet=True,
+            speaker_embedding_dim=80)
+        dummy_input = T.rand(4, 8, 256)
+        dummy_memory = T.rand(4, 2, 80)
+        dummy_embed = T.rand(4, 80)
+
+        output, alignment, stop_tokens = layer(
+            dummy_input, dummy_memory, mask=None, speaker_embeddings=dummy_embed)
+
+        assert output.shape[0] == 4
+        assert output.shape[1] == 1, "size not {}".format(output.shape[1])
+        assert output.shape[2] == 80 * 2, "size not {}".format(output.shape[2])
+        assert stop_tokens.shape[0] == 4
+
 
 class EncoderTests(unittest.TestCase):
     def test_in_out(self):